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Factors impacting faculty research productivity at a highly-ranked university

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Factors impacting faculty research productivity at a highly-ranked university

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Content Running head: RESEARCH PRODUCTIVITY FACTORS

FACTORS IMPACTING FACULTY RESEARCH PRODUCTIVITY
AT A HIGHLY-RANKED UNIVERSITY

by

Jin Lung Michael Fung

A Dissertation Presented to the
FACULTY OF THE USC ROSSIER SCHOOL OF EDUCATION
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF EDUCATION

August 2017

Copyright 2017 Jin Lung Michael Fung
RESEARCH PRODUCTIVITY FACTORS 2

Dedication
This dissertation is dedicated to my family. Your encouragement, love, and support was
the wind beneath my wings, helping me to soar through the rigors and challenges in completing
my doctorate program and dissertation study, supporting my mid-course career transition, and
smoothing the transition for our international relocation. I deeply appreciate the sacrifices that
you have made, structuring our days, weekends, and even vacation time around my classes and
academic work, and bearing with me on the many occasions when I was mentally preoccupied
and exhausted.
I would also like to dedicate this study to university presidents, who are increasingly
being pressured by stakeholders to propel their institutions up the league tables, as I have found
out in my numerous conversations with presidents from institutions around the world. I hope that
this project is helpful in some small way in your pursuit of academic excellence and enhanced
international standing for your institutions.

RESEARCH PRODUCTIVITY FACTORS 3

Acknowledgements
I am truly grateful to Dr. Melora Sundt for your ongoing support and guidance
throughout my journey. Your clarity of thought and insightful advice along the way have been
vital in helping me to find my bearings, especially in moments when I am stuck in mental knots.
I would also like to thank Deans Karen Gallagher and James Lee, for taking the time from your
busy schedules to provide me with your valuable feedback and advice as my dissertation
committee members.
Thank you to President Tony Chan and Executive Vice President Wei Shyy at the Hong
Kong University of Science and Technology, for being open and supportive of my dissertation
study at your fine university. With your support, I could study the perspectives of many of your
faculty, and they were able to share with me candid views about what makes your university
such an excellent institution. I am also thankful for the feedback on my survey and interview
instruments provided by Drs. David Mole and Tak Ha.
Finally, I want to thank all the staff at USC Rossier, who have worked tirelessly to
support the pioneering doctoral program in Organizational Change and Leadership. Despite some
hiccups along the way, I know that you have all put in your very best to make things work for
our inaugural cohort. Fight On!

RESEARCH PRODUCTIVITY FACTORS 4

Table of Contents
List of Tables .................................................................................................................................. 9
List of Figures ............................................................................................................................... 10
Abstract ......................................................................................................................................... 11
CHAPTER ONE: INTRODUCTION ........................................................................................... 12
Organizational Context and Mission ........................................................................................ 14
Organizational Performance Status ........................................................................................... 16
Related Literature ...................................................................................................................... 16
Importance of a Promising Practice Project .............................................................................. 17
Organizational Performance Goal and Current Performance ................................................... 18
Description of Stakeholder Groups ........................................................................................... 19
Stakeholders’ Performance Goals ............................................................................................. 19
Stakeholder Group for the Study .............................................................................................. 20
Purpose of the Project and Questions ....................................................................................... 22
Methodological Framework ...................................................................................................... 22
Definitions................................................................................................................................. 23
Organization of the Study ......................................................................................................... 24
CHAPTER TWO: REVIEW OF LITERATURE ......................................................................... 25
Framework ................................................................................................................................ 25
Research Faculty Knowledge, Motivation and Organizational Factors ................................... 26
Knowledge Influences .......................................................................................................... 26
Knowledge types ............................................................................................................... 26
Research faculty knowledge influences ............................................................................ 27
Motivation Influences ........................................................................................................... 30
Expectancy value theory ................................................................................................... 31
Research faculty expectancy value ................................................................................... 32
Self-efficacy theory ........................................................................................................... 34
Research faculty self-efficacy ........................................................................................... 34
Organizational Influences ..................................................................................................... 36
Cultural models for research productivity ........................................................................ 36
Cultural settings for research productivity ........................................................................ 37
Conclusion ................................................................................................................................ 40

RESEARCH PRODUCTIVITY FACTORS 5

CHAPTER THREE: METHODOLOGY ..................................................................................... 42
Purpose of the Project and Questions ....................................................................................... 42
Conceptual and Methodological Framework ............................................................................ 42
Assessment of Performance Influences .................................................................................... 44
Knowledge Assessment ........................................................................................................ 44
Motivation Assessment ......................................................................................................... 45
Organization/Culture/Context Assessment ........................................................................... 46
Participating Stakeholders and Sample Selection ..................................................................... 46
Survey Sampling Criteria ...................................................................................................... 47
Survey Recruitment Strategy ................................................................................................ 47
Interview Sampling Criteria .................................................................................................. 47
Interview Recruitment Strategy ............................................................................................ 48
Data Collection and Instrumentation ........................................................................................ 48
Surveys .................................................................................................................................. 49
Interviews .............................................................................................................................. 50
Data Analysis ............................................................................................................................ 52
Credibility and Trustworthiness ............................................................................................ 54
Role of Investigator................................................................................................................... 55
Summary ................................................................................................................................... 56
CHAPTER FOUR: RESULTS AND FINDINGS ........................................................................ 57
Participating Stakeholders and Response Analysis .................................................................. 58
Results and Findings for Knowledge Assets ............................................................................ 60
Knowledge of Research Findings in Academic Fields ......................................................... 61
Survey results .................................................................................................................... 61
Interview findings ............................................................................................................. 61
Knowledge to Generate New Research Ideas ....................................................................... 63
Survey results .................................................................................................................... 63
Interview findings ............................................................................................................. 63
Knowledge of Procedures to Publish Peer-Reviewed Articles ............................................. 65
Survey results .................................................................................................................... 65
Interview findings ............................................................................................................. 66
Knowledge of Managing Workload...................................................................................... 67
Survey results .................................................................................................................... 68
Interview findings ............................................................................................................. 68
RESEARCH PRODUCTIVITY FACTORS 6

Synthesis of Results and Findings for Knowledge Assets .................................................... 69
Results and Findings for Motivation Assets ............................................................................. 71
Motivation from Value in Publishing Research .................................................................... 71
Survey results .................................................................................................................... 71
Interview findings ............................................................................................................. 71
Motivation from Interest in Research ................................................................................... 74
Survey results .................................................................................................................... 74
Interview findings ............................................................................................................. 74
Motivation from Self-Efficacy to Publish Peer-Reviewed Articles ..................................... 76
Survey results .................................................................................................................... 76
Interview findings ............................................................................................................. 77
Synthesis of Results and Findings for Motivation Assets .................................................... 79
Results and Findings for Organization Assets .......................................................................... 80
Organizational Expectations on Faculty Research Productivity ........................................... 80
Survey results .................................................................................................................... 81
Interview findings ............................................................................................................. 81
Organizational Performance Incentives ................................................................................ 83
Survey results .................................................................................................................... 84
Interview findings ............................................................................................................. 84
Organizational Policies and Practices to Support Research. ................................................ 86
Survey results .................................................................................................................... 87
Interview findings ............................................................................................................. 87
Synthesis of Results and Findings for Organization Assets ................................................. 89
Summary ................................................................................................................................... 91
CHAPTER FIVE: RECOMMENDATIONS ................................................................................ 96
Recommendations for Practice to Address KMO Influences ................................................... 96
Knowledge Recommendations ............................................................................................. 97
Introduction ....................................................................................................................... 97
Declarative knowledge assets ......................................................................................... 100
Procedural knowledge assets .......................................................................................... 102
Metacognitive knowledge assets ..................................................................................... 103
Motivation Recommendations ............................................................................................ 104
Introduction ..................................................................................................................... 104
Expectancy value ............................................................................................................ 106
RESEARCH PRODUCTIVITY FACTORS 7

Self-efficacy .................................................................................................................... 108
Organization Recommendations ......................................................................................... 109
Introduction ..................................................................................................................... 109
Expectations and goals .................................................................................................... 112
Performance incentives ................................................................................................... 113
Policies and practices ...................................................................................................... 114
Integrated Implementation and Evaluation Plan ..................................................................... 116
Implementation and Evaluation Framework ....................................................................... 116
Organizational Purpose, Need, and Expectations ............................................................... 117
Level 4: Results and Leading Indicators ............................................................................. 117
Level 3: Behavior ................................................................................................................ 119
Critical behaviors ............................................................................................................ 119
Required drivers .............................................................................................................. 121
Monitoring ...................................................................................................................... 122
Organizational support .................................................................................................... 122
Level 2: Learning ................................................................................................................ 123
Learning goals ................................................................................................................. 123
Program ........................................................................................................................... 124
Components of learning .................................................................................................. 124
Level 1: Reaction ................................................................................................................ 126
Evaluation Tools ................................................................................................................. 127
Immediately following the program implementation ..................................................... 127
Delayed for a period after the program implementation ................................................. 128
Data Analysis and Reporting .............................................................................................. 128
Summary ............................................................................................................................. 129
Strengths and Weaknesses of the Approach ........................................................................... 129
Limitations .............................................................................................................................. 131
Future Research ...................................................................................................................... 132
Conclusion .............................................................................................................................. 134
References ................................................................................................................................... 136
Appendix A: Recruiting Script for Study ................................................................................... 147
Appendix B: Script to Arrange for Interview ............................................................................. 148
Appendix C: Recruiting Script for Interview.............................................................................. 149
Appendix D: Survey Items .......................................................................................................... 150
RESEARCH PRODUCTIVITY FACTORS 8

Appendix E: Interview Protocol ................................................................................................. 154
Appendix F: Survey Items for Post Implementation Evaluation ................................................ 158

RESEARCH PRODUCTIVITY FACTORS 9

List of Tables
Table 1: Assessment Criteria for Major International University Ranking Systems .................... 13
Table 2: Organizational Mission, Global Goal and Stakeholder Performance Goals .................. 19
Table 3: Comparison of Research Publication Growth of Top Asian Universities ...................... 21
Table 4: Summary of Promising Research Practices for Faculty: Assumed Knowledge Influences
....................................................................................................................................................... 29
Table 5: Summary of Promising Research Practices for Faculty: Assumed Motivational
Influences ...................................................................................................................................... 35
Table 6: Summary of Promising Research Practices for Faculty: Assumed Organizational
Influences ...................................................................................................................................... 39
Table 7: Summary of Assumed Knowledge, Motivation, and Organizational Influences ........... 40
Table 8: Assumed Influences and Assessment Approaches ......................................................... 44
Table 9a: Profile of Survey and Interview Respondents .............................................................. 59
Table 9b: List of Interview Respondents ...................................................................................... 59
Table 10: Survey and Interview Results for Knowledge Influences ............................................ 70
Table 11: Survey and Interview Results for Motivation Influences ............................................. 79
Table 12: Survey and Interview Results on Organization Influences ........................................... 90
Table 13: Summary of Validation of Knowledge, Motivation, and Organization Influences ...... 91
Table 14: Survey Results for Knowledge, Motivation, Organizational Influences – Breakdown
by School and Faculty Rank ......................................................................................................... 93
Table 15: Summary of Knowledge Influences and Recommendations ........................................ 98
Table 16: Summary of Motivation Influences and Recommendations ...................................... 105
Table 17: Summary of Organization Influences and Recommendations ................................... 110
Table 18: Outcomes, Metrics, and Methods for External and Internal Outcomes ..................... 118
Table 19: Critical Behaviors, Metrics, Methods, and Timing for Evaluation ............................ 119
Table 20: Required Drivers to Support Critical Behaviors ......................................................... 121
Table 21: Components of Learning for the Program. ................................................................. 125
Table 22: Components to Measure Reactions to the Program. ................................................... 126

RESEARCH PRODUCTIVITY FACTORS 10

List of Figures
Figure 1. Problem-solving approach – Gap Analysis framework ................................................. 43
Figure 2. Conceptual framework for this study ............................................................................ 43
Figure 3. Factors impacting knowledge of research findings in academic fields. ........................ 62
Figure 4. Factors impacting knowledge to generate new research ideas ...................................... 64
Figure 5. Factors impacting knowledge of procedures to publish peer-reviewed articles ............ 66
Figure 6. Factors impacting knowledge of managing workload ................................................... 68
Figure 7. Factors impacting perceived value in publishing research ............................................ 72
Figure 8. Factors impacting interest in research ........................................................................... 75
Figure 9. Factors impacting self-efficacy to publish peer-reviewed articles ................................ 77
Figure 10. Factors impacting expectations on faculty research productivity ............................... 82
Figure 11. Factors impacting performance incentives for faculty research productivity ............. 84
Figure 12. Factors impacting policies and practices for faculty research productivity ................ 87
Figure 13. Dashboard sample of Level 4 goals. .......................................................................... 129

RESEARCH PRODUCTIVITY FACTORS 11

Abstract
Universities around the world are facing increasing pressure to perform well in rankings, and
rankings results have been shown to impact institutional reputation, ability to secure funding, and
recruitment of students and faculty. Faculty research productivity is one of the main factors
impacting rankings performance, and the aim of this project was to identify the factors of
importance to faculty research productivity at a top-ranked university. Findings from 113
surveys and nine interviews with faculty at the Hong Kong University of Science and
Technology (HKUST) revealed a set of knowledge, motivation, and organization assets that
supported research productivity at the University. Knowledge assets identified were up-to-date
knowledge of developments in their academic fields, knowledge to generate new research ideas,
and knowledge of the required steps to publish peer-reviewed research articles. Motivation assets
identified were faculty valuing publishing research, having interest in research, and being self-
efficacious at producing research publications. Organization assets identified were the presence
of clear expectations and goals, and effective performance incentives to encourage faculty
research productivity. A set of recommendations were proposed to reinforce these assets and to
address identified weaknesses, supported by an integrated implementation and evaluation
package. Institutions aspiring to improve their research productivity and rankings performance
can benchmark themselves against these factors and practices, and adapt the recommendations
and implementation to suit their institutional contexts.
Keywords: rankings, research, productivity, publications, faculty, reputation

RESEARCH PRODUCTIVITY FACTORS 12

CHAPTER ONE: INTRODUCTION
Over the past three decades, university rankings have expanded in numbers and
geographical coverage. Since the inaugural publication of America’s Best Colleges by the U.S.
News and World Report in 1983, many university rankings have been published by various
private and public entities around the world (Buela-Casal, Gutiérrez-Martínez, Bermúdez-
Sánchez, & Vadillo-Muñoz, 2007). A coalition of leading Australian universities listed 57
ranking systems that were published by organizations globally (Group of Eight, 2012). Stoltz,
Hendel, and Horn (2010) listed 25 ranking systems in Europe alone. In addition, institutional
performance ranking systems have been developed by government agencies, accreditation
bodies, and higher education institutions across four continents -- Asia, Europe, North America,
and South America (Altbach, 2012; Hazelkorn, 2007). Buela-Casal et al. (2007) observed that
university rankings have evolved from a single-nation focus to international coverage, in
response to increasing student mobility, technological advancements, and economic
developments. Despite the limitations of rankings in providing accurate assessments of the
quality of institutions of higher education (Adler & Harzing, 2009; Altbach, 2012; Buela-Casal,
Gutiérrez-Martínez, Bermúdez-Sánchez, & Vadillo-Muñoz, 2007; Hou, Morse, & Chiang, 2012),
rankings are being used by higher education stakeholders to make decisions, set goals, and
manage institutions (Drewes & Michael, 2006; Hallinger, 2014; Hazelkorn, 2007).
The problem of practice addressed in this paper is how could higher education
institutions increase faculty research productivity, thereby contributing to improved rankings
performance of their institutions. Three major international university ranking systems are the
Quacquarelli-Symonds (QS) World University Rankings, the Times Higher Education (THE)
World University Rankings, and the Shanghai Jiaotong University (SJTU) Academic Ranking of
RESEARCH PRODUCTIVITY FACTORS 13

World Universities (Altbach, 2012). Table 1 lists the assessment criteria and weights for each of
these ranking systems, showing that research-related indicators form between 60% to 90% of the
assessment criteria. Hence, from an institutional perspective, faculty research productivity is one
of the main drivers of rankings performance.
Table 1
Assessment Criteria for Major International University Ranking Systems
Sources Research-Related Criteria and
Weightage
Other Criteria and Weightage
QS World
University
Rankings
• Academic reputation survey:
40%
• Citations per faculty: 20%
• Employer reputation survey:
20%
• Student-to-faculty ratio: 20%
• International faculty ratio: 5%
• International student ratio: 5%
THE World
University
Ranking
• Research – volume, income
and reputation survey: 30%
• Citations (research influence):
30%
• Teaching – staff-to-student
ratio, doctorate-to-bachelor’s
ratio, doctorates awarded,
institutional income, reputation
survey: 30%
• International outlook
(international-to-domestic-staff
ratio, international-to-domestic-
student ratio, international
research collaboration): 7.5%
• Industry income (knowledge
transfer): 2.5%
SJTU Academic
Ranking of World
Universities
• Quality of faculty (Nobel prizes
and Fields medals, highly cited
researchers): 40%
• Research output: 40%
• Per capita performance: 10%
• Quality of education (alumni
Nobel prizes, Fields medals):
10%
Note: Adapted from QS (2016c), Times Higher Education (2016c), and Shanghai Ranking
Consultancy (2016).

This chapter is organized in four sections to set the context for the study. The first section
provides background information on the organization being studied. The second section outlines
related literature and the importance of the project. The third section defines the organizational
RESEARCH PRODUCTIVITY FACTORS 14

performance goals and stakeholders for the study. The fourth section states the research
questions and describes the methodological framework used in the study.
Organizational Context and Mission
The project site is The Hong Kong University of Science and Technology (HKUST).
Permission was granted by the President of the HKUST to carry out the study and to name the
institution in this dissertation paper (T. F. Chan, personal communication, August 23, 2016).
HKUST’s main campus is located at Clear Water Bay in Hong Kong, with branch operations
across several locations in Mainland China. Founded in 1991, HKUST has 9,334 undergraduate
and 4,874 postgraduate students, of which 35.6% are non-local (international and mainland)
students. Core faculty strength stands at 663, all of whom hold doctorate degrees from renowned
universities worldwide (HKUST Facts & Figures, 2016). The University is publicly-funded, with
around half of its budget coming from government subventions (HKUST Financial Statements,
2016).
The mission of HKUST is to “advance learning and knowledge through teaching and
research, particularly: in science, technology, engineering, management and business studies; at
the postgraduate level; and to assist in the economic and social development of Hong Kong”
(HKUST Mission & Vision, n.d., para. 1). The vision of HKUST is:
To be a leading University with significant international impact and strong local
commitment. Global – To be a world-class university at the cutting edge internationally
in all targeted fields of pursuit. National – To contribute to the economic and social
development of the nation as a leading University in China. Local – To play a key role, in
partnership with government, business, and industry, in the development of Hong Kong
as a knowledge-based society. (HKUST Mission & Vision, n.d., para. 2).
RESEARCH PRODUCTIVITY FACTORS 15

Although HKUST was modeled after research-intensive universities in the US
(Postiglione, 2011), there are some fundamental differences in its operating model compared
with American universities. HKUST faculty are remunerated on a 12-month basis and
consequently do not have to earn their keep in the summer months, unlike US faculty who are
typically remunerated on a nine-month basis (Kelsky, 2016). In the Hong Kong context, public
universities receive substantial direct funding from the government for both teaching and
research, while in the US, most of the research funding comes from competitive bidding of
external research grants (Rimer, 2015). Consequently, US faculty in research-intensive
universities have relatively higher stakes in being research productive, to be able to secure
external research grants that would support their research projects and pay for their salaries. The
differences in the research funding environment are important considerations when comparing
faculty motivations for research productivity across Hong Kong and the US.
Government funding for research at the Hong Kong public universities is administered by
the University Grants Committee (UGC). The UGC conducts periodic Research Assessment
Exercises (RAE) to assess the research quality of higher education institutions in Hong Kong,
modelled after the research assessment frameworks in the U.K. and Australia. The results of the
RAE inform the distribution of research funding allocation to the institutions (UGC, 2017b).
While past RAE exercises emphasized the rating of faculty’s research outputs against
international benchmarks of research excellence, the next RAE exercise in 2020 would likely
include additional measures of research impact and environment (UGC, 2017e). Given the direct
and material impact of the RAE on research funding to institutions, organizational policies and
practices governing research activities at the institutions are substantively shaped by the
evaluation approaches adopted in the RAE.
RESEARCH PRODUCTIVITY FACTORS 16

Organizational Performance Status
HKUST is one of three research-intensive universities in Hong Kong, and ranks as one of
the top universities in Asia (QS, 2016a; Times Higher Education, 2016b) and among the top 50
universities globally (QS, 2016c; Times Higher Education, 2016c). The MBA and EMBA
programs offered by the HKUST Business School consistently rank amongst the top 20 and top 5
in the world respectively (Financial Times, 2016), and several subject disciplines such as
engineering and economics rank within the top 20 globally (Shanghai Ranking Consultancy,
2016). In just 25 years of establishment, HKUST has been recognized as having developed into a
world-class university, and is frequently cited as a benchmark and model for other universities in
the Asian region (Postiglione, 2011). Thus, HKUST is a model organization for young
institutions aspiring to become world-class universities, and for older universities aiming to
strengthen their international standing and recognition. Through studying the factors contributing
to HKUST’s faculty research productivity leading to top rankings performance, other institutions
can benchmark and adapt promising practices to be implemented within their organizations.
Related Literature
There are a number of concerns with the accounting of research productivity through the
use of rankings as an evaluation tool. Adler and Harzing (2009) and Altbach (2012) highlighted
the bias of rankings toward universities in English-speaking countries, due to major research
publication indices being English-based. As a measure of research productivity, ranking results
are, according to Adler and Harzing (2009), distorted and irrelevant, due to the aggregation of
research outputs of individual faculty, the arbitrary selection of top-rated journals, and the under-
representation of conference proceedings, books, and book chapters in publication indices. The
emphasis of international rankings on research publications and reputational surveys lead to
RESEARCH PRODUCTIVITY FACTORS 17

ranking results that only partially reflect the broader scope and contributions of universities
(Hou, Morse, & Chiang, 2012). Adler and Harzing (2009) and Hallinger (2014) criticized the
arbitrary nature of assigning weightings to rankings criteria, the subjectivity involved in selecting
research journal editors, and the decisions made by these editors on which articles to publish in
their journals. Rankings have also been shown to be subject to persistence effects, where highly-
ranked institutions tend to continue to be highly ranked in subsequent years irrespective of
changes in their performance, leading to distortions in rankings results as a measure of actual
quality (Bastedo & Bowman, 2010; Grewal & Lilien, 2008).
Despite various critiques of ranking methodologies, some scholars have argued that
international university rankings serve a useful role by providing vital information for comparing
the strengths and weaknesses of academic institutions globally (Buela-Casal, Gutiérrez-Martínez,
Bermúdez-Sánchez, & Vadillo-Muñoz, 2007; Enserink, 2007; Hazelkorn, 2008). The evolution
of rankings into a system involving multiple stakeholders (Hallinger, 2014), and the global
massification, competition, and commercialization of higher education have driven rankings to
become a permanent feature of modern education (Altbach, 2012). Given the impact of rankings
on the reputation and standing of institutions, it is important for institutions to find ways to
improve their rankings performance, with faculty research productivity being one of the main
factors impacting rankings performance.
Importance of a Promising Practice Project
The problem of how to increase faculty research productivity in higher education
institutions to perform well in international university rankings is important to address for a
variety of reasons. Rankings are being used by government agencies and higher education
policymakers to benchmark against other systems and countries (Altbach, 2012; Buela-Casal et
RESEARCH PRODUCTIVITY FACTORS 18

al., 2007), allocate resources, drive higher education reform (Hazelkorn, 2008), and even to set
national goals (Hallinger, 2014). As rankings are perceived to be a gauge of institutional
reputation and quality, stakeholders are using rankings results in making decisions about
funding, sponsorship, and recruitment (Hazelkorn, 2008). Failure to perform well in university
rankings may negatively impact an institution’s ability to bring in funding from government
agencies (Hazelkorn, 2007) and private donors (Grewal, Dearden, & Lilien, 2008).
Rankings are also used by students and parents as impartial comparisons to help them
decide on which universities to apply to (Altbach, 2012; Buela-Casal et al., 2007; Hazelkorn,
2008), and have a particularly strong influence on the university choices of international students
(Hazelkorn, 2007). Changes in ranking positions have been shown to impact the number of
applications received by universities (Drewes & Michael, 2006), thereby impacting the
selectivity and quality of student intake. The influence of rankings extends to academics
globally, and is viewed by them as external assessments of the reputation and quality of
institutions (Bowman & Bastedo, 2011). Hence, institutions that are not sufficiently research
productive would tend to perform poorly on rankings, which compromises an institution’s
attractiveness in recruiting of students and faculty (Grewal, Dearden, & Lilien, 2008; Hou,
Morse, & Chiang, 2012). The inability to recruit quality faculty and students would further
exacerbate the problem of producing too few research publications, leading to a “vicious cycle”
of downward spiraling in research productivity and rankings performance.
Organizational Performance Goal and Current Performance
The organization’s performance goal is to continue to rank among the top 10 Asian
universities and among the top 50 universities globally by December 2018. This goal embodies
HKUST’s aspirations to be competitively positioned against peer institutions within Asia and
RESEARCH PRODUCTIVITY FACTORS 19

globally, and is in line with the trajectory of HKUST’s rankings performance over the past five
years (QS, 2016c; Times Higher Education, 2016c). Hence, continually improving faculty
research productivity is required to stay ahead of the competition and to continue to be highly-
ranked.
Description of Stakeholder Groups
The stakeholders contributing toward the organizational performance goal, as shown in
Table 2, are those who are actively involved in the research activities of the university, namely
faculty, administration staff, and graduate students. Research faculty are responsible for securing
research funding, conducting academic research, and disseminating research findings through
journal publications, conference presentations, books, and trade publications. Research
administration staff provide services to assist faculty in applying for research grants, in managing
research programs and funding, and in publishing of research findings. Research graduate
students are recruited by research faculty into research project teams, to undertake academic
research work, write research papers, and co-publish research findings (White, James, Burke, &
Allen, 2012). All three stakeholder groups contribute to HKUST’s research productivity, and
consequently to HKUST’s rankings performance, as research productivity and impact are key
criteria adopted by the various rankings systems.
Stakeholders’ Performance Goals
Table 2
Organizational Mission, Global Goal and Stakeholder Performance Goals
Organizational Mission
The mission of the Hong Kong University of Science and Technology is to be a leading university
with significant international impact and strong local commitment.
RESEARCH PRODUCTIVITY FACTORS 20

Organizational Performance Goal
By December 2018, the University will continue to be ranked as a top 10 Asian university and a
top 50 global university.
Stakeholder Goals
Research Faculty Research Administration Staff Research Graduate Students
By December of 2017, faculty
will increase their total
research publications per year
by at least 10%, to stay ahead
of the competition from peer
institutions.
By end 2017, research
administrative staff will
compile and disseminate a list
of all eligible Hong Kong and
major international research
grant schemes.
By end 2017, 100% of
research graduate students will
have taken at least two
university-level courses on
academic writing and research
ethics.

Stakeholder Group for the Study
While the joint efforts of all stakeholders contributed to the achievement of the overall
organizational goal of continuing to be ranked among the top 10 Asian universities and top 50
globally, it is important to understand the promising practices and strategies utilized by
HKUST’s research faculty as they play a major role in the research outcomes of the university,
which is a key dimension being assessed through rankings. Therefore, the stakeholders of focus
for this study are all HKUST’s substantiation-track faculty members, who are expected to
actively conduct research and produce research outputs. The stakeholder goal, for consideration
by the University’s senior management team, is for faculty to increase their total research
publications by at least 10% every year, to be achieved by December 2017.
Table 3 lists the comparative data generated on the number of research publications by
each of the top 10 Asian universities, as ranked in the 2016 QS Asian University Rankings (QS,
2016a) and 2016 Times Higher Education Asian University Rankings (Times Higher Education,
2016b). Research publication data was extracted from the Elsevier’s Scopus publication
database, using the institution’s name in the affiliation search function, and specifying the
publication year as a filter criterion.
RESEARCH PRODUCTIVITY FACTORS 21

Table 3
Comparison of Research Publication Growth of Top Asian Universities
Institution Research Publications (Scopus Database)

2011 2012 2013 2014 2015 CAGR
University of Hong Kong 2985 3084 2937 3647 4487 8.49%
Peking University 7824 8678 9840 10771 10799 6.66%
Tsinghua University 10048 10762 11956 13072 12917 5.15%
Korea Advanced Institute of
Science and Technology 2374 2407 2551 2750 2835 3.61%
Seoul National University 8302 9226 9220 9864 9891 3.56%
Nanyang Technological University 5465 5830 6044 6304 6461 3.41%
City University of Hong Kong 2649 2893 2912 3148 3086 3.10%
National University of Singapore 7201 8045 8543 8574 8265 2.79%
The Hong Kong University of
Science and Technology 2212 2248 2342 2567 2,516 2.61%
Chinese University of Hong Kong 3905 4100 4352 4577 4348 2.17%
Pohang University of Science and
Technology 1490 1436 1482 1501 1507 0.23%
University to Tokyo 24379 25072 25520 25729 23521 -0.71%

In analyzing the benchmark performance of these top Asian universities, compound
annual growth rates (CAGR) of research publications from 2011 to 2015 were found to be
between -0.71% and 8.49%. HKUST produced a total of 2,516 research publications in 2015,
with a CAGR of 2.61% over the same five-year period. Hence, the goal of increasing HKUST’s
research publication growth by 10% is determined to be needed to keep pace with the
competition. Failure to accomplish this goal will negatively impact HKUST’s competitive
positioning in research output and impact, which adversely impacts the organization’s
performance in the international rankings.
Beyond contributing to rankings performance, knowledge created through faculty’s
research could be shared with scholarly and non-scholarly communities (Amo, 2007).
Publications in research journals would be read and cited by other academics, hence contributing
to the research impact of the institution. For institutions that are able to consistently publish in
RESEARCH PRODUCTIVITY FACTORS 22

highly-selective top journals in various academic fields, the institutions’ faculty would
increasingly be recognized by the scholarly community for their strengths in those fields.
Institutions can further build up their reputations among non-scholarly communities through
actively marketing their research assets, by adapting research findings into suitable forms for
dissemination through non-academic channels (Amo, 2007), such as trade publications, media
outlets, and online media. All these efforts at building an institution’s reputation hinge on
faculty’s ability to produce novel and impactful research publications.
Purpose of the Project and Questions
The purpose of this project is to study HKUST’s assets in the areas of knowledge,
motivation, and organizational resources, in relation to increasing faculty research productivity
and thereby contributing to improved rankings performance. While a complete study would
focus on all stakeholders, for practical purposes the stakeholder group focused on in this analysis
was the faculty members of HKUST.
As such, the questions that guided the promising practices study were the following:
1. What are the knowledge, motivation and organizational assets that faculty identify as
helping to increase their research productivity?
2. What solutions and recommendations in the areas of knowledge, motivation, and
organizational resources may be appropriate for solving the problem of practice at
another organization?
Methodological Framework
A mixed methods data gathering and analysis approach (Creswell, 2014) was utilized to
study the Hong Kong University of Science and Technology’s (HKUST) faculty members’
assets in the areas of knowledge, motivation, and organization resources, in relation to increasing
RESEARCH PRODUCTIVITY FACTORS 23

faculty research productivity and thereby contributing to HKUST’s performance as a top-ranked
institution globally. Faculty members’ assets were studied using both surveys and interviews.
Definitions
The terms and definitions used in this study are described in this section.
General Research Fund (GRF): Competitive research funding scheme funded by the
Hong Kong SAR Government to support academic research projects, with proposals subjected to
a rigorous peer review process via five subject panels supported by an international network of
expert reviewers (UGC, 2017a).
HKUST: The Hong Kong University of Science and Technology, a research-intensive
university established in 1991 in Hong Kong (HKUST Mission & Vision, n.d.).
Substantiation: Emplacing faculty to substantive appointments after a typical six-year
probationary period, subject to a rigorous formal review to assess the performance and potential
of the faculty (CUHK, n.d.). Broadly equivalent to tenure appointments in the U.S. higher
education system. Faculty appointed to the substantiation track are expected to be active
researchers, and are also referred to as “research faculty” in this paper.
Research Assessment Exercise (RAE): Periodic exercise conducted by the Research
Grants Council (RGC) to assess the research quality of higher education institutions in Hong
Kong, and to encourage world-class research. The results of the RAE inform the distribution of
research funding allocation to institutions (UGC, 2017b).
Research Grants Council (RGC): Committee established under the University Grants
Committee (UGC) to advise the Hong Kong SAR Government on the academic research
priorities and needs of the higher education institutions in Hong Kong, and to manage the
application and award processes for research grants, studentships and fellowships (UGC, 2017c).
RESEARCH PRODUCTIVITY FACTORS 24

University Grants Committee (UGC): Committee established by the Hong Kong SAR
government to allocate funding to institutions, and to offer impartial and respected expert advice
to the Government on the strategic development and resource requirements of higher education
in Hong Kong (UGC, 2017d).
Organization of the Study
Five chapters were used to organize this study. This chapter provided the reader with the
key concepts and terminology commonly found in discussions about international rankings of
universities and the impact of faculty research productivity on rankings performance. The
organization’s mission, goals and stakeholders, as well as the framework for reviewing
promising practices were provided. Chapter Two provides a review of current literature
surrounding the scope of the study, addressing topics on the factors influencing faculty research
productivity. Chapter Three lays out the approach for identifying factors perceived by faculty to
influence research productivity at a highly-ranked university, with the assumed influences
categorized by knowledge, motivation, or organizational assets. Chapter Four discusses the
results from the analysis of faculty’s perspectives ascertained through surveys and interviews.
Finally, research-based recommendations drawing from the study findings and research literature
are discussed in Chapter Five.

RESEARCH PRODUCTIVITY FACTORS 25

CHAPTER TWO: REVIEW OF LITERATURE
This chapter reviews the literature regarding the influencers of faculty research
productivity, to establish linkages between research performance and assumed knowledge,
motivation, and organizational causes. This chapter is divided into two sections. The first section
describes the framework utilized to analyze performance factors impacting faculty research
productivity. The second section reviews key influencers of faculty research productivity in the
context of knowledge, motivation, and organizational factors. Findings from the literature review
were used to inform this study, aimed at identifying the factors impacting faculty research
productivity at a top-ranked university.
Framework
This study used the problem-solving approach defined by Clark and Estes (2008). First,
goals were defined at the organizational and stakeholder levels, followed by an assessment of
current performance against the goals. Subsequently, the gaps between desired goals and current
performance were identified and quantified. A gap analysis framework was then applied to
identify the assumed causes of the performance gaps, using the lenses of knowledge, motivation,
and organizational factors to classify and analyze these causes (Clark & Estes, 2008).
In the context of this study, the organizational goal relates to performance in international
university rankings. Faculty research productivity is one of the key criteria contributing to
rankings performance, which was defined as the stakeholder goal for the study. In applying the
gap analysis framework described by Clark and Estes (2008), the knowledge, motivation, and
organizational factors influencing faculty research productivity were identified. Through
analyzing and validating these factors, possible solutions were proposed to improve faculty
performance in research, thereby improving the rankings performance of the university.
RESEARCH PRODUCTIVITY FACTORS 26

Research Faculty Knowledge, Motivation and Organizational Factors
The following review of the literature examines influences of knowledge, motivation, and
organizational factors on the achievement of the stakeholder goal of high faculty research
productivity. The first section discusses the assumed influences on the stakeholder goal in the
context of knowledge types. The second section considers the influences on the attainment of the
stakeholder goal from the perspective of motivation. The third section explores the
organizational influences impacting the stakeholder goal.
Knowledge Influences
Clark and Estes (2008) identified people’s knowledge and skills as one of the critical
factors to address business gaps and to improve organizational performance. This section
contains a literature review of knowledge-related influences that are pertinent to having faculty
members at the HKUST increase their research productivity. The literature was analyzed in
terms of the type of knowledge being described.
Knowledge types. An early taxonomy of knowledge types is Bloom’s Taxonomy,
categorizing knowledge as the recall of specifics and facts, the recall of methods and processes,
or the recall of trends, abstractions, and theories (Bloom, Englehart, Furst, Hill, & Krathwohl,
1956). Krathwohl (2002) subsequently adapted and revised Bloom’s Taxonomy, with knowledge
classified into factual, conceptual, procedural, and metacognitive types. According to the author,
factual knowledge is discrete simple pieces of content, including terminologies and specific
details and basic elements (Krathwohl, 2002). Conceptual knowledge is more complex and refers
to organized forms of information encompassing interrelationships between basic elements,
including classifications and categories, principles and generalizations, and theories, models, and
structures (Krathwohl, 2002). The author defined procedural knowledge as the know-how to get
RESEARCH PRODUCTIVITY FACTORS 27

something done, including subject-specific skills and algorithms, techniques and methods, and
determination of appropriate procedures (Krathwohl, 2002). Finally, Krathwohl (2002) defined
metacognitive knowledge as self-awareness of one’s thinking, including strategic knowledge,
knowledge about cognitive tasks, and self-knowledge.
A slightly different classification was offered by Mayer (2011), with five knowledge
categories defined as facts, concepts, procedures, strategies, and beliefs. Despite the minor
variations in classification and terminology, there is general alignment in the literature that
knowledge can be classified into three main categories, described by Alexander, Schallert, and
Hare (1991) as declarative knowledge (facts, concepts), procedural knowledge (methods,
processes), and metacognitive knowledge (strategies, beliefs, self-awareness).
Research faculty knowledge influences. The following sub-sections examine the
literature about the influence of knowledge in achieving the goal of faculty members increasing
their research productivity. The analysis of knowledge influences is organized by the three main
categories of declarative, procedural, and metacognitive knowledge. By examining each
knowledge category individually, better clarity can be achieved on the influence of each
knowledge type on faculty research productivity.
Declarative knowledge influences. Faculty research productivity is impacted by
declarative knowledge influences, which include both factual and conceptual knowledge
(Alexander, Schallert, & Hare, 1991). Faculty’s knowledge about their academic disciplines
gained through efforts to stay current with the developments in their fields (Azad & Seyyed,
2007), knowledge about organizational and departmental goals for research performance (Hales,
Shahrokh, & Servis, 2005; Jung, 2012), and knowledge about the types of research grants that
they have access to (Hales, Shahrokh, & Servis, 2005), are examples of factual knowledge that
RESEARCH PRODUCTIVITY FACTORS 28

could impact faculty research productivity. Factual knowledge of leading journals in respective
academic fields could also help faculty to identify publication outlets that could maximize the
impact of their research work. In a study of accounting faculty from AACSB-accredited Colleges
of Business, Chen, Nixon, Gupta, and Hoshower (2010) identified the needs for creativity/
curiosity and staying current in academic fields as relatively important outcomes to faculty at
doctoral-granting programs, illustrating the importance of factual knowledge in being productive
in research.
Conceptual knowledge includes research ideas generated by faculty, and knowledge used
in conducting research and developing research grant proposals (Azad & Seyyed, 2007), as well
as knowledge of the differences between soft and hard academic disciplines that affects research
preferences and research collaborations (Jung, 2012). Such conceptual knowledge is acquired
through faculty’s research training and experience (Azad & Seyyed, 2007), and impact their
research approaches and research productivity (Jung, 2012).
Procedural knowledge influences. Faculty research productivity is impacted by
procedural knowledge influences, as reflected in their knowledge of the steps necessary to secure
research grants, to translate research ideas into actual research, and to publish their research
findings. In a study of full-time faculty members at three AASCB-accredited business schools in
the Gulf Cooperation Council (GCC) countries, Azad and Seyyed (2007) asked faculty to rate
themselves on 13 individual competencies that could impact faculty research productivity. The
list of competencies included procedural knowledge about how to apply for research grants and
how to go about turning research ideas into publications, and the authors found that respondents
rated their competencies at moderate and above for all the competencies (Azad & Seyyed, 2007).
Another form of procedural knowledge is the techniques and methods for publishing research,
RESEARCH PRODUCTIVITY FACTORS 29

which is associated with the quality of graduate training in the case of junior faculty (Dundar &
Lewis, 1998). A study by Hales, Shahrokh, and Servis (2005) found that research productivity
among faculty from the Department of Psychiatry and Behavioral Sciences at the University of
California, Davis School of Medicine could be impacted by their procedural knowledge of how
to go about active collaboration to develop a research practice plan for faculty.
Metacognitive knowledge influences. Faculty research productivity is impacted by
metacognitive knowledge influences, specifically knowledge about how to plan their approach to
manage the workload arising from teaching, research, and service. To be research productive,
faculty need the knowledge and self-awareness to prioritize and plan their research activities, and
to manage their time between competing demands (Azad & Seyyed, 2007). Such prioritization
would be guided by what faculty perceive to be the value and costs associated with the various
activities, which will be further discussed in the motivation section below. In a study on the
research productivity of academic accountants, Levitan and Ray (1992) found that the ability to
effectively manage time was the most important factor for faculty to be research productive.
Greater knowledge of time management skills has also been associated with high-performing
researchers, termed as “research stars” (White, James, Burke, & Allen, 2012).
Table 4 lists the declarative, procedural and metacognitive knowledge influences
assumed for this study.
Table 4
Summary of Promising Research Practices for Faculty: Assumed Knowledge Influences
Promising Practices
of Research
Literature Social Science
Equivalents
Knowledge
Staying current with contemporary
issues in their respective academic /
professional fields. (Declarative-Factual)
Azad & Seyyed, 2007;
Jung, 2012.

Schraw &
McCrudden, 2006.

RESEARCH PRODUCTIVITY FACTORS 30

Generating new research ideas.
(Declarative-Conceptual)
Azad & Seyyed, 2007;
Jung, 2012).

Schraw &
McCrudden, 2006.
Knowing the required steps to take for
translating ideas into research and
acceptance into peer-reviewed
publications. (Procedural)

Azad & Seyyed, 2007;
Dundar & Lewis, 1998;
Hales, Shahrokh, &
Servis, 2005.

Schraw &
McCrudden, 2006.
Knowing how to balance research
workload with required teaching and
other responsibilities. (Metacognitive)
Azad & Seyyed, 2007;
Hu & Gill, 2000;
Levitan & Ray, 1992;
White, James, Burke,
& Allen, 2012.

Baker, 2006; Dembo
& Eaton, 2000;
Denler, Wolters, &
Benzon, 2009.

Motivation Influences
Clark and Estes (2008) identified individuals’ motivation as a second critical factor to
address business gaps and to improve organizational performance. This section contains a
literature review on motivation-related influences that are pertinent to increasing research
productivity of faculty members at HKUST in their respective fields.
Motivation is an “internal state that initiates and maintains goal directed behavior”
(Mayer, 2011, p. 39), and is personal, activating, energizing, and directed (Mayer, 2011).
Motivation is a critical determinant of choice, persistence, and effort invested by an individual to
get a job done (Clark & Estes, 2008), and is a primary influencer on the improvement of
performance (Rueda, 2011). Individuals are influenced by a combination of both extrinsic and
intrinsic motivators.
Through studying the relationships between external stimuli and observed behavior,
behavioral theorists advocate that behaviors can be strengthened or weakened through the use of
reinforcements or punishments which vary in effect for different individuals (Daly, 2009).
Desired behaviors can hence be elicited through controlling environmental influences (Tuckman,
2009), such as the provision of feedback and reinforcing incentives, as the strength and rate of
RESEARCH PRODUCTIVITY FACTORS 31

behavioral responses depend on the schedules of such reinforcements (Batsell & Grossman,
2009). However, some researchers have found that the use of rewards had only an indirect
relationship with the intrinsic motivation of individuals (Markova & Ford, 2011), and that the
use of strong incentives may not be effective at encouraging desired behaviors in particular
settings (Roberts, 2010).
Social cognitive theorists advocate that behaviors can be strengthened or weakened
through vicarious reinforcement and punishment (Mayer, 2011). Hence, modeling of desired
behaviors that are overt, credible, and similar can increase the likelihood of adoption of the
behavior by observers (Denler, Wolters, & Benzon, 2009). In addition, self-regulatory strategies
(Dembo & Eaton, 2000) and specific and timely feedback (Shute, 2008) can be used to enhance
the performance of desired behaviors.
Motivation can be enhanced through invoking personal interest, through a variety of
means such as the use of vivid materials, modeling enthusiasm, showing relevance and value,
and providing choices and control in undertaking a task (Schraw & Lehman, 2009). Goal-setting
can be used to increase motivation for tasks (Pintrich, 2003), in particular goals that focus on
mastery and improvement (Yough & Anderman, 2006), and goals that emphasize individual
achievements rather than competitive outcomes (Goette, Huffman, Meier, & Sutter, 2012).
Motivation can also be enhanced through providing performance feedback that emphasizes the
roles of effort, strategies, and self-control, instead of attributing successes and failures to an
individual’s ability (Anderman & Anderman, 2009). While there are many theories about
motivation, two specific motivational theories are discussed in the following section, namely
Expectancy Value Theory and Self-Efficacy Theory.
Expectancy value theory. Eccles (2006) described the expectancy value theory as
RESEARCH PRODUCTIVITY FACTORS 32

comprising two sets of beliefs, namely a person’s expectations to succeed at a task, and the value
the person perceives about undertaking the task. According to the author, people are most likely
to be engaged in a task if they are confident in being able to succeed at the task, and they ascribe
high value to doing the task well. The value perceived from undertaking a task is determined by
a number of factors, including the intrinsic interest (personal enjoyment expected) and the
attainment value (alignment with one’s self-identity) to an individual performing the task.
Furthermore, the utility value (expected benefit toward one’s longer-term goals and rewards)
and, the perceived cost (resources, time, and other costs) affect the value associated with
pursuing the task (Eccles, 2006).
Motivation observed in the form of positive orientation toward tasks reflects the
importance and utility value ascribed by individuals to the tasks (Eccles, 2006; Pintrich, 2003).
Expectancy of success is affected by the perceived opportunities for choice and control and the
setting of goals that convey expectations and confidence (Eccles, 2006). Other factors
influencing expectancy include feedback and past successes/failures (Borgogni et al., 2011), and
the degree of credibility and similarity of role models (Pajares, 2006) of values, enthusiasm, and
interest in the task (Eccles, 2006).
Research faculty expectancy value. A number of expectancy and value factors could
impact faculty research productivity. Some factors identified by Azad and Seyyed (2007) are
areas of academic interest (intrinsic interest), and self-perceptions of academic career success
and satisfaction with research achievements (attainment value). The authors also identified
rewards and compensation (utility value), and time allocated to research versus teaching and
other duties (perceived cost) as factors of influence (Azad & Seyyed, 2007).
According to the authors, faculty’s expectancy to succeed at research publications is
RESEARCH PRODUCTIVITY FACTORS 33

likely to be determined by how they perceive past successes of their academic career, and how
satisfied they are with their past research achievements. In addition, the value that faculty place
on research publications is likely to be impacted by their intrinsic academic interest, and the
perceived cost of undertaking research in relation to time required for teaching and other
administrative duties. In their study of three business schools in Gulf Cooperation Council
countries, the authors found that faculty preferred to allocate more time toward research and less
time toward teaching and service (Azad & Seyyed, 2007).
White, James, Burke, and Allen (2012) found that research “stars” individually place
high value on research, and that higher performing researchers value research more than lower
performing researchers. In a study on the research productivity of academic accountants, Levitan
and Ray (1992) found that there was an increasing emphasis on research productivity when
academic institutions conducted faculty appraisals for granting of tenure and making salary
decisions. The emphasis on research productivity by institutions, and the explicit linkage
between research productivity and rewards, would likely lead to higher utility value being
perceived by faculty for being research productive (Chen, Nixon, Gupta, & Hoshower, 2010).
In a study of accounting faculty from AACSB-accredited Colleges of Business, Chen,
Nixon, Gupta, and Hoshower (2010) identified several extrinsic rewards that relate to the
perceived value of research productivity, including tenure, promotion, salary raises, chaired
professorships, and reduced teaching loads. The authors also identified a number of intrinsic
rewards related to research productivity, including sense of attainment from peer recognition and
respect from students, personal need to stay current and contribute to academic fields, and
personal need for creativity/curiosity. The findings from survey responses of faculty suggested
that satisfying the needs for creativity/curiosity and staying current in academic fields were
RESEARCH PRODUCTIVITY FACTORS 34

relatively important outcomes to faculty at doctoral-granting programs, and that faculty placed
significantly greater importance on the role of research in satisfying their needs for
creativity/curiosity, among a list of 13 possible outcomes from being research productive (Nixon,
Gupta, & Hoshower, 2010).
Self-efficacy theory. Self-efficacy, the belief in one’s ability to succeed at particular
tasks, plays a major role in how a person approaches a task, and is a central concept within
social-cognitive theory. In pioneering the field of social-cognitive theory, Bandura (2005)
posited that humans operate in intentional, planned, and self-regulated ways, within the larger
context of social and cultural influences. According to social-cognitive theory, people will not be
motivated to take action if they do not believe that they can achieve the desired results (Bandura,
2000). In other words, self-efficacy impacts an individual’s motivation to undertake particular
tasks.
Pajares (2006) defined self-efficacy as the assessments made by people about their own
ability to learn or perform up to specified levels, which is a fundamental factor for their
motivation, sense of well-being, personal achievements, and self-regulation. According to the
author, self-efficacy is both personal and social, with experiences of success leading to higher
self-efficacy, and experiences of failure leading to lower self-efficacy. Information on self-
efficacy is obtained through four primary sources, namely (a) mastery experience, (b) vicarious
experience, (c) social persuasions, and (d) physiological reactions (Pajares, 2006).
Research faculty self-efficacy. Faculty’s beliefs about their skills and ability to produce
research publications in their fields could impact their research productivity, and a strong sense
of self-efficacy leads to higher levels of motivation and effort (Pajares, 2006). Blackburn and
Lawrence (1995) found that self-efficacy was a major factor accounting for variations in research
RESEARCH PRODUCTIVITY FACTORS 35

productivity of faculty, and that confidence in research abilities was closely related to faculty’s
research output. Azad and Seyyed (2007) identified faculty’s perceived satisfaction with their
academic career and past research achievements as one of the factors that could impact faculty
research productivity. With past successes increasing a person’s self-efficacy while failure
reducing it (Pajares, 2006), faculty’s research track record is likely to impact their sense of self-
efficacy toward being research productive. Formal research mentorship programs (Azad &
Seyyed, 2007), research role models (Hales, Shahrokh, & Servis, 2009), and research
collaborations (Chen, Nixon, Gupta, & Hoshower, 2010) could potentially help increase faculty
self-efficacy through feedback and modeling (Pajares, 2006).
Table 5 lists the motivational influences assumed for this study.
Table 5
Summary of Promising Research Practices for Faculty: Assumed Motivational Influences
Promising Practices
of Research
Literature Social Science
Equivalents
Motivation
Perceived value in producing
peer-reviewed research
publications. (Expectancy-
Value)

Azad & Seyyed, 2007;
Chen, Nixon, Gupta, &
Hoshower, 2010; Levitan &
Ray, 1992; White, James,
Burke, & Allen, 2012.

Eccles, 2006; Pintrich,
2003.

Intrinsic interest in conducting
research and producing
publications.

Azad & Seyyed, 2007;
Chen, Nixon, Gupta, &
Hoshower, 2010.
Markova & Ford, 2011;
Schraw & Lehman, 2009.
Belief that they have the
requisite ability and skills to
publish peer-reviewed research
within their respective fields.
(Self-Efficacy)
Azad and Seyyed, 2007;
Blackburn & Lawrence,
1995; Dundar & Lewis,
1998.
Anderman & Anderman,
2009; Bandura, 1989;
Pajares, 2006.

RESEARCH PRODUCTIVITY FACTORS 36

Organizational Influences
Clark and Estes (2008) identified organizational factors as the third critical dimension to
address business gaps and to improve organizational performance. This section contains a
literature review of organizational influences that are pertinent to increasing research
productivity of faculty members at the HKUST.
Organizational influences on performance can be analyzed using the Cultural Model-
Cultural Settings framework. Cultural models are the invisible and automated values, beliefs, and
attitudes embodied within organizations and individuals, while cultural settings are the visible
and tangible manifestations of cultural models found within organizations (Gallimore &
Goldenberg, 2001). According to this framework, learning and performance of an organization is
influenced by the interaction of organizational and individual cultural models, past and present
cultural settings, and individual beliefs, perceptions, and goals. Schein (2004) described culture
as an abstract yet powerful force within social and organizational settings, a representation of the
shared history, experiences, and learning of a given group, embodied in values and attitudes that
are passed on from generation to generation. Consequently, an understanding of cultural
elements is vital to effective leadership of organizations (Schein, 2004).
In the following sub-section, the organizational influences impacting faculty research
productivity are categorized and analyzed using the Cultural Model-Cultural Settings framework.
Cultural models for research productivity. Cultural models are mental schemas that
are shared within an organization or group of people, framing how they perceive and understand
their surrounding environments and situations (Gallimore & Goldenberg, 2001). Cultural models
are often taken for granted by members of a group, as the associated mental schema are familiar
and hence invisible and lacking salience to the members.
RESEARCH PRODUCTIVITY FACTORS 37

For a research-intensive university, the expected cultural model would be one where
research productivity and impact are highly valued at the organizational level, driven by a
fundamental belief that the creation and application of scholarly knowledge can contribute
significantly toward economic and social progress. At the faculty level, research productivity is
also expected to be highly valued by departments and individual faculty, as it reflects on their
scholarly capabilities and contributions to respective academic disciplines, and consequently
their academic reputation and accrued rewards.
Cultural settings for research productivity. Cultural settings are visible expressions of
the cultural model of an organization or group. These outward expressions of shared mental
schemas can be observed when individuals come together to engage in joint activity and to
accomplish tasks of value to the group (Gallimore & Goldberg, 1996).
In a study involving interviews of 150,000 employees from over 2,500 business units
across 24 different companies in 12 distinct industries, Buckingham and Coffman (1999)
identified the dimensions of performance expectations, recognition of good work performance,
and supportive work setting as among the most important factors impacting the productivity of
employees. Similarly, in the context of a research-intensive university, the cultural settings in the
dimensions of goals and expectations, performance incentives, and policies and practices were
expected to be among the most important factors impacting the productivity of faculty
researchers.
Expectations and goals. At the organizational level, setting clear performance
expectations and goals for faculty to conduct high-quality research could positively impact their
research productivity. One of the strongest drivers of work behavior is a sense of purpose,
observed through the general goal-orientation of individuals (Moran & Brightman, 2000). Goal
RESEARCH PRODUCTIVITY FACTORS 38

setting enhances learning and performance, and has been reported to occur with greater
frequency and consistency with higher achievers compared to low achievers (Dembo & Eaton,
2000). The setting of appropriately challenging goals facilitates the defining of desired futures
and outcomes, and the framing of action plans to achieve these outcomes (Denler, Wolters, &
Benzon, 2009). Having high performance expectations of success through the setting of goals
could positively impact learning and motivation (Eccles, 2006; Mayer, 2011). Faculty research
productivity could be influenced by organizational characteristics and factors (Jung 2012), and
academic institutions have increasingly emphasized their performance expectations on research
productivity, as such productivity enhances visibility and reputation of the institutions, thereby
attracting research grants, high quality faculty and students (Hu & Gill, 2000).
Performance incentives. Appropriate incentive systems which reward high-quality and
impactful research performance could impact faculty research productivity. According to Eccles
(2006), the perceived importance and value of a task affects the level of motivation, and work
that is seen to produce value is positively regarded. Improving organizational performance
involves the consideration of individuals’ motivations (Langley et al., 2009), and the recognition
of individuals’ performance could positively impact their motivation levels. Rewards, when
appropriately structured, could also provide a form of feedback to individuals on their level of
competence and skills (Pintrich, 2003), thereby aiding improvement efforts. In a study of
accounting faculty from business colleges, Chen, Nixon, Gupta, and Hoshower (2010) found that
faculty perceived strong linkages between research productivity and tenure and promotion, and
that universities were effectively leveraging on these perceptual linkages to motivate faculty to
be research productive. Other studies have also found that financial rewards could be used to
provide tangible incentives to influence faculty to allocate their time and effort toward research
RESEARCH PRODUCTIVITY FACTORS 39

activities (Hales, Shahrokh, & Servis, 2005; Hales, Shahrokh, & Servis, 2009).
Policies and practices. Organizational policies and practices have been identified as
factors influencing the climate of an organization, and consequently the performance of members
of the organization (Schneider, Brief, & Guzzo, 1996). Financial policies that provide adequate
resources and support for conducting rigorous research could positively impact faculty’s
production of research publications (Hales, Shahrokh, & Servis, 2005; Jung, 2012). Research
productivity could be enhanced through instituting practices that support faculty in securing
external grants and contracts, and by providing library, technology, and graduate student
resources to support academic research (Dundar & Lewis, 1998). Furthermore, policies allowing
faculty to have more time to conduct research through the reduction of course preparations and
teaching loads have been reported as contributing factors toward increased research productivity
among highly productive research faculty (White, James, Burke, & Allen, 2012).
Table 6 lists the organizational influences assumed for this study.
Table 6
Summary of Promising Research Practices for Faculty: Assumed Organizational Influences
Promising Practices
of Research
Literature Social Science
Equivalents
Organizational Culture
Clear goals and performance
expectations for conducting
high-quality research.
(Accountability)

Hu & Gill, 2000; Jung, 2012. Dembo & Eaton, 2000;
Denler, Wolters, &
Benzon, 2009; Mayer,
2011.
Appropriate incentive systems
which reward high-quality and
impactful research
performance. (Incentives)
Chen, Nixon, Gupta, &
Hoshower, 2010; Hales,
Shahrokh, & Servis, 2005;
Hales, Shahrokh, & Servis,
2009.

Eccles, 2006; Pintrich,
2003.
RESEARCH PRODUCTIVITY FACTORS 40

Supportive policies and
practices that support
performance. (Policies and
practices)
Dundar & Lewis, 1998;
Hales, Shahrokh, & Servis,
2005; Jung, 2012; White,
James, Burke, & Allen, 2012.
Schneider, Brief, &
Guzzo, 1996.

Conclusion
Faculty research productivity is one of the key criteria impacting institutional rankings
performance. In applying the gap analysis framework defined by Clark and Estes (2008), the
knowledge, motivation, and organizational factors influencing faculty research productivity were
identified. Through analyzing and validating these factors in the context of a top-ranked
institution, the HKUST, promising practices could be identified to improve faculty performance
in research, and consequently improve the rankings performance of universities.
The findings from the literature review as discussed in this chapter suggested that faculty
research productivity could be impacted by a range of knowledge, motivation, and organizational
influences, as summarized in Table 7. However, the literature did not explicitly address how
these influences operate in the context of top-ranked institutions, and in particular the
perspectives of faculty in such institutions regarding research productivity.
Table 7
Summary of Assumed Knowledge, Motivation, and Organizational Influences
Assumed Influences
Sources Knowledge Motivation Organization
Learning and
Motivation
Theory
• Need to know the list
of journals in
respective fields.
• Need to know the
emphasis and
evaluation criteria.
• Need to know the
steps necessary to
achieve outcomes.
• Need to know how to
• Need to see the value
in performing well.
• Need to have
intrinsic interest in
task.
• Need to believe that
they have the
necessary skills and
capabilities to
perform.
• Need
performance
results to be
highly valued by
the organization
and individual
faculty.
• Need to set
explicit
performance
RESEARCH PRODUCTIVITY FACTORS 41

plan approach to
manage workload.
goals.
• Need incentive
systems and
practices to
recognize and
reward
performance.
• Need supportive
policies and
practices to
facilitate
performance.
Related
Literature
• Need to stay current
with contemporary
issues in faculty’s
respective academic /
professional fields.
• Need to generate new
research ideas.
• Need to know the
required steps to take
for translating ideas
into research and
acceptance into peer-
reviewed
publications.
• Need to know how to
balance research
workload with
required teaching and
other responsibilities.
• Need to see value in
producing peer-
reviewed research
publications.
• Need to have
intrinsic interest in
conducting research
and producing
publications
• Need to believe that
they have the
requisite ability and
skills to publish peer-
reviewed research
within respective
fields.
• Need clear goals
and performance
expectations for
conducting high-
quality research.
• Need appropriate
incentive systems
which reward
high-quality and
impactful
research
performance.
• Need policies and
practices that
facilitate and
support research
publications.

The next chapter describes the actual study to validate these assumed influences on
faculty research productivity at the HKUST, and includes details about the research
methodology, the sample and population, instrumentation, data collection, and data analysis.
RESEARCH PRODUCTIVITY FACTORS 42

CHAPTER THREE: METHODOLOGY
This chapter describes the actual study to validate the assumed knowledge, motivation,
and organization influences on faculty research productivity at the HKUST. The chapter is
organized in four sections. The first section covers the research questions and methodology. The
second section describes the stakeholder population and sampling strategies. The third section
details the approach to data collection and instrumentation. The fourth section discusses the data
analysis approach for the study.
Purpose of the Project and Questions
The focus of this study is to identify the factors that were perceived by faculty to impact
research productivity at a highly-ranked university. The following research questions guided the
study:
1. What are the knowledge, motivation and organizational assets that faculty identify as
helping to increase their research productivity?
2. What solutions and recommendations in the areas of knowledge, motivation, and
organizational resources may be appropriate for solving the problem of practice at
another organization?
Conceptual and Methodological Framework
This study used the problem-solving approach defined by Clark and Estes (2008),
depicted in Figure 1. First, goals were defined at the organizational and stakeholder levels. This
was followed by an assessment of current performance, and subsequently the gaps between
desired goals and current performance were identified and quantified. A gap analysis framework
was then applied to identify the causes of the performance gaps, using the lenses of knowledge,
motivation, and organizational factors to classify and analyze these causes (Clark & Estes, 2008).
RESEARCH PRODUCTIVITY FACTORS 43

Figure 1. Problem-solving approach – Gap Analysis framework
In the context of this study, the organizational goal was to perform well in international
university rankings. Faculty research productivity is one of the key criteria contributing to
rankings performance (QS, 2016c; Times Higher Education, 2016c; Shanghai Ranking
Consultancy, 2016), and increasing faculty output in research was defined as the stakeholder
goal for the study. The conceptual framework for the study (illustrated in Figure 2) incorporated
the gap analysis framework defined by Clark and Estes (2008) to identify the knowledge,
motivation, and organizational factors influencing faculty research productivity. Through
analyzing and validating these factors, possible solutions were identified to improve faculty
performance in research, and thereby improve the rankings performance of the university.

Figure 2. Conceptual framework for this study
Current
Performance
Knowledge
Motivation
Organization
Identify &
Quantify Gaps
Evaluation
Identify
Causes
Identify &
Implement
Solutions
Performance
Goals
Rankings
Performance
Faculty Research
Productivity
Knowledge
Influences
Motivation
Influences
Organization
Influences
Declarative
Procedural
Metacognitive
Expectancy
Value
Self-
Efficacy
Goals
Resources
Incentives
Add’l Ranking Criteria
Teaching
International Outlook
Prizes and Awards
Reputation
Industry Income
Other Ranking Systems
# applicants
Endowment
Etc.
Macro Factors
Political pressures
Cultural models, beliefs
Funding, investment
Competitive landscape
Policies and regulation
Other Major
Factors
Workload –
teaching,
service
RESEARCH PRODUCTIVITY FACTORS 44

Assessment of Performance Influences
In Chapter Two, a number of assumed knowledge, motivation, and organizational
influences on faculty research productivity performance were identified. Faculty’s perspectives
on these assumed influences were assessed using a combination of surveys and interviews. The
survey items were designed to elicit ordinal ratings on the influences, while the interview items
explored the deeper underlying thinking and reasons for the responses (Creswell, 2014).
Knowledge Assessment
Through the literature review, four possible knowledge influences were identified, as
displayed in Table 8. Two of these influences pertained to declarative knowledge (factual and
conceptual), one influence pertained to procedural knowledge, and one pertained to
metacognitive knowledge. All four knowledge influences (Schraw & McCrudden, 2006) were
assessed through surveys to ascertain how faculty perceived their level of knowledge, and
through interview questions asking participants to describe the knowledge types and what could
be done to increase their knowledge.
Table 8
Assumed Influences and Assessment Approaches
Category Assumed Influence Assessment Approach
Knowledge • Stay current with contemporary issues in
faculty’s respective academic /
professional fields.
• Survey item 1
• Interview item 2, 2a
• Generate new research ideas • Survey item 2
• Interview item 2, 2b
• Know the required steps to take for
translating ideas into research and
acceptance into peer-reviewed
publications.
• Survey item 3
• Interview item 2, 2c
• Balance research workload with required
teaching and other responsibilities.
• Survey item 4, 14
• Interview item 2, 2d, 2e
RESEARCH PRODUCTIVITY FACTORS 45

Motivation • See value in producing peer-reviewed
research publications.
• Survey item 5
• Interview item 3, 3a, 3b,
4a, 4b
• Have intrinsic interest in conducting
research and producing publications.
• Survey item 6
• Interview item
• Believe that they have the requisite ability
and skills to publish peer-reviewed
research within respective fields.
• Survey items 7, 8, 12
• Interview item 4, 4c
Organizational • Institution provides clear goals and
performance expectations for conducting
high-quality research.
• Survey item 9
• Interview item 5, 5a, 5b
•
• Institution has effective policies and
practices to support research.
• Survey item 10
• Interview item 6
•
• Institution provides appropriate incentive
systems which reward high-quality and
impactful research performance.
• Survey item 11
• Interview item 7, 7a, 7b

Motivation Assessment
Through the literature review, three possible motivation influences were identified, as
displayed in Table 8. One of these influences pertained to expectancy-value, one pertained to
intrinsic interest, and one pertained to self-efficacy. The expectancy-value influence (Eccles,
2006) was assessed through a survey question to ascertain how faculty perceived the value of
publishing research, and interview questions to uncover the reasons that faculty saw value behind
conducting and publishing research and what they saw as the associated costs. The intrinsic
interest influence (Schraw & Lehman, 2009) was assessed through a survey question to ascertain
to what extent faculty enjoyed the process of conducting research and producing publications,
and interview questions to understand the underlying reasons for faculty’s intrinsic interest. The
self-efficacy influence (Pintrich, 2003) was assessed through survey questions to ascertain how
faculty perceived their ability to publish research, and interview questions to understand
faculty’s views about their strengths and weaknesses in research.

RESEARCH PRODUCTIVITY FACTORS 46

Organization/Culture/Context Assessment
Through the literature review, three possible organizational influences were identified, as
displayed in Table 8. One of these influences pertained to goal setting, one pertained to
supporting policies and practices, and one pertained to performance incentives. The goal-setting
influence (Dembo & Eaton, 2000) was assessed through a survey question to ascertain how
faculty perceived the clarity of institutional goals related to research productivity, and interview
questions to gain deeper understanding into the nature of these goals and expectations and how
they were communicated to faculty. The incentives influence (Pintrich, 2003) was assessed
through a survey question to ascertain faculty’s perception on the effectiveness of the incentive
system in encouraging research productivity, and interview questions to understand the types and
nature of incentives related to research productivity. The policies and practices influence
(Schneider, Brief, & Guzzo, 1996) was assessed through a survey question to ascertain how
faculty perceived the effectiveness of policies and practices in supporting research, and interview
questions to understand the associated enablers and barriers.
Participating Stakeholders and Sample Selection
While multiple stakeholders contributed toward the rankings performance of the
University, the study focused on the perspectives of research faculty members as they play a
major role in the research outcomes of the University, which is a key dimension in the major
ranking systems. Research faculty members at the University are organized into four Schools in
the fields of Science (SSCI), Engineering (SENG), Business and Management (SBM),
Humanities and Social Sciences (SHSS). The faculty across the Schools are highly productive
with regard to research, and have been ranked amongst the top institutions globally (QS, 2016b;
Shanghai Ranking Consultancy, 2016; Times Higher Education, 2016a). Therefore, the
RESEARCH PRODUCTIVITY FACTORS 47

stakeholder population for this study was all HKUST’s substantiation-track faculty members.
Survey Sampling Criteria
Criterion 1. Academic staff from HKUST. This criterion focused the study on the
sample population of interest.
Criterion 2. Academic staff that are substantiated (tenured) or are on the substantiation
track (tenure track). This criterion narrowed the sample to staff who were meant to be active in
academic research.
Survey Recruitment Strategy
The survey was administered to all substantiation-track faculty from HKUST, stratified
by academic departments and faculty rank (assistant professor, associate professor, and
professor/chair professor) to yield reasonable representation across the academic disciplines and
career stages of faculty. The sample criteria limited the sample to the population of interest,
namely faculty from across the four Schools who were expected to be actively involved in
academic research.
Interview Sampling Criteria
Criterion 1. Academic staff from HKUST. This criterion focused the study on the
sample population of interest.
Criterion 2. Academic staff who are substantiated (tenured) or are on the substantiation
track (tenure track). This criterion narrowed the sample to staff who are meant to be active in
academic research.
Criterion 3. Academic staff who had completed the survey and indicated their
willingness to be interviewed, or academic staff who were nominated by the Deans of the
Schools. The intent was to use interviews to gain deeper insights into the survey responses of
RESEARCH PRODUCTIVITY FACTORS 48

faculty.
Interview Recruitment Strategy
At the end of the survey phase, a subset of faculty who had completed the survey
indicated that they were willing to be interviewed. In addition, the Deans of the four Schools
were invited to provide names of faculty members whom they felt were able to contribute
representative views at the different faculty ranks. A maximum variation approach (Merriam &
Tisdell, 2009) was adopted to derive insights into the range of perspectives of faculty across
career stages (assistant professor, associate professor, professor/chair professor). The plan was to
interview one faculty member at each of the three seniority levels across the four Schools, with a
total of 12 interviews to be conducted. A snowball sampling strategy was planned for, to ask
faculty being interviewed to recommend other faculty to approach for interviews, in the event
that the desired respondent profile and numbers could not be attained.
Data Collection and Instrumentation
A mixed methods approach was used for data gathering and analysis. According to
Creswell (2014), the combining of both quantitative and qualitative research approaches draw on
the strengths of each approach, while potentially minimizing the associated limitations of each,
and hence is suited for studying complex research topics. The study adopted an explanatory
sequential approach, using surveys in a quantitative study phase, followed by interviews in a
qualitative study phase. Surveys allow for wide coverage to research a large sample with the
potential for generalizability of findings (Fink, 2013), while interviews allow for developing
more nuanced understanding of phenomena, and to get at the meaning behind observed
phenomena (McEwan & McEwan, 2003). Under the explanatory sequential approach,
quantitative findings can be used to inform the selection of participants and types of questions
RESEARCH PRODUCTIVITY FACTORS 49

for the qualitative phase, while the findings from the qualitative findings can be used to further
explain quantitative responses (Creswell, 2014).
A survey was administered to all substantiation-track faculty to establish an overall
understanding of research faculty’s perceptions of the factors that were deemed to be important
regarding research productivity. Drawing from the survey responses and faculty names suggested
by the Deans, a smaller purposive sample of faculty was interviewed to solicit deeper
explanations of faculty perspectives regarding research productivity.
Surveys
As part of the quantitative phase of the explanatory sequential design of the study
(Creswell, 2014), a survey instrument (see Appendix D) was administered to all faculty at
HKUST who fulfilled the sampling criteria (N=468). After factoring in faculty that opted out,
those who were leaving the university or yet to join the university, and those with bounced
emails, the actual sample size was reduced to 456.
The survey comprised a total of 20 questions. Eleven questions were designed to assess
the perceived knowledge, motivation, and organizational factors assumed to influence faculty
research productivity. These assumed influences were drawn from the literature review, and
phrased as questions in a purposefully constructed survey instrument. Seven background
questions were included, comprising of three demographic questions, three self-evaluation
questions, and one question on work time allocation. These background variables were used to
stratify the results to test whether there were group-based differences in perceptions, in line with
Jung’s (2012) findings that research productivity is influenced by personal and institutional
characteristics. Two final questions were included to identify faculty who were willing to be
interviewed, and those who wanted a copy of the findings of the study. For participants who
RESEARCH PRODUCTIVITY FACTORS 50

responded positively to either or both questions, two additional questions were presented to
collect their names and contact information.
Since the survey instrument was novel and purposefully constructed, it had to be pilot-
tested to establish the reliability and validity of the survey instrument, before being used for the
study (Fink, 2013). The pilot-test was meant to assess the clarity of the questions and the survey
format, and to ensure that the questions sufficiently reflected the constructs being studied. Three
academics from outside of HKUST were invited to look through the survey instrument, and to
provide specific feedback on the clarity and design of the questions. Two of the three academics
accepted the invitation, and their feedback was used to refine the instrument, before the actual
administration of the survey to the target sample. In addition, having a large sample size in the
survey increased the reliability of the findings, as larger samples help to reduce sampling errors
and to maximize the generalizability of findings (Fink, 2013).
An invitation email (see Appendix A) was sent to all substantiation-track faculty through
an email distribution list compiled from the faculty directories published on the HKUST website
(http://www.ust.hk), with a URL link provided to access the online survey form. Faculty
responses to the survey were collected anonymously. At the end of the survey, faculty who were
willing to participate in additional interviews were asked to provide their names and contact
information, without linking their names and contact information to their survey responses.
Faculty were asked to respond to the survey within two weeks. A reminder email was sent out to
faculty at the end of the two weeks, with one final email reminder sent at the end of the third
week, and the survey was closed at the end of the fourth week.
Interviews
Interviews were chosen for the qualitative explanatory phase of the study, to offer rich
RESEARCH PRODUCTIVITY FACTORS 51

descriptions and deep insights into the individual perspectives of faculty members (Creswell,
2014). The population for the interview was substantiation-track faculty who were expected to be
substantively engaged in research, and who had indicated their willingness to be interviewed,
either through their survey responses or nominations by the Deans. For faculty who indicated
their willingness to be interviewed in their survey responses, emails were sent to them to set up
the interviews (see Appendix B). For faculty who were nominated by their Deans, invitation
emails were sent to them to secure their willingness to be interviewed, and to set up the
interviews for those who responded positively (see Appendix C). A total of nine formal
interviews were conducted with this sample, with two from SSCI, one from SENG, two from
SBM, and four from SHSS. Among the faculty interviewed, there were two chair professors, four
professors, two associate professors, and one assistant professor. The interviews lasted around an
hour each, and were mostly conducted face-to-face at faculty’s offices, for the convenience of
participating faculty and to ensure privacy of the interview conversations. However, due to the
specific needs of particular faculty, two of the interviews were conducted in public spaces, and
one interview was conducted via video-conferencing. With permission from the participating
faculty, all the interview sessions were audio recorded and subsequently transcribed.
An interview protocol (see Appendix E) was used to guide the interviews. The protocol
was comprised of eight questions, with a number of probing questions associated with each
question. The first question was intended to solicit faculty’s overall perspective regarding
research productivity, and also served as a warm-up question to ease the participants into the
interview. The following six questions were intended to deepen the understanding of faculty’s
perspectives on the knowledge, motivation, and organizational factors that influence research
productivity. The final question was meant for faculty to provide concluding remarks and to
RESEARCH PRODUCTIVITY FACTORS 52

round-up the interview.
In designing the interview protocol, care was taken to phrase the questions in a manner
that faculty participants were likely to understand and respond to, while ensuring that the
interview questions were aligned with the research questions of the study. A standardized open-
ended interview design was adopted to keep the interviews highly focused and time-efficient,
and to facilitate comparisons across participants (Patton, 2002). A pilot-test of the interview
protocol was conducted, to ensure that the questions worked as intended (Maxwell, 2013). The
academics from outside of HKUST asked to review the survey design were also asked to review
the interview protocol for clarity and appropriateness of the questions. Feedback from the pilot
was used to refine the interview protocol. While the small sample size and qualitative nature of
the interviews did not allow for generalizability of the findings, a purposive sample with
representation across schools and academic ranks increased the transferability of the findings to
the broader institutional context (Merriam & Tisdell, 2009).
One potential source of bias was that the researcher conducting the interviews was
formerly a senior administrator at the University reporting directly to the President, and the
perceived power differential might have affected the responses of faculty. However, as the
researcher had left that position for a year, and faculty were accorded high levels of autonomy in
their research activities, the possibility of such a bias was reduced. In addition, triangulation was
used across the qualitative and quantitative data collection methods to assess the validity of the
findings (Maxwell, 2003).
Data Analysis
Upon closing the survey, the response data were downloaded from the online survey
system into a spreadsheet data file. Data cleaning was carried out to eliminate errors and to
RESEARCH PRODUCTIVITY FACTORS 53

improve data quality (Rahm & Hong, n.d.), by removing fields that were irrelevant to the study
analysis, and excluding survey responses with less than half of the survey questions completed.
The free-text entries on the faculty members’ academic fields were modified to follow the
standardized acronyms representing the respective academic departments, and a new column was
created to map the academics departments to the respective schools they belonged to.
Descriptive statistical analysis was conducted on the cleaned survey data. The overall
response rate was calculated, and the respondent percentages by schools and faculty ranks were
computed and compared with the university-aggregate faculty percentages. Frequency counts
were derived for the ordinal survey responses, with item response rates ranging from 81% to
100%. The percentages of respondents who indicated “strongly agree,” “agree,” and “somewhat
agree” were presented in relation to those who indicated “strongly disagree,” “disagree,” and
“somewhat disagree.” For survey items that utilized ratio scales, the means and standard
deviations were presented to identify average levels of responses. Further analysis was carried
out to compare the frequency counts and averages across schools and faculty rank, to test
whether there were differences across the stratified groupings.
For the interviews, data analysis began during data collection, in the form of written
analytic memos after each interview to document thoughts, concerns, and initial conclusions
about the data in relation to the study’s conceptual framework and research questions. Upon
leaving the field, the audio recordings from interviews were transcribed through a third-party
service provider, and the transcripts produced were further validated for accuracy by the
researcher. Subsequently, the transcripts were coded using the approach outlined by Merriam and
Tisdell (2016) and Corbin and Strauss (2008). In the first phase of analysis, open coding was
carried out by looking for empirical codes and applying a priori codes from the conceptual
RESEARCH PRODUCTIVITY FACTORS 54

framework. A second phase of analysis was conducted where empirical and a priori codes were
aggregated into analytic/axial codes. In the third phase of data analysis, pattern codes were
identified, with emergent themes constructed in relation to the conceptual framework and study
questions.
Credibility and Trustworthiness
Credibility and trustworthiness of a study pertains to whether the study produces results
that make sense and that are genuine reflections of reality (Miles, Huberman, & Saldaña, 2014).
Merriam and Tisdell (2009) state that researchers uphold credibility by conducting studies
ethically, providing descriptions with sufficient detail to show how they arrived at the
conclusions, adopting rigorous and careful design of instruments, and explicitly acknowledging
possible biases that could threaten the validity of the findings. These strategies were undertaken
to increase the credibility and trustworthiness of this study.
The survey and interview instruments used in this study were pilot-tested with academics
from outside of HKUST, to increase the validity of the questions and to maximize the reliability
of the responses. Through the pilot-tests, the instruments were refined so as to measure what was
intended, and the questions were revised to be clear and unambiguous to different respondents.
As the reflexivity of the researcher impacts the research process (Merriam & Tisdell, 2009),
there was ongoing reflection during the study to account for biases that the researcher might have
brought into the process, and to acknowledge these biases and the steps taken to mitigate them.
Miles, Huberman, and Saldaña (2014) further recommend checking for
representativeness of the sample population, checking for researcher biases, triangulating
findings across data sources, and getting participants’ feedback to validate the findings. For the
quantitative survey, a universal sampling approach was adopted, and having a good response rate
RESEARCH PRODUCTIVITY FACTORS 55

increased the representativeness of the sample. For the qualitative interviews, participants were
checked for representativeness across Schools and academic ranks. Triangulation was used to
compare findings across methods, and outliers and unexpected findings were further
investigated. Finally, member checking was carried out to ensure that qualitative findings
accurately reflected the views of the participants.
Role of Investigator
As the study involved extensive interactions with human subjects, the design and conduct
of the study must be carried out in an ethical manner, with due consideration to protect
participants from harm, respect participants’ privacy, seek informed consent, and to consider the
issues of deception (Merriam & Tisdell, 2009).
Individual informed consent was sought before commencing the study, to ensure that
participants had all the necessary information to choose whether to participate. In line with
recommendations made by Glesne (2011), participants were informed about the entirely
voluntary nature of their participation, the potential physical and emotional risks to their well-
being, and their right to withdraw at any point in time during the study without any negative
consequences. Participants’ rights to privacy must be upheld, by respecting the confidentiality of
their responses, and taking steps to ensure that confidentiality promises are kept (Glesne, 2011).
In this study, participants were assured that all discussions and participants’ responses would be
kept confidential, and that the reported findings would not be individually identifiable. Prior to
the interviews, permission was sought from participants to audio record the interviews, with the
recordings destroyed once they had been transcribed. Survey data were collected through third-
party survey tools, and stored securely on third-party servers. To avoid any coercion, no
incentives was offered for participation in the study, and the invitation to participate came
RESEARCH PRODUCTIVITY FACTORS 56

directly from the researcher rather than from university administrators. An information sheet
containing the information above was distributed to all participants. Data collection for the study
only commenced after the approval by the University of Southern California’s (USC)
Institutional Review Board (IRB), with the approval recognized by HKUST as being sufficient
for the study.
As a former senior administrator at the University reporting directly to the President, and
my current position as a senior advisor at the University, there could have been some confusion
among faculty about my role as a researcher. I made it clear to participants that I was conducting
the study as a doctoral student at USC, and that I was not representing HKUST where the study
was being conducted. While my past experience working at the University aided my
understanding of the context for my study, these experiences might lead to assumptions and
biases in my observations and listening during the research process, which Maxwell (2013)
ascribes to be due to the values and expectations that researchers bring into their research studies.
I tried my utmost to maintain neutrality and open-mindedness, and to explicitly acknowledge any
potential biases in my findings.
Summary
Chapter Three laid out the methodological approach for the study to identify factors
perceived by faculty to influence research productivity at a highly-ranked university. These
assumed influences were categorized under knowledge, motivation, or organizational assets,
using the Clark and Estes (2008) framework. Chapter Four discusses the results from the analysis
of faculty’s perspectives ascertained through surveys and interviews. Research-based
recommendations drawing on the study findings and research literature are discussed in Chapter
Five.
RESEARCH PRODUCTIVITY FACTORS 57

CHAPTER FOUR: RESULTS AND FINDINGS
The purpose of this project is to study HKUST’s assets in the areas of knowledge,
motivation, and organizational resources, in relation to increasing faculty research productivity
and thereby contributing to improved rankings performance for the University. The stakeholder
group focused on in this project was the faculty members of HKUST. As such, the questions that
guided the promising practices study were the following:
1. What are the knowledge, motivation and organizational assets that faculty identify as
helping to increase their research productivity?
2. What solutions and recommendations in the areas of knowledge, motivation, and
organizational resources may be appropriate for solving the problem of practice at
another organization?
A gap analysis approach (Clark & Estes, 2008) with mixed-methods data gathering and
analysis (Creswell, 2014) was utilized to study faculty members’ knowledge, motivation and
organizational assets that enabled them to be research productive. Drawing on the assumed
knowledge, motivation, and organizational influences on faculty research productivity
performance identified in Chapter Two and summarized in Table 7, faculty’s perspectives on
these assumed influences were assessed using a combination of surveys and interviews. The
survey items were designed to elicit ordinal ratings on the influences, while the interview items
examined the deeper underlying thinking and reasons for the responses.
This chapter describes the results and findings from the study. The chapter is organized in
five sections. The first section reports on the profile of the participants in the study. The next
three sections describe the survey results and interview findings regarding the assumed
knowledge, motivation, and organizational influences on faculty research productivity. The final
RESEARCH PRODUCTIVITY FACTORS 58

section summarizes the overall results and findings from the surveys and interviews.
Participating Stakeholders and Response Analysis
A survey was administered to 456 substantiation-track faculty at HKUST, to establish an
overall understanding of their perceptions regarding the factors that they deemed to be important
for research productivity. Faculty were surveyed across the four schools at HKUST, namely the
School of Science (SSCI), School of Engineering (SENG), School of Business Management
(SBM), and School of Humanities and Social Science (SHSS); and across four faculty ranks,
namely chair professor, professor, associate professor, and assistant professor. The population
for the interview was the substantiation-track faculty who were expected to be substantively
engaged in research, and who had indicated their willingness to be interviewed, either through
their survey responses or nominations by their Deans.
A total of 132 faculty responded to the survey, yielding a response rate of 28.9%. After
removing responses with less than half of the survey questions completed, there were 113
useable responses, representing a response rate of 24.8%. A total of nine formal interviews were
conducted, with two from SSCI, one from SENG, two from SBM, and four from SHSS. Among
the faculty interviewed, there were two chair professors, four professors, two associate
professors, and one assistant professor. Seven of the faculty were interviewed in-person at the
HKUST campus, one faculty was interviewed in-person at an off-campus location, and one
faculty was interviewed via video-conference. The faculty interviewed are referred to by
pseudonyms in the following analysis.
Table 9a details the profile of respondents across the schools and faculty ranks, and Table
9b lists the faculty interviewed with the assigned pseudonyms. The respondent sample for the
quantitative survey was deemed to be sufficiently representative for comparison of differences
RESEARCH PRODUCTIVITY FACTORS 59

between the University-aggregate and stratified categories, with the proportion of respondents in
the stratified categories falling within +/- 6% from the University profile. The respondent sample
for the qualitative interviews was small, with moderate representation across the schools and
faculty ranks. The SENG and assistant professor categories were under-represented, while the
SHSS and professor categories were over-represented, with more than +/- 10% difference from
the University profile. While the interview sample was deemed to be insufficient for
generalizability and comparison across the stratified categories, the interview responses were
used to deepen the understanding of the underlying reasons for the quantitative results.
Table 9a
Profile of Survey and Interview Respondents
Surveys Interviews University
Profile

N % N %
School
SSCI 32 33.0% 2 22.2% 27.6%
SENG 31 32.0% 1 11.1% 36.5%
SBM 22 22.7% 2 22.2% 24.1%
SHSS 12 12.4% 4 44.4% 11.7%
Faculty Rank

Chair Professor 20 18.7% 2 22.2% 14.8%
Professor 34 31.8% 4 44.4% 33.7%
Associate Professor 24 22.4% 2 22.2% 28.5%
Assistant Professor 29 27.1% 1 11.1% 23.0%

Table 9b
List of Interview Respondents
Faculty
(Pseudonym) Interview Date Mode / Location Faculty Rank
Dr. Charles December 12, 2016 In-person / HKUST Professor
Dr. Xinwang December 12, 2016 In-person / HKUST Associate Professor
Dr. Aravin December 13, 2016 In-person / HKUST Professor
Dr. Adeline December 13, 2016 In-person / HKUST Associate Professor
RESEARCH PRODUCTIVITY FACTORS 60

Dr. Jingjing December 13, 2016 In-person / HKUST Professor
Dr. Felicia December 13, 2016 In-person / HKUST Assistant Professor
Dr. Junhao December 16, 2016 In-person / HKUST Professor
Dr. Daniel December 22, 2016 Video-Conference Chair Professor
Dr. Brandon January 13, 2017 In-person / Off-campus Chair Professor

In the analysis of the survey results, the means and standard deviations of the responses
were calculated and presented at the University-level. Given the 6-point Likert scale that was
used to assess faculty perceptions, the mean scores were out of a maximum of six. Influences
with mean scores of five and above were considered as supported, and this corresponded with the
“agree” (response value of five) and “strongly agree” (response value of six) categories on the
Likert scale. The survey responses were further analyzed by school and academic rank, and
presented in a separate table (see Table 14). The mean scores of each school and academic rank
were compared with the University mean, and two-tailed t-tests assuming unequal variances
were applied to determine significance at the p<.05 and p<.01 levels.
For the analysis of the interview findings, transcripts were coded using the qualitative
analysis approach outlined by Merriam and Tisdell (2016) and Corbin and Strauss (2008). In the
first phase, open coding was carried out by looking for empirical codes and applying a priori
codes from the conceptual framework. A second phase of analysis was conducted where
empirical and a priori codes were aggregated into analytic/axial codes. In the third phase of data
analysis, pattern codes were identified, with emergent themes constructed in relation to the
conceptual framework and study questions. These themes are discussed in the following sections,
with relevant quotes from the interviews to support the identified patterns and themes.
Results and Findings for Knowledge Assets
The survey solicited faculty’s ratings on their perceived competence across the four
knowledge types, defined by Krathwohl (2002) as declarative-factual, declarative-conceptual,
RESEARCH PRODUCTIVITY FACTORS 61

procedural, and metacognitive knowledge. However, a true knowledge assessment would entail
developing methods and tools to measure learners’ use of mental schemas to recognize and
retrieve solutions to problems (Kalyuga & Sweller, 2004), which was impractical in the design of
this study. Hence, triangulation was used to increase the validity of the findings (Maxwell,
2013). The quantitative results from the survey on what faculty perceived to be their knowledge
assets were corroborated with the qualitative findings and themes about these knowledge assets
from the interviews. Survey items with mean scores of 5.0 and above were regarded as being
supported. The frequency, nature, and intensity of the responses from the interviewees were
analyzed in determining the support level from the interview findings. Knowledge assets that
were supported in both the surveys and interviews were regarded as validated factors that helped
faculty to be research productive.
Knowledge of Research Findings in Academic Fields
Knowledge of research findings in academic fields was identified as a possible
knowledge asset that helped to increase faculty research productivity. This finding is consistent
with Alexander, Schallert, and Hare’s (1991) findings that faculty research productivity is
impacted by declarative knowledge influences, which include factual knowledge.
Survey results. The survey results supported the declarative-factual knowledge
influence, with an overall mean score of 5.3 out of a 6-point scale, and the mean scores across
the schools and faculty ranks were all above 5.0.
Interview findings. The factors mentioned by faculty in the interviews to be of impact to
their asset of knowledge of research findings in their academic fields are illustrated in Figure 3.
The asset is listed in the center bubble, and the related factors mentioned in the interviews are
depicted in separate bubbles around the center bubble. Factors that are deemed to strengthen the
RESEARCH PRODUCTIVITY FACTORS 62

asset is marked as positive (+), while factors that are deemed to weaken the asset are marked as
negative (-). Factors that are deemed to have a mixed effect on the asset are marked as neutral or
mixed (=).

Figure 3. Factors impacting knowledge of research findings in academic fields. This figure
illustrates the positive (+), negative (-), and mixed (=) factors mentioned by faculty in the
interviews.

Faculty described the need to keep up-to-date with the knowledge in their respective
academic fields through various approaches. One common approach was through reading the
literature, in particular academic journals and books, as faculty deemed that it was important to
“know [their] research area really, really well, in terms of [their] understanding of the prior
literature, … and know what [they] are working on, [and] how it relates to the existing work”
(Dr. Adeline). Another approach was to engage with the international academic community by
attending international conferences, and through social media that had proliferated across the
world. For example, Dr. Charles shared that there was a need for “junior faculty to be more
engaged with the overseas, worldwide academic community, … in order to stay fresh and
Knowledge
of Research
Findings
+
Read
research
literature
Engage
internatonal
academic
community
Update
teaching
courses
Serve as
academic
reviewer
RESEARCH PRODUCTIVITY FACTORS 63

current,” and Dr. Brandon shared that by “connect[ing] with enough people in different
continents, … [faculty] know what’s going on.” The updating of teaching courses and serving as
academic reviewers were also identified as ways to stay up-to-date with academic developments
in their fields. Dr. Daniel mentioned that “preparing the courses forces [one] to go back and look
at what’s new.” Dr. Brandon mentioned that by accepting invitations from publishers to review
papers, faculty “get a sense of what people are doing, ... [and] this is a network that keeps [them]
abreast of what’s going on in the field.”
Thus, consistent with findings from Alexander, Schallert, and Hare (1991) and Azad and
Seyyed (2007), the interview findings supported the assertion that faculty’s knowledge about
their academic disciplines gained through efforts to stay current with the developments in their
fields helped them to be research productive.
Knowledge to Generate New Research Ideas
Conceptual knowledge to generate new research ideas was identified as another possible
knowledge asset that helped to increase faculty research productivity. This finding is consistent
with Azad and Seyyed’s (2007) description of declarative-conceptual knowledge used by faculty
to generate research ideas, conduct research, and develop research grant proposals; and Jung’s
(2012) finding that such conceptual knowledge impacts faculty’s research approaches and
research productivity.
Survey results. The survey results supported the declarative-conceptual knowledge
influence, with an overall mean score of 5.4 out of a 6-point scale, and the mean scores across
the schools and faculty ranks were all above 5.0.
Interview findings. The factors mentioned by faculty in the interviews to be of impact to
their asset of knowledge to generate new research ideas are illustrated in Figure 4. The asset is
RESEARCH PRODUCTIVITY FACTORS 64

listed in the center bubble, and the related factors mentioned in the interviews are depicted in
separate bubbles around the center bubble. Factors that are deemed to strengthen the asset is
marked as positive (+), while factors that are deemed to weaken the asset are marked as negative
(-). Factors that are deemed to have a mixed effect on the asset are marked as neutral or mixed
(=).

Figure 4. Factors impacting knowledge to generate new research ideas. This figure illustrates the
positive (+), negative (-), and mixed (=) factors mentioned by faculty in the interviews.

Faculty shared that interacting and collaborating with other academics and graduate
students were good sources of conceptual knowledge for generating new research ideas (Dr.
Jingjing; Dr. Junhao). In further support of this point, Dr. Charles said that senior faculty should
serve as advisors to postgraduate students, “because postgraduate students are actually a big
source of inspiration,” and Dr. Adeline emphasized the need for “finding more valuable co-
authors … to discover new ideas in new areas.” Exploring the interdisciplinary nexus across
academic fields was also identified as a good avenue for new research ideas, and there was a
“need to open up [one’s] mind, … to really embrace the interdisciplinary ideas” (Dr. Xinwang).
Knowledge
to Generate
New
Research
Ideas
+
Interact,
collaborate
with
academics
Advise post-
graduate
students
Explore
inter-
disciplinary
areas
Keep in
touch with
the field
research site
RESEARCH PRODUCTIVITY FACTORS 65

One interviewee illustrated his experience as follow:
Going to meetings with people that I actually don't know that well, but they are kind of in
related topics, and you find out what else is going on in their fields. … Initially it doesn't
look like it is relevant, but [then the] light bulb clicks. (Dr. Charles).
Keeping in touch with developments in the research sites was identified as another approach to
acquire conceptual knowledge. Dr. Daniel described how “traveling around in China to get a
sense of what are the trends” helped him with generating new research topics. He further
elaborated:
Going back to a field research site that I have been working in and visiting regularly since
the early 80s, following that same location over time and meeting people that I knew, I
can get a sense of what was changing in their lives, what were the political issues [and]
the social issues that were affecting them. (Dr. Daniel).
Thus, consistent with findings from Jung (2012) and Azad and Seyyed (2007), the
interview findings supported the assertion that faculty’s conceptual knowledge to generate new
research ideas helped them to be research productive.
Knowledge of Procedures to Publish Peer-Reviewed Articles
Procedural knowledge on how to publish peer-reviewed articles was identified as a
possible knowledge asset helping to increase faculty research productivity. This finding is
consistent with Azad and Seyyed’s (2007) identification of procedural knowledge on how to go
about turning research ideas into publications as a competency related to research productivity.
Survey results. The survey results supported the procedural knowledge influence, with
an overall mean score of 5.4 out of a 6-point scale, and the mean scores across the schools and
faculty ranks were all above 5.1.
RESEARCH PRODUCTIVITY FACTORS 66

Interview findings. The factors mentioned by faculty in the interviews to be of impact to
their asset of knowledge of procedures to publish peer-reviewed articles are illustrated in Figure
5. The asset is listed in the center bubble, and the related factors mentioned in the interviews are
depicted in separate bubbles around the center bubble. Factors that are deemed to strengthen the
asset is marked as positive (+), while factors that are deemed to weaken the asset are marked as
negative (-). Factors that are deemed to have a mixed effect on the asset are marked as neutral or
mixed (=).

Figure 5. Factors impacting knowledge of procedures to publish peer-reviewed articles. This
figure illustrates the positive (+), negative (-), and mixed (=) factors mentioned by faculty in the
interviews.

Faculty consistently mentioned the need to be knowledgeable about the publication
review process, to understand how to frame research topics in ways that appealed to publication
reviewers, and to be aware of the human dynamics involved in the publication process. For
example, Dr. Aravin mentioned the need to know “how to respond to reviews, and how to
respond to the review process,” to understand the reviewers’ criteria for “publishing a paper, …
Knowledge
of How to
Publish Peer-
Reviewed
Articles
+
Know the
publication
review
process well
Frame
research
topic to
appeal to
reviewers
Being aware
of the
politics and
human
dynamics
Tightly-
structured
process
leading to
incremental
research
RESEARCH PRODUCTIVITY FACTORS 67

[and to] check all the boxes.” Dr. Felicia talked about the importance of “knowing how to frame
[one’s] research in the right way, [so] that it appeals to the right people who are going to be
chosen as [one’s] reviewers.” She added that such knowledge could be acquired through
vicarious observations and modeling, by “co-author[ing] with more experienced scholars, …
observing them [and] how they frame [their research].” Dr. Aravin discussed the need to
understand and navigate the politics involved in the review process, observing that “the people
who publish a lot are extremely aware of that [the review process], and they know how to do that
without even appearing to be lawyers.”
However, the tightly-structured publication review process was seen to limit the
creativity of academics, given the pressure on faculty to conform to standard checklists in a bid
to get their research published. Dr. Aravin lamented that academics were “becoming very good
at producing research about … a bunch of checkboxes,” resulting in published papers that
yielded only “incremental addition to knowledge,” and hence would “tend to not get cited simply
because they are so incremental.”
Despite some misgivings about the research publication process, the interview findings
supported the assertion that faculty’s procedural knowledge on how to publish peer-reviewed
articles helped them to be research productive, consistent with findings from Azad and Seyyed
(2007).
Knowledge of Managing Workload
Metacognitive knowledge on managing workload across research, teaching, and other
activities was identified as a possible knowledge asset helping to increase faculty research
productivity. This assertion is consistent with Levitan and Ray’s (1992) finding that the ability to
effectively manage time was the most important factor for faculty to be research productive, and
RESEARCH PRODUCTIVITY FACTORS 68

White, James, Burke, and Allen’s (2012) finding that greater knowledge of time management
skills was associated with high-performing researchers.
Survey results. The survey results did not support the assertion that metacognitive
knowledge was an asset that helped HKUST faculty to be research productive, with an overall
mean score of 4.9 out of a 6-point scale falling marginally below the 5.0 validation mark. The
mean scores across the schools and faculty ranks ranged from 4.6 to 5.3.
Interview findings. The factors mentioned by faculty in the interviews to be of impact to
their asset of knowledge of managing workload are illustrated in Figure 6. The asset is listed in
the center bubble, and the related factors mentioned in the interviews are depicted in separate
bubbles around the center bubble. Factors that are deemed to strengthen the asset is marked as
positive (+), while factors that are deemed to weaken the asset are marked as negative (-).
Factors that are deemed to have a mixed effect on the asset are marked as neutral or mixed (=).

Figure 6. Factors impacting knowledge of managing workload. This figure illustrates the
positive (+), negative (-), and mixed (=) factors mentioned by faculty in the interviews.

Faculty shared that significant time commitment was required to be research productive.
Dr. Xinwang shared that faculty “have to work overtime, … [and] to work long hours.” He
Knowledge of
Managing
Workload
-
Work
overtime,
long hours
Manage
teaching load
Balanced
approach
across duties
RESEARCH PRODUCTIVITY FACTORS 69

added that to be able to devote sufficient time to research, faculty must find ways to manage their
teaching load, as “teaching really takes time,” and one approach that he suggested was to pursue
research work more intensively “during the summer when [faculty] are not teaching.” While
research productivity was an important institutional goal, a balanced approach had to be struck
across the various demands on faculty time, and faculty must not “spend all of [their] time doing
research and ignore other responsibilities” (Dr. Charles).
Although the findings from Levitan and Ray (1992) and White, James, Burke, and Allen
(2012) indicate that managing workload and time management were important factors for faculty
to be research productive, the interview findings did not fully support the assertion that
metacognitive knowledge in the form of workload management was an asset that helped HKUST
faculty to be research productive.
Synthesis of Results and Findings for Knowledge Assets
In the context of this study, the specific knowledge assets examined were declarative,
procedural, and metacognitive knowledge. The declarative knowledge assets studied were
faculty’s factual knowledge about their academic disciplines gained through efforts to stay
current with the developments in their field, and conceptual knowledge to help faculty generate
new research ideas. The procedural knowledge asset studied was the steps necessary to produce
peer-reviewed publications, and the metacognitive knowledge asset studied was on managing
workload across competing demands for faculty time.
As shown in Table 10, among the knowledge influences tested, three of the knowledge
types were supported, namely declarative-factual, declarative-conceptual, and procedural, with
mean scores ranging from 5.3 to 5.4, and standard deviations ranging from 0.7 to 0.8. Across
schools and faculty ranks, the SENG had significantly higher mean scores in the declarative-
RESEARCH PRODUCTIVITY FACTORS 70

factual and procedural knowledge influences, as shown in Table 14. There were no other
significant differences on knowledge influences across the schools and faculty ranks. The
interview findings for these three knowledge types further supported the importance of these
knowledge assets in helping faculty to be research productive. Hence, these three knowledge
types were considered as validated, as shown in Table 10.
Table 10
Survey and Interview Results for Knowledge Influences
Mean (SD)

HKUST Validated?
I am up-to-date with the research findings in my
academic field. (F)
5.3 (0.7)

Yes
I have been successful in generating new research
ideas. (C)
5.4 (0.8) Yes
I have the knowledge on how to get my research
published in peer-reviewed publications. (P)
5.4 (0.7) Yes
I have been able to manage my workload (amongst
teaching, research, and other activities). (M)
4.9 (1.0) No

However, the metacognitive knowledge type was not supported, with a mean score of 4.9
and standard deviation of 1.0, as shown in Table 10. One possible reason that this metacognitive
influence was not supported was that HKUST faculty felt that their overall workload was
overbearing, and that they found it difficult to balance between the requirements of research and
teaching. The heavy workload requirements came across distinctly in the interview findings, with
more than half of the interviewees recounting the personal and family sacrifices they had to
make to be research productive (Dr. Adeline; Dr. Brandon; Dr. Daniel; Dr. Felicia; Dr. Junhao).
Hence, metacognitive knowledge was not validated as an asset that helped faculty at HKUST to
be research productive, in contrast with the finding by White, James, Burke, and Allen (2012)
that time management skills was associated with high-performing researchers.
RESEARCH PRODUCTIVITY FACTORS 71

Based on the analysis of the survey results and interview findings, the declarative and
procedural knowledge assets were validated. In prioritizing the influences, the declarative and
procedural knowledge types were ranked higher than the metacognitive type, as the declarative
and procedural knowledge assets were validated, with specific and defined actions that could be
taken to produce positive impact on the goal of increasing faculty research productivity.
Results and Findings for Motivation Assets
The survey also solicited faculty’s ratings on their expectancy-value and self-efficacy in
relation to being research productive. Expectancy-value factors studied included factors defined
by Eccles (2006) as attainment value, utility value, and intrinsic interest. Self-efficacy was
studied through faculty’s beliefs about their own capabilities and track record in publishing
research, and how they thought they were being perceived as researchers by their colleagues.
Motivation from Value in Publishing Research
A motivation asset associated with research productivity is the value perceived by faculty
in publishing research. This finding is consistent with the expectancy-value theory described by
Eccles (2006), where people’s motivation to engage in tasks depended on the value they ascribed
to doing the tasks well.
Survey results. The survey results supported the value motivation asset, with an overall
mean score of 5.5 out of a 6-point scale, and the mean scores across the schools and faculty ranks
ranged from 5.3 to 5.9.
Interview findings. The factors mentioned by faculty in the interviews to be of impact to
their motivation asset of value they perceived in publishing research are illustrated in Figure 7.
The asset is listed in the center bubble, and the related factors mentioned in the interviews are
depicted in separate bubbles around the center bubble. Factors that are deemed to strengthen the
RESEARCH PRODUCTIVITY FACTORS 72

asset is marked as positive (+), while factors that are deemed to weaken the asset are marked as
negative (-). Factors that are deemed to have a mixed effect on the asset are marked as neutral or
mixed (=).

Figure 7. Factors impacting perceived value in publishing research. This figure illustrates the
positive (+), negative (-), and mixed (=) factors mentioned by faculty in the interviews.

All interviewed faculty reported seeing value in being research productive. For example,
faculty talked about the attainment value they derived in the “discovery of new knowledge” (Dr.
Charles) through their research work, in making “contributions to scientific knowledge” (Dr.
Jingjing), and in seeing the role as a productive researcher being “part of [their] identity” (Dr.
Adeline). Some faculty were also motivated by personal goals to prove themselves. Dr. Daniel
said “that [he] wanted to prove to [his] family that [he] could be a success in [his] own chosen
area of expertise.” Dr. Brandon shared that research provided him with the potential to have
wider influence to “shape the external environment that will influence policy,” and to “generate
some insight for people to solve some broad [societal] issues.”
Faculty also saw utility value in being research productive, helping them to earn
Value in
Publishing
Research
+
Discover,
contribute
new
scientific
knowledge
Self-identity;
prove
oneself to
others
Wider
influence on
environment,
policies
Reputation,
recognition,
respect
Better pay;
career
advancement
More
research
funding
Time
invested in
research
Family and
personal
sacrifices
Review
process time-
consuming,
tedious
RESEARCH PRODUCTIVITY FACTORS 73

“reputation, recognition and respect” (Dr. Adeline), and to secure better pay, career
advancement, and funding (Dr. Jingjing). Dr. Brandon shared that “research productivity is for
survival, … to make sure [he had] a job, … got tenured, and later … got promoted.” Other
benefits of being research productive mentioned by faculty included helping them to “get more
funding to support more exciting research” (Dr. Xinwang), and securing continued employment
beyond the retirement age. Dr. Daniel shared that what was “really important to [him] … is to try
and continue to be productive so that [he] would continue to be employed.”
However, there were costs associated with being research productive, with time being
cited by faculty as a main factor. Dr. Brandon shared that for faculty “to be productive, [they]
can’t count how many hours [they] work.” Dr. Daniel recounted the time he had put into research
by working “six and a half days a week, … and not [having] taken that much time off.” Family
and personal sacrifices had to be made to be research productive, and he went on to share:
I didn’t get married till I was forty. I don’t spend as much time thinking about my
investments as I should. It was always more about my research and publications, the next
paper and the next project. The price I’ve paid, well, missing my family sometimes. (Dr.
Daniel).
The publication review process was also seen by some faculty as being time-consuming and
tedious. One interviewee shared:
The rest of the [publication] process -- I don’t enjoy it. I think it’s a waste of my effort
and time. … I can still improve the paper, … the first draft maybe it’s like the score is 80
out of 100. After the entire review process, maybe it would [be] enhanced to 95, but for
that 15 points I wasted three years. So yeah, I would have to endure that process to get it
published. (Dr. Adeline).
RESEARCH PRODUCTIVITY FACTORS 74

Another faculty similarly shared that while she was “very interested in the research process, …
when [she] actually needed to publish stuff, … [she] would rather do something else” (Dr.
Felicia).
Despite the costs involved to be research productive, the interview findings supported the
assertion that faculty were motivated by the attainment and utility value of publishing research,
in line with findings from Eccles (2006) and Pintrich (2003) that the task value attributed by
individuals leads to motivation due to positive orientation toward these tasks.
Motivation from Interest in Research
Another motivation asset associated with research productivity is the interest or
enjoyment derived by faculty in conducting research. This finding is consistent with academic
interest being identified by Azad and Seyyed (2007) as a factor influencing research
productivity, and the findings by Chen, Nixon, Gupta, and Hoshower (2010) that faculty placed
significantly greater importance on the role of research in satisfying the need for
creativity/curiosity, among a list of possible outcomes from being research productive.
Survey results. The survey results supported the interest motivation asset, with an
overall mean score of 5.6 out of a 6-point scale, and the mean scores across the schools and
faculty ranks ranged from 5.3 to 5.7.
Interview findings. The factors mentioned by faculty in the interviews to be of impact to
their motivation asset of interest in research are illustrated in Figure 8. The asset is listed in the
center bubble, and the related factors mentioned in the interviews are depicted in separate
bubbles around the center bubble. Factors that are deemed to strengthen the asset is marked as
positive (+), while factors that are deemed to weaken the asset are marked as negative (-).
Factors that are deemed to have a mixed effect on the asset are marked as neutral or mixed (=).
RESEARCH PRODUCTIVITY FACTORS 75

Figure 8. Factors impacting interest in research. This figure illustrates the positive (+), negative
(-), and mixed (=) factors mentioned by faculty in the interviews.

Nearly all faculty mentioned intrinsic interest and the enjoyment of research as strong
influences, that “to be a researcher, it is all about intrinsic motivation, it is all about doing
something for the love of it and not for any external rewards” (Dr. Junhao). Faculty shared that
they “really enjoy the process [of] learning something new” (Dr. Adeline), driven by the
“internal motivation, curiosity, and desire to understand the world” (Dr. Daniel). One
interviewee provided a personal account:
My goal in life is to understand the world. I’m just curious about how things work, and so
being research productive is just … a side effect of exploring ideas that … strike me.
Like this is strange, this is unusual; let’s try it out and see if I can better understand this.
(Dr. Felicia).
Beyond curiosity, intrinsic motivation could also be derived from a deeper emotional connection
with the research topics, as shared by another interviewee:
The kind of things that I am working on, each of these topics all come out from my
Interest in
Research
+
Intrinsic
motivation,
curiosity, to
understand
the world
Emotional
connection
with topic
Enjoyment
Family
upbringing
Strong
determinant
of success
RESEARCH PRODUCTIVITY FACTORS 76

concern. … I mean it’s emotional, definitely. … I want to answer the question why China
is so poor, [and how] to come out from poverty, …why [its] prosperity historically …
cannot be sustained. (Dr. Brandon).
Faculty also talked about the enjoyment derived from research, that getting “research moving is
[their] therapy” (Dr. Aravin). Such intrinsic interest could be developed from a young age,
illustrated by Dr. Charles who shared that his “father was a professor, [and he] just grew up
thinking that this [research] is a really exciting thing to do.” Intrinsic motivation was observed to
be a strong determinant of success, as shared by one interviewee that the “junior people who join
[his] department are either extremely successful or extremely unsuccessful, depending on how
intrinsically motivated they are” (Dr. Aravin).
Thus, consistent with findings from Azad and Seyyed (2007) and Chen, Nixon, Gupta,
and Hoshower (2010), the interview findings supported the assertion that faculty’s interest and
enjoyment of the research process were strong motivators that helped them to be research
productive.
Motivation from Self-Efficacy to Publish Peer-Reviewed Articles
Faculty’s self-efficacy to produce peer-reviewed publications is a motivation asset
associated with their research productivity. This finding is consistent with Pajares’ (2006)
finding that a strong sense of self-efficacy leads to higher levels of motivation and effort, and
Blackburn and Lawrence’s (1995) finding that self-efficacy was a major factor accounting for
variations in research productivity of faculty, and that confidence in research abilities was
closely related to faculty’s research output.
Survey results. The survey results supported the self-efficacy motivation asset, with an
overall mean score of 5.6 out of a 6-point scale, and the mean scores across the schools and
RESEARCH PRODUCTIVITY FACTORS 77

faculty ranks ranged from 5.0 to 5.8. This result was further supported by triangulating with the
survey questions on faculty being regarded by their colleagues as productive researchers (mean
score of 5.0 out of a 6-point scale), and the number of peer-reviewed publications in the past
three years (mean of 15.5).
Interview findings. The factors mentioned by faculty in the interviews to be of impact to
their motivation asset of self-efficacy to publish peer-reviewed articles are illustrated in Figure 9.
The asset is listed in the center bubble, and the related factors mentioned in the interviews are
depicted in separate bubbles around the center bubble. Factors that are deemed to strengthen the
asset is marked as positive (+), while factors that are deemed to weaken the asset are marked as
negative (-). Factors that are deemed to have a mixed effect on the asset are marked as neutral or
mixed (=).

Figure 9. Factors impacting self-efficacy to publish peer-reviewed articles. This figure illustrates
the positive (+), negative (-), and mixed (=) factors mentioned by faculty in the interviews.

Faculty identified several strengths and weaknesses that affected their self-efficacy as
researchers. One of the strengths mentioned by faculty was the curiosity and confidence to
Self-Efficacy
to Publish
Peer-
Reviewed
Articles
+
Openness
to explore
new
questions
Match
ideas with
peers
Strong
support
from fellow
academics
Support of
field
participants
Lack
statistical
analysis
skills
Weakened
links and
recognition
by other
academics
RESEARCH PRODUCTIVITY FACTORS 78

conduct research in new areas, characterized by the “openness to exploring new questions,” the
“exploration and discovery of novel topics” (Dr. Charles), and the willingness to “try and do
different stuff” (Dr. Aravin). Faculty’s self-efficacy was also spurred by being able to match up
with their peers, and Dr. Aravin shared that “there were other people around [him], and they’re
also working very hard, and so [he] wanted to see whether [he could] match ideas with them.”
Receiving strong support from fellow academics also helped to increase faculty’s sense of self-
efficacy, and one faculty shared:
I had a group of people from my former university, who really appreciated my efforts in
that area and rewarded me with a trip to China, … [I had] strong support, and that was
really important because it gave me greater confidence in what I chose to do. (Dr.
Daniel).
Faculty’s confidence was also strengthened through the support of field participants that they
were researching on, by gaining the trust of the participants to provide access and insights for
their research (Dr. Daniel). On the other hand, the lack of deep statistical analysis skills was
identified as a weakness by Dr. Daniel, who had to enlist the “help from someone [research
assistant] from campus who was a specialist in statistics,” to aid with his quantitative analysis
work.” Another negative factor for faculty who traveled less to overseas academic conferences
and events was the weakening of links and recognition by the wider international academic
networks, and Dr. Adeline shared that “after a while, [she] became out of the circle; … they
[international academics] don’t see me in different conferences, … they don’t remember to put
me on their invitation list, on their panel.”
Thus, consistent with findings by Pajares (2006) and Blackburn and Lawrence (1995), the
interview findings supported the assertion that a strong sense of self-efficacy was a motivation
RESEARCH PRODUCTIVITY FACTORS 79

asset that helped faculty to be research productive.
Synthesis of Results and Findings for Motivation Assets
In the context of this study, the specific motivation assets examined are faculty’s
expectancy-value, interest, and self-efficacy in publishing research.
As shown in Table 11, all three motivation influences were supported, with mean scores
ranging from 5.0 to 5.6, and standard deviations ranging from 0.7 to 1.0. The self-efficacy
influence was further supported by the high mean number of 15.5 peer-reviewed research
publications over the past three years, although the standard deviation of 18.6 indicated that
individual performance varied widely across different faculty. The interview findings for these
three influences also supported the importance of these motivation assets in helping faculty to be
research productive. Hence, these three motivation assets were considered as validated, as shown
in Table 11.
Table 11
Survey and Interview Results for Motivation Influences
Mean (SD)

HKUST Validated?
Publishing research is important to me. (n=113) 5.5 (0.9)

Yes
I enjoy conducting research. (n=112) 5.6 (0.7)

Yes
I believe that I have the ability to produce peer-
reviewed research publications. (n=113)
5.6 (0.7)

Yes
I am regarded by my colleagues as a productive
researcher. (n=104)
5.0 (1.0) Yes
Number of peer-reviewed research publications in the
past three years. (n=109)
15.5 (18.6) N.A.

Across schools and faculty ranks, significant differences were found in the mean scores
for SBM on the importance of publishing research to faculty, for SENG on perceived views of
RESEARCH PRODUCTIVITY FACTORS 80

colleagues, and for professor-rank faculty’s belief in their ability to publish research, as shown in
Table 14. There were no other significant differences across the schools and faculty ranks for
motivation influences.
On the self-reported number of peer-reviewed research publications in the past three
years, the mean score of SENG was significantly higher at 27.6, while SBM and SHSS were
significantly lower at 4.1, and 8.1 respectively. The mean score for the assistant professor rank at
7.5 was also significantly lower compared with other faculty ranks. These results appear in Table
14. The wide variation in mean scores on the number of peer-reviewed publications across the
Schools could be ascribed to the different publication processes and requirements across
academic fields (Puuska, 2014). In addition, what is considered as high-quality peer-reviewed
publications vary across academic disciplines (Boise State University, n.d.), for example,
conference papers in computer science, journal articles finance, and books in humanities.
Based on the analysis of the survey results and interview findings, all three motivation
influences were validated. All of these influences were regarded as high priority, as they have
been shown in the research literature as being strong predictors of active choice, persistence, and
mental effort.
Results and Findings for Organization Assets
The survey captured faculty’s ratings on the perceived influence of organizational factors
on their research productivity. The organizational influences studied using the cultural-settings
lens were organizational expectations and goals, performance incentives, and policies and
practices.
Organizational Expectations on Faculty Research Productivity
Setting high expectations and clear goals for research performance is an organization
RESEARCH PRODUCTIVITY FACTORS 81

asset associated with faculty research productivity. This finding is consistent with Moran and
Brightman’s (2000) finding that a sense of purpose and goal-orientation is one of the strongest
drivers of work behavior, and Dembo and Eaton’s (2000) finding that goal setting enhances
learning and performance, occurring with greater frequency and consistency with higher
achievers compared to low achievers.
Survey results. The survey results supported the expectations and goals organization
asset, with an overall mean score of 5.0 out of a 6-point scale, and the mean scores across the
schools and faculty ranks ranged from 4.6 to 5.3.
Interview findings. The factors mentioned by faculty in the interviews to be of impact to
the organization asset of expectations and goals on faculty research productivity are illustrated in
Figure 10. The asset is listed in the center bubble, and the related factors mentioned in the
interviews are depicted in separate bubbles around the center bubble. Factors that are deemed to
strengthen the asset is marked as positive (+), while factors that are deemed to weaken the asset
are marked as negative (-). Factors that are deemed to have a mixed effect on the asset are
marked as neutral or mixed (=).
RESEARCH PRODUCTIVITY FACTORS 82

Figure 10. Factors impacting expectations and goals on faculty research productivity. This figure
illustrates the positive (+), negative (-), and mixed (=) factors mentioned by faculty in the
interviews.

There were mixed views amongst faculty on the clarity of the expectations and goals of
the institution regarding research productivity. About half of the interviewees expressed that
there were clear expectations on faculty to be research productive. For example, Dr. Daniel said,
“it’s very clear if you want to get ahead, you want to get promoted, you want to get tenured, you
got to produce.” In addition to setting targets for research publications, faculty cautioned that
research should be assessed in “more of a holistic view, thinking about things like impact and
productivity.” Some faculty felt that with the implementation of the Research Assessment
Exercise conducted by the University Grants Committee across the Hong Kong universities,
“now the [performance] standard is very specific” for each academic field (Dr. Brandon). For
some academic fields, interviewees mentioned specific numbers of publications in ‘A’ journals
expected of research productive faculty, while acknowledging the difficulty in drawing up a
definitive list of ‘A’ journals. For example, Dr. Aravin said, “usually what we go with is four ‘A’
Expectations
and Goals on
Faculty
Research
Productivity
=
Mixed views
on clarity of
expectations
Number of 'A'
journals
publications
Impact of
research
Specific RAE
standards
Incongruent
messages/
behaviors
Lack
transparency;
subjectivity of
evaluation
Set high
performance
bar
RESEARCH PRODUCTIVITY FACTORS 83

publications, and then there’s debate about how to define the ‘A’ publications.”
However, about half of the interviewees felt that there was a lack of clarity on the
expected performance standards, that they “got lots of mixed messages, and what was said was
often very different from what was being done,” and hence faculty had to “infer the message
from behaviors” that they observed (Dr. Aravin). Faculty also expressed concerns regarding the
limited transparency and subjectivity of faculty performance evaluations. For example, Dr.
Adeline shared that performance evaluations were “becoming more and more [the result] of
several senior personnel’s opinions and tastes, instead of a transparent process.” Two
interviewees advocated that the setting of a clear high-performance bar was a good way to set the
right expectations with faculty, and that the institution would “attract very good people” by
firmly holding the standards at a high level (Dr. Brandon; Dr. Felicia).
Despite the mixed views expressed by faculty on the clarity of expectations and goals on
faculty research performance at HKUST, the interview findings supported the assertion that
organizational expectations and goals for research performance impacted their research
productivity. This finding is further supported by Jung’s (2012) finding that faculty research
productivity could be influenced by organizational characteristics and factors, and Eccles’ (2006)
finding that having high performance expectations of success through the setting of goals could
positively impact learning and motivation.
Organizational Performance Incentives
The performance incentive to support research publications is an organization asset that is
associated with faculty research productivity. This finding is consistent with Hales, Shahrokh,
and Servis’ (2005) finding that financial rewards could be used to provide tangible incentives to
influence faculty to allocate their time and effort to research activities, and Pintrich’s (2003)
RESEARCH PRODUCTIVITY FACTORS 84

finding that appropriately structured rewards provide feedback to individuals on their level of
competence and skills to aid their improvement efforts.
Survey results. The survey results supported the incentives organization asset, with an
overall mean score of 5.1 out of a 6-point scale, and the mean scores across the schools and
faculty ranks ranged from 4.0 to 5.3.
Interview findings. The factors mentioned by faculty in the interviews to be of impact to
the organization asset of the performance incentives for research productivity are illustrated in
Figure 11. The asset is listed in the center bubble, and the related factors mentioned in the
interviews are depicted in separate bubbles around the center bubble. Factors that are deemed to
strengthen the asset is marked as positive (+), while factors that are deemed to weaken the asset
are marked as negative (-). Factors that are deemed to have a mixed effect on the asset are
marked as neutral or mixed (=).

Figure 11. Factors impacting performance incentives for faculty research productivity. This
figure illustrates the positive (+), negative (-), and mixed (=) factors mentioned by faculty in the
interviews.

Performance
Incentives
+
Gaining
tenure
Performance-
linked salary
increment
Recognition
Research
funding
allocation
Continued
employment
beyond
retirement
Teaching
relief and
flexibility
Stalled-out
faculty
RESEARCH PRODUCTIVITY FACTORS 85

The incentives for research productivity mentioned by faculty included gaining tenure,
recognition, salary increments, funding allocation, continued employment beyond retirement
age, and teaching flexibility and relief. For junior faculty, gaining tenure was seen as a strong
incentive for research productivity, and the prospect that one is “not going to get renewed after
six years provides a very strong incentive for people to publish” (Dr. Charles). However, Dr.
Charles observed that some faculty “immediately after substantiation or sometimes after
promotion … stalled out,” and was no longer research productive after gaining tenure. Dr. Daniel
referred to such stalled-out faculty as “lifetime associate professors,” and Dr. Charles observed
that there was “not much penalty for cruising” and no pressure on such faculty to remain
research productive. Some faculty shared that another incentive that led to complacency was the
provision of direct allocation grants by the University to support faculty research. While the
provision of these grants was seen as a good form of baseline support for research, the
indiscriminate availability of such grants led to the lack of incentive to apply for competitive
external grants among some faculty. Dr. Daniel observed that there were faculty “who never
apply for RGC [competitive research grants], because every year they could get 50,000 dollars
direct allocation grants.”
Increases in salary was another incentive that impacted faculty research productivity.
About half of the faculty interviewed advocated that salary increments should be used to reward
those who had demonstrated actual research performance results, “to reward people who achieve,
who have accomplishment… not just people who promise” (Dr. Brandon). However, some
interviewees felt that the differentials in the annual merit increment system were too low to
influence behaviors. Dr. Daniel said, “when it’s a one percent or two percent difference between
what I get and the guy down the hall [who is not research productive], … that is not a good
RESEARCH PRODUCTIVITY FACTORS 86

incentive.” Two interviewees mentioned that research productivity should be incentivized
through teaching load relief and flexibility in teaching arrangements, that faculty who produce
research publications should be “allowed to get some leeway … on teaching, … service, …
flexibility in choosing which courses [they] teach, or … flexibility in setting the class size” (Dr.
Aravin; Dr. Brandon). However, the current practice of assigning more teaching load to good
teachers had led to some research productive faculty being overloaded (Dr. Aravin). Some
interviewees felt that faculty should be intrinsically motivated to be research productive, even
when there were no associated external incentives. Dr. Charles observed that “two thirds of the
people here, they love what they're doing and they don't necessarily need incentives” to be
research productive, and that incentives could be better used to get the “groups of unproductive
associate professors … to essentially become productive again.”
The interview findings supported the assertion that organizational performance incentives
for research performance impacted their research productivity, consistent with findings from
Hales, Shahrokh, and Servis (2005) and Pintrich (2003). In analyzing the varied responses from
faculty on the organizational performance incentives, it was clear that the incentive systems to
support faculty research productivity had to be carefully designed to encourage the desired
behaviors, and to avoid unintentionally reinforcing undesirable behaviors by faculty.
Organizational Policies and Practices to Support Research.
Organizational policies and practices was identified as a possible organization asset
helping to increase research productivity. This assertion is consistent with Schneider, Brief, and
Guzzo’s (1996) finding that organizational policies and practices influence the climate of an
organization, and consequently the performance of members of the organization. Furthermore,
Hales, Shahrokh, and Servis (2005) and Jung (2012) found that financial policies that provide
RESEARCH PRODUCTIVITY FACTORS 87

adequate resources and support for conducting rigorous research could positively impact
faculty’s production of research publications.
Survey results. The policies and practices organizational influence was not supported by
the survey results, with the overall mean score of 4.9 out of a 6-point scale falling marginally
below the 5.0 validation mark, with the mean scores across the schools and faculty ranks ranging
from 4.4 to 5.3.
Interview findings. The factors mentioned by faculty in the interviews to be of impact to
the organization asset of policies and practices for research productivity are illustrated in Figure
12. The asset is listed in the center bubble, and the related factors mentioned in the interviews are
depicted in separate bubbles around the center bubble. Factors that are deemed to strengthen the
asset is marked as positive (+), while factors that are deemed to weaken the asset are marked as
negative (-). Factors that are deemed to have a mixed effect on the asset are marked as neutral or
mixed (=).

Figure 12. Factors impacting policies and practices for faculty research productivity. This figure
illustrates the positive (+), negative (-), and mixed (=) factors mentioned in the interviews.
Policies and
Practices
+
Delink
substantiation
from
promotion
Lack
transparency
and fairness
in evaluation
Abundant
resources to
support
research
Over-reliance
on GRF
funding
Balance
support and
striving for
excellence
Ineffective
distributed
resource
allocation
system
RESEARCH PRODUCTIVITY FACTORS 88

Faculty suggested delinking substantiation from the promotion of assistant professors to
associate professors, and to confer substantiation “later when there's evidence of independent
new projects that is distinct from the ones that [faculty] got their PhDs and carried through as
assistant professors” (Dr. Charles). Several faculty emphasized the need to be transparent and
fair in applying the internal policies for faculty performance evaluation and promotion. The
current annual merit review process was seen by Dr. Aravin as being “very flawed,” with “a few
people who make these [faculty appraisal] decisions without oversight.” The lack of
transparency led to a “toxic” system, where “nobody has an incentive to say negative things
about anyone even if they [were] true” (Dr. Aravin). One interviewee pointed out that the
University must uphold the appointment policy for department heads to serve a maximum of two
terms, as the institution “cannot let a department head dictate the entire [faculty appraisal]
process” (Dr. Adeline). One suggestion raised was to use periodic faculty surveys to “find out
where there are independent kingdoms, find out their biases, unfairness, those kinds of things,
and getting rid of those situations” (Dr. Daniel).
Policies and practices around the allocation of resources was a major area of concern, as
evidenced by the numerous comments from interviewees. Three interviewees commented that
faculty members at HKUST were provided with abundant resources to support research
compared with other top US universities, with relatively high success rates for grant proposals
made to the RGC and the availability of University-allocated research funding (Dr. Charles; Dr.
Jingjing; Dr. Xinwang). However, with the General Research Fund (GRF) from the RGC being
the only major source of funding, some faculty felt that institutions were overly reliant on GRF
funding with insufficient alternatives. The support from HKUST for “fundable but not funded
projects” -- proposals deemed to be worthy of funding but did not manage to secure GRF
RESEARCH PRODUCTIVITY FACTORS 89

funding -- was seen as a positive intervention (Dr. Jingjing). Dr. Xinwang shared that seed
funding provided to support faculty in bidding for large projects was helpful, and he advocated
that a careful balance had to be struck in managing the University’s resource allocation policies,
to “meet the basic needs of faculty members, … and to strive for excellent research.” One critical
problem area that emerged through the interviews was the current resource allocation practices,
whereby resources were allocated to the schools, which was then divided and evenly distributed
across departments. Such an arrangement created a “weird political process,” that limited the
differentiation of resource support levels across faculty based on their performance (Dr. Charles).
Suggestions were made to centralize the allocation of resources at the institutional level instead
(Dr. Junhao), and to make the allocation process competitive in nature (Dr. Jingjing), as it would
promote excellence by “forcing departments to sink or swim” (Dr. Charles).
Although the findings from Schneider, Brief, and Guzzo (1996), Hales, Shahrokh, and
Servis (2005), and Jung (2012) suggest that organizational policies and practices have an impact
on faculty research productivity, the interview findings did not fully support the assertion that the
organization policies and practices at HKUST was an asset that helped faculty to be research
productive.
Synthesis of Results and Findings for Organization Assets
In the context of this study, the specific organizational assets examined were expectations
and goals, performance incentives, and policies and practices of HKUST in relation to faculty
research productivity.
As shown in Table 12, the expectations and goals and performance incentives
organizational influences were supported, with mean scores of 5.0 and 5.1, and standard
deviations of 0.8 and 1.0 respectively. Across schools and faculty ranks, the mean scores on the
RESEARCH PRODUCTIVITY FACTORS 90

expectations and goals influence on research productivity were significantly lower for SBM and
associate professors, as shown in Table 14. There were no other significant differences across the
schools and faculty ranks on the organizational influences. The interview findings for the
expectations and goals and performance incentives influence further supported the importance of
these organization assets in helping faculty to be research productive. Hence, these two
organization assets were considered as validated, as depicted in Table 12.
Table 12
Survey and Interview Results on Organization Influences
Mean (SD)

HKUST Validated?
I know what my institution expects of me regarding
my research productivity. (n=113)
5.0 (1.0) Yes
My institution's incentive system is effective in
encouraging research productivity. (n=104)
5.1 (0.8) Yes
My institution has effective policies and practices to
support research. (n=112)
4.9 (1.0) No

However, the policies and practices influence was not supported, with a mean score of
4.9 and standard deviation of 1.0. One possible reason was that HKUST faculty felt that there
were several critical problems with the current policies and practices at the institution, in
particular the lack of transparency in the policies and practices on faculty appraisal and resource
allocation. The perceived lack of transparency came across distinctly in the interview findings,
with more than half of the interviewees recounting their own and others’ negative experiences
with the faculty appraisal and resource allocation policies and practices (Dr Adeline; Dr. Aravin;
Dr. Charles; Dr. Jingjing; Dr. Xinwang). Hence, the organization policies and practices influence
was not validated as an asset that helped HKUST faculty to be research productive. In spite of
the problems identified, Dr. Aravin felt that HKUST had policies that made it the “easiest place
in the world” to start an academic career, supported by a “fantastic research environment” with
RESEARCH PRODUCTIVITY FACTORS 91

low teaching and service load for junior faculty.
One potential contradiction in the findings on organizational assets was that the policies
and practices organizational influence was not validated, while the performance incentives
organizational influence was validated. Although performance incentives could be regarded as
part of an organization’s policies and practices, the interview findings indicated that respondents
considered a broader range of constructs beyond performance incentives when responding to
questions regarding organizational policies and practices. The broader range of constructs
mentioned by faculty in the interviews included resource allocation policies, faculty evaluation
and promotion practices, and policies on the appointment of department heads.
Based on the analysis of the survey results and interview findings, two of the three
organizational influences were validated, namely the organizational expectations and goals, and
the organizational performance incentives. Both validated influences were regarded as high
priority, as they were supported by the research literature as being strong influences on
individuals’ motivation and performance.
Summary
Based on the analysis of the survey results and interview findings, a set of knowledge,
motivation, and organization assets were validated to be of impact to faculty research
productivity, as summarized in Table 13.
Table 13
Summary of Validation of Knowledge, Motivation, and Organization Influences
Influences Validated?
Knowledge Influences
I am up-to-date with the research findings in my academic field. Yes
I have been successful in generating new research ideas. Yes
I have the knowledge on how to get my research published in peer-
reviewed publications.
Yes
RESEARCH PRODUCTIVITY FACTORS 92

I have been able to manage my workload (amongst teaching, research,
and other activities).
No
Motivation Influences
Publishing research is important to me. Yes
I enjoy conducting research. Yes
I believe that I have the ability to produce peer-reviewed research
publications.
Yes
Organization Influences
I know what my institution expects of me regarding my research
productivity.
Yes
My institution's incentive system is effective in encouraging research
productivity.
Yes
My institution has effective policies and practices to support research. No

The validated knowledge assets were the declarative-factual knowledge about research
findings in academic disciplines, the declarative-conceptual knowledge to generate new research
ideas, and the procedural knowledge on how to produce peer-reviewed publications. These
results are consistent with findings in the literature that faculty research productivity is impacted
by declarative knowledge influences (Alexander, Schallert, & Hare, 1991; Azad & Seyyed,
2007; Jung, 2012), and procedural knowledge influences (Azad & Seyyed, 2007).
The validated motivation assets were faculty’s expectancy-value, interest, and self-
efficacy in publishing research. These results are consistent with findings in the literature that
faculty research productivity is impacted by expectancy-value influences (Eccles, 2006; Pintrich,
2003), interest influences (Azad & Seyyed, 2007; Chen, Nixon, Gupta, & Hoshower, 2010), and
self-efficacy influences (Blackburn & Lawrence, 1995; Pajares, 2006).
The validated organization assets were the organizational expectations and goals, and
performance incentives of HKUST in relation to faculty research productivity. These results are
consistent with findings in the literature that faculty research productivity is impacted by the
influences of expectations and goals (Dembo & Eaton, 2000; Moran & Brightman, 2000), and
performance incentives (Hales, Shahrokh, & Servis, 2005; Pintrich, 2003).
RESEARCH PRODUCTIVITY FACTORS 93
Table 14
Survey Results for Knowledge, Motivation, Organizational Influences – Breakdown by School and Faculty Rank
Mean (SD) – By School Mean (SD) – By Faculty Rank

SSCI
n=32
SENG
n=31
SBM
n=22
SHSS
n=12
Chair
n=20
Prof.
n=34
Assoc.
n=24
Asst.
n=29
Knowledge Influences
I am up-to-date with the research
findings in my academic field.
5.3
(0.6)
5.6*
(0.5)
5.3
(0.6)
5.0
(1.3)
5.5
(0.6)
5.3
(0.6)
5.3
(1.1)
5.3
(0.5)
I have been successful in generating new
research ideas.
5.4
(0.7)
5.5
(0.7)
5.4
(0.7)
5.0
(1.4)
5.5
(0.8)
5.6
(0.6)
5.4
(1.1)
5.1
(0.7)
I have the knowledge on how to get my
research published in peer-reviewed
publications.
5.2
(0.7)
5.6*
(0.5)
5.4
(0.7)
5.1
(1.0)
5.5
(0.7)
5.4
(0.6)
5.5
(0.8)
5.2
(0.7)
I have been able to manage my
workload (amongst teaching, research,
and other activities).
4.9
(1.2)
5.1
(0.7)
5.0
(0.7)
4.6
(1.0)
4.8
(1.2)
5.0
(1.1)
5.3
(1.4)
4.8
(0.7)

Motivation Influences
Publishing research is important to me.

5.3
(0.8)
5.6
(0.5)
5.9**
(0.4)
5.5
(1.4)
5.5
(0.8)
5.5
(0.6)
5.3
(1.4)
5.7
(0.5)
I enjoy conducting research.

5.5
(0.7)
5.6
(0.6)
5.6
(0.5)
5.3
(1.4)
5.6
(0.8)
5.7
(0.5)
5.4
(1.5)
5.5
(0.6)
I believe that I have the ability to
produce peer-reviewed research
publications.
5.6
(0.7)
5.7
(0.4)
5.6
(0.5)
5.0
(1.4)
5.7
(0.7)
5.8*
(0.4)
5.3
(1.2)
5.5
(0.6)
RESEARCH PRODUCTIVITY FACTORS 94
Mean (SD) – By School Mean (SD) – By Faculty Rank

SSCI
n=32
SENG
n=31
SBM
n=22
SHSS
n=12
Chair
n=20
Prof.
n=34
Assoc.
n=24
Asst.
n=29
I am regarded by my colleagues as a
productive researcher.
4.6
(1.5)
5.3*
(1.2)
4.8
(1.2)
5.0
(1.6)
5.1
(1.1)
5.1
(1.3)
4.9
(1.3)
4.7
(1.7)
Number of peer-reviewed research
publications in the past three years.
14.9
(10.3)
27.6*
(27.1)
4.1**
(2.6)
8.1**
(6.7)
29.5
(31.8)
13.7
(9.5)
17.2
(18.4)
7.5**
(7.6)

Organizational Influences
I know what my institution expects of
me regarding my research productivity.
4.6
(1.5)
5.3
(1.2)
4.8*
(1.2)
5.0
(1.6)
5.1
(1.1)
5.1
(1.3)
4.9*
(1.3)
4.7
(1.7)
My institution's incentive system is
effective in encouraging research
productivity.
5.0
(1.8)
5.2
(1.1)
5.1
(1.6)
5.3
(1.6)
5.1
(1.4)
5.2
(1.3)
5.0
(0.9)
5.0
(2.2)
My institution has effective policies and
practices to support research.
4.9
(1.8)
5.1
(1.2)
4.7
(1.7)
5.0
(1.6)
5.3
(1.1)
5.2
(1.2)
4.8
(1.1)
4.4
(2)
Notes. *p<.05, **p<.01. Excluded 16 respondents who did not indicate their department/school, and 6 who did not indicate their rank.

RESEARCH PRODUCTIVITY FACTORS

95
In Chapter Five, recommendations will be made to address these validated and prioritized
knowledge, motivation, and organization assets, based on empirical evidence and insights from
the research literature.

RESEARCH PRODUCTIVITY FACTORS

96
CHAPTER FIVE: RECOMMENDATIONS
In Chapter Four, through the analysis of the survey results and interview findings, a set of
knowledge, motivation, and organization assets were validated to be of impact to faculty
research productivity. In this chapter, recommendations are made to address these validated
knowledge, motivation, and organization assets, based on empirical evidence and insights from
the research literature. The New World Kirkpatrick Model (Kirkpatrick & Kirkpatrick, 2016)
was used to design an integrated implementation and evaluation plan for the recommendations
made in this dissertation study.
This chapter is organized in five sections. The first section recounts the organizational
context of the study. The second section recounts the stakeholder group and research questions.
The third section describes the recommendations for practice, along with an integrated
implementation and evaluation plan. The fourth section discusses the strengths and weaknesses
of the study, and topics for future research. The final concluding section summarizes the overall
findings and implications of the dissertation study.
Recommendations for Practice to Address KMO Influences
This section contains a set of recommendations for practice to address the knowledge,
motivation, and organization influences impacting the stakeholder goal of increasing faculty
research productivity.
As this is a study on the promising practices at HKUST that helped to increase faculty
research productivity, the validated knowledge, motivation, and organization assets were
regarded as critical success factors behind HKUST’s strong performance in research publications
and consequently rankings performance. The recommendations made regarding these critical
success factors are meant as areas for continued support to enable HKUST to sustain its strong
RESEARCH PRODUCTIVITY FACTORS

97
performance in research productivity. The recommendations could also be adopted by other
institutions that aspire to improve their research productivity and rankings performance.
Regarding the knowledge and organization influences that were not validated in the study, one
possible interpretation is that these influences are less critical to faculty research productivity.
Another possibility is that these are areas of weakness in the practices at HKUST, and that
HKUST has performed well in research productivity despite these weaknesses. Further studies
are recommended to gain a clearer understanding into the nature and impact of these non-
validated influences.
Knowledge Recommendations
Introduction. Clark and Estes (2008) identified people’s knowledge and skills as one of
the critical factors to address business gaps and to improve organizational performance.
Adapting from the Bloom’s Taxonomy (Bloom, Englehart, Furst, Hill, & Krathwohl, 1956),
Krathwohl (2002) classified knowledge into factual, conceptual, procedural, or metacognitive
types. The classification can be further aggregated as declarative knowledge (facts, concepts),
procedural knowledge (methods, processes), and metacognitive knowledge (strategies, beliefs,
self-awareness) (Alexander, Schallert, & Hare, 1991).
In the context of this study, declarative knowledge influences identified included
faculty’s factual knowledge about their academic disciplines gained through efforts to stay
current with the developments in their fields (Azad & Seyyed, 2007), and conceptual knowledge
in generating research ideas acquired through faculty’s research training and experience (Azad &
Seyyed, 2007). Procedural knowledge influences identified included faculty’s knowledge of the
steps necessary to secure research grants, to translate research ideas into actual research, and to
publish research findings (Azad & Seyyed, 2007; Dundar & Lewis, 1998; Hales, Shahrokh, &
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98
Servis, 2005). Metacognitive knowledge influences identified included faculty’s knowledge to
plan their approach to manage the workload arising from teaching, research, and service (Azad &
Seyyed, 2007; Levitan & Ray, 1992; White, James, Burke, & Allen, 2012). Based on the analysis
of the survey results and interviews conducted, the declarative and procedural knowledge types
were validated, while the metacognitive knowledge type was not validated, as shown in Table
15. In prioritizing these influences, the declarative and procedural knowledge types were ranked
higher than the metacognitive type, as the declarative and procedural knowledge types were
validated, with specific actions that could be taken to produce positive impact on the goal of
increasing faculty research productivity. For each of the prioritized influences, context-specific
recommendations were proposed, grounded on principles drawn from the research literature.
Table 15
Summary of Knowledge Influences and Recommendations
Assumed
Knowledge Asset
Validated
(Y, N)
Priority
(Y, N)
Principle and Citation Context-Specific
Recommendation
Staying current with
contemporary
issues in their
respective academic
/ professional fields.
(Declarative-
Factual)

Y Y 1. Information and job
aids provide adequate
support for people to
manage familiar or
routine tasks (Clark &
Estes, 2008).

2. Decreasing
extraneous cognitive
load by effective
instruction (particularly
when intrinsic load is
high) enables more
effective learning
(Kirschner, Kirschner,
& Paas, 2006).
1. Provide information
and checklists on the
journals to read and the
conferences and events
to attend in the
respective fields.

2. Given the large
number of journals and
conferences, faculty
will learn more
effectively by reading
the most relevant
research journals, and
attend the most
informative conferences
and events.

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Generating new
research ideas.
(Declarative-
Conceptual)
Y Y 1. Information learned
meaningfully and
connected with prior
knowledge is stored
more quickly and
remembered more
accurately because it is
elaborated with prior
learning (Schraw &
McCrudden, 2006).

2. Education provides
people with knowledge
to help them tackle
novel and unanticipated
problems and situations
(Clark & Estes, 2008).
1. Attendance of events
to interact with other
fellow academics;
organize events that
bring together faculty
from within and across
disciplines

2. Interact with
academics within and
outside of the field.
Observe the needs of
community and society.
Knowing the
required steps to
take for translating
ideas into research
and acceptance into
peer-reviewed
publications.
(Procedural)

Y Y 1. Targeting training
and instruction between
the individual’s
independent
performance level and
their level of assisted
performance promotes
optimal learning (Scott
& Palincsar, 2006).

2. Training can be used
when learners need
demonstration, guided
practice, and feedback
to improve the way
things are done (Clark
& Estes, 2008).

1. Provide training on
the steps of the research
publication process for
leading journals,
matched with learners’
past training and
experience in
conducting and
publishing research.

2. One-to-one
mentoring by senior
faculty, and through
organizing sharing
sessions by faculty on
their experience of the
steps in research
publication.
Knowing how to
balance research
workload with
required teaching
and other
responsibilities.
(Metacognitive)
N N 1. Provide instruction in
metacognitive skills to
increase self-regulation
(Dembo & Eaton,
2000).

1. As part of the faculty
on-boarding process,
provide training on time
management skills and
metacognitive strategies
to self-monitor and
regulate their time
commitments on an
ongoing basis.
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2. Provide opportunities
for learners to engage in
guided self-monitoring
and self-assessment
(Baker, 2006).
2. As part of the annual
performance appraisals,
have faculty members
reflect on their
workload and time
management in the past
year, and to identify
improvement areas and
action steps.

Declarative knowledge assets. According to Krathwohl (2002), declarative knowledge
includes both factual and conceptual knowledge. Factual knowledge is discrete simple pieces of
content, including terminologies and specific details and basic elements, while conceptual
knowledge refers to organized forms of information encompassing interrelationships between
basic elements, including classifications and categories, principles and generalizations, and
theories, models, and structures (Krathwohl, 2002). The survey data indicated that HKUST’s
research faculty perceived that they had the necessary declarative knowledge assets to be
research productive, with a respondent mean score of 5.3 out of a 6-point scale indicating that
they were up-to-date with research findings in their academic fields, and a respondent mean
score of 5.4 out of a 6-point scale indicating that they were successful at generating new research
ideas.
In a study of accounting faculty members from 115 AACSB accredited Colleges of
Business, Chen, Nixon, Gupta, and Hoshower (2010) found that staying current in academic
fields is relatively important to faculty from doctoral-granting programs to be productive in
research. The interview data indicated that faculty at HKUST kept up-to-date with research
findings in their academic fields by reading research journals and attending academic
conferences. Departments could provide information and checklists to help research faculty
identify the most relevant journals to read and the most informative conferences and events to
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101
attend in their respective fields. Such information and job aids provide adequate support for
individuals to manage familiar or routine tasks (Clarke & Estes, 2008), and help to decrease
extraneous cognitive load (Kirschner, Kirschner, & Paas, 2006) due to the large number of
research journals and academic conferences, thereby enabling faculty to more effective in
acquiring contemporary knowledge in their academic fields.
Conceptual knowledge in the form of principles, theories and models are typically
acquired through faculty’s research training and experience (Azad & Seyyed, 2007), with impact
on their subsequent research approaches and research productivity (Jung, 2012), and
consequently their ability to generate new research ideas. The interview data suggested that
HKUST faculty generated new research ideas through interacting with other academics within
and outside of their fields, and through observing the needs of the society and communities.
Schraw and McCrudden (2006) advocate that information learned meaningfully and connected
with prior knowledge helps people to construct new meaning. Universities could support their
research faculty’s attendance of events to interact with other academics in their fields, and
organize events that bring together faculty from within and across disciplines to interact on a
regular basis. Interactions at these events could facilitate faculty to connect new ideas generated
through the conversations with their prior knowledge within their academic fields, thereby
strengthening their conceptual knowledge within and outside of their disciplines. Faculty’s
interactions with fellow academics and observations about their communities are forms of
education, providing faculty with knowledge to help them generate novel research ideas to
address unanticipated problems and situations (Clark & Estes, 2008). These efforts could bring
about an increase in the conceptual knowledge of faculty, and help them to generate new
research ideas.
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Procedural knowledge assets. Krathwohl (2002) defined procedural knowledge as the
know-how to get something done, including subject-specific skills and algorithms, techniques
and methods, and determination of appropriate procedures. Faculty research productivity could
be impacted by procedural knowledge influences, as reflected in their knowledge of the steps
necessary to secure research grants, translate research ideas into actual research, and publication
of research findings. The survey data indicated that HKUST’s research faculty felt that they had
the necessary procedural knowledge assets to be research productive, with a respondent mean
score of 5.4 out of a 6-point scale indicating that they knew how to get their research published
in peer-reviewed publications.
Faculty research productivity could be impacted by procedural knowledge in the
techniques and methods for publishing research, which is associated with the quality of graduate
training in the case of junior faculty (Dundar & Lewis, 1998), and how to go about active
collaboration to develop research practice plans for faculty (Hales, Shahrokh, & Servis, 2005). In
a study of full-time faculty members at three AASCB-accredited business schools in the Gulf
Cooperation Council (GCC) countries, Azad and Seyyed (2007) found that faculty rated
themselves moderate-high on procedural knowledge of how to go about turning research ideas
into publications. The interview data indicated that faculty at HKUST acquired procedural
knowledge on how to publish their research in peer-reviewed journals through mentoring by
senior faculty within and outside of HKUST, and through taking on reviewer and editorial roles
in academic journals to gain first-hand understanding of the behind-the-scenes processes.
According to Clark and Estes (2008), training is an effective form of intervention when
demonstration, guided practice, and feedback are needed for performance improvement.
Departments could provide training on the steps of the research publishing process for peer-
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103
reviewed publications in their fields, matched with faculty members’ past training and
experience in conducting and publishing research. The training could take the form of one-on-
one mentoring by senior faculty, and through organizing sharing sessions by faculty on their
experience of the steps required in publishing research. By targeting training and instruction
between faculty’s independent performance level and their level of assisted performance,
departments could optimize these learning opportunities for faculty (Scott & Palincsar, 2006).
Metacognitive knowledge assets. Krathwohl (2002) defined metacognitive knowledge
as self-awareness of one’s thinking, including strategic knowledge, knowledge about cognitive
tasks, and self-knowledge. To be research productive, faculty need the knowledge and self-
awareness to prioritize and plan their research activities, and to manage their time between
competing demands (Azad & Seyyed, 2007). The survey data was inconclusive on the role of
metacognitive knowledge assets on faculty research productivity, with a respondent mean score
of 4.9 out of a 6-point scale falling below the 5.0 validation mark, indicating a lack of support for
the assertion that faculty have been able to manage their workload across research, teaching, and
other activities.
In a study on research productivity of academic accountants, Levitan and Ray (1992)
found that the ability to effectively manage time was the most important factor for faculty to be
research productive. Greater knowledge of time management skills was associated with high-
performing researchers, termed as “research stars” (White, James, Burke, & Allen, 2012).
Despite the findings from Levitan and Ray (1992) and White et al. (2012), one possible reason
that this metacognitive influence was not validated was that HKUST faculty felt that their overall
workload was overbearing, and that they found it difficult to balance between their research and
teaching requirements. These challenges at managing workload were mentioned by several
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104
faculty in the interviews.
To help faculty acquire metacognitive knowledge, the University could provide faculty
with instruction in metacognitive skills, as such skills help to increase the self-regulation
capabilities of individuals (Dembo & Eaton, 2000). As part of the on-boarding process for
faculty, training could be provided on time management skills and metacognitive strategies to
self-monitor and regulate time commitments on an ongoing basis. To provide structured
opportunities for faculty to engage in guided self-monitoring and self-assessment (Baker, 2006),
departments could use the annual performance appraisal process to have faculty members reflect
on their workload and time management strategies over the past year, and to identify
improvement areas and action steps to take.
Based on the survey results and interview findings, it was inconclusive that faculty’s
metacognitive assets contributed to HKUST’s faculty research productivity, in contrast with
findings in the literature. Further investigation is needed to find the underlying reasons for the
incongruence.
Motivation Recommendations
Introduction. Clark and Estes (2008) identified individuals’ motivation as a critical
factor to address business gaps and to improve organizational performance. Motivation is an
“internal state that initiates and maintains goal directed behavior” (Mayer, 2011, p. 39), a critical
determinant of choice, persistence, and effort on tasks (Clark & Estes, 2008), and a primary
influencer on performance improvement (Rueda, 2011).
Individuals can be influenced by a combination of extrinsic and intrinsic motivators.
Behavioral theorists advocate that behaviors can be strengthened or weakened through the use of
reinforcements or punishments (Daly, 2009), and desired behaviors can hence be elicited through
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105
controlling environmental influences (Tuckman, 2009), such as the provision of feedback and
reinforcing incentives. Social cognitive theorists advocate that behaviors can be strengthened or
weakened through vicarious reinforcement and punishment (Mayer, 2011), and the modeling of
desired behaviors that are overt, credible, and similar can increase the likelihood of adoption of
the behavior by observers (Denler, Wolters, & Benzon, 2009). Motivation can be enhanced
through invoking personal interest (Schraw & Lehman, 2009), goal-setting (Pintrich, 2003;
Yough & Anderman, 2006), and providing performance feedback that emphasizes the roles of
effort, strategies, and self-control (Anderman & Anderman, 2009).
In the context of this study, the three specific motivational assets examined were faculty’s
perceptions on the value, interest, and self-efficacy in being research productive. Based on the
analysis of the survey results and interviews conducted, these three motivational assets were
validated, as shown in Table 16. All three assets were regarded as high priority, as they have
been shown in the research literature to be strong predictors of active choice, persistence, and
mental effort. For each of the prioritized influences, context-specific recommendations were
proposed, grounded on principles drawn from the research literature.
Table 16
Summary of Motivation Influences and Recommendations
Assumed
Motivation Asset
Validated
(Y, N)
Priority
(Y, N)
Principle and Citation Context-Specific
Recommendation
Faculty value
producing research
publications.
(Value)

Y Y Learning and
motivation are
enhanced if the learner
values the task (Eccles,
2006).

Showcase role models
at each department
level, on how research
achievements
contributed to their
success – provide
opportunities to observe
a credible, similar
model engaging in
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106
behavior that has
functional value
(Pajares, 2006).
Faculty has the
interest to conduct
research. (Interest)

Y Y Activating and building
upon personal interest
can increase learning
and motivation (Schraw
& Lehman, 2009).

In recruiting and
promoting faculty, seek
out those who have
strong intrinsic interest
in their fields and
demonstrate strong
intellectual curiosity.
Faculty believe that
they have the
requisite ability and
skills to publish
peer-reviewed
research within
their respective
fields. (Self-
Efficacy)

Y Y Feedback and modeling
increases self-efficacy
(Pajares, 2006).

Get senior faculty to
mentor junior faculty,
to guide them and
provide feedback on
their research
publication process --
provide goal-directed
practice coupled with
frequent, accurate,
credible, targeted and
private feedback on
progress in learning and
performance (Pajares,
2006).

Expectancy value. Eccles (2006) described expectancy value as a person’s expectations
to succeed at a task, and the value the person perceives about undertaking the task. Such task
value is influenced by intrinsic interest (personal enjoyment expected), attainment value
(alignment with one’s self-identity), utility value (expected benefit toward one’s longer-term
goals and rewards), and perceived cost (resources, time, and other costs) associated with
pursuing the task (Eccles, 2006). The survey data indicated that HKUST’s research faculty
valued being research productive and were interested in research work, with a respondent mean
score of 5.5 out of a 6-point scale indicating that they found it important to produce research
publications, and a respondent mean score of 5.6 out of a 6-point scale indicating that they
enjoyed conducting research.
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107
The motivation for academics to be research productive has been associated with
expectancy value factors such as areas of academic interest (intrinsic interest), self-perceptions
of academic career success and satisfaction with research achievements (attainment value),
rewards and compensation (utility value), and time allocated to research versus teaching and
other duties (perceived cost) (Azad & Seyyed, 2007). The interview data indicated that faculty at
HKUST were driven by strong intellectual curiosity and intrinsic interest in their academic
fields, that they valued producing high-quality research publications to advance their careers and
professional standing, as well as impacting the academic community through creating new
knowledge and benefiting society through addressing real-world issues.
Motivation is enhanced through individuals valuing the tasks at hand (Eccles, 2006), and
by activating and building upon their personal interest (Schraw & Lehman, 2009). Departments
could showcase role models on how research achievements contributed to the success of faculty
within their departments, to demonstrate to other faculty the tangible benefits of being research
productive. Furthermore, in recruiting and promoting faculty, departments could seek out those
who have strong intrinsic interest in their fields and who demonstrate strong intellectual
curiosity. By providing opportunities for faculty to observe credible and similar role models
(Pajares, 2006), faculty’s motivation for research publications could be enhanced. In a study on
the research productivity of academic accountants, Levitan and Ray (1992) found that there was
increasing emphasis on research productivity when academic institutions conducted faculty
appraisals for granting of tenure and making salary decisions, which translates to increased value
perceived by academics for being research productive. In another study of accounting faculty
from AACSB-accredited Colleges of Business, Chen, Nixon, Gupta, and Hoshower (2010)
identified a number of extrinsic and intrinsic rewards that related to the perceived value of
RESEARCH PRODUCTIVITY FACTORS

108
research productivity, including tenure, promotion, salary raises, reduced teaching loads, peer
recognition and respect from students, and personal need for creativity/curiosity. Hence,
motivation and performance could be enhanced through taking actions that address the
expectancy value of faculty for producing research publications.
Self-efficacy. Pajares (2006) defined self-efficacy as the assessments made by people
about their own ability to learn or perform up to specified levels. According to the author, self-
efficacy is both personal and social, with experiences of success leading to higher self-efficacy,
and experiences of failure leading to lower self-efficacy. Based on social-cognitive theory,
people will not be motivated to take action if they do not believe that they can achieve the
desired results (Bandura, 2000). In other words, self-efficacy impacts an individual’s motivation
to undertake particular tasks. The survey data indicated that HKUST’s research faculty were self-
efficacious in their capability to be research productive, with a respondent mean score of 5.6 out
of a 6-point scale indicating that they believed that they had the ability to produce peer-reviewed
research publications. This survey finding was further supported by a respondent mean score of
5.0 out of a 6-point scale indicating that faculty were regarded by their colleagues as being
productive researchers, and a mean of 15.5 peer-reviewed publications per faculty in the past
three years.
Faculty’s research productivity is impacted by their beliefs about their skills and ability to
produce research publications, and a strong sense of self-efficacy leads to higher levels of
motivation and effort (Pajares, 2006). Blackburn and Lawrence (1995) found that self-efficacy
was a major factor accounting for variations in research productivity of faculty, and that
confidence in research abilities was closely related to faculty’s research output. The interview
data indicated that faculty at HKUST believed that they were able to produce high quality
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109
research publications that were on par with the best academics globally. A strong sense of self-
efficacy was especially evident among senior faculty members. While junior faculty expressed
some concerns about the lack of clarity on the expected number of research papers in top
journals required to make tenure, they appeared to be confident about their ability to produce
such publications.
Self-efficacy can be increased through feedback and modeling (Pajares, 2006). To
increase the self-efficacy of faculty, departments could get senior faculty to mentor junior faculty
on getting their research published, and to guide and provide feedback to junior faculty
throughout the process. Motivation is increased through such goal-directed practice with
frequent, accurate, credible, targeted, and private feedback on progress in learning and
performance (Pajares, 2006). Faculty’s research track record is likely to impact their sense of
self-efficacy toward being research productive, and formal research mentorship programs (Azad
& Seyyed, 2007) and research role models (Hales, Shahrokh, & Servis, 2009) could help
increase faculty self-efficacy in producing research publications.
Organization Recommendations
Introduction. Clark and Estes (2008) identified organizational factors as the third critical
dimension to address business gaps and to improve organizational performance. Organizational
influences on performance can be analyzed using the Cultural Model-Cultural Settings
framework. Cultural models are the invisible and automated values, beliefs, and attitudes
embodied within organizations and individuals, while cultural settings are the visible and
tangible manifestations of cultural models found within organizations (Gallimore & Goldenberg,
2001). According to this framework, learning and performance of an organization is influenced
by the interaction of organizational and individual cultural models, past and present cultural
RESEARCH PRODUCTIVITY FACTORS

110
settings, and individuals’ beliefs, perceptions, and goals. Schein (2004) described culture as an
abstract yet powerful force within social and organizational settings, a representation of the
shared history, experiences, and learning of a given group, embodied in values and attitudes that
are passed on from generation to generation. Consequently, an understanding of cultural
elements is vital to effective leadership of organizations (Schein, 2004).
In the context of this study, the three specific organizational cultural influences examined
were organizational expectations and goals, performance incentives, and policies and practices.
Based on the analysis of the survey results and interviews conducted, two of the organizational
influences were validated, namely expectations and goals and performance incentives, as shown
in Table 17. Both validated influences were regarded as high priority, as they have been shown
in the research literature as being strong influences on learners’ motivation and performance. For
each of the prioritized influences, context-specific recommendations were proposed, grounded
on principles drawn from the research literature.
Table 17
Summary of Organization Influences and Recommendations
Assumed
Motivation Asset
Validated
(Y, N)
Priority
(Y, N)
Principle and Citation Context-Specific
Recommendation
The University has
clear goals and
performance
expectations for
faculty research
publications.
(Expectations and
Goals)
Y Y 1. Goal setting
enhances learning and
performance (Dembo &
Eaton, 2000). High
performance
expectations of success
through the setting of
goals can positively
impact learning and
motivation (Eccles,
2006; Mayer, 2011).
Organizational
effectiveness
1. Define clear vision
and goals at the
University, School,
department, and
individual faculty levels
for faculty research
publications.
Communicate these
goals to all faculty.

2. Benchmark key
performance indicators
with top global
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111
increases when leaders
set clear, concrete and
measurable goals,
aligned with the
organization’s vision
(Bolman & Deal, 1997;
Lipton, 1996).

2. Different types of
benchmarking
contribute data to
improve organizational
performance (Bogue &
Hall, 2003; Marsh,
2012).
institutions on research
productivity.
Communicate the
relative position and
trajectory of HKUST
key performance
indicators, versus other
leading institutions.

The University has
appropriate
incentive systems
which reward and
encourages research
productivity.
(Performance
Incentives)
Y Y 1. Perceived importance
and value derived from
completing a task
affects the level of
motivation (Eccles,
2006)

2. Rewards, when
appropriately
structured, provide a
form of feedback to
individuals on their
level of competence
and skills that could aid
improvement efforts
(Pintrich, 2003).
Establish system of
financial and non-
financial rewards to
recognize research
productivity. Rewards
have to be perceived by
faculty to be of value to
them, and performance
feedback should be
formative in nature to
help faculty improve.
The University
institution has
effective policies
and practices in
place to support
research
productivity.
(Policies and
Practices)
N N Honesty and fairness
lead to trust and work
motivation. Remove
unnecessary rules and
barriers for work
performance (Clarke &
Estes, 2008).

Review and institute
policies and practices
that effectively enable
faculty to be research
productive, and to
remove barriers that
impede their ability to
conduct and publish
research. Ensure
transparency and
accountability of
policies and practices.

RESEARCH PRODUCTIVITY FACTORS

112
Expectations and goals. Research shows that faculty research productivity is influenced
by organizational characteristics and factors (Jung, 2012), and the setting of clear performance
expectations and goals for faculty to conduct and publish high-quality research positively
impacts their research productivity (Hales, Shahrokh, & Servis, 2005). One of the strongest
drivers of work behavior is a sense of purpose, observed through the general goal-orientation of
individuals (Moran & Brightman, 2000). Goal-setting enhances learning and performance, and
has been reported to occur with greater frequency and consistency with higher achievers
compared to low achievers (Dembo & Eaton, 2000). The survey data indicated that HKUST’s
research faculty perceived that there were clear expectations and goals on research productivity
at the University, with a respondent mean score of 5.0 out of a 6-point scale indicating that they
knew what the institutional expectations were on their research productivity.
Academic institutions have increasingly emphasized their performance expectations on
research productivity, as such productivity enhances visibility and reputation of the institutions,
thereby attracting research grants, and high quality faculty and students (Hu & Gill, 2000). The
interview data indicated that academic departments at HKUST expected their faculty to be as
research productive as academics from other top institutions globally. The Research Assessment
Exercise (RAE) conducted periodically by the University Grants Committee in Hong Kong
required faculty’s research outputs to be rated against international benchmarks of research
excellence (UGC, 2014), further reinforcing the performance expectations on faculty’s research
productivity.
Having high performance expectations of success through the setting of goals positively
impacts learning and motivation (Eccles, 2006; Mayer, 2011). Organizational effectiveness is
increased when leaders set clear, concrete, and measurable goals aligned with an organization’s
RESEARCH PRODUCTIVITY FACTORS

113
vision (Bolman & Deal, 1997; Lipton, 1996), with benchmarking against similar organizations
conducted to provide relevant data to inform organizational performance improvements (Bogue
& Hall, 2003; Marsh, 2012). To enhance research productivity, clear vision and goals related to
faculty research publications could be defined at the university, school, department, and
individual faculty levels, with the vision and goals communicated effectively to all faculty. In
addition, benchmarking of key performance indicators (KPI) on research productivity with top
global institutions, coupled with communicating the relative position and trajectory of the
University versus other leading institutions, could help to inform the setting of performance
goals for faculty on research publications. The setting of appropriately challenging goals
facilitates the defining of desired futures and outcomes, and the framing of action plans to
achieve these outcomes (Denler, Wolters, & Benzon, 2009). In a study involving goal setting
using faculty practice plans at the Department of Psychiatry and Behavioral Sciences at the
University of California, Davis School of Medicine, Hales, Shahrokh, and Servis (2005) found
that the development and objective application of such practice plans was associated with an
increase in the amount of total research grants secured and research publications produced over a
four-year period.
Performance incentives. Appropriate incentive systems which reward high-quality and
impactful research performance have a positive impact on faculty research productivity.
Improving performance involves the consideration of individuals’ motivations (Langley et al.,
2009), and work that is perceived to be important and produces value is positively regarded by
individuals (Eccles, 2006). Rewards, when appropriately structured, increase the motivation of
individuals to perform on tasks, and provide a form of feedback to individuals on their level of
competence and skills (Pintrich, 2003). The survey data indicated that HKUST’s research faculty
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114
perceived that the University had an effective performance incentive system in place, with a
respondent mean score of 5.1 out of a 6-point scale indicating that they felt that the performance
incentives were effective in encouraging research productivity. The interview data indicated that
in the domain of research, senior faculty were driven to a large extent by their own intrinsic
motivation, in particular interest in their academic fields and innate desire for their research to
produce impact on society. Consequently, extrinsic motivators such as financial incentives for
performance were regarded to be of secondary importance to senior faculty. In contrast,
interviews with junior faculty suggested that gaining tenure status at the University was a strong
primary motivator for them, and this served as an important performance incentive for them to be
research productive.
The University could encourage faculty to be research productive by designing and
implementing an effective performance incentive system that appeals to faculty of different
seniority levels, based on appropriate financial and non-financial rewards to recognize and
reward research productivity. Such rewards have to be perceived by faculty to be of value to
them. In a study of accounting faculty from business colleges, Chen, Nixon, Gupta, and
Hoshower (2010) found that faculty perceived strong linkages between research productivity and
tenure and promotion, and that universities were effectively leveraging on these perceptual
linkages to motivate faculty to be research productive. Other studies have also found that
financial rewards could be used to provide tangible incentives to influence faculty to allocate
their time and effort toward research activities (Hales, Shahrokh, & Servis, 2005; Hales,
Shahrokh, & Servis, 2009).
Policies and practices. Research shows that organizational policies and practices are
factors that influence the climate of an organization, and consequently the performance of
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members of the organization (Schneider, Brief, & Guzzo, 1996). Financial policies that provide
adequate resources and support for conducting rigorous research could positively impact
faculty’s production of research publications (Hales, Shahrokh, & Servis, 2005; Jung, 2012).
Research productivity could be enhanced through instituting practices that support faculty in
securing external grants and contracts, and the provision of library, technology, and graduate
student resources to support academic research (Dundar & Lewis, 1998). The survey data was
inconclusive on the effectiveness of HKUST’s organizational policies and practices assets, with
a respondent mean score of 4.9 out of a 6-point scale falling below the 5.0 validation mark,
indicating a lack of support for the assertion that HKUST’s policies and practices were effective
in supporting faculty research productivity. The interview data indicated that HKUST had
policies and practices that engendered a conducive research environment with low teaching and
service load for junior faculty, making it easy for them to start their academic careers. However,
faculty expressed concerns over the lack of transparency in the policies and practices on faculty
appraisal and resource allocation, with more than half of the interviewees recounting their own
and others’ negative experiences in these areas.
The University could encourage faculty to be research productive by reviewing and
instituting policies and practices that effectively enable faculty to be research productive, such as
reducing teaching load to allow more time for junior faculty to conduct research. The University
could also address barriers that impede faculty’s ability to conduct and publish research, such as
the lack of transparency in the faculty performance appraisal practices. In a web-based survey of
a random sample of 236 faculty members from across a wide range of accredited business
schools, White, James, Burke, and Allen (2012) found that policies and practices allowing
faculty to have more time to conduct research, through the reduction of course preparations and
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teaching loads, were reported to be contributing factors toward increased research productivity
among highly-productive research faculty. A climate of transparency in an organization’s
policies and practices fosters trust and honesty, encourages constructive conversations, and
enforces accountability, all of which contribute toward enhanced staff performance (Lencioni,
2004). Honesty and fairness lead to greater trust and work motivation, and organizations can
remove unnecessary rules and barriers to improve work performance (Clarke & Estes, 2008).
Integrated Implementation and Evaluation Plan
To put into action the recommendations made to address the validated knowledge,
motivation, and organization assets, the New World Kirkpatrick Model (Kirkpatrick &
Kirkpatrick, 2016) was used to design an integrated implementation and evaluation plan.
Implementation and Evaluation Framework
The New World Kirkpatrick Model (Kirkpatrick & Kirkpatrick, 2016) builds upon the
original Kirkpatrick Model (Kirkpatrick & Kirkpatrick, 2006), advocating a training evaluation
model comprising four levels. Level 1:Reaction measures the extent to which participants found
the training to be favorable, engaging and relevant. Level 2:Learning measures the acquisition of
knowledge, skills, attitude, confidence, and commitment from participating in the training. Level
3:Behavior measures the extent to which participants apply what they learned to their jobs. Level
4:Results measures the extent to which training and the supporting accountability package result
in intended outcomes. According to Kirkpatrick and Kirkpatrick (2016), the evaluation of
training programs is useful for improving the programs, transferring learning to actual work
behaviors, contributing to organizational performance, and providing evidence on the benefits of
training. The New World Kirkpatrick Model was used to design the integrated implementation
and evaluation plan for the recommendations made in this dissertation study.
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Organizational Purpose, Need, and Expectations
The vision of HKUST is “to be a leading University with significant international impact
and strong local commitment” (HKUST Mission & Vision, n.d., para. 2). The organization’s
performance goal is to continue to rank among the top 10 Asian universities and among the top
50 universities globally by December 2018. This goal embodies HKUST’s aspirations to be
competitively positioned against peer institutions within Asia and globally. The stakeholders of
focus for this study are all HKUST’s research faculty members. Research faculty play a vital role
in the research outcomes of the university, which is a key dimension being assessed in several
major international university rankings. The stakeholder goal is for faculty to increase their total
research publications by at least 10% every year, to be achieved by December 2017.
Through this study of the success factors behind HKUST faculty members’ research
productivity, it is envisioned that HKUST would continue to support the relevant promising
practices and strategies to sustain its strong performance in research productivity and
international university rankings. In addition, other institutions aspiring to improve their research
productivity and rankings performance could consider adapting these practices and strategies for
their own developmental contexts.
Level 4: Results and Leading Indicators
Leading indicators are short-term measurements to assess the progress of efforts towards
desired outcomes, providing formative feedback for addressing factors that enable the
achievement of those outcomes (Kirkpatrick & Kirkpatrick, 2016). Based on the context-specific
recommendations identified to address the knowledge, motivation, and organization influences, a
set of leading indicators was identified to measure the progress toward achieving the desired
goals on faculty research productivity, as shown in Table 18. The metrics included both
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quantitative and qualitative measures, drawing upon existing reporting structures as the
assessment methods where feasible.
Table 18
Outcomes, Metrics, and Methods for External and Internal Outcomes
Outcome Metric(s) Method(s)
External Outcomes
1. Faculty recruited and promoted have
strong intrinsic interest in their fields
and demonstrate strong intellectual
curiosity.
Qualitative assessment on
the academic and
intrinsic interest of
faculty.
Faculty recruitment /
promotion reports.
2. Faculty interact with other academics
within and outside their fields.
Number of external
academic events attended
by faculty.
Faculty annual
performance reports.
3. Financial and non-financial reward
system to recognize research
productivity perceived by faculty to be
of value to them.
Perception ratings by
faculty.
Survey – Likert
scale.
Internal Outcomes
4. Faculty read articles published in the
most relevant journals and attend the
most informative conferences and events
in their academic fields.
Frequency of faculty
engaging in behavior.
Survey – Likert
scale.
5. Faculty follow the prescribed steps of
the research publication process for
leading journals, and apply learning
points from mentoring by senior faculty.
Frequency of faculty
engaging in behavior.
Survey – Likert
scale.
6. Faculty members are inspired by
departmental role models, on how
research achievements contributed to
their success.
Perception ratings by
faculty.
Survey-Likert scale.
7. Faculty members are clear about
vision, goals and competitive position
for faculty research publications.
Ability to recall and
articulate the vision,
goals, and competitive
position.
Survey / interviews /
performance review
discussions with
academic heads.
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Level 3: Behavior
Critical behaviors. Critical behaviors are specific, observable, and measurable actions
that translate learning into desired outcomes (Kirkpatrick & Kirkpatrick, 2016), thereby
providing a comprehensive, ongoing system for monitoring and improving performance. To
achieve the outcomes of the context-specific recommendations, a set of critical behaviors were
identified, as shown in Table 19. These behaviors are expected to be performed regularly to
enable the achievement of the desired results. Kirkpatrick and Kirkpatrick (2016) advocated the
necessity of narrowing down to a few critical behaviors with the most influence on Level 4
results, as too many identified behaviors would lead to confusion among staff, and would be
difficult to support and manage as a whole. The subset of critical behaviors with the most
influence on the Level 4 results defined in Table 18 are marked in asterisks in Table 19.
Table 19
Critical Behaviors, Metrics, Methods, and Timing for Evaluation
Critical Behavior Metric(s) Method(s) Timing
*1a. Communicate with
department heads and faculty on
emphasizing intrinsic interest
and intellectual curiosity in the
recruitment and promotion
evaluation processes.
Faculty awareness. Survey – Likert
scale.
Three months
post training.
1b. Redesign report templates for
recruiting and promoting faculty
to emphasize the evaluation of
intrinsic interest and intellectual
curiosity.
Percentage of
departments with
redesigned forms.
Document
analysis.
Six months
post training.
2a. Faculty attend academic
events.
Number of academic
events attended by
faculty.
Faculty annual
performance
reports.
Annually.
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2b. Faculty collaborate with
academics on research.
Percentage of faculty
with co-authors in
their research
publications.
Statistical analysis
of publication
databases.
Half-yearly.
*3a. Redesign financial rewards
for research productivity.
Completion of review
and redesign.
Document
analysis.
Six months
post training.
3b. Redesign non-financial
rewards for research
productivity.
Completion of review
and redesign.
Document
analysis.
Six months
post training.
4a. Departments maintain an
updated list of relevant journals
and conferences and events.
Faculty’s awareness
of the list of relevant
journal and academic
events.
Survey – Likert
scale.
Three months
post training.

4b. Departments communicate
the list with faculty.
Number of
communication
events.
Performance
reporting by
academic
departments.
Annually.

5a. Faculty are trained on the
research publication process.
Number of faculty
who attended training
on research
publication process.
Performance
reporting by
academic
departments.
Annually.

*5b. Faculty are mentored on the
research publication process.
Number of formal
mentoring
arrangements
established between
senior and junior
faculty.
Performance
reporting by
academic
departments.
Annually.

6a. Identify role models at each
department level.
Number of role
models identified.
Survey of
department heads.
Three months
post training.
*7a. Communicate with faculty
on the vision and goals for
research publications at the
University, School, department,
and individual faculty levels.
Percentage of faculty
communicated with.
Survey. One month
post training.
7b. Communicate with faculty on
the relative position and
trajectory of HKUST key
performance indicators, versus
other leading institutions.
Percentage of faculty
communicated with.
Survey. One month
post training.
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Note. Items marked with asterisks (*) indicate critical behaviors with the most influence on
Level 4 results for the study.

Required drivers. Required drivers are the support and accountability measures that help
to monitor the execution of critical behaviors toward achieving the intended results (Kirkpatrick
& Kirkpatrick, 2016). In alignment with the context-specific recommendations identified to
address the knowledge, motivation, and organization influences, a set of reinforcing,
encouraging, and rewarding drivers were identified, as shown in Table 20. These drivers are
targeted at the motivation and organizational influences in particular, addressing the expectancy-
value and self-efficacy of faculty in producing research publications, setting clear expectations
and goals on faculty research productivity, and having appropriate performance incentives in
place to encourage faculty performance.
Table 20
Required Drivers to Support Critical Behaviors
Method(s) Timing Critical Behaviors
Supported
Reinforcing
Provide evaluation checklist for faculty recruitment and
promotion that incorporates intrinsic interest and
intellectual curiosity dimensions.
Immediate. 1a, 1b.
Department heads to send regular reminders to faculty
on academic events.
Ongoing. 2a.
Provide checklist templates to department heads for
listing of journals and events relevant to their fields.
Three months. 4a, 4b.
Disseminate communication materials on HKUST’s
research productivity and impact.
Ongoing. 7a, 7b.
Encouraging
Provide coaching to senior faculty on how to train and
mentor junior faculty on research publication processes.
Ongoing. 5a, 5b.
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Rewarding
Recognize research collaborations in faculty
performance evaluation.
Annual
performance
appraisal.
2b.
Provide enhanced recognition awards for research
productivity.
Six months. 3a.
Implement faculty pay-for-performance system linked
to research productivity.
Six months. 3b.
Provide recognition through newsletters and university
events for faculty role models identified by
departments.
Ongoing. 6a.

Monitoring. A range of monitoring tools would be employed to ensure accountability on
the critical behaviors and the achievement of desired outcomes. Where feasible, monitoring
metrics would be drawn from quantitative and qualitative data generated through existing work
processes (Kirkpatrick & Kirkpatrick, 2016), such as annual faculty performance appraisals and
research output reports generated from publication databases. Self-monitoring through regular
reporting and checklists would be implemented to enable department heads to hold their
departments and faculty accountable. In addition, interviews, observations, and document
analyses would be carried out to monitor the ongoing implementation of the reinforcing,
encouraging, and rewarding drivers. These monitoring tools would also be deployed to assess the
execution of critical behaviors and progress toward desired outcomes. The critical behaviors and
drivers form an action plan, which would will be monitored against in tracking the
implementation of the various components of the plan.
Organizational support. The critical behaviors and required drivers have to be
supported by the implementation of the organizational-level recommendations. HKUST would
need to provide clear expectations and goals and appropriate performance incentives to support
faculty research productivity.
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The definition of Level 3 critical behaviors and required drivers provide the methods for
ongoing assessment of execution and performance, forming a comprehensive, continuous
performance monitoring and improvement system (Kirkpatrick & Kirkpatrick, 2016).
Level 2: Learning
In support of the Level 3 behaviors toward achieving the Level 4 results, it is helpful to
assess the extent to which training and mentoring had equipped stakeholders with the intended
Level 2 knowledge, skills, attitude, confidence, and commitment (Kirkpatrick & Kirkpatrick,
2016). The learning goals were first defined, followed by designing programs that deliver on the
learning goals, and subsequently detailing the components of learning to achieve the learning
goals.
Learning goals. After implementing the recommended solutions, the stakeholders would
be able to:
1. List the most relevant journals to read in their academic fields. (Declarative)
2. List the most relevant conferences and events to attend in their academic fields.
(Declarative)
3. Design an action plan and timeline for publishing their research papers. (Procedural)
4. Value being research productive in pursuit of their own success. (Value)
5. Value intrinsic academic interest in recruiting and promoting faculty. (Value)
6. Indicate confidence that they can publish peer-reviewed research. (Self-Efficacy)
7. Articulate the expectations and goals on their research productivity. (Expectations
and Goals)
8. Compare their performance relative to other benchmark institutions. (Expectations
and Goals)
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Program. The learning goals listed in the previous section would be addressed through a
faculty training and mentoring program that explores in-depth the research and publication
process, incorporating information specific to the various academic fields. Faculty would learn
about a range of topics pertaining to keeping current with knowledge in their academic fields,
generating new research ideas, and steps to take to accelerate their research publication process.
The program consists of three components -- a University-wide information session (two hours
in duration), a department-level workshop (four hours in duration), and a one-to-one mentoring
arrangement (over the span of one to two years).
At the University-wide information session, the directions and goals for faculty research
would be communicated, along with sharing by research productive faculty on their strategies
and practices. Jobs aids would be distributed to departments for listing of the relevant journals,
conferences, and events in their respective academic fields, along with revised checklists for
faculty performance evaluation. The information shared at the session would also be captured
electronically and incorporated into the faculty handbook. At the department-level workshops,
department-specific expectations and standards would be shared and discussed, including details
on the research publication processes specific to the respective academic fields of the
department. For the one-to-one mentoring arrangements, senior faculty would be assigned to
coach junior faculty along the research publication process, with the possibility of senior faculty
engaging as research collaborators and publication co-authors with their mentees.
Components of learning. To be successful at applying learning to the job, faculty must
have the prerequisite declarative knowledge, as well as procedural knowledge on how to apply
their declarative knowledge to solve problems. It is also necessary for faculty to see value in
using their newly acquired knowledge, to have a sense of confidence that they can be successful,
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and to be committed to applying their knowledge and skills at their jobs. Table 21 lists the
evaluation methods and timings for these learning components.
Table 21
Components of Learning for the Program.
Method(s) or Activity(ies) Timing
Declarative Knowledge “I know it.”
Knowledge checks through small group
discussions.
During the University-level sharing
session, documented via observation notes.
During the department-level workshop,
documented via observation notes.
Pre- and post-test assessment survey asking
participants about their level of knowledge
before and after training.
At the end of sharing session.
At the end of workshop.
Procedural Skills “I can do it right now.”
Knowledge checks through small group
discussions.
During the University-level sharing
session, documented via observation notes.
During the department-level workshop,
documented via observation notes.
Assessment survey asking participants about
their level of skills before and after training.
At the end of sharing session.
At the end of workshop.
Attitude “I believe this is worthwhile.”
Small group discussions on the value of what
they are being asked to do on the job.
During the University-level sharing
session, documented via observation notes.
During the department-level workshop,
documented via observation notes.
Assessment survey asking participants about
their attitudes before and after training.
At the end of sharing session.
At the end of workshop.
Confidence “I think I can do it on the job.”
Small group discussions on the confidence of
being able to apply learnings to the job.
During the University-level sharing
session, documented via observation notes.
During the department-level workshop,
documented via observation notes.
Assessment survey asking participants about
their confidence levels before and after training.
At the end of sharing session.
At the end of workshop.
Commitment “I will do it on the job.”
Create an individual action plan. During the department-level workshop.
Create a joint action plan with senior faculty
mentor.
After assignment of faculty mentor.
Assessment survey asking participants about
their commitment levels before and after
training.
At the end of sharing session.
At the end of workshop.
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Assessment feedback by faculty mentor. Periodic intervals (3, 6, 12 months) after
mentor assignment.

Level 1: Reaction
To confirm the quality of the training and mentoring program, the participants’
engagement, sense of relevance, and satisfaction with the training program would be assessed
through a variety of methods and timings (Kirkpatrick & Kirkpatrick, 2016). Table 22 lists the
methods used to determine participants’ reactions to the learning events, and the timings that the
methods would be applied.
Table 22
Components to Measure Reactions to the Program.
Method(s) or Tool(s) Timing
Engagement
Attendance rate. During the University-level sharing session.
During the department-level workshop.
Observations by facilitators
of discussions groups.
During the University-level sharing session.
During the department-level workshop.
Participants’ evaluation. At the end of the University-level sharing session.
At the end of the department-level workshop.
Periodic (3, 6, 12 months) feedback on mentoring arrangement.
Relevance
Brief pulse-check with
participants
During the University-level sharing session.
During the department-level workshop.
Observations by facilitators
of discussions groups.
During the University-level sharing session.
During the department-level workshop.
Participants’ evaluation. At the end of the University-level sharing session.
At the end of the department-level workshop.
Periodic (3, 6, 12 months) feedback on mentoring arrangement.
Customer Satisfaction
Brief pulse-check with
participants.
During the University-level sharing session.
During the department-level workshop.
Participants’ evaluation. At the end of the University-level sharing session.
At the end of the department-level workshop.
Periodic (3, 6, 12 months) feedback on mentoring arrangement.

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Evaluation Tools
An evaluation tool was created to assess the effectiveness of the training and mentoring
program (see Appendix F), to be administered during and immediately following the program
implementation, and delayed for a period after program implementation. The Blended Evaluation
approach advocated by Kirkpatrick and Kirkpatrick (2006) was used to design the evaluation
tools, whereby multiple levels are assessed through the same tool to draw out a range of
perspectives on the learning experience and application of learning to work settings to achieve
business results. For the assessment immediately following the program implementation, the
evaluation included questions on Level 1 reactions to the training, and Level 2 questions on the
confidence and commitment to apply learning to the job, and on expected application and results
from learning. For the delayed evaluation after program implementation, the evaluation tool
focused on learners’ Level 3 application of their learning and the support received, Level 4
results that were achieved, and a reassessment of the Level 2 confidence and commitment and
Level 1 value of the program (Kirkpatrick & Kirkpatrick, 2006).
Immediately following the program implementation. For Level 2, right after the
University-level sharing session and department-level workshops, assessment surveys would be
administered to all participants, to measure the knowledge, attitudes, confidence, and
commitment of the participants. Observations would also be carried out within the small group
discussions at the sharing session and workshops, as an additional source of data to triangulate
the findings (Maxwell, 2013). For Level 1, participants’ reactions would be solicited through
surveys administered immediately after the University-level sharing session, and after the
department-level workshops. Again, observations would be carried out by the facilitators of the
small group discussions for triangulation purposes. Brief pulse checks would also be carried out
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during the sharing session and workshops, to assess the relevance and satisfaction of faculty with
the training program (Kirkpatrick & Kirkpatrick, 2016).
Delayed for a period after the program implementation. Around three months after
the implementation of the training and mentoring program, and then again at six months and 12
months, a survey comprised of open-ended and scaled items would be administered to all faculty.
Using the Blended Evaluation approach, the survey would assess how faculty had applied their
learning and the support they had received through their mentors (Level 3), and the extent to
which their approaches to publishing research had improved (Level 4). In addition, the survey
would also evaluate faculty’s confidence and commitment to continue applying their learning
(Level 2), and to reassess their perspectives on satisfaction and relevance of the training and
mentoring program (Level 1).
Data Analysis and Reporting
The Level 4 goals for faculty would be measured by the frequency of the desired
behaviors, in:
● reading articles published in the most relevant journals in their academic fields,
● attending the most informative conferences and events in their academic fields,
● following the prescribed steps of the research publication process for leading journals,
and
● applying learning points from mentoring by senior faculty.
Data would be drawn from the delayed surveys administered after the program implementation.
The dashboard in Figure 13 would be used to report the data on these measures as a monitoring
and accountability tool. Similar dashboards would be created to monitor the Levels 1, 2 and 3
results.
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Proportion of Faculty Reporting
Frequency of Engaging in Behavior

Behavior/Result Low Medium High Rating
Reading articles published in the most
relevant journals in their academic fields.
0% 10% 90%
😄
Attending the most informative conferences
and events in their academic fields.
10% 50% 40%
😐
Following the prescribed steps of the research
publication process for leading journals.
5% 20% 75%
😄
Applying learning points from mentoring by
senior faculty.
70% 20% 10%
😞
Figure 13. Dashboard sample of Level 4 goals. Measured by proportion of faculty reporting the
frequency of engaging in desired behaviors.

Summary
The New World Kirkpatrick Model (Kirkpatrick & Kirkpatrick, 2016) was used to plan,
implement, and evaluate the recommendations identified for HKUST and other aspiring
universities to optimize actions toward achieving the goal of increased research productivity
leading to improved university rankings performance. The process began with defining the
targeted Level 4 results, followed by defining the Level 3 intended external and internal
outcomes, critical behaviors and required drivers. Subsequently, the Level 2 learning goals and
evaluation methods were drawn up in alignment with the Level 4 results and Level 3 outcomes
and behaviors, followed by defining Level 1 checks on training quality through assessing learner
engagement, relevance, and satisfaction. By applying this systematic approach, an integrated
implementation and evaluation package was designed, with tight alignment to the desired
outcomes and critical behaviors targeted at achieving the higher-level stakeholder goals.
Strengths and Weaknesses of the Approach
The Clarke and Estes (2008) Gap Analysis framework was used in this study to analyze
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the knowledge, motivation, and organization influences impacting faculty research productivity.
The design of Gap Analysis framework was particularly suited for identifying performance gaps
and finding root causes to be addressed to improve performance. The strengths of the framework
were the broad applicability of the framework to all types of performance issues, and the breadth
and inclusivity of the knowledge, motivation, and organization constructs in capturing all the
factors of importance to the topic being studied. However, difficulties were encountered in
applying the framework to analyses beyond the frame of addressing performance gaps. For this
promising practices study, the analytical frame was not centered on identified performance gaps,
but rather on the strong performance of the organization and the underlying success factors. The
definition of stakeholder performance goals in relation to current performance was not as readily
applicable to a promising practices study as compared with a performance evaluation or
improvement study. Hence, the framework had to be adapted in two ways to fit with the intent of
the study. First, a retrospective stakeholder performance goal was defined, and was positioned as
a goal to continue with the strong performance of the organization. Second, the identification of
root causes of performance issues and the underlying influences under the framework had to be
adapted to the identification of the underlying success factors, or the assets, behind the strong
performance of the organization.
The study also used the New World Kirkpatrick Model (Kirkpatrick & Kirkpatrick, 2016)
to define an integrated implementation and evaluation package to address performance issues.
The strength of the model is the systematic analysis and integration across all four levels of
implementation, leading to strong alignment across the performance goals, training approach,
implementation plan, and evaluation mechanisms. The weakness of the model is the predominant
emphasis on training as the solution to learning and performance problems. Although
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Kirkpatrick and Kirkpatrick (2016) asserts that the New World Kirkpatrick Model encompasses
broader factors beyond training to areas such as business processes and systems, role-modeling,
resource availability and other workplace issues, the methodologies and templates provided in
the model was not as well suited for planning, implementing, and evaluating non-training and
development interventions. In the context of this study, there were a number of important factors
that did not fit well with a training intervention paradigm, including motivation factors such as
intrinsic interest, and organization factors such as performance incentives and policies and
practices.
Limitations
The design of this study resulted in certain limitations. First, the findings were based on
self-reported responses that were limited by the truthfulness, objectivity, and degree of social
desirability bias among the participants. Without other objective assessment methods,
participants might have provided answers that they believed to be politically acceptable and not a
true reflection of their perspectives. This limitation applied to both the surveys and interviews.
Second, the study was limited by the design of the survey and interview instruments, and
the assumption that the survey and interview questions posed were understood and interpreted in
the manner intended. Novel instruments were designed for the purposes of this study, and the
validity and reliability of the instruments had yet to be extensively tested across multiple studies.
In analyzing the data collected through the instruments, it became clear that some of the
questions could have been better worded to get at the underlying constructs that were of interest.
For instance, the survey question on policies and practices could be replaced by more specific
and separate questions that are distinct from the question on performance incentives, such as
resource allocation policies, faculty evaluation and promotion practices, and the appointment
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policies for department heads. As the survey was conducted online, there was also the possibility
that participants could have asked someone else to complete the survey form.
Third, while the sample size of the surveys was sufficiently large to be representative of
the stakeholder groups across schools and academic ranks, the small sample size of the
interviews limited the generalizability of the interview findings. The study was also delimited to
examining the perspectives of one key stakeholder group, namely research faculty, and did not
incorporate the perspectives of other stakeholder groups, such as university administration,
research support offices, and research graduate students.
Fourth, the focus of the study was to identify the factors that were perceived by faculty to
impact research productivity in HKUST, operating within the Hong Kong higher education
environment. The delimitation of the study was that it may be context-specific to HKUST in
addressing its specific institutional mission and organizational goal, and therefore may not be
generalizable or transferable to other institutions.
Future Research
While the current study established the perspectives of faculty through surveys and
interviews, future studies could include other forms of assessments utilizing non-self-reporting
mechanisms, such as performance data, observations, and document analyses. The use of
objective assessment tools would provide additional sources of data for the triangulation of
results and findings. Given the wide variation observed in mean scores on the number of peer-
reviewed publications across academic fields, future studies could adopt field-based definitions
of high-quality peer-reviewed publications, to study field-specific differences drawn from actual
research output data from publication databases. The survey and interview instruments could be
further refined and tested, to increase the validity and reliability of the instruments.
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The current study focused on research faculty as the stakeholder group, with a reasonably
large sample size for the surveys, but a small sample size for the interviews. Future studies could
incorporate a substantially larger number of interviews, to improve the generalizability of the
findings, and to enable further exploration of the similarities and differences across academic
fields and faculty ranks. The perspectives of other stakeholder groups could also be studied, to
provide a more holistic view of the key stakeholders involved in supporting research productivity
at the University, including university administration, research support offices, and research
graduate students.
The study of promising practices could be extended to other top-ranking universities
beyond HKUST, within and outside of Hong Kong. The combined findings across multiple top-
ranked universities would offer better generalizability of the findings to other institutions
aspiring to improve their research productivity.
Two important themes that emerged from this study were the impact of the university’s
resource allocation policies and practices in eliciting desired and intended faculty behaviors in
conducting and publishing research, and the criticality of identifying faculty with strong intrinsic
interest in their academic fields to ensure continued research productivity even after achieving
tenure status. The interview findings suggested that these two factors could have significant
impact on faculty research productivity, warranting further investigation into how the resource
allocation mechanisms and faculty appraisal and recruitment practices could be further
strengthened.
Future studies could also utilize other analytical models to overcome the limitations of
the Gap Analysis framework in the study of promising practices, and supplementing the New
World Kirkpatrick Model with other approaches to identify interventions beyond the training
RESEARCH PRODUCTIVITY FACTORS

134
paradigm to improve organizational performance.
Conclusion
The problem of practice addressed in this study is how to increase the research
productivity of higher education institutions, thereby leading to improvements in institutional
rankings performance. This promising practices study focused on substantiation-track faculty at a
top-ranked institution, the HKUST. Substantiation-track faculty are expected to be substantively
engaged in conducting and publishing research, and they play a key role in the research
outcomes of the University.
Applying the Clarke and Estes (2008) Gap Analysis framework, the study examined the
knowledge, motivation, and organization assets perceived by faculty to be importance to their
research productivity. Knowledge assets validated through the study were for faculty to stay
current with developments in their academic fields, generate new research ideas, and know the
required steps to publish peer-reviewed research articles. Motivation assets validated were for
faculty to value publishing research, have interest in research, and to be self-efficacious at
producing research publications. Organization assets validated were the existence of clear
expectations and goals, and effective performance incentives to encourage faculty research
productivity. To reinforce these assets and to address identified weaknesses, a training and
mentoring plan was devised. Using the New World Kirkpatrick Model (Kirkpatrick &
Kirkpatrick, 2016), an integrated implementation and evaluation package was designed,
incorporating training, information sharing, and mentoring arrangements for faculty, with tight
alignment to the desired outcomes and critical behaviors targeted at helping faculty increase their
research productivity.
For HKUST to continue to be highly research productive and well-ranked, these
RESEARCH PRODUCTIVITY FACTORS

135
knowledge, motivation, and organization assets need to continue to be supported and reinforced,
especially for new faculty joining the University. Other universities aspiring to improve their
research productivity and rankings performance can benchmark themselves against these critical
success factors, and adapt the recommendations and implementation to suit their individual
contexts and pre-existing conditions.
By providing a conducive environment for research productivity, an institution enables its
faculty to increase their research output and impact, thereby building up the professional
standing and reputation of its faculty within the academic community. The increase in faculty
research productivity in turn contributes to improved rankings performance of the institution, and
consequently the elevation of its reputation and standing. A strong reputation will bring about
self-reinforcing benefits by enhancing the institution’s ability to attract good students, recruit and
retain quality faculty, and secure research funding, which will further contribute toward
improving the research productivity and reputation of the institution. A virtuous cycle is thereby
created, sustaining the strong performance of the institution over time.

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136
References
Adler, N. J., & Harzing, A-W. (2009). When knowledge wins: Transcending the sense and
nonsense of academic rankings. Academy of Management Learning & Education, 8(1), 72–
95.
Alexander, P. A., Schallert, D. L., & Hare, V. C. (1991). Coming to terms: How researchers in
learning and literacy talk about knowledge. Review of Educational Research, 61(3), 315-
343.
Altbach, P. G. (2012). The globalization of college and university rankings. Change: The
Magazine of Higher Learning, 44(January 2015), 26–31. doi:10.1080/00091383.2012.
636001
Amo, C. (2007). Conceptualizing research impact: The case of education research. Canadian
Journal of Program Evaluation, 22(1), 75-98.
Anderman, E., & Anderman, L. (2006). Attributions. Retrieved from http://www.education.com/
reference/article/attribution-theory/
Azad, A. N., & Seyyed, F. J. (2007). Factors influencing faculty research productivity: Evidence
from AACSB accredited schools in the GCC countries. Journal of International Business
Research, 6(1), 91-112.
Baker, L. (2006). Metacognition. Retrieved from http://www.education.com/reference/article/
metacognition/.
Bandura, A. (2000). Exercise of human agency through collective efficacy. Current Directions in
Psychological Science, 9(3), 75–78.
Bandura, A. (2005). The evolution of social cognitive theory. In K. G. Smith & M. A. Hitt
(Eds.), Great minds in management (pp. 9–35). Oxford: Oxford University Press.
RESEARCH PRODUCTIVITY FACTORS

137
Bastedo, M. N., & Bowman, N. A. (2010). College rankings: Modeling institutional effects on
organizational reputation. American Journal of Education, 116(2), 163–183. doi:10.1086/
649437
Bastedo, M. N., & Bowman, N. A. (2011). College rankings as an interorganizational
dependency: Establishing the foundation for strategic and institutional accounts. Research
in Higher Education, 52(1), 3-23.
Batsell, W., & Grossman, R. (2009). Classical conditioning. Retrieved from
http://www.education.com/reference/article/classical-conditioning/
Blackburn, R. T., & Lawrence, J. H. (1995). Faculty at work: Motivation, expectation,
satisfaction. Baltimore: The Johns Hopkins University Press.
Bloom, B. S., Engelhart, M. D., Furst, E. J., Hill, W. H., & Krathwohl, D. R. (1956). Taxonomy
of educational objectives: The classification of educational goals. Handbook I: Cognitive
Domain. New York: David McKay Company.
Bogdan, R. C., & Biklen, S. K. (2007). Qualitative research for education: An introduction to
theories and methods (5th ed.). Boston, MA: Allyn and Bacon.
Bogue, E. G., & Hall, K. (2003). College rankings and ratings: The test of reputation. In E. G.
Bogue & K. Hall (Eds.), Quality and accountability in higher education: Improving policy,
enhancing performance (pp. 51–75). Westport, CT: Praeger.
Boise State University. (n.d.). Boise State University College of Engineering Research &
Scholarly Activity Guidelines. Retrieved from http://coen.boisestate.edu/wp-
content/uploads/2012/09/Research-Scholarly-Activity-Guidelines08.pdf
Bolman, L., & Deal, T. (1997). Reframing organizations: Artistry, choice and leadership. San
Francisco, CA: Jossey-Bass.
RESEARCH PRODUCTIVITY FACTORS

138
Borgogni, L., Russo S. D., & Latham, G. P. (2011). The relationship of employee perceptions of
the immediate supervisor and top management with collective efficacy. Journal of
Leadership and Organizational Studies, 18(1), 5–13.
Bowman, N. A., & Bastedo, M. N. (2011). Anchoring effects in world university rankings:
Exploring biases in reputation scores. Higher Education, 61, 431–444. doi:10.1007/
s10734-010-9339-1
Buckingham, M., & Coffman, C. (1999). First, break all the rules: What the worlds greatest
managers do differently. New York: Simon and Schuster.
Buela-Casal, G., Gutiérrez-Martínez, O., Bermúdez-Sánchez, M. P., & Vadillo-Muñoz, O.
(2007). Comparative study of international academic rankings of universities.
Scientometrics, 71(3), 349–365. doi:10.1007/s11192-007-1653-8
Chen, Y., Nixon, M. R., Gupta, A., & Hoshower, L. (2010). Research productivity of accounting
faculty: An exploratory study. American Journal of Business Education, 3(2), 101-113.
Clark, R. E., & Estes, F. (2008). Turning research into results: A guide to selecting the right
performance solutions. Charlotte, NC: Information Age Publishing, Inc.
Corbin, J., & Strauss, A. (2008). Strategies for qualitative data analysis. Techniques and
procedures for developing grounded theory (3rd ed.). Los Angeles: SAGE.
Creswell, J.W. (2014). Research Design: Qualitative, quantitative, and mixed methods
approaches. Thousand Oaks, CA: Sage Publications.
CUHK. (n.d.). Staff handbook. Retrieved from http://www.cuhk.edu.hk/sci/TQA/doc/ch3.pdf
Daly, E. (2009). Behaviorism. Retrieved from http://www.education.com/reference/
article/behaviorism/
Dembo, M., & Eaton, M. J. (2000). Self-regulation of academic learning in middle-level schools.
RESEARCH PRODUCTIVITY FACTORS

139
The Elementary School Journal, 100(5), 473–490.
Denler, H., Wolters, C., & Benzon, M. (2009). Social cognitive theory. Retrieved from
http://www.education.com/reference/article/social-cognitive-theory/
Drewes, T., & Michael, C. (2006). How do students choose a university?: An analysis of
applications to universities in Ontario, Canada. Research in Higher Education, 47(7), 781–
800. doi:10.1007/s11162-006-9015-6
Dundar, H., & Lewis, D. R. (1998). Determinants of research productivity in higher education.
Research in Higher Education, 39(6), 607-631.
Eccles, J. (2006). Expectancy value motivational theory. Retrieved from
http://www.education.com/reference/article/expectancy-value-motivational-theory/
Financial Times (2016). Global MBA ranking 2016. Retrieved from http://rankings.ft.com/
businessschoolrankings/global-mba-ranking-2016
Fink, A. (2013). How to conduct surveys: A step-by-step guide (5th ed.). Thousand Oaks, CA:
SAGE.
Gallimore, R., & Goldenberg, C. (2001). Analyzing cultural models and settings to connect
minority achievement and school improvement research. Educational Psychologist, 36(1),
45–56.
Glesne, C. (2011). Becoming qualitative researchers: An introduction (4th ed.). Boston, MA:
Pearson.
Goette, L., Huffman, D., Meier, S., & Sutter, M. (2012). Competition between organizational
groups: Its impact on altruistic and antisocial motivations. Management Science, 58(5),
948–960.
Grewal, R., Dearden, J. A., & Lilien, G. L. (2008). The university rankings game: Modeling the
RESEARCH PRODUCTIVITY FACTORS

140
competition among universities for ranking. The American Statisician, 62(3), 232–237.
doi:10.1198/000313008X332124
Group of Eight. (2012). World university rankings: Ambiguous signals. A.C.T., Australia: Group
of Eight.
Hales, R. E., Shahrokh, N. C., & Servis, M. (2005). A department of psychiatry faculty practice
plan designed to reward educational and research productivity. Academic Psychiatry,
29(3), 244-8.
Hales, R.E., Shahrokh, N. C., & Servis, M. (2009). A progress report on a department of
psychiatry faculty practice plan designed to reward educational and research productivity.
Academic Psychiatry, 33(3), 248-51.
Hallinger, P. (2014). Riding the tiger of world university rankings in East Asia: Where are we
heading? International Journal of Educational Management, 28, 230–245. doi:10.1108/
IJEM-11-2012-0126
Hazelkorn, E. (2007). The impact of league tables and ranking systems on higher education
decision making. Higher Education Management and Policy, 19(2). doi:10.1787/hemp-
v19-art12-en
Hazelkorn, E. (2011). Rankings and the reshaping of higher education: The battle for world-
class excellence. Houndmills, Basingstoke: Palgrave Macmillan.
HKUST Facts & Figures. (December, 2016). Retrieved January 26, 2017, from
http://www.ust.hk/about-hkust/hkust-at-a-glance/facts-figures/
HKUST Financial Statements. (2016). Retrieved from https://archives.ust.hk/dspace/bitstream/
9999/44816/1/coun-03-a026.pdfHKUST Mission & Vision. (n.d.). Retrieved June 4,
2015, from http://www.ust.hk/about-hkust/hkust-at-a-glance/mission-vision/
RESEARCH PRODUCTIVITY FACTORS

141
HKUST Policy on Academic Review. (2003). Retrieved from http://www.ust.hk/~webaa/
AP_testing/Faculty_HB/ch4.pdf
HKUST Strategic Plan 2011-16. (2011). Retrieved April 2, 2016, from
http://strategicplan.ust.hk/eng/six_areas_2.html
Hou, A. Y. C., Morse, R., & Chiang, C.-L. (2012). An analysis of mobility in global rankings:
Making institutional strategic plans and positioning for building world-class universities.
Higher Education Research & Development, 31(6), 841–857. doi:10.1080/07294360.2012.
662631
Hu, Q., & Gill, T. G. (2000). IS faculty research productivity: Influential factors and
implications. Information Resources Management Journal, 13(2), 15-25.
Ioannidis, J. P., Patsopoulos, N. A., Kavvoura, F. K., Tatsioni, A., Evangelou, E., Kouri, I.,
Contopoulos-Ioannidis, D. G., & Liberopoulos, G. (2007). International ranking systems
for universities and institutions: A critical appraisal. BMC Medicine, 5, 30. doi:10.1186/
1741-7015-5-30
IREG. (2006). Berlin Principles on Ranking of Higher Education Institutions. Berlin, Germany:
International Ranking Expert Group.
Jung, J. (2012). Faculty research productivity in Hong Kong across academic discipline. Higher
Education Studies, 2(4), 1-13.
Kalyuga, S., & Sweller, J. (2004). Measuring knowledge to optimize cognitive load factors
during instruction. Journal of educational psychology, 96(3), 558-568.
Kelsky, K. (2016). Explaining the 9-month contract. Retrieved January 26, 2017, from
https://chroniclevitae.com/news/1475-explaining-the-9-month-contract
Kirkpatrick, D. L., & Kirkpatrick, J. D. (2006). Evaluating training programs: The four levels
RESEARCH PRODUCTIVITY FACTORS

142
(3rd ed.). San Francisco: Berrett-Koehler.
Kirkpatrick, J. D., & Kirkpatrick, W. K. (2016). Kirkpatrick’s four levels of training evaluation.
Alexandria, VA: ATD Press.
Kirschner, P., Kirschner, F., & Paas, F. (2006). Cognitive load theory. Retrieved from
http://www.education.com/reference/article/cognitive-load-theory/
Krathwohl, D. R. (2002). A revision of Bloom’s Taxonomy: An overview. Theory Into Practice,
41(4), 212–218.
Langley, G. J., Moen, R., Nolan, K. M., Nolan, T. W., Norman, C. L., & Provost, L. P. (2009).
The improvement guide: A practical approach to enhancing organizational performance.
John Wiley & Sons.
Lencioni, P. (2004). The Five Dysfunctions of a Team. San Francisco: Jossey-Bass.
Levitan, A. S., & Ray, R. (1992). Personal and institutional characteristics affecting research
productivity of academic accountants. Journal of Education for Business, 61(6), 335-341.
Lipton, M. (1996). Demystifying the development of an organizational vision. Sloan
Management Review, 37(4), 83–92.
Markova, G., & Ford, C. (2011). Is money the panacea? Rewards for knowledge workers.
International Journal of Productivity and Performance Management, 60(8), 813–823.
Marsh, J. A. (2012). Interventions promoting educators' use of data: Research insights and gaps.
Teachers College Record, 114(11), 1–48.
Maxwell, J. A. (2013). Qualitative research design: An interactive approach (3rd ed.). Thousand
Oaks, CA: SAGE Publications.
Mayer, R. E. (2011). Applying the science of learning. Boston, MA: Pearson Education.
McEwan, E. K., & McEwan, P. J. (2003). Making sense of research. Thousand Oaks, CA: Sage
RESEARCH PRODUCTIVITY FACTORS

143
Publications.
Merriam, S. B., & Tisdell, E. J. (2009). Qualitative research: A guide to design and
implementation (4th ed.). San Francisco, CA: Jossey-Bass.
Miles, M. B., Huberman, A. M., & Saldaña, J. (2014). Qualitative data analysis: A methods
sourcebook (3rd ed.). Thousand Oaks, CA: SAGE Publications.
Moran, J. W., & Brightman, B. K. (2000). Leading organizational change. Journal of Workplace
Learning, 12(2), 66-74.
Pajares, F. (2006). Self-efficacy theory. Retrieved from http://www.education.com/reference/
article/self-efficacy-theory/.
Patton, M. Q. (2002). Qualitative research & evaluation methods (3rd ed.). Thousand Oaks, CA:
SAGE Publications.
Pintrich, P. R. (2003). A motivational science perspective on the role of student motivation in
learning and teaching contexts. Journal of Educational Psychology, 95(4), 667–686.
Postiglione, G. A. (2011). The rise of research universities: The Hong Kong University of
Science and Technology. In P. G. Altbach & J. Salmi (Eds.), The road to academic
excellence: The making of world-class research universities (pp. 63-100). Herndon, VA,
USA: World Bank Publications. doi:10.1596/978-0-8213-8805-1
Pusser, B., & Marginson, S. (2013). University rankings in critical perspective. The Journal of
Higher Education, 84(4), 544–568. doi:10.1353/jhe.2013.0022
Puuska, H. M. (2014). Scholarly publishing patterns in Finland: A comparison of disciplinary
groups. (Unpublished doctoral dissertation). University of Tampere, Finland.
QS. (2016a). QS university rankings: Asia 2016. Retrieved from http://www.topuniversities.com/
university-rankings/asian-university-rankings/2016
RESEARCH PRODUCTIVITY FACTORS

144
QS. (2016b). QS world university rankings by subject 2016. Retrieved from
http://www.topuniversities.com/subject-rankings/2016
QS. (2016c). QS world university rankings 2016/17. Retrieved from
http://www.topuniversities.com/university-rankings/world-university-rankings/2016
Rahm, E., & Hong, H. (n.d.). Data cleaning: Problems and current approaches. Retrieved from
http://lips.informatik.uni-leipzig.de/files/2000-45.pdf on July 7, 2016.
Rimer, S. (2015). Too many postdocs, too little funding. Retrieved from http://www.bu.edu/
today/2015/helping-young-researchers-find-jobs-outside-academia/
Roberts, J. (2010). Designing incentives in organizations. Journal of Institutional Economics,
6(1), 125–132.
Rueda, R. (2011). The 3 dimensions of improving student performance. New York: Teachers
College Press.
Schein, E. H. (2004). The concept of organizational culture: Why bother? In E. H. Schein, (Ed.),
Organizational culture and leadership (3rd ed., pp. 3–24). San Francisco, CA: Jossey
Bass.
Schneider, B., Brief, A., & Guzzo, R. (1996). Creating a culture and climate for sustainable
organizational change. Organizational Dynamics, 24(4), 7–19.
Schraw, G., & Lehman, S. (2009). Interest. Retrieved from http://www.education.com/reference/
article/interest/
Schraw, G., & McCrudden, M. (2006). Information processing theory. Retrieved from
http://www.education.com/reference/article/information-processing-theory/
Scott, S., & Palinscar, A. (2006). Sociocultural theory. Retrieved from
http://www.education.com/reference/article/sociocultural-theory/
RESEARCH PRODUCTIVITY FACTORS

145
Shanghai Ranking Consultancy. (2016). Academic ranking of world universities in
engineering/technology and computer sciences – 2016. Retrieved from
http://www.shanghairanking.com/FieldENG2016.html
Shute, V. J. (2008). Focus on formative feedback. Review of Educational Research, 78(1), 153–
189.
Stolz, I., Hendel, D. D., & Horn, A. S. (2010). Ranking of rankings: Benchmarking twenty-five
higher education ranking systems in Europe. Higher Education, 60(5), 507–528.
doi:10.1007/s10734-010-9312-z
Times Higher Education. (2016a). World university rankings subject ranking 2015-2016:
Engineering and technology top 100. Retrieved from
https://www.timeshighereducation.com/world-university-rankings/2016/subject-
ranking/engineering-and-IT
Times Higher Education. (2016b). Asia University Rankings. Retrieved from
https://www.timeshighereducation.co.uk/world-university-rankings/2016/regional-
ranking#/
Times Higher Education. (2016c). World University Rankings 2016-17. Retrieved from
https://www.timeshighereducation.co.uk/world-university-rankings/2017/world-ranking/
Tuckman, B. (2009). Operant conditioning. Retrieved from http://www.education.com/
reference/article/operant-conditioning/
UGC. (2014). Results of the Research Assessment Exercise 2014. Retrieved from
http://www.ugc.edu.hk/eng/doc/ugc/rae/rae2014results.pdf
UGC. (2017a). Research funding. Retrieved from http://www.ugc.edu.hk/eng/rgc/
funded_research/funding_results/fund.html
RESEARCH PRODUCTIVITY FACTORS

146
UGC. (2017b). Research Assessment Exercise 2014 achieving international excellence.
Retrieved from http://www.info.gov.hk/gia/general/201501/27/P201501270318.htm
UGC. (2017c). RGC terms of reference. Retrieved from http://www.ugc.edu.hk/eng/rgc/about/
term_rgc.html
UGC. (2017d). UGC roles and functions. Retrieved from http://www.ugc.edu.hk/eng/ugc/about/
overview/roles.html
UGC. (2017e). Consultation on proposed framework for Research Assessment Exercise 2020.
Retrieved from http://www.ugc.edu.hk/doc/eng/ugc/rae/2020/
framework_consult_letter.pdf
White, C. S., James, K., Burke, L. A., & Allen, R. S. (2012). What makes a "research star"?
Factors influencing the research productivity of business faculty. International Journal of
Productivity and Performance Management, 61(6), 584-602.
Yough, M., & Anderman, E. (2006). Goal orientation theory. Retrieved from
http://www.education.com/reference/article/goal-orientation-theory/

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Appendix A
Recruiting Script for Study
Hello, my name is Michael Fung. I am a doctoral candidate at the University of Southern
California (USC) Rossier School of Education, and a former staff at HKUST. I am conducting a
research study on faculty research productivity at the HKUST, with permission from President
Tony Chan and Provost Wei Shyy, and the clearance of the USC Institutional Review Board. I
am inviting your participation in the study, in your capacity as a research faculty member of
HKUST.
Research productivity is vital for an institution to build up its academic reputation, and
for faculty to contribute to the wider community and to gain recognition in their respective fields.
Findings from this study will serve to inform departments, schools, and institutions on the key
factors to consider to encourage research productivity. The findings will also be shared with the
HKUST academic community, to help inform discussions to move HKUST forward. I am happy
to extend a copy of the findings of the study to you, if you are interested.
Participation in this research involves taking a survey on your perspectives about
academic research. Your time commitment for the survey should be less than 15 minutes. If you
select to be interviewed after completing the survey, I will arrange for a convenient time to meet
with you for a 30-minute conversation. To access the survey form, please go to the following
URL: https://usceducation.az1.qualtrics.com/SE/?SID=SV_0SwlcOVsad67ZiJ.
Please find attached additional information about the study, and do not hesitate to contact
me if you have any questions – I can be reached at +65 82857454 or jinlungf@usc.edu. Thank
you!

Michael Fung
Principal Investigator

Encl: Certified Information Sheet, dated 11-11-2016

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Appendix B
Script to Arrange for Interview
Dear ______,

Thank you for completing the survey for the study on faculty research productivity at
HKUST, and for agreeing to be interviewed on this topic. I am following-up to arrange for a
convenient time to interview you on your perspectives, which is expected to take around 30
minutes.

Please let me know if the following timeslot works for you: _____________. If not, please
let me know what might be a more suitable timeslot for you.

If you have any questions, I can be reached at +65 82857454 or jinlungf@usc.edu. Thank
you!

Michael Fung
Principal Investigator
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Appendix C
Recruiting Script for Interview
Hello, my name is Michael Fung. I am a doctoral student at the University of Southern
California (USC) in the Rossier School of Education. I am conducting a research study on
faculty research productivity at the Hong Kong University of Science and Technology
(HKUST), with the support of the HKUST university administration, and the clearance of the
institutional research boards at both USC and HKUST.
The Dean of your school, Prof. ____________, nominated you to be interviewed for this
study, as your Dean feels that your perspectives will contribute greatly to the study.
Research productivity is vital for an institution to build up its academic reputation, and
for faculty to contribute to the wider community and to gain recognition in their respective fields.
Findings from this study will serve to inform departments, schools, and institutions on the key
factors to consider to encourage research productivity. I would be happy to extend a copy of the
findings of the study to you, if you are interested.
Participation in this study involves an interview on your perspectives about academic
research, which is expected to take around 30 minutes. If you are agreeable, please let me know
if the following timeslot works for you: _________________. If not, please let me know what
might be a more suitable timeslot for you.
If you have any questions, I can be reached at +65 82857454 or jinlungf@usc.edu. Thank
you!

Michael Fung
Principal Investigator
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Appendix D
Survey Items
This is a brief survey about the perceptions of HKUST faculty on the factors that are
important for research productivity. Specifically, I am seeking to understand the factors that help
or hinder your ability to conduct and publish research. The purpose of my study is to identify
these influences and to offer evidence-based recommendations on ways to encourage and support
faculty research productivity. Findings from the study will be shared with the HKUST academic
community, to help inform discussions to move HKUST forward. Your responses to these
questions will provide vital information for the study. Thank you for taking part in this survey.

The following questions address your own experience with conducting and publishing
research over the past three years.

1. I am up-to-date with the research findings in my academic field.

1

2

3

4

5

6
Strongly
Disagree
Disagree
Somewhat
Disagree
Somewhat
Agree
Agree
Strongly
Agree

2. I have been successful in generating new research ideas.

1

2

3

4

5

6
Strongly
Disagree
Disagree
Somewhat
Disagree
Somewhat
Agree
Agree
Strongly
Agree

3. I have the knowledge on how to get my research published in peer-reviewed publications.

1 2 3 4 5 6
Strongly
Disagree
Disagree
Somewhat
Disagree
Somewhat
Agree
Agree
Strongly
Agree

4. I have been able to manage my workload (amongst teaching, research, and other
activities).

1 2 3 4 5 6
Strongly
Disagree
Disagree
Somewhat
Disagree
Somewhat
Agree
Agree
Strongly
Agree

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5. Publishing research is important to me.

1 2 3 4 5 6
Strongly
Disagree
Disagree
Somewhat
Disagree
Somewhat
Agree
Agree
Strongly
Agree

6. I enjoy conducting research.
1 2 3 4 5 6
Strongly
Disagree
Disagree
Somewhat
Disagree
Somewhat
Agree
Agree
Strongly
Agree

7. I believe that I have the ability to produce peer-reviewed research publications.

1 2 3 4 5 6
Strongly
Disagree
Disagree
Somewhat
Disagree
Somewhat
Agree
Agree
Strongly
Agree

8. How many peer-reviewed research publications have you produced in the past three
years? __________

9. I know what my institution expects of me regarding my research productivity.
1 2 3 4 5 6
Strongly
Disagree
Disagree
Somewhat
Disagree
Somewhat
Agree
Agree
Strongly
Agree

10. My institution has effective policies and practices to support research.

0 1 2 3 4 5 6
Don’t
Know
Strongly
Disagree
Disagree
Somewhat
Disagree
Somewhat
Agree
Agree
Strongly
Agree

11. My institution’s incentive system is effective in encouraging research productivity.

0 1 2 3 4 5 6
Don’t
Know
Strongly
Disagree
Disagree
Somewhat
Disagree
Somewhat
Agree
Agree
Strongly
Agree

The following questions are to help us understand a little about your profile.

12. In relation to what my institution expects of faculty at my rank, I am regarded by my
colleagues as a productive researcher.

0 1 2 3 4 5 6
Don’t
Know
Strongly
Disagree
Disagree
Somewhat
Disagree
Somewhat
Agree
Agree
Strongly
Agree
RESEARCH PRODUCTIVITY FACTORS

152
13. In relation to what my institution expects of faculty at my rank, I am regarded by my
colleagues as being effective at teaching.

0 1 2 3 4 5 6
Don’t
Know
Strongly
Disagree
Disagree
Somewhat
Disagree
Somewhat
Agree
Agree
Strongly
Agree

14. In relation to what my institution expects of faculty at my rank, I am regarded by my
colleagues as a strong contributor to the community (e.g. service to external bodies,
serving on university committees, providing consultancy to industry).

0 1 2 3 4 5 6
Don’t
Know
Strongly
Disagree
Disagree
Somewhat
Disagree
Somewhat
Agree
Agree
Strongly
Agree

15. What percentage of your work time do you spend on: (Please check that the three
categories add up to 100%)

a. Teaching: ________%

b. Research: ________%

c. Other work activities: __________%

16. What is your academic rank?

1 2 3 4
Assistant
Professor
Associate
Professor
Professor
Chair
Professor

17. What is your academic field? ___________________

18. How many years of research experience have you had? ____________

Thank you for taking the time to complete the survey. The following questions are to
guide follow-up actions from the survey.

19. Would you be willing to have a one-on-one interview for 30 minutes on this topic?
__YES / NO__
20. Would you like to receive a copy of the findings from the study? __YES / NO__
RESEARCH PRODUCTIVITY FACTORS

153
If you answered YES to either or both questions, please click on the following URL link
_____________ to provide your name and email address. Your name and email address will only
be used for the purposes of contacting you to set up an interview time and/or to share with you
the findings from the study. Your name and email address will not be linked to your survey
responses, i.e. your survey responses will remain anonymous.
1. What is your name? ____________________

2. What is your email contact? __________________

If you agreed to be interviewed, you will be contacted shortly via email by the researcher
to set up a convenient time for the interview. If you requested for a copy of the findings from the
study, you will be contacted in due course when the study report has been completed.

RESEARCH PRODUCTIVITY FACTORS

154
Appendix E
Interview Protocol
Good morning / afternoon / evening. Thank you for taking the time to speak with me
about your perspectives about factors that are important for research productivity. Here is an
information sheet about the study, which you should have also received previously.

Specifically, I am seeking to understand the factors that help or hinder your ability to
conduct and publish research. The purpose of my study is to identify these influences and to
offer evidence-based recommendations for ways to encourage and support faculty research
productivity. Your responses to these questions will provide vital information for the study.

With your permission, I would like to audio record this interview to help make sure that I
have an accurate account of your responses. Please be assured that no one else, other than
myself, will have access to these recordings. I will transcribe the audio recordings without
including any identifying information, and thereafter all copies of the recording will be
destroyed.

You may choose not to answer any question, or to stop this interview at any time. Your
identity will be kept strictly confidential, and you will not be named in any documents in this
research study. If you so would like to, you may review the transcripts subsequently, to make
sure that I have captured your perspectives accurately. Do you have any questions before we get
started?

Interview Questions
1. I am here to learn from you about your perspectives on academic research. Tell me how
you would describe someone who is research productive, that is, someone who produces
a lot of quality research in your field.

2. What does one need to know to be productive in research in his/her academic field?
Probes:
RESEARCH PRODUCTIVITY FACTORS

155
a. How do you keep up with trends and developments in your field?
b. How do you go about coming up with new research ideas? What do you need to
know to be able to generate new research ideas?
c. Can you describe the research publication process for your field? How did you
learn about the process?
d. How would you describe your workload? What are some approaches that you
have taken to manage your workload? How effective are these approaches?
e. How has your approach to conducting and publishing research evolved over time?
What prompted the changes?
f. What advice would you give to younger faculty on how to be productive in
research? Did you have a mentor as a young/beginning professor? What did s/he
teach you about research and publishing?

3. Among all your responsibilities as an academic, how would you rank the importance of
publishing research (e.g. at the very top, at the bottom, somewhere in the middle)?
a. In what ways is research important to you? What motivates you to productive in
research?
Probes:
i. What interests you about the field that you conduct research in?
ii. In what ways does your research contribute to the community?
iii. What benefits do you see in being productive in research?
b. In what ways is publishing research not as important to you?
Probes:
i. What areas take greater priority for you?
ii. What are the reasons that these areas take greater priority?

4. What are some things that you had experienced in the past that affected your motivation
for research? If possible, give some specific examples.
Probes:
a. Does your department and/or school have defined standards for being productive
in research? If so, what are the standards? What are the consequences of not
RESEARCH PRODUCTIVITY FACTORS

156
meeting the standards?
b. What are the tradeoffs, or perhaps even sacrifices, that one has to make to be
productive in research?
c. How would you assess your own research capabilities? What would you say are
your strengths? What would you say are your weaknesses?

5. Describe the expectations, if any, your institution has regarding faculty research
productivity? How are these expectations conveyed to faculty?
Probes:
a. What do other faculty in your department say about being productive in research?
i. What are their expectations of themselves?
ii. What are their expectations of you? How were these expectations
conveyed?
b. What are some things that university administrators say about being productive in
research?
Probes:
i. What are their expectations of academic staff? How were these
expectations conveyed?
ii. What are their expectations of you? How were these expectations
conveyed?

6. What are some of the most helpful things that your department, school, or university does
to support faculty research? What are some of the things that your department, school, or
university does that hinder faculty research?

7. From your perspective, what types of incentives are important to encourage faculty
research productivity?
Probes:
a. Which of these incentives are implemented in your institution?
b. Could you suggest some ways to improve the incentive system in your institution?

RESEARCH PRODUCTIVITY FACTORS

157
8. Given all the factors that we have discussed, what would you say is the most important
thing if someone wants to be successful as a faculty member producing and publishing
research?

Thank you so much for your valuable time, and for your responses to this interview.
Would you like to review the transcripts of the audio recording of this interview? If so, I will
send it to you via email once I have completed the draft of transcript.

Snowball Sampling (Optional – depending on size and profile of response pool)
I am looking to interview a few more faculty to strengthen my study. Would you be able
to recommend any other faculty members whom you feel I should invite to be interviewed? If
you prefer, I will not reveal your name in contacting them for the interviews.

Thank you once again, and all the best in your research endeavors.

RESEARCH PRODUCTIVITY FACTORS

158
Appendix F
Survey Items for Post Implementation Evaluation

Immediately following the program implementation.
The following questions address your experience with the faculty research sharing
session/workshop.

1. The session/workshop held my interest.
1 2 3 4 5 6
Strongly
Disagree
Disagree Somewhat
Disagree
Somewhat
Agree
Agree Strongly
Agree

2. What I learned from this session/workshop is relevant to my work.
1 2 3 4 5 6
Strongly
Disagree
Disagree Somewhat
Disagree
Somewhat
Agree
Agree Strongly
Agree

3. I will recommend this session/workshop to my faculty co-workers.
1 2 3 4 5 6
Strongly
Disagree
Disagree Somewhat
Disagree
Somewhat
Agree
Agree Strongly
Agree

4. Knowledge of the research publication process.

Before the session/workshop
1 2 3 4 5 6
None/very
low level
Very high
level

After the session/workshop
1 2 3 4 5 6
None/very
low level
Very high
level
RESEARCH PRODUCTIVITY FACTORS

159
5. What are the major concepts that you learned during this session/workshop?
_________________________________________________________________________
_________________________________________________________________________
_________________________________________________________________________

6. I believe it will be worthwhile for me to apply what I learned to my work.
1 2 3 4 5 6
Strongly
Disagree
Disagree Somewhat
Disagree
Somewhat
Agree
Agree Strongly
Agree

7. I anticipate that I will receive the necessary support to apply what I learned.
1 2 3 4 5 6
Strongly
Disagree
Disagree Somewhat
Disagree
Somewhat
Agree
Agree Strongly
Agree

8. I am committed to apply what I learned to my work.
1 2 3 4 5 6
Strongly
Disagree
Disagree Somewhat
Disagree
Somewhat
Agree
Agree Strongly
Agree

9. I believe I will see a positive impact if I consistently apply what I learned.
1 2 3 4 5 6
Strongly
Disagree
Disagree Somewhat
Disagree
Somewhat
Agree
Agree Strongly
Agree

10. How could this session/workshop be improved?
_________________________________________________________________________
_________________________________________________________________________
_________________________________________________________________________
RESEARCH PRODUCTIVITY FACTORS

160
Delayed for a period after the program implementation.
The following questions address your experience with the faculty training and mentoring
program.

1. I am seeing positive results from the applying what I learned through the program.
1 2 3 4 5 6
Strongly
Disagree
Disagree Somewhat
Disagree
Somewhat
Agree
Agree Strongly
Agree

2. What early signs of success have you noticed from your efforts?
_________________________________________________________________________
_________________________________________________________________________
_________________________________________________________________________

3. This program has positively impacted the research output of my department.
1 2 3 4 5 6
Strongly
Disagree
Disagree Somewhat
Disagree
Somewhat
Agree
Agree Strongly
Agree

4. I have been able to apply what I learned in the program to my work.
1 2 3 4 5 6
Strongly
Disagree
Disagree Somewhat
Disagree
Somewhat
Agree
Agree Strongly
Agree

5. I have received support in order to successfully apply what I learned.
1 2 3 4 5 6
Strongly
Disagree
Disagree Somewhat
Disagree
Somewhat
Agree
Agree Strongly
Agree

6. What has helped you to implement what you learned?
_________________________________________________________________________
_________________________________________________________________________
_________________________________________________________________________

RESEARCH PRODUCTIVITY FACTORS

161
7. I will continue to apply what I learned in my work.
1 2 3 4 5 6
Strongly
Disagree
Disagree Somewhat
Disagree
Somewhat
Agree
Agree Strongly
Agree

8. What were the major concepts that you learned that were particularly useful?
_________________________________________________________________________
_________________________________________________________________________
_________________________________________________________________________

9. Looking back, participating in this program was a good use of my time.
1 2 3 4 5 6
Strongly
Disagree
Disagree Somewhat
Disagree
Somewhat
Agree
Agree Strongly
Agree

10. Looking back, how could this program be improved?
_________________________________________________________________________
_________________________________________________________________________
_________________________________________________________________________

Abstract (if available)

Abstract Universities around the world are facing increasing pressure to perform well in rankings, and rankings results have been shown to impact institutional reputation, ability to secure funding, and recruitment of students and faculty. Faculty research productivity is one of the main factors impacting rankings performance, and the aim of this project was to identify the factors of importance to faculty research productivity at a top-ranked university. Findings from 113 surveys and nine interviews with faculty at the Hong Kong University of Science and Technology (HKUST) revealed a set of knowledge, motivation, and organization assets that supported research productivity at the University. Knowledge assets identified were up-to-date knowledge of developments in their academic fields, knowledge to generate new research ideas, and knowledge of the required steps to publish peer-reviewed research articles. Motivation assets identified were faculty valuing publishing research, having interest in research, and being self-efficacious at producing research publications. Organization assets identified were the presence of clear expectations and goals, and effective performance incentives to encourage faculty research productivity. A set of recommendations were proposed to reinforce these assets and to address identified weaknesses, supported by an integrated implementation and evaluation package. Institutions aspiring to improve their research productivity and rankings performance can benchmark themselves against these factors and practices, and adapt the recommendations and implementation to suit their institutional contexts.

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Asset Metadata

Creator Fung, Jin Lung Michael (author)

Core Title Factors impacting faculty research productivity at a highly-ranked university

School Rossier School of Education

Degree Doctor of Education

Degree Program Organizational Change and Leadership (On Line)

Publication Date 06/28/2017

Defense Date 06/15/2017

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag faculty,OAI-PMH Harvest,productivity,Publications,rankings,Reputation,research

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Sundt, Melora A. (committee chair), Gallagher, Karen S. (committee member), Lee, James Z. (committee member)

Creator Email jinlungf@usc.edu,mfung@alumni.cmu.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c40-391630

Unique identifier UC11264344

Identifier etd-FungJinLun-5458.pdf (filename),usctheses-c40-391630 (legacy record id)

Legacy Identifier etd-FungJinLun-5458.pdf

Dmrecord 391630

Document Type Dissertation

Rights Fung, Jin Lung Michael

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA