University of Southern California Dissertations and Theses

Data-driven culture in the consumer goods industry

(USC Thesis Other)

Data-driven culture in the consumer goods industry

PDF

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content 1
Running Head: DATA-DRIVEN CULTURE

DATA-DRIVEN CULTURE IN THE CONSUMER GOODS INDUSTRY

by

Marla M. Smith

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

May 2020

Copyright 2020 Marla M. Smith
2
DATA-DRIVEN CULTURE

ACKNOWLEDGEMENTS
I am grateful for the opportunity to spend these years in intellectual thought about this study. I
appreciate friends, family, peers, leaders, and learners who supported me through this journey. I
thought often of my father, who, at the end of his time on earth, was still using his brain to fix
clocks and be a lifelong learner. He, and others that have supported me, are my inspiration as I
continue my life’s goals.

3
DATA-DRIVEN CULTURE

DEDICATION
I dedicate this study to those who continue to innovate and impact mankind through their
thoughts and ideas.

4
DATA-DRIVEN CULTURE

TABLE OF CONTENTS
ACKNOWLEDGEMENTS ............................................................................................................ 2
DEDICATION ................................................................................................................................ 3
LIST OF TABLES .......................................................................................................................... 7
LIST OF FIGURES ........................................................................................................................ 8
ABSTRACT ................................................................................................................................... 9
CHAPTER 1 INTRODUCTION .................................................................................................. 10
Background of the Problem .................................................................................................... 10
Importance of Addressing the Problem .................................................................................. 11
Organizational Context and Mission ...................................................................................... 12
Organizational Performance Status ......................................................................................... 13
Organizational Performance Goal ........................................................................................... 13
Stakeholder of Focus............................................................................................................... 14
Purpose of the Project and Research Questions ...................................................................... 15
Definitions............................................................................................................................... 15
CHAPTER 2 LITERATURE REVIEW ....................................................................................... 17
The Growth of Data Science ................................................................................................... 17
Consumer Goods Industry Status ........................................................................................... 18
Executives’ Role in Leading Change ...................................................................................... 18
CEO Accountability ................................................................................................................ 20
Impact of Data on Organizational Culture .............................................................................. 20
Conceptual Framework ........................................................................................................... 21
Knowledge Influences ............................................................................................................ 22
5
DATA-DRIVEN CULTURE

Procedural Knowledge ..................................................................................................... 23
Knowledge and Skills Barriers ......................................................................................... 23
Metacognition ................................................................................................................... 24
Summary ........................................................................................................................... 25
Motivation Influences ............................................................................................................. 26
Self-Efficacy Theory ........................................................................................................ 27
Goal Theory ...................................................................................................................... 28
Organizational Influences ....................................................................................................... 30
Organizational Culture Gaps ............................................................................................ 31
Soft and Hard Factors ....................................................................................................... 32
Cultural Model Influence 1 ............................................................................................... 32
Cultural Model Influence 2 ............................................................................................... 33
Cultural Setting Influence 1 .............................................................................................. 34
Cultural Setting Influence 2 .............................................................................................. 35
Conclusion .............................................................................................................................. 37
CHAPTER 3 METHODOLOGY ................................................................................................. 38
Methodological Approach and Research Questions ............................................................... 38
Theoretical Framework ........................................................................................................... 39
Population and Sample ........................................................................................................... 41
Data Collection Procedures ..................................................................................................... 42
Data Analysis Procedures ....................................................................................................... 43
Credibility and Trustworthiness .............................................................................................. 43
Ethics....................................................................................................................................... 43
6
DATA-DRIVEN CULTURE

Limitations and Delimitations ................................................................................................. 44
CHAPTER 4 RESULTS AND FINDINGS ................................................................................. 45
Participant Characteristics ...................................................................................................... 45
Findings................................................................................................................................... 47
Research Question 1 ......................................................................................................... 47
Research Question 2 ......................................................................................................... 48
CHAPTER 5 DISCUSSION AND RECOMMENDATIONS ..................................................... 60
Summary of Study Findings ................................................................................................... 60
Procedure .......................................................................................................................... 61
Metacognitive ................................................................................................................... 61
Self-Efficacy ..................................................................................................................... 63
Goal Orientation ............................................................................................................... 64
Organizational Influences and Recommendations ........................................................... 65
Cultural Models ................................................................................................................ 66
Cultural Settings ............................................................................................................... 67
KMO Summary of Influences and Findings ........................................................................... 68
Knowledge Recommendations ......................................................................................... 68
Motivation Recommendations .......................................................................................... 69
Organizational Barriers Recommendations ...................................................................... 69
Limitations ........................................................................................................................ 69
Integrated Implementation and Evaluation Plan ..................................................................... 70
Level 4 .............................................................................................................................. 70
Level 3: Behavior ............................................................................................................. 72
7
DATA-DRIVEN CULTURE

Level 2: Learning .............................................................................................................. 75
Level 1: Reaction .............................................................................................................. 78
Evaluation ......................................................................................................................... 79
Summary of Recommendations and Solutions ................................................................. 80
Strengths and Weaknesses of the Approach ........................................................................... 80
Limitations and Delimitations ................................................................................................. 81
Future Research ...................................................................................................................... 82
Conclusion .............................................................................................................................. 82
REFERENCES ............................................................................................................................. 84
APPENDIX A INFORMED CONSENT ..................................................................................... 92
APPENDIX B INTERVIEW PROTOCOL .................................................................................. 93
APPENDIX C SAMPLE KIRKPATRICK SURVEY ITEMS (LEVELS 1 AND 2) .................. 94
APPENDIX D SUMMARY OF INFLUENCES ON BARRIERS TO CREATING A DATA-
DRIVEN ORGANIZATIONAL CULTURE ............................................................................... 96

LIST OF TABLES
Table 1 Knowledge Gap Analysis .................................................................................... 28
Table 2 Motivational Influences ...................................................................................... 32
Table 3 Title ..................................................................................................................... 38
Table 4 Study Participant Profile .................................................................................... 48
Table 6 Summary of Knowledge Influences ..................................................................... 61
Table 7 Summary of Motivation Influences and Recommendations ................................ 64
Table 8 Summary of Organization Influences and Recommendations ............................ 67
Table 9 Outcomes, Metrics, and Methods for External and Internal Outcomes ............. 72
8
DATA-DRIVEN CULTURE

Table 10 Critical Behaviors, Metrics, Methods, and Timing for Evaluation .................. 74
Table 11 Required Drivers to Support Critical Behaviors .............................................. 76
Table 12 Evaluation of the Components of Learning for the Program ........................... 79
Table 13 Components to Measure Reactions to the Program ......................................... 80

LIST OF FIGURES
Figure 1. Theoretical framework. ................................................................................................ 43
Figure 2. Participant organizations’ produce class. ..................................................................... 48
Figure 3. What to do with data and where it comes from. ........................................................... 53
Figure 4. Cultural settings: Gender and time on the job............................................................... 57

9
DATA-DRIVEN CULTURE

ABSTRACT
Over 90% of the world’s data have been created in the last 2 years alone (Van Loon, 2017), a
trend which shows no signs of slowing down. This case study used Clark and Estes’ (2008) gap
analysis model to examine knowledge, motivation, and organizational barriers (KMO) which
contribute to gaps in executive leadership of a data-driven culture. Literature supports the notion
that data science is critical to conduct business in today’s hyper-competitive marketplace. As
such, top executives must increasingly pave the way for this data science revolution and adopt
enhanced operational and cultural changes within their organizations. Perhaps no industry has
seen the deep impact of digital disruption as much as consumer goods manufacturers, due to hard
and soft factors and a completely new wave of global competitive threats. Therefore, the purpose
of this case study was to identify and validate the KMO gaps executives experience in reaching
organizational performance goals, and to recommend solutions to eliminate hinderances for
executives in the consumer goods industry. Using the gap analysis model (Clark & Estes, 2008),
root causes were identified through qualitative data collected from key high-level executives at
Fortune 500 ranked consumer goods companies. Validated findings indicated gaps in knowledge,
motivation and organization that would support a culture of data. The findings led to the creation
of recommendations on how to guide a data-driven culture that increases efficiency, productivity,
and competitive advantage. An implementation and evaluation plan are also included within this
study. This study adds to the body of knowledge on guiding cultural change in today’s data
permeated world. Private and government entities may find this study helpful in attempts to drive
organizational change toward data and information.

10
DATA-DRIVEN CULTURE

CHAPTER 1 INTRODUCTION
The use of big data (BD) and artificial intelligence (AI) has become paramount for
businesses. The specific problem for today’s executives is that business leaders do not have a
framework to guide a data-driven culture. Currently, these leaders do not have knowledge,
motivation, resources, or tools to make informed decisions. Consequently, leaders have a higher
risk of falling behind competitors who execute data strategies. Chen, Tang, Jin, Xie, and Li
(2014) found CEOs who embrace technology and innovation positively impact their
organizations, and data-driven companies are 6% more profitable than their competitors. There is
an urgent need for a specific framework detailing how to cultivate a data-driven organization
using knowledge, motivation, and organizational (KMO) barriers as pain points, based on Clark
and Estes’ (2008) framework. Experts believe leaders must tactically guide information as a
differentiator in the next economy (Jorge, 2017). The problem is important to address because,
by 2025, an estimated 100 billion devices will be connected (King, 2017), thus creating more
information to harness. This research study aims to guide leaders through KMO to harness
information to increased efficiency, productivity, and competitive advantage. As a result, leaders
who cultivate a deep and complex data-rich culture will develop operational and strategic
success. On the contrary, if today’s leaders remain complacent, they risk being left behind in this
data and information revolution (Minelli, Chambers, & Dhiraj, 2012).
Background of the Problem
Businesses are in the midst of what is being named the fourth Industrial Revolution—
information. Information, data science, BD, AI, and analytics are transforming all industries at
record pace. This shift is bringing about new problems. The field of BD and AI includes digital
information from retail transactions, healthcare records, satellite data, and scientific studies
11
DATA-DRIVEN CULTURE

(Tien, 2013). In other words, it is all of the bits of information floating around in computers, the
cloud, and cell phones that may be used for business intelligence. The volume, velocity, and
veracity of data are significant and growing. Van Loon (2017) reported 90% of the data in the
world today have been created in the last 2 years alone. As such, leaders now face challenges in
effectively harnessing these data to gain insight into complex parameters and risks. These
challenges directly affect organizational missions and goals, strategic planning, and daily
operations. This problem is related to the larger issue of the expanding field of BD and AI, where
AI is facilitating new methods of conducting old tasks within organizations. As a result, leaders
must engage with new knowledge and adapt and plan accordingly for the organization.
According to the literature, leaders are not equipped with strategies to improve functions such as
customer care, risk management, fiscal improvement, and strategic growth (Feldman, Martin, &
Skotnes, 2012; LaValle, Lesser, Shockley, Hopkins, & Kruschwitz, 2011).
In this dissertation, I evaluate problems in the field related to adapting to the fourth
revolution of information by creating a data-driven culture. I examine how business leaders in
the consumer goods industry may fix gaps hindering the development of a culture that integrates
information (i.e., BD and AI) effectively. The performance problem is, thus, how does a leader
create a data-driven organization to run optimally? This is a problem because of the substantial
growth in information, data science, and the ability of tools involving BD and AI to affect
organizational stakeholders significantly.
Importance of Addressing the Problem
Digital innovations such as BD and AI are recalibrating how business is conducted
(Jorge, 2017). This trend is occurring across all industries at a rapid pace (IDG Enterprise, 2014).

12
DATA-DRIVEN CULTURE

Although leaders know they need to create a culture that supports this data revolution, according
to a recent study, one-quarter of respondents had no experience in the field (Hulse, 2015).
Datacentric cultures cost money, costs leaders need to justify and fully embrace to maintain a
competitive edge (Henke et al., 2016). Factors such as how to hire and educate the workforce,
maintain fraud protection, and keep customers happy are part of the issues leaders must consider
(McAfee & Brynjolfsson, 2012). These factors specifically affect practice and, when not
addressed, create ongoing, deeper organizational problems. Unless leaders understand how the
industry is shifting toward information and the tools BD and AI offer, they risk organizational
failure. In essence, cultivating a data-driven organizational culture is critical to maintaining or
growing the organization’s position in the competitive marketplace (Minelli et al., 2012).
Organizational Context and Mission
This study focused on the field of data science as a whole rather than one organization.
The specific goal was to create a KMO-based set of recommendations (Clark & Estes, 2008) for
one specific stakeholder group: CEOs in the consumer goods sector. This group may define and
lead organizational change during the BD and AI revolution. To accomplish this goal, I studied
several consumer goods organizations to perform a gap analysis. The emphasis was on solutions
for CEO stakeholder issues. I conducted qualitative research with high-level executives to
discuss outcomes. I chose these stakeholders (CEOs) for their aptitude regarding data science and
their grasp of the important shifts taking place. This research identified the gaps, leading to the
formulation of recommendations on how to cultivate a data-driven organization within the
consumer goods industry.
13
DATA-DRIVEN CULTURE

Organizational Performance Status
A survey of 388 CEOs found inconsistency in their knowledge about how to scale a
digital corporate strategy and environment (Boulton, 2017). In the same study, 42% of the CEOs
had begun a digital business transformation and, thus, had strategic planning weaknesses.
Alternately, 56% of CEOs said improvements boosted profits but that they need to know more
tactics to continue with the process. One survey respondent stated a leader must clarify a strategy
before placing a key performance indicator (KPI) against it, which is lacking in the field. In
addition, most CEOs said they needed a top-down, cosponsored digital strategy but did not
currently have one in place (Boulton, 2017). This illustrates the gap this study addresses.
Organizations’ daily business may be improved by BD and AI, as well as deep analytical
skills guided by a data-savvy strategy (Pourshahid et al., 2014). Leaders must have full
understanding of how humans accept expanded use of technology (Hulse, 2015). Consequences
for not embracing a strategic, data-driven cultural revolution include exposure to risks and an
inability to perform against competitors within the industry. Organizational status may be
improved through a commitment to the changing landscape of BD and AI and incorporating it
through the culture.
Organizational Performance Goal
Executives’ global organizational performance goal is to increase performance in sales,
profitability, and shareholder value. Executives who lead their organizations to be digitally savvy
do so through efficiencies. Through KMO analysis (Clark & Estes, 2008), recommendations may
parallel what other savvy companies such as Wells Fargo have done, such as appointing C-level
data specialists. Most Wall Street analysts use industry benchmarking as a standard measure.
14
DATA-DRIVEN CULTURE

Deloitte (2018) reported companies in the consumer goods sector must develop newer and bolder
strategies to execute traditional levers in global economy. Innovation, digitization, and
globalization are main trends (Deloitte, 2018). Industries that embrace data outperform
competitors by 6% (Chen et al., 2014; McAfee & Brynjolfsson, 2012).
Top-down leadership initiatives including cultural change to support digitization as well
as hiring, training, and retooling organizational functions will directly enable leaders to reach this
organizational performance goal (Deloitte, 2018). The framework must ultimately affect the
bottom-line metric of the organization and will use data as a matter of practice in analysis. In this
study, I collected qualitative data from high-level executives in the consumer goods industry to
cultivate recommendations to achieve this goal.
Stakeholder of Focus
This study focused on CEOs, due to their ability to acculturate an organization to become
data-driven with volume, variety, velocity, and veracity (the four V’s) in today’s data-driven
world (see Appendix B). The particular industry of focus was the consumer goods industry. The
stakeholder performance goals are specified as follows:
• Organizational (Field of Study) Mission: To provide our consumers with the best
product.
• Organizational Performance Goal: To continuously create shareholder value, produce
goods consumers want, and outperform competitors.
• Stakeholder Goal (CEO Performance Goal): CEO has 100% understanding of what a
data-driven culture is, the ability to structure and execute a strategic plan for
organizational change toward data-driven culture within 2 years.
15
DATA-DRIVEN CULTURE

• Indicators: 90% of the data in the world today has been created in the last 2 years
alone (Van Loon, 2017). There is an urgent need for a specific framework
on how to guide a data-driven culture that increases efficacy, productivity, and
competitive advantage.
Purpose of the Project and Research Questions
The purpose of this project was to evaluate several leaders of Fortune 500 consumer
goods organizations to identify how leaders are adapting to data and cultivating a data-driven
culture to meet the demands of this new environment. Research has suggested 26% of people are
using BD and AI, while 49% are planning to use BD and AI soon (IDG Enterprise, 2014). The
analysis focused on KMO influences related to achieving the field of study organizational goals.
Furthermore, this study addressed stakeholder knowledge and motivation related to a data-driven
culture and the optimal interaction between culture, stakeholder knowledge, and motivation, in
order to formulate recommendations for field-based practice in KMO resources. The research
questions that guided this study were:
1. Does the stakeholder know facets of what makes a data-driven culture?
2. What are the CEOs knowledge and motivation related to achieving this organizational
goal to understand, structure, and execute a strategic plan to create a data-driven
organizational culture?
3. What are the recommendations for organizational practice in the areas of knowledge,
motivation, and organizational resources?
Definitions
Artificial intelligence (AI). The intelligent processing of digital data. Artificial
intelligence is the theory of developing computer systems to perform tasks that mimic human
16
DATA-DRIVEN CULTURE

intelligence. Examples include speech recognition, decision-making, planning, and natural
language processing.
Big data (BD). A term applied to digital information produced from retail transactions,
healthcare records, satellite data, and scientific studies (Tien, 2013). In the context of the field of
technology, BD refers to complex, large data and information produced and stored digitally
(Tien, 2013). Often, the terms BD, AI, data science, and digital are used interchangeably in the
industry. (See Appendices B–D for visual representations of important terms and concepts in this
field of study to expand the context of the problem.)
17
DATA-DRIVEN CULTURE

CHAPTER 2 LITERATURE REVIEW
Big data and AI have unleashed a digital disruption that impacts every facet of today’s
organizations (Demchenko, 2013). Big data is composed of huge datasets with the capacity to
exceed the current capability of conventional databases and software tools to capture, store,
manage, and analyze the information. Harnessing BD is a key challenge and a field of interest
that has grown exponentially since 2011. Big data concerns all aspects of human activities, such
as design, research, digital production, and the delivery of information. Artificial intelligence is
the processing of these data intelligently. According to O’Regan (2016), AI concepts date back to
the 1950s when Alan Turing proposed the Turing test, which asked whether computers could
communicate well enough to persuade humans of their humanity. Princeton students developed
AI at this time, while MIT, the Carnegie Institute of Technology, and others developed logic
theory (Gugerty, 2006).
The Growth of Data Science
Big data and AI are creating monumental changes in the way businesses function today
(Marz & Warren, 2015). These changes can be categorized into three problem areas: volume,
velocity, and variety. Organizations are having problems adapting to remain relevant in a
competitive market. However, researchers have linked AI to increased profit (Bughin et al.,
2017). For example, after Amazon acquired Kiva, a robotics firm, the company automated
packaging and reduced time taken to complete each cycle from 75 minutes to 15 minutes,
resulting in a reduction in operating costs by 20% and a 40% ROI. Netflix adopted an algorithm
that reduced time to find desirable content, which avoided cancelled orders and subscriptions that
would have reduced revenue by $1 billion annually.
18
DATA-DRIVEN CULTURE

Deloitte (2018) reported one of the keys to success in this field is enhancing consumers’
path to purchase, real-time engagement, e-commerce, and blockchain. Retailers such as Walmart
have capitalized on peer sharing, efficiencies, and cost savings (Bughin et al., 2017). Bughin et
al. (2017) provided further examples including how Swisslog reduced stocking time by 30%.
DeepMind can predict energy usage demands and allows for the integration of renewable energy,
thus cutting energy usage by 10%. GE uses digital wind farms with machine learners and sensors
to boost wind energy production by 20%. Mayo Clinic identifies patient issues quicker while
Civitas and Salesforce collaborate to provide services to universities that identify and engage
students at risk of quitting (Bughin et al., 2017).
Consumer Goods Industry Status
Large consumer goods manufactures have seen a decline in growth rates since 2011. The
sector has several categories that have fallen in industry rankings of economic profit, and
shareholder returns have lagged the S&P 500—a decline that has not seen a drop like this in 20
years or more (Henrich, Little, Martinez, Shah, & Sichel, 2018). Total return to shareholders has
lagged S&P because of technology, the fragmentation of sales channels, and the millennial effect
on the retail revolution (i.e., home delivery; Henrich et al., 2018). The growth model that was
once big beats small is transforming into fast beats slow, which is why this problem of practice
attunes to CEOs’ need to embrace data-driven cultural change (Henrich et al., 2018)
Executives’ Role in Leading Change
The primary stakeholders in this study are CEOs and those in executive leadership roles
more broadly. For leaders to lead and manage operations in an AI-driven world effectively, they
need to conduct early explorations and adopt new KPIs to drive AI adoption. They must also
develop recruitment and training strategies that induce creativity, empathy, collaboration, and
19
DATA-DRIVEN CULTURE

judgement skills (Kolbjornsrud, Thomas, & Amico, 2016). Big data and AI are increasingly
becoming viable because they are cheaper, more efficient, and more impartial in performing
certain tasks. Executives need to incorporate KPIs for sharing, collaborating, learning,
decisionmaking, experimenting, and reaching beyond the company’s insights effectively. Better
training and recruitment strategies allow CEOs to develop the workforce, intelligence, and
creativity.
Executives must effectively cope with the stress that BD and AI produce, which is
characterized by rapid change, rising complexity, and uncertainty (Dewhurst & Willmott, 2014).
Kolbjornsrud et al. (2016) found when leaders have a say in initial training efforts, they gain a
sense of ownership in the entire learning process as well as familiarity with AI and BD. The
authors further found that leaders’ willingness to trust AI-generated advice is based on the
leaders’ comprehension of how the system works, AI’s capability of providing a convincing
rationale for the advice it gives, and a proven record of accomplishment. This finding implies
that familiarity among leaders is vital in fostering trust. The authors thus suggested that for
leaders to incorporate AI and BD effectively, intelligent machines should be treated as
colleagues, not as competitors. In another study, Fitzgerald, Kruschwitz, Bonnet, and Welch
(2014) found where CEOs had a shared vision of digital transformation, 93% of workers felt AI
was the best thing for the business. However, only 36% of CEOs had a shared vision, indicating
CEOs are not cultivating environments of trust and may instead be hindering the efficient
adoption of AI. In a third study, Boulton (2017) similarly found only 42% of CEOs had begun a
digital transformation, though the majority said they required a top-down, cosponsored digital
strategy.
20
DATA-DRIVEN CULTURE

CEO Accountability
Leaders face new challenges in creating a culture that supports the speed and reach of
data. The liability of the CEO is rooted in the legal frameworks of the organization (Chen et al.,
2014). Generally, there are two types of CEO responsibilities. First, CEOs must maintain the
trust of the employer and implement the goals of stakeholders (Maak et al., 2016). Secondly,
CEOs must create an environment that is accountable to the employees (Coule, 2015). This
includes creating an environment that is competitive in the current world of immense data use
(Maak et al., 2016). As such, this process involves the delegation of accountability and authority
(Hou et al., 2017), which supports the achievement of specific goals, such as the stakeholder goal
of shareholder value. Modern organizations encourage employees to collaborate and work
interdependently in the business (Armitage et al., 2017; Buteau, Chaffin, & Gopal, 2017). Data
makes collaboration even more possible across the landscape of an organization. As such,
leaders are responsible for developing a clear structure for conveying information including vast
amounts of data. As the challenge of data grows and interferes with other functions in the
organization, the ability of the CEO to develop a sound culture to support all types of
communication becomes more profound (Coule, 2015).
Impact of Data on Organizational Culture
Leaders need to cultivate a data-driven organizational culture to create or maintain a
competitive edge in the marketplace and among their peers. As Dewhurst and Willmott (2014)
stated, computers do not make decisions; they follow orders. This highlights their inherent
strengths while highlighting the need for human involvement. Leaders must think of sets of
criteria upon which computers may produce results. For this reason, CEOs must have experience
in BD and AI to apply to their respective fields (self-efficacy). According to Dewhurst and
21
DATA-DRIVEN CULTURE

Willmott (2014), “the advances of brilliant machines will astound us, but they will transform the
lives of senior executives only if managerial advances enable them to” (p. 2).
Hirt and Willmott (2014) suggested leaders need to look toward opportunities created by
AI and BD adoption in leading, managing, and enhancing interactions among suppliers,
employees, stakeholders, and customers by incorporating social connectivity and digital
channelization in consumer goods. Fitzgerald et al. (2014) found CEOs and managers believe in
the ability of incorporating AI and digital transformations to facilitate major improvements; in
their survey, 78% of participants indicated they already believed that they had the skills to do so.
Conceptual Framework
Clark and Estes’ (2008) KMO process can be used to identify current challenges and
concrete performance solutions. The KMO process follows a series of steps: (a) identify business
and performance goals; (b) determine and analyze performance gaps; (c) discover knowledge,
motivation, and organizational process solutions; (d) evaluate results; and (e) “tune” the entire
system and revise goals regularly (Clark & Estes, 2008, p. 22). This KMO process classifies gaps
in or barriers to performance as well as features to facilitate change. Clark and Estes (2008)
identified six types of support: (a) clear vision and goals, (b) structure alignment, (c)
communicating plans and progress, (d) executive management involvement, (e) proper resources,
and (f) situational change process. In the context of this study, the KMO framework was used to
cultivate insight and identify gaps hindering the creation of a data-driven culture, in order to
formulate best practice recommendations.
Seminal research supports the importance of understanding how humans learn and how
those methods affect performance outcomes. Several factors impact knowledge and learning,
including self-efficacy, personal development, adaptation, and how learners use new knowledge
22
DATA-DRIVEN CULTURE

(Bandura, 2005; Batsell & Grossman, 2006; Marsh, 2012). Researchers mostly agree that people
are self-organizing, self-regulating, and self-reflecting, and learning occurs through the
reinforcement and adaptation of behaviors in a variety of ways (Clark & Estes, 2008; Dembo &
Eaton, 2000; Tuckman, 2006). Indeed, many theorists specify self-regulation is based on multiple
behaviors such as motivation, use of time, control of the environment, and performance, which
CEOs can incorporate when building a data-centric culture (Clark & Estes, 2008; Dembo &
Eaton, 2000; Rueda, 2011). With these concepts in mind, it is important to examine how to apply
these skills to solve problems, particularly enterprise-wide problems from the CEO perspective.
Knowledge Influences
Knowledge includes factual, conceptual, procedural, and metacognitive types (Bloom et
al., 1956; Krathwohl, 2002; Rueda, 2011). Factual knowledge is basic in nature and an
affirmation of what is given to solve a problem. Conceptual knowledge concerns the elements
that piece information together. Procedural knowledge includes techniques and methods to
conduct a task. Metacognitive knowledge concerns a person’s awareness of their own cognition.
These knowledge types are known as Bloom’s taxonomy (Bloom et al., 1956). Some literature
includes criticism of this taxonomy due to the limited quality of its application. Ropohl (1997)
addressed specific knowledge types in technology, building on Bloom’s original work. Ropohl’s
work is fruitful in discussing knowledge components related to the field of technology (data
science) and its ranging importance.
The literature reveals various trends in knowledge rooted in behaviorism including
Skinner’s operant conditioning (Daly, 2006; Skinner, 2011; Tuckman, 2006). Trends in
knowledge lead to new thoughts including social cognitive theory studies (Bandura, 2005;
Dembo & Eaton, 2000; Smith, 2002). Mayer’s (2011) work concerned cognitive processing
23
DATA-DRIVEN CULTURE

(selecting, organizing, and integrating) that is visible in a learner’s behavior. Mayer’s cognitive
processing parallels the inputs and outputs of data in computer processing. The ultimate way of
interpreting and deciding steps when trying to reach stakeholder goals can be traced back to this
seminal research. This research is precisely what needs to be studied to formulate solutions for
CEOs implementing data-driven tactics. The conjunction of traits and tools with BD and AI can
help achieve the organizational performance goal of 6% growth in 2 years as well as the
stakeholder goal of 70% CEO commitment to promote digital cultural transformation efforts.
The CEO will use data as a matter of decision-making practices relating to standard operating
procedures across the enterprise within 2 years.
Procedural Knowledge
Leaders entered the data-centric world without knowledge of effective data-driven
cultural practices (Hulse, 2015). Survey results have shown that although leaders know they need
to create a culture that supports this data revolution, one-quarter lack experience (Hulse, 2015).
With this knowledge gap, a need for procedural information that aligns with today’s current
datadriven environment is critical. Even when and if CEOs do know facets of what an effective
datadriven culture is, these CEOs are not familiar with how to implement specific strategies
throughout an organization. There needs to be procedural knowledge, awareness, and a process
in place to implement global goals within the field for this group of stakeholders. The procedural
knowledge will allow for the development and acceptance of standard operating procedures.
Knowledge and Skills Barriers
Leaders need a vision; clear, meaningful, and compelling answers to queries; and a better,
more efficient way of communicating new concepts (Chamorro-Premuzic, Wade, & Jordan,
2018). Mark Zuckerberg for Facebook, Wa Manlin for Tencent, Jack Ma for Alibaba, and Sundar
24
DATA-DRIVEN CULTURE

Pichai for Google are examples of CEOs with clearly articulated knowledge and skills in the
realm of BD- and AI-driven cultures. When AI completes numerous analytical and
administrative tasks, skills required include coaching, networking, and collaborating in ways that
differentiate companies. Companies must capitalize on digital technologies with knowledge and
judgment of communities, customers, and partners. Fitzgerald et al. (2014) reported in a survey
of CEOs that 63% stated the speed of technological change in their organizations was too slow.
This finding indicates most CEOs are not taking drastic enough measures to incorporate AI and
BD, with lack of urgency as a frequently cited obstacle to adoption. Only 38% of CEOs
recognized and stressed that digital transformation and adoption were a permanent fixture on
their agenda.
Metacognition
Prominent in the literature is the concept of metacognition or the individual’s capacity to
process and reflect (Krathwohl, 2002; Rueda, 2011). Additional contemporary theories on
learning include information processing theory (Schraw & McCrudden, 2006) and cognitive load
theory (Kirschner & Kirschner, 2006), among other theories that facilitate knowledge to achieve
the stakeholder rewards from data and AI. Baker (2006) positioned metacognition as a
higherlevel cognition built on prior theories of learning, including the work of Vygotsky, Piaget,
and others. Metacognition originates from interaction with more knowledgeable peers, which is
transmitted to the learners themselves (Baker, 2006).
In addressing the knowledge gap of the stakeholder, metacognition begins with CEOs
understanding themselves and using cognitive processing to solve inevitable, real-time, and
ongoing organizational problems related to the field. Building on procedural knowledge, CEOs
also must develop metacognitive mechanisms and apply concepts to adopt data-driven solutions
25
DATA-DRIVEN CULTURE

within their organizations. Although procedural knowledge establishes the organization’s tenor
with regards to data and its role within the organization, true performance derives from the
CEO’s ability to deal with prospective situations as they arise. In other words, procedural
knowledge establishes the tenor, while metacognition creates a symphonic process, allowing
organizations to lead with data-driven and metacognitive decisions that are superior to those of
their competitors.
Summary
Knowledge (factual, conceptual, procedural, and metacognitive) is a vital part of a CEO’s
performance. Executives need to understand stakeholder competencies and the market, and how
these aspects affect companies through the lens of these knowledge types. Leaders now face the
challenge of harnessing this data to gain knowledge about complex parameters and risks. These
challenges directly affect the mission and goals of organizations. However, it is risky to rely on
new technologies and information methods without true knowledge (and knowledge influences)
about the effective use of the tools (Peterson, Galvin, & Lange, 2012). In other words, the use of
knowledge types, such as procedural and metacognition, are needed to harness data technologies
to streamline operations by improving efficiency, reducing cost, minimizing the waste of
resources, and maximizing profits (Abdul Hameed & Counsell, 2012). Technological knowledge
for a CEO is quite a challenge, as indicated by the low percentages of CEO acceptance of the
data movement. Moreover, while CEOs do not need to know exactly how data, AI, and the cloud
work, they must have procedural and metacognitive knowledge. Knowledge influences that
stakeholders will need to rely upon most include procedural and metacognitive types (see Table
1).

26
DATA-DRIVEN CULTURE

Table 1
Knowledge Gap Analysis
Assumed Knowledge
Influence
(see Interview Protocol for
questions asked by influence
type)
Knowledge Type Knowledge Influence
Assessment
Proposed
Solution
CEOs findings show they
currently know some facets of
an effective data-driven culture.
However, these CEOs are not
familiar with how to implement
specific strategies procedurally.
Procedural Interviews

Chapter 5

CEOs findings show they were
reactive but not proactive and
must develop a self-reflection
mechanism and apply concepts
in real time to drive data driven
solution culture.
Metacognitive Interviews Chapter 5

Motivation Influences
The following section focuses on the literature on motivation pertinent to the stakeholder
goal. Generally, motivation is an accumulation of factors that affect a person’s desire to achieve
goals or produce the outcomes the person desires (Eccles, 2006; Pajares, 2006; Pekrun, 2011).
People produce better outcomes when they are confident in what they can achieve, are a valued
part of a social group, and have varied forms of interest (Eccles, 2006; Pajares, 2006; Shraw &
Lehman, 2009). Information abounds regarding leadership traits, styles, behaviors, and
influences CEOs use to succeed (Carmeli, Tishler, & Edmondson, 2012). Understanding how
motivational influences (active choice, persistence, and effort) affect performance and
performance goals will ultimately help to determine solutions (Clark & Estes, 2008). The
motivational theories and influences (active choice, persistence, and effort) selected for this
27
DATA-DRIVEN CULTURE

discussion concern a CEO’s ability to achieve performance goals (Clark & Estes, 2008), and
include self-efficacy theory, and goal orientation theory. Research was done on other
motivational theories that did not present themselves in findings as significant but are included in
overall thought on the study itself. They include expectancy-value theory and attribution theory
and are discussed in Additional Findings section.
Self-Efficacy Theory
Self-efficacy is an individual’s belief in his/her ability to accomplish outcomes (Bandura,
2005; Pajares, 2006). Pajares (2006) argued self-efficacy provides the foundation for human
motivation, well-being, and personal accomplishment. Leaders must have self-efficacy to
persevere and anticipate successful outcomes. With regards to the increasingly volatile, risky,
and competitive environment of data science, CEOs must achieve self-efficacy at an extremely
fast pace. Having self-efficacy to deal with situational interest (both triggered and sustained),
personal interest, and well-developed individual interest may allow CEOs to improve
performance outcomes.
The motivational influence aligning with self-efficacy theory is an active choice. In the
self-efficacy model, the CEO is driven by confidence to overcome challenges (Maddux, 2016).
Therefore, self-efficacy gives CEOs the opportunity to be internally prepared, use active choice
to make decisions, and view challenges as tasks to master (Bandura, 2005; Pajares, 2006). In
addition, they develop a more profound interest. Without self-efficacy, CEOs focus on negative
results and lose confidence, which inhibits the motivational influence of active choice (Bandura,
2005; Pajares, 2006).
Among the qualities of a leader, it is important that CEOs have self-efficacy (Carmeli et
al., 2012; Peterson et al., 2012). In creating a data-driven culture, CEOs must apply a mixture of
28
DATA-DRIVEN CULTURE

self-efficacy, active choice, and increasing reliance on data since to identify what is important,
minimize surprises, and predict possible outcomes. Here, data becomes an aide, not a substitute
to CEO leadership. Using the two in conjunction helps CEOs to plan for outcomes adequately
and allow for active choice (Goldman & Casey, 2010). This intersection of traits and tools (BD,
AI) may help CEOs achieve the organizational performance goal of 6% growth in 2 years.
Because of the fear data and AI may replace CEO intuition for decision-making, the lack of
commitment to data-driven cultures has been statistically shown. Therefore, much of the
organizational performance goal is to eliminate these influence gaps to increase commitment to
data-driven cultural strategies across the landscape of the field.
Goal Theory
Goal theory includes motivational desires to choose and persist to achieve goals actively
(Amabile, 1993; Farr, Hoffmann, & Ringenbach, 1993). In this theory, there are a variety of
causes, dimensions, and psychological as well as behavioral consequences (Amabile, 1993; Farr
et al., 1993). As highlighted earlier, procedural knowledge is key in enacting data-driven
strategies, which goal orientation facilitates. Focusing on mastery, learning, and progress
promotes motivation. Examples of motivational influences aligning with this theory include
active choice and persistence.
According to Amabile (1993), the synergy of intrinsic and extrinsic motivation is critical
in goal achievement. Intrinsic motivation (from the work of individuals) and extrinsic motivation
(from the desire to obtain outcomes that are apart from the work itself) are not contrasting
motivations. For example, in a data-driven culture, daily activities require intrinsic rewards,
especially early, highly intrinsic moments, because BD and AI are in their infancy (Amabile,
29
DATA-DRIVEN CULTURE

1993). Solving complex informational challenges must result in personal accomplishment and
pride because the person is conquering a groundbreaking, complex challenge that has never been
done before. In other words, there is little to no precedence in this field.
With respect to motivation for the stakeholder and goals, the Abilene paradox may
encourage faulty decision-making. In the case of data and AI for decision-making, complicated
root cause analysis can be even more difficult. For example, after data or AI analysis, groups that
decide to go against what the data and AI show can compound problems. Motivational influences
include active choice and persistence to contend with the group if one feels strongly about an
action (Bass & Steidlmeier, 1999). This motivational gap impacts a CEO’s ability to cultivate a
data-centric culture of group cohesiveness. This could lead to stress, coerced behavior,
dissatisfaction, and group separation (Bass & Steidlmeier, 1999).
Antlova (2009) identified motivational barriers to companies’ change and adaptability,
including leaders feeling unmotivated. This comes from threats from stronger companies, a weak
position in public spaces as an AI or technology company, or failure to or delay in allocating
budgets to costly transitions to technological advancements within a company. Antlova (2009)
highlighted factors that prevent AI and technology adoption as a lack of knowledge at an
executive level, unforeseeable benefit, and failure of clients to comply with financial, IT,
government compliance, and regulations. Ramilo and Embi (2014) highlighted various barriers
related to the psychology of leaders as motivational barriers. These include fear of work changes,
lack of psychological assurance, fear of product change, profit loss, labor costs, new marketing
strategies, identity, and trust in the technology itself. This technology trust barrier includes
insufficient knowledge of staff and leaders, inadequate maintenance, technology transfer,
30
DATA-DRIVEN CULTURE

research and development knowledge, as well as the unavailability of new digital tools and skills
in maintenance.
Table 2
Motivational Influences
Assumed Motivation Influences Motivation Influence
Self-efficacy: A CEO need to believe they
are capable of effectively differentiating their
organization’s culture to become a data-
driven industry leader.

Interview item: “How do you feel about your ability
to change culture toward this direction? Do you have
the skills and aptitude in BD, AI, and other data
initiatives currently arising in the data market?
If, not, what skills and aptitudes are needed?
GoalTheory: A CEO should want to lead
their organization (with appropriate
SMART goals showing they are doing so) to
become a leader versus a laggard or late
entrant in the data-driven culture.
Interview item: “Share your organizational mission and
goals about adapting to the data-driven cultural
revolution.”

Organizational Influences
Leaders face organizational challenges due to the rapidly changing uncharted territory of
data science. Organizations are frankly not equipped to handle the velocity, variety, and volume
of today’s data, nor harness it to improve organizational effectiveness (Boulton, 2017; Buteau et
al., 2014; Kolbjornsrud, Thomas, & Amico, 2016; O’Sullivan, 2002; Ramilo & Embi, 2014).
Even though awareness of need is growing, related literature reveals substantial organizational
barriers exist that inhibit smooth adoption of technologies (Boulton, 2017; Buteau et al., 2014;
Kolbjornsrud et al., 2016; O’Sullivan, 2002; Ramilo & Embi, 2014). These organizational
barriers include cultural models and settings (Clark & Estes, 2008). Issues with cultural models
and settings often show up in data science as poor leadership towards innovation, poor
31
DATA-DRIVEN CULTURE

knowledge management, lack of support and empowerment for digital innovation, and lack of
managers who can effectively supervise digital innovation (O’Sullivan, 2002; Ramilo & Embi,
2014). Other variables include inadequate personnel resources for implementing digital change,
insufficient team commitment, as well as the lack of budget resources (O’Sullivan, 2002; Ramilo
& Embi, 2014). However, generally speaking, in order to verify the “O” in KMO (Clark & Estes,
2008), resources, policies, culture, and time are pain points to explore. The reason these
organizational barriers are important is that without finding and fixing them, they lead to a
culture of reluctance to change, which increases failure in the rapid growth of data science. These
organizational barriers are Clark and Estes’ third cause of performance gaps— organizational
culture gap and alignment issues. These organizational barriers are further addressed under Clark
and Estes’ (2002) KMO gap analysis framework by examining cultural model and cultural
setting influences (see Table 1).
Organizational Culture Gaps
Clark and Estes (2002) stated lack of effective organizational work methods and
appropriate resources prevent organizations from achieving performance goals. The literature
provides examples of poor performance outcomes. For example, O’Sullivan (2002) surveyed
manufacturing innovation and revealed failure was due to overall cultural issues including poor
organization, leadership, knowledge management, and communication. Lack of effective work
process in innovation such as BD and AI are specifically and massively lagging (Hulse, 2015;
Jorge, 2017; LaVelle et al., 2011; Feldman et al., 2012; Pemberton, 2016). According to Sirkin,
Keenan, and Jackson (2005), managing change is difficult and lack of organizational resources
impedes positive change.
32
DATA-DRIVEN CULTURE

Soft and Hard Factors
Change management experts have typically focused on soft issues including motivation,
leadership, and culture, but these alone are not sufficient in implementing transformation projects
(Sirkin et al., 2005). Effectively removing organizational barriers requires focusing on hard
factors, which Clark and Estes (2002) identify as work process, material resources, value chains,
and value streams. Hard factors (as Sirkin et al. call them) or organizational barriers (as Clark
and Estes call them) mean the same thing—without addressing these organizational barriers, no
amount of knowledge, skill, or motivation will make the organization succeed.
Cultural Model Influence 1
There needs to be a general acceptance and willingness at executive C-level (including
CEOs) that they do not fully understand the impact of technology on their organization.
According to Boulton (2017), only 42% of CEOs have begun a digital business transformation.
The burgeoning elements of BD and AI are shaping CEO strategies. For example, a study on the
effect of AI on market strategies in Jordan revealed the companies using AI experienced positive
organizational effects as compared to their peers in leadership, cost reduction, strategy, and
alliance with suppliers (Al-Sukkar, Husein, & Jaradat, 2013). This research suggests CEOs who
learn and embrace data may reach organizational goals because they remove organizational
barriers through data-driven cultural change. This supports a cultural model because it paves the
way that what is valued and ideal at the organization is for the top-down leaders to accept
technology as important to the entire organization.
Kolbjornsrud et al. (2016) advocated for leaders to work as designers and mold a creative
culture. Leaders who work like designers bring together diverse ideas into appealing, workable,
and integrated solutions. The authors provided example of methods to improve Cultural Model
33
DATA-DRIVEN CULTURE

Influence 1. Once executives embrace this designer mentality, it will fill in the gaps of
knowledge that they do not fully know the impact of technology to their organization. The design
of a creative culture will open knowledge paths to create a new cultural model. These elements
establish the cultural model (Clark & Estes, 2008) that the normative thoughts of the
organization will include data science as a critical piece.
Cultural Model Influence 2
There needs to be a culture of trust that data-driven organizations are superior and
necessary for the future survival of organization in the tech-driven, competitive climate/world.
Survey respondents state that one quarter of employees do not trust their company (Weber,
2014). Hay Group (2009) confirmed we are less engaged today, thus widening the gap in
creating a culture of trust that data-driven decision-making is necessary for the future survival of
the organization. The literature suggests several ways to improve organizational trust. This
cultural model influence generally focuses on improvement from the top-down (CEOs) in
addressing issues of trust and commitment to the vision (Korsgaard, Brodt, & Whitener, 2008;
Moran & Brightman, 2000; Schneider, Brief, & Guzzo, 1996).
Sirkin et al. (2005) proposed a focus on four factors: duration, integrity, commitment, and
effort. Duration calls for scheduling the project milestones, while integrity calls for team
cohesiveness, clarification of accountability, commitments, and roles. Commitment is required
from most influential executives, such as CEOs, as well as people who deal with the new
processes, systems, and ways of thinking. Top-level involvement is necessary, for if the project
does not receive such backing, it will not impart change among employees. Pertaining to effort,
project teams should calculate how much work employees should go beyond their assigned quota
to change over to new processes or systems. Face-to-face meetings, freedom to share, and a
34
DATA-DRIVEN CULTURE

genuine shared vision and belief system across the organization are suggested solutions. Other
methods include having leaders model the way and functioning as data-driven advocates in their
day-to-day functions. Being a key advocate of the tools leads to cultural setting influence 1—
CEOs need time to learn, adapt, and build facets of effective data-driven culture.
Cultural Setting Influence 1
C-level executives (including CEOs) need time to learn, adapt, and build facets of
effective data-driven culture and specific strategies to do so throughout the entire organization.
Leaders must prioritize time spent to learn, adapt, and build strategic shifts from the top-down to
maintain and grow the organization’s position in a competitive marketplace (Minelli et al., 2012).
Strategies to drive this culture through the organization must consider the rapid change and rising
complexity and uncertainty that goes along with change (Dewhurst & Wilmott, 2014). These
facets relate back to leadership accountability—a cultural setting of accountability is important
for success. Concepts of collaboration, interdependency (Buteau et al., 2014), and supporting
communication in data form (Coule, 2015) are ways for CEOs to improve Cultural Setting
Influence 1. This cultural setting indicates how a group interacts within a model (Clark & Estes,
2008); accountability from leadership down is an example of this.
Kezar (2001) discussed strategies for change in the 21
st
century including using common
language for the change process in the cultural settings by answering the what and how (Kezar,
2001). Leaders should use scale, foci, timing, and degree when considering strategies to weave
through the organization. The outcome of a dramatic 21
st
-century organizational change such as
creating a data-centric culture falls under Kezar’s evolutional theory, in that it is a response to
external circumstances of growing emphasis on data science.
35
DATA-DRIVEN CULTURE

Porter and Miller’s (1985) five forces framework is another change identification tool that
helps leaders to identify cultural settings affecting the organization. Here, external threat is the
threat of new entry, the competitive field of data science. Data science is being used to improve
knowledge and cost of scale (Porter & Miller, 1985). What is occurring externally requires a
second-order transformation or paradigm shift, as leaders must embrace the knowledge that it is
irreversible and momentous (Kezar, 2001). Recognizing that leaders need to become directly
involved in new technology is paramount (Porter & Miller, 1985), and raises the questions this
study also begets: how do advances in technology affect sources of competitive advantage? What
strategies should leaders exploit and what are the implications? (Porter & Miller, 1985). The
impact of data to organizations is happening at the place of work and its nuances are part of daily
work life, hence it is a cultural setting issue (Clark & Estes, 2008).
Cultural Setting Influence 2
C-level executives need effective peer groups from which to model (Amazon) CEO
summits, DAVOS, and other executive-level forums. Cultural Setting Influence 2 is a critical
piece for CEOs, as they must model themselves from the early adopters of the data science field
at the highest level. According to Stone (2013), the best practices of creating a cultural setting for
success in data science and driving data culture is Jeff Bezos and his success as Amazon’s CEO.
Stone stated corporate Darwinism will occur unless leaders model effective, rapid cultural
changes to address competitive data-driven environments. The examples specified in this story
relate to the retail sector having the most dramatic shift and the lessons learned from entrants
such as Amazon, EBay, and others, as well as failures such as the Target credit card breach
(Provost & Fawcett, 2013; Stone, 2013). As such, Amazon, eBay, and Alibaba CEOs may
36
DATA-DRIVEN CULTURE

provide effective peer groups with which leaders may engage. This would be a setting (actual
place) that groups of leaders should get together.

Table 3
Organizational Influences

Assumed Organizational
Influence
Organizational
Influence
Cultural Model Influence 1: The
organization needs to have general
acceptance and willingness at the
executive C level (including
CEOs) that they do not fully know
the impact of technology on their
organization
Interview questions
regarding barriers
and awareness of
issues related to
willingness to change
Cultural Model Influence 2: The
organization needs to have a
culture of trust that data-driven
organizations are superior and
necessary for the future survival
of the organization in the tech
driven, competitive climate/world.
Interview internal
external best practices
Cultural Setting Influence 1: C
level executives (including CEOs)
need time to learn, adapt, and
build facets of effective data
driven culture and specific
strategies to do so throughout the
entire organization.
Procedural
Cultural Setting Influence 2: C
level executives need effective
peer groups from which to model
(Amazon) CEO summits,
DAVOS, and other executive level
forums.
Interview internal
external best practices

37
DATA-DRIVEN CULTURE

Conclusion
Data is fundamentally impacting the consumer goods industry in various areas including
profitability, competitive power, and culture of innovation. As such, top executives must
increasingly pave the way for the data-driven cultural revolution in order to survive and thrive. In
investigating the knowledge, motivation, and organizational barriers through the lens of Clark
and Estes’ (2008) KMO framework, several key themes surfaced particularly related to CEOs.
Leaders have knowledge gaps in procedural and metacognitive skills. Leaders also need to
resolve motivation gaps of self-efficacy and goal orientation. Thirdly, there are gaps in cultural
models and settings related to a culture of trust, allocation of proper resources, and the
importance of learning from peers in the data space. Effectively removing organizational barriers
requires focusing on hard factors such as work process, material resources, value chains, and
value streams. For these reasons, this study aimed to identify how CEOs may spearhead digital
transformation by eliminating these barriers.
38
DATA-DRIVEN CULTURE

CHAPTER 3 METHODOLOGY
The purpose of this project was to find solutions to performance gaps that affect CEOs’
goal achievement in leading data-driven transformations in the consumer goods industry.
Specifically, I examined KMO influences that affect CEOs’ abilities to embrace and lead
datadriven culture effectively, in order to identify strategies CEOs may implement to improve
organizational performance outcomes. Spotting obstacles is the first step toward developing a
data-driven culture and using analytic tools to build profitability (Pemberton, 2016). The key to
the success of this study will be the ability of CEOs to lead data culture as a source of
competitive advantage. Creating the right culture to realize the value of BD and AI is crucial for
companies to thrive in today’s competitive marketplace.
Methodological Approach and Research Questions
The methodological approach of this study comprised an exploratory sequential method.
The purpose of this approach was to understand the social phenomena of meaning, context, and
process related to the stakeholder goal of the CEOs. This method comprised of conducting
qualitative interviews with a small, purposeful sample of high-level executives (i.e., CEOs). As a
subjective questioner, I utilized an inductive means to comprehend gaps in information and learn
pain points that were preventing CEOs from achieving performance goals, and specifically those
related to data-driven cultural transformation. I then utilized an inductive approach to reveal the
gaps in KMO systems deterring CEOs from embracing a data-driven culture in their respective
fields. I thematically coded and interpreted data to enable the development recommendations for
CEOs concerning adopting a data-driven culture. This sequential exploratory research process
provided the means to gather information continually for use in subsequent contentions. The
research questions that guided this study were:
39
DATA-DRIVEN CULTURE

1. Does the stakeholder know facets of what makes a data-driven culture?
2. What are the CEOs knowledge and motivation related to achieving this organizational
goal to understand, structure, and execute a strategic plan to create a data-driven
organizational culture?
3. What are the recommendations for organizational practice in the areas of knowledge,
motivation, and organizational resources?
Theoretical Framework
Chen et al. (2014) characterize a conceptual framework as a visual or written product that
explains (graphically or in narrative form) key items to be studied such as concepts, variables,
and presumed relationships among them. This may also be known as a hypothetical system or
thought setting for the examination (Hulse, 2015). Merriam and Tisdell (2015) emphasized the
role of a theoretical framework in designing a study to provide deeper analysis and articulate the
framework through visuals. Frameworks thus help to assess and refine objectives, create
reasonable and significant research questions, select proper strategies, and distinguish potential
legitimacy dangers to your decisions. This framework helped me as the researcher to legitimize
my exploration (Lane et al., 2006).
In this study, I analyzed the KMO impacts that influence CEOs’ capacity to lead a
datadriven culture (Research Question 1). Spotting and fixing hindrances (or gap analysis
according to Clark and Estes) addresses how to improve leadership to foster change toward a
data-driven culture (Research Question 2). As such, stakeholder KMO barriers were represented
in the framework. Specifically, the knowledge and motivation factors for the CEO are enveloped
within the organization’s cultural settings and cultural models. In this specific case, because the
40
DATA-DRIVEN CULTURE

CEO is charged with leading cultural settings and models, the other two factors reside within this
greater component of organization (Clark & Estes, 2008).
The intersectionality of KMO issues was important for me as the researcher to
understand. In particular, I aimed to understand how knowledge and motivation relate to one
another and whether changes to certain factors improve conditions for stakeholder goal
achievement. For example, what are the knowledge factors that improve CEO metacognition
skills allowing them to solve real-time queries regarding data successfully? Does the
organizational setting allow the CEO to use metacognition easily? Motivation factors such as
self-efficacy must also be analyzed in conjunction with both knowledge and cultural settings and
models (i.e., do CEOs have enough knowledge to feel self-efficacy on the topic?).
Finally, the literature suggests CEO knowledge and motivation influence organizational
success (Antlova, 2009; Fitzgerald et al., 2014). Knowledge has been identified as a way to
enable an organization to achieve (Rühli, Sachs, & Schneider, 2015). Additionally, scholars point
to organizational design, cultural models, and settings as critical in driving change such as a data-
driven one (Al-Sukkar et al., 2013; Pemberton, 2016). This framework therefore acknowledges
interconnectivity of these barriers is salient in determining solutions that embrace data-driven
cultural transformation.

41
DATA-DRIVEN CULTURE

Figure 1. Theoretical framework.
Population and Sample
The stakeholder population of focus for this study was narrowly focused on CEOs in the
consumer goods industry. Since this was a field study, I did not examine one specific
organization for internal performance. Instead, as the literature uncovered, there was an urgent
need for specific recommendations on how CEOs should guide a data-driven culture to increase
efficiency, productivity, and competitive advantage across the field—a task that falls under the
responsibility of CEOs (Hulse, 2015). The participants interviewed for the study comprised a
42
DATA-DRIVEN CULTURE

small, purposeful sample of CEOs of traditionally nontechnical companies in the consumer
goods sector, specifically of manufacturing, sports team, and insurance companies. Leaders who
were not C-level executives were excluded from the study. Leaders of technical companies (e.g.,
IBM, Amazon) were excluded from the study due to their emphasis on data within the company.
Data Collection Procedures
To collect data, I conducted qualitative, semi structured interviews with four CEOs
utilizing open-ended questioning. This strategy was chosen because it gives the researcher an
opportunity to use an open, flexible, oral, narrative, and conversational approach to collecting
data (Edwards & Holland, 2013). The interviews were conducted over a 30-day period and lasted
approximately 45 minutes each. Two interviews were held at my office, while the other two
participants chose to be interviewed at their offices in South Florida. The interview locations
allowed participants to be comfortable and engage in dialogue (Maxwell, 2013). All interviews
were audio recorded with notes taken as well.
The interviews followed an interview protocol (see Appendix X), which specified an
introduction to the study, content questions, and probes per Creswell and Creswell (2017). The
goal of these interviews was to ascertain participants’ knowledge and motivation elements
related to technology and data adaptation in their organization, as well as organizational barriers.
Although there was some crossover of functionality to the participants’ leadership positions in
the consumer goods market, there were distinct difference as to their performance goals, targeted
audiences, and current financial outlooks. The semi structured questions allowed the CEOs to
provide reliable information on their KMOs regarding the use of data-driven frameworks within
their organizations. The conversational nature of the interviews also helped the CEOs to open up
and provide more in-depth information concerning their knowledge on the use of data in their
43
DATA-DRIVEN CULTURE

organization. The ability to use a data-led framework requires some level of technical
knowledge. As such, I also evaluated the levels of technical proficiency among the CEOs. After
initial interviews were completed, follow-up interviews were conducted as needed to address any
issues that arose during the research process requiring further clarification.
Data Analysis Procedures
To analyze the data, I reviewed the interviews to identify codes and themes. The findings
were coded a priori by KMO then aggregated back to the research questions. This inductive
method allowed me to find patterns across the field from the lens of a CEO. Once I had a
comprehensive set of themes, I looked for emergent themes to take further during my exploratory
study (Creswell & Creswell, 2017). I coded with several steps in mind: get a sense of the whole,
list, codes, descriptive wording, categorize and perform preliminary analysis.
Credibility and Trustworthiness
As a researcher, I used the criteria of dependability and confirmability to ensure the
credibility and reliability of the results (Creswell, 2008). Strategies included peer group, member
checking, and triangulation. I performed member checking by going back to the interview
subjects to confirm data during points along the way of research study. The design of the
interviews included using background research and peer group comparisons to ensure
trustworthiness. In data analysis, I checked my work from preliminary to finalized data and
looked for patterns. I paid attention to discrepancies and was willing to give a second look to
inconsistencies which might have caused credibility issues.
Ethics
This study involved the use of human participants. As such, it was important for me to
maintain ethical approaches during the study. First, I ensured that I did not coerce any CEO to
44
DATA-DRIVEN CULTURE

participate in the study. Only those who agreed to take part in the study formed part of my
chosen sample. Secondly, I ensured that I explained the purpose of the study fully to make sure
that my participants understood what they intended to take part in to make concrete decisions. I
also assured them of confidentiality by telling them that their names and the names of the
organizations they worked for would not appear anywhere in the research. I was not be biased in
conversing with one participant and then moving to another, despite the knowledge I had gained.
Limitations and Delimitations
Due to the time constraints of the study, data collection was limited to qualitative
interviewing with a small sample of CEOs. I could not control participants’ truthfulness in the
answers provided in the interviews, though I believe the participants provided effective and
reliable answers.

45
DATA-DRIVEN CULTURE

CHAPTER 4 RESULTS AND FINDINGS
The purpose of this study was to use gap analysis to understand the factors and influences
that affect consumer goods manufacturers in an era of significant data and information growth.
The research questions that guided this study were:
1. Does the stakeholder know facets of what makes a successful data-driven
organizational culture?
2. What are the CEOs knowledge and motivation related to achieving this organizational
goal to understand, structure, and execute a strategic plan to create a data-driven
organizational culture?
3. What are the recommendations for organizational practice in the areas of knowledge,
motivation, and organizational resources in achieving the stakeholder goal of creating
and executing cultural change that is data-driven for optimized organizational
performance?
I conducted a qualitative study with a small but purposeful sample in order to answer the
research questions and create a set of recommendations to improve the gap.
Participant Characteristics
The findings for this study stem from qualitative interviews conducted with four
stakeholders, who were senior (C-level) executives of consumer goods industries. The executives
held the roles of President, Senior Vice-President of Sales and Marketing, CEO, and Vice
President Global IT and Business Market Development for companies with approximately $70
billion in total revenue and 100,000 of employees. All executives were in consumer goods
manufacturing of items including personal care, food and beverage consumables, and other
consumer products. Three (75%) were in premium or luxury goods segment of their industries,
46
DATA-DRIVEN CULTURE

having the highest price point in their given sector for their product line (i.e., not a price value,
generic, or budget product). The small but purposeful group of participants was due to the higher
level of targeted stakeholder who fit the study’s protocol (see Appendix X for the study
protocol).

Figure 2. Participant organizations’ produce class.

Table 4
Study Participant Profile
Participant
Pseudonym
Job Title Number of
Employees
Company Revenue Gender Age (< or > 50)
C1 President 5000 1.9 billion Male > 50
C2 Senior Vice
President/VP Global IT
2500 .5 billion Male > 50
C3 CEO 1000 .5 billion Female > 50
C4 Senior Vice
President/VP Global IT
95,000 66 billion Male > 50

75
25
Premium or Luxury Goods Price Value Brand
47
DATA-DRIVEN CULTURE

Findings
I present the findings below as they relate to the three research questions. The findings
were coded a priori by KMO influence, then calibrated by theme and ultimately grouped by
research question. I then propose recommendations following the conceptual framework of Clark
and Estes’ KMO model.
Research Question 1
Research Question 1 asked: Does the stakeholder know facets of what makes a successful
data-driven organizational culture? Although the executives stated they know what makes a
successful data-driven organization, they felt they learned about the use of data or integrating it
into culture too late or after market conditions had deteriorated their brand’s position in the
market. C2 stated, “We did not see that coming well enough. . . . We didn’t listen to changing
consumer preferences.” C1 reported his company “lost market share over about a decade.” C2
stated, “We’ve used AI and a lot of marketing research firms and a lot of data to determine where
and what will be in demand in the future, to not get caught like we were before.” C4 stated, “We
didn’t do enough, we did not prepare enough and we’re kind of playing catch up.”
The executive believed facets of a successful data-driven culture are focused more on
data as an information tool for the sales and marketing department, and not necessarily always or
wholly used by every department in an organization. Examples of data use included sales and
budget forecasting, manufacturing decisions, and competitive analysis. C1 stated:
We use a lot of data to provide analytics on sales competitors, sales and penetration
gains, market share. . . . We would take that data and develop promotions. . . . The data
showed exact volumes sold by every single brand (ours and competitors) then showed
48
DATA-DRIVEN CULTURE

trends, changes and even regional or seasonal preferences. . .. All of that data
predominately was used by the marketing department.
C1 stated, “I looked at it, and our sales guys looked at it and read it, but the marketing
department are the ones . . . who predominately used that information.” C2 stated, “I believe the
leaders here are comfortable with technology, and they rely on it and they accept it. I don’t know
if they’re doing enough, but I believe, it’s not for lack of not knowing what it is.”
Hence, in answering Research Question 1, the answer would be generally no. Although
the executives were at the beginning of grasping technology and its impact, they lacked
knowledge and motivation of the broader concept of what makes a successful data-driven
organizational culture. Additionally, cultural models and settings were not in place to foster a
successful data-driven organization.
Research Question 2
Research Question 2 asked: What are stakeholders’ knowledge and motivation related to
achieving the organizational goals of shareholder value, competitive advantage?
Data-driven culture is not tied to organizational goals. Several statements were made
regarding organizational goals of shareholder value and competitive advantage. Mainly, the
organizational goal of producing a product that elicits shareholder value was reinforced in all
interviews. There were no links between creating the organizational goals of shareholder value or
competitive advantage and creating a data-driven culture. In other words, the value of a
datadriven culture toward achieving organizational goals did not strongly exist. Generally, the
way to achieve goals was to make numbers, increase stock value, and look at it in choppier,
numbersbased goal achievement. C3 stated, “[The] job was to increase volume, grow revenue.”
C1 stated:
49
DATA-DRIVEN CULTURE

My position reported to the CEO. His entire job, more or less, is to shareholder value and
to grow our company’s stock. That also rolls down to me because if my group are not
making the numbers, we then have a problem with the CEO being able to continue to
grow that company stock, shareholder value. . . . The CEO took the offer to the board and
then eventually the price got to substantial, at $6.50 a share on all outstanding stock
options, which is roughly $10 billion. That’s what you’d call increasing substantially
shareholder value. So, everyone holding stock was happy. . . . Always keep in mind what
makes your company strong is the people who are buying your stock. You’ve got to
create shareholder value.
The connection between the value of a data-driven culture and achieving organizational
performance goals was support in the literature as a needed tool for stakeholder goal
achievement. The gap in appreciation of this link was found in literature review and supported in
100% of the participants. The participants knew about data and how it is molding the future, but
did not have the knowledge (procedural or metacognitive) or motivation (self-efficacy and goal
orientation) to move their organizations toward a data-driven culture that would improve their
competitive advantage.
Procedural knowledge influences. Research Question 1 also addressed procedural
knowledge influence. These leaders’ procedural knowledge gave them the ability to run the
organization, reach sales goals, and reach the overarching organizational goal of improving
shareholder value and competitive advantage. Specifically, in the consumer goods industry, the
procedural knowledge concerned how to identify product sales opportunities and use knowledge
and motivation to obtain customers’ business. The participants stated they spent much of their
time on sales opportunities, understanding and listening to consumers, but lacked an organized or
50
DATA-DRIVEN CULTURE

optimized method. They also desired better tools to measure and achieve goals set forth in their
organizational plans. C2 stated:
I provide direction on procedures; we have to achieve our team goals. I don’t go back and
check out their work, I’m spread too far and too wide to do that, so they have to execute
procedures, and I trust them to do that because that’s my job.
C1 further stated:
Our job was to increase volume, grow revenue, and we had goals and objectives of
certain percentages that you had to obtain. Also, we were very responsible for moving
budgets to give tools to do more with our products. . .. We would take data and develop
promotions. . .. Shareholder success is the main goal of the CEO; he actually did his job
and met his objective shareholder value by following procedures and using his procedural
knowledge.
Procedural knowledge of how to establish action items and goal setting. The
interviews provided evidence of procedural knowledge on how executives set sales goals and
interact with staff to achieve their goals. The participants reported limited knowledge of technical
(or data-based) tools and capabilities. All participants relied on outside firms to supply them with
the data information they required. These outside firms provided reports of varying types that
executives internally digested and attempted to use to improve their ability to achieve goals and
action items.
The findings indicated all participants knew about data, and most agreed needed to learn
more about procedural knowledge of data. Half of participants stated they had received recent
procedural training on conducting job and specifically on data and analytics. C2 provided
evidence of a procedural knowledge gap that may be remedied through skill enhancement by
51
DATA-DRIVEN CULTURE

way of education (Clark & Estes, 2008). Participant C3 provided an example of the lack of
knowledge in analytics by himself and team. C4, on the other hand, acknowledged and supported
the importance of procedural training and investments in ongoing procedural knowledge training
for all employees in technology and data as well as other job functions. C2 stated:
We do trust the data. We trust each other. . .. They (CEOs) see what’s happening in the
retail sector, the know they have to do something, we have to rely on it, and they use it in
their decision making. They use outside consulting services and they put it in useable
formats for us.
C2 further identified a problem in the organizational setting regarding age diversity in
management, which impacted procedural knowledge as well. This created a mix of aptitudes on
data tools with younger individuals being stronger and older individuals being less strong.

Figure 3. What to do with data and where it comes from.

0
20
40
60
80
100
120
Know what data is Confident they know
what to do with it
Gets data from internal
source
Gets data from external
source
C1 C2 C3 C4
52
DATA-DRIVEN CULTURE

Connection between data tools and improving product sales must be shown for buy-
in.
Knowledge – Metacognition. Executives need to know how to decide and reflect on data
decisions with metacognitive skills that enable them to create shareholder value, produce goods
consumers want, and outperform competitors. Using metacognition, executives may observe data
points to decide what is most important and reflect on the relevance or usefulness of the data
(Kale, 2018). Executives may also make real-time decisions based on metacognition, which will
directly improve organizational performance goals. In this study, all participants needed more
metacognitive knowledge about how to adjust to real-time data-driven decisions and actions to
utilize information during the course of a workday. While all participants identified analytics as
necessary, three participants (C1, C2, and C3) highlighted discrepancies in what decisions should
be made and by whom once the data have been collected.
Motivation – Self-efficacy Executives need to feel confident they can understand how to
create and execute a data-driven culture, as well as the reasoning behind data science and its
ability to help improve organizational performance. The research found that Enhancing an
individual’s belief in his/her ability to complete a function will spark additional confidence as
they complete future tasks. The research supports what Clark and Estes (2008) found that
motivation, learning, and performance are enhanced if a person values the task. So, data-driven
culture has to be valued for self-efficacy to take place. Self-efficacy theory suggests an
individual’s perception of what he/she can do influences his/her capacity to accomplish a task
(Elias, Barney, & Bishop, 2013; Unau et al., 2018). Findings show:

53
DATA-DRIVEN CULTURE

Internal data sources will improve the culture of trust and data-driven culture.
Instead of relying fully on outside firms or agencies, internal data gathering by experts improves
effective data-driven solutions and creates a stronger overall internal data-driven culture.
Motivation. Research indicates four factors for increasing motivation: “personal and team
confidence, beliefs about organizational and environmental barriers to achieving goals, the
emotional climate people experience in their work environments, and the personal and team
values for their performance goals” (Clark & Estes, 2008, p. 90). Four themes were gleaned from
participant interviews pertaining to motivational influences: mapping data-driven culture
strategies, performance to goal attainment, changing culture within the organization and industry,
and other cultural settings and models. Approximately 75% of participants were not confident in
using tools to achieve specific goals.
Motivation influence. All executive teams need to feel confident they can understand the
data science reports that assist in strategy creation, implementation, and analysis. Eccles
identified four elements of value which include cost, attainment, intrinsic and utility value
(Akcaoglu, Rosenberg, Ranellucci, & Schwarz, 2018; Eccles, 2014; Eccles & Wigfield, 2002).
Attainment value is believing that a personal achievement had been met, while intrinsic value
relies upon finding gratification by doing an activity (Eccles & Wigfield, 2002). Cost equates to
the ramifications of an individual’s actions, while utility value refers to the ability of an exercise
to identify with the individual’s goals (Eccles & Wigfield, 2002; Galla, Amemiya, & Wang,
2018; Wang, 2012). Overall, the findings showed approximately 80% of team members did not
value the usefulness of data for sales improvement. Team members need encouragement to
combat feeling helpless regarding lack of knowledge in data science.
54
DATA-DRIVEN CULTURE

Multigenerational workforce impacts culture of trust and acceptance of technology
and change. Time on job impacts the culture of trust and acceptance of technology and change.
Half of the participants indicated the need to address the cultural setting of a multigenerational
workforce to improve trust between older and younger workers. The smaller company (C4) had a
better grasp of the multigenerational workforce than the larger organizations. Three-quarters
(75%) of the participants (comprised of the larger companies) did not have goals concerning
creating a culture of trust (of data and supporting a multigenerational environment). C2 stated:
One thing we are experiencing is the diversity of older or younger people. So, change and fresh
new eyes are good, but not at the expense of everything we have accomplished before, such as
providing XXX to the White House and all these traditional aspects of our business for a long
time. . . . There are definitely divisions and political factions that are getting in the way of trust
within the organization.
C4 stated:
We’ve got employees into their 70s that are still on top of their game and we keep people
as long as they want to work if they’re doing a good job. Retention in this job market is
important and that is why we have a mix of older and younger workers. I try to lead with
trust. They can trust me if they need anything. . . . The labor market it tight, so we really
have to offer a lot to employees. We’ve been together a long time and we try to work
with them and be sympathetic.

55
DATA-DRIVEN CULTURE

Figure 4. Cultural settings: Gender and time on the job.

0
10
20
30
40
50
60
70
80
Male Female Experienced Worker Mid
and Late Career
Entry Level or Early
Career
C1 C2 C3 C4
56
DATA-DRIVEN CULTURE

Table 5

Cultural model age of company impacts model of culture that is less
likely to accept change such as technology and √ data

Motivation to feel empowered by data reports and to act
Organizational Barriers
Cultural setting of multi-generational workforce impacts
culture of trust of accepting technology
√
Motivation

Self - efficacy CEOs confident under s tand data and how it
can improve organization
√

Self - efficacy of CEO creates confidence in future tasks

√

Self - efficacy on reading data report s

X

Motivation to map data strategies, performance
√

√
Cultural setting of older workers and training for technology
√

Cultural setting of older CEOs and tra ining for technology
√

57
DATA-DRIVEN CULTURE

Cultural setting of enough resources and support from CEO
to have data driven strategies showed that they are willing to X support
initiatives with money

Common culture. One surprising finding concerned the link the majority of these
organizations unknowingly shared. Three of the businesses were established companies founded
in 1837, 1889, and 1882 with elements of mature market conditions. Although products were
very different, they shared this common culture of established brand with historical ties to
American culture with lore and legacy known in the market. Examples include being a product
used by George Washington in the White House or being a product ingrained in early Americana
culture from the 1800s. These affected the cultural models.
Additional Research but Not Studied Deeper Due to Limitations of Scope
Expectancy-Value Theory. Eccles (2006) argued expectations for success and the value
individuals attach to tasks are strong predictors of achievement. This is known as the expectancy-
value motivational theory (Eccles, 2006). The motivational influence aligning with expectancy-
value theory is value. There are four dimensions of value: attainment, intrinsic quality, utility,
and cost (Eccles, 2006). Enterprise-wide, the CEO must use goal orientation to establish
expectation value to attain superior organizational performance. Because of the burgeoning
influence of data within all aspects of organizations, the CEO must ensure that all stakeholders
understand the value of data and why data is critical, such as using risk analysis data on a
customer prior to releasing inventory to that customer. This example relates to utility value, as
the data usage helps employees to perform tasks aligned with their goals.
The constructs of utility and intrinsic value can lead CEOs to achieve their vision. Utility
and intrinsic value can also enable employees to perform their jobs better despite the challenges
58
DATA-DRIVEN CULTURE

and risks inherent in the complicated use of BD and AI. Furthermore, CEOs using
expectancyvalue theory to motivate themselves may successfully show how data-driven actions
improve employees’ beliefs that they can and want to do a task. This is especially important with
challenges and fears people face regarding technology acceptance. In their research on
technology acceptance models, Venkatesh and Davis (Davis 1985; Ventkatesh & Davis, 2000)
discussed this fear, which utility and intrinsic value can help to eliminate.
Attribution Theory. With the theory of attribution, Weiner (2010) proposed individual
attributes determine the effort a person is willing to exert in achieving a specific future goal.
According to this model, an individual’s effort or luck is not as important as attribution.
Attribution theory concerns the ways people interpret different events and how these experiences
relate to behavior and their thinking. Attributes explain the behavior that causes people to do
what they do. Leaders can use this theory to link effort, self-concept, and potential behaviors.
Situationally, a CEO may attribute success or failure to a causal reason, such as opening new
locations based on data, unsupportive employees, or a poor working environment. However, a
CEO who succeeds may attribute success to reasons such as luck, knowledge, competence, or
other causal attributes (Weiner, 2010). In this case, success would most align with the
motivational influence of attributes. An example would be successfully developing a new
product in the R&D department based on the parameters the data, AI, and past successes
established. For CEOs who attribute success to effort, knowledge, and confidence, there is a high
likelihood they will demonstrate improvement in future outcomes. However, if CEOs rank poor
in their performance, there is a tendency to attribute failure to internal factors such as employees,
instability, or the complexity of the task (Weiner, 2010). Situating these facts into the
59
DATA-DRIVEN CULTURE

stakeholder goal that CEOs need to raise their percentage of commitment to data strategies, if the
attribute of self-efficacy is strong, their motivation improves.

60
DATA-DRIVEN CULTURE

CHAPTER 5 DISCUSSION AND RECOMMENDATIONS
The purpose of this project was to evaluate several leaders of Fortune 500 consumer
goods organizations. I analyzed how executives were adapting to data cultivating a data-driven
culture to meet the demands of their environments. Evidence suggests 26% of people are using
BD and AI, while 49% are planning to use BD or AI soon (IDG Enterprise Big Data Study,
2014). The analysis focused on KMO influences related to achieving the field-study
organizational goals. The research questions guided the study in addressing the knowledge,
skills, motivation, and organizational influences of the executives.
Summary of Study Findings
Table 6
Summary of Knowledge Influences
Assumed Knowledge
Influence
Knowledge Type Knowledge Influence
Assessment
Proposed
Solution
CEOs currently do not know
facets of an effective data
driven culture and, even
when/if CEOs do know, these
CEOs are not familiar with how
to implement specific strategies
procedurally.

Procedural Observations and code
these for process
improvements,
benchmarking
achievements

In Plan
CEOs must develop a self
reflection mechanism and apply
concepts in real time to drive
data-driven solution culture.
Metacognitive Interviews In Plan

61
DATA-DRIVEN CULTURE

Procedure
Increasing CEOs’ knowledge about data-driven culture. The findings indicated 56%
of executives did not have proper knowledge of creating data-driven cultural practices. The
theory that supports this procedural knowledge influence comes from Schraw and McCrudden
(2006). Schraw and McCrudden stated mastery is achieved when acquiring component skills,
practicing integrating them, and knowing when to apply them. The recommendation is to provide
CEOs with a visual informational document or pamphlet that defines the data-driven culture with
its critical characteristics.
Evidence from the study showed although leaders know they need to create a culture that
supports the data revolution, one-quarter of respondents had zero experience (Hulse, 2015). As
Schraw and McCrudden (2006) stated, individuals must master how to identify and understand
important points. The current findings show CEOs do know some facets of what an effective
data-driven culture is but have a hard time integrating them or applying what they have learned.
Remedying this gap will allow for the development and acceptance of a standard operating
procedure led by the CEO to be used throughout the organization. Chen et al. (2014) found CEOs
who used technology and innovation positively impacted their organizations, and that data-driven
companies were 6% more profitable than competitors. The meaningful organization of
knowledge and connecting new knowledge to prior knowledge to construct meaning will help
CEOs to guide information tactically as a differentiator in the next economy, which Jorge (2017)
stated is critical for organizations to survive.
Metacognitive
Improving CEOs’ metacognitive skills will enable them to capitalize on data knowledge.
62
DATA-DRIVEN CULTURE

The findings of this study indicated all CEOs needed to improve metacognitive skills. Schraw
and McCrudden (2006) stated to develop mastery (i.e., metacognition), individuals must acquire
component skills, practice integrating them, and know when to apply what they have learned.
The recommendation is to provide CEOs with education to create learning environments that
support independent decision making based on data and information tools.
Evidence from contemporary theories on learning, which encompass metacognition,
include information processing theory and cognitive load theory (Schraw & McCrudden, 2006).
Leaders need metacognition to learn new concepts more efficiently (Chamorro-Premuzic, Wade,
& Jordan, 2018). Executives must have good metacognitive skill to understand queries and what
to do about them. This metacognition is built on prior theories including the work of Vygotsky,
Piaget, and others (Baker, 2006; Krathwohl, 2002; Rueda, 2011). In this study, CEOs used
metacognitive processing to solve inevitable, real-time, and ongoing organizational problems.
Peer-to-peer transfer of knowledge during established sessions will help CEOs to gain cognitive
processing skills to drive a data culture. Methods including check ins, interviews, peer
conversations, and surveys may be used to close this knowledge gap.

Table 7
Summary of Motivation Influences and Recommendations
Assumed Motivation
Influence
Validated as a
Gap
(Yes, High
Probability, No)
Priority
(Yes,
No)
Principle and Citation Context-Specific
Recommendation
63
DATA-DRIVEN CULTURE

CEOs need self
efficacy to enact
policies that support
the organizational
goal of a 100%
permeated data driven
culture.
High Probability Yes Make it clear that
individuals are capable
of being taught or are
capable of performing a
task (Pajares, 2006).
Provide goal-directed
practice coupled with
frequent, accurate,
credible targeted and
private feedback on
progress in learning and
performance (Pajares,
2006).
Peer modeling,
instruction, guided
practice, and
immediate feedback.
Overconfidence
checkpoints in peer to-
peer meetings to
facilitate mental
effort.
Goal Orientation:
CEOs need to develop
and master
competencies to
create and execute a
data-driven culture.
High Probability Yes Focusing on mastery,
individual improvement,
learning, and progress
promotes
positive motivation
(Young & Anderman,
2006).
Make it safe to take risks
(Anderman).
Team meetings to
promote learning
through feedback
sessions using new
situations to solve
problems using verbal
report methods.

Self-Efficacy
Executives need self-efficacy to execute a data strategy. The findings of this study
showed CEOs did not have 100% self-efficacy to persevere in the increasingly volatile data
driven world. A recommendation rooted in self-efficacy theory has been selected to close this
self-efficacy gap: peer modeling, instruction, guided practice, and immediate feedback.
Overconfidence checkpoints may be used in peer-to-peer meetings to facilitate mental effort.
Pajares (2006) found high self-efficacy may positively influence motivation. This suggests
providing leaders with modeling, guided practice, and targeted feedback will support their
confidence. The recommendation then is to provide peer modeling, instruction on how to
integrate technology-based activities, guided practice, and immediate feedback. Bandura (2000)
and Pajares (2006) support the solution of peer modeling, instruction, guided practice, and
64
DATA-DRIVEN CULTURE

immediate feedback. In the self-efficacy model, the CEO is driven by confidence to overcome
challenges (Maddux, 2016). Having self-efficacy via guided practice will allow CEOs to
improve performance outcomes (Bandura, 2000; Pajares, 2006). Therefore, self-efficacy will
give CEOs an opportunity to be internally prepared, use active choices to make decisions, and
view challenges as tasks to master (Bandura, 2000; Pajares, 2006) while executing data strategy.
Without immediate feedback on self-efficacy, CEOs focus on negative results and lose
confidence, which inhibits the motivational influence of active choice (Bandura, 2000; Pajares,
2006). From a theoretical perspective, then, it would appear increasing self-efficacy in
technology integration would increase technology acumen in activities. Evidence suggests tools
such as instruction, guided practice, and immediate feedback help CEOs to plan for outcomes
and allow for active choice adequately (Goldman & Casey, 2010).
Goal Orientation
Increasing CEOs’ goal orientation will develop competencies to execute a data-driven
culture. The findings showed all executives needed to develop and master their competencies in
creating and executing a data-driven culture. A principle from theory has been selected to close
the gap. Young and Anderman (2006) indicated focusing on mastery, individual improvement,
learning, and progress promote positive motivation. Additionally, Young and Anderman (2006)
concluded leaders must sense it is safe to take risks. The recommendation then is to provide
CEOs team meetings to promote learning through feedback sessions using new situations to
solve problems using verbal reporting methods on organizational data.
The results and findings of this study indicated all participants needed to view technology
and data as valuable for improving organizational performance. Executives need goal orientation
tools such as peer modeling to persist in mastery of skills to lead a successful data-driven
65
DATA-DRIVEN CULTURE

organization (Amabile, 1993; Farr et al., 1993). A recommendation for CEOs to become
competent is mastery, individual improvement, learning, and progress promote positive
motivation. Executives should learn through feedback sessions using new situations to solve
problems and use verbal report methods.
Evidence shows daily activities need intrinsic rewards (such as the reward of immediate
feedback or verbal reporting; Amablie, 1993). Evidence further states early, highly intrinsic
rewards are especially important in data because of its infancy stage. (Amabile, 1993; Bass &
Steidlmeier, 1999). This suggests providing feedback and verbal reporting early and often will
achieve favorable results in motivation toward goal orientation. The recommendation is to hold
feedback sessions and verbal report methods.
Organizational Influences and Recommendations
The assumed organizational needs for this study were as follows: common use and
trusting results of data-based decision making being standard organizational culture, CEOs
needing to trust data-driven culture to achieve organizational performance objectives, and
organization setting allowing data to be a critical part of all decisions made. Because data
collection is not completed for this study, the needs are still assumed. These organizational needs
are high priority in that each affects all stakeholders. Here, the framework of Clark and Estes
(2008) helps to make recommendations. Table 6 summarizes the assumed organizational needs,
their probability of validation and prioritization, and contextual recommendations based on
identified cited principles.
Table 8
Summary of Organization Influences and Recommendations
66
DATA-DRIVEN CULTURE

Assumed
Organization
Influence
Validated as
a Gap
(Yes, High
Probability,
No)
Priority
(Yes,
No)
Principle and Citation

Context-Specific
Recommendation
Cultural Model:
Common use and
trust in results of
data-based decision
making as standard
organizational
culture.
High
Probability
Yes Creating and maintaining a culture
of trust is critically important to
achieving stakeholder goals,
ongoing communication,
solicitation of feedback;
demonstration of concern by
leadership consistent with
organizational policies will facilitate
such trust (Agocs, 1997; Clark &
Estes, 2006).
Cultural models are so familiar that
they are often invisible and
unnoticed by those who hold them
(Gallimore & Goldenberg, 2001).
Leaders need to
communicate
constantly and
candidly while
asking for
feedback in a loop
to build trust.
Cultural Setting
Influence:
Organizational
setting allows
strategic data plan to
be a critical part of
all decisions made.
High
Probability
Yes Model learning from own errors by
accepting mistakes as opportunities
to learn (Anderman & Anderman,
2006).
Make it safe to take risks
(Anderman & Anderman, 2006).
Use heterogeneous cooperative
groups to foster peer interaction; use
individual work to convey progress
(Young & Anderman, 2006).
Provide
professional
development
training with
follow-up
feedback and team
meetings.

Cultural Models
The organization needs common use of and trust in results of data-based decision making.
This theory is rooted in Clark and Estes framework (2008). Creating and maintaining a culture
of trust is critical in achieving stakeholder goals, positive feedback loops, and leadership that
reflects organizational policies (Agocs, 1997; Clark & Estes, 2006). Clark and Estes (2008)
stated when policies and procedures are aligned and communicated from the top with all
stakeholders, organizational performance increases. Hence, top-down communication of need
67
DATA-DRIVEN CULTURE

for and trust in data-based decision making will create a cultural model that envelops data.
The recommendation that leaders communicate constantly and candidly while asking for
feedback in a loop to build trust will build this cultural model. Applying the principles of
Clark and Estes, leaders must continually state policies and procedures on how important data
use is in the company. Furthermore, leaders must continuously communicate from the top that
use of data will help achieve stakeholder goals, as reflected in organizational policies.
According to Tuckman (2009), an environment that fosters desirable behaviors helps
learning. Therefore, top-down communication on the importance of trusting data will create a
culture of trust and reinforce learning. Daly (2009) confirmed behavior that is reinforced is
strengthened. Mayer (2011) also stated integrating auditory and visual information maximizes
working memory capacity. Hence, continuous communication (auditory or visually) will build a
cultural model. Executives must have a top-down digital strategy which includes policies and
procedures that are communicated from the top (Boulton, 2017). As mentioned, creating a
community of learners where everyone supports everyone else’s attempts to learn (Youth &
Anderman, 2006) will lead to improvement in this performance gap.
Cultural Settings
Executives need an organizational setting that allows data to be a critical part of all
decisions made. Results show the organizational setting needs to allow data to be used all the
time for improved accuracy in decision making. Ways to incorporate these actions are to model
learning from errors by accepting mistakes as opportunities to learn (Anderman & Anderman,
2006) and make it safe to take risks (Anderman & Anderman, 2006). Additionally, the use of
heterogeneous cooperative groups to foster peer interaction and individual work to convey
progress (Young & Anderman, 2006) will effect desired change. Further theories that support
68
DATA-DRIVEN CULTURE

this belief come from Kirshner et al. (2006), in that data offloads cognitive load. This is
supported by findings that decreasing cognitive load enables more effective learning, and
learning is enhanced when the learner’s working memory capacity is not overloaded (Sweller,
1994). Allowing tools such as data (which can be used to create schemas) to be part of decision
making will help organizational effectiveness (Sweller, 1994).
Positive emotional environments support motivation (Clark & Estes, 2008). The trusting
of data as a tool in the setting will provide a positive emotional environment. This will create
more motivated, confident employees. Scott and Palincsar (2006) discussed how using the tool of
data as part of social interaction, cooperative learning, and cognitive apprenticeships (such as
reciprocal teaching) facilitates construction of new knowledge. When the CEO uses these
methods to introduce and use data in the workplace culture, success across all job functions will
be improved based on sociocultural theory. Therefore, the recommendation is to create a setting
that facilitates taking risks in a positive emotional environment in order to support data-based
decision making.
KMO Summary of Influences and Findings
Knowledge Recommendations
Krathwohl’s revised Bloom’s taxonomy (2002), the levels of knowledge are analyzed.
These knowledge factors were used in this study and attributed to the participants’ knowledge
gaps, namely procedural knowledge and metacognition. Creating a strategic plan and allowing
for stakeholders to improve procedural and metacognitive knowledge are addressed. Changing
the culture to support using technology (data and information) will improve knowledge gaps.
69
DATA-DRIVEN CULTURE

Motivation Recommendations
Motivation recommendations include improving self-efficacy and goal orientation. Goal
orientation involves typing data-driven culture goals toward the overall organizational
performance goals as well as to daily activities that support goals. Executives need improved
self-efficacy on the topic and from a higher level that includes specific organizational resource
additions. As with the knowledge recommendations, the motivation recommendations are also
situated within cultural models. Hence, changes to the culture should include revamping the
culture to trust data and support data-based decisions and give credence and resources to do so.
Organizational Barriers Recommendations
Organizational culture and models are the broader place where this study’s participants
influence resided. Recommendations include a 21
st
-century Kezar change (Kezar, 2017). This
includes improving culture to trust data and encourage settings for use of data. To do so, they
must address cultural settings of multigenerational workforce and retention/time on job issues
among other themes. One issue with cultural models and settings in data science is poor
leadership toward innovation (O’Sullivan, 2002; Ramilo & Embi, 2014). This is why CEOs must
deal with workforce retention and age issues in consumer goods. Lack of support for digital
innovation and lack of managers who can effectively supervise digital innovation were also
mentioned (O’Sullivan, 2002; Ramilo & Embi, 2014). Hence, the recommendation is to hire
appropriately educated personnel to help CEOs in this respect.

Limitations
This study did not have time or scope to allow for development of all influences.
Therefore, as I researcher, I chose the priority influences and focused on them for best outcomes.
70
DATA-DRIVEN CULTURE

Integrated Implementation and Evaluation Plan
The new world model (NWM; Kirkpatrick & Kirkpatrick, 2006) will guide this
implementation and evaluation plan. This model is broken into four levels of evaluation with the
goal being to improve performance in the organization. The NWM builds from Level 4 to Level
1. The fourth level is the preplanned overall organizational goals and leading indicators that most
create the environment for organizational goals to be met. Level 3 incorporates analysis of
behavior changes and reinforcing, monitoring, encouraging, and rewarding behaviors or drivers.
The final levels evaluate learning and reactions with the goal of learning transfer of knowledge
within the organization (Kirkpatrick & Kirkpatrick, 2006). The organizational performance goal
was to create continuous shareholder value, produce goods consumers want, and outperform
competitors in consumer goods manufacturing sector by 6% in 2 years.
Level 4
According to Kirkpatrick and Kirkpatrick (2016), Level 4 results are the degree to which
targeted outcomes occur as a result of training and support. This is a combination of the
organizational mission while maintaining profitability in bringing product to market.

71
LEADING DATA-DRIVEN CULTURAL TRANSFORMATION

Table 9
Outcomes, Metrics, and Methods for External and Internal Outcomes
Outcome
Type
Outcome Metric(s) Method(s)
External The CEO receives positive press,
and positive feedback from Wall
Street Analysts for this initiative
Media and press articles.
BUY recommendations
from Wall Street, press
and media responses
Shareholder value
increases 6% in 2 years
Analysis data
The strategic plan is executed
resulting in profit margin
increased and happier
shareholders and customers
Annual report Analysis data

Customers comment and state
they enjoy the company, products
and processes better than they did
before initiative. Customer
satisfaction rates
shown increases
Sales team forecast and
final reports, customer
ratings higher than before
Analysis forms of
accounting reports
Market share percentage
improved versus competitor
Comparison to RAD data RAD data analysis
Internal The CEO hires a chief data
officer. This gives the CEO a
resource for 100% understanding
of what a data-driven culture is
and to gain internal knowledge of
procedures to use data in
organizational strategy with a
strategic plan.
Strategic plan created
from insights within 6
months
Strategic plan and budget
analysis components
The CEO has created a culture
where employees conduct jobs
with data usage.
Number of employees
utilizing data to conduct
job
Data login information
Organizational job satisfaction
and culture at workplace
improved upon enactment of new
strategic plan.
Culture of data
acceptance among
employees
Surveys
Employee retention rates
correlate to organizational
optimal functioning.
Percentage of retention
year over year

72
LEADING DATA-DRIVEN CULTURAL TRANSFORMATION

Level 3: Behavior
Critical behaviors. Critical behaviors at Level 3 include key behaviors that the primary
group will have to perform consistently to bring targeted outcomes (Kirkpatrick, 2016). The key
behaviors start with CEOs following the newly established procedural manual to execute
strategy. This will be assessed by observing CEO behaviors and errors in execution aggressively
in the first 30 days and with ongoing evaluation from Day 1–90. The CEO will communicate
readily and consistently in weekly meetings with department heads. This will keep performance
outcomes directly on the CEO’s leadership behavior. The CEO will coach the message on
procedures as well as lead meetings that monitor the organization’s effectiveness toward
performance indicators. This critical behavior will be backed up by a consistent bonus program
specifically tied to performing these behaviors to change culture to be data driven. As a
reminder, the CEO stakeholder goal is that the CEO has 100% understanding of what a
datadriven culture is, the ability to structure and execute a strategic plan for organizational
change toward data-driven culture within 2 years.

Table 10
Critical Behaviors, Metrics, Methods, and Timing for Evaluation
Critical Behaviors Metrics Methods Timing
1. CEOs follow
procedural manual to
execute strategy. (ability
to understand what
culture is)
Less than 10% of errors in
executing accurate actions
stated procedural manual
Assessed by those who
observe CEO performance
90 days
73
LEADING DATA-DRIVEN CULTURAL TRANSFORMATION

2. CEOs continuously
communicate this strategy
in face-to-face (live or
online) meetings with
department heads each
week. (ability to execute
plan)
CEOs organize and have
100% attendance in weekly
meetings with each
department head
Tracked in web-based
calendar app
60 days
3. CEOs coach employees
consistently on new
procedures.
(ability to structure and
execute plan)
100% attendance in all
coaching sessions
Assessed by follow-up
surveys
30 days
4. CEOs establish
consistent bonus payment
structure based on
SMART
performance. (ability to
tie goals to encourage
plan’s strategic
effectiveness)
Published document on
bonuses structure
Published and filed document
on record with HR
90 days

Required drivers. The success of this change initiative will have stakeholders in full
support of desired behavior (see Table 6). In the case of the primary stakeholder CEO, the job
aid (visual) will be more sophisticated, extremely high level, and specific to the language and
tone of the CEO level. Drawing from previous KMO understanding, the CEO will require a
unique method of peer review due to his/her position of power. The CEO will also require
unique feedback, collaboration, and meetings that tie back to the overarching goals of the
organization, which are for shareholder value and tend to be numbers focused.
Organizational support. The organization will attend to cultural models and settings
that support the Kirkpatrick levels in behavior modification. First, time will be given for daily,
weekly, monthly, and quarterly sessions for feedback discussions. Second, money will be
allocated for communication tools, meeting spaces, and other capacities needed to ensure
success. Third, money will be allocated for bonus payments that align appropriate behavior
74
LEADING DATA-DRIVEN CULTURAL TRANSFORMATION

toward the data-driven culture. In summary, the model and settings will involve ongoing
meetings, feedback, and reward systems to change behavior toward desired outcomes.

75
LEADING DATA-DRIVEN CULTURAL TRANSFORMATION

Table 11
Required Drivers to Support Critical Behaviors
Methods Timing Critical Behaviors
Supported
(1, 2, 3, etc.)
Reinforcing Job Aid (visual) that summarizes the strategic plan
that was created to guide this change
Ongoing 1, 2

Use existing modes of communication to reinforce
themes; email, intranet, website, face to face
Ongoing 1, 2, 3

Chief Data Officer working throughout the
company on reinforcing
After hire,
ongoing
1, 2
Encouraging
Feedback and coaching with the C-level executives
Ongoing 1, 2, 3, 4

Collaborating with other industry leaders peer-
topeer
Ongoing 1, 2
Rewarding
Roundtable discussions, rewarding behaviors, call
outs of examples when data-driven strategies were
used during that time frame effectively
Weekly 2, 3

Monetary bonus for performance toward KPI of
driving data culture
Quarterly 2, 3
Monitoring Roundtable discussions Weekly 1, 2, 3

Self-reporting and checking in Weekly 1, 2, 3

Level 2: Learning
Following the above listed recommendations, stakeholders will be able to:
1. Understand with 100% certainty what the components of a successful data-driven
culture consists of.
2. Integrate a strategic learning plan toward data. P
76
LEADING DATA-DRIVEN CULTURAL TRANSFORMATION

3. Carry out this strategic learning plan within the organization to achieve
organizational goals. P
4. Use the job aids to guide this strategic learning plan. M
5. Use time dedicated within the organization for learning and manifesting decisions
with this strategy. M
6. Integrate the resources for implementing this strategy within the organization. P
7. Identify all elements of the process to all internal and external stakeholders. M
The learning goals will be achieved through a strategic learning plan. The plan will be
led by the CEO and supported as a top-down digital strategy. This plan is then enacted by using
blended learning tools such as visual aids, pamphlets, feedback sessions, meetings, and follow
up to ensure the plan’s metrics are met. The CEO will commit to the technical and peer-to-peer
training sessions to identify Learning Goal 1 prior to rolling them out to the organization. This
will take 3 weeks of ongoing meetings and sessions with the CEO including interviews and
follow-up feedback to ensure learning transfer occurs.
Learning Goals 2 and 3 will be accomplished through the CEO’s ability to integrate
learning obtained in the first 3 weeks from Goal 1 into a customized plan that works for
organizational performance goals. This phase will be finalized within the first month (30 days).
Using job aides, decisions, and resources in daily activities are part of the activation phase of the
strategic learning plan (Learning Goals 4–6). These will be analyzed weekly in post-activity
meetings, which discuss if visual aids, resources, and subsequent decision making
(metacognition) are being done effectively. There will also be a macrolevel look back to identify
whether behaviors align with the overarching goal of the stakeholder and organizational
performance goal.
77
LEADING DATA-DRIVEN CULTURAL TRANSFORMATION

In summary, the CEO will have a three-week intense introduction to data-driven culture
and strategies. The strategic learning plan will then take shape within 30 days (the first full
month) encompassing all of the learning facets. The program rollout will take place next. There
will be constant weekly, monthly, and quarterly analysis and feedback to modify and grow the
robustness of the plan. This rollout will be ongoing with the initial thrust completed within the
first 12 months. A larger evaluation in 24 months will be buttressed against the organizational
performance objective of 6% improved performance within 24 months. A post-plan analysis will
take place to see what can be done to make this a continuing, iterative plan.
Learning program evaluation. The learning goals will be evaluated through the lens of
Clark and Estes (2008) KMO framework as well as the four levels (Kirkpatrick & Kirkpatrick,
2016). These influences include declarative knowledge, procedural skills, attitude, confidence,
and commitment. These components will be evaluated weekly, monthly, and other timeframes to
ensure success.

Table 12
Evaluation of the Components of Learning for the Program
Methods or Activities Timing
Declarative Knowledge– “I
know it.”
Knowledge checks using
meetings face to face
Weekly after any training or
information session

Knowledge checks after training
sessions
After any training or
information session
Procedural Skills– “I can do it
right now.”
Pre and post skills assessment During information sessions
78
LEADING DATA-DRIVEN CULTURAL TRANSFORMATION

Real-world application sessions
Observation of practice with job
aids
During mentoring sessions

Attitude– “I believe this is
worthwhile.”
Exit ticket reflections After information sessions
Observations of peer feedback
sessions

During and after information
sessions

Confidence– “I think I can do it
on the job.”
One on one interviews of
confidence in tasks and concepts
Monthly after strategic plan
rollout
Commitment– “I will do it on
the job.”
Creation of SMART goals
Creation of personal action plan

Level 1: Reaction
Kirkpatrick and Kirkpatrick (2016) stated Level 1 (reaction) is the degree participants
find training favorable, engaging, and relevant to the job. Stakeholder evaluation and
participation in training sessions are formative and summative. Both methods of reaction are
critical in training evaluation. First, learning at this level is measured in learner comfort,
engagement, and satisfaction. Table 11 shows as the program progresses, data documents
reactions such as “does participant satisfaction with this information session meet expectations?”

Table 13
Components to Measure Reactions to the Program
Methods or Tools Timing
Engagement Observed behavior of the CEO to incorporate data
culture in weekly meetings
Ongoing
79
LEADING DATA-DRIVEN CULTURAL TRANSFORMATION

CEO attendance in peer-to-peer learning sessions
internal, CDO
Ongoing
Relevance Plan evaluation CEO and CDO After every weekly meeting,
broad evaluation at 12 months
and 24 months
Plan’s success to organization’s KPIs Quarterly check
Customer
Satisfaction
Reflective survey after each weekly meeting
One-on-one time with CEO and CDO and technology
experts to ensure consistency and alignment
Weekly
Monthly

Evaluation
After weekly meetings, feedback reporting is critical to determine if topics are grasped
and learning transfer has occurred. Levels 1 and 2 (Kirkpatrick & Kirkpatrick, 2016) use
methods such as attendance and surveys. This will be a simple pulse check survey of no more
than five questions, which take a small amount of stakeholders’ time via email after weekly
meetings. Every 6 months, a larger behavioral analysis will be compiled from the various results
which looks to tie Levels 1 and 2 to behavioral changes. This will analyze what effectively
brings about change in the organization. The data analysis and reporting are geared to support
whether the activities conducted in the training program are affecting the primary stakeholder
goal that the CEO has 100% understanding of what a data-driven culture is, and the ability to
structure and execute a strategic plan for organizational change toward a data-driven culture
within 2 years. The ultimate outcome is that the CEO effectively leads a data-driven culture in
the organization specifically from what was learned in this training program.
Data are critical to the success of any program, and in particular one that emphasizes use
of data within the organization. Evaluation tools of the initial and ongoing program can be found
in the Appendices. Results will be tallied and analyzed as to the program’s effectiveness. The
80
LEADING DATA-DRIVEN CULTURAL TRANSFORMATION

key learning components looked at will include whether learning transfer took place and
whether the behaviors are repeated effectively. Formative and summative data will be tied back
to the original learning goals discussed. Those learning goals include understand with 100%
certainty the components of a successful data-driven culture, integrate a strategic learning plan
toward data, carry out this strategic learning plan, use job aids, have time dedicated for learning,
and integrate resources for success.
Summary of Recommendations and Solutions
In summary, the NWM will be used from Level 4 to 1 to focus on improving behavior
toward the desired outcome of CEOs creating and executing a data driven culture within the
organization for success. The framework’s advantages are the multitude of monitoring tools and
time sensitive goals that are defined clearly. This process will keep behavior in line with what is
expected by the CEO, in particular because the CEO may be deterred by a vast array of fires or
other operational time zappers. This tool will require the CEO to commit to and monitor the
amount of time actually dedicated to this cause. This will be a return on expectation but
ultimately will lead to a return on investment (of time and money) by improving competitive
position in market by 6% in 2 years through having a data driven culture.
Strengths and Weaknesses of the Approach
All methodological approaches have strengths and weaknesses. The Clark and Estes
(2008) framework focuses on assessing needs tied to the organizational performance goal. This
was a strength in looking at this problem of practice. Because it assesses things that most
organizations skip through more quickly in the hyper quest to solve the problem, the KLM was a
weakness in this problem specifically due to the microlevels of Levels 1 and 2 when discussing a
high-level stakeholder such as the CEO. For example, suggesting a job aid “pamphlet” for a
81
LEADING DATA-DRIVEN CULTURAL TRANSFORMATION

CEO to read on a highly sophisticated and technical concept is not strong or appropriate, even
though the NWM recommends solutions at this level. A case study design utilizing qualitative
interviews with a small but purposeful sample was appropriate for this study. Sending a
seniorlevel executive of a Fortune 500 company would be inadequate and sometimes insulting to
the person in that position. This may result in a lack of responses or curt and skewed responses
which do give rich or useable data. The time to conduct and code the findings was reasonable
considering the topic, and financial, human, and social capital was used efficiently. In dealing
with C-level executives, efficient use of their time and resources is paramount to the success of
the research study. The weakness would be bias toward superior KMO by the CEO as the
position gives them locus of control, power, and feelings of self-efficacy that may be overstated.
Limitations and Delimitations
This study experienced several limitations. A larger sample size would have yielded more
data. A broader focus of more diverse stakeholders would allow for comparison of thoughts and
experiences as well as perceptions of practice gaps. A longitudinal study would help establishing
trends in the use of data, information, and technology (AI, robotics, etc.). A two-year timeframe
on the goal should be followed up with a longer goal after the longitudinal study results are
analyzed. Recommendations may crossover to multiple businesses, especially changes to
organizational culture. However, aspects including the industry would be a factor. For example,
Amazon is already the leader in this field and does not necessarily need this study’s
recommendations as a mature market consumer goods company would. As such, the study
results are most relevant to organizations that are not yet technologically advanced.
82
LEADING DATA-DRIVEN CULTURAL TRANSFORMATION

Future Research
Recommendations for future research are based on the aforementioned limitations. A
larger study size, expanded stakeholder analysis, and longitudinal research are important future
research considerations. A specific area delineated in this study is the impact of data information
and human rights. Privacy concerns of data are a further topic that could be studied for future
research, specifically the responsibility of an organization to protect or use personal data in
ethical ways. An example is China’s use of facial recognition and social credit system (Cheng &
Shao, 2019).
Conclusion
The fields of BD and AI are creating monumental changes in the way businesses
function today. The CEO as primary stakeholder for this research study was chosen due to
his/her locus of control and ability to steer the organization toward this new horizon. This
research study was conducted to assess the gaps CEOs have in the consumer goods
manufacturing industry in adapting to the changing landscape. Findings included KMO barriers
(Clark & Estes, 2008). These were then addressed through the NWM solution framework, which
included creating and executing a strategic plan toward data and hiring a chief data officer to
address KMO needs. The recommendations suggested the need to reinforce new solutions,
encourage others to support, reward through financial compensation tied to data-driven goals,
and monitor success across the entire organization. Specific learning goals were then
recommended including: understand components of successful data-driven culture, integrate
strategic learning plan toward data, carry out strategic learning plan toward organizational goals,
use job aids to guide strategic learning plan, use dedicated time for learning and manifesting
decisions, integrate resources, and identify all elements to stakeholders. Creating the right
83
LEADING DATA-DRIVEN CULTURAL TRANSFORMATION

organization to realize the value of BD and AI is crucial (Hulse, 2015), and companies that
digitally and culturally transform will thrive in this new world. This study provides evidence-
and data-based solutions for organizations to use as a guide to navigate this exciting and
uncharted new territory of the Information Revolution, which is truly here.

84
LEADING DATA-DRIVEN CULTURAL TRANSFORMATION

REFERENCES
Abbott, D. (2014). Applied predictive analytics: Principles and techniques for the professional
data analyst. John Wiley & Sons.
Abdul Hameed, M., & Counsell, S. (2012). Assessing the influence of environmental and CEO
characteristics for adoption of information technology in organizations. Journal of
Technology Management & Innovation, 7(1), 64–84.
Aiken, P., & Gorman, M. M. (2013). The case for the chief data officer: Recasting the c-suite to
leverage your most valuable asset. Newnes.
Al-Sukkar, A. S., Husein, A., & Jaradat, A. (2013). The impact of social network sites determine
students orientations: Field study on jordanian private universities.
Amabile, T. M. (1993). Motivational synergy: Toward new conceptualizations of intrinsic and
extrinsic motivation in the workplace. Human Resource Management Review, 3(3), 185–
201.
Anderman, E. M., & Young, A. J. (1994). Motivation and strategy used in science: Individual
differences and classroom effects. Journal of Research in Science Teaching, 31(8), 811–
831.
Anderman, L. H., & Anderman, E. M. (2006). Social predictors of changes in students’
achievement goal orientations. Contemporary educational psychology, 24(1), 21–37.
Antlova, K. (2009). Motivation and barriers of ICT adaption in small and medium-size
enterprises.
Arain, M., Campbell, M. J., Cooper, C. L., & Lancaster, G. A. (2010). What is a pilot or
feasibility study? A review of current practice and editorial policy. BMC Medical
Research Methodology, 10(1), 67.
85
LEADING DATA-DRIVEN CULTURAL TRANSFORMATION

Average chief data officer salary. (2019, February 2). Retrieved from https://www.payscale
.com/research/US/Job=Chief_Data_Officer/Salary
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, United Kingdom: Oxford
University Press.
Bass, B. M., & Steidlmeier, P. (1999). Ethics, character, and authentic transformational
leadership behavior. The Leadership Quarterly, 10(2), 181–217.
Batsell, W., & Grossman, R. (2006). Classical conditioning. Retrieved from http://www.
education.com/reference/article/classical-conditioning/
Bloom, B. S., Engelhart, M. D., Furst, E. J., Hill, W. H., & Krathwhol, D. R. (1956). Taxonomy
of educational objectives: The classification of educational goals. Handbook I: Cognitive
domain. New York, NY: David McKay Company.
Bosch, G., & Lehndorff, S. (1995). Annual working hours in Germany (Report No.
993116963402676). International Labour Organization, 58(2), 73–75.
Boulton. (2017). CEO’s lack of success metrics dampens digital prospects. Retrieved from
http://www.cio.com/article/3191873/cio-role/ceos-lack-of-success-metrics-dampen
-digital-prospects.html
Buteau, E., Chaffin, M., & Gopal, R. (2014). Transparency, performance assessment, and
awareness of nonprofits’ challenges: Are foundations and nonprofits seeing eye to eye?
The Foundation Review, 6(2), 7.
Buyl, T., Boone, C., Hendriks, W., & Matthyssens, P. (2011). Top management team functional
diversity and firm performance: The moderating role of CEO characteristics. Journal of
Management Studies, 48(1), 151–177.
86
LEADING DATA-DRIVEN CULTURAL TRANSFORMATION

Carmeli, A., Tishler, A., & Edmondson, A. C. (2012). CEO relational leadership and strategic
decision quality in top management teams: The role of team trust and learning from
failure. Strategic Organization, 10(1), 31–54.
Celeux, G., Forbes, F., Robert, C. P., & Titterington, D. M. (2006). Deviance information
criteria for missing data models. Bayesian Analysis, 1(4), 651–673.
Chen, Y., Tang, G., Jin, J., Xie, Q., & Li, J. (2014). CEO’s transformational leadership and
product innovation performance: The roles of corporate entrepreneurship and technology
orientation. Journal of Product Innovation Management, 31(Suppl. 1), 2–17.
Clark, R. E., Estes, F., Middlebrook, R. H., & Palchesko, A. (2004). Turning research into
results: A guide to selecting the right performance solutions. Performance Improvement,
43(1), 44–46.
Coule, T. M. (2015). Nonprofit governance and accountability: Broadening the theoretical
perspective. Nonprofit and Voluntary Sector Quarterly, 44(1), 75–97.
Creswell, J. W. (2008). Research design: Qualitative, quantitative, and mixed methods
approaches. Thousand Oaks, CA: Sage.
Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user
information systems: Theory and results (Doctoral dissertation). Available from
Massachusetts Institute of Technology.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use
computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132.
Deloitte. (2018). Consumer products industry outlook. Retrieved from https://www2.deloitte
.com/us/en/pages/consumer-business/articles/consumer-products-industry-outlook .html
Edwards, R., & Holland, J. (2013). What is qualitative interviewing. A&C Black.
87
LEADING DATA-DRIVEN CULTURAL TRANSFORMATION

Farr, J. L., Hofmann, D. A., & Ringenbach, K. L. (1993). Goal orientation and action control
theory: Implications for industrial and organizational psychology. International Review
of
Industrial and Organizational Psychology, 8(2), 193–232.
Feldman, B., Martin, E. M., & Skotnes, T. (2012). Big data in healthcare hype and hope. Dr.
Bonnie, 360.
Fitzgerald, M., Kruschwitz, N., Bonnet, D., & Welch, M. (2014). Embracing digital technology:
A new strategic imperative. MIT Sloan Management Review, 55(2), 1.
Goldman, E. F., & Casey, A. (2010). Building a culture that encourages strategic thinking.
Journal of Leadership & Organizational Studies, 17(2), 119–128.
Griffin, J. (2008). The role of the chief data officer. Information Management, 18(2), 28.
Henke, N., Bughin, J., Chui, M., Manyika, J., Wiseman, B., & Sethupathy, G. (2016). The age of
analytics: Competing in a data-driven world. McKinsey Global Institute.
Hulse, J. (2015). Driving a data-centric culture: The leadership challenge. The Economist. IDG
Enterprise. (2014). Growing trends and emerging opportunities.
Jones, G. R., & Jones, G. R. (2010). Organizational theory, design, and change.
Jorge. (2017). 10 emergent technologies that will drive the next economy. Retrieved from
http://www.game-changer.net/2016/08/29/10-emerging-technologies-that-will-drive
-the-next-economy/#.WUBkm-vyupq
Kirkpatrick, D., & Kirkpatrick, J. D. (2011). The Kirkpatrick four levels. Kirkpatrick Partners.
Krathwohl, D. R. (2002). A revision of Bloom’s taxonomy: An overview. Theory Into Practice,
41(4), 212–218.
88
LEADING DATA-DRIVEN CULTURAL TRANSFORMATION

Lane, P. J., Koka, B. R., & Pathak, S. (2006). The reification of absorptive capacity: A critical
review and rejuvenation of the construct. Academy of Management Review, 31(4), 833–
863.
Langley, G. J., Moen, R. D., 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.
LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data,
analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21.
Lee, Y., Madnick, S. E., Wang, R. Y., Wang, F., & Zhang, H. (2014). A cubic framework for the
chief data officer: Succeeding in a world of big data. 7(14), 79.
Lohr, S. (2012). The age of big data. New York Times, 11(20), 54.
Maddux, J. E. (2016). Self-efficacy. In Interpersonal and intrapersonal expectancies (pp. 55–
60). Routledge.
Marsh, J. A. (2012). Interventions promoting educators’ use of data: Research insights and gaps.
Teachers College Record, 114(11), 1–48.
Marz, N., & Warren, J. (2015). Big data: Principles and best practices of scalable real-time data
systems. Manning.
Maxwell, J. A. (2013). Qualitative research design. An interactive approach (3
rd
ed.). Thousand
Oaks, CA: Sage.
McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard
Business Review, 90(10), 60–68.
McCrudden, M. T., & Schraw, G. (2007). Relevance and goal-focusing in text processing.
Educational Psychology Review, 19(2), 113–139.
89
LEADING DATA-DRIVEN CULTURAL TRANSFORMATION

McGrath, M. (2014). Target profit falls 46% on credit card breach and the hits could keep on
coming. Forbes Magazine.
McIvor, R. (2005). The outsourcing process: Strategies for evaluation and management.
Cambridge University Press.
McKinsey Global Institute. (n.d.). The 4 V’s of big data.
Merriam, S. B., & Tisdell, E. J. (2015). Qualitative research: A guide to design and
implementation. John Wiley & Sons.
Minelli, M., Chambers, M., & Dhiraj, A. (2012). Big data, big analytics: Emerging business
intelligence and analytic trends for today’s businesses. John Wiley & Sons.
Moolayil, J. (2016). Smarter decisions: The intersection of internet of things and decision
science. Birmingham, United Kingdom: Packet.
Noe, R. A., Hollenbeck, J. R., Gerhart, B., & Wright, P. M. (2006). Human resource
management: Gaining a competitive advantage. 48(7), 225–318.
Pajares, F. (2006). Sources of science self-efficacy beliefs of middle school students. Journal of
Research in Science Teaching, 43(5), 485–499.
Pazzaglia et al. (2016).
Pemberton, C. (2016). Cultivate a data-driven culture. Gartner.
Peterson, S. J., Galvin, B. M., & Lange, D. (2012). CEO servant leadership: Exploring executive
characteristics and firm performance. Personnel Psychology, 65(3), 565–596.
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.
Press, G. (2016, July 20). Artificial intelligence rapidly adopted by enterprises, survey
says. Forbes.
90
LEADING DATA-DRIVEN CULTURAL TRANSFORMATION

Price Waterhouse Cooper. (2016). PwC’s global data and analytics survey 2016. Retrieved from
http://www.pwc.com/us/en/advisory-services/data-possibilities/big-decision
-survey.html
Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven
decision making. Big Data, 1(1), 51–59.
Ropohl, G. (1997). Knowledge types in technology. In Shaping concepts of technology (pp. 65–
72). Dordrecht: Springer.
Rueda, R. (2011). The three dimensions of improving student performance. New York, NY:
Teachers College Press.
Rühli, E., Sachs, S., & Schneider, T. (2015). Innovation in multi-stakeholder settings: The case
of a wicked issue in health care. Journal of Business Ethics, 1–17.
Sabine, P. (2017, June 22). Firms use big data and AI to solve complex problems. South China
Morning Post.
Schein, E. H. (2010). Organizational culture and leadership (Vol. 2). John Wiley & Sons.
Schmid, S. (2015). Organizational learning in innovation networks: Exploring the role of
cognitive distance and absorptive capacity: An agent-based model. Marburg,
Deutschland: Metropolis-Verlag.
Skinner, B. F. (2011). About behaviorism. Vintage.
Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning
and Instruction, 4(4), 295–312.
Tambe, P. (2014). Big data investment, skills, and firm value. Management Science, 60(6),
1452–1469.
Tien, J. M. (2013). Big data: Unleashing information. Journal of Systems Science and Systems
91
LEADING DATA-DRIVEN CULTURAL TRANSFORMATION

Engineering, 22(2), 127–151.
Van Loon, R. (2017). Retrieved from https://mobile.twitter.com/i/web/status/78733069734
2181376
Veeramachaneni, K. (2016). Why you’re not getting value from your data science. Boston, MA:
Reprint Service, Harvard Business Review.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance
model: Four longitudinal field studies. Management Science, 46(2), 186–204.
Weiner, B. (2010). Attribution theory. In Corsini encyclopedia of psychology.
Weiss, R. S. (1994). Learning from strangers.
Wheelen, T. L., Hunger, J. D., Hoffman, A. N., & Bamford, C. E. (2017). Strategic management
and business policy. Person.
Whitler, K. A. (2018, July 28). Why companies are turning to chief data officers to generate
more value out of data. Retrieved from https://www.forbes.com/sites/kimberlywhitler
/2018/07/28/why-companies-are-turning-to-chief-data-officers-to-generate-more
-value-out-of-data/#46da8e914b6a

92
LEADING DATA-DRIVEN CULTURAL TRANSFORMATION

APPENDIX A INFORMED CONSENT
This was conducted at the beginning of interviews with the following verbiage.
INTERVIEW PROTOCOL
Hello, my name is Marla Smith and I am a graduate student at USC. I am conducting a research
study for my dissertation and the purpose of my study is to understand the interaction between
organizational culture and context and stakeholder knowledge and motivation related to today’s
data-driven world. I seek to understand the conditions, perceived causes, and consequences of
making change. The information collected will be held confidential, I will use a pseudonym
instead of your name. You can decide not to answer any question as you wish, or withdraw from
the study at any time for any reason. If these items are agreeable to you and you are comfortable,
we can begin.

93
LEADING DATA-DRIVEN CULTURAL TRANSFORMATION

APPENDIX B INTERVIEW PROTOCOL
Hello, my name is Marla Smith and I am a graduate student at USC. I am conducting a research
study for my dissertation and the purpose of my study is to understand the interaction between
organizational culture and context and stakeholder knowledge and motivation related to today’s
data-driven world. I seek to understand the conditions, perceived causes, and consequences of
making change. The information collected will be held confidential, I will use a pseudonym
instead of your name. You can decide not to answer any question as you wish, or withdraw from
the study at any time for any reason. If these items are agreeable to you and you are comfortable,
we can begin.
Interview Questions
Leadership, Vision, Style
1. Tell me about yourself as a leader. Knowledge– self-reflection

2. What is the nature of your business?

3. Tell me about your organizational vision, mission, and goals. Cultural model
a. In your opinion, how are you aligning your efforts to achieve goals? Knowledge– self-
efficacy
b. Share your organizational mission and goals about adapting to the data-driven cultural
revolution.

And now, I would like to hear about change and culture change…
Change, Culture Change
4. Give me a sense of how is change executed. Knowledge– procedural
a. What initiatives were successful? Why? Knowledge– metacognitive
b. What initiatives were unsuccessful? Why? Knowledge– metacognitive

5. How are you aligning efforts of yourself and team to achieve change? Cultural setting–
culture of trust

A big topic in today’s competitive business landscape is the use of data. So, at this point, I
would like to understand what you can tell me about data and its use in your organization.

Data-Driven

6. How do you measure your organization to peers and competitors? (Will likely open up
details about their use of data)
94
LEADING DATA-DRIVEN CULTURAL TRANSFORMATION

a. “How do you feel about your ability to change culture toward this direction? Do you have
the skills and aptitude in BD, AI, and other data initiatives currently arising in the data
market? If, not, what skills and aptitudes are needed?

7. How else is data used in your organization? Knowledge– procedural

8. In what ways are the organization’s decisions informed by data? Knowledge– procedural
9. (Probing in case they withhold) Being a devil’s advocate, how would people say your
organization’s decisions are made?
a. Please give examples of when data has driven an organizational decision. Cultural
setting– trust, Cultural models– resources

10. In an ideal world, what ways would data improve decisions and performance?
Motivation– goal orientation

11. How do you feel about your knowledge about this field of data we have been discussing?
Motivation– self-efficacy

12. Are there areas you or your organization could learn more about within the field that
would help? Cultural setting– trust

13. Hypothetically, if resources were not an issue, how would you outlay resources (i.e.,
budget, personnel, training) to enable data tools to achieve performance goals? Cultural
models– resources

14. Do you have any other questions about what we have discussed today or anything else
you want to share?

APPENDIX C SAMPLE KIRKPATRICK SURVEY ITEMS (LEVELS 1 AND 2)
Note: Questions derived from Kirkpatrick and Kirkpatrick (2006, pp. 140–144).

I. Immediately After Training
1. The training was interesting to me. (L1: Engagement) Strongly Disagree, Disagree,
Agree, Strongly Agree
2. My participation was encouraged by the facilitator. (L1: Engagement) Strongly
Disagree, Disagree, Agree, Strongly Agree
3. This training was relevant to my work. (L1: Relevance) Strongly Disagree,
Disagree, Agree, Strongly Agree
95
LEADING DATA-DRIVEN CULTURAL TRANSFORMATION

4. During the training I was taught how to apply what I learned (L1: Relevance)
Strongly Disagree, Disagree, Agree, Strongly Agree
5. I found my training experience today satisfying. (L1: Customer Satisfaction)
Strongly Disagree, Disagree, Agree, Strongly Agree
6. I would recommend this training to others. (L1: Customer Satisfaction) Strongly
Disagree, Disagree, Agree, Strongly Agree
7. What material needed more time or less time? (Open-Ended)

8. Understanding the importance of data-driven culture is valuable to my
organization’s success (L2: Attitude)
Strongly Disagree, Disagree, Agree, Strongly Agree
9. I feel confident I can use what I learned in this training to create a data-driven
procedure manual. (L2: Confidence)
Strongly Disagree, Disagree, Agree, Strongly Agree
10. I am committed to executing data driven procedures (L2: Commitment) Strongly
Disagree, Disagree, Agree, Strongly Agree
II. 90 Days After Training
1. What I learned in training has been valuable to creating data driven culture Strongly
Disagree, Disagree, Agree, Strongly Agree
2. My metacognition on issues that arise on topic of data in my job has improved Strongly
Disagree, Disagree, Agree, Strongly Agree
3. I am seeing a greater acceptance data as a matter of daily use at my organization
Strongly Disagree, Disagree, Agree, Strongly Agree
4. What plans need to be enacted to further improve these knowledge, motivation and
organization issues toward a data-driven culture? (Open-Ended)

96
LEADING DATA-DRIVEN CULTURAL TRANSFORMATION

APPENDIX D SUMMARY OF INFLUENCES ON BARRIERS TO CREATING A DATA-
DRIVEN ORGANIZATIONAL CULTURE
KMO Influences Description General Literature
Knowledge (K)

Leaders need vision and clarity with meaningful
and compelling answers to queries and a better,
more efficient way of communicating these new
concepts.
Skills required include coaching, networking, and
collaborating, which allows them to stand out in a
world where AI has been tasked with carrying out
numerous analytical and administrative tasks.
(Baker, 2006; Chamorro-
Premuzic, Fitzgerald,
Kruschwitz, Bonnet, &
Welch, 2014; Daly, 2006;
Hulse, 2015; Ropohl, 1997;
Schraw & McCrudden, 2006;
Skinner, 2011; Tuckman,
2006; Wade & Jordan, 2018)
Motivation (M) Leaders have various barriers to motivation that
prevent AI and tech adoption including lack of
executive-level vision, unforeseeable benefits,
threats from stronger companies already in space,
among others.
Motivational factors create an environment that
inhibits growth, risk, and compliance. These
include fear of work changes, lack of psychological
assurance, fear of product change, profit loss, labor
costs, new marketing strategies, identity, and trust
in the technology itself. This tech trust barrier
includes insufficient knowledge by staff and
leaders, inadequate maintenance, technology
transfer, and a lack of motivation to create an
environment that supports a new, data driven
culture.
(Antlova, 2009; Carmeli,
Tishler, & Edmondson, 2012;
Davis, 1985; Goldman &
Casey, 2010; Maddux, 2016;
Peterson, Galvin, & Lange,
2012; Ramilo & Embi, 2014;
Venkatesh & Davis, 2000;
Weiner, 2010)
Organization (O) Leaders have new challenges organizationally in
the field of data science due to uncharted territory.
Organizations are not equipped to handle the
velocity, variety, and volume of today’s data, nor
harness the data and AI to improve business
functions such as customer care, risk management,
fiscal improvement, and strategic growth.
(Al-Sukkar, Hussein, & Jalil,
2013; Abdul Hameed &
Counsell, 2012; Boulton,
2017; Buteau, Chaffin, &
Gopal, 2014; Kolbjornsrud,
Thomas, & Amico, 2016)

Abstract (if available)

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

PDF

Creating the conditions for change readiness in higher education: an innovation study

PDF

The moderating role of knowledge, motivation, and organizational influences on employee turnover: A gap analysis

PDF

Leveraging the power of data-driven storytelling: C-suite executive leadership approaches to improving business performance

PDF

Utilizing data analytics in the field of physical security: an exploratory study

PDF

Leadership education and development that incorporates e-leadership: the “e” factor in leading in an electronic (virtual) work environment

PDF

Building teacher competency to work with middle school long-term English language learners: an improvement model

PDF

Key factors for successful adoption of culturally relevant and impact driven grantmaking practices

PDF

The utilization of data analytics in the entertainment sector

PDF

Closing the achievement gap for marginalized students using the college-going culture: a promising practices study

PDF

Improving veterans employment outcomes through increasing enrollment in vocational rehabilitation and employment program

PDF

A qualitative examination of the methods church leaders use to increase young adult attendance in Christian churches: an evaluation study

PDF

Mitigating low employee engagement through improved performance management: an evaluation study

PDF

Winning the organizational leadership game through engagement: a gap analysis

PDF

Analyzing the implementation of a learning management system into a post-merger & acquisition organization and its effects on sales performance (improvement model)

PDF

The role of middle manager alignment in achieving effective strategy execution: an evaluation study

PDF

The implementation of data driven decision making to improve low-performing schools: an evaluation study of superintendents in the western United States

PDF

Lack of culturally relevant teaching in international bilingual schools: a gap analysis

PDF

Knowledge, motivation, and organizational influences within leadership development: a study of a business unit in a prominent technology company

PDF

Competency in a blended workforce: an evaluation study of contracted labor development challenges

PDF

An evaluation study: quality contextual professional development

Asset Metadata

Creator Smith, Marla M. (author)

Core Title Data-driven culture in the consumer goods industry

School Rossier School of Education

Degree Doctor of Education

Degree Program Organizational Change and Leadership (On Line)

Publication Date 02/17/2020

Defense Date 05/01/2020

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

Tag change management,consumer goods,data science,data-driven culture,digital disruption,executive leadership,gap analysis,global business,leadership,OAI-PMH Harvest,organizational change,organizational culture,organizational leadership,Technology

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Tobey, Patricia (committee chair)

Creator Email mmillonas@aol.com,smarla@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-270232

Unique identifier UC11674881

Identifier etd-SmithMarla-8182.pdf (filename),usctheses-c89-270232 (legacy record id)

Legacy Identifier etd-SmithMarla-8182.pdf

Dmrecord 270232

Document Type Dissertation

Rights Smith, Marla M.

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