University of Southern California Dissertations and Theses

Industry 4.0 impacts on U.S. food industry executive self-directed learning

(USC Thesis Other)

Industry 4.0 impacts on U.S. food industry executive self-directed learning

PDF

Download

Open document

Flip pages

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content

Industry 4.0 Impacts on U.S. Food Industry Executive Self-Directed Learning
by
Michael S. Hackney

Rossier School of Education
University of Southern California
A dissertation submitted to the faculty
in partial fulfillment of the requirements for the degree of
Doctor of Education

December 2021

© Copyright by Michael Shayde Hackney 2021

All Rights Reserved

The Committee for Michael S. Hackney certifies the approval of this Dissertation

Cathy Krop

Alexandra Wilcox

Jennifer L. Phillips, Committee Chair

Rossier School of Education

University of Southern California

2021

iv

Abstract

The Fourth Industrial Revolution, or Industry 4.0, refers to the prolific digital transformation
creating Artificial Intelligence and Big Data programs and their business impact on marketing,
logistics and robotics. The Industry 4.0 transformative challenges have created numerous
knowledge gaps for U.S. food executives as they address the impacts on their industry and
individual organizations. There are limited 4.0 formal academic programs available which these
executives may utilize. The manner of how these executives are responding to Industry 4.0
knowledge gaps has not been widely studied. To gain insights to U.S. food industry executives’
response to knowledge gaps, this qualitative research study examined the sentiment of targeted
U.S. food industry executives to the impact of Industry 4.0 on their organizations, the broader
food industry, and how they viewed addressing knowledge gaps for the current state and looking
forward. The findings indicate individual executives have a keen awareness of the potentials and
costs of Industry 4.0 technologies and utilize self-directed learning (SDL) to stay current with the
speed of change. They are concerned at the economic costs to keep up and failure if they do not.
They see a reliance upon industry technology companies and Industry 4.0 trained key employees
as the forward-looking crucial elements to organization survival. From these findings and
concerns three recommendations for U.S. food industry organizations were developed:
organization executives should utilize self-directed learning to bridge formal learning gaps and
create a learning culture; invest resources to develop Industry 4.0 innovation experts; and utilize
best-practice Industry 4.0 technologies.

v

Dedication

To my wife, Dr. Karen Malamut, I thank you. You demonstrated time and again through your
commitment as a physician providing free pediatric care to the underserved children of Chicago
that the mark of a career is always what one does for the benefit of others most in need, not what
one does for oneself. Your guidance, love, and support are the rock upon which I have labored to
complete this degree and I am profoundly grateful for your encouragement and sacrifice to this
academic adventure!

vi

Acknowledgements
This has been a very unusual journey, one that I never anticipated embarking, that has
become more amazing and satisfying with each passing day and week as a member of Cohort 14
of the Doctor of Education program. To the fellow members of the Cohort, I stand in awe of your
individual and collective brilliance. Your humor, creativity, and perspectives are inspiring, and I
am eternally grateful for what I learned from each of you.
This research and dissertation would not have been possible, credible, or completed
without the guidance of the Dissertation Chair Dr. Jennifer Phillips, and committee members Dr.
Cathy Krop and Dr. Alexandra Wilcox. The coaching, scolding, and encouragement each
provided during this program and this research study was amazing. They each told me to think—
not how or what—just to think, with incredible focus and expertise. I am profoundly grateful to
each of you for the time and energy you provided as this project evolved from concept to
completion. I would also like to thank the study participants; each is a busy executive who took
time away from their responsibilities to provide candid information and feedback through the
research process.
I also thank my immediate family and the members of my extended family for their
support and encouragement as I began and travelled this journey: it would not have been possible
without them. To my brothers in spirit, Colonel (Retired) John Foster, U.S. Army, and Colonel
(Retired) Dave McNeil, U.S. Army: my adult life has been centered around our common
experiences as cadets, then officers serving our country, and now as businesspersons—you have
been with me every step of the way.

vii

Table of Contents
Abstract……………………………………………………………….……………………….… iv
Dedication…………….………..……………………………………………….………...……… v
Acknowledgements…………………………………………………………………………..… vi
List of Tables……...…………………………………………...………………………………… x
List of Figures………………………………………………………...……………...…..……… xi
List of Abbreviations………………………………………………….……...………………… xii
Chapter One: Introduction………………………………………………….………………….… 1
Background of the Problem………………………………….…………………………... 2
Field Context and Mission…………………………………………………….……….… 5
Overview of the Theoretical Framework and Methodology……………………..………. 6
Study Purpose and Research Questions……………………………………………..…… 7
Importance of the Study………………………………………………………..………… 8
Definition of Terms………..………………………………………………………….… 11
Organization of the Dissertation………………………………………………...……… 14
Chapter Two: Literature Review……………………………………………………….………. 16
Management Change in the First, Second, and Third Industrial Revolutions……….…. 17
The Fourth Industrial Revolution……..……………………………………….……..… 23
Executive Responsibilities to Foster Self-Developed Learning………………………... 35
Theoretical Framework: Social Cognitive Theory………………………………...…… 41
Self-Directed Learning…………………………………………………………….…… 43
SCT and SDL in Academic and Business Applications……………………………...… 46
Conceptual Framework……………………………………………………………….… 51
viii

Summary……………………………………………………………………….……….. 56
Chapter Three: Methodology…………………………………………………………………… 58
Research Questions………………………………………………………...…………… 58
Overview of Methodology……………………………………………………………… 59
Data Sources………………………………………………………………….………… 61
The Researcher ………………………………………………………………………… 66
Ethics ……………………………………………………………………………..…… 68
Chapter Four: Findings………..………………………………………………...……………… 69
Participant Background…………………………………………………………………. 71
Emergent Findings……………………………………………………………………… 75

Research Question 1: How Are U.S. Food Industry Executives Responding to
Change Created by Industry 4.0?................................................................................... 76

Research Question 2: How Does the U.S. Food Industry Organizational Environment
Impact Food Industry Executives’ Knowledge and Motivation With Respect to
Responding to Change Created by Industry 4.0?........................................................... 85

Research Question 3: What Role Do U.S. Food Industry Executives See for Either
Either Self-Directed Learning or Other Forms of Learning in Response to
Industry 4.0 and Beyond?.............................................................................................. 95

Summary………...…………………………………………………………………….. 104

Chapter Five: Discussion and Recommendations………….…………………………….……. 108

Discussion of Findings………………………………………………………………… 109
Recommendations for Practice…………………………………………………...…… 118
Limitations and Delimitations….…………………………………………….……….. 124
Recommendations for Future Research………………………………………….……. 125
Conclusion…………………………………………………………………………...... 127
References……………………………………………………………………………..……… 130
ix

Appendix A: Pre-Interview Protocol..…...……………………………………………….....… 150
Appendix B: Information Sheet for Exempt Research………………………………………... 153
Appendix C: Interview Protocol……………………………………………………….……… 155

x

List of Tables

Table 1: Data Sources 60
Table 2: Characteristics of Participation 72
Table 3: Executive Position Breakdown of Participants 73
Table 4: U.S. Food Industry Types Represented 74
Table 5: U.S. Food Ownership Types Represented 75
Table 6: Categorization of Research Question One Response 77
Table 7: Categorization of Research Question Two Response 86
Table 8: Categorization of Research Question Three Response 96

xi

List of Figures
Figure 1: Picciano’s Data-Driven Decision-Making Process 27
Figure 2: Bandura’s Reciprocal Triadic Determinism 42
Figure 3: Awareness and Impact Utilization through Self-Directed Learning and
Other Learning Methods 52

xii

List of Abbreviations
4.0 Industry 4.0
AI Artificial Intelligence
BD Big Data
BI Business Intelligence
CLT Cognitive Load Theory
IRB Institutional Review Board
PVC Private or Venture Capital ownership of organization
PT Publicly Traded ownership of organization
SCT Social Cognitive Theory
SDL Self-Directed Learning
SME Subject Matter Expert

1

Chapter One: Introduction to the Problem of Practice
The U.S. food industry is in constant change and evolving even more rapidly as it charges
into the Fourth Industrial Revolution, or Industry 4.0, which is the transformation of marketing,
business analytics, manufacturing, and logistics. This transformation is occurring through the
incorporation of robotics and artificial intelligence (Dombrowski, 2014; Machado, 2019; Marr,
2018; Pfeiffer, 2018). U.S. food industry executives—those who make or influence policy
decisions for their respective organizations—have more information available to them and more
competitive requirements for utilizing that information properly than at any time in prior years.
This proliferation of information and transformation of how data is used, referred to as “Big
Data,” has reached overwhelming levels (Carillo, 2019). These levels can hinder, rather than
help, decision makers due to an inability for them to effectively glean through the accumulation
of information (Carillo, 2017; Schulz, 2019; Subramanian, 2019). The need for food industry
executives to respond to the competitive pressures of this transformation is of critical concern to
their constituencies of business owners, business customers, product consumers, and employees
at all levels of their organizations (Chuang, 2018).
The U.S. food industry is transforming as consumer tastes, food service industry
procedures and consumer preparation methods are rapidly changing. These areas of preparation
methods and consumer tastes are experiencing novel utilizations of ingredients and consumer
desires to try other cultural food-influences driven by an ever-growing fascination with food
(Hemphill, 2012; Olayanju, 2019). These transformations in preferences and willingness to try
new recipes, dishes, and cooking methods are dominating the United States food marketing and
manufacturing sectors (Martin, 2019; Singh, 2019). These industry shaping changes present a
major challenge for food industry executives in prioritizing and responding to the transforming
world around them. As seen through the prism of the individual for-profit corporations that
2

comprise most of the U.S. food industry, food sector business leaders are beset by hyper-
accelerating challenges within the marketplace (Carillo, 2019; Kerr, 2020; Shoup, 2019).
In an era of increasing accessibility of information available to U.S. food industry
executives, how these executives can utilize this data is enhanced (Kerr, 2018) and can be
extended to impact disparities such as equity in food availability. Equity issues exist for
economically disadvantaged areas as convenience to food retailers diminishes and the types of
food products available in these specific markets becomes even more constrained (Beaulac,
2009; Demetrakakes, 2021; Walker, 2010). In quantitative studies by Black (2014) and Caspi
(2012) the impacts of these food market constraints disproportionally impact the health and
dietary practices of Persons of Color (POC). Alvarado (2018, 2019) identified that there are
chronic impacts for the children growing up in these economically disadvantaged areas to future
earnings and employment potentials.
Little research has been done to examine food industry executive response—through
either formal or self-directed avenues—to the current business climate brought on by the
transformational changes of Industry 4.0. It is unknown how executives view the industry
transformation and their responsibility to adapt. The purpose of this study was to examine how
these decision-making executives are engaging in self-directed learning (SDL) and other learning
methods through formal or informal activities to acquire tools to address current or future
escalating Industry 4.0 business challenges.
Background of the Problem
The need for effective decision-making is high in the current Big Data era (Carillo,
2019). Such decision-making, acknowledged as the primary obligation and function of industry
business leaders (Dubnick, 2011; Melone, 1994), should be based upon the best information
3

possible (Hemphill, 2012). Formal methods, such as programs within academia for executives to
acquire the emerging slate of skill sets to utilize the benefits of the Big Data era, are not yet
widely in place (Picciano, 2012, 2015; Wixom, 2014). Self-directed and other learning options
can bridge the limited formal academic options with informal options (Kyvliuk, 2019).
Self-directed and other learning options involve pro-active efforts to increase one’s
capabilities, knowledge, or skill sets. To better understand or address a changing situation
(Parker, 2010), SDL is an option in the absence of extensive formal learning programs as they
are being developed and introduced at select universities. A variety of SDL options exist and are
available to executives in bridging capability gaps (Allen, 2020; O’Roark 2002). These options
can include topic-specific business consultants, industry-led symposia, and on-line open courses.
In addition, utilization of in-house subject matter experts, individual readings, and directed intra-
industry peer activities serves as a potential bridge to this shortfall of formal programs (Allen,
2019; Brahimi, 2015; Burksaitiene, 2011; Hong, 1999; O’Roark, 2002).
Currently there is little known in regard to how SDL or other non-formal learning efforts
amongst food industry executives are addressed or identified, either through formal offerings or
informal options dating back to studies on this topic by Melone (1994). The lack of empirical
studies on this issue indicates that the rapidity of the Industry 4.0 transformation is exceeding the
ability of academia to provide sufficient formal learning alternatives for food industry executives
to stay current and acquire Big Data-related knowledge (Picciano, 2012, 2015). Pro-active food
industry executives may therefore look to self-directed and other non-formal learning methods to
address perceived skill gaps for data-based decision making for their organizations.
The information age, fueling Industry 4.0, has transcended all facets of business. Timely,
effective, and ethically responsive decision-making, within the maelstrom of information
4

overload, places a large burden on decision makers to develop, assess, and utilize the most
pertinent information available (Bridges, 2018; N. White, 2020). Rupert Murdoch, CEO of 21st
Century Fox, regarding the information age, stated “The world is changing very fast. Big will not
beat small anymore. It will be the fast beating the slow” (Rohrbeck, 2009, p.421).
Research has demonstrated that effectively keeping up with changes affecting Industry
4.0 provides a competitive advantage that benefits the “fast” organizations over the “slow.” A
quantitative study by Müller (2018) indicated that highly competitive firms that use industry data
effectively, and whose decision-making executives therefore made timely decisions, increased
their productivity by 5.7%. An industry consulting firm determined that 56% of business
executives interviewed in a qualitative study were incapable of keeping up with available data as
the rate of data increase was doubling every eighteen months and these same executives were
seeking strategies to improve their capabilities (Avanade, 2010). Merendino (2018) and Müller
(2018) have since found that the acceleration of Big Data is progressing even more rapidly with
the quantity of available information doubling every twelve months or less.
The movement into Industry 4.0, along with the requirements for keeping up with the
information available to executives, has reached an overload level (Dombrowski, 2014;
Merendino, 2018). Yet the need to utilize available information in the manner most beneficial to
the organization is even keener, with the ability to sort that information for relevancy now more
difficult than previous eras (Müller, 2018). A better understanding, as posited by Abatecola
(2018) and Stubbart (1989), is needed of how food industry executives view self-directed and
other non-formal learning as a means to keep up with changes affecting their industry.

5

Field Context and Mission
This study examines the field of United States food industry executives. These
individuals are defined as principal decision makers, or influencers, for strategic direction setting
within their respective food industry organizations (Melone, 1994). The food industry, which
comprises those companies developing, manufacturing, marketing and selling food and beverage
items to retailers, is a major part of the United States economy; the industry accounts for some
thirty cents of each dollar spent on food and beverage items by the average consumer
(Committee for Economic Development, 2017). The other seventy cents is apportioned to the
retailers—such as grocery stores and restaurants, for their costs and profit to merchandise and
distribute the food items. According to the Committee for Economic Development (2017), a U.S.
food industry research group,
The food sector plays an essential role in the US economy, accounting for about 5 percent
of gross domestic product, 10 percent of total US employment, and 10 percent of US
consumers’ disposable personal income (DPI). The food sector has total sales of $1.4
trillion, including food consumed at home and away from home. (p. 5)
The economic responsibility of food industry executives is quite large both in dollars and
stakeholders affected; how those executives adapt to their changing industry dynamics is critical
to the success of their respective organizations. U.S. food industry executives who participated in
this study are targeted to work within a cross section of food industry organizations ranging from
publicly traded Fortune 500 companies to smaller, entrepreneurial privately-owned companies.
The common threads for the participants are their recognition of the quickly evolving state of the
food industry, and that they make—or directly influence—how their organizations anticipate and
respond to these changes.
6

Overview of the Theoretical Framework and Methodology
This research utilizes the paradigm of Bandura’s Social Cognitive Theory (SCT)
(Bandura, 2001), using a pragmatic analysis (Creswell, 2018) of participant qualitative
interviews. The pragmatic model (Bybee, 1984; Pierce, 1905 [1998]) is utilized in that what is
sought is simply truth with no ancillary or ideological agendas. As this study seeks to identify
sentiment at a base level concerning SDL by food industry executives, other models that are
agenda driven are not preferred for the study.
Social Cognitive Theory, as developed by Bandura (1989), addresses the role of the
“agent” who through individual motivation can affect their individual situation: in this study the
agent(s) are food industry executives. Bandura further states that individuals make an active
contribution to their individual world around them, they are not just a product of that world but
an active participant (Bandura, 1989, 2001). Individual choice and self-determination are key
factors to how the individual’s circumstances develop. The model developed by Wood and
Bandura (1989a) referred to as Triadic Reciprocal Determinism concerning the inter-relationship
of behavior, personal, and environmental factors is the theoretical foundation for SCT. Triadic
Reciprocal Determinism (TRD) held that individual choice of how one responds to these three
factors—singularly and interchangeably—empowers an individual to affect their circumstances.
It can be posited that the decision-making behavior of food industry executives is related to their
SDL of personal skills. As associated to the business-environment changes currently facing the
food industry, this in turn can be seen as corresponding to the base definitions of Social
Cognitive Theory (Bandura, 1989, 2011).
Food industry executives have a subjective expertise and a pragmatic understanding of
how their industry-based perspectives affect their organizations’ practices. SCT would posit that
7

persons who take advantage of self-directed and other non-formal learning opportunities will see
a benefit to themselves—and their organizations—by doing so (Bandura, 2011). This general
concept should likewise be applicable to food industry executives. From an assessment of the
participant perspectives, the research goal is to determine food industry executives’ sentiments,
viewpoints, and perceived results from SDL.
Study Purpose and Research Questions
Food industry change driven by Industry 4.0 poses unique challenges for how U.S. food
industry executives should respond, as changes are occurring at an accelerated pace compared to
prior business eras. From an industry executive perspective, prior formal education and
knowledge bases that pre-dates Industry 4.0, have been surpassed by these changes. The scope of
transformation and rate of food industry reaction has affected market expectations, consumers,
manufacturing capabilities, and response to competitive pressures (Pfeiffer, 2018). For those
organizations seeking to lead the food industry 4.0 transformation, the abundance of information
on consumer preferences, customer responses, and artificial intelligence (AI) analytical tools
provide a treasure trove of data that can be utilized to identify new product and consumer
opportunities (Chuang, 2018). Whether one’s organization is an industry leader or follower, how
U.S. food industry executives react to their knowledge gaps is unknown.
Bandura (2016) and Mourtzis (2019) determined changes in knowledge awareness and
circumstances combine to make the roles and reliance on past skill sets and knowledge tenuous,
and this can be extended to food industry executives. Understanding the manner these executives
are processing new information, while coping and adapting to Industry 4.0 and its impact on the
food industry, has not been specifically researched. The purpose of this study was to examine
how these decision-making executives are engaging in self-directed learning (SDL) and other
8

learning methods through formal or informal activities to acquire tools to address current or
future escalating Industry 4.0 business challenges. The study focused on the following research
questions:
1. How are U.S. food industry executives responding to change created by Industry 4.0?
2. How does the food industry organizational environment impact food industry
executives’ knowledge and motivation related to responding to change created by
Industry 4.0?
3. What role do U.S. food industry executives see for either self-directed learning or other
forms of learning in response to Industry 4.0 and beyond?
Underlying these questions is the relation of current actions by the participants, and the results
and sentiment each perceives about their actions, within the premise of Bandura’s Social
Cognitive Theory. Does the study participants’ sentiment support the concept that individually
self-directed learning can positively result in greater achievement for agentic efficacy (Bandura,
2011), or in this case, food industry executive efficacy.
Importance of the Study
It is important to study this problem of practice to understand the sentiment of food
industry business executives concerning utilization of SDL opportunities, because of how these
executives impact the current and future performance of their respective organizations. There are
three key areas of importance to this study: they include the perceptions of food industry
executives concerning the changes to the food industry; their perceptions of why it is important
to respond to those changes; and the manner in which they individually choose to address their
own need for growth in light of the industry challenges. The acceleration of change is impacted
by Industry 4.0 and unpredictability of world events such as the global Covid-19 viral pandemic
9

of 2019-2021(Ayseli, 2020). That pandemic, which derailed the U.S. economic learnings of
2017-2020, accentuates the importance of individual executives successfully adapting their
individual capabilities for the benefit of their organizations (Hippold, 2020; Kerr, 2020;
Machado, 2019).
Food industry executives are dealing with a transformational era to their industry as
competitors employ new tools such as Big Data driven artificial intelligence programs for
determining consumer demands (Azzaretti, 2017; Singh, 2019; Wirth, 2018). Marketing
innovations increase consumer awareness and industry competitive pressures. The dynamic
expansion of choices and demands on food industry retailers, such as grocery stores and
restaurants, are creating increasing pressures for food marketers and manufacturers to respond
(Bayona-Saez, 2017; Carillo, 2017). The effect of Industry 4.0 (Marr, 2018; Peters, 2012;
Pfeiffer, 2018), with impacts of robotics, logistical planning, and utilization of labor, is creating
cost pressures that are unique to the new era.
These pressures on costs and competitiveness have shaped a need for creative methods to
address competitive pressures. These pressures include market share and profitability and how
food industry executives must adapt to respond. Pessôa (2020) has identified how product design
has changed due to Industry 4.0. These changes are launching a completely new requirement for
requisite organizational-moving responsive thought, action, and directional skill sets that
incorporate the benefits of Big Data. Industry insiders (Martin, 2019; Shoup, 2019) view the
current business climate as the most dramatic and transformative period the food industry has
faced. This is requiring food industry executives to recognize the differences and importance of
the current business environment to navigate these tenuous times.
10

How executives recognize—or fail to recognize—these challenges can impact their
stakeholders (Velasquez, 2011). The subsequent response to close any knowledge or skill gaps
that are identified, such as advents in robotics or use of AI systems (Sharp, 2019), are
technologies that are still emergent in academia. However, mastery of these skills—and
recognition of competitive advantages and disadvantages they represent—is critical to their
organizations (Chuang, 2018; Olayanju, 2019).
Aguinis (2009) and Kirkpatrick (2016) have demonstrated the value of formal and
informal training within organizations as both the employee base and external factors impact the
manner that current or traditional organization-specific information is imparted to stakeholders:
this study expands these perceived values to the food industry executive participants. Their self-
perception about the relevancy of their own individual needs for SDL was examined against the
backdrop of the three key areas of industry changes, importance of response, and manner of
response. In the emergent age of Industry 4.0, the demands on these executives to keep up with
all the factors that can impact their organizations is a monumental task (Kerr, 2020; Marr, 2018).
For those executives who finished their academic or professional education a decade or more
ago, it is unknown how they perceive the need to bridge their formal education time gap to the
Industry 4.0 new requirements and challenges.
Identification of how these individuals add new capabilities to their personal skill sets is
of value to others of similar responsibility seeking to refine and emulate, and to stakeholders
seeking to have confidence in their executives (Aguinis, 2009). Stakeholders can ask if their
executives are embracing SDL strategies. If not, do their organizations suffer a competitive
disadvantage to other organizations employing executives who avail themselves to improving
individual capabilities (Bennink, 2020; Müller, 2018).
11

For food industry executive hiring authorities, understanding the challenges facing
today’s executives can ensure that those with the right skill set and initiative for SDL are placed
in appropriate roles. Additionally, for business owners and boards of directors, understanding the
challenges of SDL and other organizational internal learning can lead to resourcing of enhanced
opportunities for their key decision makers (Martin, 2019). The consequence for those
organizations for indifference to this area of the need for continued learning within the
organization includes being surpassed by more nimble competition who are responding
effectively to business changes. When reflecting on the body of knowledge related to this area of
continued learning there is little research into this area extending back to Melone (1994): this
study offers an opportunity to provide further insights into implications for the U.S. food
industry to the benefits of Social Cognitive Theory in practice.
Definitions of Terms
Artificial Intelligence (AI) refers to the ability to use computer process tools—
algorithms—to independently coalesce “learning, knowledge representation, reasoning, and
prediction/planning” (Wirth, 2018, p.436) about a certain area of information in such a manner
that Big Data can be utilized more readily than left simply to individual human capabilities.
Big Data refers to the massive amount of information now available through the internet
across the wide-ranging numbers of topics that can be applicable to a given field of interest such
as business, medicine, engineering concepts and other topics. The key to Big Data is that the
sheer immensity of the information available can make it difficult to assess, synthesize or
effectively utilize to specific queries of need (Allen, 2019; Carillo, 2019).
Business Intelligence (BI) refers to the ability to gather information related to targeted
business activities from a variety of sources be it customer sales data, third party information
12

gathering or other data troves, then to refine it categorically for subsequent use in activity and
decision making (Ponelis, 2012).
Cognitive Load Theory refers to the ability of individuals to leverage cognitive demands
given limitations in working memory for both capacity and duration of retention (Feldon, 2019).
Consumer-Food Industry refers to those persons who individually consume food or
beverage products or make the purchasing decision on behalf of another—such as a parent or
caregiver. (Wunderlich, 2017).
Customer-Food Industry refers to the companies that purchase food and beverage
products from manufacturers for further resale to consumers (Wilding, 2004).
Food Industry refers to the manufacturing, distribution and marketing organizations that
develop and sell food and beverage products for institutional or individual consumption
(Bayona-Saez, 2017).
Industry 4.0 refers to the “Fourth Industrial Age” where the use of AI and robotics,
combined with Big Data and hyper competition, completely revolutionizes how organizations
conduct their business activities when compared to the previous eras (Mourtzis, 2019; Pfeiffer,
2018).
Organization General Administration refers to the administrative requirements to support
a business including the areas of human resources, legal support, safety, financial and accounting
services, information technology, and all other service functions and Big Data analysis that allow
the sales team to sell and the operations team to produce (Blazquez-Resino, 2019).
Private Company Ownership refers to the ownership of a (U.S. food industry related)
organization by an individual, family, equity group, or some other form of limited partnership
13

such that the financial performance of the company is not subject to public disclosure (Tuovila,
2021).
Publicly Traded Company refers to a company that issues shares of stock for public sale
and resales such that the ownership of a (U.S. food industry related) organization is subject to
regulatory requirements associated with the United States Securities and Exchange Commission
(SEC), and, as such, is required to make compulsory disclosures to ownership (shareholders) of
corporate activities and financial performance. (Bowman, 2021).
Robotics refers to the use of computerized machines to perform tasks formerly performed
by human workers or tradespersons, usually to a faster or more precise capability removing
drudgery, tedious or dangerous tasks, and improving efficiencies for manufacturing companies
(Mourtzis, 2019; Sharp, 2019).
Sales and Marketing refers to all facets of Big Data analysis for business intelligence,
development and support of customer relationships; consumer identification and marketing; new
product development, and goods and services development; direct selling and customer relations
(Ungerman, 2018).
Self-Directed Improvement refers to the concepts of individuals seeking to increase skill
sets by whatever tools or means may be available (Clark, 2005; Sedikides, 2009).
Self-Directed Learning (SDL) refers to self-directed improvement that occurs via
structured and unstructured, formal and informal, academic and non-academic, instruction or
coaching one receives that increases their capabilities to understand or respond to a piece of
information (Bandura, 2011).
14

Social Cognitive Theory refers to the ability of individuals, through deliberate intention
and design—referred to as personal agency, to make causal contributions to activities that affect
their cognitive and other personal factors (Bandura, 1989).
Supply Chain refers to the functions and Big Data analysis within an organization for
procurement of materials; manufacturing or product/service production; quality systems;
operation of physical facilities such as warehousing, logistics fleets, and manufacturing facilities;
movement and shipment of goods and services; engineering and design of future machines and
facilities to be acquired; and all other aspects with the physical operation of the business
(Tjahjono, 2017).
Subject Matter Expert (SME) refers to an ubiquitous capability or acknowledgement of
expertise to a specific task, function, or body of knowledge by an individual utilized at an
organization or business to impart that specific knowledge to others (Jeffcoat, 2019).
Venture Capital refers to investment by individuals or groups of investors (venture
capitalists) in privately owned organizations where ownership is in whole, or in part with another
private entity such as the business founder. The venture capitalists provide funds that allow the
business to start, expand, or find fiscal stability during the time of investment. Typically, the
financial interest in the business or organization is focused upon achieving specific financial
goals to recover the value of the initial investment and gain a return on that investment (Deeb,
2016; Puri, 2012).
Organization of the Dissertation
This study is organized in five chapters. The first chapter outlined the scope of the
problem to understand how-if food industry executives are keeping up with the challenges posed
to the food industry as it accelerates into the Industry 4.0 era. Chapter Two addresses the current
15

literature influencing and defining the scope of the study. The conceptual framework is also
introduced. Chapter Three reviews the methodology used for the study, research questions,
selection of participants and data collection, and analysis processes used for interviews and other
data evaluation. Chapter Four presents the study findings. Chapter Five details the study
recommendations, strengths and weaknesses of the approach, and opportunities for future
research.

16

Chapter Two: Literature Review
This chapter addresses a comprehensive review of the literature relating to U.S. food
industry executive self-directed learning and the changes to the industry that compel the need for
individual improvement (Popkova, 2019). This literature review briefly examines past executive
leadership response during transformational change during the first, second, and third phases of
the Industrial Revolution. The literature review surveys the emergence of current changes in
production methods, marketing, logistics, and new product development in the emerging
environment of Big Data, artificial intelligence, and robotics, referred to as the Fourth Industrial
Revolution or “Industry 4.0” in the trade and academic literature. The impacts of how U.S. food
industry business executives need to respond to this emergence of Industry 4.0 was also
examined.
The background and tenets of Social Cognitive Theory and literature on the concepts of
SDL, in the context of the work of Bandura, Wood, and other theorists over the last four decades,
was utilized as the theoretical framework for the study. How social cognitive theory and SDL has
been applied, primarily to formal education settings, but is posited to applications for other types
of organizations was also part of this framework for review. Literature concerning how business
executives seek learning of skills and capabilities within the context of their business-
organizational setting was examined. Understanding how these decision-making executives,
specifically in the U.S. food industry, are engaging in SDL through formal or informal activities
to acquire effective and timely tools to address escalating business challenges in this emergent
era of Industry 4.0 is the purpose of the study. Accordingly, the chapter concludes with the
conceptual framework and literature that describes how business executives can view the
connection of Industry 4.0 impacts on their organizations and their individual responses.

17

Management Change in the First, Second, and Third Industrial Revolutions
The need for change is nothing new to the food industry in the United States (Risch,
2009). Each of the first three industrial revolution phases brought great changes to the mechanics
of making food for sale to others, and the management of those resulting businesses, leading to
the current evolution referred to as the Fourth Industrial Revolution. U.S. food industry
organizations have responded to the history of western civilization over the last three centuries:
advances in food science, of government and regulation, and the parallel capabilities that have
emerged with the evolution of technology for each given period have defined the subsequent eras
of food production (Currier, 2018). Those organizations that responded effectively survived, and
the nonresponsive perished (Porter, 1986).
Transformational Change and the Advent of Modern Factories
Western society began the transformation to the industrial age in the mid-18
th
century
with the First Industrial Revolution (Logan, 2009; Pollard, 1963). The ability to use machines to
make cloth and other items was developed, and England, as well as its colonies including the yet-
emerging Canada and the United States, were caught up in this new capability (Sweet, 2007). If
cloth and garments could be created, why not other products, thus beginning the entrepreneurial
quest to produce any type of merchandise that the public would buy, or was decreed by
government (Currier, 2018; Pollard, 1963; Popkova, 2019).
Entering this era, the industrial mass-production of food simply was not possible other
than for a few items such as crackers and some attempts at pickled vegetables or cured meats,
which had little appeal and a short shelf life (Risch, 2009). Most food items produced for later
consumption had always been hampered by rapid spoilage (Currier, 2018; Rankin, 2017).
18

However, from the 1860’s the use of pasteurization enabled food to be stored, transported, re-
cooked, and safely consumed; and the modern food industry began (Currier, 2018; Risch, 2009).
The need for the modern food industry had its basis in the urban areas that were attracting
people to work in factories. Laborers emerged from the more-rural areas and drudgery of
agriculture and could now reside in cities where perhaps a better life could be found (Allen,
2012; Baumard, 2019; Lindert, 1983). These populations provided the work force for the factory
creation that accelerated as both labor and consumers increased (Allen, 2012; Logan, 2009;
Pollard, 1963). The ability to buy prepared foods and food ingredients now stored in glass jars
and later metal cans, and manufactured using early pasteurization processes (Risch, 2009) gave
this labor force the ability to assume new toils at the factory with an alternative to subsistence
level agriculture.
Workers could buy food items at reasonably inexpensive prices at emerging food focused
shops and stores (Baumard, 2019; Pollard, 1963). However, the fear of contaminated prepared
foods was still a major concern for consumers and for the food industry executives attempting to
market their products to a broader audience (Petrick, 2009). As food science and manufacturing
processes became more controlled and illness less frequent, consumer confidence improved and
sales volume, revenue, and profit increased (Petrick, 2009). The ability to move beyond their
primary needs to simply find enough food to eat each day allowed the people of this era to focus
their attentions on more “abstract things” such as factory work (Baumard, 2019; Lindert, 1983).
As with all industries during the First (steam power) and Second (electrical power and the
assembly line) phases of the industrial revolution, the management of companies dealt with
significant advances in the capabilities and technology of the day (Allen, 2012; Baumard, 2019).
For food manufacturing companies, these changes were many. They ranged from manufacturing
19

capabilities allowed by pasteurization (Currier, 2018; Risch, 2009) to the creation of food
product specific assembly lines. The rapid escalation of demand for their products and the means
of distribution to get the products—by wagon train or rail—to virtually anywhere in the
Americas through the 19
th
Century, led to an increase in food companies and a seemingly
boundless market (Currier, 2018; Sweet, 2007), devoid of the types of competitive pressures of
the current day U.S. food industry (Cheffins, 2015).
The late 19
th
Century onward saw the evolution of the Second Industrial Revolution for
the U.S. food industry. What became the modern factory of the food industry included assembly
lines powered by electricity and utilized refrigeration and freezing capabilities. These
technologies remained much the same through the end of the Second World War (Collins, 1961;
Currier, 2018; Risch, 2009). During this era, the methods of storage and distribution changed
significantly with the emergence of stable electricity and refrigeration at both the point of
manufacture and at the points of sale or consumption (Hartel, 2017). This allowed the storage
and sale to both individual consumers and restaurants of perishable products with a short shelf
life such as pre-cut uncooked meats, produce, eggs, butter, and milk (Currier, 2018; Rankin,
2017). New refrigerated and frozen dishes could now be developed (Hartel, 2017). Reliable
refrigerated trucking and delivery vehicles made use of the paved roadways, expanding markets
and product availability outside of the central urban areas.
As commented on by Cheffins (2015), there was little ability to fail during this era of
high demand and limited competition, so the necessity for “great” management was not needed:
simply getting goods to market would be enough to succeed. Trademarks and protection of food
brand names emerged as competition increased for consumers: companies with proprietary
interests sought to prevent other companies from copying their recipes and formulations
20

(Horowitz, 2019). Large, market-dominating food companies, utilizing formal management
tools, increased their size and market share (Horowitz). The management of food organizations
was about to take on an even greater sense of urgency as U.S. food companies moved out of the
2
nd
Industrial Revolution and into the rapidly changing post-World War II era.
Transformational Change with the Emergence of the Computer Age
The third phase of the industrial revolutions is sometimes referred to as the “Third
Industrial Revolution” (Basu, 2008). This phase is grounded in the creation and evolution of
information technology (IT) since the mid-20
th
century. While improvements in mechanization
of processes and improvements in efficiency continued at a steady pace, Basu identified this new
phase was about sorting and using data in a completely different manner. It encompassed
affordable computer expertise and expanding expectations by manufacturers, their suppliers, and
customers of IT based interactions (Senge, 2001).
The ability to digitize the vast amounts of paper associated with business and
organization operations became possible due to early computers. These improving computer
capabilities expanded at a hectic rate. To this point, large staffs of clerks and office workers were
required to keep up with the labors of production scheduling, accounts receivable, accounts
payable, and all the other “sea of paper” needs of business. The way all these business functions
occurred began to change; computer and data-transfer technology could do these jobs faster,
more accurately, and more cheaply (Basu, 2008; Senge, 2001).
As these digitized capabilities developed, expertise emerged in a variety of new areas.
These areas included data driven plans, use of rudimentary computer modeling, and developing
marketing profiles of specific target demographics with some degree of statistical confidence.
Other capacities such as the ability to measure-predict-create trends, became the capability of
21

marketing teams using rudimentary and quickly evolving computer programs for all types of
organizations (Brynjolfsson, 2012). With those enhanced capabilities competition increased and
the ability of new, upstart competitors to enter the markets caused larger companies to lose total
dominance (Porter, 1986).
In food manufacturing, the ability to use computer systems to assess food production
process efficiencies, quality performance, and other operating techniques allowed shelf life and
other freshness characteristics to be more accurately monitored (Rankin, 2017). New capabilities
to make and synthesize food additives emerged. This created an entirely new category of food
ingredient manufacturing with the subsequent controversies of what is synthetic versus natural,
and what is considered merely a “filler” to minimize natural ingredients costs (BeMiller, 2009).
During this era of emergent computer systems utilization by some companies, the ability
of the swift to overcome the position of the slow became apparent. Perhaps the most well-known
example of two upstarts displacing an industry leader is the supplanting of IBM, the giant of
corporate computing, by the entrée of personal and small business computing by Microsoft and
Apple through the last quarter of the 20
th
century (Cusumano, 2011). In the U.S. food industry
companies like Whole Foods also approached their business model utilizing the new computer
tools. Whole Foods effectively utilized marketing and product inventory control systems that
their predecessors had not grasped in the same manner (Johnston, 2007; S. White, 2020). All
these advances, now rapidly accelerating into a complete change of what organizations could do
because of computer systems, led to yet another phase of global transformation.
Transformational Change for Management Education and Other Learning
As food companies became bigger during the second industrial revolution period, the
need to run them more cost effectively increased. One of the consequences of changes to
22

markets, production, and logistics, was a realization that “management” required its own
academic platform (Amdam, 2016; Augier, 2007; Kelly, 2015) beyond on-the-job and SDL. Just
as with the formal academic focus on medicine, law, and science, the identification of a need for
formal instruction of management tools for how to structure and operate business organizations
became increasingly important (Amdam, 2016). Topics of instruction included business
management, industrial accounting, fledgling marketing classes, and industrial engineering.
Research by academics of business and organization efficiency took on a new sense of
immediacy and practical relevancy (Augier, 2007).
These formal academic and skills development initiatives in turn led to instruction for
management and business topics developed at public and “private-for profit” colleges and
universities across the United States in the latter 19
th
century (Weiss, 1981). While these inroads
to develop managerial knowledge and executive capabilities were initially limited and
developing, they did provide the basis for the professional management and business programs
now found in all levels of academia (Augier, 2007; Weiss, 1981). At higher learning institutions,
degrees in these areas of business and industrial engineering, and the subsequent development of
the “Master of Business Administration” occurred (Amdam, 2016). Task and skillset training
were identified as a need. Emerging companies required employees with day-to-day skillsets;
bookkeeping, stenography, typing, and accounting schools developed to meet the demands of the
changing times (Weiss, 1981). These capabilities offered by trained employees through these
programs provided business organizations an ability to manage themselves with some degree of
planned application rather than simply reacting to issues.
Self-directed learning as well as more structured opportunities to learn outside of formal
academia also began to emerge in the 20
th
century and have carried forward in the form of trade
23

shows and specific topic symposia (Maskell, 2006). The use of trade fairs and conventions taps
into a variety of vocational and professional disciplines (Kresse, 2003). At these events
organizations and management are able to identify knowledge streams or product concepts that
are not yet within the realm of formal academia (Bathelt, 2003; Zhu, 2020). U.S. food industry
executives have a plethora of regional and national opportunities presented annually by
organizations such as the National Restaurant Association, the Pack Expo offered alternate years
in Chicago or Las Vegas, and the Refrigerated Foods Association Exhibition and Conference
(Food Industry Executive, 2021).
The Fourth Industrial Revolution
The introduction of new generations of computers, programmed to act seemingly
autonomously, and the integration of IT into all facets of business applications, has presented
what is called the Fourth Industrial Revolution, or Industry 4.0 (Machado, 2019; Marr, 2018;
Pugna, 2019). IT capabilities and how they are used have significantly advanced from the digital
age of the Third Industrial Revolution. These advances have extended into areas of AI and
robotics that were not possible twenty years ago. These advances are what makes the Fourth
Industrial Revolution distinctive from the Third Industrial revolution in that the autonomous
interconnectivity of devices, not just people, is happening for the first time (Schwab, 2017).
These subsequent information technology innovations have revolutionized what “data” is and
what data, now known as Big Data, can do (Carillo, 2017; Marr, 2018). However, the Fourth
Industrial Revolution as a topic of academic study has only developed since 2010 (Maresova,
2018).
The term “Industry 4.0” did not come into broad use until 2011. It was first used by the
German government to characterize future economic policy given the potentials of automation,
24

robotics, and artificial intelligence to transform the-then-current state of German commerce
(Piccarozzi, 2018). From an academic study perspective, the emerging era of Industry 4.0 has not
yet been broadly examined. As of 2018, only 2% of the research papers in this area are from the
United States, while 32% are from Germany, 9% are from Russia, and the remainder in single
digits from other industrial nations (Piccarozzi, 2018). The impact of Industry 4.0 on industry
innovations and data systems-based communications is being acknowledged in a variety of
industry and trade driven journals outside of emergent academic interest (Brynjolfsson, 2012;
Xu, 2018).
The Tenets of Industry 4.0
The context of what U.S. food industry executives are confronting as their business
capabilities and competitive pressures evolve to meet Industry 4.0 challenges is one of
transformational change to earlier eras of operations. As an evolution from the advances of the
Third Industrial Revolution, Industry 4.0 is marked by the gains in capabilities that are
permeating all facets of technology for both personal and industrial applications (Klotz, 2018).
These areas include how Big Data—the vast amount of computer system information—is created
and used (Kambhampati, 2020). Big Data impacts on the creation and manufacture of products
are also a factor in the impact to supply chain planning, identification of potential product
creation ideas, and changes in manufacturing processes through the introduction of robotics
(Merendino, 2018; Tjahjono, 2017). The changes in marketing and consumer insights
(Merendino, 2018), and the accompanying psychological impacts on stakeholders (Dombrowski,
2014), are also considerations.
The technical aspects of how artificial intelligence programs work to create and utilize
Big Data is outside the scope of this study. However, it is significant to distinguish that the
25

computer capabilities of the Third Industrial Revolution gave these programs the ability to amass
and compute tremendous amounts of data, while Industry 4.0 technology gives these AI
programs the ability to “think” about that amassed data, then respond to it (Oudeyer, 2017).
These AI processes utilize formerly “human-brain-only” conceptual areas of game theory and
anticipatory thinking that are converted into computer program algorithms (LeCun, 2015). These
programs can abstract data and independently learn from the conceptualizations in a manner that
mimics how the human brain learns and responds as identified in separate studies by LeCun
(2015) and Oudeyer (2017). Because of these new capabilities, and the speed at which they
occur, new awareness is required, and new skills are needed by decision makers, to keep up with
industry advances (Klotz, 2018).
U.S. food industry executives are challenged to identify personal knowledge
shortcomings and bridge them (Tjahjono, 2017). How these executives choose to obtain
additional information, and the manner with which they use it, ties directly to the utilization of
information (Parker, 2010; Sedikides, 2016). Subramanian (2019) identified that major
companies such as Amazon and Netflix have utilized dynamic information and strategies. Each
company employed technology and cutting-edge logistics tools, developed resilience and
adaptation to opportunities, and gained a major strategic advantage to surpass their competition.
Subramanian stated that the examples demonstrated by Amazon and Netflix provide templates of
innovation for U.S. food industry companies to examine.
In separate studies by Bridges (2018) and McCall (2002), each identified that it takes the
teams of an organization to develop the resilience and flexibility to excel, and those teams must
be effectively led. While Industry 4.0 may be based upon incredibly capable AI and robotics
systems, each function is nothing more than a tool for the people who comprise society, not an
26

end in itself. Executives must approach differing areas of information and the way it is evaluated
to influence the outcomes of their organizations and motivate their teams to succeed, even in
daunting or stressful circumstances (Bridges, 2018).
An unwillingness of an organization’s leadership to pursue industry knowledge affects
their teams quite negatively which can be de-motivating (O’Roark, 2002). Maintaining
motivation and credibility with one’s team relies upon attainment of the relevant knowledge
needed to facilitate the efforts of the organization, and to make sound decisions (Parker, 2010).
This is further emphasized by Carillo (2017) who posited that effectiveness in mastering the new
skills of Big Data utilization is much more beneficial to the organization when it manifests as
part of the organizational culture.
The ongoing evolution of Big Data and its impact on all facets of analytics is discussed
by Picciano (2012) who posited that data must be accurate and relevant to the situation to be
usable and timely. The data driven process illustrates that for a decision maker, based upon the
reality of the internal situation and the sources of information, other external information or
factors creates the need for a decision process that then results in revised or new courses of
action. Influences and the decision process as shown in Figure 1, deriving from the information
system, or Big Data, become the parameters for action.

27

Figure 1
Data-Driven Decision-Making Process

Note. Adapted from “The Evolution of Big Data and Learning Analytics in American Higher
Education,” by A. Picciano, 2012, Online Learning, 16(3), 9–. Copyright 2012 by Online
Learning.

Bandura (2011) identified the influence of external and personal factors for individual
behavior and decision making in Social Cognitive Theory. Picciano’s 2012 model also reflects
how the consideration of internal and external factors impacts the decision process. This is
consistent to the Industry 4.0 emergent world as information is holistically evaluated (Klotz,
2018; Müller, 2018).
28

The dilemma for organizations becomes apparent. What do the organizations and key
decision makers need to do to effectively address the issues and opportunities for their
companies given change, individual competencies, and competitive pressures? Merendino (2018)
and Pugna (2019) regard this dilemma as the most significant strategic issue facing boards of
directors and corporate decision makers in today’s evolving business environment.
An Enhancement of Decision Capability
AI is evolving the ability to think in the manner of humans. Driving an automobile is a
very human activity: because of AI the self-driving car from Tesla is redefining the future of
automotive engineering (Cuthbertson, 2020). In 2010 and in 2016 the worlds of Chess and Go
were shocked when their grand masters were defeated by AI programs (Hassabis, 2017). These
were games that no one, outside of the AI industry, believed a computer-AI system could win.
At the business level, the plethora of computer systems collecting data, sorting it and
cataloguing it, have created Big Data capabilities that are transformational where the AI systems
can and do make some decisions (Carillo, 2019). How organizations choose to use or not use Big
Data has major potential competitive consequences. Ignore what Big Data is stating and get
squashed by the competition, try to use it—poorly—and also get squashed. As posited by Carillo
(2019) the only real survival strategy is to invest in Big Data capabilities and use them to out-
perform the competition, but this requires a significant systems and people investment.
An Enhancement of Computer Utilization
The utilization of computers in Industry 4.0 has far surpassed the capabilities developed
during the Third Industrial Revolution (Lasi, 2014). These capabilities were still mainly human
interaction focused and dependent upon human collaboration. The Big Data driven interface with
the human experience, has, is, and will continue to change all facets of business organization
29

operations (Schulz, 2019). These interfaces are marked by the expansion of proficiencies into
robotics and miniaturization, the near-flawlessly transparent functionality of AI systems that can
“grow and learn” autonomously, and how all of that now is becoming integrated into “normal”
life (Lasi, 2014; Schulz, 2019). With this functionality also comes new risks in areas such as
cybersecurity (Culot, 2019). An examination of three operational areas that impact U.S. food
companies identifies the effect Industry 4.0 is having. These areas are supply chain, product
development and sales and marketing, and organization general administration.
An Enhancement of Supply Chain
In the area of supply chain Tjahjono (2017) posited that smart factory machines will
utilize information exchange across global networks, allowing these factories to operate
autonomously. Big Data also impacts sales and marketing efforts and efforts to enhance supply
chain forecasting capabilities and production speed to market (Ponelis, 2012). Establishment of
robust sales networks to optimize marketing trends and opportunities by utilizing business
intelligence (BI) and point of sale data in a proactive manner all enhance the value received from
Big Data optimization (Ungerman, 2018).The administrative functions of an organization will
likely become more decentralized, require significantly fewer employees, and the skill sets
required for employees will be more focused on supporting the analytics derived through Big
Data (Blazquez-Resino, 2019).
Supply chain development with Industry 4.0 is affected by the clock “speed”
(Subramanian, 2019) of differing functions within organizations. For example, at a food industry
organization, the time for development of a new product concept can be achieved in weeks.
However, the time to build the appropriate robotized and high-performance production line for
that new product idea can be a year or more depending upon complexity, before the concept can
30

be brought to market (Bayona-Saez, 2017; Saenz, 2020). These varying clock speeds are also
relevant from a standpoint of smart design engineering (Pessôa, 2020) which entails fully
integrated mechanical systems that can be synchronized and managed by AI programs. Pessôa’s
(2020) study examined the value chain of companies as they look at the “smart design” of new
machinery and tooling, through production and subsequent distribution to customers, and
ongoing flexibility for yet to be developed products. The need for synchronicity is critical for
both the balance of costs as well as employee utilization. This integration of machine capabilities
into human dialogue, where the AI inputs of the machine, tool, or data program are viewed as
accurate, credible, and useful, adds a vastly different dynamic to how industry executives
formerly viewed these areas but need to think about them now (Allen, 2019; Janseen, 2017).
An Enhancement of Product Development and Sales and Marketing
For food industry organizations, new product development and marketing is the basis for
virtually all business opportunities. Using AI to make trend assessments and assess customer
feedback is a new capability for marketers (Merendino, 2018). Wirth (2018) stated that the
ability of AI to make human-like decisions based upon product and marketing insight is not yet
the equivalent of human thinking, but that the ability of AI to provide large amounts of data for
executives to ponder increases at a rapid pace.
Big Data is increasing in breadth and focus as systems refine their capabilities but are still
somewhat fragmented in how information is collected and assessed within the AI system
(Frizzo-Barker, 2016). How executives approach all facets of decision making was hindered in
2010 by the wealth of data available unless the AI initial analysis is sufficiently sophisticated to
assess what is really needed for this decision (Avanade, 2010). However, over the last decade the
amount of Big Data has doubled in each of the intervening years according to Müller (2018).
31

What was noted as difficult and overwhelming by Avanade is today even broader and more
complex. As new product concepts are considered, and marketing methods are evaluated based
upon Big Data, business executives must develop better abilities to assess if customer sentiment
will still be there a year or more from now (Carillo, 2019). As the pace of trends accelerates but
the timeframe for production capability development remains more constrained, balancing the
clock speed differences will become even more critical to meeting competitive pressures
(Subramanian, 2019).
An Enhancement of People Investment
Industry 4.0 is causing a transition to the structure of organizations which impacts
organizational psychology (Dombrowski, 2014). Organization structures, the use of machinery
and robotics, the changes in tasks performed from factory and warehouse to office areas, and
how workers will interact in a modern Industry 4.0 business is being revolutionized (Jiao, 2020).
While the shape of the future business world is dynamically changing as companies sort through
their potentials, the impact of this level of change has been shown to be destabilizing and
demotivating to both individuals and teams (Dombrowski, 2014; Jiao, 2020). The key is the
uncertainty of how automation and robotics, administrative tasks supplanted by Big Data
systems, and other potential changes will disrupt the individuals affected (Dombrowski, 2014;
Jiao, 2020).
The employment of unskilled and semi-skilled workers can be impacted by companies
moving to more automation and robotics (Piccarozzi, 2018). Pfeiffer (2018) determined that how
industries qualify “routine versus non-routine” work requirements is key to leveraging people
power versus robot power. For those tasks that are as simple as putting an item in a box—or as
routine, the likelihood of utilization of robotics is not only high but probably required to control
32

costs if one’s competitors are already doing so. But non-routine tasks, such as troubleshooting
process flow disruptions or the set up and changeover of activities, are not yet within the realm
of automation and workers will still be required (Dombrowski, 2014; Frey, 2017; Pfeiffer).
While the movement to the positives offered by Industry 4.0, or simply demanded in response to
competitive pressures will continue unabated, Kaczmarek (2019) has posited that organizations
need to proactively anticipate the impact on employees at all levels.
Industry 4.0 and the U.S. Food Industry
The academic focus on Industry 4.0 is only now emerging, and few specific research
studies have been conducted about the food industry concerning Industry 4.0 impacts on the U.S.
food industry. However, the studies that have been undertaken show that the U.S. food industry
is seeing a change in how companies do business because of the impacts of Industry 4.0 (Frey,
2017). Some of this change is in response to competitive pressures to not be left behind, but
other change is due to companies seeking to create or maximize market share competitive
advantages (Hemphill, 2012). AI data is helping U.S. food companies do both on the marketing
and supply chain fronts. The new technologies are critical to culture change both to take
advantage of the opportunities that are possible as well as not to be left behind by more
ambitious competitors (Singh, 2019; Wirth, 2018).
The manner differing size companies are approaching new product development is a
function of organizational size and resources. Large food companies have competitive advantage
to utilizing Industry 4.0 tools than smaller food companies, according to a quantitative study by
Bayona-Saez (2017) in the European Union. Although there has not been a similar study
identified that looked at U.S. food companies, the Bayona-Saez study results point to the
innovation capability of larger companies to be positively influenced by the greater financial
33

capabilities than those enjoyed by their smaller counterparts. This, however, points back to the
opportunity for the fast to overtake the slow, as smaller companies may demonstrate greater
nimbleness than their larger competitors, particularly with quality related initiatives (Bayona-
Saez, 2017). This position is cautioned by Janssen (2017) who posited that the quality of Big
Data can be messy and difficult for decision makers to correctly sort through and utilize unless
they have a firm grasp on the requisite skill sets. Lack of expertise or becoming bogged down by
the data and unable to make a decision places an organization in an ineffective position.
From a sales and marketing emphasis, industry analyst Shoup (2019) posited that the rate
of change facing the food industry over the coming decade will be greater than what was faced in
the last 50 years. Key to these changes will be the ability of U.S. food manufacturing companies
to read possible trends based upon new food items being created across the globe (Olayanju,
2019). For business-intelligence (BI) programs, sifting through data to identify potentials is a key
tool derived from AI (Vercellis, 2009). In the food industry a benefit of utilizing BI could be
using critics’ reviews or small food company announcements to glean possible new items for
development and testing: this can be done faster than research and development teams might
create them starting from scratch (Mourtzis, 2019; Ponelis, 2012). Manufacturing lines with
flexibility to be quickly modified (Sharp, 2019) for new ingredients and methods of cooking can
then turn these concepts into mass-market ready products in a matter of weeks. In factories using
earlier generation processing lines it can require months to re-tool or develop and test ingredient
combinations to bring a new food item to consumers (Mourtzis, 2019).
Food products must be manufactured then transported to market. No matter how much
emphasis is made on merchandising, selling, product creation, and vendor relationships, there is
a simple reality that if the product is not on a shelf, it cannot be sold (Hemphill, 2012). Someone
34

must make the food and put it on a truck for the consumer to ever have a purchase opportunity.
Industry 4.0 is both a blessing and a curse for today’s manufacturing companies (Avanade,
2010). The blessing is the potential to improve food safety, quality, speed to market, and reduce
manufacturing waste. Firms that utilize supply chain enhancements for robotics, automation,
faster ingredient supply systems to ensure best possible freshness, and management of
employees will thrive (Business Transformation, 2019; Martin, 2019). The curse is to those that
do not and are left behind by their competition. However, these innovations require capital
investment and retooling or replacement of manufacturing plants and equipment as well as the
potential displacement of employees (Chuang, 2018; Risch, 2009). These innovations also
require the willingness of corporate decision makers to embrace, then lead, new ideas to their
teams (Bridges, 2018; Dubnick, 2003).
How industry overall pursues and supports environmental sustainability is seen by
Machado (2019) as an emerging academic issue, with research studies, journals, and symposia
all on the rise over the last decade by over 100%. Industry needs to approach all facets of
sustainable resource usage. Additionally, organizations must be proactive with suppliers to
ensure the full supply chain is seen as critical to future commercial acceptance by consumers and
for profitability (Jovane, 2008). Sustainability initiatives for food industry companies are vital
because sustainability is important for consumers (Olayanju, 2019). The performance initiatives
utilized to meet consumer interest in how sustainability initiatives are identified, then presented
for public discourse, can be highly influenced by the decision making based upon sustainability
related Big Data (Jovane, 2008; Machado, 2019).
Using BI and AI to assess consumer trends is a key part of forward-looking marketing for
U.S. food companies. It is also as essential to know how consumers perceive what companies are
35

doing related to ingredients, manufacturing methods, and how they are portrayed to the public. In
a quantitative study Wunderlich (2017) found that consumers pay more attention to these factors
of ingredients and methods than U.S. food companies may have realized. This consumer
attentiveness to label declarations and comparisons to alternative products can affect the
profitability of those companies if the companies are not responsive to positives or negatives
revealed by the consumer perceptions. Using AI to assess how the media is portraying
ingredients and processes, so that food companies can more quickly respond to potential negative
consequences or positive trends, is a new opportunity for food companies. This type of analysis
is a benefit only recently developed (Wirth, 2018).
Several researchers and industry analysts cited in this section have noted common
themes about the emergent capabilities of Industry 4.0. Characterizations of complexity, messy,
difficult, and overwhelming appear in the descriptions of how to analyze what to do with all the
data sets and automation opportunities now possible. And still others have noted that without
effective utilization of AI and robotics, forward progress will be limited while competitors blaze
by. It raises the conundrum for what U.S. food industry executives should do in this accelerating
environment, and how heuristic individual abilities should be enhanced when former skill sets
may now be outmoded.
Executive Responsibilities to Foster Self-Directed Learning
The pathways for achieving SDL are varied with individual degrees of effectiveness.
Whether through formal in-person or online learning at an academic institution (Picciano, 2015;
Wixom, 2014) or combining personal growth strategies within the structures of prior academic
learning technique (Burksaitiene, 2011), executives have a variety of options to improve their
knowledge of virtually any topic. A study by Boyer (2014) identified that SDL can occur through
36

fully individual structuring, organizational initiative or compulsion, or some synthesis of each.
Regardless, the motivation of the individual and tenaciousness to complete learning opportunities
is central to effectiveness (Kyvliuk, 2019). Whether the motivation is personal survival or a
desire to ensure all capabilities to one’s organization are fully realized, executives should
recognize how Industry 4.0 has created changes to the landscape of their organizations, their
employees, and the individual interactional structure to both (Chuang, 2018; Maglio, 2019).
Self-directed learning and other learning by U.S. food industry executives is situational,
diverse in its application, and fueled by general industry awareness (Pfeifer, 2018). How the
changing requirements of Industry 4.0 influence events that cause a proactive or reactive
response from these executives is driven by the events’ circumstances and the willingness of the
executive to formulate a response. Burksaitiene (2011) determined that the synthesis of academic
study techniques enabled individuals to approach SDL more effectively if their knowledge of the
area studied was reinforced by their individual determination to improve. Boyer (2014)
determined that formal academic preparation should include teaching students the skills needed
for effective SDL once they enter the realms of their livelihoods.
Due to the pace of innovation, there is an organizational need to keep up with what is
happening as it is happening in relation to their industry. Boyer (2014) posited that it is essential
for future business leaders to learn the requisite skill sets needed to adapt as opportunities
develop. From this, executive leadership at organizations should foster a culture of SDL that
utilizes the formal academic preparation and awareness of the benefits of individual members of
organizations utilizing SDL to benefit all stakeholders. O’Roark (2002) identified that these
general qualities of willingness to learn and to avoid becoming “mentally stagnant,” combined
37

with the insights to motivate their teams to stay fresh and abreast of internal and external
organizational challenges, leads to more effective organizations.
Factors of environment, behavior, and personal attributes in a livelihood environment, as
posited by Artis (2007), must be appreciated and understood if SDL is to be effectively utilized.
Artis determined that when these factors are understood and managed as a part of promoting
self-efficacy in a SDL supportive organization, these criteria are an innate predilection to SDL
effectiveness. Technical skills relative to the topic to be studied, familiarity with that subject, and
motivation to start and complete the SDL task are influential to organizations achieving a SDL
environment. The Artis study aligned in its findings to the Kirkpatrick New World Model
(Kirkpatrick, 2016) determination that senior leadership buy-in was essential to positive
influence on individuals’ motivation, as well as establishing supporting resources.
A separate study on learning organizations by Rana (2016) had similar findings for
development of self-efficacy through SDL and the need for organizational support. These studies
call to a dual role for organization executives: that of individual self-directed learners and as
empowering a culture of SDL for their teams. The role of SDL as a tool to improve
organizational capabilities and success appears expandable to other areas for personal
development in any given organization (Artis, 2007). These studies, if extrapolated to the U.S.
food industry, indicate that the U.S. food industry requires a revised SDL approach for
individuals and teams in response to Industry 4.0 to likewise achieve similar positive
organization benefits.
The Changing Information Landscape
How information becomes available, how great the amount of information and how
relevant it is to an executive or organization, can either inspire or stifle capabilities (Merendino,
38

2018; Ransbotham, 2015). Big Data, in its abundance, can either motivate an executive to dig in
and learn or be so overwhelming that there is no place to start. This can lead to issues of
cognitive overload or indecisiveness; additionally, while it is easy to produce data, knowing how
to effectively consume it is the biggest shortcoming for U.S. executives (Ransbotham, 2015). But
against this backdrop executives are compelled to make decisions: Fleming (2017) asserted that
executives must be able to assess their decisions, learn from them, improve upon them, and do so
in an honest and effective manner. An executive must make some rational attempt to assess self-
knowledge, utilization of information on a given topic, the efficacy of a decision made, then self-
evaluate how they did (Picciano, 2012). Throw in the manner in which accountability will be
assessed (Bovens, 2014; Church, 2018; De Langhe, 2011) in a Big Data-Industry 4.0
environment, and that is a lot to ask of a third-party observer, much less an individual executive
attempting to make sense of it all (Bridges, 2018).
How information is examined and by whom, can affect how it is used. A quantitative
research study of U.S. food industry executives by Melone (1994) validated that practical
experiential knowledge and operational perspective makes a difference in decision making.
Melone (1994) determined that, when presented the same data, role and function perspective
resulted in quite different assessments and subsequent decision recommendations based upon
that data. Melone determined that function expertise bias, for instance the difference in
perspective between a finance executive to that that of a marketing executive, causes differences
in recommendation strategies for the same data. Moving forward 30 years, Industry 4.0 presents
even greater complexities (Xu, 2018). The benefit of functional expertise and cross functional
alignment can be posited by the overlap of capabilities, ethics, and perspectives (Stubbart, 2015).
However, the speed intensity for action or response and amount of data available to be assessed
39

can create disruptive impacts to an organization, reinforcing the requirement for executive
expertise (Pugna, 2019).
The Human Impact of Big Data
The need for executives to be aware of biases that impact their heuristic outlooks, as
identified by Abatecola (2018), can have risk implications when utilizing impact information
related to Industry 4.0. Reactions based on heuristic bias, ignorance of relevant facts, or
impulsive response can be detrimental to organizations. Accountability and learning are
interrelated: Langhe (2011) posited that the role of accountability is highly influenced by the
complexities of cognitive load, the heuristics impacts on bias, and the process that decision
making processes can follow. Langhe’s study found that the more abstract the information,
relative to an individual’s capabilities, the greater the impact on memory disaffection, such that
judgmental accountability becomes more difficult. All these factors impact executive decision-
making effectiveness.
As identified by Lerner (2003) and Tetlock (1992), the combination of personal
influences and environment impact decision making, and that cognitive load impacts biases
which can lead to poor decision making. Lerner posited that dealing with increased cognitive
effort impacts how individual biases affect decision making. This reinforces that executive
decision making, particularly in a fast-paced environment, is fraught with peril and difficulty
(Shoup, 2019). While many studies predict how one’s choices and judgements may be affected
by the factors discussed, there is yet no mechanism to teach an executive all these techniques
such that heuristic bias is no longer a factor: relying on a Big Data algorithm is not a substitute
for executive capability.

40

Team Benefits of Shared Perspective
Industry 4.0 increases the challenges to U.S. food organizations as stakeholders face new
situations that can lead to poor decisions. Ensuring that all members of the team have a voice and
understand the stakes is fundamental to the organization’s success (Kirkpatrick, 2016). Ebrahim
(2005) posited that accountability in an organization involves all stakeholders, and the need for
organizational learning must be emphasized at all levels, or decision makers will be neither
aware of issues nor capable of addressing them. This viewpoint was also supported by a Langhe
(2011) study on the impact of teams and individual judgement, which found shared perspective
impacts accountability and learning efficacy.
For executives, creating an environment of shared capabilities and perspectives is seen as
essential (Jones, 2001; Müller, 2018). Individual executives and their teams are looking for the
mutual self-interests of their organization, demonstrating self-efficacy and encouraging team
members to develop higher levels of individual and team self-efficacy. Liou (2018) determined
in a quantitative study that in an academic environment, instructors who were from learning
organizations that promoted self-efficacy were more likely to foster higher performing and more
capable students, reinforcing the benefit to encouraging high self-efficacy competencies in
organizational leaders. In the U.S. food industry, executives who can inspire a similar team
performance to that of their academic counterparts, should likewise achieve greater organization
success. Whether directed to individuals or teams, promoting SDL opportunities to achieve
enhanced individual and team self-efficacy, as identified by Müller (2018), is a critical
responsibility for executives.

41

Theoretical Framework: Social Cognitive Theory
Social Cognitive Theory (SCT) provides the theoretical framework for this study. Wood
and Bandura (1989a, 1989b), and subsequently expanded by Bandura (1989, 2001, 2011, 2016),
posited that individuals can successfully pursue knowledge on their own initiative, which can
increase their ongoing capabilities, achieving varying degrees of self-efficacy. Wood and Badura
(1989b) wrote “It requires a strong sense of efficacy to deploy one's cognitive resources
optimally and to remain task oriented in the face of organizational difficulties and failures” (p.
1176). This sentence defines the challenges that today’s U.S. food manufacturing executives
face with the movement to Industry 4.0.
Outside of the structure of a formal learning system, research has demonstrated an
individual can assess their current state of being and self-initiate a learning process (Wood &
Bandura, 1989). They posited with their model of Triadic Reciprocal Determinism (TRD) that
three specific influences of personal, behavioral and environmental determinants affect how an
individual pursues personal improvement. Because these three influences are unique to each
person’s circumstance, which constitutes an agentic perspective (Bandura, 1989, 2011, 2016),
what works for one individual will not necessarily work for another, and the key catalyst to
individual improvement is the desire and will of the person to make the best of their special
circumstance to achieve enhanced knowledge or capability.
The individual is an “agent” who is an active part of the situation, not merely a passive
participant with no influence over a situation. These three influences are the behavioral, personal,
and environmental factors associated with the individual and the type of knowledge they are
pursuing. Bandura (2011) stated “Social cognitive theory subscribes to a casual structure
grounded in triadic reciprocal causation. In this triadic codetermination, human functioning is a
42

product of the interplay of intrapersonal influences, the behavior individuals engage in, and the
environmental forces that impinge upon them” (p. 11).
The three factors of TRD, as shown in Figure 2, are self-reinforcing: the agent is affected
by and can change or influence their environment, which is the social or physical conditions in
which the agent exists. The agent is affected by personal factors, such as how they are
emotionally feeling about a given situation, either positively or negatively. The agent’s behavior,
by actions, words, or response, can influence both the environment, the personal sentiment, or
the outcome of what is being sought or determined by pursuing the task or giving up the
endeavor (Wood & Bandura, 1989).

Figure 2
Triadic Reciprocal Determinism

Note. Adapted from “Social Cognitive Theory of Organizational Management” by R. Wood and
A. Bandura, 1989, The Academy of Management Review, 14(3), 361–. Copyright 1989 by The
Academy of Management Review.
43

The increase in knowledge that arises with an individual can lead to a higher level of self-
efficacy, or competence, to deal with the specific subject area (Bandura, 2009). The new agentic
perspective of the individual leads to actions based on self-belief in competency or capability
with an increased willingness and ability to demonstrate that efficacy. Additionally, an individual
demonstrating self-efficacy can also recognize the need to acquire knowledge.
An impact of self-efficacy initiative is that those persons who believe self-efficacy is due
primarily to intellectual capacity find themselves unable to cope as well with adversity. Those
who see ability and performance as acquirable achieve a high sense of self-efficacy and find
ways to work through adversities (Wood & Bandura, 1989). The achievement of self-efficacy
leads to a feedback loop: mistakes and failures cause the individual to reassess their knowledge
such that those with low self-efficacy will find their efforts useless, while high self-efficacy
individuals will look at the lack of success to an issue and seek to overcome it. In both cases how
individuals actively choose to respond to the triadic factors affects their self-efficacy position
(Bandura, 1986, 2011). The capability to embrace SDL learning is also affected.
Self-Directed Learning
Self-directed learning (SDL) and self-efficacy are reinforcing (Garrison, 1997). Self-
efficacy is enhanced when individuals take on an agentic perspective as discussed by Bandura
(2011). The outcomes of SDL positive results have been researched extensively.
Brookfield (1984) and Tough (1978, 1989) are among the initial scholars who
investigated how individuals seek increased knowledge and skill sets outside of formal academic
settings. Each determined a high success rate and individual efficacy were achievable. Tough’s
seminal work dating to the 1960’s challenged the existing paradigm that SDL in a non-formal or
non-structured environment was not effective. His research demonstrated the exact opposite, that
44

SDL could be an effective alternative to formal instruction. Tough showed positive results, even
with blue collar workers of initial low formal education. Chandler (1991) determined that
cognitive load significantly affected the ability to learn and retain new or challenging
information. Chandler defined cognitive capability as the capacity of individuals to learn or
participate in discovering based upon total cognitive activities being conducted, or intensity of
the cognitive effort required. Merriënboer (2009) stated that acknowledgement of professional
task complexities, or cognitive load, is a first step. By breaking down those complexities into
more easily understood and learned sub-part, better efficacy is realized, reinforcing research by
Tough (1978).
Self-efficacy from TRD and as a part of SCT, has been demonstrated as a critical and
measurable component of self-directed learning. In a study of medical residents, it was found
that personal self-efficacy and motivation to pursue individual improvement defined the
effectiveness of SDL efforts amongst the participants (Sawatsky, 2017). Comparing the
Sawatsky study findings with those of Tough (1978) and Brookfield (1984), highly educated and
motivated medical doctor residents and blue-collar workers both demonstrated the ability to
benefit from SDL when individually self-motivated. In a study by Dunning (2016), the analysis
found that high self-efficacy individuals tended to hold others in a light of being more-capable,
and themselves as less-capable, than what actual test results revealed. This reinforced the
findings of Wood and Bandura (1989a, 1989b) concerning self-assessment of capabilities.
Dunning also demonstrated that self-efficacy limitations of self-deception can affect how SDL is
approached. One’s belief of capability in many things, because of the high ability to do one thing
may not be correct; conversely an inability to do one thing does not mean an inability to do many
other things (Dunning, 2016).
45

Prior to the emergence of Industry 4.0, business community reaction to changing
circumstances was researched within the context of the Third Industrial Revolution in separate
studies by Hong (1999) and Jones (2001). These studies noted the need for all levels of
management to improve inherent skills and capabilities, to innovate, to react faster and in a
controlled and disciplined manner. In a study by Hong, three types of learning were identified as
central to an organization: survival, benchmark, and leading. These areas are broken down as
understanding how we use what we have, how do we learn from our competition or professional
state-of-the-art, and how do we take what we know and transform to what others in our
respective industry are not. Self-study in the form of SDL is seen by Hong as integral to all three
types of learning within an organization. When looking to organizational learning, Jones wrote,
The following elements are seen as important to organizational learning processes: a) an
organizational culture which not only allows but actively encourages questions by
employees at all levels; b) the development throughout the organization of the skills of
critical refection; c) regular and varied opportunities for sharing questions and refection;
d) a continuous search for opportunities for learning from the organization’s ongoing
operations; e) taking action based on such learning; and f) critical refection on the
outcomes of action. (p. 93)
How these six concepts align, in whole or in part, to both the academic and practical
study outcomes of SDL, is reflected in other studies such as by Merriënboer (2009) and
Sawatsky (2017) which demonstrated positive impacts to both individuals and organizations
deriving from SDL activities. Overlaid with SCT and self-efficacy, these concepts of culture,
critical reflection, shared opportunities, continuous learning opportunities, data-based action, and
impact assessment can provide a basis for U.S. food industry executive SDL
46

SCT and SDL in Academic and Business Applications
The expansive study of SCT and SDL over the last fifty years has generated different
perspectives on the role of environment and the self-initiative of the individual. A review of
study findings is indicative of how SDL can potentially affect U.S. food executives as they
respond to skill gaps. SCT defines and establishes the concept of individuals’ assessing
situational issues then developing actions in response to learning opportunities (Bjork, 2013;
Dunlosky, 2012). The relevance of academic setting findings is important to establishing how
SDL can be examined in organizational or business settings.
SDL is not a panacea that replaces formal education or training programs but can
supplement in their absence and lend to learning techniques that reinforce individual learning
prowess. Studies by Aguinis (2009) and Kirkpatrick (2016) demonstrate the relevancy and value
of training programs for organizations, yet when something is emergent and no training
programs yet exist or are just becoming available, how to approach that deficit can impact an
organization’s future standing.
For U.S. food industry executives seeking to bridge that dearth of information, efforts at
SDL may not come easily or effectively (Loyens, 2008). In a 2019 study, Markant determined
that self-based hypothesis’ can introduce a bias that can cause one to either not learn relevant
information or disregard what is learned in a self-learning environment. Markant (2019) stated
“Self-directed learning may be limited not because people do not know how to test their
hypotheses, but because they fail to consider the right kinds of hypotheses in the first place”
(p.1565). The impact of complex environments was further elaborated: one can overlay the
intricacies of the Industry 4.0 changes and note the difficulty for U.S. food industry food
47

executives as they grapple with these challenges in a completely unstructured self-learning
environment.
In reviewing the efficacy of SDL, Bjork (2012) found that personal behavior is key to
developing effective learning. A student—or potentially, a food industry executive—must
understand that an ability to immediately recall a fact or perform a task does not mean that
knowledge is embedded in long term memory for subsequent retrieval. Repetition and spacing of
the learned information are key to future retention and appropriate retrieval. Bjork, and Dunlosky
(2011) posited that the willingness to monitor learning through acquisition of knowledge and
accepting mistakes in that process, then adapting from them, is key to long term self-learned
success. This aligns with the Bandura (2011) SCT findings on self-efficacy and the need for
acknowledgement of learning ability—not just self-perception of innate intelligence.
Not all academic approaches to SDL are affirming, however. Hepper (2010) reviewed the
frailties of individual ego as it pertains to comfort level with associations, knowledge areas, and
self-serving attitudes that can affect one’s biases and openness to both feedback and information.
This tendency to self-enhancement can negatively impact the reality of one’s self-efficacy.
With the tendency to self-enhancement in mind, Kirschner (2006) determined that formal
and directed instruction, as opposed to SDL, achieved the best results for learners. Additionally,
the ability of individuals to approach learning was higher, particularly for complex or problem-
based tasks. The study posited that SDL is highly influenced by the biases and ill-informed
beliefs of that individual: without formal and directed instruction that minimizes these
influences, effective learning is less achievable. Kirschner’s study did acknowledge that some
individuals, because of specific and mature knowledge in a particular area, may have sufficient
intuitive knowledge to overcome these limitations. Schmidt (2007) adopted a contrary position
48

that complex concepts, or problem-based learning, can occur in less structured environments.
When the learners approach tasks in a scaffolded manner—one that is relative to their prior
knowledge that is being built upon, and that flexibility and awareness of cognitive load are the
keys to the effective learning experience.
Innovation, Social Cognitive Theory, and Self-Directed Learning
Innovation is a hallmark of business improvement and societal benefits that thus arise
(Hong, 1999). When innovation occurs, and businesses seek to either emulate or adapt to it, it
requires decision makers to reflect, learn, adapt, and implement. Gureckis (2012) determined that
“the very act of planning interventions or deciding which information to collect may necessitate
a more thorough evaluation of the problem structure” (p. 470). In the absence of academic
research due to the novel innovation, an executive’s self-efficacy, or adoption of a self-directed
agentic perspective, becomes the basis for pursuing the SDL reflection such decisions may
require (Bandura, 2011).
The effectiveness of an innovation can completely transform an industry requiring other
businesses in that industry to adapt their own operations or service offerings to meet competitive
pressures. That adaptation within those other organizations occurs as a result of learning from the
actions of the competitor before sufficient time has elapsed for academic appraisal (Lieberman,
2006). An example of a transformative mechanical innovation is the use of the jet engine for
commercial air service (Cohen, 1957). As one or two airlines adopted this innovation, others
were required to both emulate the technology to keep customers, and to learn how to implement
the technology. Early transatlantic airplane trips were fifteen or more hours in piston driven early
airliners (Cummins, 2019). The jet engine made airplane transit from New York to London a
journey of less than nine hours; when introduced this was further transcended by the Concorde
49

supersonic jetliner which could do the same trip in one third the time (Cookson, 1976). This
innovation of jet travel redefined the entire commercial airline industry both operationally and
from a standpoint of consumer expectations; affected businesses were compelled to adapt all
facets of their operational and passenger experience (Cohen, 1957; Cookson, 1976; Janic, 2020).
Such change prompted the affected executives to embrace new knowledge and business practices
and do so with immediacy devoid of academic assessment (Cohen, 1957; Cookson, 1976)
aligning with the SCT initiative of becoming a self-directed agent. As the jet-age and Concorde-
age were initially immediate and transformational to airline innovation and the airline industry
paradigm, industry executives of the organizations directly affected adapted while others learned
and changed as needed (Cookson, 1976).
While contrasting jet engines and airline travel to food is quite dissimilar, the common
thread within both sectors is the recognition by the responsible executives of either the
opportunity or the need to respond to competitive pressures by utilization of self-directed
learning or other learning systems (Rana, 2016). U.S. food industry companies have offered
unique initiatives which required competitive response. These innovations, whether in
marketing, internal operations, or product offerings—initiated by one company—have triggered
competing food companies to adapt, emulate, and learn to implement their own responses.
Adoption of Total Quality Management (TQM) methodologies by U.S. food industry firms such
as RJR Nabisco, Anheuser-Busch and PepsiCo lead to increases in capabilities and profitability
that caused other companies to take notice and initiate TQM changes to stay competitive on
operational costs and practices (Staw, 2000). Similarly, in a study of soft drink firms in Asia,
Asaba (1999) determined that both small and large companies followed each other’s product
introductions to retain market share as customers trialed differing product taste profiles. United
50

States beverage giants Pepsi and Coca Cola have had similar battles for new product
introductions, competitive marketing and pricing strategies, and synergies with other food
product types and distribution networks (McKelvey, 2006).
These fast-paced changes caused the executives in each company to learn, emulate, then
implement counterstrategies in very short order (McKelvey, 2006; Yoffie, 2009). However, the
pace of change and availability of information driving innovation and competitive responses
during the last fifty years since the advent of the third industrial revolution pales to the amount of
data and pace of change brought on by Industry 4.0 (Carillo, 2017). In an extensive literature
review of how dynamic capabilities such as knowledge acquisition, product and process
development, and reflexive control initiatives are demonstrated, Beske (2014) confirmed the
continuous effort of differing U.S. food companies to keep up with, or try to gain advantage,
over their competitors.
Whether leading innovation or reacting to that of others in the U.S. food industry, timely
and effective responsiveness requires flexibility by decision making executives (Nesbit, 2012;
Rana, 2016; Wirth, 2018). Innovation, adaptation, and learning to new environments is essential
if industry executives are going to assure their organizations are not left behind (Bennink, 2020;
Müller, 2018). One U.S. food industry corporate leader, ConAgra Brands CEO Sean Connolly,
holds that operational capability changes for speed to market and consumer expectations are the
keys to innovation and are the crucial drivers of future success (Cao, 2017). Taking the
theoretical framework of Bandura’s SCT—the acknowledgement of individual ability to address
individual, personal, and behavioral factors to choose and affect a course of action—one can
build confidence and self-belief in the outcome. That self-belief and resulting self-efficacy can
reinforce each attribute and lead to the ability to address unforeseen situations (Bandura, 2016;
51

Bjork, 2012). It is an emergent time of industry change brought on by Industry 4.0 for U.S. food
industry executives. With the speed of change out-pacing formal academic preparation, these
executives are faced with confronting business situations and innovations utilizing both their
formal preparation and their self-efficacy to utilize SDL and other learning tools. A means of
how that may be occurring is addressed with a conceptual framework that identifies the key role
of SDL and other learning tools.
Conceptual Framework
The research on self-directed learning demonstrated that an individual can learn new
information and skills, with or without specific organizational directive, when the organization
environment supports the effort. Developing new knowledge and skills gives the individual an
ability to better address the needs of the organization’s stakeholders and in the absence of a
formal academic environment, may require self-directed initiative. The conceptual framework
for this study extrapolates from the theoretical model of Social Cognitive Theory (Bandura,
2011), data-driven decision making (Picciano, 2012), and self-directed learning (Bjork, 2012;
Dunlosky, 2011). The purpose of this study was to examine how the participating U.S. food
industry decision-making executives are engaging in SDL and other forms of learning through
formal or informal activities to acquire tools to address current or future escalating Industry 4.0
business challenges. Aligning with an approach that examines SDL through the lens of SCT,
Parker (2010) determined that individual inspiration is central to and meaningful accountability.
Performance and accountability can be linked to future-oriented identities; these also align with
the perspective of Augier (2007) and the need for executives to assure self-relevance.
These areas of awareness leading to performance improvement efficacy and subsequent
actions directing accountability guide the three key concepts for this study, which shape the
52

conceptual framework. The first concept is exploring the perception of food industry executives
concerning the changes to the food industry as brought on by Industry 4.0. The second is
discovering the heuristic perceptions of these executives as to why—or why not—it is important
to respond to these changes. And third, is understanding the manner these executives
individually choose to address their own need for new capabilities and knowledge considering
the industry changes, with contemplation of future change yet identified. These three ideas frame
the conceptual model that encompass industry awareness, impact to one’s business, and the
learning actions taken. The conceptual model, referred to as “Awareness and Impact Utilization
through Self-Directed Learning and Other Learning Methods” is depicted in Figure 3.

Figure 3
Awareness and Impact Utilization Through Self-Directed Learning and Other Learning Methods

53

The Awareness and Impact Utilization Through Self-Directed Learning and Other
Learning Methods Model
Awareness and impact utilization through SDL and other learning methods as a
conceptual framework has three stages. The first stage of the conceptual model, awareness of
general industry developments, refers to the general knowledge that is held by a singular
individual or organization of what is occurring in the U.S. food industry from an overall industry
standpoint. This aligns to the external environmental factors of SCT (Bandura, 2011) that shape
an individual’s awareness of associative circumstances. As changes and events drive industry-
wide response, knowledge gaps and response requirements of what is occurring within the U.S.
food industry emerge. Events, such as those prompted by Industry 4.0, affect executive
understanding of the macro-level impacts to the industry, which is the first part of self-efficacy.
Recognizing and acknowledging that changes are occurring becomes the first step of the
conceptual framework.
The second stage, realization of impact on specific organization, refers to the issues and
opportunities recognized as affecting a singular individual or organization. In the case of a U.S.
food executive, it refers to their specific business. Each individual organization is unique. This
aligns to the internal personal factors of SCT (Bandura, 2011) that shape an individual’s self-
efficacy, among other factors, to respond to a specific circumstance. The roles of U.S. food
industry executives are differentiated by function, level of responsibility, and level of authority.
Upon assessing the macro-level impacts to the industry, the individual executive assesses the
micro-level impact to their specific level of responsibility (Melone, 1994).
When the impact is outside the standard knowledge level of the organization, bridging
that knowledge gap must be accomplished whether through formal or informal means. For novel
54

areas, individual self-efficacy to make decisions about SDL may occur. Selecting appropriate
SDL supports closing that knowledge gap. The individual U.S. food executive, having assessed
the micro-level impacts to their functional area, utilizes SDL to develop an action strategy.
The third stage, action taken due to utilization of information, refers to the actions taken
by a singular individual or organization in response to the first and second leg impacts. Prior to
taking actions it is presumed that decisions are achieved after reflective discovery, supported by
SDL activities. This aligns to the behavioral factors of SCT (Bandura, 2011) that shape an
individual’s response and action. That strategy or decision action plan, developed from the
macro and micro knowledge awareness, then assessed through SDL initiatives, is implemented
for the organization.
The Conceptual Framework and Role of Organizational Emphasis and Sound Judgement
Awareness and impact utilization through SDL and other learning methods as a
conceptual framework, suggests that U.S. food industry executives understand and effectively
respond to their business demands, utilizing SDL as a means of closing knowledge gaps. One
area that is an unknown factor related to the efforts and results of U.S. food executives to resolve
Industry 4.0 related issues is the role of the executive’s organization to create or support a
learning environment. When such an organizational direction is not present, how individual
effort and organizational result is aligned will be due to the motivation of the individual
executive or decision maker (Fleming, 2017; Liguori, 2018; Tseng, 2013).
The need for organizations to promote learning situations (Kirkpatrick, 2016) may be
broadly acknowledged as a positive or even expected environment, but individual organization
efficacy to that end creates differing situational foci for individual executives (Stubbart, 1989).
How organizations balance the need to achieve and the need to have learning cultures can impact
55

the motivation of individual executives to respond to Industry 4.0 challenges. While the
challenges of the present are immediate and primary executive considerations (Dubnick, 2011;
Melone, 1994) it is asserted by Bennink (2020) that responsible executives should also address
future impacts of their decisions or decision paths as considerations. Bennink writes “Decisions
to innovate are complex, containing many moral issues. It involves processes including problem
analysis, preliminary design, simulation, evaluation, decision, marketing, production, and
launching.” (p.328). How an organization emphasizes support of, or even requires, a learning
environment (Ebrahim, 2005), can impact the direction of individual executive leadership and
sound judgement decision making that addresses current issues, but also contemplates future
impacts for all stakeholders.
Self-efficacy, demonstrated by SCT and subsequent studies on Bandura’s research, places
the key on the ability of U.S. food executives to recognize knowledge gaps and the confidence in
their ability to address them. Executives must then use sound judgement and decision processes
to successfully move their organizations forward in a period of business uncertainty, dramatic
business transformations, and unpredictable change in an absence of formal educational
resources (Kyvliuk, 2019). Awareness and impact utilization through SDL becomes a relevant
tool. Singh (2019) posited that U.S. food industry executives and their teams address
stakeholder requirements in circumstances of speed of change that are unlike any their
predecessors have confronted. Stubbart (1989) wrote of business executives,
The rationality of managers is often limited, their knowledge often incomplete, and their
attention often overloaded. Yet simultaneously, many managers are skilled at strategy
making, adept organizational experts, and ingenious innovators. Nor do managers all
think alike in terms of their vision, expertise, risk-profiles, motivations, or goals. (p. 326)
56

Stubbart’s assertion aligns with SCT (Bandura, 2011) and the awareness and impact utilization
conceptual framework of this study.
U.S. food industry executives deal with a variety of changing conditions, identifying the
need to model behaviors from an agentic perspective and capability. Stubbart emphasized that
there were enablers for executives to realize new concepts and achieve sound decision making or
innovations. These enablers included a need for business executives to deliberately take the time
to think, to use their sound judgement and cognitive skills, then combine those with a reliance on
their practical experiential knowledge. This cognitive ability leads to sound decision making,
which leads to self-efficacy. This self-efficacy is shaped by a variety of personal and
environmental factors that in turn influences subsequent behaviors. When viewed through the
conceptual framework, the U.S. food executive utilizes the enablers identified by Stubbart to
assess macro level information and micro-level impacts, reconcile knowledge gaps to develop
courses of actions, then implement decisions.
Summary
The U.S. food industry has faced constant innovation and new concepts to create,
manufacture, and distribute its products from the First Industrial Revolution until current times.
The Fourth Industrial Revolution (Industry 4.0), which is still unfolding, saw the rise of Artificial
Intelligence and robotics to transform all facets of modern human interaction. The U.S. food
industry is seeing increased competitive pressures to the technologies and capabilities made
possible by the emergence of Industry 4.0. With each generational change in food industry
capabilities, the executives running those organizations have been confronted with the need to
develop new levels of knowledge. However, breadth of Industry 4.0 has outpaced formal
57

academia and impacted the U.S. food industry with complexities and speed of utility that are far
beyond anything previously encountered.
The pertinent literature addressed the history of transformation of the first three phases of
the Industrial Revolution, and the manner of how Industry 4.0 changes are currently affecting the
U.S. food industry. It explored the research on self-directed learning and studies that suggest
how executives approach SDL. The research looked at the effectiveness of SDL to promote and
achieve improved individual and organizational self-efficacy. Lastly, the literature review
explored the relevancy of Social Cognitive Theory as the basis for the overarching theoretical
framework, leading to the conceptual framework of awareness and impact utilization through
SDL. This conceptual framework and model provides the basis for examining how U.S. food
industry executives may be developing new—if any—SDL actions to meet emergent industry
challenges such as those resulting from Industry 4.0.

58

Chapter Three: Methodology
This study examined how U.S. food industry executives are engaging in self-directed
learning. This chapter presents the research design and methodology utilized to collect and assess
data related to SDL among U.S. food industry executives. A semi-structured interview format
conducted with 13 U.S. food executives was utilized to support a thematic analysis of how SDL
or other learning types are or are not pursued to gain Industry 4.0 related capabilities. It was
unknown how these executives were formally or informally acquiring effective and timely tools
to address escalating business challenges brought on by Industry 4.0. It was determined from the
participants that the manner individuals or organizations approach new information, specifically
related to Industry 4.0, can be complex. The manner of how choices are made to learn,
understand, and make use of opportunities derived from new information has been broadly
studied in a number of fields (Bandura, 2011; Müller, 2018). Yet, within the context of the U.S.
food industry, little formal study has been undertaken to determine the context, methods, or
manner of use of new Industry 4.0 information for individual or business organization impacts,
and whether it was obtained through formal academic applications or self-directed efforts.
Research Questions
How U.S. food industry executives acknowledge awareness of overall industry trends, the
impact of those trends on individual organizations, and actions taken by decision-making
executives in response to those impacts were the focus areas of the research. Self-directed
learning or other learning methods and formal-academic institution attendance were identified as
being utilized by these executives to address knowledge deficits for informed decision making.
The following questions guided the study:
1. How are U.S. food industry executives responding to change created by Industry 4.0?
2. How does the food industry organizational environment impact food industry
59

executives’ knowledge and motivation related to responding to change created by
Industry 4.0?
3. What role do U.S. food industry executives see for either self-directed learning or other
forms of learning in response to Industry 4.0 and beyond?
Overview of Methodology
The methodological manner used a semi-structured fully anonymous interview approach
(Creswell, 2018). The qualitative, versus the quantitative or mixed methods approaches, was
utilized as this method best provided subjective perspectives from the participants that are rich
and detailed. As the probable information from the participants was not yet categorizable due to
this topic as an emergent area of interest, a grounded-theory approach was utilized. The semi-
structured interviews allowed a script of pre-determined narrative questions, derived from
Patton’s (2002) six types of interview questions, that were asked to all participants. This allowed
for clarifying, probing, and other exploratory avenues to also be incorporated, adding depth and
context to each participant’s perspective as well as aiding in establishing credibility and
trustworthiness (Merriam, 2016). The high level of professional expertise of the target
participants also created additional directions of inquiry not initially anticipated.
Thirteen participants were interviewed. These participants were drawn from the executive
groups of various U.S. food industry companies. A series of 13 questions, listed as part of the
interview protocol in Appendix C, was used with each participant. Additional elaborative and
probing questions as follow-ons were also used as the researcher deemed it appropriate to the
interview context.
At the time of the research study development, as shown in Table 1, it was unknown if
there would be any documents or artifacts offered by the participants or indicated through
60

interviews that would benefit this study. The potential for such documents or artifacts was
anticipated. However, none were identified.

Table 1
Data Sources
______________________________________________________________________________
Research questions Interview Document and
artifact analysis*
______________________________________________________________________________
How are U.S. food industry executives
responding to change created by Industry 4.0? X X*

How does the U.S. food industry organizational
environment impact food industry executives’ X X*
knowledge and motivation with respect to responding
to change created by Industry 4.0?

What role do U.S. food industry executives see for
self-directed learning or other forms of learning in X X*
response to Industry 4.0 and beyond?
______________________________________________________________________________

Note. *Document and artifact analysis was not conducted as determined by outcome of

participant interviews.

61

Data Sources
Interviews
The data was collected from the targeted participants utilizing initial, follow-on, probing,
and elaborating questions in a recorded, semi-structured format. Thirteen participants were
interviewed. The initial and probing questions are listed in Appendix C. Interviews lasted 45-60
minutes, were recorded with consent for accuracy of transcription purposes and are confidential.
Interviews were not conducted until approval from the Institutional Review Board (IRB) was
received.
Participants
This study was conducted as a qualitative study based upon interviews with participants
who make executive decisions for their organizations and based upon the impacts to the U.S.
food industry driven by external factors such as Industry 4.0. Executive level responsibility was a
specific criteria for the targeted participants of this study. These individuals were drawn from
differing segments of the U.S. food industry types of organizations. The food industry
encompasses a broad spectrum of industries that are privately owned or publicly traded. These
range from ingredient developers and manufacturers to food processing and manufacturing
companies that develop, manufacture, then distribute the United States food supply.
Additionally, there are varied retailers for both direct consumer consumption with restaurants or
cafeterias, or grocery shoppers for later use.
The participants were 13 U.S. food industry executives. Job titles for these executives
included the following: director, vice president, chief marketing officer, chief human resources
officer, chief executive officer, and president. What each executive potential participant had in
common is that they were decision making, or direct influencing, leaders of their respective
62

organizations. Additionally, they set the direction for their businesses as they individually
responded to industry events. Each had a differing perspective on how to execute organization
responsibilities, plans, and activities based on respective role viewpoints, which aligned with the
research by Melone (1994) on executive perspective differences based upon functional
responsibility. These food industry executives set the tone for the daily, tactical, and strategic
directions of their respective organizations to achieve short-term and long-term plans, and profit
objectives.
Potential individual participants were identified through U.S. food industry organizations
such as the Food Marketing Institute and the North American Meat Institute. Other potential
participants were identified through LinkedIn professional contacts. One-to-one solicitations
were sent to 47 individual executives from differing types of U.S. food industry organizations
with 16 executives expressing initial willingness to participate in the study, and 13 executives
actually participating.
Instrumentation
As the researcher for the study, I was the primary instrument for data gathering utilizing
electronic recoding for future transcription. A semi-structured interview method was used
(Creswell, 2018) to assess the sentiment of the participants to the impact of issues affecting the
U.S. food industry, such as Industry 4.0. The interviews also probed for the participants’ means
of response to bridging Industry 4.0 knowledge gaps and their individual and organizational
utilization of varied learning methodologies such as SDL. The protocol for the interviews
questions was adapted from a reported interview with Dr. Thomas Malone of MIT who posed
questions on the consequences of business executives not understanding the changes around
63

them and the impact of Industry 4.0 (Hopkins, 2010). The interview questions were also
informed by the conceptual framework.
The instrument developed for the study included the interview schedule with the
participants and the interview questions (Appendix C). An initial beta of the interview questions
was conducted with pilot interviews of two U.S. food industry executives as part of a doctoral
level research methods course. Data from the beta interviews was not included as a part of the
study. Feedback from those interviews, along with dissertation committee feedback, identified
changes needed to the questions for the subsequent study participant interviews (Merriam, 2016).
Data Collection Procedures
Accurate data collection is essential to determining the broad sentiment of the
participants to utilization of self-directed learning. I conducted all participant interviews using
Zoom meetings. Additionally, I oversaw the transcription of the interview recordings.
To secure interview participants the following method was used. Participants were
selected in a two-stage manner. Stage one involved a one-by-one solicitation of general interest
to participate in the study to 47 U.S. food industry executives identified through professional
associations who met the criteria described in the Participants section above. This was in the
form of a one-to-one email, identifying that a study was being conducted to determine how
changes to the food industry brought on by Industry 4.0 are affecting industry executives. It
stated confidentiality and manner of conduct (Zoom meeting), and an offer to share findings to
the participants. Sixteen executives indicated an initial interest to participate in the study. This
email format is shown in Appendix A.
Stage two involved ensuring the potential participant respondents—by type of
organization represented (size, public, private, type of sub-industry)—was sufficient to provide a
64

broad overall industry representation. The sixteen executives who indicated interest in
participating met this overall-industry representation goal. A notification email was sent to those
individuals requesting to set up the formal date and time for the interview, including a statement
of their rights to confidentiality, to refrain from answering any particular question, and to opt out
of the study at any point, which is reflected in Appendix B. Interviews were subsequently
scheduled based upon participant availability with 13 individuals confirming participation.
Interviews were recorded for transcription and analysis.
Participants were interviewed remotely from their respective office locations across the
United States. Data was captured via audio recordings of the interview for further transcription.
None of the participants declined to be recorded, and the researcher additionally took notes
during the interview. Scheduling for interview appointments was respectfully based upon the
availability of the participants.
The interviews lasted 45-60 minutes and were broken down into the following sections:
1. Introductions and restatement of the purpose of the interview.
2. Affirmation of participation willingness, the ability to ask questions at any time, and
to discontinue the interview at any time.
3. A re-confirmation of confidentiality and permission to record the conversation for
future review.
4. Conduct of the interview questions as outlined in Appendix C.
5. If offered by the participant, or it is appropriate to request, confirmation of how to
receive any documents or artifacts.
6. Conclusion of the interview with solicitation of participant questions to me.
65

7. Conveyance of gratitude to participant and request for possible follow-up questions if
there is any clarification of responses needed due to transcript analysis, or if
documents or artifacts are offered.
Data Analysis
This was a qualitative study based upon the data gathered from participant interviews.
Merriam (2016) stated that coding and identifying themes from the data is the means to having a
proper analysis to answer the research questions. Following the completion of the interviews the
recordings were transcribed. Those individual transcriptions were assessed individually for
relevancy, clarity, depth, and participant perspective Transcripts of the interviews were used to
begin coding the data. Coding and categorization were linked to variables of interest expressed in
the research questions and the study’s conceptual framework. Researcher reflexivity, which
refers to the reflection I gave to the participant responses to assure that biases or
misunderstanding, did not taint my interpretation of participant responses and was the primary
method of addressing researcher biases and assumptions (Creswell, 2018; Gibbs, 2018). No
requests were received from participants to conduct transcript reviews. The data gathered was
assessed utilizing the Creswell (2018) six steps method. Those steps are as follows:
1. Organizing and transcribing interviews and other document data (if any).
2. Identification of major themes.
3. Organization of the data by identifiable themes.
4. Generate detailed descriptions from the identified themes.
5. Create a qualitative narrative of the participants’ perspectives.
6. Interpret the data for conclusions, association to the research questions and prior
literature, and suggest further study queries.
66

Credibility and Trustworthiness
Veracity of the participants was key to the study; the researcher has the responsibility to
gather detail from numerous participants to establish credibility (Merriam, 2016). Utilization of
participant direct quotes when appropriate to reflect dependability was utilized in reporting the
study findings (Eldh, 2020; Gibbs, 2018; Labuschagne, 2015). It was expected that using the
questions identified in Appendix C with each participant would also authenticate the credibility
of responses. However, as this is a qualitative study and there was no working hypothesis to what
participants may state in regard to the utilization of SDL or other learning methods, the potential
for the study to reveal many and varying perspectives did occur.
The Researcher
I am a United States business executive who is the owner and CEO of an operations-
focused consulting group in Nashville, Tennessee. During the research phase of this dissertation
I was employed by a privately owned food manufacturing firm in Nashville, Tennessee as its
vice president of operations and prior to that role I was division vice president for a multinational
food manufacturing firm founded in Mexico, leading the U.S. divisional headquarters in the
Oklahoma City, Oklahoma area. My business career has been principally in the food industry
after an initial term of service as a commissioned officer in the United States Army following
undergraduate university. I earned an MBA at the age of 55, much later in my business career.
My privately owned and publicly traded corporate business career has focused principally
on operations and general management leadership roles from supervisory to executive levels.
The study participants work within my food industry field. I have had past direct business
associations with some study participants as either a peer, direct report, or co-worker. With some
others I have enjoyed an indirect association due to the relatively small nature of the food
67

executive community, and with others I have had no prior interactions before this research
project. None of the participants have a reporting relationship to me in the business unit for
which I am responsible, or the firm I was with at the initiation of the study. However, it is
possible that a power relationship given social capital relationships (Seibert, 2001) as either a
customer or employer of anticipated study participants, while unlikely, is possible and was
respected.
My biases extend to a desire for business improvement and learning as usually
demonstrated in the direct and task focused traditional United States model, or U.S. work culture.
I recognize that “culture” is, in itself, an oftentimes pejorative perspective that can mask the
ability to perceive other points of view (McDermott, 1995). I define the U.S. model as customer
focused, employee based, for-profit, with an emphasis on integrity, reliability, and fast but
deliberate responsiveness to market demands. I have a personal quest for knowledge, and I am a
lifelong learner. I recognize that not all with whom I work, or who participated in this study,
have the same perspective based upon their own business experience or as influenced by their
personal positionality. This study is being pursued with a simple desire to learn what others do
related to self-directed learning in the food industry. What, in the simplest of pragmatic terms,
works (Bryant, 2009; Lerner, 2003; Pierce, 1905)? Accordingly, I am not approaching this
research from other than a pragmatic perspective relative to positionality questions of gender
identification, ethnicity, ageism, or nationality. However, further research would be appropriate
based upon those stratifications to see if any differences emerge. To mitigate potential biases and
assumptions, I am approaching this study with a complete dedication to seeking viewpoints
different from my own and diligently paying attention to all viewpoints to assure that I do not
overlook or dismiss them.
68

Ethics
The guidelines for ethical research of human subjects as defined by the University of
Southern California’s Institutional Review Board (IRB) were followed for this study.
Participants were not compensated but will receive a report of the study findings following
approval of this dissertation by the dissertation committee. Participants received an email from
me detailing the purpose of the study, the manner it would be conducted via a synchronous
platform such as Zoom or Teams, and their confidentiality and right to decline further
participation and withdrawal from the study at any time (Lo Iacono, 2016; Merriam, 2016). The
participants were further reminded of their ability to not participate further at the initiation of the
recorded—with permission—interview sessions (Nespor, 2009). As an addendum to that email,
the participants received a copy of the information sheet for exempt research (IRB Template
Version: dated 1/30/2021) that had been approved for this study, shown in Appendix B. While I
am a part of the broad U.S. food industry, I am not in a leadership or business to business
position for any of the participants.

69

Chapter Four: Findings
The purpose of this qualitative study was to examine how food industry executives are
engaging in self-directed actions to acquire tools to address current or future escalating Industry
4.0 business challenges. These actions included self-directed learning (SDL) and other learning
methods through formal or informal activities. Self-directed learning presumes the individual
takes on an agentic perspective in accordance with Social Cognitive Theory (SCT; Bandura,
2001) to acquire new knowledge, skills, or capabilities. As SCT serves as the theoretical
framework for this study, the research questions explored how participants were responding to
Industry 4.0, whether the participants were assuming agentic self-responsibility as it related to
the impacts of Industry 4.0: and if so, how were they acquiring new knowledge, skills, or
capabilities relevant to their respective organizations. The study identified two emergent findings
that influenced the overall study. There were seven key themes also identified that addressed
these three research areas, which align to the specific research questions. Additionally, many of
the participants echoed sentiments that aligned with the conceptual framework and model
discussed in Chapter 2.
These diverse observations and approaches concerning the impact of Industry 4.0 on the
U.S. food industry and the participants’ individual organizations had many similarities but some
specific differences. The participants spoke to how they were addressing company-
transformational challenges, the role of self-directed learning each was utilizing—or not
utilizing—to gain the expertise to close knowledge gaps, and technology benchmarks that were
affordable for some companies, but not yet for others. As these areas were echoed by multiple
executives, these areas evolved into the themes that shaped the study findings.
70

The conceptual framework premised industry executives’ awareness of the impacts of
Industry 4.0 on their respective organizations, leading to finding appropriate new knowledge to
focus future action. This premise was reinforced during the interviews with the responses to the
research questions that framed the study:
1. How are U.S. food industry executives responding to change created by Industry 4.0?
2. How does the food industry organizational environment impact food industry
executives’ knowledge and motivation related to responding to change created by
Industry 4.0?
3. What role do U.S. food industry executives see for either self-directed learning or other
forms of learning in response to Industry 4.0 and beyond?
The study was conducted with 13 participants employing individual virtually conducted
semi-structured interviews. When cited or referred to directly these participants are referred to as
P1, P2, P3…P13. These identifiers were chosen to provide participant confidentiality for both
their individual information as well as the organizations they represent.
Thirteen primary questions, with additional probing and follow-on questions, were used
to address the three research questions (Appendix C). Using the methodology outlined in Chapter
3, the content of the interviews was analyzed to develop the key themes identified as they related
to each research question. As this was an inductive, qualitative study to determine the sentiment
of the participants, there was no pre-conceived hypothesis of what the participants may or may
not be doing to change or enhance their individual knowledge and capabilities in response to
Industry 4.0 challenges. However, several key trends emerged from the participant interviews
that indicate diverse responses and approaches to Industry 4.0 challenges and opportunities.
71

This chapter will first review the material-background of the targeted participants. It will
then present the emergent findings and research question findings. Themes identified for each
research question will be further discussed as reinforced by the participants’ perspectives. Lastly,
the chapter will conclude with a summary of the study findings.
Participant Background
The participants for the research study were all individuals employed across a broad
spectrum of U.S. food industry companies. None of the participants worked for the same
company. Each is an executive with their specific firm. These individuals serve as the principal
decision makers, or influencers, for strategic direction setting within their respective food
industry organizations and had unique and pertinent insights on the research questions.
Participants Solicited and Interviewed
Individual executives were solicited for participation in the study. Forty-seven executives
who met the criteria of serving as key decision makers or decision influencers for their respective
organizations outlined in Chapter 3 were invited to participate in interviews. Of the 16
executives who initially accepted the request to participate in the study, three decided not to
participate for reasons unknown. Thirteen participants were interviewed. As the original study
goal of 10 to 15 participants was met, additional solicitations to increase the number of
participants were not undertaken. A breakdown of solicitation and participation is shown in
Table 2.

72

Table 2
Characteristics of Participation (N = 47)
______________________________________________________________________________
Participants n % of N
______________________________________________________________________________
Persons solicited 47 100

Persons accepted 16* 34

Persons interviewed 13 28
______________________________________________________________________________

Note. *Non-participants (3) initially accepted but did not respond to follow-on emails to finalize
an interview time.

Executive Position Breakdown of Participants
The 13 executives who participated in the study comprised a broad cross-function of
roles, position titles, educational backgrounds, and experience. Over half were the senior leaders
or C-suite executives for their respective organizations (Table 3). As the study was conducted in
such a manner as to protect both participant confidentiality and the organizations each
represented, the background information is presented in a generic fashion. Individual U.S. food
industry experience levels ranged from five to 30+ years as indicated by public-domain
information available for each participant on LinkedIn. Three of the participants were female,
two were of Hispanic heritage, and all held undergraduate degrees in business or engineering
related fields. Two participants additionally held master’s degrees in engineering fields, seven
had a Master of Business Administration, and two of those with an MBA also held a doctorate.

73

Table 3
Executive Position Breakdown of Participants (N = 13)
______________________________________________________________________________
Position title n % of N
______________________________________________________________________________
President-CEO-COO 5 38

Other C-Level position 2 15

Vice president 5 38

Director 1 8
______________________________________________________________________________

U.S. Food Industry Types Represented and Type of Ownership
The participants represented a broad range of companies that fall within the U.S. food
industry (Table 4). All the participants were associated with for-profit organizations. These
organizations comprised both publicly traded companies and privately owned companies. Sales
of the companies range from $10 million to multi-billion $USD in annual sales, and employee
levels of 100 to greater than 50,000. Five of the sub-industry companies are related to
operational support in areas associated with IT infrastructure, logistics, retail, and food
manufacturing facility operations. The other eight sub-industry company types create,
manufacture, sell and distribute processed foods, baked goods, beverages, meat-protein products,
and other food items that are common to the food service, convenience store, grocery, and
restaurant points of sale.

74

Table 4
U.S. Food Industry Types Represented (N = 13)
______________________________________________________________________________
Type n % of N
______________________________________________________________________________
Food Sales, marketing, and manufacturing 8 62
(protein, beverage, processed, baked goods)

Food industry support companies 5 38
(sanitation, IT, logistics)
______________________________________________________________________________

An additional breakdown of the participants is the corporate ownership type related to
their organizations (Table 5). Seven organizations were private or venture capital owned (PVC),
and six were publicly traded (PT). Executives in each group often characterized their individual
responses to Industry 4.0 issues with a personal to organization caveat. Their personal thoughts
as to relevance of response to Industry 4.0 issues was tempered by the attitude of their corporate
ownership. According to some participants, some PVC organizations, with a keener focus to
short term profitability, were less inclined to invest in Industry 4.0 activities due to the expense
and unknown abilities to forecast return on investment. All PT organizations contrasted with a
more deliberate awareness of Industry 4.0 impacts, and due to a longer-term profitability focus,
were more likely to make internal development investments for individuals and teams for
Industry 4.0 responses such as in self-directed learning per the interview data.

75

Table 5
U.S. Food Industry Types Represented (N = 13)
______________________________________________________________________________
Type n % of N
______________________________________________________________________________
Private or Venture Capital Owned (PVC) 7 54

Publicly Traded Corporations (PT) 6 46

______________________________________________________________________________

Emergent Findings

Two emergent findings were noted that shaped the overall study. One finding concerned
the type of company ownership and how company ownership influenced SDL and Industry 4.0
behaviors by the participants. The other finding concerned participant self-perception of
capabilities related to SDL and bridging Industry 4.0 knowledge gaps. These two findings
influenced the seven themes that are discussed with each specific research question.
The first emergent finding is that the participants’ actions on behalf of their organizations
were influenced by the type of ownership of their organization. The participants from publicly
traded companies were generally more focused on utilization of Industry 4.0 capabilities from a
high investment, long term, and sustainable perspective. Participants from private or venture
capital group (PVC) owned organization stated their companies did not invest in Industry 4.0
activities at the level each participant would prefer. Participants from the PVC organizations
stated their business decisions minimizing most investment for Industry 4.0 activities, per their
corporate ownership guidance, did not reflect their personal sentiment concerning SDL or
Industry 4.0 to invest more in both technologies and training.
76

The second emergent finding was a universal admission by all participants that the
breadth of expertise needed for learning and mastering the extent of Industry 4.0 capabilities was
far too great for any of them to grasp as individual experts. The participants stated this inability
to master Industry 4.0 necessitated internal development of team members or hiring of key
employees with a specific Industry 4.0 capability. These executives stated that, through using
SDL and other learning methods, they had achieved a limited generalist knowledge of Industry
4.0 that was adequate to oversee their responsibilities, but it was inadequate to be the primary
subject matter experts for their respective organizations.
Research Question 1: How Are U.S. Food Industry Executives

Responding to Change Created by Industry 4.0?

Participants were asked a series of questions that tied to RQ1. The two themes that
emerged were responding to Industry 4.0 changes “tentatively and deliberately,” and “Amazon is
the benchmark.” Each theme is discussed in detail, with evidence, in the sub-sections.
A categorization of the responses to RQ1 was made of the participants’ sentiment to the
degree of response to industry change created by Industry 4.0. From this categorization of
responses to RQ1, nine of the 13 participants indicated that they, as individuals, were either very
responsive or somewhat responsive to the changes posed by Industry 4.0 on their respective
companies (Table 6). Four participants indicated they were either aware, or aware but indifferent.

77

Table 6
Categorization of Research Question One Response (N = 13)
______________________________________________________________________________
How are U.S. food industry
executives responding to change
created by Industry 4.0? n % of N
______________________________________________________________________________
Very responsive 3 23

Somewhat responsive 6 46

Aware 2 15

Aware but indifferent 2 15

Unaware 0 0
______________________________________________________________________________

All participants indicated that their awareness and subsequent level of action was also
influenced by corporate environmental factors that are discussed in the section on RQ2.
Participants also detailed the manner they were using individually, and the methods they were
recommending to their teams, to learn. These items, whether SDL or other learning methods, are
discussed more specifically in the section on RQ3.
Executives Tentatively and Deliberately Responding to Industry 4.0 Change

U.S. food industry executives are quite aware of how the factors that comprise Industry
4.0 are affecting the industry at a macro level and their own organizations at a micro level.
Participants indicated individually weighted responses to RQ1 ranging from tentatively to
deliberating approaching both personal response to Industry 4.0 as well as how that manifested
to their teams and organizations. Awareness and understanding of the impact of Industry 4.0 was
keenly felt, but the reactions of participants varied in many instances. Those participants who
78

were part of PT companies had differing sentiments than those from PVC organizations
concerning their individual application of SDL to meet knowledge gaps, and how to deliberately
move their organizations forward from a capital-technology perspective. The participants’ views
on the impact of cost and time factors, perceived needs to respond, and differences in response to
self-directed learning and thus framing individual and teams response, are discussed in this
section on RQ1.
Participants cited individual organization preferences and decision-conflicts concerning
how to best respond to Industry 4.0 challenges. Factors of time, costs, and indecision on what
response elements to prioritize were revealed during the interviews. Consequently, there were a
broad range of responses on the need to move forward with deliberate haste or to wait, watch,
and tentatively initiate Industry 4.0 response actions and investment among participants.
Cost of investment was contrasted by the participants to the needs, by company
ownership type, to demonstrate short-term profits. According to the interview data, those
organizations with a shorter-term profitability motivation were more tentative to investing in
Industry 4.0 related endeavors. Highlighting this view, P4 stated "(my) company is private equity
owned—focus is on what will increase immediate profitability versus longer term interests for
owner investment.” This was contrasted by P3, from a PT organization, who discussed how their
company viewed investments required by Industry 4.0, “our board of directors is interested, but
more commercially focused. Improvements are appreciated and a more stable company is
sought.”
Some PT participants stated in the interviews that their organizations, looking to be
sustainably competitive for the long term, were more acutely aware of the need to invest
79

deliberately but actively in Industry 4.0 improvements. Regarding deliberate actions to respond
to changes brought forward by Industry 4.0, P12 stated,
Certainly, (4.0) has had an impact. It's forcing us to think about our capital allocations,
you know, in terms of the amount of money typically we would implement for a
technology. And we would look at the useful life of that technology and say “hey we're
going to put in a big enterprise system or we're going to put in a planning system,” we
would look at that and say “okay, we put it in now move on to the next thing” and now I
think we're looking at it much more in a build into these platforms and expanding the
build off of those platforms, every year, so there's an investment or ongoing required
investment for each of those because the technology is changing so rapidly.
This sentiment, influenced by type of corporate ownership and the asserted need by participants
to invest in their own personal knowledge gap-closure, was illustrated by P7 who stated, “to
really understand what's driving (these changes) I’ve probably gotten a little more—a lot more
involved than I would have even liked to have been involved.”
When contrasting the differing elements of Industry 4.0 such as Big Data, AI, and
robotics, there was little consensus among participants about how to narrow organizational focus,
and thus personal knowledge development, because the differing organizations viewed the
myriad of alternative technology opportunities with differing priorities. However, determining
prioritization was a shared concern of the participants. From the participants’ perspectives,
response to the myriad Industry 4.0 elements that would be most favorable for their
organizations varied. Five participants indicated priorities such as the use of AI for analytics
while four other participants prioritized the use of AI for logistics or human resources related
areas. P9 stated, regarding challenges of what technologies the U.S. food industry should focus
80

on, “people think robotics are kind of the game changer and in many situations, they are. But
when you get into food situations, what I found is you're dealing with variation and variability
that drives robotics crazy.”
Participant consensus viewed Industry 4.0 response investment costs as high—but
necessary. Nine participants saw hardware costs, employee training, retooling factories and fleets
to be AI and robotics capable in some manner, as unavoidably expensive. Yet they viewed these
measures as vital to competitiveness, even if their respective organizations in some cases, were
not yet financially vested to those actions. P8 stated,
I think that some of the bigger players are investing more and it's, not to say that the
smaller players can't because there are certainly ways that they can you know they can
look at robotics and there's ways that they can look at data as well, but I think for folks
that are just not playing at all I think they're going to find themselves like lagging behind.
The cost of personal investment, whether individually undertaken or financed by the company
was also noted by eleven participants. Their collective position was that personal investment by
the company and, in some cases by the individual, to utilize self-directed learning to close
knowledge gaps in response to Industry 4.0 changes was essential. P13, regarding encouraging
team members to make the personal time investment to acquire Industry 4.0 related skills related,
“I set the tone then fund the initiatives my team members bring to me. Then we come together
and see what we can all learn from the individuals’ new knowledge.”
For all participants, the timing and cost of capital investment and time requirements for
personal improvement impacted fellow executive buy-in to Industry 4.0 response. The
participants indicated that their peers disagreed on the utility of different available elements to
respond to Industry 4.0 changes and how to prioritize their adoption relative to perceived benefit.
81

Twelve participants stated their organizations were examining Industry 4.0 related enhancements
and cost reduction opportunities and were assessing the financial commitment to achieve these
goals. However, these participants also stated that how those costs fit into the profitability
strategy for their organization was a key influencer. According to P11, “There are so many
companies that need to get that solid foundation, and if they don't get it they're going to either go
out of business or become inconsequential.” Participants from both PVC and PT organizations
stated that implementation costs versus benefits were being examined. More of the participants
from PT organizations were inclined to make the subsequent investments in Industry 4.0
responses or enhancing skill sets of their employees, but most PVC participants were not. Three
PVC executives indicated they would be continually reassessing the need while other PVC
participants had no deliberate plans beyond awareness of what others were doing.
The size of the company, whether $10 million or $10 billion in revenues, did not appear
to play a significant role in the participant perspectives: larger companies were both moving
forward with deliberate speed while others were holding back and waiting to see what directions
their competitors were taking. P1, who was formerly at a large publicly traded company and now
runs a private equity owned company stated, “a big cultural change is needed to the deep-rooted
way industries are looked at: too many executives aren’t making the cultural change.” Smaller
companies were dealing with more significant financial resource restraints. For some, the
participants indicated this was a reason to move slowly, tentatively, or not move at all. Yet other
participants from smaller companies indicated there was an advantage to moving quickly with
Industry 4.0 technologies. Speaking to the need to take advantage of newly enhanced capability
for their small company, P7 stated,
82

We would be eventually outpaced so there's no question the acquisition of some of this
big data (capability) has helped us to grow—it’s helped us to form a strategic platform
for us on how we're going to market our product.
These smaller company participants saw the ability to be nimble, because AI driven data
capabilities to respond to customer wants and rapidly changing consumer trends, gave their
companies a competitive advantage.
All participants, whether from large or small companies, publicly traded or private-
venture capital businesses, looked at the transformative nature of Industry 4.0 on the U.S. food
industry and how it was impacting short-term and longer-term situations for their companies.
Within the financial parameters available to them, all saw the need to develop Industry 4.0
awareness individually and for their teams, deliberatively focused on their organizations’ needs.
They also looked to move tentatively with investment in technologies and systems as they
explored differing technologies and gained ownership approval for the associated financial costs.
Executives View Amazon as the Benchmark for Responding to Industry 4.0 Change

Participants stated they were waiting to see what directions their competitors were taking,
as well as how they were dealing with the costs of new and untried technologies, as reasons to
proceed tentatively with individual or corporate Industry 4.0 investments. With so many
alternatives being introduced for new and emergent technologies, there were questions of
prioritization for what to invest in with company capital dollars and themselves for self-directed
focus. With that in mind, seven participants identified the Amazon corporation as the current
benchmark for responding to Industry 4.0 challenges and opportunities. This section reviews
how participants stated they were studying both their own organizational capabilities and the
83

personal knowledge gaps they would need to fill based upon their awareness of Amazon
developed capabilities.
Reluctance was shared by eight participants to develop experimental technologies or
invest in brand new applications. Avoiding these unknown or limited validation opportunities,
while looking to companies like Amazon for validated technologies, were a shared sentiment
which influenced reticence to be the first to adopt newer technologies that had not yet been
adopted by peer companies in the U.S. food industry. P8 spoke of being too slow to act also had
significant negatives to keeping existing business or growing with potential customers. For
example, P8 spoke of the need for Amazon-like analytics adoption to improve logistics
capabilities or lose business,
Many (rail and other logistics) companies that service the food industry are so incapable
they're unreliable in areas like time of shipment and when things are going to happen, and
scheduling. But they are suddenly realizing that they have to be better or they're going to
get beat out by the Amazons and trucking and other modes of transportation, and so they
are all of a sudden starting to get interested in AI and analytics programs to get better.
Several participants referred to adopting proven technologies developed by the Amazon
corporation as their preferred choice once other companies were seen as making the investment.
These participants cited Amazon’s ability to both invest in technology development, validation,
then incorporation to operating systems. P2 stated, “Amazon is a great example of how data is
being leveraged for every factor—versus some companies (that) pick and choose what they wish
to utilize.” The participants then stated that emulation of proven capabilities—without the
expense of research and development—was seen as the best manner of investing wisely. On
emulation, P1 stated, “I see the biggest impacts as in logistics. Amazon is the benchmark of what
84

supply chain has achieved. Their logistics model and automation are defining the state of the art
for the business.” This sentiment was further amplified by P12. They stated that Amazon was
redefining what Business Intelligence (BI) systems and delivery systems could do, but P12 did
not see this as all necessarily positive if a company was using Amazon as their point of sale:
You know if you sell through Amazon you're now at risk because of their pricing
algorithms and the way that they work. You've now declared that that's your that's your
best price. And I think you're going to find many alternatives to Amazon popping up
more and more and more that are viable for companies that don't want to lose control
because Amazon doesn't give you data on who they sold to or at what price.
The role of Amazon’s technology efforts was noted by other participants. P9 summed up
many of their viewpoints stating “Amazon was leading the pack” from the logistics innovation
perspective and that other food companies needed to improve or get left behind. This was also
reflected by many participants: whether they emulated Amazon or not, they individually needed
to define their next steps and either enhance their own 4.0-logistics skills or hire a subject-
matter-expert (SME) to lead their teams and bring their organizations to an “Amazon-like”
customer service and logistics capability.
The sentiments identified by the participants in response to RQ1 fell into differing
perspectives influenced by type of company ownership. This type of company ownership
difference was noted as influencing the participants’ individual SDL and company Industry 4.0
actions, and were subsequently amplified in responses related to RQ2 and RQ3. Participants
hailed from a broad range of U.S. food industry types and individual responsibilities, yet those
with similar roles or from similar types of companies still provided divergent responses based
85

upon ownership categories. The emergent distinctiveness of actions between public versus
private ownership was unequivocally voiced.
Research Question 2: How Does the U.S. Food Industry Organizational Environment
Impact Food Industry Executives’ Knowledge and Motivation With
Respect to Responding to Change Created by Industry 4.0?
Participants were asked a series of questions related to RQ2. The two themes that
emerged were “forward thinking and financial capability,” and “competitive pressure.” Each
theme is discussed in detail, with evidence, in the sub-sections.
A categorization of the responses to RQ2 was made of the participants’ sentiment to the
impact of organizational environment influencing response to industry change created by
Industry 4.0. From this categorization of responses to RQ2, only one of the participants indicated
that the organizational environment of the U.S. food industry was very impactful to their
motivation to acquire new knowledge, skills, or capabilities relative to Industry 4.0 changes as an
individual (Table 7). Ten of the 13 participants’ responses were categorized as somewhat
impactful or simply aware that the industry was having an impact on their need to develop new
knowledge. Two participants stated the impact was minimal. This research question also evoked
participant responses that focused more on the Industry 4.0 tools they could utilize to enhance
their organizational capabilities or concerns about how competitors were using capabilities that
placed them at a disadvantage. However, all participants had viewpoints on the organizational
environment impact as it affected their forward-looking responses which is covered in the
section on RQ3.

86

Table 7
Categorization of Research Question Two Response (N = 13)
______________________________________________________________________________
How does the U.S. food industry organizational
environment impact food industry executives’
knowledge and motivation with respect to
responding to change created by Industry 4.0? n % of N
______________________________________________________________________________
Very impactful 1 8

Somewhat impactful 6 38

Aware 4 31

Minimally impactful 2 15

No impact 0 0
______________________________________________________________________________

Forward Thinking and Financially Responding to Industry 4.0 Change

The study participants were all quite candid—and varied—as to how the organizational
environment impacted their knowledge and motivation to changes wrought by Industry 4.0. P10
stated that “improving quality, or removing costs by reducing waste, are the only two ways
investment is returned.” While generic, this was a common outlook that the participants echoed
as they explained what they were individually doing to increase personal capabilities, or to
respond to competitive needs yet be financially prudent for their respective organizations. How
the participants related their forward thinking about Industry 4.0 impacts, how to financially
respond in a prudent and considered manner, and how those impacts affected their personal and
organizational response evaluations, are outlined in this section.
Awareness of the balance between Industry 4.0 capabilities and financial positions
affecting their individual organizations was a common perspective among participants. The types
87

of companies, based upon goods produced, markets serviced, size and profit focus of their
ownership, created differing perspectives for individual self-directed learning responses and the
manner each was directing their teams. Individually, the participants cited several examples of
how they had used self-directed learning to improve knowledge of differing Industry 4.0
capabilities that would have quick application within their financial capabilities or limitations.
The transformation of data analytics because of AI, which refers to how computer
programs can think through items such as sales or manufacturing performance data, or customer
trend information, and make projections and recommendations (Müller, 2018; Punga, 2019) was
noted specifically by 10 of the participants. Use of data analytics was a central theme for these
10 participants as allowing them to be more proactive—and forward thinking, while
demonstrating a tangible return on their capital investments to their respective ownership. This
proactive stance was stated by the participants as affecting the way they sought to bridge
knowledge gaps. One of the capabilities that Industry 4.0 AI technology provides is the ability to
run very accurate projection models of future business requirements for the next period, quarter,
or year, using business analytics programs (Bordeleau, 2020; Hahn, 2020). Three participants
stated that data analytics systems were more affordable than other Industry 4.0 technologies such
as a new robotic line or other major brick and mortar capital investment.
An example of how being forward thinking on the purchase of new analytics capabilities
benefited their company was provided by P10, who stated that their self-directed research into
the technology options had resulted in the purchase and implementation of the analytics software
capability. This new capability improved the logistics and sales modeling for movement and sale
of their perishable product. P10 stated this also extended to modeling sales forecasts for differing
regions of the United States, and future weather, a capability they did not formerly have:
88

I’m shipping perishable goods, like bananas, from Central America. By utilizing the
weather forecasts the logistics analytics can identify how routing product to the upper
Midwest, via Chicago, when there are pending road closures or delays to unloading
aircraft. This means I will be better off shipping this perishable product—that I cannot
hold until the weather is better in Chicago—to alternate locations that will not have the
same kinds of weather impacts and where I can hold my pricing. Before, I might not have
shipped to Chicago just because of common sense looking at the weather forecasts, but I
would not have had the kind of analytics data that lets me identify the best alternative
locations for the product.
Shared sentiment was offered by P7 in describing their organization’s recent venture into
Industry 4.0 analytics capabilities two years prior and how it had benefited their sales
capabilities. They were competing for market share, losing, and needed to find a capability the
company could afford to reverse the trend. After self-directed learning about the different types
of business analytics packages appropriate to their business, P7 received approval from their
private-venture capital (PVC) ownership to upgrade their capabilities. While costly to purchase
and install, and with employee learning issues for implementation, P7 stated it had already paid
for itself and kept their company from not only falling behind competitors but losing customers:
Yes, we would be walking away from business, and I think not only would we be
walking away from business but additionally we would be eventually outpaced so there's
no question the acquisition of big data capability has helped us to grow, it's helped us to
form a strategic platform for how we're going to market our product. Being able to keep
up with the competition is critical so there's no question, we would have stalled in our
growth and there's just no doubt in my mind, we would have failed.
89

The desire to respond to Industry 4.0 opportunities with usable and affordable
technologies was specifically cited by nine of the participants. One participant, P4, who also
oversees a PVC organization, stated that while their smaller company was resource constrained,
looking for affordable technology opportunities was always ongoing. P4 stated, “larger
companies can do more; they are better resourced, and margins drive the investment capability.
But we still have to innovate as we can.” This position to innovate in a forward-thinking manner,
against resource constraints, was echoed by P1. “There was little interest by the PVC ownership
to invest broadly, so I, as the leader, was directing funds for other key team members to attend
seminars and gain knowledge in anticipation of ownership allowing future capital investment,”
stated P1. Based upon personal interactions with other company leaders, P1’s view was that
“many executives (in my industry) aren’t making the cultural change, so more has to be invested
in management level skills improvements.” P5 stated a similar concern that longer term priorities
at their PVC organization were defined solely by ownership’s priorities, which were mainly short
term focused, and this required P4 to seek inexpensive knowledge and tools to improve
capabilities. P4 stated that avoiding financial “bad times” was the best impetus to investing in
capabilities, but convincing PVC ownership of that was not an easy task.
Competitive differences between the U.S. approach and global executive outlooks were
brought up by five participants. These participants viewed the European food industry leaders
specifically as more forward thinking than the U.S food industry executives overall in forward
thinking to Industry 4.0 responses, with impacts as well on demand growth from China. The
Europeans were seen as more innovative in areas such as introducing robotics into factories,
reducing hazardous labor practices through robotics, and developing analytics tools to improve
consumer-related quality control for aesthetic preferences. P2 stated, regarding using Industry
90

4.0 capabilities to reduce costs, “the U.S. is doing a lot of innovation—but in other areas
(compared to Europe) not so much. Really large-scale food manufacturing is different in the US
than in Europe, and European companies are leading in food innovation.” P6 stated that trying to
understand international trends from a global viewpoint, and how to learn from them for U.S.
implementation, was becoming a larger area of interest:
With the declining workforce in the US as well with the middle class in China growing
by, I think it's something like 40 to 50 million people over the next couple of years, the
level of disposable income that will afford will have a dramatic effect on food imports
and exports globally.
How U.S. companies were starting to ask for help in designing effective and forward-looking
solutions was posited by P2. Ongoing improvements to how U.S. food companies are handling
data analytics have developed because U.S. food companies are identifying the advantages to the
advances in Big Data and AI and new systems are being put in place. P2 stated,
Food companies are asking us for how to go beyond perceived self-knowledge to actually
using available data with confidence. How to change personal perspective from “I think”
to acknowledging what the data is actually telling us. Learning how to effectively bridge
the gap between theoretical capability and practical applications that will actually assist
fixing a company issue.
Six participants addressed use of technology testbeds, even on a limited scale, to identify
affordable and usable Industry 4.0 technology improvements for the manufacturing factories.
The collective goal was limiting financial exposure within the overall organizational networks
but still seeking the tools and capabilities that would provide their respective companies with the
ability to stay even with their competitors. P3 stated they were “acquiring new knowledge. (They
91

were) motivated by what is out there, but they must be cautious: with AI—AI is about aligning
the problem to the right tool.”
Five of the participants also addressed the use of remote training. Advances in interactive
technology and the ability to mimic “in the same room” instruction through the use of virtual
headsets and other similar technologies, were seen as reducing expense while improving learning
capabilities for management and operators at their factories. P2 observed this was a good way to
also talk to peer companies in these virtual training environments and observe best practices.
While the need for being smart with capital budgeting for forward thinking responses to Industry
4.0 challenges were noted by most of the study executives, all indicated that competitive
pressures, in one way or another, were driving their response initiatives.
Forward thinking concerning what was needed for their organizations and financial
prudence as to how to respond to those needs were noted amongst all the participants as they
utilized self-directed learning to bridge knowledge gaps. The participants indicated their focus to
acquire better organizational proficiencies, utilizing Industry 4.0 capabilities, for improving
quality and reducing waste while also enhancing organizational teams’ responsiveness. Their
perspectives indicated a pragmatic and deep reflection on how the influences of Industry 4.0
defined the manner they are or will use Industry 4.0 related self-directed learning, and how that
reflection was shown by the actions they had subsequently taken with their organizations.
Participants’ Describe Responses to Industry Competitive Pressures

How competitors are faring, what they are doing, and what they might be doing was a
key focus of all the study participants as each articulated their motivation for self-directed
learning activities. Several perspectives on how to address competitive pressures were offered by
the participants, with examples given of the means they had used to gain new knowledge of
92

Industry 4.0 capabilities on competitive capabilities. These perspectives ranged from use of data
to learn, understand, and predict what competitor actions were or could be, to what proactive
steps could participant’s organizations take to seize their own competitive advantage for their
organization. Participants reviewed a number of interwoven reactions to the topic of competitive
pressures which are reviewed in this section. These reactions included prioritizing self-directed
learning for themselves and their teams in response to Big Data, bridging Covid-19 impacts to
methods of bridging knowledge gaps, and the negative impacts to competition such as cyber-
security.
Examples of how Big Data can be used were offered by several participants. P10
exemplifies this perspective from a marketing and product offerings viewpoint, “the best use of
big data is to look at trends and do comparative analyses of types of products that are winners.”
This position from P10 tied back to the previous section on being forward thinking and using
capital funds wisely, but the focus was also on how does my company innovate, whether there
are large budgets or practically no budget, in response to competitive pressures, as expressed by
all of the participants. The participants also tied to a commonly expressed introspective question
of “what do I, as a leader of my organization, need to do to have the knowledge skills necessary
to affect and lead appropriate responses to these competitive pressures.” Competitive response to
direct competitors, in addition to understanding what their competitors were doing, was seen by
nine of the participants as critical to keeping their organizations profitable and growing.
A recurring sentiment of these nine participants was the distinction between revamping
existing means of production such as factories, and instead focusing on the “bookends” of the
product-production process. These bookends were seen as the predictive tools for minimizing
waste in the scheduling of the factories and the purchase of the needed materials to make
93

products, and the logistics—warehousing and shipping—aspects of the business. “Paying
attention to Big Data” was noted by P13 in reference to predictive analytics for factory capacity
scheduling and materials acquisition. P1 observed “leadership teams are exploring what to invest
for logistics. A big cultural change is needed to the deep-rooted way industries are looked at.”
Citing an example that 10 years ago would have been quite far-fetched, P1 further stated “who
would envision drones doing your inventory? We now do that” in reference to using AI
programs to have drones count the quantities and types of products in a warehouse, minimizing
the former expense of multiple workers doing that activity.
Concerns about improving personal knowledge that focused on the “right direction and
priority” given the current and potential competitive pressures was a sentiment voiced by all the
participants, even if the company ownership was less keen on Industry 4.0 investment. P5, a
chief operating officer for a PVC organization, stated they had a smaller company and lacked the
means to invest in technology testing. But the need to stay abreast defined P5’s SDL so they
could keep the PVC ownership apprised of opportunities and risks. P5 stated,
The bigger CPGs* are resourced to invest in technology trials. Smaller companies are
playing it nimble with AI innovation and use, or waiting to see what the bigger
companies validate.
*CPGs—companies making Consumer Packaged Goods for food consumption.
Determining what competitors are doing or planning to do was stated by all participants
as a central part of their individual SDL prioritizations. Each offered differing methods of
obtaining new knowledge. These methods included consultations with vendors and other
contractors who shared to what degree advanced technologies were being purchased within the
U.S. food industry. Other methods included use of trade journal articles and friendly interactions
94

with direct competitors. Attendance at industry think-tank events, such as those conducted by the
Gartner company, was cited by three participants as an additional way of assessing current and
potential competitive pressures. To improve the ability to analyze the multi-axis reality of
competitive pressures and how to approach them, both as an individual and to assist their teams,
one participant had taken Design Thinking classes at a prominent University of California
location.
Seven of the participants identified trade show events as a primary source of gaining new
insights on competitive trends and potentials. At the time of this research, most events had been
cancelled in the 2020-2021 timeframe due to the international response to Covid-19. These
participants noted the information gap that had resulted and were looking forward to the
resumption of these events at some point in the coming business year. To bridge the absence of
these events, but maintain an information gathering and self-improvement focus, P5 stated “I
have used webinars and other online forums that are most prevalent on LinkedIn feeds.” Other
participants stated that increased use of trade journals and vendor interaction via technologies
such as Zoom or WebEx had become a routine means of maintaining interactions and awareness
of industry events.
Responding to competitive pressures also examined the downsides of Industry 4.0
technologies. Participants were reflective on how AI and other Industry 4.0 technologies created
potential vulnerabilities. Specifically, four of the participants voiced concerns about data breach
of their respective organizations, compromise of customer’s data bases, and other IT fears. For
example, P3 expressed concerns about cybersecurity:
Cybersecurity and data analytics are a major concern. Reliance on cyber systems is
great—but how do we secure them? How do we make (IT-Cyber) security more robust
95

with interconnectivity both for our organization and with our customers and suppliers and
still meet competitive pressures?
The participants’ responses to RQ2 provided strong insights on these individuals’
sentiments on the state of overall U.S. food industry awareness and response to Industry 4.0. The
participants reviewed proactive measures for increasing individual awareness through self-
directed learning and how finding alternatives to normally recurring events such as trade shows,
provided the ability to be financially prudent while being forward thinking. These sentiments
continued to be echoed from other perspectives as the central themes to RQ3, as individual
actions related to company obligations were identified.
Research Question 3: What Role Do U.S. Food Industry Executives See for
Either Self-Directed Learning or Other Forms of Learning
in Response to Industry 4.0 and Beyond?
Participants were asked a series of questions that tied to RQ3. The three themes that
emerged were “highly defined,” “fast follower,” and “self-directed learning and key-employee
focused.” Each theme is discussed in detail, with evidence, in the sub-sections.
A categorization of the responses to RQ 3 was made of the participants’ sentiment to the
roles of learning in response to industry change created by Industry 4.0. From a categorization of
responses to RQ3, nine of the 13 respondents saw a significant role or active role that each would
need to play for ensuring Industry 4.0 changes were addressed by themselves, their teams, and
their organizations (Table 8). Four participants saw the role as moderate or limited. All
participants acknowledged that they, as executives, had a responsibility to both personal learning
and directing learning events among their teams. All participants expressed that their leadership
was key to current and future Industry 4.0 response activities within their organizations.
96

Table 8
Categorization of Research Question Three Response (N = 13)
______________________________________________________________________________
What role do U.S. food industry executives see for
either self-directed learning or other forms of learning
in response to Industry 4.0 and beyond? n % of N
______________________________________________________________________________
Significant role 3 23

Active role 6 38

Moderate role 2 15

Limited role 2 15

No role 0 0
______________________________________________________________________________

Highly Defined Role for Industry 4.0 Change

The U.S. food industry was perceived by the participants as being at an industry-
transformational crossroads between old ways of doing things that the participants framed as ill-
fitting to the emerging consumer demands and customer needs of today’s marketplace. Eleven
participants itemized areas of manufacturing capabilities, the speed of change related to logistics
capabilities and limitations, and the utilization of business analytics as outmoded compared to
advanced technology and robotics. How participants stated improvements in these areas as keys
to their organizations’ survival, characterized the need to enhance their personal knowledge of
the utilization of Industry 4.0 capabilities, and viewed central to organizational improvement and
withstanding competitive pressures, are identified in this section.
For those participants from the PVC owned companies, they viewed the need for
improvements capitalizing on Industry 4.0 capabilities as vital. They viewed the advances as key
97

for their organizations’ competitive survival even if they had not yet persuaded their ownership
groups of the current and future utility to such investment. In all cases, for both PVC and PT
organizations, the participants viewed their perspectives as having been driven by their self-
directed knowledge enhancements related to the general Industry 4.0 inroads over the last
decade.
For those participants who were not C-Level executives or those C-Level participants
who identified a need to influence others in those roles, financing Industry 4.0 change initiatives
and creating a sense of urgency about them to permeate the organization was seen as difficult. P1
discussed these challenges:
This is CEO level driven to get the rest of the executives to understand what they need to
be looking at (these needs). But lower-level executives are bringing awareness to the C-
Level and prompting their attention.
Ensuring organization ownership is aware of potentials and costs was viewed by nine of
the participants as specifically incumbent to their roles. P3 said they had conveyed to their
ownership what they had learned through self-directed study and, in collaboration with vendors
and other SME’s, “I have created a factory of the future team that is looking at 4.0 to make all
equipment investments to meet what is the future state of the art, not what is the current state of
the art.” P6 voiced that sentiment of looking forward to incorporating the appropriate Industry
4.0 capabilities into their business model:
If you're talking factories that are designed for mass production that are now shifting
towards a consumer facing type scenario. That that change current perception. (It) really
changes how your capital spending and how your operating expense is really driven and
98

that's a very different way of looking at the businesses than historically we've seen in the
food industry.
Sentiment from all participants recognized that there are many different areas that
Industry 4.0 capabilities touch and can improve their organizations’ current practices, so the
question for the participants becomes what areas should be prioritized first. Accordingly, the
participants stated scope of each organizations’ focus must be specifically defined and ranked to
the most immediate needs and affordability of the organization. With that focus and prioritization
of financial resources, 11 of the participants stated that their own self-directed learning, and
utilization of other more formal online or institutional directed learning, had been key to their
teams embracing the need for improvement and mastering the capabilities to do so.
Fast Follower Role for Industry 4.0 Change
The concept of being a “fast follower” emerged from several directions as described by
the participants; the scope of innovative and transformational nature of Industry 4.0 technologies
was stated to be increasing at a very rapid pace. P1 defined being a fast follower as
acknowledging that one does not have to be the first innovator for a technology but identifying
and incorporating relevant technology as quickly as possible maintains competitiveness. How the
participants viewed impacts of self-directed learning as influenced by varying technologies and
their costs, the impact of competitive pressures, and the utilization of external resources to bridge
knowledge and capability gaps, are reviewed in this section.
Because of competitive pressures, deciding priorities for organizational Industry 4.0
enhancements and individual self-directed learning was a daily challenge. For fast followers,
uniquely developing technologies to lead or keep up was seen as outside their organization’s
financial capabilities, so the emphasis turned to emulation. P1 and P3 both defined this concept
99

as watching-learning-evaluating what other companies were doing with novel Industry 4.0
technologies.
Nine participants viewed themselves as fast followers to the major food industry
organizations and Amazon. These participants stated ownership direction made responding as a
fast follower difficult: they cited ownership emphasis as lacking in some cases, and insufficient
available capital in most cases. Competitive pressures and affordable response were identified as
motivators to identify what others had successfully initiated, then copy it as quickly and
affordably as possible. P1 stated, “we want to be a fast follower—(because) we can’t be a
laboratory. We test in a location and as successful scale to the rest of the organization.” P3 stated
that one SDL technique they utilized is the Gartner company meetings; there they were able to
hear about best-in-class utilizations and that validates their internal test-mode requirements, and
then introduced their own versions to their organizations.
For two of the publicly traded (PT) organizations represented in the study, internal and
unique development was being conducted. Each organization had multi-million-dollar research
and development budgets as referenced by the participants and validated through examination of
the company’s Securities and Exchange Commission filings. The participants of these two
companies were developing Industry 4.0 capabilities and sought to achieve industry-leading
competitive advantage through their innovation. P12, who runs an industry leading multi-billion
dollars in annual sales food manufacturing company, spoke to how AI was changing their
traditional business model for manufacturing, distribution, and sales, and how they were
developing innovative analytics to increase their market responsiveness:
Industry 4.0 is moving how we think about doing things. Our portfolio of products is
dramatically different than it was five years ago. And it starts with where is that demand
100

where's that pocket of demand (coming from), that growth in the US markets is coming
in very micro markets of ethnicity and a certain region it's coming in things like social
influencers, and so I think this notion of big food developing these mass products and
marketing it to the masses, those days are gone. It's much more going to be about
personalized and individualized products and delivery, and I think those companies that
can do that and leverage their scale to do it at a cost that's affordable will find huge
markets.
However, these PT participants also saw the need to be fast followers. P12 stated “we absolutely
watch what others, like Amazon, are doing.” As other companies, such as Amazon, introduce
validated ideas, these participants’ companies had also acquired replica or approximate
capabilities of others’ new capabilities or technologies and placed them into their own
organizations.
Utilization of vendors, contractors, and suppliers to identify potential technologies was
also seen by five participants as a means of test-mode validation. P4 stated that using those types
of resources was beneficial when developing concepts and considering or implementing new
technology purchases. P4 added that they utilized those agencies as third-party trainers for on-
site training, or sent employees to the third-party location for formal skill transfer. This technique
was stated by five other participants as an efficient way to move their organizations forward,
achieve critical training and skills transfer for employees, but do so affordably as a fast follower.
Development of network contacts and being a self-directed learner was identified by P7
as an effective way to be a fast follower to a critical task for their organization. In P7’s
circumstance, the situation involved a lack of effective data analytics capability:
101

I actually reached out to people who are doing the jobs (had the skills and background for
what I needed to do at my organization), so I used LinkedIn. I went to a couple of
Operations summits across the country, networked, found people who were doing the
roles and eventually ended up hiring a consulting firm.
At P7’s small, PVC company, securing investment authority was a daunting task, but the reward
was worth the effort:
All we were ever doing even at the biggest companies was trend analysis—that’s
it…..So, we learned what others were doing and modified it to what we needed on our
own and gained those predictive capabilities we did not have.
According to P7, the adoption of the Industry 4.0 data analytics capabilities allowed their
organization, as a fast follower, to remain viable and to increase both sales efficiency and
leverage production materials procurement.
Self-Directed Learning and Key-Employee Focused Role for Industry 4.0 Change

A variety of ideas were expressed by the study participants concerning the future of the
U.S. food industry and the need for individual executives to continuously improve their personal
Industry 4.0 skill sets through self-directed learning. However, it was recognized by all
participants that it was not possible to make inroads for all areas SDL offered given time and
breadth of the topic, placing a greater need on broadening the capabilities of their teams. This
section reviews areas voiced by the participants concerning needs for self-directed learning,
specializing key employees’ capabilities using self-directed learning, and development of subject
matter experts (SMEs) through self-directed learning and external hiring.
The participants viewed the broadening number of Industry 4.0 areas as equivocal to the
specialization of medical practices: the requirement was evolving to recognize and support
102

specialization by key employees to the various Industry 4.0 areas. The need to emphasize key
employees delivering within focus areas was discussed extensively by P1 who stated,
I stay current on trends, but I want to hire the best people who know the current
capabilities—it is too much (for me) to learn as a SME but find the people who can
deliver winning solutions. I look for sustainable P&L (profit and loss) growth with these
team members rather than chasing the peaks and valleys of business cycles.
This requirement for subject matter expertise was identified by all the participants. The
need to stress SDL-capable current employees and new hires to take on the SME roles was
emphasized by P6, “for my organization I need folks with the self-motivations to tackle SDL
opportunities in addition to the company directed programs we send them to.” P4 added
additional perspective, stating, “self-directed tends to be more important (than directed) learning
for me or my teams. But hiring curious people is the key to getting new ideas generated.” P4
continued that these individuals become the key-employees who can focus on a specific area of
Industry 4.0 technology, rather than everyone trying to be an all-knowing generalist. P2 stated
that the need is to identify those internal SMEs or hire SMEs to become the key-employees to
bridge the needs for specialization: then “let them excel in those areas.”
All of the participants identified that their individual limitations, despite using self-
directed learning to varying extents to bridge knowledge gaps, affected their overall
organizations. The gulf in time between their own formal education, and that of other
organization executives, was seen as driving their forward-thinking strategies for utilization of
other key employees to offset those knowledge gaps. The participants felt that failure to adopt
key employee strategies would limit meeting Industry 4.0 competitive pressures and
opportunities. How other key employees acquired knowledge about the Industry 4.0 environment
103

through either SDL or other learning methods to enhance organizational capabilities, given the
knowledge gaps of senior executives, was seen by eight participants as a concern. P10 addressed
this,
The use of SME’s is key-- yes, I think a lot of it has to do with fear of looking stupid, you
know when you've been working for 30 years you don't (want to) look stupid because
somebody showed you a trick on the computer…The notion of being able to retrain
people of my generation who have not kept pace with technology, the likelihood of
success is very, very low in my experience.
The need to bridge this gulf of formal education that predated the advent of Industry 4.0 was also
identified by P1, “management is the gap---they are not finding the right systems. Leadership has
to find and engage employees is the key—yet everyone is all over the board.”
How to address the need to bridge organization knowledge gaps for Industry 4.0 areas,
outside of the participants’ individual SDL efforts was identified as a key concern by
participants. Six participants stated their organization’s future hinged on their ability to find new,
highly trained, key employees who could become Industry 4.0 innovation experts. These
participants were actively seeking new employees with formal training in Industry 4.0 related
areas, such as those from business and AI engineering universities such as MIT or Cal Tech. It
was also recognized by participants that Industry 4.0 specialized areas are becoming more
nuanced. Accordingly, the expertise of an organization’s new SMEs could be daunting when it is
outside an executive’s current SDL knowledge, or their formal education areas: P2 added, “when
I don’t understand what they are doing, I simply ask them to explain it to me like I’m a six-year-
old.”
104

Of note was the desire of the participants to hire individuals who were open to individual
improvement. These participants were seeking those “who had the innate curiosity” to be a self-
directed agent to learn more. P7 observed their company was better because of “the self-directed
that are trying to stay current on their knowledge—people hold certifications and they have
continuing education credits, but they're doing that on their own.” One participant, who also
serves as an adjunct professor for an MBA program shared, “our university is in the process of
adding AI and Industry 4.0 areas to the curriculum.” However, until such time as formally
trained individuals from academic backgrounds become the norm for hiring within the industry,
the participants all stated that a reliance on SDL or other informal learning methods would be
their approach for training themselves and their key employees.
Summary
Three research questions were utilized for this study. As the responses to those questions
were assessed, two emergent findings became apparent that framed the research question
responses, in addition to the themes identified by each research question. The first emergent
finding concerned the business ownership model applicable to each participant’s unique
organization. Those participants at publicly traded companies were generally able to devote more
company money to Industry 4.0 related technologies and training, and individual focus on
Industry 4.0. Those at private equity owned companies stated they were generally unable to
spend company money on longer term initiatives related to Industry 4.0, and consequently did
not spend as much time developing Industry 4.0 individual capabilities. The second finding
related to self-identification to master Industry 4.0 capabilities. While all participants stated they
had used self-directed learning to some extent to increase their Industry 4.0 related knowledge,
all stated that the needs are too broad for anything more than a generalist capability in their
105

executive roles. Consequently, they viewed future reliance on key employees who would
specialize in focused identification and implementation of Industry 4.0 capabilities as the key to
their respective organizations’ future competitive survival.
The first research question was: How are U.S. food industry executives responding to
change created by Industry 4.0? The two themes that emerged were approaching the changes
tentatively and deliberately, and Amazon is the benchmark. The approach to changes was
focused, with a strong reliance on self-directed research, because of the breadth of available
options. The untried aspects of many industry-specific unique solutions being developed, and the
limitations of time and capital further increased focal intensity. The collective view of
participants related to the Amazon corporation as the benchmark was associated with perceptions
of industry leading investment in research and capabilities related to utilization of Industry 4.0
advantages for logistics and sales
The second research question was: How does the food industry organizational
environment impact food industry executives’ knowledge and motivation related to responding
to change created by Industry 4.0? Two themes also emerged: forward thinking and financial
capability, and competitive pressure. Participants stated that looking at individual response to
bridging Industry 4.0 knowledge gaps was related to type of company ownership, so their
forward thinking involved finding the find the most reasonable solutions within the financial
parameters of their specific organizations. Competitive pressures tempered or defined for the
participants how they individually approached, using self-directed learning methods, what those
reasonable solutions might be as the organizations looked to avoid being left behind by the
actions of their market competitors or sought their own competitive advantage.
106

The third research question was: What role do U.S. food industry executives see for
either self-directed learning or other forms of learning in response to Industry 4.0 and beyond?
Three themes that emerged were: highly defined, fast follower, and self-directed learning and
key-employee focused. Participants viewed the U.S. food industry’s Industry 4.0 activities as
transformational and therefore their individual knowledge-gap closure and organizations’
financial response as requiring very specific and defined focus, which they had bridged using
self-directed learning. The breadth of capabilities offered by Industry 4.0 technologies was seen
as too great to approach without very directed and researched specificity. Participants viewed
themselves, generally, as fast followers to the major food industry organizations and Amazon.
The participants cited a lack of ownership emphasis in some cases, and available capital in most
cases, to set out on their own unique and individual Industry 4.0 response efforts: consequently,
they utilized self-directed learning to identify what other organizations successfully implemented
then emulated in their own manner. The participants stated their individual time and learning
capabilities were too limited to become subject matter experts on all the capabilities of industry
4.0. Due to those limitations, they were increasingly looking to hire—or internally develop
utilizing self-directed learning—key employees to become specific area innovation experts in the
various technology areas related to Industry 4.0.
Lastly, the research findings indicated that the participant’s behavior aligned with the
conceptual model (Figure 3) concerning deliberate, agentic action by the participants to acquire
new knowledge and close gaps related to Industry 4.0. Awareness of change impacts on their
individual organizations prompted self-directed learning as the basis for developing and
implementing subsequent actions. Moving forward from these findings, Chapter Five will review
the recommendations identified from the findings, limitations and delimitations of the study,
107

discussion of further research and opportunities indicated for developing additional knowledge
from these findings, potential impacts of these findings on marginalized communities, and the
research conclusion.

108

Chapter Five: Discussion and Recommendations

This chapter discusses the study findings and recommendations from those findings. A
field study was conducted on U.S. food industry executives’ self-directed learning (SDL)
response to factors associated with Industry 4.0. The Fourth Industrial Revolution, or Industry
4.0, refers to the prolific business impact of digitization (Dombrowski, 2014; Machado, 2019).
Study participants identified that U.S. food industry executives do not have focused academic
programs to bridge Industry 4.0 knowledge gaps but must stay abreast of the 4.0 impacts on their
industry and individual organizations. How U.S. food industry executives are responding to
Industry 4.0 related knowledge gaps has not been widely studied. To gain insights on their
actions, this qualitative research study examined the sentiment of food industry executives to the
impact of Industry 4.0 on their organizations and the broader food industry and how they viewed
addressing knowledge gaps for the current state and looking forward.
The study was based upon a theoretical framework from Albert Bandura’s Social
Cognitive Theory and triadic reciprocal determinism research. This framework was incorporated
into a conceptual model concerning deliberate, agentic action by the participants to acquire new
knowledge and close perceived knowledge gaps related to Industry 4.0. This theoretical
framework and conceptual model were the basis for study research questions.
The research questions for the study were as follows:
1. How are U.S. food industry executives responding to change created by Industry 4.0?
2. How does the food industry organizational environment impact food industry
executives’ knowledge and motivation related to responding to change created by
Industry 4.0?
109

3. What role do U.S. food industry executives see for either self-directed learning or other
forms of learning in response to Industry 4.0 and beyond?
All three research questions were answered by the study. At the study onset it was
unknown if these participants were addressing Industry 4.0 related knowledge gaps, and if they
were addressing them, how they were doing so. A perceived awareness of new knowledge
achieved by using self-directed learning to address Industry 4.0 challenges was noted by all of
the participants. Chapter Four described two emergent findings and seven themes that were
identified through data collection and analysis. The two emergent findings were noted to
significantly influence and categorize how the participants responded to the interview protocol
questions. The first emergent finding concerned how the type of public or private company
ownership influenced SDL and Industry 4.0 behaviors by the participants. The second emergent
finding concerned participant self-perception of capabilities related to SDL and bridging Industry
4.0 knowledge gaps to meet their respective organizations’ needs.
The chapter begins with a discussion of the study findings and their direct implications
for the U.S. food industry. Three recommendations that derived from those findings are detailed
and concern how U.S. food industry organizations should utilize self-directed learning, develop
key employees to meet specialized Industry 4.0 challenges, and optimize Industry 4.0
technologies to meet competitive business challenges. The chapter reviews the limitations and
delimitations that affected the study, recommendations for future research, and the study
conclusion.
Discussion of Findings
This field study determined several findings from the U.S. food industry executive
participants. The utilization of self-directed learning by the participants was indicated as a means
110

each had applied to address knowledge gaps related to Industry 4.0. Their sentiments revealed
two unanticipated emergent findings: the participants’ responses were influenced by their
respective organizations’ type of ownership, and the participants’ self-realized inability to master
the breadth of Industry 4.0 topics. The participants’ fervor in these two areas weighted their
subsequent responses to the semi-structured interview questions. The findings for the study can
be summarized as follows: the participants’ response to utilization of self-directed learning to
address Industry 4.0; the impact of U.S. food industry environment and how that influenced the
participants’ use of self-directed learning to respond; and the requirements for continued reliance
on self-directed learning to address Industry 4.0 issues in the future. These findings are
elaborated in this section.
The theoretical framework of Social Cognitive Theory that individuals are not just
passive participants in their interactions but purposefully look to affect their specific situation by
their individual efforts (Bandura, 2011; Nevid, 2009) was demonstrated by the participant
responses and relevant for all of the findings. U.S. food industry organizations were competitive,
but co-dependent, when the relationships of the supply chain of materials suppliers, service
companies, and technology companies, and their interactions with manufacturers and distributors
to retail and food service customers are examined (Beske, 2014). This industry based social
environment (Bandura, 2016), particularly with the type of ownership and recognition of
individual cognitive capabilities identified in the emergent findings, affected the executives of
these varied organizations. This affectation aligns with the construct of SCT (Bandura, 2016)
demonstrated by the participants responding to the business (environmental) demands and
actively engaging themselves to find solutions to their organizational issues outside of their
formal training.
111

Emergent Findings
The participants’ sentiments on their individual and organizational responses to industry
4.0 and self-directed learning were tempered by two emergent findings. The first emergent
finding regarded the type of ownership for the participants’ organizations. Differing ownership
models were determined to influence the degree to which the participants conducted their
individual and organization Industry 4.0 SDL responses. Six of the participants were employed
by publicly traded companies and seven were employed by privately owned companies (Table
5).
Those participants from publicly traded companies expressed that their organizations’
willingness was high to prioritize and invest in 4.0 technology responses. The participants from
this ownership category stated prioritization motivated them to utilize SDL to learn what
Industry 4.0 capabilities were available, affordable, and would best address their organization’s
needs. These six public company participants stated they used self-directed learning methods to
bridge their Industry 4.0 knowledge gaps in very comprehensive ways and made significant
investments in time to do so.
The responses of the seven participants from privately owned companies reflected
different organizational financial priorities and individual sentiment about Industry 4.0
opportunities than the responses from participants at publicly owned organizations. These
participants stated their organizations’ immediate profit goals did not always focus on longer
term profitability. The participants further related that their ownership did not usually view
Industry 4.0 opportunities as needed to meet the short-term profit goals. Accordingly, the
participants stated that their necessity to learn Industry 4.0 capabilities through SDL was
mitigated by their respective organizations’ financial priorities and allocations of funding
112

resources. However, each of the seven participants stated they invested personal time in varying
degrees through self-directed learning to learn Industry 4.0 capabilities. They also stated they
recommended Industry 4.0 capabilities identified through their SDL initiatives to their
organization owners even if their proposals were not followed. These participants’ sentiments
aligned with research on privately owned companies by Deeb (2016) and Tuovila (2021) and the
differing profit and internal investment actions compared to publicly traded companies.
A second emergent finding concerned the participants’ acknowledgement of their
personal cognitive limitations related to the broad range of Industry 4.0 information. This
cognitive limitation was also impacted by the length of time that had elapsed between their
formal education and assuming their current executive responsibilities, according to all of the
participants. This finding additionally influenced the participants’ sentiments to the manner each
addressed personal self-directed learning to meet Industry 4.0 challenges.
The differential influence of the type of company ownership also emerged as an influence
on how participants’ addressed their perceived cognitive limitations. Participants from publicly
owned companies looked at self-directed learning investments differently from their privately
owned company counterparts. The participants from companies that were highly engaged with
Industry 4.0 technology adoption stated an acknowledgement of personal limitation motivated
them to utilize SDL to look beyond themselves to meet the 4.0 needs of their organizations. In
looking beyond their limitations, these participants identified the need to resource Industry 4.0
innovation experts to give their organizations needed expertise. For those participants from
privately owned companies, they stated that internal development of 4.0 innovation experts or
hiring of external 4.0 innovation experts on a limited scale was also part of their forward
thinking for Industry 4.0 requirements. This sentiment, as identified from the data and thematic
113

analysis of the participant responses, was influential to the other research themes that were
identified. This finding on cognitive limitation and developing others within the team to respond
to Industry 4.0 initiatives aligned with the five learning organization constructs model developed
by Senge (1990). The Senge model posits personal mastery, mental models, team learning,
shared vision, and systems thinking as needed to advance organizational capabilities and build
cohesive teams in the business culture (Reese, 2020).
Response to Industry 4.0
All of the study participants stated they utilized self-directed learning to address their
knowledge gaps related to Industry 4.0 in varying degrees. These degrees ranged from all
participants having general topic inquiries, to having comprehensive conversations with Industry
4.0 technology experts, seeking information through internet articles and professional
publications, and (for three participants) enrolling in formal academic business and technology
opportunities. All the participants stated that the outcome of using SDL to increase knowledge
influenced subsequent Industry 4.0 actions at their organizations, as noted by their interview
responses to the protocol questions.
All participants perceived the results of their self-directed learning about Industry 4.0 as
framing how they approached adopting 4.0 capabilities for their organizations. That framing,
influenced by competitive pressures, financial capabilities, and ownership parameters, defined
their SDL methodology to Industry 4.0 challenges. The participants stated that the desire to
respond was also tempered by the financial goals and investment capabilities of their
organizations.
Adopting or not adopting Industry 4.0 capabilities was viewed by participants as defining
the future potential of their organizations. All participants recognized the challenges of
114

contending in a business environment that was being defined by those companies that were using
Industry 4.0 technologies and those that were not. The participants’ sentiments aligned with the
challenges of using or not using Industry 4.0 technologies outlined in studies by Olayanju (2019)
and Popkova (2019) on the nascent consequences from Industry 4.0 competitiveness: those
companies adopting 4.0 solutions were emerging as more viable. These participant sentiments
also influenced Industry 4.0 acquisition solutions: participants related that the untried aspects of
many vendor-developed food industry 4.0 technologies were too risky. They stated that their
limitations of time and capital further increased intensity to only invest in demonstrated
capabilities. These participants’ sentiments were consistent with research conducted by Tang
(2019) on acquisition risks of timely Industry 4.0 capabilities, and Lieberman (2006) on business
capabilities emulation.
Environment Impacts on Executives’ Knowledge and Motivation Responses to Industry 4.0
The study participants viewed the U.S. food industry environment as changing due to the
emergence of Industry 4.0 technologies. The business environment changes were stated by all
the participants as motivating their actions for individual and organizational 4.0 actions. The
participants all stated that addressing U.S. food industry knowledge gaps for how they could
direct their organizations response to the Industry 4.0 emerging environment could not occur
without self-directed learning. Industry 4.0 technologies impacting the U.S. food industry were
described by participants as “daunting and challenging” to their self-directed learning efforts yet
rewarding to how individual knowledge enhancements provided participants new and definitive
skill sets. The participants revealed that the sheer number of new technologies and capabilities
and the speed of change of the Industry 4.0 capabilities made good decision making very
complex considering factors of the value of competitive responses and affordability.
115

Enhancing self-efficacy through SDL was stated by participants as playing a key role in
their response to the changing U.S. food industry environment. All the participants stated that
their self-efficacy was increased through their self-directed Industry 4.0 research for individual
and organizational capabilities. The participants’ perspective on utilization of SDL was
consistent with a study by Feldon (2019) on cognitive load and motivation concluded that
perceived self-efficacy had been shown to increase when cognitive load increases were
accompanied by motivational attributes.
Several methods of self-directed learning were identified by the participants that
influenced their individual awareness of the U.S. food industry environment, their personal SDL
actions, and their ability to shape their internal organization environments as decision makers.
Face-to face interactions with their peers, industry equipment vendors, and attendance at trade
group meetings were cited by various study participants as means to gauge how their
organization compared to others. Studies conducted by Kresse (2003), Maskell (2006), and Zhu
(2020) identified the benefit of business decision makers utilizing such interactions for
enhancing new knowledge through interactive settings.
Future Role of Self-Directed Learning for Industry 4.0
The future role of self-directed learning related to the U.S. food industry and Industry 4.0
was collectively endorsed by the study participants. These areas were seen by all participants as
needed and essential to bridge gaps in formal academic programs. How to build organizational
capacity to address the future challenges was a central concern of the participants. Three themes
emerged regarding support for individual and organization-wide use of self-directed knowledge
methods to bridge knowledge gaps, specificity of responses to technology opportunities and
116

adopting proven technologies, and development of specialized employees to meet industry 4.0
challenges.
Participants viewed the U.S. food industry’s Industry 4.0 activities as transformational.
All of the participants observed that their individual knowledge-gap closure and organizations’
financial response required specific and defined focus. The participants considered the breadth of
capabilities offered by Industry 4.0 technologies as too great to approach without very directed
and researched specificity. All participants viewed themselves, generally, as fast followers to the
major food industry organizations and Amazon for adopting proven Industry 4.0 technologies.
The participants stated their individual time and learning capabilities were too limited to
become subject matter experts on all the capabilities of industry 4.0 and involvement of other
employees was seen as critical. Due to those limitations, they were increasingly looking to hire—
or internally develop utilizing self-directed learning—key employees to become specific area
innovation experts in the various technology areas related to Industry 4.0. Two of the participants
stated diversity in hiring opportunities for 4.0 technology positions would align with their
organizational diversity and inclusiveness initiatives. Participants were aware of the need for
imposing transformative change on their organizations as they adopted Industry 4.0 technologies,
and of the changing power dynamic that such transformative change would potentially have on
their companies (Bolman, 2017). The viewpoints of the participants aligned with a study by
Tortorella (2021) on the role of increasing employee involvement in Industry 4.0
transformational organizations.
Other Studies on the Role of Industry 4.0 Learning
There are few studies published on the impact of self-directed learning and other
knowledge gap closure methods related to Industry 4.0. Studies specifically focused on United
117

States industry responses to Industry 4.0 using self-directed learning, outside of the educational
or medical students and allied health vocations, are also limited. Two recent studies published in
2020 were identified that look at the role of training and self-directed learning to bridge Industry
4.0 knowledge gaps. The first was a qualitative study addressing self-directed learning concerned
Information and Communications Technology (ICT) sector workers in Finland (Lemmetty,
2020). The second was a mixed-methods study examining a U.S. owned multi-national
construction company requirements to address technology opportunities and train a broad range
of key employees to identify needs and uses for Industry 4.0 technologies (Patrucco, 2020). In
both studies the roles of identifying appropriate, timely, and affordable Industry 4.0 related
technologies to enhance productivity were identified as critical tasks. Additionally, both studies
identified the role of training and involvement at all levels of the organizations to bridge
knowledge gaps. The Lemmetty study specifically addressed the role of self-directed learning for
knowledge gap closure that was rapidly adapting due to the speed of change.
Findings were similar for the Lemmetty (2020) and Patrucco (2020) studies to this study.
Both reached analogous findings concerning the need to respond to Industry 4.0 impacts on those
other industries, the impact on the business environment for each, and the future role of
enhancing training and learning at all organizational levels to meet Industry 4.0 challenges and
seize 4.0 opportunities. These two studies dealt with industries and locations that are quite
different than those associated with the U.S. food industry, but both were conducted to garner
participants’ sentiments on what each was currently doing and could do in the future to address
Industry 4.0 opportunities.

118

Recommendations for Practice
The findings of this study indicated that the impact of Industry 4.0 on the U.S. food
industry created a need for knowledge gap closure that self-directed learning fulfilled with the
study participants. The study participants stated that their actions to seek new knowledge and
gain new skills concerning Industry 4.0 improved their capacity as executive decision makers.
The findings from the participants related to their statements of perceived benefit for utilizing
self-directed learning and their self-initiative to close knowledge gaps align to the literature in
such areas as taking on an agentic perspective (Bandura, 1989) and improving perceived self-
efficacy (Bandura, 2011; Feldon, 2019) in practice.
Three recommendations were identified from the study findings. As formal education
lags the speed of change associated with Industry 4.0 advances (Kyvliuk, 2019), the role of self-
directed learning (Allen, 2019; O’Roark 2002) will continue to be relevant to U.S. food industry
organizations. The three recommendations are mutually supporting. Utilizing self-directed
learning on an ongoing basis is the first recommendation, but SDL plays a major role in each of
the other two recommendations for resource development of key employees and adoption of
relevant industry leading technologies.
Recommendation 1: Executives Should Utilize Self-Directed Learning to Bridge Formal
Learning Gaps and Create a Learning Culture
It is recommended that U.S. food industry executives create a learning culture within
their organizations by focusing on their personal practice of self-directed learning and
announcing their support for their organization employees to utilize self-directed learning actions
to close Industry 4.0 knowledge gaps. These executives should exemplify the utilization of SDL.
The study participants stated they learned or developed response methodologies to Industry 4.0
119

based upon strong reliance on self-directed research. Kyvliuk (2019) identified that there are few
if any formal academic options available. To address the challenges of Industry 4.0, new courses
of action to meet knowledge gaps are needed. Past research on self-directed learning (Bandura,
2001, 2011) and knowledge awareness (Mourtzis, 2019; Rana, 2016) determined self-directed
efforts are effective to close knowledge gaps. Availing of the formal academic options that may
be available, as three of the participants had done to gain 4.0 technology related skills, should
also be supported by organizations as those academic programs become more widely accessible
for all learning levels.
In order to make this transition to active support and encouragement of employee
engagement with SDL, it is recommended that Amabile’s (2008) methodology for initial
assessment of how to lead change be reviewed. As a leader, determining the appropriate manner
of communicating the need for an organizational emphasis is a critical first step. To execute the
SDL emphasis, it is recommended that the decision-making executives utilize both the Ling
(2018) model for change leadership and the Kotter (2018) model for the 8-steps to accelerate
organizational change. The Ling model emphasizes leader-developed understanding of goals and
process to design to then actively and inclusively lead implementation to achieve the desired
change strategies. Kotter’s 8-steps approach to conducting a structured change management
emphasizes how to form and address the phases of a change environment based upon vision and
empowerment within the affected team. With its emphasis on the creation of a sense of urgency,
Kotter’s model aligns with the speed of change associated with Industry 4.0 technology
development.
These two strategies, if intertwined to best address the needs and financial capabilities of
the respective organization, provide a flexibility to tailor response to either publicly owned
120

companies or privately owned company dynamics. Using these two strategies, tailored for the
unique requirements of an individual organization, provides an adaptable approach to addressing
self-directed learning implementation. The participants stated the need to avoid single or
uncompromising approaches to their organization opportunities was stressed as key to addressing
issues in a sustainable manner. This approach should be specific to the executive decision
makers’ individual practice then expanded to a guided practice for their teams utilizing task and
standard setting (Jones, 2001; Müller, 2018).
Recommendation 2: Invest Resources to Develop Industry 4.0 Innovation Experts

It is recommended that U.S. food industry executives adopt and resource a plan to close
Industry 4.0 organizational knowledge gaps by developing or hiring key employees as 4.0
innovation experts. These innovation experts should specialize in specific technologies and
practices that are aligned to each key function within the organization. These key functions
include areas such as marketing, human resources, logistics, manufacturing, operations planning,
procurement, and quality systems. To ensure that these individuals’ contributions are not lost due
to inherent resistance to organizational change, creation of appropriate organizational
departments focused on Industry 4.0 opportunities should also occur. If appropriate for the
organization, creation of a Chief 4.0 Innovation Officer should be considered as an addition to
the C-Level. Each of the study participants stated their individual time and learning capabilities
were too limited to become subject matter experts on all the capabilities of Industry 4.0. Due to
those limitations, the participants stated their recourse to bridge organizational skill gaps was the
internal development of key employees as subject matter experts for Industry 4.0 capabilities. If
internal development proved inadequate, the participants stated external hiring of individuals
who already possessed Industry 4.0 skills, or who could develop them, was their next step.
121

The first step to implementing the addition of Industry 4.0 innovation experts through
internal development or external hiring in a U.S. food industry organization is for organizations
to assess current and foreseeable key employee needs contrasted against the organization’s
current employee capabilities and limitations. Conducting a gap analysis, using the Clark and
Estes (2008) gap analysis framework, is a first step to determine what type of innovation experts
are needed and the critical timing for their placement. Once the gaps are identified a training
program should be developed and implemented for the needed industry 4.0 specialties.
Flexibility for organizations would be achieved by taking a synthesis of the Kirkpatrick (2018)
model and the Bolman and Deal (2017) four frames model to develop the training standard most
appropriate for the respective organization’s requirements. Allen (2019) and O’Roark (2002)
identified that utilization of in-house subject matter experts, individual readings of relevant
literature, and directed intra-industry peer activities serve as a potential bridge to develop key
employees for needed roles.
The approach executives take for focusing on differing areas of information, and the
manner it is evaluated to influence the outcomes of their organizations and motivate their teams
to succeed, must embrace key employees even in daunting or stressful circumstances (Bridges,
2018; Corts, 2007; Senge, 2001). Changing an organizations’ current responsibilities alignment
and resultant power structure can be fraught with peril for other team members (Ebrahim, 2005;
Langhe, 2011). To affect a positive climate for such change, decision making executives are
recommended to develop a corresponding organizational structure that provides Industry 4.0
innovation experts the opportunities for individual empowerment and success in those expertise
areas (Magistretti, 2021).
122

Developing these new Industry 4.0 innovation expert roles is a novel opportunity for an
organization. The scope of these roles should include identification, implementation, and
developing Industry 4.0 capabilities into the organization at contributor, management, and
executive levels, with subsequent training and sustainability activities. Hiring for these roles
provides a potential to incorporate hiring practices that focus on diversity and inclusiveness
(Usher, 2018) that could benefit the hiring company. New ideas requiring new employees
provides organizations the ideal window to pursue diversity in hiring goals if so desired (Boone,
2009; Mazzei, 2012). Hiring organizations can take this opportunity to structure diversity goals
for innovation expert hiring as part of building their Industry 4.0 subject matter expert key
employees.
Recommendation 3: Utilize Best Practice Industry 4.0 Technologies
It is recommended that a needs-based identification of available Industry 4.0 technology
be stringently utilized by U.S. food industry organizations. Utilizing the capabilities afforded by
implementation of Recommendation 2 and investing in 4.0 innovation experts, organizations can
offset potentially losing competitive position. Potential acquiring stakeholders should assess
Industry 4.0 status and internal investment as part of their valuation criteria for purchase or
investment. This needs-based identification should be used to optimize the balance between
competitive need and affordability by organizations to address their unique limitations, while
optimizing Industry 4.0 opportunities (Bordeleau, 2020; Pfeiffer, 2018). The study participants
identified that the breadth of available and developing Industry 4.0 technologies was
overwhelming; decision making was hampered by too many options and insufficient expertise to
assess them. Seven participants stated the emphasis from those who owned their respective
organizations to acquire Industry 4.0 technologies was lacking, but the participants perceived the
123

need to pursue 4.0 opportunities still existed. Eleven participants stated insufficient capital
motivated their need to identify emergent and validated technologies for their organizations to
emulate.
As with Recommendation 1, it is recommended that the decision-making executives
utilize the Ling (2018) model for change leadership assessment then utilize the Kotter (2018)
model for the 8-steps program to accelerate organizational change. Industry 4.0 needs of the
organization identified by the members of the new organizational structure will allow
identification of 4.0 technology options. After such an evaluation is conducted, and within the
limiting parameters of business objectives, financial resources, and strategic plan adherence,
initial purchase recommendations for the prioritized 4.0 technologies should be submitted
(Chuang, 2018; Müller, 2018).
Innovations to respond to change require capital investment and retooling or replacement
of manufacturing plants and equipment (Chuang, 2018; Risch, 2009). Speed of change is
increasing the complexity of the Industry 4.0 environment (Xu, 2018). The effect of Industry 4.0,
with impacts of robotics, logistical planning, and utilization of labor, is creating cost pressures
that are unique to the new era (Marr, 2018; Peters, 2012; Pfeiffer, 2018). Determining the
efficacy of new technology, as identified by Lasi (2014) and Schulz (2019), should be
incorporated when developing concepts and considering or implementing new technology
purchases. The participants identified utilization of outside agencies such as vendors, contractors,
and suppliers to identify potential technologies as efficient and cost-effective. These agencies
were seen as optimal sources by the participants when conducting self-directed leaning on
Industry 4.0 knowledge gaps to identify best practices that were already optimized and validated
by other organizations.
124

Recommendations Summary
Executives within the U.S. food industry can utilize the findings related by the study
participants to achieve their own personal and organizational improvements. The themes
identified by the study participants led to three recommendations for application across the U.S.
food industry; each is based upon the utilization of self-directed learning methodologies. The
recommendations are to create a learning culture by organizational executives with and for their
employees to bridge Industry 4.0 knowledge gaps, invest to develop Industry 4.0 innovation
experts, and identify validated technologies that provide individual companies the Industry 4.0
tools they can affordably adopt to remain competitive in the market.
Limitations and Delimitations
Limitations to qualitative studies are outside the researcher’s control (Simon, 2011). They
reflect aspects such as the breadth and willingness of interview participation and the veracity and
expertise of the participants (Creswell, 2018; Merriam, 2016). For this study participants’
candor, memory, and willingness to openly share experiences were potential limitations. The
first limitation was addressed by focusing on targeted participation of interview participants
based upon identified criteria. These individuals reflected diversity of sub-industries within the
overall U.S. food industry which included manufacturing and sales, logistics, warehousing,
materials procurement, and the associated administrative functions with each of these sub-
industries. The second limitation considering candor or openness was addressed by the use of a
semi-structured interview format that allowed me to interview the selected participants from
varied executive functions using the same proposed questions. Because of the senior level and
broad tenure and expertise of the proposed participants, it was apparent that information gathered
was extensive, had appropriate depth, and was rich with context and perspective. A final
125

limitation related to the study was the collection of documents and artifacts. None of the
participants identified documents or artifacts for themselves or their organizations within the
scope of this study’s research questions. This absence precluded the ability to use triangulation
or other corroborative validation based upon documents or artifacts.
Delimitations, as contrasted to limitations, are in the control of the researcher and outline
the boundaries of a study: the breadth of the study as constrained to a reasonable scope, given the
topic, so that the research can be realistically conducted (Merriam, 2016; Simon, 2011).
Delimitations of this study included the limited focus on the U.S. food industry and targeted
selection of food industry executives to be the study participants. Other organizational levels of
these companies below the executive level were not examined. Individuals in these other levels
may have completely different perspectives on self-directed learning than those in the senior
levels of their respective companies. Other researchers utilizing this study and research findings
may not find repeatable results with additional research participants representing different U.S.
food industry organizations. This does suggest opportunities to seek distinct types of inquiry both
in regard to levels of responsibility below the executive level within the U.S. food industry and
to other for-profit organizations impacted by rapid corporate change.
Recommendations for Future Research
The findings of this narrowly focused study identified recommendations for the specific
area of utilization of self-directed learning in the U.S. food industry as related to Industry 4.0
advancements, and these findings and methods may be the basis for future research. The scope of
Industry 4.0 is so broad as to make similar studies relevant in virtually any field that is touched
by technology. Impacts of Industry 4.0 affect nearly all, if not all, facets in the digitized world of
commerce and human interaction. Other business, societal, and communications general areas
126

can also be examined for inherent impact and the manner or response by the affected consumers
or utilizers of those areas. Industry 4.0 capabilities and advances, and the manner they are
acknowledged and understood, will define how the interactions of virtually any organization and
its members are responded to and measured.
Specifically for the food industry, and taking a global perspective and broader individual
organization perspective, the role of self-directed learning to address Industry 4.0 advancements
can be examined for its impact in other nations outside the United States. Additionally, a
comparative assessment of differences between the approach to Industry 4.0 by United States
food industry companies and those of Europe (or other world-areas) could be examined. The role
of self-directed learning can be examined for its impact for other organizational levels of
responsibility outside the executive group. Studies can be defined on specific organizational
segments, and with specific attention to those individuals identifying with historically
marginalized groups. Examination through prisms of innovation and profitability, contrasting
companies that adopt self-directed learning practices to those that do not, can also be conducted.
Another area of recommended focus is the determination of what specific types of self-directed
or other forms of informal learning are most effective in addressing Industry 4.0 knowledge
gaps, and with what sub-groups within an organization.
One area not addressed by this study is the impact of Industry 4.0 influenced decision
making on the ability of U.S. food companies to equitably service historically marginalized
communities and areas. This potential for disparate impact is a topic that could and should be
examined. How Industry 4.0 technologies are developed and then utilized to offset service issues
to these locales should be scrutinized as the quest for maximum profitability and efficiency
should not come at the expense of these communities.
127

Because of the larger scope of these recommendations for future research, utilizing these
(and other) identified areas may be more appropriate for a quantitative study. While the findings
of this study on the U.S. food industry were limited to the qualitative perspectives of this study’s
participants, utilizing a quantitative methods study with a broader group of participants is
recommended. Such a study would identify to what degree this study’s findings are corroborated
or generalized to the U.S. food industry or are shown to be unconfirmed. An additional benefit to
broadening this type of inquiry, framed as a quantitative study, is the ability to further minimize
any unconscious bias on my part that affected the description of findings or recommendations.
Research questions like “Does Industry 4.0 affect area X?” and “Do the stakeholders of area X
find relevance or success with self-directed learning to bridge knowledge gaps?” are relevant
areas of consideration for any affected group of individuals or field of endeavor.
Conclusion
Industry 4.0 technologies are reshaping the capabilities of the U.S. food industry to
create, make, and deliver food items. The findings of this qualitative field study established that
the participating U.S. food industry executives viewed Industry 4.0 technologies as relevant to
their industry, to their individual organizations, and to their ability to be the key decision-makers
for those organizations. Utilizing the lens of Social Cognitive Theory, the findings also
demonstrated how the participants’ actions aligned with the tenets of SCT of self-motivation to
develop capabilities to affect industry and organizational environments, and adoption of an
agentic perspective to do so. The purpose of this study was to examine how these decision-
making executives were engaging in self-directed learning (SDL) and other learning methods
through formal or informal activities to acquire tools to address current or future escalating
Industry 4.0 business challenges. The study findings revealed that the 13 executives interviewed
128

in this study utilized self-directed learning opportunities to bridge knowledge gaps in varying
degrees, enabling Industry 4.0 decision making to occur in a data driven manner. The study
participants viewed their own level of Industry 4.0 self-directed learning and that of their peers
and teams as critical to the competitive survival of their respective organizations.
The study also identified that the participants viewed their application of self-directed
learning, and the subsequent actions of their organizations to acquire or utilize Industry 4.0
related capabilities, as strongly influenced by the ownership model of their companies. All
participants stated they utilized SDL, yet those participants from publicly traded companies were
more likely to significantly invest in their own Industry 4.0 knowledge development and acquire
Industry 4.0 technologies for their organizations than their counterparts from privately owned
companies. Additionally, the study found that the participating executives recognized the
influence of time between their formal educations in business and engineering and today’s
Industry 4.0 advances on their knowledge gaps. Lastly, the study identified that the participants
viewed their own capabilities to master Industry 4.0 topics were limited, necessitating the
external hiring or internal development of key employees who could specialize in Industry 4.0
specific areas.
How the broad range of stakeholders for the U.S. food industry will individually or
collectively respond to the challenges of Industry 4.0 transformation will play out over the
coming decades. As academic institutions race to keep up with the Industry 4.0 speed of change
for relevant formal offerings, the role of self-directed learning will continue to assist decision
makers and their teams to bridge knowledge gaps. The competitive pressures for U.S. food
industry organizations to make high quality, food safe products that address the demands of their
consumers are high. Doing so, while meeting the cost structures of local economies for their
129

distribution networks of retailers and food providers, requires continuous improvement in all
areas of food manufacturing and sales. Transformational advances with the advent of Industry
4.0 technologies are significant to reduce costs and increase efficiencies in the food industry,
affecting areas such as product creation, manufacturing, logistics, and administrative costs. The
literature, and the sentiments of the participants, reflects that companies which invest and react
quickly to transformational advances have a much higher potential to survive market competitive
pressures than companies that do not invest and react. The consequences for those organizations
that do not remain competitive will negatively impact all stakeholders and beneficiaries of those
companies in the markets they currently serve. Those U.S. food executives who embrace the
agentic perspective for themselves and their teams, within the financial parameters of their
companies to adopt those transformational advances, are much more likely to see their
businesses grow and thrive.

130

References
Abatecola, C. (2018). Reviewing cognitive distortions in managerial decision making: toward
an integrative co-evolutionary framework. The Journal of Management Development,
37(5), 409–424.
Aguinis, K. (2009). Benefits of training and development for individuals and teams,
organizations, and society. Annual Review of Psychology, 60(1), 451–474.
Allen, H. (2020). Applying a social theory of learning to explain the possible impacts of
continuing professional development (CPD) programs. Medical Teacher, 42(10), 1–8.
Allen, M. (2012). The colonial origins of the divergence in the Americas: a labor market
approach. The Journal of Economic History, 72(4), 863–894.
Allen, R. (2019). Transformational and digital change: a UK perspective. Organisational and
Social Dynamics, 19(2), 143-167,289.
Alvarado, S. (2018). The impact of childhood neighborhood disadvantage on adult joblessness
and income. Social Science Research, 70(February), 1–17.
Alvarado, S. (2019). The indelible weight of place: childhood neighborhood disadvantage,
timing of exposure, and obesity across adulthood. Health & Place, 58, 102159–102159.
Amabile, T. (2008). Creativity and the role of the leader. Harvard Business Review, 86(10),
100–142.
Amdam, R. (2016). Executive education and the managerial revolution: the birth of executive
education at Harvard business school. Business History Review, 90(4), 671–690.
Artis, H. (2007). Self-directed learning and sales force performance: an integrated
framework. The Journal of Personal Selling & Sales Management, 27(1), 9–24.
Asaba, L. (1999, August). Why do firms behave similarly? A study on new product introductions
in the Japanese soft-drink industry. Academy of Management Proceedings (Vol. 1999,
131

No. 1, pp. M1-M6). Academy of Management.
Augier, M. (2007). The pursuit of relevance in management education. California Management
Review, 49(3), 129–146.
Avanade. (2010). The business impact of big data. Avanade.com. https://www.avanade.com
/-/media/asset/point-of-view/big-data-executive-summary-final-seov.pdf
Ayseli, A. (2020). Food policy, nutrition and nutraceuticals in the prevention and management of
COVID-19: Advice for healthcare professionals. Trends in Food Science &
Technology, 105, 186–199.
Azzaretti, M. (2017). How digital data is transforming the food industry. Food Manufacturing,
30(6), 22–.
Bandura, A. (1989). Human agency in social cognitive theory. The American
Psychologist, 44(9), 1175–1184.
Bandura, A. (2001). Social cognitive theory: an agentic perspective. Annual Review of
Psychology, 52(1), 1–26.
Bandura, A. (2011). On the functional properties of perceived self-efficacy revisited. Journal
of Management, 38(1), 9–44.
Bandura, A. (2016). Toward a psychology of human agency. Perspectives on Psychological
Science, 1(2), 164–180.
Basu, F. (2008). Information and communications technology as a general purpose technology:
evidence from U.S. industry data. Economic Review, 1–.
Bathelt, H. (2003). Success in the local environment–local buzz, global pipelines and the
importance of clusters. Think on, 2, 28-33.
Baumard, N. (2019). Psychological origins of the industrial revolution. The Behavioral and
132

Brain Sciences, 42, 1–e189.
Bayona-Saez, C. (2017). Open innovation in the food and beverage industry. Management
Decision, 55(3), 526–546.
Beaulac, K. (2009). A systematic review of food deserts, 1966-2007. Preventing Chronic
Disease, 6(3), A105–A105
BeMiller, J. (2009). One hundred years of commercial food carbohydrates in the United
States. Journal of Agricultural and Food Chemistry, 57(18), 8125–8129.
Bennink, H. (2020). Understanding and managing responsible innovation. Philosophy of
Management, 19(3), 317–348.
Beske, S. (2014). Sustainable supply chain management practices and dynamic capabilities in the
food industry: A critical analysis of the literature. International journal of production
economics, 152, 131-143.
Bjork, D. (2013). Self-regulated learning: beliefs, techniques, and illusions. Annual Review of
Psychology, 64(1), 417–444.
Black, M. (2014). Dietary inequalities: what is the evidence for the effect of the neighbourhood
food environment? Health & Place, 27, 229–242.
Blazquez-Resino, G. (2019). Dealing with human resources in the age of consumer 4.0: aiming
to improve service delivery. Frontiers in Psychology, 10, 3058–3058.
Bolman, L. (2017). Power and the power simulation: then and now. Journal of
Management Education, 41(5), 627–633.
Bolman, D. (2017). Reframing Organizations: Artistry, Choice, and Leadership. John Wiley &
Sons, Incorporated.
Boone, H. (2009). Top management team diversity and firm performance: moderators of
133

functional-background and locus-of-control diversity. Management Science, 55(2), 165–
180.
Bordeleau, F. (2020). Business intelligence and analytics value creation in Industry 4.0: a
multiple case study in manufacturing medium enterprises. Production Planning &
Control, 31(2-3), 173–185.
Bovens, S. (2014). Meaningful accountability. In the Oxford handbook of public accountability.
Bowman, J. (2021). Publicly traded companies: definition and examples. The Motley Fool.com.
Boyer, E. (2014). Self-directed learning: a tool for lifelong learning. Journal of Marketing
Education, 36(1), 20–32.
Brahimi, S. (2015). Learning outside the classroom through MOOCs. Computers
in Human Behavior, 51, 604–609.
Bridges, E. (2018). Executive ethical decisions initiating organizational culture and values.
Journal of Service Theory and Practice, 28(5), 576–608.
Brookfield, S. (1984). Self-directed adult learning: a critical paradigm. Adult Education
Quarterly (American Association for Adult and Continuing Education), 35(2), 59–71.
Bryant, A. (2009). Grounded theory and pragmatism: the curious case of Anselm Strauss.
Forum, Qualitative Social Research, 10(3).
Brynjolfsson, M. (2012). Winning the race with ever-smarter machines. MIT Sloan Management
Review, 53(2), 53–.
Burksaitiene, T. (2011). Portfolio use for documentation of personal and professional growth
gained outside academia. Baltic Journal of Management, 6(2), 245–.
Business Transformation. (2019). Transforming the food and beverage industry with digital
technologies. Business Transformation.
134

Bybee, M. (1984). James’s theory of truth as a theory of knowledge. Transactions of the
Charles S. Peirce Society, 20(3): 253–267.
Cao, R. (2017). Conagra ceo: innovation is as important in the food business as it is in anybody’s
business. CNBC.com.
Carillo, K. (2017). Let’s stop trying to be “sexy” – preparing managers for the (big) data-driven
business era. Business Process Management Journal, 23(3), 598–622.
Carillo, G. (2019). How to turn managers into data-driven decision makers: measuring attitudes
towards business analytics. Business Process Management Journal, 25(3), 553–578.
Caspi, K. (2012). The relationship between diet and perceived and objective access to
supermarkets among low-income housing residents. Social Science & Medicine (1982),
75(7), 1254–1262.
Chandler, S. (1991). Cognitive load theory and the format of instruction. Cognition and
Instruction, 8(4), 293–332.
Cheffins, B. (2015). Corporate governance since the managerial capitalism era. Business History
Review, 89(4), 717–744.
Chuang, S. (2018). Embracing the sobering reality of technological influences on jobs,
employment and human resource development. European Journal of Training and
Development, 42(7/8), 400–416.
Church, D. (2018). Agile feedback drives accountability and sustained behavior change.
Strategic HR Review., 17(6), 295–302.
Clark, R. (2005). Research‐tested team motivation strategies. Performance Improvement, 44(1),
13-16.
Clark, E. (2008). Turning research into results: A guide to selecting the right performance
135

solutions. IAP. Chapter 2, 21-39.
Cohen, S. (1957). Political and economic prospects of the jet age. The Journal of Air Law and
Commerce, 24, 398–.
Collins, P. (1961). The structure of food-processing industries 1935-55. The Journal of Industrial
Economics, 9(3), 265–279.
Committee for Economic Development. (2017). Economic Contribution of the Food and
Beverage Industry.
Cookson, B. (1976). Concord - What’s new? The Journal of Air Law and Commerce, 42, 1–.
Corts, K. S. (2007). Teams versus individual accountability: solving multitask problems
through job design. The RAND Journal of Economics, 38(2), 467–479.
Creswell, J. D. (2018). Research design: qualitative, quantitative, and mixed methods
approaches. Fifth edition. SAGE
Cummins, N. (2019). The evolution of transatlantic flight. Simple Flying.
Culot, G. (2019). Addressing industry 4.0 cybersecurity challenges. IEEE Engineering
Management Review, 47(3), 79–86.
Currier, W. (2018). A brief history of milk hygiene and its impact on infant mortality from
1875 to 1925 and implications for today: a review. Journal of Food Protection, 81(10),
1713–1722.
Cusumano, M. (2011). The platform leader’s dilemma. Communications of the ACM, 54(10),
21–24.
Cuthbertson, A. (2020). World’s first fully self-driving car will be ready this year.
World's first fully self-driving car will be ready this year, Elon Musk claims | The
Independent.
136

Deeb, G. (2016). What exactly is venture capital? Forbes.com.
Demetrakakes, P. (2021). Is big food too big? Food Processing, 82(1), 21-25.
De Langhe, O. (2011). The effects of process and outcome accountability on judgment process
and performance. Organizational Behavior and Human Decision Processes, 115(2), 238-
252.
Dombrowski, W. (2014). Mental strain as field of action in the 4th industrial revolution.
Procedia CIRP, 17, 100–105.
Dubnick, M. (2003). Accountability and ethics: reconsidering the relationships. International
Journal of Organization Theory and Behavior, 6(3), 405–441.
Dubnick, M. (2011). Move over Daniel: we need some “accountability space”. Administration &
Society, 43(6), 704-716.
Dunlosky, R. (2012). Overconfidence produces underachievement: inaccurate self-evaluations
undermine students’ learning and retention. Learning and Instruction, 22(4), 271–280.
Dunning, J. (2016). Why people fail to recognize their own incompetence. Current Directions
in Psychological Science: A Journal of the American Psychological Society, 12(3), 83–
87.
Ebrahim, A. (2005). Accountability myopia: Losing sight of organizational learning. Nonprofit
and voluntary sector quarterly, 34(1), 56-87.
Eldh, Å. (2020). Quotations in qualitative studies: reflections on constituents, custom, and
purpose. International Journal of Qualitative Methods, 19.
Feldon, D. (2019). Cognitive load as motivational cost. Educational Psychology Review, 31(2),
319–337.
Fleming, D. (2017). Self-evaluation of decision-making: A general Bayesian framework for
137

metacognitive computation. Psychological Review,124(1), 91–114.
Food Industry Executive. (2021). Trade shows. Food Industry Events & Trade Shows – Food
Industry Executive News
Frey, O. (2017). The future of employment: how susceptible are jobs to computerisation?
Technological Forecasting & Social Change, 114(January), 254–280.
Frizzo-Barker, C. (2016). An empirical study of the rise of big data in business
scholarship. International Journal of Information Management, 36(3), 403-413.
Garrison, D. (1997). Self-directed learning: toward a comprehensive model. Adult Education
Quarterly (American Association for Adult and Continuing Education), 48(1), 18–33.
Gibbs, G. R. (2018). Analyzing qualitative data (Vol. 6). Sage.
Gureckis, M. (2012). Self-directed learning: a cognitive and computational perspective.
Perspectives on Psychological Science, 7(5), 464–481.
Hahn, G. (2020). Industry 4.0: a supply chain innovation perspective. International Journal of
Production Research, 58(5), 1425–1441.
Hartel, R. (2017). A 100-year review: milestones in the development of frozen desserts. Journal
of Dairy Science, 100(12), 10014–10025.
Hassabis, D. (2017). Artificial intelligence: chess match of the century. Nature,
544(7651), 413–414.
Hemphill, P. (2012). A U.S. manufacturing strategy for the 21st century: what policies yield
national sector competitiveness? Business Economic, 47(2), 126–147.
Hepper, G. (2010). Individual differences in self‐enhancement and self‐protection strategies: an
integrative analysis. Journal of Personality, 78(2), 781–814.
Hippold, S. (2020). Gartner says supply chain leaders must develop a culture of quality to sustain
138

quality levels during periods of disruption. Gartner.
Hong, K. (1999). Knowledge management in the learning organization. Leadership &
Organization Development Journal, 20(4), 207–215.
Hopkins, M. (2010). A billion brains are better than one: interview with Thomas W.
Malone. MIT Sloan Management Review, 51(3), 1–.
Horowitz, R. (2019). Creating the kosher brand: Orthodox Judaism as business strategy.
American Jewish History, 103(4), 407–428.
Janic, M. (2020). Modelling performance an air transport network operated by subsonic and
supersonic aircraft. Aeronautical Journal, 124(1281), 1–38.
Janssen, V. (2017). Factors influencing big data decision-making quality. Journal of Business
Research, 70, 338–345.
Jeffcoat, K. (2019). A conceptual framework for increasing innovation through improved
selection of specialized professionals. Engineering Management Journal, 31(1), 22–34.
Jiao, Z. (2020). Towards augmenting cyber-physical-human collaborative cognition for human-
automation interaction in complex manufacturing and operational environments.
International Journal of Production Research, 58(16), 5089–5111.
Johnston, J. (2007). The citizen-consumer hybrid: ideological tensions and the case of Whole
Foods Market. Theory and Society, 37(3), 229–270
Jones, M. (2001). Sustainable organizational capacity building: is organizational learning a key?.
The International Journal of Human Resource Management, 12(1), 91-98.
Jovane, Y. (2008). The incoming global technological and industrial revolution towards
competitive sustainable manufacturing. CIRP Annals, 57(2), 641–659.
Kaczmarek. S. (2019). Mastering fourth industrial revolution through innovative personnel
139

management – a study analysis on how game-based approaches affect competence
development. Ifac-Papers Online, 52 (13), 2332-2337.
Kambhampati, S. (2020). Challenges of human-aware AI systems. The AI Magazine, 41(3), 3–
17.
Kelly, M. (2015). Finding meaning in the business environment. Philosophy of Management,
14(2), 135–156.
Kerr, W. (2020). The COVID‐19 pandemic and agriculture: Short‐ and long‐run implications
for international trade relations. Canadian journal of agricultural economics.
2020;68(2):225-229. doi:10.1111/cjag.12230
Kirkpatrick, K. (2016). Kirkpatrick’s four levels of training evaluation. ATD Press.
Kirschner, S. (2006). Why minimal guidance during instruction does not work: an analysis
of the failure of constructivist, discovery, problem-based, experiential, and inquiry-
based teaching. Educational Psychologist, 41(2), 75–86.
Klotz, F. (2018). How AI can amplify human competencies. MIT Sloan Management
Review, 60(1), 14–15.
Kotter, J. (2018). 8 steps to accelerate change in your organization. Kotterinc.com.
Kresse, H. (2003). The European trade fair industry: current situation and challenges. Mimeo.
AUMA.
Kyvliuk, Z. (2019). The modern paradigm of non-academic education in the context of unlimited
professional education. Humanitarnyĭ Visnyk Zaporizʹkoï Derz
͡ h
︡ avnoï Akademiï, 76, 112–
131.
Labuschagne, A. (2015). Qualitative research - airy fairy or fundamental? Qualitative Report.
Langhe, O. (2011). The effects of process and outcome accountability on judgment process and
140

performance. Organizational Behavior and Human Decision Processes, 115(2), 238–252.
Lasi, F. (2014). Industry 4.0. Business & Information Systems Engineering, 6(4), 239–242.
LeCun, B. (2015). Deep learning. Nature, 521(7553), 436–444.
Lemmetty, S. (2020). Self-directed learning as a practice of workplace learning: interpretative
repertoires of self-directed learning in ICT work. Vocations and Learning, 13(1), 47–70.
Lerner, T. (2003). Bridging individual, interpersonal, and institutional approaches to judgment
and decision making: the impact of accountability on cognitive bias (pp. 431–457).
Cambridge University Press.
Lieberman, A. (2006). Why do firms imitate each other?. Academy of management review,
31(2), 366-385.
Liguori, B. (2018). Revisiting entrepreneurial intentions: a social cognitive career theory
approach. International Entrepreneurship and Management Journal, 14(1), 67–78.
Lindert, W. (1983). English workers’ living standards during the industrial revolution: a new
look. The Economic History Review, 36(1), 1–25.
Ling, B. (2018). Change leadership and employees’ commitment to change: A multilevel
motivation approach. Journal of Personnel Psychology, 17(2), 83–93.
Liou, D. (2018). Investigating leader self-efficacy through policy engagement and
social network position. Educational Policy, 34(3),
089590481877390–089590481877448.
Lo Iacono, S. (2016). Skype as a tool for qualitative research interviews. Sociological
Research Online, 21(2), 103–117.
Logan, T. (2009). The transformation of hunger: the demand for calories past and present. The
Journal of Economic History, 69(2), 388–408.
Loyens, M. (2008). Self-directed learning in problem-based learning and its relationships
141

with self-regulated learning. Educational Psychology Review, 20(4), 411–427.
Machado, W. (2019). Sustainable manufacturing in Industry 4.0: an emerging research agenda.
International Journal of Production Research, 58(5), 1462–1484.
Magistretti, S. (2021). The microfoundations of design sprint: how Johnson & Johnson cultivates
innovation in a highly regulated market. Journal of Knowledge Management, 25(11), 88–
104.
Maglio, R. (2019). Feeling certain: gut choice, the true self, and attitude certainty.
Emotion, 19(5), 876–888.
Maresova, S. (2018). Consequences of industry 4.0 in business and economics. Economies, 6(3),
46–.
Markant, D. (2019). Effects of biased hypothesis generation on self-directed category
learning. Journal of Experimental Psychology. Learning, Memory, and Cognition, 45(9),
1552–1568.
Marr, B. (2018). What is industry 4.0? here is a super easy explanation for anyone. Forbes.
Martin, N. (2019). How technology is transforming the food industry. Forbes.
Maskell, B. (2006). Building global knowledge pipelines: the role of temporary clusters.
European Planning Studies, 14(8), 997–1013.
Mazzei, A. (2012). Leveraging variety for creativity, dialogue and competition.
Journal of Communication Management (London, England), 16(1), 59–76.
McCall, J. (2002). Leadership and ethics: corporate accountability to whom, for what and by
what means?. Journal of Business Ethics, 38(1-2), 133-139.
McDermott, V. (1995). Culture “as” disability. Anthropology & Education Quarterly, 26(3),
324–348.
142

McKelvey, S. M. (2006). Coca-Cola vs. PepsiCo-a “super" battleground for the cola wars?.
Sport Marketing Quarterly, 15(2), 114.
Melone, N. (1994). Reasoning in the executive suite: the influence of role/experience-based
expertise on decision processes of corporate executives. Organization Science, 5(3), 438–
455.
Merendino, D. (2018). Big data, big decisions: the impact of big data on board level decision-
making. Journal of Business Research, 93, 67–78.
Merriam, T. (2016). Qualitative research: a guide to design and implementation (4th ed.).
Jossey-Bass.
Merriënboer, S. (2009). Toward a synthesis of cognitive load theory, four-component
instructional design, and self-directed learning. Educational Psychology Review, 21(1),
55–66.
Mourtzis, F. (2019). Modelling and quantification of industry 4.0 manufacturing complexity
based on information theory: a robotics case study. International Journal of Production
Research, 57(22), 6908–6921.
Müller, F. (2018). The effect of big data and analytics on firm performance: an econometric
analysis considering industry characteristics. Journal of Management Information
Systems, 35(2), 488–509.
Nesbit, P. (2012). The role of self-reflection, emotional management of feedback, and self-
regulation processes in self-directed leadership development. Human Resource
Development Review, 11(2), 203–226.
Nespor, G. (2009). Ethics, problem framing, and training in qualitative inquiry. Qualitative
Inquiry, 15(6), 996–1012.
143

Nevid, J. S. (2009). Psychology: concepts and applications (3rd ed.). Houghton Mifflin
Company.
O’Roark, A. (2002). The quest for executive effectiveness: consultants bridge the gap between
psychological research and organizational application. Consulting Psychology Journal,
54(1), 44–54.
Olayanju, J. (2019). Top trends driving change in the food industry. Forbes.
Oudeyer, P. (2017). Autonomous development and learning in artificial intelligence and robotics:
Scaling up deep learning to human-like learning. The Behavioral and Brain Sciences, 40,
e275–e275.
Parker, B. (2010). Making things happen: a model of proactive motivation. Journal of
Management, 36(4), 827–856.
Patrucco, A. (2020). Industry 4.0 and supply chain process re-engineering: A coproduction study
of materials management in construction. Business Process Management Journal, 26(5),
1093–1119.
Patton, M. Q. (2002). Qualitative interviewing. In qualitative research and evaluation methods
(3rd ed.). SAGE Publications.
Pessôa, B. (2020). Smart design engineering: a literature review of the impact of the 4th
industrial revolution on product design and development. Research in Engineering
Design, 31(2), 175–195.
Peters, S. (2012). The impact of technology-push and demand-pull policies on technical change –
does the locus of policies matter? Research Policy, 41(8), 1296–1308.
Petrick, G. (2009). Feeding the masses: H.J. Heinz and the creation of industrial food. Endeavour
(New Series), 33(1), 29–34.
Pfeiffer, S. (2018). The “future of employment” on the shop floor: why production jobs are
144

less susceptible to computerization than assumed. International Journal for Research in
Vocational Education and Training, 5(3), 208–225.
Picciano, A. (2012). The evolution of big data and learning analytics in American higher
education. Online Learning,16(3), 9–.
Picciano, A. (2015). Planning for online education: a systems model. Journal of Asynchronous
Learning Networks JALN, 19(5), 142–.
Piccarozzi, A. (2018). Industry 4.0 in management studies: a systematic literature
review. Sustainability, 10(10), 3821–.
Pierce, C. (1905 [1998]). What pragmatism is. Monist, 15(2): 161–181; reprinted in The
Essential Peirce (Volume 2), The Peirce Edition Project (eds.), Indiana
University Press, 1998, pp. 331–345. doi:10.5840/monist190515230
Pollard. S. (1963). Factory discipline in the industrial revolution.1. The Economic History
Review, 16(2), 254–271.
Ponelis, S. (2012). A descriptive framework of business intelligence derived from definitions by
academics, practitioners and vendors. Mousaion, 30(1), 103–119.
Popkova, R. (2019). Fundamental differences of transition to industry 4.0 from previous
industrial revolutions. In Industry 4.0: Industrial Revolution of the 21st Century (pp. 21-
29). Springer, Cham.
Porter, M. (1986). Changing patterns of international competition. California Management
Review, 28(2), 9–40.
Pugna, D. (2019). Corporate attitudes towards big data and its impact on performance
management: a qualitative study. Sustainability, 11(3), 684–.
Puri, M. (2012). On the life cycle dynamics of venture-capital- and non-venture-capital-
145

financed firms. The Journal of Finance, 67(6), 2247–2293.
Rana, A. (2016). Promoting self-directed learning in a learning organization: tools and
practices. European Journal of Training and Development, 40(7), 470–489.
Rankin, B. (2017). A 100-year review: a century of dairy processing advancements—
pasteurization, cleaning and sanitation, and sanitary equipment design. Journal of Dairy
Science, 100(12), 9903–9915.
Ransbotham, K. (2015). Minding the analytics gap. MIT Sloan management review.
2015;56(3):63-.
Reese, S. (2020). Reflecting on impacts of Peter Senge’s fifth discipline on learning
organizations. The Learning Organization, 27(1), 75–80.
Risch, S. (2009). Food packaging history and innovations. Journal of Agricultural and Food
Chemistry, 57(18), 8089–8092.
Rohrbeck, H. (2009). Opening up for competitive advantage-how Deutsche Telekom creates an
open innovation ecosystem. R&D Management, 39(4). 420-430.
Saenz, B. (2020). Methods for considering safety in design of robotics applications featuring
human-robot collaboration. International Journal of Advanced Manufacturing
Technology, 107(5-6), 2313–2331.
Sawatsky, R. (2017). A model of self-directed learning in internal medicine residency: a
qualitative study using grounded theory. BMC Medical Education, 17(1), 31–31.
Schmidt, H. (2000). Assumptions underlying self-directed learning may be false. Medical
Education, 34(4), 243–245.
Schnotz, W. (2016). Learning and instruction: a review of main research lines during recent
decades. Zeitschrift Für Erziehungswissenschaft, 19(1), 101–119.
146

Schraw, D. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology,
19, 460–475.
Schulz, E. (2019). Everything you need to know about the fourth industrial revolution.
CNBC.com.
Schwab, K. (2017). The fourth industrial revolution. Currency.
Sedikides, H. (2009). Self‐improvement. Social and Personality Psychology
Compass, 3(6), 899–917.
Sedikides, L. (2016). Enhancing feedback and improving feedback: subjective perceptions,
psychological consequences, behavioral outcomes. Journal of Applied Social Psychology,
46(12), 687–700.
Seibert, K. (2001). A social capital theory of career success. Academy of Management
Journal, 44(2), 219–237.
Senge, P. (1990). The fifth discipline. Doubleday
Senge, C. (2001). Innovating our way to the next industrial revolution. MIT Sloan
Management Review, 42(2), 24–.
Staw, E. (2000). What bandwagons bring: effects of popular management techniques on
corporate performance, reputation, and CEO pay. Administrative Science Quarterly,
45(3), 523-556.
Sharp, M. (2019). Observations on developing reliability information utilization in a
manufacturing environment with case study: robotic arm manipulators. International
Journal of Advanced Manufacturing Technology, 102(9-12), 3243–3264.
Shoup, M. (2019). More change to come in the food industry in the next 10 years than in the last
50, says accenture strategy. Food Navigator USA.com.
147

Simon, M. (2011). Dissertation and scholarly research: recipes for success (2011 Ed.).
Singh, S. (2019). The soon to be $200b online food delivery is rapidly changing the global food
industry. Forbes.
Stubbart, C. I. (1989). Managerial cognition: a missing link in strategic management
research. Journal of Management Studies, 26(4), 325-347.
Subramanian, R. (2019). How to build disruptive strategic flywheels. Strategy + Business.
96(Autumn, 2019).
Sweet, T. (2007). Factors affecting the evolution of manufacturing in Canada: an historical
perspective. Journal of Operations Management. 2007;25(2):260-283.
doi:10.1016/j.jom.2006.05.014
Tang, C. (2019). The strategic role of logistics in the industry 4.0 era. Transportation Research.
Part E, Logistics and Transportation Review, 129, 1–11.
Tetlock, P. (1992). The impact of accountability on judgment and choice: toward a
social contingency model (Vol. 25, pp. 331–376). Elsevier Science & Technology.
Tjahjono, E. (2017). What does industry 4.0 mean to supply chain? Procedia Manufacturing, 13,
1175–1182.
Tortorella, G. (2021). The mediating effect of employees’ involvement on the relationship
between Industry 4.0 and operational performance improvement. Total Quality
Management & Business Excellence, 32(1-2), 119–133.
Tough, A. (1978). Major learning efforts: recent research and future directions. Adult
Education (Chapel Hill), 28(4), 250–263.
Tough, A. (1989). Self-directed learning: concepts and practice. In Lifelong Education for adults
(pp 256-260). Pergamon
148

Tseng, C. (2013). Connecting self-directed learning with entrepreneurial learning to
entrepreneurial performance. International Journal of Entrepreneurial Behaviour &
Research, 19(4), 425–446.
Tuovila, A. (2021). Privately owned. Investopedia.com
Ungerman, D. (2018). The impact of marketing innovation on the competitiveness of enterprises
in the context of industry 4.0. Journal of Competitiveness, 10(2), 132–148.
Usher, E. (2018). Acknowledging the whiteness of motivation research: seeking cultural
relevance. Educational Psychologist, 53(2), 131–144.
Velasquez, A. (2011). Thinking ethically: a framework for moral decision making. Markkula
Center for Applied Ethics at Santa Clara University.
Vercellis, C. (2009). Business intelligence: data mining and optimization for decision making. In
Business Intelligence (1st ed.). Wiley.
Walker, R. (2010). Disparities and access to healthy food in the United States: a review of food
deserts literature. Health & Place., 16(5), 876–884.
Weiss, J. (1981). Educating for clerical work: the nineteenth-century private commercial
school. Journal of Social History, 14(3), 407–423.
White, N. (2020). Good deeds and cheap marketing-the food industry in the times of COVID-
19. Obesity. Published online May 22, 2020. doi:10.1002/oby.22910
White, S. (2020). Amazon and Whole Foods: adventures in grocery shopping. The CASE
Journal.
Wixom, A. (2014). The current state of business intelligence in academia: the arrival of big data.
Communications of the Association for Information Systems, 34, pp-pp.
Wilding, J. (2004). Customer perceptions on logistics outsourcing in the European consumer
149

goods industry. International Journal of Physical Distribution & Logistics Management,
34(8), 628–644.
Wirth, N. (2018). Hello marketing, what can artificial intelligence help you with? International
Journal of Market Research, 60(5), 435–438.
Wood, B. (1989). Impact of conceptions of ability on self-regulatory mechanisms and
complex decision making. Journal of Personality and Social Psychology.
1989;56(3):407-415. doi:10.1037/0022-3514.56.3.407
Wood, B. (1989). Social cognitive theory of organizational management. The Academy of
Management Review, 14(3), 361–.
Wunderlich, G. (2017). Consumer knowledge about food production systems and their
purchasing behavior. Environment, Development and Sustainability, 20(6), 2871–2881.
Yoffie, W. (2009). Cola wars continue: Coke and Pepsi in 2006. Harvard Business
School Case 706-447, May 2006. (Revised April 2009.)
Xu, D. (2018). The fourth industrial revolution: opportunities and challenges. International
Journal of Financial Research, 9(2), 90-95.
Zhu, B. (2020). Are trade fairs relevant for local innovation knowledge networks? Evidence
from Shanghai equipment manufacturing. Regional Studies, 54(9), 1250–1261.

150

Appendix A: Pre-Interview Protocol

Pre-Interview—Initial Email to Determine Potential Interest for Study Participation

The following email will be sent to targeted food industry executives to recruit volunteers
for the study.

Good day, Dr., Ms., or Mr. ___________

I am also a food industry executive and I am working on my doctorate at the
University of Southern California with a focus area of how changes to the food industry
brought on by Industry 4.0 are affecting executives.
This spring I will be conducting interviews of approximately one-hour with food
industry executives about this topic as part of my dissertation research. Everything in the
dissertation will be completely confidential about participants and their organizations. I
am gathering initial interest of participants at this point. If you would consider assisting
me with this research, I would be most appreciative.
Best regards,

/s/ Mike

Michael Hackney
Doctoral Candidate
Rossier School of Education
The University of Southern California

151

Pre-Interview—Email to Request Participation for Study

Following review of potential participants who have responded affirmatively to the initial
email, a follow-on email will be sent to initiate interview appointments.

Good day, Dr., Ms., or Mr. ___________

Thank your agreeing to participate in my research study concerning the topic of
how changes to the food industry brought on by Industry 4.0 are affecting executives.
We will conduct an interview with either a Zoom call or telephone call format. As a
reminder your identity will be known only to me, and I am conducting this study for my
doctoral dissertation at the University of Southern California. I would like to set up our
45-to-60-minute interview at your convenience during the weeks of TBA to TBA. There
is no need for any pre-work.
Please reply with any proposed best dates and times for you, and I will
accommodate your schedule. In the event your preferred time is not available to me
please also give me an alternative time. I will respond back to you within one day of your
proposal with a confirmation.
I am attaching a pdf file to this email: the pdf is for your information concerning
the formal notice of participant rights and the protocol surrounding how the information
you provide will be used and protected.
Again, I thank you for taking time out of your schedule to assist me with this
research and I look forward to our conversation!
Best regards,

/s/ Mike

Michael Hackney
152

Doctoral Candidate
Rossier School of Education
The University of Southern California
847.420.5117
mhackney@usc.edu

153

Appendix B: Information Sheet for Exempt Research

University of Southern California
Rossier School of Education
3470 Trousdale Pkwy Ste 1100, Los Angeles, CA 90089

INFORMATION SHEET FOR EXEMPT RESEARCH

STUDY TITLE: Impact of Industry 4.0 on U.S. Food Industry Executives
PRINCIPAL INVESTIGATOR: Michael S. Hackney, Doctoral Candidate
FACULTY ADVISOR: Dr. Jennifer Phillips

You are invited to participate in a research study. Your participation is voluntary. This document
explains information about this study. You should ask questions about anything that is unclear to
you.
PURPOSE
The purpose of this study is to determine how U.S. food industry executives are responding to
industry transformative changes such as those brought on by Industry 4.0. We hope to learn how
U.S. food industry executives, such as yourself, are coping with these changes both individually
and for your organizational teams. You are invited as a possible participant because of your
prominence as a U.S. food industry executive responsible for, or as an influencer of, the
corporate decision-making process for your organization.
PARTICIPANT INVOLVEMENT
You are asked to participate in a Zoom meeting or telephone conversation to be interviewed
about the research topic. The interview is expected to last no more than one hour. While it is
unlikely, it is possible that the researcher may have a desire to conduct a subsequent follow-on
conversation. Should that need arise you will be contacted for permission for such additional
activity and a time to your convenience will be scheduled. All interaction for you and your
organization is confidential and anonymous. Neither your or your organization will be named or
alluded to in a manner that would provide identification.
While it is the desire of the researcher to record the conversation for subsequent confidential and
anonymous transcription so that your responses can be accurately analyzed, such recording is
purely voluntary on your part and is not a condition for participation. The researcher will take
notes as an alternative during the interview as needed.
There is no “prework” necessary for the interview, and it will be held at a time that is to your
convenience and with respect to your schedule and responsibilities.

154

CONFIDENTIALITY
The members of the research team and the University of Southern California Institutional
Review Board (IRB) may access the data. The IRB reviews and monitors research studies to
protect the rights and welfare of research subjects.
When the results of the research are published or discussed in conferences, no identifiable
information will be used.
Audio recordings, if made, will not have any direct reference to the full name or organization of
the participant and will be used solely for the purpose of analyzing the transcript for relevant
content. The recordings will remain in the sole possession of the research team and will be
destroyed not later than one year from completion and final approval of the study. The study is
expected to be fully completed by December 2021. For this study the Research Team is the
researcher and the Chair of the researcher’s dissertation committee.
Audio recordings, if made, will not be started until the preliminary and identifying remarks of
the participant, and their organization, are concluded. The researcher will refer to the participant
by an arbitrary identification to maintain confidentiality and anonymity. As a part of the research
study the recordings will be transcribed by a bonded academic paper transcription company. If a
participant desires a copy of that transcript will be provided for review, editing, of declination of
participation.
INVESTIGATOR CONTACT INFORMATION
If you have any questions about this study, please contact Michael S. Hackney:
mhackney@usc.edu , (847) 420-5117, or Dr. Jennifer Phillips: jlp62386@usc.edu .
IRB CONTACT INFORMATION
If you have any questions about your rights as a research participant, please contact the
University of Southern California Institutional Review Board at (323) 442-0114 or email
irb@usc.edu.

USC IRB Information Sheet Template Version Date: 01/30/2021

155

Appendix C: Interview Protocol
Introduction
‘Good afternoon Dr. / Ms. / Mr. _____________, I greatly appreciate your assistance
with this study. I certainly understand how valuable your time is and I would like to keep
our conversation to the 45 minutes to 1-hour time frame we had discussed in our prior
correspondence.
Also, are you still comfortable with the conditions of confidentiality for both you
and your company that we had agreed to?
Do you have any questions concerning my agreement not to disclose your
participation or anything about your organization as a part of my study?
I will be recording our interview in lieu of active note taking so I can focus on
your answers: is that still agreeable to you?
Before we begin do you have any other questions of me?
Also, if you have any questions during our interview, please ask: just a reminder,
at any time that you desire to discontinue our interview or withdraw from this study, that
is your right.
May I have your permission to begin recording and get started? I will refer to this
interview as being with Person “Alpha-Omicron” for purposes of cataloging the
recording while protecting anonymity.
“I am beginning recording now with Person “Alpha” on DATE-TIME.
Interview Questions
I would like to begin by asking about what you see as going on in the U.S. food industry:
the phrase Industry 4.0 is used by academics to refer to the changes in Artificial
Intelligence, robotics, and use of data to affect numerous areas in the food industry. I will
156

be using that “Industry 4.0” term during our interview. I’d like to ask you questions about
how those changes are impacting the industry overall, your organization specifically, and
what tools you are finding or creating to improve your capabilities and those of your
teams. What questions about this “Industry 4.0” term do you have before we get started?
1. How do you see Industry 4.0 as impacting your organization, if at all (RQ1)?
Probe with follow on questions concerning:
What is your company doing differently, if anything, in response to Industry 4.0?
What, if anything, are you personally doing differently in response to Industry
4.0?
2. How do you see the U.S. food industry, as an overall industry, responding to Industry
4.0 (RQ2)?
Probe with follow on questions concerning:
How, if at all, has that impacted how you do your job?
In what ways, if any, has that impacted how your teams or peers do their jobs?
3. How do you see these general industry responses to industry 4.0 as causing you to
develop new knowledge (RQ2)?
Probe with follow on questions concerning:
How, at all, have you sought new self-efficacy?
How, at all, have you teams indicated interest in new knowledge?
4. Can you please tell me a time that you identified a personal knowledge gap related to
Industry 4.0 impacts on the food industry (RQ1; RQ2).
Probe with follow on question concerning:
How did you find resources you needed to address this?
157

5. Have you participated in any programs concerning AI or robotics (RQ1, RQ3)?
Probe with follow on question concerning:
Did you seek these program(s) out on your own?
Was this participation because of an organizational directive?
6. In what ways, if at all, does your organization actively support events for you or other
executives to improve skill sets (RQ2, RQ3)?
Probe with the following questions concerning:
Is awareness of these events initiated by senior management to you?
Are these events deemed mandatory for participation by senior management?
7. What self-directed learning activities, if any, have you conducted within the last two
years to enhance your skill sets to meet Industry 4.0 changes (RQ2, RQ3)?
Probe with follow on questions concerning:
Did it enhance specific job tasks in your organization?
How did it improve your organization’s capabilities, if at all?
8. As an organizational leader, what activities, if any, do you engage in to enhance the
capabilities of your current team as it relates to Industry 4.0 (RQ2)?
Probe with follow on question concerning:
Do you share your current or new skill sets with them in some way?
9. What plans do you have to enhance your new skill sets, if at all, to improve the
capabilities of your current team (RQ3)?
Probe with follow on question concerning:
Why is enhancing those skill sets important to the organization?
158

10. What has been of greater value to your organization’s performance—organization
driven activities to enhance executive skills or self-directed activities you have
undertaken on your own initiative (RQ1; RQ2; RQ3)?
Probe with follow on question concerning:
Which do you see as personally having more value?
Why?
11. Can you please tell me any self-directed activities you have recommended to your
peers for their Industry 4.0 skill enhancement (RQ2, RQ3)?
Probe with follow on question concerning:
Have you recommended that subordinate members of your team seek self-directed
activities?
12. Can you please describe any planned or contemplated self-directed initiatives you see
yourself doing in the next two years (RQ3).
Probe with follow on questions concerning:
Will these be self-funded?
13. We have a covered a lot of ground relative to the impact of Industry 4.0 on the food
industry and personal or organizational responses to the changes you and your teams
have encountered: are there other areas or considerations that I have not asked about
that you can share with me (RQ1; RQ2; RQ3)?
Probe with follow on questions concerning:
Elaboration of open-ended responses.
Clarification of open-ended responses.

159

Closing and Follow-Up
Dr. / Ms. / Mr. _____________, this concludes my questions at this time; after I
review our conversation, should I need to ask a follow up or clarification to anything I
did not understand about your answers, may I please email you for a suitable time to
conduct a brief phone call?
Do you have any other questions for me?
I sincerely thank you for your assistance with this study, and I look forward to sharing with
you the results and conclusions once I’ve completed the project.
Again, thank you, and please have a wonderful rest of your day. (End Recording.)

Abstract (if available)

Abstract The Fourth Industrial Revolution, or Industry 4.0, refers to the prolific digital transformation creating Artificial Intelligence programs and Big Data, and their business impact on marketing, logistics and robotics. U.S. food industry executives are limited for academic programs available to them at this transformative time to bridge knowledge gaps but must still stay abreast of impacts on their industry and individual organizations. The manner of how these executives are responding to knowledge gaps has not been widely studied. To gain insights to U.S. food industry executives’ response to knowledge gaps, this qualitative research study examined the sentiment of targeted U.S. food industry executives to the impact of Industry 4.0 on their organizations, the broader food industry, and how they viewed addressing knowledge gaps for the current state and looking forward. The findings indicate individual executives have a keen awareness of the potentials and costs of Industry 4.0 technologies and utilize self-directed learning (SDL) to stay current with the speed of change. They are concerned at the economic costs to keep up, and failure if they do not. They see a reliance upon industry technology companies and Industry 4.0 trained key employees as the forward-looking crucial elements to organization survival. Further research, outside the targeted scope of this study, is recommended to determine if other U.S. or global industries are affected by, and responding to, Industry 4.0 impacts in the same or differing manners, and if other stakeholders in the organization have the same perceptions of SDL utility.

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

PDF

Prescription for change: gender barriers impact on healthcare leadership equity

PDF

Corporate America after Ursula Burns: building a pipeline of Black female executives

PDF

Increasing workplace civility in higher education: a field study

PDF

Keeping virtual team members engaged in their roles: an impossible task or a paramount necessity for an increasingly globalized world

PDF

The failure to attract and hire Millennials and Gen Zs within the U.S. aerospace & defense industry creates workforce scarcity

PDF

The path to satisfaction, connection, and persistence: implementing a strategic and structured employee onboarding program: an innovation study

PDF

Double jeopardy: the influence of prevalent race and gender bias on women of color executive leadership promotions

PDF

Underrepresentation of women in the U.S. banking industry’s top executive roles: why doesn’t the CEO look like me?

PDF

Addressing employee retention in the technology industry: improving access to corporate social responsibility programs

PDF

Digital transformation in the resources industry: an exploration of promising management practices

PDF

Preparing for the future of work: exploring worker perceptions of the impact of automation

PDF

The role of healthcare volunteers in addressing social determinants of health

PDF

Leadership and patient safety culture

PDF

Understanding teacher leadership styles in relation to student outcomes

PDF

The lack of gender diversity in executive leadership ranks within higher education

PDF

Increasing female representation in the finance and banking sector in Qatar

PDF

Exploring the experience of acquired employees within an organization following acquisition

PDF

Building equity for English language learners: technology employees in Fortune 500 companies

PDF

The role of U.S. historic sites and museums in supporting social studies instruction in K-12 classrooms

PDF

The underrepresentation of Black, indigenous, and people of color in executive-level information technology positions

Asset Metadata

Creator Hackney, Michael Shayde (author)

Core Title Industry 4.0 impacts on U.S. food industry executive self-directed learning

School Rossier School of Education

Degree Doctor of Education

Degree Program Organizational Change and Leadership (On Line)

Degree Conferral Date 2021-12

Publication Date 12/09/2021

Defense Date 11/08/2021

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

Tag artificial intelligence,automation,big data,Communication,critical thinking,executive bandwidth,executive development,executive engagement,executive skills,Food industry,Fourth Industrial Revolution,Industry 4.0,OAI-PMH Harvest,privately owned companies,publicly traded companies,robotics,self-directed learning,social cognitive theory,speed of change

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Phillips, Jennifer (committee chair), Krop, Cathy (committee member), Wilcox, Alexandra (committee member)

Creator Email mhackney@usc.edu,mikehackney1@aol.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC18010012

Unique identifier UC18010012

Legacy Identifier etd-HackneyMic-10291

Document Type Dissertation

Format application/pdf (imt)

Rights Hackney, Michael Shayde

Type texts

Source 20211210-wayne-usctheses-batch-903-nissen (batch), 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 author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given.

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

Repository Email cisadmin@lib.usc.edu