Posted: June 17th, 2022

interview transcript2

The Automated Enterprise:
A Narrative Inquiry Exploring Worker Perceptions of Robotic Process Automation (RPA) in a Global Life Sciences Organization

A Dissertation Proposal
Submitted to the Faculty
of Drexel University
By
Mantel P. Featherson
in partial fulfillment of the
requirements for the degree
of
Doctor of Education
Field of Educational Leadership and Management
October 2020

© Copyright 2020
Mantel P. Featherson. All Rights Reserved

Abstract
The Automated Enterprise: A Narrative Inquiry Exploring Worker Perceptions of Robotic Process Automation (RPA) in a Global Life Sciences Organization
Mantel Featherson
Chairperson: Deanna Hill, PhD

The Ed.D. Dissertation Committee from the School of Education at Drexel University certifies that this is the approved version of the following dissertation:
The Automated Enterprise:
A Narrative Inquiry Exploring Worker Perceptions of Robotic Process Automation (RPA) in a Global Life Sciences Organization

Mantel P. Featherson

Committee:
______________________________
Deanna Hill, J.D., Ph.D.

______________________________
Bruce Levine, J.D.

______________________________
Chris Finnin, Ed.D.

Dedication

Acknowledgments

Table of Contents

Chapter 1: Introduction to the Research 11
Introduction to the Problem 11
Statement of the Problem to be Researched 18
Purpose and Significance of the Problem 19
Research Questions 20
Conceptual Framework 21
Definition of Terms 28
Assumptions and Limitations 30
Summary 31
Chapter 2: The Literature Review 32
Introduction to Chapter 2 32
Literature Review 34
Summary 56
Chapter 3. Methodology 58
Introduction to Chapter Three 58
Research Design and Rationale 59
Research Methods 60
Research Methods 62
Ethical Considerations 65
Chapter 4: Findings, Results and Interpretations 68
Introduction 68
Findings and Assignment help – Discussion 68
Participant Overview 69
Findings 75
Results and Interpretations 83
Summary 88
Chapter 5: Conclusions, Implications, and Recommendations 89
Introduction 89
Conclusions 91
Implications and Recommendations 94
Practice 95
Future Research 96
Summary 96
References 98
Appendix A 106

Chapter 1: Introduction to the Research
Introduction to the Problem
In the summer of 2018, Merck and Co., a global life sciences organization headquartered in the suburbs of Philadelphia, Pennsylvania, conducted a competitive analysis to determine the types of research and technology investments that its competitors were making. Upon completion of the competitive analysis, along with consultations with several industry advisory firms with deep experience in the technology selection and adoption, the executive board decided to invest in the implementation of several advanced technology platforms and tools. These tools were dubbed “Smart Technology” by the company leadership as they consisted of several tools that would completely automate key company processes, either completely removing or severely reducing the need for human intervention in the execution of those processes.
One element of these Smart Technologies is known as Robotic Process Automation (RPA). RPA is unlike traditional Enterprise Resource Planning (ERP) systems that businesses adopted to streamline the processes of workers in the performance of their daily tasks. RPA is designed to replace many of the manual processes that have been traditionally completed by human labor. Its use enables those workers to focus on completing higher complexity tasks and harmonizes them to derive increasingly higher levels of organizational productivity (Akst, 2013).
There is much speculation around the impact of RPA on the economy and jobs. Workers have concerns about their long-term employment prospects. As tools like RPA are increasingly adopted, workers fear their jobs could be in jeopardy. Schwab (2016), founder of the World Economic Forum, stated “We stand on the brink of a technological revolution that will fundamentally alter the way we live, work, and relate to one another” (p. 12). His proclamation encapsulates a growing understanding by leaders in both government and business that the emergence of intelligent technology (i.e., artificial intelligence, automation, bots, etc.) has the potential to dramatically reshape sectors, industries, economies and societies. More specifically, the global emergence and adoption of intelligent technologies has the potential to significantly disrupt the productivity of global organizations. This truth has far reaching implications in our society generally and in the business world specifically.
Throughout the generations, our society has rapidly evolved through the advancement and adoption of technology. A germane example is the global ubiquity of the smart phone. Smart phones enable millions of people to seamlessly connect through applications that inform, educate and entertain. Ross (2017), author of Industries of the Future, refers to the current wave of technology advancement as the Innovation Revolution. Ross contends that, similar to the industrial ages that have impacted our society in the past, there are winner and losers. As innovations are developed and grow, the winners are the investors, entrepreneurs, and highly skilled laborers who took advantage of fast-growing markets and new inventions. The losers are the low-skilled workers and those unwilling to embrace the emerging systems and tools that will becomes essential to thriving in the world of work, including business owners that rejected technology. Schwab (2016), Ross (2017) and other thought leaders contend that a similar theme of winners and losers will emerge as intelligent technologies grow in adoption within the business sector, and it will take a coordinated effort to effectively manage the disruption that the emergence of intelligent technology is likely to bring.
For more than 25 years, “technology acceptance” research has focused on the complexity of adoption within social systems (Fador, 2014). The use of the phrase “technology acceptance” identifies two distinct beliefs that have been shown to predict a user’s attitude toward using a system, namely: (1) perceived usefulness; and (2) perceived ease of use (Fador, 2014). According to Fador (2014), these beliefs can be used to determine user readiness of newly implemented technology. The rate of acceptance will ultimately depend on users’ perceptions of its ease of use and whether it produces effective results.
Life Sciences Industry Overview
Life sciences organizations tend to be very conservative in terms of how they conduct business as the products they produce are highly regulated by the government. Life sciences as an industry sector is comprised of bio-technology companies, which are organizations that use living organisms to develop drugs, and pharmaceutical companies, which are organizations that use chemicals to develop drugs (Styhre, 2012).
Merck & Co. is a pharmaceutical manufacturer and a member of Big Pharma – a descriptor applied to sophisticated, high-revenue, globally-dispersed pharmaceutical manufacturers. Big Pharma is comprised of the following ten companies, listed in order of highest revenue through 2018:
1. Johnson & Johnson
2. Novartis
3. Merck & Co
4. Eli Lilly
5. Pfizer
6. Roche
7. GlaxoSmithKline
8. Sanofi
9. AstraZeneca
10. AbbVie

Pharmaceutical manufacturers are part of the healthcare sector, which is a significant part of the US economy. Spending on healthcare-related products and services totaled $3.4 trillion dollars as recently as 2017 (Pellegrini, 2018). Healthcare as a sector includes physical care facilities, psychological care facilities, medical equipment, biotechnology, insurance and more. Healthcare as an industry is tightly regulated by government bodies. Regulation plays a major role in the healthcare industry and there are a number of regulatory bodies whose role is to protect the public from the risks of poorly designed medical products and performed practices. These regulatory agencies protect and regulate public health at every level, and health care regulations are developed and implemented not only by all levels of government (federal, state and local) but by private organizations as well (Almgren, 2017).
As part of a regulated industry, pharmaceutical companies are accountable to the Food and Drug Administration (FDA). The FDA is a government agency that provides oversight to medical companies by ensuring the safety, efficacy, and security of human and veterinary drugs, biological products, and medical devices (Almgren, 2017). FDA regulations govern a large part of how pharmaceutical organizations function. This is particularly true when it comes to the development and approval of drugs. The FDA utilizes a twelve-step process that governs the process for drug development and approval. Adhering to the process is a critical part of getting a drug approved and pharma companies have invested millions of dollars in the development of processes and technologies to ensure that their drug applications are consistently approved (Feldman, Robin, Frondorf & Evan, 2017).
Pharmaceutical Technology Overview
The emergence of widely adopted technology in the pharmaceutical industry began with Enterprise Resource Planning (ERP) systems. The term ERP was coined in the early1990s by the Gartner Group, a technology research firm. However, the original roots of ERP date back to the 1960s (Roberts, 2007). Back then, ERP technologies were mostly applied to inventory management and controls in the manufacturing or drugs. Software companies created programs to monitor inventory, reconcile balances, and perform status reporting. In the 1970s, the systems evolved into Material Requirements Planning (MRP) systems for managing the scheduling of production processes. In the 1980s, MRP evolved to encompass a complete suite of manufacturing processes, prompting many to call it MRP-II or Manufacturing Resource Planning. By the mid-1990s, the MRP systems had grown beyond the management of inventory control and other operational processes to other back-office functions like finance accounting, supply chain, sales, and human resources, and that became the ERP system that we have today (Roberts, 2007).
According to Saunders (2017), the specific applications of an ERP system that are tailored to the pharmaceutical industry include: 1) recipe management, tracking, control and security; 2) Title 21 CFR Part 11 management and general compliance management; 3) inventory management and control; 4) production transparency and traceability; and 5) quality control. ERP systems remain the core technology used within the pharmaceutical industry to manage essential business operations; however smart technologies are increasingly being implemented and utilized along with ERP systems (Hossain, 2019).
The Emergence of Smart Technology
Smart technologies are among the technologies that are beginning to be adopted to streamline discrete processes and enhance productivity within the pharmaceutical industry. This is following what is occurring in other industries such as media, retail, banking, insurance and telecommunications (Hossain, 2019). Pharmaceutical executives, industry researchers, innovators, and policy makers are increasing their awareness of the disruptive potential of smart technologies and are evaluating a wide range of digital technologies. However, it is not easy to determine which projects to pursue and to what extent, as the future impact of these technologies is still very unclear (Hossain, 2019). Although there is still a lack of clarity around the specific smart technologies that will be adopted in the pharma industry, leaders are gradually leading their organizations to try these emerging technologies in very discrete ways. The technologies include: 1) artificial intelligence and machine learning (AI/ML); 2) digitization of medicine; 3) augmented reality/virtual reality (AR/VR); 4) Internet of Things (IoT) integration; 5) blockchain; 6) organ-on-chips; and 7) Robotic Process Automation (RPA) (Hossain, 2019).
Artificial Intelligence and Machine Learning (AI/ML) will streamline the clinical trials process – rather than taking months to see the effect of a particular drug on thousands of people, it will take seconds to see the effect of a drugs on billions of simulations of the human body’s physiology from the past records of patients. AI will also help accurately identify the subset of patients who will benefit from a particular drug. This could reduce the failure rates and streamline FDA approvals.
Digitization of Medicine enables pharmaceutical companies to go beyond the manufacturer of the medicines themselves and offer a complete package. Currently referred to as “around the pill” digital offerings – this package includes digital health mobile apps, devices or services that could be prescribed by a doctor or bundled with a drug.
AR and VR can cater to a wide range of needs of the pharmaceutical industry. For example, an AR tool can be used to simulate an affliction or illness in a person to better help researchers understand what how that illness impacts the patient experience.
IoT integration aides the drug manufacturing in areas like material tracking and compound processing of medicines. IoT-enabled data-gathering devices can collect information from RFID tags and barcodes and correlate the information from multiple locations, including production facilities and warehouses, to verify whether data is consistent.
Blockchain provides an opportunity to decrease costs and increase transparency and create trust for all participants by protecting intellectual property information. Current intellectual property management systems sometimes hampers the internal technical and financial collaboration that is needed in a research project. Blockchain provides a platform for the protection and facilitation of intellectual property, including the facilitation of royalties, payments, and incentivization models that facilitate the research and development process. For example, Aprecia Pharmaceuticals developed Zipdose, patented technology for 3D printing drugs to enable high-dose medications in a rapidly disintegrating form. Using this technology, they then produced Spritam, which treats Epilepsy. This was the world’s first 3D printed drug approved by the FDA. As this manufacturing method gains popularity, it will completely transform how the pharmaceutical industry operates its supply chain.
Organ-on-Chips technology mimics the characteristics of living organisms so that they can be simulated and substituted for actual humans in clinical trials. This enables trials to be carried out in less time, with less money and still good outcomes. This method is called the “silico trial” and it is an individualized computer simulation used in the development or regulatory evaluation of a medication product, device, or intervention.
RPA is comprised of software robots that work with existing business systems and applications (i.e., ERP) to simplify processes and reduce the administrative burden on employees through the automation of discrete, rote tasks. RPA is of greatest value when used for rule-based, repetitive processes in drug development operations. Its use can support consistency in data entry, quality control and audit readiness in several targeted activities comprising clinical, regulatory, safety and laboratory operations (Hossain, 2019).
Merck like many of its competitors is exploring the use of various smart technologies across the enterprise. One particular area of focus is RPA. The company is exploring automation through the implementation of five discrete functions: 1) Reporting Workflow; 2) Power Point Automation; 3) Foreign Exchange (FX) Trade Optimization; and 4) On-boarding Automation. 5) Automated Vision Inspection. These five automated functions have been developed, implemented and managed by Merck’s internal AI and Automation team – a small division of Merck’s Global Information Technology (IT) organization.
Statement of the Problem to be Researched
The implementation of RPA in Merck is positioned to fundamentally transform the way the organization operates, as RPA enables the organization to remove or significantly decrease the need for human labor in the functional areas in which automation is being deployed. The core problem is the unknown impact that the implementation of RPA will have on the workers. This study seeks to uncover the perceptions of workers as they relate to their experiences with RPA. The findings from this study are expected to yield data that leaders at Merck can use to enhance future implementation of RPA to minimize negative impact to workers.
Purpose and Significance of the Problem
Purpose
The purpose of this qualitative narrative inquiry is to explore the worker perceptions of RPA technology implemented for packaged drug inspection (Automated Vision Inspection) in the manufacturing division of Merck, a global life sciences organization. The use of RPA technology in Merck is a new phenomenon and, as a result, its impact on worker performance factors such as productivity, motivation and autonomy has yet to be fully determined. The intent is to provide Merck with an understanding of how workers perceive the influence of RPA on their day-to-day work.
Significance of the Problem
Many organizations in the life sciences industry are working to transition into the digital era where advanced technologies such as AI are enabling organizations across industries to realize gains in productivity and transforming how leaders operate the business now and in the future. Technologists contend that RPA has grown in importance across all industry segments and according to Gartner, 85 percent of big organizations will have deployed some form of RPA by the year 2022 (Supriya, 2019).
Technologists further contend that RPA is meant to bring about efficiency and reduce errors made in mundane and tedious work done manually by humans. However, this thinking gives rise to the question: how will the shift from human to digital labor impact the worker? For example, will there be a loss of jobs across industries due to democratization of RPA? Leaders of The World Economic Forum believe that the widespread adoption of RPA will result in significant workforce shifts – up to 35% of the skills currently in demand likely to change by 2025 (Supriya, 2019).
As the adoption of automation becomes more widespread, businesses will need to adapt quickly to workforce transitions. One thought is that businesses will need to empower employees to adapt their jobs to create a productive mix of human and digital labor that meets the needs of the enterprise. This rapidly the evolving business environment is moving towards new productivity-enhancing processes alongside the creation of entirely new job roles. Specialist roles such as AI, ML or IoT specialists, Big Data specialists, automation experts, security specialists, machine interaction specialists, robotics engineers are emerging (Supriya, 2019).
Leaders in Merck are concerned with losing ground to competitors and seek to, at a minimum, maintain their leadership position within the industry. However, the desired goal of leaders is to gain a competitive advantage through the use of advanced technology and at the same time maximize their workforce (human labor) in a way that compliments the advanced automated technologies. Within Merck, RPA is still in its infancy in terms of adoption across the organization, yet there are perceptions among employees that have been exposed to RPA within the organization. These perceptions must be explored and managed effectively if the organization seeks to extend the adoption of RPA and minimize the disruption and frustration within the workforce.
Research Questions
This is a qualitative narrative study exploring the perceptions of workers in a life sciences organization who are experiencing the influence of an RPA implementation within the enterprise. The central research question is: How do workers perceive the influence of RPA on their overall performance in the workplace? Sub-questions that guide the study are as follows:
1. How do life sciences professionals perceive the influence of RPA on their motivation?
2. How do life sciences professionals perceive the influence of RPA on their productivity?
3. How do life sciences professionals perceive the influence of RPA on their autonomy?
These questions will enable participants to share a variety of opinions and behaviors that will enable the researcher to develop a comprehensive narrative of the participants’ experiences (Clandinin, 2013).

Conceptual Framework
Researcher Stance
My professional background as a management consultant and new researcher was initially formulated through numerous consulting engagements and academic training in research. Effective consulting is based on the development of creative solutions to complex problems. The problem-solving process is often collaborative and involves significant input from clients. This involves grounding in the current state process and then ideating on what is possible. The outcome of these sessions was often a recommendation to implement a technology, revise or create a new business process.
In addition to my role as a management consultant, I have had several years of experience as an employee and a contractor with several life sciences organizations. This experience has enabled me to gain perspective as to the pharmaceutical business and how technology is used to support business operations. I was exposed to the technology and its gradual evolution within both the clinical and non-clinical sides of the organization. The clinical side of the organization is what is comprised of the drug development process (including the research, development, testing and manufacture of pharmaceuticals), while the non-clinical side is comprised of business operations that include financial management, marketing, sales and distribution. Being exposed to these distinct yet related areas enabled me to experience how the organizations prepared members for technology change and the members reacted to the strategies that were used to facilitate their adoption of technology and their subsequent reactions to its use to get their work done. What I have observed through these experiences has crystallized my thinking about how we as human beings develop in a professional setting and as a result, I have aligned to the constructivist view of learning.
Constructivists focus on how people make meaning of or construct knowledge when interacting with content and the active processes of this interaction (Stabile & Asher, 2016). Constructivism is a way of building knowledge about self, school, everyday experience, and society through reflection and meaning making. One of the primary goals of constructivism is to provide a democratic and critical learning experience for students. It serves to open boundaries through inquiry, not through unquestioned acceptance of prevailing knowledge. It is the realization that knowledge is never neutral, that the ways in which knowledge is mediated and created are as dynamic and important as the knowledge itself (Flockhart, 2016).
In a learning community grounded in constructivism, learners mediate knowledge within a social context. The role of language in a constructivist environment is that of mediator between the learner and the world, shaping and extending thought (Flockhart, 2016). In managing organizational change initiatives as a former consultant, I observed workers continuously attempt to make meaning of their environment as change initiative progressed and technologies and processes evolved.
Experiential Base
I am member of the organization that is the subject of this study. Specifically, I am employed as an Associate Director with the Global Human Resources Department of Merck. My role is as a Learning and Development consultant, in this role I lead the design, development and deployment of professional development programs for leaders and managers. The programs I manage are focused on three areas: 1) leadership, which includes topics such as executive presence and managing change; 2) professional skills, which includes topics such as financial reporting and analysis; and 3) functional skills, which covers technical or systems-based training such as ERP systems applications (i.e., financial accounting). When the finance organization decides to adopt a new technology, they will often ask me and other me mbers of my team to oversee the development and deployment of the training that the employees will need to effectively operate the new systems. The implementation of RPA is a bit different in that the training of employees is not as extensive as it would be for mature systems such as ERP. In ERP training, the users would be taught to execute procedures such as creating a credit note through an Accounts Receivable application (A/R). Users would learn what fields to complete and what data to enter to complete a very manual procedure.
Conversely, RPA technology will take that same procedure – creating a credit note and enter the all required fields with the correct information and complete the transaction without the intervention of a human worker. This distinction means that the training required for employees is minimal in terms of how the technology handles discreet tasks. Employees will need to be made aware of their new responsibilities now that parts of their jobs have been automated. In this case, my role would be to learn how RPA technology has impacted their roles and then train them in the newly required responsibilities that may include tasks such as error checking, trouble-shooting, holistic process management. These types of tasks are what compliment the implementation of RPA and enable workers to remain productive contributors to the overall success of the organization. It is my role to help Merck’s leaders enable the employees to transition from the tasks that have been made obsolete through automation to tasks that are now adding more value to the organization that are outside the scope of automation.
Conceptual Framework
The current technological landscape in most life sciences organizations is mainly comprised of ERP systems. Companies use these systems to manage the core business processes (i.e., finance, human resources, manufacturing, procurement etc.). Other technologies such as cloud computing and mobile are also rising in prevalence, however ERP still dominates as roughly 65.6 percent of businesses with over 1000 employees used ERP so ftware as opposed to mobile 27% and cloud 12% (Panorama, 2019). The rise of intelligent technologies such as RPA are threatening to transform the technological landscape in life sciences organizations. The adoption of intelligent technologies (i.e., RPA) is at a slower rate than that of ERP systems and the likely reason for that is organizations are still learning how to effectively apply RPA technologies to maximize their return on investment. As a result, it may take up to 10 or more years until these technologies gain widespread adoption and replace the ERP and other legacy technologies and the human labor that is currently in place and becomes the industry standard (Seidel, Langner & Sims, 2017).
Innovation is a natural function in modern organizations, in fact they must innovate to feed the customer requirements for increasingly advanced products and services (Danaher, 2017). However, the emergence of intelligent technology (i.e., RPA) is different in that it threatens to transform not only the products and services that an organization produces but the very way an enterprise does its business. This includes core functions that include finance, operations, production, human resources and supply and distribution (Brynjolfsson, 2014).
The emergence of intelligent technology – RPA – is being considered by the leaders of the global life sciences organization as they know that they must prepare to manage its impact to the business. To that end, this study offers a framework that organizes the key concepts for structuring and understanding the influence of RPA within an organizational context. In order to facilitate the development of knowledge that supports the adoption of RPA, it is important to understand the supporting forces that have a role in influencing the effective adoption of RPA. The supporting forces as identified by the researcher are the Diffusion of Innovations and Self-Determination Theory.
Modern organizations innovate to create greater efficiency and gain a competitive advantage (Wunderlich, 2014). Innovation is often viewed within the context of technology and, within an enterprise, the success or failure of a technological innovation is determined by how well it is adopted by the stakeholders (Wunderlich, 2014). However, it is not uncommon for large scale organizational change projects to fail, as change leaders opt not to take a holistic change approach towards technology transformation initiatives (Kogetsidis, 2012). In order to have a successful change initiative, the individual workers must become consumers and advocates of the technological innovation (Straub, 2015).
The conceptual framework presented in Figure 3 represents the three pillars of study in this research by exploring RPA as a technology from both a development and application perspective, how it influences worker motivation and autonomy, and the diffusion of RPA as an innovation within the context of a life sciences organization.

Figure 3. Conceptual framework for the present study.
These three areas form an approach to the study of how the adoption of RPA technology influences the workers in the global life sciences organization.
RPA encompasses software tools that partially or fully automate human activities; usually those which are manual, rule-based and monotonously repetitive (Cooper, 2019). While that represents the practical definition of RPA, business leaders have begun to think of RPA as a digital workforce that can carry out business processes currently completed by human workers, but more quickly, efficiently, reliably, and accurately, freeing employees up to spend more time on activities that add real value to the organization such as engaging with customers, creating propositions, and setting strategic direction (Courie 2019).
The Diffusion of Innovation model is a structured approach to the adoption of innovation in organizations and supports the development of people competencies, process management and performance improvement from both a technological and process perspective (Lindgren & Emmitt, 2017). Diffusion serves as a form of communication about an innovation and the primary focus is to share new information or ideas within a social system. As the knowledge of an innovation is implemented, employees make evaluations of the -innovation based on the information from their peers. Workers assess an innovation on certain characteristics (i.e., ease of use) and those perceptions determine how quickly it will be adopted into the enterprise (Lindgren &m, Emmitt, 2017).
Self Determination Theory is a concept of human motivation, development, and wellness. Self-determination theory proposes that individuals experience distinct types of motivation to varying degrees (Howard, Gagné, Morin, & Van den Broeck, 2016). The theory focuses on types of motivation and pays particular attention to autonomous motivation, controlled motivation, and amotivation as predictors of performance, relational, and well-being outcomes. SDT also examines the social conditions that enhance or diminish these types of motivation and the degrees to which the basic human needs for autonomy, competence, and relatedness are supported versus diminished (Howard et al., 2016).
Researcher Organization of the Literature Review
The organization of the literature review is based on three concepts: (1) Robotic Process Automation (Cooper, 2019); (2) Diffusion of Innovations (Lindgren & Emmitt, 2017); and (3) Self-determination Theory (Howard et al., 2016). This organization is intended to provide a structured approach for the exploration of the relevant literature that supports the three key areas indicated.
Robotic Process Automation. The RPA stream explores the history of the technology, its key elements and the primary argument for its adoption by business leaders and technologists – who believe that it provides organizations with a digital workforce, that can perform business processes currently delivered by human workers, but more quickly, efficiently, reliably, and accurately. This frees workers up to spend more time on activities that add real value to the organization such as interacting with customers, creating propositions, and setting strategic direction (Evan-Lee Courie. 2019).
Diffusion of Innovations. The Diffusion of Innovations stream explores the process for adopting an innovation in an organization. This process involves the communication of a new idea through certain channels is a key activity in the innovation process and it relates to the adoption and diffusion of new ideas, methods and products within a social system (Lindgren & Emmitt, 2017).
Self Determination Theory. The Self Determination Theory (SDT) stream explores SDT as a prominent empirically-driven psychological theory that examines human flourishing within the context of technology supported organizations. Further, the literature will examine the perspective of humans as inherently oriented toward actualizing their capabilities, through processes including intrinsic motivation, social internalization and integration, and connecting with others (Dehaan, Hirai, & Ryan, 2016).
Definition of Terms
Diffusion of Innovations: The formal communication of a new idea through specific channels with the goal of driving the distribution and adoption (diffusion) of the new idea, method and/or product within a social system (Howard et al., 2016).
Innovation: innovation is thinking of, and then implementing, a better way of doing things. An innovation doesn’t have to be something completely new and different. It can be any sort of incremental improvement (Lindgren & Emmitt, 2017).
Robotic process automation (RPA): The use of technology and ‘bots’ to automate work traditionally done by humans. RPA describes the continuum of technologies used to automate business processes and operations (KPMG, 2017). At one end, it includes the basic automation of parts of a business process, such as auto claim adjudication. At the other end, it covers the application of sophisticated technologies involving cognitive machine processing and elements of artificial intelligence.
Self Determination Theory: The growing and flourishing psychological metatheory of human self-regulation, motivation, personal growth and well-being (Martos & Sallay, 2017)
Cognitive process automation: The convergence of RPA, machine learning, cognitive platforms and advanced analytics. It is one of the most important — and potentially disruptive — changes facing businesses today (KPMG, 2017).
Software bots: The robots that perform pre-programmed tasks and ‘learn’ how to get better at performing more intricate and varied tasks to move on to even more complex ones (KPMG, 2017). For example, cognitive processing can interface with humans, thanks to a combination of artificial intelligence and cognitive technologies that mimic human thought processes and communication.
Cognitive augmentation: Augmentation that mimics human activities such as perceiving, inferring, gathering evidence, hypothesizing, and reasoning (KPMG, 2017). When combined with advanced automation, analytics, mobile, and cloud technologies, these systems can be trained to execute judgment-intensive tasks.
Assumptions and Limitations
Assumptions
Assumptions are a key aspect of a management consulting engagement. As a consultant the researcher brings a certain perspective established through experience with a range of clients in a variety of industries. The key assumption the researcher holds is that the pace of the development and the subsequent adoption will require several years and will extend beyond scope and timing of this study.
Another assumption is that participants will answer questions fully and truthfully. This assumption must exist in order for the study to proceed as planned.
Limitations
One of the primary limitations of the study is that it is limited to a single organization within a life sciences sector. The study is not intended to be generalizable across organizations or industry sectors but is intended to add to a growing body of literature on the adoption of RPA and a limited body of literature on RPAs and worker satisfaction.
Another limitation of the study is the limited population of workers within the organization that have been exposed to RPA. The total employee population of Merck is nearly 70,000, however, the number of workers that have access to the RPA functionality is limited to approximately 200 people. Of those 200 people, the researcher has received permission to study up to 15 people (of which the researcher will select 10). The small sample size of this study means the results may not be generalizable to the broader employee population within Merck.
Summary
Smart Technologies specifically RPA threaten to change the very nature of work by influencing how organizations execute core processes through a mix of human and digital labor (RPA). The purpose of the section was to define the problem which is the lack of knowledge of workers’ perceptions of the organizational adoption of RPA. In order to explore that problem, the researcher intends to explore research questions that include how workers perceive the organizational adoption of RPA, how worker productivity is impacted by the implementation of RPA, and how workers perceive their value to the organization is influenced by the implementation of RPA. These questions must be deeply considered, if organizations like Merck are to continue to thrive both within and outside of the life sciences industry.

Chapter 2: The Literature Review
Introduction to Chapter 2
This chapter synthesizes research on the three key themes of this study: Robotic Process Automation, Diffusion of Innovation and Self Determination Theory. These themes are the basis of the research that is intended to analyze and assess the influence that the implementation of RPA is likely to have on the workers at Merck – global pharmaceutical company. This review begins by exploring the literature around the key elements of RPA and their application with an organizational context. The second part of the review examines how the innovations like RPA are adopted within an organizational context through the Diffusion of Innovation framework. Finally, the review concludes with an exploration of Self Determination Theory (SDT) by covering areas that include job satisfaction and performance. This section will conclude with a summary of the literature.
Intelligent technologies have the potential to transform the business and government sectors and other parts of our society over the next decade and beyond (Sparrow, Hird, & Cooper, 2015). Leading researchers contend that the continued emergence of intelligent technology could trigger the most expansive economic disruption in history since industrialization (Schwab, 2016).
Intelligent technologies like RPA are fostering the creation of a new type of organization. Organizations that rely on technology to perform more and more tasks that require little to no human intervention will become more common over the next decade and more. These organizations will be at the forefront of a movement that changes the nature of work (Schwab, 2016).
Admittedly, organizations are currently in the earliest phases of adopting these technologies as business leaders are still working to understand how technologies will influence the performance of their overall businesses. One example and the subject of this study is Merck – a life sciences organization that has recently implemented RPA technology to streamline and automate various processes in its operations.
RPA is the automation of repeatable and redundant, rule-based human action through the use of software bots. RPA can mimic a human worker’s actions and replicate these activities on their own and once the bot has performed its designated tasks, it can then report, notify, or handoff for additional work to be performed by another bot (Cooper, 2019).
The intent of RPA is to enable organizations to reduce their reliance on human workers to perform repeatable tasks. The implication is that as these tasks are removed from human ownership – how are the employees perceiving the removal of work that they previously performed in terms of their overall satisfaction and performance of their jobs.
The problem this research seeks to address is uncover and understand worker perceptions about the organizational adoption of RPA and in turn how that phenomena influences worker satisfaction and performance at work. The study is informed by three streams of theory and research emerging from the literature: (1) Robotic Process Automation, (2) Self-determination Theory and (3) Diffusion of Innovations.

Figure 4. Literature Map
Literature Review
Stream 1: Robotic Process Automation (RPA)
The RPA stream explores the history of automation technology, its key elements and the rationale for its growing adoption by business leaders. These leaders believe that RPA provides their organizations with a digital workforce, that is capable of executing business processes currently performed by human workers, but more quickly, efficiently, reliably, and accurately. Advocates of RPA contend that one of its key benefit is that it frees workers up to spend more time on activities that add real value to the organization such as interacting with customers, creating propositions, and setting strategic direction (Evan-Lee Courie. 2019).
Throughout history of our society, industrialization has led to the progressive automation of tasks aimed at continuously driving productivity efficiencies and improving the overall quality of products. The rise of heavy equipment manufacturers such as Henry Ford sparked the creation of production lines and emergence of business disciplines such as industrial engineering where once bespoke processes were formalized into series of repetitive activities. Through complex studies initiated by management scientists such as Frederick Taylor, he and others were among the first to see these processes as primarily discrete constructs consisting of highly repetitive human work.
This process of industrial engineering has largely not been applied in the world of life sciences outside of where the core products (i.e., drugs) are developed. However, recently, (the last few years) several of the major life sciences organizations have started to rethink their operations and look to emerging technologies to streamline some of their processes and supplement human performance in the execution of these processes through the use of advanced automation technologies with analytics and cognitive technologies.
One of the key advanced automation technologies that is being gradually adopted in life sciences organizations is Robotic Process Automation, the IEEE (Institute of Electrical and Electronics Engineers) Standards Association defines Robotic Process Automation (RPA): as a preconfigured software instance that uses business rules and predefined activity choreography to complete the autonomous execution of a combination of processes, activities, transactions, and tasks in one or more unrelated software systems to deliver a result or service with human exception management (IEEE, 2017).
These preconfigured software instances replicate the work that humans perform and they are often referred to as “bots” or “software robots”. RPA possesses unique characteristics that set it apart from other automation paradigms contained in business process automation, business process reengineering, or business process management systems (IEEE, 2017).
RPA Functional Overview
Functionally, RPA robots work in the same way that humans do, through the use of a software presentation layer. The presentation layer establishes the way in which information is presented, typically for display or accessing the application (IEEE, 2017). Humans access the presentation layer through what is commonly known as the “logon screen”.
One of the more common ways that technologists use to describe RPA is to compare it to functionality of Microsoft Excel. The recorded macros in Microsoft Excel are designed to automate specific tasks such as “deleting rows and “removing duplicates”. However, the key difference between them is that RPA “macros” can be recorded to work with virtually any existing desktop or server software whereas Excel macros are only designed to automate Excel tasks (IEEE, 2017).
RPA software generally includes an interface with a execute button that, when activated, generates a script, that the robot follows to complete the task automatically. Additional value of RPA lies in its flexibility, as many RPA experts contend that with some minor configuration it can “learn” to do things like –
• Read emails, identify and highlight salient information
• Create and modify PDFs
• Enter data into ERP systems
• Create and send an email to specific supervisors when ambiguity or errors are encountered while executing a process
All of these actions can be (but do not have to be) monitored in real time by the user that designed the script, or by other software robots.

Types of Automation
Business Process Automation (BPA), Robotic Process Automation (RPA) and Cognitive Process Automation (CPA) are the three most common types of automation (Lacity, Willcocks, Craig, 2015). Business Process Automation and Robotic Process Automation are often used interchangeably within the business community as leaders and lay people are generally unfamiliar with the distinction between them. However, these stakeholders do understand that both promise to streamline work by enhancing efficiencies. However, BPA is a broader use of the concept of automation as it considers integration and automation across the business to enhance value and efficiency (Lacity et al., 2015). RPA promises to execute routine, repeatable tasks more quickly, efficiently, reliably, and accurately, freeing employees up to spend more time on activities that add greater value to the organization – such as engaging with customers, generating sales, and setting strategic direction (Evan-Lee Courie, 2019).
Finally, Cognitive Process Automation (CPA) leverages Artificial Intelligence to take BPA and RPA a step further to enhance both the execution and results of automation endeavors. For example, CPA can pull and correlate data from multiple sources such as internal and external databases to provide an in-depth analysis that could not be easily performed easily completed via human efforts (IEEE, 2017).
Overall, automation technology enables technologists to create flexible and highly productive interactions with diverse range of applications. However, each automation tool operates differently and is highly dependent on what the objective of the automation (Evan-Lee Courie, 2019). Figure 5 details the types of automations and how they are best used within a business context. While there are dependencies related to each automation type, there are some standard steps and they include –
1. User shows the platform an application interface
2. Automation tool automatically retrieves the commands that allow it to operate the application
3. The commands (scripts) that an automation application learns from the interaction are retained for the next instance that the specific process is initiated (Sharma, 2019).
An automated process represents a discreet business activity that is definable, repeatable, and rules-based (Lacity et al., 2015). Examples of common automated business processes include:
• invoice reconciliation
• purchase order processing
• customer inquiry response
• account closures (Lacity, et al, 2015)
Figure 5 below displays five categories of automation. The chart shows from left to right the type of automation and its level of complexity. The office automation category is the lowest level of automation that is implemented in a business context, whereas automation supported by Artificial Intelligence (AI) is the highest level (Sharma, 2019). AI supported automation is also referred to as Intelligent Automation (IA) and it’s the least common form of automation utilized in a business context. The primary reason for the low utilization of IA in organizations is that it is the most advanced form automation and the most complex and expensive to adopt (Sharma, 2019). IA differs from RPA in that IA includes a form of intelligence for learning the initiation and execution process which is unlike RPA where scripts are needed to initiate and execute the process (Sharma, 2019).

Figure 5. Types of Automation.
RPA Influence on Organizational Structure
A key measurement of success for any business is its ability to respond to change – in demand, in technology, or in size and scale (Evan-Lee Courie, 2019). For traditional businesses such as those in the life sciences sector, effecting change means adopting one of a number of unpalatable or high risk approaches: perhaps increasing headcount to satisfy an increased number of customers; undertaking large-scale IT programs to introduce or integrate new technologies; or carrying out extensive consultancy engagements to shift business focus. Organizations empowered with RPA are able to minimize the impact of these challenges or avoid them altogether because the flexibility that RPA adds to the business (Sharma, 2019).
Figure 6 below displays symbolically the effect of replacement of devices into human-based workflows. These replacements must be considered both in terms of technological process reframing as well as in terms of the effects of workforce replacement

Figure 6. Organizational Structure.
Side A of Figure 6 displays the structure of a typical organizational structure. This structure is comprised of a mix of high skilled and lower skilled workers. However, the typical organization includes are more lower-skilled workers than higher skilled workers and that results in a pyramid shape. Side B of Figure 6 displays the structure of an organization structure when digital labor incorporated. In this scenario, robots replace more low-level jobs than higher-level skilled positions. The resulting organizational structure is fundamentally the same shape as the Side A structure, but the human component resembles a pillar instead of a pyramid (IEEE, 2017).
As RPA evolves as a business productivity technology, the identification of appropriate tasks and processes that can be executed by RPA is increasing. These tasks possess common attributes that include –
1. Well-defined processes (robots still need precise instructions in order to successfully complete tasks).
2. High-volume repetitious tasks (tasks associated with payroll, accounts payable, and accounts receivable are recurring)
3. Tasks should be targeted (these tasks have more predictable outcomes and the costs are known) (IEEE, 2017).
The most obvious benefit of RPA is the reduction of time spent within highly repetitive processes. returns more value-creating work back to the workers. Other benefits include greater reliability, enhanced service quality, and improved security (McClimans 2016). Assuming perfect training, robots can perform tasks error free, which leads to higher-quality data, improved reports, and fewer downline error-correction functions. In addition, robotic work can leave reliable records of accomplished tasks. Since the robot must perform within the scope of a prescribed script, auditing a robot is theoretically simpler than auditing a human.
RPA-enabled processes also lead to superior service. By simply reducing the amount of time in such functions as invoice processing, application and loan approval, or purchase order and fulfillment, satisfaction increases for both the customer and supplier (McClimans 2016).
In addition to the benefits outlined above, RPA implementations carry intrinsic risks. According to Knowledge Capital Partners (Hindle, 2018), about 30–50 percent of RPA projects fail. They also identify eight areas of manageable risk related to strategy, sourcing, tool selection, project time estimates, operations and execution, change management, maturity, and stakeholder buy in (Hindle, 2018).
Another concern with RPA is that software robots will replace human jobs. In fact, one RPA key performance indicator (KPI) is the number of human labor hours that are saved per month, or the number full-time equivalent (FTE) employees whose work is now being conducted by robots. However, in current available white papers, vendors and proponents of RPA do not address the factor of robots replacing human workers. Instead, they tout the benefits of allocating mundane, repetitive tasks to software agents, which frees up human workers to perform tasks that require creativity, complex decision making, and emotional insight. However, the risk is ever present and is subtly acknowledged in one white paper that predicts RPA will allow businesses to expand without hiring more employees (Chappell 2017).
Stream 2: Diffusion of Innovation
The Diffusion of Innovations stream explores the process for adopting an innovation in an organization. This process involves the communication of a new idea through certain channels is a key activity in the innovation process and it relates to the adoption and diffusion of new ideas, methods and products within a social system (Lindgren & Emmitt, 2017). This section includes a discussion of the qualities that lead workers to either accept or reject an innovation and the rate at which they do so. Within the broad scope of enterprise technologies RPA is unique in that it is the first that doesn’t require significant human intervention to complete tasks, so in a sense it doesn’t have to be “adopted” in the traditional way that is technology is introduced within an organization. However, its best for business stakeholders to think of RPA as a contributor to the completion of a whole business process, in other words – humans and RPA will work together as equal contributors to the successful completion of the discrete steps that comprise holistic business processes (KPMG, 2017).
Diffusion of innovation is the process of moving an innovation through an organization by facilitating adoption through different mediums, distributing information between individuals and groups (Lindgren & Emmitt, 2017). Traditionally, diffusion of innovation research has mainly focused on technological innovations. However, an innovation does not have to be a technology rather it can also simply be an idea or practice that is perceived as new (Lindgren & Emmitt, 2017).
The research suggests that the key variables that either constrain or facilitate the adoption of a new idea/technology is time and people. As diffusion of innovation is dependent on people to adopt the technology, idea or practice – their reactions and willingness to explore the idea/technology at its earliest stages are critical to its ability to be diffused (Risquez & Moore, 2013).
Rogers first proposed Diffusion of Innovations theory in 1962. He theorized that innovation diffusion happens when several individuals go through the decision-making process over time. That process is comprised of four stages:
1. Knowledge – where awareness and knowledge about the innovations arises
2. Persuasion – where a favorable or unfavorable attitude is formed about the innovation
3. Decision – where decision about adoption of the innovation takes place
4. Implementation – and confirmation, which is about reinforcing the decision already made (Lindgren & Emmitt, 2017).
Although presented in sequential steps, it is common for many of the activities to within the stages to occur concurrently. For example, in the implementation stage, innovations can be changed or modified based on feedback received from stakeholders (adopter groups).
Adopter Groups
Adopter groups define the characteristics of the target population that will either help or hinder adoption of the innovation. Tidd (2010) identifies five different categories of adopters that move a new idea through a social system:
1. Innovators – The people that are the first to try the innovation. They are always interested in new ideas, willing to take risks, and are often the first to develop new ideas. Very little needs to be done to appeal to this population.
2. Early Adopters – These are people who represent opinion leaders. They occupy leadership roles, embrace change opportunities and are aware of the need to change. Things that appeal to this population include communications such as how-to manuals, fact sheets and quick reference cards.
3. Early Majority – These people are rarely leaders, but they do adopt new ideas before the masses. However, they typically need evidence that the innovation works before adopting it themselves. Appealing to this population requires success stories that provide evidence of the innovation’s effectiveness.
4. Late Majority – This group is skeptical of change and will only adopt an innovation after it has been adopted by the majority. Strategies that appeal to this population include information on how many other people have adopted the innovation successfully.
5. Laggards – This group is bound by tradition and very conservative. They are skeptical of change and are the hardest group to convince to try new things. Strategies to appeal to this population include statistics, fear appeals, and peer pressure.
Adopter groups indicate that innovations are not adopted by all individuals in a social system at the same time. Instead, they tend to adopt in a time sequence, and can be classified into adopter categories based upon how long it takes for them to begin employing the new idea. Adoption of a new idea is initiated by human interaction through interpersonal networks. Tidd further states that if the initial adopter of an innovation discusses it with two members of a given social system, and these two become adopters who pass the innovation along to two peers, and so on, the resulting distribution follows a binomial expansion. Expect adopter distributions to follow a bell-shaped curve over time which is depicted in Figure 7 below (Tidd, 2010).

6.
Figure 7. Adopter Group Percentage.
The above figure shows the normal frequency distributions divided into five categories: innovators, early adopters, early majority, late majority and laggards. Innovators are the first 2.5 percent of a group to adopt a new idea. The next 13.5 percent to adopt an innovation are labeled early adopters. The next 34 percent of the adopters are called the early majority. The 34 percent of the group to the right of the mean are the late majority, and the last 16 percent are considered laggards (Tidd, 2010).
The adopter groups and percentage are important to understand as they inform how RPA might be adopted and integrated within an enterprise. The adopter groups reflect the how individual groups may perceive and accept the introduction of RPA in an organization. For example, workers that are inclined to embrace change are likely to fall into the innovators category while workers that are less receptive to change may tend to fall into the late majority and laggards categories. Understanding the distinctions and where impacted employees may fall on the curve will help change managers and business leaders prepare the workforce to adoption of RPA.
Diffusion of Technology Innovation
In addition to the general diffusion of innovation that typically refers to an adoption of an idea within an organization, the diffusion of innovation theory also includes a process for managing the adoption of technological innovations. The process is extremely critical for those planning to introduce new products driven by emerging technological innovations. The process is depicted in Figure 8 below.

Figure 8. Diffusion of Innovation for Technology
Technology assessment enables change leaders to combine traditional financial discounting procedures for known cost data for various technologies with expert opinions for estimating their benefit values; and incorporates engineering performance improvement, project volumes ranging from one to infinity, various time horizons and a comprehensive sensitivity analysis. Determining diffusion characteristics includes developing and implementing a process by which an innovation is communicated through certain organizational channels over time among the members of a social system. Determining forecasting methodologies is a systematic process for logically analyzing the technical attributes and the economic attributes of a specific technology. Future possible diffusion paths include the Bass epidemiological model for the diffusion of innovations – an alternative method for the adoption of technology innovation. This model is useful in predicting technology introduction rates from a set of estimated values for the innovation factors. The model assumes that the probability that someone will adopt a technology, given that he or she has not yet adopted it, consists of two factors: 1) the peoples’ intrinsic tendency to adopt the new product; and 2) the word of mouth or social contagion (i.e., the larger the proportion of the market that has already adopted the technology, the more likely people are to adopt it) (Tidd, 2010).
At one time, the introduction of new technologies created jobs for people who managed their use. The opposite is predicted to be true now: technological innovations will pull humans out of control positions and replace them with intelligent machines leaving skilled workers with fewer options for earning a reasonable income (de Ven, Adner, Barley, Dougherty, Fountain, Hargadon, Schilling, 2017).
One example of this emerging phenomena is Amazon. Through the online retailer, consumers can now easily order and pay for goods via sophisticated computational devices that can be activated from nearly any computer or smartphone. Once Amazon’s computers receive the order, they instruct robots to retrieve the ordered goods from shelving in a warehouse, which then move those goods to a mailing station, where a human touches the items for the first time-picking them up off the cart and sending them to other machines to prepare for shipping (de Ven et al., 2017).
Up to 47% of U.S. employment could be at risk over the next two decades. The occupational categories most at risk, are predicted to be service, sales, office and administrative support, production, transportation, and materials handling. Researchers further indicate that automation based on machine learning will render obsolete additional jobs with midlevel wages and thereby further exacerbate income inequality (Antonescu, 2018). These types of predictions may make business leaders and employees anxious about jobs and the future of their organization. However, technology innovation is an ever-present force in our society and business leaders and workers must learn how to adopt emerging technologies while minimizing the impact to enterprise productivity. Diffusion of innovation is one strategy that facilitates the integration of technology, however, there are others strategies that work in conjunction with diffusion to help organizations effectively manage technological innovation.
In 1944, President Roosevelt asked his science advisor, Vannevar Bush, how wartime investments in science might contribute to peacetime society. In response, Bush’s 1945 report, Science: The Endless Frontier, laid out the government’s first formal innovation policy, outlining a bold new vision of how federally funded basic research could and would solve the nation’s-and the world’s-problems of disease, hunger, poverty, and national security (de Ven et al., 2017). Bush defined the innovation process itself-by creating a language that still dominates public and private sector understandings of the process-as a set of distinct linear stages moving from research (both basic and applied) to development, then demonstration, and finally deployment. Bush developed a three phased approach for managing the integration of technology: 1) development, 2) demonstration, and 3) deployment.
Development comes up with the actual products or processes that can be put into practice. Demonstration, as the term suggests, represents the activities associated with installing, running, and monitoring the performance of these products or processes. Deployment (or diffusion) is the successful culmination of the process: the manufacturing, selling, installing, using, and maintaining of a new technology across a broad market (de Ven et al., 2017). Corporations continue to manage their research and development with stage-gate processes that follow this linear model, and the federal government allocates funds with these stages in mind (de Ven et al., 2017).

Stream 3: Self Determination Theory (SDT)
The Self Determination Theory (SDT) stream explores SDT as a prominent psychological theory that examines human flourishing within the context of technology supported organizations. Further, the literature will examine the perspective of humans as inherently oriented toward actualizing their capabilities, through processes including intrinsic motivation, social internalization and integration, and connecting with others (Dehaan, Hirai, & Ryan, 2016). The section includes an overview of the key drivers of worker performance such as motivation, autonomy and productivity and their supporting qualities that include job satisfaction and worker engagement.
Self-determination theory (SDT) is the notion that motivation drives key outcomes of workers such as well-being, high performance, creative problem-solving and more (Deci & Ryan, 2000). SDT enables individuals to pursue their goals as they align with key desires and behaviors that include psychological needs such as autonomy, competency and relationships (Deci & Ryan, 2000).
Researchers use SDT to investigate of people’s inherent growth tendencies and innate psychological needs that are the basis for self-motivation and personality integration. SDT also examines situational factors that hinder or undermine self-motivation, social functioning, and personal wellbeing. Moreover, SDT focuses on positive developmental tendencies and social environments that are antagonistic towards these tendencies (Coccia, 2018).
The meeting of psychological needs is essential for employee growth and well-being and when the needs are met, it facilitates optimal levels of employee performance. The psychological need for competence is the need to feel capable in one’s efforts to achieve desired outcomes (Van Beek et al., 2011). The need for relationship is a need to feel understood and accepted by others. The need for autonomy refers to the individual worker’s need to feel that their actions and behaviors are of their own choice and having these three core needs met results in enhanced employee performance (Deci & Ryan, 2008).
Another central element of SDT is its theories of motivation. Researchers Deci and Ryan (2008) refer to their theories as autonomous motivation and controlled motivation. Autonomous motivation is described as an individual’s behavior as being self-directed and self-endorsed and that comes from employees that have met their basic psychological needs for autonomy.
Controlled motivation is the overall sense that their actions and decisions are externally governed based on rewards and punishment (Deci & Ryan, 2008). Within controlled motivation, an employee is focused completing tasks based on perceived reward and punishment (Van Beek et al., 2011). For example, an employee may complete tasks at work, not because they feel a sense of value in reward in completing the task, but because they feel that the punishment for not completing the task will threaten their sense of self-esteem.
Another more common way of thinking about motivation as it relates to SDT is intrinsic and extrinsic motivation. Intrinsic motivation exists in the job itself and gives personal satisfaction to individuals, such as autonomy, recognition, expense preference (e.g., leeway to invest monetary resources), trust and empowerment (Benati & Coccia, 2018). Extrinsic motivation is driven by external factors such as pay and benefits (i.e., paid time off, health coverage, etc.) gifts, promotion or advancement opportunities, recognition etc. (Benati & Coccia, 2018).
The key factor of understanding motivation is how it informs an employee’s satisfaction and performance on the job. Researchers have been interested in the relationship between job satisfaction and job performance with much of the interest based on the idea that satisfied employees perform better than do dissatisfied employees (Coccia, 2018). The uniquely human qualities of intrinsic and extrinsic motivation and their relationship to the factors of job satisfaction and performance are leading indicators of employee engagement.
Employee engagement is a key factor in the development and maintenance of a successful enterprise (Jain, 2019). Between 2000 and 2010, one of the first studies conducted identified a quantifiable increase in productivity per hour (6.6%) among happy employees as compared to unhappy employees. A more recently published Harvard Business Review (2015) paper indicated that happy employees lead to an average of 31% productivity and 37% sales increase (Jain, 2019).
Researchers view the emergence and integration of RPA across global enterprises as a threat to employee engagement and by extension – organizational performance. With the rise of automation tools, business leaders and workers hold a dystopian view that technologies like RPA will increasingly replace the tasks of human workers and they will lose value in the eyes of management (UiPath, 2019). Organizations are increasingly concerned with the employee experience as they grapple with the forces of automation and that operating model issues and psychological barriers held by workers hold back RPA adoption efforts. Ultimately, researchers and business leaders agree that keeping employees engaged will enable organizations to capitalize on the transformative potential of RPA; The key factors that drive employee engagement are job satisfaction and job performance (UiPath, 2019).

Engaging Employees
Employee engagement is defined as the harnessing of an organization’ members to express themselves physically, cognitively and emotionally in the context of both task execution and social connection. This is derived from William Kahn’s influential grounded theory of engagement and disengagement where he posited that employee engagement was the synchronized expression of one’s preferred self and the promotion of connections to others (Krishnaveni & Monica, 2016). According to Kahn’s work, people tend to use varying degrees of themselves in their work and detach themselves too. It is only when there is congruence between self and the role performance, they can create stunning work performance (Krishnaveni & Monica, 2016).
Kahn identifies the key drivers of employee engagement as: 1) job characteristics, 2) supervisor and co-worker relationship, 3) development and growth opportunities, and 4) rewards and recognition. When these drivers are reinforced, it enriches engagement in the minds and hearts of workers (Krishnaveni & Monica, 2016).
Job characteristics include the key attributes of an individual’s work, which include clearly identified and challenging tasks, creative and autonomous roles, self-image and status, and meaningfulness which is generally understood as one’s feeling of being “worthwhile, useful, and valuable”. This implies that they add value to the work they do (Monesson, 2013).
Supervisor and co-worker relationship is a complex driver. An employee perceives a situation as safe only when there is freedom to express him/herself without the risk of consequences from supervisors and co-workers. Organizations that encourage and sustain positive relational elements are built on concepts such as cooperation, support, trust and partnerships. Key themes that engaged workers cite as critical to their performance include – “trust in co-workers”, “trust that the co-workers had the best interest in mind” and “support from co-workers and managers”. When such relationships were not provided, employees tend to disengage from their work. Organizations that are supportive and that endorse positive emotion widen an employee’s skill to think and briefly build their available emotional and psychological resources. Such emotions have evolutionary roots and are linked to the basic human needs all employees have when they are at work (Pei-Li Yu, Shih-Chieh Fang & Yu-Lin Wang 2015).
Development and growth opportunities are also complex drivers. Employees’ engagement increases when there is a sense of involvement and meaningfulness that is associated with their jobs. Opportunities like robust training and development and flexible working arrangements are things that raise engagement and help instill a sense of loyalty that can be further cemented through challenging tasks, development opportunities and a fun environment (Cattermole, 2013).
Finally, the rewards and recognition driver is critical. Rewards are an integral part of engagement in the minds of employees as an emotional driver of engagement. Kahn (2013) asserts that workers vary their engagement level according to the way they perceive the benefits that are received for their job performance.

Job Satisfaction and Job Performance
Employee engagement is a significant driver of job satisfaction and job performance. The four qualities referenced in the previous section (job characteristics, supervisor and co-worker relationship, development and growth opportunities and rewards and recognition) serve as the pillars for enhancing the engagement of employees.
Job satisfaction is defined as a cognitive and behavioral perspective of the level of enjoyment that an employee derives from their job (Edwards, Bell, & Decuir, 2008). While there are a number of contributing factors to how satisfied an employee is with their job (i.e., benefits, schedule flexibility, work-life balance, organizational vision, etc.), it is primarily perceived as how a worker feels and how they think about their work (Wright & Cropanzano, 2000).
Job performance is inclusive of both task and contextual behaviors that are congruent with the role expectations that are determined by the organization that created the role. Task performance is defined by the extent to which an employee fulfills the formal requirements and duties of the job (Judge & Kammeyer-Muller, 2012). Contextual performance is comprised of those activities that fall outside of the formal job duties that support the organizational environment, such as helping a co-worker. Unlike formal task-based behaviors, contextual performance activities are generally not recognized as part of the formal requirements of the job, but they are relevant in that they contribute to the organizational culture and its overall effectiveness (Judge & Kammeyer-Muller, 2012).
Motivation, Employee Engagement, Job Satisfaction & Performance and SDT
In terms of how the factors of motivation, employee engagement and job satisfaction and performance inform Self-determination theory (SDT), it is best to think of these factors as building blocks that lead to the formation of a worker’s self-perception and desire to perform. SDT has been applied to many environmental contexts and researchers have found its application of understanding human motivation with consideration of innate psychological needs for competence, autonomy and relatedness and individual goal-pursuit associative with outcomes in work engagement (Van Beek, Schaufeli, Taris, & Schreurs, 2012).
Researchers further contend that a manager’s support of employees’ self-determination, by being supportive of the autonomy of workers, has a positive effect on both performance and satisfaction. Researchers indicate that employees with high autonomy also have high competence and relationships and higher levels of job-related satisfaction (Van Beek et al., 2012). Similarly, results in a study done on the motivation correlates to higher work engagement or a positive work-related state of mind characterized by dedication (Van Beek et al., 2012).
SDT gives business leaders a framework to better understand the needs of workers as organizations evolve to accommodate the introduction and adoption of smart technologies. The elements of job satisfaction and job performance, along with the supporting elements of motivation, employee engagement, and job satisfaction and performance, are key to keeping the workforce engaged during times of significant organizational change.
Summary
Succeeding with an enterprise digital transformation initiative requires an appropriate mix of three things – a productivity enhancing digital technology, an engaged workforce and a comprehensive strategy for facilitating adoption of the digital tools. RPA is an emerging tool that has a proven record of success in the transforming specific care areas of an enterprise by automating critical but routine tasks. The near immediate benefit is that automating tasks minimizes human intervention, thus freeing workers to focus on doing work that adds greater value to the business. The inherent benefits of automation are that it increases accuracy and completes tasks faster than human workers.
The same benefits that make automation so compelling are also the ones that make workers anxious about the emergence and adoption of RPA. Business leaders must recognize the need to keep employees at the core of their digital transformation efforts. Adopting RPA is a multifaceted task that must not be taken lightly. Ensuring that employees are engaged is a key priority to enable organizations to capitalize on the transformative potential of RPA.
The companion approach to driving employee engagement is to implement a strategy that enables an organization to adopt technology that while minimizing the impact to the overall employee experience. Diffusion of innovation is the process by which an innovation is communicated to the participants in an enterprise. Communication is a key element of driving technology adoption and the engagement or employees to maximize the investment in emerging technologies like RPA.

.
Chapter 3. Methodology
Introduction to Chapter Three
Robotic Process Automation (RPA) is designed to execute some of the discrete manual processes traditionally completed by human workers, thus enabling those workers to focus on completing (and harmonizing) higher complexity tasks to derive increasingly higher levels of organizational productivity (Akst, 2013). Unlike traditional Enterprise Resource Planning (ERP) systems adopted by businesses to streamline manual processes humans execute daily, RPA is designed to replace many of the manual processes that have traditionally been completed by human labor. While there is speculation about the long-term impact of RPA, few studies have explored the perceptions of the workers who are experiencing the adoption and implementation of RPA in their daily workflow. The purpose of this qualitative narrative inquiry is to better understand the perceptions of workers in a global life sciences organization on how RPA influences their daily workflow.
Research questions in a qualitative study identify the phenomenon to be studied and support an in-depth examination (Clandinin, 2013). The focus of the research question is to formulate and articulate a clear issue to study (Creswell, 2012). Based on the problem and purpose of this study, the central research question for this study is: How do workers perceive the influence of RPA on their overall performance in the workplace? Three sub-questions include:
1. How do life sciences professionals perceive the influence of RPA on their motivation?
2. How do life sciences professionals perceive the influence of RPA on their productivity?
3. How do life sciences professionals perceive the influence of RPA on their autonomy?
These open-ended questions will enable participants to share a variety of
opinions and behaviors that will enable the researcher to develop a comprehensive narrative of the participants’ experiences (Clandinin, 2013).
This chapter includes an outline of the proposed methodology for this study. The chapter begins with a discussion of the qualitative methodology and the appropriateness of narrative inquiry as the chosen method. As a social constructivist, the researcher chose narrative inquiry as it was deemed the best approach to answer the research questions within a real-world context (Clandinin, 2018). With regard to narrative inquiry, this chapter discusses data collection and analysis procedures, as well as the timeline for the research and ethical considerations.
Research Design and Rationale
The appeal of taking a qualitative research approach lies in the fact that “it is a source of well-grounded, rich descriptions and explanations of processes occurring in local contexts”(Corrado, 2019). In this way, qualitative research brings depth and context to the research in a way that a quantitative research cannot (Corrado, 2019). Qualitative researchers explore and attempt to understand a central phenomenon (as opposed to explaining and predicting variable relationships) and focus on describing and explaining that phenomenon (Merriam & Grenier, 2019). In a qualitative study, the researcher works to describe the situation that exists as viewed primarily through the perspective of the study’s subjects. Corrado (2019) contends that qualitative inquiry is mainly a way of seeing things in a way that satisfies and is useful for the things we value. The findings from qualitative research are used to persuade by such characteristics as its weight, the coherence of the study, and the cogency of the final analysis. In qualitative research, there is no statistical test of significance to determine if the results “count”; in the end what counts is a matter of judgment (Corrado, 2019).
The qualitative approach proposed for this study is narrative inquiry. Narrative inquiry is suitable for exploring a phenomenon, event, or situation (Corrado, 2019). A narrative inquiry is an empirical inquiry in which researchers explore contemporary phenomena within a real-life context when the boundaries between phenomenon and context are not evident (Merriam & Grenier, 2019). The researcher will serve as the key instrument for data collection and bring personal connection through his passion for the subject to the overall research experience (Creswell, 2009).
Research Methods
Population and Sample Description
The target population for this study will be processors at Merck Pharmaceuticals. Currently, there are a few hundred processors at Merck. Processors are the individuals that perform the current manual activities that have been or soon will be automated. All processors possess an average of 4.5 years of experience with Merck and 1.5 years working with RPA. The overall gender makeup across both groups is 60% male and 40% percent female. The primary population of study participants are located in various Merck sites across the world however, the primary location of the study participants will be in Pennsylvania and New Jersey of the study. The sample will be 10 processors who interact with the Automated Visual Inspection RPA technology.
Site Description
The location of the study will be at Merck Pharmaceuticals – a global life sciences company. The site is located in North Wales, PA which is approximately 20 miles north of Philadelphia, PA. Merck is a globally-dispersed organization with over 69,000 employees located across thirty countries with revenue of $11 billion dollars in 2018.
There are three key factors that make Merck a highly useful organization to study
1. It is of significant size both in terms of employees and annual revenue (approximately 69k employees across thirty countries, 2018 revenue was $11 billion).
2. It is a global organization that is vertically integrated (i.e. it designs, manufactures, markets, and distributes its own life sciences products).
3. It is currently implementing an RPA in five functional areas – Reporting Workflow; Power Point Automation; Foreign Exchange (FX) Trade Optimization and Automated Vision Inspection (AVI). The focus of the study will be Automated Vision Inspection (AVI).
Site Access
As an employee of Merck, the researcher has access to the primary site, which is located in North Wales, PA. However, the execution strategy of the study will be comprised mainly of virtual interactions with subjects based in the PA and NJ sites.
The researcher identified the study participants through contacting senior leaders in Merck’s IT organization. A coordinator of Merck’s early stage RPA initiative was identified, and the researcher and the RPA technology coordinator worked together to determine a preliminary scope of the research project, which is further detailed below.

Research Methods
This proposed narrative study will utilize research methods recommended by Clandinin (2013) for narrative data collection and analysis. The researcher will utilize what Marshall and Rossman (2014) identify as one of the most common sources of data collection researchers use in qualitative research: interviews (Marshall & Rossman, 2014).

Semi-structured Interviews
The primary method of data collection is semi-structured interviews with processors and process executors. These interviews will capture the stories of participants as they discuss their perceptions of how RPA has and will impact their work.
Instrument description. Instrumentation is the measurement device in qualitative research (Pezalla, Pettigrew & Miller-Day 2012). The researcher has developed an ninterview protocol with open-ended questions (see Appendix X). Open-ended or constructed interviews are suitable for recording data that is richer and more meaningful than data collected in surveys or questionnaires, and the researcher will gain insight into how participants construct reality (Clandinin, 2013).
Participant selection. The researcher will select participants through purposeful, criterion sampling. Creswell (2019) states that purposeful sampling is where “researchers intentionally select individuals and sites to learn or understand the central phenomenon” (p. 206). The researcher will utilize a type of purposeful sampling labeled criterion sampling. In criterion sampling, participants are selected based on specific criteria (Given, 2008). In this study, the specific criteria are: 1) employees of Merck; 2) currently working as processors or process executors; and 3) whose day-to-day work is impacted by RPA.
Identification and invitation. Once the researcher has identified a sample of participants based on the criteria describe above, the researcher will email each participant an invitation letter. The letter will explain the research and assure them that participation is voluntary, and they may decline to participate or withdraw from the study at any time without consequence. The researcher will select participants from among those who volunteer so as to represent the sites and approximate the workforce demographics.
Data collection. The researcher will conduct the interviews using the interview protocol. The interview protocol is designed for a 45 – 60 minute interview. Interviews will be conducted over Zoom for participants who are not located at the primary site and in person in a private office for participants located at the primary site. With the participants’ permission, the researcher will record the sessions for notetaking purposes only. The researcher will utilize a transcription service. Once the transcripts are received, the researcher will destroy the recordings. Before, during and after the interview, the researcher will take notes on the participant’s non-verbal behavior; note additional questions and clarifications made during the interview; and add any other observations relevant to the inquiry. These notes will be added to the transcripts and considered as data for the analysis.
Member checking will be used to validate the accuracy of responses by cross-checking data sets. The researcher will use member checking during the interview by restating and summarizing the participants’ response to ensure understanding, interpretation, and accuracy. Additionally, the researcher will verify the transcripts for accuracy. Verifying the transcripts for accuracy will involve providing each participant the opportunity to review the transcribed interview and make changes if necessary. Participants will receive the transcribed interviews via e-mail and the participants will be given seven days for the review.
Data Analysis
Semi-structured interviews. According to Clandinin (2013), qualitative research analysis in a narrative inquiry begins by reviewing the research question and by basing the data analysis on the central question. Clandinin (2013) compares building an explanation from collected data to the process of refining a collection of ideas. This narrative inquiry study will include the biographical narrative interpretive method (BNIM) to allow participants to share stories and experiences in their own words and perspective (Corbally & O’Neill, 2014).
The BNIM method of interviewing begins with asking one open-ended question
to start the narrative. The researcher will then ask follow-up questions after the participant finishes the story. The interview questions will be open-ended. Transcribing the interview data verbatim preserved information for analysis.
The participants’ narratives will need to be coherent and meaningful. The researcher will organize, classify, and code the data. According to Gale, Heath, Cameron, Rashid, and Redwood (2013), the categorization process of organizing and coding data is an integral process in data analysis. Coding is a process that requires the reduction of data into labels to explore, compare, and categorize (Gale et al., 2013). Once the researcher has identified and outlined patterns, themes will be developed through a general interpretation of the data.
NVivo will be used for organizing and interpreting the data into thematic representations. Researchers use NVivo software to classify, sort, and arrange information from textual interviews. The use of Nvivo10 will facilitate the consolidation and transformation of multiple interviews into comprehensive answers to each of the interview questions and later merge those initial answers across the entire interview script to develop answers to the research questions. This process will involve grouping the relevant data and coding categories to form the basis for theme development to uncover themes, as well as develop meaningful conclusions.
Stages of Data Collection
The following timeline is appropriate for the study:
Event Data
Proposal Defense March 2020
IRB Approval April – May 2020
Participant Recruitment May 2020
Data Collection June – July 2020
Data Analysis July – August 2020
Draft Chapters 4 and 5 September 2020
Revise November 2020
Dissertation Defense December 2020

Table 1: Plan of Events
Ethical Considerations
Qualitative researchers must consider ethical issues related to conducting research
studies by following established ethical procedures to promote effective research ethics. Researchers face ethical challenges in all stages of a study from writing to designing to reporting. Challenges in research include ensuring ethics by maintaining anonymity and confidentiality, as well as providing and obtaining informed consent (Corrado, 2019). Narrative inquiry researchers have an additional responsibility when adhering to ethical considerations. Due to the relational aspects of narrative inquiries, ethical considerations are important throughout the inquiry. According to Merriam & Grenier (2019), narrative inquirers should adopt a learning attitude of empathic listening by refraining from being judgmental and by suspending personal beliefs.
The rights and privacy of research participants in this qualitative narrative inquiry will include protecting research participants’ rights and privacy throughout the study. Qualitative researchers must provide participants with an informed consent form for the participants to sign before the research study takes place. Researchers agree to inform participants of the requirements of the study and agreed to prevent harm to any participants by protecting their anonymity and privacy (Corrado, 2019). Researchers have an obligation to participants to provide accurate information by detailing the contents of the study and the expectations for the participants. Researchers must not deceive or mislead participants while conducting a research project.
The researcher has completed the required training from the Collaborative Institutional Training Initiative (CITI). Further, in order ensure data integrity and to keep the subjects from possible abuse through misuse of the data collected, the researcher will obtain approval from Drexel University’s Institutional Review Board (IRB). Appropriate steps will be taken to ensure the safety and confidentiality of the participants, as well to guard against retaliation for expressing potentially unpopular opinions.
Specifically, the researcher will inform participants that their participation in the research is voluntary and they may decline to participate or withdraw from the study at any time without consequence. Additionally, the researcher will utilize a verbal consent protocol prior to conducting semi-structured interviews with participants. The researcher will record the interviews for transcription and will destroy the recording once the transcriptions are received. The research will protect confidentiality by assigning participants pseudonyms. Also, the researcher will not include information that would identify participants as individuals. All data will be stored on a password protected computer and on Drexel University’s Microsoft 365 password protected, secure server.

Chapter 4: Findings, Results and Interpretations
Introduction
In this narrative inquiry study, product inspectors within working in a drug manufacturing facility described their experiences when Automated Vision Inspection (AVI) an RPA technology was introduced to the organization. This technology changed their work through transitioning approximately 60% of the manual activities performed by human inspectors to the AVI technology. Through querying the inspectors and listening to their stories about how the technology impacted their work within the production environment, a clear picture of a deep and rich narrative exploring human motivation, productivity and autonomy unfolded.

Findings and Assignment help – Discussion
The purpose of this qualitative, narrative inquiry was to understand the influence of the Automated Vision Inspection (AVI) on the work of the human inspectors as described through their experiences as workers performing the detailed process of manual inspection of pharmaceutical products. Participant reflections provided insight into how AVI is impacting the autonomy, productivity and motivation of the human product inspectors. The compelling descriptions, through the stories the participants shared, reflected their perception of how AVI personally impacted them. This chapter presents themes that responded to the central research question: How do workers perceive the influence of automation on their overall performance in the workplace? To provide context, the chapter begins with a brief recap of the study’s narrative inquiry methodology as implemented. The next section profiles each participant, followed by an explanation of the superordinate and subthemes identified. The chapter concludes with the research themes.
Through a narrative inquiry methodology, research participants expressed their individual experiences to the researcher. The data generated a rich collection of experiences that aligned with the subjective and interpretative foundation of qualitative research. As previously discussed, qualitative research does not prescriptive. Rather, the authenticity of qualitative narrative research is based on a researcher finding meaning within the data. Above all, the responsibility to process the depth and complexities of participants’ experiences remained with the researcher. Narrative inquiry enabled the researcher to construct the context that gave the stories of the participants experience and meaning.
Ultimately, through these stories, themes and subthemes emerged and were identified by the researcher. The next section introduces to the ten study participants.
Participant Overview
During this qualitative research, ten product inspectors with an average age of 42 from a global pharmaceutical company participated in ten separate, virtual, one-on-one interviews. Each interview lasted 45–60 minutes. The average years of product inspection experience was 14.5 years.
The ten inspectors who became the study’s participants were those who replied first to the recruitment invitation. Ethnicity and gender were not criteria, yet five of the ten participants were female, and seven were white. Three female inspectors identified their ethnicity as Black, and another two inspectors identified as Asian. The participants ranged in age from 35–52, Table 2 contains pertinent participant demographic information.

Pseudonym Age Gender Ethnicity Years of Experience
Participant 1 35 Male White 7
Participant 2 43 Female White 15
Participant 3 39 Female White 13
Participant 4 40 Male Hispanic 15
Participant 5 47 Female Black 12
Participant 6 52 Male White 23
Participant 7 44 Male Black 14
Participant 8 37 Female Black 13
Participant 9 42 Male Hispanic 15
Participant 10 36 Male White 16

Table 2: Participant Demographics
Participant Profiles
During the interview, each participant shared some personal data and experiences related to their roles as inspectors and what they are the most important aspects of the role as it pertains to their personal level of autonomy, productivity and motivation. The overall role of pharmaceutical quality inspector is to examine drug products and related materials for defects or deviations from pre-determined specifications. Specific tasks include – Read and understand blueprints and specifications; Monitor or observe operations to ensure that they meet production standards; Recommend adjustments to the process or assembly; Inspect, test, or measure materials or products being produced; Measure products with rulers, calipers, gauges, or micrometers; Accept or reject finished items; Remove all products and materials that fail to meet specifications; and Assignment help – Discuss inspection results with those responsible for products; Report inspection and test data.
The compensation range for the inspectors is $29,000 to $55,000 per year. As a full-time employee of Merck, each inspector receives a pension plan which is fully funded by the organization, medical and dental care and a 401k in which investments made by the employee are is matched at 50%. The minimum level of education required to be an inspector is a high school diploma. Though six of the inspectors of the ten inspectors interviewed did attend college none of the ten have earned a college degree.
Participant 1 is a 35-year-old white male, from Lansdale, PA with seven years of experience as a product inspector with Merck. Before joining Merck as an inspector, he worked as a packaging specialist for a global freight company. This participant grew weary of his job as a package specialist and saw a job opening on Merck’s job board for Quality Control Inspector. The role required 0-2 years of experience. He applied and about two month later he was offered job as an entry-level Quality Control Inspector. As entry level inspector he attended new inspector bootcamp that was a required training program that all new inspectors had to attend. The participant described it as an intense two-week program where he learned the end to end process of pharmaceutical product inspection. That included learning the inspection equipment, understanding GMP (Good Manufacturing Processes) and adherence to FDA regulations. After the two-week course was completed, the participant received on-the-job training and served as an inspector in training for 8 months. During that period he gained experience using the equipment and applying the guidelines and regulations. After the eight-month training period, the participant was assigned to perform package inspection of drug products.
Participant 2 is a 43-year-old white female from Philadelphia, PA with fifteen years of experience as a product inspector with Merck. Before joining Merck as an inspector, she was homemaker. She stayed home with her twin girls while her husband worked as a project manager for a construction company. Once her children entered the first grade, she decided to return to work. She was referred for an inspector job by a family friend that worked at Merck as a Quality Assurance Manager. Over her fifteen-year tenure she has been promoted three times and holds the role senior quality assurance specialist. This participant mentors junior level inspectors and also serves as an instructor in the inspector on-boarding program where new instructors are trained on core processes and equipment.
Participant 3 is a 39-year-old white female from King of Prussia, PA with thirteen years of experience as a product inspector with Merck. Before joining Merck as an inspector she worked as an inspector for a medical products manufacturing company in Westchester, PA before joining Merck. Over her thirteen-year tenure she has performed all manner of inspections and supported FDA reviews and submissions. Over her thirteen she has participated in all manner of inspections. She finds her job interesting and enjoys the work as she says its meaningful because she contributes to the wellbeing of society.
Participant 4 is a 40-year-old Hispanic male from Norristown, PA with fifteen years of experience as a product inspector with Merck. Before joining Merck as an inspector he worked as a lab technician for medical diagnostics firm located in Norristown, PA. He joined Merck through a local job fair where recruiters for sourcing inspectors. Participant 4 focuses on quality control of GMP (Good Manufacturing Practices) and SOP validation. He is also the lead instructor in the areas for the two-week inspector bootcamp.
Participant 5 is a 47-year-old Black female from Lansdale, PA with twelve years of experience as a product inspector with Merck. Before joining Merck she was as an quality assurance specialist with a global software development company located in Newtown, PA. Participant 5 attended college for three years for before dropping out due to the lack of finances. She would like to complete her degree and move into another role in the company. Her goal is to move into a position in the Human Resources area. However, she is concerned that she is too old and has no experience that would qualify her for a position in the HR area. She also expressed some dissatisfaction with her role as an inspector in that there are limited opportunities for promotion or lateral moves.
Participant 6 is a 52-year-old white male from Ambler, PA with twenty-three years of experience as a product inspector with Merck. This participant has spent their entire career as an inspector with the company. Participant 6 is one of five Quality Assurance Managers in the department. He has been a manager for ten years and directly manages six of the participants included in the study. The participant is very satisfied with his role in the organization. Over the last twenty there years he has worked his way up trough the ranks by starting as junior quality assurance specialist, QA specialist to senior QA specialist to QA assistant manager to QA manager. This participant has seen process and tools of inspection evolve over the years and he has some apprehension over the automated tools that are changing the work of inspectors.
Participant 7 is a 44-year-old black male from Philadelphia, PA with fourteen years of experience as a product inspector with Merck. Before joining Merck as an inspector he worked as a documentation specialist for medical device consulting firm located in Plymouth Meeting, PA. He joined Merck through a responding to job opening of the Merck career website. Over his fourteen year career he has participated in all types of inspections but has focused on the review of SOPs (Standard Operating Procedures). He enjoys his work but has some concerns over the growing influence of automation. She isn’t concerned about the increasing automation of inspection as he believes that his work will change but he isn’t sure how.
Participant 8 is a 37-year-old black female from Lansdale, PA with thirteen years of experience as a product inspector with Merck. Her role is that of a Sr QA specialist. Before joining Merck as an inspector she worked as a QA specialist for a pharmaceutical firm located in King of Prussia, PA. She joined Merck through a responding to job opening of the Merck career website. Over his thirteen-year career she has participated in all types of inspections but has focused on the review of GMP practices. She doesn’t have strong feelings about her work in either a positive or negative sense. However, she is very threatened by the idea of her work being impacted by automated vision inspection technology. She believes that the technology will eventually cause her to lose her job. As a result she is actively seeking to leave her job for another role in or out of the company.
Participant 9 is a 36-year-old black male from Philadelphia, PA with sixteen years of experience as a product inspector with Merck. His role is that of a Senior QA specialist. He joined Merck through a referral and responding to job opening on the Merck career website. Over his fifteen-year career he has participated in all types of inspections but has focused on the review of product hygiene protocols and practices. He has very positive feelings about his work and he believes that his work plays a part in make members of our society healthier and that is important to him. However, he is concerned how his work will change as more and more automation used in inspection processes. He wonders if he will offer the same level of value to the organization after automation is implemented.
Participant 10 is a 36-year-old White male from Philadelphia, PA with sixteen years of experience as a product inspector with Merck. His role is that of a Sr QA specialist. He has spent his entire career as an inspector at Merck. He joined Merck as intern through an apprenticeship program established with the local high-school. Over his sixteen-year career he has participated in all types of inspections but has primarily focused on the review of equipment and the production environment. He thoroughly enjoys his work and he believes that what he does contributes to making our society happy and healthier and he considers that a great honor. He is concerned that his work will change as more and more automation is used in inspection processes.

Findings
A set of three themes emerged from the data analysis based on the research question: How do workers perceive the influence of RPA on their overall performance in the workplace?

The three themes that emerged were –Introduction of AVI – this first theme explored how the leaders planned to introduce into the inspections department and documented the experiences of QA inspectors as it relates to communications and engagement. The second theme is AVI in the Workplace – this theme explores how the QA Inspectors experienced the impact of AVI on their work – including the how AVI affected core inspection processes. The third theme, AVI the Future – explored the QA inspectors expectation of the future -including the skills that may be needed by inspectors in order to be productive.
The themes reflect the descriptions, stories, and lived experiences of the QA inspectors experiencing the emergence and implementation of Automated Visual Inspection (AVI) – an RPA technology within their daily work.

Introduction of AVI
The QA Inspectors shared their experiences with Automated Visual Inspection (AVI). Their stories included first learning about the acquisition of the AVI technology through their leaders and some high-level plans for its implementation within the QA department. These initial experiences were the first to begin shaping their perceptions of AVI and these initial perceptions became filters for how the QA inspectors interacted with and reacted to AVI as an addition to their daily work.
Implementation strategy. The implementation strategy was developed by the leadership team. It was designed to be a phased approach with certain functions of AVI implemented in the organization at six-month intervals over a span of eighteen months. The six-month interval was purposely established to give inspectors time understand and maximize the use of the AVI technology in their daily work.
Participant 1 recalled when he learned about the strategy from the leadership team –
The leadership team held a meeting in 2018 and in that session, they showed a roadmap of the implementation strategy. I recall thinking that the strategy for implementing seemed to unnecessarily long. Is the tool that complicated that we need six months to learn each component?

Communication. Communication was and continues to be a key aspect of the introduction and subsequent implementation of AVI. Communication took a variety of forms (email, meetings, announcements, presentations, etc.) was mostly one way – meaning that the leadership team-initiated communication far more frequently with the inspectors than the inspectors-initiated communication with the leadership team. Participant 3 recalled that the early communication from the leaders mainly consisted of short announcements.
Early in 2018, I recall receiving a brief email announcement that described that a new tool was coming that would completely transform the way we currently work. It mentioned the name of the Automated Visual Inspection (AVI) but had no critical details such as timing or scope.

Several of the participants expressed similar experiences with the initial emails and other messaging shared by the leadership team. The QA inspectors thought many of those early messages were often too vague and too brief to offer any sufficient information to get prepared for the change. When Participant 9 recalled those early messages, he stated –
The initial communications from leaders regarding the announcement of the AVI tool were vague. Those messages were never clear about their purpose. Where they meant to get us excited for what was coming or inform us about changes to our work. The lack intent was a challenge as it I wasn’t sure what I was supposed to do, if anything or what to expect and when to expect it.

In addition to the lack of clarity contained in those initial messages the QA inspectors also noticed inconsistency in the sharing of information around AVI. Participant 7 stated –
In the initial communications it was stated by leadership that updates pertaining to AVI would be shared on a bi-weekly interval. However, we sometimes did not receive updates for over a month. When we did get an update, it lacked the details that we were interested in, such as what would AVI do, when would it be implemented and how it might impact my work.

The lack of clear and consistent communication from leadership grew to be a point of frustration among the QA inspectors. Their frustration led several of them to set up time with the QA inspection manager to discuss their concerns regarding the communication. The meeting prompted Participant 6 (QA Manager) craft a send a communication to the leadership team –
I wrote an email to the leadership team to get a status of AVI and express our concerns with the inconsistent communication. I wanted to convey to the leadership team that we (QA inspectors) are ready to support the implementation of AVI, but the lack of consistent and detailed information makes that extremely challenging. A few days after sending the email, the leadership team requested to set up a meeting me and the other inspectors.

The QA Inspectors met with the leadership team approximately two weeks after the QA manager sent the email to the leadership team. During the meeting the QA inspectors shared their concerns with how the information around has been shared with them. Several attendees spoke of their experiences of the meeting. Participant 8 shared impressions –
I feel that the meeting went well and in speaking with other inspectors most felt the same way. We were able to share our frustrations with the way the communication process has been deployed. The leaders listened and agreed that communication process could and would be improved.

Employee Engagement. Another concern of the QA inspectors was around the area of engagement. Specifically, related to how the leadership team chose to engage with the inspectors. This is closely related to communication experiences that are shared by the inspectors. Participant 2 shared their experience regarding engagement –
I do not feel that the leadership team did a good job of involving us in the selection, decision and implementation of AVI. The way they communicated with us in the early stages of the process reflected their engagement with us.

Overall, the research group of QA inspectors shared a similar overall sentiment with the way the leadership team involved in the project. Participant 8 voiced concerns about the leaders’ approach to the communication and their engagement with AVI.
Honestly, I have been disappointed with how the leaders have managed the project. There has been very little communication about how AVI was selected, the timeline for its implementation, the role it will play the overall inspection process and how we as inspectors will be impacted. How can I be expected to support something when I have no say in how it will be implemented and utilized.

A key aspect of engagement was the inspector’s perceived influence over technology. The participants’ collective sentiment was that it was the leadership team should ensure that inspectors are prioritized over technology. However, the research participants believed that the leaders of the QA department were overly enthusiastic about the implementation of the AVI technology – and that lead to the seeming lack of willingness to engage the inspectors more fully.
Participant 7 mentioned overhearing leaders discuss how AVI would reduce product inspection discrepancies typically made human inspectors.
On multiple occasions I have heard members of our leadership team proclaim that the AVI technology will be a significant upgrade in terms of both cost and performance over the current QA inspection process. They anticipate that they expect to be able to reduce overall costs by a minimum of 20%.

Participant 3 shared a similar sentiment through a conversation with a leader –
In a recent conversation with a leader, he revealed to me that several directors have shared the belief that AVI is the first step in the long overdue transformation of the organization’s QA process and that transformation would make the process significantly faster, more efficient and would generate cost savings through a reduction in the need for QA inspectors.

The actions of the leaders related to the planning and implementation of AVI brought about a multitude of concerns expressed by the QA inspectors. Their approach to the implementation of AVI left the inspectors with the perception that the leaders didn’t think that their input and suggested guidance was necessary in the planning process, even though they would be among the primary teams impacted by the implementation of AVI.
Participant 4 highlighted the value that inspectors bring to their work –
We (inspectors) possess qualities such as creativity, problem solving and empathy and these are advantages over AVI and other related automated technologies. However, we have largely been ignored by our leaders throughout the implementation process. Our leaders seem to believe that our value is best realized post-implementation rather than pre or in process-implementation.

Leadership focus. The lack of communication and engagement caused the QA inspectors to openly question the focus of leaders. The main reason that the QA inspectors began to scrutinize the actions of the leaders was due to at least in part to their seeming inability to adhere to a schedule for communicating and proactively engaging with the QA inspectors. They (the leaders) seemed to be preoccupied with the acquisition and implementation of the AVI technology rather supporting the inspectors in adopting the AVI tool. Participant 3 stated –
The relative ineffectiveness of the communication and engagement caused me to consider if they (the leaders) were truly focused on the success of the AVI project. I believe that they were committed to AVI as a tool to drive productivity, but I am not sure that they were as focused on the impact on us as inspectors.

Participant 5 offered a similar sentiment regarding the overall focus of the leaders –
At times the leaders didn’t seem as engaged in the project as I believe that they should have been. The inconsistency of communication was a on-going problem, however, I think that their failure to involve us (the QA inspectors) more actively in the project was the bigger problem.

AVI in the Workplace
Impact to process efficiency. Inspectors possess deep knowledge of all aspects of the inspection process. As AVI was implemented, it raised some concerns among the some of the inspectors about AVI would impact the QA process and the overall efficiency of the inspectors.
Several participants (QA inspectors) shared views that claimed that AVI contributed to lowering their overall productivity, while others claimed that the AVI technology maintained their level productivity or in some cases even improved it. While the variation in experiences of the inspectors may seem unusual as it relates to their productivity when compared with the inspectors’ experiences in autonomy and motivation – as they have been decidedly negative.
The researcher recognized that productivity is the space where subjectivity is less of a factor because the work of the inspectors is measured more objectively. In the case of productivity, leaders have historical performance data as it relates to the QA inspectors task completion. That data was used to compare report on the efficiency of AVI as it was used in conjunction with the human owned and performed processes.
Some of the participants held a perspective that inspectors must hold more value than a technology, even if it is “intelligent technology” and therefore they (inspectors) should be able to exert more control over how technologies are implemented that impact their work.
Participant 6 stated –
The implementation of AVI impacts my efficiency in that it influences the way I approach my work. With a tool like AVI, that is capable of performing aspects of inspections that were previously done manually through human control, it requires me to think differently about the work I do verses what AVI can and should do. That is liberating to me however, I can also see how it can be restrictive to other inspectors that are not accustomed to or comfortable with such planning.

Sentiments shared by the inspectors showed a perspective of autonomy that is one-part perception and one-part reality based on their personal experience. They contended that the key tasks of their jobs are very structured and require a very ordered approach to ensure that the work is completed efficiently and accurately.

Error identification and resolution. AVI functions be primarily deployed within two areas – Quality Control and/or the Production Line. Quality control is the testing of pharmaceutical product to determine if they are within the specifications for the final product. The purpose of the testing is to determine any needs for corrective actions in the manufacturing process. The production line is the main element of the pharmaceutical product creation and packaging process. The line is established as a linear sequence of mechanical or manual operations. AVI can a play either a significant or minimal role in either or both quality control and the production line. These were two primary areas where a errors derived from AVI would be identified and need to be resolved. Participant 2 said:
We were told that the AVI technology would be faster, more accurate and provide a more comprehensive inspection output. However, that has not been my experience in working the AVI tool. It does complete the inspection faster than I would normally do it, however, I found a couple of errors in packaging that it didn’t recognize. This happened on several occasions and I had to spend more time rechecking the inspections completed by AVI.

Participant 4 had a similar experience with AVI:
My productivity has taken a hit because AVI isn’t working as promised. We were told that AVI performs inspections more accurately and faster than a human inspector, but so far that has not been then case. All it has done has increased my workload because I must spend a significant amount to check the results of AVI and at time reperform the inspections.

Input to decisions. The QA inspectors experience with AVI impacted their decision-making responsibility with their roles. The implementation and adoption of AVI impacted key tasks in the inspection process. The inspectors remarked that several tasks were adjusted, redesigned or reordered to accommodate the use of AVI. In several ways the revision of these tasks impacted how the QA inspectors performed their work and the decisions they were able to make related to its completion. Participant 3 highlights in the statement below –
It is becoming more difficult to complete the inspections in an effective way. The implementation of AVI has radically changed the traditional inspection process. That was more efficient in my view and now that the order has changed through a revision of the process its negatively impacted my efficiency. More importantly however, is that the ability to make changes to the existing process is now no longer within my ability to control.

Several inspectors held the sentiment that having diminished input on decisions about how to structure their work left them feeling than energized and motivated about their work. Participant 1 had similar concerns and that also affected his motivation. He has developed feelings of distrust with management in large part because they have done a poor job of implementing AVI in the QA department. Participant 1, stated
The fact that AVI directly affects my role and associated responsibilities and yet I minimal input on many of the decisions around the adoption of the tool is frustrating. I believe that the leadership team deliberately set up to minimize input from us as they believed that our input would have slowed the implementation process more than they preferred.

Loss of control over the work. Similar to the lack of input on decisions, the QA inspectors experienced a noticeable lack of control over their work. AVI was acquired to perform specific inspection activities that were previously performed by human workers. The inspectors shared that they preferred to control their work and its one of the key aspects of their role as inspectors. The loss of control manifested within inspectors in several ways – frustration, disappointment, and a lack of motivation. Participant 9 stated –
I believe that AVI was adopted because of the leaders’ desire to increase production and efficiency. This is a good initiative for the company, and I believe it will end up in higher output than before. However, this tool had significantly reduced the level of control that I have over my inspections. As a result, my motivation for doing the job has diminished.

The extensive capabilities of AVI, combined with a lack of clear information and advanced notice of what AVI will bet set up to do exacerbated feelings of the loss of control among the inspectors. The inspectors attributed significant value to their tenure with the organization. As previously mentioned, the participants average 14.5 years of employment experience with the organization. On the whole, the notion of long tenure tended to illicit feelings of loyalty and disloyalty – loyalty on the part of the inspectors, for the time that they have devoted to their jobs and disloyalty on the part on the leadership team for their seeming unwillingness to involve the inspectors more fully in the AVI implementation process.
Participant 10 stated the following –
I like working in a positive and productive environment and I believe that that type of environment is created when information is shared and I’m able to make key decisions about how my work is performed. The implementation of AVI has hindered that in some ways as I’ve lost control of some facets of the inspection process. For example, it’s more difficult adjust the order of inspections because AVI is setup to perform what it’s considers the most complex inspections first. There is no way to override the AVI sequence without reprogramming the tool.

AVI in the Future
The QA inspectors experience with AVI coupled with interactions with the senior leaders informed their perspective about how the future of AVI would impact the inspection process and the inspection organization. While the QA inspectors are obviously unable to accurately predict the future, their experience with AVI informs their perspectives of what the future could be in terms of how the people, processes and technology will be impacted in the weeks, months and years ahead.

Expectations in the months and years to come. Several of the inspectors shared the experiences where they found that AVI contributed to lowering their overall productivity, while others suggested that the AVI technology maintained their level productivity or in some cases even improved it. While the variation in experiences of the inspectors may seem unusual as it relates to their productivity when compared with the inspectors’ experiences in autonomy and motivation – as they have been decidedly negative.
The researcher realized that productivity is the space where subjectivity is less of a factor because the work of the inspectors is measured more objectively. In the case of productivity, leaders have historical performance data as it relates to the QA inspector’s completion of inspections. In response to the claims that AVI negatively impacted QA inspection completion, the leaders reviewed historical performance data to compare performance both pre and post AVI implementation.
The leaders determined that there was in fact a drop in the productivity of the QA inspectors in the initial implementation of AVI. However, this reduction was attributed to the learning and practice required to utilize the tool within the inspection process at the individual inspector level. Once inspectors became acclimated with the AVI tool the analysis showed that productivity increased.
Participant 8 confirmed the findings of leaders in recounting their experience with AVI –
I have been using AVI for several months and my productivity was lower when I first began completing inspections that incorporated AVI. However, within thirty days my productivity was returned to the same level it was prior to implementing AVI. Sixty days later my productivity had measurably increased. AVI has lead to increased performance for me and I expect that continue in the years to come. In fact, I can imagine that leaders will incorporate more AVI functionality as well as other intelligent tools into the inspection process to achieve further gains in productivity.

The QA inspectors recalled that the leadership team has always from the outset of the planning and implementation of AVI that it was never intended to replace the inspectors. Rather it was designed to complement and enhance their work.
Participant 2 stated that her productivity has been lower since the implementation of AVI because she has experienced errors with the AVI completed inspections and that has required her to spend time verifying those products to ensure quality.

My initial experience with AVI was not productive. Although the tool completed efficiently it often generated false errors in completed inspections and the required me to manual review of the inspections completed by AVI. That was extremely tedious and time-consuming process. I complained to my manager about the tool and few days later a support specialist came and observed my process and checked AVI. By the end of the day the tool was recalibrated and producing near error free inspections. This level of performance leads me to expect that AVI will become a tool that becomes the main driver of the inspection process.

Evolution of the Relationship between AVI and QA Inspectors. The key element in a successful technology implementation is how well the workers adopt the technology so that it enhances their overall productivity. The majority of the QA inspectors were resistant to the implementation of AVI and that resistance stemmed mainly from the leadership team’s ineffective communication in the early stages of the project.
However, as the QA inspectors began to interact with the tool, they soon realize that AVI would have a profound impact on the inspection process as a whole and their jobs in particular.
Participant 10 stated –
AVI has noticeably improved my productivity. The tool is highly efficient and I’m finding that on simple inspections it completes them more rapidly than I do. On complex inspections it works well with some intervention from me. However, I can see that in the very-near future (six-months) it will be able to perform complex inspections without any intervention from me. As AVI becomes integrated into in the inspection process, I expect that my role in the inspections will change as my job will change as a result.

Participant 7 held viewed his productivity in alignment with the productivity of the overall organization. He said that his main concern was that the inspection work should be to contribute to the overall output of the enterprise and any process or technology that is aligned with that goal has full support from him.
Participant 7 stated –
I have worked in the QA department for a little over fourteen years. I consider my role as QA inspector critical to the fulfillment of the company’s primary mission of – discovering, developing and providing innovative products and services that save and improve lives around the world. My work is one of final steps to getting our products the customer. I see AVI as a means to increase efficiency so that we can get our products to ur customers in faster and safer. While I haven’t noticed any measurable gains in my productivity so far I believe that the AVI technology will streamline our inspection work over the long-term. The QA department will be better as it will be more productive and innovative – and I envision that my productivity will improve.

It became clear to the inspectors that AVI represented a significant shift in their work – both in the present and in the future. Many began to envision how AVI would impact not only their work but the work of the QA organization as a whole.
Participant 5 stated –
Although we have experienced some early challenges with AVI, (mostly related to the way the project has been run) the technology itself has been fairly effective at doing what it was originally intended for – to perform product inspections with minimal human input. If we can rely on the tool to complete inspections more efficiently than a QA inspector it completely transforms our work by freeing us up to do more to support the more complex tasks in the production process such as – monitoring production practices to ensure compliance with Quality Control procedure, design and implement new protocols and procedures in response to deviations and more.

Skills needed to succeed as a QA Inspector. In the earliest stages of the planning for AVI, the leaders of the QA organization held the expectation that AVI would change the way inspections were done and significantly impact the QA inspectors as a result. As AVI implemented the leaders of the QA organization and the QA inspectors realized that as AVI became more efficient it would change the work of the QA inspectors and that change would necessitate the inspectors performing new and different types of work.
Participant 6, the QA Manager, stated –
Currently, the QA inspectors spend approximately 90% of their time performing product inspections of both the simple and complex variety. However, with the adoption of AVI that 90% has begun to decrease and I expect that trend to continue as AVI is programmed and expended to perform all of the simple inspections and at least half of all complex inspections.

That statement is an indication that the primary tasks of the QA inspectors is evolving and will continue to evolve in the future. It also underscores the need for the QA inspectors to identify and develop new skills that facilitate the shift in their core responsibilities.
Participant 2, stated –
The implementation of AVI has given me more time to get involved in more strategic tasks within the QA organization, such as – the review and revision of inspection procedures, plans, product overview documents for quality production standards and support the continual improvement in production run quality by identifying common issues for resolution.

The new tasks that will be performed by the QA inspectors will require them to develop new skills. The senior leaders of the QA organizations expect that many of the needed new skills can be acquired through on-the-job. Examples of those skills include working in or leading teams, attention to detail, written and verbal communication skills, ability to prioritize. However, the leaders also anticipate that formal retraining will also be needed to complement what workers already know and can do.

Participant 6, the QA manager stated –
While much of the new work that we expect QA inspectors to perform can accomplished with their existing skillset. We as a leadership team have to be ready and willing to provide the formal training resources and opportunities that will enable to grow and evolve in new roles so that they are able to adapt to a QA organization that is far different from the one that they are currently working in.

Skills that are expected to require additional training beyond what is developed through on-the-job training include – production order management, recipe management and track and trace. While these skills are related to the QA process, they require competencies that are not within the current skillset of the QA inspectors.

Participant 4, stated –
I along with the other QA inspectors welcome the opportunity to expand our skills and get involved in other areas. It’s an unexpected benefit for us and the organization as we can now support an area(s) of need and continue to add value by supporting the meeting of our mission to provide life-changing and life-saving products to our customers.

Results and Interpretations
The research adopted a narrative inquiry methodology with a set of questions that focused on addressing the main question: How do workers perceive the influence of RPA on their overall performance in the workplace. The questions for the interviews were structured in a way that addressed in a way that the participants could address three themes – Introduction to AVI, AVI in the workplace and AVI in the Future. The questions, even though they were addressing different themes, aimed at understanding the QA inspectors’ overall experience and interpretations with regard to the influence of AVI on their work.
The question of how the members of a global life sciences organization perceive the influence of RPA on their work was designed to capture experiences of QA inspectors. The study findings indicated that the QA inspectors perspectives revolved around how they perceived their work was impacted from outset of the implementation of AVI, how it influences their day to day activities and what the future could look like as their jobs and the QA organization evolves. To that point, the QA inspectors expressed a range of sentiments regarding the impact of AVI on their work. Most were positive with respect to the utilization of AVI as they tend to see it as an overall enhancement to their work and it’s likely to result in the QA inspectors expanding their scope of responsibilities and developing a range of new skills to support their work in the transformed QA organization.
According to IIEE (2017), the organizational structure of a company before implementing the RPA technology changes significantly once that intelligent technology (like RPA) is implemented. The key difference is in the number of human resources involved in a process before the implementation of the technology, the number of human workers is typically reduced after an RPA technology has been implemented. However, the commitment from the QA leadership team is that human inspectors will not lose their jobs – rather their current jobs will be changed to incorporate new responsibilities and they will trained so that they can perform their new roles effectively.
One of key sub-themes of the study was that leadership team failed to involve the QA inspectors was that in the early planning and implementation process of AVI. The leaders had also failed to communicate in detail how AVI would impact the primary work of the QA inspectors. These two failings lead to much of the negative experiences of the QA inspectors had with their early experience with AVI. Those failings also contributed to creating the prevailing perception of the inspectors was that the management team viewed the efficiency of the AVI technology to be higher than that of the QA inspectors. The general perception lead to lower levels of motivation and productivity experienced by some inspectors.
According to KPMG (2017), humans and RPA can work together as equal contributors to the good of an organization. However, the efficient cooperation between the human workers and RPA needs a strategy for it to happen. The QA inspectors likely would have been more engaged with AVI on a broader level had they been involved in the planning and implementation of AVI technology. The direct participation of the QA inspectors is important because they are the workers that the AVI technology is intended to support.
The research question on how life science professionals perceive the influence of AVI on their productivity drew a range of shared experiences. The findings to the question showed a variability on the part of the QA inspectors in that their productivity and that’s an indicator of how each inspector has experienced AVI technology within their individually assigned tasks.
Several QA inspectors were threatened by the AVI technology and held that their expertise of the QA inspectors is in doing the work that the AVI technology is doing and not supervising the AVI technology to do the work. That view seemed to be related to the inspectors that had decreases in their productivity. These same inspectors indicated that redundancy of tasks or having the verify task completion of AVI was a drain on their productivity.
According to Evan-Lee Courie (2019), RPA frees up workers so that they can spend more time on activities that add actual value to the organization such as interacting with customers. The QA inspectors efficiently execute the tasks within their role of examining drug products and related materials for defects from pre-determined specifications. Their efficiency in doing this task is high because of specialization and experience. The changing of the QA inspectors’ roles by the management to roles like interacting with the production teams means that they are leaving tasks which they are highly experienced to other tasks that they may be less experienced in but are a much higher value add to the overall organization, at least in the short-term.
After AVI was implemented some the QA inspectors remarked how the tool had impacted their productivity. This subject elicited a range of responses – some inspectors experienced lower productivity, while others experienced neither a drop nor an improvement in productivity. There were also a inspectors that experienced productivity gains from AVI almost from the outset of the implementation.
The factors that contributed to this variability seemed to a mix of task and attitude. In terms of task – inspectors that owned more challenging inspection projects where the variability of the product necessitated a more complex inspection was more likely to elicit challenges for AVI to complete it successfully. Simpler more straight-forward inspections proved to be more in line with the capability of AVI and inspectors owning more of these types of inspections either maintained or improved their productivity.
In terms of the general attitude of the inspectors – those that held a more positive attitude towards the implementation of AVI tended to maintain or improve their productivity, while those that had a generally negative view of AVI tended to have lower productivity. Generally, a negative attitude in the working environment is not conducive to achieving productivity gains. The inspectors’ experience with AVI was individualized as their reflections were direct result of how AVI was deployed to support their individual tasks and in many cases the limitations of AVI was a key factor in fostering their negative feelings. However, the inspectors that chose to view the addition of AVI as net-positive rather than a net-negative seemed described themselves as optimistic about the future their roles and the application of the AVI technology. They didn’t expect to have immediate productivity gains as they saw to implementation of AVI as long-term proposition that would greatly benefit the department over the long-term. Interesting, this is the same sentiment the leadership shared with the inspectors early in the planning process of AVI.
The key research question of how the life sciences professionals perceive the influence of RPA on their work showed findings that indicated a more uniform view that the QA inspectors’ autonomy was negatively affected. The QA inspectors had the view that they have lost control of their work and they were not consulted in the whole process of adopting and implementing AVI.
According to Akst (2013), RPA enables workers to focus on other tasks and those new tasks often result in gains in overall organizational productivity. The research findings in the three research questions complement many of the studies discussed in the literature review. The findings augment the self-determination theory as expounded by Deci and Ryan (2000). The findings are also in line with the assertions by Van Beek et al (2011) that an employee’s optimal performance is directly affected by their psychological needs. The psychological needs of employees need to be addressed first if they are to perform their duties optimally. The research is in line with the findings by Coccia (2018) and Jain (2019), that the productivity of engaged employees is higher than that of disengaged employees.
The findings also showed that leadership performed in a way that was not inclusive of the QA inspectors input at the outset of the of the planning and implementation of AVI. The effect is this is seen mainly within Introduction of AVI phase and shaped the workers early impressions of AVI until they were able to experience the tool directly.

Summary
Chapter 4 provided the findings generated from the research questions used in the study. The data analysis resulted in a discussion of three themes that were derived from the research questions. The themes that have formed the foundation of the chapter are – The Introduction of AVI, AVI in the Workplace and AVI in the Future. These themes and the subsequent findings represent the how modern technological advancements are becoming common within enterprises large, medium and small and the workers are expected to be flexible enough to effectively manage that results to their work.
The ability of key constituents – leaders, managers and individual contributors to effectively manage the change that technology transformations bring will be the difference between organizational success and failure. The subsequent chapter will grapple with this issue by giving some recommendations that are meant to enable the key constituents to develop and implement strategies to build success while minimizing the inherent challenges of a large scale technology transformation.

Chapter 5: Conclusions, Implications, and Recommendations
Introduction
Technological advancements have been on the rise since the industrial revolution. The myriad of technologies that have developed since the industrial revolution have transformed the way businesses operate around the world. Over the last two decades technology has gradually evolved from the personal computer and databases that have been chiefly responsible for managing data across an enterprise so that workers were informed to make better decisions to a range of tools and systems commonly referred to as Smart Technologies – tools that perform entire business processes without the need for any human intervention.
An example of Smart Technologies is Robotic Process Automation (RPA). RPA is a technology that automates repeatable and redundant, rule-based human action through the use of software bots. The real-world application of RPA and its impact to human workers is what forms the crux of this paper because of its ability to replicate human performed activities.
The study uses Merck and Co., a pharmaceutical manufacturing organization that recently adopted an RPA technology as the site for the research study. The research narrows down to how QA inspectors within Merck perceive the influence of an RPA technology known as Automated Visual Inspection (AVI) on their overall performance in the workplace as the primary research question.
The implementation of AVI in the QA department is positioned to fundamentally transform the way the department operates, as AVI enables the organization to transform the QA organization by implementing a new tool that streamline the inspection processes. AVI does that by significantly decreasing the need for human labor in the functional areas in which automation is being deployed. The core problem is the unknown impact that the implementation of AVI will have on the workers. The purpose of this study was to uncover the perceptions of workers as they relate to their experiences with AVI.
The research is conducted through a narrative inquiry in an effort to understand the workers’ perception of the influence of AVI on their overall performance. The questions in the interview are structured to address the themes of Introducing AVI, Working with AVI and AVI in the Future. These are the themes that run across the study’s attempt to understanding the view of the workers with regard to AVI. The findings of the interviews show the diverse experiences of the QA inspectors as they worked closely with AVI.
The findings support the self-determination theory, discussed in chapter three of this study. The findings also complement the study that an employee’s optimal performance is directly affected by their psychological needs. The psychological needs of employees should be addressed first if they are to perform their duties optimally.
The study concludes by giving recommendations on the best way that technological advancements can be implemented in business while minimizing the negative effects on the workforce.
Succeeding with an enterprise digital transformation initiative requires an appropriate mix of three things – a productivity-enhancing digital technology, an engaged workforce, and a comprehensive strategy for facilitating the adoption of digital tools. RPA is an emerging technology that has a proven record of success in transforming specific areas of an enterprise by automating critical but routine tasks. The near-immediate benefit is that automating tasks minimizes human intervention, thus freeing workers to focus on doing work that adds greater value to the business. The inherent benefits of automation are that it increases accuracy and completes tasks faster than human workers. The significance of RPA in the business world shows that there is a need to come up with recommendations that will bridge the needs of task-focused workers and those of strategic leaders.

Conclusions
The study central research question is: How do workers perceive the influence of RPA on their overall performance in the workplace?
Research confirms that technological innovations are gradually replacing the aspects of human work and the leading thinkers predict that millions of unskilled and semi-skilled workers will be rendered jobless (de Ven, Adner, Barley, Dougherty, Fountain, Hargadon, Schilling, 2017). In situations where the fear isn’t effectively addressed by the management in a given company, the workers’ negative feelings towards the technological change grows.
The leaders of the QA department failed to effectively engage the QA inspectors early in the selection and planning process. The QA inspectors should have been informed about the implementation of AVI in advance since they are directly affected by the technology. The failure to engage on the part of the leadership made the QA inspectors feel like their value is not being recognized or appreciated. Consequently, the QA inspectors perspective towards AVI were initially much more negative than they probably would have they been engaged in project earlier.

Implications and Recommendations
Practice
A key measurement of success for any business is its ability to respond to change – in demand, in technology, or size and scale (Evan-Lee Courie, 2019). Technological advancements that result in automation of work are important to the business leaders because they often result in cost reductions because it reduces the need for humans to do the work. Business leaders are adopting and implementing RPA systems as a way of streamlining their businesses and raising productivity and profitability.
On the part of the employees, technological advancements are often considered threats because they often result in affected workers losing their jobs. The workers who survive being replaced by the automation of services technologies are skilled who are given the tasks of supervising the technologies (IEEE, 2017). Leaders of The World Economic Forum believe that the widespread adoption of RPA will result in significant workforce shifts – up to 35% of the skills currently in demand likely to change by 2025 (Supriya, 2019).
This study is designed to help leaders better understand the implications of a smart technology implementation on the workers. The results of this study are designed to assist business leaders on effective ways of adopting and implementing an RPA technology with minimal negative impact to workers.

There are two key recommendations that are a result of the study –
1. Involve the affected employees in the implementation process early and often

2. Monitor the attitudes of potentially affected employees and intervene should negative feelings emerge

The first recommendation – involve the affected employees in the implementation process early and often – is that business leaders must provide sufficient support to their workers when implementing a technology transformation. That support should come in the form of communication about and involvement in the implementation of the project. One the primary areas of challenge that afflicted the AVI project was the lack of the communication and involvement at the outset of project. In future projects, leaders should take steps to engage employees early in the technology implementation process. Examples of this could be – involving the employees in planning discussions with the vendor, communicating weekly status of the implementation in detail, adding employees to the project as team members and power users. Power users are team members that get access to test the application(s) and they act as liaisons to their internal teams and provide information and updates to those team on a more frequent basis than the leadership team. They also are trusted to provide details around the functionality and the process that leaders may be unwilling or unable to provide.
The second recommendation – monitor the attitudes of potentially affected employees and intervene should negative feelings emerge – is a more nuanced observation and evaluation of a worker’s feelings towards the adoption of intelligent technology. The prevailing research as indicated throughout this study asserts that there is concern amongst the global workforce around the emergence of intelligent technology. That chief concern is that the emergence and adoption of intelligent technology will destroy jobs. The global media frequently reports that intelligent technology is a job-killer and it will eliminate upwards of 50 million jobs over the next ten years. While there is truth in that statement – these same media outlets often fail to report the other side of the argument that predicts that a similar number of new jobs will be created that because of technologies. The task for leaders is to monitor their workers attitudes and behaviors for signs of fear and then take the necessary steps to intervene. These steps could include – surveying employees, holding focus groups, offering re-training, communicating openly and honestly, creating formal paths to roles and more.
These strategies will foster an environment where the employees’ fear of losing their jobs will be made known and addressed. Also, it cultivates a culture of flexibility within the workforce so that they can more easily see how their roles could evolve within the business.

Future Research
The study has contributed to the existing knowledge on how workers perceive the influence of RPA on their overall performance in the workplace. However, the study cannot be fully relied on as the only paper demonstrating the workers’ perception in this subject. There is a need for further research to augment and complement the existing data and findings discussed in this study. The researcher suggests the following recommendations for further study.
There should be a study that will provide for a large number of companies that will enable the recording of various views from workers in different industries and business areas. A wider pool of workers than the ones used for this study will provide for a more holistic view of how the workers perceive the influence of RPA on their overall performance.

Summary
Technological advancements will continue to evolve our society. More so, many sophisticated technologies are yet to be created, adopted and implemented. The impact of many of these technologies which are automating most of the services provided by the unskilled and semi-skilled workers will likely result in a loss of jobs.
The complexity of balancing the interests of the business leaders and those of the workers with regard to the technologies automating many services is here. This study has grappled with this question and it has propose an approach that is a strategic way of adopting and implementing the RPA technology will not just leave the business leaders with more effective and profitable enterprises – but also workers that productive, motivated and in control of their work.

References
Akst, D. (2013, Summer). What can we learn from past anxiety over automation Retrieved from https://www.wilsonquarterly.com/quarterly/summer-2014-where-have-all-the-jobs-gone/theres-much-learn-from-past-anxiety-over-automation/
Almgren, G. (2017). Health care politics, policy, and services: A social justice analysis (3rd ed.). New York, NY: Springer Publishing Company, LLC.
Antonescu, M. (2018). Are business leaders prepared to handle the upcoming revolution in business artificial intelligence? Quality: Access to Success, 19, 15-19.
Aoun, J. E. (2017). Robot-proof: Higher education in the age of artificial intelligence. Cambridge, MA: MIT Press.
Blankenhorn, R. (2015). The influence of technology on advanced manufacturing, private R&D, and infrastructure. In M. Pagano (Ed.), Technology and the Resilience of Metropolitan Regions (pp. 84-89). Champaign, IL: University of Illinois Press.
Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. New York, NY: WW Norton and Co.
Calvano, L. (2008). Multinational corporations and local communities: A critical analysis of conflict. Journal of Business Ethics, 82(4), 793-805.
Clandinin, D. J. (2019). Journeys in narrative inquiry: The selected works of D. Jean Clandinin. New York, NY: Routledge.
Clandinin, D. J. (2013). Developing qualitative inquiry. Engaging in narrative inquiry. Walnut Creek, CA: Left Coast Press.
Coccia, M. (2018). Motivation and theory of self-determination: Some management implications in organizations. Journal of Economics Bibliography, 5(4), 223-230.
Cooper, Z. (2019, Oct. 09). What is robotic process automation? How does RPA work, how is it used and what are the pros and cons? Retrieved from https://www.itpro.co.uk/automation/34592/what-is-robotic-process-automation
Corrado, M. (2019). Qualitative research methods and evidential reasoning. Philosophy of the Social Sciences, 49(5), 385-412.
Corbally, M., & O’Neill, C. S. (2014). An introduction to the biographical narrative interpretive method. Nurse Researcher, 21(5), 34.
Courie, E. (2019). What you need to know about robotic process automation (part 1). Retrieved from https://www.bizcommunity.com/Article/196/794/194041.html
Danaher, J. (2017). Will life be worth living in a world without work? Technological unemployment and the meaning of life. Science and Engineering Ethics, 23(1), 41-64.
Davis, F. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. (Doctoral dissertation). Massachusetts Institute of Technology, Cambridge, MA. Retrieved from http://dspace.mit.edu/handle/1721.1/15192
Dehaan, C. R., Hirai, T., & Ryan, R. M. (2016). Nussbaum’s capabilities and self-determination theory’s basic psychological needs: Relating some fundamentals of human wellness. Journal of Happiness Studies, 17(5), 2037-2049.
Feldman, R. & Frondorf, E. (2017). Drug wars: How big pharma raises prices and keeps generics off the market. New York, New York: Cambridge University Press.
Ford, M. (2015). The rise of the robots: Technology and the threat of a jobless future. New York: Basic Books.

Flockhart, T. (2016). The problem of change in constructivist theory: Ontological security seeking and agent motivation. Review of International Studies, 42(5), 799-820. doi:http://dx.doi.org.ezproxy2.library.drexel.edu/10.1017/S026021051600019X
IEEE Corporate Advisory Group. (2017). IEEE guide for terms and concepts in intelligent process automation. New York, NY: IEEE
International Auditing and Assurance Standards Board (IAASB). (2016). Exploring the growing use of technology in the audit, with a focus on data analytics. New York, NY: IFAC.
Gross, N. (2018). Pragmatism and the study of large-scale social phenomena. Theory and Society, 47(1), 87-111.
Gilbert, C. (2005). Unbundling the structure of inertia: Resource versus routine rigidity. Academy of Management Journal, 48, 741–763.
Hoske, M. T. (2013). Robots vs. jobs: Jobs win. Plant Engineering, Retrieved from http://ezproxy2.library.drexel.edu/login?url=https://search-proquest-com.ezproxy2.library.drexel.edu/docview/1317448748?accountid=10559
Hossain, F. (2019). Pharmaceutical industry: Top 7 technologies to reshape the future Retrieved from https://medium.com/brainstation23/pharmaceutical-industry-top-7-technologies-to-reshape-the-future-ebf167340985
Howard, J., Gagné, M., Morin, A. J. S., & Van den Broeck, A. (2016). Motivation profiles at work: A self-determination theory approach. Journal of Vocational Behavior, 95-96, 74.
Jain, T. (2020, Feb. 25). Why employee’s happiness at work matters. Retrieved from http://www.businessworld.in/article/Why-Employee-s-Happiness-At-Work-Matters-/28-12-2019-181137/
de Ven, A. V., Adner, R., Barley, S., Dougherty, D., Fountain, J., Hargadon, A., . . . Schilling, M. (2017). Increasing benefits & reducing social costs of technological innovations. Behavioral Science & Policy, 3(1), 93-103.
Krishnaveni, R., & Monica, R. (2016). Identifying the drivers for developing and sustaining engagement among employees. IUP Journal of Organizational Behavior, 15(3), 7-15.
KPMG. (2017). Rise of the humans: The integration of human and digital labor. Retrieved from https://advisory.kpmg.us/content/dam/advisory/en/pdfs/rise-of-the-humans-1.pdf
KPMG. (2017). Rise of the humans 2: Practical advice for shaping a workforce of bots and their bosses. Retrieved from https://assets.kpmg/content/dam/kpmg/xx/pdf/2017/10/rise-of-the-humans-2.pdf
Kurzweil, R. (2006, Mar). Reinventing humanity: The future of machine-human intelligence. The Futurist, 40, 39-40,42-46.
Lacity, M., Willcocks, L. & Craig, A. (2015). Robotic process automation at Telefonica 02. Retrieved from https://www.umsl.edu/~lacitym/TelefonicaOUWP022015FINAL.pdf
Lala (Fador), G. (2014). The emergence and development of the technology acceptance model (TAM). Marketing from Information to Decision, 7, 149-160.
Latta, G. F. (2009). A process model of Organizational Change in Cultural Context (OC3 Model): The impact of organizational culture on leading change. Journal of Leadership & Organizational Studies, 16(1), 19–37. https://doi.org/10.1177/1548051809334197
Lindgren, J., & Emmitt, S. (2017). Diffusion of a systemic innovation. Construction Innovation, 17(1), 25-44.
Madakam, S., Holmukhe, R. M., & Jaiswal, D. K. (2019). The future digital workforce: Robotic Process Automation (RPA). Journal of Information Systems and Technology Management, 16, 1-17.
Merriam, S. B., & Grenier, R. S. (Eds.). (2019). Qualitative research in practice: Examples for discussion and analysis. Hoboken, NJ: Wiley.
Miller, T. (2018). The new world of human resources and employment: How artificial intelligence and process redesign is driving dramatic change. New York, NY: Business Expert Press.
Monesson, P. E. (2013). The benefits of an anabolic culture: Understanding your leadership style and its effect on employee engagement (Part 2)”, CPA Practice Management Forum.
Nigh, D., & Cochran, P. (2011). Issues Management and the Multinational Enterprise. Management International Review, 27(1), 4-12.
North, R. (2019, Aug 27). Top 5 benefits of RPA for enterprises/businesses. Retrieved from https://opensourceforu.com/2019/08/top-5-benefits-of-rpa-for-enterprises-businesses/
O’Brien, F. & Meadows, M. (2001). How to develop visions: a literature review, and a revised CHOICES approach for an uncertain world. Journal of Systemic Practice and Action Research, 14(4), 495-515.
O’Loghlin, J. (2016). Innovation is a state of mind: Simple strategies to be more innovative in what you do. Hoboken, NJ: Wiley.
Panorama Consulting Group. (2019, November 12). Executive Team. Retrieved from https://www.panorama-consulting.com/company/executive-team/
Parasuraman, R., & Riley, V. (2007). Humans and automation: Use, misuse, disuse, abuse. Human Factors, 39(2), 230.
Paul, J., Graffam, B., & Fowler, K. (2005). Perspectivism and critique of research: An overview. In J. L. Paul (Ed.), Introduction to the philosophies of research and criticism in education and the social sciences (pp. 43-48). Upper Saddle River, N.J.: Pearson/Merrill/Prentice Hall.
Smith, A. (1776). An inquiry into the nature and causes of the wealth of nations. The Glasgow Edition of the Works and Correspondence of Adam Smith, Vol. 2: An Inquiry into the Nature and Causes of the Wealth of Nations, Vol. 1. https://doi.org/10.1093/oseo/instance.00043218

Yu, P., Fang, S., & Wang, Y. (2015). Improving IT professionals job skills development: The use of management styles and individual cultural value orientation. Asia Pacific Management Review, 21(2), 63-73.
Pellegrini, L. C., Rodriguez-Monguio, R., & Qian, J. (2018). The US healthcare workforce and the labor market effect on healthcare spending and health outcomes. International Journal of Health Care Finance and Economics, 14(2), 127-141.
Pezalla, A. E., Pettigrew, J., & Miller-Day, M. (2012). Researching the researcher-as-instrument: An exercise in interviewer self-reflexivity. Qualitative Research, 12(2), 165-185.
Rahimnia, F., Moghadasian, M., & Mashreghi, E. (2011). Application of grey theory approach to evaluation of organizational vision. Grey Systems, 1(1), 33-46.
Reyes, J. R., & Kleiner, B. H. (1990). How to establish an organisational purpose. Management Decision, 28(7), 51.
Jacobs, F. R., & Weston, F. C. Jr. (2007). Enterprise resource planning (ERP)—A brief history. Journal of Operations Management, 25(2), 357-363.
Ross, A. (2017). The industries of the future. London: Simon & Schuster.
Rogers, E. (1995). Diffusion of innovations (4th ed.). New York: Free Press
Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. New York: Guilford Publishing.
Saunders, D. (2017). Enterprise systems and software validation: What pharma needs to know. Retrieved from https://www.pharmaceuticalprocessingworld.com/enterprise-systems-and-software-validation-what-pharma-needs-to-know/
Seidel, V. P., Langner, B., & Sims, J. (2017). Dominant communities and dominant designs: Community-based innovation in the context of the technology life cycle. Strategic Organization, 15(2), 220-241.
Schwab, K. (2016). The fourth industrial revolution. New York: Crown Business.
Silva, P. (2016). Human, machines, and the interpretation of formal systems. AI & Society, 31(2), 157-169.
Stabile, C., & Ershler, J. (Eds.). (2016, January). Constructivism reconsidered in the age of social media: New directions for teaching and learning, number 144. San Francisco, CA: Jossey-Bass.
Styhre, A. (2012). Organizations and the bioeconomy: The management and commodification of the life sciences (1st ed.). New York, NY: Routledge.
Saifullah, A., & Sajjad, S. (2016). Intra organizational factors that impact employee’s loyalty. Journal of Business Strategies, 10(1), 129-146. Retrieved from http://ezproxy2.library.drexel.edu/login?url=https://search-proquest-com.ezproxy2.library.drexel.edu/docview/1815438193?accountid=10559
Supriya, R. (2019). Impact of RPA on the existing workforce and workplace. Retrieved from https://www.dqindia.com/impact-rpa-existing-workforce-workplace/
Tidd, J. (Ed.). (2010). Gaining momentum: Managing the diffusion of innovations. London, UK: Imperial College Press.
UiPath (2019, Mar. 30). New study finds that nearly 70 percent of organizations believe RPA is enabling employees to have more human interaction. Retrieved from https://www.aithority.com/news/new-study-finds-that-nearly-70-percent-of-organisations-believe-rpa-is-enabling-employees-to-have-more-human-interaction/
Van Beek, I., Hu, Q., Schaufeli, W. B., Taris, T. W., & Schreurs, B. J. (2012). For fun, love, or money: What drives workaholic, engaged, and burned-out employees at work? Applied Psychology: An International Review, 61(1), 30-55.

Appendix A
Interview Protocol

Anticipated Interview Duration: 60 – 75 minutes

First, I want to talk with you about your role and how you have come to interact with Robotic Process Automation or what I will be referring to as “RPA.”

Role

1. What is your role?

2. Describe for me how RPA was implemented in your area?
[Probes: How was it rolled out in your area? How was it communicated to you? What kind of training did you receive and from whom? How is RPA implementation monitored by the organization?]

3. How do you interact with RPA?
[Probes: What does RPA do for you? How often do you use RPA? Is the use of RPA mandatory or optional? Does RPA affect how you are evaluated?]

Next, I want to talk with you about how RPA has impacted your role.

Motivation

4. How has the implementation of RPA affected your motivation toward your work, if at all?
[Probe: What about RPA makes you more or less motivated?]

5. How has the implementation of RPA affected your engagement in your work, if at all?
[Probe: Describe how RPA makes your work more or less engaging?]

Productivity

6. How has RPA affected the quality of your work, if at all?
[Probe: How is the quality of your work better? How did RPA facilitate that improvement?]

7. Has RPA enabled you to spend time on more complex, value-added tasks?
[Probe: If yes, what is the nature of the work? What value does it provide to the organization? If no, is there a way that you could imagine it doing that for you?]

Autonomy
8. How has RPA influenced how your work gets completed, if at all?
[Probe: Are you able to make decisions about how your work gets completed?]

9. Has RPA enabled you to spend less time completing any tasks?
[Probe: If yes, how/which tasks? What did you spend the additional time on? If no, why not?]

Expectations

10. What do you think is next for RPA in your area? In the company?

Those are really all of the questions I have for you. To wrap up:

Wrap Up

11. Is there anything I have not asked you about that you think would be important for me to know about RPA in your area?

Order | Check Discount

Tags: custom written college papers, essay custom writer service writing paper, essay writer free generator, essay writing service online free, free essay typer