Goals of the Dissertation

Introduction

The manufacturing industry is undergoing a significant transformation driven by rapid technological advancements, including AI. A recent survey by Deloitte found that 79% of CEOs are implementing or likely to implement AI to gain a competitive edge in a dynamic and challenging business environment. However, while the strategic benefits of AI adoption are clear, its implementation presents unique challenges as there are multiple stakeholders with differing priorities within organizations. Decision-making in the manufacturing sector is often complex and fragmented, and so executives need to balance the act of integrating AI into the existing management system of the company while minimizing operational disturbance. At the same time, employees frequently express concerns about AI adoption, including issues of trust, discomfort, and fears of job displacement.

Goals

The overarching goal of this dissertation is to explore how executive leadership teams in manufacturing organizations can successfully implement AI to enhance strategy execution and achieve competitive advantage while ensuring workplace well-being and ethical AI practices.

Framework

This framework analyzes AI adoption across three levels: employees, leadership, and the organization. It examines how employees perceive AI through innovation characteristics, trust, and discomfort, how leadership navigates ethics and risk in decision-making, and how organizational factors—including technology, structure, and environment—shape AI integration and its impact on performance, competitive advantage, and employee well-being.

Research Questions

These research questions form the foundation for developing hypotheses that will be tested through empirical analysis. They examine employee adoption factors, including trust and workplace well-being, and analyze how AI influences decision-making at both strategic and operational levels. Additionally, this study addresses leadership challenges, particularly in balancing ethics and risk management, while evaluating AI’s broader impact on organizational outcomes, competitive advantage, and firm performance.

  • Research Question 1: What factors influence employee adoption of AI and its impact on workplace well-being?

  • Research Question 2: How does AI shape decision-making processes at operational and strategic levels?

  • Research Question 3: What leadership strategies address ethical concerns and manage AI-related risks?

  • Research Question 4: How does AI adoption affect organizational outcomes like firm value and competitive advantage?

Research Design

This research comprises three interconnected studies exploring AI adoption in manufacturing. Study 1 examines employee perspectives on AI, focusing on trust, innovation characteristics, and workplace well-being. Study 2 investigates leadership insights through qualitative interviews, analyzing decision-making, ethical challenges, and risk management. Study 3 evaluates AI’s impact on firm performance using financial and operational data. Together, these studies provide a comprehensive understanding of AI adoption’s implications for employees, leadership, and organizational outcomes.

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Study 1: Understanding Employee Perspectives

This quantitative survey focuses on employees in the U.S. manufacturing sector to explore their perceptions of AI adoption. It investigates how innovation characteristics, trust, and discomfort influence current and future AI use and examines how these factors impact workplace well-being. The study will employ advanced statistical methods to uncover the relationships between employee experiences and AI adoption.

  • To be announced
  • Online Survey
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Study 2: Leadership Insights on AI Integration

Through in-depth qualitative interviews with senior executives, this study will explore leadership perspectives on AI’s strategic role. It focuses on decision-making, ethical challenges, risk management, and how leaders address employee concerns about work-life balance and engagement. Thematic analysis highlights leadership strategies and narratives surrounding AI adoption.

  • To be announced
  • Interviews
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Study 3: Measuring Impact on Organizations

This quantitative secondary analysis evaluates the impact of AI adoption on firm value and competitive advantage. Analyzing financial and operational data from U.S. manufacturing companies over a decade, the study will use advanced statistical analysis to assess changes in key metrics, comparing companies that have adopted AI with those that haven’t.

  • To be announced
  • Online Research

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