Abstract
Due to technological advances and disruptions in the business environment, digital transformation has become imperative rather than a choice for organizations, attracting significant interest from researchers and practitioners. Despite the growing number of firms undertaking digital transformation, there is still limited understanding of how to realize business value, and many fail to implement it effectively, thereby facing a digital paradox and technological failures. Digital transformation goes beyond merely digitizing processes; it represents a comprehensive digital disruption that affects customers, business models, and the entire organization. This phenomenon has been explored from various perspectives in academic literature, including information systems management, marketing management, strategic management, innovation management, and operations management. Digital transformation consists of three key stages: digitization, digitalization, and digital transformation, all driven ...
Due to technological advances and disruptions in the business environment, digital transformation has become imperative rather than a choice for organizations, attracting significant interest from researchers and practitioners. Despite the growing number of firms undertaking digital transformation, there is still limited understanding of how to realize business value, and many fail to implement it effectively, thereby facing a digital paradox and technological failures. Digital transformation goes beyond merely digitizing processes; it represents a comprehensive digital disruption that affects customers, business models, and the entire organization. This phenomenon has been explored from various perspectives in academic literature, including information systems management, marketing management, strategic management, innovation management, and operations management. Digital transformation consists of three key stages: digitization, digitalization, and digital transformation, all driven by the innovative use of digital technologies. Among others, Artificial Intelligence (AI) emerged as a key driver in the last period, ushering in a new wave of business transformation. The main objective of this research is to understand how digital technologies can create business value and drive innovation through business transformation. Notably, it examines how the impact of certain technologies can be framed under the lens of digital transformation to map expected and achieved economic, capability, and innovation-driven outcomes and necessary changes. This study also investigates the strategic role of digital transformation in driving business innovation. In addition, this thesis aims to study the impact of Artificial Intelligence on businesses and introduce the concept of AI transformation to address the lack of consensus around this term. The thesis research model is grounded upon a unified definition, a multi-layered theoretical digital transformation framework, and a practitioner-centric framework providing salient industry perspectives, enriching the analytical depth. We combine, validate, and extend both utilizing empirical evidence in real settings. The research model is based on six key building blocks that can be used to conceptualize digital transformation: the nature of change, the entity, the means (such as the appropriate resources, capabilities, and technologies), the expected outcome, the impact, and the scope of changes. This thesis employs a multiple-stage, mixed-method approach utilizing a quantitative and qualitative research strand to address the research objective and questions. Firstly, a preliminary quantitative study was conducted in small businesses (500 companies) to provide empirical evidence, deepening our understanding and enabling us to decide the more suitable entities for examining the digital transformation phenomenon. The study focused on measuring key dimensions of the research model, particularly technology adoption and key capabilities (such as the existence of basic digital skills). Results reveal that despite the various business benefits, including resilience in times of crisis, most small businesses are in the early stages of digitization, which is the preliminary phase of digital transformation. Small businesses utilize only a few available technologies, have limited digital skills, and face significant challenges, such as a lack of resources and awareness, which hinder their ability to adopt and leverage technologies for a comprehensive digital transformation. As a result, the following studies focus on bigger organizations and organizations with adequate resources (human, financial, and technological), so there is a higher probability of extracting meaningful insights and exploring digital transformation as organizations are involved in digital transformation initiatives. Main Study 1 aims to provide empirical evidence to support the research model, highlighting the strategic role of digital transformation as a catalyst for achieving business innovation. A quantitative study with 250 medium and big businesses in Greece is implemented to validate the above-mentioned relationship employing Partial Least Squares Structural Equation Modeling. The study’s findings confirm the significant impact of digital transformation on business model innovation, demonstrating that firms with higher digital transformation maturity can systematically redefine how they create, deliver, and acquire value to a greater extent. The study also highlights that digital transformation is significantly enhanced by an appropriate digital culture shaped by narrow strategic and organizational changes. In parallel, Main Study 2 leverages all dimensions of the research model to examine four real-life manufacturing transformation cases. We aim to combine theoretical insights and empirical evidence to validate the unified digital transformation framework (all dimensions of the research model) as a lens to frame the implementation of an advanced technological solution based on digital twins following a multiple-case study approach. We purposively selected these four case studies based on their data availability, engagement in the study, and relevance to the research objective. The findings show that the research model of this thesis can be used to frame and evaluate certain technologies and transformation projects under the digital transformation lens, highlighting key expected and observed outcomes and revealing essential future strategic roles and prerequisites for organizational transformation. In recent years, companies’ transformation efforts have increasingly focused on harnessing business value from AI, ushering in a new era of AI-driven transformation. To this end, this thesis aims to examine the impact of AI under a digital transformation lens, introducing the term ‘AI transformation’ by extending the identified unified definition of digital transformation and providing a conceptualization building upon the thesis research model. The study’s strength lies in its empirical foundation, as Main Study 3 concerns a global survey with 1594 big businesses across different geographies and industries. The findings of the study offer valuable insights regarding the key dimensions of our research model, such as the AI technologies adopted by businesses, the most critical capabilities/resources, and the expected outcomes that companies aim to achieve as well as have achieved. The study analyzes the differences, along these dimensions, between firms that have achieved substantial impact through AI transformation and those that, despite its use, have failed to reap the expected benefits. This comparative analysis highlights the critical conditions, i.e., organizational capabilities necessary for a successful transformation, such as resource prioritization, organizational support, and linking business needs with appropriate AI solutions. This PhD thesis offers multidisciplinary theoretical and empirical contributions primarily in information systems, innovation, and strategy management literature, as well as marketing and operations management literature. Firstly, it contributes to the literature by providing empirical evidence on the digitization of small businesses, revealing key business benefits and challenges. Secondly, it investigates and validates the real-life impact of digital transformation on business innovation as an essential business outcome of digital transformation, while most existing studies have focused on economic-driven outcomes, as well as it supports the strong effect of digital organizational culture on driving digital transformation and highlights the critical role of organizational transformation for businesses. Additionally, this thesis contributes by developing a methodological approach to studying and understanding the impact of digital technologies through the lens of digital transformation, bridging the gap between the theoretical understanding of digital transformation and its practical implications. It presents the applicability of this proposed approach by applying it to four case studies of advanced technology transformation projects (the case of digital twins in production). As a result, the study presents a mixed-method qualitative and quantitative evaluation of digital twins’ applications in manufacturing to inform relevant literature on the obtained and expected outcomes and the challenges of introducing them in the field. Finally, this thesis contributes to the literature by introducing, defining, and conceptualizing the new term ‘AI Transformation’ to address the ambiguity observed in both academic research and practical applications, presenting also prominent expected outcomes, achieved outcomes, challenges, and critical competencies required for enterprises to integrate AI. This PhD provides managerial implications for practitioners. Specifically, it introduces a digital readiness index that small businesses can use to assess their digital readiness and insights for deepening the awareness of employees and owners on small business digitalization. Additionally, it offers guidance for manufacturing firms on leveraging digital twins as a catalyst for digital transformation. The research also presents a structured approach for analyzing technologies and transformation projects through a digital transformation lens to map expected and observed outcomes, future considerations, and organization requirements. Lastly, it conceptualizes the impact of artificial intelligence on businesses, highlighting key capabilities that organizations should prioritize to drive AI-enabled growth. It also offers practical insights and a primer that companies can use to inform their AI strategies and successfully implement them in the context of digital transformation. This thesis provides some future research directions. Future studies should investigate where and under which circumstances small businesses can achieve a comprehensive digital transformation. Additionally, by building upon the thesis approach, future studies could replicate digital transformation analysis in various industries and other technologies, such as real-life AI transformation cases, comparing the findings with the thesis results. For instance, future research can shed light on companies’ diverse AI transformation paths, examining multiple cases from diverse industries, geographies, and sizes. Additionally, while the thesis focuses on large firms and high-turnover industries, there is a need to understand further the unique challenges SMEs face in AI transformation, given their limited financial and human capital resources. In this context, collaborative networks, innovation ecosystems, and public policy interventions could be explored. Finally, the socio-technical transformation of AI technologies is significant. As the new paradigm of human-AI collaboration presents both opportunities and challenges, there is a need for a deeper analysis of trust, workforce transformation and augmentation, leadership adaptation, and the well-being of employees in AI-augmented workplaces, balancing business efficiency. Finally, as sustainability becomes a central focus in business discourse, future research should investigate how AI technologies can simultaneously drive economic growth and environmental sustainability, particularly regarding AI’s carbon footprint, ESG compliance, and sustainable business practices.
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