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As technology-enabled innovations have become ever more common, successful firms have struggled to stay ahead of their imitators. One way for an innovating firm to defend itself from imitators is to activate lock-in mechanisms. These mechanisms discourage actors in the innovating firm’s business model from migrating to imitators. Current business model theory explains how to establish lock-in through loyalty, sunk costs, direct network effects, indirect network effects, and data network effects. This paper extends the current understanding of lock-in by integrating business ecosystem theory with business model theory. The objective is to outline how firms can strategically configure their business ecosystems to activate lock-in mechanisms that discourage actors (e.g., customers, suppliers, and partners) from migrating to competitors. When an innovating firm’s business model is deliberately configured to activate lock-in by establishing a business ecosystem, it can withstand imitation. Real-life examples are provided to illustrate this mechanism in action. The paper also provides managerial recommendations for how to activate lock-in by establishing a business ecosystem. Finally, it highlights topics that deserve further research.

Some firms have successfully harnessed artificial intelligence (AI) to create unparalleled wealth, while most around them have failed to do so. This managerial challenge has led to recent calls for research to answer the question of how firms can use AI to create and appropriate economic value. This paper answers that question. The paper reviews the existing research and discusses its merits. This review highlights the need for subsequent conceptual reconfigurations of business model theory, the theory of data network effects, and the theory of situated AI for competitive advantage. The integration of these three theories leads to a novel theory: AI-enabled business models for competitive advantage. This paper contributes to the broad literature on technology management, and more specifically to literature on technology-enabled business models and the use of AI. Several important managerial implications are outlined to help firms ensure successful AI use.

Firms are increasingly investing in the use of artificial intelligence (AI). Some succeed in creating and appropriating substantial economic value, but many fail. There is no consensus as to how a firm should use AI to create and appropriate economic value. This paper provides an answer to that question. A novel research model is advanced based on the notion of data network effects being realized within a firm’s business model. This research model is tested in a unique and natural industrial setting of two competing firms that simultaneously adopt the use of similar predictive AI. This setting is researched with two distinct empirical studies that employ mixed-methods research. The results shows that one firm fails to convert its AI use into economic value creation and appropriation while the other succeeds. Value is created and appropriated by ensuring that AI users perceive high user value that realize data network effects while being located in the firm’s business model architecture so as to activate business value drivers. These findings confirm the here proposed research model and offer novel theoretical contributions and specific managerial implications.

This chapter explores the revolutionary effects of artificial intelligence (AI) on modern business and technology. The chapter first presents a historical overview of AI development and varying definitions of AI, highlighting its central role in propelling business model innovation and technological progress. The multifaceted ethical challenges of AI are discussed, particularly within business contexts, in light of its potential to revolutionise the global economic landscape. A central idea is AI’s transformative power to reshape industry norms and create novel economic possibilities. The discussion covers the dual function of AI (automation and augmentation of business operations), the ethical spectrum of AI application, and the implications for management and workforce dynamics. Emphasis is placed on the capacity of AI to enhance decision-making processes and its pivotal role in the development of strategies aligned with data-driven business models. The complex interplay between technical innovation and ethical considerations is examined. The chapter outlines a future where AI integration is transformative and responsible. By providing a thorough analysis of the technical opportunities and challenges of AI, the chapter offers a nuanced perspective of how AI continues to redefine the link between business and technology.

Objective: This study investigates the impact of omnichannel practices in retail, driven by both the challenge of product commodification and the opportunities arising from digital technology adoption by consumers. The primary aim is to understand how these practices enhance customer purchase processes through seamless interactions across multiple channels and contribute to value creation.

Methodology: Employing a qualitative multi-case study approach, the research examines 14 retailers to explore the implementation and outcomes of omnichannel practices. This methodology provides in-depth insights into the operational and strategic aspects of omnichannel retailing.

Results: The study reveals that omnichannel practices contribute to value creation in four distinct themes: novelty, efficiency, complementarity, and lock-in. Contrary to the prevailing assumption in managerial orthodoxy that primarily associates omnichannel practices with efficiency, this research demonstrates that these practices can be leveraged in a more multifaceted manner. It also shows that activation of these value creation themes is not binary, as previously suggested, but varies in degrees and combinations.

Practical Implications: Managers in the retail sector are advised to carefully consider which operational solutions to integrate into their omnichannel practices. This strategic selection should aim to activate a specific combination of value creation themes in a certain order and to a certain degree. The research is limited by its reliance on a qualitative approach and a relatively small sample size of 14 retailers. In the future, independent studies with large empirical and quantitative analyses should be done in order to confirm these insights.

This chapter discusses the challenges and opportunities of digital transformation from the perspective of three organizations based on continuous interview and observation data gathered between late 2018 and the end of 2020. The purpose of such a discussion is to understand the importance of spatial co-existence and the significance of spatiality for meaningful organizational interaction. In other words—what happens to organizational members and the constitution, preservation and development of organizations, when the activity of “work” becomes detached from space? Of course, space or location matters more for some than others. For a telemarketer, space might be entirely insignificant, while for the priest and church-goer or the actor and theater-goer, the act of “going” somewhere to experience something is as important as what happens at the location, making the church and stage places that in themselves carry significance. This importance is represented in how churches and theater buildings often are majestic architectural achievements similar to other institutions of cultural, societal or financial importance that are housed in impressive locations such as banks, governments, court houses and universities.

To discuss these questions, we draw on theories and perspectives of the communicative constitution of organizations (CCO) (e.g., Brummans et. al., 2014; Cooren, 2006; Putnam & Nicotera, 2009; Schoeneborn et al., 2018; Taylor & Van Every, 2000), and present a Sweden-based multisite case study (Ekström et al., 2021) of digital transformation consisting of three organizations whose common trait is that their core activities have historically speaking been dependent on either a physical location, physical spaces or face-to-face meetings. The three organizations in question are a major European financial institute, a church organization and a city theater—all institutions of traditional architectural and societal importance. As our relationship with these organizations deepened over the course of the pandemic, it allowed us as researchers to glimpse how digitalization has affected each one over time, and how each organization’s perspective of the challenges and opportunities of digitalization shifted and evolved before and throughout the pandemic.

This study focuses on how start-ups use machine learning technology to create and appropriate value. A firm’s use of machine learning can activate data network effects. These data network effects can then create perceived value for users. This study examines the interaction between the activation of data network effects by start-ups and the value that they are able to create and appropriate based on their business model. A neo-configurational approach built on fuzzy-set qualitative comparative analysis (fsQCA) explores how the design of a firm’s business model interacts with various aspects to explain value creation and appropriation using machine learning. The study uses a sample of 122 European start-ups created between 2019 and 2022. It explores the system of interactions between business model value drivers and value creation factors under the theory of data network effects. The findings show that start-ups primarily activate the efficiency and novelty elements of value creation and value capture.

Highly valued firms exploit machine learning to activate data network effects. Data is gathered and analyzed to generate predictions and recommendations. This loop locks in existing service users and locks out potential competitors, thus creating a sizeable entry barrier, particularly for small and medium-sized (SME) enterprises. The literature does not describe the possible pathways to enter markets protected by incumbents’ data network effects. This study examines an SME that successfully entered such a market. A key finding is that, for successful market entry, an SME can focus on different stakeholders from those that are targeted by incumbents, provided such stakeholders can legitimize the SME's use of user data generated by incumbents.

Human cognitive time has become a key asset of the digital economy, yet we lack the means to manage it. In response to that need, we propose a technology to manage cognitive time in economic organizations. This technology is called cognitive time-driven activity-based costing (CTABC), and it extends the established time-driven activity-based costing technology. CTABC accounts for human agents' cognitive time and the fact that cognitive time typically does not equal physical clock time for a given economic activity. CTABC also unearths a hidden lever effect that leads to considerable economic inefficiencies. An illustration of the proposed CTABC shows the limitations of contemporary approaches to cost assessments, which ignore errors caused by workers' cognitive time estimation. This paper contributes to the literature on cost accounting technology and the future of the digital economy. Specifically, it enriches the literature on the problem of the accuracy of employees' time estimates.

Studies focus on the process of business model innovation as performed by start-up firms, while incumbent industrial firms' attempts to innovate their business models often fail, being hindered by path-dependency. There is a lack of understanding of what in a business model of such firms is modified to produce an innovation that gives rise to value creation. Based on explorations of twenty-two incumbent industrial firms, five dimensions of a business model are identified that, when modified, may result in business model innovation by incumbents. These dimensions are exchangeable, activity, actor, transaction mechanism, and governance setup. The results show how business model innovation can be systemically characterized in terms of several dimensions that must be modified in concert to produce an innovative business model. The results also show that such business model innovations require novel uses of digital technologies that enable new activities to be incorporated into existing business models.