Logo: to the web site of Uppsala University

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.

Purpose This study aims to contribute to the enrichment of the literature by examining the impact of cross-cultural training on expatriates' adjustment of Nigerian bankers on expatriate assignments in other African countries.

Design/methodology/approach Data on cross-cultural adjustment from expatriate employees in five banks that collectively accounted for over 80 per cent of Nigerian banks with subsidiaries in other African were systematically selected for the investigation. This data was collected quantitatively via a survey instrument. Independent sample t-test, analysis of variance (ANOVA) and regression analysis were deployed in analyzing the data.

Findings The study found that cross-cultural adjustment varied significantly across the different categories of gender, age, marital status, previous expatriate training, previous expatriate experience, and duration of expatriation. The study concludes that in order to attain higher levels of adjustment African banks and other organizations should provide a more comprehensive cross-cultural training program that mirrors the needs of employees following a detailed needs analysis. Also, the training must be sequential and not a one-off approach.

Originality/value The literature though still nascent is largely focused on expatriate preparation and adjustment for expatriates moving from Western-to-western contexts and very little exists in the literature on how multinationals from Non-Western contexts like Africa prepare their staff for expatriation and its consequent impact on their adjustment. This study aims to enrich the literature by examining the impact of cross-cultural training on expatriates' adjustment of Nigerian bankers on expatriate assignments in other African countries.

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 research provides a novel answer to the following question: How can firms use artificial intelligence (AI), specifically predictive AI, to create economic value? A novel theory is developed by integrating, in a specific way, two complementary theoretical bodies: the theory of data network effects and business model theory. The former accounts for AI’s unique learning ability to generate users’ perceived value but offers no insight into economic value creation at the firm level. To remedy this limitation, business model theory accounts for economic value creation from digital technology use, while not accounting for AI’s unique learning ability. The process of integrating these two theories is carried out using a multi-method research approach, presented here in nine papers. Regarded together, the papers manifest an evolutionary research process with conceptual and empirical work and exploratory and hypothesis-based studies. Spanning several industrial contexts, these studies jointly develop and test the new theory proposed in this thesis. The findings show that when AI is used to create perceived value for its users, and thereby realise data network effects, and when such AI use is positioned adequately within a firm’s business model architecture, one or more of the four business model themes (novelty, efficiency, complementarity and lock-in) can be activated to achieve economic value creation. The key contribution of this thesis lies in explaining how a firm can use AI to create economic value. The findings have practical implications for managers’ investment decisions regarding the use of AI.

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.

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.