Pathways to Developing Digital Capabilities within Entrepreneurial Initiatives in Pre-Digital Organizations

To enable new digital business models, pre-digital organizations launch entrepreneurial initiatives. However, in developing the required digital capabilities, pre-digital organizations often face challenges as they are marked by the ways they have historically established their organizational identity. Research on how pre-digital organizations can develop digital capabilities remains scarce. This study draws on a single case study to illustrate potential pathways for the development of digital capabilities. Two key characteristics are identified: the source of digital capability development and the set-up of the actors involved. The authors synthesize four possible pathway manifestations, discuss the dynamic nature of pathway combinations, and suggest that managing a portfolio of pathways may be crucial for pre-digital organizations. Therefore, the study contributes to a better understanding of digital transformation in pre-digital organizations. Furthermore, it provides guidance for practitioners to reflect on when deciding which pathways to follow.

Gameful Learning for a More Sustainable World

Municipal waste sorting is an important but neglected topic within sustainability-oriented Information Systems research. Most waste management systems depend on the quality of their citizens pre-sorting but lack teaching resources. Thus, it is important to raise awareness and knowledge on correct waste sorting to strengthen current efforts. Having shown promising results in raising learning outcomes and motivation in domains like health and economics, gamification is an auspicious approach to address this problem. The paper explores the effectiveness of gameful design on learning outcomes of waste sorting knowledge with a mobile game app that implements two different learning strategies: repetition and elaboration. In a laboratory experiment, the overall learning outcome of participants who trained with the game was compared to that of participants who trained with standard analogue non-game materials. Furthermore, the effects of two additional, learning-enhancing design elements – repetition and look-up – were analyzed. Learning outcome in terms of long-term retention and knowledge transfer were evaluated through three different testing measures two weeks after the training: in-game, through a multiple-choice test and real-life sorting. The results show that the game significantly enhanced the learning outcome of waste sorting knowledge for all measures, which is particularly remarkable for the real-life measure, as similar studies were not successful with regard to knowledge transfer to real life. Furthermore, look-up is found to be a promising game design element that is not yet established in IS literature and therefore should be considered more thoroughly in future research and practical implementations alike.

AI Startup Business Models

We currently observe the rapid emergence of startups that use Artificial Intelligence (AI) as part of their business model. While recent research suggests that AI startups employ novel or different business models, one could argue that AI technology has been used in business models for a long time already—questioning the novelty of those business models. Therefore, this study investigates how AI startup business models potentially differ from common IT-related business models. First, a business model taxonomy of AI startups is developed from a sample of 100 AI startups and four archetypal business model patterns are derived: AI-charged Product/Service Provider, AI Development Facilitator, Data Analytics Provider, and Deep Tech Researcher. Second, drawing on this descriptive analysis, three distinctive aspects of AI startup business models are discussed: (1) new value propositions through AI capabilities, (2) different roles of data for value creation, and (3) the impact of AI technology on the overall business logic. This study contributes to our fundamental understanding of AI startup business models by identifying their key characteristics, common instantiations, and distinctive aspects. Furthermore, this study proposes promising directions for future entrepreneurship research. For practice, the taxonomy and patterns serve as structured tools to support entrepreneurial action.

Eras of Digital Entrepreneurship

While recent research continues to emphasize the importance of digital entrepreneurship, the historical terminology of this field is often overlooked. Digital entrepreneurship tends to be considered a new phenomenon despite emerging in the early 1990s. Building on a scoping literature review, this study analyzes 1354 publications that use nine different terms interchangeably to describe the phenomenon of digital entrepreneurship. Based on the number of publications per year, three eras in the historical development of digital entrepreneurship research are outlined. Digital technologies are identified as external enablers, and certain practical events are considered to be influencing factors. The results show that recent research has not adequately recognized the contributions of previous publications and that the understanding of digital entrepreneurship is quite similar with regard to the terms used and over time. This study shows how emerging digital technologies, such as artificial intelligence, blockchain technology, and big data analytics, might shape the future of digital entrepreneurship research. The study occupies the intersection between entrepreneurship and information systems literature and its main contribution is to provide new insights into the eras of digital entrepreneurship from the past to the present and into the future.

Archetypes of Digital Twins

Currently, Digital Twins receive considerable attention from practitioners and in research. A Digital Twin describes a concept that connects physical and virtual objects through a data linkage. However, Digital Twins are highly dependent on their individual use case, which leads to a plethora of Digital Twin configurations. Based on a thorough literature analysis and two interview series with experts from various electrical and mechanical engineering companies, this paper proposes a set of archetypes of Digital Twins for individual use cases. It delimits the Digital Twins from related concepts, e.g., Digital Threads. The paper delivers profound insights into the domain of Digital Twins and, thus, helps the reader to identify the different archetypical patterns.