The Internet of Things (IoT) is recognized as one of the most disruptive technologies in the market as it integrates physical objects into the networked society. As such, the IoT also transforms established business-to-customer interactions. Remote patient monitoring, predictive maintenance, and automatic car repair are examples of evolving business-to-thing (B2T) interactions. However, the IoT is hardly covered by theoretical investigations. To complement the predominant technical and engineering focus of IoT research, we developed and evaluated a taxonomy of B2T interaction patterns. Thereby, we built on sociomateriality as justificatory knowledge. We demonstrated the taxonomy's applicability and usefulness based on simple and complex real-life objects (i.e. Nest, RelayRides, and Uber). Our taxonomy contributes to the descriptive knowledge on the IoT as it enables the classification of B2T interactions and facilitates sense-making as well as theoryled design. When combining weak and strong sociomateriality, we found that the IoT enables and requires a new perspective on material agency by considering smart things as independent actors.
Taxonomies are classification systems that help researchers conceptualize phenomena based on their dimensions and characteristics. To address the problem of ‘ad-hoc’ taxonomy building, Nickerson et al. (2013) proposed a rigorous taxonomy development method for information systems researchers. Eight years on, however, the status quo of taxonomy research shows that the application of this method lacks consistency and transparency and that further guidance on taxonomy evaluation is needed. To fill these gaps, this study (1) advances existing methodological guidance and (2) extends this guidance with regards to taxonomy evaluation. Informed by insights gained from an analysis of 164 taxonomy articles published in information systems outlets, this study presents an extended taxonomy design process together with 26 operational taxonomy design recommendations. Representing an update for taxonomy designers, it contributes to the prescriptive knowledge on taxonomy design and seeks to augment both rigorous taxonomy building and evaluation.
Recognizing opportunities enabled by digital technology (DT) has become a competitive necessity in today’s digital world. However, opportunity recognition is a major challenge given the influence of DT, which not only disperses agency across various actors, but also blurs boundaries between customers, companies, products, and industries. As a result, traditional entrepreneurship knowledge needs to be rethought and the effects of DT on opportunity recognition need to be better understood. Drawing from opportunity recognition theory – as one of the central theories in the entrepreneurship domain – this study builds on a structured literature review to identify and explain three direct as well as three transitive effects of DT on opportunity recognition. These effects have been validated with real-world cases as well as interviews with academics and practitioners. In sum, this study contributes to descriptive and explanatory knowledge on the evolution from traditional to digital entrepreneurship. As a theory for explaining, the findings extend opportunity recognition theory by illuminating how and why DT influences opportunity recognition. This supports research and practice in investigating and managing opportunities more effectively.
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