Information Systems and e-Business Management最新文献

Virtual reality (VR) is increasingly being used in the corporate environment. Benefits of using VR have also already been identified in the area of combined workplace and process design. However, whether organizations should invest in VR for this use case is only feasible with knowledge of all operational and strategic costs and benefits. Since previous methods for simulating the costs and benefits of information systems rely strongly on prior knowledge and experience, these approaches are not effective for novel technologies such as VR for less tested use cases due to low empirical databases. In order to provide a more accurate cost–benefit analysis (CBA) of the use of VR for strategical planning like workplace and process design, design science research is applied. Subsequently, by including task technology fit theory, a suitability- and utilization-based CBA method emerged. The contribution thus provides, first, a systematically derived method for quantification and simulation of costs and benefits of strategic VR use in organizations. Second, it provides concrete insights into factors influencing profitability of an investment in a specific VR system for strategic planning projects for workplace and process design based on case study insights.

Business analytics is considered in research and practice as a promising approach to support organizations in the increasing complexity and dynamics in the strategic planning and decision making, which arise for example through the integration of product-service systems. However, literature is lacking a comprehensive analysis to what extant business analytics supports the strategic planning and decision making. Thus, coming from the affordance and socio-technical system theory, we are linking business analytics affordances to strategic planning outcomes. In doing so, we identified 20 affordances which we have assigned to the dimensions of the socio-technology system theory. Based on this, we have derived implications and propositions for research and practice. The results can be used as guidelines for practice and directions for future research.

In the past years we have observed enormous adoption and use of digital technologies in almost all domains of human life. In this context, researchers and practitioners have been using the term “digital transformation” (DT) to characterize phenomena related to the changes caused by increased use of digital technologies. The progression and contributions of corresponding discussions significantly depend on a common understanding and conceptualization of DT. Accordingly, the information systems research community has started working toward conceptual clarity. Importantly, the current debate focuses on theoretical contributions by academic researchers. Hence, data on the practitioners’ perspective on DT is missing. Against this background, we investigated general understandings of DT in practice. Our analysis comprises data of N = 529 business decision-makers in the UK. Our results show a significant discrepancy between science (i.e., conceptualizations and definitions of DT in the academic literature) and practice (i.e., business decision-makers’ perceptions and corresponding survey responses). Specifically, for a large proportion of the respondents their decision to classify concrete digitalization examples from the literature as DT or no DT was largely independent of the degree of organizational change caused by the use of digital technologies. This is a surprising result, as high-caliber academic literature proposes this degree to be a critical factor to characterize DT. Another key finding is that a remarkable number of practitioners consider a digitalization initiative as DT based on the mere occurrence of specific buzzwords related to digital technologies in the corresponding descriptions. In particular, the buzzword “digital technology” led the survey participants to classify a definition as instance for DT. We discuss implications of our results and limitations.

The sharing economy is considered to be an important pathway for economic growth, increasing employment and household income, and the transition toward sustainability in the twenty-first century. In spite of the widespread use of the sharing economy concept, the understanding varies, and there is disagreement about what constitutes an analytical framework of the sharing economy business model (SEBM). This research quantitatively analyzes and categorizes the fundamental elements of the current SEBM literature. A comprehensive analytical framework is presented, identifying three distinct segments of SEBM: (1) the knowledge of sharing economy (incl. innovation, new technologies, determinants, and ecosystems); (2) supplier-oriented studies (incl. hospitality-oriented, transportation-oriented, and other platforms and models); and (3) consumer-socioeconomic-sustainability-focused research (incl. consumption and customer satisfaction, collaborative consumption, sustainability, social effects, and economic growth). In addition to contributing to a better understanding of SEBM research, the framework has implications for SEBM development policy and practice. It can also be used as an instrument for evaluating business models. Furthermore, the paper identifies six key research directions for the post-COVID-19 era, covering the studies of determinants and ecosystems, emerging economies, new SEBMs, location- and culture-specific models, sustainability, and combined impacts on socioeconomics.

Startups are becoming increasingly important in the mobility sector and are putting established companies under pressure to adapt their business models. Especially, the automotive industry faces a significant change from an automotive manufacturer towards a mobility provider. However, literature does not provide a comprehensive understanding of the elements and archetypes of product-service system-oriented business models of startups in the mobility sector. This paper provides a taxonomy with six dimensions and 55 characteristics based on the analysis of 34 startups’ business models. Four clusters can be described as the result of a cluster analysis. Thus, a deeper understanding of the business models and a starting point for future research and practical application is provided. Furthermore, based on the results, implications for research and practice like dominated characteristics of product-service system-oriented business models as well as future research directions are pointed out.

With a continuous stream of emerging technologies, organizations are increasingly seeking to digitally transform their business towards ever efficient, intelligent, and self-learning business models. As the recognized head of IT, the Chief Information Officer (CIO) is often tasked with the responsibility of successfully implementing Digital Transformation (DT) initiatives. While there are independent studies on CIO role, and on the impact of DT, the role of CIO is not sufficiently examined in the DT context. This study fills this gap by conducting multiple case study of six organizations engaged in DT. Following the role theory, the study emphasizes the CIO as an actor focused on digital strategy and empirically delineates the multidimensional task demands associated with DT regarding the CIO role under the new strategic realities of the digital age. We find that while traditional CIO roles still apply, the DT context adds more nuances in such roles. Moreover, some new roles (e.g., legacy systems integration, risk management and IT security) have become crucial that are not adequately captured in extant literature on CIO roles.