Information Systems and e-Business Management最新文献

Business process redesign has received considerable attention in research and practice. An important task contributing to redesign is idea generation. It has been argued that the choice of technique influences redesign outcomes, but empirical evidence is largely missing. In this paper, we examine the role of idea generation in business process redesign by comparing two different kinds of techniques: exploitative and explorative redesign techniques. Often, exploitative techniques are expected to yield more appropriate solutions, while explorative techniques are associated with more creative outcomes. To investigate such propositions, we consider the Best Practices of Process Redesign as an example of an exploitative redesign technique and the Business Process Design Space as an explorative redesign technique. We conducted a free-simulation experiment to study the empirical impact of the choice of redesign techniques on outcomes. We find that the Business Process Design Space leads to a higher number of redesign ideas, which are also more diverse. These are more creative and novel than ideas produced by the Best Practices of Process Redesign. Against expectation, the best practices as an exploitative approach do not produce more appropriate nor impactful ideas in our task design. Since both approaches cover different areas of improvement, a key implication of our work is that they should not be seen as isolated approaches to redesign; rather, they should be purposefully combined to not only create novel but also operational business process redesigns.

Driven by technological progress, business analytics is gaining momentum while paving the path for next-generation business process management. Especially, embedded real-time analytics offers new opportunities for business process intelligence and value creation. However, there are several obstacles that organizations face in their adoption process. A key challenge is to identify business processes that are suitable for embedded analytics and hold relevant value potential. Our research addresses this need by introducing an exploratory BPM method, namely a process selection method. Applying action design research and situational method engineering, we iteratively built, used, evaluated, and refined the theory-ingrained method artifact. The method provides organizations with guidance in selecting operational business processes, for which a reengineering project should be initiated.

The core success of digital transformation among small and medium enterprises (SMEs) still needs to be clarified and comprehensively discussed. Meanwhile, SMEs cannot exploit digital advantages due to their limited strategic empirical and practical knowledge. This study aims to (1) describe the research pattern in the SME’s digitalization area; (2) identify the core success factors of SMEs' digital transformation to gain business success; (3) search for relevant business aspects that are necessary for SME digital transformation; and (4) develop further research agenda in the field of SMEs’ digital transformation. This study uses a systematic literature review and thematic analysis to determine core success factors suggested by primary studies. The literature review suggests that (1) SMEs should consider their baseline, limitation, and idiosyncratic to develop an aligned digitalization strategy; (2) SMEs should consider starting with incremental and gradually improving digitalization; and (3) SMEs should commit to investing in education and continuous learning.

Past generations of BPM involved the efficient and effective management of business processes. Yet, we currently face a turning point. The technological facets of data-driven BPM add complexity to traditional BPM applications. As a result, organizations face intended and unintended technology-related changes across all business process initiatives. Using the term BP-x, we summarize the recent changes in BPM knowledge that has hitherto been fragmented across academic literature. To address the challenges of BP-x initiatives, we envision a holistic model that focuses on managing related cutting-edge technologies and BPM. Thus, we propose the operationalized BP-x management model as a valuable IT meta-artifact. We develop the model using a two-cycled Design Science Research methodology and conduct a threefold summative evaluation. The results of our study indicate that creating awareness of potentials and opportunities accelerates the process toward action and fosters new business outcomes in terms of performance and innovation. By strategically aligning BP-x endeavors, organizations promote visibility, shared understanding, and culture among stakeholders. Our model guides managers throughout the BP-x adoption journey in conjunction with organizational, managerial, and technological prerequisites.

Many enterprises are currently engaged in developing blockchain-based business models. Enterprise networks offer a variety of potential applications for blockchain solutions as they benefit from transparency and security as well as automation of handling data, material, and financial flows along their supply chains. Despite profound potentials, the indicated business models are still in their early stages and need further investigation. To provide an overview of existing blockchain-based business models in the context of enterprise networks, the underlying paper designs a multidimensional taxonomy and identifies several archetypes of blockchain-based businesses. For the taxonomy development, data from 101 blockchain start-ups serves as a basis for empirical validation. Using hierarchical clustering and the k-means method, seven archetypes that sharpen the understanding of how blockchain solutions affect business models in enterprise networks and enable new business models are derived. The proposed work results are intended to be applied in future research and practice to classify and assess the integration of blockchain solutions into existing business models and to support developing new ones that leverage emerging technological capabilities.

When studying information systems (IS) phenomena, scholars increasingly aim to take a socio-technical approach. This means that instead of focusing exclusively on the technical side, they also study them from a human perspective. An underrecognized yet powerful tool for examining the opinions and attitudes of individuals is the Q methodology because it makes subjective viewpoints on IS phenomena objectively measurable. Despite its benefits and wide application in other disciplines, the use of Q methodology in top IS journals is still rare. Based on a systematic literature review, this article explores the potential and fit of Q methodology within the sociotechnical systems framework. This analysis leads to two main insights. First, Q methodology enables the integration of the social and the technical component as well as instrumental and humanistic outcomes. Second, this qualiquantilogical technique enriches the understanding of IS phenomena by objectifying the approach to exploring subjective viewpoints. Thus, our work highlights the potential of the method for conducting IS research. And it also provides clear guidelines on how to use the method to uncover new patterns inherent in the data being studied.

In dynamic business environments, many organizations need to acquire a process innovation capability (PIC) that caters to less-structured business processes (LSBP) in order to remain relevant in the market. However, such organizations are still in need of appropriate frameworks that enable them to identify and evaluate their PIC in LSBP. To fill this gap, we have followed the Delphi method with three rounds of semi-structured interviews, including twelve practitioners and fifteen scholars across five continents. The findings reveal five major differences and barriers for PIC in LSBP, as compared to innovating structured business processes. In addition, we refine and empirically validate an innovation capability framework covering six main capabilities and 18 sub-areas, configured into three major pillars: (1) a people-process-technology pillar, (2) an organizational pillar, and (3) an ecosystem pillar. Besides being one of the first studies to offer a conceptual framework for PIC in LSBP, we provide practical brainstorming with reflective questions per capability, for practitioners to reflect on their organization’s current PIC in LSBP.

Case study research is one of the most widely used research methods in Information Systems (IS). In recent years, an increasing number of publications have used case studies with few sources of evidence, such as single interviews per case. While there is much methodological guidance on rigorously conducting multiple case studies, it remains unclear how researchers can achieve an acceptable level of rigour for this emerging type of multiple case study with few sources of evidence, i.e., multiple mini case studies. In this context, we synthesise methodological guidance for multiple case study research from a cross-disciplinary perspective to develop an analytical framework. Furthermore, we calibrate this analytical framework to multiple mini case studies by reviewing previous IS publications that use multiple mini case studies to provide guidelines to conduct multiple mini case studies rigorously. We also offer a conceptual definition of multiple mini case studies, distinguish them from other research approaches, and position multiple mini case studies as a pragmatic and rigorous approach to research emerging and innovative phenomena in IS.

The risk of peer to peer lending (P2P) platform is predicted based on text data on the Internet to avoid the risk of social network financing and improve the security of social network financing. First, the transaction and review text information of a third-party P2P platform are classified for the time series of emotional changes. Second, the Granger causal relation test is used to verify the correlation between the time series of emotional changes and trading volume. Finally, a long short-term memory (LSTM) forecasting model is proposed based on investors’ emotional changes to predict the trading volume of P2P platforms using emotional changes as a reference for social network financing to avoid risks. The results show that the value of Pearson correlation coefficient between the trading volume of P2P platforms and negative emotions is -0.2088, with a P value less than 1%, indicating a correlation between emotional changes and trading volume. The Pearson correlation coefficient between the predicted and actual values is 0.7995, whereas the mean square error is 0.2190 with a fitting degree of 0.6532. This shows that the LSTM forecasting model can accurately predict the trading volume of P2P platforms with good performance in comparison with other forecasting models.

Economic, political, and societal pressure forces companies to act more sustainably. New legislation and regulation such as the Supply Chain Act, mandates companies to take responsibility for their actions as well as those of their supply chain collaborators. Meeting these requirements demands transparency. Digital technologies along the supply chain have the potential to collect diverse types of data and raise awareness for sustainability purposes. For example, data obtained from multiple supply chain participants can be used to estimate CO₂ emissions or optimize resource utilization. Following guidelines for systematically reviewing a body of literature, this paper therefore investigates the distribution of digital technologies in supply chains for sustainability. Based on a collected corpus of 70 articles, we identify seven key digital technologies and indicate how they can be employed to contribute to economic, environmental, and social sustainability. The results show that (1) blockchain is the most used technology for sustainability within the supply chain and (2) the majority of articles on digital technologies in our corpus address the social dimension of sustainability. Our findings advance the understanding of how digitalization can transform supply chains into more sustainable entities and help practitioners decide which technology fits a certain sustainability dimension.