Information Systems and e-Business Management最新文献

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.

This study investigated the correlation between piglet performance and sow body weight change (BWC) during two gestational periods: 35-70, 70-105, and 35-105 days. A cohort of 70 sows was evaluated for BWC, backfat thickness change (BFC), caliper score change (CALC), feed intake, and weaning-to-estrus interval (WEI). The collected data were then analyzed according to the two specified periods. Our findings highlighted that piglet birth weight, weaning weight, and average daily weight gain (ADG) correlated with sow body characteristics, including BFC and CALC. The strongest correlation was observed with BWC. Piglet mortality was intimately associated with BFC. Piglet birth weight, weaning weight, and ADG showed a positive correlation with sow BWC, particularly during the 35-70 day period. Furthermore, sows displaying a higher BWC during the 70-105 day period, and also exhibiting a higher BW gain from 35-70 days, registered greater piglet weight gains and higher weaning weights. These trends became more apparent as the sow's BWC increased during the 70-105 day period. Piglet mortality increased when the sow exhibited a lower BWC during both the 35-70 and 70-105 day periods. No significant observations were found concerning the number of stillborn piglets, live-born piglets, or weaned piglets, and no interaction effects were detected between these periods. In conclusion, our findings underscore the significance of sow BWC during the early stages of gestation (d 35-70) for enhancing piglet performance from birth to weaning.

Industrial organizations of all kinds increasingly recognize the industrial internet of thing’s (IIoT) capabilities to enable valuable business process improvement (BPI). However, both theoretically and practically, there is a lack of clarity regarding the systematic and successful identification, specification, and implementation of corresponding applications. This article aims to bridge this research gap by presenting a comprehensive metamodel encompassing all relevant aspects and elements of IIoT applications with BPI propositions. The metamodel is the foundation for deriving generic yet practical patterns that can assist organizations in effectively executing IIoT projects. To evaluate the usefulness of the approach, five initial patterns were designed and applied by a market-leading organization. The metamodel and patterns contribute to the descriptive knowledge of the IIoT and facilitate sense-making, theory-led design, and practical project execution. To ensure rigor, the research endeavor followed fundamental principles of the design science research (DSR) methodology.

Adopting the dynamic capabilities (DC) perspective, this study proposes a model to assess the impact of organizational capabilities on business analytics (BA) use and addresses the moderating role played by environmental dynamism (ED). Based on 457 surveys of BA experts at Saudi Arabian firms and a partial least squares analysis of the model, the results show that data-driven decision-making has a direct and significant influence on BA use. The findings also show that ED has a direct influence on BA use and positively moderates the link between the knowledge aspect and BA use, which indicates that knowledge is required in a volatile environment for successful BA use. Together, these findings extend the dynamic capability view and provide a theory-based perspective on the impacts of organizational aspects of BA use, building a better understanding of the DC of firms and providing guidance to align the necessary capabilities in dynamic environments.

Abstract

Online Communities have been growing with increasing intervention of social media. The digital natives have also gradually started to spend a fairly large time on the internet and their contributions have grown significantly in shaping discussions and future of online communities. With an increase in contribution, it is also witnessed that these communities have also been responsible for promoting cyberbullying; trolling; fake news; discriminant; harassment and many more such pressing issues. While studies have focused on each of these important issues, their lies a dearth of contributions from the academicians and practitioners that would extend the debate on the theoretical and practical side of these issues. Furthermore, with increasing complexity of data and forms of data, the problem becomes multifold. The current special issue aims to build discussions and debate on these critical issues.