Information Systems Journal最新文献

The online phenomenon of social commerce (i.e., s-commerce) platforms has emerged as a combination of online social networking and e-commerce. On s-commerce platforms, consumers can observe others' behavioural decisions and can distinguish those made by their friends from those made by their followees (i.e., the people a focal consumer follows but who do not follow that consumer back). Given this distinction, our study examines how consumers' behavioural decisions—regarding, for example, purchases, ratings, or “likes”—are made on s-commerce platforms, with a focus on how they are influenced by prior decisions of friends and followees. Combining panel data from a large s-commerce platform and two controlled experiments, we identify a strong normative social influence pattern in which consumers tend to follow others' prior decisions to gain social approval. Because the occurrence of normative social influence depends on both consumer behaviours with high public visibility and strong consumer needs to establish social ties, the unique information concerning behaviour visibility and consumers' social needs in the panel data allows us to identify normative social influence and to distinguish it from informational confounding mechanisms. Our panel data results show that on a friend network, where consumers' behavioural decisions are visible, females exhibit a greater tendency to follow others' prior decisions than males. We attribute this result to the stronger social needs of females. However, on a followee network, where behavioural decisions are invisible, these differences become less evident. Moreover, the two experiments demonstrate that making decision contexts private or activating social needs via a priming procedure can thwart (or even turn off) normative social influence. Our findings challenge prior research that identifies informational social influence as the predominant driver of conformity behaviours and thus have important implications for practice related to normative social influence, such as the development of techniques for satisfying consumers' different social needs depending on their gender or any other situational factors on s-commerce platforms.

Online freelancing, an alternative form of work where independent workers offer services on digital labor platforms, gains increasing importance in IS research. While the general understanding of this form of work is growing, research lacks understanding careers on digital labor platforms. However, these differ from careers in offline labor markets due to volatility, global matching and platform mediation, the digital and temporary nature of work, and algorithmic management as particular platform working conditions. Therefore, to understand how working conditions on digital labor platforms influence the dynamic career paths of freelancers, we conduct an exploratory analysis using 35 interviews with freelancers and clients on digital labor platforms. We thus contribute to the body of knowledge on alternative forms of work on digital labor platforms by developing a long-term freelancing career model and outlining the dynamics of advancement, decline, and exit within platform careers. We also illustrate mechanisms between career phases in terms of platform lock-in effects, which arise from the career advancement dynamics and career exit dynamics.

Crowdfunding platforms have emerged as a promising contemporary means for mobilising collective civic actions to address local or social issues, improve community cohesion and develop the public good. This empirical study taps into the understudied civic crowdfunding platforms (CCP) developed to facilitate such actions, proposing, supporting and funding public-interest projects through crowdsourcing and microfinancing. Previous studies have shown that individuals' characteristics affect their level of civic engagement with social issues. Considering the diversity of contributor motivations, we aim to shed light on the dynamics of emergent subpopulations of citizens who participate in CCPs. To this end, we use a sequential mixed-methods approach to integrate our fuzzy set Qualitative Comparative Analysis (fsQCA) findings with the results of an in-depth qualitative study, to gain rich and robust inferences and meta-inferences. In Study 1 (n = 316), we used fsQCA to explore five distinctive configural profiles that display the heterogeneity of civic backers' motivations, including civic champions, prosocial advocates, normative supporters, reward seekers and regret-averse contributors. In Study 2, we corroborated and complemented our fsQCA inferences through an extreme-case study and identified four boundary conditions. Taken together, our inferences and meta-inferences address the heterogeneity of motivations for participating in CCPs, by understanding and theorising about diverse profiles of citizen backers. Finally, we offer practical implications for successful civic crowdfunding initiatives.

Chatbots offer customers access to personalised services and reduce costs for organisations. While some customers initially resisted interacting with chatbots, the COVID-19 outbreak caused them to reconsider. Motivated by this observation, we explore how disruptive situations, such as the COVID-19 outbreak, stimulate customers' willingness to interact with chatbots. Drawing on the theory of consumption values, we employed interviews to identify emotional, epistemic, functional, and social values that potentially shape willingness to interact with chatbots. Findings point to six values and suggest that disruptive situations stimulate how the values influence WTI with chatbots. Following theoretical insights that values collectively contribute to behaviour, we set up a scenario-based study and employed a fuzzy set qualitative comparative analysis. We show that customers who experience all values are willing to interact with chatbots, and those who experience none are not, irrespective of disruptive situations. We show that disruptive situations stimulate the willingness to interact with chatbots among customers with configurations of values that would otherwise not have been sufficient. We complement the picture of relevant values for technology interaction by highlighting the epistemic value of curiosity as an important driver of willingness to interact with chatbots. In doing so, we offer a configurational perspective that explains how disruptive situations stimulate technology interaction.

Knowledge is an invaluable resource and a key to organisational success. To leverage this resource adequately, organisations must encourage their employees to share what they know with their peers. Enterprise social media (ESM) has emerged as an ideal venue for achieving this goal, and numerous studies have examined the drivers of work-related knowledge contributions on these platforms. The present study contributes to this body of research by examining a prevalent yet underexplored form of knowledge sharing that often occurs on ESM: nonwork-related knowledge contributions. We argue that contrary to a commonly held belief, this presumably hedonic employee behaviour can benefit organisations through its spillover effect on the work domain. In other words, we argue that nonwork-related knowledge contributions on ESM can foster work-related ones. Building on social exchange theory and on the associative–propositional evaluation model in social psychology, we also show that the employee–employer (EE) relationship—conceptualised in terms of perceived organisational support and perceived employee psychological safety—moderates the relationship between the two forms of knowledge contributions. The analysis of field data collected from 269 employees of a French e-commerce company confirmed that nonwork-related knowledge contributions are positively associated with work-related ones and that this positive association is moderated by the EE relationship. We discuss the theoretical contributions of our results and explain key managerial implications for organisations hoping to reap the benefits of ESM in a sustainable way.

In late 2023, Wiley, the publisher of the ISJ, informed the editor in chief that from volume 34 no. 3 (May 2024) onwards, changes would be introduced to the way accepted papers are assigned to an issue. Prior to May 2024, the editor in chief selected articles to be included in an issue and communicated this information to a production editor, who then compiled the issue and published it. The editor would also write up an editorial introduction to that issue, with a commentary on a topic relevant to the IS community and an introduction to the articles published in that issue. Although it was common practice for the articles that had been accepted longer ago (and that had therefore been in Early View for a longer time) to be published first, articles accepted for special issues were generally held back until all articles for that special issue had been accepted. The time lag between the acceptance of the first article accepted for a special issue and the last article for the same special issue could be quite considerable, in the order of 12–24 months. Such held back articles would still be visible in Early View, with their own DOI, which meant that they could be cited accurately.

Starting from May 2024, a new arrangement is in place. First, control over which articles are included in which issue is transferred from the editor in chief to the production editor. The production editor will still select the articles that have been in Early View longest, but will not hold back articles accepted for special issues. Instead, these special issue articles will appear in regular issues, mixed in with other regular articles. Since, at the time of writing, there is a considerable backlog of special issue articles in Early View, I expect that the first couple of issues to appear following this change will consist entirely of special issue articles, but from multiple special issues. In order to preserve the integrity of the special issue, a new feature called a virtual issue will be created. Virtual issues, which will be accessible via the standard menu on the journal's home page, will then capture all the articles accepted for the special issue. Virtual issues will also have an editorial introduction, created by the guest editors of the special issue. This editorial introduction will have its own DOI and so will be citable in its own right. Indeed, it will also be possible to curate accepted articles that were not published as part of a special issue into a new virtual issue on the basis of their collective contribution to a particular part of the discourse in IS research. This may bring together articles published many years apart.

Given the above changes to the management of issue production, the institution of writing an editorial to introduce the articles published in each regular issue will cease and the nature of an editorial at the ISJ will also change. Although we will no longer use editorials to introduce the articles published in an iss

It is important to note that the text of this editorial is entirely written by humans without any Generative Artificial Intelligence (GAI) contribution or assistance. The Editor of the ISJ (Robert M. Davison) was contacted by one of the ISJ's Associate Editors (AE) (Marjolein van Offenbeek) who explained that the qualitative data analysis software ATLAS.ti was offering a free-of-charge analysis of research data if the researcher shared the same data with ATLAS.ti for training purposes for their GAI1 analysis tool. Marjolein believed that this spawned an ethical dilemma. Robert forwarded Marjolein's email to the ISJ's Senior Editors (SEs) and Associate Editors (AEs) and invited their comments. Nine of the SEs and AEs replied with feedback. We (the 11 contributing authors) then engaged in a couple of rounds of brainstorming before amalgamating the text in a shared document. This was initially created by Hameed Chughtai, but then commented on and edited by all the members of the team. The final version constitutes the shared opinion of the 11 members of the team, after several rounds of discussion. It is important to emphasise that the 11 authors have contrasting views about whether GAI should be used in qualitative data analysis, but we have reached broad agreement about the ethical issues associated with this use of GAI. Although many other topics related to the use of GAI in research could be discussed, for example, how GAI could be effectively used for qualitative analysis, we believe that ethical concerns overarch many of these other topics. Thus, in this editorial we exclusively focus on the ethics associated with using GAI for qualitative data analysis.

The emergence and ready availability of GAI has profound implications for research. This powerful technology, capable of generating human-like text, has the potential to create many opportunities for researchers in all disciplines. However, the technology brings ethical challenges and risks. We unearth and comment on many facets of qualitative data-related ethics. Our goal is to engage with and inform the many stakeholders of the ISJ, including other editors, (prospective) authors, reviewers and readers.

We intend that this discussion serves as a starting point for a broader conversation on how we can responsibly navigate the evolving landscape of GAI in research. It is important to point out that we are not advocating for or against the use of GAI in research, nor are we attempting to find ways to make it easier (or harder) for researchers to incorporate GAI in their research designs and practices. Our focus relates to the ethical issues associated with GAI use in analysing qualitative data that scholars, in the conduct of their academic research, may encounter and should consider.

One of the allures of GAI lies in its capability to discover patterns to produce new codes in a data corpus faster and more comprehensively than humans, by drawing from its trained data. This c