Knowledge that Transforms

Digital platform ecosystems thrive on their ability to acquire and leverage user data across multiple data-driven services. This enables dominant platforms to harness insights obtained from their primary markets, where user data is collected, thus gaining a competitive advantage in secondary markets, where they exploit this data. While data cross-use brings about efficiencies, policymakers worldwide have expressed concerns about the economic power and the potential distortion of competition and innovation incentives associated with it. To address these concerns, two distinct and targeted policy interventions have been suggested: data siloing, which restricts the cross-use of data within platform ecosystems, and mandated data sharing with competitors. Using an analytical model that examines data cross-use in digital platform ecosystems, we analyzed the impact of data siloing and data sharing obligations, and their interaction on competition, innovation, consumer welfare, and overall social welfare. Our findings indicate that an optimal policy involves data sharing without data siloing, whereas the EU’s Digital Markets Act currently mandates both types of data cross-use regulation.

As online reviews become increasingly indispensable for consumers, they have attracted significant attention from both practitioners and researchers. It is a common belief that the persuasive effect of online reviews involves a deliberative and conscious process. Drawing on dual-process theories and the persuasion literature, we challenge this conventional wisdom, distinguish Type 2 processing (which requires deliberation) and Type 1 processing (which occurs automatically), and disentangle their relative impacts. With a focus on review elaborateness and review exposure, we propose that the automatic process of review exposure may play a greater role than elaborateness in changing consumers’ attitudes and purchase intentions. In addition, in line with the negativity bias, we posit that the persuasive impact of review exposure (vs. elaborateness) is moderated by the valence of highly exposed reviews. The results of the two experiments provide consistent support for these predictions. Our findings complement and extend the emerging literature starting to explore the role of automatic Type 1 processing in consumers’ use of online reviews, reveal the primary driver of persuasion and its boundary condition in online word-of-mouth, and provide important implications for review platforms, product manufacturers, and retailers.

Only a fraction of people with mental health issues seek medical care, in part because of fear of judgment, so deploying text-based conversational agents (i.e., chatbots) for mental health screening is often viewed as a way to lower barriers to mental health care. We conducted four experiments and a qualitative study and, contrary to common assumptions, consistently found that participants perceived a text-based chatbot as more judgmental than a human mental health care professional, even though the interactions were identical. This greater judgmentalness reduced the willingness to use the service, disclose information, and follow the agent’s recommendations. Participants described judgmentalness as a rush to judgment without fully grasping the issues. The chatbot was perceived as more judgmental because it was less capable of deeply understanding the issues (e.g., emotionally and socially) and conveying a sense of being heard and validated. It has long been assumed that chatbots can address the real or imagined fear of being judged by others for stigmatized conditions like mental health. Our study shows that perceptions of judgmentalness are actually the opposite of what has been assumed and that these perceptions significantly influence patients’ acceptance of chatbots for mental health screening.

Depression is a common mental disorder involving a depressed mood or loss of pleasure for long periods, which induces grave financial and societal ramifications. Social media-based depression detection is an effective method for early intervention to mitigate those consequences. Such a high-stake decision inherently necessitates interpretability. Although a few studies explain this decision based on the importance of linguistic or demographic features, these explanations do not directly relate to depression diagnosis criteria that are based on symptoms. To fill this gap, we develop a Focused Temporal Prototype Network (FTPNet) to detect depression and provide interpretations based on depressive symptoms as well as their temporal distributions. Extensive evaluations using large-scale datasets show that FTPNet outperforms comprehensive benchmark methods with an F1-score of 0.864. Our result also reveals fine-grained and emerging manifestations of depressive symptoms, such as sharing admiration for a different life, that are unnoted in traditional depression surveys like the Patient Health Questionnaire-9 (PHQ-9). We further conduct a user study to demonstrate improved interpretability over the benchmark. This study contributes to the Information Systems (IS) literature by introducing an interpretable depression detection approach that models the temporal distribution of depressive symptoms. In practice, multiple stakeholders, such as social media platforms and volunteers, can apply our approach to identify depressed users and deliver targeted assistance.

Augmented reality (AR) is rapidly emerging as a transformative display technology, blending computer-generated content with the real-world environment in real time. Using divided attention theory, this study investigates how different information delivery channels (i.e., AR vs. mobile phone) and the nature of information (i.e., dependence on specific physical context and complexity) affect work performance. A field experiment in the aircraft maintenance context demonstrates that the effect on work performance of providing information via AR vs. a mobile phone is mediated by work attentiveness. The findings reveal that the effectiveness of AR is particularly pronounced when information is highly dependent on the specific physical context but diminishes when information complexity is high. This research deepens our understanding of how presenting information directly in front of users’ eyes (i.e., via AR) affects their attention management and work performance. The findings have significant implications for firms in terms of how to leverage AR to enhance work performance in industrial settings.

Algorithms are increasingly seen as capable of autonomously initiating and managing interactions with humans—for example, through delegating the rights and responsibilities for successful outcomes of shared tasks without human intervention. While research into such interactions primarily focuses on dyadic configurations, complex settings where multiple agents work together have become a nexus of more nuanced interactions that go beyond the dyad. This paper explores such interactions through the lens of delegation by investigating how many algorithms delegate to many humans in a multi-agent setting. Analyzing patent data and interviews with drivers and passengers, we unpack delegation in the context of the ride-hailing application Uber. We theorize distributed delegation as a construct capturing collective hybrid appraisal, collective hybrid distribution, and collective hybrid coordination, in which a collective of algorithms delegates by drawing on inputs from multiple human agents. Our findings highlight that distributed delegation is collective, hybrid, and relational by nature, and demonstrate the extent to which human inputs are necessary for collectives of algorithms to exercise the capacity to delegate. Distributed delegation as a continuum of algorithmic and human involvement poses a challenge for recent theories suggesting the unprecedented autonomy of algorithms from humans.

Although the prevalence of artificial intelligence (AI) innovations is on the rise, firms frequently report failures and setbacks in their development and implementation of AI innovation efforts. One common issue behind many failing AI initiatives is that they are organized just like other information technology (IT) innovation efforts. To elucidate why and how the production of AI and IT innovations may need to be managed differently, this study juxtaposes these two types of innovations based on two key dimensions of the Schumpeterian framework: the form (product vs. process) and magnitude (radical vs. incremental) of innovations. By analyzing a matched sample of AI and IT patents, we found robust evidence that AI innovations are less radical and more process oriented than comparable IT innovations. Drawing upon our empirical discovery, we developed a conceptual framework to suggest a new way to think about organizing AI innovation. Our research contributes to the literature and practice on AI innovation by illuminating the comparative differences between AI innovations and other IT innovations and advancing a set of empirically derived propositions on how firms may be able to better manage their AI innovation activities.

Researchers must ensure that the claims about the knowledge produced by their work are valid. However, validity is neither well-understood nor consistently established in design science, which involves the development and evaluation of artifacts (models, methods, instantiations, and theories) to solve problems. As a result, it is challenging to demonstrate and communicate the validity of knowledge claims about artifacts. This paper defines validity in design science and derives a “design science validity framework” and a process model for applying it. The framework comprises three high-level claim and validity types— criterion, causal, and context—as well as validity subtypes. The framework guides researchers in integrating validity considerations into projects employing design science and contributes to the growing body of research on design science methodology. It also provides a systematic way to articulate and validate the knowledge claims of design science projects. We apply the framework to examples from existing research and then use it to demonstrate the validity of knowledge claims about the framework itself.

This research investigates the interplay of top-down control and bottom-up self-organization within digital platform ecosystems (DPEs), focusing on the formation and management of complementor coalitions. Although these coalitions can increase a DPE’s generativity, they can also threaten its integrity. We investigate this tension by employing information ecology (IE) theory, which allows us to examine complementor coalitions as holons that navigate between self-assertiveness and integration within the structural hierarchies of DPEs. Utilizing an inductive, embedded case-study approach, we analyze the interplay between top-down control exerted by platform owners and the bottom-up self-organization of complementors in two enterprise software platform ecosystems. Our findings identify three distinct interaction modes—mandated, supported, and autonomous self-organization—each presenting hierarchical trade-offs between platform owner control and complementor autonomy. Our findings extend the prevalent owner-centric theory of platform governance by highlighting the significant impact of bottom-up self-organization on the governance and evolution of DPEs. We propose an integrated theory that accommodates these new dynamics, suggesting soft power as an effective governance mechanism. This study contributes to a deeper understanding of the complexities in governing DPEs and offers practical insights for managing top-down control and bottom-up self-organization in the evolving landscape of enterprise software DPEs.

This study investigates how “Hashtag Dance Challenges” (HDCs), a phenomenon popularized on the short-video platform TikTok, are instrumental in helping music artists gain traction in the digital music marketplace. HDCs represent an appealing combination of music and dance, designed to engage users and achieve virality, thereby benefiting artists whose music is featured. This research focuses on how HDCs contribute to the success of women artists, as compared to men, in an industry known for its diversity but challenged by gender inclusivity. We apply role congruity theory to posit that women artists are in a better position to derive benefits from being featured on HDCs, relative to male artists, particularly in cases of gender concordance—when both the creator and the artist are women. We measure the benefits of HDCs using daily changes in the artist’s followership on Spotify, a leading music streaming service, and test our hypotheses using song and artist-level data collected from Spotify and TikTok. We found that artists featured in a new HDC achieve a significant increase in followership on Spotify, relative to similar artists not featured in an HDC. Further, we observed that women creators drive this effect, enhancing the daily growth of Spotify followers by approximately 3% more for women artists, underscoring the value of gender concordance. Our findings shed light on the role of short videos, especially through the vehicle of HDCs, in advancing women artists, while also promoting inclusivity within the digital music industry.