Knowledge that Transforms

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.

We studied the effect of a major bank adopting an AI-enabled credit scoring model on financial inclusion as measured by changes to the approval rate, default rate, and utilization level of a personal loan product for an underserved population. The bank serves over 50 million customers and previously used a traditional rule-based model to evaluate the default risk of each loan application. It recently developed an AI model with a higher prediction accuracy of default risk and used the AI model and the traditional model together to assess loan applications for one of its personal loan products. Although the AI model may be more accurate in estimating default risk, little is known about its impact on financial inclusion. We investigated this question using a difference-in-differences approach by comparing changes in financial inclusion of the personal loan product that adopted the AI model to that of a similar personal loan product that did not adopt the AI model. We found that the AI model enhanced financial inclusion for the underserved population by simultaneously increasing the approval rate and reducing the default rate. Further analysis attributed the enhancement in financial inclusion to the use of weak signals (i.e., data not conventionally used to evaluate creditworthiness) by the AI model and its sophisticated machine learning algorithms. Our findings are consistent with statistical discrimination theory, as the use of weak signals and sophisticated machine learning algorithms improves prediction accuracy at the individual level, thus reducing the reliance on group characteristics that often lead to financial exclusion. We elaborated on the development process of the AI model to illustrate how and why the AI model can better evaluate members of underserved populations. We also found the impacts of the AI model to be heterogeneous across subgroups, and those with missing weak signals saw smaller improvements in the approval rate. A simulation-based analysis showed that simplified AI models were also able to increase the approval rate and reduce the default rate for this population. Our findings provide rich theoretical and practical implications for social justice by documenting how an AI model designed for improving prediction accuracy can enhance financial inclusion.

While marketing on social media fan pages has received widespread research attention, few studies have investigated the impact of adding a showroom to a social media fan page. Showrooms on social media fan pages are unique in that they can amplify the conflicts between businesses’ commercial purposes (selling) and customers’ expectations (socializing) on social media, making it unclear how they might influence customer behavior. In this study, we open this black box by using data from a leading fashion retailer. We found that adding a showroom to a fan page has both positive implications, in that it leads to more user engagement and purchases, and negative implications, in that it leads users to “unfollow” the retailer’s social media fan page. We further found that such impact is moderated by customer willingness to disclose private information. Specifically, the positive (negative) implication is significantly greater (smaller) for customers who are willing to disclose their private information to the retailer on social media. Mechanism-level analyses suggest that adding a showroom to a fan page can increase customer purchases both directly and indirectly by facilitating their engagement with the fan page and that customer willingness to disclose private information negatively moderates the mediation effect of user engagement on purchase behavior. In addition, results from an online experiment indicate that such showrooms can increase unfollowing by undermining users’ social perception of the fan page and raising users’ privacy concerns. Our findings suggest that even when firms see a significant increase in user purchase and activities after adding a showroom on their fan pages, they should carefully consider the potential risk of driving away customers and strategically target users who are less privacy sensitive.

Firms must strategically manage their responses to user comments to keep users engaged on their social media business pages. The question of whether, how, and when a firm should respond to user comments to achieve favorable outcomes is of great interest to researchers and practitioners. We focus on these questions and study the effects of initial user comments and firm responses on subsequent user engagement on social media business pages. In particular, we theorize and examine how two features of initial user comments (i.e., sentiment and controversialness) and two features of firm responses (i.e., uniqueness and timeliness) jointly affect the volume and sentiment of subsequent user comments. By analyzing data from the Facebook business pages of multiple U.S. retailers (10,312 firm posts from 37 firms and over 1 million user comments), we found that firms are more likely to respond to negative comments (than positive or neutral comments) but less likely to respond to controversial comments (which evoke diverse opinions and emotions). Further, we found that engaging with negative and controversial comments and promptly responding to comments are linked to an increase in the volume of subsequent user comments but also to a more negative sentiment in these comments. We also found that providing unique responses improves the volume and sentiment of subsequent user comments. Our findings offer theoretical and practical insights into firms’ response management on social media.

Both research and public discourse have recently drawn attention to the downsides of algorithmic decision-making (ADM), highlighting how it can produce biased and discriminatory outcomes and also pose threats to social justice. We address such threats that emanate from but also go beyond algorithms per se, extending to how public agencies and legal institutions respond or fail to respond to the consequences of ADM. Drawing on a case study of the use of an ADM system in public school administration, we explore the practices through which public institutions avoided engagement with the detrimental consequences of ADM, leading to injustice. We provide a conceptual model outlining how organizational ignoring practices can lead to social and institutional blackboxing of an ADM system, engendering both social and legal injustice. Our work paves the way for interdisciplinary research on the multilayered blackboxing of ADM. We also extend algorithmic injustice research to include a legal dimension and provide practical implications in the form of a legal framework for ADM in the public sector.

Understanding how technostressors lead to technostrain, such as high job burnout or low job performance, has become a core question in information systems (IS) research and practice. To unpack this relationship, we build on general systems theory to argue that the next step for technostress research is to go beyond examining the independent influences of technostressors and discuss how their interdependencies lead to technostrain. To illustrate our argument empirically, we use fuzzy-set qualitative comparative analysis (fsQCA) and identify four configurations of high- and low-intensity technostressors that lead to high job burnout and one that leads to low job performance. We show that three types of interdependencies among technostressors, i.e., complementarity, contingency, and substitution, form configurations that lead to technostrain. Within these configurations, high-intensity technostressors can mutually enhance their effects and low-intensity technostressors can buffer the impact of other high-intensity technostressors on technostrain. The results help to explain why organizational interventions that address independent technostressors may fail if they do not account for the interdependencies among technostressors. Our work provides evidence of the need to further develop theories that explain how and why interdependencies among technostressors lead to technostrain.

We study how Apple and independent repair service providers used different physical, regulatory, and digital instruments to influence each other’s abilities to control the repair aftermarket of the Apple iPhone between 2007 and 2020. We show how the emergence of digital instruments for enacting control, made possible through emerging functionality for tethering, encryption, and temporary binding implemented in the iPhone itself, was shaped by and shaped the actions of Apple and the independent repair service providers, and led to a dominant tension between encrypted authorization and inscription of control through Apple and collective legal action by independent repair service providers. Our analysis provides a more nuanced understanding of control enactment by highlighting the implications of using different mediums for exercising control, and we provide a new way to understand the dialectics involved in enacting control in digital product aftermarkets. Our study provides insights that can inform the regulation of digital product aftermarkets—in particular, the ongoing debate about rights-to-repair legislation.

People have conflicting responses for support from algorithms or humans in decision-making. On the one hand, they fail to benefit from algorithms due to algorithm aversion, as they reject decisions provided by algorithms more frequently than those made by humans. On the other hand, many prefer algorithmic over human advice, an effect of algorithm appreciation. However, currently, we lack a shared understanding of these constructs’ meaning and measurements, resulting in a lack of theoretical integration of empirical findings. Thus, in this research note, we conceptualize algorithm aversion as the preference for humans over algorithms in decision-making and analyze approaches in current research to measure this preference. First, we outline the implications of focusing on a specific understanding of algorithms as computational procedures or as embedded in material or nonmaterial objects. Then, we classify four decision configurations that distinguish individuals’ evaluations of algorithms, human advisors, their own judgments, or combinations of these. Consequently, we develop a classification scheme that provides guidance for future research to develop more specific hypotheses on the direction of preferences (aversion vs. appreciation) and the effect of moderators.