The 10 most-cited articles in MIS Quarterly, 2024

Design science research (DSR) aims to generate knowledge about innovative solutions to real-world problems. Consequently, DSR needs to deal with the complexity related to problem and solution spaces involving sociotechnical phenomena that people perceive differently and are subject to constant change. This complexity poses challenges to sequential, process-based approaches—specifically, the existing DSR methodology. We designed a DSR methodology that extends existing approaches by adding a complementary organizing logic to address complexity. Based on the theory of hierarchical, multilevel systems, we suggest organizing DSR based on the concept of “echelons”—meaning decomposing DSR projects into smaller logically coherent self-contained parts—and suggest a set of five design echelons that imply a hierarchical organizing logic for DSR projects. The echeloned DSR (eDSR) methodology was developed in five iterations, involving seven design and evaluation episodes.

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…

Decentralized autonomous organizations (DAOs)—collectively owned human-machine systems deployed on a blockchain that self-govern through smart contracts and the voluntary contributions of autonomous community members—exhibit the potential to facilitate collective action in managing digital commons. Yet the promise of decentralization and collective action is difficult to sustain. To this end, this paper critically examines the transformational potential of DAOs in the case of decentralized finance. Using a polycentric governance lens, we contribute to the literature on technology-enabled forms of organizing with a model explaining the transformational potential of DAOs to facilitate collective action in digital commons. Our study highlights that (1) DAOs are a new form of organizing enabled by blockchain technology in which individuals are free to pursue their objectives within a general system of rules enforced by smart contracts, (2) collective action for managing digital commons can be sustained through a set of three mechanisms—sustained participation, collective direction, and…

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…

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,…

The increasing societal concern for consumer information privacy has led to the enforcement of privacy regulations worldwide. In an effort to adhere to privacy regulations such as the General Data Protection Regulation (GDPR), many companies’ privacy policies have become increasingly lengthy and complex. In this study, we adopted the computational design science paradigm to design a novel privacy policy evolution analytics framework to help identify how companies change and present their privacy policies based on privacy regulations. The framework includes a self-attentive annotation system (SAAS) that automatically annotates paragraph-length segments in privacy policies to help stakeholders identify data practices of interest for further investigation. We rigorously evaluated SAAS against state-of-the-art machine learning (ML) and deep learning (DL)-based methods on a well-established privacy policy dataset, OPP-115. SAAS outperformed conventional ML and DL models in terms of F1-score by statistically significant margins. We demonstrate the proposed framework’s practical utility…

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…

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…

Social media influencer marketing has grown substantially in the last decade and is a major advertising channel for many brands. Social media influencers weave sponsored posts with organic content into their feeds, which raises concerns among regulators and consumer advocates that users may not be able to clearly distinguish between sponsored and organic influencer content. Thus, regulators often mandate the explicit disclosure of sponsored content. However, there is little empirical evidence based on field data about the effects of explicit sponsorship disclosure. Therefore, we empirically investigate the effects of explicitly disclosing sponsorship in influencers’ content on users’ engagement using a large-scale field dataset collected from Facebook and Instagram. Our empirical results suggest that explicit sponsorship disclosure increases user awareness of the advertising nature and earns users’ favorability by enhancing transparency about the sponsored content. We further designed two online experiments to corroborate our empirical results and directly test the underlying…

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…