Knowledge that Transforms

Prevailing information privacy theories have advanced understanding of how users evaluate privacy risks. Yet, they leave important aspects of user behavior insufficiently explained, particularly why some consumers proactively protect their personal information, whereas others remain passive despite expressing similar privacy concerns. Much of this research relies on privacy concerns as the primary indicator of privacy attitudes. Privacy concerns are crucial, but they largely capture users’ risk-focused evaluations of data collection, use, and disclosure. They therefore explain how users react to perceived privacy threats better than how users develop sustained engagement in protecting their privacy. We propose and test the Theory of Privacy Interests to explain this consumer-side engagement. By privacy interests, we mean users’ experiential engagement with protecting their personal information—the extent to which privacy protection is meaningful to them, they feel competent to pursue it, and they believe their actions can influence privacy outcomes. This construct refers to users’ privacy-protective interests, not to the economic or strategic interests of firms, platforms, or other producers that supply privacy-adjacent digital environments. Drawing on the Heideggerian onto-epistemological framework, we conceptualize privacy interests as experience-based, skillful engagement with privacy protection that develops through users’ repeated interactions with digital environments, privacy risks, and privacy-protective practices. We empirically examine privacy interests and privacy concerns as distinct but complementary constructs. Across three scale-development data collections and four survey studies with 3,922 participants, we find that privacy interests are more effective in explaining proactive privacy actions. In contrast, privacy concerns are more effective in explaining reactive privacy actions. This research operationalizes the Heideggerian onto-epistemological framework to shift attention from users’ risk evaluation to users’ sustained engagement in actualizing privacy protection. Our research offers a complementary explanation for variation in consumer privacy behavior and advances research on privacy attitudes, privacy actions, and the privacy paradox.

Generative AI (GenAI) has advanced rapidly and made significant impacts. However, its effect on developers remains a topic of industry debate. Companies want to know whether GenAI can enhance developers’ coding performance, as an unclear understanding may put companies at a disadvantage. While the literature has begun addressing this issue, a formal understanding of GenAI’s impact remains incomplete. Moreover, existing findings are often short-term, fragmented, or lack explanatory mechanisms. To fill these gaps, we designed a multimethod research program comprising a longitudinal field study and a randomized controlled experiment. In Study 1, we collaborated with a global information technology organization and applied a difference-in-differences approach to over 27 weeks of proprietary data. In Study 2, we designed a randomized experiment involving 253 software developers. From these studies, we find that GenAI usage affects both developers’ coding quantity and quality. These effects, however, depend critically on how the tool is used. While reduced cognitive effort can be associated with diminished quality, interestingly, GenAI usage enables developers to produce higher-quality code with less cognitive effort. In this current study, we explain the paradoxical findings through cognitive load theory, showing that GenAI reduces extraneous load while preserving germane processing during ideation and debugging. Using a multimethod research design that integrates longitudinal field data with a randomized controlled experiment, we link observed performance effects to underlying cognitive mechanisms and usage strategies. We also offer guidance on effective usage styles and propose boundary conditions for realizing GenAI’s benefits in practice.

This paper examines the use of large language models (LLMs) for human-LLM co-creation, wherein humans use LLMs to accomplish text-based tasks requiring knowledge and understanding of a topic. Task factuality, the correspondence of the human-LLM co-creation task output to reality and verifiable facts, is an important outcome of such tasks, yet is difficult to achieve. We investigate how the human’s reflection enhances task factuality in such tasks. Theorizing two aspects of reflection, namely, the human’s cognitive state and the interaction mode with the LLM, we develop hypotheses explaining how: (1) two types of interaction modes (adversarial and conversational) differentially enhance task factuality; and (2) three types of cognitive states (shallow, dialogic and critical) mediate the differential effect of interaction mode on task factuality. We test our hypotheses through a randomized experiment on a task in which participants wrote a short essay on a specific topic by working with an LLM. Integrating data from experimental manipulations (interaction mode), survey measures (cognitive state) and objective assessment (task factuality and cognitive state) drawn from 280 LLM users, the paper makes theoretical contributions by: explaining how reflection can enhance epistemic integration between humans and LLMs by increasing task factuality in human-LLM co-creation tasks, theoretically unpacking the concept of reflection in the context of human-LLM co-creation, and providing insights for LLM design that can lead to higher factuality of such tasks. Practical implications for LLM users are to engage in reflection when working with LLMs to generate more factual outputs, for organizations to develop employee capacity for reflection, and for LLM companies to design features that foster reflection for users.

The quality of online review content is a central factor in its perceived helpfulness to consumers. Yet platforms’ efforts to enhance the quality of online reviews through extrinsic incentives have shown mixed results. In this paper, we address this issue by showing that a subtle change in the design of the review generation process can improve review quality. Via four randomized controlled experiments (n = 1,101), we specifically examine whether the task order in which platforms solicit numerical ratings and textual reviews can shape reviewers’ reasoning processes and, in turn, the deliberativeness of textual reviews and the characteristics of subsequent numerical ratings. Results show that changing the task order in the online review generation process, from the current “rating first” to a “review first” task order, enhances reviewers’ deliberation in their textual reviews, reduces their ambivalence toward the product/service, and improves their decisiveness and confidence in numerical ratings. Moreover, we show that these effects are contingent on the device (PC vs. mobile) used to generate textual reviews, as a situational boundary condition. In response to calls for research on the design of review systems, these findings offer actionable insights for online review platforms by presenting novel ways to elicit more deliberative textual reviews and more decisive, confident numerical ratings. They also clarify the important distinction between PC reviews and mobile reviews.

Although opportunities are central to firm innovation and performance, prior research lacks a scalable, theory-grounded approach to measuring them. Existing measures are either context-specific or detached from explicit relational mechanisms, limiting their generalizability and interpretability. Leveraging computational methods including text analytics, unsupervised machine learning, and network analysis of large-scale digitized data, we propose a computational design framework guided by structural hole theory that enables fine-grained strategic opportunity measures: hole-opening, hole-entering, and non-hole positions. We validate this framework through systematic analysis of initial public offering (IPO) outcomes using U.S. public firm panel data. The results show that hole-opening positions are associated with higher post-IPO valuations, but with a lower likelihood of mergers and acquisitions (M&A) exits, whereas hole-entering and non-hole positions are linked to lower IPO valuations but higher probabilities of M&A outcomes. These patterns reveal distinct opportunity roles based on firms’ relative structural positions. This computational framework contributes to IS research by offering a replicable, theory-driven foundation for opportunity measurement.

On online platforms, new ideas often emerge by recombining existing ones within idea networks. Unlike traditional knowledge networks, idea networks represent curated, meaning-based associations among ideas, offering a distinct lens on recombination. Drawing upon a hypergraph perspective, we investigate how new idea success depends on their structural and content attributes, and how collaborative participation shapes these attributes. Using data from an ideation platform, we find that both structural embeddedness and bridging benefit new idea success. Content diversity has no direct effect, but it amplifies the benefits of bridging while constraining those of embeddedness. Both crowd contributions and ideator expertise strengthen ideas’ structural positions, whereas they shape content diversity in opposite ways: crowd contributions increase diversity, while ideator expertise reduces it through selective integration. These findings advance research on networks, recombination, and online collaboration by showing how structure, content, and collaborative participation jointly shape new idea success.

This research examines how startup complementors’ product security influences their funding from external investors in platform ecosystems. The longitudinal analysis of the Hadoop ecosystem shows that the release of product security features is positively associated with startup funding. The effect is stronger when public media pays more attention to the platform ecosystem’s security issues and for complementors with a central position in the platform ecosystem’s technological proximity network. We further find that the effect depends on the strength and interpretability of the product security signals. Specifically, the effect is driven by product-embedded security efforts rather than by partnership-based initiatives, and is more pronounced among platform-native complementors. These findings advance our understanding of how product security functions as a strategic signal in ecosystems characterized by interdependent risks and networked competition. We offer practical implications for complementors and platforms seeking to translate security investments into competitive advantage and strengthen security incentives.

Artificial intelligence (AI) developers have leveraged open source software (OSS) to accelerate AI’s progress. However, this has introduced security issues, including newly developed machine learning open-source software (MLOSS) repositories inheriting vulnerabilities from each other, typically lacking any explicit signal. In this study, we adopted the computational design science paradigm to design a novel MLOSS Link Prediction framework to map the spread of vulnerabilities across AI. We propose a Self-Supervised AI-Feature Aware Graph Attention Autoencoder (SSAIF-GATE) to learn from a sparsely labeled network, a novel AI-Feature Aware attention mechanism that captures shared AI terms, and a multilevel pretext task to leverage multiple components of a network’s structure. SSAIF-GATE outperforms prevailing graph embedding methods with an area-under-the-curve of 94.8 percent and an average precision of 96.1 percent. SSAIF-GATE helps address extensive vulnerability spread among MLOSS and contributes design principles that can inform future information technology artifact design for broader domains including business intelligence and healthcare.

We theorize that designing conversational agents (CAs) to appear more humanlike will make minor errors appear more normal because to err is human. When errors appear more normal, users are less likely to strive to identify their cause (a process called attribution) and thus are less likely to respond negatively. We conducted two experiments to test our theoretical model, and the results generally support our theorizing: greater perceived humanness preserves the perception of situational normality when an error occurs, thereby reducing error attribution and mitigating the negative effects of errors on service satisfaction. Our research contributes to the theory by identifying a theoretical mechanism that underlies users’ responses to errors (a reduction in situational normality triggers error attribution). It also has important implications for practice by showing that designing CAs to be more humanlike is important for CAs more likely to make errors (e.g. CAs controlled by large language models) and less important for other CAs (e.g. those controlled by robust rule-based scripts).