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.
Medical crowdfunding depends on visible donation signals that help prospective donors assess campaign credibility. Yet platform design choices that allow donors to remain anonymous may weaken these signals. Drawing on signaling theory and information cue theory, we conceptualize donor anonymity as a double-edged sword: it protects donor privacy and intrinsic giving motives, but it also reduces the diagnosticity of donation histories for subsequent donors. Using donation-level data from GoFundMe medical campaigns, we find that each anonymous donation among the three most recent donations reduces subsequent donation amounts by approximately 5.5 percent, net of campaign heterogeneity. We term this pattern the anonymity penalty effect. This effect is moderated by signal substitutes and campaign stage. Large cumulative anonymous donations and large individual anonymous donations attenuate the penalty, suggesting that donation size can partially compensate for missing identity cues. By contrast, the penalty intensifies in late-stage campaigns, when donors appear to rely more heavily on visible identity signals as campaigns approach their funding goals. A randomized controlled experiment provides evidence consistent with the archival findings and identifies reduced signal diagnosticity as the mechanism underlying the anonymity penalty effect. Theoretically, this study extends signaling theory and information cue theory to medical crowdfunding by showing that donor identity operates as a diagnostic legitimacy cue in settings without institutional verification, identifying anonymity as a platform-generated signal externality, and revealing how identity, donation size, and campaign stage interact in shaping donor behavior. Practically, the findings suggest that crowdfunding platforms should design anonymity options and recent-donation displays jointly, balancing donor privacy with the need to preserve signal diagnosticity through features that contextualize anonymous donations, highlight large anonymous contributions, or otherwise compensate for withheld donor identities.
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.
