Pub Date : 2026-01-13DOI: 10.1016/j.im.2026.104303
Musen Xue , Bei Zhang , Xiaojie Sun
In a dual-channel structure, consumer reviews impact product demand and capacity allocation, which are crucial for operational decisions. The oversight of capacity constraints in existing literature has prompted this study to investigate its impact on the preference of chain members for consumer review strategies, specifically considering hindering and facilitating strategies. Findings show strategy preference hinges on capacity constraints and consumers’ updated quality perception from online reviews. Particularly, opting to facilitate consumer reviews in the online channel can emerge as the optimal strategy for the supplier when confronted with moderate capacity and negative product quality perceptions from online reviews. Our results further indicate that capacity constraints play a pivotal role in shaping supply chain members’ consensus on consumer review strategy preferences. While moderate capacity leads supply chain members to favor a hindering strategy when consumer reviews trigger poor quality perception, they converge on a facilitating strategy if consumers’ updated perception, although relatively low, is framed within a context of perceived good product quality.
{"title":"To facilitate consumer reviews or not? The role of capacity constraint","authors":"Musen Xue , Bei Zhang , Xiaojie Sun","doi":"10.1016/j.im.2026.104303","DOIUrl":"10.1016/j.im.2026.104303","url":null,"abstract":"<div><div>In a dual-channel structure, consumer reviews impact product demand and capacity allocation, which are crucial for operational decisions. The oversight of capacity constraints in existing literature has prompted this study to investigate its impact on the preference of chain members for consumer review strategies, specifically considering hindering and facilitating strategies. Findings show strategy preference hinges on capacity constraints and consumers’ updated quality perception from online reviews. Particularly, opting to facilitate consumer reviews in the online channel can emerge as the optimal strategy for the supplier when confronted with moderate capacity and negative product quality perceptions from online reviews. Our results further indicate that capacity constraints play a pivotal role in shaping supply chain members’ consensus on consumer review strategy preferences. While moderate capacity leads supply chain members to favor a hindering strategy when consumer reviews trigger poor quality perception, they converge on a facilitating strategy if consumers’ updated perception, although relatively low, is framed within a context of perceived good product quality.</div></div>","PeriodicalId":56291,"journal":{"name":"Information & Management","volume":"63 3","pages":"Article 104303"},"PeriodicalIF":8.2,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-03DOI: 10.1016/j.im.2026.104301
Anne Zöll , Anjuli Franz , Ofir Turel
Previous research has emphasized online decisions by individual owners about disclosure of private information. Less is known about the disclosure of co-owned information by multiple owners (e.g., romantic partners) in digital environments, despite such sharing being common and risky. We posit that in such contexts, individuals’ decisions to share co-owned information are shaped not only by their self-centered privacy concerns and perceived benefits, but also by shared (e.g., dyadic) privacy norms. These norms, we posit, act beyond individual-level, self-centered privacy reflections in two significant ways: (1) They directly influence co-owned information disclosure and (2) they influence the weights assigned to individual privacy concerns. We further theorize that dyadic privacy norm accessibility is impacted by social identity salience, which can be manipulated through priming. The findings based on a realistic paradigm largely support our assertions but also produce surprising results. They highlight the need for further study of the role of dyadic privacy norms and social identities in multilevel privacy management.
{"title":"Dyadic privacy management: The influence of dyadic privacy norms on the disclosure of co-owned information in romantic relationships","authors":"Anne Zöll , Anjuli Franz , Ofir Turel","doi":"10.1016/j.im.2026.104301","DOIUrl":"10.1016/j.im.2026.104301","url":null,"abstract":"<div><div>Previous research has emphasized online decisions by individual owners about disclosure of private information. Less is known about the disclosure of co-owned information by multiple owners (e.g., romantic partners) in digital environments, despite such sharing being common and risky. We posit that in such contexts, individuals’ decisions to share co-owned information are shaped not only by their self-centered privacy concerns and perceived benefits, but also by shared (e.g., dyadic) privacy norms. These norms, we posit, act beyond individual-level, self-centered privacy reflections in two significant ways: (1) They directly influence co-owned information disclosure and (2) they influence the weights assigned to individual privacy concerns. We further theorize that dyadic privacy norm accessibility is impacted by social identity salience, which can be manipulated through priming. The findings based on a realistic paradigm largely support our assertions but also produce surprising results. They highlight the need for further study of the role of dyadic privacy norms and social identities in multilevel privacy management.</div></div>","PeriodicalId":56291,"journal":{"name":"Information & Management","volume":"63 2","pages":"Article 104301"},"PeriodicalIF":8.2,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145893714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-02DOI: 10.1016/j.im.2025.104299
Prashant Palvia, Vanessa Cooper, Mohammad Daneshvar Kakhki
{"title":"The Flight of Women from the Information Technology Profession: Nuances and Global Perspectives","authors":"Prashant Palvia, Vanessa Cooper, Mohammad Daneshvar Kakhki","doi":"10.1016/j.im.2025.104299","DOIUrl":"https://doi.org/10.1016/j.im.2025.104299","url":null,"abstract":"","PeriodicalId":56291,"journal":{"name":"Information & Management","volume":"23 1","pages":""},"PeriodicalIF":9.9,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145893715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-30DOI: 10.1016/j.im.2025.104300
Zhao Du , Mengxiang Li , Kanliang Wang , Bin Gu
The challenge of motivating potential donors to contribute is the primary hurdle to the success of online charitable crowdfunding. Anchoring on warm glow theory, we investigate how fundraisers’ self-donations influence donors to contribute to online charitable crowdfunding projects and examine the underlying mechanism through activating potential donors’ warm glow feeling. Using a large-scale dataset from a leading crowdfunding platform in China and a laboratory experiment, we find that fundraisers’ self-donations have a significant positive impact on potential donors’ contributions through developing their warm glow feeling. Our results show that charitable crowdfunding projects with fundraisers’ self-donations perform better in attracting donations from donors. Fundraisers’ self-donations not only improve the probability of success for charitable crowdfunding projects but also facilitates the collection of additional donations. Implications for theory and practice are discussed.
{"title":"Investigating the impacts of fundraisers’ self-donations on donors’ contribution in charitable crowdfunding: A warm glow perspective","authors":"Zhao Du , Mengxiang Li , Kanliang Wang , Bin Gu","doi":"10.1016/j.im.2025.104300","DOIUrl":"10.1016/j.im.2025.104300","url":null,"abstract":"<div><div>The challenge of motivating potential donors to contribute is the primary hurdle to the success of online charitable crowdfunding. Anchoring on warm glow theory, we investigate how fundraisers’ self-donations influence donors to contribute to online charitable crowdfunding projects and examine the underlying mechanism through activating potential donors’ warm glow feeling. Using a large-scale dataset from a leading crowdfunding platform in China and a laboratory experiment, we find that fundraisers’ self-donations have a significant positive impact on potential donors’ contributions through developing their warm glow feeling. Our results show that charitable crowdfunding projects with fundraisers’ self-donations perform better in attracting donations from donors. Fundraisers’ self-donations not only improve the probability of success for charitable crowdfunding projects but also facilitates the collection of additional donations. Implications for theory and practice are discussed.</div></div>","PeriodicalId":56291,"journal":{"name":"Information & Management","volume":"63 2","pages":"Article 104300"},"PeriodicalIF":8.2,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Crowdfunding, initially designed for individuals and entrepreneurs to gather small-scale investments for profit-oriented ventures, has evolved into a valuable platform for nonprofit organizations and individuals to raise funds for diverse causes. In online charitable crowdfunding, fundraisers often rely on compelling project narratives to capture donors' attention and drive contributions. Despite their importance, prior research on textual features has largely overlooked the role of external resource allocation signals in reshaping donors’ narrative expectations, which in turn influence their response to textual messages. This oversight limits our understanding of why identical narrative strategies generate heterogeneous persuasive effects across contexts. We investigate the effectiveness of project narratives in driving donor contributions empirically and further bridge this gap by examining how resource allocation signals interact with narrative appeals to shape donors’ decision-making behavior. Using a unique longitudinal dataset from a prominent United States-based charitable crowdfunding platform, we provide causal evidence and identify the mechanisms underlying the effects of project narration on donor contributions. Our findings reveal that rational appeals in project narratives significantly enhance donor contributions, while emotional appeals tend to reduce them. Additionally, value expectations, formed by both anticipated and realized resource signals, influence donors’ normative language expectations. These expectations moderate the effects of appeals, weakening the impact of rational appeals while amplifying the effectiveness of emotional appeals. These insights provide valuable implications for crowdfunding stakeholders, offering practical guidance to fundraisers crafting effective narratives, donors evaluating projects, and platform managers optimizing fundraising outcomes.
{"title":"The persuasive art of linguistics: an empirical study of project narration and donors’ contributions in the online charitable crowdfunding market","authors":"Hailiang Huang , Yanhong Li , Liangfei Qiu , Shengsheng Xiao , Xiaorong Zhang","doi":"10.1016/j.im.2025.104298","DOIUrl":"10.1016/j.im.2025.104298","url":null,"abstract":"<div><div>Crowdfunding, initially designed for individuals and entrepreneurs to gather small-scale investments for profit-oriented ventures, has evolved into a valuable platform for nonprofit organizations and individuals to raise funds for diverse causes. In online charitable crowdfunding, fundraisers often rely on compelling project narratives to capture donors' attention and drive contributions. Despite their importance, prior research on textual features has largely overlooked the role of external resource allocation signals in reshaping donors’ narrative expectations, which in turn influence their response to textual messages. This oversight limits our understanding of why identical narrative strategies generate heterogeneous persuasive effects across contexts. We investigate the effectiveness of project narratives in driving donor contributions empirically and further bridge this gap by examining how resource allocation signals interact with narrative appeals to shape donors’ decision-making behavior. Using a unique longitudinal dataset from a prominent United States-based charitable crowdfunding platform, we provide causal evidence and identify the mechanisms underlying the effects of project narration on donor contributions. Our findings reveal that rational appeals in project narratives significantly enhance donor contributions, while emotional appeals tend to reduce them. Additionally, value expectations, formed by both anticipated and realized resource signals, influence donors’ normative language expectations. These expectations moderate the effects of appeals, weakening the impact of rational appeals while amplifying the effectiveness of emotional appeals. These insights provide valuable implications for crowdfunding stakeholders, offering practical guidance to fundraisers crafting effective narratives, donors evaluating projects, and platform managers optimizing fundraising outcomes.</div></div>","PeriodicalId":56291,"journal":{"name":"Information & Management","volume":"63 2","pages":"Article 104298"},"PeriodicalIF":8.2,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145844733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-23DOI: 10.1016/j.im.2025.104297
Tao Wang , Chen Chen , Ximeng Jia
With the rapid development of digital technologies, enterprises increasingly regard digital transformation as a key strategic choice, and its impact on the quality of information disclosure has attracted growing attention. This study combines theoretical analysis with empirical testing to examine the effects of digital transformation on corporate information disclosure quality and its underlying mechanisms systematically. The results indicate that digital transformation improves disclosure quality significantly, a conclusion that remains robust across multiple empirical tests. Further analysis reveals that market competition and corporate governance strength enhance this positive effect significantly. Digital transformation improves disclosure quality primarily through three pathways: reducing information asymmetry, mitigating agency problems, and improving resource allocation efficiency. By examining the moderating effects on these theoretical mechanisms, the study finds that the moderating variables mainly amplify the impact of digital transformation by influencing the mechanisms of reducing information asymmetry and enhancing resource allocation efficiency. In addition, different types of digital technologies demonstrate distinct transmission mechanisms, with the cognitive-intelligence and support-system layers primarily enhancing disclosure by alleviating information asymmetry, while the implementation layer plays a more critical role in optimizing resource allocation. This study enriches the theoretical framework linking digital transformation and information disclosure, uncovers the mechanisms and boundary conditions of this relationship, and offers valuable insights for improving corporate transparency and optimizing institutional design in capital markets.
{"title":"Can digitalization drive corporate transparency? Exploring the impact of digital transformation on information disclosure quality","authors":"Tao Wang , Chen Chen , Ximeng Jia","doi":"10.1016/j.im.2025.104297","DOIUrl":"10.1016/j.im.2025.104297","url":null,"abstract":"<div><div>With the rapid development of digital technologies, enterprises increasingly regard digital transformation as a key strategic choice, and its impact on the quality of information disclosure has attracted growing attention. This study combines theoretical analysis with empirical testing to examine the effects of digital transformation on corporate information disclosure quality and its underlying mechanisms systematically. The results indicate that digital transformation improves disclosure quality significantly, a conclusion that remains robust across multiple empirical tests. Further analysis reveals that market competition and corporate governance strength enhance this positive effect significantly. Digital transformation improves disclosure quality primarily through three pathways: reducing information asymmetry, mitigating agency problems, and improving resource allocation efficiency. By examining the moderating effects on these theoretical mechanisms, the study finds that the moderating variables mainly amplify the impact of digital transformation by influencing the mechanisms of reducing information asymmetry and enhancing resource allocation efficiency. In addition, different types of digital technologies demonstrate distinct transmission mechanisms, with the cognitive-intelligence and support-system layers primarily enhancing disclosure by alleviating information asymmetry, while the implementation layer plays a more critical role in optimizing resource allocation. This study enriches the theoretical framework linking digital transformation and information disclosure, uncovers the mechanisms and boundary conditions of this relationship, and offers valuable insights for improving corporate transparency and optimizing institutional design in capital markets.</div></div>","PeriodicalId":56291,"journal":{"name":"Information & Management","volume":"63 2","pages":"Article 104297"},"PeriodicalIF":8.2,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-22DOI: 10.1016/j.im.2025.104296
Wenhua Li , Hongtao Li , Junpeng Guo , Weiguo Fan
With the rapid development of Internet technology, information overload has become increasingly severe. Recommendation systems, as effective information filtering tools, can provide users with personalized recommendations to reduce their decision-making costs. Although recommendation algorithms have achieved great progress in prediction accuracy with the advance of deep learning technologies, the reliability of recommendation results has been relatively underexplored. "Reliability" refers to the likelihood of users accepting the final recommended results, and it is one of the important aspects influencing user experience. Many existing recommendation systems primarily optimize predictive accuracy metrics, while relatively fewer works explicitly model the likelihood of user acceptance, leading to potential gaps in user satisfaction. To address this, we propose the deep ensemble reliable recommendation algorithm (DERA). DERA integrates reliability into both the data preprocessing and model training phases of recommendation models. Drawing on the ensemble learning concept, DERA trains multiple weak learners and employs voting to determine the final prediction result. Additionally, a data preprocessing method is designed to alleviate the imbalance of training data. Experiments on four real-world datasets demonstrate that DERA can enhance recommendation performance by considering reliability. In summary, our work presents a novel framework to integrate reliability estimation into the training pipeline without extra side-information.
{"title":"A reliability-enhanced deep ensemble learning framework for recommendation","authors":"Wenhua Li , Hongtao Li , Junpeng Guo , Weiguo Fan","doi":"10.1016/j.im.2025.104296","DOIUrl":"10.1016/j.im.2025.104296","url":null,"abstract":"<div><div>With the rapid development of Internet technology, information overload has become increasingly severe. Recommendation systems, as effective information filtering tools, can provide users with personalized recommendations to reduce their decision-making costs. Although recommendation algorithms have achieved great progress in prediction accuracy with the advance of deep learning technologies, the reliability of recommendation results has been relatively underexplored. \"Reliability\" refers to the likelihood of users accepting the final recommended results, and it is one of the important aspects influencing user experience. Many existing recommendation systems primarily optimize predictive accuracy metrics, while relatively fewer works explicitly model the likelihood of user acceptance, leading to potential gaps in user satisfaction. To address this, we propose the deep ensemble reliable recommendation algorithm (DERA). DERA integrates reliability into both the data preprocessing and model training phases of recommendation models. Drawing on the ensemble learning concept, DERA trains multiple weak learners and employs voting to determine the final prediction result. Additionally, a data preprocessing method is designed to alleviate the imbalance of training data. Experiments on four real-world datasets demonstrate that DERA can enhance recommendation performance by considering reliability. In summary, our work presents a novel framework to integrate reliability estimation into the training pipeline without extra side-information.</div></div>","PeriodicalId":56291,"journal":{"name":"Information & Management","volume":"63 2","pages":"Article 104296"},"PeriodicalIF":8.2,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-19DOI: 10.1016/j.im.2025.104295
Min Yu, Mingyue Zhang, Baojun Ma
The challenge of encouraging knowledge contribution has led many knowledge-sharing communities to implement incentive mechanisms. While rule-based incentives are widely used, bounty awards—a novel form that allows knowledge seekers to set customized reward amounts and is subject to a fixed expiration period—remain underexplored. We investigate how these two features of bounty awards influence knowledge contribution, drawing on the idea that bounty amount signals both reward attractiveness and question difficulty, while expiration deadline introduces temporal scarcity. Using data from Stack Overflow, we assess outcomes in terms of answer quantity, average quality, and problem-solving likelihood. We find that offering a bounty award increases the quantity and quality of answers, as well as the likelihood of problem-solving. However, the bounty amount yields diminishing marginal returns in answer quantity, while it has a positive and linear effect on the relevance to the question. Meanwhile, it exhibits an inverted U-shaped effect on problem-solving likelihood and answer scores—possibly due to the perceived difficulty of higher-reward questions. Temporal scarcity exhibits a U-shaped relationship with both quantity and solving likelihood, while the U-shaped pattern in answer quality is only partially supported. We also uncover insightful heterogeneous effects, demonstrating that high-quality or under-answered questions may intensify the impact of bounty amount on answer volume, while low-reputation contributors exhibit greater sensitivity to temporal scarcity regarding answer volume. Our study advances the understanding of incentive design in knowledge-sharing communities by theorizing and empirically validating how bounty awards—with their seeker-customized amounts and time-sensitive nature—shape contributor behavior.
{"title":"Answers are wanted: The role of bounty amount and temporal scarcity in knowledge contribution","authors":"Min Yu, Mingyue Zhang, Baojun Ma","doi":"10.1016/j.im.2025.104295","DOIUrl":"10.1016/j.im.2025.104295","url":null,"abstract":"<div><div>The challenge of encouraging knowledge contribution has led many knowledge-sharing communities to implement incentive mechanisms. While rule-based incentives are widely used, bounty awards—a novel form that allows knowledge seekers to set customized reward amounts and is subject to a fixed expiration period—remain underexplored. We investigate how these two features of bounty awards influence knowledge contribution, drawing on the idea that bounty amount signals both reward attractiveness and question difficulty, while expiration deadline introduces temporal scarcity. Using data from Stack Overflow, we assess outcomes in terms of answer quantity, average quality, and problem-solving likelihood. We find that offering a bounty award increases the quantity and quality of answers, as well as the likelihood of problem-solving. However, the bounty amount yields diminishing marginal returns in answer quantity, while it has a positive and linear effect on the relevance to the question. Meanwhile, it exhibits an inverted U-shaped effect on problem-solving likelihood and answer scores—possibly due to the perceived difficulty of higher-reward questions. Temporal scarcity exhibits a U-shaped relationship with both quantity and solving likelihood, while the U-shaped pattern in answer quality is only partially supported. We also uncover insightful heterogeneous effects, demonstrating that high-quality or under-answered questions may intensify the impact of bounty amount on answer volume, while low-reputation contributors exhibit greater sensitivity to temporal scarcity regarding answer volume. Our study advances the understanding of incentive design in knowledge-sharing communities by theorizing and empirically validating how bounty awards—with their seeker-customized amounts and time-sensitive nature—shape contributor behavior.</div></div>","PeriodicalId":56291,"journal":{"name":"Information & Management","volume":"63 2","pages":"Article 104295"},"PeriodicalIF":8.2,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-17DOI: 10.1016/j.im.2025.104294
Hui Yang , Haoyuan Zheng , Lingfei Lu , Peng Hu
People increasingly rely on generative AI systems for content creation. However, the uncertainty of AI-generated responses poses new challenges for effective usage. A common strategy is clicking “regenerate” in search of better outputs. Based on uncertainty reduction theory, this study develops a model to understand this phenomenon, tested using data from a large-scale survey (n = 2,582). The findings reveal an overgeneration effect, which refers to the counterproductive pattern of excessive regeneration. Response uncertainty in these systems can amplify users’ tendency to regenerate content. This effect is weaker among users with higher AI literacy. Importantly, frequent regeneration is not costless exploration, as we find that it can induce cognitive fatigue and reduce task efficiency. This study redirects attention from process-level to output-level uncertainty and introduces regeneration as a distinct behavioral mechanism with unintended consequences. Practically, the findings call for interface designs that limit excessive regeneration and guide users toward evaluating and refining existing outputs before regenerating.
{"title":"Next output is better? Overgeneration effect in generative artificial intelligence interaction","authors":"Hui Yang , Haoyuan Zheng , Lingfei Lu , Peng Hu","doi":"10.1016/j.im.2025.104294","DOIUrl":"10.1016/j.im.2025.104294","url":null,"abstract":"<div><div>People increasingly rely on generative AI systems for content creation. However, the uncertainty of AI-generated responses poses new challenges for effective usage. A common strategy is clicking “regenerate” in search of better outputs. Based on uncertainty reduction theory, this study develops a model to understand this phenomenon, tested using data from a large-scale survey (<em>n</em> = 2,582). The findings reveal an overgeneration effect, which refers to the counterproductive pattern of excessive regeneration. Response uncertainty in these systems can amplify users’ tendency to regenerate content. This effect is weaker among users with higher AI literacy. Importantly, frequent regeneration is not costless exploration, as we find that it can induce cognitive fatigue and reduce task efficiency. This study redirects attention from process-level to output-level uncertainty and introduces regeneration as a distinct behavioral mechanism with unintended consequences. Practically, the findings call for interface designs that limit excessive regeneration and guide users toward evaluating and refining existing outputs before regenerating.</div></div>","PeriodicalId":56291,"journal":{"name":"Information & Management","volume":"63 2","pages":"Article 104294"},"PeriodicalIF":8.2,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study examines how facial shape, as an anthropomorphic cue, influences consumers' acceptance of investment advice from robo-advisors. Results from three studies demonstrate that a square facial shape of the robo-advisors leads to a higher intention to accept investment advice compared to a round shape. This effect is mediated by perceived competence. Additionally, the social role and algorithm transparency moderate the relationship between facial shape and perceived competence. These findings contribute to our understanding of consumers' social cognition of facial shape influencing their acceptance of robo-advisors’ investment recommendations and advance knowledge on anthropomorphic and communication cues in the field of robo-advisory services.
{"title":"Round versus square: Exploring the influence of facial shape in robo-advisors on consumer acceptance of investment advice","authors":"Zhongpeng Cao , Kexin Yu , Lijun Guo , Yanyan Zhang","doi":"10.1016/j.im.2025.104291","DOIUrl":"10.1016/j.im.2025.104291","url":null,"abstract":"<div><div>This study examines how facial shape, as an anthropomorphic cue, influences consumers' acceptance of investment advice from robo-advisors. Results from three studies demonstrate that a square facial shape of the robo-advisors leads to a higher intention to accept investment advice compared to a round shape. This effect is mediated by perceived competence. Additionally, the social role and algorithm transparency moderate the relationship between facial shape and perceived competence. These findings contribute to our understanding of consumers' social cognition of facial shape influencing their acceptance of robo-advisors’ investment recommendations and advance knowledge on anthropomorphic and communication cues in the field of robo-advisory services.</div></div>","PeriodicalId":56291,"journal":{"name":"Information & Management","volume":"63 2","pages":"Article 104291"},"PeriodicalIF":8.2,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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