Pub Date : 2025-11-19DOI: 10.1016/j.dss.2025.114575
Zhichao Wu , Xi Zhao , Xiaoni Lu
The Drop feature on OpenSea provides creators with a standardized tool for designing NFT collection (NFTC) initial offering campaigns. This study examines the impact of campaign design elements on sales performance. Analyzing 693 NFTCs, we reveal an inverted U-shaped relationship between target size and sales outcomes, attributable to the balance between social proof and scarcity. Additionally, we observe a positive effect of incorporating pre-sale stages, which is driven by social proof. Notably, OpenSea's official certification, as a significant credibility signal, moderates these effects. This research advances the understanding of social proof theory within the Web3.0 context, offering actionable insights for NFT creators to optimize campaign strategies and for platform managers to enhance the effectiveness of the Drop feature.
{"title":"What makes a well-performing NFT collection initial offering campaign: Evidence from OpenSea Drop","authors":"Zhichao Wu , Xi Zhao , Xiaoni Lu","doi":"10.1016/j.dss.2025.114575","DOIUrl":"10.1016/j.dss.2025.114575","url":null,"abstract":"<div><div>The Drop feature on OpenSea provides creators with a standardized tool for designing NFT collection (NFTC) initial offering campaigns. This study examines the impact of campaign design elements on sales performance. Analyzing 693 NFTCs, we reveal an inverted U-shaped relationship between target size and sales outcomes, attributable to the balance between social proof and scarcity. Additionally, we observe a positive effect of incorporating pre-sale stages, which is driven by social proof. Notably, OpenSea's official certification, as a significant credibility signal, moderates these effects. This research advances the understanding of social proof theory within the Web3.0 context, offering actionable insights for NFT creators to optimize campaign strategies and for platform managers to enhance the effectiveness of the Drop feature.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"200 ","pages":"Article 114575"},"PeriodicalIF":6.8,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145553605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-13DOI: 10.1016/j.dss.2025.114574
Mengxiao Zhu , Lin Liu , Chunke Su
Creators on social media platforms are increasingly engaging in collaborative content generation. Given the recognized value of integrating diverse perspectives and expertise from different domains, such as fostering innovation, improving content quality, and expanding audience engagement, this study aims to investigate the decision-making dynamics among creators involved in cross-domain collaboration. Drawing on social identity theory, we examine the effect of content domain differentiation on the formation of collaborative relationships and how creators' attributes of content diversity and influencing power alter these effects. Our data were collected from Bilibili, one of the largest Chinese video-sharing platforms, which offers a joint submission feature allowing multiple creators to publish their generated videos. We employ exponential random graph models (ERGMs) to analyze the formation of a collaboration network comprising 2490 creators. The findings reveal that content domain differentiation is negatively related to the formation of collaborative relationships, indicating that cross-domain collaborative relationships are less likely to occur compared to within-domain ones on social media. Furthermore, content diversity mitigates the negative effect of content domain differentiation, suggesting that creators with higher content diversity are more inclined to engage in cross-domain collaborations. Regarding influencing power, creators with less reach and activeness are more likely to participate in cross-domain collaboration. Interestingly, creators with institutional authority are less likely to form cross-domain collaborations, whereas those with individual authority are more likely, compared to non-authority creators. This study highlights the challenges in fostering cross-domain collaborative relationships on social media and elucidates actionable strategies to promote such collaborations.
{"title":"Breaking boundaries: Investigating the formation of cross-domain collaboration on social media platforms","authors":"Mengxiao Zhu , Lin Liu , Chunke Su","doi":"10.1016/j.dss.2025.114574","DOIUrl":"10.1016/j.dss.2025.114574","url":null,"abstract":"<div><div>Creators on social media platforms are increasingly engaging in collaborative content generation. Given the recognized value of integrating diverse perspectives and expertise from different domains, such as fostering innovation, improving content quality, and expanding audience engagement, this study aims to investigate the decision-making dynamics among creators involved in cross-domain collaboration. Drawing on social identity theory, we examine the effect of content domain differentiation on the formation of collaborative relationships and how creators' attributes of content diversity and influencing power alter these effects. Our data were collected from Bilibili, one of the largest Chinese video-sharing platforms, which offers a joint submission feature allowing multiple creators to publish their generated videos. We employ exponential random graph models (ERGMs) to analyze the formation of a collaboration network comprising 2490 creators. The findings reveal that content domain differentiation is negatively related to the formation of collaborative relationships, indicating that cross-domain collaborative relationships are less likely to occur compared to within-domain ones on social media. Furthermore, content diversity mitigates the negative effect of content domain differentiation, suggesting that creators with higher content diversity are more inclined to engage in cross-domain collaborations. Regarding influencing power, creators with less reach and activeness are more likely to participate in cross-domain collaboration. Interestingly, creators with institutional authority are less likely to form cross-domain collaborations, whereas those with individual authority are more likely, compared to non-authority creators. This study highlights the challenges in fostering cross-domain collaborative relationships on social media and elucidates actionable strategies to promote such collaborations.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"200 ","pages":"Article 114574"},"PeriodicalIF":6.8,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-13DOI: 10.1016/j.dss.2025.114564
Xiaowei Shi , Cong Wang , Qiang Wei
Online recruitment platforms have revolutionized labor markets by enabling bidirectional engagement between job seekers and employers, but this transformation has also introduced complex decision-making challenges due to information overload and parallel decision processes. Existing research and algorithms often focus on static and one-way models, neglecting the dynamic feedback loops and preference adjustments inherent in two-way proactive recruitment. This study introduces ProMatch, a novel person-job matching approach designed to support decision-making for both sides. ProMatch formalizes recruitment as a multi-stage process involving intention formation, preference updates, and bilateral matching, capturing the sequential dependencies between decision outcomes. It also incorporates a dynamic preference learning mechanism grounded in self-regulation theory, which iteratively refines preferences using textual profiles, historical interactions, and feedback. Validation using a real-world IT enterprise dataset and a two-week field experiment demonstrates ProMatch’s effectiveness. Results show a 9% increase in click-through rates and a 20% improvement in interview-through rates, highlighting its ability to enhance prediction accuracy by dynamically modeling evolving preferences. ProMatch’s innovations offer actionable decision support for both job seekers and employers, ultimately improving recruitment efficiency and cost-effectiveness in modern recruitment ecosystems.
{"title":"ProMatch: A novel dynamic process-unpacking approach for two-way proactive recruitment","authors":"Xiaowei Shi , Cong Wang , Qiang Wei","doi":"10.1016/j.dss.2025.114564","DOIUrl":"10.1016/j.dss.2025.114564","url":null,"abstract":"<div><div>Online recruitment platforms have revolutionized labor markets by enabling bidirectional engagement between job seekers and employers, but this transformation has also introduced complex decision-making challenges due to information overload and parallel decision processes. Existing research and algorithms often focus on static and one-way models, neglecting the dynamic feedback loops and preference adjustments inherent in two-way proactive recruitment. This study introduces ProMatch, a novel person-job matching approach designed to support decision-making for both sides. ProMatch formalizes recruitment as a multi-stage process involving intention formation, preference updates, and bilateral matching, capturing the sequential dependencies between decision outcomes. It also incorporates a dynamic preference learning mechanism grounded in self-regulation theory, which iteratively refines preferences using textual profiles, historical interactions, and feedback. Validation using a real-world IT enterprise dataset and a two-week field experiment demonstrates ProMatch’s effectiveness. Results show a 9% increase in click-through rates and a 20% improvement in interview-through rates, highlighting its ability to enhance prediction accuracy by dynamically modeling evolving preferences. ProMatch’s innovations offer actionable decision support for both job seekers and employers, ultimately improving recruitment efficiency and cost-effectiveness in modern recruitment ecosystems.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"200 ","pages":"Article 114564"},"PeriodicalIF":6.8,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145531617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-06DOI: 10.1016/j.dss.2025.114573
Meng An , Jiabao Lin , Jose Benitez
Many antecedents of organizational innovation have been examined in isolation, overlooking their synergistic and threshold effects. To address this gap, this study draws on resource orchestration theory to investigate how AI usage and knowledge-based dynamic capabilities, i.e., knowledge generation capability, knowledge acquisition capability, and market-sensing capability, jointly drive exploratory and exploitative innovation. Using survey data from 218 Chinese firms, we apply fuzzy-set qualitative comparative analysis (fsQCA) to identify multiple sufficient configurations that generate high innovation, highlighting heterogeneous pathways shaped by firm size and industry context. To complement these findings, we conduct necessary condition analysis (NCA), which reveals critical threshold levels for AI usage and knowledge capabilities that should be met regardless of the chosen configuration. Furthermore, we map fsQCA results with three types of interdependencies among AI usage and knowledge-based capabilities—complementarity, contingency, and substitution—to form configurations that lead to different organizational innovations. This study enriches configurational theory on organizational innovation, expands the theoretical boundaries of AI-enabled innovation, and provides actionable decision support for resource allocation and capability development under digital transformation.
{"title":"Effects of artificial intelligence usage and knowledge-based dynamic capabilities on organizational innovation: A configurational approach","authors":"Meng An , Jiabao Lin , Jose Benitez","doi":"10.1016/j.dss.2025.114573","DOIUrl":"10.1016/j.dss.2025.114573","url":null,"abstract":"<div><div>Many antecedents of organizational innovation have been examined in isolation, overlooking their synergistic and threshold effects. To address this gap, this study draws on resource orchestration theory to investigate how AI usage and knowledge-based dynamic capabilities, i.e., knowledge generation capability, knowledge acquisition capability, and market-sensing capability, jointly drive exploratory and exploitative innovation. Using survey data from 218 Chinese firms, we apply fuzzy-set qualitative comparative analysis (fsQCA) to identify multiple sufficient configurations that generate high innovation, highlighting heterogeneous pathways shaped by firm size and industry context. To complement these findings, we conduct necessary condition analysis (NCA), which reveals critical threshold levels for AI usage and knowledge capabilities that should be met regardless of the chosen configuration. Furthermore, we map fsQCA results with three types of interdependencies among AI usage and knowledge-based capabilities—complementarity, contingency, and substitution—to form configurations that lead to different organizational innovations. This study enriches configurational theory on organizational innovation, expands the theoretical boundaries of AI-enabled innovation, and provides actionable decision support for resource allocation and capability development under digital transformation.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"200 ","pages":"Article 114573"},"PeriodicalIF":6.8,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145461300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-04DOI: 10.1016/j.dss.2025.114565
Yuxiao Luo , Nanda Kumar , Adel Yazdanmehr
This study explores the impacts of AI nudging on customer purchase decisions. Digital nudging is a well-established technique used to alter people's behaviors in a predictable way. With the rapid development of Artificial Intelligence/Machine Learning (AI/ML) and the widespread integration of the “black box” algorithm in the digital choice architecture, personalized targeting nudges can vastly influence individual and collective behaviors and lead to undesired consequences. AI nudge refers to the situation when human outsources developing and implementing nudges to AI/ML systems. Drawing upon the literature on nudge and recommendation agents/systems in IS, this study investigated the impact of two types of recommendation badges on user decision quality: AI nudge (e.g., Amazon's Choice) and non-AI nudge (e.g., Best Seller). We found that these two badges can lead to different user perceptions of transparency and thus affect the choice confidence of product selection. In addition, the effect of perceived transparency on choice confidence is contingent upon the mismatch/match between the recommendation and users' preferences, with perceived transparency exerting significantly higher influence on choice confidence in the preference match condition. We tested our research model using a randomized experiment and post-task survey data collected from 837 US-based college students with online shopping experience. This is the first empirical study examining the impact of AI nudging on user decision-making on e-commerce platforms and will contribute to the nudge literature and biased recommendation research in IS. The study also brings ethical implications to the use of AI/ML models and calls for careful oversight on delegating the power of nudging to AI in guiding online user behavior.
{"title":"AI nudging and decision quality: Evidence from randomized experiments in online recommendation setting","authors":"Yuxiao Luo , Nanda Kumar , Adel Yazdanmehr","doi":"10.1016/j.dss.2025.114565","DOIUrl":"10.1016/j.dss.2025.114565","url":null,"abstract":"<div><div>This study explores the impacts of AI nudging on customer purchase decisions. Digital nudging is a well-established technique used to alter people's behaviors in a predictable way. With the rapid development of Artificial Intelligence/Machine Learning (AI/ML) and the widespread integration of the “black box” algorithm in the digital choice architecture, personalized targeting nudges can vastly influence individual and collective behaviors and lead to undesired consequences. AI nudge refers to the situation when human outsources developing and implementing nudges to AI/ML systems. Drawing upon the literature on nudge and recommendation agents/systems in IS, this study investigated the impact of two types of recommendation badges on user decision quality: AI nudge (e.g., <em>Amazon's Choice</em>) and non-AI nudge (e.g., <em>Best Seller</em>). We found that these two badges can lead to different user perceptions of transparency and thus affect the choice confidence of product selection. In addition, the effect of perceived transparency on choice confidence is contingent upon the mismatch/match between the recommendation and users' preferences, with perceived transparency exerting significantly higher influence on choice confidence in the preference match condition. We tested our research model using a randomized experiment and post-task survey data collected from 837 US-based college students with online shopping experience. This is the first empirical study examining the impact of AI nudging on user decision-making on e-commerce platforms and will contribute to the nudge literature and biased recommendation research in IS. The study also brings ethical implications to the use of AI/ML models and calls for careful oversight on delegating the power of nudging to AI in guiding online user behavior.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"200 ","pages":"Article 114565"},"PeriodicalIF":6.8,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145441667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-04DOI: 10.1016/j.dss.2025.114563
Humam Kourani , Alessandro Berti , Jasmin Hennrich , Wolfgang Kratsch , Robin Weidlich , Chiao-Yun Li , Ahmad Arslan , Wil M.P. van der Aalst , Daniel Schuster
In Business Process Management (BPM), effectively comprehending process models is crucial yet poses significant challenges, particularly as organizations scale and processes become more complex. This paper introduces a novel framework utilizing the advanced capabilities of Large Language Models (LLMs) to enhance the comprehension of complex process models. We present different methods for abstracting business process models into a format accessible to LLMs, and we implement advanced prompting strategies specifically designed to optimize LLM performance within our framework. Additionally, we present a tool, AIPA, that implements our proposed framework and allows for conversational process querying. We evaluate our framework and tool through: i) an automatic evaluation comparing different LLMs, model abstractions, and prompting strategies; ii) a qualitative analysis assessing the ability to identify critical quality issues in process models; and iii) a user study designed to assess AIPA’s effectiveness comprehensively. Results demonstrate our framework’s ability to improve the comprehension and understanding of process models, pioneering new pathways for integrating AI technologies into the BPM field.
{"title":"Leveraging large language models for enhanced process model comprehension","authors":"Humam Kourani , Alessandro Berti , Jasmin Hennrich , Wolfgang Kratsch , Robin Weidlich , Chiao-Yun Li , Ahmad Arslan , Wil M.P. van der Aalst , Daniel Schuster","doi":"10.1016/j.dss.2025.114563","DOIUrl":"10.1016/j.dss.2025.114563","url":null,"abstract":"<div><div>In Business Process Management (BPM), effectively comprehending process models is crucial yet poses significant challenges, particularly as organizations scale and processes become more complex. This paper introduces a novel framework utilizing the advanced capabilities of Large Language Models (LLMs) to enhance the comprehension of complex process models. We present different methods for abstracting business process models into a format accessible to LLMs, and we implement advanced prompting strategies specifically designed to optimize LLM performance within our framework. Additionally, we present a tool, AIPA, that implements our proposed framework and allows for conversational process querying. We evaluate our framework and tool through: i) an automatic evaluation comparing different LLMs, model abstractions, and prompting strategies; ii) a qualitative analysis assessing the ability to identify critical quality issues in process models; and iii) a user study designed to assess AIPA’s effectiveness comprehensively. Results demonstrate our framework’s ability to improve the comprehension and understanding of process models, pioneering new pathways for integrating AI technologies into the BPM field.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"200 ","pages":"Article 114563"},"PeriodicalIF":6.8,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145441665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-30DOI: 10.1016/j.dss.2025.114562
Wei Wang , Yao Tong , Jian Mou
Although Artificial Intelligence (AI) agents are being increasingly deployed in crowdfunding platforms to address labor shortages, knowledge about their scope and limits is still limited. Across a secondary data analysis and three experiments (total N = 1027), we reveal that AI (vs. human) agents are more effective in reward-based (vs. donation-based) crowdfunding. This effect can be parallelly mediated by perceptions of warmth and competence, with AI agents evoking higher competence but weaker warmth perceptions. Importantly, anthropomorphic AI agents serve as an effective intervention to alleviate AI's negative impact on donation-based crowdfunding by enhancing warmth perceptions. Finally, we show that human agents outperform AI agents in boosting donation-based funding performance only for those with an interdependent versus independent self-construal. Overall, these findings expand the theoretical framework on AI applications in crowdfunding and offer actionable insights for fundraisers and platform operators to optimize agent deployment.
{"title":"Artificial intelligence agents or human agents? Impact of online customer service agents on crowdfunding performance","authors":"Wei Wang , Yao Tong , Jian Mou","doi":"10.1016/j.dss.2025.114562","DOIUrl":"10.1016/j.dss.2025.114562","url":null,"abstract":"<div><div>Although Artificial Intelligence (AI) agents are being increasingly deployed in crowdfunding platforms to address labor shortages, knowledge about their scope and limits is still limited. Across a secondary data analysis and three experiments (total <em>N</em> = 1027), we reveal that AI (vs. human) agents are more effective in reward-based (vs. donation-based) crowdfunding. This effect can be parallelly mediated by perceptions of warmth and competence, with AI agents evoking higher competence but weaker warmth perceptions. Importantly, anthropomorphic AI agents serve as an effective intervention to alleviate AI's negative impact on donation-based crowdfunding by enhancing warmth perceptions. Finally, we show that human agents outperform AI agents in boosting donation-based funding performance only for those with an interdependent versus independent self-construal. Overall, these findings expand the theoretical framework on AI applications in crowdfunding and offer actionable insights for fundraisers and platform operators to optimize agent deployment.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"200 ","pages":"Article 114562"},"PeriodicalIF":6.8,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145382617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-27DOI: 10.1016/j.dss.2025.114561
Jun Yang , Hongchen Duan , Demei Kong
Multi-dimensional (MD) rating systems are increasingly adopted by online platforms to capture product evaluations across multiple attributes. While this structured format enriches product information, it also makes intra-review inconsistencies salient, raising new questions about how such inconsistencies shape review helpfulness—a topic largely overlooked in prior research dominated by single-dimensional (SD) reviews. This study examines the effects of cross-dimensional inconsistencies (in ratings, sentiment, and informativeness) and a cross-modal inconsistency (rating–sentiment misalignment within a dimension) on the perceived helpfulness of MD reviews, drawing on cognitive dissonance theory. Using a large dataset from a leading Chinese automobile review platform, we find that cross-dimensional rating inconsistency can enhance review helpfulness by signaling realistic product trade-offs, whereas sentiment, informativeness, and cross-modal inconsistencies reduce helpfulness by triggering unresolved dissonance. We further uncover interactive effects among cross-dimensional inconsistencies: the positive effect of rating inconsistency diminishes in the presence of high sentiment or informativeness inconsistencies. Conversely, the negative effects of sentiment and informativeness inconsistencies are mitigated when they co-occur. Additionally, the impact of these inconsistencies varies depending on reviewer characteristics, product characteristics, and review order. These findings advance the literature on review helpfulness and MD rating systems by introducing cross-dimensional and cross-modal inconsistencies as key determinants and clarifying when inconsistency serves as a credibility signal versus a cognitive burden.
{"title":"Consistency matters: Impacts of dimension-level characteristics on the helpfulness of multi-dimensional reviews","authors":"Jun Yang , Hongchen Duan , Demei Kong","doi":"10.1016/j.dss.2025.114561","DOIUrl":"10.1016/j.dss.2025.114561","url":null,"abstract":"<div><div>Multi-dimensional (MD) rating systems are increasingly adopted by online platforms to capture product evaluations across multiple attributes. While this structured format enriches product information, it also makes intra-review inconsistencies salient, raising new questions about how such inconsistencies shape review helpfulness—a topic largely overlooked in prior research dominated by single-dimensional (SD) reviews. This study examines the effects of cross-dimensional inconsistencies (in ratings, sentiment, and informativeness) and a cross-modal inconsistency (rating–sentiment misalignment within a dimension) on the perceived helpfulness of MD reviews, drawing on cognitive dissonance theory. Using a large dataset from a leading Chinese automobile review platform, we find that cross-dimensional rating inconsistency can enhance review helpfulness by signaling realistic product trade-offs, whereas sentiment, informativeness, and cross-modal inconsistencies reduce helpfulness by triggering unresolved dissonance. We further uncover interactive effects among cross-dimensional inconsistencies: the positive effect of rating inconsistency diminishes in the presence of high sentiment or informativeness inconsistencies. Conversely, the negative effects of sentiment and informativeness inconsistencies are mitigated when they co-occur. Additionally, the impact of these inconsistencies varies depending on reviewer characteristics, product characteristics, and review order. These findings advance the literature on review helpfulness and MD rating systems by introducing cross-dimensional and cross-modal inconsistencies as key determinants and clarifying when inconsistency serves as a credibility signal versus a cognitive burden.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"199 ","pages":"Article 114561"},"PeriodicalIF":6.8,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145382618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-27DOI: 10.1016/j.dss.2025.114560
Qianqian Wang , Qiang Chen , Sai-Ho Chung , Junmei Rong
Within platform ecosystems, data protection transparency remains insufficient, and research on the dynamic interaction mechanisms governing user data authorization and utilization remains limited. This study develops a stylized analytical model to investigate three interrelated dimensions: platforms' optimal data protection capability (DPC) disclosure strategies, their capacity to enhance user experience, and complementors' utilization levels of user data for product improvement. Key findings are as follows: Platforms voluntarily disclose DPC when their DPC exceeds a critical threshold and disclosure costs are sufficiently low. Platform reputation diminishes disclosure propensity, whereas government reward mechanisms enhance it. Complementors' utilization of reasonably priced user data achieves Pareto improvements by boosting profits for both platforms and complementors. Lower user privacy sensitivity elevates user data authorization ratio, which in turn increases the platform's capability to enhance user experience, and complementors' data utilization levels to improve the product, creating a self-reinforcing cycle of enhanced user utility. While user subsidy and cost-sharing strategies effectively increase user demand and utility, they concurrently reduce platforms' propensity for active DPC disclosure.
{"title":"Data protection capability disclosure strategies and data utilization decisions in platform ecosystems","authors":"Qianqian Wang , Qiang Chen , Sai-Ho Chung , Junmei Rong","doi":"10.1016/j.dss.2025.114560","DOIUrl":"10.1016/j.dss.2025.114560","url":null,"abstract":"<div><div>Within platform ecosystems, data protection transparency remains insufficient, and research on the dynamic interaction mechanisms governing user data authorization and utilization remains limited. This study develops a stylized analytical model to investigate three interrelated dimensions: platforms' optimal data protection capability (DPC) disclosure strategies, their capacity to enhance user experience, and complementors' utilization levels of user data for product improvement. Key findings are as follows: Platforms voluntarily disclose DPC when their DPC exceeds a critical threshold and disclosure costs are sufficiently low. Platform reputation diminishes disclosure propensity, whereas government reward mechanisms enhance it. Complementors' utilization of reasonably priced user data achieves Pareto improvements by boosting profits for both platforms and complementors. Lower user privacy sensitivity elevates user data authorization ratio, which in turn increases the platform's capability to enhance user experience, and complementors' data utilization levels to improve the product, creating a self-reinforcing cycle of enhanced user utility. While user subsidy and cost-sharing strategies effectively increase user demand and utility, they concurrently reduce platforms' propensity for active DPC disclosure.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"199 ","pages":"Article 114560"},"PeriodicalIF":6.8,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145382620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-27DOI: 10.1016/j.dss.2025.114559
Mi Chang , Eun Hye Jang , Woojin Kim, Daesub Yoon, Do Wook Kang
In Level 3 autonomous driving, drivers must quickly regain manual control when the vehicle exceeds its operational limits. Assessing driver readiness in real-time is crucial, especially under cognitive distraction, as delayed reactions can compromise safety. However, most vehicle systems rely on simple behavioral indicators, such as head movements from visual distractions, and struggle to predict driver readiness under complex cognitive distractions. Moreover, existing studies on cognitive distraction are primarily limited to laboratory settings or surveys, which limits their applicability to real-world driving conditions that require real-time decision making. To address these limitations, this study proposes an in-vehicle decision support system that analyzes cognitive distraction before take-over and predicts driver readiness in real-time. Phase 1 involved experiments with varying levels of cognitive distraction to collect data on driver behavior as well as psychological and physiological states to examine their relationship with driver readiness. Phase 2 used these findings to evaluate and compare deep learning models for predicting driver readiness. The results indicate that driver readiness can be predicted using eye-tracking data, with a model combining a transformer with a Random Forest Regressor achieving the best performance. This study enhances the understanding of the relationship between cognitive distraction and driver readiness. It applies these insights to an in-vehicle decision support system, improving the safety and reliability of autonomous vehicles. Furthermore, it provides a crucial foundation for advancing autonomous system design and driver monitoring technologies.
{"title":"Driver readiness prediction: Bridging cognitive distraction monitoring and in-vehicle decision support systems","authors":"Mi Chang , Eun Hye Jang , Woojin Kim, Daesub Yoon, Do Wook Kang","doi":"10.1016/j.dss.2025.114559","DOIUrl":"10.1016/j.dss.2025.114559","url":null,"abstract":"<div><div>In Level 3 autonomous driving, drivers must quickly regain manual control when the vehicle exceeds its operational limits. Assessing driver readiness in real-time is crucial, especially under cognitive distraction, as delayed reactions can compromise safety. However, most vehicle systems rely on simple behavioral indicators, such as head movements from visual distractions, and struggle to predict driver readiness under complex cognitive distractions. Moreover, existing studies on cognitive distraction are primarily limited to laboratory settings or surveys, which limits their applicability to real-world driving conditions that require real-time decision making. To address these limitations, this study proposes an in-vehicle decision support system that analyzes cognitive distraction before take-over and predicts driver readiness in real-time. Phase 1 involved experiments with varying levels of cognitive distraction to collect data on driver behavior as well as psychological and physiological states to examine their relationship with driver readiness. Phase 2 used these findings to evaluate and compare deep learning models for predicting driver readiness. The results indicate that driver readiness can be predicted using eye-tracking data, with a model combining a transformer with a Random Forest Regressor achieving the best performance. This study enhances the understanding of the relationship between cognitive distraction and driver readiness. It applies these insights to an in-vehicle decision support system, improving the safety and reliability of autonomous vehicles. Furthermore, it provides a crucial foundation for advancing autonomous system design and driver monitoring technologies.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"199 ","pages":"Article 114559"},"PeriodicalIF":6.8,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145382621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号