Pub Date : 2025-09-01Epub Date: 2025-06-24DOI: 10.1016/j.dss.2025.114497
Arslan Rafi , Sanjit K. Roy , Mohsin Abdur Rehman , Muhammad Junaid Shahid Hasni
In the original article, we examined various factors, including social, spatial, and self-presence, influencing user well-being in metaverse communities. We intended to examine the symmetrical and asymmetrical relationships between types of presence and user well-being. However, discrepancies emerged in reporting the final measurement items and their validity assessment. We provide details on how we corrected the errors in the article.
{"title":"Corrigendum to “Impact of multidimensional presence on user well-being in metaverse communities”","authors":"Arslan Rafi , Sanjit K. Roy , Mohsin Abdur Rehman , Muhammad Junaid Shahid Hasni","doi":"10.1016/j.dss.2025.114497","DOIUrl":"10.1016/j.dss.2025.114497","url":null,"abstract":"<div><div>In the original article, we examined various factors, including social, spatial, and self-presence, influencing user well-being in metaverse communities. We intended to examine the symmetrical and asymmetrical relationships between types of presence and user well-being. However, discrepancies emerged in reporting the final measurement items and their validity assessment. We provide details on how we corrected the errors in the article.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"196 ","pages":"Article 114497"},"PeriodicalIF":6.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503547","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 : 2025-09-01Epub Date: 2025-06-28DOI: 10.1016/j.dss.2025.114498
Sen Yan, Zhiyi Wang, David Dobolyi
The recent development of generative AI (GenAI) algorithms has allowed machines to create new content in a realistic way, driving the spread of AI-generated content (AIGC) on the Internet. However, generative AI models and AIGC have exacerbated several societal challenges such as security threats (e.g., misinformation), trust issues, ethical concerns, and intellectual property regulation, calling for effective detection methods and a better understanding of AI-generated vs. human-written content. In this paper, we focus on AI-generated texts produced by large language models (LLMs) and extend prior detection methods by proposing a novel framework that combines semantic information and linguistic features. Based on potential semantic and linguistic differences in AI vs. human writing, we design our Semantic-Linguistic-Detector (SemLinDetector) framework by integrating a transformer-based semantic encoder and a linguistic encoder with parallel linguistic representations. By comparing a series of benchmark models on datasets collected from various LLMs and human writers in multiple domains, our experiments show that the proposed detection framework outperforms other benchmarks in a consistent and robust manner. Moreover, our model interpretability analysis showcases our framework's potential to help understand the reasoning behind prediction outcomes and identify patterns of differences in AI-generated and human-written content. Our research adds to the growing space of GenAI by proposing an effective and responsible detection system to address the risks and challenges of GenAI, offering implications for researchers and practitioners to better understand and regulate AIGC.
{"title":"An explainable framework for assisting the detection of AI-generated textual content","authors":"Sen Yan, Zhiyi Wang, David Dobolyi","doi":"10.1016/j.dss.2025.114498","DOIUrl":"10.1016/j.dss.2025.114498","url":null,"abstract":"<div><div>The recent development of generative AI (GenAI) algorithms has allowed machines to create new content in a realistic way, driving the spread of AI-generated content (AIGC) on the Internet. However, generative AI models and AIGC have exacerbated several societal challenges such as security threats (e.g., misinformation), trust issues, ethical concerns, and intellectual property regulation, calling for effective detection methods and a better understanding of AI-generated vs. human-written content. In this paper, we focus on AI-generated texts produced by large language models (LLMs) and extend prior detection methods by proposing a novel framework that combines semantic information and linguistic features. Based on potential semantic and linguistic differences in AI vs. human writing, we design our Semantic-Linguistic-Detector (SemLinDetector) framework by integrating a transformer-based semantic encoder and a linguistic encoder with parallel linguistic representations. By comparing a series of benchmark models on datasets collected from various LLMs and human writers in multiple domains, our experiments show that the proposed detection framework outperforms other benchmarks in a consistent and robust manner. Moreover, our model interpretability analysis showcases our framework's potential to help understand the reasoning behind prediction outcomes and identify patterns of differences in AI-generated and human-written content. Our research adds to the growing space of GenAI by proposing an effective and responsible detection system to address the risks and challenges of GenAI, offering implications for researchers and practitioners to better understand and regulate AIGC.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"196 ","pages":"Article 114498"},"PeriodicalIF":6.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518575","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 : 2025-09-01Epub Date: 2025-06-20DOI: 10.1016/j.dss.2025.114495
Abhipsa Pal , Rahul Dé , H. Raghav Rao
Although the diffusion of mobile payment technology has been historically governed by contextual events that trigger anxiety, accentuating either the risks of mobile payments or the risks of its conflicting alternative, cash, literature neglects the importance of examining the risks associated with the alternatives. To address this gap, we develop the risk-risk trade-off (R2T) framework, drawing from the theory of substitutes of hazardous substances, and examine how individuals make usage decisions by balancing two sets of risks – for mobile payments and cash, respectively. On one side, the framework weighs contactless [mobile payment] risks related to potential thefts and losses, heightened by the rise in cybercrime. Conversely, on the other side, it weighs the risks from its substitute, contact [cash] payment, carrying the health hazard of infectious disease transmission through contact, with this risk magnified during the global pandemic. To validate the model, we used survey responses from 1403 participants in India and triangulated the quantitative results using their qualitative comments. This study theoretically contributes to the mobile payment usage literature by moving beyond technology risks as the sole risks to be considered for usage decision-making and includes the analysis of risks of the technology's substitute, cash, as well. The framework can support analysis of users' decisions towards consciously choosing the technology against its alternatives, in various risky contexts.
{"title":"The risk-risk trade-off (R2T) framework: Examining contact [cash] versus contactless [mobile] payment usage","authors":"Abhipsa Pal , Rahul Dé , H. Raghav Rao","doi":"10.1016/j.dss.2025.114495","DOIUrl":"10.1016/j.dss.2025.114495","url":null,"abstract":"<div><div>Although the diffusion of mobile payment technology has been historically governed by contextual events that trigger anxiety, accentuating either the risks of mobile payments or the risks of its conflicting alternative, cash, literature neglects the importance of examining the risks associated with the alternatives. To address this gap, we develop the risk-risk trade-off (R<sup>2</sup>T) framework, drawing from the theory of substitutes of hazardous substances, and examine how individuals make usage decisions by balancing two sets of risks – for mobile payments and cash, respectively. On one side, the framework weighs contactless [mobile payment] risks related to potential thefts and losses, heightened by the rise in cybercrime. Conversely, on the other side, it weighs the risks from its substitute, contact [cash] payment, carrying the health hazard of infectious disease transmission through contact, with this risk magnified during the global pandemic. To validate the model, we used survey responses from 1403 participants in India and triangulated the quantitative results using their qualitative comments. This study theoretically contributes to the mobile payment usage literature by moving beyond technology risks as the sole risks to be considered for usage decision-making and includes the analysis of risks of the technology's substitute, cash, as well. The framework can support analysis of users' decisions towards consciously choosing the technology against its alternatives, in various risky contexts.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"196 ","pages":"Article 114495"},"PeriodicalIF":6.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144335630","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 : 2025-09-01Epub Date: 2025-06-22DOI: 10.1016/j.dss.2025.114496
Yu Han , Ziqiong Zhang , Carol X.J. Ou , Zili Zhang
Review helpfulness is crucial for assessing the quality of online reviews and mitigating information overload. Although numerous studies have explored the impact of textual and reviewer characteristics on review helpfulness, the role of photo aesthetics remains important but underexplored. This study addresses this gap by investigating the impact of photo aesthetics on perceived review helpfulness and its underlying mediating effects. The hotel review data from TripAdvisor.com exhibit an inverted U-shaped effect of photo aesthetics on perceived review helpfulness, in which review text length moderates this relationship. To further validate this causal relationship and explore the underlying mediating effects, an experimental study is conducted. The experimental results confirm the causal impact of photo aesthetics on perceived review helpfulness and reveal that perceived pleasure, reviewer effort and review authenticity mediate the relationship. These novel insights challenge the notion that “the more aesthetic, the better” for review photos, offering new theoretical and practical implications.
{"title":"The more aesthetic, the better? The impact of photo aesthetics on perceived review helpfulness","authors":"Yu Han , Ziqiong Zhang , Carol X.J. Ou , Zili Zhang","doi":"10.1016/j.dss.2025.114496","DOIUrl":"10.1016/j.dss.2025.114496","url":null,"abstract":"<div><div>Review helpfulness is crucial for assessing the quality of online reviews and mitigating information overload. Although numerous studies have explored the impact of textual and reviewer characteristics on review helpfulness, the role of photo aesthetics remains important but underexplored. This study addresses this gap by investigating the impact of photo aesthetics on perceived review helpfulness and its underlying mediating effects. The hotel review data from <span><span>TripAdvisor.com</span><svg><path></path></svg></span> exhibit an inverted U-shaped effect of photo aesthetics on perceived review helpfulness, in which review text length moderates this relationship. To further validate this causal relationship and explore the underlying mediating effects, an experimental study is conducted. The experimental results confirm the causal impact of photo aesthetics on perceived review helpfulness and reveal that perceived pleasure, reviewer effort and review authenticity mediate the relationship. These novel insights challenge the notion that “the more aesthetic, the better” for review photos, offering new theoretical and practical implications.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"196 ","pages":"Article 114496"},"PeriodicalIF":6.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502471","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 : 2025-09-01Epub Date: 2025-06-03DOI: 10.1016/j.dss.2025.114474
Michael Khavkin, Eran Toch
Differential Privacy (DP) has emerged as the standard for privacy-preserving analysis of individual-level data. Despite growing attention in the research community to the operationalization of DP through the selection of the privacy budget , little is known about how DP obfuscation affects users’ disclosure decisions in data market scenarios. These decisions may be context-specific and vary with privacy preferences, eliciting disparate data valuations across individuals. Through a choice-based conjoint analysis (), simulating realistic data markets, we analyzed how varying DP protection levels influence individual decision-making of participation in data collection under DP. Our findings show that personal reward and the guaranteed DP protection had the strongest influence on participants’ selection of a data collection scenario. Surprisingly, the type of disclosed data had the least influence on participants’ decisions to disclose personal data, a trend consistent across participants from different countries. Furthermore, increasing the DP protection level by a single unit reduced the preferred compensation price by over 60% for the same level of user utility, with marginal effects diminishing exponentially at higher DP levels. Our results were then confirmed in an online study () involving real data disclosure with actual payments, using our original scenario framing. Our findings can support context-specific DP configuration and help data practitioners improve decision-making associated with privacy protection in differentially private systems, balancing the trade-off between DP and compensation costs.
{"title":"Investigating the impact of differential privacy obfuscation on users’ data disclosure decisions","authors":"Michael Khavkin, Eran Toch","doi":"10.1016/j.dss.2025.114474","DOIUrl":"10.1016/j.dss.2025.114474","url":null,"abstract":"<div><div>Differential Privacy (DP) has emerged as the standard for privacy-preserving analysis of individual-level data. Despite growing attention in the research community to the operationalization of DP through the selection of the privacy budget <span><math><mi>ɛ</mi></math></span>, little is known about how DP obfuscation affects users’ disclosure decisions in data market scenarios. These decisions may be context-specific and vary with privacy preferences, eliciting disparate data valuations across individuals. Through a choice-based conjoint analysis (<span><math><mrow><msub><mrow><mi>N</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>=</mo><mn>588</mn></mrow></math></span>), simulating realistic data markets, we analyzed how varying DP protection levels influence individual decision-making of participation in data collection under DP. Our findings show that personal reward and the guaranteed DP protection had the strongest influence on participants’ selection of a data collection scenario. Surprisingly, the type of disclosed data had the least influence on participants’ decisions to disclose personal data, a trend consistent across participants from different countries. Furthermore, increasing the DP protection level by a single unit reduced the preferred compensation price by over 60% for the same level of user utility, with marginal effects diminishing exponentially at higher DP levels. Our results were then confirmed in an online study (<span><math><mrow><msub><mrow><mi>N</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>=</mo><mn>146</mn></mrow></math></span>) involving real data disclosure with actual payments, using our original scenario framing. Our findings can support context-specific DP configuration and help data practitioners improve decision-making associated with privacy protection in differentially private systems, balancing the trade-off between DP and compensation costs.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"196 ","pages":"Article 114474"},"PeriodicalIF":6.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254153","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 : 2025-09-01Epub Date: 2025-07-08DOI: 10.1016/j.dss.2025.114503
Peide Liu , Ran Dang , Peng Wang , Yingcheng Xu , Yunfeng Zhang
With the proliferation of tourism websites, online reviews have become indispensable for offline decision-makers when selecting hotels. Solely relying on personal judgment poses risks amid diverse preferences. Thus, this study aimed to create a hotel recommendation system that integrates online reviews and ratings with offline travel groups. First, the sentiment analysis of online reviews was integrated with ratings using heterogeneous reviewer weights, transforming them into probabilistic linguistic term sets. Second, by predicting reviewers' travel types and clustering them, a method was devised to calculate subgroup weights, considering online group size and offline social trust networks. Third, attribute importance was determined via an online–offline method (attribute importance optimization model) considering the intensity and ordinal information. Subsequently, an adaptive consensus optimization model was developed based on a novel measurement method. This study offers personalized recommendations for offline decision-makers, providing essential guidance for travel agencies and platforms to enhance services and holding significant practical value.
{"title":"Online–offline combined adaptive hotel recommendation system considering attribute importance and group consensus","authors":"Peide Liu , Ran Dang , Peng Wang , Yingcheng Xu , Yunfeng Zhang","doi":"10.1016/j.dss.2025.114503","DOIUrl":"10.1016/j.dss.2025.114503","url":null,"abstract":"<div><div>With the proliferation of tourism websites, online reviews have become indispensable for offline decision-makers when selecting hotels. Solely relying on personal judgment poses risks amid diverse preferences. Thus, this study aimed to create a hotel recommendation system that integrates online reviews and ratings with offline travel groups. First, the sentiment analysis of online reviews was integrated with ratings using heterogeneous reviewer weights, transforming them into probabilistic linguistic term sets. Second, by predicting reviewers' travel types and clustering them, a method was devised to calculate subgroup weights, considering online group size and offline social trust networks. Third, attribute importance was determined via an online–offline method (attribute importance optimization model) considering the intensity and ordinal information. Subsequently, an adaptive consensus optimization model was developed based on a novel measurement method. This study offers personalized recommendations for offline decision-makers, providing essential guidance for travel agencies and platforms to enhance services and holding significant practical value.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"196 ","pages":"Article 114503"},"PeriodicalIF":6.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633372","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 : 2025-09-01Epub Date: 2025-07-13DOI: 10.1016/j.dss.2025.114508
Yuhong Zhan , Chaoyue Gao , Alvin Chung Man Leung , Qiang Ye
Investors increasingly use online investment communities to acquire financial market information before making trading decisions to reduce the cost of information acquisition and get more abundant content. Due to limited attention, investors tend to focus their trading only on a subset of assets that align with their personal investment preferences. Thus, the attention behavior of investors in the communities can reflect their focus trends and indicate future stock movements. Unlike previous research that mainly focused on investor common search and viewing behaviors, we constructed stock clusters based on different common attention behaviors data (i.e., common follow behavior by investors and common mention behavior by content contributors) and compared their predictive capabilities on stock returns. After controlling for some deterministic factors, we verified the existence of comovement among stocks within the clusters (i.e., stock habitats) and found that investors' common attention behaviors can better predict stock returns compared to content contributors. To explore the mechanism, we found a possible direction of information flow between different stock habitats and revealed the leading role of content contributors in online investment communities. This study enriches the literature on stock habitats and information diffusion in online investment communities and provides practical decision support on portfolio management for investors. Moreover, online platform managers can also use our conclusions to provide better decision-making assistance for market participants.
{"title":"Stock habitats and information flow: How do different co-attention behaviors in online communities shape market reactions?","authors":"Yuhong Zhan , Chaoyue Gao , Alvin Chung Man Leung , Qiang Ye","doi":"10.1016/j.dss.2025.114508","DOIUrl":"10.1016/j.dss.2025.114508","url":null,"abstract":"<div><div>Investors increasingly use online investment communities to acquire financial market information before making trading decisions to reduce the cost of information acquisition and get more abundant content. Due to limited attention, investors tend to focus their trading only on a subset of assets that align with their personal investment preferences. Thus, the attention behavior of investors in the communities can reflect their focus trends and indicate future stock movements. Unlike previous research that mainly focused on investor common search and viewing behaviors, we constructed stock clusters based on different common attention behaviors data (i.e., common follow behavior by investors and common mention behavior by content contributors) and compared their predictive capabilities on stock returns. After controlling for some deterministic factors, we verified the existence of comovement among stocks within the clusters (i.e., stock habitats) and found that investors' common attention behaviors can better predict stock returns compared to content contributors. To explore the mechanism, we found a possible direction of information flow between different stock habitats and revealed the leading role of content contributors in online investment communities. This study enriches the literature on stock habitats and information diffusion in online investment communities and provides practical decision support on portfolio management for investors. Moreover, online platform managers can also use our conclusions to provide better decision-making assistance for market participants.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"196 ","pages":"Article 114508"},"PeriodicalIF":6.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144664745","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 : 2025-09-01Epub Date: 2025-07-03DOI: 10.1016/j.dss.2025.114502
Sheng-Wei Lin , Shin-Yuan Hung , Kai-Teng Cheng
Given its context orientation, service quality is a core issue in the study of smart retail. This paper examines service quality in smart retail through the lens of the cues–images–impressions model. The objective is to analyze the influence of service characteristics of smart retail stores (SRSs) on customers' perceived service quality and organizational impressions. Using a mixed-methods design and a fuzzy-set qualitative comparative analysis approach, the study highlights customer orientation, SRS employee characteristics, and the SRS servicescape as mechanisms driving service quality and enabling positive organizational impression. The findings have both theoretical and practical implications for future research.
{"title":"Does warm care matter? Exploring the effects of service characteristics on organizational impression in smart retail stores","authors":"Sheng-Wei Lin , Shin-Yuan Hung , Kai-Teng Cheng","doi":"10.1016/j.dss.2025.114502","DOIUrl":"10.1016/j.dss.2025.114502","url":null,"abstract":"<div><div>Given its context orientation, service quality is a core issue in the study of smart retail. This paper examines service quality in smart retail through the lens of the cues–images–impressions model. The objective is to analyze the influence of service characteristics of smart retail stores (SRSs) on customers' perceived service quality and organizational impressions. Using a mixed-methods design and a fuzzy-set qualitative comparative analysis approach, the study highlights customer orientation, SRS employee characteristics, and the SRS servicescape as mechanisms driving service quality and enabling positive organizational impression. The findings have both theoretical and practical implications for future research.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"196 ","pages":"Article 114502"},"PeriodicalIF":6.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144580302","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 : 2025-09-01Epub Date: 2025-07-21DOI: 10.1016/j.dss.2025.114506
Yan Zhang , Guofang Nan , Jian Luo , Jing Zhang
Online review datasets are always imbalanced and contain numerous outliers or noise, making the accurate and efficient identification of helpful reviews a critical challenge in the digital age. To address this issue, the optimal feature set is first obtained from numerous constructed possible features (including ones based on the knowledge adoption model) by a feature selection method, and then a novel fuzzy nonparallel quadratic surface support vector machine (FNQSSVM) model is proposed for identifying helpful online reviews in this study. For well handling the imbalanced data with outliers or noise, a novel fuzzy membership function is first developed based on the K-nearest neighbor method with respect to the cosine distance, and then incorporated with the kernel-free nonlinear and nonparallel separating ideas to propose the FNQSSVM model by directly using two nonparallel quadratic surfaces for nonlinear classification. Computational results on three crawled real-life datasets in different domains show that the proposed FNQSSVM model outperforms the well-known and state-of-the-art classification methods in terms of classification accuracy for identifying helpful online reviews, within competitive computational time. The proposed method can be integrated into the decision support systems to assess the helpfulness of online reviews and facilitate the ranking of helpful reviews. Our findings can provide valuable managerial insights for online platforms, merchants and customers.
{"title":"A novel fuzzy nonparallel support vector machine for identifying helpful online reviews","authors":"Yan Zhang , Guofang Nan , Jian Luo , Jing Zhang","doi":"10.1016/j.dss.2025.114506","DOIUrl":"10.1016/j.dss.2025.114506","url":null,"abstract":"<div><div>Online review datasets are always imbalanced and contain numerous outliers or noise, making the accurate and efficient identification of helpful reviews a critical challenge in the digital age. To address this issue, the optimal feature set is first obtained from numerous constructed possible features (including ones based on the knowledge adoption model) by a feature selection method, and then a novel fuzzy nonparallel quadratic surface support vector machine (FNQSSVM) model is proposed for identifying helpful online reviews in this study. For well handling the imbalanced data with outliers or noise, a novel fuzzy membership function is first developed based on the K-nearest neighbor method with respect to the cosine distance, and then incorporated with the kernel-free nonlinear and nonparallel separating ideas to propose the FNQSSVM model by directly using two nonparallel quadratic surfaces for nonlinear classification. Computational results on three crawled real-life datasets in different domains show that the proposed FNQSSVM model outperforms the well-known and state-of-the-art classification methods in terms of classification accuracy for identifying helpful online reviews, within competitive computational time. The proposed method can be integrated into the decision support systems to assess the helpfulness of online reviews and facilitate the ranking of helpful reviews. Our findings can provide valuable managerial insights for online platforms, merchants and customers.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"196 ","pages":"Article 114506"},"PeriodicalIF":6.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686121","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 : 2025-09-01Epub Date: 2025-07-05DOI: 10.1016/j.dss.2025.114504
Qian Liu , Qianzhou Du , Chuang Tang , Yili Hong , Weiguo Fan
Idea screening in collaborative crowdsourcing communities poses significant challenges for firms. These challenges are primarily attributable to issues of prediction accuracy and information overload. The rapid expansion of idea pools generates a vast amount of data, making it difficult to effectively identify valuable ideas for new product development. This study introduces an interpretable framework for machine learning that integrates a novel exploration and exploitation perspective within the value cocreation model to enhance idea screening. The framework incorporates six theoretical dimensions of the exploration and exploitation of value cocreation (EEVC): the exploration and exploitation of digital resources, direct interactions, and ideas and their comments. Our evaluation reveals that the EEVC-based idea-screening system significantly outperforms the traditional 3Cs model in terms of prediction accuracy. SHAP value analysis further reveals that the exploration and exploitation of digital resources are the most influential predictors of idea implementation. The EEVC framework advances open innovation theory by clarifying how value cocreation dynamics influence idea implementation. Practically, it proposes a human–machine collaboration system that enhances expert decision-making for more effective idea selection.
{"title":"An exploration and exploitation of value cocreation-based machine learning framework for automated idea screening","authors":"Qian Liu , Qianzhou Du , Chuang Tang , Yili Hong , Weiguo Fan","doi":"10.1016/j.dss.2025.114504","DOIUrl":"10.1016/j.dss.2025.114504","url":null,"abstract":"<div><div>Idea screening in collaborative crowdsourcing communities poses significant challenges for firms. These challenges are primarily attributable to issues of prediction accuracy and information overload. The rapid expansion of idea pools generates a vast amount of data, making it difficult to effectively identify valuable ideas for new product development. This study introduces an interpretable framework for machine learning that integrates a novel exploration and exploitation perspective within the value cocreation model to enhance idea screening. The framework incorporates six theoretical dimensions of the exploration and exploitation of value cocreation (EEVC): the exploration and exploitation of digital resources, direct interactions, and ideas and their comments. Our evaluation reveals that the EEVC-based idea-screening system significantly outperforms the traditional 3Cs model in terms of prediction accuracy. SHAP value analysis further reveals that the exploration and exploitation of digital resources are the most influential predictors of idea implementation. The EEVC framework advances open innovation theory by clarifying how value cocreation dynamics influence idea implementation. Practically, it proposes a human–machine collaboration system that enhances expert decision-making for more effective idea selection.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"196 ","pages":"Article 114504"},"PeriodicalIF":6.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144595701","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}
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