Decision Support Systems最新文献

Can comments and dialogues make sense? The effect of two-way interactions on sales and followers in live streaming commerce 评论和对话有意义吗？直播商业中双向互动对销售和粉丝的影响

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2025-04-05 DOI: 10.1016/j.dss.2025.114451

Xiaoping Lang , Sheng Lin , Xiangyang Ma , Tieshan Li

{"title":"Can comments and dialogues make sense? The effect of two-way interactions on sales and followers in live streaming commerce","authors":"Xiaoping Lang , Sheng Lin , Xiangyang Ma , Tieshan Li","doi":"10.1016/j.dss.2025.114451","DOIUrl":"10.1016/j.dss.2025.114451","url":null,"abstract":"<div><div>This research employs interaction ritual model to explore the two-way interaction between streamers and viewers on a live streaming commerce platform. To be specific, the study investigates the impact of viewers' real-time comments and streamer's dialogue on product sales and follower growth, using minute-level data for detailed analysis. The results show that real-time comments exhibit a nonlinear inverted U-shaped relationship with product sales and increment in followers. Such relationship indicates that the “overloaded comments” during live streams could cause a problem on live streaming commerce platforms. In addition, we find that streamer's dialogue has a positive effect on product sales and can mitigate the inverted U-shaped relationship between real-time comments and product sales. Furthermore, we identify that streamer's dialogue has a delayed effect on product sales, with sales typically occurring 2.5 to 5 min after the dialogue ends. The findings provide valuable guidance for optimizing the management of live streaming commerce platforms.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"193 ","pages":"Article 114451"},"PeriodicalIF":6.7,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800434","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}

引用次数: 0

Remote work in the metaverse: The impact of gamification and online social connectedness on job satisfaction

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2025-04-04 DOI: 10.1016/j.dss.2025.114447

Khadija Ali Vakeel , Saurav Chakraborty , Lamont Black

{"title":"Remote work in the metaverse: The impact of gamification and online social connectedness on job satisfaction","authors":"Khadija Ali Vakeel , Saurav Chakraborty , Lamont Black","doi":"10.1016/j.dss.2025.114447","DOIUrl":"10.1016/j.dss.2025.114447","url":null,"abstract":"<div><div>This study explores the potential of the metaverse in enhancing job satisfaction for remote employees. With the increasing shift towards remote work, firms are investing more in the metaverse to create dynamic and immersive digital environments. Drawing upon media richness theory, we investigate the roles of gamification and online social connectedness within the metaverse, which are crucial factors shaping employee job satisfaction. Survey results show that remote employees perceive higher gamification in the metaverse but have similar online social connectedness to video conferencing platforms. Contrary to linear assumptions, polynomial regression analysis reveals an intriguing S-shaped relationship between gamification and job satisfaction in the metaverse, highlighting an optimal threshold beyond which excessive gamification in the metaverse may diminish job satisfaction. Additionally, online social connectedness in the metaverse significantly strengthens job satisfaction. This study contributes to theoretical and practical knowledge by expanding the application of the metaverse to remote work. Our findings provide valuable guidance for firms navigating the evolving landscape of remote work and technology adoption, paving the way for more engaging and satisfying remote work experiences.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"193 ","pages":"Article 114447"},"PeriodicalIF":6.7,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143790851","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}

引用次数: 0

Unravelling the effects of two inconsistencies on online review helpfulness: Evidence from TripAdvisor

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2025-04-04 DOI: 10.1016/j.dss.2025.114450

Dujuan Wang , Qianyang Xia , Yi Feng , T.C.E. Cheng

{"title":"Unravelling the effects of two inconsistencies on online review helpfulness: Evidence from TripAdvisor","authors":"Dujuan Wang , Qianyang Xia , Yi Feng , T.C.E. Cheng","doi":"10.1016/j.dss.2025.114450","DOIUrl":"10.1016/j.dss.2025.114450","url":null,"abstract":"<div><div>Facing the challenge of information overload, some travel websites have introduced systems for travelers to vote on helpful reviews, prompting researchers to focus on the determinants of review helpfulness. While evaluations from multiple reviews may provide travelers with more perspectives, inconsistent information within the reviews may cause confusion. Studies exploring the effects of multiple inconsistencies on review helpfulness are relatively rare. Grounded in the heuristic-systematic model, we explore the relationships between systematic cues, i.e., review and rating inconsistencies, and review helpfulness. We also investigate how reviewer expertise and hotel rank moderate these inconsistency-helpfulness links, serving as heuristic cues. Applied to a real-world hotel dataset collected from TripAdvisor, our findings show that review inconsistency negatively influences review helpfulness, while rating inconsistency positively affects it. Furthermore, we find that reviewer expertise negatively moderates the review and rating inconsistency-helpfulness links, while hotels that rank low positively moderate both links. These findings offer both theoretical insights for research and practical implications for consumers, reviewers, and platform managers.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"193 ","pages":"Article 114450"},"PeriodicalIF":6.7,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821319","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}

引用次数: 0

Dynamic model selection in enterprise forecasting systems using sequence modeling

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2025-03-29 DOI: 10.1016/j.dss.2025.114439

Jinhang Jiang , Kiran Kumar Bandeli , Karthik Srinivasan

{"title":"Dynamic model selection in enterprise forecasting systems using sequence modeling","authors":"Jinhang Jiang , Kiran Kumar Bandeli , Karthik Srinivasan","doi":"10.1016/j.dss.2025.114439","DOIUrl":"10.1016/j.dss.2025.114439","url":null,"abstract":"<div><div>Enterprise forecasting systems often involve modeling a large scale of heterogeneous time series using a pool of candidate algorithms, such as in the case of simultaneous sales forecasts of thousands of stock-keeping units. In such cases, it can be advantageous to automatically monitor and replace algorithms for each time series. We introduce TimeSpeaks, a framework that adapts sequence modeling in natural language processing to the problem of dynamic model selection in enterprise forecasting. We instantiate our framework using sequential (BiLSTM) and transformer-based (TimeXer) deep learning models to learn the temporal dependencies between candidate algorithms. We compare the performance of our framework with state-of-the-art forecasting models using two public benchmarking datasets. We further demonstrate its practical application on two retail case studies, while comparing them to alternative model selection scenarios. TimeSpeaks has superior predictive performance and scalability across different scenarios and datasets. Its ability to adapt to evolving data patterns and its minimal reliance on exogenous information make TimeSpeaks a suitable framework for large-scale enterprise forecasting applications.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"193 ","pages":"Article 114439"},"PeriodicalIF":6.7,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ESG-KIBERT: A new paradigm in ESG evaluation using NLP and industry-specific customization

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2025-03-29 DOI: 10.1016/j.dss.2025.114440

Haein Lee , Jang Hyun Kim , Hae Sun Jung

{"title":"ESG-KIBERT: A new paradigm in ESG evaluation using NLP and industry-specific customization","authors":"Haein Lee , Jang Hyun Kim , Hae Sun Jung","doi":"10.1016/j.dss.2025.114440","DOIUrl":"10.1016/j.dss.2025.114440","url":null,"abstract":"<div><div>This study presents a significant advancement in Environmental, Social, Governance (ESG) evaluation by addressing critical gaps in transparency, consistency, and industry-specific relevance. The ESG-Keyword integrated bidirectional encoder representations from transformers (ESG-KIBERT) model, developed using advanced natural language processing (NLP) techniques, enhances ESG classification performance and sets a new standard for automated ESG analysis. With robust performance metrics, it supports reliable and consistent assessments across industries. Additionally, incorporating Sustainability Accounting Standards Board's materiality map offers a customized evaluation framework that accounts for industry-specific factors affecting corporate sustainability. Furthermore, the integration of sentiment analysis enriches ESG evaluations by capturing market and investor perceptions, contributing to a more transparent assessment. This study offers a comprehensive, standardized ESG evaluation framework that improves both the methodological rigor and practical utility of corporate sustainability assessments, enabling more informed decision-making for companies, investors and policymakers.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"193 ","pages":"Article 114440"},"PeriodicalIF":6.7,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738406","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}

引用次数: 0

Predicting stock price movement using social network analytics: Posts are sometimes less useful

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2025-03-24 DOI: 10.1016/j.dss.2025.114438

Wanyun Li , Alvin Chung Man Leung , Ka Wai Choi (Stanley) , Shuk Ying Ho

{"title":"Predicting stock price movement using social network analytics: Posts are sometimes less useful","authors":"Wanyun Li , Alvin Chung Man Leung , Ka Wai Choi (Stanley) , Shuk Ying Ho","doi":"10.1016/j.dss.2025.114438","DOIUrl":"10.1016/j.dss.2025.114438","url":null,"abstract":"<div><div>Contemporary research has leveraged social network data as a predictive tool for decision-making process in the capital market. Yet, its effectiveness may be compromised by social contagion. This study addresses this problem by introducing conversation-level measures that capture how interactions among investors affect market predictions. Drawing on social contagion theory, we identified three conversation conditions—argument similarity, sentiment similarity, and conversation size—and examined their association with the likelihood of abrupt stock price changes, which indicate a loss of collective wisdom. Our analysis of 18 million StockTwits posts for 859 Initial Public Offerings (2008–2017) reveals that conversations with highly similar arguments, highly similar sentiments, and larger size are significantly associated with an increased likelihood of abrupt stock price changes in the subsequent week. Moreover, out-of-sample tests confirm that monitoring conversational dynamics enhances the predictive power of social network analytics, offering valuable guidance for investors and practitioners. Our study extends the theoretical framework of social contagion by highlighting the importance of the conversation level and provides practical recommendations for refining trading strategies based on social media data.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"192 ","pages":"Article 114438"},"PeriodicalIF":6.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

To disclose or not? The impact of prosocial behavior disclosure on the attainment of social capital on social networking sites

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2025-03-21 DOI: 10.1016/j.dss.2025.114437

Jiayuan Zhang , Koray Özpolat , Gulver Karamemis , Dara Schniederjans

{"title":"To disclose or not? The impact of prosocial behavior disclosure on the attainment of social capital on social networking sites","authors":"Jiayuan Zhang , Koray Özpolat , Gulver Karamemis , Dara Schniederjans","doi":"10.1016/j.dss.2025.114437","DOIUrl":"10.1016/j.dss.2025.114437","url":null,"abstract":"<div><div>While some donors and volunteers do not publicize their prosocial behaviors because of humility, many others fear that disclosing their prosocial behaviors may be perceived as bragging. With the rise of social networking sites (SNSs), this has become an essential issue with important business implications. As more companies encourage employees to volunteer a small portion of their work time and match their charitable contributions, disclosing these prosocial acts on social media platforms has become more common. Building upon social capital theory, we apply a mixed-method approach to investigate the relationship between the disclosure of prosocial behaviors and the attainment of social capital on SNSs. Our first exploratory study applies qualitative interviews to explore the factors that moderate the relationship between the disclosure of prosocial behaviors and the attainment of social capital. Our second study utilizes a randomized online experiment in the U.S. to test the causal effect of prosocial behavior disclosure on social capital attainment online, as well as two moderators of this relationship. A post-hoc replication study of our experiment is conducted in China. We find that the disclosure of prosocial behavior increases relational and structural social capital on SNSs but find no evidence of the impact of the disclosure of prosocial behavior on cognitive social capital. The effect becomes stronger when one's prosocial behavior is disclosed by others (rather than by oneself) in the U.S. sample. Our findings inform SNSs users to make informed decisions regarding disclosing prosocial behaviors to attain structural and relational social capital. Businesses encouraging their employees to donate/volunteer and charities on the receiving end could also benefit from our findings.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"192 ","pages":"Article 114437"},"PeriodicalIF":6.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706230","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}

引用次数: 0

Not easy to “like”: How does cognitive load influence user engagement in online reviews?

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2025-03-17 DOI: 10.1016/j.dss.2025.114436

Yuqiu Wang , Kai Li

{"title":"Not easy to “like”: How does cognitive load influence user engagement in online reviews?","authors":"Yuqiu Wang , Kai Li","doi":"10.1016/j.dss.2025.114436","DOIUrl":"10.1016/j.dss.2025.114436","url":null,"abstract":"<div><div>User engagement (e.g., likes, shares, and comments) is widely recognized as critical to business success. Although existing studies have explored the determinants of user engagement, relatively little attention has been paid to cognitive load. This study, based on cognitive load theory, expectation confirmation theory, and the stressor-strain-outcome framework, examines the heterogeneous effects of intrinsic, extraneous, and germane cognitive load on user engagement in Study 1, the moderating effects of intrinsic, extraneous, and germane cognitive load variance (i.e., described as cognitive load changes induced by processing from one review to another review) in Study 1, and their underlying mechanisms in Study 2. Results indicate that: (1) intrinsic and extraneous cognitive load negatively influence user engagement, while germane cognitive load has a positive effect, (2) intrinsic/extraneous cognitive load variance accentuates the negative effect of intrinsic/extraneous cognitive load, while germane cognitive load variance strengthens the positive effect of germane cognitive load, and (3) perceived fatigue negatively mediates the effects of intrinsic and extraneous cognitive load but positively mediates the effect of germane cognitive load. Our findings contribute to the literature on user engagement and offer practical insights for optimizing online review systems.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"192 ","pages":"Article 114436"},"PeriodicalIF":6.7,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143682319","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}

引用次数: 0

Re-evaluating causal inference: Bias reduction in confounder-effect modifier scenarios

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2025-03-13 DOI: 10.1016/j.dss.2025.114435

Xuan Wang , Tamer Oraby , Xi Mao , Geng Sun , Helmut Schneider

{"title":"Re-evaluating causal inference: Bias reduction in confounder-effect modifier scenarios","authors":"Xuan Wang , Tamer Oraby , Xi Mao , Geng Sun , Helmut Schneider","doi":"10.1016/j.dss.2025.114435","DOIUrl":"10.1016/j.dss.2025.114435","url":null,"abstract":"<div><div>Propensity Score Matching (PSM) is a widely used method for estimating causal treatment effects, but its performance can be limited in complex scenarios. This paper examines cases where a confounder also serves as an effect modifier and compares the bias-reduction performance of PSM with Inverse Probability Weighting (IPW). Using the University of California, Berkeley graduate admission data as an illustrative example, we show that PSM can produce biased estimates of the Average Treatment Effect (ATE) in such contexts. Through a simulation study, we demonstrate that PSM generally fails to adequately reduce bias for the ATE when a confounder is also an effect modifier, while IPW yields less biased estimates with lower Mean Squared Error (MSE). To validate these findings in a more real-world setting, we analyse data generated from a well-known matched-pairs experimental study of Mexico's Seguro Popular de Salud (Universal Health Insurance) Program. From this experiment we derive observational data that incorporates confounders and effect modifiers and compare the performance of PSM and IPW estimators. Our results confirm that IPW consistently provides more accurate and reliable estimates of the ATE, with smaller bias, compared to PSM.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"192 ","pages":"Article 114435"},"PeriodicalIF":6.7,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143654745","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}

引用次数: 0

Optimal advertising strategy for streaming platforms: Whether to purchase external consumer data

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2025-03-03 DOI: 10.1016/j.dss.2025.114427

Jiahe Wang , Nan Feng , Haiyang Feng , Minqiang Li

{"title":"Optimal advertising strategy for streaming platforms: Whether to purchase external consumer data","authors":"Jiahe Wang , Nan Feng , Haiyang Feng , Minqiang Li","doi":"10.1016/j.dss.2025.114427","DOIUrl":"10.1016/j.dss.2025.114427","url":null,"abstract":"<div><div>By utilizing consumer behavioral data, targeted ads can enhance the click-through rates (CTRs) but, at the same time, cause consumer privacy concerns. In this paper, we investigate whether a streaming platform should purchase external consumer data to improve ad-targeting levels, whereby it gain revenue from cost-per-mille (CPM) and cost-per-click (CPC) advertising. We explore how advertising intensity and consumer advertising fatigue interactively determine the data purchase decision and the optimal ad-targeting level of the streaming platform. Our findings indicate that the platform with low advertising intensity or high consumer advertising fatigue is more likely to purchase external consumer data because the ad-driven CTRs are low in these cases. An unexpected finding is that the amount of consumer data purchased by the platform increases with the intensity of privacy concern when advertising intensity is low or when both advertising intensity and fatigue level are high. In these scenarios, the privacy invasion effect resulting from purchasing external consumer data is low due to the low ad-driven CTRs. After purchasing consumer data, to compensate for the privacy invasion effect of targeted ads on consumers, the platform should lower the advertising intensity if and only if the advertising fatigue level is low. Furthermore, we demonstrate that if the platform simultaneously decides on advertising intensity and ad-targeting level, purchasing external consumer data results in a Pareto improvement when the organic CTR is low, benefiting both consumers and the streaming platform. Our findings highlight the importance of balancing advertising precision and consumer privacy on a streaming platform.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"192 ","pages":"Article 114427"},"PeriodicalIF":6.7,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578331","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}

引用次数: 0