Pub Date : 2026-02-01Epub Date: 2025-12-10DOI: 10.1016/j.dss.2025.114590
Xinyao Yu , David Jingjun Xu , Kai Li
AI reviews, defined as product reviews generated by artificial intelligence, represent a novel application of AIGC in e-commerce. This study investigates how AI reviews influence consumers' purchase intention, considering the roles of product category, review breadth, and consumer review volume. Results show that AI reviews significantly increase purchase intention for search products but have no effect on experience products. Moreover, consumer review volume strengthens the effect of AI reviews for search products but weakens it for experience products. In addition, an inverted U-shaped relationship is identified between the breadth of AI reviews and consumers' purchase intention for experience products. These findings highlight the context-dependent effectiveness of AI reviews and extend the literature on AI-generated content, while offering practical implications for e-commerce platforms seeking to leverage AI reviews strategically.
{"title":"Effects of AI reviews on consumers' purchase intention: Influence of product category, review breadth, and consumer review volume","authors":"Xinyao Yu , David Jingjun Xu , Kai Li","doi":"10.1016/j.dss.2025.114590","DOIUrl":"10.1016/j.dss.2025.114590","url":null,"abstract":"<div><div>AI reviews, defined as product reviews generated by artificial intelligence, represent a novel application of AIGC in e-commerce. This study investigates how AI reviews influence consumers' purchase intention, considering the roles of product category, review breadth, and consumer review volume. Results show that AI reviews significantly increase purchase intention for search products but have no effect on experience products. Moreover, consumer review volume strengthens the effect of AI reviews for search products but weakens it for experience products. In addition, an inverted U-shaped relationship is identified between the breadth of AI reviews and consumers' purchase intention for experience products. These findings highlight the context-dependent effectiveness of AI reviews and extend the literature on AI-generated content, while offering practical implications for e-commerce platforms seeking to leverage AI reviews strategically.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"201 ","pages":"Article 114590"},"PeriodicalIF":6.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731027","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 : 2026-02-01Epub Date: 2025-11-26DOI: 10.1016/j.dss.2025.114579
Zhe Jing, Xin Xu, Yong Jin, Jie Shen
Emerging technologies such as neural networks, cloud computing, big data, and blockchain have paved the way for the development of artificial intelligence (AI), enabling AI to facilitate business operations. In particular, some organizations seek to leverage AI to replace human agents in positions involving sensitive customer information, with the aim of enhancing privacy protection. However, AI-human interaction tends to fall short of expectations in real-world settings due to the difference between humans and AI. To address this, a study will be conducted to explore the effect of implementing an AI-powered call system on potential customers compared to human agent calls. Leveraging a randomized field experiment conducted at a call center of a large securities company and a randomized online experiment, we investigated the mechanism resulting in the different impacts on customer behavior between humans and AI. The results show that voice-based AI calls trade off emotional and informational support: AI's informational advantages can raise intention, but empathy gaps can suppress it. These findings contribute to the literature on the application of technology in organizations and provide guidance to organizations on the effective implementation of AI systems, highlighting both the advantages and limitations of AI in customer-facing roles.
{"title":"Emotion vs. information: Understanding the effect of AI-powered call systems on potential customer decision from a field experiment","authors":"Zhe Jing, Xin Xu, Yong Jin, Jie Shen","doi":"10.1016/j.dss.2025.114579","DOIUrl":"10.1016/j.dss.2025.114579","url":null,"abstract":"<div><div>Emerging technologies such as neural networks, cloud computing, big data, and blockchain have paved the way for the development of artificial intelligence (AI), enabling AI to facilitate business operations. In particular, some organizations seek to leverage AI to replace human agents in positions involving sensitive customer information, with the aim of enhancing privacy protection. However, AI-human interaction tends to fall short of expectations in real-world settings due to the difference between humans and AI. To address this, a study will be conducted to explore the effect of implementing an AI-powered call system on potential customers compared to human agent calls. Leveraging a randomized field experiment conducted at a call center of a large securities company and a randomized online experiment, we investigated the mechanism resulting in the different impacts on customer behavior between humans and AI. The results show that voice-based AI calls trade off emotional and informational support: AI's informational advantages can raise intention, but empathy gaps can suppress it. These findings contribute to the literature on the application of technology in organizations and provide guidance to organizations on the effective implementation of AI systems, highlighting both the advantages and limitations of AI in customer-facing roles.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"201 ","pages":"Article 114579"},"PeriodicalIF":6.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145609095","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 : 2026-02-01Epub Date: 2025-11-30DOI: 10.1016/j.dss.2025.114581
Qian Wang , Xixi Li , Xiangbin Yan
Despite the immense popularity of live commerce, many streamers struggle in conveying appealing signals to viewers and realizing expected commercial returns. It is also confusing that the same signal often delivers differential impacts on viewers across virtual and human streaming settings. Toward this end, we integrate signaling theory with consumption value theory and propose a comprehensive framework to explain how streamers' multidimensional signals collectively shape viewers' impulsive purchases and how such signaling processes differ across virtual and human streaming contexts. Analyzing survey data from 557 experienced livestreaming shoppers, we observe that aesthetic, social, and task signals all significantly enhance viewers' product value perceptions, which in turn motivate their impulsive purchases. Moreover, aesthetic signal displays no differential impacts on viewers' product value perceptions across virtual and human live-show settings. Social signal and task signal respectively exert a stronger and a weaker influence on product value perceptions in virtual live shows than in human ones. Our findings provide nuanced insights to help optimize streaming strategies and signal investments, and ultimately enhance commercial effectiveness of live-streaming ventures.
{"title":"Digital charisma or human appeal: A comparative study on how streamers' multidimensional signals affect viewers' impulsive purchases in live commerce","authors":"Qian Wang , Xixi Li , Xiangbin Yan","doi":"10.1016/j.dss.2025.114581","DOIUrl":"10.1016/j.dss.2025.114581","url":null,"abstract":"<div><div>Despite the immense popularity of live commerce, many streamers struggle in conveying appealing signals to viewers and realizing expected commercial returns. It is also confusing that the same signal often delivers differential impacts on viewers across virtual and human streaming settings. Toward this end, we integrate signaling theory with consumption value theory and propose a comprehensive framework to explain how streamers' multidimensional signals collectively shape viewers' impulsive purchases and how such signaling processes differ across virtual and human streaming contexts. Analyzing survey data from 557 experienced livestreaming shoppers, we observe that aesthetic, social, and task signals all significantly enhance viewers' product value perceptions, which in turn motivate their impulsive purchases. Moreover, aesthetic signal displays no differential impacts on viewers' product value perceptions across virtual and human live-show settings. Social signal and task signal respectively exert a stronger and a weaker influence on product value perceptions in virtual live shows than in human ones. Our findings provide nuanced insights to help optimize streaming strategies and signal investments, and ultimately enhance commercial effectiveness of live-streaming ventures.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"201 ","pages":"Article 114581"},"PeriodicalIF":6.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145650856","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 : 2026-02-01Epub Date: 2025-12-02DOI: 10.1016/j.dss.2025.114580
Arthur Carvalho , Liudmila Zavolokina , Suman Bhunia , Gerhard Schwabe
Regulatory changes have enabled American student-athletes to profit from their name, image, and likeness (NIL). However, only a fraction of the student-athlete population is actually profiting from their NIL, which raises questions concerning fairness and inclusiveness. Motivated by that scenario, we look at technological solutions capable of sharing a limited amount of financial resources fairly and inclusively. Following a design science methodology, we define design requirements for such technological solutions after interviewing student-athletes, which leads us to establish the inclusive-meritocratic fairness criterion. Subsequently, we determine design principles that artifacts aiming at helping student-athletes should satisfy. We find that a solution that satisfies the proposed design principles is to associate student-athletes with digital collectibles represented as non-fungible tokens (NFTs). The core idea behind our artifact is that student-athletes receive royalties in primary markets after NFTs are randomly minted, plus deterministic royalties in secondary markets whenever a transaction involving their collectibles happens. Interviews with student-athletes validate our design. We conclude the paper by discussing how our ideas give rise to a new NIL design theory.
{"title":"Designing a fair and inclusive digital asset-based name-image-likeness marketplace","authors":"Arthur Carvalho , Liudmila Zavolokina , Suman Bhunia , Gerhard Schwabe","doi":"10.1016/j.dss.2025.114580","DOIUrl":"10.1016/j.dss.2025.114580","url":null,"abstract":"<div><div>Regulatory changes have enabled American student-athletes to profit from their name, image, and likeness (NIL). However, only a fraction of the student-athlete population is actually profiting from their NIL, which raises questions concerning fairness and inclusiveness. Motivated by that scenario, we look at technological solutions capable of sharing a limited amount of financial resources fairly and inclusively. Following a design science methodology, we define design requirements for such technological solutions after interviewing student-athletes, which leads us to establish the inclusive-meritocratic fairness criterion. Subsequently, we determine design principles that artifacts aiming at helping student-athletes should satisfy. We find that a solution that satisfies the proposed design principles is to associate student-athletes with digital collectibles represented as non-fungible tokens (NFTs). The core idea behind our artifact is that student-athletes receive royalties in primary markets after NFTs are randomly minted, plus deterministic royalties in secondary markets whenever a transaction involving their collectibles happens. Interviews with student-athletes validate our design. We conclude the paper by discussing how our ideas give rise to a new NIL design theory.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"201 ","pages":"Article 114580"},"PeriodicalIF":6.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145657173","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 : 2026-01-01Epub Date: 2025-11-19DOI: 10.1016/j.dss.2025.114576
Esther Yanfei Jin , Wei Jiang , Zhiqiang Zheng
Adverse selection remains a significant challenge in the insurance industry, often resulting in substantial financial losses for insurers. The primary hurdle in addressing the issue lies in accurately identifying and quantifying adverse selection. Traditional methods often fail to adequately account for the heterogeneity of insurance purchasers and the endogenous nature of their insurance decisions. This study introduces an innovative approach that integrates the Gaussian Mixture Model and the regression-based model from Dionne et al. [18] to assess adverse selection, addressing the limitations of previous methods. Through comprehensive simulations, we demonstrate that our method yields unbiased estimates, outperforming existing approaches. Applied to China's automobile insurance market, this method leverages IoT-based telematics data to capture risk heterogeneity among policyholders more effectively than relying solely on traditional policy information. The results offer robust evidence of adverse selection, in contrast to conventional methods that fail to detect this phenomenon due to their inability to account for underlying risk and insurance choice heterogeneity. Our approach offers insurers a robust framework for identifying information asymmetries in the market, thereby enabling the development of more targeted policy interventions and risk management strategies.
{"title":"A novel method for testing adverse selection with IoT data: Evidence from China's auto insurance market","authors":"Esther Yanfei Jin , Wei Jiang , Zhiqiang Zheng","doi":"10.1016/j.dss.2025.114576","DOIUrl":"10.1016/j.dss.2025.114576","url":null,"abstract":"<div><div>Adverse selection remains a significant challenge in the insurance industry, often resulting in substantial financial losses for insurers. The primary hurdle in addressing the issue lies in accurately identifying and quantifying adverse selection. Traditional methods often fail to adequately account for the heterogeneity of insurance purchasers and the endogenous nature of their insurance decisions. This study introduces an innovative approach that integrates the Gaussian Mixture Model and the regression-based model from Dionne et al. [18] to assess adverse selection, addressing the limitations of previous methods. Through comprehensive simulations, we demonstrate that our method yields unbiased estimates, outperforming existing approaches. Applied to China's automobile insurance market, this method leverages IoT-based telematics data to capture risk heterogeneity among policyholders more effectively than relying solely on traditional policy information. The results offer robust evidence of adverse selection, in contrast to conventional methods that fail to detect this phenomenon due to their inability to account for underlying risk and insurance choice heterogeneity. Our approach offers insurers a robust framework for identifying information asymmetries in the market, thereby enabling the development of more targeted policy interventions and risk management strategies.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"200 ","pages":"Article 114576"},"PeriodicalIF":6.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145553606","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 : 2026-01-01Epub 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":"2026-01-01","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 : 2026-01-01Epub Date: 2025-09-02DOI: 10.1016/j.dss.2025.114529
Yinghui Huang , Jinyi Zhou , Wanghao Dong , Weiqing Li , Maomao Chi , Changbin Jiang , Weijun Wang , Shasha Deng
The proliferation of fake online reviews, a long-standing threat to platform trust, is now exacerbated by large language models (LLMs) capable of generating highly convincing deceptive text. Understanding the linguistic strategies LLMs employ is crucial for developing effective mitigation. To address this gap, we develop an explainable artificial intelligence (XAI)-based computational framework, grounded in deception detection theories, to analyze and distinguish the deceptive patterns of LLMs. A core component of our methodology is a novel Turing-style test designed for LLM-generated online reviews. When applied to three purpose-built datasets, our framework not only achieves high detection accuracy for both human-authored fakes (96.57 %) and LLM-generated fakes (96.13 %)—substantially outperforming two current general-purpose detectors—but also indicates that LLMs possess a human-level deceptive capability (metric gaps <0.72 %). The analysis reveals that while cues related to cognitive load and perceptual-contextual details are powerful discriminators for both human and machine deception, certainty uniquely signals LLM-generated text, whereas emotion is a primary predictor only for human fakes. These findings support a central dissociation hypothesis between linguistic generation and cognitive representation: LLM deception is characterized by strategies like surface-level fluency, content realism without experiential grounding, and positivity bias. This study probes the mechanistic differences between human and machine deception, delivers a robust computational detection framework, and advances the theoretical discourse on AI's capacity for deceit.
{"title":"Decoding LLMs' verbal deception in online reviews","authors":"Yinghui Huang , Jinyi Zhou , Wanghao Dong , Weiqing Li , Maomao Chi , Changbin Jiang , Weijun Wang , Shasha Deng","doi":"10.1016/j.dss.2025.114529","DOIUrl":"10.1016/j.dss.2025.114529","url":null,"abstract":"<div><div>The proliferation of fake online reviews, a long-standing threat to platform trust, is now exacerbated by large language models (LLMs) capable of generating highly convincing deceptive text. Understanding the linguistic strategies LLMs employ is crucial for developing effective mitigation. To address this gap, we develop an explainable artificial intelligence (XAI)-based computational framework, grounded in deception detection theories, to analyze and distinguish the deceptive patterns of LLMs. A core component of our methodology is a novel Turing-style test designed for LLM-generated online reviews. When applied to three purpose-built datasets, our framework not only achieves high detection accuracy for both human-authored fakes (96.57 %) and LLM-generated fakes (96.13 %)—substantially outperforming two current general-purpose detectors—but also indicates that LLMs possess a human-level deceptive capability (metric gaps <0.72 %). The analysis reveals that while cues related to cognitive load and perceptual-contextual details are powerful discriminators for both human and machine deception, certainty uniquely signals LLM-generated text, whereas emotion is a primary predictor only for human fakes. These findings support a central dissociation hypothesis between linguistic generation and cognitive representation: LLM deception is characterized by strategies like surface-level fluency, content realism without experiential grounding, and positivity bias. This study probes the mechanistic differences between human and machine deception, delivers a robust computational detection framework, and advances the theoretical discourse on AI's capacity for deceit.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"200 ","pages":"Article 114529"},"PeriodicalIF":6.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145468745","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 : 2026-01-01Epub 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":"2026-01-01","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 : 2026-01-01Epub 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":"2026-01-01","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 : 2026-01-01Epub 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.
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