Pub Date : 2025-04-24DOI: 10.1016/j.im.2025.104153
Lina Zhou , Zhe Fu
Offensive language is a significant detriment to social media environments. Existing research predominantly assumes monolingual expression, overlooking the prevalent behavior of code-switching (CS). To address this critical knowledge gap, this study identifies and empirically validates the distinct stylometric characteristics of code-switched (CSed) offensive language. Additionally, we developed methods to construct the first social media dataset specifically for CSed offensive content. Our analysis of this dataset reveals that CSed offensive language exhibits unique stylometric characteristics; moreover, these characteristics vary between the language segments involved in the CS. Furthermore, incorporating these features significantly enhances the performance of offensive language detection models. These findings offer significant research and practical implications for social media researchers, platforms, moderators, and users.
{"title":"Stylometric characteristics of code-switched offensive language in social media","authors":"Lina Zhou , Zhe Fu","doi":"10.1016/j.im.2025.104153","DOIUrl":"10.1016/j.im.2025.104153","url":null,"abstract":"<div><div>Offensive language is a significant detriment to social media environments. Existing research predominantly assumes monolingual expression, overlooking the prevalent behavior of code-switching (CS). To address this critical knowledge gap, this study identifies and empirically validates the distinct stylometric characteristics of code-switched (CSed) offensive language. Additionally, we developed methods to construct the first social media dataset specifically for CSed offensive content. Our analysis of this dataset reveals that CSed offensive language exhibits unique stylometric characteristics; moreover, these characteristics vary between the language segments involved in the CS. Furthermore, incorporating these features significantly enhances the performance of offensive language detection models. These findings offer significant research and practical implications for social media researchers, platforms, moderators, and users.</div></div>","PeriodicalId":56291,"journal":{"name":"Information & Management","volume":"62 6","pages":"Article 104153"},"PeriodicalIF":8.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-15DOI: 10.1016/j.im.2025.104154
Xiao-Yu Xu , Qing-Dan Jia
This study offers a holistic interpretation of signaling theory by demonstrating a four-stage research rationale innovatively to unveil the mechanism of five signaling elements in resolving the information asymmetry in social commerce-facilitated cross-border e-commerce. Using different data sources and methods, stages 1 and 2 cross-validate the significance of the signaling elements that are deconstructed as second-order constructs and mirror the dual routes in the elaborated likelihood model. The artificial neural networking analysis in stage 4 reveals the importance ranks of the information factors in three consumer segments that are identified via unobserved heterogeneity analysis regarding the receiver's moderating role in stage 3.
{"title":"A new exploration of signaling theory in social commerce facilitated cross-border retailing: A four-stage approach","authors":"Xiao-Yu Xu , Qing-Dan Jia","doi":"10.1016/j.im.2025.104154","DOIUrl":"10.1016/j.im.2025.104154","url":null,"abstract":"<div><div>This study offers a holistic interpretation of signaling theory by demonstrating a four-stage research rationale innovatively to unveil the mechanism of five signaling elements in resolving the information asymmetry in social commerce-facilitated cross-border e-commerce. Using different data sources and methods, stages 1 and 2 cross-validate the significance of the signaling elements that are deconstructed as second-order constructs and mirror the dual routes in the elaborated likelihood model. The artificial neural networking analysis in stage 4 reveals the importance ranks of the information factors in three consumer segments that are identified via unobserved heterogeneity analysis regarding the receiver's moderating role in stage 3.</div></div>","PeriodicalId":56291,"journal":{"name":"Information & Management","volume":"62 5","pages":"Article 104154"},"PeriodicalIF":8.2,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143833563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-11DOI: 10.1016/j.im.2025.104155
Kyootai Lee , Kailash Joshi , Jin-Gyu Kim , Jongweon Kim
Drawing from the literature on collective information system (IS) usage and social learning theory, we identified how early-stage team IS usage characteristics shape the evolution of individuals’ IS usage. We obtained actual IS usage data from a news organization, covering 504 employees across 43 teams over 14 weeks. The results showed that early-stage team IS usage characteristics created assimilation gaps among individuals after the early stage. However, the impact of these team characteristics declines over time. This study contributes to the literature by investigating the time-dependent roles of collective IS usage characteristics for individuals’ usage and providing an integrated view of switching and post-adoption behaviors.
{"title":"Examining the evolving influence of early-stage team context on individuals’ IS usage over time during implementation","authors":"Kyootai Lee , Kailash Joshi , Jin-Gyu Kim , Jongweon Kim","doi":"10.1016/j.im.2025.104155","DOIUrl":"10.1016/j.im.2025.104155","url":null,"abstract":"<div><div>Drawing from the literature on collective information system (IS) usage and social learning theory, we identified how early-stage team IS usage characteristics shape the evolution of individuals’ IS usage. We obtained actual IS usage data from a news organization, covering 504 employees across 43 teams over 14 weeks. The results showed that early-stage team IS usage characteristics created assimilation gaps among individuals after the early stage. However, the impact of these team characteristics declines over time. This study contributes to the literature by investigating the time-dependent roles of collective IS usage characteristics for individuals’ usage and providing an integrated view of switching and post-adoption behaviors.</div></div>","PeriodicalId":56291,"journal":{"name":"Information & Management","volume":"62 6","pages":"Article 104155"},"PeriodicalIF":8.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-11DOI: 10.1016/j.im.2025.104156
Aihui Chen , Feifei Han , Xinyi Zhang , Yaobin Lu
The use of artificial intelligence (AI) has significantly enhanced the efficiency of resume screening; however, discrepancies in person–job fit assessments between AI and human evaluators can adversely affect the recruitment process. This study introduces the concept of "person–job fit perception difference" to describe these discrepancies and proposes a theoretical model outlining the relationships among person–job fit perception difference, AI transparency, and algorithmic literacy. Based on data from a 2 × 3 factorial-design experiment (N = 286), the findings reveal that both external transparency and functional transparency of AI recruitment systems negatively influence the person–job fit perception difference. Additionally, two distinct aspects of algorithmic literacy moderate different pathways in this process.
{"title":"Cracking the AI recruitment code: Striving for transparency in finding the right person–job fit","authors":"Aihui Chen , Feifei Han , Xinyi Zhang , Yaobin Lu","doi":"10.1016/j.im.2025.104156","DOIUrl":"10.1016/j.im.2025.104156","url":null,"abstract":"<div><div>The use of artificial intelligence (AI) has significantly enhanced the efficiency of resume screening; however, discrepancies in person–job fit assessments between AI and human evaluators can adversely affect the recruitment process. This study introduces the concept of \"person–job fit perception difference\" to describe these discrepancies and proposes a theoretical model outlining the relationships among person–job fit perception difference, AI transparency, and algorithmic literacy. Based on data from a 2 × 3 factorial-design experiment (<em>N</em> = 286), the findings reveal that both external transparency and functional transparency of AI recruitment systems negatively influence the person–job fit perception difference. Additionally, two distinct aspects of algorithmic literacy moderate different pathways in this process.</div></div>","PeriodicalId":56291,"journal":{"name":"Information & Management","volume":"62 5","pages":"Article 104156"},"PeriodicalIF":8.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143854910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-11DOI: 10.1016/j.im.2025.104157
Jooho Kim , Joohun (Justin) Lee , Hyun Jong (Henry) Na
This paper examines how online platforms enhance the resilience of affiliated properties during crises, using the COVID-19 pandemic as a natural experiment. Employing difference-in-differences approach, we compare the performance of platform-listed hotels and their non-listed counterparts in New York City before and after the pandemic's onset. Our findings reveal that platform-affiliated hotels achieved significantly higher booking rates during the pandemic by leveraging operational adaptability and building trust at a greater scale. These results are robust to various specifications, including propensity score matching, synthetic difference-in-differences, and several sensitivity analyses. By identifying the unique role of platforms in fostering resilience, this study extends the literature on digital resilience and offers actionable insights for traditional businesses navigating external shocks.
{"title":"Resilience through an online platform: Evidence from airbnb and hotels","authors":"Jooho Kim , Joohun (Justin) Lee , Hyun Jong (Henry) Na","doi":"10.1016/j.im.2025.104157","DOIUrl":"10.1016/j.im.2025.104157","url":null,"abstract":"<div><div>This paper examines how online platforms enhance the resilience of affiliated properties during crises, using the COVID-19 pandemic as a natural experiment. Employing difference-in-differences approach, we compare the performance of platform-listed hotels and their non-listed counterparts in New York City before and after the pandemic's onset. Our findings reveal that platform-affiliated hotels achieved significantly higher booking rates during the pandemic by leveraging operational adaptability and building trust at a greater scale. These results are robust to various specifications, including propensity score matching, synthetic difference-in-differences, and several sensitivity analyses. By identifying the unique role of platforms in fostering resilience, this study extends the literature on digital resilience and offers actionable insights for traditional businesses navigating external shocks.</div></div>","PeriodicalId":56291,"journal":{"name":"Information & Management","volume":"62 5","pages":"Article 104157"},"PeriodicalIF":8.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143833446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-07DOI: 10.1016/j.im.2025.104152
Chih-Ping Wei , Evana Szu-Han Fang , Chin-Sheng Yang , Pin-Jun Liu
Startups play a crucial role in driving economic growth, job creation, regional development, and technological innovation. However, they often encounter risks stemming from uncertainties in technology, unfamiliar markets, and limited resources. Given these challenges, effectively predicting startup success, defined as achieving a successful exit within a specific observation window, is vital for shaping investment decisions and facilitating the strategy formulation of stakeholders such as venture capitalists and startups themselves. In this study, we are interested in startups in high-tech industries. Existing startup success prediction research primarily focuses on exploiting features related to company profile, funding, founder, and top management team, and pays less attention to technological and venture-capital-related (VC-related) features that are prominent to high-tech startups. Furthermore, prior studies do not assess the effectiveness of startup success prediction over different prediction time points. To address these gaps, we design a startup success prediction method that incorporates three categories of features: basic, technological, and VC-related. For empirical evaluation purposes, we collected a dataset comprising 4415 startup cases and their corresponding feature values from the Securities Data Company's VentureXpert database and the USPTO database. Our evaluation results indicate the superiority of our method over the literature model, suggesting the predictive value of our proposed technological and VC-related features. Our results also show that the VC-related features are more salient in predicting high-tech startup success than the technological and basic features. Finally, our exploratory study of the deep learning approach reveals that using deep learning (e.g., graph convolutional network) to extract VC features automatically may not enhance prediction effectiveness at the very early stage of startups but shows a potential advantage over statistical and machine learning methods at a later prediction time point due to the increased number of VCs investing in the startups.
{"title":"To shine or not to shine: Startup success prediction by exploiting technological and venture-capital-related features","authors":"Chih-Ping Wei , Evana Szu-Han Fang , Chin-Sheng Yang , Pin-Jun Liu","doi":"10.1016/j.im.2025.104152","DOIUrl":"10.1016/j.im.2025.104152","url":null,"abstract":"<div><div>Startups play a crucial role in driving economic growth, job creation, regional development, and technological innovation. However, they often encounter risks stemming from uncertainties in technology, unfamiliar markets, and limited resources. Given these challenges, effectively predicting startup success, defined as achieving a successful exit within a specific observation window, is vital for shaping investment decisions and facilitating the strategy formulation of stakeholders such as venture capitalists and startups themselves. In this study, we are interested in startups in high-tech industries. Existing startup success prediction research primarily focuses on exploiting features related to company profile, funding, founder, and top management team, and pays less attention to technological and venture-capital-related (VC-related) features that are prominent to high-tech startups. Furthermore, prior studies do not assess the effectiveness of startup success prediction over different prediction time points. To address these gaps, we design a startup success prediction method that incorporates three categories of features: basic, technological, and VC-related. For empirical evaluation purposes, we collected a dataset comprising 4415 startup cases and their corresponding feature values from the Securities Data Company's VentureXpert database and the USPTO database. Our evaluation results indicate the superiority of our method over the literature model, suggesting the predictive value of our proposed technological and VC-related features. Our results also show that the VC-related features are more salient in predicting high-tech startup success than the technological and basic features. Finally, our exploratory study of the deep learning approach reveals that using deep learning (e.g., graph convolutional network) to extract VC features automatically may not enhance prediction effectiveness at the very early stage of startups but shows a potential advantage over statistical and machine learning methods at a later prediction time point due to the increased number of VCs investing in the startups.</div></div>","PeriodicalId":56291,"journal":{"name":"Information & Management","volume":"62 6","pages":"Article 104152"},"PeriodicalIF":8.2,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-04DOI: 10.1016/j.im.2025.104144
Chaitanya Sambhara , Arun Rai , Sean Xin Xu
We examine how the impact of accounting and finance enterprise systems (AFES) use on internal control process failures is influenced by the competence (or lack thereof) of internal controls. We draw on resource- and knowledge-based theories to motivate our hypotheses. We test our model using a six-year multisource panel dataset for 143 firms. We find that when firms lack requisite competence for internal controls, AFES use has a damaging effect on internal control processes, widening the scope of process failures. Our findings show that inopportune pursuit of information technology use initiatives can have far-reaching adverse impacts on firms.
{"title":"Why pill becomes poison? Examining the damaging effect of increased information technology use on internal control process failures","authors":"Chaitanya Sambhara , Arun Rai , Sean Xin Xu","doi":"10.1016/j.im.2025.104144","DOIUrl":"10.1016/j.im.2025.104144","url":null,"abstract":"<div><div>We examine how the impact of accounting and finance enterprise systems (AFES) use on internal control process failures is influenced by the competence (or lack thereof) of internal controls. We draw on resource- and knowledge-based theories to motivate our hypotheses. We test our model using a six-year multisource panel dataset for 143 firms. We find that when firms lack requisite competence for internal controls, AFES use has a damaging effect on internal control processes, widening the scope of process failures. Our findings show that inopportune pursuit of information technology use initiatives can have far-reaching adverse impacts on firms.</div></div>","PeriodicalId":56291,"journal":{"name":"Information & Management","volume":"62 5","pages":"Article 104144"},"PeriodicalIF":8.2,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-04DOI: 10.1016/j.im.2025.104140
Yuting Wang , Jie Fang , Bojue Xu , Shuning Zheng , Zhao Cai
{"title":"Corrigendum to ‘Disentangling the impact of vendor in-role and extra-role performance on client citizenship behavior in enterprise system projects: A response surface analysis’ [Information & Management 62/2 (2025) 104104]","authors":"Yuting Wang , Jie Fang , Bojue Xu , Shuning Zheng , Zhao Cai","doi":"10.1016/j.im.2025.104140","DOIUrl":"10.1016/j.im.2025.104140","url":null,"abstract":"","PeriodicalId":56291,"journal":{"name":"Information & Management","volume":"62 4","pages":"Article 104140"},"PeriodicalIF":8.2,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-02DOI: 10.1016/j.im.2025.104145
Aihui Chen , Yunshuang Yu , Yaobin Lu
Evaluations of AI services may be adversely influenced by speciesism due to AI's distinctiveness from humans. We conducted four studies to investigate the impact of speciesism on AI evaluations. Our findings suggest that AI's capacity for affection expression (AE) increases people's satisfaction with AI services by reducing their speciesism toward AI. Additionally, we found that the influence of speciesism on AI satisfaction is moderated by the type of task. This study offers novel recommendations for AI service providers and designers.
{"title":"Speciesism toward AI: The mechanism of AI affection expression on user satisfaction","authors":"Aihui Chen , Yunshuang Yu , Yaobin Lu","doi":"10.1016/j.im.2025.104145","DOIUrl":"10.1016/j.im.2025.104145","url":null,"abstract":"<div><div>Evaluations of AI services may be adversely influenced by speciesism due to AI's distinctiveness from humans. We conducted four studies to investigate the impact of speciesism on AI evaluations. Our findings suggest that AI's capacity for affection expression (AE) increases people's satisfaction with AI services by reducing their speciesism toward AI. Additionally, we found that the influence of speciesism on AI satisfaction is moderated by the type of task. This study offers novel recommendations for AI service providers and designers.</div></div>","PeriodicalId":56291,"journal":{"name":"Information & Management","volume":"62 5","pages":"Article 104145"},"PeriodicalIF":8.2,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-29DOI: 10.1016/j.im.2025.104143
Vivien K.G. Lim , Jing Xi Ng , Thompson S.H. Teo
This study examines how employees utilize resources in a psychologically safe environment, as well as how the presence of techno-stressors affects resource conservation. Drawing on the conservation of resources (COR) theory, we developed and tested a model for how techno-stressors moderate the relationships between psychological safety and both psychological (i.e., future work self) and behavioral (i.e., social and structural job crafting) outcomes. Results indicated that psychological safety was positively related to social job crafting. Techno-invasion moderated the relationship between psychological safety and future work self, while techno-overload moderated the relationship between psychological safety and structural job crafting. Post hoc analyses unveiled a three-way interaction effect on social job crafting that involved psychological safety, techno-overload, and gender. The findings offer insights into how employees manage techno-stressors in resource-rich environments, providing implications for both research and practice in organizational settings.
{"title":"Psychological safety, job crafting, and future work self: The moderating effect of techno-stressors","authors":"Vivien K.G. Lim , Jing Xi Ng , Thompson S.H. Teo","doi":"10.1016/j.im.2025.104143","DOIUrl":"10.1016/j.im.2025.104143","url":null,"abstract":"<div><div>This study examines how employees utilize resources in a psychologically safe environment, as well as how the presence of techno-stressors affects resource conservation. Drawing on the conservation of resources (COR) theory, we developed and tested a model for how techno-stressors moderate the relationships between psychological safety and both psychological (i.e., future work self) and behavioral (i.e., social and structural job crafting) outcomes. Results indicated that psychological safety was positively related to social job crafting. Techno-invasion moderated the relationship between psychological safety and future work self, while techno-overload moderated the relationship between psychological safety and structural job crafting. Post hoc analyses unveiled a three-way interaction effect on social job crafting that involved psychological safety, techno-overload, and gender. The findings offer insights into how employees manage techno-stressors in resource-rich environments, providing implications for both research and practice in organizational settings.</div></div>","PeriodicalId":56291,"journal":{"name":"Information & Management","volume":"62 5","pages":"Article 104143"},"PeriodicalIF":8.2,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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