Pub Date : 2025-04-23DOI: 10.1016/j.jbusres.2025.115409
Davide Burkhart, Christoph Bode
The management literature traditionally views ‘force majeure’ in business relationships as the result of exogenous events, that is, the consequence of external unforeseeable and irresistible catastrophes outside of human control. Yet, recent events suggest that companies frequently invoke force majeure for purposes beyond excusing non-performance due to genuine force majeure events. Drawing on expectancy violation theory and employing a sequential empirical research design – including an analysis of force majeure declarations at a focal firm, semi-structured interviews, and an experiment – this study examines the expectations and outcomes associated with force majeure in buyer–supplier relationships. Contrary to the extant literature, our findings suggest that force majeure declarations are, under certain conditions, used as a pretext or strategic tool to address other underlying issues in the business relationship. Our study broadens the understanding of force majeure declarations in business relationships offers significant managerial insights for effectively navigating force majeure-related challenges.
{"title":"Force majeure in business relationships","authors":"Davide Burkhart, Christoph Bode","doi":"10.1016/j.jbusres.2025.115409","DOIUrl":"10.1016/j.jbusres.2025.115409","url":null,"abstract":"<div><div>The management literature traditionally views ‘<em>force majeure’</em> in business relationships as the result of exogenous events, that is, the consequence of external unforeseeable and irresistible catastrophes outside of human control. Yet, recent events suggest that companies frequently invoke force majeure for purposes beyond excusing non-performance due to genuine force majeure events. Drawing on expectancy violation theory and employing a sequential empirical research design – including an analysis of force majeure declarations at a focal firm, semi-structured interviews, and an experiment – this study examines the expectations and outcomes associated with force majeure in buyer–supplier relationships. Contrary to the extant literature, our findings suggest that force majeure declarations are, under certain conditions, used as a pretext or strategic tool to address other underlying issues in the business relationship. Our study broadens the understanding of force majeure declarations in business relationships offers significant managerial insights for effectively navigating force majeure-related challenges.</div></div>","PeriodicalId":15123,"journal":{"name":"Journal of Business Research","volume":"195 ","pages":"Article 115409"},"PeriodicalIF":10.5,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-23DOI: 10.1016/j.obhdp.2025.104405
Oliver Schilke , Martin Reimann
As generative artificial intelligence (AI) has found its way into various work tasks, questions about whether its usage should be disclosed and the consequences of such disclosure have taken center stage in public and academic discourse on digital transparency. This article addresses this debate by asking: Does disclosing the usage of AI compromise trust in the user? We examine the impact of AI disclosure on trust across diverse tasks—from communications via analytics to artistry—and across individual actors such as supervisors, subordinates, professors, analysts, and creatives, as well as across organizational actors such as investment funds. Thirteen experiments consistently demonstrate that actors who disclose their AI usage are trusted less than those who do not. Drawing on micro-institutional theory, we argue that this reduction in trust can be explained by reduced perceptions of legitimacy, as shown across various experimental designs (Studies 6–8). Moreover, we demonstrate that this negative effect holds across different disclosure framings, above and beyond algorithm aversion, regardless of whether AI involvement is known, and regardless of whether disclosure is voluntary or mandatory, though it is comparatively weaker than the effect of third-party exposure (Studies 9–13). A within-paper meta-analysis suggests this trust penalty is attenuated but not eliminated among evaluators with favorable technology attitudes and perceptions of high AI accuracy. This article contributes to research on trust, AI, transparency, and legitimacy by showing that AI disclosure can harm social perceptions, emphasizing that transparency is not straightforwardly beneficial, and highlighting legitimacy’s central role in trust formation.
{"title":"The transparency dilemma: How AI disclosure erodes trust","authors":"Oliver Schilke , Martin Reimann","doi":"10.1016/j.obhdp.2025.104405","DOIUrl":"10.1016/j.obhdp.2025.104405","url":null,"abstract":"<div><div>As generative artificial intelligence (AI) has found its way into various work tasks, questions about whether its usage should be disclosed and the consequences of such disclosure have taken center stage in public and academic discourse on digital transparency. This article addresses this debate by asking: Does disclosing the usage of AI compromise trust in the user? We examine the impact of AI disclosure on trust across diverse tasks—from communications via analytics to artistry—and across individual actors such as supervisors, subordinates, professors, analysts, and creatives, as well as across organizational actors such as investment funds. Thirteen experiments consistently demonstrate that actors who disclose their AI usage are trusted less than those who do not. Drawing on micro-institutional theory, we argue that this reduction in trust can be explained by reduced perceptions of legitimacy, as shown across various experimental designs (Studies 6–8). Moreover, we demonstrate that this negative effect holds across different disclosure framings, above and beyond algorithm aversion, regardless of whether AI involvement is known, and regardless of whether disclosure is voluntary or mandatory, though it is comparatively weaker than the effect of third-party exposure (Studies 9–13). A within-paper meta-analysis suggests this trust penalty is attenuated but not eliminated among evaluators with favorable technology attitudes and perceptions of high AI accuracy. This article contributes to research on trust, AI, transparency, and legitimacy by showing that AI disclosure can harm social perceptions, emphasizing that transparency is not straightforwardly beneficial, and highlighting legitimacy’s central role in trust formation.</div></div>","PeriodicalId":48442,"journal":{"name":"Organizational Behavior and Human Decision Processes","volume":"188 ","pages":"Article 104405"},"PeriodicalIF":3.4,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860102","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 : 2025-04-23DOI: 10.1016/j.annals.2025.103959
Huicai Gao , Hengyun Li , Chen Jason Zhang
Choosing appropriate weights for individual models represents a major challenge in combination forecasting. Most research has used constant or time-varying weights during stable periods, ignoring dynamic weights that account for the latent features in multisource data during uncertain periods. We introduce an innovative approach that employs a time- and feature-varying ensemble learning–based meta-learner to consolidate individual model forecasts. The proposed model integrates statistical, machine learning, and deep learning models, along with economic and search engine data, to forecast visitor arrivals in Hong Kong and Sanya City, China. Results show that the proposed model surpasses most individual models and typical combination methods in stable and uncertain times. The findings highlight the proposed model's ability to yield consistent and reliable predictions across a variety of scenarios, particularly during volatile periods.
{"title":"Time and feature varying tourism demand forecasting","authors":"Huicai Gao , Hengyun Li , Chen Jason Zhang","doi":"10.1016/j.annals.2025.103959","DOIUrl":"10.1016/j.annals.2025.103959","url":null,"abstract":"<div><div>Choosing appropriate weights for individual models represents a major challenge in combination forecasting. Most research has used constant or time-varying weights during stable periods, ignoring dynamic weights that account for the latent features in multisource data during uncertain periods. We introduce an innovative approach that employs a time- and feature-varying ensemble learning–based meta-learner to consolidate individual model forecasts. The proposed model integrates statistical, machine learning, and deep learning models, along with economic and search engine data, to forecast visitor arrivals in Hong Kong and Sanya City, China. Results show that the proposed model surpasses most individual models and typical combination methods in stable and uncertain times. The findings highlight the proposed model's ability to yield consistent and reliable predictions across a variety of scenarios, particularly during volatile periods.</div></div>","PeriodicalId":48452,"journal":{"name":"Annals of Tourism Research","volume":"112 ","pages":"Article 103959"},"PeriodicalIF":10.4,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-23DOI: 10.1016/j.jbusres.2025.115395
Xin Qi , Xiyao Liu , Xiaoyan Zhang , Yuhuan Xia , Shasha Liu , Hui Lin , Ziyao Wang
Organizations are increasingly incorporating innovation into job requirements to encourage employees to generate and implement novel ideas (i.e., enhance employees’ creativity). Despite extensive research on the impact of innovation job requirements on creativity, the findings remain inconsistent, being either positive or negative. To address this issue, drawing on the regulatory focus theory, we focused on employees’ different types of creativity and explored how innovation job requirements impact employees’ radical versus incremental creativity. Through a questionnaire survey of 485 employees and 124 direct supervisors from two high-tech firms and one manufacturing firm in China, the empirical results indicated that innovation job requirements promoted expected image gains for promotion-focused employees, whereas for prevention-focused employees, innovation job requirements fostered incremental creativity by heightening expected image risks. These findings contribute valuable insights to research on innovation job requirements and creativity and provide practical guidance for organizations to foster employees’ different types of creativity.
{"title":"One requirement, multiple insights: The impact of innovation job requirement on employee radical and incremental creativity","authors":"Xin Qi , Xiyao Liu , Xiaoyan Zhang , Yuhuan Xia , Shasha Liu , Hui Lin , Ziyao Wang","doi":"10.1016/j.jbusres.2025.115395","DOIUrl":"10.1016/j.jbusres.2025.115395","url":null,"abstract":"<div><div>Organizations are increasingly incorporating innovation into job requirements to encourage employees to generate and implement novel ideas (i.e., enhance employees’ creativity). Despite extensive research on the impact of innovation job requirements on creativity, the findings remain inconsistent, being either positive or negative. To address this issue, drawing on the regulatory focus theory, we focused on employees’ different types of creativity and explored how innovation job requirements impact employees’ radical versus incremental creativity. Through a questionnaire survey of 485 employees and 124 direct supervisors from two high-tech firms and one manufacturing firm in China, the empirical results indicated that innovation job requirements promoted expected image gains for promotion-focused employees, whereas for prevention-focused employees, innovation job requirements fostered incremental creativity by heightening expected image risks. These findings contribute valuable insights to research on innovation job requirements and creativity and provide practical guidance for organizations to foster employees’ different types of creativity.</div></div>","PeriodicalId":15123,"journal":{"name":"Journal of Business Research","volume":"195 ","pages":"Article 115395"},"PeriodicalIF":10.5,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-23DOI: 10.1016/j.jik.2025.100704
Weiran Lin , Haijing Yu , Liugen Wang
Global financial markets frequently experience extreme volatility, which poses significant challenges in forecasting stock returns, particularly following market crashes. Traditional models often falter under these conditions due to heightened investor sentiment and strong regulatory interventions. Predicting individual stock returns after a crash is especially challenging in China's A-share market, which is characterized by high volatility and active government involvement. Although deep learning has advanced stock return forecasting, most studies have focused on general market conditions or relied solely on sentiments extracted from texts, leaving firm-level government intervention metrics largely unaddressed. To bridge this gap, we propose a novel deep learning framework that leverages historical post-crash data ("distant relative data") to forecast future stock returns. Unlike conventional methods that rely on recent pre-crash data—often overlooking government interventions—our approach leverages post-crash data, where investor sentiment and regulatory responses are already reflected, to model stable relationships between financial and momentum factors and subsequent returns, thereby implicitly integrating the effects of government interventions on investor behavior. We validate our framework using data from four distinct "thousand-stock limit-down" events in China's A-share market from 2018 to 2023. For the Fully Connected Neural Network (FCNN) model, training with close neighbor data yielded average F1-scores of 0.219 (2019), 0.106 (2020), and 0.282 (2022), whereas using distant relative data improved these to 0.571 (2019), 0.311 (2020), and 0.412 (2022). Notably, incorporating two distant relative datasets further boosted the FCNN F1-scores to 0.627 and 0.533 for 2020 and 2022, respectively. Additionally, Long Short-Term Memory (LSTM) networks consistently outperform FCNN models, underscoring their advantages in capturing temporal dependencies. Overall, our findings indicate that leveraging multiple historical crisis data sets significantly enhances post-crash stock return predictions. This data-driven approach, analogous to the stand-alone application of SMOTE for data balancing, offers a robust framework that can be integrated with other post-crisis models, thereby providing promising directions for future research and practical implementation.
{"title":"A data-driven deep learning approach incorporating investor sentiment and government interventions to predict post-crash stock return in China's A-share market","authors":"Weiran Lin , Haijing Yu , Liugen Wang","doi":"10.1016/j.jik.2025.100704","DOIUrl":"10.1016/j.jik.2025.100704","url":null,"abstract":"<div><div>Global financial markets frequently experience extreme volatility, which poses significant challenges in forecasting stock returns, particularly following market crashes. Traditional models often falter under these conditions due to heightened investor sentiment and strong regulatory interventions. Predicting individual stock returns after a crash is especially challenging in China's A-share market, which is characterized by high volatility and active government involvement. Although deep learning has advanced stock return forecasting, most studies have focused on general market conditions or relied solely on sentiments extracted from texts, leaving firm-level government intervention metrics largely unaddressed. To bridge this gap, we propose a novel deep learning framework that leverages historical post-crash data (\"distant relative data\") to forecast future stock returns. Unlike conventional methods that rely on recent pre-crash data—often overlooking government interventions—our approach leverages post-crash data, where investor sentiment and regulatory responses are already reflected, to model stable relationships between financial and momentum factors and subsequent returns, thereby implicitly integrating the effects of government interventions on investor behavior. We validate our framework using data from four distinct \"thousand-stock limit-down\" events in China's A-share market from 2018 to 2023. For the Fully Connected Neural Network (FCNN) model, training with close neighbor data yielded average F1-scores of 0.219 (2019), 0.106 (2020), and 0.282 (2022), whereas using distant relative data improved these to 0.571 (2019), 0.311 (2020), and 0.412 (2022). Notably, incorporating two distant relative datasets further boosted the FCNN F1-scores to 0.627 and 0.533 for 2020 and 2022, respectively. Additionally, Long Short-Term Memory (LSTM) networks consistently outperform FCNN models, underscoring their advantages in capturing temporal dependencies. Overall, our findings indicate that leveraging multiple historical crisis data sets significantly enhances post-crash stock return predictions. This data-driven approach, analogous to the stand-alone application of SMOTE for data balancing, offers a robust framework that can be integrated with other post-crisis models, thereby providing promising directions for future research and practical implementation.</div></div>","PeriodicalId":46792,"journal":{"name":"Journal of Innovation & Knowledge","volume":"10 3","pages":"Article 100704"},"PeriodicalIF":15.6,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-23DOI: 10.1016/j.techfore.2025.124132
Anna Cabigiosu
The adoption of electric vehicles in public transport will be essential for the transition of the world's economies toward more sustainable mobility. For this reason, we need an in-depth understanding of the variables that affect the choice of a specific electric technology for public transport: successful implementation of electric transport is highly sensitive to operational context and there could be different types of challenges regarding the electrification of public transport in our cities. This study, by analysing the introduction process of fully electric buses and hybrid vaporetti in the Venice Municipality, contributes to the existing debate on EV adoption. The study explores why and how space can generate path dependency and embeddedness moving from an ICE public transport mobility service toward an electrified mobility service and identifies the specific space attributes that affect the adoption of electric vehicles for public transport. Findings help managers and policymakers in understanding which type of technological innovation, why and how, can support sustainable mobility in each place, while at the same time preserving existing service levels. Interestingly, our findings suggest that space availability in cities can generate barriers of entry for fully electric vehicles, especially for most performing vehicles with higher range.
{"title":"The adoption of electric vehicles in public transport services: Space, path dependency and embeddedness: The Venice case","authors":"Anna Cabigiosu","doi":"10.1016/j.techfore.2025.124132","DOIUrl":"10.1016/j.techfore.2025.124132","url":null,"abstract":"<div><div>The adoption of electric vehicles in public transport will be essential for the transition of the world's economies toward more sustainable mobility. For this reason, we need an in-depth understanding of the variables that affect the choice of a specific electric technology for public transport: successful implementation of electric transport is highly sensitive to operational context and there could be different types of challenges regarding the electrification of public transport in our cities. This study, by analysing the introduction process of fully electric buses and hybrid vaporetti in the Venice Municipality, contributes to the existing debate on EV adoption. The study explores why and how space can generate path dependency and embeddedness moving from an ICE public transport mobility service toward an electrified mobility service and identifies the specific space attributes that affect the adoption of electric vehicles for public transport. Findings help managers and policymakers in understanding which type of technological innovation, why and how, can support sustainable mobility in each place, while at the same time preserving existing service levels. Interestingly, our findings suggest that space availability in cities can generate barriers of entry for fully electric vehicles, especially for most performing vehicles with higher range.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"216 ","pages":"Article 124132"},"PeriodicalIF":12.9,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143858812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study intends to bridge an important research gap in the existing literature by extending academic knowledge on the relationship between national cultural dimensions and corporate environmental, social and governance (ESG) disclosure practices via social media. To this end, this study relies upon a sample of 96 of the world's largest companies from the Fortune Global 500 list over the 2018–2020 period. A dictionary‐based content analysis was conducted on a total of 122,739 tweets extracted from the official Twitter accounts of sampled companies to identify and codify those containing ESG themes according to a glossary created on the basis of the Bloomberg ESG Index. Accordingly, several panel regression models were estimated to examine the influence of national culture represented by the six Hofstede's cultural dimensions, namely, power distance (PD), individualism (IDV), masculinity (MAS), uncertainty avoidance (UA), long‐term orientation (LTO) and indulgence (IND), and the level of ESG disclosure provided by sampled companies via Twitter. The study's findings reveal that companies operating in countries with less power distance are more likely to disclose ESG information via Twitter. Also, companies from countries with individualist and masculine cultures tend to provide more ESG disclosure via Twitter.
{"title":"The Impact of National Cultural Dimensions on Corporate Environmental, Social and Governance Disclosure: Evidence From Social Media Practices","authors":"Giuseppe Nicolo, Lukasz Bryl, Diana Ferullo","doi":"10.1002/bse.4304","DOIUrl":"https://doi.org/10.1002/bse.4304","url":null,"abstract":"This study intends to bridge an important research gap in the existing literature by extending academic knowledge on the relationship between national cultural dimensions and corporate environmental, social and governance (ESG) disclosure practices via social media. To this end, this study relies upon a sample of 96 of the world's largest companies from the Fortune Global 500 list over the 2018–2020 period. A dictionary‐based content analysis was conducted on a total of 122,739 tweets extracted from the official Twitter accounts of sampled companies to identify and codify those containing ESG themes according to a glossary created on the basis of the Bloomberg ESG Index. Accordingly, several panel regression models were estimated to examine the influence of national culture represented by the six Hofstede's cultural dimensions, namely, power distance (PD), individualism (IDV), masculinity (MAS), uncertainty avoidance (UA), long‐term orientation (LTO) and indulgence (IND), and the level of ESG disclosure provided by sampled companies via Twitter. The study's findings reveal that companies operating in countries with less power distance are more likely to disclose ESG information via Twitter. Also, companies from countries with individualist and masculine cultures tend to provide more ESG disclosure via Twitter.","PeriodicalId":9518,"journal":{"name":"Business Strategy and The Environment","volume":"15 1","pages":""},"PeriodicalIF":13.4,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-22DOI: 10.1016/j.techfore.2025.124160
Jose Nicolas Pacheco , Andreu Turro , David Urbano
Open Social Innovation (OSI) has garnered significant attention in recent years as a collaborative approach to addressing societal challenges. However, the field remains fragmented, with divergent definitions, methods, and theoretical underpinnings across disciplines. Through bibliometric and multi-level content analysis, we analyze 115 studies to address these tensions and propose a systems-based framework that bridges conceptual and practical divides. We map the intellectual structure and synthesize OSI research's antecedents, processes, relationships, and outcomes. Unlike prior reviews focused on particular OSI initiatives (e.g., Living Labs) or single levels of analysis, our study integrates dispersed knowledge to highlight actionable insights for practitioners and policymakers. Finally, our review establishes a thematic agenda for future research, targeting multi-level investigations into OSI drivers, mechanisms, and impacts.
{"title":"Open social innovation: A systematic literature review and future research agenda","authors":"Jose Nicolas Pacheco , Andreu Turro , David Urbano","doi":"10.1016/j.techfore.2025.124160","DOIUrl":"10.1016/j.techfore.2025.124160","url":null,"abstract":"<div><div>Open Social Innovation (OSI) has garnered significant attention in recent years as a collaborative approach to addressing societal challenges. However, the field remains fragmented, with divergent definitions, methods, and theoretical underpinnings across disciplines. Through bibliometric and multi-level content analysis, we analyze 115 studies to address these tensions and propose a systems-based framework that bridges conceptual and practical divides. We map the intellectual structure and synthesize OSI research's antecedents, processes, relationships, and outcomes. Unlike prior reviews focused on particular OSI initiatives (e.g., Living Labs) or single levels of analysis, our study integrates dispersed knowledge to highlight actionable insights for practitioners and policymakers. Finally, our review establishes a thematic agenda for future research, targeting multi-level investigations into OSI drivers, mechanisms, and impacts.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"216 ","pages":"Article 124160"},"PeriodicalIF":12.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143854765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Applicants generally react less favorably to asynchronous video interviews (AVIs) in the selection process than synchronous interviews; however, explanations may improve reactions. This study applied a justice model of applicants' reactions, including formal characteristics, information given, and interpersonal treatment, to influence applicants' perceptions of AVIs. Data were collected from 380 individuals through online platforms. Participants took an AVI, were informed the interview would be scored automatically, and were rejected with either a consistency-centric (i.e., emphasizing the consistency of the selection process), opportunity-centric (i.e., emphasizing the flexibility of the process and the opportunity to perform), combined, or a simple message saying they did not score high enough. While the hypothesized main effects of explanations were not supported, the use of a combined explanation indirectly influenced organizational attraction, pursuit intentions, and recommendation intentions through perceptions of procedural justice and interpersonal treatment. This study underscores the importance of comprehensive rejection information to enhance applicant reactions to AVIs (A data transparency [Supporting Information S1: Table S1] is provided).
{"title":"The Impact of Explanations on Applicant Reactions to Automated Asynchronous Video Interviews","authors":"Benjamin Falls, Colin Willis, Joshua Liff","doi":"10.1111/ijsa.70009","DOIUrl":"https://doi.org/10.1111/ijsa.70009","url":null,"abstract":"<div>\u0000 \u0000 <p>Applicants generally react less favorably to asynchronous video interviews (AVIs) in the selection process than synchronous interviews; however, explanations may improve reactions. This study applied a justice model of applicants' reactions, including formal characteristics, information given, and interpersonal treatment, to influence applicants' perceptions of AVIs. Data were collected from 380 individuals through online platforms. Participants took an AVI, were informed the interview would be scored automatically, and were rejected with either a consistency-centric (i.e., emphasizing the consistency of the selection process), opportunity-centric (i.e., emphasizing the flexibility of the process and the opportunity to perform), combined, or a simple message saying they did not score high enough. While the hypothesized main effects of explanations were not supported, the use of a combined explanation indirectly influenced organizational attraction, pursuit intentions, and recommendation intentions through perceptions of procedural justice and interpersonal treatment. This study underscores the importance of comprehensive rejection information to enhance applicant reactions to AVIs (A data transparency [Supporting Information S1: Table S1] is provided).</p></div>","PeriodicalId":51465,"journal":{"name":"International Journal of Selection and Assessment","volume":"33 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-22DOI: 10.1016/j.respol.2025.105243
Andrew Yizhou Liu
I demonstrate that the adoption of the Uniform Trade Secrets Act (UTSA) results in a 5.1% increase in employment among public firms in Compustat. The effects are concentrated in firms with below-median initial employment, higher debt costs, and greater potential for knowledge spillovers. R&D expenditures and the accumulation of intangible assets emerge as key drivers of these employment effects. An analysis of labor demand reveals a 9.4% rise in vacancy postings, particularly for skilled workers, following UTSA adoption. Counties with higher initial exposure to public firms’ labor demand experience declines in unemployment rates, underscoring the UTSA’s non-uniform impact on local labor markets and employment growth.
{"title":"Trade secrets protection and employment of public firms: Evidence from the Uniform Trade Secrets Act","authors":"Andrew Yizhou Liu","doi":"10.1016/j.respol.2025.105243","DOIUrl":"10.1016/j.respol.2025.105243","url":null,"abstract":"<div><div>I demonstrate that the adoption of the Uniform Trade Secrets Act (UTSA) results in a 5.1% increase in employment among public firms in Compustat. The effects are concentrated in firms with below-median initial employment, higher debt costs, and greater potential for knowledge spillovers. R&D expenditures and the accumulation of intangible assets emerge as key drivers of these employment effects. An analysis of labor demand reveals a 9.4% rise in vacancy postings, particularly for skilled workers, following UTSA adoption. Counties with higher initial exposure to public firms’ labor demand experience declines in unemployment rates, underscoring the UTSA’s non-uniform impact on local labor markets and employment growth.</div></div>","PeriodicalId":48466,"journal":{"name":"Research Policy","volume":"54 6","pages":"Article 105243"},"PeriodicalIF":7.5,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143854649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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