Pub Date : 2022-07-03DOI: 10.1080/07421222.2022.2096551
M. Jensen, Ryan T. Wright, Alexandra Durcikova, Shamya Karumbaiah
ABSTRACT Phishing is an increasing threat that causes billions in losses and damage to productivity, trade secrets, and reputations each year. This work explores how security gamification techniques can improve phishing reporting. We contextualized the cognitive evaluation theory (CET) as a kernel theory and constructed a prototype phishing reporting system. With three experiments in a simulated work setting, we tested gamification elements of validation, attribution, incentives, and public presentation for improvements in experiential (e.g., motivation) and instrumental outcomes (e.g., hits and false positives) in phishing reporting. Our findings suggest public attribution with rewards and punishments best balance the competing necessities of accuracy with widespread reporting. Furthermore, our results demonstrate the unique benefits of security gamification to phishing reporting over and above other phishing mitigation techniques (e.g., training and warnings). However, we also noted that unintended consequences in false alarms might arise from shifts in motivation resulting from public display of incentives. These findings suggest that carefully calibrated external incentives (rather than intrinsic rewards) are most likely to improve the ancillary task of phishing reporting.
{"title":"Improving Phishing Reporting Using Security Gamification","authors":"M. Jensen, Ryan T. Wright, Alexandra Durcikova, Shamya Karumbaiah","doi":"10.1080/07421222.2022.2096551","DOIUrl":"https://doi.org/10.1080/07421222.2022.2096551","url":null,"abstract":"ABSTRACT Phishing is an increasing threat that causes billions in losses and damage to productivity, trade secrets, and reputations each year. This work explores how security gamification techniques can improve phishing reporting. We contextualized the cognitive evaluation theory (CET) as a kernel theory and constructed a prototype phishing reporting system. With three experiments in a simulated work setting, we tested gamification elements of validation, attribution, incentives, and public presentation for improvements in experiential (e.g., motivation) and instrumental outcomes (e.g., hits and false positives) in phishing reporting. Our findings suggest public attribution with rewards and punishments best balance the competing necessities of accuracy with widespread reporting. Furthermore, our results demonstrate the unique benefits of security gamification to phishing reporting over and above other phishing mitigation techniques (e.g., training and warnings). However, we also noted that unintended consequences in false alarms might arise from shifts in motivation resulting from public display of incentives. These findings suggest that carefully calibrated external incentives (rather than intrinsic rewards) are most likely to improve the ancillary task of phishing reporting.","PeriodicalId":50154,"journal":{"name":"Journal of Management Information Systems","volume":"39 1","pages":"793 - 823"},"PeriodicalIF":7.7,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42857484","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 : 2022-07-03DOI: 10.1080/07421222.2022.2096544
B. Flyvbjerg, Alexander Budzier, Jong Seok Lee, M. Keil, Daniel Lunn, Dirk W. Bester
ABSTRACT If managers assume a normal or near-normal distribution of Information Technology (IT) project cost overruns, as is common, and cost overruns can be shown to follow a power-law distribution, managers may be unwittingly exposing their organizations to extreme risk by severely underestimating the probability of large cost overruns. In this research, we collect and analyze a large sample comprised of 5,392 IT projects to empirically examine the probability distribution of IT project cost overruns. Further, we propose and examine a mechanism that can explain such a distribution. Our results reveal that IT projects are far riskier in terms of cost than normally assumed by decision makers and scholars. Specifically, we found that IT project cost overruns follow a power-law distribution in which there are a large number of projects with relatively small overruns and a fat tail that includes a smaller number of projects with extreme overruns. A possible generative mechanism for the identified power-law distribution is found in interdependencies among technological components in IT systems. We propose and demonstrate, through computer simulation, that a problem in a single technological component can lead to chain reactions in which other interdependent components are affected, causing substantial overruns. What the power law tells us is that extreme IT project cost overruns will occur and that the prevalence of these will be grossly underestimated if managers assume that overruns follow a normal or near-normal distribution. This underscores the importance of realistically assessing and mitigating the cost risk of new IT projects up front.
{"title":"The Empirical Reality of IT Project Cost Overruns: Discovering A Power-Law Distribution","authors":"B. Flyvbjerg, Alexander Budzier, Jong Seok Lee, M. Keil, Daniel Lunn, Dirk W. Bester","doi":"10.1080/07421222.2022.2096544","DOIUrl":"https://doi.org/10.1080/07421222.2022.2096544","url":null,"abstract":"ABSTRACT If managers assume a normal or near-normal distribution of Information Technology (IT) project cost overruns, as is common, and cost overruns can be shown to follow a power-law distribution, managers may be unwittingly exposing their organizations to extreme risk by severely underestimating the probability of large cost overruns. In this research, we collect and analyze a large sample comprised of 5,392 IT projects to empirically examine the probability distribution of IT project cost overruns. Further, we propose and examine a mechanism that can explain such a distribution. Our results reveal that IT projects are far riskier in terms of cost than normally assumed by decision makers and scholars. Specifically, we found that IT project cost overruns follow a power-law distribution in which there are a large number of projects with relatively small overruns and a fat tail that includes a smaller number of projects with extreme overruns. A possible generative mechanism for the identified power-law distribution is found in interdependencies among technological components in IT systems. We propose and demonstrate, through computer simulation, that a problem in a single technological component can lead to chain reactions in which other interdependent components are affected, causing substantial overruns. What the power law tells us is that extreme IT project cost overruns will occur and that the prevalence of these will be grossly underestimated if managers assume that overruns follow a normal or near-normal distribution. This underscores the importance of realistically assessing and mitigating the cost risk of new IT projects up front.","PeriodicalId":50154,"journal":{"name":"Journal of Management Information Systems","volume":"39 1","pages":"607 - 639"},"PeriodicalIF":7.7,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49035034","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 : 2022-07-03DOI: 10.1080/07421222.2022.2096547
Jing Peng, Juheng Zhang, R. Gopal
ABSTRACT Firms and investors often react to financial news on social media. However, how they react to news of different nature and whether their reactions influence the stock market is far from clear. Employing data on financial news, tweets posted by firms and investors, and daily stock prices, we find that firms are more responsive to news with positive sentiment and low uncertainty, whereas investors are more responsive to news with high uncertainty. Moreover, the increased tweeting activities of firms and investors can improve the stock returns of firms. We further show that investors’ social media reactions to news and the subsequent influence on stock returns depend on firm size. This paper provides a fuller picture of how firms, investors, and the stock market react to financial news, and reveals the nuanced interactions among them. We discuss how firms and investors can better leverage social media to improve stock performance.
{"title":"The Good, the Bad, and the Social Media: Financial Implications of Social Media Reactions to Firm-Related News","authors":"Jing Peng, Juheng Zhang, R. Gopal","doi":"10.1080/07421222.2022.2096547","DOIUrl":"https://doi.org/10.1080/07421222.2022.2096547","url":null,"abstract":"ABSTRACT Firms and investors often react to financial news on social media. However, how they react to news of different nature and whether their reactions influence the stock market is far from clear. Employing data on financial news, tweets posted by firms and investors, and daily stock prices, we find that firms are more responsive to news with positive sentiment and low uncertainty, whereas investors are more responsive to news with high uncertainty. Moreover, the increased tweeting activities of firms and investors can improve the stock returns of firms. We further show that investors’ social media reactions to news and the subsequent influence on stock returns depend on firm size. This paper provides a fuller picture of how firms, investors, and the stock market react to financial news, and reveals the nuanced interactions among them. We discuss how firms and investors can better leverage social media to improve stock performance.","PeriodicalId":50154,"journal":{"name":"Journal of Management Information Systems","volume":"39 1","pages":"706 - 732"},"PeriodicalIF":7.7,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47886951","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 : 2022-07-03DOI: 10.1080/07421222.2022.2096546
Sabine Matook, A. Dennis, Y. Wang
ABSTRACT Social media firestorms (SMF) are commonly seen as destructive forces of toxic comments hurled at a target for perceived wrongdoing. Yet some research suggests that SMF can provide beneficial outcomes for the target. In two studies, we qualitatively examine SMF comments (in terms of purpose and tone) and quantitatively examine users’ motivations for making different types of comments. Results show that SMF comments are diverse, either supporting or condemning the target and being either aggressive or cordial in tone. Further, the results show that users’ understanding of the triggering event in the real world influences the purpose of their comment (support or condemn) while disinhibition and others’ online comments (i.e., herd influence) shape how they comment (tone). We conclude with an expanded SMF definition as “A digital artifact created by large numbers of user comments of multiple purposes (condemnation and support) and tones (aggressive and cordial) that appear rapidly and recede shortly after”. Some SMF persist as destructive and harmful firestorms; some exist to condemn the target but without aggressive language; and some support the target’s behavior. Thus, SMF are not always abusive and toxic. The implications of our research are that SMF can be positive, enable collective actions, and require a detailed examination of their elements (purpose and tone) to understand their effects in the digital and real world.
{"title":"User Comments in Social Media Firestorms: A Mixed-Method Study of Purpose, Tone, and Motivation","authors":"Sabine Matook, A. Dennis, Y. Wang","doi":"10.1080/07421222.2022.2096546","DOIUrl":"https://doi.org/10.1080/07421222.2022.2096546","url":null,"abstract":"ABSTRACT Social media firestorms (SMF) are commonly seen as destructive forces of toxic comments hurled at a target for perceived wrongdoing. Yet some research suggests that SMF can provide beneficial outcomes for the target. In two studies, we qualitatively examine SMF comments (in terms of purpose and tone) and quantitatively examine users’ motivations for making different types of comments. Results show that SMF comments are diverse, either supporting or condemning the target and being either aggressive or cordial in tone. Further, the results show that users’ understanding of the triggering event in the real world influences the purpose of their comment (support or condemn) while disinhibition and others’ online comments (i.e., herd influence) shape how they comment (tone). We conclude with an expanded SMF definition as “A digital artifact created by large numbers of user comments of multiple purposes (condemnation and support) and tones (aggressive and cordial) that appear rapidly and recede shortly after”. Some SMF persist as destructive and harmful firestorms; some exist to condemn the target but without aggressive language; and some support the target’s behavior. Thus, SMF are not always abusive and toxic. The implications of our research are that SMF can be positive, enable collective actions, and require a detailed examination of their elements (purpose and tone) to understand their effects in the digital and real world.","PeriodicalId":50154,"journal":{"name":"Journal of Management Information Systems","volume":"39 1","pages":"673 - 705"},"PeriodicalIF":7.7,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49536319","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 : 2022-07-03DOI: 10.1080/07421222.2022.2096542
Mei Xue, Xing Cao, Xu Feng, Bin Gu, Yongjie Zhang
ABSTRACT As a general-purpose technology, artificial intelligence (AI) is expected to transform almost all industries and aspects of our society. Thus, it is important to understand the potential changes within the firms related to how AI applications change their labor force. Using a panel dataset with over 1,300 publicly-traded companies in China from 2007 to 2018, we examine the relationship between AI applications and firm labor structure with workers with or without formal college education. The study indicates that AI applications were positively associated with the overall employment as well as the employment of nonacademically- trained workers with no college degrees at the firm level. These associations were more significant in the service sector than in the manufacturing sector. Further causal analysis shows increasing AI applications have a positive effect on a firm’s employment of nonacademically-trained workers and its overall employment but a negative effect on academically-trained workers. We attribute the findings to the technology deskilling effect of AI. The findings suggest that, in response to the potential labor force transformation with increasing AI applications, information-systems research needs to focus on structural changes of labor forces and the implications for preparing human employees to work with AI side by side.
{"title":"Is College Education Less Necessary with AI? Evidence from Firm-Level Labor Structure Changes","authors":"Mei Xue, Xing Cao, Xu Feng, Bin Gu, Yongjie Zhang","doi":"10.1080/07421222.2022.2096542","DOIUrl":"https://doi.org/10.1080/07421222.2022.2096542","url":null,"abstract":"ABSTRACT As a general-purpose technology, artificial intelligence (AI) is expected to transform almost all industries and aspects of our society. Thus, it is important to understand the potential changes within the firms related to how AI applications change their labor force. Using a panel dataset with over 1,300 publicly-traded companies in China from 2007 to 2018, we examine the relationship between AI applications and firm labor structure with workers with or without formal college education. The study indicates that AI applications were positively associated with the overall employment as well as the employment of nonacademically- trained workers with no college degrees at the firm level. These associations were more significant in the service sector than in the manufacturing sector. Further causal analysis shows increasing AI applications have a positive effect on a firm’s employment of nonacademically-trained workers and its overall employment but a negative effect on academically-trained workers. We attribute the findings to the technology deskilling effect of AI. The findings suggest that, in response to the potential labor force transformation with increasing AI applications, information-systems research needs to focus on structural changes of labor forces and the implications for preparing human employees to work with AI side by side.","PeriodicalId":50154,"journal":{"name":"Journal of Management Information Systems","volume":"39 1","pages":"865 - 905"},"PeriodicalIF":7.7,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47637482","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 : 2022-07-03DOI: 10.1080/07421222.2022.2096549
Sepideh Ebrahimi, Maryam Ghasemaghaei, I. Benbasat
ABSTRACT Research on recommendation agents (RAs) originally focused on interactive RAs, which rely on explicit methods, i.e., eliciting user-provided inputs to learn about consumers’ needs and preferences. Recently, due to the availability of large amounts of data about individuals, the focus shifted toward non-interactive RAs that use implicit methods rather than explicit ones to understand users’ needs. This paper examined the differences between interactive and non-interactive RA types in terms of how they influence the impacts of two important antecedents of RA adoption, namely recommendation quality and trust on users’ cognitive and affective attitudes and behavioral intention. To that end, we developed a set of hypotheses and tested them empirically using a meta-analytic structural equation modeling approach. Our findings provide strong support for the influence of interactivity on RA users’ attitudes and cognitions. While we found that recommendation quality exerts a strong influence on consumers’ cognitive attitudes toward interactive RAs, this influence is statistically non-significant in the context of non-interactive RAs, in which recommendation quality mainly drives consumers’ affective attitudes toward the agent. Furthermore, while we found that cognitive attitudes exert a stronger influence than affective ones on consumers’ adoption of non-interactive RAs, our results indicate that the reverse is true with interactive RAs. Given the recent rise in the popularity of non-interactive RA tools, our results carry important implications for researchers and practitioners. Specifically, this study contributes to the extensive literature on consumers’ use of RAs by providing a better understanding of the differences between interactive and non-interactive RAs. For practitioners, the findings provide guidance for designers and providers of RAs on developing and improving RAs that are more likely to be adopted by consumers.
{"title":"The Impact of Trust and Recommendation Quality on Adopting Interactive and Non-Interactive Recommendation Agents: A Meta-Analysis","authors":"Sepideh Ebrahimi, Maryam Ghasemaghaei, I. Benbasat","doi":"10.1080/07421222.2022.2096549","DOIUrl":"https://doi.org/10.1080/07421222.2022.2096549","url":null,"abstract":"ABSTRACT Research on recommendation agents (RAs) originally focused on interactive RAs, which rely on explicit methods, i.e., eliciting user-provided inputs to learn about consumers’ needs and preferences. Recently, due to the availability of large amounts of data about individuals, the focus shifted toward non-interactive RAs that use implicit methods rather than explicit ones to understand users’ needs. This paper examined the differences between interactive and non-interactive RA types in terms of how they influence the impacts of two important antecedents of RA adoption, namely recommendation quality and trust on users’ cognitive and affective attitudes and behavioral intention. To that end, we developed a set of hypotheses and tested them empirically using a meta-analytic structural equation modeling approach. Our findings provide strong support for the influence of interactivity on RA users’ attitudes and cognitions. While we found that recommendation quality exerts a strong influence on consumers’ cognitive attitudes toward interactive RAs, this influence is statistically non-significant in the context of non-interactive RAs, in which recommendation quality mainly drives consumers’ affective attitudes toward the agent. Furthermore, while we found that cognitive attitudes exert a stronger influence than affective ones on consumers’ adoption of non-interactive RAs, our results indicate that the reverse is true with interactive RAs. Given the recent rise in the popularity of non-interactive RA tools, our results carry important implications for researchers and practitioners. Specifically, this study contributes to the extensive literature on consumers’ use of RAs by providing a better understanding of the differences between interactive and non-interactive RAs. For practitioners, the findings provide guidance for designers and providers of RAs on developing and improving RAs that are more likely to be adopted by consumers.","PeriodicalId":50154,"journal":{"name":"Journal of Management Information Systems","volume":"39 1","pages":"733 - 764"},"PeriodicalIF":7.7,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41583349","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 : 2022-07-03DOI: 10.1080/07421222.2022.2096545
N. Berente, C. Salge, Venkata K.P. Mallampalli, Kenneth J. Park
ABSTRACT Project escalation involves the continued, persistent commitment to a failing project. Through a qualitative meta-analysis of 15 published cases of large information systems (IS) projects in escalation situations, we develop an institutional perspective on IS projects in escalation situations. This perspective describes how project persistence emerges from a plurality of legitimizing institutional logics that decision-makers draw upon at different project stages to maintain and reduce their commitment to the project. Logics related to the project’s approval are not the same logics that guide decisions throughout the project. For example, while we find that innovation and economic logics of return on investment are salient before approval, economic costs tend to be more salient after approval, along with technical impositions and managerial concerns. We further find that managerial logics are particularly salient in reducing commitment to projects, and we detail the differences and point out contextual triggers of external scrutiny and leadership changes that can contribute to reduced commitment to a project and eventual de-escalation.
{"title":"Rethinking Project Escalation: An Institutional Perspective on the Persistence of Failing Large-Scale Information System Projects","authors":"N. Berente, C. Salge, Venkata K.P. Mallampalli, Kenneth J. Park","doi":"10.1080/07421222.2022.2096545","DOIUrl":"https://doi.org/10.1080/07421222.2022.2096545","url":null,"abstract":"ABSTRACT Project escalation involves the continued, persistent commitment to a failing project. Through a qualitative meta-analysis of 15 published cases of large information systems (IS) projects in escalation situations, we develop an institutional perspective on IS projects in escalation situations. This perspective describes how project persistence emerges from a plurality of legitimizing institutional logics that decision-makers draw upon at different project stages to maintain and reduce their commitment to the project. Logics related to the project’s approval are not the same logics that guide decisions throughout the project. For example, while we find that innovation and economic logics of return on investment are salient before approval, economic costs tend to be more salient after approval, along with technical impositions and managerial concerns. We further find that managerial logics are particularly salient in reducing commitment to projects, and we detail the differences and point out contextual triggers of external scrutiny and leadership changes that can contribute to reduced commitment to a project and eventual de-escalation.","PeriodicalId":50154,"journal":{"name":"Journal of Management Information Systems","volume":"39 1","pages":"640 - 672"},"PeriodicalIF":7.7,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45420533","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 : 2022-04-03DOI: 10.1080/07421222.2022.2063549
Weifeng Li, Yidong Chai
ABSTRACT As predictive analytics increasingly applies supervised machine learning (SML) models to inform mission-critical decision-making, adversaries become incentivized to exploit the vulnerabilities of these SML models and mislead predictive analytics into erroneous decisions. Due to the limited understanding and awareness of such adversarial attacks, the predictive analytics knowledge and deployment need a principled technique for adversarial robustness assessment and enhancement. In this research, we leverage the technology threat avoidance theory as the kernel theory and propose a research framework for assessing and enhancing the adversarial robustness of predictive analytics applications. We instantiate the proposed framework by developing a robust text classification system, the ARText system. The proposed system is rigorously evaluated in comparison with benchmark methods on two tasks extensively enabled by SML: spam review detection and spam email detection, which then confirmed the utility and effectiveness of our ARText system. Results from numerous experiments revealed that our proposed framework could significantly enhance the adversarial robustness of predictive analytics applications.
{"title":"Assessing and Enhancing Adversarial Robustness of Predictive Analytics: An Empirically Tested Design Framework","authors":"Weifeng Li, Yidong Chai","doi":"10.1080/07421222.2022.2063549","DOIUrl":"https://doi.org/10.1080/07421222.2022.2063549","url":null,"abstract":"ABSTRACT As predictive analytics increasingly applies supervised machine learning (SML) models to inform mission-critical decision-making, adversaries become incentivized to exploit the vulnerabilities of these SML models and mislead predictive analytics into erroneous decisions. Due to the limited understanding and awareness of such adversarial attacks, the predictive analytics knowledge and deployment need a principled technique for adversarial robustness assessment and enhancement. In this research, we leverage the technology threat avoidance theory as the kernel theory and propose a research framework for assessing and enhancing the adversarial robustness of predictive analytics applications. We instantiate the proposed framework by developing a robust text classification system, the ARText system. The proposed system is rigorously evaluated in comparison with benchmark methods on two tasks extensively enabled by SML: spam review detection and spam email detection, which then confirmed the utility and effectiveness of our ARText system. Results from numerous experiments revealed that our proposed framework could significantly enhance the adversarial robustness of predictive analytics applications.","PeriodicalId":50154,"journal":{"name":"Journal of Management Information Systems","volume":"39 1","pages":"542 - 572"},"PeriodicalIF":7.7,"publicationDate":"2022-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48722390","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 : 2022-04-03DOI: 10.1080/07421222.2022.2063553
Sangseok You, C. Yang, Xitong Li
ABSTRACT We propose a theoretical model based on the judge-advisor system (JAS) and empirically examine how algorithmic advice, compared to identical advice from humans, influences human judgment. This effect is contingent on the level of transparency, which varies with whether and how the prediction performance of the advice source is presented. In a series of five controlled behavioral experiments, we show that individuals largely exhibit algorithm appreciation; that is, they follow algorithmic advice to a greater extent than identical human advice due to a higher trust in an algorithmic than human advisor. Interestingly, neither the extent of higher trust in algorithmic advisors nor the level of algorithm appreciation decreases when individuals are informed of the algorithm’s prediction errors (i.e., upon presenting prediction performance in an aggregated format). By contrast, algorithm appreciation declines when the transparency of the advice source’s prediction performance further increases through an elaborated format. This is plausibly because the greater cognitive load imposed by the elaborated format impedes advice taking. Finally, we identify a boundary condition: algorithm appreciation is reduced for individuals with a lower dispositional need for cognition. Our findings provide key implications for research and managerial practice.
{"title":"Algorithmic versus Human Advice: Does Presenting Prediction Performance Matter for Algorithm Appreciation?","authors":"Sangseok You, C. Yang, Xitong Li","doi":"10.1080/07421222.2022.2063553","DOIUrl":"https://doi.org/10.1080/07421222.2022.2063553","url":null,"abstract":"ABSTRACT We propose a theoretical model based on the judge-advisor system (JAS) and empirically examine how algorithmic advice, compared to identical advice from humans, influences human judgment. This effect is contingent on the level of transparency, which varies with whether and how the prediction performance of the advice source is presented. In a series of five controlled behavioral experiments, we show that individuals largely exhibit algorithm appreciation; that is, they follow algorithmic advice to a greater extent than identical human advice due to a higher trust in an algorithmic than human advisor. Interestingly, neither the extent of higher trust in algorithmic advisors nor the level of algorithm appreciation decreases when individuals are informed of the algorithm’s prediction errors (i.e., upon presenting prediction performance in an aggregated format). By contrast, algorithm appreciation declines when the transparency of the advice source’s prediction performance further increases through an elaborated format. This is plausibly because the greater cognitive load imposed by the elaborated format impedes advice taking. Finally, we identify a boundary condition: algorithm appreciation is reduced for individuals with a lower dispositional need for cognition. Our findings provide key implications for research and managerial practice.","PeriodicalId":50154,"journal":{"name":"Journal of Management Information Systems","volume":"39 1","pages":"336 - 365"},"PeriodicalIF":7.7,"publicationDate":"2022-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47541999","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 : 2022-04-03DOI: 10.1080/07421222.2022.2063556
W. Cram, Martin Wiener, Monideepa Tarafdar, Alexander Benlian
ABSTRACT This study examines how the use of algorithmic control within gig economy platforms relates to the well-being and behavior of workers. Specifically, we explore how two different forms of algorithmic control—gatekeeping and guiding—correspond with (positive) challenge technostressors and (negative) threat technostressors experienced by Uber drivers. We also examine the moderating impact of algorithmic control transparency on these relationships, as well as the outcomes of technostressors in terms of continuance intentions and workaround use. Based on a survey of 621 U.S.-based Uber drivers, we find that gatekeeping and guiding algorithmic control positively relate to both challenge and threat technostressors. The study bridges the literature on control and technostress by conceptualizing algorithmic control as a condition that puts workers under stress. This stress is found to contribute to important behavioral consequences pertaining to both continuance intentions and workaround use. Findings from our work suggest that gig economy organizations can use algorithmic control to enhance challenge technostressors for their workers, thereby contributing to the cultivation of a more committed workforce. Furthermore, we find evidence disputing the assumption that algorithmic control transparency can mitigate the negative effects of threat technostressors.
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