Pub Date : 2024-03-01DOI: 10.25300/misq/2023/16499
Hani Safadi
Enterprise social media (ESM) is changing how knowledge workers interact and share information; however, a debate persists as to whether ESM is an adequate knowledge management system. ESM provides a rich set of affordances for organizational knowledge work, such as improved organizational memory, but also constrains knowledge work performance because of digital interruptions. Extending and complementing existing scholarship, this study asks the following research question: How can organizations design ESM to realize its knowledge work benefits? Using a computational agent-based model that incorporates the design features of ESM, workers’ attitudes, and resulting ESM-use affordances and constraints, this study shows how ESM-use outcomes are contingent both on the design of and users’ attitudes toward ESM. Specifically, the negative effects of ESM interactivity are mitigated when employees have a low transparency preference and access ESM without posting as much. The study further unpacks asymmetric engagement as the mechanism that leads low transparency configurations to be more resilient to the negative effects of interruptions driven by ESM interactivity. Asymmetric engagement—learning from posted content without interacting often—enables the gradual creation of organizational memory while maintaining a broad user base by minimizing interruptions. These results ultimately contribute a multilevel model of ESM use and knowledge work outcomes, enhancing the theoretical understanding of previously studied mechanisms such as communication visibility and providing implications for organizations designing ESM.
{"title":"Balancing Affordances and Constraints: Designing Enterprise Social Media for Organizational Knowledge Work","authors":"Hani Safadi","doi":"10.25300/misq/2023/16499","DOIUrl":"https://doi.org/10.25300/misq/2023/16499","url":null,"abstract":"<style>#html-body [data-pb-style=MMV4CPM]{justify-content:flex-start;display:flex;flex-direction:column;background-position:left top;background-size:cover;background-repeat:no-repeat;background-attachment:scroll}</style>Enterprise social media (ESM) is changing how knowledge workers interact and share information; however, a debate persists as to whether ESM is an adequate knowledge management system. ESM provides a rich set of affordances for organizational knowledge work, such as improved organizational memory, but also constrains knowledge work performance because of digital interruptions. Extending and complementing existing scholarship, this study asks the following research question: How can organizations design ESM to realize its knowledge work benefits? Using a computational agent-based model that incorporates the design features of ESM, workers’ attitudes, and resulting ESM-use affordances and constraints, this study shows how ESM-use outcomes are contingent both on the design of and users’ attitudes toward ESM. Specifically, the negative effects of ESM interactivity are mitigated when employees have a low transparency preference and access ESM without posting as much. The study further unpacks asymmetric engagement as the mechanism that leads low transparency configurations to be more resilient to the negative effects of interruptions driven by ESM interactivity. Asymmetric engagement—learning from posted content without interacting often—enables the gradual creation of organizational memory while maintaining a broad user base by minimizing interruptions. These results ultimately contribute a multilevel model of ESM use and knowledge work outcomes, enhancing the theoretical understanding of previously studied mechanisms such as communication visibility and providing implications for organizations designing ESM.","PeriodicalId":49807,"journal":{"name":"Mis Quarterly","volume":"3 1","pages":""},"PeriodicalIF":7.3,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140015673","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 : 2024-03-01DOI: 10.25300/misq/2023/16470
Inmyung Choi, David E. Cantor, Kunsoo Han, Joey F. George
Digital strategic posture (DSP) is defined as a firm’s overall strategic stance toward investing in information technology (IT) initiatives relative to that of rival firms. This study examines how a firm’s DSP affects firm performance. Drawing on the competitive dynamics perspective and contingency view, we demonstrate that DSP influences competitive actions through dual pathways. First, DSP enables firms to take competitive actions that are more appropriate given the level of environmental uncertainty (captured by industry dynamism). In particular, our findings suggest that a proactive DSP enables relatively more innovation-oriented actions in dynamic industries while enabling relatively more operations-oriented actions in less dynamic industries. Second, DSP plays a facilitating role in firms’ execution of competitive actions such that a firm’s value from its proactive DSP is enhanced when there is a fit between the type of the firm’s competitive actions and its level of environmental uncertainty. Specifically, we find that firms with a more proactive DSP achieve superior firm performance from innovation-oriented actions in dynamic industries and from operations-oriented actions in less dynamic industries. Taken together, our findings suggest that a proactive DSP not only allows firms to take appropriate competitive actions that fit their environmental conditions but also contributes to firms’ performance by facilitating the execution of these appropriate actions, thus enhancing their efficacy.
{"title":"Dual Pathways of Value Creation from Digital Strategic Posture: Contingent Effects of Competitive Actions and Environmental Uncertainty","authors":"Inmyung Choi, David E. Cantor, Kunsoo Han, Joey F. George","doi":"10.25300/misq/2023/16470","DOIUrl":"https://doi.org/10.25300/misq/2023/16470","url":null,"abstract":"<style>#html-body [data-pb-style=V0XPLF9]{justify-content:flex-start;display:flex;flex-direction:column;background-position:left top;background-size:cover;background-repeat:no-repeat;background-attachment:scroll}</style>Digital strategic posture (DSP) is defined as a firm’s overall strategic stance toward investing in information technology (IT) initiatives relative to that of rival firms. This study examines how a firm’s DSP affects firm performance. Drawing on the competitive dynamics perspective and contingency view, we demonstrate that DSP influences competitive actions through dual pathways. First, DSP enables firms to take competitive actions that are more appropriate given the level of environmental uncertainty (captured by industry dynamism). In particular, our findings suggest that a proactive DSP enables relatively more innovation-oriented actions in dynamic industries while enabling relatively more operations-oriented actions in less dynamic industries. Second, DSP plays a facilitating role in firms’ execution of competitive actions such that a firm’s value from its proactive DSP is enhanced when there is a fit between the type of the firm’s competitive actions and its level of environmental uncertainty. Specifically, we find that firms with a more proactive DSP achieve superior firm performance from innovation-oriented actions in dynamic industries and from operations-oriented actions in less dynamic industries. Taken together, our findings suggest that a proactive DSP not only allows firms to take appropriate competitive actions that fit their environmental conditions but also contributes to firms’ performance by facilitating the execution of these appropriate actions, thus enhancing their efficacy.","PeriodicalId":49807,"journal":{"name":"Mis Quarterly","volume":"32 1","pages":""},"PeriodicalIF":7.3,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140015597","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 : 2024-03-01DOI: 10.25300/misq/2023/17012
Zhou Zhou, Lingling Zhang, Marshall Van. Alstyne
Extant research has popularized the perspective that strong network effects produce “winner-take-all” outcomes, which leads platforms to invest in user growth and encourages investors to subsidize these platforms. However, user growth does not necessarily imply strong user stickiness. Without user stickiness, strong network effects in the current period may fade in future periods, thus rendering a user growth strategy ineffective. By adding a time dimension to network effects, we developed a model of cross-period and within-period network effects to explain how different types of network effects drive value. We emphasize that the cross-period same-side network effect contributes to user stickiness, while the within-period cross-side network effect persists conditional on user stickiness. We propose that one reason for platforms having heterogeneous cross-period same-side network effects is because of the “product learning” mechanism: it is expected that products with higher uncertainty have a stronger cross-period same-side network effect. Based on different drivers, we extend the customer lifetime value model (CLV2) to two-sided platform markets, allowing us to measure how different interventions drive platform value. Using Groupon data, we verify our insights and discuss platform design choices that enhance user stickiness when the cross-period same-side network effect is weak.
{"title":"How Users Drive Value in Two-Sided Markets: Platform Designs That Matter","authors":"Zhou Zhou, Lingling Zhang, Marshall Van. Alstyne","doi":"10.25300/misq/2023/17012","DOIUrl":"https://doi.org/10.25300/misq/2023/17012","url":null,"abstract":"<style>#html-body [data-pb-style=T2VF659]{justify-content:flex-start;display:flex;flex-direction:column;background-position:left top;background-size:cover;background-repeat:no-repeat;background-attachment:scroll}</style>Extant research has popularized the perspective that strong network effects produce “winner-take-all” outcomes, which leads platforms to invest in user growth and encourages investors to subsidize these platforms. However, user growth does not necessarily imply strong user stickiness. Without user stickiness, strong network effects in the current period may fade in future periods, thus rendering a user growth strategy ineffective. By adding a time dimension to network effects, we developed a model of cross-period and within-period network effects to explain how different types of network effects drive value. We emphasize that the cross-period same-side network effect contributes to user stickiness, while the within-period cross-side network effect persists conditional on user stickiness. We propose that one reason for platforms having heterogeneous cross-period same-side network effects is because of the “product learning” mechanism: it is expected that products with higher uncertainty have a stronger cross-period same-side network effect. Based on different drivers, we extend the customer lifetime value model (CLV2) to two-sided platform markets, allowing us to measure how different interventions drive platform value. Using Groupon data, we verify our insights and discuss platform design choices that enhance user stickiness when the cross-period same-side network effect is weak.","PeriodicalId":49807,"journal":{"name":"Mis Quarterly","volume":"27 1","pages":""},"PeriodicalIF":7.3,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140015704","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 : 2024-03-01DOI: 10.25300/misq/2023/16752
Pranav Gupta, Young Ji Kim, Ella Glikson, Anita Williams Woolley
The dramatic expansion of internet communication tools has led to the increased use of temporary online groups to solve problems, provide services, or produce new knowledge. However, many of these groups need help to collaborate effectively. The rapid development of new tools and collaboration forms requires ongoing experimentation to develop and test new ways to support this novel form of teamwork. Building on research demonstrating the use of nudges to shape behavior, we report the results of an experiment to nudge teamwork in 168 temporary online groups randomly assigned to one of four different nudge treatments. Each nudge was designed to spur one of three targeted collaborative processes (collaborator skill use, effective task strategy, and the level of collective effort) demonstrated to enhance collective intelligence in extant research. Our results support the basic notion that digitally nudging collaborative processes can improve collective intelligence. However, to our surprise, a couple of nudges had unintended negative effects and ultimately decreased collective intelligence. We discuss our results using structured speculation to systematically consider the conditions under which we would or would not expect the same patterns to materialize in order to clearly articulate directions for future research.
{"title":"Using Digital Nudges to Enhance Collective Intelligence in Online Collaboration: Insights from Unexpected Outcomes","authors":"Pranav Gupta, Young Ji Kim, Ella Glikson, Anita Williams Woolley","doi":"10.25300/misq/2023/16752","DOIUrl":"https://doi.org/10.25300/misq/2023/16752","url":null,"abstract":"<style>#html-body [data-pb-style=F1YMG1X]{justify-content:flex-start;display:flex;flex-direction:column;background-position:left top;background-size:cover;background-repeat:no-repeat;background-attachment:scroll}</style>The dramatic expansion of internet communication tools has led to the increased use of temporary online groups to solve problems, provide services, or produce new knowledge. However, many of these groups need help to collaborate effectively. The rapid development of new tools and collaboration forms requires ongoing experimentation to develop and test new ways to support this novel form of teamwork. Building on research demonstrating the use of nudges to shape behavior, we report the results of an experiment to nudge teamwork in 168 temporary online groups randomly assigned to one of four different nudge treatments. Each nudge was designed to spur one of three targeted collaborative processes (collaborator skill use, effective task strategy, and the level of collective effort) demonstrated to enhance collective intelligence in extant research. Our results support the basic notion that digitally nudging collaborative processes can improve collective intelligence. However, to our surprise, a couple of nudges had unintended negative effects and ultimately decreased collective intelligence. We discuss our results using structured speculation to systematically consider the conditions under which we would or would not expect the same patterns to materialize in order to clearly articulate directions for future research.","PeriodicalId":49807,"journal":{"name":"Mis Quarterly","volume":"22 1","pages":""},"PeriodicalIF":7.3,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140018928","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 : 2024-03-01DOI: 10.25300/misq/2023/17690
Eleunthia Wong Ellinger, Robert Wayne Gregory, Tobias Mini, Thomas Widjaja, Ola Henfridsson
Decentralized autonomous organizations (DAOs)—collectively owned human-machine systems deployed on a blockchain that self-govern through smart contracts and the voluntary contributions of autonomous community members—exhibit the potential to facilitate collective action in managing digital commons. Yet the promise of decentralization and collective action is difficult to sustain. To this end, this paper critically examines the transformational potential of DAOs in the case of decentralized finance. Using a polycentric governance lens, we contribute to the literature on technology-enabled forms of organizing with a model explaining the transformational potential of DAOs to facilitate collective action in digital commons. Our study highlights that (1) DAOs are a new form of organizing enabled by blockchain technology in which individuals are free to pursue their objectives within a general system of rules enforced by smart contracts, (2) collective action for managing digital commons can be sustained through a set of three mechanisms—sustained participation, collective direction, and scaled organizing, and (3) DAOs tend to strike a balance between centralized and fully decentralized or community-based governance by implementing a polycentric governance system involving a combination of human and machine agency that creates skin in the game.
#html-body [data-pb-style=GXH662Y]{justify-content:flex-start;display:flex;flex-direction:column;background-position:left top;background-size:cover;background-repeat:no-repeat;background-attachment：scroll}去中心化自治组织（DAOs）--部署在区块链上的集体所有的人机系统，通过智能合约和自治社区成员的自愿贡献进行自我管理--展示了促进集体行动管理数字公域的潜力。然而，去中心化和集体行动的承诺很难持久。为此，本文以去中心化金融为例，批判性地研究了 DAOs 的变革潜力。利用多中心治理视角，我们通过一个模型解释了 DAO 在促进数字公地集体行动方面的转型潜力，为有关技术驱动的组织形式的文献做出了贡献。我们的研究强调：(1) DAO 是区块链技术促成的一种新的组织形式，在这种组织形式中，个人可以在智能合约执行的通用规则体系内自由追求自己的目标；(2) 管理数字公域的集体行动可以通过一系列三种机制来维持--持续参与、集体指导和规模化组织；(3) DAO 倾向于通过实施多中心治理系统，在中心化治理和完全去中心化治理或基于社区的治理之间取得平衡，这种多中心治理系统涉及人类和机器机构的结合，从而在游戏中创造出利益。
{"title":"Skin in the Game: The Transformational Potential of Decentralized Autonomous Organizations","authors":"Eleunthia Wong Ellinger, Robert Wayne Gregory, Tobias Mini, Thomas Widjaja, Ola Henfridsson","doi":"10.25300/misq/2023/17690","DOIUrl":"https://doi.org/10.25300/misq/2023/17690","url":null,"abstract":"<style>#html-body [data-pb-style=GXH662Y]{justify-content:flex-start;display:flex;flex-direction:column;background-position:left top;background-size:cover;background-repeat:no-repeat;background-attachment:scroll}</style>Decentralized autonomous organizations (DAOs)—collectively owned human-machine systems deployed on a blockchain that self-govern through smart contracts and the voluntary contributions of autonomous community members—exhibit the potential to facilitate collective action in managing digital commons. Yet the promise of decentralization and collective action is difficult to sustain. To this end, this paper critically examines the transformational potential of DAOs in the case of decentralized finance. Using a polycentric governance lens, we contribute to the literature on technology-enabled forms of organizing with a model explaining the transformational potential of DAOs to facilitate collective action in digital commons. Our study highlights that (1) DAOs are a new form of organizing enabled by blockchain technology in which individuals are free to pursue their objectives within a general system of rules enforced by smart contracts, (2) collective action for managing digital commons can be sustained through a set of three mechanisms—sustained participation, collective direction, and scaled organizing, and (3) DAOs tend to strike a balance between centralized and fully decentralized or community-based governance by implementing a polycentric governance system involving a combination of human and machine agency that creates skin in the game.","PeriodicalId":49807,"journal":{"name":"Mis Quarterly","volume":"10 1","pages":""},"PeriodicalIF":7.3,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140015687","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 : 2024-03-01DOI: 10.25300/misq/2023/16789
Aron Lindberg, Aaron Schecter, Nicholas Berente, Phil Hennel, Kalle Lyytinen
In this study we identify a process of “entrainment” around open source software (OSS) development release cycles to capture patterns of self-organized task allocation among developers. We conducted an abductive, computationally intensive study of eight OSS projects, using relational event modeling to analyze 1,169,489 actions covering 93 major software releases. The process of entrainment that we identify involves three task allocation mechanisms: (1) developer-issue inertia, (2) developer contribution frequency, and (3) issue popularity. Our analysis demonstrates that these mechanisms and the phases of the release cycle entrain each other. Before a major release, developers engage in a concentrated mobilization phase, whereby they democratize development activity and increasingly allocate community contributions to the set of issues related to the release. After a major release, the extended cleanup phase garners a greater share of development work from recently highly active developers and dilutes the activity of these developers across a wider range of issues. Our theorizing suggests that major releases constitute important events around which OSS communities self-organize and we characterize how this occurs. Our research contributes to theorizing on organizing in OSS communities by explaining how self-organizing task allocation interacts with release cycles through the mechanism of entrainment. We also contribute to the literature on entrainment by showing how it may unfold in the context of online peer production communities such as OSS.
{"title":"The Entrainment of Task Allocation and Release Cycles in Open Source Software Development","authors":"Aron Lindberg, Aaron Schecter, Nicholas Berente, Phil Hennel, Kalle Lyytinen","doi":"10.25300/misq/2023/16789","DOIUrl":"https://doi.org/10.25300/misq/2023/16789","url":null,"abstract":"<style>#html-body [data-pb-style=FOXJBQ7]{justify-content:flex-start;display:flex;flex-direction:column;background-position:left top;background-size:cover;background-repeat:no-repeat;background-attachment:scroll}</style>In this study we identify a process of “entrainment” around open source software (OSS) development release cycles to capture patterns of self-organized task allocation among developers. We conducted an abductive, computationally intensive study of eight OSS projects, using relational event modeling to analyze 1,169,489 actions covering 93 major software releases. The process of entrainment that we identify involves three task allocation mechanisms: (1) developer-issue inertia, (2) developer contribution frequency, and (3) issue popularity. Our analysis demonstrates that these mechanisms and the phases of the release cycle entrain each other. Before a major release, developers engage in a concentrated mobilization phase, whereby they democratize development activity and increasingly allocate community contributions to the set of issues related to the release. After a major release, the extended cleanup phase garners a greater share of development work from recently highly active developers and dilutes the activity of these developers across a wider range of issues. Our theorizing suggests that major releases constitute important events around which OSS communities self-organize and we characterize how this occurs. Our research contributes to theorizing on organizing in OSS communities by explaining how self-organizing task allocation interacts with release cycles through the mechanism of entrainment. We also contribute to the literature on entrainment by showing how it may unfold in the context of online peer production communities such as OSS.","PeriodicalId":49807,"journal":{"name":"Mis Quarterly","volume":"53 1","pages":""},"PeriodicalIF":7.3,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140015574","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 : 2024-03-01DOI: 10.25300/misq/2023/17707
W. Alec Cram, John D'Arcy, Alexander Benlian
Many of the theories used in behavioral cybersecurity research have been applied with a nomothetic approach, which is characterized by cross-sectional data (e.g., one-time surveys) that identify patterns across a population of individuals. Although this can provide valuable between-person, point-in-time insights (e.g., employees who use neutralization techniques, such as denying responsibility for cybersecurity policy violations, tend to comply less), it is unable to reveal within-person patterns that account for varying experiences and situations over time. This paper articulates why an idiographic approach, which undertakes a within-person analysis of longitudinal data, can: (1) help validate widely used theories in behavioral cybersecurity research that imply patterns of behavior within a given person over time and (2) provide distinct theoretical insights on behavioral cybersecurity phenomena by accounting for such within-person patterns. To these ends, we apply an idiographic approach to an established theory in behavioral cybersecurity research—neutralization theory—and empirically test a within-person variant of this theory using a four-week experience sampling study. Our results support a more granular application of neutralization theory in the cybersecurity context that considers the behavior of a given person over time. We conclude the paper by highlighting the contexts and theories that provide the most promising opportunities for future behavioral cybersecurity research using an idiographic approach.
{"title":"Time Will Tell: The Case for an Idiographic Approach to Behavioral Cybersecurity Research","authors":"W. Alec Cram, John D'Arcy, Alexander Benlian","doi":"10.25300/misq/2023/17707","DOIUrl":"https://doi.org/10.25300/misq/2023/17707","url":null,"abstract":"<style>#html-body [data-pb-style=K5SA9L3]{justify-content:flex-start;display:flex;flex-direction:column;background-position:left top;background-size:cover;background-repeat:no-repeat;background-attachment:scroll}</style>Many of the theories used in behavioral cybersecurity research have been applied with a nomothetic approach, which is characterized by cross-sectional data (e.g., one-time surveys) that identify patterns across a population of individuals. Although this can provide valuable between-person, point-in-time insights (e.g., employees who use neutralization techniques, such as denying responsibility for cybersecurity policy violations, tend to comply less), it is unable to reveal within-person patterns that account for varying experiences and situations over time. This paper articulates why an idiographic approach, which undertakes a within-person analysis of longitudinal data, can: (1) help validate widely used theories in behavioral cybersecurity research that imply patterns of behavior within a given person over time and (2) provide distinct theoretical insights on behavioral cybersecurity phenomena by accounting for such within-person patterns. To these ends, we apply an idiographic approach to an established theory in behavioral cybersecurity research—neutralization theory—and empirically test a within-person variant of this theory using a four-week experience sampling study. Our results support a more granular application of neutralization theory in the cybersecurity context that considers the behavior of a given person over time. We conclude the paper by highlighting the contexts and theories that provide the most promising opportunities for future behavioral cybersecurity research using an idiographic approach.","PeriodicalId":49807,"journal":{"name":"Mis Quarterly","volume":"34 1","pages":""},"PeriodicalIF":7.3,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140015671","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 : 2024-03-01DOI: 10.25300/misq/2023/17316
Benjamin M. Ampel, Sagar Samtani, Hongyi Zhu, Hsinchun Chen
The rapid proliferation of complex information systems has been met by an ever-increasing quantity of exploits that can cause irreparable cyber breaches. To mitigate these cyber threats, academia and industry have placed a significant focus on proactively identifying and labeling exploits developed by the international hacker community. However, prevailing approaches for labeling exploits in hacker forums do not leverage metadata from exploit darknet markets or public exploit repositories to enhance labeling performance. In this study, we adopted the computational design science paradigm to develop a novel information technology artifact, the deep transfer learning exploit labeler (DTL-EL). DTL-EL incorporates a pre-initialization design, multi-layer deep transfer learning (DTL), and a self-attention mechanism to automatically label exploits in hacker forums. We rigorously evaluated the proposed DTL-EL against state-of-the-art non-DTL benchmark methods based in classical machine learning and deep learning. Results suggest that the proposed DTL-EL significantly outperforms benchmark methods based on accuracy, precision, recall, and F1-score. Our proposed DTL-EL framework provides important practical implications for key stakeholders such as cybersecurity managers, analysts, and educators.
#html-body[data-pb-style=YM00THS]{justify-content:flex-start;display:flex;flex-direction:column;background-position:left-top;background-size:cover;background-repeat:no-repeat;background-attachment:scroll}随着复杂信息系统的迅速发展，可造成不可挽回的网络漏洞的漏洞数量也在不断增加。为了减轻这些网络威胁，学术界和工业界都非常重视主动识别和标记国际黑客社区开发的漏洞。然而，对黑客论坛中的漏洞进行标注的主流方法并没有利用漏洞暗网市场或公共漏洞库中的元数据来提高标注性能。在本研究中，我们采用计算设计科学范式开发了一种新型信息技术工具--深度转移学习漏洞利用标签器（DTL-EL）。DTL-EL 融合了预初始化设计、多层深度迁移学习（DTL）和自我关注机制，可自动标记黑客论坛中的漏洞。我们对照基于经典机器学习和深度学习的最先进的非DTL基准方法，对所提出的DTL-EL进行了严格评估。结果表明，基于准确度、精确度、召回率和 F1 分数，拟议的 DTL-EL 明显优于基准方法。我们提出的 DTL-EL 框架为网络安全管理人员、分析师和教育工作者等关键利益相关者提供了重要的实际意义。
{"title":"Creating Proactive Cyber Threat Intelligence with Hacker Exploit Labels: A Deep Transfer Learning Approach","authors":"Benjamin M. Ampel, Sagar Samtani, Hongyi Zhu, Hsinchun Chen","doi":"10.25300/misq/2023/17316","DOIUrl":"https://doi.org/10.25300/misq/2023/17316","url":null,"abstract":"<style>#html-body [data-pb-style=YM00THS]{justify-content:flex-start;display:flex;flex-direction:column;background-position:left top;background-size:cover;background-repeat:no-repeat;background-attachment:scroll}</style>The rapid proliferation of complex information systems has been met by an ever-increasing quantity of exploits that can cause irreparable cyber breaches. To mitigate these cyber threats, academia and industry have placed a significant focus on proactively identifying and labeling exploits developed by the international hacker community. However, prevailing approaches for labeling exploits in hacker forums do not leverage metadata from exploit darknet markets or public exploit repositories to enhance labeling performance. In this study, we adopted the computational design science paradigm to develop a novel information technology artifact, the deep transfer learning exploit labeler (DTL-EL). DTL-EL incorporates a pre-initialization design, multi-layer deep transfer learning (DTL), and a self-attention mechanism to automatically label exploits in hacker forums. We rigorously evaluated the proposed DTL-EL against state-of-the-art non-DTL benchmark methods based in classical machine learning and deep learning. Results suggest that the proposed DTL-EL significantly outperforms benchmark methods based on accuracy, precision, recall, and F1-score. Our proposed DTL-EL framework provides important practical implications for key stakeholders such as cybersecurity managers, analysts, and educators.","PeriodicalId":49807,"journal":{"name":"Mis Quarterly","volume":"66 4 1","pages":""},"PeriodicalIF":7.3,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140015658","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 : 2023-12-01DOI: 10.25300/misq/2022/17961
Ofir Turel and Shivam Kalhan
Algorithm aversion is an important and persistent issue that prevents harvesting the benefits of advancements in artificial intelligence. The literature thus far has provided explanations that primarily focus on conscious reflective processes. Here, we supplement this view by taking an unconscious perspective that can be highly informative. Building on theories of implicit prejudice, in a preregistered study, we suggest that people develop an implicit bias (i.e., prejudice) against artificial intelligence (AI) systems, as a different and threatening “species,” the behavior of which is unknown. Like in other contexts of prejudice, we expected people to be guided by this implicit bias but try to override it. This leads to some willingness to rely on algorithmic advice (appreciation), which is reduced as a function of people’s implicit prejudice against the machine. Next, building on the somatic marker hypothesis and the accessibility-diagnosticity perspective, we provide an explanation as to why aversion is ephemeral. As people learn about the performance of an algorithm, they depend less on primal implicit biases when deciding whether to rely on the AI’s advice. Two studies (n1 = 675, n2 = 317) that use the implicit association test consistently support this view. Two additional studies (n3 = 255, n4 = 332) rule out alternative explanations and provide stronger support for our assertions. The findings ultimately suggest that moving the needle between aversion and appreciation depends initially on one’s general unconscious bias against AI because there is insufficient information to override it. They further suggest that in later use stages, this shift depends on accessibility to diagnostic information about the AI’s performance, which reduces the weight given to unconscious prejudice.
{"title":"Prejudiced against the Machine? Implicit Associations and the Transience of Algorithm Aversion","authors":"Ofir Turel and Shivam Kalhan","doi":"10.25300/misq/2022/17961","DOIUrl":"https://doi.org/10.25300/misq/2022/17961","url":null,"abstract":"<style>#html-body [data-pb-style=TE8QKQW]{justify-content:flex-start;display:flex;flex-direction:column;background-position:left top;background-size:cover;background-repeat:no-repeat;background-attachment:scroll}</style>Algorithm aversion is an important and persistent issue that prevents harvesting the benefits of advancements in artificial intelligence. The literature thus far has provided explanations that primarily focus on conscious reflective processes. Here, we supplement this view by taking an unconscious perspective that can be highly informative. Building on theories of implicit prejudice, in a preregistered study, we suggest that people develop an implicit bias (i.e., prejudice) against artificial intelligence (AI) systems, as a different and threatening “species,” the behavior of which is unknown. Like in other contexts of prejudice, we expected people to be guided by this implicit bias but try to override it. This leads to some willingness to rely on algorithmic advice (appreciation), which is reduced as a function of people’s implicit prejudice against the machine. Next, building on the somatic marker hypothesis and the accessibility-diagnosticity perspective, we provide an explanation as to why aversion is ephemeral. As people learn about the performance of an algorithm, they depend less on primal implicit biases when deciding whether to rely on the AI’s advice. Two studies (n1 = 675, n2 = 317) that use the implicit association test consistently support this view. Two additional studies (n3 = 255, n4 = 332) rule out alternative explanations and provide stronger support for our assertions. The findings ultimately suggest that moving the needle between aversion and appreciation depends initially on one’s general unconscious bias against AI because there is insufficient information to override it. They further suggest that in later use stages, this shift depends on accessibility to diagnostic information about the AI’s performance, which reduces the weight given to unconscious prejudice.","PeriodicalId":49807,"journal":{"name":"Mis Quarterly","volume":"116 6","pages":""},"PeriodicalIF":7.3,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138455780","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 : 2023-12-01DOI: 10.25300/misq/2023/17200
Mario Nadj, Raphael Rissler, Marc T. P. Adam, Michael T. Knierim, Maximilian X. Li, Alexander Maedche, René Riedl
Flow, the holistic sensation people experience when they act with total involvement, is a known driver for desired work outcomes like task performance. However, the increasing ubiquity of IT at work can disrupt employees’ flow. Thus, the impact of IT-mediated interruptions on flow warrants more attention in research and practice. We conducted a NeuroIS laboratory experiment focusing on a typical office work task—an invoice matching task (i.e., matching customer payments to invoices). We manipulated interruption frequency (low, high) and content relevance (irrelevant, relevant) to study the impact of interruptions on self-reported flow, its dimensions, and high-frequency heart rate variability (HF-HRV; calculated from electrocardiography recordings) as a proxy for parasympathetic nervous system (PNS) activation. We found that content relevance moderated the relationship between interruption frequency and self-reported flow and that these results vary along flow dimensions. Content relevance also moderated the relationship between interruption frequency and PNS activation. Furthermore, self-reported flow was positively associated with both perceived and objective task performance, while PNS activation was not related to either performance measure. Lastly, we found no relationship between PNS activation (measured by HF-HRV) and self-reported flow, contributing to an important debate in the NeuroIS literature on whether physiological evidence constitutes an alternative or a complement to self-reports. Overall, our findings indicate that frequent interruptions are not harmful per se. Rather, considering content relevance is critical for a more comprehensive understanding of the effects on self-reported flow, its dimensions, and the underlying physiology.
{"title":"What Disrupts Flow in Office Work? The Impact of Frequency and Relevance of IT-Mediated Interruptions","authors":"Mario Nadj, Raphael Rissler, Marc T. P. Adam, Michael T. Knierim, Maximilian X. Li, Alexander Maedche, René Riedl","doi":"10.25300/misq/2023/17200","DOIUrl":"https://doi.org/10.25300/misq/2023/17200","url":null,"abstract":"<style>#html-body [data-pb-style=PPEGXSK]{justify-content:flex-start;display:flex;flex-direction:column;background-position:left top;background-size:cover;background-repeat:no-repeat;background-attachment:scroll}</style>Flow, the holistic sensation people experience when they act with total involvement, is a known driver for desired work outcomes like task performance. However, the increasing ubiquity of IT at work can disrupt employees’ flow. Thus, the impact of IT-mediated interruptions on flow warrants more attention in research and practice. We conducted a NeuroIS laboratory experiment focusing on a typical office work task—an invoice matching task (i.e., matching customer payments to invoices). We manipulated interruption frequency (low, high) and content relevance (irrelevant, relevant) to study the impact of interruptions on self-reported flow, its dimensions, and high-frequency heart rate variability (HF-HRV; calculated from electrocardiography recordings) as a proxy for parasympathetic nervous system (PNS) activation. We found that content relevance moderated the relationship between interruption frequency and self-reported flow and that these results vary along flow dimensions. Content relevance also moderated the relationship between interruption frequency and PNS activation. Furthermore, self-reported flow was positively associated with both perceived and objective task performance, while PNS activation was not related to either performance measure. Lastly, we found no relationship between PNS activation (measured by HF-HRV) and self-reported flow, contributing to an important debate in the NeuroIS literature on whether physiological evidence constitutes an alternative or a complement to self-reports. Overall, our findings indicate that frequent interruptions are not harmful per se. Rather, considering content relevance is critical for a more comprehensive understanding of the effects on self-reported flow, its dimensions, and the underlying physiology.","PeriodicalId":49807,"journal":{"name":"Mis Quarterly","volume":" 732","pages":""},"PeriodicalIF":7.3,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138475761","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：