Pub Date : 2023-03-01DOI: 10.25300/misq/2022/16959
Amin Sabzehzar, Gordon Burtch, Y. Hong, T. Raghu
Biases on online platforms pose a threat to social inclusion. We examine the influence of a novel source of bias in online philanthropic lending, namely that associated with religious differences. We first propose religion distance as a probabilistic measure of differences between pairs of individuals residing in different countries. We then incorporate this measure into a gravity model of trade to explain variation in country-to-country lending volumes. We further propose a set of contextual moderators that characterize individuals’ offline (local) and online social contexts, which we argue combine to determine the influence of religion distance on lending activity. We empirically estimate our gravity model using data from Kiva.org, reflecting all lending actions that took place between 2006 and 2017. We demonstrate the negative and significant effect of religion distance on lending activity, over and above other established factors in the literature. Further, we demonstrate the moderating role of lenders’ offline social context (diversity, social hostilities, and governmental favoritism of religion) on the aforementioned relationship to online lending behavior. Finally, we offer empirical evidence of the parallel role of online contextual factors, namely those related to community features offered by the Kiva platform (lending teams), which appear to amplify the role of religious bias. In particular, we show that religious team membership is a double-edged sword that has both favorable and unfavorable consequences, increasing lending in general but skewing said lending toward religiously similar borrowers. Our findings speak to the important frictions associated with religious differences in individual philanthropy; they point to the role of governmental policy vis-à-vis religious tolerance as a determinant of citizens’ global philanthropic behavior, and they highlight design implications for online platforms with an eye toward managing religious bias.
{"title":"Putting Religious Bias in Context: How Offline and Online Contexts Shape Religious Bias in Online Prosocial Lending","authors":"Amin Sabzehzar, Gordon Burtch, Y. Hong, T. Raghu","doi":"10.25300/misq/2022/16959","DOIUrl":"https://doi.org/10.25300/misq/2022/16959","url":null,"abstract":"Biases on online platforms pose a threat to social inclusion. We examine the influence of a novel source of bias in online philanthropic lending, namely that associated with religious differences. We first propose religion distance as a probabilistic measure of differences between pairs of individuals residing in different countries. We then incorporate this measure into a gravity model of trade to explain variation in country-to-country lending volumes. We further propose a set of contextual moderators that characterize individuals’ offline (local) and online social contexts, which we argue combine to determine the influence of religion distance on lending activity. We empirically estimate our gravity model using data from Kiva.org, reflecting all lending actions that took place between 2006 and 2017. We demonstrate the negative and significant effect of religion distance on lending activity, over and above other established factors in the literature. Further, we demonstrate the moderating role of lenders’ offline social context (diversity, social hostilities, and governmental favoritism of religion) on the aforementioned relationship to online lending behavior. Finally, we offer empirical evidence of the parallel role of online contextual factors, namely those related to community features offered by the Kiva platform (lending teams), which appear to amplify the role of religious bias. In particular, we show that religious team membership is a double-edged sword that has both favorable and unfavorable consequences, increasing lending in general but skewing said lending toward religiously similar borrowers. Our findings speak to the important frictions associated with religious differences in individual philanthropy; they point to the role of governmental policy vis-à-vis religious tolerance as a determinant of citizens’ global philanthropic behavior, and they highlight design implications for online platforms with an eye toward managing religious bias.","PeriodicalId":18743,"journal":{"name":"MIS Q.","volume":"10 1","pages":"33-62"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91208382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Online health communities (OHCs) play an important role in enabling patients to exchange information and obtain social support from each other. However, do OHC interactions always benefit patients? In this research, we investigate different mechanisms by which OHC content may affect patients’ emotions. Specifically, we notice users can read not only emotional support intended to help them but also emotional support targeting other persons or posts that are not intended to generate any emotional support (auxiliary content). Drawing from emotional contagion theories, we argue that even though emotional support may benefit targeted support seekers, it could have a negative impact on the emotions of other support seekers. Our empirical study on an OHC for depression patients supports these arguments. Our findings are new to the literature and have critical practical implications since they suggest that we should carefully manage OHC-based interventions for depression patients to avoid unintended consequences. We design a novel deep learning model to differentiate emotional support from auxiliary content. Such differentiation is critical for identifying the negative effect of emotional support on unintended recipients. We also discuss options to alter the intervention volume, length, and frequency to tackle the challenge of the negative effect.
{"title":"Unintended Emotional Effects of Online Health Communities: A Text Mining-Supported Empirical Study","authors":"Jiaqi Zhou, Qingpeng Zhang, Sijia Zhou, Xin Li, X. Zhang","doi":"10.2139/ssrn.3394398","DOIUrl":"https://doi.org/10.2139/ssrn.3394398","url":null,"abstract":"Online health communities (OHCs) play an important role in enabling patients to exchange information and obtain social support from each other. However, do OHC interactions always benefit patients? In this research, we investigate different mechanisms by which OHC content may affect patients’ emotions. Specifically, we notice users can read not only emotional support intended to help them but also emotional support targeting other persons or posts that are not intended to generate any emotional support (auxiliary content). Drawing from emotional contagion theories, we argue that even though emotional support may benefit targeted support seekers, it could have a negative impact on the emotions of other support seekers. Our empirical study on an OHC for depression patients supports these arguments. Our findings are new to the literature and have critical practical implications since they suggest that we should carefully manage OHC-based interventions for depression patients to avoid unintended consequences. We design a novel deep learning model to differentiate emotional support from auxiliary content. Such differentiation is critical for identifying the negative effect of emotional support on unintended recipients. We also discuss options to alter the intervention volume, length, and frequency to tackle the challenge of the negative effect.","PeriodicalId":18743,"journal":{"name":"MIS Q.","volume":"5 1","pages":"195-226"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75040302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.25300/MISQ/2022/17248
Yinghao Liu, Xin Xu, Yong Jin, Honglin Deng
The COVID-19 pandemic has underscored the urgent need for healthcare entities to develop resilient strategies to cope with disruptions caused by the pandemic. This study focuses on the digital resilience of certified physicians who adopted an online healthcare community (OHC) to acquire patients and conduct telemedicine services during the pandemic. We synthesize the resilience literature and identify two effects of digital resilience—the resistance effect and the recovery effect. We use a proprietary dataset that matches online and offline data sources to study the digital resilience of physicians. A difference-in-differences (DID) analysis shows that physicians who adopted an OHC had strong resistance and recovery effects during the pandemic. Remarkably, after the COVID-19 outbreak, these physicians had 35.0% less reduction in medical consultations in the immediate period and 31.0% more bounce-back in the subsequent period as compared to physicians who did not adopt the OHC. We further analyze the sources of physicians’ digital resilience by distinguishing between new and existing patients from both online and offline channels. Our subgroup analysis shows that, in general, digital resilience is more pronounced when physicians have a higher online reputation rating or have more positive interactions with patients on the OHC platform, providing further support for the mechanisms underlying digital resilience. Our research has significant theoretical and managerial implications beyond the context of the pandemic.
{"title":"Understanding the Digital Resilience of Physicians during the COVID-19 Pandemic: An Empirical Study","authors":"Yinghao Liu, Xin Xu, Yong Jin, Honglin Deng","doi":"10.25300/MISQ/2022/17248","DOIUrl":"https://doi.org/10.25300/MISQ/2022/17248","url":null,"abstract":"The COVID-19 pandemic has underscored the urgent need for healthcare entities to develop resilient strategies to cope with disruptions caused by the pandemic. This study focuses on the digital resilience of certified physicians who adopted an online healthcare community (OHC) to acquire patients and conduct telemedicine services during the pandemic. We synthesize the resilience literature and identify two effects of digital resilience—the resistance effect and the recovery effect. We use a proprietary dataset that matches online and offline data sources to study the digital resilience of physicians. A difference-in-differences (DID) analysis shows that physicians who adopted an OHC had strong resistance and recovery effects during the pandemic. Remarkably, after the COVID-19 outbreak, these physicians had 35.0% less reduction in medical consultations in the immediate period and 31.0% more bounce-back in the subsequent period as compared to physicians who did not adopt the OHC. We further analyze the sources of physicians’ digital resilience by distinguishing between new and existing patients from both online and offline channels. Our subgroup analysis shows that, in general, digital resilience is more pronounced when physicians have a higher online reputation rating or have more positive interactions with patients on the OHC platform, providing further support for the mechanisms underlying digital resilience. Our research has significant theoretical and managerial implications beyond the context of the pandemic.","PeriodicalId":18743,"journal":{"name":"MIS Q.","volume":"43 1","pages":"391-422"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80665554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-01DOI: 10.48550/arXiv.2209.05943
Xiaohang Zhao, Xiao Fang, Jing He, Lihua Huang
Industry assignment, which assigns firms to industries according to a predefined industry classification system (ICS), is fundamental to a large number of critical business practices, ranging from operations and strategic decision-making by firms to economic analyses by government agencies. Three types of expert knowledge are essential to effective industry assignment: definition-based knowledge (i.e., expert definitions of each industry), structure-based knowledge (i.e., structural relationships among industries as specified in an ICS), and assignment-based knowledge (i.e., prior firm-industry assignments performed by domain experts). Existing industry assignment methods utilize only assignment-based knowledge to learn a model that classifies unassigned firms to industries, overlooking definition-based and structure-based knowledge. Moreover, these methods only consider which industry a firm has been assigned to, ignoring the time-specificity of assignment-based knowledge, i.e., when the assignment occurs. To address the limitations of existing methods, we propose a novel deep learning-based method that not only seamlessly integrates the three types of knowledge for industry assignment but also takes the time-specificity of assignment-based knowledge into account. Methodologically, our method features two innovations: dynamic industry representation and hierarchical assignment. The former represents an industry as a sequence of time-specific vectors by integrating the three types of knowledge through our proposed temporal and spatial aggregation mechanisms. The latter takes industry and firm representations as inputs, computes the probability of assigning a firm to different industries, and assigns the firm to the industry with the highest probability. We conduct extensive evaluations with two widely used ICSs and demonstrate the superiority of our method over prevalent existing methods.
{"title":"Exploiting Expert Knowledge for Assigning Firms to Industries: A Novel Deep Learning Method","authors":"Xiaohang Zhao, Xiao Fang, Jing He, Lihua Huang","doi":"10.48550/arXiv.2209.05943","DOIUrl":"https://doi.org/10.48550/arXiv.2209.05943","url":null,"abstract":"Industry assignment, which assigns firms to industries according to a predefined industry classification system (ICS), is fundamental to a large number of critical business practices, ranging from operations and strategic decision-making by firms to economic analyses by government agencies. Three types of expert knowledge are essential to effective industry assignment: definition-based knowledge (i.e., expert definitions of each industry), structure-based knowledge (i.e., structural relationships among industries as specified in an ICS), and assignment-based knowledge (i.e., prior firm-industry assignments performed by domain experts). Existing industry assignment methods utilize only assignment-based knowledge to learn a model that classifies unassigned firms to industries, overlooking definition-based and structure-based knowledge. Moreover, these methods only consider which industry a firm has been assigned to, ignoring the time-specificity of assignment-based knowledge, i.e., when the assignment occurs. To address the limitations of existing methods, we propose a novel deep learning-based method that not only seamlessly integrates the three types of knowledge for industry assignment but also takes the time-specificity of assignment-based knowledge into account. Methodologically, our method features two innovations: dynamic industry representation and hierarchical assignment. The former represents an industry as a sequence of time-specific vectors by integrating the three types of knowledge through our proposed temporal and spatial aggregation mechanisms. The latter takes industry and firm representations as inputs, computes the probability of assigning a firm to different industries, and assigns the firm to the industry with the highest probability. We conduct extensive evaluations with two widely used ICSs and demonstrate the superiority of our method over prevalent existing methods.","PeriodicalId":18743,"journal":{"name":"MIS Q.","volume":"67 1","pages":"1147-1176"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80917464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-25DOI: 10.25300/misq/2022/14596
W. He, J. J. Po-An, A. Schroeder, Yulin Fang
Contemporary IT project teams demand that individual members generate and implement novel ideas in response to the dynamic changes in IT and business requirements. Firms rely on multidisciplinary, geographically distributed IT project teams to gather the necessary talent, regardless of their locations, for developing novel IT artifacts. In this team context, individuals are expected to leverage dissimilar others’ expertise for creating ideas during idea generation (IG) and then implement their ideas during idea implementation (II), known as the IGII process. Although much has been done to explain individual creativity, the extant literature offers little theoretical understanding on how to address the double-edged effects of dispersions in both functional expertise (ExpDisp) and geographical locations (GeoDiss)—the two defining characteristics of multi-disciplinary, cross-locational IT project teams—on individual creativity and subsequent performance. Drawing on the IGII framework, we propose transactive memory systems (TMSs) as a plausible team-level solution to tackle the challenge. With a multi-wave multi-level dataset from 141 members and their supervisors from 35 IT project teams, we found that team-level TMS and GeoDiss interactively moderate individual-level IGII processes in multi-disciplinary geographically-distributed IT project teams during both II and IG, but in qualitatively different ways.
{"title":"Attaining Individual Creativity and Performance in Multidisciplinary and Geographically Distributed IT Project Teams: The Role of Transactive Memory Systems","authors":"W. He, J. J. Po-An, A. Schroeder, Yulin Fang","doi":"10.25300/misq/2022/14596","DOIUrl":"https://doi.org/10.25300/misq/2022/14596","url":null,"abstract":"Contemporary IT project teams demand that individual members generate and implement novel ideas in response to the dynamic changes in IT and business requirements. Firms rely on multidisciplinary, geographically distributed IT project teams to gather the necessary talent, regardless of their locations, for developing novel IT artifacts. In this team context, individuals are expected to leverage dissimilar others’ expertise for creating ideas during idea generation (IG) and then implement their ideas during idea implementation (II), known as the IGII process. Although much has been done to explain individual creativity, the extant literature offers little theoretical understanding on how to address the double-edged effects of dispersions in both functional expertise (ExpDisp) and geographical locations (GeoDiss)—the two defining characteristics of multi-disciplinary, cross-locational IT project teams—on individual creativity and subsequent performance. Drawing on the IGII framework, we propose transactive memory systems (TMSs) as a plausible team-level solution to tackle the challenge. With a multi-wave multi-level dataset from 141 members and their supervisors from 35 IT project teams, we found that team-level TMS and GeoDiss interactively moderate individual-level IGII processes in multi-disciplinary geographically-distributed IT project teams during both II and IG, but in qualitatively different ways.","PeriodicalId":18743,"journal":{"name":"MIS Q.","volume":"32 1","pages":"1035-1072"},"PeriodicalIF":0.0,"publicationDate":"2022-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76736203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-25DOI: 10.25300/misq/2022/15631
Chengxin Cao, Gautam Ray, M. Subramani, Alok Gupta
This paper examines the relationship between enterprise systems (ES) and the likelihood of mergers and acquisitions (M&A). The key argument is that since ES can reduce agency costs associated with internal coordination and the transaction cost of coordinating with external partners, ES may be related to the likelihood of M&A. Using a sample of 3,289 firms headquartered in North America from 2010 to 2018 that made 8,373 M&A deals, the empirical analysis suggests that ES are positively related to horizontal acquisitions and negatively related to conglomerate acquisitions. However, as internal coordination costs increase, ES are becoming associated with more conglomerate M&A, especially when the goal is to introduce new products and enter new markets. Also, in contexts where partners require transaction specific investments, ES are associated with a relative decrease in the number of horizontal and vertical M&A. These findings suggest that ES create options for managers to engage in ownership as well as information-based coordination, depending on the internal and external coordination costs as well as the goals of the organization.
{"title":"Enterprise Systems and the Likelihood of Horizontal, Vertical, and Conglomerate Mergers and Acquisitions","authors":"Chengxin Cao, Gautam Ray, M. Subramani, Alok Gupta","doi":"10.25300/misq/2022/15631","DOIUrl":"https://doi.org/10.25300/misq/2022/15631","url":null,"abstract":"This paper examines the relationship between enterprise systems (ES) and the likelihood of mergers and acquisitions (M&A). The key argument is that since ES can reduce agency costs associated with internal coordination and the transaction cost of coordinating with external partners, ES may be related to the likelihood of M&A. Using a sample of 3,289 firms headquartered in North America from 2010 to 2018 that made 8,373 M&A deals, the empirical analysis suggests that ES are positively related to horizontal acquisitions and negatively related to conglomerate acquisitions. However, as internal coordination costs increase, ES are becoming associated with more conglomerate M&A, especially when the goal is to introduce new products and enter new markets. Also, in contexts where partners require transaction specific investments, ES are associated with a relative decrease in the number of horizontal and vertical M&A. These findings suggest that ES create options for managers to engage in ownership as well as information-based coordination, depending on the internal and external coordination costs as well as the goals of the organization.","PeriodicalId":18743,"journal":{"name":"MIS Q.","volume":"8 1","pages":"1227-1242"},"PeriodicalIF":0.0,"publicationDate":"2022-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89537253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-24DOI: 10.25300/misq/2022/15596
Eric Bachura, Rohit Valecha, Rui Chen, H. Rao
This paper investigates the shared emotional responses of Twitter users in the aftermath of a massive data breach, a crisis event known as the Office of Personnel Management (OPM) data breach of 2015. This breach impacted the lives of several million individuals due to the exposure of sensitive and personally identifying information. We take a data exploration approach to analyzing over 18,000 tweet messages of the ensuing discussion that took place after public notification that the breach had occurred. The resulting analysis reveals that although the emotions of anxiety, anger, and sadness may initially appear erratic, at an aggregate level, the public display of these emotions corresponds to the situational awareness of the breach event. Further, our analysis finds that this relationship extends to the sharing of emotions, indicating that those participating in the conversation congregate around a sense of shared emotional experience. Finally, an in-depth analysis of the ensuing dialogue identifies the most salient conversational drivers of these emotions, revealing breach concepts most significantly related to each emotion. Based on the results, we present propositions that draw from this analysis to inform emotional response characteristics that emerge over the duration of such crisis events. The results of this study can inform organizational practices and policy making in the context of response to crisis events such as data breaches.
{"title":"The OPM Data Breach: An Investigation of Shared Emotional Reactions on Twitter","authors":"Eric Bachura, Rohit Valecha, Rui Chen, H. Rao","doi":"10.25300/misq/2022/15596","DOIUrl":"https://doi.org/10.25300/misq/2022/15596","url":null,"abstract":"This paper investigates the shared emotional responses of Twitter users in the aftermath of a massive data breach, a crisis event known as the Office of Personnel Management (OPM) data breach of 2015. This breach impacted the lives of several million individuals due to the exposure of sensitive and personally identifying information. We take a data exploration approach to analyzing over 18,000 tweet messages of the ensuing discussion that took place after public notification that the breach had occurred. The resulting analysis reveals that although the emotions of anxiety, anger, and sadness may initially appear erratic, at an aggregate level, the public display of these emotions corresponds to the situational awareness of the breach event. Further, our analysis finds that this relationship extends to the sharing of emotions, indicating that those participating in the conversation congregate around a sense of shared emotional experience. Finally, an in-depth analysis of the ensuing dialogue identifies the most salient conversational drivers of these emotions, revealing breach concepts most significantly related to each emotion. Based on the results, we present propositions that draw from this analysis to inform emotional response characteristics that emerge over the duration of such crisis events. The results of this study can inform organizational practices and policy making in the context of response to crisis events such as data breaches.","PeriodicalId":18743,"journal":{"name":"MIS Q.","volume":"35 1","pages":"881-910"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74166434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-24DOI: 10.25300/misq/2022/15392
S. Samtani, Yidong Chai, Hsinchun Chen
Black hat hackers use malicious exploits to circumvent security controls and take advantage of system vulnerabilities worldwide, costing the global economy over $450 billion annually. While many organizations are increasingly turning to cyber threat intelligence (CTI) to help prioritize their vulnerabilities, extant CTI processes are often criticized as being reactive to known exploits. One promising data source that can help develop proactive CTI is the vast and ever-evolving Dark Web. In this study, we adopted the computational design science paradigm to design a novel deep learning (DL)- based exploit-vulnerability attention deep structured semantic model (EVA-DSSM) that includes bidirectional processing and attention mechanisms to automatically link exploits from the Dark Web to vulnerabilities. We also devised a novel device vulnerability severity metric (DVSM) that incorporates the exploit post date and vulnerability severity to help cybersecurity professionals with their device prioritization and risk management efforts. We rigorously evaluated the EVA-DSSM against state-of-theart non-DL and DL-based methods for short text matching on 52,590 exploit-vulnerability linkages across four testbeds: web application, remote, local, and denial of service. Results of these evaluations indicate that the proposed EVA-DSSM achieves precision at 1 scores 20% - 41% higher than non-DL approaches and 4% - 10% higher than DL-based approaches. We demonstrated the EVA-DSSM’s and DVSM’s practical utility with two CTI case studies: openly accessible systems in the top eight U.S. hospitals and over 20,000 Supervisory Control and Data Acquisition (SCADA) systems worldwide. A complementary user evaluation of the case study results indicated that 45 cybersecurity professionals found the EVADSSM and DVSM results more useful for exploit-vulnerability linking and risk prioritization activities than those produced by prevailing approaches. Given the rising cost of cyberattacks, the EVA-DSSM and DVSM have important implications for analysts in security operations centers, incident response teams, and cybersecurity vendors.
{"title":"Linking Exploits from the Dark Web to Known Vulnerabilities for Proactive Cyber Threat Intelligence: An Attention-Based Deep Structured Semantic Model","authors":"S. Samtani, Yidong Chai, Hsinchun Chen","doi":"10.25300/misq/2022/15392","DOIUrl":"https://doi.org/10.25300/misq/2022/15392","url":null,"abstract":"Black hat hackers use malicious exploits to circumvent security controls and take advantage of system vulnerabilities worldwide, costing the global economy over $450 billion annually. While many organizations are increasingly turning to cyber threat intelligence (CTI) to help prioritize their vulnerabilities, extant CTI processes are often criticized as being reactive to known exploits. One promising data source that can help develop proactive CTI is the vast and ever-evolving Dark Web. In this study, we adopted the computational design science paradigm to design a novel deep learning (DL)- based exploit-vulnerability attention deep structured semantic model (EVA-DSSM) that includes bidirectional processing and attention mechanisms to automatically link exploits from the Dark Web to vulnerabilities. We also devised a novel device vulnerability severity metric (DVSM) that incorporates the exploit post date and vulnerability severity to help cybersecurity professionals with their device prioritization and risk management efforts. We rigorously evaluated the EVA-DSSM against state-of-theart non-DL and DL-based methods for short text matching on 52,590 exploit-vulnerability linkages across four testbeds: web application, remote, local, and denial of service. Results of these evaluations indicate that the proposed EVA-DSSM achieves precision at 1 scores 20% - 41% higher than non-DL approaches and 4% - 10% higher than DL-based approaches. We demonstrated the EVA-DSSM’s and DVSM’s practical utility with two CTI case studies: openly accessible systems in the top eight U.S. hospitals and over 20,000 Supervisory Control and Data Acquisition (SCADA) systems worldwide. A complementary user evaluation of the case study results indicated that 45 cybersecurity professionals found the EVADSSM and DVSM results more useful for exploit-vulnerability linking and risk prioritization activities than those produced by prevailing approaches. Given the rising cost of cyberattacks, the EVA-DSSM and DVSM have important implications for analysts in security operations centers, incident response teams, and cybersecurity vendors.","PeriodicalId":18743,"journal":{"name":"MIS Q.","volume":"32 1","pages":"911-946"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84400245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-24DOI: 10.25300/misq/2022/15979
Yulia W. Sullivan, Fred D. Davis, Chang Koh
Information systems (IS) are complex and effortful, placing ever-greater demands on humans’ executive functions. Executive functions, general-purpose control processes that regulate one’s thoughts and behaviors, are the subject of growing investigation in cognitive psychology. The present research examines the relationship between individuals’ executive functions and IS learning. Using neuropsychological methods from cognitive psychology, we measured three key dimensions of executive functions: working memory, shifting, and inhibition. Two empirical studies were conducted. Study 1 tested the relationship between executive functions and IS learning in a self-paced offline learning environment. Study 2 replicated Study 1 and extended it to include a comparison of two self-paced online learning methods: behavior modeling and text-based learning. Both studies found significant effects of executive functions on IS learning after controlling for known IS learning determinants. Study 2 also showed that declarative knowledge was higher for behavior modeling than for text-based learning. Overall, our research highlights the influence of executive functions on IS learning. This research advances knowledge about determinants of IS learning and opens important research avenues for gaining deeper insights into cognitive mechanisms underlying effective IS learning.
{"title":"Executive Functions and Information Systems Learning","authors":"Yulia W. Sullivan, Fred D. Davis, Chang Koh","doi":"10.25300/misq/2022/15979","DOIUrl":"https://doi.org/10.25300/misq/2022/15979","url":null,"abstract":"Information systems (IS) are complex and effortful, placing ever-greater demands on humans’ executive functions. Executive functions, general-purpose control processes that regulate one’s thoughts and behaviors, are the subject of growing investigation in cognitive psychology. The present research examines the relationship between individuals’ executive functions and IS learning. Using neuropsychological methods from cognitive psychology, we measured three key dimensions of executive functions: working memory, shifting, and inhibition. Two empirical studies were conducted. Study 1 tested the relationship between executive functions and IS learning in a self-paced offline learning environment. Study 2 replicated Study 1 and extended it to include a comparison of two self-paced online learning methods: behavior modeling and text-based learning. Both studies found significant effects of executive functions on IS learning after controlling for known IS learning determinants. Study 2 also showed that declarative knowledge was higher for behavior modeling than for text-based learning. Overall, our research highlights the influence of executive functions on IS learning. This research advances knowledge about determinants of IS learning and opens important research avenues for gaining deeper insights into cognitive mechanisms underlying effective IS learning.","PeriodicalId":18743,"journal":{"name":"MIS Q.","volume":"49 1","pages":"813-880"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76377863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-24DOI: 10.25300/misq/2022/15252
Gongtai Wang, O. Henfridsson, J. Nandhakumar, Youngjin Yoo
Digital product innovation involves a meaning-making process. Designers of digital innovations often challenge established product meanings as they digitize physical products, such as cars, toothbrushes, and water bottles. A significant problem for product designers, however, is striking the right balance between the newness and comprehensibility of product meanings. Failure to do so may result in a digital product innovation that is too conventional or difficult to relate to or understand. Yet, the extant digital product innovation literature pays little, if any, attention to product meaning. To fill this void, this study examines a digital product innovation project in which product designers created a digital theater with product meanings beyond those of the traditional movie theater. Our theory, grounded in in-depth data collection and analysis, explains how product designers attribute meanings to their products in the process of digital innovation by enacting two meaning-making loops: a reinforcing loop that makes the product meaning comprehensible, and a differentiating loop that captures emerging product meanings. The two loops come together via meaning sedimentation, through which a new core product meaning is created. Our study contributes to the digital product innovation literature by shedding light on the essential role of meaning-making in innovation and offers an explanatory process theory.
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