Pub Date : 2022-03-01DOI: 10.25300/misq/2022/16301
G. Adomavicius, J. Bockstedt, S. Curley, Jingjing Zhang
Online retailers use product ratings to signal quality and help consumers identify products for purchase. These ratings commonly take the form of either non-personalized, aggregate product ratings (i.e., the average rating a product received from a number of consumers such as “the average rating is 4.5/5 based on 100 reviews”), or personalized predicted preference ratings for a product (i.e., recommender-system-generated predictions for a consumer’s rating of a product such as “we think you’d rate this product 4.5/5”). Ratings in either format can provide decision aid to the consumer, but the two formats convey different types of product quality information and operate with different psychological mechanisms. Prior research has indicated that each recommendation type can significantly affect consumer’s post-experience preference ratings, constituting a judgmental bias, but has not compared the effects of these two common product-rating formats. Using a laboratory experiment, we show that aggregate ratings and personalized recommendations create similar biases on post-experience preference ratings when shown separately. Shown together, there is no cumulative increase in the effect. Instead, personalized recommendations tend to dominate. Our findings can help retailers determine how to use these different types of product ratings to most effectively serve their customers. Additionally, these results help to educate the consumer on how product-rating displays influence their stated preferences.
{"title":"Effects of Personalized Recommendations Versus Aggregate Ratings on Post-Consumption Preference Responses","authors":"G. Adomavicius, J. Bockstedt, S. Curley, Jingjing Zhang","doi":"10.25300/misq/2022/16301","DOIUrl":"https://doi.org/10.25300/misq/2022/16301","url":null,"abstract":"Online retailers use product ratings to signal quality and help consumers identify products for purchase. These ratings commonly take the form of either non-personalized, aggregate product ratings (i.e., the average rating a product received from a number of consumers such as “the average rating is 4.5/5 based on 100 reviews”), or personalized predicted preference ratings for a product (i.e., recommender-system-generated predictions for a consumer’s rating of a product such as “we think you’d rate this product 4.5/5”). Ratings in either format can provide decision aid to the consumer, but the two formats convey different types of product quality information and operate with different psychological mechanisms. Prior research has indicated that each recommendation type can significantly affect consumer’s post-experience preference ratings, constituting a judgmental bias, but has not compared the effects of these two common product-rating formats. Using a laboratory experiment, we show that aggregate ratings and personalized recommendations create similar biases on post-experience preference ratings when shown separately. Shown together, there is no cumulative increase in the effect. Instead, personalized recommendations tend to dominate. Our findings can help retailers determine how to use these different types of product ratings to most effectively serve their customers. Additionally, these results help to educate the consumer on how product-rating displays influence their stated preferences.","PeriodicalId":18743,"journal":{"name":"MIS Q.","volume":"28 1","pages":"627-644"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74031539","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 : 2022-02-28DOI: 10.25300/misq/2022/15420
Jianqing Chen, Zhiling Guo
We have recently witnessed two important trends in online retailing: The advent of new media (e.g., social media and search engines) has made advertising affordable for small sellers, and large online retailers (e.g., Amazon and JD.com) have opened their platforms to allow even direct competitors to sell on their platforms. We examine how new-media advertising affects retail platform openness. We develop a game-theoretic model in which a leading retailer, who has both valuation and awareness advantages, and a third-party seller, who sells an identical product, engage in price competition. We find that the availability of relatively low-cost advertising through new media plays a critical role in influencing the leading retailer to open its platform and to form a partnership with the third-party seller, which is impossible when the cost of advertising is relatively high. Low-cost advertising can increase consumer surplus either directly via the third-party seller’s advertising or indirectly via the partnership on the leading retailer’s platform. We also find that the leading retailer has a greater incentive to open its platform and that the partnership is more likely to be formed when there are network effects, when the leading retailer can control the third-party seller’s exposure on its platform, or when the leading retailer can offer a direct advertising service to the third-party seller. Meanwhile, the constraint on the third-party seller’s advertising budget can reduce the leading retailer’s incentive to open its platform, making a partnership less likely. Our analysis offers important insights into the underlying economic incentives that help explain the emerging open retail platform trend in the era of new-media advertising.
{"title":"New-Media Advertising and Retail Platform Openness","authors":"Jianqing Chen, Zhiling Guo","doi":"10.25300/misq/2022/15420","DOIUrl":"https://doi.org/10.25300/misq/2022/15420","url":null,"abstract":"We have recently witnessed two important trends in online retailing: The advent of new media (e.g., social media and search engines) has made advertising affordable for small sellers, and large online retailers (e.g., Amazon and JD.com) have opened their platforms to allow even direct competitors to sell on their platforms. We examine how new-media advertising affects retail platform openness. We develop a game-theoretic model in which a leading retailer, who has both valuation and awareness advantages, and a third-party seller, who sells an identical product, engage in price competition. We find that the availability of relatively low-cost advertising through new media plays a critical role in influencing the leading retailer to open its platform and to form a partnership with the third-party seller, which is impossible when the cost of advertising is relatively high. Low-cost advertising can increase consumer surplus either directly via the third-party seller’s advertising or indirectly via the partnership on the leading retailer’s platform. We also find that the leading retailer has a greater incentive to open its platform and that the partnership is more likely to be formed when there are network effects, when the leading retailer can control the third-party seller’s exposure on its platform, or when the leading retailer can offer a direct advertising service to the third-party seller. Meanwhile, the constraint on the third-party seller’s advertising budget can reduce the leading retailer’s incentive to open its platform, making a partnership less likely. Our analysis offers important insights into the underlying economic incentives that help explain the emerging open retail platform trend in the era of new-media advertising.","PeriodicalId":18743,"journal":{"name":"MIS Q.","volume":"55 1","pages":"431-456"},"PeriodicalIF":0.0,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89663783","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 : 2022-02-28DOI: 10.25300/misq/2022/14861
Nan (Andy) Zhang, Chong Wang, Elena Karahanna, Yan Xu
Privacy needs on today’s internet differ from the information privacy needs in traditional e-commerce settings due to their focus on interactions among online peers rather than merely transactions with an online vendor. Peer-oriented online interactions have critical implications for an individual’s virtual presence and self-cognition. Yet existing conceptualizations of internet privacy concerns have solely focused on the control of personal information release and on online interactions with online vendors. Drawing on the theory of personal boundaries, this study revisits the theoretical foundation of online privacy and proposes a multidimensional peer-related privacy concern construct, that focuses on privacy violations from online peers. We term this new construct “Peer Privacy Concern” (PrPC) and define it as the general feeling of being unable to maintain functional personal boundaries in online activities as a result of the behavior of online peers. This construct consists of four dimensions comprised of a reconceptualization of information privacy concerns to also reflect privacy concerns with respect to peers’ handling of self-shared information and with respect to peer-shared information about one’s self, and three new dimensions that tap into the arising privacy needs from virtual interactions (i.e., virtual territory privacy concern and communication privacy concern) as well as from the need to maintain psychological independence (i.e., psychological privacy concern). These new dimensions, which are rooted in the theory of personal boundaries, are prominent privacy needs in online social interactions with peers. However, they are absent from previous privacy concern conceptualizations. Scales for measuring this new construct are developed and empirically validated.
{"title":"Peer Privacy Concerns: Conceptualization and Measurement","authors":"Nan (Andy) Zhang, Chong Wang, Elena Karahanna, Yan Xu","doi":"10.25300/misq/2022/14861","DOIUrl":"https://doi.org/10.25300/misq/2022/14861","url":null,"abstract":"Privacy needs on today’s internet differ from the information privacy needs in traditional e-commerce settings due to their focus on interactions among online peers rather than merely transactions with an online vendor. Peer-oriented online interactions have critical implications for an individual’s virtual presence and self-cognition. Yet existing conceptualizations of internet privacy concerns have solely focused on the control of personal information release and on online interactions with online vendors. Drawing on the theory of personal boundaries, this study revisits the theoretical foundation of online privacy and proposes a multidimensional peer-related privacy concern construct, that focuses on privacy violations from online peers. We term this new construct “Peer Privacy Concern” (PrPC) and define it as the general feeling of being unable to maintain functional personal boundaries in online activities as a result of the behavior of online peers. This construct consists of four dimensions comprised of a reconceptualization of information privacy concerns to also reflect privacy concerns with respect to peers’ handling of self-shared information and with respect to peer-shared information about one’s self, and three new dimensions that tap into the arising privacy needs from virtual interactions (i.e., virtual territory privacy concern and communication privacy concern) as well as from the need to maintain psychological independence (i.e., psychological privacy concern). These new dimensions, which are rooted in the theory of personal boundaries, are prominent privacy needs in online social interactions with peers. However, they are absent from previous privacy concern conceptualizations. Scales for measuring this new construct are developed and empirically validated.","PeriodicalId":18743,"journal":{"name":"MIS Q.","volume":"10 1","pages":"491-530"},"PeriodicalIF":0.0,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82434270","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 : 2022-02-15DOI: 10.25300/misq/2021/14962
Honglin Deng, Weiquan Wang, Siyuan Li, Kai H. Lim
Online social cues that utilize user-generated data, such as user reviews and product ratings, have become one of the key factors influencing online user behavior and decisions. Online users who shared their reviews and ratings about a product (or a seller) become an abstract reference group to a focal user interested in the same product. This study focuses on sponsored search results (SSRs), a type of unsolicited information that matches users’ search queries and receives high evaluations from prior consumers. We investigate the effects of positive social cues on alleviating users’ avoidance responses toward an encountered SSR when searching for a product in a C2C e-commerce context. We synthesize the avoidance literature and identify three forms of SSR avoidance, namely, cognitive, behavioral, and affective avoidance. We apply users’ implicit concerns on SSRs to explain users’ avoidance of an encountered SSR. In addition, we extend social influence theory to online settings where abstract reference groups are posited to trigger social influence. We examine how and under what conditions the three forms of SSR avoidance can be reduced by various positive online social cues (i.e., product- and seller-related). We conduct three laboratory experiments. Results attest to users’ implicit concerns on SSRs and their avoidance of SSRs and reveal different effects of various social cues on reducing the three forms of SSR avoidance. This study uncovers the theoretical mechanisms of social influence on reducing SSR avoidance in online settings. It also offers practical implications for online search service providers to help online users’ decision making in their search process.
{"title":"Can Positive Online Social Cues Always Reduce User Avoidance of Sponsored Search Results?","authors":"Honglin Deng, Weiquan Wang, Siyuan Li, Kai H. Lim","doi":"10.25300/misq/2021/14962","DOIUrl":"https://doi.org/10.25300/misq/2021/14962","url":null,"abstract":"Online social cues that utilize user-generated data, such as user reviews and product ratings, have become one of the key factors influencing online user behavior and decisions. Online users who shared their reviews and ratings about a product (or a seller) become an abstract reference group to a focal user interested in the same product. This study focuses on sponsored search results (SSRs), a type of unsolicited information that matches users’ search queries and receives high evaluations from prior consumers. We investigate the effects of positive social cues on alleviating users’ avoidance responses toward an encountered SSR when searching for a product in a C2C e-commerce context. We synthesize the avoidance literature and identify three forms of SSR avoidance, namely, cognitive, behavioral, and affective avoidance. We apply users’ implicit concerns on SSRs to explain users’ avoidance of an encountered SSR. In addition, we extend social influence theory to online settings where abstract reference groups are posited to trigger social influence. We examine how and under what conditions the three forms of SSR avoidance can be reduced by various positive online social cues (i.e., product- and seller-related). We conduct three laboratory experiments. Results attest to users’ implicit concerns on SSRs and their avoidance of SSRs and reveal different effects of various social cues on reducing the three forms of SSR avoidance. This study uncovers the theoretical mechanisms of social influence on reducing SSR avoidance in online settings. It also offers practical implications for online search service providers to help online users’ decision making in their search process.","PeriodicalId":18743,"journal":{"name":"MIS Q.","volume":"53 1","pages":"35-70"},"PeriodicalIF":0.0,"publicationDate":"2022-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83197212","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 : 2021-12-01DOI: 10.25300/misq/2021/15262
Monika Malinova, J. Mendling
Models play an important role in systems analysis and design (SAD). A diagrammatic model is defined as a mapping from a domain to a visual representation in such a way that relevant information is preserved to meet a specific goal. So far, cognitive research on diagram criteria in relation to task performance has been fragmented. The aim of this paper is to (1) consolidate research on the cognitive processing steps involved during understanding and task performance with diagrams, (2) consolidate corresponding criteria for such diagrams to best support cognitive processing, and (3) demonstrate the support effective diagrams provide for performing SAD tasks. Addressing the first aim, we develop a theoretical cognitive framework of task performance with diagrams called CogniDia. It integrates different cognitive theories from research on diagrams in software engineering and information systems. Regarding the second aim, we review the literature to organize criteria for effective cognitive processing of diagrams. We identify research gaps on verbal and task processing. Regarding the third aim, we use the theoretical cognitive framework to investigate how diagrams support the SAD process effectively.
{"title":"Cognitive Diagram Understanding and Task Performance in Systems Analysis and Design","authors":"Monika Malinova, J. Mendling","doi":"10.25300/misq/2021/15262","DOIUrl":"https://doi.org/10.25300/misq/2021/15262","url":null,"abstract":"Models play an important role in systems analysis and design (SAD). A diagrammatic model is defined as a mapping from a domain to a visual representation in such a way that relevant information is preserved to meet a specific goal. So far, cognitive research on diagram criteria in relation to task performance has been fragmented. The aim of this paper is to (1) consolidate research on the cognitive processing steps involved during understanding and task performance with diagrams, (2) consolidate corresponding criteria for such diagrams to best support cognitive processing, and (3) demonstrate the support effective diagrams provide for performing SAD tasks. Addressing the first aim, we develop a theoretical cognitive framework of task performance with diagrams called CogniDia. It integrates different cognitive theories from research on diagrams in software engineering and information systems. Regarding the second aim, we review the literature to organize criteria for effective cognitive processing of diagrams. We identify research gaps on verbal and task processing. Regarding the third aim, we use the theoretical cognitive framework to investigate how diagrams support the SAD process effectively.","PeriodicalId":18743,"journal":{"name":"MIS Q.","volume":"20 1","pages":"2101-2158"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72994135","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 : 2021-12-01DOI: 10.25300/misq/2022/15964
E. Trauth, R. Connolly
How and why do societal, organizational and individual factors affecting gender equity in the IT field change over time? To answer this question a longitudinal investigation of the nature of change in factors affecting the position of women in the IT profession was undertaken. It was conducted in Ireland against the backdrop of fluctuations in the nation’s socio-economic status. The individual differences theory of gender and IT was used to analyze life history interviews conducted at four points in time with a total of 63 women whose stories cover the decades from the 1970s to the 2010s. What resulted is a dynamic extension of this theory through the addition of seven themes that characterize the nature of change in factors affecting women IT professionals. The effect on women of economic changes in Ireland is shown to occur through changes in other factors: environmental (i.e., policy, infrastructural, and cultural), identity (e.g., motherhood) and individual (e.g., family). The results reveal both gradual and dramatic changes in an evolving picture of women in this sector, against the back drop of the peaks and valleys of Ireland’s economy. Both transformational and enduring images emerge from this look at Ireland over five decades.
{"title":"Investigating the Nature of Change in Factors Affecting Gender Equity in the IT Sector: A Longitudinal Study of Women in Ireland","authors":"E. Trauth, R. Connolly","doi":"10.25300/misq/2022/15964","DOIUrl":"https://doi.org/10.25300/misq/2022/15964","url":null,"abstract":"How and why do societal, organizational and individual factors affecting gender equity in the IT field change over time? To answer this question a longitudinal investigation of the nature of change in factors affecting the position of women in the IT profession was undertaken. It was conducted in Ireland against the backdrop of fluctuations in the nation’s socio-economic status. The individual differences theory of gender and IT was used to analyze life history interviews conducted at four points in time with a total of 63 women whose stories cover the decades from the 1970s to the 2010s. What resulted is a dynamic extension of this theory through the addition of seven themes that characterize the nature of change in factors affecting women IT professionals. The effect on women of economic changes in Ireland is shown to occur through changes in other factors: environmental (i.e., policy, infrastructural, and cultural), identity (e.g., motherhood) and individual (e.g., family). The results reveal both gradual and dramatic changes in an evolving picture of women in this sector, against the back drop of the peaks and valleys of Ireland’s economy. Both transformational and enduring images emerge from this look at Ireland over five decades.","PeriodicalId":18743,"journal":{"name":"MIS Q.","volume":"1 1","pages":"2055-2100"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76354463","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 : 2021-10-14DOI: 10.25300/misq/2021/15297
S. Venkatesan, Rohit Valecha, Niam Yaraghi, Onook Oh, H. Rao
Through the lens of social movement theory, this paper investigates the drivers of individual users’ social influence on Twitter during the Egyptian Revolution of 2011. Following this lens, we suggest an extended model of sustained social influence (that considers retweets as the measure of user influence) as a function of the duality of individual Twitter users’ social actions and the underlying facilitating Twitter network structure. Based on an analysis of organic large-scale Twitter data on this social movement, we examine how characteristics of individuals’ social actions, namely activity and tenure on Twitter, and characteristics facilitated by the network (i.e., the number of followers as well as centrality in the community structure of Twitter), impact retweet influence in time windows spanning the movement. Utilizing a mixed methods approach consisting of machine learning and human coding we conceptualize social movement-related engagement activities of Twitter users, which map to generic frames of social movement mobilization. The analysis reveals interesting patterns across different contexts of the Egyptian Revolution. Regarding individual social action, social movement related to “who” and “where” activities, as well as tenure, were found to contribute to individual social influence. In terms of the facilitating structure, the follower network (an observed network structure) and centrality (an unobserved network structure) were both found to contribute significantly to sustained influence.
{"title":"Influence in Social Media: An Investigation of Tweets Spanning the 2011 Egyptian Revolution","authors":"S. Venkatesan, Rohit Valecha, Niam Yaraghi, Onook Oh, H. Rao","doi":"10.25300/misq/2021/15297","DOIUrl":"https://doi.org/10.25300/misq/2021/15297","url":null,"abstract":"Through the lens of social movement theory, this paper investigates the drivers of individual users’ social influence on Twitter during the Egyptian Revolution of 2011. Following this lens, we suggest an extended model of sustained social influence (that considers retweets as the measure of user influence) as a function of the duality of individual Twitter users’ social actions and the underlying facilitating Twitter network structure. Based on an analysis of organic large-scale Twitter data on this social movement, we examine how characteristics of individuals’ social actions, namely activity and tenure on Twitter, and characteristics facilitated by the network (i.e., the number of followers as well as centrality in the community structure of Twitter), impact retweet influence in time windows spanning the movement. Utilizing a mixed methods approach consisting of machine learning and human coding we conceptualize social movement-related engagement activities of Twitter users, which map to generic frames of social movement mobilization. The analysis reveals interesting patterns across different contexts of the Egyptian Revolution. Regarding individual social action, social movement related to “who” and “where” activities, as well as tenure, were found to contribute to individual social influence. In terms of the facilitating structure, the follower network (an observed network structure) and centrality (an unobserved network structure) were both found to contribute significantly to sustained influence.","PeriodicalId":18743,"journal":{"name":"MIS Q.","volume":"43 1","pages":"1679-1714"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74059254","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 : 2021-10-14DOI: 10.25300/misq/2021/16033
C. Maier, Sven Laumer, D. Joseph, Jens Mattke, Tim Weitzel
Recent statistics indicate that most organizations prefer to fill IT vacancies by rehiring IT professionals who previously worked in the organization. Less is known about what drives IT professionals to “turnback,” a term we define as returning to employment with a former employer. To explain this important and rarely considered IT job mobility behavior, we build on job embeddedness theory and on the concepts of shocks and job dissatisfaction from, among others, the unfolding model of voluntary turnover to develop the theory of IT professional turnback. We perform fuzzy-set qualitative comparative analysis (fsQCA) of data collected from 248 IT professionals to draw conclusions about the intention among IT professionals to return to work for a former employer, and develop a midrange theory. Our results reveal two configurations contributing to high turnback intention and three configurations contributing to low turnback intention. Our model distinguishes between work shocks, personal shocks, and IT work shocks. IT shocks are a new category of shocks specific to the IT profession. We contribute theoretically by theorizing a behavior relevant to IT professionals and explaining attributes driving turnback intention.
{"title":"Turnback Intention: An Analysis of the Drivers of IT Professionals' Intentions to Return to a Former Employer","authors":"C. Maier, Sven Laumer, D. Joseph, Jens Mattke, Tim Weitzel","doi":"10.25300/misq/2021/16033","DOIUrl":"https://doi.org/10.25300/misq/2021/16033","url":null,"abstract":"Recent statistics indicate that most organizations prefer to fill IT vacancies by rehiring IT professionals who previously worked in the organization. Less is known about what drives IT professionals to “turnback,” a term we define as returning to employment with a former employer. To explain this important and rarely considered IT job mobility behavior, we build on job embeddedness theory and on the concepts of shocks and job dissatisfaction from, among others, the unfolding model of voluntary turnover to develop the theory of IT professional turnback. We perform fuzzy-set qualitative comparative analysis (fsQCA) of data collected from 248 IT professionals to draw conclusions about the intention among IT professionals to return to work for a former employer, and develop a midrange theory. Our results reveal two configurations contributing to high turnback intention and three configurations contributing to low turnback intention. Our model distinguishes between work shocks, personal shocks, and IT work shocks. IT shocks are a new category of shocks specific to the IT profession. We contribute theoretically by theorizing a behavior relevant to IT professionals and explaining attributes driving turnback intention.","PeriodicalId":18743,"journal":{"name":"MIS Q.","volume":"12 1","pages":"1777-1806"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74349206","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 : 2021-10-14DOI: 10.25300/misq/2021/15684
E. McFowland, Sandeep Gangarapu, R. Bapna, Tianshu Sun
We define a prescriptive analytics framework that addresses the needs of a constrained decision-maker facing, ex ante, unknown costs and benefits of multiple policy levers. The framework is general in nature and can be deployed in any utility-maximizing context, public or private. It relies on randomized field experiments for causal inference, machine learning for estimating heterogeneous treatment effects, and on the optimization of an integer linear program for converting predictions into decisions. The net result is the discovery of individual-level targeting of policy interventions to maximize overall utility under a budget constraint. The framework is set in the context of the four pillars of analytics and is especially valuable for companies that already have an existing practice of running A/B tests. The key contribution of this work is to develop and operationalize a framework to exploit both within- and between-treatment arm heterogeneity in the utility response function in order to derive benefits from future (optimized) prescriptions. We demonstrate the value of this framework as compared to benchmark practices—i.e., the use of the average treatment effect, uplift modeling, as well as an extension to contextual bandits—in two different settings. Unlike these standard approaches, our framework is able to recognize, adapt to, and exploit the (potential) presence of different subpopulations that experience varying costs and benefits within a treatment arm while also exhibiting differential costs and benefits across treatment arms. As a result, we find a targeting strategy that produces an order of magnitude improvement in expected total utility for the case where significant within- and between-treatment arm heterogeneity exists.
{"title":"A Prescriptive Analytics Framework for Optimal Policy Deployment Using Heterogeneous Treatment Effects","authors":"E. McFowland, Sandeep Gangarapu, R. Bapna, Tianshu Sun","doi":"10.25300/misq/2021/15684","DOIUrl":"https://doi.org/10.25300/misq/2021/15684","url":null,"abstract":"We define a prescriptive analytics framework that addresses the needs of a constrained decision-maker facing, ex ante, unknown costs and benefits of multiple policy levers. The framework is general in nature and can be deployed in any utility-maximizing context, public or private. It relies on randomized field experiments for causal inference, machine learning for estimating heterogeneous treatment effects, and on the optimization of an integer linear program for converting predictions into decisions. The net result is the discovery of individual-level targeting of policy interventions to maximize overall utility under a budget constraint. The framework is set in the context of the four pillars of analytics and is especially valuable for companies that already have an existing practice of running A/B tests. The key contribution of this work is to develop and operationalize a framework to exploit both within- and between-treatment arm heterogeneity in the utility response function in order to derive benefits from future (optimized) prescriptions. We demonstrate the value of this framework as compared to benchmark practices—i.e., the use of the average treatment effect, uplift modeling, as well as an extension to contextual bandits—in two different settings. Unlike these standard approaches, our framework is able to recognize, adapt to, and exploit the (potential) presence of different subpopulations that experience varying costs and benefits within a treatment arm while also exhibiting differential costs and benefits across treatment arms. As a result, we find a targeting strategy that produces an order of magnitude improvement in expected total utility for the case where significant within- and between-treatment arm heterogeneity exists.","PeriodicalId":18743,"journal":{"name":"MIS Q.","volume":"1 1","pages":"1807-1832"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80633435","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 : 2021-09-01DOI: 10.25300/misq/2021/16024
Emmanuelle Vaast, A. Pinsonneault
Occupations are increasingly embedded with and affected by digital technologies. These technologies both enable and threaten occupational identity and create two important tensions: they make the persistence of an occupation possible while also potentially rendering it obsolete, and they magnify both the similarity and distinctiveness of occupations with regard to other occupations. Based on the critical case study of an online community dedicated to data science, we investigate longitudinally how data scientists address the two tensions of occupational identity associated with digital technologies and reach transient syntheses in terms of “optimal distinctiveness” and “persistent extinction.” We propose that identity work associated with digital technologies follows a composite life-cycle and dialectical process. We explain that people constantly need to adjust and redefine their occupational identity, i.e., how they define who they are and what they do. We contribute to scholarship on digital technologies and identity work by illuminating how people deal in an ongoing manner with digital technologies that simultaneously enable and threaten their occupational identity.
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