Individuals are increasingly using novel fitness technologies, such as running applications (apps), to support their workouts. The literature has primarily focused on the use of fitness apps at the individual level (i.e., to improve individuals’ exercise levels) and few studies have investigated the role of fitness apps in facilitating group exercise. Consequently, there is a paucity of information on how to enhance the exercise participation of individuals using fitness apps through the use of groups (i.e., how to entice more individuals to engage in exercise). We selected a running app as the context and focused on a particular feature of this app called “Running Spot,” which facilitates members’ offline group engagement, a topic that has thus far received scant attention in the literature. Drawing on the perspective of psychological distance and relational cohesion theory, we propose that the Running Spot feature facilitating offline group engagement improved group participation in running. To advance this line of research, we utilized a panel dataset of 151 running groups from the running app platform over a period of 38 weeks. The aim was to empirically evaluate the effects of offline group engagement facilitation (e.g., Running Spot) using a combination of the difference-indifferences approach and the propensity score matching technique. Our findings suggest that Running Spot indeed promoted groups’ participation in running. Furthermore, the impact of Running Spot was magnified with smaller groups and groups that were moderately closely located to the designated running spots. Our study contributes to the growing body of knowledge on fitness technologies by revealing ways to support group participation and uncovering the complex impact of offline group engagement facilitation (e.g., Running Spot). Our study has important implications for fitness app developers in that it demonstrates that features facilitating offline group engagement should be prioritized to improve group participation in fitness activities.
{"title":"Be Together, Run More: Enhancing Group Participation in Fitness Technology","authors":"Zilong Liu, Xuequn Wang, X. Luo, Xiaolong Song, Na Liu, Yuan Zhang","doi":"10.17705/1jais.00779","DOIUrl":"https://doi.org/10.17705/1jais.00779","url":null,"abstract":"Individuals are increasingly using novel fitness technologies, such as running applications (apps), to support their workouts. The literature has primarily focused on the use of fitness apps at the individual level (i.e., to improve individuals’ exercise levels) and few studies have investigated the role of fitness apps in facilitating group exercise. Consequently, there is a paucity of information on how to enhance the exercise participation of individuals using fitness apps through the use of groups (i.e., how to entice more individuals to engage in exercise). We selected a running app as the context and focused on a particular feature of this app called “Running Spot,” which facilitates members’ offline group engagement, a topic that has thus far received scant attention in the literature. Drawing on the perspective of psychological distance and relational cohesion theory, we propose that the Running Spot feature facilitating offline group engagement improved group participation in running. To advance this line of research, we utilized a panel dataset of 151 running groups from the running app platform over a period of 38 weeks. The aim was to empirically evaluate the effects of offline group engagement facilitation (e.g., Running Spot) using a combination of the difference-indifferences approach and the propensity score matching technique. Our findings suggest that Running Spot indeed promoted groups’ participation in running. Furthermore, the impact of Running Spot was magnified with smaller groups and groups that were moderately closely located to the designated running spots. Our study contributes to the growing body of knowledge on fitness technologies by revealing ways to support group participation and uncovering the complex impact of offline group engagement facilitation (e.g., Running Spot). Our study has important implications for fitness app developers in that it demonstrates that features facilitating offline group engagement should be prioritized to improve group participation in fitness activities.","PeriodicalId":51101,"journal":{"name":"Journal of the Association for Information Systems","volume":"54 1","pages":"3"},"PeriodicalIF":5.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81484840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Backstory of \"An Adversarial Dance\"","authors":"D. Leidner, A. Burns, P. Balozian","doi":"10.17705/1jais.00804","DOIUrl":"https://doi.org/10.17705/1jais.00804","url":null,"abstract":"<jats:p />","PeriodicalId":51101,"journal":{"name":"Journal of the Association for Information Systems","volume":"30 1","pages":"9"},"PeriodicalIF":5.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77823388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The main purpose of the Sentencing Reform Act of 1984 was to provide more uniformity in sentencing and reduce interjudge disparity. Subsequently, the act created the federal sentencing guidelines to offer judges a possible sentencing range for offenses. However, since these recommendations were based on historical data, the guidelines amplified existing biases and increased inequality and the disproportionate sentencing of minorities. To address this problem, we developed an artifact called “ShowCase”—a data-driven dashboard—that is grounded in penal theory, organizational context theory, social bonds theory, and triangulation notion in design theory. The artifact helps judges make fairer and more objective decisions by integrating a variety of data points. We used a design science research methodology and mixed methods to guide the development and evaluation of the proposed dashboard. Our research inquiry revealed the legal and extralegal factors that contribute to more equitable judicial decisions. We also found support for integrating data science and more diverse viewpoints in the sentencing process. Our study shows that a validated data-driven dashboard can be used to promote fairness, objectivity, and transparency in the criminal justice system.
{"title":"Showcase: A Data-Driven Dashboard for Federal Criminal Sentencing","authors":"Ace Vo, Miloslava Plachkinova","doi":"10.17705/1jais.00796","DOIUrl":"https://doi.org/10.17705/1jais.00796","url":null,"abstract":"The main purpose of the Sentencing Reform Act of 1984 was to provide more uniformity in sentencing and reduce interjudge disparity. Subsequently, the act created the federal sentencing guidelines to offer judges a possible sentencing range for offenses. However, since these recommendations were based on historical data, the guidelines amplified existing biases and increased inequality and the disproportionate sentencing of minorities. To address this problem, we developed an artifact called “ShowCase”—a data-driven dashboard—that is grounded in penal theory, organizational context theory, social bonds theory, and triangulation notion in design theory. The artifact helps judges make fairer and more objective decisions by integrating a variety of data points. We used a design science research methodology and mixed methods to guide the development and evaluation of the proposed dashboard. Our research inquiry revealed the legal and extralegal factors that contribute to more equitable judicial decisions. We also found support for integrating data science and more diverse viewpoints in the sentencing process. Our study shows that a validated data-driven dashboard can be used to promote fairness, objectivity, and transparency in the criminal justice system.","PeriodicalId":51101,"journal":{"name":"Journal of the Association for Information Systems","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135448387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Data science has been described as the fourth paradigm of scientific discovery. The latest wave of data science research, pertaining to machine learning and artificial intelligence (AI), is growing exponentially and garnering millions of annual citations. However, this growth has been accompanied by a diminishing emphasis on social good challenges—our analysis reveals that the proportion of data science research focusing on social good is less than it has ever been. At the same time, the proliferation of machine learning and generative AI has sparked debates about the sociotechnical prospects and challenges associated with data science for human flourishing, organizations, and society. Against this backdrop, we present a framework for “data science for social good” (DSSG) research that considers the interplay between relevant data science research genres, social good challenges, and different levels of sociotechnical abstraction. We perform an analysis of the literature to empirically demonstrate the paucity of work on DSSG in information systems (and other related disciplines) and highlight current impediments. We then use our proposed framework to introduce the articles appearing in the JAIS special issue on data science for social good. We hope that this editorial and the special issue will spur future DSSG research and help reverse the alarming trend across data science research over the past 30-plus years in which social good challenges are attracting proportionately less attention with each passing day
{"title":"Data Science for Social Good","authors":"Ahmed Abbasi, Roger H. L. Chiang, Jennifer Xu","doi":"10.17705/1jais.00849","DOIUrl":"https://doi.org/10.17705/1jais.00849","url":null,"abstract":"Data science has been described as the fourth paradigm of scientific discovery. The latest wave of data science research, pertaining to machine learning and artificial intelligence (AI), is growing exponentially and garnering millions of annual citations. However, this growth has been accompanied by a diminishing emphasis on social good challenges—our analysis reveals that the proportion of data science research focusing on social good is less than it has ever been. At the same time, the proliferation of machine learning and generative AI has sparked debates about the sociotechnical prospects and challenges associated with data science for human flourishing, organizations, and society. Against this backdrop, we present a framework for “data science for social good” (DSSG) research that considers the interplay between relevant data science research genres, social good challenges, and different levels of sociotechnical abstraction. We perform an analysis of the literature to empirically demonstrate the paucity of work on DSSG in information systems (and other related disciplines) and highlight current impediments. We then use our proposed framework to introduce the articles appearing in the JAIS special issue on data science for social good. We hope that this editorial and the special issue will spur future DSSG research and help reverse the alarming trend across data science research over the past 30-plus years in which social good challenges are attracting proportionately less attention with each passing day","PeriodicalId":51101,"journal":{"name":"Journal of the Association for Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135448624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Silviana Tana, Christoph F. Breidbach, Andrew Burton-Jones
Digital technologies are reshaping both organizations and our lives as members of society. The resulting changes are often referred to as “digital transformation” (DT)—a concept that has proven difficult to define and explain theoretically. This is especially true because the nature and scope of DT is evolving beyond clearly defined organizational boundaries. Today, information technology (IT) is becoming increasingly accessible and relevant for many more members of society, creating the potential for previously unseen transformations beyond organizations. Accordingly, new theoretical lenses are needed to explain how a broader set of social actors instigate and are affected by transformative change induced by IT. We conceptualize DT as collective social action, a novel lens that aims to expand the focus of DT research beyond organizations as its unit of analysis, with a complementary perspective centered on social actors, their actions, and their impact. We delineate a typology of digital transformation as collective social action that considers the locus of transformation (what?), types of social actor(s) (who?), and the underlying mechanisms (how?). Collectively, the four ideal types constitute a new theory to help apprehend, explain, and predict how DT as collective social action occurs within and outside formally defined organizational boundaries.
{"title":"Digital Transformation as Collective Social Action","authors":"Silviana Tana, Christoph F. Breidbach, Andrew Burton-Jones","doi":"10.17705/1jais.00791","DOIUrl":"https://doi.org/10.17705/1jais.00791","url":null,"abstract":"Digital technologies are reshaping both organizations and our lives as members of society. The resulting changes are often referred to as “digital transformation” (DT)—a concept that has proven difficult to define and explain theoretically. This is especially true because the nature and scope of DT is evolving beyond clearly defined organizational boundaries. Today, information technology (IT) is becoming increasingly accessible and relevant for many more members of society, creating the potential for previously unseen transformations beyond organizations. Accordingly, new theoretical lenses are needed to explain how a broader set of social actors instigate and are affected by transformative change induced by IT. We conceptualize DT as collective social action, a novel lens that aims to expand the focus of DT research beyond organizations as its unit of analysis, with a complementary perspective centered on social actors, their actions, and their impact. We delineate a typology of digital transformation as collective social action that considers the locus of transformation (what?), types of social actor(s) (who?), and the underlying mechanisms (how?). Collectively, the four ideal types constitute a new theory to help apprehend, explain, and predict how DT as collective social action occurs within and outside formally defined organizational boundaries.","PeriodicalId":51101,"journal":{"name":"Journal of the Association for Information Systems","volume":"294 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135448630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We develop principles that facilitate socially inclusive design-oriented research with marginalized groups. Building on the recognition that the research process must be informed by theoretical perspectives about social inclusion, our effort begins with an empirical investigation of a multiyear research project that designed several IT-based solutions for people with intellectual and developmental disabilities. We treat the efforts to design each solution as a “case,” capture primary data from multiple sources, and analyze it in light of three facets of social inclusion drawn from prior work: self-determination, belongingness, and social capital. The findings are interpreted to derive five principles for a socially inclusive design-oriented research process: (1) respecting multiperspective problem ownership and integrated solution design, (2) surfacing emic contributions to guide artifact design, (3) leveraging the support network to shape artifact design and refine research conduct, (4) customizing design-evaluate cycles with inclusive practices, and (5) pursuing authenticity in research collaborations. We elaborate each principle with connections to different facets of social inclusion, guidelines suggested by our empirical investigation, and a mapping against contemporary design-oriented research approaches. The five principles suggest key directions to facilitate a socially inclusive design-oriented research process when working with marginalized groups. The paper concludes with a discussion of implications for IS scholars, and pointers for using design-oriented approaches for greater social inclusion of marginalized populations.
{"title":"Principles to Facilitate Social Inclusion for Design-Oriented Research","authors":"Sofie Wass, Elin Thygesen, Sandeep Purao","doi":"10.17705/1jais.00814","DOIUrl":"https://doi.org/10.17705/1jais.00814","url":null,"abstract":"We develop principles that facilitate socially inclusive design-oriented research with marginalized groups. Building on the recognition that the research process must be informed by theoretical perspectives about social inclusion, our effort begins with an empirical investigation of a multiyear research project that designed several IT-based solutions for people with intellectual and developmental disabilities. We treat the efforts to design each solution as a “case,” capture primary data from multiple sources, and analyze it in light of three facets of social inclusion drawn from prior work: self-determination, belongingness, and social capital. The findings are interpreted to derive five principles for a socially inclusive design-oriented research process: (1) respecting multiperspective problem ownership and integrated solution design, (2) surfacing emic contributions to guide artifact design, (3) leveraging the support network to shape artifact design and refine research conduct, (4) customizing design-evaluate cycles with inclusive practices, and (5) pursuing authenticity in research collaborations. We elaborate each principle with connections to different facets of social inclusion, guidelines suggested by our empirical investigation, and a mapping against contemporary design-oriented research approaches. The five principles suggest key directions to facilitate a socially inclusive design-oriented research process when working with marginalized groups. The paper concludes with a discussion of implications for IS scholars, and pointers for using design-oriented approaches for greater social inclusion of marginalized populations.","PeriodicalId":51101,"journal":{"name":"Journal of the Association for Information Systems","volume":"49 1","pages":"9"},"PeriodicalIF":5.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88334805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Luisa Pumplun, F. Peters, J. Gawlitza, Peter Buxmann
Clinical decision support systems (CDSSs) based on machine learning (ML) hold great promise for improving medical care. Technically, such CDSSs are already feasible but physicians have been skeptical about their application. In particular, their opacity is a major concern, as it may lead physicians to overlook erroneous outputs from ML-based CDSSs, potentially causing serious consequences for patients. Research on explainable AI (XAI) offers methods with the potential to increase the explainability of black-box ML systems. This could significantly accelerate the application of MLbased CDSSs in medicine. However, XAI research to date has mainly been technically driven and largely neglects the needs of end users. To better engage the users of ML-based CDSSs, we applied a design science approach to develop a design for explainable ML-based CDSSs that incorporates insights from XAI literature while simultaneously addressing physicians’ needs. This design comprises five design principles that designers of ML-based CDSSs can apply to implement user-centered explanations, which are instantiated in a prototype of an explainable ML-based CDSS for lung nodule classification. We rooted the design principles and the derived prototype in a body of justificatory knowledge consisting of XAI literature, the concept of usability, and an online survey study involving 57 physicians. We refined the design principles and their instantiation by conducting walk-throughs with six radiologists. A final experiment with 45 radiologists demonstrated that our design resulted in physicians perceiving the ML-based CDSS as more explainable and usable in terms of the required cognitive effort than a system without explanations.
{"title":"Bringing Machine Learning Systems into Clinical Practice: A Design Science Approach to Explainable Machine Learning-Based Clinical Decision Support Systems","authors":"Luisa Pumplun, F. Peters, J. Gawlitza, Peter Buxmann","doi":"10.17705/1jais.00820","DOIUrl":"https://doi.org/10.17705/1jais.00820","url":null,"abstract":"Clinical decision support systems (CDSSs) based on machine learning (ML) hold great promise for improving medical care. Technically, such CDSSs are already feasible but physicians have been skeptical about their application. In particular, their opacity is a major concern, as it may lead physicians to overlook erroneous outputs from ML-based CDSSs, potentially causing serious consequences for patients. Research on explainable AI (XAI) offers methods with the potential to increase the explainability of black-box ML systems. This could significantly accelerate the application of MLbased CDSSs in medicine. However, XAI research to date has mainly been technically driven and largely neglects the needs of end users. To better engage the users of ML-based CDSSs, we applied a design science approach to develop a design for explainable ML-based CDSSs that incorporates insights from XAI literature while simultaneously addressing physicians’ needs. This design comprises five design principles that designers of ML-based CDSSs can apply to implement user-centered explanations, which are instantiated in a prototype of an explainable ML-based CDSS for lung nodule classification. We rooted the design principles and the derived prototype in a body of justificatory knowledge consisting of XAI literature, the concept of usability, and an online survey study involving 57 physicians. We refined the design principles and their instantiation by conducting walk-throughs with six radiologists. A final experiment with 45 radiologists demonstrated that our design resulted in physicians perceiving the ML-based CDSS as more explainable and usable in terms of the required cognitive effort than a system without explanations.","PeriodicalId":51101,"journal":{"name":"Journal of the Association for Information Systems","volume":"23 1","pages":"8"},"PeriodicalIF":5.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90856094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Online gamified competition utilizes competition as a core gamification design element with affordances from wearables and mobile applications to track competitive activities and visualize information in an integrated way to shape users’ exercise behaviors. However, a clear understanding of how online gamified competition cultivates exercise behaviors in different types of individuals is still lacking. We take into account the individual differences in exercise behaviors and categorize exercisers into three groups (active, moderate, and inactive) based on an adapted recency, frequency, and monetary value framework using key exercise behavior metrics. Theorizing online gamified competition as a means of social and temporal self-comparison, we examine the effect of performance feedback from two distinct modes of comparison (performance rankings and performance gap), and participants’ relationships with their social comparison referents (i.e., rivalry intensity), on the exercise behaviors of different exerciser groups. Our results reveal that online gamified competition has differential effects on exercise behaviors across different exerciser groups. Specifically, we find that positive performance improvements are more motivational for active and moderate exercisers, while performance deterioration relative to historical exercise performance level is more discouraging for inactive exercisers. Performance rankings exhibit a more salient effect for moderate and inactive exercisers, and rivalry intensity has a stronger positive effect on active exercisers’ exercise behavior. The strengthening effect of awareness affordances in mobile fitness apps is more notable with regard to the impact of rivalry intensity on moderate and inactive exercisers. We derive theoretical and practical implications of gamified information systems that use competition as a core design element for shaping the exercise behavior of individuals in different exerciser groups.
{"title":"Compete with Me? The Impact of Online Gamified Competition on Exercise Behavior","authors":"Yang Yang, K. Goh, H. Teo, S. Tan","doi":"10.17705/1jais.00806","DOIUrl":"https://doi.org/10.17705/1jais.00806","url":null,"abstract":"Online gamified competition utilizes competition as a core gamification design element with affordances from wearables and mobile applications to track competitive activities and visualize information in an integrated way to shape users’ exercise behaviors. However, a clear understanding of how online gamified competition cultivates exercise behaviors in different types of individuals is still lacking. We take into account the individual differences in exercise behaviors and categorize exercisers into three groups (active, moderate, and inactive) based on an adapted recency, frequency, and monetary value framework using key exercise behavior metrics. Theorizing online gamified competition as a means of social and temporal self-comparison, we examine the effect of performance feedback from two distinct modes of comparison (performance rankings and performance gap), and participants’ relationships with their social comparison referents (i.e., rivalry intensity), on the exercise behaviors of different exerciser groups. Our results reveal that online gamified competition has differential effects on exercise behaviors across different exerciser groups. Specifically, we find that positive performance improvements are more motivational for active and moderate exercisers, while performance deterioration relative to historical exercise performance level is more discouraging for inactive exercisers. Performance rankings exhibit a more salient effect for moderate and inactive exercisers, and rivalry intensity has a stronger positive effect on active exercisers’ exercise behavior. The strengthening effect of awareness affordances in mobile fitness apps is more notable with regard to the impact of rivalry intensity on moderate and inactive exercisers. We derive theoretical and practical implications of gamified information systems that use competition as a core design element for shaping the exercise behavior of individuals in different exerciser groups.","PeriodicalId":51101,"journal":{"name":"Journal of the Association for Information Systems","volume":"746 1","pages":"1"},"PeriodicalIF":5.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85413025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To realize value from their wealth of digital data, organizations are investing in data-driven organizational initiatives—efforts in which they must draw expertise in data, algorithms, and visualization together with knowledge and skills in business domains such as marketing and human resources. However, they face the challenge of crossing the knowledge divide between analytics groups and business groups. Exploring relationships between the two groups in 37 data-driven organizational initiatives, we develop a configuration-based model that explains analytics and businessdomain knowledge integration through the lens of synergy. Our configurational analyses revealed five configurations of relationships between the two, which bring about two distinct change outcomes: “dedicated data groups” and “multidisciplinary teams” lead to the emergence of new datadriven ways to work, and “analytics institutionalization,” “analytics resource optimization,” and “networked communities” produce convergence, through the sharing of data-driven ways to work. Each configuration displays a distinct element of the core processes identified (“developing group connectedness,” “exchanging analytics and business domain knowledge,” and “incentivizing organizational data use”) and yields either an emergence or convergence of data-driven ways of working. The findings demonstrate how data-driven organizational initiatives can benefit from a pervasive form of organizing that entwines analytics groups and business groups such that their members’ tools, mindsets, and behaviors are merged to profoundly change ways of working. Together, these findings and the configurational methodology used provide a nuanced picture of how organizations integrate the requisite specialist knowledge across domains to realize value from data.
{"title":"Configuring Relationships between Analytics and Business Domain Groups for Knowledge Integration","authors":"I. Someh, B. Wixom, Michael J. Davern, G. Shanks","doi":"10.17705/1jais.00782","DOIUrl":"https://doi.org/10.17705/1jais.00782","url":null,"abstract":"To realize value from their wealth of digital data, organizations are investing in data-driven organizational initiatives—efforts in which they must draw expertise in data, algorithms, and visualization together with knowledge and skills in business domains such as marketing and human resources. However, they face the challenge of crossing the knowledge divide between analytics groups and business groups. Exploring relationships between the two groups in 37 data-driven organizational initiatives, we develop a configuration-based model that explains analytics and businessdomain knowledge integration through the lens of synergy. Our configurational analyses revealed five configurations of relationships between the two, which bring about two distinct change outcomes: “dedicated data groups” and “multidisciplinary teams” lead to the emergence of new datadriven ways to work, and “analytics institutionalization,” “analytics resource optimization,” and “networked communities” produce convergence, through the sharing of data-driven ways to work. Each configuration displays a distinct element of the core processes identified (“developing group connectedness,” “exchanging analytics and business domain knowledge,” and “incentivizing organizational data use”) and yields either an emergence or convergence of data-driven ways of working. The findings demonstrate how data-driven organizational initiatives can benefit from a pervasive form of organizing that entwines analytics groups and business groups such that their members’ tools, mindsets, and behaviors are merged to profoundly change ways of working. Together, these findings and the configurational methodology used provide a nuanced picture of how organizations integrate the requisite specialist knowledge across domains to realize value from data.","PeriodicalId":51101,"journal":{"name":"Journal of the Association for Information Systems","volume":"72 1","pages":"1"},"PeriodicalIF":5.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84362385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we focus on the early-stage diffusion of codependent IT innovations, which are a type of innovation in which the overall innovation consists of two complementary parts that are adopted by two different adopter communities but where both parts need to be jointly adopted by the two coadopter communities for successful diffusion of the overall innovation. Using innovation diffusion, organizing vision (OV), and institutional entrepreneurship theories as the key theoretical lenses, and an in-depth case study reconstructed using 20 years of discourse surrounding Walmart’s campaign in the early stage of diffusion of the RFID-in-retailing technology, we develop a fourphase process model for the early-stage diffusion of codependent IT innovations. We make three specific contributions to the IS discipline, specifically to the literature on IS innovation adoption and diffusion. First, we add the notion of coadopter relative advantage and posit that the organization in the coadopter community with a higher coadopter relative advantage that perceives the highest degree of coadopter relative advantage will emerge as an institutional entrepreneur (IE) and will influence the early-stage diffusion of the codependent IT innovation. Second, we add the notion of an internal-external influencer and posit that the IE may be an actor who is internal to the overall adoption phenomenon, which involves two different coadopter communities, but external to the coadopter community with a lower coadopter relative advantage that adopts the innovation component. Third, we divide the early-stage diffusion process into four phases—emergence, structuralization, evolution, and chasm—and identify the institutional entrepreneurship strategies used and the OV functions enacted by the IE during each phase. We propose that the IE for a codependent innovation will: (1) use the rationale development strategy and enact the interpretation OV function during the emergence phase, (2) use the resource mobilization strategy and enact the mobilization OV function during the structuralization phase, (3) use the relationship development strategy and enact the legitimation OV function during the evolution phase, and (4) use all the three institutional entrepreneurship strategies and enact all the three OV functions during the chasm phase.
{"title":"Theorizing about the Early-Stage Diffusion of Codependent IT Innovations","authors":"S. Parameswaran, R. Kishore, X. Yang, Zhenyu Liu","doi":"10.17705/1jais.00789","DOIUrl":"https://doi.org/10.17705/1jais.00789","url":null,"abstract":"In this paper, we focus on the early-stage diffusion of codependent IT innovations, which are a type of innovation in which the overall innovation consists of two complementary parts that are adopted by two different adopter communities but where both parts need to be jointly adopted by the two coadopter communities for successful diffusion of the overall innovation. Using innovation diffusion, organizing vision (OV), and institutional entrepreneurship theories as the key theoretical lenses, and an in-depth case study reconstructed using 20 years of discourse surrounding Walmart’s campaign in the early stage of diffusion of the RFID-in-retailing technology, we develop a fourphase process model for the early-stage diffusion of codependent IT innovations. We make three specific contributions to the IS discipline, specifically to the literature on IS innovation adoption and diffusion. First, we add the notion of coadopter relative advantage and posit that the organization in the coadopter community with a higher coadopter relative advantage that perceives the highest degree of coadopter relative advantage will emerge as an institutional entrepreneur (IE) and will influence the early-stage diffusion of the codependent IT innovation. Second, we add the notion of an internal-external influencer and posit that the IE may be an actor who is internal to the overall adoption phenomenon, which involves two different coadopter communities, but external to the coadopter community with a lower coadopter relative advantage that adopts the innovation component. Third, we divide the early-stage diffusion process into four phases—emergence, structuralization, evolution, and chasm—and identify the institutional entrepreneurship strategies used and the OV functions enacted by the IE during each phase. We propose that the IE for a codependent innovation will: (1) use the rationale development strategy and enact the interpretation OV function during the emergence phase, (2) use the resource mobilization strategy and enact the mobilization OV function during the structuralization phase, (3) use the relationship development strategy and enact the legitimation OV function during the evolution phase, and (4) use all the three institutional entrepreneurship strategies and enact all the three OV functions during the chasm phase.","PeriodicalId":51101,"journal":{"name":"Journal of the Association for Information Systems","volume":"32 1","pages":"7"},"PeriodicalIF":5.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86058360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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