Pub Date : 2023-04-03DOI: 10.1080/07421222.2023.2196780
Jiaheng Xie, Yidong Chai, Xinyu Liu
ABSTRACT As video-sharing sites emerge as a critical part of the social media landscape, video viewership prediction becomes essential for content creators and businesses to optimize influence and marketing outreach with minimum budgets. Although deep learning champions viewership prediction, it lacks interpretability, which is required by regulators and is fundamental to the prioritization of the video production process and promoting trust in algorithms. Existing interpretable predictive models face the challenges of imprecise interpretation and negligence of unstructured data. Following the design-science paradigm, we propose a novel Precise Wide-and-Deep Learning (PrecWD) to accurately predict viewership with unstructured video data and well-established features while precisely interpreting feature effects. PrecWD’s prediction outperforms benchmarks in two case studies and achieves superior interpretability in two user studies. We contribute to IS knowledge base by enabling precise interpretability in video-based predictive analytics and contribute nascent design theory with generalizable model design principles. Our system is deployable to improve video-based social media presence.
{"title":"Unbox the Black-Box: Predict and Interpret YouTube Viewership Using Deep Learning","authors":"Jiaheng Xie, Yidong Chai, Xinyu Liu","doi":"10.1080/07421222.2023.2196780","DOIUrl":"https://doi.org/10.1080/07421222.2023.2196780","url":null,"abstract":"ABSTRACT As video-sharing sites emerge as a critical part of the social media landscape, video viewership prediction becomes essential for content creators and businesses to optimize influence and marketing outreach with minimum budgets. Although deep learning champions viewership prediction, it lacks interpretability, which is required by regulators and is fundamental to the prioritization of the video production process and promoting trust in algorithms. Existing interpretable predictive models face the challenges of imprecise interpretation and negligence of unstructured data. Following the design-science paradigm, we propose a novel Precise Wide-and-Deep Learning (PrecWD) to accurately predict viewership with unstructured video data and well-established features while precisely interpreting feature effects. PrecWD’s prediction outperforms benchmarks in two case studies and achieves superior interpretability in two user studies. We contribute to IS knowledge base by enabling precise interpretability in video-based predictive analytics and contribute nascent design theory with generalizable model design principles. Our system is deployable to improve video-based social media presence.","PeriodicalId":50154,"journal":{"name":"Journal of Management Information Systems","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48297299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-03DOI: 10.1080/07421222.2023.2196778
W. Jabr, Abhijeet Ghoshal, Yichen Cheng, P. Pavlou
ABSTRACT Online retailers are increasingly focused on maintaining a long-term relationship with customers, encouraging repeat visits rather than single-time purchases to increase customer lifetime value. To help retailers maximize the probabilities of customers’ revisiting and purchasing, we develop a two-stage model to better characterize and predict these two fundamental customer activities. In the first stage, we characterize the propensity of a customer revisiting the retailer’s website. In the second stage, we develop a stochastic model that predicts revisits while also incorporating individual customer heterogeneity in exerted search effort during repeated visits. This heterogeneity is based on individual customer preferences in the choice of consideration sets, product information, pricing, and the search environment. Using customer level clickstream data, we show that our approach is not only better at predicting repeat customer visits, compared to existing methods, but also explainable and managerially interpretable. Most importantly, using computationally efficient simulation-based prescriptive analytics, we leverage our modeling approach to propose practical intervention strategies that maximize the joint likelihoods of customers revisiting and purchasing at the individual customer level.
{"title":"Maximizing Online Revisiting and Purchasing: A Clickstream-Based Approach to Enhancing Customer Lifetime Value","authors":"W. Jabr, Abhijeet Ghoshal, Yichen Cheng, P. Pavlou","doi":"10.1080/07421222.2023.2196778","DOIUrl":"https://doi.org/10.1080/07421222.2023.2196778","url":null,"abstract":"ABSTRACT Online retailers are increasingly focused on maintaining a long-term relationship with customers, encouraging repeat visits rather than single-time purchases to increase customer lifetime value. To help retailers maximize the probabilities of customers’ revisiting and purchasing, we develop a two-stage model to better characterize and predict these two fundamental customer activities. In the first stage, we characterize the propensity of a customer revisiting the retailer’s website. In the second stage, we develop a stochastic model that predicts revisits while also incorporating individual customer heterogeneity in exerted search effort during repeated visits. This heterogeneity is based on individual customer preferences in the choice of consideration sets, product information, pricing, and the search environment. Using customer level clickstream data, we show that our approach is not only better at predicting repeat customer visits, compared to existing methods, but also explainable and managerially interpretable. Most importantly, using computationally efficient simulation-based prescriptive analytics, we leverage our modeling approach to propose practical intervention strategies that maximize the joint likelihoods of customers revisiting and purchasing at the individual customer level.","PeriodicalId":50154,"journal":{"name":"Journal of Management Information Systems","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45495428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-03DOI: 10.1080/07421222.2023.2196771
Ruiyun Xu, Hailiang Chen, J. Zhao
ABSTRACT While venture capital firms are increasingly relying on recommendation models in investment decisions, existing startup recommendation models fail to consider the uniqueness of venture capital context, including two-sided matching between investing and investee firms and a lack of information disclosure requirements on startups. Following the design science research paradigm and guided by the proximity principle from social psychology, we develop a novel framework called SocioLink by depicting and analyzing various relations in a knowledge graph via machine learning. Our experimental results show that SocioLink significantly outperforms state-of-the-art startup recommendation methods in both accuracy and quality. This improvement is driven by not only the inclusion of social relations but also the superiority of modelling relations via knowledge graph. We also develop a web-based prototype to demonstrate explainable artificial intelligence. This work contributes to the FinTech literature by adding an innovative design artifact—SocioLink—for decision support in the investment context.
{"title":"SocioLink: Leveraging Relational Information in Knowledge Graphs for Startup Recommendations","authors":"Ruiyun Xu, Hailiang Chen, J. Zhao","doi":"10.1080/07421222.2023.2196771","DOIUrl":"https://doi.org/10.1080/07421222.2023.2196771","url":null,"abstract":"ABSTRACT While venture capital firms are increasingly relying on recommendation models in investment decisions, existing startup recommendation models fail to consider the uniqueness of venture capital context, including two-sided matching between investing and investee firms and a lack of information disclosure requirements on startups. Following the design science research paradigm and guided by the proximity principle from social psychology, we develop a novel framework called SocioLink by depicting and analyzing various relations in a knowledge graph via machine learning. Our experimental results show that SocioLink significantly outperforms state-of-the-art startup recommendation methods in both accuracy and quality. This improvement is driven by not only the inclusion of social relations but also the superiority of modelling relations via knowledge graph. We also develop a web-based prototype to demonstrate explainable artificial intelligence. This work contributes to the FinTech literature by adding an innovative design artifact—SocioLink—for decision support in the investment context.","PeriodicalId":50154,"journal":{"name":"Journal of Management Information Systems","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42910401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-03DOI: 10.1080/07421222.2023.2196770
Da Xu, P. J. Hu, Xiao Fang
ABSTRACT Popular online business directory (OBD) platforms, such as Yelp and TripAdvisor, depend on voluntarily user-submitted data about various businesses to assist consumers in finding appropriate options for transactions. Yet the crowdsourced nature of such data restricts the availability of attribute values for many businesses on the platform. Crowdsourced data often suffer serious completeness and timeliness constraints, with negative implications for key stakeholders such as users, businesses, and the platform. We thus develop a novel, deep learning–based imputation method, premised in institutional theory, to estimate missing attribute values of individual businesses on an OBD platform. The proposed method leverages a deep model architecture and considers both inter-business and inter-attribute relationships for imputations. An application to a Yelp data set reveals our method’s greater imputation effectiveness relative to prevalent methods. To illustrate the method’s practical utilities and values, we further examine the efficacy of business recommendations empowered by its imputed business attribute values, in comparison with those enabled by data imputed by benchmark methods. The results affirm that the proposed method substantially outperforms benchmarks for imputing missing attribute values and empowers more effective business recommendations. This study addresses crucial, prominent completeness and timeliness constraints in crowdsourced data on OBD platforms and offers insights for downstream applications that can improve user experiences, firm performance, and platform services.
{"title":"Deep Learning-Based Imputation Method to Enhance Crowdsourced Data on Online Business Directory Platforms for Improved Services","authors":"Da Xu, P. J. Hu, Xiao Fang","doi":"10.1080/07421222.2023.2196770","DOIUrl":"https://doi.org/10.1080/07421222.2023.2196770","url":null,"abstract":"ABSTRACT Popular online business directory (OBD) platforms, such as Yelp and TripAdvisor, depend on voluntarily user-submitted data about various businesses to assist consumers in finding appropriate options for transactions. Yet the crowdsourced nature of such data restricts the availability of attribute values for many businesses on the platform. Crowdsourced data often suffer serious completeness and timeliness constraints, with negative implications for key stakeholders such as users, businesses, and the platform. We thus develop a novel, deep learning–based imputation method, premised in institutional theory, to estimate missing attribute values of individual businesses on an OBD platform. The proposed method leverages a deep model architecture and considers both inter-business and inter-attribute relationships for imputations. An application to a Yelp data set reveals our method’s greater imputation effectiveness relative to prevalent methods. To illustrate the method’s practical utilities and values, we further examine the efficacy of business recommendations empowered by its imputed business attribute values, in comparison with those enabled by data imputed by benchmark methods. The results affirm that the proposed method substantially outperforms benchmarks for imputing missing attribute values and empowers more effective business recommendations. This study addresses crucial, prominent completeness and timeliness constraints in crowdsourced data on OBD platforms and offers insights for downstream applications that can improve user experiences, firm performance, and platform services.","PeriodicalId":50154,"journal":{"name":"Journal of Management Information Systems","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42158749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-02DOI: 10.1080/07421222.2023.2172768
G. de Vreede, J. Nunamaker
Recent years have proven to be among the most challenging on record for organizations and society at large. A global multi-year pandemic, violence resulting from polemic political discourse, and social-justice movements borne from (deadly) racial inequality are but a few examples of the major events that have changed how we work and live. The pandemic has changed where we perform our work duties and how we collaborate across space and time using technology. The political discourse has given rise to new social media phenomena like fake news and deep fake videos. The social-justice movements have put issues of diversity, equity, and inclusion front and center for many organizations. At the same time, information systems and technology have seen accelerated changes as well. Artificial Intelligence (AI) has become a mainstream application for organizations and households. Social media applications keep evolving, changing how we share information, interact with each other, and form communities. Information systems (IS) professionals versed in analytics and data science have become one of the scarcest organizational resources. Together these societal challenges and technological advances have changed how organizations and individuals create, receive, interpret, analyze, and act on information. The essence of value creation in communities and organizations is shifting as we find new work structures, new technologyhuman relationships, and new analytical techniques to find insight and extract knowledge from huge amounts of information. This special issue presents advanced research studies that share insights on new approaches, new techniques, and new understandings of how communities, organizations, and individual use information and information systems to create value The first paper focuses on a design method: “Act and Reflect: Integrating Reflection into Design Thinking,” by Thorsten Schoormann, Maren Stadtländer, and Ralf Knackstedt, demonstrates the criticality of adding a reflection lens to development methods. Specifically, the authors report on a multi-method study that includes a literature review, semi-structured interviews, a case study, and a software prototype, to develop prescriptive design knowledge on how to integrate reflection into design thinking. Their contribution to the Design Thinking discourse is significant as it accommodates and structures teams that experience divergent values, knowledge, and preferences to actively learn from their experiences and inform future design efforts. The next paper, “Formation and Action of a Learning Community with Collaborative Learning Software,” by Evren Eryilmaz, Brian Thoms, Zafor Ahmed, and Howard Lee presents a mixed-methods field study that is grounded in group cognition, knowledge building, and learning analytics to demonstrate how learning community development can be facilitated by specialized asynchronous online discussion (AOD) tools. The authors show participants operate in different co
{"title":"Introduction to the Special Issue","authors":"G. de Vreede, J. Nunamaker","doi":"10.1080/07421222.2023.2172768","DOIUrl":"https://doi.org/10.1080/07421222.2023.2172768","url":null,"abstract":"Recent years have proven to be among the most challenging on record for organizations and society at large. A global multi-year pandemic, violence resulting from polemic political discourse, and social-justice movements borne from (deadly) racial inequality are but a few examples of the major events that have changed how we work and live. The pandemic has changed where we perform our work duties and how we collaborate across space and time using technology. The political discourse has given rise to new social media phenomena like fake news and deep fake videos. The social-justice movements have put issues of diversity, equity, and inclusion front and center for many organizations. At the same time, information systems and technology have seen accelerated changes as well. Artificial Intelligence (AI) has become a mainstream application for organizations and households. Social media applications keep evolving, changing how we share information, interact with each other, and form communities. Information systems (IS) professionals versed in analytics and data science have become one of the scarcest organizational resources. Together these societal challenges and technological advances have changed how organizations and individuals create, receive, interpret, analyze, and act on information. The essence of value creation in communities and organizations is shifting as we find new work structures, new technologyhuman relationships, and new analytical techniques to find insight and extract knowledge from huge amounts of information. This special issue presents advanced research studies that share insights on new approaches, new techniques, and new understandings of how communities, organizations, and individual use information and information systems to create value The first paper focuses on a design method: “Act and Reflect: Integrating Reflection into Design Thinking,” by Thorsten Schoormann, Maren Stadtländer, and Ralf Knackstedt, demonstrates the criticality of adding a reflection lens to development methods. Specifically, the authors report on a multi-method study that includes a literature review, semi-structured interviews, a case study, and a software prototype, to develop prescriptive design knowledge on how to integrate reflection into design thinking. Their contribution to the Design Thinking discourse is significant as it accommodates and structures teams that experience divergent values, knowledge, and preferences to actively learn from their experiences and inform future design efforts. The next paper, “Formation and Action of a Learning Community with Collaborative Learning Software,” by Evren Eryilmaz, Brian Thoms, Zafor Ahmed, and Howard Lee presents a mixed-methods field study that is grounded in group cognition, knowledge building, and learning analytics to demonstrate how learning community development can be facilitated by specialized asynchronous online discussion (AOD) tools. The authors show participants operate in different co","PeriodicalId":50154,"journal":{"name":"Journal of Management Information Systems","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42247970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-02DOI: 10.1080/07421222.2023.2172771
Scott Thiebes, Fangjian Gao, R. Briggs, Manuel Schmidt-Kraepelin, A. Sunyaev
ABSTRACT Multiorganizational, multistakeholder (MO-MS) collaborations that may span organizational and national boundaries, present design challenges beyond those of smaller-scale collaborations. This study opens an exploratory research stream to discover and document design concerns for MO-MS collaboration systems beyond those of the single-task collaborations that have been the primary focus of collaboration engineering research. We chose the healthcare industry as the first target for this research because it has attributes common to many MO-MS domains, and because it faces significant challenges on a global scale, like the recent COVID-19 pandemic, for which MO-MS collaboration could offer solutions, as, for example, evidenced by the rapid collaborative development and distribution of COVID-19 vaccines. To this end, we reviewed 6,609 articles to find 100 articles that offered insights about the design of MO-MS collaboration systems, then conducted 50 semi-structured interviews in two countries with expert practitioners in the field. From those sources, we derived an eleven-category set of design concerns for MO-MS collaboration systems and argue their generalizability to other MO-MS domains. We offer exemplar probe questions that designers can use to increase the breadth and depth of requirements gathering for MO-MS collaboration systems.
{"title":"Design Concerns for Multiorganizational, Multistakeholder Collaboration: A Study in the Healthcare Industry","authors":"Scott Thiebes, Fangjian Gao, R. Briggs, Manuel Schmidt-Kraepelin, A. Sunyaev","doi":"10.1080/07421222.2023.2172771","DOIUrl":"https://doi.org/10.1080/07421222.2023.2172771","url":null,"abstract":"ABSTRACT Multiorganizational, multistakeholder (MO-MS) collaborations that may span organizational and national boundaries, present design challenges beyond those of smaller-scale collaborations. This study opens an exploratory research stream to discover and document design concerns for MO-MS collaboration systems beyond those of the single-task collaborations that have been the primary focus of collaboration engineering research. We chose the healthcare industry as the first target for this research because it has attributes common to many MO-MS domains, and because it faces significant challenges on a global scale, like the recent COVID-19 pandemic, for which MO-MS collaboration could offer solutions, as, for example, evidenced by the rapid collaborative development and distribution of COVID-19 vaccines. To this end, we reviewed 6,609 articles to find 100 articles that offered insights about the design of MO-MS collaboration systems, then conducted 50 semi-structured interviews in two countries with expert practitioners in the field. From those sources, we derived an eleven-category set of design concerns for MO-MS collaboration systems and argue their generalizability to other MO-MS domains. We offer exemplar probe questions that designers can use to increase the breadth and depth of requirements gathering for MO-MS collaboration systems.","PeriodicalId":50154,"journal":{"name":"Journal of Management Information Systems","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44708969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-02DOI: 10.1080/07421222.2023.2172776
Edona Elshan, P. Ebel, Matthias Söllner, J. Leimeister
ABSTRACT Smart personal assistants (SPAs), such as Alexa for example, promise individualized user interactions owing to their varying interaction possibilities, knowledgeability, and human-like behaviors. To support the widespread adoption and use of SPAs, organizations such as Google or Amazon provide low code environments that support the development of SPAs (e.g., for Google Home or Amazon’s Alexa). These so-called low code platforms enable domain experts (e.g., business users without programming skills or experience) to develop SPAs for their purposes. However, using these platforms alone does not guarantee a useful and good conversation with novel SPAs due to non-intuitive design choices. Following a design science research approach, we propose the Smart Personal Assistant for Domain Experts (SPADE) method to address the missing link. This method supports domain experts in the development and contextualization of sophisticated SPAs for various application scenarios and focuses especially on conversational and anthropomorphic design steps. Our proof of concept and proof of value results show that SPADE is useful for supporting domain experts to create effective SPAs in different domains beyond private set-ups.
{"title":"Leveraging Low Code Development of Smart Personal Assistants: An Integrated Design Approach with the SPADE Method","authors":"Edona Elshan, P. Ebel, Matthias Söllner, J. Leimeister","doi":"10.1080/07421222.2023.2172776","DOIUrl":"https://doi.org/10.1080/07421222.2023.2172776","url":null,"abstract":"ABSTRACT Smart personal assistants (SPAs), such as Alexa for example, promise individualized user interactions owing to their varying interaction possibilities, knowledgeability, and human-like behaviors. To support the widespread adoption and use of SPAs, organizations such as Google or Amazon provide low code environments that support the development of SPAs (e.g., for Google Home or Amazon’s Alexa). These so-called low code platforms enable domain experts (e.g., business users without programming skills or experience) to develop SPAs for their purposes. However, using these platforms alone does not guarantee a useful and good conversation with novel SPAs due to non-intuitive design choices. Following a design science research approach, we propose the Smart Personal Assistant for Domain Experts (SPADE) method to address the missing link. This method supports domain experts in the development and contextualization of sophisticated SPAs for various application scenarios and focuses especially on conversational and anthropomorphic design steps. Our proof of concept and proof of value results show that SPADE is useful for supporting domain experts to create effective SPAs in different domains beyond private set-ups.","PeriodicalId":50154,"journal":{"name":"Journal of Management Information Systems","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44462040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-02DOI: 10.1080/07421222.2023.2172773
Thorsten Schoormann, Maren Stadtländer, R. Knackstedt
ABSTRACT Teams working on creative projects, such as design thinking, mostly face complex problems as well as challenging situations characterized by uniqueness and value conflicts. To cope with these characteristics, teams usually start doing something by drawing on their current store of experiences and professional knowledge, and then (re-)assess the outcomes produced, and adjust future actions based on insights obtained during the process. In reflecting on actions, tacit knowledge is revealed that enables designers to handle challenging situations. Although there is great potential to support design thinking by adding a reflection lens, we lack guidance on how, when, and on what to perform reflection. Based on scientific and theoretical literature, semi-structured interviews, a case study and a software prototype, prescriptive design knowledge on how to integrate reflection into design thinking is deduced, which enriches the scarce body of knowledge at the intersection of reflection and (digital) design thinking.
{"title":"Act and Reflect: Integrating Reflection into Design Thinking","authors":"Thorsten Schoormann, Maren Stadtländer, R. Knackstedt","doi":"10.1080/07421222.2023.2172773","DOIUrl":"https://doi.org/10.1080/07421222.2023.2172773","url":null,"abstract":"ABSTRACT Teams working on creative projects, such as design thinking, mostly face complex problems as well as challenging situations characterized by uniqueness and value conflicts. To cope with these characteristics, teams usually start doing something by drawing on their current store of experiences and professional knowledge, and then (re-)assess the outcomes produced, and adjust future actions based on insights obtained during the process. In reflecting on actions, tacit knowledge is revealed that enables designers to handle challenging situations. Although there is great potential to support design thinking by adding a reflection lens, we lack guidance on how, when, and on what to perform reflection. Based on scientific and theoretical literature, semi-structured interviews, a case study and a software prototype, prescriptive design knowledge on how to integrate reflection into design thinking is deduced, which enriches the scarce body of knowledge at the intersection of reflection and (digital) design thinking.","PeriodicalId":50154,"journal":{"name":"Journal of Management Information Systems","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47843725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-02DOI: 10.1080/07421222.2023.2172770
A. Kathuria, Prasanna P. Karhade, Xue Ning, B. Konsynski
ABSTRACT Family-owned businesses differ in their strategic intent and behavior as they serve as a reservoir of wealth and social status for their family owners. Family-owned businesses demonstrate relatively conservative strategic decision making that aspires long-term wealth preservation and enhancement. For family owners, investments in information technology (IT) raise a predicament as they are risky, yet a long-term imperative. We propose three hypotheses that build upon the thesis that family owners combine a deep understanding of the business with a strong influence on stakeholders within and beyond the firm’s boundaries to exert strategic control in the extended enterprise. First, family ownership negatively influences IT investment, because family owners are likely to avoid investments in IT that are frivolous, reduce information asymmetry, or leave auditable digital trails. Second, the negative influence of family ownership on IT investment is weakened when a career professional is appointed in the senior-most executive position of a family-owned business. This is because professional executives strive to utilize IT for control and performance benefits, and family owners desire to use IT to monitor and control the non-family professional executive. Third, family ownership weakens the negative influence of environmental hostility on the relationship between IT investment and firm performance, as family-owned businesses incur less dynamic adjustment costs and maintain better alignment between IT and business strategy. Empirical analysis, consisting of panel regression estimations, on archival data of publicly listed Indian firms in the years 2006 to 2018 provides support for our theory that highlights how IT for control acts as a noneconomic motivation for the strategic IT behavior of firms. In doing so, we bring family ownership into the theoretical foreground for future IS scholarship. We contribute to theory and practice by advancing the nature of ownership and executive management as sources of heterogeneity in IT investment and its business value.
{"title":"Blood and Water: Information Technology Investment and Control in Family-owned Businesses","authors":"A. Kathuria, Prasanna P. Karhade, Xue Ning, B. Konsynski","doi":"10.1080/07421222.2023.2172770","DOIUrl":"https://doi.org/10.1080/07421222.2023.2172770","url":null,"abstract":"ABSTRACT Family-owned businesses differ in their strategic intent and behavior as they serve as a reservoir of wealth and social status for their family owners. Family-owned businesses demonstrate relatively conservative strategic decision making that aspires long-term wealth preservation and enhancement. For family owners, investments in information technology (IT) raise a predicament as they are risky, yet a long-term imperative. We propose three hypotheses that build upon the thesis that family owners combine a deep understanding of the business with a strong influence on stakeholders within and beyond the firm’s boundaries to exert strategic control in the extended enterprise. First, family ownership negatively influences IT investment, because family owners are likely to avoid investments in IT that are frivolous, reduce information asymmetry, or leave auditable digital trails. Second, the negative influence of family ownership on IT investment is weakened when a career professional is appointed in the senior-most executive position of a family-owned business. This is because professional executives strive to utilize IT for control and performance benefits, and family owners desire to use IT to monitor and control the non-family professional executive. Third, family ownership weakens the negative influence of environmental hostility on the relationship between IT investment and firm performance, as family-owned businesses incur less dynamic adjustment costs and maintain better alignment between IT and business strategy. Empirical analysis, consisting of panel regression estimations, on archival data of publicly listed Indian firms in the years 2006 to 2018 provides support for our theory that highlights how IT for control acts as a noneconomic motivation for the strategic IT behavior of firms. In doing so, we bring family ownership into the theoretical foreground for future IS scholarship. We contribute to theory and practice by advancing the nature of ownership and executive management as sources of heterogeneity in IT investment and its business value.","PeriodicalId":50154,"journal":{"name":"Journal of Management Information Systems","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48175227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-02DOI: 10.1080/07421222.2023.2172778
Matthew Johnson, D. Murthy, Brett W. Robertson, W. R. Smith, K. Stephens
ABSTRACT Social media platforms are increasingly used during disasters. In the United States, users often consider these platforms to be reliable news sources and they believe first responders will see what they publicly post. While having ways to request help during disasters might save lives, this information is difficult to find because non-relevant content on social media completely overshadows content reflective of who needs help. To resolve this issue, we develop a framework for classifying hurricane-related images that have been human-annotated. Our approach uses transfer learning and classifies each image using the VGG-16 convolutional neural network and multi-layer perceptron classifiers according to the urgency, relevance, and time period, in addition to the presence of damage and relief motifs. We find that our framework not only successfully functions as an accurate method for hurricane-related image classification but also that real-time classification of social media images using a small training set is possible.
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