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        <description>Current issue information for the Journal of Management Information Systems.</description>
        <title>Journal of Management Information Systems</title>
        <link>http://jmis-web.org</link>
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            <title>Journal of Management Information Systems</title>
            <link>http://jmis-web.org</link>
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        <webMaster>dave@daveeargle.com (Dave Eargle)</webMaster>
        <managingEditor>dave@daveeargle.com (Dave Eargle)</managingEditor>
        <pubDate>Thu, 13 Apr 2023 03:09:44 +0000</pubDate>
        <lastBuildDate>Thu, 13 Apr 2023 03:09:44 +0000</lastBuildDate>
                <item>
            <title>Editorial Introduction</title>
            <link>http://jmis-web.org/articles/1601</link>
                            <dc:creator>Vladimir Zwass</dc:creator>
                        <description><![CDATA[ ABSTRACT: 
<p>The Journal of Management Information Systems (JMIS) is celebrating its 40th year of publication. Over this period, we have witnessed how information systems have evolved into a driving force of societal change. We have also seen the burgeoning of our research field into a mature contributor to the knowledge about the organizational and societal computing, communication, and general information processing, now increasingly assisted by artificial intelligence (AI). With information technology (IT) permeating the global society, with the affordability of its networked end points supported by the clouds, with virtuality and machine learning radically changing the realm of possibility, our field has new heights to climb. We are still quite remote from the understanding of the effects of AI on human work, from knowing how the social media can more strongly become a force for the good, how to move toward more transparent AI, or how online platforms can be differentiated in their influence on the communities and marketplaces. JMIS has always stood for the broad understanding of the compass of our discipline and for the methodological diversity in the generation of knowledge that moves the field of Information Systems (IS) forward.</p>
<p>JMIS is a leading journal in the IS field. Just one of the indicators of this standing is the Journal’s inclusion in the FT50 list of the top scholarly venues by the Financial Times. That is a responsibility we should understand. The leading journals of a scholarly discipline have three essential roles to play. These are the epistemic, instrumental, and societal purposes.</p>
<p>First and foremost, in the epistemic domain, a top-tier scholarly journal is responsible for the refinement, validation, accumulation, and dissemination of reliable and relevant knowledge. To a large degree, the leading journals define the discipline as it evolves and as the journals evolve the contours of the discipline. A generalist journal such as JMIS should be open to all the legitimate subjects and methodologies of the field, as represented by the best papers. It should champion the inclusion in every sense and at every level. For example, early on, JMIS championed the introduction of IS economics and design research into good currency in the IS field. These are now flourishing subfields of our field. The highly international and balanced Editorial Board of JMIS is composed of leaders of the IS discipline. That is one of the ways of assuring not only the quality and integrity of our stringent refereeing process, but also tending to the balanced scope of our evolving coverage.</p>
<p>Second, we have to recognize the instrumental role of the leading journals. They are the means of certification of intellectual achievement. In this credentialing role, scholarly journals impact the advancement, grantsmanship, and intellectual influence of individual scholars. Such journals undergird the functioning of the informal scholarly networks. They should also aim at exerting the influence of the field on the external constituencies, including the cognate disciplines, business, and government. Reflective practitioners should be able to benefit from the journals’ content.</p>
<p>Third, and increasingly important, in the societal domain, the leading journals have to contribute responsibly to the inclusive wellbeing of the society at large. Our societies are undergoing epochal changes and are responding to epochal challenges, and we cannot stand aloof. To a large degree, this transformation and its rapidity have been precipitated or facilitated by the effects of the IT we study. Responsiveness to the societal needs is a commandment in the stewardship of the journals. Our filtering function has to be complemented by fostering our positive contribution to society. As editors, we have at our disposal special issues, editorials, and reviewing guidance.</p>
<p>With this in mind, we have recently championed new subfields of our field, often with special issues guest-edited by leading scholars and guided by members of our Editorial Board. JMIS papers have significantly contributed to health informatics, the understanding of AI as a complement to human efforts, prevention of deception in social media and in other settings, and to behavioral information security, to give only some examples. A special section on the role of IT in fostering mental health is in preparation. The special issue on fostering the metaverse, also now in preparation by its guest editors, will not approach it as a millenarian ideal. Rather, we will show how the present and emerging technologies can undergird the progressive stages of what can become a globally inclusive virtual environment of work, private life, and play.</p>
<p>The present issue of JMIS aptly illustrates the understanding of our field as delivering value with information and information systems, in the definition of the Guest Editors, who bring to you a multifaceted special issue. The Guest Editors, Gert-Jan de Vreede and Jay F. Nunamaker, Jr., include a set of papers that show how this value can be created with the contemporary IS to benefit individuals, communities, and organizations. The issue showcases the objects of our study in action, ranging from collaboration systems and business analytics to deep learning in action.</p>
<p>As we are entering another decade of JMIS publication, we are welcoming to our Editorial Board its new members: Juan (Jane) Feng of Tsinghua University, Susanna Ho of Australian National University, Shirish C. Srivastava of HEC Paris, and Ofir Turel of University of Melbourne.</p>
<p>We mourn the passing of two Editorial Board members. Phillip Ein-Dor was one of the creators of the intellectual and organizational underpinnings of our discipline. Makoto Nagao, a former President of Kyoto University, was a pioneer in machine translation and in several other subfields of IS and Computer Science. We also wish all the best in his future pursuits to Joey F. George, who is stepping down from the Board.</p>
<p>At this milestone, we have every reason to look forward to a new decade of our intellectual development, and of our contribution to scholarship and society.</p> ]]></description>
            <guid>http://jmis-web.org/articles/1601</guid>
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                <item>
            <title>Special Issue: Information Systems, Artificial Intelligence, and Analytics to Support Value Creation in Communities and Organizations</title>
            <link>http://jmis-web.org/articles/1602</link>
                            <dc:creator>Gert-Jan de Vreede</dc:creator>
                            <dc:creator>Jay F Nunamaker</dc:creator>
                        <description><![CDATA[ ABSTRACT: 
<p>Gert-Jan de Vreede (gdevreede@usf.edu; corresponding author) is Interim Dean and Professor of Information Systems at the Muma College of Business at the University of South Florida. He received his Ph.D. from Delft University of Technology in The Netherlands. He is co-founder of Collaboration Engineering as a scholarly discipline and co-inventor of the ThinkLets design pattern language. Dr. de Vreede’s research focuses on crowdsourcing, collaboration engineering, and behavioral AI. He has published over 300 refereed journal and conference papers, and book chapters. His research has appeared in journals such as Journal of Management Information Systems, Information Systems Research, Journal of the Association for Information Systems, MIS Quarterly Executive, Communications of the Association for Information Systems, Small Group Research, and Communications of the ACM. He is Editor-in-Chief of Group Decision &amp; Negotiation.</p>
<p>Jay F. Nunamaker, Jr. is Regents and Soldwedel Professor of MIS, Computer Science and Communication, and director of the Center for the Management of Information and the National Center for Border Security and Immigration at the University of Arizona. He received his Ph.D. in Operations Research and Systems Engineering from Case Institute of Technology. Dr. Nunamaker has held a professional engineer’s license since 1965. He was inducted into the Design Science Hall of Fame and received the LEO Award for Lifetime Achievement from the Association for Information Systems. He was featured in the July 1997 issue of Forbes Magazine on technology as one of eight key innovators in information technology. His specialization is in the fields of system analysis and design, collaboration technology, and deception detection. The commercial product GroupSystems ThinkTank, based on his research, is often referred to as the gold standard for structured collaboration systems. He founded the MIS Department at the University of Arizona and served as department head for 18 years.</p>
<p>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 technology-human relationships, and new analytical techniques to find insight and extract knowledge from huge amounts of information.</p>
<p>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</p>
<p>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.</p>
<p>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 community layers—central, intermediate, and peripheral layers—when they engage in a discourse to co-create knowledge based on the feedback on raw ideas. They further show that a message’s lexical complexity does not correlate to the stages of knowledge building.</p>
<p>Next, Mateusz Dolata, Dzmitry Katsiuba, Natalie Wellnhammer, and Gerhard Schwabe, in their paper “Learning with Digital Agents: An Analysis Based on the Activity Theory,” propose a detailed conceptual model describing how people interact with digital agents. They specifically focus on pedagogical agents that support natural-language interaction with learners. Their conceptual model is grounded in a model of learning based on activity theory. Based on their model and an extensive literature review, they show how characteristics of the learning, the agent, and the activity correlate to different learning outcomes. These insights form the basis for a detailed IS research and development agenda for pedagogical agents and digital agents in general.</p>
<p>The fourth paper, “Leveraging Low- Code Development of Smart Personal Assistants: An Integrated Design Approach with the SPADE Method,” by Edona Elshan, Philipp Ebel, Matthias Söllner, and Jan Marco Leimeister, focuses on a different type of digital agent: the smart personal assistant (SPA). SPAs can be designed and programmed to provide individualized user interactions while displaying human-like behaviors. The authors follow a design science research approach to develop a proof of concept and proof of value of the Smart Personal Assistant for Domain Experts (SPADE) method. This design method supports domain experts without coding or programming skills to use low-code platforms to develop specific SPAs. The authors illustrate the effectiveness of their method by showing how a large number of experts in the field of education was successfully guided through the SPA development process.</p>
<p>Sheila O’Riordan, Bill Emerson, Joseph Feller, and Gaye Kiely, in their paper “The Road to Open News: A Theory of Social Signaling in an Open News Production Community” take a deep dive into the realm of peer-production communities. Their study of WikiTribune—a collaborative journalism project—shows how social signals can address motivation, coordination, and integration challenges in a hybrid peer-production setting. Using a rich set of empirical data, they develop a social signaling model that extends Benkler’s theory of commons-based peer production and presents three constructs that shape user engagement through the different participation levels: system signals, normative signals, and behavioral signals. Their model explains how address challenges and leverage advantages in commons-based peer production.</p>
<p>The paper “Moving Emergency Response Forward: Leveraging Machine-Learning Classification of Disaster-Related Images Posted on Social Media,” by Matthew Johnson, Dhiraj Murthy, Brett Robertson, William Roth Smith, and Keri Stephens, shows how social media postings with images can be effectively classified using neural networks and multi-layer perceptron classifiers. Specifically, they propose a framework that uses a small training set of human-annotated hurricane-related images. The authors demonstrate that this framework can be successfully used to classify hurricane-related images which helps first responders to offer assistance to those that need it most urgently.</p>
<p>In their paper “Trust in Online Ride-Sharing Transactions: Impacts of Heterogeneous Order Features,” Xusen Cheng, Shixuan Fu, Jianshan Sun, Meiyun Zuo, and Xiangsong Meng use an expansive set of real “sharing economy” transaction data to investigate different factors that are associated with trust development in ride-sharing. Using trust distribution maps based on order location data, their results show historical order completion rate and ride-distance are positively associated with mutual trust, while order time and departure density are negatively associated with mutual trust. They further demonstrate how the presence of trust can be predicted using machine learning algorithms.</p>
<p>Abhishek Kathuria, Prasanna Karhade, Xue Ning, and Benn Konsynski provide a unique and in-depth inquiry into IT investments into publicly listed, family-owned businesses. Their paper “Blood and Water: IT Investment and Control in Family-Owned Businesses” uses an extensive set of archival data of Indian firm to demonstrate how family owners make strategic IT investment decisions. Their results show that (1) family ownership is negatively associated with IT investments, (2) that this association is weakened when the business has a career professional in the senior-most executive position, and (3) that family ownership weakens the negative association of environmental hostility on the relationship between IT investment and firm performance. Their work demonstrates the nuances of ownership and senior management as they influence a firm’s IT investment decisions.</p>
<p>The paper “Design Concerns for Multiorganizational, Multistakeholder Collaboration: A Study in the Healthcare Industry” by Scott Thiebes, Fangjian Gao, Robert Briggs, Manuel Schmidt-Kraepelin, and Ali Sunyaev proposes an exploratory research stream on design concerns for multiorganizational, multistakeholder (MO-MS) collaborations that span organizational and national boundaries. Against the backdrop of the Covid-19 pandemic, in particular the collaborative development and distribution of vaccines, the authors focus on the health sector as they perform an extensive literature review and rich collection of semi-structure expert interviews to derive a comprehensive, eleven-category set of design concerns for MO-MS collaboration systems. They further provide question guidelines for MO-MS collaboration system requirements engineers and articulate the generalizability of their design concerns to other MO-MS domains.</p>
<p>The final paper in this Special Issue, “Deep Learning for Information Systems Research,” by Sagar Samtani, Hongyi Zhu, Balaji Padmanabhan, Yidong Chai, Hsinchun Chen, and</p>
<p>Jay Nunamaker provides a thorough treatment of the role of Deep Learning (DL) in the information systems discipline. The authors propose a conceptual model of DL contribution types and DL development guidelines. Based on a review of past DL research in information systems, they also develop a Knowledge Contribution Framework to distinguish between DL contributions for computational, behavioral, or economic information systems research. Finally, they offer and illustrate ten guidelines for scholars to design, execute, and present their DL research.</p>
<p>The Special issue consists of a selection of the best research that initially was presented at the Hawaiian International Conference on System Sciences. We invited 40 author teams of the best papers to submit their revised work to the Special Issue. Out of these initial submissions, several papers were selected and underwent several rounds of refereed revisions.</p>
<p>We conclude by expressing our gratitude to the authors, reviewers, and the Journal’s editorial team for their contributions. Each of the papers provides a unique perspective on the way in which information systems researchers contribute to our understanding of the role of information systems and methods in supporting value creation in communities and organizations. We warmly commend them to your reading and trust that they will inspire a broad array of future research.</p>

<p>Disclosure Statement</p>
<p>No potential conflict of interest was reported by the authors.</p>
 ]]></description>
            <guid>http://jmis-web.org/articles/1602</guid>
        </item>
                <item>
            <title>Act and Reflect: Integrating Reflection into Design Thinking</title>
            <link>http://jmis-web.org/articles/1603</link>
                            <dc:creator>Thorsten Schoormann</dc:creator>
                            <dc:creator>Maren Stadtländer</dc:creator>
                            <dc:creator>Ralf Knackstedt</dc:creator>
                        <description><![CDATA[ ABSTRACT: 

<p>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.</p>
 ]]></description>
            <guid>http://jmis-web.org/articles/1603</guid>
        </item>
                <item>
            <title>Formation and Action of a Learning Community with Collaborative Learning Software</title>
            <link>http://jmis-web.org/articles/1604</link>
                            <dc:creator>Evren Eryilmaz</dc:creator>
                            <dc:creator>Brian Thoms</dc:creator>
                            <dc:creator>Zafor Ahmed</dc:creator>
                            <dc:creator>Howard Lee</dc:creator>
                        <description><![CDATA[ ABSTRACT: 

<p>This paper explores the formation of a learning community facilitated by custom collaborative learning software. Drawing on research in group cognition, knowledge building discourse, and learning analytics, we conducted a mixed-methods field study involving an asynchronous online discussion consisting of 259 messages posted by 50 participants. The cluster analysis results provide evidence that the recommender system within the software can support the formation of a learning community with a small peripheral cluster. Regarding knowledge building discourse, we identified the distinct roles of central, intermediate (i.e., middle of three clusters), and peripheral clusters within a learning community. Furthermore, we found that message lexical complexity does not correlate to the stages of knowledge building. Overall, this study contributes to the group cognition theory to deepen our understanding about collaboration to construct new knowledge in online discussions. Moreover, we add a much-needed text mining perspective to the qualitative interaction analysis model.</p>
 ]]></description>
            <guid>http://jmis-web.org/articles/1604</guid>
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            <title>Learning with Digital Agents: An Analysis based on the Activity Theory</title>
            <link>http://jmis-web.org/articles/1605</link>
                            <dc:creator>Mateusz Dolata</dc:creator>
                            <dc:creator>Dzmitry Katsiuba</dc:creator>
                            <dc:creator>Natalie Wellnhammer</dc:creator>
                            <dc:creator>Gerhard Schwabe</dc:creator>
                        <description><![CDATA[ ABSTRACT: 

<p>Digital agents are considered a general-purpose technology. They spread quickly in private and organizational contexts, including education. Yet, research lacks a conceptual framing to describe interaction with such agents in a holistic manner. While focusing on the interaction with a pedagogical agent, that is, a digital agent capable of natural-language interaction with a learner, we propose a model of learning activity based on activity theory. We use this model and a review of prior research on digital agents in education to analyze how various characteristics of the activity, including features of a pedagogical agent or learner, influence learning outcomes. The analysis leads to identification of information systems research directions and guidance for developers of pedagogical agents and digital agents in general. We conclude by extending the activity theory-based model beyond the context of education and show how it helps designers and researchers ask the right questions when creating a digital agent.</p>
 ]]></description>
            <guid>http://jmis-web.org/articles/1605</guid>
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            <title>Leveraging Low Code Development of Smart Personal Assistants: An Integrated Design Approach with the SPADE Method</title>
            <link>http://jmis-web.org/articles/1606</link>
                            <dc:creator>Edona Elshan</dc:creator>
                            <dc:creator>Philipp Ebel</dc:creator>
                            <dc:creator>Matthias Söllner</dc:creator>
                            <dc:creator>Jan Marco Leimeister</dc:creator>
                        <description><![CDATA[ ABSTRACT: 

<p>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.</p>
 ]]></description>
            <guid>http://jmis-web.org/articles/1606</guid>
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            <title>The Road to Open News: A Theory of Social Signaling in an Open News Production Community</title>
            <link>http://jmis-web.org/articles/1607</link>
                            <dc:creator>Sheila O’Riordan</dc:creator>
                            <dc:creator>Bill Emerson</dc:creator>
                            <dc:creator>Joseph Feller</dc:creator>
                            <dc:creator>Gaye Kiely</dc:creator>
                        <description><![CDATA[ ABSTRACT: 

<p>This study theorizes the role of social signals in overcoming the motivation, coordination, and integration challenges in a hybrid peer production community, WikiTribune. WikiTribune was a collaborative journalism project that combined elements of firm-based production with that of commons-based peer production. Empirical data (article metrics, project documentation, and user communications) was used to examine the first 18-months of building and developing the collaborative journalism platform and community. The study’s primary contribution is a social signaling model that extends the theory of commons-based peer production and presents three constructs that inform the socially productive behavior in these communities. These constructs (1) system signals, (2) normative signals, and (3) behavioral signals are theorized to shape user engagement through the different levels of project participation. The alignment/misalignment of these signals with project strategy produce positive or negative outcomes. The social signaling model seeks to explain how challenges are overcome and advantages leveraged in commons-based peer production, in both pure and hybrid forms.</p>
 ]]></description>
            <guid>http://jmis-web.org/articles/1607</guid>
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            <title>Moving Emergency Response Forward: Leveraging Machine-Learning Classification of Disaster-Related Images Posted on Social Media</title>
            <link>http://jmis-web.org/articles/1608</link>
                            <dc:creator>Matthew Johnson</dc:creator>
                            <dc:creator>Dhiraj Murthy</dc:creator>
                            <dc:creator>Brett W Robertson</dc:creator>
                            <dc:creator>William Roth Smith</dc:creator>
                            <dc:creator>Keri K Stephens</dc:creator>
                        <description><![CDATA[ ABSTRACT: 

<p>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.</p>
 ]]></description>
            <guid>http://jmis-web.org/articles/1608</guid>
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            <title>Trust in Online Ride-Sharing Transactions: Impacts of Heterogeneous Order Features</title>
            <link>http://jmis-web.org/articles/1609</link>
                            <dc:creator>Xusen Cheng</dc:creator>
                            <dc:creator>Shixuan Fu</dc:creator>
                            <dc:creator>Jianshan Sun</dc:creator>
                            <dc:creator>Meiyun Zuo</dc:creator>
                            <dc:creator>Xiangsong Meng</dc:creator>
                        <description><![CDATA[ ABSTRACT: 

<p>With the development of the sharing economy, online ride-sharing has become a primary form of commuting. Using secondary transaction data, this study investigates the associations between the heterogeneous features and mutual trust in sharing economy-driven online ride-sharing transactions. Based on an examination of 12,404 ride-sharing orders in Beijing, we propose a set of trust distribution maps using order location data to reveal heterogeneous spatial patterns of the relationship between online ride-sharing transactions and mutual trust. The results show that the historical order completion rate and order distance are positively associated with mutual trust in ride-sharing transactions, whereas order time and departure density negatively and significantly influence mutual trust. Furthermore, we use machine learning algorithms to predict trust. The implications for theory and practice and future research directions are discussed.</p>
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            <guid>http://jmis-web.org/articles/1609</guid>
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            <title>Blood and Water: Information Technology Investment and Control in Family-owned Businesses</title>
            <link>http://jmis-web.org/articles/1610</link>
                            <dc:creator>Abhishek Kathuria</dc:creator>
                            <dc:creator>Prasanna P Karhade</dc:creator>
                            <dc:creator>Xue (Nancy) Ning</dc:creator>
                            <dc:creator>Benn R Konsynski</dc:creator>
                        <description><![CDATA[ ABSTRACT: 

<p>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.</p>
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            <guid>http://jmis-web.org/articles/1610</guid>
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            <title>Design Concerns for Multiorganizational, Multistakeholder Collaboration: A Study in the Healthcare Industry</title>
            <link>http://jmis-web.org/articles/1611</link>
                            <dc:creator>Scott Thiebes</dc:creator>
                            <dc:creator>Fangjian Gao</dc:creator>
                            <dc:creator>Robert O Briggs</dc:creator>
                            <dc:creator>Manuel Schmidt-Kraepelin</dc:creator>
                            <dc:creator>Ali Sunyaev</dc:creator>
                        <description><![CDATA[ ABSTRACT: 

<p>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.</p>
 ]]></description>
            <guid>http://jmis-web.org/articles/1611</guid>
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            <title>Deep Learning for Information Systems Research</title>
            <link>http://jmis-web.org/articles/1612</link>
                            <dc:creator>Sagar Samtani</dc:creator>
                            <dc:creator>Hongyi Zhu</dc:creator>
                            <dc:creator>Balaji Padmanabhan</dc:creator>
                            <dc:creator>Yidong Chai</dc:creator>
                            <dc:creator>Hsinchun Chen</dc:creator>
                            <dc:creator>Jay F Nunamaker</dc:creator>
                        <description><![CDATA[ ABSTRACT: 

<p>Modern artificial intelligence (AI) is heavily reliant on deep learning (DL), an emerging class of algorithms that can automatically detect non-trivial patterns from petabytes of rapidly evolving “Big Data.” Although the information systems (IS) discipline has embraced DL, questions remain about DL’s interface with a domain and theory and DL contribution types. In this paper, we present a DL information systems research (DL-ISR) schematic that reviews DL while considering the role of the application environment and knowledge base, summarizes extant DL research in IS, a knowledge contribution framework (KCF) to position DL contributions, and ten guidelines to help IS scholars design, execute, and present DL for computational, behavioral, or economic IS research. We illustrate a research contribution to DL for cybersecurity. This article’s contribution to theory resides in the conceptual DL-ISR schematic and KCF, while its contributions to practice are based on its practical guidelines for executing DL-based projects.</p>
 ]]></description>
            <guid>http://jmis-web.org/articles/1612</guid>
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