Gert-Jan de Vreede ([email protected]; 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 & Negotiation.
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.
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
No potential conflict of interest was reported by the authors.