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As COVID-19 continues to wreak havoc in everyday lives, the need to limit the spread of the virus remains a challenge, even with advances in medical knowledge, patient care, and vaccine development and distribution. Furthermore, COVID-19 is one in a recent series of airborne diseases, and probably not the last, given the ongoing encroachment of humans into animal habitat. This paper addresses the challenge of managing physical distancing, a highly effective, yet unnatural and contentious, mitigation strategy against infectious diseases. It presents a Pandemic Tech Stack and proposes that physical distancing management technologies are underutilized to fight pandemics. The latter can help ensure that people remain apart when they need to, support the transfer of activities to an online format, and, ultimately, facilitate the gradual reopening of our economies. The challenges associated with the development and use of these technologies are identified and discussed from both the technical and socio-psychological perspectives.

Artificial intelligence (AI) is beginning to transform traditional research practices in many areas. In this context, literature reviews stand out because they operate on large and rapidly growing volumes of documents, that is, partially structured (meta)data, and pervade almost every type of paper published in information systems research or related social science disciplines. To familiarize researchers with some of the recent trends in this area, we outline how AI can expedite individual steps of the literature review process. Considering that the use of AI in this context is in an early stage of development, we propose a comprehensive research agenda for AI-based literature reviews (AILRs) in our field. With this agenda, we would like to encourage design science research and a broader constructive discourse on shaping the future of AILRs in research.

Elevator Pitch A quiet revolution is happening in the offices, cubicles, and boardrooms of the world. Non-IT professionals are becoming empowered by leveraging organizational data for analytics. We support this movement by offering a powerful way to make data more usable via a combination of graphical conceptual models with narratives. Longer Version We are witnessing a quiet revolution—the rise of empowered users. These non-IT professionals increasingly seek to leverage the ever-expanding amount of organizational data for analytics to support their initiatives, decisions, and actions. All too often, however, the enthusiasm of these users collides against the harsh reality—many of them lack sophisticated IT skills, and they struggle to find/access relevant data, understand their meaning, and extract and adapt them to meet their needs. We propose a powerful way to support empowered users with a combination of conceptual models and narratives. Conceptual models are diagrams that accurately and succinctly represent rules and patterns captured in data. Although somewhat intuitive, these models alone do not suffice, as interpreting them still requires some specialized IT knowledge. Hence, we add narratives—stories written in natural language. The narratives intuitively explain some of the challenging aspects of the conceptual models. We conducted a series of experiments and interviews with empowered users to assess our idea. These studies show the value of the conceptual models with narratives for understanding organizational data. Our work unlocks an important missing puzzle for further user empowerment—a way for non-IT professionals to get the most out of the data.

The role of information systems (IS) as representations of real-world systems is changing in an increasingly digitalized world, suggesting that conceptual modeling is losing its relevance to the IS field. We argue the opposite: Conceptual modeling research is more relevant to the IS field than ever, but it requires an update with current theory. We develop a new theoretical framework of conceptual modeling that delivers a fundamental shift in the assumptions that govern research in this area. This move can make traditional knowledge about conceptual modeling consistent with the emerging requirements of a digital world. Our framework draws attention to the role of conceptual modeling scripts as mediators between physical and digital realities. We identify new research questions about grammars, methods, scripts, agents, and contexts that are situated in intertwined physical and digital realities. We discuss several implications for conceptual modeling scholarship that relate to the necessity of developing new methods and grammars for conceptual modeling, broadening the methodological array of conceptual modeling scholarship, and considering new dependent variables.

As crowdsourced user-generated content becomes an important source of data for organizations, a pressing question is how to ensure that data contributed by ordinary people outside of traditional organizational boundaries is of suitable quality to be useful for both known and unanticipated purposes. This research examines the impact of different information quality management strategies, and corresponding data collection design choices, on key dimensions of information quality in crowdsourced user-generated content. We conceptualize a contributor-centric information quality management approach focusing on instance-based data collection. We contrast it with the traditional consumer-centric fitness-for-use conceptualization of information quality that emphasizes class-based data collection. We present laboratory and field experiments conducted in a citizen science domain that demonstrate trade-offs between the quality dimensions of accuracy, completeness (including discoveries), and precision between the two information management approaches and their corresponding data collection designs. Specifically, we show that instance-based data collection results in higher accuracy, dataset completeness, and number of discoveries, but this comes at the expense of lower precision. We further validate the practical value of the instance-based approach by conducting an applicability check with potential data consumers (scientists, in our context of citizen science). In a follow-up study, we show, using human experts and supervised machine learning techniques, that substantial precision gains on instance-based data can be achieved with post-processing. We conclude by discussing the benefits and limitations of different information quality and data collection design choices for information quality in crowdsourced user-generated content.

While many conceptual modelling grammars have been developed since the 1970s, they share the general assumption of representation by abstraction; that is, representing generalised knowledge about the similarities among phenomena in a domain (classes) rather than about domain objects (instances). This assumption largely ignores the fundamental role that instances play in the constitution of reality and in human psychology. In this paper, we argue there is a need for a grammar that explicitly recognises the primary role of instances. We examine the limitations of traditional class-based approaches to conceptual modelling, especially for modern information environments. We then explore theoretical and practical motivations for instance-based modelling, and show how such an approach can address the limitations of traditional modelling approaches. We conclude by calling for the engineering of instance-based grammars as an important direction for conceptual modelling research to address the limitations ...

The rapid proliferation of online content producing and sharing technologies resulted in an explosion of user-generated content (UGC), which now extends to scientific data. Citizen science, in which ordinary people contribute information for scientific research, epitomizes UGC. Citizen science projects are typically open to everyone, engage diverse audiences, and challenge ordinary people to produce data of highest quality to be usable in science. This also makes citizen science a very exciting area to study both traditional and innovative approaches to information quality management. With this paper we position citizen science as a leading information quality research frontier. We also show how citizen science opens a unique opportunity for the information systems community to contribute to a broad range of disciplines in natural and social sciences and humanities.

This study investigates the legitimation strategies adopted by information technology (IT) vendors and their respective influence on market share. We conducted an analysis of the public discourse on websites of top Electronic Medical Record (EMR) vendors in Ontario, Canada. A total of 815 segments extracted from these websites were analyzed. Our findings indicate that strategies under the cognitive and pragmatic forms of legitimacy were strongly represented in the EMR vendors’ discourses compared with regulative and normative strategies. Furthermore, the link between legitimation strategies and market share has not yet been clearly established. Implications for practice and research are discussed.

User-generated content (UGC) is becoming a valuable organizational resource, as it is seen in many cases as a way to make more information available for analysis. To make effective use of UGC, it is necessary to understand information quality (IQ) in this setting. Traditional IQ research focuses on corporate data and views users as data consumers. However, as users with varying levels of expertise contribute information in an open setting, current conceptualizations of IQ break down. In particular, the practice of modeling information requirements in terms of fixed classes, such as an Entity-Relationship diagram or relational database tables, unnecessarily restricts the IQ of user-generated data sets. This paper defines crowd information quality (crowd IQ), empirically examines implications of class-based modeling approaches for crowd IQ, and offers a path for improving crowd IQ using instance-and-attribute based modeling. To evaluate the impact of modeling decisions on IQ, we conducted three experiments. Results demonstrate that information accuracy depends on the classes used to model domains, with participants providing more accurate information when classifying phenomena at a more general level. In addition, we found greater overall accuracy when participants could provide free-form data compared to a condition in which they selected from constrained choices. We further demonstrate that, relative to attribute-based data collection, information loss occurs when class-based models are used. Our findings have significant implications for information quality, information modeling, and UGC research and practice.

As information technology becomes an integral part of daily life, increasingly, people understand the world around them by turning to digital sources as opposed to directly interacting with objects in the physical world. This has ushered in the age of Ubiquitous Digital Intermediation (UDI). With the explosion of UDI, the scope of Information Quality (IQ) research is due to expand dramatically as the challenge becomes to capture the wealth and nuances of human experience. This article presents three key changes to the IQ landscape brought about by UDI, including expansion of the scope of traditional IQ dimensions, digital to physical mapping challenge, and the increased need to manage content authenticity. UDI generates many novel questions and opportunities for the IQ research community.