ABSTRACT As human reliance on information technology (IT) increases, having a clear, precise, and comprehensive understanding of the nature of digital objects, digital systems, and digitalized systems becomes more critical. Otherwise, our ability to research, manage, control, and make reliable predictions about them will be limited. Accordingly, in this paper, we propose a new theory of digital objects, digital systems, and digitalized systems based on the adoption, adaptation, and extension of existing theories of ontology, semantics, and semiotics. The theory provides precise explanations of the nature of digital objects, digital systems, and digitalized systems. Ours is a realist theory that does not countenance the independent existence of nonmaterial or hybrid objects in the world. Accordingly, we are at odds with much of the prevailing discourse about digital phenomena. We show how our theory generates different insights and predictions from this the dominant discourse on the nature of the digital. Our predictions lay the groundwork for further empirical studies on the design, use, and impact of IT on individuals, organizations, and society.

Abstract The paper proposes universal conceptual modeling, conceptual modeling that strives to be as general-purpose as possible and accessible to anyone, professionals and non-experts alike. The idea of universal conceptual modeling is meant to catalyze new thinking in conceptual modeling and be used to evaluate and develop conceptual modeling solutions, such as modeling languages, approaches for requirements elicitation, or modeling tools. These modeling solutions should be usable by as many people and design agents as possible and for as many purposes as possible, aspiring to the ideals of universal conceptual modeling. We propose foundations of universal conceptual modeling in the form of six principles: flexibility, accessibility, ubiquity, minimalism, primitivism, and modularity. We then demonstrate the utility of these principles to evaluate existing conceptual modeling languages and understand conceptual modeling practices. Finally, we propose future research opportunities meant to realize the ideals of universal conceptual modeling.

BACKGROUND: All aspects of our society, including the life sciences, need a mechanism for people working within them to represent the concepts they employ to carry out their research. For the information systems being designed and developed to support researchers and scientists in conducting their work, conceptual models of the relevant domains are usually designed as both blueprints for a system being developed and as a means of communication between the designer and developer. Most conceptual modelling concepts are generic in the sense that they are applied with the same understanding across many applications. Problems in the life sciences, however, are especially complex and important, because they deal with humans, their well-being, and their interactions with the environment as well as other organisms. RESULTS: This work proposes a "systemist" perspective for creating a conceptual model of a life scientist's problem. We introduce the notion of a system and then show how it can be applied to the development of an information system for handling genomic-related information. We extend our discussion to show how the proposed systemist perspective can support the modelling of precision medicine. CONCLUSION: This research recognizes challenges in life sciences research of how to model problems to better represent the connections between physical and digital worlds. We propose a new notation that explicitly incorporates systemist thinking, as well as the components of systems based on recent ontological foundations. The new notation captures important semantics in the domain of life sciences. It may be used to facilitate understanding, communication and problem-solving more broadly. We also provide a precise, sound, ontologically supported characterization of the term "system," as a basic construct for conceptual modelling in life sciences.

The digitalization of human society continues at a relentless rate. However, to develop modern information technologies, the increasing complexity of the real-world must be modeled, suggesting the general need to reconsider how to carry out conceptual modeling. This research proposes that the often-overlooked notion of “system” should be a separate, and core, conceptual modeling construct and argues for incorporating it and related concepts, such as emergence, into existing approaches to conceptual modeling. The work conducts a synthesis of the ontology of systems and general systems theory. These modeling foundations are then used to propose a CESM+ template for conducing systems-grounded conceptual modeling. Several new conceptual modeling notations are introduced. The systemist modeling is then applied to a case study on the development of a citizen science platform. The case demonstrates the potential contributions of the systemist approach and identifies specific implications of explicit modeling with systems for theory and practice. The paper provides recommendations for how to incorporate systems into existing projects and suggests fruitful opportunities for future conceptual modeling research.

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.

Abstract General ontology is a prominent theoretical foundation for information technology analysis, design, and development. Ontology is a branch of philosophy which studies what exists in reality. A widely used ontology in information systems, especially for conceptual modeling, is the BWW (Bunge–Wand–Weber), which is based on ideas of the philosopher and physicist Mario Bunge, as synthesized by Wand and Weber. The ontology was founded on an early subset of Bunge’s philosophy; however, many of Bunge’s ideas have evolved since then. An important question, therefore, is: do the more recent ideas expressed by Bunge call for a new ontology? In this paper, we conduct an analysis of Bunge’s earlier and more recent works to address this question. We present a new ontology based on Bunge’s later and broader works, which we refer to as Bunge’s Systemist Ontology (BSO) . We then compare BSO to the constructs of BWW. The comparison reveals both considerable overlap between BSO and BWW, as well as substantial differences. From this comparison and the initial exposition of BSO, we provide suggestions for further ontology studies and identify research questions that could provide a fruitful agenda for future scholarship in conceptual modeling and other areas of information technology.

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.

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 of traditional approaches, and articulate five challenges to overcome in such efforts.

The emergence of crowdsourcing as an important mode of information production has attracted increasing research attention. In this article, the authors review crowdsourcing research in the data management field. Most research in this domain can be termed tasked-based, focusing on micro-tasks that exploit scale and redundancy in crowds. The authors' review points to another important type of crowdsourcing – which they term observational – that can expand the scope of extant crowdsourcing data management research. Observational crowdsourcing consists of projects that harness human sensory ability to support long-term data acquisition. The authors consider the challenges in this domain, review approaches to data management for crowdsourcing, and suggest directions for future research that bridges the gaps between the two research streams.