Crafting theory to satisfy the requirements of systems science

Just as Lee, Briggs & Dennis (2014) showed that a rigorous conception of “explanation” leads to requirements for a positivist theory to satisfy, and just as Lee & Hovorka (2015) showed that a rigorous conception of “interpretation” leads to requirements for an interpretive theory to satisfy, we show that a rigorous conception of “systems” leads to certain requirements for a systems theory to satisfy. We apply basics of systems science in general, as well as basics of Luhmann’s (Luhmann, 1995; Moeller, 2006) systems perspective in particular. We illustrate these basics with empirical material from a case about the role of information technology in anti-money laundering. The example demonstrates that research in information systems, which has been informed by positivism, interpretivism, and design, can be additionally and beneficially informed by systems science – which, ironically, has been largely absent in information “systems” research

Taking the first step with systems theorizing in information systems : a response

We address the commentaries of Robey and Mikhaeil, of Mingers, and of Schultze which provided responses to our paper, “Crafting theory to satisfy the requirements of systems science.” We find their responses useful for reflecting on the development of the role of systems theorising within information systems research and provide our reaction in order to clarify several fundamental considerations pertaining to 1) our proposed set of requirements for systems theorizing, 2) the need for explicit systems theorizing, 3) the supposed overall neglect of systems science, 4) the communicability of systems theory and the path of grand theories, 5) emergence, the observer, and other considerations, and 6) systems theory from the perspective of sociomateriality

Back to the future: A critique of Demetis and Lee's "Crafting theory to satisfy the requirements of systems science"

Demetis and Lee's paper outlines criteria for constructing theory in accordance with systems science. This is a laudable aim but in this comment I suggest that their view of systems thinking is both narrow and somewhat dated. Demetis and Lee equate systems science with only one aspect of it – General Systems Thinking (GST) – and they discuss in detail only one theorist – Niklas Luhmann. I draw attention to a range of other systems approaches including system dynamics, soft systems methodology, complexity theory, critical systems thinking, critical realism and multimethodology. I conclude with tentative guidelines of my own

In Pursuit of Systems Theories for Describing and Analyzing Systems in Organizations

This research essay illustrates how the IS discipline might pursue systems theories with the goal of understanding IS in new ways, generating innovative and useful systems theories, and achieving more impact in the world. It discusses recent articles that compare different perspectives and expectations related to theories and theorizing in the IS discipline. It uses the term domain-specific systems theory (DSST) to accentuate the difference between general systems theory (GST) and specific systems theories. It provides examples illustrating how DSSTs can illuminate important concerns that variance and process perspectives do not address directly. It shows how work system theory (WST) and several of its extensions are DSSTs that provide useful lenses for understanding, analyzing, and theorizing about systems in organizations. It concludes by summarizing ways in which the IS discipline might welcome systems theories more wholeheartedly

A Systems Model of IS Success Using Agent-Based Simulation

Measuring the value realized from information systems (IS) and understanding the factors which influence success are critical to organizations. DeLone and McLean’s IS success model is one of the most well-known theories in IS literature; however, the model has been primarily examined from a variance perspective and this offers an opportunity to explore ways to improve its explanatory capability. This study presents an agent-based simulation model of the IS success model based on complex adaptive systems theory. Principles from the unified theory of acceptance and use of technology, social learning theory, and expectation disconfirmation theory are incorporated in the model to capture individual behavior and interactions, feedback loops and emergent effects. The model is under development in the context of a hospital surge management system with the goal of extending the IS success model and to improve understanding of IS success in a complex digital ecosystem. The next steps are to calibrate the model and to conduct multiple case studies

Cultural dynamics: the interplay of culture, leadership and performance in Information Systems projects

Despite a long interest in Information Systems (IS) development, the need to improve the success rates of IS projects remains relevant. Continuing disappointment with project performance has led to suggestions that a broadening of the project management (PM) conceptual base could bring new insights to this enduring problem. Consequently, this study acknowledges the sociological nature of IS projects and will explore the dynamic interaction of culture and leadership to expose better explanations for project performance. This interpretive study will use the Cultural Dynamics Model (CDM) as a theoretical lens, and will privilege a view of ‘data as text’ over ‘data as fact’ by accentuating reflexivity in the research. Four IS projects in two organizations will serve as cases in a multiple case study approach. This paper argues for a dynamic, reflexive study of culture and leadership and positions the CDM as an appropriate theoretical framework to support this approach

An observer-relative systems approach to information

By returning to the foundational principles of second-order cybernetics and resting on the central role of the observer, this essay explores how the distinction between data/information can be conceptualized. Using systems theory, we derive a series of systemic principles for the distinction between data/information and we illustrate them with a case study from Anti-Money Laundering

When humans using the IT artifact becomes IT using the human artifact

Following Demetis & Lee (2016) who showed how systems theorizing can be conducted on the basis of a few systems principles, in this conceptual paper, we apply these principles to theorize about the systemic character of technology and investigate the role reversal in the relationship between humans and technology. By applying systems-theoretical requirements outlined by Demetis & Lee, we examine conditions for the systemic character of technology and, based on our theoretical discussion, we argue that humans can now be considered artifacts shaped and used by the (system of) technology rather than vice versa. We argue that the role reversal has considerable implications for the field of information systems that has thus far focused only on the use of the IT artifact by humans. We illustrate these ideas with empirical material from a well-known case from the financial markets: the collapse (“Flash Crash”) of the Dow Jones Industrial Average

When Humans Using the IT Artifact Becomes IT Using the Human Artifact

Following Lee & Demetis [20] who showed how systems theorizing can be conducted on the basis of a few systems principles, in this paper, we apply these principles to theorize about the systemic character of technology and investigate the role-reversal in the relationship between humans and technology. By applying systems-theoretical requirements outlined by Lee & Demetis, we examine conditions for the systemic character of technology and, based on our theoretical discussion, we argue that humans can now be considered artifacts shaped and used by the (system of) technology rather than vice versa. We argue that the role-reversal has considerable implications for the field of information systems that has thus far focused only on the use of the IT artifact by humans. We illustrate these ideas with empirical material from a well known case from the financial markets: the collapse (“Flash Crash”) of the Dow Jones Industrial Average