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Final Version of 16 October 2025
For the Provocations Track at ACIS'25
? Xamax Consultancy Pty Ltd, 2025
Available under an AEShareNet 
licence or a Creative
Commons 
licence.
This document is at http://rogerclarke.com/SOS/RNFP.html
The accompanying slide-set is at http://rogerclarke.com/SOS/RNFP.pdf
Disruptive information technologies (IT) are impactful in scope and in scale. Many stakeholders are affected, and their concerns are more intense than in the past. Yet the Information Systems (IS) discipline continues to be preoccupied with the interests of the organisations that sponsor IS, and pays little attention to the interests of those affected. It is untenable for the academic IS discipline to sustain that myopia.
In addition, the conception of research projects, rather than addressing the needs of practitioners, has become inward-looking. It prioritises rigour and intellectual depth ahead of relevance, comprehensibility and implementability. Disruptive IT causes rapid change, and hence near-future phenomena are materially different from those observable today. The obsession of IS research with empirical rigour, combined with the slowness of research conduct, write-up and publication, lead to researchers reporting on phenomena of the past, not even of the present, let alone of the near future. On the one hand, near-future phenomena cannot be subjected to observation, and hence it is infeasible to conduct empirical research into them. On the other hand, IS research in contexts that exhibit rapid change needs to deliver insights into precisely those kinds of things.
This paper argues that two major changes are necessary for the IS discipline to rediscover its mojo:
Provocation #1: The IS discipline must regain relevance by expanding its scope to encompass near-future phenomena, and embracing the application of futures studies research techniques to them.
Provocation #2: The IS discipline must confront the reality that large-scale IS applying disruptive IT embody intense competition among multiple stakeholders, all of whose perspectives are legitimate for researchers to adopt.
This paper puts flesh on those criticisms of contemporary IS research, outlines research techniques employed in futures studies, and argues for their application to domains that are both dynamic and rich in conflict among stakeholder interests.
A provocation is an essay that presents "critical reflection and argumentation on issues pertinent to ... [a] community ... [to] push the reader to think differently about an issue pertinent to [that community]" (Reed et al. 2024), and "intended to challenge existing paradigms and encourage innovative thinking ..." (Davern & Black 2025, p.2). This paper identifies two ways in which the information systems (IS) discipline has lost its way, and as a result has become increasingly less relevant, less interesting and less likely to flourish.
The contemporary context of the IS discipline is radically different from that which prevailed during the first half of its existence, viz. 1965-95. During that period, the focus shifted from 'data processing' to support records management, to 'information systems' to support management planning and control, and then 'decision support systems' to serve the needs of executives. IT applications then escaped the confines of a single organisation to serve pairs, chains and networks of organisations (Clarke & Wigan 2018). Many systems are extra-organisational (Clarke 1992a), in that individuals are direct participants, not only as employees, but also as consumers and citizens. Most data is 'born digital', the scale of data-holdings is massive, and the scramble for competitive advantage from it borders on the frantic (Piccoli & Watson 2008). In the early 21st century, digitisation has spawned digitalisation, whereby interpretation and management of the world is now far less through human perception and cognition, and instead heavily dependent on computer-performed manipulation of digital data (Brennen & Kreiss 2016).
Entering the 21st century's second quarter, IT-based systems generate new data by drawing inferences from available data and models, and are delegated the power to make decisions. They increasingly act directly in the real world, and with increasing degrees of autonomy. This is accompanied by decreasing degrees of transparency, exacerbated by the uncontrolled application of machine-learning forms of artificial intelligence (AI/ML) and Generative AI, which deliver inferences, decisions and even actions based on data alone, devoid of rationale (Clarke 2019, 2025a).
The scale and scope of functions that are supported, combined with the transformational and disruptive impacts of IT applications, mean that no organisation is free to make decisions as it sees fit, solely in pursuit of its own interests. IS generally involve a great many stakeholders, their interests differ and conflict, many direct conflicts among interests arise, and some stakeholders are subject to substantial negative impacts. Simplistic cost-benefit trade-off analyses are a thing of the past.
Given the magnitude of the changes, it is untenable to cling to the approaches and traditions that served the discipline's practitioners during the formative years. Yet this is what we have done. From a methodological perspective, commitment to empirical research in a world of rapidly-changing phenomena condemns the IS discipline to having limited relevance to the world in which organisations now operate. Section 2 identifies ontological and epistemological assumptions that are no longer appropriate in a rapidly-changing environment. Section 3 considers the methodological implications, and outlines futures studies techniques that enable contributions to be made in conditions where the assumptions of empiricism do not hold.
In section 4, an axiological perspective is adopted. IS researchers generally demonstrate a strong commitment to the values of corporate board-rooms, and to treatment of other value-sets as being constraints on the achievement of corporate objectives. This results in inadequately narrow research domains, and condemns the IS discipline to having limited relevance to a world in which the varied and conflicting interests of multiple stakeholders need to be taken into account.
Empiricism involves "primary reliance on evidence derived from observation, investigation, or experiment [ of or in the real world ] rather than on abstract reasoning, theoretical analysis, or speculation" (OED 6). For five centuries, the practice of knowledge-seeking has placed great emphasis on the application of disciplined imagination to empirical data (data gained by "direct observation, investigation, or experiment [of a perceived reality]", OED 3), and comparison of the results of exercises in disciplined imagination against new, purposefully-collected empirical data. This has resulted in theories about the real world that have had sufficient explanatory and predictive power to enable accumulations in many fields in both knowledge and standard of living.
To frame the argument presented below, it is necessary to go one level deeper into the underpinnings of IS theory. "Researchers and practitioners alike unavoidably make 'metatheoretic assumptions', often implicitly, and sometimes consciously. Where the assumption is not merely conscious, but is also intentional, the term 'metatheoretic commitments' is appropriate" (Clarke 2021b, p.2. See also Johnson & Duberley 2003). Metatheory relevant to the present discussion draws on four branches of philosophy: ontology (concerned with existence), epistemology (concerned with knowledge), methodology (concerned with processes) and axiology (concerned with value). Table 1 identifies key metatheoretic assumptions in the first three of those branches.
In Clarke (2021b), a set of pragmatic ontological assumptions is proposed as being mainstream in IS practice. One is that real-world phenomena comprise relatively stable 'things' and inherently dynamic 'events'. Properties of phenomena (such as the wavelength of electromagnetic radiation, hardness and brittleness of things, and the duration of events) provide a basis for categorising them into particular groups. Ideas (such as numbers, colours, hardness, brittleness and time), on the other hand, are notions within the abstract world of data and models. Phenomena are assumed to be observable by humans or artefacts produced by humans. Very importantly, phenomena also treated as being reasonably consistent across space, and reasonably stable over time. These form items 1-4 of Table 1.
In the epistemological area, pragmatlc assumptions include that 'codified knowledge' exists in the form of "data expressed and recorded in a more or less formal language ... that is disembodied from individuals, but communicable among them" and that 'tacit knowledge' "exists only in the mind and/or body of a particular person, is informal and intangible, and hence is not readily communicated to others" (Clarke 2021b, p.3). Items 5-6 in Table 1 identify the further assumptions of empiricism that data can be generated more or less directly from observations of phenomena, and that the resulting data bears a reliable relationship with some aspect of real-world phenomena. On the basis of the first six assumptions in Table 1, it can be readily argued that scientific research methods can be applied (item 7).
These assumptions have become engrained, to the point that they are seldom-discussed, and it is very easy for researchers to overlook circumstances in which they do not hold. The rapidity of technologically-enabled and technologically-driven change is such that there is a pressing need for research into near-future phenomena, by which is meant phenomena that it is reasonably anticipated may soon exist, but presently do not. The term 'near-future' is intentionally relative rather than absolute, and is primarily intended to focus on highly tenable patterns rather than more distant and more speculative new conditions and behaviours. This might be operationalised as 1-2 years hence, or perhaps within the length of the research project cycle, from conception to publication.
Near-future phenomena are by definition not observable and not influencable, yet. This undermines ontological assumption 2. Further, given the common experience of change being progressive, future states seldom come into existence all at once, in a mature form, and hence near-future phenomena may well also fail assumptions 3 -- consistency -- and 4 -- stability. That in turn undermines epistemological assumptions 5 and 6 about the capacity to generate observational data that reliably represents phenomena, and the methodological assumption 7 of refutable propositions generated from theories that can be tested directly through the observation of phenomena.
At any given time, particular kinds of technologically-enabled and -driven change will be in public focus, or in laboratories, transitioning from dreams to experiments and pilots. Some are challenging to grasp, and require immersion in emergent technologies before trajectories can be considered and research conceived. Current examples include various applications of quantum computing, new features of wearable computing, and various forms and uses of human-implanted computing-and-communications. Rather than bleeding-edge contexts, it's more tenable for writer and reader of this paper alike to think in terms of recent-and-familiar instances rather than current-and-still-strange sources of major change. Less dramatic and uncertain examples are yet more forms of electronic commerce (such as AI-agentic services), electronic value-storage and -transmission, and alternative ways of embedding data collection and IS-driven actions within smart grids. These forms of IT are giving rise to rapid changes in phenomena. How can these near-future phenomena be researched?
The study of future worlds may be supported by some mainstream research techniques. This section, however, has its focus on some examples of relevant research techniques that have been devised specifically for circumstances in which empirical techniques are ineffective. The techniques discussed here are a sub-set, selected with a view to a sufficient degree of diversity to support many different research domains. Fuller lists and discussions are available, such as Bell (2003) and Glenn & Gordon (2009).
This section provides a brief overview of a number of research techniques, listed in Table 2, that have at least some reasonable degree of recognition within the IS discipline, commencing with longstanding techniques, and encompassing various forms of conceptual research, quasi-empirical techniques, business case development, and risk and impact assessment tools.
Early notions of trends, and forecasting by projecting statistical data along a timeline to postulate future time-series data, suffer from the confusion of correlation with causality, and make implicit assumptions about underlying stability of both processes and the environment in which they occurred.
Various forms of modelling have proven more useful, initially in the econometric arena. However, challenges of complexity and obscurity need to be addressed, and suitable concepts and constructs discovered or invented. Generally, modelling makes assumptions, often only implicitly, about some kinds of at least second-order stability. Even modelling that featured multi-layer feedback loops could not fulfil all needs (Forrester 1961). Gradually, acceptance emerged of less quantitative approaches, and there was increased focus on discontinuities (Drucker 1968) or shifts, shocks and divergence (Toffler 1970, 1980). An application of discrete event simulation modelling (DES), designed to assist public health policy-makers during the 'shock' of the COVID-19 pandemic, is reported in Clarke (2021a).
In the absence of a real world that can be subjected to observation, it is common to resort to 'conceptual research' or 'arm-chair reasoning'. Works of speculative fiction are an extreme form, but some exemplars within 'sci-fi' genres have effectively imagined technologically-driven aspects of future societies. Effective theoretical work in the social sciences depends on adequate grounding in the techno- as well as the social aspects of systems. Hovorka & Peter (2021) futher articulate such ideas as 'speculatively engaging futures'. Drawing on work of Rob Kling, I coined the expression 'instrumentalist futurism' to refer to categories of conceptual research that appear capable of delivering informational value (Clarke 1997b). An approach to instrumentalist futurism is described by Kling & Lamb (1997) as 'visionary depiction'. This delivers a snapshot of an imagined future state. Such descriptions are commonly 'utopian' (strongly emphasing 'good' aspects), sometimes 'dystopian' (strongly emphasing 'bad' aspects), and occasionally balanced, ambivalent or intentionally ambiguous.
Utopian descriptions abound. Examples of the primarily dystopian form, applied in a surveillance society context, include analyses of the impacts of the imposed digital persona (Clarke 1994), of chip-based ID cards (Clarke 1997a), and more recently of AI applications Clarke (2022). However, many examples can be found, across many disciplines. An example of a 'balanced' 'visionary depiction' of a static future situation, in this case of university administrative computing, is in Clarke (1992b). Hovorka & Mueller (2024) discuss other forms of 'instrumentalist futurism'. One is 'truth-irrelevant speculation' such as metaphors, in the sense of Ågfictions useful for explaining, predicting, and unifying certain observable phenomenaÅh (Achinstein, 2018 p.8), which support 'analogous thinking' (p.6450). They also identify 'socio-technical imaginaries', and 'speculative design' "as a means of entering into future design spaces" (p.6451).
A related technique, with an express 'agenda', is critical theory research. This directly addresses the value-conflict challenge, by recognising the effects of power and the tendency of some stakeholders' interests to dominate those of other stakeholders (Myers 1997, Alvesson & Deetz 2000, Cecez-Kezmanovic 2005).
Conceptual research can offer a great deal of value, provided that writers and readers all remember that all propositions are of necessity speculative and untested. It can provide what might be termed 'bootstrap hypotheses', which fall somewhere between 'ad hoc hypotheses' and 'theory-based hypotheses'. It can also underpin the development of research frameworks, which provide structure to themes and issues, including descriptions of fundamental concepts and processes, and of research agendas, which combine a broad conceptual framework, a process model, a set of broad research questions and a research program whereby those broad questions can be further articulated and addressed (Wand & Weber 2002, Ahuja 2002, Avgerou 2008, Newell & Marabelli 2015, Clarke 2019).
Beyond conceptual research approaches, a second cluster can be reasonably described as quasi-empirical techniques. This term has been used in methodological commentaries in several disciplines, but with various meanings. It is used here to refer to synthetic data that resembles empirical data, or is seemingly but not actually representative of real-world phenomena. The techniques considered under this category commence with some more or less formalised model of current circumstances, and some degree of appreciation of the nature of a new technology (including its features and intended impacts, and perhaps even of its more apparent affordances). They then postulate the effects that the technological intervention might have, based on previously-observed or theorised processes of political economy and/or social and psychological behaviour. By utilising such empirical base as is available, and placing constraints on the speculative component, the outcomes are less likely to be mere 'flights of fancy', and more likely to deliver value to the analysis team, system sponsors , and society as a whole. Examples of such techniques are:
Scenarios analysis, in contrast to visionary depiction of a snapshot of a future state, involves the development of a set of story-lines. The value derives from a combination of the depth of insights achieved within each scenario, together with the diversity among the different scenarios' trajectories. It is discussed at length in relation to the digital surveillance economy in Clarke (2019). An individual scenario may be developed forwards in time starting with a present (or perhaps hypothesised) state, applying some postulated contextual and/or strategic changes together with known or postulated dynamic patterns (environmental, economic, social and/or political, depending on the context), and inferring what further changes would occur over time. An alternative approach, referred to as 'backcasting', involves postulating a future state (often one that is highly desirable or highly undesirable), and proposing actions (e.g. decisions, changes, and interventions) that would be needed in order to result in transition from the present to the future state (Bibri 2020).
There are also well-established organisation-internal techniques that are much-used in individual business enterprises and government agencies, including:
Business-case and RA techniques are myopic, in that they are dedicated to the interests of a single player, with interests of other stakeholders ignored or marginalised, except where they are perceived as threatening the primary player's interests, e.g. through non-adoption, active resistance or even countermeasures. That weakness is serious and even fatal, at least for the purposes of the present discussion. The scope may exist for refining RA, in order to deliver an effective Multi-Stakeholder Risk Assessment (MSRA) technique (Clarke & Michael 2024).
A further category comprises techniques, widely used in public policy contexts, that are loosely referred to as 'impact assessment'. These evidence multiple flavours, depending on the degree of emphasis on a technology, a project, the impacts, or compliance with particular regulatory requirements. The origins of the genre lie in Environmental Impact Assessment (EIA) (Morgan 2012). This evaluates the effects on the physical environment (air, land and water) of a development project such as a mine, a dam, transport infrastructure, a manufacturing facility, a built-up area, a campus or a single large building. Techniques relevant to IS include:
All of these techniques are applied in other disciplines and professions. In order to address the pressing need for research into near-future phenomena, their use in IS needs to be greatly expanded. Sections 2 and 3 have together provided supporting information for:
Provocation #1: The IS discipline must regain relevance by expanding its scope to encompass near-future phenomena, and embracing the application of futures studies research techniques to them.
The scope for publication of research using futures techniques appears to be improving, given the existence of editorials and calls for papers in multiple leading journals (e.g. Schlagwein et al. 2024, Davison & Schwabe 2025). The following section shifts the focus to another area of need that has to date attracted less attention. It considers three further meta-theoretic assumptions inherent in empirical research, which relate to the value-sets that are evident in most reported IS research, and which lead to Provocation #2.
The term axiology was coined early in the 20th century to refer to a branch of philosophy concerned with the nature and classification of value and how value is imputed to things (Hart 1971). The sense of 'values' is broad, encompassing at least individual, social, economic and environmental aspects. "The ethicist approach considers value on what might be called a 'virtue' dimension of 'good / bad'. Arguments also exist for a 'deontic' approach, i.e. related to duty or obligation, on a 'mandated / optional / forbidden' dimension ... The term 'utility' is concerned with impacts or outcomes. The notion of 'teleology' has to do with purpose, and 'consequentialism' with the proposition that an action must be evaluated on the basis of its impacts or outcomes in relation to purpose" (Clarke 2021b, p.4). See SEP (2024).
In the private sector, the values of shareholders have always been the primary focus. The notion of 'stakeholders' (Freeman & Reed 1983) was created as a counterpoint to shareholders, to refer to participants in the process or intervention, in such roles as investor, data source, technology provider, system sponsor and user (Seddon et al. 1999). Some categories of entity can have an interest in an IS despite not being direct participants (Pouloudi & Whitley 1997, p.3). The term 'usees' is descriptive of such parties (Berleur & Drumm 1991 p.388, Clarke 1992a, Fischer-Huebner & Lindskog 2001, Baumer 2015).
In section 1, attention was drawn to the massive impact of transformational and disruptive IT. Threads in computation, data management, communications, robotics, data analytics, data manipulation, data creation, and materials creation, have matured, and have interwoven. With each phase, the impactfulness has broadened and deepened. Yet the IS discipline remains stuck in a time-warp, acting as though decisions about an IS were an organisation-internal matter, there were limited external effects, and the interests of external stakeholders were of little importance. With inter- and extra-organisational IS now the norm, multiple stakeholders exist, many have legitimacy, some have power, they have conflicting objectives, value-sets clash, and a whole range of public policy issues arises. Boards, IS practitioners, and the IS discipline, have no choice but to adapt to the new world of public responsibility as a substantial constraint on the freedom of choice of corporate and governmment executives.
An enabler of the necessary adaptation is the researcher perspective extension of stakeholder theory. Attention is drawn to the existence of multiple levels of abstraction on each of the economic, social and environmental dimensions in Clarke & Davison (2020, pp.487-488) and Clarke et al. (2020). Those authors also observe that about 90% of IS research adopts the perspective of a single entity, and in about 90% of that 90%, that entity is the system sponsor. Table 3 lists this dominance of system sponsor interests as axiological assumption 8.
Single-perspective research that prioritises the interests of some other stakeholder other than the system sponsor is much less common, but it appears in small quantities in many IS journals, and hence is not regarded by the discipline as illegitimate (axiological assumption 9). Dual-perspective research (e.g. balancing the interests of both entities within a dyad, such as employer and employees) is consistent with the enablement of win-win relationships, but is methodologically challenging. Multi-perspective research is a fit to networked industry sectors, and more broadly to economies, societies and polities in which the interests of many players are interwoven. In such contexts, it is very difficult to achieve the standards of rigour demanded by A and A* journals, and hence very little such research is reported in the IS literature. That gives rise to axiological assumption 10, that such research is essentially unpublishable in IS venues.
The author's contention is that the current situation in which system sponsors' interests dominate IS research (assumption 8) is both ethically challenged and an untenable position for the IS discipline to sustain in the contemporary world of continual waves of transformational and disruptive IT leading to IS being highly impactful on the interests of large numbers of stakeholders. This leads to the second proposition advanced in this paper:
Provocation #2: The IS discipline must confront the reality that large-scale IS applying disruptive IT embody intense competition among multiple stakeholders, all of whose perspectives are legitimate for researchers to adopt.
This paper has advanced two propositions. The first is:
Provocation #1: The IS discipline must regain relevance by expanding its scope to encompass near-future phenomena, and embracing the application of futures studies research techniques to them.
That provocation is supported by argument to the effect that conventional empirical research cannot deliver useful information about emergent, rapidly-changing and unstable phenomena, because ontological, epistemological and methodological assumptions underlying empiricism are unjustifiable.
The second proposition arises from the undermining of a crucial meta-theoretic assumption of an axiological nature:
Provocation #2: The IS discipline must confront the reality that large-scale IS applying disruptive IT embody intense competition among multiple stakeholders, all of whose perspectives are legitimate for researchers to adopt.
The author contends that these acceptance of these propositions for the IS discipline are not only necessary, but also feasible. The cited works on researcher perspective theory provide examples of research reflecting diverse stakeholder concerns. A companion project illustrates the application of futures studies in digiveillance. New technological intrusions into the workplace are emerging following the impetus given to Work From Home patterns by the COVID pandemic. Research can be conducted in advance of deployment:
A further working paper is emergent in the eInteraction field (encompassing digital commerce, business, government, currency, etc.), which extends though chains and networks to collaborating service providers and to various categories of users and usees. Meanwhile, gthe impacts of each new digital technology in clinical settings has impacts on many stakeholders across that most interwoven of all networked sectors, healthcare.
The ground has shifted beneath the IS discipline. There are research domains in which the inherent assumptions of empirical research outlined in Tables 1 and 2 still hold sufficiently well for it to remain viable. In many other research domains, on the other hand, IS research is irrelevant unless it copes with the contemporary realities of rapid change, unstable phenomena, and multiple stakeholders with conflicting interests. To recover the vibrancy that it once exhibited, the scope of IS must be rapidly extended to futures studies of policy-ridden domains. We need the boldness to seek relevance, and to risk-manage the impacts on rigour.
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The genesis of this paper was a challenge posed to me by Fred Niederman of St Louis University Missouri. I greatly appreciated the challenge, the ensuing discussions and his suggestions about the ideas it contains. I valued the deep, considered and informative comments of the ACIS'25 reviewers. They are being applied to the ongoing projects in both streams of thought in this Provocation piece.
Roger Clarke is Principal of Xamax Consultancy Pty Ltd, Canberra. He is also a Visiting Professorial Fellow associated with UNSW Law & Justice, and a Visiting Professor in Computing in the College of Systems & Society at the Australian National University.
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