The importance of theory at the Information Systems Journal
这篇社论由《信息系统期刊》的资深编辑团队撰写，阐述了该期刊对理论贡献的多元包容立场，并为不同研究范式（如演绎、归纳、设计科学等）的作者提供了如何融入理论的具体指南。
Theory is a crucial aspect of the information systems (IS) discipline. Authors draw from articles on how to develop theory and from the theories themselves to anchor knowledge contributions. Editors and reviewers expect to see novel theoretical insights in conjunction with empirical rigour and sophistication (cf. Hardin et al., 2022). The thinking of PhD students is shaped by discussions on the importance of theory through formal coursework and research seminars, as well as socialisation with peers, supervisors and senior scholars in the field. Journals often solicit submissions to special issues that champion particular kinds of theory or theories on specific topics, for example indigenous theory (Davison, 2021). Advice is given to authors in different ways that they can theorise (Hassan et al., 2022; Hong et al., 2014; Sandberg & Alvesson, 2021; Weick, 1989). The peer review process emphasises the importance of theory and tends to reject research articles that lack substantial theoretical contribution. However, assessing theoretical contributions is often a challenging task. IS scholars research a variety of topics with a pluralistic set of methods and epistemological approaches (Tarafdar et al., 2022), which have several implications for our engagement with theory. Traditionally, reference disciplines have informed the diversity of topics IS scholars investigate. The IS field is at a point in its disciplinary evolution where we are seeing an even greater ambit of the application and use of IS, which fosters new topics being investigated from different epistemological and methodological viewpoints as well as new types of contributions (Tarafdar & Davison, 2018). Consequently, IS theories take on different roles for different types of epistemologies and methods, and not understanding or respecting these differences can lead to unreasonable or unbalanced evaluation of papers. In addition to the diversity of theoretical approaches, we also perceive differences in the nature of engagement with theory. For example, papers that analyse large amounts of secondary data (textual and numerical, structured and unstructured) often focus on complex empirical techniques to analyse such datasets, often engaging minimally with theory (Miranda et al., 2022). We believe that sophisticated data analysis does not relieve IS researchers from the obligation to make a theoretical contribution. In this context, we believe, that we should take heed of the advice by Gurbaxani and Mendelson (1994) who warned, almost 30 years ago, about ‘the risks of ignoring the guidance of theory’ and recommended that IS researchers refrain from tinkering with ‘atheoretical “black box” extrapolation techniques’ (p. 180). In an earlier editorial in this journal, Davison and Tarafdar (2018) noted how baselines for what is an acceptable contribution in a discipline shift over time. However, it is our view that a robust theoretical contribution should be (and is) a consistent expectation, even if the nature of the theoretical contribution varies. Journals play a key role in establishing baselines and in that spirit, recent and emerging intellectual trends in IS and other disciplines have implications for how we apply and develop theory in IS and point to an evolving and multi-focused role of theory in IS research. Therefore, in this editorial, we revisit and explicate why theory is important at the Information Systems Journal (ISJ) in these emerging scenarios. Seven of the ISJ's regular senior editors (Andrew Hardin, Angsana Techatassanasoontorn, Antonio Díaz Andrade, Gerhard Schwabe, Monideepa Tarafdar, Paul Benjamin Lowry and Sutirtha Chatterjee) join the editor-in-chief (Robert Davison) to craft a position statement regarding the ISJ's view on theory. It is applicable, with sensitivity, to the empirical research articles that we consider for publication. Specifically, we provide a set of guidelines to help ISJ authors consider the role of theory in crafting papers of different genres and different epistemological and methodological approaches. Consistent with the journal's cultural values (Davison & Tarafdar, 2022), we lay out a pluralistic and inclusive view of theory and theoretical contributions. The guidelines are broadly indicative of what we believe are key points that authors should consider. We encourage authors submitting their research to the ISJ to consider these guidelines carefully, as we expect that reviewers will be aware of them, and senior and associate editors may also consider them as they craft their reports. However, these guidelines are not meant to serve as a comprehensive checklist, and least of all as a template for rejection. Theory lies at the heart of a scholarly discipline, supporting its scholarly relevance, identity and legitimacy. Without theory and the associated cumulative contribution to knowledge, the viability of a discipline is jeopardised because its scholarly distinctiveness is lost. As Suddaby (2014) puts it, ‘To cede theory means to give up legitimacy (of knowledge)’ (p. 409). Similarly, Van de Ven (1989, p. 486) states that ‘Good theory is practical precisely because it advances knowledge in a scientific discipline, guides research toward crucial questions, and enlightens the profession’. Weick (1989) emphasises that a good theory should be plausible and correspondent with reality. Thus, theory helps us ‘organise our thoughts, generate coherent explanations and improve our predictions’ (Hambrick, 2007, p. 1346). At the same time, there is recognition that theory can be performative (Burton-Jones et al., 2021), that is, theories influence practice as well as other theories. Because of this, we have the obligation to avoid making ‘excessive truth claims based on extreme assumptions and partial analysis of complex phenomena’ that can result in theories that mislead researchers and practitioners (Ghoshal, 2005; p. 87). Theories are employed to make sense of phenomena and are useful if they guide and structure both the research and the telling of the research story. In research designs that utilise a deductive and positivist approach with respect to data, theory guides the development of relationships to be tested in the form of hypotheses, analytical models, and so on. Campbell's (1990) definition of theory fits well under a deductive and positivist epistemological approach: ‘a collection of assertions, both verbal and symbolic, that identifies what variables are important and for what reasons, specifies how they are interrelated and why, and identifies the conditions under which they should be related or not’ (p. 65). In a deductive approach, theory plays a distinctive role in conceptualising concepts and constructs, thus defining the empirical benchmarks of what is measured and what data is collected. For inductive and interpretive research designs, the emphasis is on the process of generating theories or theoretical understanding (Strübing, 2007). Theories constitute ‘temporarily acceptable generalisations about the influences on and consequent variations in human action’ (Kearney, 2007, p. 148). Yet, existing theory can play the role of sensitising the data collection endeavour (i.e., guide the researcher toward what data to collect) or be applied toward sense-making and analysis of the data (i.e., help the researcher in anchoring the patterns and relationships emerging from the data). In both cases, theory gives meaning to the data (Illari et al., 2011). However, not understanding the respective roles of theory is likely to result in incorrect evaluation and review of the theoretical contribution of manuscripts. We illustrate with two examples. Consider research that collects primary data expressly for the purpose of the project (e.g., a theory-driven survey) versus that which utilises secondary data not collected specifically for the research (e.g. data scraped from user activity on social media websites or collected by organisations in anticipation of future functional value it may bring). The latter is not collected according to the rigorous standards essential to the conceptualisation and operationalisation of constructs in a theorising process and is thus subject to issues of incomplete observations and/or noisy data (Stieglitz et al., 2018). Consequently, theoretical concepts, constructs and propositions from such data may not be developed based on the theory that specifically informs the data collection; rather in many cases, theoretical engagement is somewhat eschewed, thus creating a more serious problem where such data is replete with issues such as endogeneity bias (Wooldridge, 2010). Quantitative research designs based on such data are thus subject to a slew of robustness tests to address the natural endogeneity bias that results from (1) omitted variables (missing portions of the nomological network of constructs), (2) measurement error, (3) simultaneity, and (4) selection bias (Wooldridge, 2010; Zaefarian et al., 2017). However, not understanding the role of theory and how it can dramatically reduce endogeneity bias, can lead reviewers and editors to unnecessarily and incorrectly ask authors using the first type of research design to conduct robustness checks only appropriate for the second type. Such requests, and any attempts to address them, frequently result in frustrations among authors, reviewers and editors. Relatedly, consider research that seeks to generate insights from secondary datasets through qualitative or computational analysis, for example ML-based pattern generation (Miranda et al., 2022). Our ability to analyse vast amounts of data in nearly all forms has spotlighted this second kind of research. The role of theory in such research is ideally to serve as a guiding light to understand the generated concepts and relationships and assess their novelty. However, the absence of understanding of this role of theory can lead to research designs that jettison theory altogether and focus on finding patterns in an exploratory way without building theoretical understanding in parallel with data analysis. Rigorous and essential conceptual understanding is not generated in these instances. We recognise that there are different types of theory (Gregor, 2006), different forms of theorising (Cornelissen et al., 2021; Sandberg & Alvesson, 2021) and different objects of theorising (Hassan et al., 2022; Rivard, 2014). However, for IS research we submit that theoretical engagement should follow the sociotechnical tradition. IS phenomena arise at the confluence of social and technical factors. Our discipline has, since its early days, described this fused approach as the sociotechnical approach (Mumford, 2006), one that has hues that can be described along a continuum (Sarker et al., 2019). Although the extent to which each component (the technical and the social) is present in a phenomenon varies qualitatively, each is present. The cumulative IS literature points to several typical and desirable characteristics of IS-centric, theoretical understanding. Such understanding is developed around the traditional IT artefact, and the greater IS artefact (Chatterjee et al., 2021; Lowry et al., 2020; Orlikowski & Iacono, 2001), and spans phenomena relating to their design, development and use. The theoretical insight includes both a social component (i.e., what happens and why when the artefact is designed, developed or used) and a technical component (i.e., the nature of the explicit influence of the artefact characteristics). IS scholars develop and advance theoretical understanding of IS phenomena through novel constructs, associations, processes, and design artefacts that adhere to these characteristics. Moreover, IS-centric theoretical understanding is critical to the transformation of social theories because of such advances. At ISJ, we expect authors to explicitly articulate theoretical insights that offer novel interpretations or challenge and problematise conventional understanding of the phenomenon under investigation (Sandberg & Alvesson, 2021), broadly adhering to the general criteria articulated above. In addition, given the wide range of phenomena, problems, methods, topics, data types and contexts in IS scholarship, we lay out practical guidelines for developing theoretical knowledge, based specifically on the particular focus of research. The guidelines are intended to help prospective ISJ authors frame and articulate the theoretical treatment of their work; they can also assist editors and reviewers in evaluating the theoretical merits of these works. Theorisation should precede primary data collection. It should include the conceptual development of anticipated relationships among concepts (i.e., hypotheses or analytical models) and the development or adaptation from literature, of appropriate operationalisation for measuring the concepts. All this involves logical and nomological argumentation based on engagement with theories and general engaged scholarship with industry and other researchers, such that it is not primarily driven by mere gap-spotting in literature reviews. Context-driven theoretical arguments are a crucial aspect of developing new theoretical insights or extending existing ones in novel directions. To give a simplified example, if the goal is to test an existing theory with a new population of IS users, the focus of theorisation should be to hypothesise new relationships (e.g., moderated and mediated relationships) based on the new population, which extend or alter the theory's predicted relationships rather than replicate them (Hong et al., 2014). Theoretical engagement after data is collected and during analysis has pitfalls in that it may lead to constructs and relationships that are not theoretically defensible or novel, even if significant statistical effects exist. In such a case, if published, there is the risk that readers will interpret the results as if the data were collected in a theoretically appropriate way and accept this imprecisely defined data as accurately representing the original conceptual and theoretical definitions in the literature, potentially propagating erroneous constructs through domino effects. 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