Advancing Organizational Science: Construct and Process Theorizing, Layers of Theoretical Explanation, and Implications for Causality and Intervention

The popular and professional press is filled with discussions of major changes on the organizational landscape, including organizational design experiments at entrepreneurial firms as well as at major corporations, the slashing of corporate staffs, the downsizing, delayering and revitalization of firms, the emerging electronic organization, mergers and acquisitions, failures of high reliability organizations, and time-based competition. Each of these issues has been associated with the redesign of organizations, yet these redesigns seem far removed from academic research, and they do not typically utilize the academic body of knowledge. Although the field has progressed enormously in new methods and insights during a century of research, it seems to us that organization studies have been a source of recurrent disappointment for practitioners and academics alike (Bedian 1989; Cummings 1983; Luthans 1986; Slocum 1984). For example, Miner (1984) analyzed 32 established organizational science theories and concluded that with the exception of theories of motivation there is no relationship between usefulness and validity. Is the field of organization studies irrelevant? Organizations have become the dominant institution on the social landscape. Yet the body of knowledge published in academic journals has practically no audience in business or government. Unlike a field such as economics, research on organizations has not typically focused on problems relevant to business and government organizations, and the real world of organizations has not drawn on the work undertaken by organizational scientists. From colleagues within our field and in allied disciplines, we hear complaints that manuscripts espousing radical ideas, or topics outside the mainstream, are difficult to publish. Reviewers for established journals seem to value papers whose theses are anchored in established theories or that use "legitimate" methods, thus implicitly creating a publication barrier for research that falls outside mainstream topics or methods. Moreover, we observe that scholars with interests in organizations span many disciplines and fields of inquiry such as anthropology, economics, history, information science, communication theory, artificial intelligence, systems theory, psychology, sociology, political science, policy sciences, as well as organization behavior, strategic management and organization theory. We sense that a new discipline of organization science is evolving and we envision that a new journal can become a forum for a discipline defined more broadly. The purpose of this essay is to discuss these issues and the need for reorienting research away from incremental, footnote-on-footnote research as the norm for the field. Although current research approches have made solid contributions, they do not

Editor's Comments: Construct Clarity in Theories of Management and Organization

Geisler argues that the over-reliance on co-variation techniques and statistical methods, instead of process approach and in-depth analysis, produces meaningless knowledge in the managerial and organizational sciences, and indeed throughout all the social sciences. He offers instead a new and different approach, based on the notion of what he calls dynamic morphologies—an architecture of slicing complex phenomena. This way it is possible to explain many inconsistencies in research findings, and to find a cohesive, systematic outlook on research, research design, and knowledge creation. Intellectually challenging and following in the footsteps of Kuhn, Argyris, and Popper, Geisler's approach is frankly revolutionary in research design and contains its own notions, terms, and nomenclature. A provocative discussion for academics and others well trained in the organizational, managerial, and social sciences. Geisler's dynamic morphologies provide a means to research complex phenomena and gain knowledge about them. They are composed of a chain of events, combined logically and temporally, and a method by which this process is studied. Geisler also contends that knowledge in the organizational and managerial sciences is only viable when it describes and explains the complex, higher-order phenomena. Therefore, theory building and research in these fields must be linked to higher-order constructs and the phenomena that they attempt to explain. This is the central notion of amplitude that Geisler introduces and describes. His book also criticizes the evolutionary epistemology view of knowledge creation and contends that knowledge in all of these fields of study in general is not evolutionary, but instead, cumulative and expansive.

Advancing Organizational Science With Computational Process Theories

PurposeRecent criticisms of organizational science theory have lamented a lack of depth and a growing “maturity” that is impeding empirical advances. The purpose of this paper is to propose that organizational scientists can address this problem by embracing “evolutionary awareness” (EA). EA builds on theories and constructs developed in the evolutionary sciences that serve to add depth to theory building.Design/methodology/approachThe design of the paper is first to introduce the concept of EA and identify its four key constructs. Next, the authors apply EA to three areas of research within organizational science: human motivation, interpersonal communication and leadership. The authors’ intent is to show that EA constructs extend and deepen traditional organizational science theorizing. Thereby, the authors show that the problems noted above, i.e., lack of depth and maturing theories, can be addressed by embracing EA.FindingsThe findings are that EA substantially enhances and freshens theorizing in the organizational sciences in the areas of human motivation, communication and leadership. By extension, other areas of interest will also benefit by embracing the EA perspective.Research limitations/implicationsThe implications of the research are many. Organizational scientists can advance theory building, research and practical prescriptions by embracing EA. They can also engage in interdisciplinary research programs with scholars in the evolutionary sciences eager to see their work having practical implications. The limitation of this work is that the authors were only able to show a limited application of EA to three areas of interest to organizational science scholars.Practical implicationsThe practical implications of this research are potentially far reaching. At this very moment, scholars in a wide array of disciplines are re-casting their views of humanity, cognition, values and other constructs based on the acceptance of evolution and its primary mechanism, variation and selection based on consequences. These changes will usher in new ideas about leadership, work-life balance, organizational purpose and many others.Social implicationsA much-needed “consilience” across the human sciences through embracement of the EA perspective may provide insights that will advance human flourishing in organizations and beyond. The authors believe that an increasingly veridical understanding of humanity will produce substantial social impact.Originality/valueThis work will provide an encompassing perspective that will assist organizational scholars in advancing their theory building and research questions. A much-needed “consilience” across the human sciences may provide insights that will advance human flourishing in organizations and beyond.

Current theoretical, empirical, and methodological efforts in organizational science are increasingly concerned with causal inference and system dynamics. The strengths and weakness of recent approaches to causal inference are reviewed. It is demonstrated that causal inference techniques derived from both the graph-theoretic and potential outcomes perspectives provide only limited support for the majority of focal inferences in organizational science. System dynamic methods such as state space and computational modeling are then presented as viable inferential methods for advancing organizational science.

Well-developed theory provides a strong foundation for empirical work in the organizational sciences. Theory drives the focus of core research questions, shapes inferences drawn from analyses, and guides a cumulative organizational science. This chapter considers varying definitions of theory in the organizational sciences and a taxonomy of theory building concepts is presented (e.g., pre-theory, construct theory, taxonomy, phenomenon theory, causal models, process theory). Process theory is on the scientific frontier. Theory, the methods used to generate data, and the statistical analyses used to make sense of data are deeply intertwined. A case is made that the foundation for the dominant methods and analyses used in organizational science were in place a century ago and, although far more sophisticated, are essentially the same today. Contemporary theorizing is primarily focused on factor (construct) relationships. Indeed, the dominant methods largely constrain theorizing to a focus on relationships, rather than on the underlying processes – action sequences and process mechanisms – that generate them. The implications of the data revolution for advancing process theories in organizational science are considered, and methods for linking big data approaches with process theories are discussed.

“Most reviewers’ checklists of leading management journals list the criterion, “relevance for practice.” Authors comply with this criterion by pointing out what implications their results might have for practice. Evidence in the form of successful implementations of the results in practice is not required. Essentially, the authors are only supposed to point out what implications practitioners, as they socially construct them, can possibly draw from their results. If the reviewers’ construction of relevance is in accordance with the author’s, the criterion of relevance has been met . . .” (Kieser & Leiner, 2009: 522–523; italics in original).

The initial public offering (IPO) is one of the most critical events in the lifetime of a young firm. Prior research has shown that firms tend to have successful IPOs if they go public with the endorsement of a prestigious lead underwriter. This paper examines the antecedents to receiving endorsement by a prestigious underwriter and links this to the experience base of a firm's upper echelon. We theorize that the amount and type of upper echelon experience serve as important symbols of a young firm's legitimacy to critical outsiders. We introduce a typology of upper echelon experience that distinguishes between upper echelon upstream, horizontal, and downstream employment-based affiliations and suggest that these different types of upper echelon affiliations allay different types of endorser concerns regarding firm legitimacy, affecting the endorsement process. Further, we theorize that the relationships between upper echelon experience and investment bank prestige will be moderated by technological uncertainty. We test our assertions on a comprehensive sample of public and private biotechnology firms that were founded between 1961 and 1994 and that went public between 1979 and 1996. Analyses of the five-year career histories of the over 3,200 executives and directors that make up the upper echelons of these firms show that firms with upper echelons with affiliations with prominent downstream organizations (i.e., pharmaceutical and/or healthcare companies) and with prominent horizontal organizations (i.e., biotechnology companies) are more likely to attract the endorsement of a prestigious investment bank. We also find that the greater the range of upper echelon affiliations across the categories of upstream, horizontal, and downstream affiliations, the more prestigious the firm's lead underwriter. We also find that these latter results are moderated by technological uncertainty. The present research has implications for the study of organizational legitimacy, interorganizational endorsements, and entrepreneurship.

The author, editor of the publication, looks at the developmental approach to peer review of academic articles, described as practices which focus not only on evaluating or identifying weaknesses in an article but on providing guidance to the original author on how the article can be improved and on how he or she can become a better scholar. She contrasts effective developmental reviewing with practices she considers ineffective including ghostwriting the article. Other topics include the role of developmental reviewing in expanding inclusion and diversity in an academic field and its benefits for long-time as well as young scholars.

According to recent psychological theories of situation model construction, readers routinely and quickly construct inferences that elaborate causal antecedents of explicit events in the text, but not inferences about causal consequences. The process of forecasting lengthy causal chains into the future is taxing on working memory, so these inferences are either not constructed or their construction consumes a comparatively large amount of reading time. This study collected self-paced sentence reading times from younger and older adults who read expository texts on scientific and technological mechanisms. Readers were also measured on working memory span, general world knowledge, reasoning ability, and reading frequency. Multiple regression analyses on the reading times revealed that (a) causal consequence inferences were more time consuming than causal antecedent inferences and (b) noncausal elaborative inferences were not constructed. The pattern of beta weights for inference variables was remarkably similar for younger and older adults and was unaffected by other measures of individual differences. The process of constructing causal inferences is therefore stable and predictable across different groups of readers.

Causality assessment is vital in patient safety and pharmacovigilance (PSPV) for safety signal detection, adverse reaction management, and regulatory submission. Large language models (LLMs), especially those designed with transformer architecture, are revolutionizing various fields, including PSPV. While attempts to utilize Bidirectional Encoder Representations from Transformers (BERT)-like LLMs for causal inference in PSPV are underway, a detailed evaluation of "fit-for-purpose" BERT-like model selection to enhance causal inference performance within PSPV applications remains absent. This study conducts an in-depth exploration of BERT-like LLMs, including generic pre-trained BERT LLMs, domain-specific pre-trained LLMs, and domain-specific pre-trained LLMs with safety knowledge-specific fine-tuning, for causal inference in PSPV. Our investigation centers around (1) the influence of data complexity and model architecture, (2) the correlation between the BERT size and its impact, and (3) the role of domain-specific training and fine-tuning on three publicly accessible PSPV data sets. The findings suggest that (1) BERT-like LLMs deliver consistent predictive power across varied data complexity levels, (2) the predictive performance and causal inference results do not directly correspond to the BERT-like model size, and (3) domain-specific pre-trained LLMs, with or without safety knowledge-specific fine-tuning, surpass generic pre-trained BERT models in causal inference. The findings are valuable to guide the future application of LLMs in a broad range of application.

Purpose – The current state of theory-building in the field of operations and supply chain management (OSCM) is in a strong need of rigorous, empirically based theories that enhance understanding of the causal relationships between the structural elements and properties of the business processes. In this research note the authors propose the critical realism (CR) philosophy of science as a particularly suitable philosophical position (not to the exclusion of others) to review the mechanisms of OSCM knowledge generation and to provide philosophical grounding and methodological guidance for both OSCM theory building and testing. Design/methodology/approach – To demonstrate potential benefits of CR-based structured approach to knowledge generation in OSCM research, this conceptual paper uses a case study that illustrates the adoption of one of the OSCM theories – i.e. the theory of swift, even flow. Findings – CR interprets the accumulated empirical information about OSCM phenomena as observable manifestations of the underlying causal mechanisms that cannot be perceived otherwise. CR can provide epistemological support to the choice of performance measures that manifest the underlying causal mechanisms of interest. Extensive accumulation of empirical data from multiple innovative sources will not dramatically add to understanding of the system under investigation, unless and until the underlying causal mechanisms that trigger the observed behaviour are identified and tested. The CR abductive mode of reasoning emphasises the role of uncertainty in complex process behaviours and can facilitate enrichment and refutation of OSCM theories. Originality/value – CR has a clear potential to contribute to OSCM research by enabling better understanding of causal relationships underlying complex behaviours of different elements of business process by providing robust and relevant mechanisms of generating knowledge about business processes that explicitly link empirical and causal aspects of theory building and testing.

Multi-level issues in evolutionary theory, organization science, and leadership

The last decades have seen an enormous growth in published research and evaluations, which makes it difficult for a researcher to stay up-to-date in their own field, let alone complement their knowledge with insights from other fields. In this paper we give an elaborate overview of a methodology that aims to tackle this task. It builds on the realist evaluation science approach and combines it with qualitative comparative analysis (QCA), hence its name: the ResQ approach. Central to the approach are generative mechanisms that can be found across fields, domains, sectors and contexts. The approach sets out to synthesize the evidence on the circumstances linked to the triggering of these mechanisms. QCA is used to identify the most relevant conditions, leading to theories around these mechanisms, called ‘mechanism concepts’. New studies can test, and refine the mechanism concepts, setting up a continuous cycle of theory-building across disciplines enabling us to learn from other fields, disciplines and contexts in a systematic way.