The War Over What Counts as Knowledge
Epistemology, Speculation, and the Case for Ambitious Theorizing in Organizational Science
Part 2
Part 1 mapped the terrain: five foundational voices that still define what theory means; the rise of phenomenon-driven research; four methodological traditions for building theory; and the strategic implications for positioning a new theoretical framework. That landscape reveals a field locked in productive disagreement about standards, methods, and contribution types. The question now is not which debates exist but what they reveal about the kind of knowledge management science can and should produce. The epistemological ground on which a novel theoretical framework stands determines whether the field will receive it as a contribution or curiosity.
The Double Bind
The field demands theory in every paper, along with data. The first requirement prevents the reporting of important empirical phenomena that lack explanation. The second prevents the publication of ambitious conceptual frameworks that lack accompanying empirics. Together, they create a double bind that suppresses both observational discovery and the speculative theorizing that built the field’s foundational paradigms.
The numbers tell the story. In 2005, every article published in AMJ, ASQ, and Organization Science contained the word “theory,” averaging 18 mentions per article (Hambrick, 2007). Finance, marketing, and accounting journals used the word in only 78% of articles, averaging 8 mentions per article. The blanket requirement forced scholars to attach ill-fitting theoretical frameworks to empirical work that would stand on its own merits, producing contorted prose and tortured argumentation. Only 9% of theoretical presentations published in AMR received subsequent empirical testing, yielding a ratio of ideas to tests that serves no cumulative scientific purpose (Hambrick, 2007). A 1930s epidemiologist who discovered the smoking-cancer link would face rejection from management journals for lacking theory.
The problem extends beyond inefficiency. Management theories have exerted significant negative influences on management practice (Ghoshal, 2005). Agency theory posits that managers cannot be trusted. Transaction cost theory assumes opportunistic behavior as the default. These theoretical commitments do not merely describe organizational reality. They produce it. Business schools propagate these assumptions through their curricula, and their graduates build organizations that reflect them. The 2008 financial crisis validated this warning (Khurana, 2007; Pfeffer, 2005). The question of what kind of theorizing management science should pursue, therefore, determines which organizations and managers society will produce (Ferraro, Pfeffer, & Sutton, 2005).
Defenders of the theory answer with equal force. Theory serves three value propositions: knowledge accumulation (capturing phenomena to enable progressive science), knowledge abstraction (providing perceptual lenses that structure experience), and normative vision (allowing scholars to see the world as it might be) (Suddaby, 2014). Abstract knowledge also protects jurisdictional legitimacy for the management disciplines. When explicit frameworks recede into the background, theory becomes implicit, and implicit theories operate without scrutiny (Suddaby, 2014).
The defense is correct on its own terms but misidentifies the threat. The anti-theory position does not advocate abandoning explanation. It advocates relaxing the institutional requirement that every paper contain novel theory, so that robust empirical patterns can reach publication without forced theoretical wrapping (Hambrick, 2007). The pro-theory position does not advocate the current gatekeeping regime. It advocates rigorous causal explanation over the five common substitutes: references, data, lists, diagrams, and hypotheses (Sutton & Staw, 1995). Both camps want better scholarship. They disagree about whether the institutional machinery of journals helps or hinders that goal. The demand for “rigor” won the argument, but the field interpreted rigor as data rather than logic, precisely the opposite of what the pro-theory camp intended. Five things that are not theory became the gatekeeping standard, while the “why” that constitutes theory’s most critical element languished (Whetten, 1989).
A foundational definition clarifies what was lost. Theory is a system of constructs linked by propositions, where constructs are abstract terms approximated by variables that can be observed or measured (Bacharach, 1989). Two criteria govern the system: falsifiability (can the relationships between constructs be disconfirmed?) and utility (does the system explain something that matters?). The current regime collapses this architecture. Scholars produce propositions (X is positively related to Y) without specifying the strength of the relationship, the conditions under which it holds, or the pattern of evidence that would constitute disconfirmation. The constructs remain vague. The boundary conditions remain unstated. The result meets the institutional requirement for “theory” while violating every substantive standard that the requirement was designed to enforce.
A third diagnosis cuts deeper still. Foundational paradigms in organizational science, including population ecology, institutional theory, resource dependence, and transaction costs, all emerged in the 1970s and have accumulated increasing citations without replacement (Davis, 2015). Theories rise and decline as a function of fashion rather than evidence, and the scholarly career incentive system promotes novelty rather than truth and impact rather than coherence (Davis, 2015). The field is solving the wrong problem. A crucial distinction separates unit theory (specific empirical frameworks tested in individual studies) from programmatic theory (the settled science that unit theories collectively support) (Cronin, Stouten, & Van Knippenberg, 2021). Individual studies refine individual frameworks. Nobody integrates the results across studies into a cumulative understanding. The “theory crisis” is not a shortage of theories but the absence of infrastructure for connecting them.
Bayesian updating provides a formal mechanism for precisely this kind of cross-study integration, because the posterior from one study becomes the prior for the next, creating a mathematical architecture for programmatic knowledge accumulation. That architecture also restores the standard that the double bind corrupted. A Bayesian prior is the only mathematical way to respect the construct-proposition system fully: it quantifies the relationship between constructs before data touch them, specifying direction, magnitude, uncertainty, and boundary conditions as formal commitments rather than verbal gestures (Bacharach, 1989; Vanpaemel & Lee, 2012). The prior forces the theorist to pay rent on every claim.
The Epistemological Distinctiveness of Management Science
Management science differs from natural science in ways that alter what theorizing can and should accomplish.
Social science studies reflexive phenomena (Giddens, 1984). Management and economic theories do not merely describe reality but actively constitute it. When the Chicago Board Options Exchange opened in 1973, the Black-Scholes formula did not accurately predict option prices, with deviations of 30 to 40 percent. As traders adopted the formula and the exchange institutionalized it through automated systems, actual prices converged toward the model’s predictions, with deviations dropping to roughly 2% by 1978 (MacKenzie, 2006). The theory became true because people acted as if it were true. Three mechanisms drive this self-fulfilling dynamic: institutional design (theories shape organizational structures and reward systems), social norms (theoretical assumptions become normative expectations), and language (naming the same prisoner’s dilemma game “Wall Street Game” versus “Community Game” dramatically alters cooperation rates) (Ferraro et al., 2005).
A stratified ontology distinguishes three domains: the empirical (what we observe), the actual (what occurs whether or not observed), and the real (the underlying generative mechanisms that produce events) (Bhaskar, 1975). Social structures, unlike natural structures, are activity-dependent, concept-dependent, and transient. Reducing reality to observable correlations commits the “epistemic fallacy.” This ontology redirects attention from surface-level statistical relationships to the generative mechanisms underneath them, a redirection that organizational researchers have translated into practical methodology (Fleetwood, 2005; Edwards, O’Mahoney, & Vincent, 2014).
Social science has tried and failed to produce episteme, the universal, context-independent knowledge that characterizes natural science (Flyvbjerg, 2001). Its distinctive strength lies instead in phronesis, the practical wisdom necessary for deliberating about values, power, and how societies ought to organize themselves. Abduction complements this orientation as the only logical operation that introduces any new idea (Peirce, 1903/1998). Unlike deduction (which tests) or induction (which generalizes), abduction generates novel explanations when existing theories fail. Recent work has incorporated abduction within the hypothetico-deductive tradition as particularly informative for researchers studying complex, evolving phenomena that rarely fit cleanly into prespecified models (Sætre & Van de Ven, 2021; Wickert, Cornelissen, Schultz, Gehman, & Haack, 2025).
Management theories are not neutral descriptions awaiting confirmation. They are interventions in the reality they study. Any epistemological framework adequate to management science must account for reflexivity rather than treating it as a methodological nuisance, attend to generative mechanisms rather than limit itself to observable correlations, and recognize practical wisdom as a legitimate form of knowledge production.
Grand Theories Emerged from Speculation, Not Data
Every major management theory arrived theory-first, built from conceptual reasoning, interdisciplinary synthesis, and subjective theoretical priors rather than systematic empirical observation.
Transaction cost economics emerged from PhD training at Carnegie Mellon between 1960 and 1963, absorbing bounded rationality, organizational routines, and the architecture of complexity from Simon, March, and Cyert. The core concepts of opportunism, asset specificity, and governance structures derive from conceptual reasoning and interdisciplinary synthesis. Empirical testing followed the establishment of the framework (Williamson, 1975, 1985, 2010).
“The Iron Cage Revisited” ran 14 pages, contained no original empirical data, and proposed 12 testable hypotheses derived entirely from conceptual reasoning (DiMaggio & Powell, 1983). The paper synthesized three mechanisms, coercive, mimetic, and normative isomorphism, from conversations at Yale’s Institution for Social and Policy Studies. It drew on prior work on formal structure as myth (Meyer & Rowan, 1977) and cultural persistence through institutionalization (Zucker, 1977). It has since accumulated over 68,000 citations, making it one of the most cited papers in all of social science, entirely on the strength of its conceptual architecture (Powell & DiMaggio, 2023).
The resource-based view followed an even more extended trajectory of pure theorizing. The Theory of the Growth of the Firm was empirically informed but primarily theoretical (Penrose, 1959). “A Resource-Based View of the Firm” was a conceptual paper by Wernerfelt (1984). The 1991 paper crystallizing the VRIN criteria (valuable, rare, inimitable, non-substitutable) contained no original empirical data (Barney, 1991). Meta-analyses confirming the RBV’s tenets arrived 15 to 20 years later. The tautological risk was noted early (Priem & Butler, 2001), but the theory’s influence was established long before empirical validation.
Population ecology began as an explicit theoretical transplant from biological ecology (Hannan & Freeman, 1977). Resource dependence theory was closer to a mixed model, but the core framework was still derived from a conceptual synthesis of exchange theory, political economy, and open systems thinking (Pfeffer & Salancik, 1978). Transaction cost economics (1975), agency theory (1976), population ecology (1977), institutional theory (1977/1983), and resource dependence (1978) all emerged within a single compressed period. The most influential theories emerged at disciplinary boundaries, through interdisciplinary synthesis rather than narrow empirical specialization.
The contemporary publishing system, which demands rigorous empirics alongside theory, would likely have rejected every one of them.
Two evaluation logics illustrate the point. Under the prevailing frequentist regime, an editor receives “The Iron Cage Revisited”: 14 pages, no original data, 12 hypotheses, a theoretical argument synthesized from three existing sources. Reviewer 1 notes the absence of any empirical test. Reviewer 2 acknowledges the elegance but demands at least a preliminary dataset confirming one of the isomorphism mechanisms. The paper received rejection with encouragement to collect data and resubmit. The most consequential theoretical contribution in the history of organizational studies dies in the review process.
Under a Bayesian epistemological framework, the same paper is read differently. The three mechanisms function as structured priors over organizational behavior. Coercive isomorphism encodes a prior expectation that organizations subject to regulatory mandates will converge in form, with the strength of that prior reflecting accumulated theoretical reasoning about state power, legal compliance, and resource dependence on government funding. Mimetic isomorphism encodes a priori about uncertainty: organizations facing ambiguous environments will model themselves on perceived successful peers. Normative isomorphism encodes a priori about professionalization: common training and credentialing among personnel will homogenize organizational practices. Each prior can be formalized not as a single parameter estimate but as a family of predictions over observable organizational characteristics, specifying the direction, magnitude, and boundary conditions of expected convergence. The 12 original hypotheses become testable predictions with explicit expected effect sizes derived from the strength of the theoretical reasoning supporting each mechanism.
This translation is not easy. Operationalizing a priori about professionalization demands specifying what “professionalization” means observationally (credentialing rates, standard curricula, professional association membership), determining a baseline convergence rate against which to measure isomorphic effects, and eliciting from domain experts a plausible distribution over effect magnitudes. The field currently lacks the infrastructure to perform this translation routinely. Few doctoral programs teach prior elicitation methods. No established conventions govern how to report the justification linking a theoretical mechanism to a specific distributional form. Building this infrastructure will require the same kind of sustained methodological investment that structural equation modeling demanded in the 1980s and hierarchical linear modeling demanded in the 1990s (Kruschke, Aguinis, & Joo, 2012; McKee & Miller, 2015). The difficulty of the translation identifies a capability gap, not a logical flaw.
The epistemological logic of the original contribution already mirrors the Bayesian structure: firm theoretical commitments, derived from synthesis and reasoning, offered to the field for subsequent empirical updating. What changed between 1983 and 2025 is not the quality of speculative theorizing but the institutional willingness to publish it without accompanying data.
Bayesian Epistemology and the Formalization of Sensemaking
The theory wars have proceeded as though scholars must choose between ambitious speculation and rigorous explanation. Bayesian epistemology bridges the divide.
In Bayesian reasoning, probability represents the degree of belief rather than long-run frequency. The updating process (prior belief multiplied by the likelihood of data yields a posterior belief) maps directly onto the scientific process: existing theory (prior) encounters new evidence (data) to produce a revised understanding (posterior). The posterior from one study becomes the prior for the next, creating a formal mechanism for cumulative science (Davis, 2015; Vanpaemel & Lee, 2012). Informative priors simultaneously decrease model flexibility and increase empirical content, making theories more falsifiable, not less. When priors encode theory, Bayesian model selection via Bayes factors directly evaluates competing theoretical frameworks (Vanpaemel & Lee, 2012).
A legitimate objection arises. The “priors” that animated transaction cost economics or institutional theory were mechanistic beliefs about human nature and social structure, not probability distributions over parameter spaces. Conflating qualitative causal assertions with stochastic expectations risks a category error. The bridge requires recognizing that every theoretical commitment generates empirical predictions, and every empirical prediction has a corresponding probability structure. The assertion that asset-specific investments increase the likelihood of hierarchical governance was a directional claim with an implicit magnitude. A Bayesian prior does not replace the qualitative reasoning. It formalizes the downstream empirical expectations that the qualitative reasoning generates. The semantic content of the theory lives in the justification for the prior, not in the prior itself.
Precision matters here. The Bayesian framework proposed in this essay operates at the level of epistemological orientation, not at the level of methodological prescription. The claim is not that every management theorist must write out probability distributions. The claim is that the logic of prior specification, evidential updating, and posterior revision describes what good theorizing already does, and that making this logic explicit, whether through formal mathematics or through structured verbal commitments about direction, magnitude, conditions, and disconfirmation criteria, produces better theory than the current regime of vague propositions. A scholar who states “ALC fluency predicts coordination depth with moderate effect size (d = 0.4 to 0.6) among workers with less than six months of platform tenure, weakening to negligible effects among workers with more than two years” has made a Bayesian commitment in substance if not in notation. A scholar who states “ALC fluency is positively related to coordination outcomes” has not committed at all.
The deeper connection runs through sensemaking. Theorizing is an ongoing, provisional, self-correcting process in which scholars construct plausible accounts, test them against anomalies, and revise them (Weick, 1995). Pragmatic Bayesian practice follows the same loop: model specification, confrontation with data, identification of misfit through posterior predictive checks, and model expansion to address discrepancies (Gelman & Shalizi, 2013). Both accounts reject the notion of a final settled theoretical product. Both treat the encounter between theoretical expectation and empirical evidence as generative rather than merely confirmatory.
The structural correspondence is precise. Enactment (constructing a plausible account of the environment) corresponds to prior specification. Selection (testing the account against experience and anomalies) corresponds to likelihood evaluation. Retention (preserving the revised account for future use) corresponds to posterior updating. Anomaly detection (identifying where the retained account fails) corresponds to posterior predictive checking. And iteration (re-enacting with the revised account) corresponds to using the posterior as the prior for subsequent analysis (Weick, 1995; Gelman & Shalizi, 2013).
The critical difference is formalization. What sensemaking describes as a social and cognitive process, Bayesian inference formalizes as a mathematical one. Not to replace qualitative judgment but to make its logic explicit, cumulative, and comparable across scholars and studies. Bayesian epistemology is, in this reading, the formalized methodology of sensemaking.
Bold theories (sharp priors) are rewarded when confirmed and penalized when disconfirmed, creating incentives for theoretical ambition rather than incremental hedging. Under the current regime, a scholar can write pages of dense theoretical argumentation connecting constructs through elaborate causal chains, arrive at a hypothesis that says only “X is positively related to Y,” and submit the paper with no commitment about how strong the effect should be, under what conditions it should strengthen or weaken, or what pattern of data would cause the scholar to abandon the theory entirely. The Bayesian framework forbids this evasion. The prior is a commitment. It has mathematical consequences. A theoretical justification that produces no distributional commitment, that refuses to specify direction, magnitude, or uncertainty, fails on its own terms.
Despite a decade of advocacy, Bayesian methods remain effectively absent across thousands of articles in 15 organizational science journals (Kruschke et al., 2012). Doctoral training remains overwhelmingly frequentist. No established norms for reporting Bayesian results exist (McKee & Miller, 2015). Recent guides for strategy scholars are beginning to change this landscape and demonstrate substantively different conclusions from Bayesian versus frequentist analyses (McCann & Schwab, 2023; Certo, Busenbark, Kalm, & LePine, 2024). A reviewer trained exclusively in frequentist null-hypothesis significance testing will likely view explicit priors as “biasing the results.” This objection confuses a legitimate claim (that a poorly chosen prior can dominate a posterior when sample sizes are small) with an illegitimate one (that having any prior expectation constitutes bias). Every frequentist analysis embeds theoretical assumptions in model specification, variable selection, and functional form. These assumptions are priors in everything but name.
The Performativity Problem
Performativity poses a serious challenge to any updating framework. If management theories shape the reality they describe, then the data against which priors are updated may themselves be artifacts of the theory (Ferraro et al., 2005; MacKenzie, 2006). A practitioner who holds a prior that managers are opportunists, designs a governance system reflecting that prior, and then observes opportunistic behavior has not learned anything about human nature. The posterior confirms the prior because the prior shaped the data-generating process.
This circularity is not a Bayesian problem. It is a management science problem that afflicts frequentist inference equally. A null-hypothesis significance test on data generated by a performative theory is no less circular. The Bayesian framework makes the circularity visible. Because the prior is explicit and the updating process is transparent, a critic can identify precisely where the self-fulfilling dynamic enters the analysis. Frequentist inference buries theoretical assumptions in unstated model choices and treats the resulting p-value as theory-free evidence (Gelman & Shalizi, 2013).
The Bayesian response has two parts. Prior predictive checks (simulating data from the prior and comparing them to observed patterns) expose cases in which the theory generates data it expects, triggering model criticism rather than confirmation. And the iterative cycle of fit, check, and expand treats every posterior as provisional. When posterior predictive checks reveal that the model reproduces the data too well, or in the wrong ways, the practitioner revises the model rather than declaring victory (Gelman & Shalizi, 2013).
ALC theory faces a specific version of this challenge, and the standard interventionist framing understates its severity. ALC does not merely intervene in the phenomenon it studies. It is constitutive. If ALC is a literacy, then the theory does not simply predict that competent users will coordinate better. It defines the competence itself, specifies the acquisition process, and (once applied) actively shapes the cognitive development it purports to measure. An ALC-derived training program changes how people interact with platforms. Subsequent measurement of “ALC fluency” will partly reflect the theory’s own intervention in the phenomenon.
The constitutive character of ALC creates a specific empirical design problem. The “control group” in an ALC study is not simply an untreated comparison condition. It consists of users who lack the literacy that the theory defines as necessary for coordination. Comparing literate and illiterate users does not test a mechanism in the conventional sense. It tests a pedagogy. The empirical design must therefore account for the learning curve, not just the static outcome. The relevant priors concern not only whether fluent users coordinate better than non-fluent users (a trivial prediction) but also how quickly fluency develops, through what cognitive stages, with what transfer characteristics across platforms, and at what threshold fluency begins to predict coordination outcomes that tenure alone does not explain.
The Bayesian framing makes this tractable. The prior specifies what coordination outcomes ALC predicts before intervention. The training constitutes a theoretically motivated manipulation. Posterior predictive checks assess whether the observed improvement exceeds, falls short of, or matches the theory’s specific predictions about rate, trajectory, and transfer. A theory that merely confirms itself is detectable because it generates no predictions beyond the tautological. A theory that predicts with specificity about temporal dynamics is informative even in a constitutive context.
Institutional Barriers to Ambitious Theorizing
No epistemological framework can solve an institutional problem. The barriers to ambitious theorizing in management science are sociological, not statistical.
The dominant mode of constructing research questions is gap-spotting: identifying understudied areas within existing paradigms. A review of 52 published articles found that gap-spotting dominated across paradigmatic camps, manifesting in three variants: confusion spotting, neglect spotting, and application spotting (Alvesson & Sandberg, 2011). Problematization (identifying and challenging assumptions underlying existing theories) produces more influential work but remains politically riskier because it threatens the assumptions on which prominent scholars have built careers. Gap-spotting serves a legitimate function in normal science (Kuhn, 1962). The problem is not that gap-spotting exists but that it dominates to the near-exclusion of paradigm-challenging work. When every article in the top journals must contain “theory,” and the safest way to meet that requirement is to spot a gap in an existing framework, the system selects for incremental contributions. It selects against the ambitious speculation that built the field.
Business schools themselves exhibit exactly the isomorphic processes that institutional theory predicts: coercive pressures from accreditation bodies (AACSB, EQUIS), mimetic pressures from rankings, and normative pressures from disciplinary PhD training (Wilson & McKiernan, 2011). The assumed dichotomy between basic and applied research is false. A matrix distinguishing pure basic research, pure applied research, and use-inspired fundamental research reveals a third option. Pasteur studied fermentation among French winemakers and discovered microbiology. Business schools are uniquely positioned for this kind of use-inspired basic research, producing work that is simultaneously theoretically fundamental and practically relevant. Most management research, however, pursues theoretical elegance without consideration of use (Stokes, 1997; Tushman & O’Reilly, 2007).
The publish-or-perish culture encourages questionable research practices, including HARKing (hypothesizing after results are known) and p-hacking (Tourish, 2019). Elite management journals ignore the significant problems facing humanity (Harley & Fleming, 2021).
Recent institutional developments signal partial openings. AMR’s editorial team has provided sustained guidance on theory contributions and positioning (Barney, 2018, 2020), including practical guidance on the mechanics of writing theory papers (Thatcher, Fisher, & Criado, 2021). The journal Organization Theory, launched circa 2020, is explicitly dedicated to theory development. Its inaugural issue traced how positivist scientism creates “mechanistic self-dehumanization” and called for approaches that make science more humane (Petriglieri, 2020). A 2024 Journal of Management Studies debate argued that the field over-relies on describing “how things are” at the expense of theorizing “how things should be,” creating blind spots for grand challenges, including climate change and inequality (Hanisch, 2024). Construct clarity continues to receive vigorous defense against calls for more action and less abstraction (Suddaby, 2024). The meaning of theory has been further clarified through typification (Sandberg & Alvesson, 2021), and the masculinized discourse of theory-building has received sustained critique (Cunliffe, 2022).
An honest reckoning must also confront what might be called the relocation risk of any reform. The current theory fetish demands that scholars justify their hypotheses through elaborate theoretical argumentation. A Bayesian regime relocates that demand: scholars must now justify their priors. Reviewer 2 will not stop demanding 15 pages of prose. The prose will move from the “Hypothesis Development” section to the “Prior Specification” section. The theory fetish does not disappear. It migrates. The advantage is that in the Bayesian regime, the theory must pay rent. A theoretical justification that produces no distributional commitment fails on its own terms. The debate over whether the prior is well-justified is more productive than the field currently conducts, because it forces theories to make predictions specific enough to be wrong (Gelman & Shalizi, 2013).
A fundamental tension also persists between the open science movement’s emphasis on reproducibility and pre-registration and the ambitious theorizing community’s embrace of speculation, creative leaps, and initially fuzzy constructs. Management lags behind psychology in adopting open science practices, although awareness is growing through the Responsible Research in Business and Management (RRBM) network. Pre-registered Bayesian analyses with explicit priors may resolve this tension, representing both transparency and theoretical commitment simultaneously. The prior is itself a form of pre-registration: it commits the scholar to a specific expectation before the data are collected, making the updating process publicly auditable.
Metaphor also functions as a generative device in early-stage theorizing, enabling scholars to see unfamiliar phenomena through familiar conceptual lenses (Cornelissen, 2005; Cornelissen, Oswick, Christensen, & Phillips, 2008). Bayesian formalization operates downstream of metaphor, at the point where a metaphorical insight generates specific empirical predictions. The two are sequential rather than competing. ALC’s literacy metaphor (treating algorithmic communication as analogous to historical literacy transitions) performs exactly this generative function. The Bayesian framework does not demand immediate quantification of the metaphor. It provides architecture for formalizing the empirical expectations that emerge once the metaphorical reasoning has done its work.
Implications for Introducing ALC as a Novel Coordination Mechanism
The epistemological landscape surveyed across both parts of this essay converges on several strategic insights for a dissertation introducing Application Layer Communication as a coordination mechanism alongside markets, hierarchies, and networks.
The field’s most influential theories emerged through the same process ALC follows: interdisciplinary synthesis producing a conceptual framework with testable predictions, offered to the field for subsequent empirical updating. Transaction cost economics synthesized economics, organization theory, and law: institutional theory synthesized Weber, organizational sociology, and cognitive psychology. ALC synthesizes coordination theory, literacy studies, and communication scholarship. The pattern is not merely a historical analogy. It is the documented path through which organizational science produces its most consequential knowledge.
The Bayesian framing, operating here as an epistemological orientation rather than a statistical method, clarifies what the ALC framework offers. Each of its five properties (asymmetric interpretation, intent specification, machine orchestration, implicit acquisition, stratified fluency) encodes a structured theoretical commitment about observable coordination outcomes. Asymmetric interpretation predicts that meaning determination in platform coordination will be unilateral rather than negotiated, producing specific, measurable patterns in how user inputs map to algorithmic outputs. Implicit acquisition predicts that competency development will follow learning curves rather than instruction-driven step functions, with specifiable rates and plateau characteristics. Stratified fluency predicts that differential competence levels will yield systematic coordination advantages, measurable across users with equivalent platform access.
These commitments are not mathematical priors in the technical sense. They are structured verbal predictions about direction, magnitude, conditions, and disconfirmation criteria. They occupy the space between a vague proposition (“ALC fluency is positively related to coordination”) and a fully formalized probability distribution. The epistemological argument does not require that Paper 2 produce distributional parameters. It requires that Paper 2 produce theoretical commitments specific enough to be wrong: commitments about when ALC predicts coordination improvement, how large that improvement should be, for whom it should hold, and what empirical patterns would constitute falsification. This is the standard the construct-proposition system demands (Bacharach, 1989). The Bayesian vocabulary articulates it with precision. The empirical work in Paper 3 can then test these commitments using either Bayesian or frequentist methods, because the epistemological orientation constrains the theoretical claims regardless of the statistical framework applied to the data.
The counterintuitive prediction that distinguishes ALC from competing frameworks (that general conceptual training about platform coordination should equal or exceed platform-specific procedural training) functions as a sharp commitment. Transfer of learning theory predicts the opposite: far transfer rarely occurs, and platform-specific training should outperform general training. If ALC’s prediction holds, it identifies a boundary condition for transfer theory. If transfer theory’s prediction holds, it confirms specificity requirements and identifies where ALC’s mechanisms break down. Either outcome advances knowledge. The Bayesian epistemological structure rewards this kind of specific, falsifiable theoretical commitment by treating bold predictions as more informative than hedged ones.
The problematization framework specifies the positioning strategy (Alvesson & Sandberg, 2011). The most powerful contribution challenges an underlying assumption rather than filling a gap. ALC challenges the assumption that coordination mechanisms operate through structural adaptation: market design, hierarchical authority, network ties, and platform governance features. The alternative is that coordination operates through literacy acquisition in communication systems. The structural features of platforms create the conditions for coordination. Still, the coordination itself occurs through communicative competence that users develop, or fail to develop, through sustained interaction with algorithmically mediated systems. The assumption being challenged sits at the center of how coordination theory conceptualizes the relationship between organizational structure and organizational outcomes.
Construct clarity requires precise definition across four elements: clear definitions, explicit scope conditions, semantic relationships to existing constructs, and logical consistency (Suddaby, 2010). ALC must specify how it relates to existing coordination mechanisms, including plans, routines, roles, proximity, and objects (Okhuysen & Bechky, 2009). It must articulate when, where, and for whom it operates (Johns, 2006; Busse, Kach, & Wagner, 2017). And it must anticipate the lifecycle dynamics in which new constructs face initial championing by “umbrella advocates” followed by scrutiny from “validity police” (Hirsch & Levin, 1999). The construct must be defined clearly enough to survive scrutiny while remaining broad enough to capture the phenomenon’s full scope.
The three-paper dissertation format provides structural advantages for ambitious theorizing. Paper 1 establishes through a literature review why existing coordination mechanisms cannot explain the variance puzzle (users with equivalent platform access producing dramatically different coordination outcomes). Paper 2 develops the ALC framework as a conceptual contribution in the tradition of the field’s most influential works, offering structured theoretical commitments that subsequent work can update (DiMaggio & Powell, 1983; Barney, 1991; Williamson, 1975). Paper 3 provides initial empirical validation through psychometric development, longitudinal testing, and qualitative process validation. The format mirrors the historical trajectory of the field’s most influential theories: speculation first, then empirical engagement, with the conceptual architecture bearing the primary weight. The dissertation committee functions as a review body that can evaluate conceptual architecture on its own merits before empirical validation follows, providing structural protection for exactly the kind of speculative theorizing that a single-paper submission to AMR would not receive.
The field in 2025 is more receptive to novel theoretical frameworks than at any point since the 1970s. Phenomenon-driven research has institutional support through the Academy of Management Discoveries and multiple editorial mandates. Methodological pluralism has gained ground through proposals for theoretical triangulation across propositional, narrative, and computational grammars (Cornelissen, 2023). AI-assisted theorizing has opened new possibilities for exploratory work (Tranchero, Uetake, & Kominers, 2024). And the persistent inability of existing theories to explain coordination outcomes on digital platforms, where identical structural features produce wildly different results, creates exactly the kind of empirical puzzle that the field has historically rewarded with paradigm-level theoretical innovation.
The war over what counts as knowledge in organizational science remains unresolved. That irresolution is not a weakness. It is the condition that makes new theoretical contributions possible. The field does not require different theoretical quantities. It needs different theorizing: more transparent in its assumptions, more ambitious in its scope, more honest about its performative and constitutive effects, and more formally connected to cumulative knowledge-building. ALC enters a field primed to receive exactly this kind of work. It generates specific falsifiable predictions about coordination variance. It challenges a foundational assumption about the mechanism through which coordination operates. It connects to observed phenomena across multiple platform contexts. And it does what the field’s foundational theories did: speculate with discipline, commit with precision, and offer the result for collective updating.
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