An update to Duncan’s theory: uncertainty as a function of complexity, environmental turbulence, and managerial approach to decision-making

An update to Duncan’s theory: uncertainty as a function of complexity, environmental turbulence, and managerial approach to decision-making | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An update to Duncan’s theory: uncertainty as a function of complexity, environmental turbulence, and managerial approach to decision-making Sasanka Sekhar Chanda This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5207078/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract In a seminal paper, Duncan found that managers feel higher degree of uncertainty in a low-complexity, turbulent (simple-dynamic) environment, compared to the extent of uncertainty they feel in a complex and low turbulence (complex-static) environment. Using a computational simulation model, I find that Duncan’s proposition holds good for longer duration tasks, but reverses for shorter duration tasks. The simulation results further suggest that switching from maximizing to a satisficing approach is helpful in lowering uncertainty. This research has the potential to pave the way for resolving the deadlock between perceptual and objective measures of uncertainty. Thereby, it enables giving credit to managers where due, e.g. when an organizational outcome is obtained while facing higher levels of uncertainty. The research also highlights relative efficacies of strategies for lowering uncertainty. Complexity computational simulation decision dynamism maximizing satisficing turbulence uncertainty Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Accelerating change, dynamic markets, new technology, shorter product lifecycles, digital information revolution, decentralization, globalization and environmental decay are part of everyday life in the world of business organizations (Halal and Taylor, 1999 ; McKelvey, 2004 ; Trevor-Roberts et al., 2019 ). There is renewed interest among scholars regarding constructs of environmental dynamism, like stability-instability or turbulence , complexity , uncertainty , and so forth. Yet, in the extant literature, the measurement of dimensions of environmental dynamism have followed two separate paths with little in common between them (Boyd et al., 1993 ). One stream of research focuses on objective measures of environmental dynamism . Industry characteristics (or archival data) are studied to measure environmental dynamism along the dimensions munificence-hostility , stability-instability , complexity and competitive intensity (Aldrich, 1979 ; Dess and Beard, 1984 ) . A second stream deals with perceptual measures of environmental uncertainty , anchored in Milliken’s characterization of environmental uncertainty as distinguished in terms of state , effect and response uncertainty (Milliken, 1987 ). Measurement of perceptual uncertainty entails interviewing managers regarding their perceptions. Proponents of the objective measures of environmental dynamism criticize the perceptual approach on ground of lack of objectivity, because data on managerial perception is situated, contextual, and prone to be bias-ridden (Boyd et al., 1993 ). Proponents of perceptual measures contend that what matters in organizational decision-making is what goes on in the minds of the managers who are making important organizational decisions, so-called objective measures count for less (Anderson and Paine, 1975 ; Hambrick and Snow, 1977 ; Miller, 1988 referred in Boyd et al., 1993 ). For example, objective measures computed from industry data fail to reflect the fact that a manager at Intel (a company having 80% + market share in microprocessors) would perceive much less uncertainty than a manager at AMD (a company having less than 10% market share in microprocessors). The competing research streams churn out significant research supporting their respective positions. In this intellectual debate, however, an important goal of research is overlooked: what is the guidance to practicing managers from academia? Should managers’ actions disregard perceptions of uncertainty altogether and be solely based on objective characteristics of environmental dynamism measured from industry data? Or, does academia recommend that the applicable levels of uncertainty—based on study of managers’ own perceptions of uncertainty—ought to be factored in, for devising courses of actions? Or, perhaps, could there be a third approach, where constituencies of interest of both perspectives are given reasonable consideration? In this study I set out to find this middle-ground. Resolution of above questions is important for practicing managers. Managers are judged based on actions they take to run organizations as well as the results their actions obtain for organizations. Environmental turbulence upsets plans, thwarting measurement based on accomplishment on pre-agreed goals. Environmental turbulence, in conjunction with complexity, also tests managers’ abilities in steering an organization in trying circumstances. It is imperative that managers get a fair evaluation. A competent manager may obtain somewhat ordinary organizational performance, after battling a high degree of environmental dynamism. Such a manager should be recognized and rewarded more than, say, a manager who obtains better organizational outcomes simply because the environment happened to be less uncertain. Thus, the level of uncertainty overcome to deliver organizational outcomes is a reasonable indicator of the extent of difficulty countenanced by managers in performing their jobs. Higher the uncertainty, greater is the value of a given level of organizational performance attained. This calls for more research in the dynamism-uncertainty link. In order to get around the fruitless debate as to whether objective or perceptual measures are more representative of the managerial situation in organizational life, a fresh approach is necessary, one that links uncertainty to measures of environmental dynamism. Indeed, Duncan ( 1972 ) published a study relating environmental variability (stability-instability or turbulence) and complexity with uncertainty. He defined complexity as the number of relevant tasks that must be correctly executed, in order to stand a chance of obtaining success. We note that this characterization incorporates the fact that a sophisticated organization—having well developed systems and processes and cushion of finances—may find some tasks less complex, compared to an organization with less resources. Hence this characterization addresses an important complaint of the proponents of perceptual measures of uncertainty—that managers of all companies in an industry do not necessarily face the same extent of uncertainty. Duncan ( 1972 ) suggests that managers operating in a turbulent, low-complexity environment perceive a higher level of uncertainty than managers operating in a higher complexity, stable environment. Duncan drew above conclusion even though his data did not show statistical significance; rather, analysis of variance strongly indicated the relation. Duncan’s seminal work inspires my research. I posit that it may be possible to get sharper results in the uncertainty-dynamism link by considering two additional contingencies: time available for completing decision task (short vs. long duration), and managerial decision-making style (satisficing vs. maximizing). I consider task duration as a relevant contingency because longer exposure to environmental turbulence erodes organizational knowledge more, creating uncertainties in managers’ minds as to whether they are keeping up with the extent of learning necessary. In contrast, availability of more time is likely to allow mastering a higher degree of complexity, something that is less feasible when less time is available. Moreover, a satisficing approach calls for lesser cognitive resources compared to a maximizing approach, and hence is likely to impact uncertainty experienced by decision-makers. I invoke the computational simulation model proposed by March ( 1991 )—and enhanced by Chanda ( 2017 ) and Chanda and Miller ( 2019 )—to compute uncertainty as a function of complexity and environmental turbulence, taking into account maximizing or satisficing managerial approach to decision-making. I find that Duncan’s conjecture is upheld for strategic tasks of longer duration, and reversed for shorter duration tasks. Further, the model informs on relative suitability of strategies for uncertainty reduction under stylized conditions of environmental dynamism. This research makes three contributions. Firstly, it creates a bridge between perceptual and objective measures enabling researchers discard tribalistic attachment to one camp or the other, thereby clearing the path to find superior truths. Second, it highlights relative efficacies of strategies for lowering uncertainty. Third, it addresses the interrelation between V ( Variability or turbulence ), U ( Uncertainty ), C ( Complexity ) of V-U-C-A, an important concern in current times. 2. Theoretical foundations 2.1. Objective measures of environmental dynamism Research dealing with the (so-called) objective measures of the task environment consider the following dimensions of the dynamic environment (Dess and Beard, 1984 and research citing them, drawing from Aldrich, 1979 ): munificence-hostility, stability-instability, and complexity. Munificence or capacity relates to the ease of doing business. Stability-instability is measured by intermediate market orientation comprising the proportion of industry shipments sold to intermediate markets or as investments. Dess and Beard ( 1984 ) measure complexity by specialization ratio [Ratio of primary product shipments to total {primary and secondary, excluding miscellaneous} product shipments for the establishments classified in the industry] and geographical concentration of (i) total sales (ii) value added by manufacture (iii) total employment (iv) industry establishments. Consideration of industry characteristics for these measures confers high integrity to the data. A downside is that if all managers in an industry are deemed to be countenancing the same level of dynamism—as derived from industry data—there is injustice to managers of some businesses and unwarranted leniency to managers of others, as highlighted by the Intel-AMD example. 2.2. Subjective (perceptual) measures of uncertainty Milliken ( 1987 ) suggests that state uncertainty arises from a low level of appreciation regarding the state of the environment, specifically with regard to which all elements should managers consider as relevant and understand in detail. Once state uncertainty is resolved, effect uncertainty comes in play. This uncertainty is with regard to the effect of the identified environmental state on the organization. After effect uncertainty is resolved, response uncertainty becomes salient. Response uncertainty pertains to indeterminacy regarding what organizational responses are appropriate. In order to assess each kind of uncertainty, managers are surveyed for their perceptions. An upside of this approach is that actual decision-makers are given a say regarding the nature of uncertainty experienced. However, if managers’ self-reported accounts constitute the only source of data to measure uncertainty, inflated accounts of uncertainty may come in the way of a proper assessment. 2.3. Bridging the divide In order to find a way out of the impasse, I consider it productive to look into the sources of uncertainty. Uncertainty definitely is a matter of perception. Uncertainty is salient at the time of commencement of a decision-task that will bear fruit in the immediate term or longer-term future. I observe that Dess and Beard’s objective measure of complexity does not incorporate any a-priori attribute specific to the task facing given organization; rather it incorporates parameters that are known post-facto (i.e., after the result of a prior decision is known) e.g., concentration ratios. The implicit premise—that past occurrences in the industry constitute important arbiters of uncertainty countenanced by all mangers in all companies—is questionable. There is a case for framing the complexity parameter not in terms of post-facto values pertaining to the entire industry (for instance concentration ratio), but rather with respect to an individual business’s a-priori situation. This is what Duncan ( 1972 ) did, in his study. A changing environment partly outdates organizational knowledge accessible to managers (March 1991 ). This makes managers wary about putting faith in the efficacy of administrative measures previously known to have been successful. Uncertainty stems from apprehension of failure in noticing an environmental change and /or omission in taking corrective action to address the changed situation. Moreover, when complexity (given by the minimum number of relevant tasks that must be correctly executed, in order to succeed with a decision) increases, cognizing which specific tasks make into the must-do list require more effort. Anxiety about misreading the new dimensions where effort is necessary, contributes to uncertainty. When both complexity and instability are present, environmental change may alter a dimension of minimum requirement necessary for crossing the complexity barrier. Unknown to managers, a must-have may become non-significant (meriting lower effort); alternately, an earlier good-to-have dimension may come into the list of dimensions that must be correctly fashioned (meriting greater attention, provided it is noticed in a timely manner). Further, higher complexity may be difficult to cognize and attend to, when environmental change itself is monopolizing a major chunk of managerial attention. Taking above factors into consideration, and aligned to Duncan ( 1972 ), I advocate discarding of the mapping of the complexity parameter in terms of prior output values pertaining to the entire industry (for instance concentration ratios). Moreover, given the deadlocked discourse on the dynamism-uncertainty link, taking recourse to a computational simulation model is ideal, such that the assumptions leading to particular outcomes become clearer (Adner et al. 2009 ; Boisot and McKelvey 2010 ; Harrison et al. 2007 ). 2.4. Some further considerations The extent of time available for making a decision has a bearing on the level of uncertainty experienced by managers. Blandin and Brown (1997) and Boyd and Fulk ( 1996 ) site length of time for feedback as one of the three major factors contributing to perceived uncertainty. Besides, Ancona et al. ( 2001 ) also suggest making time an important variable in theory development. The length of window available for making a decision is important because (a) managers are often required to make decisions within a limited time-window that an opportunity is available for action and (b) under conditions of uncertainty managers are unsure whether relevant information can be obtained from limited or extensive search efforts and (c) a dynamic environment may erode knowledge that helps making judgments regarding the salience of information (Chanda and Ray, Forthcoming ). Hence, I consider two archetypes regarding time —short-duration and longer-duration decision-making. Managerial decision-making style—satisficing vs. maximizing (Heshmat, 2015 ; Simon 1957 , 1959 ; Vergová et al., 2023)—also contributes to the level of uncertainty experienced. A strategic decision can be perceived as either an opportunity or threat depending on how information is processed (Schwarz et al., 2020 ). A maximizing tendency pertains to striving to select the best option. In an uncertain situation, options that are available are not clear (Knight 1921 ). Hence striving for the best will contribute to uncertainty whether the best option can indeed be found. A satisficing approach constitutes the willingness to settle for a sufficient (“good enough”) or “fairly good” option. Mangers can make decisions more confidently regarding whether a chosen recourse is “good enough” or “fairly good” for a particular purpose, compared to a situation where they are called upon to judge whether a recourse is the best one (Simon, 1957 ). Thus, the preferred decision-making style of top management—satisficing vs. maximizing—is likely to have an effect on the extent of uncertainty experienced by company managers. Hence, I include these archetypes in the computational simulation model. Lastly, given that participation and interaction have a big role to play in countenancing uncertainty (Schwarz et al., 2020 ), I invoke the multi-agent, genetic algorithm model from the seminal work by March ( 1991 ), illustrating exploration and exploitation through an organizational learning perspective. 3. Computational simulation model In the simulation model, the environment or external reality R is an M -bit string. At the beginning of simulation, each bit of R is given a random value from the set S1 = {-1, + 1}, with 50 percent probability for occurrence of any given value. A stable environment is motivated by having the values in R the same throughout T time steps of a simulation experiment. Environmental turbulence is modeled by assigning a non-zero probability to each bit of R flipping its value (from + 1 to -1 and vice-versa) in any given time step. This probability is stored in a parameter p 4 . 3.1. The organization An organization comprises of N members, and one central entity, the organizational code OC (that functions as a repository of organizational knowledge ). Each member is represented by means of an M -bit belief string. At the beginning of any simulation experiment, belief-strings of all members are populated by randomly assigning values from the set S2 = {-1, 0, + 1}, with one-third probability for occurrence of any given value. The organizational code OC is also an M -bit string. At the start of a simulation experiment, all bit positions of OC are given a value “0”. A value of “0” in a belief dimension of a member or OC signifies neutral belief or “no opinion” (March, 1991 ). 3.2. Learning processes The organizational code is endowed with an ability to identify members who are more knowledgeable about R than itself. In each time step, the organizational code consults those members, to elicit their recommendation regarding what value ought to be assigned to a given bit-position of OC . If the recommendation for a non-zero value is obtained by a majority of K , the organizational code updates its value to the recommended value with probability [1 – (1 – p 2 ) K ], where p 2 (0 < p 2 < 1) is a measure of the code’s learning rate, whose value is set exogenously. Moreover, in each time-step, each member of the organization learns from OC at a rate p 1 (0 < p 1 < 1). For any given bit, if a member finds the value in OC to be non-zero, the member updates the value in his/her bit by the value in the corresponding bit of OC with a probability p 1 . 3.3. Exploration and exploitation In the orthogonal conception of exploitation and exploration, p 1 (0 < p 1 < 1) is a measure of the rate of exploitation (Chanda and Ray, 2015 ; Chanda, 2017 ; March, 1991 ), given that higher the value of p 1 , greater is the erosion of diversity of knowledge. Exploration is motivated by allowing inflow of heterogeneous knowledge from outside the organization at a rate given by p 3 (0 < p 3 < 1), given that higher p 3 entails higher diversity of knowledge coming into the organization. The parameter p 3 represents the probability that any given member’s belief string gets replaced by a string fashioned by random draws from the set S2 —with one-third probability for materialization of any value of S2 —in any time-step of a simulation experiment. 3.4. Measurement of uncertainty Managers of the organization are given the ability to set the rates of exploration ( p 3 ) and exploitation ( p 1 ). Simulation experiments are run, to observe an organizational outcome—the probability of organizational success (Chanda, 2017 )—in the entire state space constituted by variation of p 1 and p 3 from zero to one, in steps of 0.05, comprising 441 (21 X 21) cells in all. Uncertainty is given by the fraction of cells where organizational outcome is below a mandated level. Thus, I define uncertainty as follows: If N levers for action are available to managers, out of which only m yield desired outcomes—and managers are not sure which are those m desirable options—uncertainty is given by ( N – m ) / N . Alternately: Uncertainty ( U ) = 1 – (Number of exploration-exploitation combinations that qualify as having accomplished organizational objectives / Total number of exploration-exploitation combinations) The mandated value of organizational outcome is a function of the probability of organizational success . The probability of organizational success is computed by using a complexity parameter ( C ) to assess the extent of match between the reality R and the organizational code OC . At the conclusion of the last time-step of a simulation experiment, C * M bit positions are randomly selected a large number of times ( L ). A pay-off of one unit is assigned if all the values in the selected bit positions of OC match the values in the corresponding positions of R . Otherwise the payoff assigned is zero. The proportion of exact matches—i.e., the sum of pay-offs divided by L —constitutes the probability of organizational success. Lastly, an organization exhibits maximizing behavior when top management mandates that the probability of obtaining organizational success has to be within one percent of the maximum attainable. Satisficing behavior is associated with a mandate that the probability of obtaining organizational success is within five percent of the maximum attainable. 3.5. Model Parameters The parameter values used largely draw from March ( 1991 ). The number of agents is 50 (N = 50). The external reality R has 30 bits ( M = 30). The parameter representing the code’s learning rate ( p 2 ) is given a value of 0.50. For determining the probability of organizational success, L = 500 trials are carried out. Short and long duration tasks are motivated by setting a parameter, T , to 20 and 100, respectively. The complexity dimension uses five and ten percent values for characterization as simple and complex , respectively. The static and turbulent conditions are implemented by setting the turbulence parameter ( p 4 ) to zero and two percent, respectively. I list the parameter values in Table 1 , along with the values used for robustness checks. Table 1 Model Parameters and Robustness Checks Parameter Value Robustness Check Number of dimensions of R M = 30 M = 25 and M = 35 Number of agents N = 50 N = 40 and N = 60 Learning rate of the code p 2 = 0.50 p 2 = 0.40 and p 2 = 0.60 Rate of environmental turbulence p 4 = 0.02 p 4 = 0.01 Number of trials for determining probability of organizational success L = 500 L = 1000 Exploration ( p 3 ) and exploitation ( p 1 ) 0 to 1, in steps of 0.05 N/A [Figures referred in the text are provided in the next page] >>>>>>>>>>> Insert Table 1 about here <<<<<<<<<< 4. Results Following March ( 1991 ), a turbulent environment is fashioned by setting the parameter p 4 to a value of 0.02, and a stable environment is fashioned by setting the parameter p 4 to a value zero. The simulation experiments are carried out for T = 20 time-steps and for T = 100 time-steps, respectively, in order to obtain results for short duration and long duration tasks. The graphs under Fig. 1 and Fig. 2 provide information regarding model behavior. The subsequent graphs display the main findings. >>Insert Fig. 1 A and 1 B about here << 4.1. Model descriptive characteristics Given a satisficing approach—where top management considers reaching 95% of the maximum outcome attainable as acceptable—the variation of uncertainty with complexity is shown for a stable environment ( p 4 = 0) in Fig. 1 A and for a turbulent environment ( p 4 = 0.02) in Fig. 1 B. Figure 1 A suggests that, in a stable environment, the extent of uncertainty is comparable, for short and long duration decision tasks. Figure 1 B highlights that, in a turbulent environment, uncertainty is much higher as far as longer duration tasks are concerned. >>>>Insert Fig. 2 A and 2 B about here <<<< For the maximizing approach (where top management mandates reaching 99% of the maximum outcome attainable as necessary for success) the variation of uncertainty with complexity for a stable environment ( p 4 = 0) is shown in Fig. 2 A and that for a turbulent environment ( p 4 = 0.02) is shown in Fig. 2 B. We observe that task duration does not appear to make a big difference in the level of uncertainty involved. However, onset of turbulence increases uncertainty (as seen from higher y-values in Fig. 2 B). Moreover, comparing the y-axis values in Fig. 2 with those from Fig. 1 , we note that the level of uncertainty is markedly higher, under the maximizing approach. >>>>Insert Fig. 3 about here <<<< 4.2. Comparison with the results from Duncan ( 1972 ) Duncan considered four environmental archetypes: simple-static, complex-static, simple-dynamic and complex-dynamic. I use five and ten percent values for complexity for simple and complex environment, respectively. For static and dynamic (turbulent) environments, the turbulence parameter ( p 4 ) is set to zero and two percent, respectively. The key finding in Duncan ( 1972 ) is that managers feel higher degree of uncertainty in a simple-dynamic environment, compared to the extent of uncertainty they feel in a complex-static environment. Accordingly, I plot the difference in uncertainty values between simple-dynamic and complex-static environments in the vertical axis of Fig. 3 , allowing task duration to vary on the horizontal axis. We observe that Duncan’s conjecture holds good where the values in the y-axis are positive, i.e., for T ≥ 50. For shorter duration tasks, the values in the y-axis are negative, i.e., Duncan’s results are reversed. Thus: Proposition P1 : For short duration decision tasks, the uncertainty experienced by managers is higher for complex-static environments, compared to the uncertainty experienced in simple-dynamic environments . Proposition P2 : For longer-duration decision tasks, the uncertainty experienced by managers is lower for complex-static environments, compared to the uncertainty experienced in simple-dynamic environments . >>>>Insert Fig. 4 about here <<<< 4.3. Benefit of switching from maximizing to satisficing approach I compute a metric—benefit of switching from maximizing to satisficing approach—by the ratio, reduction in uncertainty / reduction in probability of success. Higher the value of this metric, more desirable it is, for an organization, to implement the switchover. In Fig. 4 I present graphs comparing benefits of switching from a maximizing to a satisficing approach, for all four archetypes considered by Duncan ( 1972 ), for tasks of varying duration. The greatest benefits accrue for the shortest task duration ( T = 10). Thus: Proposition P3 : Switching from a maximizing approach to a satisficing approach is beneficial to organizations, and the greatest extent of benefits accrue for short-duration tasks . 5. Discussion This research enhances Duncan’s theory by demonstrating the important role of task duration and of the approach to decision-making by managers (concerning satisficing / maximizing behavior). Duncan’s results are seen to hold for longer-duration tasks and reverse for shorter-duration tasks. Further, the findings show that significant benefits can be obtained by espousing a satisficing approach in lieu of a maximizing approach, and particularly so for shorter-duration managerial decision tasks. Moreover, for longer duration decision tasks, preferring satisficing over maximizing is desirable when environmental turbulence is high. 5.1. Limitations of the study First, the model of uncertainty largely conforms to the description of response uncertainty (Milliken, 1987 ). State uncertainty and effect uncertainty have been kept out of scope. Besides, managerial routines and heuristics (e.g., falling back on organizational identity—please see Whetten 2006 ) may influence how uncertainty is handled. Moreover, certain other factors that could influence the extent of uncertainty experienced by the managers—say, internal turbulence in the organization (originating in factors like change in senior management, labor unrest, etc.) or on account of volatility in the regulatory environment (particularly where rule-making struggles to keep up with change in technology and/or preferences of members of society)—are presently out of scope of the study. We note here that change in the external environment could engender higher uncertainty than changes in the internal environment (Duncan, 1972 ; Jones, 2010 , Schwarz et al., 2020 ). Lastly, I use one particular definition of complexity following Duncan ( 1972 ); other definitions exist (Chanda, 2017 ; Shannon, 1948 ) and are presently out of scope. 5.2. Implications The streams of research referring to objective vs. perceptual measures need no longer be pitted against each other, since it is now possible to assess uncertainty from the stability-instability and complexity dimensions, given managerial orientation towards the satisficing or maximizing decision-making approach. For this to materialize though, it is necessary that the complexity parameter is not measured in terms of output values relative to entire industry (for instance by concentration ratios), but rather with respect to an input parameter relevant to a business’s a-priori situation, as suggested by Duncan ( 1972 ). We note that, aligned with theorization by Duncan ( 1972 ), the simple-dynamic case indeed engenders higher uncertainty compared to the complex-static case, for decision tasks of longer duration. Moreover, the complex-static case engenders higher uncertainty compared to the simple-dynamic case, for decision tasks of shorter duration. Theoretical confirmation of this nature helps fixing responsibility and helps prevent managers being judged inappropriately. 5.3. Directions for further research March’s versatile simulation model can be invoked to compare how uncertainty varies when exploration-exploitation tasks are separated spatially or temporally. It can enrich the attention-based-view by comparing uncertainty experienced under varying conditions of executive attention and attentional vigilance (Ocasio et al., 2020 ). The model may be extended to study uncertainty regarding fiscal, regulatory, or monetary issues (Jiang et al., 2023 ) in terms of complexity and dynamism or unpredictability in policy. 5.4. Implications for practice Mangers deserve more credit when higher extent of uncertainty is overcome in delivering on a strategic objective. Measuring uncertainty by interviewing managers may be ridden with biases. However, assessing uncertainty based on industry-wide parameters for demand variability and concentration ratios is also flawed. For example, managers of well-endowed firms face much lower uncertainty than firms having scarce resources. Therefore, I enhance Duncan’s method of measuring complexity from firm-specific parameters, incorporating the role of task duration and managerial approach to decision-making, satisficing or maximizing. I also demonstrate that switching from maximizing to satisficing reduces uncertainty, and particularly so for shorter duration tasks. 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J Manage Inq 15:219–234 Footnotes Turbulence (March, 1991 ) or stability-instability (Dess and Beard, 1984 ) denotes extent of unpredictable environmental changes. Environmental complexity refers to the number of relevant tasks that must be correctly executed, in order to stand a chance of obtaining success Duncan ( 1972 ) or to the heterogeneity and range of environmental activities which are relevant to an organization’s operations (Child, 1972 ). True or Knightian uncertainty refers to situations where options facing a decision-maker are not clear, let alone probabilities of materialization and related payoff distributions (Knight, 1921 ). Munificence-hostility refers to ease of doing business. Stability-instability refers to demand variability. Complexity refers to concentration ratios. Competitive intensity was dropped by Dess and Beard ( 1984 ) as an environmental characteristic. The “A” in V-U-C-A is ambiguity . In layman terms, ambiguity connotes multiple feasible interpretations of a single signal. Ambiguity is addressed by using redundant bits of information. For example, when one says “Charlie Delta” instead of “C-D”, the characters are clearly distinguished from similar sounding alphabets. Likewise, in the simulation model, I invoke a complexity parameter to assess the probability of success of an organization based on the compatibility of its own organizational knowledge with various probable configurations of salience with corresponding segments of the external reality. Rules, norms, forms, routines etc., present in databases, manuals, standard operating procedures comprise organizational knowledge. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 14 Oct, 2025 Reviews received at journal 30 Sep, 2025 Reviewers agreed at journal 31 Aug, 2025 Reviewers invited by journal 18 Oct, 2024 Editor assigned by journal 05 Oct, 2024 Submission checks completed at journal 05 Oct, 2024 First submitted to journal 05 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5207078","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":529591515,"identity":"d711ea9c-066e-474e-a0a8-d5168b45280c","order_by":0,"name":"Sasanka Sekhar Chanda","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYNCCAwxybMzMBx8AmTx8xGox5mdnSzYAaWEjVkvizH4eMwkQm6AW+fbjDz8XnLFj3HCYwazya46dDBsD88NHN/BoMTiTYyw940Yys8FhhrTbstuSgQ5jMzbOwaeFIYdBmucDMxtQy7HbktuYgVp42KTxaZHvf/74N8+Heh6Dw4xtxZLb6glrYbiRYCbNc+OwhGQzMxvjx22HCWsxuPHGzJrnzHEDfmY2ZmnGbcd52JgJ+EW+P/3xbZ5j1fVt/Oc/fvy5rdqen7354WO8DkMGzDxgkljlIMD4gxTVo2AUjIJRMGIAAJbhRMct4yNOAAAAAElFTkSuQmCC","orcid":"","institution":"Indian Institute of Management Indore","correspondingAuthor":true,"prefix":"","firstName":"Sasanka","middleName":"Sekhar","lastName":"Chanda","suffix":""}],"badges":[],"createdAt":"2024-10-05 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1","display":"","copyAsset":false,"role":"figure","size":70186,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e1A\u003c/strong\u003e. Uncertainty vs. Complexity, Stable Environment, Satisficing approach\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1B\u003c/strong\u003e. Uncertainty vs. Complexity, Turbulent Environment, Satisficing approach\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5207078/v1/f8a9a9d734a42e46b9c4ffa8.jpg"},{"id":94888665,"identity":"64695375-b87c-4d6f-b4ec-a802e30ef3e8","added_by":"auto","created_at":"2025-10-31 19:29:31","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":68526,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e2A\u003c/strong\u003e. Uncertainty vs. Complexity, Stable Environment, Maximizing approach\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2B\u003c/strong\u003e. Uncertainty vs. Complexity, Turbulent Environment, Maximizing approach\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5207078/v1/cbed686a0b58119e214b2791.jpg"},{"id":94986492,"identity":"e2690437-e4fb-4302-b7d5-43c6baec9fd7","added_by":"auto","created_at":"2025-11-03 07:00:22","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":18747,"visible":true,"origin":"","legend":"\u003cp\u003eDifference in uncertainty (simple, dynamic) and (complex, stable) environments\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5207078/v1/ef692bbaca7406e3903d2771.jpg"},{"id":94987209,"identity":"3839ccd9-6a7f-414d-92dd-c0c6bada9ad9","added_by":"auto","created_at":"2025-11-03 07:01:28","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":27715,"visible":true,"origin":"","legend":"\u003cp\u003eBenefit of switching to satisficing\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5207078/v1/e119f3d459ed787f6b97ed82.jpg"},{"id":94990496,"identity":"cd944d8b-85f6-4ecf-8c51-6fb9972f0800","added_by":"auto","created_at":"2025-11-03 07:17:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1023976,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5207078/v1/bcf33329-36d3-4d15-a628-f81457071f3e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An update to Duncan’s theory: uncertainty as a function of complexity, environmental turbulence, and managerial approach to decision-making","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAccelerating change, dynamic markets, new technology, shorter product lifecycles, digital information revolution, decentralization, globalization and environmental decay are part of everyday life in the world of business organizations (Halal and Taylor, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; McKelvey, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Trevor-Roberts et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). There is renewed interest among scholars regarding constructs of environmental dynamism, like stability-instability or \u003cem\u003eturbulence\u003c/em\u003e\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e, \u003cem\u003ecomplexity\u003c/em\u003e\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e, \u003cem\u003euncertainty\u003c/em\u003e\u003ca class=\"FNLink\" href=\"#Fn3\" id=\"#FNLinkFn3\"\u003e\u003c/a\u003e, and so forth. Yet, in the extant literature, the measurement of dimensions of environmental dynamism have followed two separate paths with little in common between them (Boyd et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). One stream of research focuses on \u003cem\u003eobjective measures of environmental dynamism\u003c/em\u003e. Industry characteristics (or archival data) are studied to measure environmental dynamism along the dimensions \u003cem\u003emunificence-hostility\u003c/em\u003e, \u003cem\u003estability-instability\u003c/em\u003e, \u003cem\u003ecomplexity\u003c/em\u003e and \u003cem\u003ecompetitive intensity\u003c/em\u003e (Aldrich, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1979\u003c/span\u003e; Dess and Beard, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1984\u003c/span\u003e)\u003ca class=\"FNLink\" href=\"#Fn4\" id=\"#FNLinkFn4\"\u003e\u003c/a\u003e. A second stream deals with \u003cem\u003eperceptual measures of environmental uncertainty\u003c/em\u003e, anchored in Milliken\u0026rsquo;s characterization of environmental uncertainty as distinguished in terms of \u003cem\u003estate\u003c/em\u003e, \u003cem\u003eeffect\u003c/em\u003e and \u003cem\u003eresponse\u003c/em\u003e uncertainty (Milliken, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). Measurement of perceptual uncertainty entails interviewing managers regarding their perceptions.\u003c/p\u003e\u003cp\u003eProponents of the objective measures of environmental dynamism criticize the perceptual approach on ground of lack of objectivity, because data on managerial perception is situated, contextual, and prone to be bias-ridden (Boyd et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Proponents of perceptual measures contend that what matters in organizational decision-making is what goes on in the minds of the managers who are making important organizational decisions, so-called objective measures count for less (Anderson and Paine, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Hambrick and Snow, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; Miller, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1988\u003c/span\u003e referred in Boyd et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). For example, objective measures computed from industry data fail to reflect the fact that a manager at Intel (a company having 80% + market share in microprocessors) would perceive much less uncertainty than a manager at AMD (a company having less than 10% market share in microprocessors). The competing research streams churn out significant research supporting their respective positions. In this intellectual debate, however, an important goal of research is overlooked: what is the guidance to practicing managers from academia? Should managers\u0026rsquo; actions disregard perceptions of uncertainty altogether and be solely based on objective characteristics of environmental dynamism measured from industry data? Or, does academia recommend that the applicable levels of uncertainty\u0026mdash;based on study of managers\u0026rsquo; own perceptions of uncertainty\u0026mdash;ought to be factored in, for devising courses of actions? Or, perhaps, could there be a third approach, where constituencies of interest of both perspectives are given reasonable consideration? In this study I set out to find this middle-ground.\u003c/p\u003e\u003cp\u003eResolution of above questions is important for practicing managers. Managers are judged based on actions they take to run organizations as well as the results their actions obtain for organizations. Environmental turbulence upsets plans, thwarting measurement based on accomplishment on pre-agreed goals. Environmental turbulence, in conjunction with complexity, also tests managers\u0026rsquo; abilities in steering an organization in trying circumstances. It is imperative that managers get a fair evaluation. A competent manager may obtain somewhat ordinary organizational performance, after battling a high degree of environmental dynamism. Such a manager should be recognized and rewarded more than, say, a manager who obtains better organizational outcomes simply because the environment happened to be less uncertain. Thus, the level of uncertainty overcome to deliver organizational outcomes is a reasonable indicator of the extent of difficulty countenanced by managers in performing their jobs. Higher the uncertainty, greater is the value of a given level of organizational performance attained. This calls for more research in the dynamism-uncertainty link.\u003c/p\u003e\u003cp\u003eIn order to get around the fruitless debate as to whether objective or perceptual measures are more representative of the managerial situation in organizational life, a fresh approach is necessary, one that links uncertainty to measures of environmental dynamism. Indeed, Duncan (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1972\u003c/span\u003e) published a study relating environmental variability (stability-instability or turbulence) and complexity with uncertainty. He defined \u003cem\u003ecomplexity\u003c/em\u003e as the number of relevant tasks that must be correctly executed, in order to stand a chance of obtaining success. We note that this characterization incorporates the fact that a sophisticated organization\u0026mdash;having well developed systems and processes and cushion of finances\u0026mdash;may find some tasks less complex, compared to an organization with less resources. Hence this characterization addresses an important complaint of the proponents of perceptual measures of uncertainty\u0026mdash;that managers of all companies in an industry do not necessarily face the same extent of uncertainty. Duncan (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1972\u003c/span\u003e) suggests that managers operating in a turbulent, low-complexity environment perceive a higher level of uncertainty than managers operating in a higher complexity, stable environment. Duncan drew above conclusion even though his data did not show statistical significance; rather, analysis of variance strongly indicated the relation. Duncan\u0026rsquo;s seminal work inspires my research. I posit that it may be possible to get sharper results in the uncertainty-dynamism link by considering two additional contingencies: time available for completing decision task (short vs. long duration), and managerial decision-making style (satisficing vs. maximizing). I consider task duration as a relevant contingency because longer exposure to environmental turbulence erodes organizational knowledge more, creating uncertainties in managers\u0026rsquo; minds as to whether they are keeping up with the extent of learning necessary. In contrast, availability of more time is likely to allow mastering a higher degree of complexity, something that is less feasible when less time is available. Moreover, a \u003cem\u003esatisficing\u003c/em\u003e approach calls for lesser cognitive resources compared to a \u003cem\u003emaximizing\u003c/em\u003e approach, and hence is likely to impact uncertainty experienced by decision-makers.\u003c/p\u003e\u003cp\u003eI invoke the computational simulation model proposed by March (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1991\u003c/span\u003e)\u0026mdash;and enhanced by Chanda (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Chanda and Miller (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u0026mdash;to compute uncertainty as a function of complexity and environmental turbulence, taking into account maximizing or satisficing managerial approach to decision-making. I find that Duncan\u0026rsquo;s conjecture is upheld for strategic tasks of longer duration, and reversed for shorter duration tasks. Further, the model informs on relative suitability of strategies for uncertainty reduction under stylized conditions of environmental dynamism.\u003c/p\u003e\u003cp\u003eThis research makes three contributions. Firstly, it creates a bridge between perceptual and objective measures enabling researchers discard tribalistic attachment to one camp or the other, thereby clearing the path to find superior truths. Second, it highlights relative efficacies of strategies for lowering uncertainty. Third, it addresses the interrelation between V (\u003cem\u003eVariability\u003c/em\u003e or \u003cem\u003eturbulence\u003c/em\u003e), U (\u003cem\u003eUncertainty\u003c/em\u003e), C (\u003cem\u003eComplexity\u003c/em\u003e) of V-U-C-A, an important concern in current times.\u003ca class=\"FNLink\" href=\"#Fn5\" id=\"#FNLinkFn5\"\u003e\u003c/a\u003e\u003c/p\u003e"},{"header":"2. Theoretical foundations","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Objective measures of environmental dynamism\u003c/h2\u003e\u003cp\u003eResearch dealing with the (so-called) objective measures of the task environment consider the following dimensions of the dynamic environment (Dess and Beard, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1984\u003c/span\u003e and research citing them, drawing from Aldrich, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1979\u003c/span\u003e): munificence-hostility, stability-instability, and complexity. Munificence or capacity relates to the ease of doing business. Stability-instability is measured by intermediate market orientation comprising the proportion of industry shipments sold to intermediate markets or as investments. Dess and Beard (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1984\u003c/span\u003e) measure complexity by specialization ratio [Ratio of primary product shipments to total {primary and secondary, excluding miscellaneous} product shipments for the establishments classified in the industry] and geographical concentration of (i) total sales (ii) value added by manufacture (iii) total employment (iv) industry establishments. Consideration of industry characteristics for these measures confers high integrity to the data. A downside is that if all managers in an industry are deemed to be countenancing the same level of dynamism\u0026mdash;as derived from industry data\u0026mdash;there is injustice to managers of some businesses and unwarranted leniency to managers of others, as highlighted by the Intel-AMD example.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Subjective (perceptual) measures of uncertainty\u003c/h2\u003e\u003cp\u003eMilliken (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1987\u003c/span\u003e) suggests that \u003cem\u003estate uncertainty\u003c/em\u003e arises from a low level of appreciation regarding the \u003cem\u003estate\u003c/em\u003e of the environment, specifically with regard to which all elements should managers consider as relevant and understand in detail. Once \u003cem\u003estate\u003c/em\u003e uncertainty is resolved, \u003cem\u003eeffect\u003c/em\u003e uncertainty comes in play. This uncertainty is with regard to the effect of the identified environmental state on the organization. After \u003cem\u003eeffect\u003c/em\u003e uncertainty is resolved, \u003cem\u003eresponse\u003c/em\u003e uncertainty becomes salient. Response uncertainty pertains to indeterminacy regarding what organizational responses are appropriate. In order to assess each kind of uncertainty, managers are surveyed for their perceptions. An upside of this approach is that actual decision-makers are given a say regarding the nature of uncertainty experienced. However, if managers\u0026rsquo; self-reported accounts constitute the only source of data to measure uncertainty, inflated accounts of uncertainty may come in the way of a proper assessment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Bridging the divide\u003c/h2\u003e\u003cp\u003eIn order to find a way out of the impasse, I consider it productive to look into the sources of uncertainty. Uncertainty definitely is a matter of perception. Uncertainty is salient at the time of commencement of a decision-task that will bear fruit in the immediate term or longer-term future. I observe that Dess and Beard\u0026rsquo;s objective measure of complexity does not incorporate any a-priori attribute specific to the task facing given organization; rather it incorporates parameters that are known post-facto (i.e., after the result of a prior decision is known) e.g., concentration ratios. The implicit premise\u0026mdash;that past occurrences in the industry constitute important arbiters of uncertainty countenanced by all mangers in all companies\u0026mdash;is questionable. There is a case for framing the complexity parameter not in terms of post-facto values pertaining to the entire industry (for instance concentration ratio), but rather with respect to an individual business\u0026rsquo;s a-priori situation. This is what Duncan (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1972\u003c/span\u003e) did, in his study.\u003ca class=\"FNLink\" href=\"#Fn6\" id=\"#FNLinkFn6\"\u003e\u003c/a\u003e\u003c/p\u003e\u003cp\u003eA changing environment partly outdates organizational knowledge accessible to managers (March \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). This makes managers wary about putting faith in the efficacy of administrative measures previously known to have been successful. Uncertainty stems from apprehension of failure in noticing an environmental change and /or omission in taking corrective action to address the changed situation. Moreover, when complexity (given by the minimum number of relevant tasks that must be correctly executed, in order to succeed with a decision) increases, cognizing which specific tasks make into the must-do list require more effort. Anxiety about misreading the new dimensions where effort is necessary, contributes to uncertainty.\u003c/p\u003e\u003cp\u003eWhen both complexity and instability are present, environmental change may alter a dimension of minimum requirement necessary for crossing the complexity barrier. Unknown to managers, a must-have may become non-significant (meriting lower effort); alternately, an earlier good-to-have dimension may come into the list of dimensions that must be correctly fashioned (meriting greater attention, provided it is noticed in a timely manner). Further, higher complexity may be difficult to cognize and attend to, when environmental change itself is monopolizing a major chunk of managerial attention.\u003c/p\u003e\u003cp\u003eTaking above factors into consideration, and aligned to Duncan (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1972\u003c/span\u003e), I advocate discarding of the mapping of the complexity parameter in terms of prior output values pertaining to the entire industry (for instance concentration ratios). Moreover, given the deadlocked discourse on the dynamism-uncertainty link, taking recourse to a computational simulation model is ideal, such that the assumptions leading to particular outcomes become clearer (Adner et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Boisot and McKelvey \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Harrison et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Some further considerations\u003c/h2\u003e\u003cp\u003eThe extent of time available for making a decision has a bearing on the level of uncertainty experienced by managers. Blandin and Brown (1997) and Boyd and Fulk (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) site length of time for feedback as one of the three major factors contributing to perceived uncertainty. Besides, Ancona et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) also suggest making \u003cem\u003etime\u003c/em\u003e an important variable in theory development. The length of window available for making a decision is important because (a) managers are often required to make decisions within a limited time-window that an opportunity is available for action and (b) under conditions of uncertainty managers are unsure whether relevant information can be obtained from limited or extensive search efforts and (c) a dynamic environment may erode knowledge that helps making judgments regarding the salience of information (Chanda and Ray, \u003cem\u003eForthcoming\u003c/em\u003e). Hence, I consider two archetypes regarding \u003cem\u003etime\u003c/em\u003e\u0026mdash;short-duration and longer-duration decision-making.\u003c/p\u003e\u003cp\u003eManagerial decision-making style\u0026mdash;satisficing vs. maximizing (Heshmat, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Simon \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1957\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1959\u003c/span\u003e; Vergov\u0026aacute; et al., 2023)\u0026mdash;also contributes to the level of uncertainty experienced. A strategic decision can be perceived as either an opportunity or threat depending on how information is processed (Schwarz et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A \u003cem\u003emaximizing\u003c/em\u003e tendency pertains to striving to select the best option. In an uncertain situation, options that are available are not clear (Knight \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1921\u003c/span\u003e). Hence striving for the best will contribute to uncertainty whether the best option can indeed be found. A satisficing approach constitutes the willingness to settle for a sufficient (\u0026ldquo;good enough\u0026rdquo;) or \u0026ldquo;fairly good\u0026rdquo; option. Mangers can make decisions more confidently regarding whether a chosen recourse is \u0026ldquo;good enough\u0026rdquo; or \u0026ldquo;fairly good\u0026rdquo; for a particular purpose, compared to a situation where they are called upon to judge whether a recourse is the best one (Simon, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1957\u003c/span\u003e). Thus, the preferred decision-making style of top management\u0026mdash;satisficing vs. maximizing\u0026mdash;is likely to have an effect on the extent of uncertainty experienced by company managers. Hence, I include these archetypes in the computational simulation model. Lastly, given that participation and interaction have a big role to play in countenancing uncertainty (Schwarz et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), I invoke the multi-agent, genetic algorithm model from the seminal work by March (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), illustrating exploration and exploitation through an organizational learning perspective.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Computational simulation model","content":"\u003cp\u003eIn the simulation model, the environment or external reality \u003cb\u003eR\u003c/b\u003e is an \u003cem\u003eM\u003c/em\u003e-bit string. At the beginning of simulation, each bit of \u003cb\u003eR\u003c/b\u003e is given a random value from the set \u003cem\u003eS1\u003c/em\u003e = {-1, +\u0026thinsp;1}, with 50 percent probability for occurrence of any given value. A stable environment is motivated by having the values in \u003cb\u003eR\u003c/b\u003e the same throughout \u003cem\u003eT\u003c/em\u003e time steps of a simulation experiment. Environmental turbulence is modeled by assigning a non-zero probability to each bit of \u003cb\u003eR\u003c/b\u003e flipping its value (from +\u0026thinsp;1 to -1 and vice-versa) in any given time step. This probability is stored in a parameter \u003cb\u003ep\u003c/b\u003e\u003csub\u003e\u003cb\u003e4\u003c/b\u003e\u003c/sub\u003e.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1. The organization\u003c/h2\u003e\u003cp\u003eAn organization comprises of \u003cem\u003eN\u003c/em\u003e members, and one central entity, the organizational code \u003cb\u003eOC\u003c/b\u003e (that functions as a repository of organizational knowledge\u003ca class=\"FNLink\" href=\"#Fn7\" id=\"#FNLinkFn7\"\u003e\u003c/a\u003e). Each member is represented by means of an \u003cem\u003eM\u003c/em\u003e-bit belief string. At the beginning of any simulation experiment, belief-strings of all members are populated by randomly assigning values from the set \u003cem\u003eS2\u003c/em\u003e = {-1, 0, +\u0026thinsp;1}, with one-third probability for occurrence of any given value. The organizational code \u003cb\u003eOC\u003c/b\u003e is also an \u003cem\u003eM\u003c/em\u003e-bit string. At the start of a simulation experiment, all bit positions of \u003cb\u003eOC\u003c/b\u003e are given a value \u0026ldquo;0\u0026rdquo;. A value of \u0026ldquo;0\u0026rdquo; in a belief dimension of a member or \u003cb\u003eOC\u003c/b\u003e signifies neutral belief or \u0026ldquo;no opinion\u0026rdquo; (March, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1991\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Learning processes\u003c/h2\u003e\u003cp\u003eThe organizational code is endowed with an ability to identify members who are more knowledgeable about \u003cb\u003eR\u003c/b\u003e than itself. In each time step, the organizational code consults those members, to elicit their recommendation regarding what value ought to be assigned to a given bit-position of \u003cb\u003eOC\u003c/b\u003e. If the recommendation for a non-zero value is obtained by a majority of \u003cem\u003eK\u003c/em\u003e, the organizational code updates its value to the recommended value with probability [1 \u0026ndash; (1 \u0026ndash; \u003cb\u003ep\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e)\u003csup\u003e\u003cem\u003eK\u003c/em\u003e\u003c/sup\u003e], where \u003cem\u003ep\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e (0\u0026thinsp;\u0026lt;\u0026thinsp;\u003cb\u003ep\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1) is a measure of the code\u0026rsquo;s learning rate, whose value is set exogenously.\u003c/p\u003e\u003cp\u003eMoreover, in each time-step, each member of the organization learns from \u003cb\u003eOC\u003c/b\u003e at a rate \u003cb\u003ep\u003c/b\u003e\u003csub\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sub\u003e (0\u0026thinsp;\u0026lt;\u0026thinsp;\u003cb\u003ep\u003c/b\u003e\u003csub\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sub\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1). For any given bit, if a member finds the value in \u003cb\u003eOC\u003c/b\u003e to be non-zero, the member updates the value in his/her bit by the value in the corresponding bit of \u003cb\u003eOC\u003c/b\u003e with a probability \u003cb\u003ep\u003c/b\u003e\u003csub\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sub\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Exploration and exploitation\u003c/h2\u003e\u003cp\u003eIn the orthogonal conception of exploitation and exploration, \u003cb\u003ep\u003c/b\u003e\u003csub\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sub\u003e (0\u0026thinsp;\u0026lt;\u0026thinsp;\u003cb\u003ep\u003c/b\u003e\u003csub\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sub\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1) is a measure of the rate of exploitation (Chanda and Ray, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Chanda, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; March, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), given that higher the value of \u003cb\u003ep\u003c/b\u003e\u003csub\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sub\u003e, greater is the erosion of diversity of knowledge. Exploration is motivated by allowing inflow of heterogeneous knowledge from outside the organization at a rate given by \u003cb\u003ep\u003c/b\u003e\u003csub\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sub\u003e (0\u0026thinsp;\u0026lt;\u0026thinsp;\u003cb\u003ep\u003c/b\u003e\u003csub\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sub\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1), given that higher \u003cb\u003ep\u003c/b\u003e\u003csub\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sub\u003e entails higher diversity of knowledge coming into the organization. The parameter \u003cem\u003ep\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e represents the probability that any given member\u0026rsquo;s belief string gets replaced by a string fashioned by random draws from the set \u003cem\u003eS2\u003c/em\u003e\u0026mdash;with one-third probability for materialization of any value of \u003cem\u003eS2\u003c/em\u003e\u0026mdash;in any time-step of a simulation experiment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Measurement of uncertainty\u003c/h2\u003e\u003cp\u003eManagers of the organization are given the ability to set the rates of exploration (\u003cb\u003ep\u003c/b\u003e\u003csub\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sub\u003e) and exploitation (\u003cb\u003ep\u003c/b\u003e\u003csub\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sub\u003e). Simulation experiments are run, to observe an organizational outcome\u0026mdash;the \u003cem\u003eprobability of organizational success\u003c/em\u003e (Chanda, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u0026mdash;in the entire state space constituted by variation of \u003cb\u003ep\u003c/b\u003e\u003csub\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sub\u003e and \u003cb\u003ep\u003c/b\u003e\u003csub\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sub\u003e from zero to one, in steps of 0.05, comprising 441 (21 X 21) cells in all. Uncertainty is given by the fraction of cells where organizational outcome is below a mandated level. Thus, I define uncertainty as follows: If \u003cem\u003eN\u003c/em\u003e levers for action are available to managers, out of which only \u003cem\u003em\u003c/em\u003e yield desired outcomes\u0026mdash;and managers are not sure which are those \u003cem\u003em\u003c/em\u003e desirable options\u0026mdash;uncertainty is given by (\u003cem\u003eN\u003c/em\u003e \u0026ndash; \u003cem\u003em\u003c/em\u003e) / \u003cem\u003eN\u003c/em\u003e. Alternately:\u003c/p\u003e\u003cp\u003eUncertainty (\u003cb\u003eU\u003c/b\u003e)\u0026thinsp;=\u0026thinsp;1 \u0026ndash; (Number of exploration-exploitation combinations that qualify as having accomplished organizational objectives / Total number of exploration-exploitation combinations)\u003c/p\u003e\u003cp\u003eThe mandated value of organizational outcome is a function of the \u003cem\u003eprobability of organizational success\u003c/em\u003e. The \u003cem\u003eprobability of organizational success\u003c/em\u003e is computed by using a complexity parameter (\u003cem\u003eC\u003c/em\u003e) to assess the extent of match between the reality \u003cb\u003eR\u003c/b\u003e and the organizational code \u003cb\u003eOC\u003c/b\u003e. At the conclusion of the last time-step of a simulation experiment, \u003cem\u003eC\u003c/em\u003e *\u003cem\u003eM\u003c/em\u003e bit positions are randomly selected a large number of times (\u003cem\u003eL\u003c/em\u003e). A pay-off of one unit is assigned if all the values in the selected bit positions of \u003cb\u003eOC\u003c/b\u003e match the values in the corresponding positions of \u003cb\u003eR\u003c/b\u003e. Otherwise the payoff assigned is zero. The proportion of exact matches\u0026mdash;i.e., the sum of pay-offs divided by \u003cem\u003eL\u003c/em\u003e\u0026mdash;constitutes the probability of organizational success. Lastly, an organization exhibits \u003cem\u003emaximizing\u003c/em\u003e behavior when top management mandates that the probability of obtaining organizational success has to be within one percent of the maximum attainable. \u003cem\u003eSatisficing\u003c/em\u003e behavior is associated with a mandate that the probability of obtaining organizational success is within five percent of the maximum attainable.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Model Parameters\u003c/h2\u003e\u003cp\u003eThe parameter values used largely draw from March (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). The number of agents is 50 \u003cem\u003e(N\u003c/em\u003e\u0026thinsp;=\u0026thinsp;50). The external reality \u003cb\u003eR\u003c/b\u003e has 30 bits (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30). The parameter representing the code\u0026rsquo;s learning rate (\u003cb\u003ep\u003c/b\u003e\u003cb\u003e2\u003c/b\u003e) is given a value of 0.50. For determining the probability of organizational success, \u003cem\u003eL\u003c/em\u003e\u0026thinsp;=\u0026thinsp;500 trials are carried out. Short and long duration tasks are motivated by setting a parameter, \u003cem\u003eT\u003c/em\u003e, to 20 and 100, respectively. The complexity dimension uses five and ten percent values for characterization as \u003cem\u003esimple\u003c/em\u003e and \u003cem\u003ecomplex\u003c/em\u003e, respectively. The static and turbulent conditions are implemented by setting the turbulence parameter (\u003cb\u003ep\u003c/b\u003e\u003csub\u003e\u003cb\u003e4\u003c/b\u003e\u003c/sub\u003e) to zero and two percent, respectively. I list the parameter values in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, along with the values used for robustness checks.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModel Parameters and Robustness Checks\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRobustness Check\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of dimensions of \u003cb\u003eR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;25 and \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of agents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;40 and \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLearning rate of the code\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.40 and \u003cb\u003ep\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRate of environmental turbulence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003csub\u003e\u003cb\u003e4\u003c/b\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003csub\u003e\u003cb\u003e4\u003c/b\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of trials for determining probability of organizational success\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eL\u003c/em\u003e\u0026thinsp;=\u0026thinsp;500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eL\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExploration (\u003cb\u003ep\u003c/b\u003e\u003csub\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sub\u003e) and exploitation (\u003cb\u003ep\u003c/b\u003e\u003csub\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 to 1, in steps of 0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003e[Figures referred in the text are provided in the next page]\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u003c/b\u003e Insert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e about here \u003cb\u003e\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eFollowing March (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), a turbulent environment is fashioned by setting the parameter \u003cem\u003ep\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e to a value of 0.02, and a stable environment is fashioned by setting the parameter \u003cem\u003ep\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e to a value zero. The simulation experiments are carried out for \u003cem\u003eT\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20 time-steps and for \u003cem\u003eT\u003c/em\u003e\u0026thinsp;=\u0026thinsp;100 time-steps, respectively, in order to obtain results for short duration and long duration tasks. The graphs under Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e provide information regarding model behavior. The subsequent graphs display the main findings.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u0026gt;\u0026gt;Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eB about here \u0026lt;\u0026lt;\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Model descriptive characteristics\u003c/h2\u003e\u003cp\u003eGiven a \u003cem\u003esatisficing\u003c/em\u003e approach\u0026mdash;where top management considers reaching 95% of the maximum outcome attainable as acceptable\u0026mdash;the variation of uncertainty with complexity is shown for a stable environment (\u003cb\u003ep\u003c/b\u003e\u003csub\u003e\u003cb\u003e4\u003c/b\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0) in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and for a turbulent environment (\u003cb\u003ep\u003c/b\u003e\u003csub\u003e\u003cb\u003e4\u003c/b\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.02) in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eB. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eA suggests that, in a stable environment, the extent of uncertainty is comparable, for short and long duration decision tasks. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eB highlights that, in a turbulent environment, uncertainty is much higher as far as longer duration tasks are concerned.\u003c/p\u003e\u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eB about here \u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/p\u003e\u003cp\u003eFor the \u003cem\u003emaximizing\u003c/em\u003e approach (where top management mandates reaching 99% of the maximum outcome attainable as necessary for success) the variation of uncertainty with complexity for a stable environment (\u003cb\u003ep\u003c/b\u003e\u003csub\u003e\u003cb\u003e4\u003c/b\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0) is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and that for a turbulent environment (\u003cb\u003ep\u003c/b\u003e\u003csub\u003e\u003cb\u003e4\u003c/b\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.02) is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eB. We observe that task duration does not appear to make a big difference in the level of uncertainty involved. However, onset of turbulence increases uncertainty (as seen from higher y-values in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Moreover, comparing the y-axis values in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e with those from Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e, we note that the level of uncertainty is markedly higher, under the maximizing approach.\u003c/p\u003e\u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e about here \u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Comparison with the results from Duncan (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1972\u003c/span\u003e)\u003c/h2\u003e\u003cp\u003eDuncan considered four environmental archetypes: simple-static, complex-static, simple-dynamic and complex-dynamic. I use five and ten percent values for complexity for \u003cem\u003esimple\u003c/em\u003e and \u003cem\u003ecomplex\u003c/em\u003e environment, respectively. For static and dynamic (turbulent) environments, the turbulence parameter (\u003cb\u003ep\u003c/b\u003e\u003csub\u003e\u003cb\u003e4\u003c/b\u003e\u003c/sub\u003e) is set to zero and two percent, respectively. The key finding in Duncan (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1972\u003c/span\u003e) is that managers feel higher degree of uncertainty in a simple-dynamic environment, compared to the extent of uncertainty they feel in a complex-static environment. Accordingly, I plot the difference in uncertainty values between simple-dynamic and complex-static environments in the vertical axis of Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e, allowing task duration to vary on the horizontal axis. We observe that Duncan\u0026rsquo;s conjecture holds good where the values in the y-axis are positive, i.e., for T\u0026thinsp;\u0026ge;\u0026thinsp;50. For shorter duration tasks, the values in the y-axis are negative, i.e., Duncan\u0026rsquo;s results are reversed. Thus:\u003c/p\u003e\u003cp\u003eProposition \u003cb\u003eP1\u003c/b\u003e: \u003cem\u003eFor short duration decision tasks, the uncertainty experienced by managers is higher for complex-static environments, compared to the uncertainty experienced in simple-dynamic environments\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eProposition \u003cb\u003eP2\u003c/b\u003e: \u003cem\u003eFor longer-duration decision tasks, the uncertainty experienced by managers is lower for complex-static environments, compared to the uncertainty experienced in simple-dynamic environments\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e about here \u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Benefit of switching from maximizing to satisficing approach\u003c/h2\u003e\u003cp\u003eI compute a metric\u0026mdash;benefit of switching from maximizing to satisficing approach\u0026mdash;by the ratio, reduction in uncertainty / reduction in probability of success. Higher the value of this metric, more desirable it is, for an organization, to implement the switchover. In Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eI present graphs comparing benefits of switching from a maximizing to a satisficing approach, for all four archetypes considered by Duncan (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1972\u003c/span\u003e), for tasks of varying duration. The greatest benefits accrue for the shortest task duration (\u003cem\u003eT\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10). Thus:\u003c/p\u003e\u003cp\u003eProposition \u003cb\u003eP3\u003c/b\u003e: \u003cem\u003eSwitching from a maximizing approach to a satisficing approach is beneficial to organizations, and the greatest extent of benefits accrue for short-duration tasks\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis research enhances Duncan\u0026rsquo;s theory by demonstrating the important role of task duration and of the approach to decision-making by managers (concerning satisficing / maximizing behavior). Duncan\u0026rsquo;s results are seen to hold for longer-duration tasks and reverse for shorter-duration tasks. Further, the findings show that significant benefits can be obtained by espousing a satisficing approach in lieu of a maximizing approach, and particularly so for shorter-duration managerial decision tasks. Moreover, for longer duration decision tasks, preferring satisficing over maximizing is desirable when environmental turbulence is high.\u003c/p\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e5.1. Limitations of the study\u003c/h2\u003e\u003cp\u003eFirst, the model of \u003cem\u003euncertainty\u003c/em\u003e largely conforms to the description of \u003cem\u003eresponse\u003c/em\u003e uncertainty (Milliken, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). \u003cem\u003eState\u003c/em\u003e uncertainty and \u003cem\u003eeffect\u003c/em\u003e uncertainty have been kept out of scope. Besides, managerial routines and heuristics (e.g., falling back on organizational identity\u0026mdash;please see Whetten \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) may influence how uncertainty is handled. Moreover, certain other factors that could influence the extent of uncertainty experienced by the managers\u0026mdash;say, internal turbulence in the organization (originating in factors like change in senior management, labor unrest, etc.) or on account of volatility in the regulatory environment (particularly where rule-making struggles to keep up with change in technology and/or preferences of members of society)\u0026mdash;are presently out of scope of the study. We note here that change in the external environment could engender higher uncertainty than changes in the internal environment (Duncan, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1972\u003c/span\u003e; Jones, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Schwarz et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Lastly, I use one particular definition of complexity following Duncan (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1972\u003c/span\u003e); other definitions exist (Chanda, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Shannon, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1948\u003c/span\u003e) and are presently out of scope.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e5.2. Implications\u003c/h2\u003e\u003cp\u003eThe streams of research referring to objective vs. perceptual measures need no longer be pitted against each other, since it is now possible to assess uncertainty from the stability-instability and complexity dimensions, given managerial orientation towards the \u003cem\u003esatisficing\u003c/em\u003e or \u003cem\u003emaximizing\u003c/em\u003e decision-making approach. For this to materialize though, it is necessary that the \u003cem\u003ecomplexity\u003c/em\u003e parameter is not measured in terms of output values relative to entire industry (for instance by concentration ratios), but rather with respect to an input parameter relevant to a business\u0026rsquo;s a-priori situation, as suggested by Duncan (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1972\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe note that, aligned with theorization by Duncan (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1972\u003c/span\u003e), the simple-dynamic case indeed engenders higher uncertainty compared to the complex-static case, for decision tasks of longer duration. Moreover, the complex-static case engenders higher uncertainty compared to the simple-dynamic case, for decision tasks of shorter duration. Theoretical confirmation of this nature helps fixing responsibility and helps prevent managers being judged inappropriately.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e5.3. Directions for further research\u003c/h2\u003e\u003cp\u003eMarch\u0026rsquo;s versatile simulation model can be invoked to compare how uncertainty varies when exploration-exploitation tasks are separated spatially or temporally. It can enrich the attention-based-view by comparing uncertainty experienced under varying conditions of executive attention and attentional vigilance (Ocasio et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The model may be extended to study uncertainty regarding fiscal, regulatory, or monetary issues (Jiang et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) in terms of complexity and dynamism or unpredictability in policy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e5.4. Implications for practice\u003c/h2\u003e\u003cp\u003eMangers deserve more credit when higher extent of uncertainty is overcome in delivering on a strategic objective. Measuring uncertainty by interviewing managers may be ridden with biases. However, assessing uncertainty based on industry-wide parameters for demand variability and concentration ratios is also flawed. For example, managers of well-endowed firms face much lower uncertainty than firms having scarce resources. Therefore, I enhance Duncan\u0026rsquo;s method of measuring complexity from firm-specific parameters, incorporating the role of task duration and managerial approach to decision-making, satisficing or maximizing. I also demonstrate that switching from maximizing to satisficing reduces uncertainty, and particularly so for shorter duration tasks.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSingle author did all the work.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eMy heartfelt thanks to late Prof. James March and late Prof. Bill McKelvey for inspiring me to carry out this project. All errors remain my sole responsibility.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdner R, Polos L, Ryall M et al (2009) The case for formal theory. 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Aust J Manage 44:50\u0026ndash;69\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVargov\u0026aacute; L, Zibr\u0026iacute;nov\u0026aacute; L, Ban\u0026iacute;k G (2023) The way of making choices: Maximizing and satisficing and its relationship to well-being, personality, and self-rumination. \u003cem\u003eCambridge University Press.\u003c/em\u003e [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cambridge.org/core/journals/judgment-and-decision-making/article/way-of-making-choices-maximizing-and-satisficing-and-its-relationship-to-wellbeing-personality-and-selfrumination/64424CFEE4D1BB8C8E6D875D0F84CD8E\u003c/span\u003e\u003cspan address=\"https://www.cambridge.org/core/journals/judgment-and-decision-making/article/way-of-making-choices-maximizing-and-satisficing-and-its-relationship-to-wellbeing-personality-and-selfrumination/64424CFEE4D1BB8C8E6D875D0F84CD8E\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e] Accessed: September 2, 2023\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWhetten DA (2006) Albert and Whetten revisited: Strengthening the concept of organizational identity. J Manage Inq 15:219\u0026ndash;234\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Turbulence (March, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) or stability-instability (Dess and Beard, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1984\u003c/span\u003e) denotes extent of unpredictable environmental changes.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Environmental complexity refers to the number of relevant tasks that must be correctly executed, in order to stand a chance of obtaining success Duncan (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1972\u003c/span\u003e) or to the heterogeneity and range of environmental activities which are relevant to an organization\u0026rsquo;s operations (Child, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1972\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e True or Knightian uncertainty refers to situations where options facing a decision-maker are not clear, let alone probabilities of materialization and related payoff distributions (Knight, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1921\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Munificence-hostility refers to ease of doing business. Stability-instability refers to demand variability. Complexity refers to concentration ratios. Competitive intensity was dropped by Dess and Beard (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1984\u003c/span\u003e) as an environmental characteristic.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The \u0026ldquo;A\u0026rdquo; in V-U-C-A is \u003cem\u003eambiguity\u003c/em\u003e. In layman terms, \u003cem\u003eambiguity\u003c/em\u003e connotes multiple feasible interpretations of a single signal. Ambiguity is addressed by using redundant bits of information. For example, when one says \u0026ldquo;Charlie Delta\u0026rdquo; instead of \u0026ldquo;C-D\u0026rdquo;, the characters are clearly distinguished from similar sounding alphabets.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Likewise, in the simulation model, I invoke a complexity parameter to assess the probability of success of an organization based on the compatibility of its own organizational knowledge with various probable configurations of salience with corresponding segments of the external reality.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Rules, norms, forms, routines etc., present in databases, manuals, standard operating procedures comprise organizational knowledge.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"computational-and-mathematical-organization-theory","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cmot","sideBox":"Learn more about [Computational and Mathematical Organization Theory](https://link.springer.com/journal/10588)","snPcode":"10588","submissionUrl":"https://submission.springernature.com/new-submission/10588/3","title":"Computational and Mathematical Organization Theory","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Complexity, computational simulation, decision, dynamism, maximizing, satisficing, turbulence, uncertainty","lastPublishedDoi":"10.21203/rs.3.rs-5207078/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5207078/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn a seminal paper, Duncan found that managers feel higher degree of uncertainty in a low-complexity, turbulent (simple-dynamic) environment, compared to the extent of uncertainty they feel in a complex and low turbulence (complex-static) environment. Using a computational simulation model, I find that Duncan\u0026rsquo;s proposition holds good for longer duration tasks, but reverses for shorter duration tasks. The simulation results further suggest that switching from maximizing to a satisficing approach is helpful in lowering uncertainty. This research has the potential to pave the way for resolving the deadlock between perceptual and objective measures of uncertainty. Thereby, it enables giving credit to managers where due, e.g. when an organizational outcome is obtained while facing higher levels of uncertainty. The research also highlights relative efficacies of strategies for lowering uncertainty.\u003c/p\u003e","manuscriptTitle":"An update to Duncan’s theory: uncertainty as a function of complexity, environmental turbulence, and managerial approach to decision-making","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-31 19:29:27","doi":"10.21203/rs.3.rs-5207078/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-14T16:01:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-30T11:24:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"276082166228874970490384464653601722315","date":"2025-08-31T13:45:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-18T04:24:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-05T09:59:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-05T09:58:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Computational and Mathematical Organization Theory","date":"2024-10-05T05:08:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"computational-and-mathematical-organization-theory","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cmot","sideBox":"Learn more about [Computational and Mathematical Organization Theory](https://link.springer.com/journal/10588)","snPcode":"10588","submissionUrl":"https://submission.springernature.com/new-submission/10588/3","title":"Computational and Mathematical Organization Theory","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0eb5a7c9-16f8-4b29-bf09-79e7304e02db","owner":[],"postedDate":"October 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-26T05:24:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-31 19:29:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5207078","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5207078","identity":"rs-5207078","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}