When clarity clouds the view: Introducing a decision style framework for assessing task-related effectiveness in analysis and intuition

Julmi, Christian Article When clarity clouds the view: Introducing adecision style framework for assessing task-related effectiveness in analysis and intuition Schmalenbach Journal of Business Research (SBUR) Provided in Cooperation with: Schmalenbach-Gesellschaft für Betriebswirtschaft e.V. Suggested Citation: Julmi, Christian (2025) : When clarity clouds the view: Introducing adecision style framework for assessing task-related effectiveness in analysis and intuition, Schmalenbach Journal of Business Research (SBUR), ISSN 2366-6153, Springer, Heidelberg, Vol. 77, Iss. 2, pp. 267-308, https://doi.org/10.1007/s41471-025-00207-8 This Version is available at: https://hdl.handle.net/10419/323728 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ ORIGINAL ARTICLE https://doi.org/10.1007/s41471-025-00207-8 Schmalenbach Journal of Business Research (2025) 77:267–308 When Clarity Clouds the View: Introducing a Decision Style Framework for Assessing Task-Related Effectiveness in Analysis and Intuition Christian Julmi Received: 30 June 2023 / Accepted: 11 January 2025 / Published online: 19 February 2025 © The Author(s) 2025 Abstract Recent scholarly discussions suggest the potential superiority of intuition over analysis in tasks with high equivocality. However, it remains unclear when and whether intuition is preferable when both uncertainty and equivocality are considered. Addressing this gap, the article introduces a framework linking the effectiveness of individual decision styles with configurations of uncertainty and equivocality, adopting an information processing perspective. Within this framework, intuitiveness and adaptiveness are treated as independent dimensions of decision styles, associating intuitiveness with equivocality and adaptiveness with uncertainty in terms of effectiveness. To demonstrate the value of the framework, the article discusses implications for research. Keywords Equivocality · Uncertainty · Intuition · Analysis · Decision style · Management effectiveness JEL classification D81 · D91 · M10 1 Introduction In research, it is considered common sense that the characteristics of a decision task have an influence on whether the task should be accomplished rather intuitively or analytically. One characteristic of the task that researchers have discussed in this context is the uncertainty of the task-related environment. Because uncertain decision tasks often do not allow an analytical derivation of a clearly correct solution, Christian Julmi Fakultät für Wirtschaftswissenschaft, Lehrstuhl für Betriebswirtschaftslehre, insb. Organisation und Planung, FernUniversität in Hagen, 58084 Hagen, Germany E-Mail: [email protected] K 268 Schmalenbach Journal of Business Research (2025) 77:267–308 researchers have suggested that intuition may be an effective strategy for dealing with uncertain decision tasks (Agor 1986; Gigerenzer 2008;Parikh1994; Sadler-Smith and Shefy 2004). Scholars argue that intuition helps decision makers to react timely and accurately to the environment’s changing stimuli (Eisenhardt 1989; Manesh et al. 2022), to integrate large amounts of data and cope with incomplete information (Khatri and Ng 2000), and to perceive possibilities that have not been identified previously (Crossan et al. 1999). Sinnaiah et al. (2023) argue that utilizing intuition as a decision style can enhance organizational performance, particularly when faced with limited resources or knowledge. According to Goldberg (1990, p. 73), any “forecaster has to use intuition in gathering and interpreting data and in deciding which unusual future events might influence the outcome”. Conversely, other researchers propose that intuition may falter under conditions of uncertainty, leading to biased or unreliable decisions. Most prominently, Kahneman and Tversky (1973) observed several cognitive biases that occur in intuitive predictions in the context of uncertainty and probabilities. Schirrmeister et al. (2020) emphasize that, in uncertain environments, the use of intuition can lead to cognitive biases because it is based on incomplete information and implicit assumptions. Luoma and Martela (2021) generally consider intuition as an inferior strategy in novel or rapidly changing environments. In the context of clinical decision making, Hall (2002) states that clinicians rely on intuition particularly in uncertain situations, despite this may result in lower performance and is susceptible to cognitive bias. To address this ostensible inconsistency and the ongoing discussion on intuition effectiveness, we propose incorporating equivocality as an additional criterion to uncertainty for assessing the effectiveness of intuition. This corresponds with a nascent trend in the literature that ties intuition effectiveness to equivocality. For example, Sonenshein (2007) argues from a sensemaking perspective that intuitive sensemaking enables individuals to respond to the various interpretations when equivocality is high. Following this perspective, Fisher and Neubert (2023) conclude that when equivocality is high, a person must rely on intuitive judgments because the information needed to make a rational-analytic decision is confusing. For Julmi (2019), the effectiveness of intuition increases with equivocality because the analyzability of the problem simultaneously decreases. Likewise, Flores-Garcia et al. (2021) pose that intuitive decision making is effective under conditions of equivocality “because intuition does not rely on rules to cope with a problem” (Flores-Garcia et al. 2021, p. 4). However, these approaches do not examine how different constellations of equivocality and uncertainty relate to the effectiveness of intuition. The only exception we know of is Julmi’s (2024) recent article on the effectiveness of intuition in moral judgments. He considers different constellations of moral uncertainty and moral equivocality, and views intuition as superior to analysis in the presence of moral equivocality. This approach has important implications for the present study, but is limited to the moral domain. In light of this context, the aim of this article is to develop a framework that links the effectiveness of individual decision styles with different constellations of uncertainty and equivocality from an information processing perspective. Through our decision style framework, inferences can be drawn from the characteristics of the K Schmalenbach Journal of Business Research (2025) 77:267–308 269 decision task to the appropriate degree of intuitiveness inherent in the individual’s decision style. In addition, we consider the degree of the decision style’s adaptiveness, which we conceptualize as independent of the degree of intuitiveness. Within our framework, we align different decision styles (considering their levels of intuitiveness and adaptiveness) with distinct patterns of uncertainty and equivocality. More specifically, we associate high intuitiveness with high equivocality and high adaptiveness with high uncertainty regarding the effectiveness of the decision style. As we will show, the distinction between uncertainty and equivocality is crucial in assessing the appropriateness of either an intuitive or analytic approach to decision making. Our work reflects a theoretical contribution to research into intuition effectiveness. Although Dane and Pratt’s (2007) seminal work on intuition effectiveness has been cited more than two and a half thousand times (according to Google scholar), almost no discussion has developed around the core connections of the article on the interplay of intuition effectiveness, task characteristics and domain knowledge. With our article, we would like to revive this discussion and look at this interplay from a fit perspective. Our framework proposes several new assumptions in this context. First, it implies that equivocality and not uncertainty is a predictor of intuition effectiveness. This explains why the effectiveness of intuition can vary in high-uncertainty situations, being either high or low, depending on the presence of equivocality. Second, although the literature acknowledges that intuition can both be reproductive and adaptive (Akinci and Sadler-Smith 2012; Hodgkinson and SadlerSmith 2018), this distinction has not yet been systematically applied to the issue of intuition effectiveness. As will be shown, this distinction is crucial in determining the effectiveness of either analysis or intuition for a given task properly. Third, unlike Dane and Pratt’s model, our framework not only predicts when intuition effectiveness is high but also when it surpasses the effectiveness of analysis. This allows for comparative analyses of intuition and analysis effectiveness. Fourth, by demonstrating that intuition can be more effective than analysis in certain constellations, the framework contributes to the literature by combating the still widespread belief that intuition is generally inferior to analysis (e.g., Korherr et al. 2023). Since the framework derives constellations in which high intuitiveness is assumed to be more effective than low intuitiveness, there are potential cognitive biases if an analytic approach is chosen in these constellations. To illustrate this point, we devote a section of the research implications to potential cognitive biases. Our article is organized as follows. First, building on a dual-process view, we define the difference between an intuitive and an analytical approach. Then we outline the relevant theoretical concepts of our framework. From these foundations, we proceed to link four types of decision styles with four types of tasks in terms of effectiveness. In the following section, the task-related effectiveness of these four decision styles is illustrated through four mini case studies. To demonstrate the value of our framework, we divide the section on research implications into three parts. First, we examine the area of strategic decision making, highlighting how our framework addresses and extends existing controversies in this area. Second, we propose that future research should explicitly investigate cognitive biases that arise during analytical decision making, as opposed to intuitive decision making. Third, K 270 Schmalenbach Journal of Business Research (2025) 77:267–308 we are exploring approaches to test the assumptions of our framework, enhance its development, and implement it in practical applications. 2 A Parallel-Competitive View on Intuition and Analysis Intuition is often characterized as the innate ability to act or make decisions adeptly, devoid of deliberate consideration of alternatives, without adherence to specific rules, and, potentially, without conscious awareness (Harteis and Billett 2013). In our approach, we follow Dane and Pratt’s (2007, p. 33) well-established definition of intuition “as affectively charged judgments that arise through rapid, nonconscious, and holistic associations”. In this sense, intuition refers to a form of “knowing that emerges out of self-organizing holistic associations” (Adinolfi and Loia 2022, p. 9). Analysis, in contrast to intuition, is defined as slow, conscious, and rulebased deliberations. Whereas intuitive decisions follow an overall impression of the decision task, analytic decisions rest upon logically decomposing the decision task and sequentially recombining its elements (Hogarth 2010). In research, there is “considerable agreement among researchers” (Rusou et al. 2013, p. 608) and “an emerging consensus that a useful distinction can be made between two basic systems of information processing” (Hodgkinson et al. 2008,p.8). Although such so-called dual-process theories come in many flavors, these views or theories commonly assume that human information processing is accomplished by two substantially dissimilar, yet complementary systems: the intuitive and the analytic system. In this context, the field of psychology distinguishes between a defaultinterventionist and a parallel-competitive view (St. Evans 2007,2008). The default-interventionist perspective posits that the intuitive and analytic systems function sequentially and in a hierarchical structure. The lower-level intuitive system generates judgments by default, requiring validation or correction by the higher-level analytic system to rectify any inaccuracies. In practical terms, individuals tend to initially depend on intuitive thinking when making a decision. Subsequently, this default process may be, but is not obligatory to be, assessed by analytical reasoning for intervention if deemed necessary. According to the parallel-competitive view, the two systems have access to distinct forms of knowledge. The intuitive system draws on implicit knowledge. It can process a large amount of information simultaneously, although this kind of information processing is mostly beyond conscious control and often difficult to articulate. In contrast, the analytic system is characterized by explicit information processing that draws on explicit knowledge. It is rule based and operates in a sequential step-by-step manner. Unlike the intuitive system, the analytic system allows for a conscious and deliberate control of the sequence and direction of information processing (Baldacchino et al. 2015; Betsch and Glöckner 2010). The concept of parallel-competitive dual-process theory originates from the notion of two types of learning, resulting in two types of knowledge: implicit and explicit knowledge (St. Evans 2008). When decision making follows an analytical approach, the tacitness of underlying knowledge is low. In case of intuitive decision making, tacitness is high. In between, there exist varying degrees of utilizing both types of knowledge. K Schmalenbach Journal of Business Research (2025) 77:267–308 271 For example, there are decisions predominantly driven by analytical processes that, to some extent, incorporate implicit information. This also corresponds to a broader body of knowledge within intuition research that acknowledges that intuition utilizes tacit knowledge (Deters 2023; Harteis and Billett 2013; Polanyi 1966;PretzandTotz2007; Reber 1989). Through experiential learning, implicit knowledge is gained, subsequently stored in long-term memory. This reservoir of implicit knowledge serves as input for the implicit information processing involved in intuitive decision making (Betsch 2008; Betsch and Glöckner 2010; Heimstädt et al. 2024;PretzandTotz2007). From a process view, intuition “is the end product of an implicit learning experience” (Reber 1989, p. 232). Empirical research has demonstrated a positive correlation between implicit learning and intuitive preference (Woolhouse and Bayne 2000). Additionally, a link has been established between implicit learning and a reduced inclination for deliberation (Kaufman et al. 2010). Whereas in “the field of psychology the jury is still out regarding the relative merits of default-interventionist and parallel-competitive accounts” (Hodgkinson and Sadler-Smith 2018, p. 477), we favor a parallel-competitive view for at least four reasons. First, the default-interventionist view seems, due to its hierarchical structure, to overemphasize the deficiency of intuition while simultaneously overrating analysis: Because the intuitive system produces errors, the analytical system must intervene and correct them (Adinolfi 2021; Baldacchino et al. 2023; Fabio et al. 2024;Julmi2019). For example, such a view is applied in the heuristics and biases program which focuses on the detrimental use of intuition in decision making (Kahneman 2003,2011). However, we want to include constellations where an analytical approach may be more error-prone than an intuitive approach. Second, a parallelcompetitive view is consistent with growing evidence from experimental and neurological studies (Alós-Ferrer and Strack 2014; Healey et al. 2015; Howarth et al. 2019; Kuo et al. 2009;Lieberman2007). Third, the adoption of a parallel-competitive view corresponds with recent trends in management and organization studies, where the focus is about to slightly shift from a dominance of default-interventionist accounts towards a parallel-competitive view (Adinolfi 2021; Hodgkinson and Sadler-Smith 2018; Keller and Sadler-Smith 2019; Luoma and Martela 2021;Pretorius et al. 2024; Thanos 2023; Zaitsava et al. 2022). Fourth, Hodgkinson and SadlerSmith (2018) point out that a parallel-competitive view is far better suited to capturing the interplay of intuitive and analytical processes than the default-interventionist view. While the sequentiality of both systems in the default-interventionist view only conceptualizes their interaction in one direction, the parallel-competitive view enables the conceptualization of different degrees of mutual interaction between the two systems, encompassing variations in the extent to which each system is engaged or involved. In fact, pure intuition or analysis are ideal types rather than real types. In most cases, there is an interplay between analysis and intuition, even though the emphasis often is on one side or the other. For example, analysis may support the intuitive decision maker to assess partial aspects of the decision problem. On the other hand, St. Evans (2010, p. 320) argues “that even when people are reasoning, their focus may be restricted by preconscious intuitive processes”. Intuition can play a significant role in plausible reasoning, as individuals draw upon their past experiK 272 Schmalenbach Journal of Business Research (2025) 77:267–308 ences and implicit knowledge to arrive at conclusions that feel plausible or sensible. Additionally, the perception of coherence, which is essential for plausibility, is inherently tied to an intuitive impression (Betsch and Glöckner 2010).Sinceweassume in our framework that decision styles differ in their degree of intuitiveness, a parallel-competitive view is better suited for our purposes. Accordingly, the framework to be developed in the following sections is based on a parallel-competitive view. 3 Theoretical Concepts of the Decision Style Framework 3.1 Ecological Rationality in Decision Making Decision making effectiveness is often tied to its ecological rationality. The concept of ecological rationality stems from Simon’s metaphor of rationality as a pair of scissors “whose two blades are the structure of the task environment and the computational capabilities of the actor” (Simon 1990, p. 7). According to Gigerenzer and colleagues, the ecological rationality of a decision making approach refers to the degree it is adapted to the structure of the environment (Gigerenzer 2019; Gigerenzer and Gaissmaier 2011; Todd and Gigerenzer 2007). Effective decision making therefore emerges from the fit between the structure of information processing of the individual and the structure of information in relation to a decision problem or task (Chater et al. 2018, p. 804). Even though Simon (1987,1993) and Gigerenzer and colleagues (Gigerenzer 2010; Gigerenzer and Regier 1996; Kruglanski and Gigerenzer 2011) are wellknown critics of dual-process theories, there is no contradiction between a parallelcompetitive view of information processing and the basic idea of ecological rationality. From such a view, both the information processing of the intuitive and the analytic system can be ecological rational “if it turns out that the behavior prescribed is well adapted to its goals—whatever those goals might be” (Simon 1993, p. 393). In general, effectiveness arises when the information processing requirements from a task correspond to the information processing capacities of the decision maker (Daft and Macintosh 1981; Winkler et al. 2015). If implicit information processing is better suited to processing task-relevant information than explicit information processing, an intuitive approach would be more effective (and vice versa), provided the decision-maker has the appropriate capacities. Consequently, the question to address is which task structures align with which type of information processing capacities. In order to approach this question, we examine three established and interconnected frameworks: Perrow’s (1967,1970)framework for the comparative analysis of organizations, Daft and Lengel’s (1986)framework of equivocality and uncertainty on information requirements and McIver et al.’s (2012,2013)knowledge-inpractice framework. Whereas the former two frameworks focus on task structure and associated information cues, the latter (third) framework is concerned with the knowledge structure of individuals engaged in accomplishing tasks. Additionally, we introduce a (fourth) decision style framework grounded in the knowledge-in-practice framework, aligning preferred decision making behavior with task structures. The fundamental characteristics or dimensions of the framework will be discussed at K Schmalenbach Journal of Business Research (2025) 77:267–308 273 the end of the chapter, while the derived types of decision styles will be elaborated in the subsequent chapter, in harmony with the respective types of the other three frameworks. Following the ideas of Driver and Mock (1975) and Macintosh (1981), we assume that the decision style of an individual is a cognitive trait which effects the way information is processed to accomplish tasks. This view proposes “that human information processing models should also consider the nature of the task faced by the individual as well as the way the individual interacts with the task” (Macintosh 1981, p. 41), with the latter representing the decision style. Although decision style is often regarded as a relatively stable individual trait, it is important to emphasize that decision styles are not fixed or unchangeable aspects of a person’s character. In research, it is common to assume that individuals have metacognitive control over choosing between analytical or intuitive judgments (Betsch and Glöckner 2010;Luoma and Martela 2021; Salas et al. 2010; Shapiro and Spence 1997; Thompson et al. 2011). While individuals may tend to adopt a specific decision style, experiments reveal that decision styles are flexible and can be influenced by situational factors (Ayal et al. 2015; Rusou et al. 2013). The decision of whether to trust one’s intuition or analytical results, even when contradictory, is a common scenario both within and outside organizations (Salas et al. 2010). The same applies to the question of whether to fall back on tried-and-tested solutions or try something new. We therefore assume that the degrees of intuitiveness and adaptiveness can vary within an individual. Nevertheless, while people can learn to modify their decision style to some extent, such adaptations may not completely override their predominant style. For example, someone with a predominantly analytical style might improve their intuitive responses through practice, yet under critical conditions they are still likely to rely more on analysis. Hence, while it may be difficult to change the nature of a task itself, our framework identifies two main approaches to ensuring effectivevaried unvaried elbazy l ana unanalyzable FITTask Cues Decision Knowledge Effectiveness achieved by matching information processing requirements and capacities uncertain certain unequivocal equivocal adaptive repetitive analytic intuitive unlearnable learnable explicit implicit Decision problem Perrow (1967) Daft and Lengel (1986) McIver et al. (2013) Fig. 1 Connections between the frameworks and their relation to decision making effectiveness K 274 Schmalenbach Journal of Business Research (2025) 77:267–308 Table 1 Dimensions across frameworks Reference First dimension Definition Second dimension Definition Author Task Analyzability Task analyzability refers to the degree to which there is an objective, computational procedure currently in place or potentially available for solving a given task. Variety Task variety refers to the number of exceptions encountered in a task, i.e. the degree to which the task is perceived as unfamiliar. Perrow (1967) Information cues Equivocality Equivocality refers to the existence of multiple and potentially conflicting interpretations about the nature and completion of a task and correlates with the degree the task is ill-structured. Uncertainty Uncertainty refers to the amount of information that is needed to complete a task and can be reduced by gathering and/or interpreting new information. Daft and Lengel (1986) Knowledge Tacitness The tacitness of a practice refers to the degree the necessary knowledge to accomplish a task is implicit in nature. Learnability The learnability of a practice reflects the type and amount of effort, study, accumulated comprehension, and expertise required to understand taskrelevant information. McIver et al. (2012) Decision style Intuitiveness Decision intuitiveness is the propensity of individuals to make decisions based on implicit knowledge within the intuitive system. Adaptiveness Decision adaptiveness pertains to the tendency and ability to adjust and thrive in varying circumstances by effectively responding to changes. Our extension K Schmalenbach Journal of Business Research (2025) 77:267–308 281 Decision intuitiveness describes the tendency of individuals to make decisions based on the implicit knowledge of the intuitive system. The higher the decision intuitiveness, the greater the tendency to make decisions based on implicit knowledge, and vice versa. As the intuitive system is based on the processing of implicit information and due to the “intuitive nature of tacit knowledge” in general (Nonaka and Takeuchi 1995, p. 9), the tacitness of the knowledge used is directly related to decision intuitiveness. The literature widely acknowledges that individuals differ in their style towards analytical and intuitive decision making. Individuals with a preference for intuition are assumed to make better intuitive decisions, and vice versa (Alaybek et al. 2022; Bullini Orlandi and Pierce 2020; Phillips et al. 2016; Woolhouse and Bayne 2000). From the perspective of dual-process theory, individuals exhibit varying preferences for either using the intuitive or the analytic system, and the degree to which a person tends to rely on either of these cognitive systems is considered an individual differences variable (Salas et al. 2010). Regarding Perrow’s and Daft and Lengel’s frameworks, several clues can be found in the literature that suggest a fit between decision intuitiveness on the one side and task analyzability and information equivocality on the other side. For Perrow (1967), the accomplishment of unanalyzable tasks is associated with experience or intuition, whereas analyzable tasks can be accomplished “on a logical, analytical basis” (Perrow 1967, p. 196). Concerning Perrow’s task analyzability concept, Nutt (1976) contends that high task analyzability corresponds to an analytical foundation for decision making, while low task analyzability is characterized by the lack of such a basis, relying instead on intuition and experience. Similarly, Daft and Lengel (1986) argue that, when tasks are unanalyzable, individuals “rely on judgment and experience rather than on rules or computational routines” (Daft and Lengel 1986, p. 564). Daft later expanded this statement to include the aspect of intuition, arguing that, when analyzability is low, “employees rely on accumulated experience, intuition, and judgment” (Daft 2021, p. 338). Moreover, Withey et al. (1983) argue that decisions based on intuition and experience play an important role in unanalyzable tasks, while high analyzability is suited to the application of a logical-analytical procedure. Thus, when tasks can be decomposed and solved in a step-by-step manner (high analyzability), effective decisions can be reached by analytically executing a set of rules and sequences. When tasks are relatively non-decomposable (low analyzability), intuition may prove effective as it allows holistically approaching atask. In addition, Daft and Lengel state that equivocal situations are “ill-defined to the point where a clear answer will not be forthcoming” (Daft and Lengel 1986, p. 557). In the intuition literature, the structuredness of a task—ranging from well-defined tasks (high structuredness) to ill-defined tasks (low structuredness)—is a widely accepted criterion to determine the appropriateness of an intuitive approach in light of a given task. The less a task is structured, the more intuition becomes crucial (Adinolfi 2021; Dane and Pratt 2007;Julmi2019; Rusou et al. 2013; Shapiro and Spence 1997). We therefore assume that a high level of decision intuitiveness is best suited for processing equivocal information. K 282 Schmalenbach Journal of Business Research (2025) 77:267–308 Although the relationship between intuitiveness and equivocality has hardly been investigated empirically to date, initial study results support the assumptions discussed here. For instance, Rusou et al. (2013) have demonstrated through experiments that intuition is more effective than analysis in poorly structured tasks, whereas the opposite is true for well-structured tasks. In another experimental setting, Fellnhofer and Deng (2024) found that in unstructured environments like crowdfunding, where emotional aspects play a significant role, intuition can be more effective, fostering gender equality among investors. Furthermore, Adjerid et al. (2023) show in their longitudinal study that the effectiveness of clinical data analytics in relation to patient experience quality is lower in hospitals dealing with complex cases characterized by high levels of equivocality. This suggests that the effectiveness of an analytical approach (where intuitiveness is low) decreases with increasing equivocality. We suggest that decision styles also differ in their degree of adaptiveness. With the term decision adaptiveness, we refer to the tendency and ability of an individual to adjust and thrive in varying circumstances by effectively responding to changes. Like decision intuitiveness, decision adaptiveness describes a continuum from low to high adaptiveness. In most adaptations, there is a reference to the past, and in most repetitions, there is some degree of variation (Barron 1988), with various degrees of adaptiveness within the continuum (Sternberg et al. 2002). In their study, Fasolo et al. (2003) show that individuals with higher openness to experience and reasoning abilities exhibit more adaptability in their decision making. Since new experiences generate implicit knowledge and the reasoning ability requires analytical skills, this finding suggests that the adaptiveness of decisions can be conceptualized as independent of their intuitiveness. The study further reveals that between-subjects differences in adaptiveness remain stable across various tasks. This underlines the importance of matching the type of information to be processed with the decision style in terms of adaptiveness. When the adaptiveness of a decision is low, the decision is merely a reproduction of previous solutions, i.e. individuals tend to make decisions in a repetitive style. In case adaptiveness is high, the individual seeks to creatively develop new solutions to meet the actual requirements of the decision task. We thus assume that it is not sufficient to look at analytical versus intuitive decision making alone to make claims about their ecological rationality. From our perspective, individuals with a highly adaptive decision style are presumed to align well with tasks characterized by high variability. On the other hand, individuals with a low adaptiveness in their decision style are presumed to be suitable for tasks characterized by low variability. This is also in line with Perrow’s framework. For him, variability is a lack of stability. When variability is high, “continual adjustment” to accomplish tasks is necessary, whereas tasks of low variability “can be treated in a standardized [i.e. non-adaptive] fashion” (Perrow 1967, p. 197). As a consequence of the postulated independence of the intuitiveness and adaptiveness of a decision style, intuition can be a highly adaptive process but may also be a reproduction of previously acquired learning patterns. This aligns with literature recognizing that intuition has both the capacity to reproduce established solutions and to synthesize elements to generate innovative solutions (Akinci and SadlerK Schmalenbach Journal of Business Research (2025) 77:267–308 283 Smith 2012; Glöckner and Witteman 2010; Gobet and Chassy 2009; Sleesman et al. 2024). In situations that are unfamiliar and unique, individuals with an intuitive, but not an adaptive decision style may overly rely on established solutions that worked in the past but no longer do. On the other hand, individuals with an intuitive and adaptive decision style may fail to show the necessary consistency in tasks with low variability. Likewise, an analytical approach can consist of both the simple application of existing rules (low adaptiveness) and the complex transfer of a large amount of explicit information to a new situation (high adaptiveness). Thus, and analogously to the frameworks discussed, four types of decision styles can be derived from the two dimensions of decision intuitiveness and decision adaptiveness. We specify these in the following chapter and discuss them in connection with the dimensions of tasks, information cues and knowledge introduced in this chapter. 4 Types of Decision Styles and Their Task-Related Effectiveness 4.1 A Reductionist Perspective of Fit In the following, the article develops a framework on decision making that is in line with the outlined models of task characteristics, information cues and knowledge structure. By considering perspectives on task characteristics and knowledge structure, the framework takes into account the demand that “the elements of a psychological theory of decision making must include a concern for task structure, the representation of the task, and the information processing capabilities of the organism” (Einhorn and Hogarth 1981, p. 61). Our framework follows a reductionist perspective of fit (as opposed to a holistic perspective). This perspective is based on the assumption that the fit between constructs (such as information cues and decision style) can be conceptualized in terms of pairwise coalignment among the individual dimensions of the constructs (Venkatraman and Prescott 1990). From the dimensions of decision intuitiveness and adaptiveness, we derive four types of decision styles (Scherm et al. 2016): (1) mechanistic decision style (low adaptiveness, low intuitiveness), (2) evidence-based decision style (high adaptiveness, low intuitiveness), (3) habitual decision style (low adaptiveness, high intuitiveness), (4) improvisational decision style (high adaptiveness, high intuitiveness). In line with the parallel-competitive view, the framework assumes that different types of decision styles draw on different types of knowledge. Figure 2depicts our decision style framework. In accordance with the discussed frameworks, the four types are treated as extreme positions on a continuum. In practice, the various decision styles encompass a spectrum that relies both on the analytic and intuitive systems to different extents, resulting in decisions that can be characterized as more or less adaptive. For example, a highly intuitive decision style may still consider explicit information and process them holistically, just as analytical decision styles may consider intuitive judgments as discrete variables and process them sequentially (Julmi 2024). As we will show, each of the four decision styles can be aligned with different degrees of uncertainty and equivocality. Our framework provides insights into those task charK 284 Schmalenbach Journal of Business Research (2025) 77:267–308 low high ytniatrecn U low high Equivocality Mechanistic decision style is based on explicit sets of rules that cover all potential cases of a decision problem Evidence-based decision style proficiently utilizes explicit knowledge to recombine rules in a productive way Improvisational decision style meaningfully links existing patterns and uniquely applies them to the current context Habitual decision style relies on implicit knowledge and uses familiar aspects to reproduce patterns Repetitive decision making Adaptive decision making gnikamnoisiced lacityl a nA Intuitive decision making Fig. 2 Effectiveness of decision styles in relation to uncertainty and equivocality acteristics that best suit a specific decision style (and vice versa). The central idea underscores the importance of ensuring that decision approaches align harmoniously with the diverse organizational contexts they aim to serve. We will now discuss the four types of decision style in more detail. 4.2 Mechanistic Decision Style A mechanistic decision style refers to an approach to decision making characterized by a systematic and rule-based process that follows established procedures with minimal deviation. Individuals inclined toward this style tend to strictly adhere to unequivocally predefined rules, procedures, or algorithms, and they “prefer to work under organizational arrangements which feature well understood rules, clear reporting lines and short spans of control” (Macintosh 1981, p. 40). The style is characterized by a reliance on explicit information, treated as templates for decision making, and involves processing a relatively low volume of information. Those who lean toward this style find standardized decisions easily manageable, and individuals equipped with sufficient task-related explicit information can readily make such decisions. In this sense, the decision making process is mostly carried out in a manner akin to a mechanical or predetermined system. An example of a mechanistic decision style is observed in choices like accepting or declining a loan offer, where the decision stages are well-documented and should be strictly followed in a replicable manner, aligning with the inherent predisposition of individuals toward this pre-structured decision style. Another example is withdrawing money at a bank counter. The bank teller checks whether the customer’s account balance and credit rating are sufficient. If this is the case, the employee hands over the money. The task is easy to learn and leaves no room for interpretation (Macintosh 1981). The derivation of the optimal order quantity, which K Schmalenbach Journal of Business Research (2025) 77:267–308 285 minimizes the problem of stocking too many or too few products, is also an instance of a setting that matches a mechanistic decision style. Here, the optimal solution can be unequivocally determined, while research has shown that intuitive inventory decisions lead to human errors and biases (Yamini 2021). In such settings, intuition is an inferior strategy, which at best coincides with the optimal solution. In sectors like manufacturing, a mechanistic decision style is assumed to be highly effective. Consider a production line where tasks are well-defined, and each step is pre-structured. Employees follow standardized procedures, relying on explicit rules and clear guidelines. This approach ensures efficiency and consistency in output. For instance, in quality control processes, the use of predetermined criteria and systematic checks allows for reliable and replicable decision making. The mechanistic decision style’s effectiveness lies in its ability to streamline operations, minimize errors, and maintain a high level of predictability in outcomes (Choi and Lee 2003). Moreover, in situations requiring rule adherence, the mechanistic decision style is ideal, particularly for compliance problems. For instance, when organizations set clear rules or codes of conduct, individuals with this decision style tend to excel. In such scenarios, where there’s no room for interpretation or moral equivocality, this style ensures that individuals conform to established guidelines effortlessly. For instance, legal compliance programs benefit from the straightforward approach of this decision style, fostering predictability in employee behavior and addressing conformity challenges associated with rule compliance (Julmi 2024). A well-known experimental setting that matches a mechanistic decision style is the so-called “Linda-problem” (Tversky and Kahneman 1983). In the associated experiment, test persons must decide which of the following two statements is more probable: (A) Linda is a bank teller, or (B) Linda is a bank teller and is active in the feminist movement. Although the correct solution (A) can be unequivocally derived from the rule of probability theory that a subset cannot be more likely than a larger set that includes the subset, most persons intuitively chose (B). Such a conjunction fallacy can also be found in organizational settings. Take, for example, a project manager who must assess two presented potential risks associated with a product launch: (A) The product launch will face significant supply chain delays, and (B) The product launch will face significant supply chain delays and increased regulatory scrutiny. Although incorrect, the project manager may intuitively assess (B) as more probable because it paints a more vivid and comprehensive picture of potential issues. In such settings, intuitive decision styles are a mismatch, as intuitive outcomes at best coincide with the optimal solution. To summarize, the mechanistic decision style is assumed to be effective when both uncertainty and equivocality are low. Explicit rules, procedures and standards provide a fixed and objective body of knowledge which individuals can easily learn to make proper decisions. There is no room for interpretation. Cause and effect relations between decisions and outcomes are clear and reliable. As exceptions occur only occasionally, decisions can be pre-structured in an easy-to-follow way. K 286 Schmalenbach Journal of Business Research (2025) 77:267–308 4.3 Evidence-Based Decision Style Like the mechanistic decision style, the evidence-based decision style relies on explicit sets of rules. However, in this decision style, these rules are not simply followed to make a pre-structured decision. Instead, individuals exhibiting this style combine and connect existing rules in an adaptive manner. This adaptive approach results in decisions that align with existing knowledge but are unique in their recombination. While the individual steps of decision making remain comprehensible to outsiders, the approach showcases the skillful utilization of a comprehensive and domainspecific body of explicit knowledge, which is open to further extensions. McIver et al. (2013, p. 601) characterize the underlying knowledge of such an approach as “10,000h of study”; whereas the tacitness of seized knowledge is low, “the amount of information is vast, and new information is often needed both to fit new work conditions and to accommodate exceptions during execution”. Aligned with Mintzberg’s perspective, the evidence-based decision style holds particular significance in roles characterized as “complex, involving difficult, yet specified skills and sophisticated recorded bodies of knowledge—jobs essentially professional in nature” (1979, p. 99). These include, for example, doctors, lawyers or engineers. Furthermore, the evidence-based decision style is integral to the contemporary evolution of evidence-based management, which is about making decisions through the conscientious, explicit and judicious use of the best available evidence from multiple sources by: asking an answerable question; acquiring research evidence; appraising the quality of the evidence; aggregating the evidence; applying the evidence in decision making, and assessing the outcomes of the previous steps (Rynes and Bartunek 2017, p. 239). Professional jobs, as defined by Mintzberg, necessitate a predominant reliance on analysis. The complex and specialized nature of these roles demands a systematic approach to problem-solving, with rigorous analysis taking the lead. While intuition may have a supportive role, it should be considered a supplement rather than a primary driver. The intricacies of professional jobs underscore the importance of precision and reliability that analytical methods bring. Simultaneously, adaptive decision making becomes imperative, accommodating the unique demands of each situation. Daft (2021) contends that tasks in engineering and accounting, despite their high variety, also exhibit high analyzability, allowing for systematic breakdown into manageable components. Even though engineering and accounting tasks may involve a wide range of elements, they still allow for systematic analysis and can be approached methodically. For example, in engineering, designing a complex system may involve various components, each with unique features. Despite this variety, engineers can apply analytical methods to break down the design process into specific steps, ensuring a systematic approach to problem-solving. Similarly, in accounting, financial transactions and statements can vary in complexity and nature. Despite this variability, accountants can employ analytical frameworks and standardized procedures to analyze and process financial data systematically, maintaining a structured and organized approach to their tasks. In such tasks, analytical methods provide K Schmalenbach Journal of Business Research (2025) 77:267–308 287 a systematic and objective framework for breaking down complex problems into manageable steps. This systematic analysis ensures a thorough understanding of underlying contingencies and interactions, reducing the reliance on subjective judgment or intuition. In contrast, an intuitive approach may lack the precision and structured process needed to handle the diverse elements and intricacies involved in these tasks. Therefore, an analytical approach is considered more appropriate for maintaining accuracy and reliability in decision making within the realms of engineering and accounting. At the same time, adaptive decision making is favored over repetitive decision making because of the high variety inherent in these tasks. Adaptability enables professionals to address the diverse and evolving nature of challenges in engineering and accounting, accommodating changes in circumstances or unique aspects of each situation. This flexibility is crucial for navigating the complexities of tasks that involve a broad range of elements, making adaptive decision making more effective than a rigid, repetitive approach. Building upon these arguments, we posit that evidence-based decision styles demonstrate effectiveness in situations characterized by high uncertainty but low equivocality. In high uncertainty scenarios, where exceptions are frequent and taskrelevant information undergoes constant change, an adaptive approach that renews and recombines information is crucial. In such contexts, mechanistic decision styles are expected to be ineffective, as their rigidity hinders the processing of new information and adaptation to a dynamic environment. However, effectiveness is attributed to evidence-based decision styles only when, in addition, equivocality is low. In contexts where multiple or conflicting interpretations are possible, primarily relying on explicit and unequivocal information becomes problematic. This is because, in situations open to various valid interpretations, favoring one interpretation over equally valid alternatives can distort the decision task. 4.4 Habitual Decision Style In the habitual decision style, individuals exhibit a preference for relying on implicit knowledge and making decisions based on well-established patterns ingrained through experience. The decision-maker adeptly replicates patterns from earlier situations, seamlessly applying them in the current context. Decisions effortlessly emerge from implicit knowledge, allowing for instant responses without the need for additional information. With a focus on familiarity rather than uniqueness, individuals embracing this style confidently lean on ingrained habits and past experiences to guide their decisions. Because similar decisions have been made over and over again in the past, they can be executed in an automatic fashion that is beyond conscious control (Betsch 2008). Although the underlying knowledge is mostly tacit, it can be acquired “by recreating and rehearsing the experiences needed to gain this know-how. Learning takes place by doing and is based on experiencing what works and what doesn’t and recreating activities through repetition” (McIver et al. 2013, p. 601). For example, the decisions of fashion retailers about how to deal with customers are supposed to be highly repetitive, but should mostly rely on acquired implicit knowledge that includes the recognition of the customers’ identification K 288 Schmalenbach Journal of Business Research (2025) 77:267–308 with a brand and a qualitative understanding of their associated lifestyle (Cutcher and Achtel 2017). The notion of intuition as habits largely stems from Simon, recognizing the value of habitual patterns in decision making (Simon 1987,1997). For Simon, intuition “enables the expert’s rapid recognition of and response to situations that are marked by familiar cues, and thereby give access to large bodies of knowledge assembled through training and experience” (Simon 1997, p. 331). From this perspective, habit becomes internalized through repeated experience, manifesting as unconscious and automatic responses to situational cues. Daft (2021) provides several examples of tasks with low variability and low analyzability that match an habitual decision style: Steel furnace engineers persist in blending steel relying on intuition and experience; pattern makers in renowned fashion houses like Louis Vuitton or Zara adeptly transform rough designer sketches into marketable garments; teams of writers for television series such as Game of Thrones or This is Us skillfully translate conceptual ideas into compelling storylines. These tasks involve a notable degree of equivocality, suggesting a lack of clear, objective information or predetermined procedures. While these tasks may not be inherently associated with uncertainty in the traditional sense, their equivocal nature requires a decision making approach that leans towards intuition rather than strict analysis. Given the equivocal nature of these tasks, an intuitive decision making approach becomes essential. This approach relies on the tacit knowledge and experience of individuals involved in these creative processes. Furthermore, a repetitive decision style, rather than an adaptive one, is suitable for such tasks. In contrast, an adaptive decision style may introduce variability that could compromise the reliability and consistency required in these processes. Repetition, based on accumulated knowledge and successful past practices, provides a stable foundation for decision making in these tasks, making a rather repetitive style more favorable for achieving consistent and successful outcomes. Therefore, our framework suggests that the habitual decision style is effective when uncertainty is low, but equivocality is high. Due to the low uncertainty, the individual with such a decision style can easily fall back on previous experience. The context remains stable, so there is no need to alter routines or consider new aspects. At the same time, the situation is ill-structured and can hardly be formalized. The decision maker thus frequently faces information cues that are equivocal and need to be adequately interpreted in a holistic manner. 4.5 Improvisational Decision Style An improvisational decision style is characterized by its adaptability, creativity, and the ability to make rapid decisions in messy and unpredictable situations. Decisionmakers utilizing this style embrace equivocality and are comfortable navigating uncertainty. This decision making approach relies on the individual’s experience and intuition, allowing for spontaneous solutions without strict adherence to predetermined rules, extensive planning, or habitual patterns. We use the term improvisational decision style to consider adaptive aspects of intuition in decision making. This is in line with the literature viewing intuition as K Schmalenbach Journal of Business Research (2025) 77:267–308 289 a defining aspect of improvisation (Fisher and Barrett 2019; Leybourne and SadlerSmith 2006; Müller 2011; Roux-Dufort and Vidaillet 2003). For example, Hatch defines improvisation as “intuition guiding action upon something in a spontaneous but historically contextualized way” (Hatch 1997, p. 181). In improvisation, the decision maker processes implicit information in an adaptive way. The focus is on knowledge about how to find unique solutions in the sense of “improvisatory capabilities” (Leybourne and Sadler-Smith 2006, p. 483), as opposed to exploiting existing knowledge in the habitual decision style. Similarly, scholars draw distinctions between technical craft and creative craft. Technical craft primarily relies on apprenticed know-how, reflecting a habitual decision style, while creative craft leans more on talent and adaptivity, aiming for exceptional outcomes and embodying an improvisational decision style (Kroezen et al. 2021). An instance where the improvisational decision style is particularly suitable in organizations is crisis management. In situations marked by high equivocality, where the reality of a crisis can be rapidly shifting and challenging to define, and in the face of uncertainty, where the unfolding events are unknown and unpredictable, decisionmakers need to adapt swiftly. For example, during a cybersecurity breach or a natural disaster, the inherent multifacetedness and unpredictability demand an improvisational approach. Individuals employing this decision style can navigate through the equivocality and uncertainty by relying on their experience and intuition. However, they are also capable of creatively formulating on-the-spot solutions, acknowledging that the reality of the crisis might evolve rapidly, and strict adherence to predefined rules or detailed planning may not align with the dynamic nature of the unfolding events. The adaptability and comfort with equivocality in the improvisational decision style make it particularly effective in managing crises where the unknown and rapidly changing circumstances require quick and creative responses. The framework from McIver et al. indicates that the improvisational decision style may be reserved for individuals with unique talent, such as scientific geniuses or famous artists. They provide the example of “competent leaders who know instinctively how to diffuse dysfunctional conflict while maintaining teamwork and collaborative relationships” (McIver et al. 2012, p. 96). We may not fully endorse that viewpoint, but we recognize that achieving effectiveness in an improvisational decision style often demands an intuitive mastery. On the one hand, existing patterns are meaningfully linked and applied to the current context in a unique way. On the other hand, new experiences lead to new patterns that may even contradict existing patterns. In negotiations, for example, the parties try to identify familiar patterns, while at the same time these are related to the actual situation in a flexible way, since the motives and intentions of the others are precisely not apparent from what has been explicitly stated. We propose that the improvisational decision style is effective in a context that is unpredictably equivocal (Colville et al. 2012). As exceptions frequently occur, it is not sufficient to reproduce previous solutions. A habitual decision style neglects the changing and new aspects of the decision making context, treating new problems with old solutions that worked in the past but no longer do (Brunsson and Brunsson 2017). Although habitual decision making is fast and effortless, it can be quite maladaptive (Alaybek et al. 2022). An evidence-based decision style should be inefK 290 Schmalenbach Journal of Business Research (2025) 77:267–308 fective as well because it is based on explicit facts that do not correspond to the illstructured nature of the task. Only an improvisational decision style meets the taskrelated requirement to consider new solutions in a holistic and context-sensitive way. However, while the improvisational decision style offers agility and adaptability, it may not be suitable for all situations, particularly those requiring a more structured or evidence-based approach. The effectiveness of this style depends on the decision task and the decision-maker’s ability to navigate complexity while embracing the spontaneity inherent in the decision making process. 5 Illustration of the Framework Through Four Mini Case Studies To illustrate the proposed fit between decision styles and task characteristics, this chapter presents four prototypical tasks, each corresponding to one decision style in our framework in terms of effectiveness. 5.1 Case 1: The Compliance Task St. Alfonzo Investments, a leading financial services firm, places a high priority on adherence to compliance principles. Due to the sensitive nature of financial transactions and the regulatory environment, ensuring compliance with established rules and regulations is critical. To streamline this process, the company has a dedicated compliance task force that reviews and approves all transactions. The compliance task involves reviewing financial transactions to ensure they adhere to regulatory requirements and internal company policies. The task requires precision, consistency, and a strict adherence to predefined rules and procedures. Any deviation from these rules can result in severe penalties for the company. In such an environment, the mechanistic decision style proves to be the most effective. The nature of compliance tasks demands strict adherence to predefined rules to ensure accuracy and consistency. A systematic and rule-based approach minimizes errors and ensures that all transactions meet the required compliance standards. While the evidence-based style brings valuable adaptability and context consideration, it introduces variability and can be time-consuming. The habitual style, although fast, lacks the necessary accuracy and consistency, making it less suitable for tasks requiring strict compliance. In environments where adherence to compliance principles is critical, a mechanistic decision style is the most effective approach. This style ensures that all regulatory and policy requirements are meticulously followed, thereby safeguarding the organization against potential compliance breaches. 5.2 Case 2: The Accounting Task Apostrophe Worldwide, a large multinational company, deals with a wide range of financial transactions and statements that vary greatly in complexity and nature. This variability introduces a high level of uncertainty, but the underlying financial principles and regulatory requirements remain clear and well-defined. To handle these K Schmalenbach Journal of Business Research (2025) 77:267–308 297 trast, thwarting heuristic impulses and embracing cognitive conflict when making decisions was shown to reduce cognitive bias. Beyond heuristic processing, research has only recently started to identify cases in which cognitive biases are caused by analytical thinking (Ayal et al. 2015; Macpherson and Stanovich 2007; Rusou et al. 2013; Wong et al. 2008), although “there is ample evidence to show that even effortful, persistent, and highly conscious and controlled attempts to solve logical problems can produce strong biases” (Fiedler and v. Sydow 2015, p. 154). A prototypical example of an analysis bias is what Harris and Tayler (2019) have labeled the surrogation snare. The term surrogation refers to the tendency to cognitively replace strategy with metrics. As a result, people lose sight of the company’s strategy and instead focus on the metrics designed to represent it. Whilst this creates analytical clarity, the holistic view of the strategy gets lost. March (1978, p. 603) has already pointed out that this is problematic: Where contradiction and confusion are essential elements of the values, precision misrepresents them. The more precise the measure of performance, the greater the motivation to find ways of scoring well on the measurement index without regard to the underlying goals. And precision in objectives does not allow creative interpretation of what the goal might mean. To prevent surrogation, Harris and Tayler (2019) recommend loosening the connection between metrics and incentives, employing multiple metrics, and involving individuals in strategy formulation. In our interpretation, to mitigate cognitive bias, they propose maintaining equivocality to allow for a more improvisational and, consequently, more effective engagement with the strategy. In general, our framework allows recognizing constellations in which an analytical approach is exposed to an increased risk of cognitive biases compared to an intuitive one. In examining habitual and improvisational decision styles and the prototypical cases 3 and 4 (retail task and crisis management task), we have given several examples where an analytical approach is ineffective and, in this sense, subject to greater cognitive bias when compared to an intuitive approach. In this way, the assumptions of the framework oppose the belief that systematic errors only occur when people make intuitive decisions (instead of analytic ones). Nevertheless, the effectiveness of habitual and improvisational decision styles warrant closer examination, as decision effectiveness in conditions of high equivocality depends not only on the degree of intuitiveness but also on the degree of adaptiveness. For example, the surrogation snare highlights a cognitive bias that may emerge either through analytical decision making or intuitive decision making characterized by low adaptiveness. While existing research has extensively cataloged cognitive biases tied to intuitive decision making (see, for example, Kahneman 2011), we advocate for future studies to work on a comparable taxonomy of cognitive biases associated with the shortcomings of analytical decision making. However, such efforts should also account for the level of uncertainty in tasks, emphasizing the role of adaptiveness as a critical factor influencing whether a decision style is likely to cause cognitive biases or not. In some tasks, a habitual decision style may be more likely to cause cognitive bias as compared to an improvisational decision style, and vice versa. It is therefore not sufficient to compare an intuitive K 298 Schmalenbach Journal of Business Research (2025) 77:267–308 and an analytic decision style in a given task without reference to decision style’s adaptiveness. 6.3 Testing, Enhancing and Applying the Framework With our work, we call on future research to empirically test the predictions of our framework. In particular, we urge researchers to investigate the relative effectiveness of the four decision styles under varying levels of uncertainty and equivocality. In addition, the conceptualization of our framework gives rise to further interesting research questions. As we have outlined earlier, our framework proposes two key strategies for ensuring decision making effectiveness. The first strategy involves selecting individuals whose decision style and corresponding knowledge base suits the demands of a particular task. This approach focuses on identifying people whose tendencies, expertise, and cognitive patterns align with the requirements of a task. The second strategy involves adapting a person’s decision style so it aligns more closely with the needs of the task, an approach that can be facilitated through training programs, mentorship, or structured feedback loops. Although this route may require a greater investment of time and resources, it enables organizations to develop internal talent and foster versatility among team members. Within these two broad strategies, research could examine the organizational and contextual conditions under which it is more prudent to select an individual with a preexisting style suited to the task versus attempting to adapt someone already in place. This might involve assessing the extent to which urgency, training costs, and an individual’s inherent adaptability influence the likelihood of successful outcomes. An additional area for future research concerns the variability in individuals’ decision style preferences. While some individuals strongly favor a single decision making approach, others display greater flexibility, using multiple styles or showing no dominant preference. In two recent studies, Bakken et al. (2024) found that individuals who strongly favored either an intuitive or an analytic cognitive style were outperformed by those who held a strong preference for both styles or those with no clear preference for either. However, these studies primarily focus on the intuitive–analytic distinction. Extending this perspective, our framework allows for a broader examination of decision making effectiveness across various styles. Such investigations promise to provide a more comprehensive understanding of how multiple decision styles interact and influence task performance. Moreover, we would like to briefly discuss a task type or, more precisely, a problem type that extends our frameworks scope: wicked problems. The term ‘wicked problem’ in managerial decision making is defined as a complex, intractable challenge that defies traditional problem-solving approaches (Hogarth 2001; McMillan and Overall 2016). The distinctive characteristic of wicked problems with regard to our framework is that the level of uncertainty is so high that it merges with equivocality, blurring their distinction (Julmi 2024). According to Raadschelders and Whetsell (2018, p. 1132), wicked problems “are unique, involve many different stakeholders, concern issues of which the causes are uncertain, and can only be resolved partially and temporarily since changing time and context will demand continuous adaptation of policy”. Adequate coping with wicked problems involves considering K Schmalenbach Journal of Business Research (2025) 77:267–308 299 the broader social-ecological context, co-evolutionary dynamics, and transformative change, rather than focusing solely on decision making effectiveness (Grewatsch et al. 2023). While there is no doubt that a decision style suitable for wicked problems should be characterized by high adaptiveness, the question of a suitable degree of intuitiveness cannot be answered unambiguously from the framework. In line with Julmi’s (2024) arguments, we assume that relying solely on intuition is ineffective in wicked problems, as the problem-relevant environment lacks adequate validity. That is, the environment lacks the necessary causal and statistical structure, preventing the provision of representative sets of cases essential for the development of intuitive skills (Kahneman and Klein 2009). Conversely, an excess of unorganized explicit information could potentially overload the information processing capacity of the analytical system, necessitating a holistic approach to information processing (Pretz 2011). Moreover, in wicked problems, “there are no ‘solutions’ in the sense of definite and objective answers” (Rittel and Webber 1973, p. 155). Wicked problems often demand interpretive evaluations and an integrated consideration of various interpretations, underscoring the ongoing significance of adaptive intuition in assessing such complex issues. Hence, it appears that neither a strictly evidencebased nor an entirely improvisational decision style proves effective in handling wicked problems. Rather, a blend of adaptive analysis and adaptive intuition seems essential to some extent. Future research could investigate how both evidence-based and improvisational decision styles perform in tackling wicked problems or expand our framework by proposing a new hybrid style that merges the two. Although we position our work as a theoretical contribution to research, we also recognize its significant practical relevance. First, our framework provides a structured approach for determining how a specific decision task should be effectively approached. To determine the structure of a task, managers could apply specific criteria to assess the degrees of uncertainty and equivocality inherent in a task. For instance, McCaskey (1982) offers a set of indicators that can help evaluate a problem’s level of equivocality, such as the presence of multiple or conflicting interpretations of the same information or ambiguity regarding the nature of the problem itself. By applying these criteria, managers can enhance the effectiveness of decision making processes, both proactively and retrospectively, ensuring decisions are better aligned with the nature of the task (Julmi 2018). Second, the framework can also serve as a valuable staffing tool by helping organizations align individuals with tasks that match their capabilities and decision styles. By assessing an individual’s capacities, knowledge, and preferred decision style, organizations can ensure that each task is assigned to personnel equipped to handle it effectively and confidently. This targeted alignment can lead to more successful task completion and enhanced organizational performance. Third, our framework draws attention to the development of implicit knowledge and strategies for managing equivocality for improving intuitive decision making. Practitioners can enhance implicit knowledge through hands-on experience and on-the-job learning (Salas et al. 2010). Furthermore, they can improve their ability to handle equivocality through training in ambiguity tolerance (Neill and Rose 2007), mindfulness, and both/and thinking (Smith et al. 2012). In light of these practical implications, future research could expand on these points by developing a more detailed and nuanced methodology for applying our framework in organizaK 300 Schmalenbach Journal of Business Research (2025) 77:267–308 tional settings. Such advancements would further bridge the gap between theoretical insights and practical utility, enhancing the framework’s relevance and applicability in real-world contexts. 7Conclusion Our framework implies that uncertainty alone is not a predictor of the effectiveness of a decision style that is high in intuitiveness. Instead, the effectiveness of such a style is tied to equivocality. In unanalyzable decision tasks, the individual is confronted with equivocal cues that call for an intuitive approach building on implicit information processing. This is consistent with Dane and Pratt’s (2007) proposition that the relationship between uncertainty and intuition effectiveness is mediated by the structuredness of the task as the latter is associated with equivocality here. The more the decision maker is confronted with equivocal cues, the less the task can be solved analytically, and the more intuition becomes effective. In contrast, a decision style that is low in intuitiveness is assumed to be preferable when uncertainty is unequivocal. However, when uncertainty and equivocality co-occur, information processing requirements match with the improvisational decision style which is both adaptive and intuitive. The article has shown that the distinction between uncertainty and equivocality is crucial in assessing the appropriateness of either an intuitive or an analytic approach. Furthermore, it highlights that there is not a single intuitive or analytic style; instead, it is essential to also consider the extent to which the decision style in question exhibits adaptiveness. Thus, the introduced framework is designed to help researchers assess the effectiveness of decision styles in a specific task. From our point of view, the presented approach has a high explanatory power with respect to the inconsistency of effectiveness under uncertainty outlined at the beginning. For example, the intuitive biases observed by Kahneman and Tversky (1973)inthe context of uncertainty can be explained by the fact that the authors focus on wellstructured tasks where there is an unequivocal correct solution (i.e. equivocality is low). Accordingly, a mechanistic decision style would be assumed to be more effective compared to decision styles with a high degree of intuitiveness. In the case of clinical decisions considered by Hall (2002), we assume that clinical practice is professional in the sense Mintzberg proposed. We therefore hypothesize that, despite high uncertainty, there is little equivocality and clinical decisions matches with an evidence-based decision style. In contrast, those approaches that view intuition as beneficial in the presence of uncertainty consider situations characterized by both high uncertainty and high equivocality. In the end, we also see a practical value in our theoretical arguments. In organizations, intuition still lacks legitimacy compared to analysis. To gain or maintain legitimacy, decision makers often have to rationalize their intuitive decisions posthoc. As Brunsson and Brunsson (2017, p. 65) put it: “Were decision makers to refer to their intuition or their feelings, they would probably be greeted with either compassion or contempt”. But when even successful entrepreneurs like Jeff Besos or Steve Jobs have repeatedly emphasized the unique value of intuition, why should inK Schmalenbach Journal of Business Research (2025) 77:267–308 301 tuition not be a legitimate way of making decisions within organizations in practice? To support this thought, our framework allows decision makers and organizations to analytically justify the effectiveness of intuition. Acknowledgements Many thanks to Ewald Scherm and Florian Lindner for their invaluable contributions through countless, yet always fruitful discussions on intuition effectiveness and for their pivotal role in developing the decision style framework. His beate! Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Availability of data and materials Not applicable. Declarations Conflict of interest C. Julmi declares no conflict of financial or non-financial interests that are directly or indirectly related to the work. Ethical standards Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4. 0/. References Adinolfi, Paola. 2021. A journey around decision-making: Searching for the “big picture” across disciplines. European Management Journal 39(1):9–21. Adinolfi, Paola, and Francesca Loia. 2022. Intuition as emergence: Bridging psychology, philosophy and organizational science. Frontiers in Psychology 12:787428. Adjerid, Idris, Corey M. Angst, Sarv Devaraj, and Nicholas Berente. 2023. Does analytics help resolve equivocality in the healthcare context? Contrasting the effects of analyzability and differentiation. Journal of the Association for Information Systems 24(3):882–911. Agor, Weston H. 1986. The logic of intuition: how top executives make important decisions. Organizational Dynamics 14(3):5–18. Akinci, Cinla, and Eugene Sadler-Smith. 2012. Intuition in management research: a historical review. International Journal of Management Reviews 14(1):104–122. Alaybek, Balca, Yi Wang, S. Dalal Reeshad, Samantha Dubrow, and Louis S.G. Boemerman. 2022. The relations of reflective and intuitive thinking styles with task performance: a meta-analysis. Personnel Psychology 75(2):295–319. Alós-Ferrer, Carlos, and Fritz Strack. 2014. From dual processes to multiple selves: Implications for economic behavior. Journal of Economic Psychology 41:1–11. Ayal, Shahar, Zohar Rusou, Dan Zakay, and Guy Hochman. 2015. Determinants of judgment and decision making quality: the interplay between information processing style and situational factors. Frontiers in Psychology 6:1088. Bakken, Bjørn T., Mathias Hansson, and Thorvald Hærem. 2024. Challenging the doctrine of “non-discerning” decision-making: investigating the interaction effects of cognitive styles. Journal of Occupational and Organizational Psychology 97(1):209–232. K 302 Schmalenbach Journal of Business Research (2025) 77:267–308 Baldacchino, Leonie, Deniz Ucbasaran, Laure Cabantous, and Andy Lockett. 2015. Entrepreneurship research on intuition: a critical analysis and research agenda. International Journal of Management Reviews 17(2):212–231. Baldacchino, Leonie, Deniz Ucbasaran, and Laure Cabantous. 2023. Linking experience to intuition and cognitive versatility in new venture ideation: a dual-process perspective. Journal of Management Studies 60(5):1105–1146. Barron, Frank. 1988. Putting creativity to work. In The nature of creativity: contemporary psychological perspectives, ed. Robert J. Sternberg, 76–98. Cambridge: Cambridge University Press. Bazerman, Max H., and Dan A. Moore. 2012. Judgment in managerial decision making, 8th edn., Chichester: Wiley. Bennett, Robert H. 1998. The importance of tacit knowledge in strategic deliberations and decisions. Management Decision 36(9):589–597. Betsch, Tilmann. 2008. The nature of intuition and its neglect in research on judgement and decision making. In Intuition in judgement and decision making, ed. Henning Plessner, Cornelia Betsch, and Tilmann Betsch, 3–22. New York, London: Erlbaum. Betsch, Tilmann, and Andreas Glöckner. 2010. Intuition in judgment and decision making: extensive thinking without effort. Psychological Inquiry 21(4):279–294. Brown, John S., and Paul Duguid. 2001. Knowledge and organization: a social-practice perspective. Organization Science 12(2):198–213. Brunsson, Karin, and Nils Brunsson. 2017. Decisions: the complexities of individual and organizational decision-making. Cheltenham: Edward Elgar. Bullini Orlandi, Ludovico, and Paul Pierce. 2020. Analysis or intuition? Reframing the decision-making styles debate in technological settings. Management Decision 58(1):129–145. Cabantous, Laure, and Jean-Pascal Gond. 2011. Rational decision making as performative praxis: explaining rationality’s Éternel Retour. Organization Science 22(3):573–586. Calabretta, Giulia, Gerda Gemser, and Nachoem M. Wijnberg. 2017. The interplay between intuition and rationality in strategic decision making: a paradox perspective. Organization Studies 38(3–4):365–401. Carson, Stephen J., Anoop Madhok, and Tao Wu. 2006. Uncertainty, opportunism, and governance: the effects of volatility and ambiguity on formal and relational contracting. Academy of Management Journal 49(5):1058–1077. Chater, Nick, Teppo Felin, David C. Funder, Gerd Gigerenzer, Jan J. Koenderink, Joachim I. Krueger, Denis Noble, Samuel A. Nordli, Mike Oaksford, Barry Schwartz, Keith E. Stanovich, and Peter M. Todd. 2018. Mind, rationality, and cognition: an interdisciplinary debate. Psychonomic Bulletin & Review 25(2):793–826. Choi, Byounggu, and Heeseok Lee. 2003. An empirical investigation of KM styles and their effect on corporate performance. Information & Management 40(5):403–417. Choi, Ellen, Lieke L. ten Brummelhuis, and Hannes Leroy. 2024. Honesty is not always the best policy: The role of self-esteem based on others’ approval in qualifying the relationship between leader transparency and follower voice. Journal of Leadership & Organizational Studies 31(2):192–210. Clarke, Ian, and William Mackaness. 2001. Management ‘intuition’: an interpretative account of structure and content of decision schemas using cognitive maps. Journal of Management Studies 38(2):147–172. Colville, Ian, Andrew D. Brown, and Annie Pye. 2012. Simplexity: Sensemaking, organizing and storytelling for our time. Human Relations 65(1):5–15. Covin, Jeffrey G., Dennis P. Slevin, and Michael B. Heeley. 2001. Strategic decision making in an intuitive vs. technocratic mode: structural and environmental considerations. Journal of Business Research 52(1):51–67. Cristofaro, Matteo, Pier L. Giardino, Riccardo Camilli, and Ivo Hristov. 2024. Understanding behavioral strategy: a historical evolutionary perspective in “management decision”. Management Decision 62(13):426–455. Crossan, Mary M., W. Lane Henry, and Roderick E. White. 1999. An organizational learning framework: from intuition to institution. Academy of Management Review 24(3):522–537. Cutcher, Leanne, and Pamela Achtel. 2017. ‘Doing the brand’: Aesthetic labour as situated, relational performance in fashion retail. Work, Employment and Society 31(4):675–691. Daft, Richard L. 2021. Organization theory & design, 13th edn., Boston: Cengage. Daft, Richard L., and Robert H. Lengel. 1986. Organizational information requirements, media richness and structural design. Management Science 32(5):554–571. K Schmalenbach Journal of Business Research (2025) 77:267–308 303 Daft, Richard L., and Norman B. Macintosh. 1981. A tentative exploration into the amount and equivocality of information processing in organizational work units. Administrative Science Quarterly 26(2):207–224. Daft, Richard L., and Karl E. Weick. 1986. Toward a model of organizations as interpretation systems. Academy of Management Review 9(2):284–295. Dane, Erik, and Michael G. Pratt. 2007. Exploring intuition and its role in managerial decision making. Academy of Management Review 32(1):33–54. Dean, James W., Jr., and Mark P. Sharfman. 1993. Procedural rationality in the strategic decision-making process. Journal of Management Studies 30(4):587–610. Dean, James W., Jr., and Mark P. Sharfman. 1996. Does decision process matter? A study of strategic decision-making effectiveness. Academy of Management Journal 39(2):368–396. Deters, Jürgen. 2023. Analytics and intuition in the process of selecting talent. Berlin, Boston: De Gruyter. Driver, Michael J., and Theodore J. Mock. 1975. Human information processing, decision style theory, and accounting information Systems. The Accounting Review 50(3):490–508. Einhorn, Hillel J., and Robin M. Hogarth. 1981. Behavioral decision theory: processes of judgement and choice. Annual Review of Psychology 32(1):53–88. Eisenhardt, Kathleen M. 1989. Making fast strategic decisions in high-velocity environments. Academy of Management Journal 32(3):543–576. Eisenhardt, Kathleen M. 1998. Strategic decision making as improvisation. In Strategic decisions,ed. Vassilis Papadakis, Patrick Barwise, 251–257. Boston: Springer. Elbanna, Said, and Yasir Fadol. 2016. The role of context in intuitive decision-making. Journal of Management & Organization 22(5):642–661. Fabio, Rosa A., Dalila Verzì, and Amelia Gangemi. 2024. A contribute to the default-interventionist and parallel accounts in deductive reasoning: The effect of decisional styles on logic and belief. The Journal of General Psychology 151(2):209–222. Fasolo, Barbara, Raffaella Misuraca, and Gary H. McClelland. 2003. Individual differences in adaptive choice strategies. Research in Economics 57(3):219–233. Fellnhofer, Katharina, and Yu Deng. 2024. Investor intuition promotes gender equality in access to rewardbased crowdfunding. Entrepreneurship Theory and Practice 48(2):675–718. Fiedler, Klaus, and Momme v. Sydow. 2015. Heuristics and biases: beyond Tversky and Kahneman’s (1974) judgment under uncertainty. In Cognitive psychology: revisiting the classic studies,ed. Michael W. Eysenck, David Groome, 146–161. London: SAGE. Fisher, Colin M., and Frank J. Barrett. 2019. The experience of improvising in organizations: a creative process perspective. Academy of Management Perspectives 33(2):148–162. Fisher, Greg, and Emily Neubert. 2023. Evaluating ventures fast and slow: sensemaking, intuition, and deliberation in entrepreneurial resource provision decisions. Entrepreneurship Theory and Practice 47(4):1298–1326. Flores-Garcia, Erik, Jessica Bruch, Magnus Wiktorsson, and Mats Jackson. 2021. Decision-making approaches in process innovations: an explorative case study. Journal of Manufacturing Technology Management 32(9):1–25. Forbes, Daniel P. 2007. Reconsidering the strategic implications of decision comprehensiveness. Academy of Management Review 32(2):361–376. Fredrickson, J.W. 1984. The comprehensiveness of strategic decision processes: extension, observations, future directions. Academy of Management Journal 27(3):445–466. Fredrickson, James W., and Terence R. Mitchell. 1984. Strategic decision processes: comprehensiveness and performance in an industry with an unstable environment. Academy of Management Journal 27(2):399–423. Gigerenzer, Gerd. 2008. Gut feelings: Short cuts to better decision making. London: Penguin. Gigerenzer, Gerd. 2010. Personal reflections on theory and psychology. Theory & Psychology 20(6):733–743. Gigerenzer, Gerd. 2019. Axiomatic rationality and ecological rationality. Synthese 3(2):20. Gigerenzer, Gerd, and Wolfgang Gaissmaier. 2011. Heuristic decision making. Annual Review of Psychology 62:451–482. Gigerenzer, Gerd, and Terry Regier. 1996. How do we tell an association from a rule? Comment on Sloman (1996). Psychological Bulletin 119(1):23–26. Glöckner, Andreas, and Cilia Witteman. 2010. Beyond dual-process models: a categorisation of processes underlying intuitive judgement and decision making. Thinking & Reasoning 16(1):1–25. Gobet, Fernand, and Philippe Chassy. 2009. Expertise and intuition: a tale of three theories. Minds and Machines 19(2):151–180. K 304 Schmalenbach Journal of Business Research (2025) 77:267–308 Goldberg, Philip. 1990. The many faces of intuition. In Intuition in organizations, ed. Weston H. Agor, 62–77. Newbury Park: SAGE. Govindarajan, Vijay. 2016. Planned opportunism: using weak signals to spur innovation. Harvard Business Review 94(5):54–61. Grewatsch, Sylvia, Steve Kennedy, and Pratima Bansal. 2023. Tackling wicked problems in strategic management with systems thinking. Strategic Organization 21(3):721–732. Guarana, Cristiano L., and Morela Hernandez. 2016. Identified ambivalence: when cognitive conflicts can help individuals overcome cognitive traps. The Journal of Applied Psychology 101(7):1013–1029. Hall, Katherine H. 2002. Reviewing intuitive decision-making and uncertainty: the implications for medical education. Medical Education 36:216–224. Hammond, Kenneth R., Robert M. Hamm, Janet Grassia, and Tamra Pearson. 1987. Direct comparison of the efficacy of intuitive and analytical cognition in expert judgment. IEEE Transactions on Systems, Man, and Cybernetics 17(5):753–770. Harris, Michael, and Bill Tayler. 2019. Don’t let metrics undermine your business. Harvard Business Review 97(9–10):62–69. Harteis, Christian, and Stephen Billett. 2013. Intuitive expertise: theories and empirical evidence. Educational Research Review 9:145–157. Hatch, Mary J. 1997. Jazzing up theory of organizational improvisation. In Advances in strategic management, Vol. 14, ed. James P. Walsh, Anne S. Huff, 181–191. Greenwich: JAI Press. Hayashi, Alden M. 2001. When to trust your gut. Harvard Business Review 79(2):59–65. Healey, Mark P., and Gerard P. Hodgkinson. 2017. Making strategy hot. California Management Review 59(3):109–134. Healey, Mark P., Timo Vuori, and Gerard P. Hodgkinson. 2015. When teams agree while disagreeing: reflexion and reflection in shared cognition. Academy of Management Review 40(3):399–422. Heimstädt, Maximilian, Tomi Koljonen, and Kasper T. Elmholdt. 2024. Expertise in management research: a review and agenda for future research. Academy of Management Annals 18(1):121–156. Hodgkinson, Gerard P., and Eugene Sadler-Smith. 2018. The dynamics of intuition and analysis in managerial and organizational decision making. Academy of Management Perspectives 32(4):473–492. Hodgkinson, Gerard P., Nicola J. Bown, A.J. Maule, Keith W. Glaister, and Alan D. Pearman. 1999. Breaking the frame: an analysis of strategic cognition and decision making under uncertainty. Strategic Management Journal 20(10):977–985. Hodgkinson, Gerard P., Janice Langan-Fox, and Eugene Sadler-Smith. 2008. Intuition: a fundamental bridging construct in the behavioural sciences. British Journal of Psychology 99(1):1–27. Hogarth, Robin M. 2001. Educating intuition. Chicago: University of Chicago Press. Hogarth, Robin M. 2010. Intuition: a challenge for psychological research on decision making. Psychological Inquiry 21(4):338–353. Howarth, Stephanie, Simon Handley, and Clare Walsh. 2019. The logic sense: exploring the role of executive functioning in belief and logic-based judgments. Thinking & Reasoning 25(4):416–448. Julmi, Christian. 2018. Management jenseits der Rationalität: Intuitive Entscheidungen und deren Reflexion mit der FIRSt-Matrix. Controlling 30(3):39–46. Julmi, Christian. 2019. When rational decision-making becomes irrational: a critical assessment and reconceptualization of intuition effectiveness. Business Research 12(1):291–314. Julmi, Christian. 2024. Analysis and intuition effectiveness in moral problems. Journal of Business Ethics 191:179–193. Kahneman, Daniel. 2003. Maps of bounded rationality: psychology for behavioral economics. The American Economic Review 93(5):1449–1475. Kahneman, Daniel. 2011. Thinking, fast and slow. New York: Farrar, Straus and Giroux. Kahneman, Daniel, and Gary Klein. 2009. Conditions for intuitive expertise. A failure to disagree. American Psychologist 64(6):515–526. Kahneman, Daniel, and Amos Tversky. 1973. On the psychology of prediction. Psychological Review 80(4):237–251. Kaufman, Scott B., Colin G. Deyoung, Jeremy R. Gray, Luis Jiménez, Jamie Brown, and Nicholas Mackintosh. 2010. Implicit learning as an ability. Cognition 116(3):321–340. Keller, Josh, and Eugene Sadler-Smith. 2019. Paradoxes and dual processes: a review and synthesis. International Journal of Management Reviews 21(2):162–184. Khatri, Naresh, and H.A. Ng. 2000. The role of intuition in strategic decision making. Human Relations 53(1):57–86. K Schmalenbach Journal of Business Research (2025) 77:267–308 305 Korherr, Philipp, Dominik K. Kanbach, Sascha Kraus, and Paul Jones. 2023. The role of management in fostering analytics: the shift from intuition to analytics-based decision-making. Journal of Decision Systems 32(3):600–616. Koufteros, Xenophon, Mark Vonderembse, and Jayanth Jayaram. 2005. Internal and external integration for product development: the contingency effects of uncertainty, equivocality, and platform strategy. Decision Sciences 36(1):97–133. Kowalczyk, Martin, and Peter Buxmann. 2014. Big data and information processing in organizational decision processes. Business & Information Systems Engineering 6(5):267–278. Kroezen, Jochem, Davide Ravasi, Innan Sasaki, Monika ˙ Zebrowska, and Roy Suddaby. 2021. Configurations of craft: alternative models for organizing work. Academy of Management Annals 15(2):502–536. Kruglanski, Arie W., and Gerd Gigerenzer. 2011. Intuitive and deliberate judgments are based on common principles. Psychological Review 118(1):97–109. Kuo, Wen-Jui, Tomas Sjöström, Yu-Ping Chen, Wang Yen-Hsiang, and Chen-Ying Huang. 2009. Intuition and deliberation: two systems for strategizing in the brain. Science 324(5926):519–522. Lee, Jung W., Hal G. Rainey, and Young H. Chun. 2010. Goal ambiguity, work complexity, and work routineness in federal agencies. The American Review of Public Administration 40(3):284–308. Lewis, Michael. 2017. The undoing project: a friendship that changed our minds. New York: Norton. Lewis, Marianne W., and Wendy K. Smith. 2014. Paradox as a metatheoretical perspective: sharpening the focus and widening the scope. The Journal of Applied Behavioral Science 50(2):127–149. Leybourne, Stephen, and Eugene Sadler-Smith. 2006. The role of intuition and improvisation in project management. International Journal of Project Management 24(6):483–492. Lieberman, Matthew D. 2007. Social cognitive neuroscience: a review of core processes. Annual Review of Psychology 58:259–289. Luoma, Jukka, and Frank Martela. 2021. A dual-processing view of three cognitive strategies in strategic decision making: Intuition, analytic reasoning, and reframing. Long Range Planning 54(3). Macintosh, Norman B. 1981. A contextual model of information systems. Accouting, Organizations and Society 6(1):39–53. Macpherson, Robyn, and Keith E. Stanovich. 2007. Cognitive ability, thinking dispositions, and instructional set as predictors of critical thinking. Learning and Individual Differences 17(2):115–127. Manesh, Mohammad F., Giulia Flamini, Damiano Petrolo, and Rocco Palumbo. 2022. A round of dancing and then one more: embedding intuition in the ballet of entrepreneurial decision making. International Entrepreneurship and Management Journal 18(2):499–528. March, James G. 1978. Bounded rationality, ambiguity, and the engineering of choice. The Bell Journal of Economics 9(2):587–608. March, James G. 1994. Primer on decision making: How decisions happen. New York: Simon & Schuster. McCaskey, Michael B. 1982. The executive challenge: managing change and ambiguity. Boston: Pitman. McIver, Derrick, Cynthia A. Lengnick-Hall, Mark L. Lengnick-Hall, and Indu Ramachandran. 2012. Integrating knowledge and knowing: a framework for understanding knowledge-in-practice. Human Resource Management Review 22(1):86–99. McIver, Derrick, Cynthia A. Lengnick-Hall, Mark L. Lengnick-Hall, and Indu Ramachandran. 2013. Understanding work and knowledge management from a knowledge-in-practice perspective. Academy of Management Review 38(4):597–620. McMillan, Charles, and Jeffrey Overall. 2016. Wicked problems: turning strategic management upside down. Journal of Business Strategy 37(1):34–43. Mintzberg, Henry. 1979. The structuring of organizations. Englewood Cliffs: Prentice-Hall. Mintzberg, Henry, Duru Raisinghani, and Andre Theoret. 1976. The structure of “unstructured” decision processes. Administrative Science Quarterly 21(2):246–275. Mintzberg, Henry, Bruce W. Ahlstrand, and Joseph Lampel. 1998. Strategy safari: a guided tour through the wilds of strategic management. Upper Saddle River: Prentice-Hall. Müller, David. 2011. Antecedences and determinants of improvisation in firms. Problems and Perspectives in Management 9(4):117–130. Narayanan, V.K., J. Zane Lee, and Benedict Kemmerer. 2011. The cognitive perspective in strategy: an integrative review. Journal of Management 37(1):305–351. Neill, Stern, and Gregory M. Rose. 2007. Achieving adaptive ends through equivocality: a study of organizational antecedents and consequences. Journal of Business Research 60(4):305–313. Nicolai, Alexander T., and David Seidl. 2010. That’s relevant! Different forms of practical relevance in management science. Organization Studies 31(9/10):1257–1285. K 306 Schmalenbach Journal of Business Research (2025) 77:267–308 Nonaka, Ikujir¯ o, and Hirotaka Takeuchi. 1995. The knowledge creating company: how Japanese companies create the dynamics of innovation. New York: Oxford University Press. Nutt, Paul C. 1976. Models for decision making in organizations and some contextual variables which stipulate optimal use. Academy of Management Review 2(1):84–98. Nutt, Paul C. 2002. Making strategic choices. Journal of Management Studies 39(1):67–96. Papadakis, Vassilis M., Spyros Lioukas, and David Chambers. 1998. Strategic decision-making processes: the role of management and context. Strategic Management Journal 19(2):115–147. Parikh, Jagdish. 1994. Intuition. The new frontier of management. Oxford, Cambridge: Blackwell Business. Perrow, Charles. 1967. A framework for the comparative analysis of organizations. American Sociological Review 32(2):194–208. Perrow, Charles. 1970. Organizational analysis: a sociological view. London: Tavistock. Phillips, Wendy J., Jennifer M. Fletcher, Anthony D.G. Marks, and Donald W. Hine. 2016. Thinking styles and decision making: A meta-analysis. Psychological Bulletin 142(3):260–290. Polanyi, Michael. 1966. The tacit dimension. London: Routledge & Kegan Paul. Pretorius, Carianne, Maryam Razavian, Katrin Eling, and Fred Langerak. 2024. When rationality meets intuition: a research agenda for software design decision-making. Journal of Software: Evolution and Process 36(9):. Pretz, Jean E. 2011. Types of intuition: inferential and holistic. In Handbook of intuition research,ed. Marta Sinclair, 17–28. Cheltenham: Edward Elgar. Pretz, Jean E., and Kathryn S. Totz. 2007. Measuring individual differences in affective, heuristic, and holistic intuition. Personality and Individual Differences 43(5):1247–1257. Putnam, Linda L., and Ritch L. Sorenson. 1982. Equivocal messages in organizations. Human Communication Research 8(2):114–132. Quinn, Robert E. 1988. Beyond rational management: mastering the paradoxes and competing demands of high performance. San Francisco: Jossey-Bass. Raadschelders, Jos C.N., and Travis A. Whetsell. 2018. Conceptualizing the landscape of decision making for complex problem solving. International Journal of Public Administration 41(14):1132–1144. Reber, Arthur S. 1989. Implicit learning and tacit knowledge. Journal of Experimental Psychology: General 118(3):219–235. Rittel, Horst W.J., and Melvin M. Webber. 1973. Dilemmas in a general theory of planning. Policy Sciences 4:155–169. Rönnberg Sjödin, David, Johan Frishammar, and Per E. Eriksson. 2016. Managing uncertainty and equivocality in joint process development projects. Journal of Engineering and Technology Management 39:13–25. Roux-Dufort, Christophe, and Bénédicte Vidaillet. 2003. The difficulties of improvising in a crisis situation. International Studies of Management & Organization 33(1):86–115. Rusou, Zohar, Dan Zakay, and Marius Usher. 2013. Pitting intuitive and analytical thinking against each other: the case of transitivity. Psychonomic Bulletin & Review 20(3):608–614. Rynes, Sara L., and Jean M. Bartunek. 2017. Evidence-based management: foundations, development, controversies and future. Annual Review of Organizational Psychology and Organizational Behavior 4(1):235–261. Sadler-Smith, Eugene, and Erella Shefy. 2004. The intuitive executive: Understanding and applying ‘gut feel’ in decision-making. Academy of Management Executive 18(4):76–91. Salas, Eduardo, Michael A. Rosen, and Deborah DiazGranados. 2010. Expertise-based intuition and decision making in organizations. Journal of Management 36(4):941–973. Samba, Codou, Pooya Tabesh, Ioannis C. Thanos, and Vassilis M. Papadakis. 2021. Method in the madness? A meta-analysis on the strategic implications of decision comprehensiveness. Strategic Organization 19(3):414–440. Scherm, Ewald, Christian Julmi, and Florian Lindner. 2016. Intuitive versus analytische Entscheidungen – Überlegungen zur situativen Stimmigkeit. In Nachhaltiges Entscheiden, ed. Heinz Ahn, Marcel Clermont, and Rainer Souren, 299–318. Wiesbaden: Springer. Schirrmeister, Elna, Anne-Louise Göhring, and Philine Warnke. 2020. Psychological biases and heuristics in the context of foresight and scenario processes. Futures & Foresight Science 2(2):e31. Schwenk, Charles R. 1995. Strategic decision making. Journal of Management 21(3):471–493. Shapiro, Stewart, and Mark T. Spence. 1997. Managerial intuition: a conceptual and operational framework. Business Horizons 40(1):63–68. Simon, Herbert A. 1987. Making management decisions: the role of intuition and emotion. Academy of Management Executive 1(1):57–64. Simon, Herbert A. 1990. Invariants of human behavior. Annual Review of Psychology 41:1–19. K