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One of the main challenges for managers in the 21st century is the correct identification and exploitation of business opportunities in the face of “high velocity” such as rapidly changing market conditions. This challenge is particularly crucial in “blockbuster” economies, where relatively low earnings typically follow very high levels of upfront investments, and where only a handful of economic actors capture the most returns in clear “winners take all” environments. Thus, in the pharmaceutical sector, drug discovery is often frustratingly unsuccessful due to tedious developmental, market or regulatory complexities, and very few products eventually take to market and prove profitable (Stonebraker, 2002). Similarly, oil companies never know for sure whether a well will contain oil, and if so, how much they will be able to drill out of it. Faced with the decision “to drill or not to drill,” they commonly base their predictions on a small set of probabilistic seismic data (Skaf, 1999). And in the cultural industries (e.g., recorded film and music), the managerial evaluation of creative talent only generates a small number of “hits” every year, which are all the more difficult to predict as the underlying technology is currently evolving at a very fast pace, just like cultural consumers’ preferences. In the music industry, only less than 10% of artists’ records signed by a major record company ever break even (Vogel, 2004). Dealing with uncertainty is increasingly complex, in particular in such high-velocity contexts, where the pace and rate of discontinuous change in both internal and external firm conditions (including change in demand, competitors, technology, and regulation) is reflected in the degree of rationality characterizing the organization’s decision capability.
Taking the managerial challenge of coping with extreme uncertainty into consideration, past strategy research has argued that key drivers for market success and long-term competitive advantage lie in the development and deployment of superior organizational resources, defined as inputs in the firm’s production processes (Amit & Schoemaker, 1993) and capabilities. The latter are defined as unique configurations of organizational resources (Itami & Roehl, 1987; Amit & Schoemaker, 1993) that combine human skills and technological expertise and determine specific problem-finding or problem-solving heuristics (Hitt & Tyler, 1991; Teece, Pisano, & Shuen, 1997). Resource-based view (RBV) authors also emphasize the importance of embedding resources and capabilities in a higher level, strategic firm architecture and “dominant logic” (Prahalad & Bettis, 1986) and establishing an organizational culture of learning and improvement in order to continuously nurture and expand core capabilities (Prahalad & Hamel, 1990).
Behavioral theories of the firm consider organizations as hierarchical accumulations of decisions. Strategic decisions at the top of this hierarchy also have a larger impact on firm success than operational decisions at its bottom (Bower & Gilbert, 2005). Following Langley et al. (1995) we refer to a “decision” as an organizational commitment to action and use “importance” as a criterion for identifying “strategic” decisions (Eisenhardt & Zbaracki, 1992). Recent research argues that strategic decision processes can be understood as dynamic capabilities (Eisenhardt, 2001). In particular, Bower and Gilbert (2005) suggest that at the core of these decision capabilities lies the organizational task of linking strategy making to the allocation of organizational resources and, hence, configuring the tacit decision skills of key employees with the organization’s explicit, formal decision-making structure (e.g., frame, technology, tools, and processes). The combination of both tacit, managerial intuition and explicit, rational organizational processes characterizes strategic choice in organizations and describes how the latter process information in the face of external and internal uncertainty. A firm’s strategic decision-making system may therefore be defined as a dynamic capability (Eisenhardt, 2001) and may be assessed directly in terms of immediate effectiveness of its outcome and indirectly (related to its potential for creating and sustaining a competitive advantage) in terms of firm performance.
In consideration of these developments, how strategic decision capabilities should be identified and managed is one of the fundamental questions that senior executives face in high-velocity contexts. The content of this research-paper addresses this challenge by discussing current research findings in the RBV and the strategic decision-making theory and by providing an integrated view to characterize the fundamental components of such capabilities. We start by discussing the notion of uncertainty and its impact on the degree of rationality in strategic decision making. In the next section of this research-paper, we introduce and define the four fundamental components of strategic decision capabilities. Among these, a higher level strategic decision-making system embedding specific decision processes is essential. We introduce it, before shifting our focus to the firm’s tacit and explicit decision competences and providing an overview of their general characteristics as moderators of decision rationality. In the next section, we discuss key behavioral issues in the strategic choice process, which result from the interaction between the firm’s explicit and tacit decision competences. Last, we summarize our discussion on strategic decision capabilities and offer suggestions for further research.
The Role Of Uncertainty In Strategic Decision Making
This research-paper focuses on two fundamental characteristics of uncertainty: ambiguity and complexity, both internal and external (see for instance Cohen, March, & Olsen, 1972; Reed & De Fillippi, 1990; Porter, 1990). While ambiguity addresses the difficulty of recognizing causality behind certain observable processes, complexity refers to the amount of information in the decision maker’s environment and accounts for his or her available time frame and cognitive ability to identify and process relevant types of data. Internal uncertainty relates to all those resources, processes, skills, and technologies that reside inside the organization. In contrast, external uncertainty comprises all regulatory, technological, and market-related factors likely to impact the choice process.
Uncertainty is the difference between the information needed by and available to the decision maker (Fredrickson & Mitchell, 1984). The external environment can increase or decrease this gap. Decision rationality is the firm’s ability to act in pursuit of its goals in light of internal causal ambiguity and external complexity (Rajagopalan, Rasheed, & Datta, 1993; Priem, Rasheed, & Kotulic, 1995). It is therefore closely linked to the firm’s dynamic environment and performance. Last, uncertainty is usually given ex ante, and the decision maker finds it difficult to control, in particular considering external complexity.
The stability (Fredrickson & Iaquinto, 1989) and velocity (Bourgeois & Eisenhardt, 1988) of an organization’s external environment play a crucial role in achieving effective decision outcomes. While environmental stability concerns the likelihood that critical pieces of information are accessible (Fredrickson & Mitchell, 1984), environmental “velocity” refers to the pace and rate of discontinuous change in demand, competitors, technology, and regulation that result in flawed, incomplete information available to reach a decision (Bourgeois & Eisenhardt, 1988). The concept of “comprehensiveness” describes the extent to which strategic decision processes in a particular environment follow a rational planning process (Fredrickson & Iaquinto, 1989). There is no consensus in research on the decision-specific factors that determine ambiguity and complexity. As a result, researchers refer to a number of different concepts. Among them, the level of technical uncertainty, degree of outcome uncertainty, or criticalness to decision makers (Rajagopalan et al., 1993); multiple conflicting objectives (Clemen, 2003); decision urgency (Pinfield, 1986); decision motive (Fredrickson, 1985) information source (Schilit & Paine, 1987), and problem specification (Volkema, 1986) have all been argued to have a direct impact on decision process characteristics (Rajagopalan et al., 1993).
Uncertainty And Rationality
As stated in the introduction to this research-paper, the degree of rationality in an organization’s decision-making capability mirrors the pace and rate of discontinuous change in both internal and external firm conditions in high-velocity contexts. It is also a central debate in research on strategic decision making. Traditionally, researchers split into two camps.
Defenders of the first camp come mainly from the strategy formulation tradition. They believe that an alignment between the firm’s external environment and internal structure and processes is best achieved using a formal, rational planning process (see for instance Andrews, 1971). Their fundamental assumptions are that decision makers have clear, known objectives, that they can take difficult decisions in a rational way by gathering relevant information and developing sets of alternative courses of action, and that they can choose the best alternative out of these courses of action (Eisenhardt & Zbaracki, 1992). The rational tradition of decision-making research is rooted in classical economic theory and shares its core postulate that human beings are utility maximizers (Kahneman & Tversky, 1979). In particular, these models assume that decision makers act as “economic men” who are not restricted by their cognitive limitations, emotions, or other individual characteristics.
The second camp gathers proponents of Simon’s (1955) bounded rationality view and of Cyert and March’s (1963) behavioral theory of the firm. They challenge this rational model by emphasizing cognitive limitations in human behavior and by arguing that goals can be inconsistent and relevant information difficult to get. In particular, information can be ambiguous or exceed the cognitive capacity of decision makers, making the choice of an optimal course of action impossible. In an extreme form of uncertainty, Cohen, March, and Olsen (1972) suggest a garbage-can model of organizational anarchy, in which problems, solutions, and decision makers are disconnected and outcomes can be understood as the results of several relatively independent streams of events within the organization. More moderate authors suggest that managers can only create strategies in small, incremental steps (Lindblom, 1959; Fredrickson & Mitchell, 1984). Kahneman and Tversky (1979) also provide significant evidence of differences in individual decision makers’ attitudes to risk. Under the notion of “prospect theory,” they reject the definition of individuals as purely rational utility maximizers and argue that decision makers adjust their attitudes to risks according to factors such as “current state of wealth,” or “win or loss framing.”
Subsequent research (see for instance Fredrickson & Mitchell, 1984) reconciles both camps and suggests that the adoption of rational or incremental decision behavior is related to key characteristics of the firm’s external environment. It thereby proposes that the relationship between firm performance and decision comprehensiveness is negative in unstable environments and positive in stable environments. Other approaches no longer perceive perfect and bounded rationality in strategic decisions as a dichotomy, but understand rationality as a cognitive continuum on which decision models can be positioned. They thus acknowledge the simultaneous existence of both aspects and present substantial empirical evidence of at least partially rational structures in highly uncertain environments (Eisenhardt & Zbaracki, 1992). Bourgeois and Eisenhardt (1988) and Eisenhardt (1989) argue that rationality is multidimensional. In order to be successful, decision makers in high-velocity environments consequently need to adopt rational behaviors in some ways, but not in others. Similarly, Fredrickson and Iaquinto (1989) show that rationality creeps in as organizational size, top management tenure, or top management team continuity increase. Building on this view, subsequent studies investigate specific aspects of rationality (Eisenhardt, 1989) and define an optimal degree of rationality on the cognitive continuum (Dean & Sharfman, 1996). Some authors further suggest that firms need to develop their managers’ “intuition,” which is defined as a skill that translates experience into action (Simon, 1987; Klein & Weick, 2000; Miller & Ireland, 2005) to improve the efficiency of their strategic decision-making processes (Eisenhardt, 1989; Dean & Sharfman, 1996).
Components Of Strategic Decision Capabilities
As previously stated, Eisenhardt (2001) suggests that strategic decisions can be understood as dynamic firm capabilities that hold the potential to be sources of sustainable competitive advantages. The notion of decision capabilities proposed next builds on her perspective and draws on concepts from both the RBV and decision-making theory.
By definition, the specific strategic decision processes that are of superior value to the organization may be interpreted as “core” capabilities (Prahalad & Hamel, 1990). The notion of “organizational importance” also implies that the actions to be taken, resources to be committed, and any precedents set when a strategic decision is implemented must critically affect the organization’s internal and external ability to compete (Eisenhardt & Zbaracki, 1992). In practice, organizational decision processes represent such “core capabilities” if they are embedded in a higher level strategic decision-making system, which clearly outlines the underlying firm strategy and ensures a culture of learning within the organization (Prahalad & Hamel, 1990; Eisenhardt & Martin, 2000).
The immediate value of a decision-core capability can be assessed in terms of decision quality, as evidenced by Matheson and Matheson’s (1998) large-scale demonstration of the existence of a link between the quality of the decision capability dimensions and superior firm performance within hundreds of organizations in highly uncertain environments. Similarly, Amason (1996) describes decision quality as a key driver of sustainable performance. Both studies argue that quality does not refer to a particular outcome of the decision process, but rather to the effectiveness and efficiency of this process in view of the firm’s limited resources. Other researchers assess the quality of a decision process on specific individual dimensions (see for instance Eisenhardt & Bourgeois, 1988 on velocity; Fredrickson & Mitchell, 1984 on rationality; Pfeffer & Salancik, 1974 on power and politics; or Hambrick & Mason, 1984 on upper echelons). So far, only a few studies have analyzed the concept of decision quality as a multiplicity of dimensions. Matheson and Matheson (1998) argue that the holistic effectiveness of strategic decisions must be described and evaluated in terms of the cognitive ability of the decision makers and of their interaction during the decision process. Since the different components of decision capabilities are closely linked, they conclude that the overall quality of the decision process is only as strong as its weakest dimension.
The following section builds on the RBV and behavioral decision-making theory to discuss the implications of several fundamental components of decision capabilities on their quality. These components are a higher level strategic decision-making system and tacit and explicit decision competences and their interaction in the strategic choice process. External uncertainty and internal ambiguity influence the configuration of these decision capability elements substantially. Hence, decision quality refers to the direct measure of the fit between capability configuration, internal ambiguity, and external uncertainty, while the performance of the organization indirectly reflects the quality of its decision processes.
A Higher Level Strategic Decision-Making System
According to RBV scholars, bundles of capabilities (as opposed to processes taken in isolation) drive firm success and sustainable competitive advantage. They consequently argue that there is a need for installing a higher level system capable of identifying and deploying existent and emerging firm resources. Dynamic decision capabilities aim at appropriately adapting, integrating, and reconfiguring internal and external organizational skills, resources, and functional competences in a changing environment (Eisenhardt, 2001). Moreover, a higher level strategic decision-making system comprises the firm’s overall strategy and value system and its culture of organizational learning and improvement. In the following discussion, we consider them sequentially.
A higher level firm architecture outlines clear strategies for capability development and makes resource-allocation issues transparent to the entire organization (Prahalad & Hamel, 1990; Teece et al., 1997). More specifically, these strategies provide a plan for linking the firm’s values, overall strategy, structure, and culture to its managers’ knowledge and expertise. To do so, the higher level strategy system specifies needs in the organizational structure; identifies system gaps between the current and the desired system; provides a plan for sourcing competences; and enhances them through institutionalizing training and coaching. Underinvestment in time, energy, and financial resources may lead to imprisoned resources and constrained innovation, thus giving both incumbents and new entrants an opportunity to outperform the company in the long run.
The second key characteristic of strategic decision systems refers to the existence of a culture of organizational learning and improvement, which enables firms to communicate and “memorize” successes and failures in the minds of key employees (Prahalad & Hamel, 1990). By creating a shared understanding, firms are also more likely to critically reflect upon their own performance and to ensure commitment to action once a decision has been taken (Matheson & Matheson, 1998). Such a culture allows the firm to build an organizational memory capable of storing decision heuristics and, ultimately, to create a corporate vision encouraging decision makers to strive for continuous improvement (Lei, Hitt, & Bettis, 1996). It emerges by developing basic dynamic routines rooted in an organizational structure which allows for and institutionalizes collective intuition and improvisation on the one hand, and promotes change and adaptability on the other hand. Such routines also rely on shared understandings, knowledge, experience, and learning to create decision skills and channel resources into core capabilities (Fiol & Lyles, 1985).
Skills accumulation typically happens through experimentation with new processes both as “learning by doing” and “learning by using” (Lippman & Rumelt, 1982; Dierickx & Cool, 1989; Lei et al.,1996; Teece, Pisano, & Shuen, 1997). The collective experience gathered through these learning processes creates unique historical path dependencies in the organization, which eventually may themselves become sources of competitive advantages (Lei et al., 1996). This “positional factor of the competence” (Coyne, 1987) is asset based and highlights constructive past actions and decisions. A culture of organizational learning is therefore a fundamental element in the strategy of an organization (Prahalad & Hamel, 1990), and effectively integrating it into its core capabilities is crucial to its long-term competitive advantage (Lei et al., 1996). Miller and Shamsie (2001) demonstrate, in a study of product-line experimentation in the movie industry, that tenure plays a key role in organizational learning. Last, Martin de Holan and Phillips (2003) define organizational forgetting as a complementary mechanism to organizational learning that firms both implement voluntarily and endure against their resolve. A key success factor for organizational learning therefore becomes finding the right balance among employees’ inexperience, established routines, and organizational forgetting.
Explicit Decision Competence
According to Matheson and Matheson (1998), one of the key characteristics of successful companies lies in their setting up an appropriate formal frame representing the organization’s rational decision system. This formal decision frame aims to enhance organizational learning by formally building effective, complex problem-defining and problem-solving heuristics (Fiol, 1991; Bower & Gilbert, 2005). A formal decision frame provides the infrastructure for decision heuristics in terms of information processing tools, outcome measures, processes, and decision-support technology. Furthermore, it creates a unique configuration of resources and capabilities, which is constantly monitored and revised when new market conditions emerge. An explicit decision competence also requires the organization to understand on which hierarchical level the decision should be taken in order to clearly define responsibilities and accountabilities of decisions taken (Bower & Gilbert, 2005). Lastly, an explicit decision competence contributes to a high degree of decision quality if the firm effectively avoids resolving the “wrong” types of problems. It can do so by providing decision tools that match the characteristics of the decision problem at hand. Decision makers’ beliefs and prejudices influence the interpretation of decision outcomes. Therefore, in order to perceive alternatives within its higher level strategy-making system, the organization must provide rational tools, measures, and processes that challenge conventional thinking and result in multiple perspectives (Matheson & Matheson, 1998).
Tacit Decision Competence
The flexibility of the explicit decision competence determines, to a large extent, how rational the strategic choice process is allowed to be in the organization. However, research has shown that even if the explicit tools, measures, and processes seem to be perfectly capable of taking external ambiguity and complexity into account, decision effectiveness can still be poor due to decision makers’ collective interactions and individual characteristics (Amason, 1996).
On the collective level, intuition, speed, conflict, politics, power, and procedural justice are all important moderators of the quality of decision capabilities and, consequently, of the effectiveness of decision outcomes (Quinn, 1980; Simon, 1987; Eisenhardt & Bourgeois, 1989; Kim & Mauborgne, 1998). These moderators do not necessarily represent an impediment to high decision quality. Indeed, some of them (e.g., intuition, speed, conflict, and justice) may enhance the quality of decision making (Schweiger & Sandberg, 1989; Amason, 1996; Kim & Mauborgne, 1998), whereas others (e.g., politics and power plays) need to be averted during the strategic choice process (Pfeffer & Salancik, 1974).
On the individual level, Parikh (1994) describes managerial intuition as a multidimensional, multicontextual, and multilevel concept. Due to its intangible form of aggregation, many different connotations originating from philosophy, arts, epistemology, psychology, mysticism, and neuroscience have been attached to it. Management research suggests the existence of two meta-categories: intuition as expertise and intuition as sensing (Miller & Ireland, 2005). Kahneman and Frederick (2005) distinguish intuition from rational analysis in terms of its cognitive tacit aggregation, its speed, its degree of controllability, and the content on which it operates.
Intuition usually occurs effortlessly and without conscious attention and allows individuals to learn from experience (Hogarth, 2001). In the scope of this research-paper, we conform to Kahneman and Frederick’s (2003) distinction of intuition from rational analysis with respect to aggregation, speed and content, but argue, in accordance with Klein and Weick’s (2000) epistemological definition of intuition as a “skill,” that intuition may be controlled to a limited extent through training and experience—a commonly used notion in management research (see for instance Simon, 1987; Parikh, 1994; Klein, 2003; Sadler-Smith & Shefy, 2004; Miller & Ireland, 2005).
The key to effectively utilize decision capabilities is therefore to correctly identify and synthesize the decision makers’ skills and perspectives that are most appropriate for the decision (Amason, 1996). This allows the organization to accelerate its learning patterns and to develop “improvisation skills” to rapidly adapt to its changing environment (Eisenhardt, 1989; Eisenhardt & Martin, 2000). In this research-paper, we refer to such improvisation skills in organizations as the “tacit decision competence.” Employees’ individual and collective decision skills and their underlying cognitive reasoning processes determine this competence, which in turn reflects their ability to interpret and process explicit information provided by the decision environment and tacit information rooted in the employees’ expertise and experience (Matheson & Matheson, 1998). Adopting a multidimensional perspective on decision processes, Eisenhardt and Zbaracki (1992) argue that decision makers in organizations tend to follow basic rational models. Yet, at the same time, they make use of intuition and improvisation skills in order to cope with a dynamic and rapidly changing environment. Klein (2003) consequently suggests that the adoption of meaningful behavior in strategic decision making requires organizations to strike the right balance between rational analysis and intuition.
However, due to the limited time frame of organizational decisions and managers’ limited ability to consider all risks and uncertainties involved, decision-making skills must be based on experience and learning, since intuition in isolation commonly leads to a number of “decision traps” inducing bias into the process (Schoemaker & Russo, 1993). Hence, there is a need to build and enhance organizational intuition and supplement it with more objective, explicit, rational knowledge. In practice, this may be done by accumulating managerial experience and constantly updating the mental rules that managers use to make sense of the world (Klein, 2003; Miller & Ireland, 2005).
Strategic Choice Process
A significant stream of research in strategic decision making focuses on the set of sequential actions taken and the dynamic factors involved from stimulus for action to a specific postdecision commitment (Mintzberg, Raisinghani, & Théorêt, 1976; Nutt, 1984). Most researchers in this stream use in-depth analyses of empirical data to intuitively identify underlying sequential patterns of decision activities (Soélbêrg, 1967; Mintzberg et al., 1976; Hitt & Tyler, 1991). However, they do so at the cost of external validity, on small samples only and with findings contingent on the reliability of their intuition (Nutt, 1984). Alternatively, Nutt applies a classification approach similar to the one developed by linguistic researchers, who typically start out with a minimal set of conceptual elements used as reference points for the study and then use elaborate subconcepts when analyzing the data. This approach allows for larger samples, but assumes the existence of a basic normative framework applied in all decision processes (Nutt, 1984).
Mintzberg et al. (1976) remark that activities carried out in strategic decision processes do not follow a fixed, undisturbed sequence. Rather, they form a dynamic, open system subject to disruptions, feedback loops, dead ends, and other interferences. This kind of turbulence in strategic choice is caused by the interaction of both tacit and explicit decision competences in the face of external and internal uncertainty. Furthermore, this interaction of both systems can be characterized by the firm’s attempts to guide and control the process stages; to communicate input and output information; and to use political routines to reach a solution (Mintzberg et al., 1976). Nutt (1984) finds variations in the search, synthesis, and analysis stages of strategic decision-making processes.
To this day, Simon’s (1965) trichotomy of intelligence, design, and choice remains the dominant and most frequently cited decision-process morphology model. The original trichotomy starts out with the recognition and diagnosis of a decision problem. It then proceeds with the design of possible solutions. Last, a choice is made. Nutt’s (1984) conceptualization of decision process types also reflects these phases, yet elaborates them in a higher number of decision sequences. To better account for varying levels of stimulus, process, and solutions associated with the strategic decision, Mintzberg et al. (1976) redefine them as a sequence of identification, development, and selection. Mintzberg et al. (1976) also argue that, within each phase, the intensity of efforts induced varies depending on the type of decision problem at hand. Thus, “ready-made” or “off-the-shelf” decision processes are characterized by low-effort information gathering, as alternatives are readily available. In contrast, “custom-made” or “nova” processes require the organization to engage in extensive information search and alternative generation (Mintzberg et al., 1976; Nutt, 1984).
Identification is the starting point of every decision process (Simon, 1965). During this first phase, the problem or opportunity is recognized and diagnosed. The overwhelming amount of often ambiguous or tacit data decision makers have to deal with makes existing problems, opportunities, and crises difficult to understand straightaway and renders identification necessary (Mintzberg et al., 1976). This phase involves a number of different activities (e.g., listening to stakeholders, environmental scanning involving internal and external databases, initial brainstorming for gaps between status quo and future conditions, and performing an analysis of internal and external strengths and weaknesses).
The second phase is one of development. As reliable information is often inaccessible and alternative courses of action are unclear, from an organizational point of view, the design stage contains the most resource-intense and time-consuming activities of the whole strategic choice process (Mintzberg et al., 1976; Nutt, 1984). Decision makers adopt two types of routines in the development phase: information search and alternative generation.
During the information search phase, decision makers draw upon internal and external sources (using both explicit and tacit decision competences) to gather data on potential alternatives and to narrow down available courses of action. Thus, the search phase rests on the assumption that real choice always requires a number of different possible alternatives, and that high-quality decisions depend on meaningful, reliable information (Matheson & Matheson, 1998). Nevertheless, information quality is inherently problematic when speculating on future uncertainties about markets, technology, competitors, and regulatory changes. Organizations are consequently constrained to rely on partial information, which may or may not give them insights on future uncertainties. This information must be sufficiently meaningful to make inferences and reliable enough to motivate large investments implied by the decision. In this regard, decision makers may obtain meaningful, reliable data by carefully identifying the uncertainties involved and by understanding influential factors in the decision process. They may then use the alternative-generation phase to modify potential courses of action depending on the problem, crisis, or opportunity at hand. Creativity, which allows the firm to frame problems differently, is a core requirement for creating multiple actionable alternatives. It should be possible to make a judgment about the marketability of the product under development (Matheson & Matheson, 1998). Last, high-quality strategic decisions do not produce a plethora of alternatives: they generate only a few that are feasible and core to the company’s business.
In the final, selection stage, decision makers determine choice criteria, evaluate the likely consequences of alternatives in the light of these criteria, and reach a decision (Simon, 1965). The selection phase is multidimensional, iterative, and involves a deeper investigation of the alternative courses of action (Mintzberg et al., 1976; Nutt, 1984). In particular, Mintzberg et al., describe three different selection approaches. The first one, “screen routine,” involves rejecting infeasible alternatives. The second one, “evaluation routine,” is less frequently adopted. It uses judgment, bargaining, or analysis to prioritize alternatives (Mintzberg et al., 1976). The evaluation of alternatives requires the decision maker to consider a large number of often non-quantifiable criteria likely to make the decision process ambiguous and complex. The individual decision maker makes judgments through his or her cognitive processes, and due to their tacit nature, can seldom explain them. In contrast, bargaining involves a group of decision makers, whose selection is influenced as much by conflicting goals, power, and politics as it is by each individual’s cognitive judgments. Lastly, in the analysis mode, a formal, rational decision model is used to generate alternative courses of action. Authorization is the last routine associated with the selection phase. It is required to approve completed solutions across the organizational hierarchy and typically occurs as a binary choice.
Summary And Directions For Further Research
The purpose of this research-paper was to provide a conceptual overview of strategic decision capabilities in high-velocity contexts. We started out by discussing the notion of uncertainty and its impact on strategic decision processes. We split uncertainty into two basic dimensions (ambiguity and complexity) that affect both the internal and external environment of decision capabilities, and discussed the moderating effect of uncertainty on the degree of rationality in strategic decision processes. Next, we built on the RBV and strategic decision-making literature to identify and define four fundamental components of decision capabilities in organizations: higher level strategic decision system, explicit decision competence, tacit decision competence, and strategic choice process. We argued that through the interactions of these components, unique path dependencies are created, which are likely to become core capabilities of the organization.
The higher level system incorporates a firm’s strategic values and corporate culture of learning and improvement, and serves as a metaframework for its specific strategic decision processes. The second component, the explicit decision system, provides tools, rational measures, technology, and processes to support strategic decisions with formal, fact-based data. In contrast, the tacit decision system of the firm, which is represented by employees’ intuitive decision skills and cognitive ability to process information relevant to the decision problem, is quick, based on experience, and uses basic rational models to adapt to the rapidly changing decision environment. Explicit and tacit systems interact in the strategic choice process to result in the selection of alternatives in the face of external and internal uncertainties. We argued that the quality of the strategic decision capability largely depends on the effectiveness of managing these interactions. The identification of a problem or opportunity, the development of alternative courses of action, and their evaluation leading to a strategic decision all characterize strategic choice.
Further research could look into the way managers make sense of strategic decision capabilities. In particular, dual-process models from behavioral decision theory could be used to investigate the interplay between intuition and analysis in the decision-maker’s mind. Additional studies could focus, for instance, on the degree of rationality employed when making strategic decisions. There is also a need for substantial empirical evidence on variations in the components of strategic decision capabilities and on the general nature of strategic decision capabilities across industries, environments, and velocity conditions.
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