Designing a Decision-Making Model

# 2009 The Braybrooke Press Ltd. Journal of General Management Vol. 35 No. 2 Winter 2009/10 43

Strategic decision-making: models and methods in the face of complexity and time pressure Noushi Rahman

Associate Professor of Management, Lubin School of Business, Pace University, New York, USA

George L. De Feis

Visiting Assistant Professor of Management, Hagan School of Business, Iona College, New York, USA

The aim of this paper is to organise decision-making models and methods into one coherent matrix, using complexity (high to low) and time pressure (high to low) dimensions as relevant axes. Eight case vignettes are used to demonstrate the ®t of four decision-making models and four decision making methods within high-low complexity and high-low time pressure. The arguments and the vignettes suggest that a particular decision-making model or method becomes an appropriate tool for strategic decision-makers under varying complexity and time pressure. The appropriate model or method would change when the characteristics of the environment change. Decision-making models and methods can be systematically assessed with the proposed framework.

Introduction

Decision-making has been a key focus in strategic management literature for over ®ve decades. This paper distinguishes between models and methods of strategic decision-making. Since labelling confusions abound in the literature, it is imperative to de®ne these concepts a priori. Lyles and Thomas (1988: 134) conceptualised strategic decision-making models in terms of `the dominant conceptual frameworks' within which decision-makers operate. Similarly, Loewenstein (2001) has conceptualised decision-making models in terms of deep-rooted paradigms that dictate decision-makers' orientations. Thus, in this paper, decision-making models refer to the unique set of assumptions that a�ect decision makers' analytic and executive orientations. In contrast, decision-making methods refer to procedures and techniques utilised to arrive at a decision. Whether it is cluster analysis (e.g. Bittman and Gelbard, 2007), fuzzy logic (e.g. Gu and Zhu, 2006), con¯ict resolution (e.g. Bose and Paradice, 1999), consensus building (Choudhury, Shankar and Tiwari, 2006),

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simulation (e.g. Eldabi, Irani, Paul and Dove, 2002), or some other approach, they consistently refer to procedures and techniques of arriving at a decision by either an individual or a group.

Various decision-making models and methods have been introduced over the years. The planning school of strategy was largely based on the rational and boundedly-rational models of decision-making (Anso�, 1965; Simon, 1958). In later years, scholars started to challenge these models, suggesting the use of incremental (Quinn, 1981), garbage can (Cohen, March and Olsen, 1972), and random choice models (Mintzberg, 1978). Parallel to the research on decision- making models, several decision-making methods have been devel- oped during the past decades. Decision-making methods, such as the Delphi technique, nominal group technique, environmental scanning, and manage- ment science, have received considerable attention by academics and practi- tioners (Dean and Sharfman, 1996; Furrer, Thomas and Goussevskaia, 2007; Huber, 1984).

The rich varieties of decision-making models and methods have left practitioners more confused than composed about analysing and enacting their strategic decisions. The purpose of this paper is to develop a framework of decision-making models and methods to aid strategic decision makers in their decision-making process. To that end, the authors map the various decision-making models and methods into a two-by-two matrix. Such a framework would allow managers and academics to compare and contrast the relative value of di�erent models and methods under relevant decision- making conditions. More speci®cally, this paper shows a framework built on two distinct dimensions of decision-making, namely complexity (Swait and Adamowicz, 2001) and time pressure (Sheremata, 2000). It is argued here that models and methods of decision-making can be mapped in terms of the relative complexity and time pressure involved. Such a map would allow managers to determine the most appropriate decision-making model and corresponding method to use to make e�ective strategic decisions. The paper is organised into four sections ± a brief review of strategic decision-making, dimensions of the decision-making environment, decision-making models and methods, and concluding remarks.

Prefatory note on strategic decision-making

Decision-making permeates throughout all levels of an organisation ± from line level, to functional level, to business units, to corporate headquarters (Hall, 1999). Strategic decision-making is contingent upon many factors, such as market variability, opportunistic behaviour of a partner, natural calamity and internal riot. These factors can add layers of complexity to the decision- making process. Similarly, these factors can increase the time pressure on strategic decision-makers (Eisenhardt, 1989; Eisenhardt and Zbaracki, 1992). A substantial part of the literature on decision-making has attended to cognitive biases of decision-makers. Cognitive biases are observer e�ects (e.g. statistical errors, social attribution errors and memory errors) common in all humans that skew the reliability of other evidences (Haselton, Nettle and Andrews, 2005). Decision-makers may become more susceptible to cognitive

Strategic decision-making: models and methods in the face of complexity and time pressure

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biases in the face of additional complexity and time pressures. Such conditions may prompt decision-makers to be anything from extra careful and con- servative to reckless and daring. Strategic management scholars have elabo- rated on the adverse e�ects of cognitive biases on decision-making processes (Das and Teng, 1999; Schwenk, 1986).

Strategic decision-makers can under- or over-estimate the values of their decision to adhere to their initial position or hypothesis when dealing with many decision-making variables. In the face of time pressure, they may be prone to use over-simpli®ed analogies to conceptualise a complex situation. Decision-makers may also undermine partially-described alternatives by ignoring that information as incomplete. Too many issues, limited time, and existing and potential competitors make decision-making complex, unstable and risky (Simon, 1979). Moreover, in fast-changing environments, time pressure has emerged as a salient force (Kocher and Sutter, 2006). The authors contend that complexity and time pressure would make di�erent decision-making models and methods more or less aligned with the decision- making environment. Consequently, complexity and time pressure are reviewed in the next section.

Decision-making environment: complexity and time pressure

Complexity in¯uences decision-making because when there are too many issues with uncertain and unstable potential outcomes, the decision-making tree would yield very complex and uncertain conclusions (Siggelkow and Rivkin, 2005). Decision-making would be tantamount to selecting a potential outcome from a garbage can (Cohen et al., 1972). Complexity theorists state that the interdependencies among various decision-making inputs increase the level of complexity (Mischen and Jackson, 2008). Time pressure could also impact decision-making, as bounded-rationality and satis®cing play a role when the clock is ticking, particularly when stress comes into the picture (Simon, 1947, 1962; Svenson and Maule, 1993). Sports, wars and business decisions (in a competitive marketplace) are a�ected by time pressures. In football, basketball and soccer there is a time clock. Similarly, in a highly competitive environment, where `does Google tell Microsoft' takes on a new meaning from `does Macy's tell Gimbels', time seems to pass faster. The dimensions of complexity and time pressure are critical in the area of decision- making. Complexity refers to the number and interdependencies of di�erent components that exist in any decision-making process: e.g., what type of product should one choose, from what vendor, what do the many competitors say, what industry is a ®rm in, could that industry be changed, what new entrants are there, etc. (Mischen and Jackson, 2008). For the sake of simplifying the demonstration here, the authors ignored the interdependen- cies and focused only on the number of decision-making components when selecting vignettes as examples.

Kocher and Sutter (2006: 375) aptly note that `[m]any decisions in economics and ®nance have to be made under severe time pressure'. In a fast-evolving competitive marketplace, making the right decision by itself

does not give a ®rm any competitive advantage, but making a right decision quickly may lead to temporary competitive advantage (Porter and Millar, 1985). For this reason companies in high velocity industries continuously spend a great deal on research and development (Eisenhardt and Bourgeois, 1988), as the competitive advantage they achieve is only sustainable when they keep inventing new products and services in quick succession (Reed and De®llippi, 1990). These companies routinely engage in strategic decision- making under time pressure.

Models and methods

This section will examine four decision-making models and four decision- making methods in terms of their ability to deal with complexity and time pressure. Figure 1 below shows how a decision-making model and a decision- making method are paired in each of the four cells. The authors posit that each of these pairings is a strong ®t, albeit not the only logical one. For the sake of exposition, the four models are discussed ®rst, followed by the four methods.

Figure 1: Analytical framework for strategic decision-making models and methods

Rational model

The roots of the rational model of decision-making can be traced back to the thoughts of Frederick Taylor and Henry Fayol. In the rational model, decision-makers make decisions based upon complete information. Rational decision-makers identify the objective, develop possible alternatives, evaluate the alternatives, and select the best possible alternative (Schwenk, 1986). Clearly, few business problems present themselves with a complete, de®nitive frame within which to work. Thus, scholars assert that the rational decision- making model is less e�ective under uncertain conditions (Lyles and Thomas, 1998; Simon, 1979). Heracleous (1994: 16) aptly notes that `[t]he applicability of a rational decision-making model is limited to relatively simple problems, where objectives are clear, unambiguous, and agreed, and cause-e�ect

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relations are clearly known.' There are business situations where the circum- stances can be somewhat simpli®ed, so that data on variables within the simpli®ed context are fully known. Within the boundaries of the rational model, Simon (1979) has suggested the use of satis®cing, whereby satisfactory (but not optimum) solutions can be reached in a more realistic environment.

Procter and Gamble's (P&G) strategic decision regarding employee bene®ts o�ers a telling example of rational decision-making. At P&G Canada, the strategic decision was to adopt a new way to balance the competing work- family obligations of its employees. To ease the worry of employees ®nding time to cook for their children, spouses or even for themselves, P&G now o�ers a catering service which delivers frozen meals to the o�ce so employees can pick up their dinner as they are leaving. According to Jane Lewis, the Director of Human Resources at P&G, `Providing a supportive workplace and helping employees balance their professional and personal lives is a pillar of our HR philosophy at P&G Canada' (Harston, 2007). In keeping with its philosophy of work-family balance, P&G tried to add a novel bene®t for its employees. Given that no other ®rm had this bene®t, this idea of o�ering a catering service to employees was a unique one. Since costs of the meal/service were borne by the employees, there are few variables to deal with in this strategic decision. Hence, the decision-making environment was not com- plex. In addition to the low complexity, this strategic decision did not deal with any signi®cant time pressure. Had P&G been a follower in o�ering this unique kind of employee bene®t, it would need to be more concerned about catching up with the ®rst mover. However, since P&G was the ®rst mover in this employee bene®ts program, P&G could not have experienced a high level of time pressure in enacting such a program.

Guideline 1: Under conditions of low complexity and low time pressure, the rational model will be an appropriate decision-making model.

Incremental model

The incremental model reduces complex decisions to a series of simple decisions. Thus, when a complex decision's implementation path is unpre- dictable, the implementation path of a series of simpler decisions seems more conceivable by the decision-makers. Two kinds of incrementalism exist: disjointed incrementalism (Braybrooke and Lindblom, 1963) and logical incrementalism (Quinn, 1980). Disjointed incrementalism would advocate that managers should muddle through (Lindblom, 1959), whereas logical incrementalism would prescribe that managers muddle through with a purpose (Wrapp, 1967, emphasis added). According to Rajagopalan and Rasheed (1995: 294), `[t]he models are relevant to di�erent domains of policy making, varying from `incremental government politics' (disjointed incre- mentalism) to strategic decision-making in business organisations (logical incrementalism).' This paper will focus on logical incrementalism from here on. According to Wrapp (1967), the utility of logical incrementalism model arises when the eventual goal is known but the path cannot be determined a priori due to unavailability of the facts. Along a similar view, Rajagopalan and

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Strategic decision-making: models and methods in the face of complexity and time pressure

Rasheed (1995: 293) explain, `excellent managers have a very good sense of their objectives, but lack precise ideas about how to realize them. Hence, through a trial and error process they grope intelligently towards their goals.' The complexity of the decision path appears less dramatic when a long-term, complex decision is deconstructed into a series of shorter-term, simpler decisions. For the logical incrementalism model to be relevant under complex settings, the decision at stake must be collapsible into smaller parts. Decisions that cannot be collapsed (e.g. whether to merge with a competitor) cannot be e�ectively accomplished through incremental sequenced steps.

For example, Google recently announced its continuing international expansion with the launch of two Latin American operation centres in SaÄo Paulo, Brazil and Mexico City, Mexico. These new o�ces would enable Google to provide the best advertising services and search experience to its users, advertisers and partners in Brazil, Mexico, and throughout Latin America. Google's recent international expansion was highly complex yet the time pressure was low. As a part of its internationalising strategy, Google needed to decide on the speci®c markets that it intended to enter and the most e�ective and e�cient path to establish its presence in those markets. Clearly, such expansion required substantial capital resources as well as time. The incremental steps of Google's decision-making manifests in two ways. First, Google conducted extensive research of the Latin American market, identi®ed critical success factors in each country within that market, and ran cost-bene®t analysis for those countries. Each of these activities moved Google closer to its eventual decision to expand to the Latin American market. Second, Google did not decide to enter all Latin American countries at the same time. Rather, in keeping with the incremental model, Google is expanding to the Latin Americas one country at a time (United Press International, 2005).

Guideline 2: Under conditions of high complexity and low time pressure, the logical incrementalism model will be an appropriate decision-making model.

Boundedly-rational model

The boundedly-rational model emerges from the behavioural assumption of bounded rationality, which acknowledges the cognitive limitations of decision-makers (Simon, 1957). Thus, in addition to limited availability of information that makes rational model applications di�cult, Simon (1957, 1979) argues that decision-makers would not be able to cognitively process all of the subjective information even if all pertinent information was made available to them. The boundedly-rational model allows for ample room to not know of decision parameters. As Simon (1979: 500±501) describes, the boundedly-rational model strives to `replace the classical model with one that would describe how decisions could be (and probably actually were) made when . . . the decision maker did not possess a general and consistent utility function for comparing heterogeneous alternatives.' Within this model, decision-makers do not attempt optimisation of outcome. Rather, satisfactory outcomes are deemed acceptable. Also, when organisation goals are abstract in nature, collapsing such a goal to more tangible sub-goals can allow for feasible

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decision-making for boundedly-rational decision-makers. Lastly, the deci- sion-making task can be divided among specialists who may be most knowledgeable within their narrow band of expertise, thereby overcoming some of the challenges for a decision-maker to be an expert of many di�erent areas all at once. Rather, co-ordinating specialist work can overcome many situations where individual managers would not be able to make satisfactory decisions on their own.

Consider the case of UK-based National Fruit Collection (NFC), the world's largest collection of apple cultivars. Located at Brogdale Farm, near Kent, NFC is home to more than 2000 named varieties of apples (NFC website). If the members of NFC do not follow up with simple production- related activities in a timely manner, the apples will neither taste delicious nor look attractive. To respond to time pressure, NFC farmers deliberately choose to deal with only few key factors to produce apples. To get the best from each orchard, NFC farmers focus on seven factors: site selection, orchard design, planting and crop establishment, tree management, crop management, crop protection and crop nutrition (Farm Advisory Services Team website). Farmers cannot realistically account for all the unknown variables that a�ect apple production. Thus, they choose to make production decisions based on a simpli®ed model. This paper argues that apple production is a satis®ced outcome for NFC; perhaps under a more controlled environment even better apples can be grown, but in the face of too many unknown factors that may a�ect apple production, NFC farmers make production decisions following the boundedly-rational model.

Guideline 3: Under conditions of low complexity and high time pressure, the boundedly-rational model will be an appropriate deci- sion-making model.

Garbage can model

The garbage can model of decision-making builds on the chancy and complex interaction of problems, solutions, participants and choice opportunities (Cohen et al., 1972). The chancy nature of interaction stems from the recognition that the business environment is anarchic, inconsistent and ill- de®ned where participants can freely enter and exit. The environment is assessed to be highly dynamic; hence, there is a need to make and act on decisions within a limited time frame. Amit and Schoemaker (1993) explain how under uncertainty, complexity, and con¯ict, managers make imperfect and discretionary decisions to develop and deploy selected resources and capabilities to generate organisational rent. Therefore, while the garbage can model e�ectively accounts for complexity, the decision made using this model can at best be sub-optimal.

Building his argument around the case of the National Bipartisan Commis- sion on the Future of Medicare, Kalu (2005: 50) concludes that `although the garbage can model could provide a basis for analysing the dynamics of the Medicare reform debate, it cannot o�er a complete picture for capturing the practical implication of the policy process.' He admits that policy calculations of political actors and exogenous conditions a�ecting the political

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Strategic decision-making: models and methods in the face of complexity and time pressure

environment o�er a stronger explanation of why some policy decisions are made and others ®zzle out. Consider Washington Mutual's (WAMU) stra- tegic choices in the face of the sub-prime mortgage crisis of 2007. WAMU could either (a) tighten its sub-prime lending standards, or (b) sell its riskiest loans to investors at a deep discount, or (c) focus more on its other businesses that are unrelated to the mortgage business. Note that none of these choices that WAMU could extract from its `garbage can' would e�ectively address the actual sub-prime related challenges. Nevertheless, in an extremely turbulent business environment WAMU executives had to decide from these anarchic and inconsistent alternatives a possible solution to the company's sub-prime related problem. The highly complex world of sub-prime mortgage and the shortage of adequate time forced WAMU and several other ®rms within the ®nancial industry to make decisions adopting the garbage can model.

Guideline 4: Under conditions of high complexity and high time pressure, the garbage can model will be an appropriate decision- making model.

So far, four decision-making models have been discussed. The remainder of this section will address four decision-making methods.

Management science method

The management science method of decision-making (Simon, 1959, 1977) uses the sequential decision-making steps in a clear and precise way:

� Define the problem. � Identify alternatives. � Develop some criteria. � Evaluate alternatives (relative to those criteria). � Choose an alternative. � Implement the decision. � Analyse the results.

Thus, management science may be viewed as a decision-making approach or procedure. This approach or procedure is the way all decisions are made in this method, though sometimes all of these steps can be made very quickly. However, time is generally not a pressing issue for the management science method. Analysts who are seeking to identify how many, what size and what dimensions would more readily be able to use this quantitative approach. Management science is a science, in that it utilises the scienti®c methodology just as other sciences (e.g. biology, chemistry, etc.). Namely, it is objective, uses measurement, applies theories or models, uses experiments, and is valid and reliable. By using mathematical models, linear programming, PERT charts and computer simulations, the management science method presents the quantitative side of management (De Feis, 2007). Examples of modern methods in management science include the ELECTRE series (Roy, 1968), Rough Set Theory (Pawlak, 1991), NAIADE (Novel Approach to Imprecise Assessment and Decision Environments) (Munda, 2006), MAVT (Multi- attribute Value Theory) (Simpson, 1996), three-point discrete-distribution

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approximations (Keefer and Bodily, 1983; Keefer, 1994) and others. ELEC- TRE is designed to handle decision-making based on multiple criteria with thresholds and a ®nite number of alternative decision paths. Rough Set Theory gives a formal estimation of the conventional set of numbers, when some numbers are missing. NAIADE is a discrete multi-criteria method that uses crisp, stochastic, or fuzzy measurement metrics and assumes a steady judgment criterion. MAVT `is a method used for making decisions in an environment of certainty. It has its basis in an aggregative model and gives a ranking of all options from worst to best' (Simpson, 1996: 919). The three- point discrete-distribution approximations are `used in decision and risk analyses to represent probability distributions for continuous random vari- ables ± e.g., as probability nodes in decision or probability trees' (Keefer, 1994: 760). These modern methods use some approximations and estimations to facilitate rational thoughts. While these modern management science methods are capable of handling more decision-making variables, a ®nite number of variables is always assumed. Thus, notwithstanding their merits, modern management science tools may not be the optimum decision-making method when the number of decision-making variables is many and inde®- nite.

Consider Ford Motors' search for an alliance partner. In strategic alliances, the number of variables to consider from both relational (Das and Rahman, 2009; Rahman, 2007, 2008) and operational angles (Eisner, Rahman and Korn, 2009; Rahman, 2007; Rahman and Korn, 2009) is many and inde®nite. If Ford attempts to apply modern management science tools, there will be more questions than actual answers. In such a highly complex environment, Ford is better o� adopting either the Delphi method or the environmental scanning method to ®nd the most suitable alliance partner. In contrast, Ford has to consider a ®nite number of variables to determine whether to produce more or less of a speci®c product line. In this scenario, Ford can e�ectively utilise the management science method. To illustrate the management science approach, Ford may look at the problem the following way:

� Define the problem ± should Ford produce more or less of the 2006 models of its Escape and Escape Hybrid SUVs?

� Identify alternatives ± should Ford increase production of the Escape Hybrid, maintain production of the Escape Hybrid, or retrench produc- tion of the Escape Hybrid?

� Develop some criteria (based on 2005 conditions) ± what if market conditions get better (reduction of gas prices, increased consumer demand for SUVs and increased consumer demand for hybrid vehicles)?

� Evaluate alternatives (relative to criteria) ± what if demand increases for hybrid vehicles due to increasing gas prices and environmental awareness?

� Choose an alternative ± Ford chose to increase production of Escape Hybrid.

� Implement the decision ± Ford implemented the decision to increase production of the Escape Hybrid (11,446 [2005]; 20,000 [2006] ).

� Analyse the results ± Ford increased production of both the Escape and Escape Hybrid from the 2005 models to the 2006 models. The Escape 2006

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Strategic decision-making: models and methods in the face of complexity and time pressure

was successful with increasing demand for the mid-size SUV, which became the top of its class of SUVs by 2007.

Guideline 5: Under conditions of low complexity and low time pressure, the management science approach will be an appropriate decision-making method.

Delphi method

The Delphi method for decision-making is an iterative process whereby each new decision is impacted by the previous decisions made. The decision process is channelled through a point-person who proposes the changes that are put forth in the next round of the decision process. This decision-making method consists of a structured procedure for collecting and distilling knowledge from a group of experts by means of a series of questionnaires and interviews arranged with controlled opinion feedback (Adler and Ziglio, 1996). The Delphi method represents a useful group of decision-making methods with a group of experts, facilitating the formation of a group judgment (Helmer, 1977). However, due to the Delphi method's group nature, it takes longer to reach a consensus or compromise; hence, the temporal elements are not critical (Landeta, 2006). The Delphi Method works well when urgency is not of the essence because there is substantial time required for the iteration process to take place. The Delphi method has been developed in order to make use of experts in di�erent locations, time zones and cultures, without permitting certain social behaviours that may happen during a normal group discussion. Lack of full scienti®c knowledge forces decision-makers to rely on their own intuitions or expert opinions. The Delphi method has been widely used to generate forecasts in technology (e.g. deciding on memory sizes that will be required in computers) and education (e.g. deciding on changes in distance learning technology), and other ®elds (Cornish, 1977).

Consider the example of Boeing, whose objective is to deliver superior design, e�ciency and support to airline customers to allow passengers to conveniently travel to just about anywhere in the world. The Boeing 787 program is expected to produce some 3,500 aircrafts over the next 15 years (ending in 2023) for a total cost of $400 billion. With Delphi, when time constraints are low and complexity is high, this slow iterative process works best. For Boeing, high time pressure is not an in¯uential force in its decision- making environment. Not only is it time-consuming to make new aircraft, but also the aerospace industry has always had long design time for its techno- logically sophisticated products. There is an ongoing iterative process which allows the company to learn from models which they have previously tested (i.e. what the previous processes or previous models are which have been successful). These professionals are the experts who can facilitate the forma- tion of group judgment. Finally, there is high decision complexity, which stems from numerous safety, construction/engineering and logistical issues.

Guideline 6: Under conditions of high complexity and low time pressure, the Delphi method will be an appropriate decision-making method.

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Nominal group method

According to DeSanctis and Gallupe (1987), the nominal group method is a decision-making method used by groups of di�erent sizes, who want to make their decision quickly (e.g. through a voting process), and yet want all opinions to count. This is unlike a traditional voting mechanism, where only the largest group is considered (i.e. majority rules). In the nominal group technique, the method of scoring is di�erent. The process can be described as follows. First, every member of the group gives their view of the solution, e.g. how much time should a red light endure? Then, duplicate solutions are eliminated from the list of all solutions, and the members proceed to rank the solutions as ®rst, second, third, etc. Finally, the numbers each solution receives are totalled, and the solution most favoured is selected as the ®nal decision.

There are variations on how this technique is used. For example, it can identify strengths of the argument versus areas in need of development. It can be used as a decision-making voting alternative. Also, options do not always have to be quantitatively ranked, but may be evaluated more qualitatively with descriptions elaborated thereon. To perform this `group consultation', the group appoints a group leader (Jones and Hunter, 1995). The leader asks the individuals in the group to brainstorm for the solution. Only positive comments are to be made during this step in the brain- storming process. After the brainstorming session concludes, all comments ± positive and negative ± are made. Every opinion is an important one, as one does not know which brainstormed suggestion is correct. In other words, a negative comment given during the brainstorming process prevents the next suggestion, which may be the right answer. When the nominal group method is used for qualitatively identifying strengths and weaknesses, the larger clusters are where the strengths of the group lie, and the smaller clusters become areas for development (McMurray, 1994; Stewart and Shamdasani, 2004).

Pressure to make the decision in a timely manner and the low complexity of the decision-making process characterises the Mattel Chinese toy recall decision. In the face of bad publicity, Mattel had to act quickly to remedy the situation to ensure no ill-will persisted toward the company, as that could potentially depress future sales. In summer of 2007, Mattel was forced to recall nearly 15 million toys due to lead paint and small magnets. These toys had been produced in China and a number of children were seriously injured by ingesting the toxic lead paint or by swallowing tiny magnets. Mattel issued a massive recall of these harmful toys. The company even broadened the scope of the recall to be on the safe side. This kind of decision is low in complexity because the decision was obvious (simple) to recall the toys, although the steps taken to ensure the safety of the toys in subsequent batches needed some analysis. This is where the nominal group technique could have been very useful; both quantitative as well as qualitative assessments of alternatives are plausible in this case. To avoid tainting its own reputation any further, Mattel quickly recalled the toys and admitted the wrongdoing.

Guideline 7: Under conditions of low complexity and high time

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Strategic decision-making: models and methods in the face of complexity and time pressure

pressure, the nominal group method will be an appropriate decision- making method.

Environmental scanning method

The environmental scanning method is the acquisition and use of information about events, trends, and relationships in an organisation's external environ- ment. This information would assist management in planning the future course of action for the organisation (Aguilar, 1967; Choo and Auster, 1993). Organisations scan the environment in order to understand the external forces of change so that they may develop e�ective responses to improve their position in the future. An organisation will scan in order to avoid surprises, identify threats and opportunities, gain competitive advantage, and improve long-term and short-term planning (Sutton, 1988). The extent to which an organisation is adaptable to its outside environment depends on knowing and interpreting the external changes that are taking place. Environmental scan- ning is a primary tool for organisational learning. Environmental scanning includes looking at known information and looking for unknown informa- tion. It could range from an unplanned observation (e.g. casual conversation at the `water cooler' or a chance observation of an angry customer), to a planned observation (e.g. formal data gathering for a market research program). Environmental scanning is a description of information behaviour that is composed of information needs, information seeking and information use. Information needs refer to the focus and scope of scanning a given industry environment. Information seeking has been examined in terms of the sources that are used to scan the environment, as well as the organisational methods and systems deployed to monitor the environment. Lastly, informa- tion use is usually viewed in relation to decision- making or strategic planning (Choo and Auster, 1993).

Pioneer Electronics is an example of how a company uses the environ- mental scanning method, which is required when complexity is high and time pressure is high. The company looks at trends going on in the marketplace and focuses on what the consumers want to determine the design of its products and to acquire a competitive advantage over rivals. With technology changing so rapidly in the electronic world, it is daunting for a company to continuously stay ahead of the competition. Impressively enough, Pioneer has been able to excel in this area and is regarded as a world leader in entertainment. By investing a percentage of its revenue back into R&D the company is constantly doing more advanced research, exploring what other companies are doing, and listening to what the consumers want. Pioneer has been so innovative that it was the ®rst to introduce the laser disc player in 1980 as the precursor to the DVD player; CD players in cars in 1984; CD-based GPS systems in 1990; and combined DVD/LD/CD for the home in player in 1996. This portrays high decision complexity and high time pressure because the company needs to come out with its products before rival ®rms introduce their versions.

Guideline 8: Under conditions of high complexity and high time pressure, the environmental scanning method will be an appropriate decision-making method.

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Concluding remarks

The authors have organised four strategic decision-making models and four strategic decision- making methods into an analytical framework that can be used by both researchers and practitioners. In developing this framework, the authors thought of real-life decision-making models that are frequently used and popular decision-making methods that senior managers employ. The examples given in each of the eight models and methods show the range of decision complexity (low to high) mapped against the range of time pressure (low to high) (see Table 1 below).

Admittedly, these models and methods are juxtaposed in four di�erent cells for their strong relevance and appropriateness under the corresponding conditions. However, other models and methods may also be useful, albeit to a lesser degree under those conditions. For example, it is argued here that the management science method would be an appropriate selection when the decision-making environment was characterised as relatively straightforward and not pressed for time. However, strategic decision-makers may ®nd the Delphi method to be a useful alternative under such conditions. This paper is limited in that there is no recognition of managers' individual di�erences when interpreting the decision-making contexts. Mackenzie and Barnes (2007) explain how the lack of boundary conditions propels scholars to theorise based on the axiom that place or context is not important. In this paper, we have not been able to recognise the place or context of decision- makers faced with varying levels of complexity and time pressure. Rather, the authors have assumed that complexity and time pressure are estimable constructs that are determined before the decision-maker engages in the decision. Future scholars ought to recognise the role of place or context in decision-making to further the state of the literature in this subject.

Another limitation stems from the adoption of long-standing models and methods in the decision-making literature. The classic models and methods reviewed in this paper do not explicitly account for modern salient issues in the strategy literature. For instance, the ®eld of strategy has shifted its focus from decision-making processes to values, cultures and routines of the ®rm. How these axioms would a�ect the choice of decision-making models and/or

# 2009 The Braybrooke Press Ltd. Journal of General Management Vol. 35 No. 2 Winter 2009/10 55

Strategic decision-making: models and methods in the face of complexity and time pressure

Table 1: Industry examples ± strategic decision-making models and methods

Model/Method Complexity Time pressure Company

Rational model Low Low Procter & Gamble

Incremental model High Low Google

Boundedly-rational rational model Low High National Fruit Collection (NCF)

Garbage can model High High Washington Mutual

Management science method Low Low Ford

Delphi method High Low Boeing

Nominal group method Low High Mattel

Environmental scanning method High High Pioneer Electronics

methods remains an unexplored yet highly interesting area of scholarly inquiry. Many interconnected and interdependent relationships exist in complex and dynamic business environments. Whether it is a high-velocity industry (e.g. telecommunications or biotechnology) or a stable and matured industry (e.g. aluminum or defence-related products), some decision-making models and methods will be more appropriate than others. To aid managers in selecting the most appropriate decision-making tools, this paper delineates eight clear guidelines.

The usefulness of this framework may be more obvious from the following vignette. In recent months, the business press has covered the news of Microsoft tendering a bid for Yahoo! The strategic decision on Yahoo!'s part is both complex and highly pressed for time. With a high-level market volatility in both ®nancial and technology sectors, Yahoo!'s future is rather uncertain and mathematically determining the appropriateness of Microsoft's o�er is practically impossible. Additionally, Yahoo!'s main rival Google has started to thwart Microsoft's bid soon after Google learned of Microsoft's bid. Google has even o�ered to share some of its technology with Yahoo! to help the ailing ®rm revive and thwart Microsoft's acquisition bid.

Notwithstanding whether it is to become acquired by Microsoft or it is to ally with Google in a technology-sharing project, timing is of the essence for Yahoo! top executives because a delay may mean an attractive deal passing by. Given this circumstance, senior executives can easily note from this frame- work that the rational model or the management science method would be highly inappropriate. One may argue that the boundedly-rational model may be useful to some extent. Similarly, the Delphi method can be a useful tool in this instance. However, senior executives can look up this model and quickly determine that the most appropriate model and method would be a garbage can model and an environmental scanning method. In other words, armed with the two-by-two decision-making framework, given a certain decision- making context, a senior executive can immediately determine which model and method to use to maximise both e�ectiveness and e�ciency. Knowing the relative appropriateness of di�erent models and methods, managers will be able to better engage in strategic decision-making. Consequently, more time would be spent on the strategic issues and less time on the decision making process itself. This is a key managerial implication of our framework, as strategic decision makers are always hard-pressed for time and being able to singularly focus on strategic issues at hand could make them more productive. The authors hope that the strategic decision-making framework becomes a useful platform for future research extensions. Finally, the creators of this framework believe that it will serve senior decision-makers with a convenient and useful guide to determine relevant decision-making tools (i.e. models and methods) to use.

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Noushi Rahman is an Associate Professor of Management at the Lubin School of Business, Pace University. His work has been published in a variety of journals including the Journal of Business and Psychology, Asia-Paci®c Business Review and Journal of General Management. He received his PhD from the City University of New York.

George De Feis is Visiting Assistant Professor of Management at the Hagan School of Business, Iona College. He has also taught in various capacities in Monroe College and Pace University and served as the Chief Executive O�cer of the US Chess Federation. His textbook Management Science with Spreadsheets is in its second edition with Thomson Custom Publishing.

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