Alignment: the duality of decisionproblems

Christopher M. ScherpereelNorthern Arizona University, College of Business Administration,

Flagstaff, Arizona, USA

Abstract

Purpose – Identifying the state of alignment, when there is misalignment, and the path to achievealignment are of central importance to decision makers today. This paper seeks to offer decisionmakers some actionable guidance in narrowing the search for possible solution methodologies and todevelop a generalized decision alignment framework that can be applied to real decision problems.

Design/methodology/approach – Alignment is viewed as a goal of decision makers and thecorrect matching of decision and action is essential to achieving consistently high performance.Drawing on parallels with the duality problem in linear programming, decision alignment is defined.The decision alignment framework is theoretically developed using examples from a diverseapplication set, including quantitative research, decision making, education, and e-commerce.

Findings – The evidence shows that good research conforms to the decision alignment frameworkand poor research violates it. Similarly, good decisions conform to the decision alignment frameworkand poor decisions violate it. The decision alignment framework guides decision makers inconstraining and redefining problems to optimize outcome performance, and shows the importance ofaddressing the dual problem of learning and understanding the phenomena.

Research limitations/implications – The theoretical foundation developed can be used topromote future research in decision alignment. By providing a theoretically derived framework, richopportunities for empirical testing are offered. Researchers are also given guidance on how alignmentresearch can be conducted.

Practical implications – The examples presented highlight the prescriptive, communicative, anddescriptive value of the decision alignment framework. Practitioners are provided with examples forusing the decision alignment framework to build toolboxes of approaches that can be aligned to acharacterization of real-world decision problems to improve performance.

Originality/value – The introduction of a decision alignment framework is a significantcontribution to the management decision literature. By introducing a decision alignmentframework, the rather ambiguous term alignment is precisely defined as the matching of decisionproblem characterization (primal problem) with the approach possibility set (dual problem).

Keywords Strategic alignment, Decision making, Modelling

Paper type Conceptual paper

Developing a prescriptive model for decision-making is an intellectually satisfyingendeavor; unfortunately, the complexities of most business decision problems makethis a seemingly impossible task (Beach, 1993; Beach and Mitchell, 1978; Cooksey,2000; Edwards, 1954; Einhorn and Hogarth, 1986; Kahneman and Tversky, 1974, 1979;Klein, 1998; Lindblom, 1959, 1971; Russo and Schoemaker, 1989; Simon, 1979; Teigen,1996). Studies in naturalistic decision-making expose researchers to the reality thatmost decisions are made using a combination of observation, intuition, and experience(Klein, 1993, 1998; Lipshitz et al., 2001; Lipshitz and Strauss, 1997; Orasanu andConnolly, 1993; Zsambok, 1997). Decision-making guidance comes in the form of

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Received November 2005Revised May 2006Accepted June 2006

Management DecisionVol. 44 No. 9, 2006pp. 1258-1276q Emerald Group Publishing Limited0025-1747DOI 10.1108/00251740610707721

heuristics that help the decision maker gain experience, sharpen intuition, and improveobservation skills. This paper provides a framework to assist the decision maker innarrowing the search for possible solution methodologies, and it offers some actionableguidance on selecting the best approach to real decision problems.

Central to this framework is the concept of alignment. Labovitz and Rosansky(1997) have formally applied the alignment concept to the management andorganization of companies. They defined alignment “as both a noun and a verb – astate of being and a set of actions . . . alignment . . . refers to the integration of keysystems and processes and responses to changes in the external environment” (p. 5). Inan article on developing a business’s core competencies, McCrackin and Carroll (1998)state that “realizing the full benefit of competencies is only possible through theintegrated process of aligning.” These points are supported in the book Built to Last:Successful Habits of Visionary Companies by Collins and Porras (1994), whereempirical evidence is presented showing that the companies best able to sustainsuccess over long periods of time are those that aligned their business processes andcapabilities to the dynamically changing marketplace. The sustainability of alignedorganizations is further supported by research (Joyce et al., 2003) identifying the needto align strategy, execution, culture, and organization.

What is alignment and how is it defined? Beal and Yasai-Ardekani (2000) observedthat “the concept of alignment underlies the many contingency theories of strategy andorganizations” (p. 735). Markman and Medin (1995), in researching choice, highlightthe relationship between alignment and correspondence:

A body of current research suggests that in making judgments about alternatives, peoplebuild structured representations of items and compare them by placing like structures intocorrespondence (p. 117).

Often the concept of alignment when used in business is referred to as strategic fit(Chorn, 1991; Smaczny, 2001), strategic match (Mintzberg et al., 1998), or simply theinterface between two things (van der Zee and De Jong, 1999). Beal and Yasai-Ardekani(2000) identified alignment as “moderation, mediation, profile deviation, gestalts,covariation, and matching” (p. 735).

Clearly, alignment is an expressed goal of many managerial decision processes:

Organizations are re-engineering, collapsing delivery times, leveraging knowledge,attempting to create the learning organization, striving to implement TQM, working atdefining core competencies, delighting the customer, conducting 360 reviews, etc., etc., etc. Allmeaningful interventions, and all for naught unless alignment is an underlying theme(Burdett, 1994).

As Fred Smith, Chairman of Federal Express once said:

Alignment is the essence of management (Labovitz and Rosansky, 1997).

The literature is full of claims that alignment is necessary for strategy to be successful:

For any strategy to be successful, there must be management systems and processes in placewhich are aligned with and which reinforce the strategy (Bart et al., 2001, p. 22).

Despite all these researchers emphasizing the importance of alignment in business itremains a difficult concept to implement. A framework is needed that clearly definesalignment and provides a methodology for its consistent attainment.

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The usage of the alignment concept has received its greatest support in the strategyand information systems literature (Burdett, 1994; Henderson and Venkatraman, 1991;Lefebvre, 1992; Mockler, 2001; Smaczny, 2001; Strassmann, 1998). Since informationsystems are key components defining the infrastructure of an organization, it isreasonable to assume that alignment between an organization’s information systemsand business processes would be an executive’s priority. A Strassmann (1998) surveysupports this hypothesis, concluding that “aligning information systems to corporategoals . . . as the number one concern over the last five years”(p. 1). Further support isoffered by van der Zee and De Jong (1999) in a survey of 67 senior IT executives fromthree different continents, which confirms that aligning information technology andcorporate goals was their most important task.

For most businesses, achieving alignment is a continuous process (Smaczny, 2001).While alignment is a state, its definitional opposite – misalignment – is an infinitenumber of states in a multidimensional space. Thus, a discussion of the alignmentprocess requires both identifying the state of alignment and monitoring the dynamicsof misalignment. As concluded by Lefebvre (1992), “most organizations are generallyin a state of misalignment.” Lefebvre empirically confirms that “misalignedorganizations operate at decreased levels of performance . . . the more severe themisalignment, the worse the performance” (p. 52). Although Lefebvre uses the term todiscuss the specific decision problem of aligning a business’s information structure, theobservation can be broadened to the alignment of all decision problems. Thus,Lefebvre’s misalignment conclusion might be analogously applied to decisionproblems as follows: misaligned decisions will result in inferior outcomes and the moresevere the misalignment the worse the performance. This paper offers practitioners analignment framework that will help identify misalignment and will offer guidance inmaking the best realignment decisions.

The decision alignment framework presented here is developed theoretically usingexamples from a diverse application set, including quantitative research,decision-making, education, and e-commerce. Although empirical validation is notcurrently offered, future research is suggested to confirm the theory presented.Alignment is viewed as central to management decisions – the correct matching ofdecision and action is essential to achieving consistently high performance. Given thatthe link between alignment and performance is supported by the literature (Lefebvre,1992), if the practitioner is desirous of improving performance, then understanding theconcept of alignment is critical. The decision alignment framework offers thepractitioner a tool for understanding alignment, correcting problems of misalignment,and ultimately, improving performance.

Decision problem classificationIdentifying a formal classification methodology is the starting point for developing aguiding framework for decision makers. As noted by Bowker and Star (1999), “toclassify is human” (p. 1). In decision making practice the classification system is oftentacit and informal, however to develop a guiding framework for alignment, theclassification must be made explicit and formal. An explicit classification system fordecision problems has been attempted in most disciplines with some of the richestdebates still continuing in economics[1]. Many of these debates center on how to makedecisions under conditions that roughly correspond to the terms uncertainty,

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probability, and certainty; and the exact meaning of these terms (see Helfat and Teece,1987; Kogut and Kulatilaka, 2004; Dosi and Egidi, 1991; Sanchez, 2003). Although theterms are conveniently simple, they do not capture the multidimensionality associatedwith many decision problems. Therefore, a more descriptive multidimensionalclassification has been developed by Scherpereel (2006) that identifies decisionproblems as either first, second, or third order.

First-order decision problems typically have static properties and are associatedwith high levels of certainty and simplicity. These are often described in the literatureusing words such as simple, reversible, certain, low risk, static, small, short term,understood, and common. First-order decisions typically have well-establishedsolution methodologies, characterized by rational deterministic if-then rules anddeductive procedures. The objective is to find an exact solution.

Second-order decision problems have probabilistic uncertainty, are oftencomplicated, and follow definable dynamic processes. These are characterized in theliterature using such words as complicated, stochastic, probabilistic, optimizing,efficient, frequent, irreversible, medium risk, and medium term. These decisions rely onprobability theory and inductive logic for solutions. They are typically approachedusing axioms, computer simulations, and a constrained model of the actual phenomenaof interest. The objective is to find the most likely solution.

Third-order decision problems have genuine uncertainty, complexity, anddynamics. These are characterized with words like complex, irrevocable, ambiguous,high-risk, important, big, long term, subjective, and tacit. Third-order decisions rely onabductive logic (Peirce, 1998) and heuristic solutions. The objective is to findacceptability and effectiveness in the results. This decision orders classification formsthe basis for the decision alignment framework.

The duality of alignmentIt should be noted that each of the three decision orders (first, second, and third) havetwo distinct descriptors – one describing the problem characteristics and oneidentifying the approach methodology. These two descriptors form the basis foralignment. Borrowing some rich terminology from management science, the twodescriptors will be identified as the primal and dual elements of the decision problem.The terms “primal” and “dual” are central terms used in the classic operations researchmethodology called linear programming. In linear programming, the problem is solvedby the simultaneous solution to both the primal and dual problem formulations: oranalogously, the alignment of the primal and dual problems. For example, if the primalformulation is about maximizing the sales revenue from the seller’s viewpoint, thecorresponding dual formulation may be about minimizing the purchasing cost from thebuyer’s viewpoint. If both the primal and dual problems converge to the same solution,the solution will be optimal for both parties in the transaction.

In an analogous decision problem formulation, it is the alignment, or matching, ofthe primal and dual elements that dictates the optimal solution. The primal elementsare identified with the characteristics of the decision problem, whereas the dualelements represent the solution approach. To achieve alignment, the dual elementsmust align to the primal elements to achieve optimal performance. In other words, thedecision maker’s objective is to achieve alignment between the decision problem’scharacterization and the approach methodologies. The decision maker must solve both

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the characterization problem and the approach problem simultaneously to achievealignment. The mechanics of alignment will be discussed further in the followingsections.

Primal elements (characteristics)The attributes of the decision problem are called its characteristics; these describe itsessential quality or nature. Reviewing the definitions of first-order, second-order, andthird-order decision problems exposes many of these characteristic descriptors. Forexample, a second-order decision is characterized as complicated, stochastic,probabilistic, optimizing, efficient, frequent, irreversible, medium risk, and mediumterm. The classification of a new decision problem requires the identification of certaindescribable characteristics, which are used to place it as a member of a particulardecision order. The characteristics become the essence of the decision problem. Sincecharacteristics are observable physically or through a mental visualization, they arecalled the primal elements. The primal elements take the first priority in how thedecision problem is classified. They define the dimension of the vertical axis for thestate of alignment in Figure 1.

Figure 1.Decision alignmentframework

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Dual elements (approaches)Unlike characteristics, which are inherent descriptors of the decision problem,approaches represent the derived elements: approaches are derived in the sense thatthey are manifestations of the decision maker’s previous attempts at solving theparticular decision problem. Since they are derived from historical applications and arethe result of a process called mental simulation[2], approaches have an inherent degreeof uncertainty. In a situation in which alignment has occurred, the decisioncharacteristics will match the approach methodology. Thus, the approach elements arederived from the decision characterization and can be defined as dual to thecharacteristic elements. The dual elements define the horizontal axis for the state ofalignment in Figure 1.

Basic alignmentIn alignment, the same decision classification is made regardless of whether theapproach elements are specified but not the characteristic elements, or the dualproblem is developed where the characteristic elements are specified but not theapproach elements. This highlights the basic property of decision problem duality.Alignment requires that a decision problem’s primal classification be the same as itsdual classification. This occurs along the forty-five degree bisecting line in Figure 1. Ifthe primal classification and the dual classification are not equivalent, then the decisionproblem is in a state of misalignment. There is only one state of alignment but there aremultiple states of misalignment.

Basic misalignmentTwo generic causes are identified for decision problem misalignment. First, the dualelements, or the approaches, are not appropriate for the decision problem of interest.For example, the decision maker might approach the decision problem assuming astatic environment when the actual environment is dynamic. Second, the decisionmaker might misidentify the primal elements, or the characteristics. For example, thedecision maker may have characterized the decision problem as complex when inactuality it is simple, and thus has a deterministic solution that will go unrecognized.Both these states of misalignment result in decision errors and should be corrected by arealignment process. The decision alignment framework heuristic that follows,describes this realignment process as a choice between either constraining/redefiningthe problem or learning/understanding the problem.

Decision alignment framework heuristicThe primal and dual elements can be used to define a two-dimensional space in whichdecision problems can be located. Application of the decision problem classificationdefines precisely where decision problems lie within this space (see Figure 1). Bydefinition, if a decision problem is located on the 45 degree bisecting line within thisspace it is considered aligned, or having approaches (dual elements) that match thecharacteristics (primal elements). The decision problems located above the bisectingline can be repositioned into alignment by either constraining or redefining the decisionproblem’s primal elements. This is indicated in Figure 1 by the vertical arrows and isconceptually bringing the primal elements into alignment with the dual elements.

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For example, the problem of eliminating mice from a house can generate an infinitenumber of proposals from using dynamite to getting a cat. The decision maker’sallergy to cats and desire to retain use of the house after the removal of the mice startsthe process of constraining and redefining the problem. This is represented bymovement vertically along the arrows indicated in Figure 1. Eventually, thisredefinition process will converge on a set of aligned approach solutions that areacceptable. The approach selected by the decision maker may be the use of atraditional mousetrap. In this process, the decision problem, defined as eliminatingmice from a house, is systematically constrained so that acceptable solutionmethodologies become available. The example illustrates the process of bringing theprimal elements into alignment with the dual elements.

Decision problems that fall to the right or left of the 45 degree bisecting line areconsidered to be in misalignment. Moving them into alignment requires a change in thedecision maker’s knowledge or understanding of the phenomena. The horizontalarrows in Figure 1 represent the process of learning and understanding the problem.The process involves matching the dual elements to the fixed primal elements. In thistype of movement, the decision problem’s characteristics are fixed and new approachesare sought.

Returning to the mice elimination example, the decision problem may beconstrained through the vertical alignment process in such a way that no acceptablesolution methodology can be identified. The decision maker is an animal rightsactivist, and cannot allow any harm to come to the mice. The decision maker alsobelieves that traps that simply incarcerate the mice will psychologically traumatizethem. The decision problem characteristics are fixed, but no acceptable approach isavailable. Thus, the problem remains in misalignment, off the 45 degree line until thedecision maker can obtain new knowledge. The decision maker now faces the classicproblem of building a better mousetrap. Fortunately, the decision maker can movehorizontally along the arrows by learning. Through extensive research into mousepsychology, the decision maker discovers that mice dislike atonal music[3] and willquickly leave the area where atonal music is being played. Thus, a new solutionapproach is identified; the dual elements have been brought into alignment with theprimal elements.

Figure 1 provides a simple visual representation of the decision problem alignmentproblem. Solid arrows that are parallel to the primal and dual axes indicate incrementalmovements toward alignment within the decision – order space. Seminal movementsare also allowed in this framework but cannot be represented statically. A seminalmove implies that the decision problem is fundamentally changed, and can bevisualized as a discontinuity in the incremental progression. These changes areperhaps the result of breaking an established paradigm and are indicative of a changein the taxonomic classification. Typically, a seminal discovery will come from therecognition that a decision problem can be decomposed into lower order problems thatperhaps have known solution methodologies. For example, tropical disease treatmentmight be considered as a third order decision problem until the scientists are able todecompose the problem and find the underlying causes. Once found, the lower orderproblems are solved using aligned scientific methods where vaccines, antibiotics, andtreatments can then be developed.

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The key insight to be taken from the decision alignment framework is that thedecision problems lying above or below (right or left of) the bisecting line indicate amisalignment and require some further action by the decision maker/problem solver.The following examples will provide some validity to the alignment construct, inaddition to illustrating how numerous decision problems might be fit within theframework.

Alignment in academic researchAcademia is a world where logical thinkers are supposed to be able to explore theproblem solution space without bounds; it should therefore be rich in incrementalprogress and seminal discovery. In the decision alignment framework, incrementalprogress is visualized as the alignment of the primal characteristics with the dualapproaches, following the constraining or learning arrows in Figure 1. The 45 degreebisection of the decision-order space contains the academic’s aligned metaphoricaltoolbox. A “toolbox” is simply the set of properly positioned approaches within thedecision orders space. Or, alternatively an aligned toolbox contains all approaches thatin the past have proven successful in solving problems that are properly characterized.The toolbox contains all techniques known to the decision maker for making decisionsand solving problems. A representative academic toolbox is illustrated in Figure 2.

The mission of academics and scientists is to better understand the problem to besolved or the decision to be made and to find new approaches that aid in this effort.This mission, that is a focus of the decision alignment framework, is indicated inFigure 2 by a progression along the arrows (in a similar way to that described forFigure 1). Movements that are counter to the arrows represent poor research. Forexample, if the decision problem can be reasonably characterized as first-order, scienceshould not be searching for methodologies designed for second and third-orderproblems. Pursuing such a search is equivalent to taking a problem with a known,deterministic, first-order solution and attempting to apply a third-order heuristic. Thisis perhaps an interesting intellectual exercise, but there is no advancement becausethe aligned first-order solution is still superior – it solves the problem exactly. Thisargument can be illustrated with the problem of finding the third angle of a trianglewhen two other angles are known. The problem can be solved using the first-ordermathematical formula from geometry: 180 2 angleðknownÞ

1 2 angleðknownÞ2 ¼

angleðunknownÞ3 . To develop or apply a third-order heuristic that depends on the

current sun spot activity might be interesting but it is not likely to provide asuperior solution methodology that is 100 percent accurate. Figure 3 illustrates poorresearch of this type by a movement in the direction of the horizontal arrows. Thus,the search for solution methodologies is a horizontal movement away from thealignment axis in Figure 3.

A more common pursuit by academic researchers is attempting to apply tools fromthe known toolbox to problems of different decision orders. This is the trap ofsearching for applications that do not fit the methodology in the hope that throughserendipity and luck the known tool might solve the problem. Poor research of thistype is represented by the vertical arrows. Often this approach requires the actualproblem to be arbitrarily redefined and constrained to fit the available tool. Rather thenconstraining and redefining the problem a priori based on theory (represented by thevertical arrows in Figure 2), the problem is redefined after the solution methodological

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choice is made. This is the theoretical equivalent to collecting and analyzing empiricaldata before formulating the hypothesis and then claiming that the data prove thehypothesis. Thus, the search is vertical in Figure 3 for applications that do not fit themethodology.

This example illustrates that good research conforms to the decision alignmentframework (movement with the arrows in Figure 2) and poor research violates it, asshown by the arrows in Figure 3. Although seminal advancements could perhaps comefrom poor research practices, such advancements are more likely attributed to purechance. Those who have studied major seminal advancements like Einstein’s theory ofrelativity will recognize that the process used conforms to good research practices[4].The decision alignment framework prescribes those good practices. Thus, decisionalignment is achieved in academic research by bringing the primal characterization ofthe research problem into alignment with the dual approach methodologies that areavailable or can be developed.

Alignment in decision researchWhile the previous example provided an introduction in the use of the decisionalignment framework to properly align academic research, this second example showshow specific adjustments are made within the framework. Here alternative

Figure 2.A generic academicdecision toolbox

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decision-making theories are identified from the decision literature and are placed liketools into the decision alignment framework’s metaphorical toolbox (within theapplication space). Some possible placements are illustrated in Figure 4. These are notabsolute placements, but instead represent the decision-order region where the theorieshave garnered the greatest success. The decision theories that fall along the alignmentaxis have received some level of general acceptance within the field of study. Althoughthe accepted paradigms may give these theories greater application reach than theirstatic locations indicate, the decision alignment framework suggests limiting theapplication to decision problems of a particular characterization.

The theories indicated off the alignment axis represent theories that affordsignificant challenges to the existing paradigms or that require additional developmentto fit within the current paradigms. Notably, Shackle’s (1949) theory on potentialsurprise challenges the probability theory paradigm, which maintains a central role ingame theory and Bayesian decision analysis. To reach wider acceptance, Shackle’stheory will require advancement in the generally accepted understanding ofsecond-order problems (a movement along the horizontal arrow in Figure 4) or itwill be grudgingly, and perhaps appropriately, repositioned[5] to the less rigoroustoolbox for heuristic third-order solution methodologies (a movement along the verticalarrow in Figure 4).

Heiner’s (1983) C-D gap probability theory is also positioned off the alignment axisor outside the aligned toolbox. Although not a direct attack on the established

Figure 3.Poor research in the

decision alignmentframework

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methodologies, it requires some significant changes in the underlying assumptions andconstraints common to traditional methodologies. The decision problem addressed byHeiner is clearly third-order, incorporating genuine unknowable uncertainty; howeverthe probabilistic tools utilized are second-order. This misalignment could be the reasonthat Heiner’s quantitative techniques have not yet been incorporated into a majortheory of decision-making[6]. Thus, Heiner’s C-D gap theory is viewed as requiringeither some additional problem constraints to align it within the second-order problemspace (a movement along the vertical arrow in Figure 4), or a loosening of themathematical rigor to allow the qualitative insights to be supported in the third-orderproblem space (a movement along the horizontal arrow in Figure 4).

The solution methodologies along the alignment axis have proven valuable inapproaching decision problems within their defined decision-order space. Starting inthe lower left corner (first-order decision space) of Figure 4 are the stage models. Theseare generally applicable to all decision orders, but they find their greatest success inassisting decision makers/problem-solvers with first-order decision problems. Themodels in the first-order decision space typically use normative techniques that claimprescriptive capabilities. Aligned research in this region tends to define decisionproblems as solvable by following an orderly sequence of stages (Dewey, 1933).

Figure 4.Decision-making toolbox

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The middle region is arguably the most actively researched, at least among thequantitatively inclined research establishment. In this region are found a voluminousnumber of theories based on logical reasoning, rational argument, and probabilitycalculus. Aligned research in this region tends to view decision problems as choicesamong “a set of conceivable actions which an individual could take, each of which leadto certain consequences” (Arrow, 1951).

The promoters of qualitative research typically pursue third-order decisionproblems, which appear in the upper right-hand region of Figure 4. The focus inthird-order decision problem research is on providing good descriptive heuristics thatapply in real-world settings. Third-order decision problems are considered holisticallyand therefore they tend to support some type of systems perspective. Since there islittle attempt at rigorous proof – only circumstantial support – this toolbox tends tocontain a hodgepodge of sometimes-conflicting methodologies. The point being madehere is that because third order decision problems have so much genuine uncertaintyassociated with them the approaches tend to receive less supporting consensus withinthe literature, not that they are inferior theories. The goal of these third-orderapproaches is not an optimal solution but a solution that works. Decision theoriesaligned with this region are perceived as occurring in “a world in which there isautonomous or creative decision-making . . . one in which the future is not merelyunknown, but unknowable” (O’Driscoll and Rizzo, 1985, p. 2). An unknown futureprecludes prescriptive prediction. Instead, third-order tools claim to offer prescriptiveguidance in crafting an acceptable future.

Alignment in educationThis example is designed to demonstrate how the decision alignment framework canbe used to build customized toolboxes of approaches. Once constructed, decisionmakers can use similarly constructed toolboxes to align their decisions and improveperformance. Specifically, this example presents a possible toolbox for decision makersentrusted with the problem of selecting appropriately educated individuals oreducation pedagogy to accomplish specific objectives. The decision alignmentframework is used to organize the different education methodologies so that alignedselections can be made based on the decision maker’s hiring or education objectives. Ifthe objective is to increase the organization’s basic skills through education, thedecision maker can select aligned methodologies from the first-order region of thedecision alignment framework’s toolbox. In contrast, if the objective is to hire creativepeople then the decision maker should be guided toward individuals educated in usingthe methodologies found in the third-order region.

This hypothetical education-orders toolbox is illustrated in Figure 5. Thehypothetical toolbox in Figure 5 was created by identifying all the knowneducational approaches and researching empirical data as to each approaches’effectiveness. Those approaches where a consensus in the literature was clear wereplaced appropriately along the alignment axis within the toolbox. The alignedfirst-order region contains basic skills training methodologies needed for the doers inan organization. The focus in this region is on deductive logic, defined rules, and exactresults. Decision makers interested in this region can typically obtain real-timequantitative verification of success in reaching their objectives.

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The second-order region of the toolbox offers tactical skills education for the solvers inthe organization. The focus is placed on developing inductive logic, establishingprocedures, and obtaining consistent results. Measuring success in meeting thedecision maker’s objectives often lags behind the education; therefore, meaningfulquantitative measurement linked exclusively to the education becomes difficult.

The last region in the toolbox is focused on developing abductive logic, synthesizingcomplex information, and obtaining usable results. This type of education is targetedat the strategic formulators, or leaders, in the organization. To measure the success ofthese education methodologies is extremely difficult because of the long lag timebetween that actual education and the realization of a measurable result. Instead,third-order education requires the identification of indicator objectives from whichsuccess can be inferred.

A logical extension of this education-orders toolbox would be aligned measurementand evaluation methodologies. A number of these methodologies were alluded to in thediscussion of each decision-order region. However, this extension is not pursuedbecause the purpose of this example is to simply illustrate the possibilities of using thedecision alignment framework to build toolboxes for real-world decision problems.

Once constructed, the education-orders toolbox can be used to select individualswith the appropriate type of educational background that aligns with the specific task

Figure 5.Education toolbox

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or job duties to be performed. Similarly, the same toolbox can be used to aligncorporate training objectives with different pedagogy. Maintaining the clear separationbetween the primal and the dual problems actually simplifies the problem and makesthe condition of alignment clear for the decision maker. Thus, the decision maker istasked with solving the primal problem of characterizing the training (or hiring)objectives and the alignment framework is then used to match this primal solutionwith its dual approach.

Alignment in e-commerceThe last example illustrates the use of the decision alignment framework tocommunicate a major shift in the order of a decision problem. After the shift isidentified and the new classification has been communicated, the decision alignmentframework provides guidance in searching for understanding. To illustrate this point,the concept of changing business models is presented. The term “business model” isused in this discussion as a descriptor for the business’s physical organization and howit responds to marketplace dynamics.

The growth of the internet trade, or e-commerce, has created a new marketplace thatis dramatically different from the one faced by traditional businesses. As pointed outby Evans and Wurster (2000) the internet has deconstructed the traditional tradeoffbetween richness and reach. This implies that products and services available on theinternet can potentially reach geographically distributed consumers withcustomer-intimate personal richness. Achieving the same levels of richness possibleon the internet requires traditional business to physically locate near selected largerconsumer populations, and often sacrificing the reach into smaller but relatedpopulation groups.

How can this change be represented in the decision alignment framework? Thedeconstruction of the richness and reach tradeoff changes the marketplace so that thefirst and second-order rules and procedures established in the traditional marketplaceno longer apply. The traditional business models no longer function effectively. Thenet result is a decision problem that is blown to the third-order boundaries ofdecision-order space. The vertical arrows in Figure 6 represent this movement. Thebusiness models take on perceived third-order characterization but the availableapproaches and traditional thinking are first and second order. Thus, to achievealignment initially, the decision alignment framework requires the application ofthird-order approaches.

The unresolved issue is whether or not e-commerce business models will continue torequire third-order methodologies. If the Internet marketplace dynamics stabilize, willthe perception of e-commerce business models evolve to be characterized as more of asecond or even first-order issue? If this characterization evolves, will standardmethodologies and practices be developed for competing in the Internet marketplace?The answers are still to be determined. However, the decision-alignment frameworktoolbox is available to help the decision maker cope while waiting for seminaldiscoveries to be made. Reflecting on Figure 4, the decision alignment framework willalso guide researchers in their search for better definitions of the Internet businessmodel and appropriate solution methodologies.

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Conclusion and future researchIdentifying the state of alignment, determining whether there is misalignment, andchoosing which path will achieve alignment are of central importance to decisionmakers today. Alignment is an important goal of decision makers; and the correctmatching of characterization and approach is essential to achieving consistently highperformance. The decision alignment framework offers decision makers someactionable guidance in narrowing the search for possible solution methodologies, and itdevelops a generalized decision alignment framework that can be applied to realdecision problems.

Drawing on parallels with the duality problem in linear programming, decisionalignment is precisely defined as matching of decision problem characterization(primal elements) with the approach possibility set (dual elements). The evidencepresented in paper shows that good research conforms to the decision alignmentframework and poor research violates it. Similarly, examples illustrate that gooddecisions conform to the decision alignment framework and poor decisions violate it.The decision alignment framework offers the foundation upon which a prescriptivetool can be built for use by decision makers to correct for decision errors.

The theoretically derived decision alignment framework can be used to promotefuture research. A theoretically derived framework enables rich opportunities forempirical testing and theory extension. Additional empirical research should addressthe validity of the decision alignment framework. While the link between alignmentand performance has been empirically validated, this link has not been tested withinthe context of the decision alignment framework. This testing can be conducted bycomparing the performance of actual decision problems solved in conformance withthe framework (aligned) and those solved in non-conformance with the framework(misaligned).

Figure 6.Communication of thee-commerce decisionproblem

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It is also presumptuous to believe that all decision problems can be subjectedsmoothly to the decision alignment framework, as currently developed. A majority ofdecision problems retain some characteristics from each of the first, second, and thirdorder classifications; or along some multidimensional continuum containing all threedecision orders. Thus, according to the taxonomy classification most decisionproblems would be identified as perceived third order problems. Strict adherence to thedecision alignment framework mandates pursuing third order approaches unless theproblem can be somehow decomposed. Without having any known third orderapproaches, alignment may never be achieved. This leaves the practitioner with anincomplete framework and a difficult time achieving optimal performance. Thus,further investigation into the decomposability of decision problems will help to extendthe decision alignment framework.

The first three examples presented in this paper: alignment in academic research,alignment in decision research, and alignment in education: are developed to serve anumber of purposes. First, they are used principally to support the theory developmentpresented. Secondly, they provide illustrations on the use and implementation of thetheory. Thirdly, the examples offer practitioners the concept of using toolboxes ofapproaches to address classes of decision problems once they have been properlycharacterized (or diagnosed). Finally, the field specific examples (decision education,e-commerce) provide excellent opportunities for historical research. This researchcould further validate the framework by tracing the progress of individual approachesand particular applications of those approaches over time.

The introduction of a decision alignment framework is a significant contribution tothe management decision literature. By developing such a framework, the ratherambiguous term “alignment” is made explicit for the decision maker as the matching ofdecision problem characterization (primal) with the approach possibility set (dual).Following the prescriptions suggested by the alignment framework, the decisionmaker can confidently adjust the primal and dual problems of characterization andapproach selection into an aligned strategy.

The decision alignment framework introduced in this paper makes a strong casethat there exists an underlying pattern to decision problems. To exploit this pattern,the decision alignment framework can guide researchers and decision makers towardappropriate solution methodologies. Ultimately, it is the alignment of decision problemcharacterization (or primal elements) with available approaches (or dual elements) thatdetermine the adequacy and efficacy of a decision. The examples presented in thispaper highlight the framework’s prescriptive, communicative, and descriptive value.Future research will extend the prescriptive capabilities of the decision alignmentframework. However, at present the practitioner is provided with sufficient examplesfor using the decision alignment framework to build toolboxes to aid in the alignmentof real-world decision problems.

Notes

1. For the start of an interesting debate on this topic see Keynes (1921) and Knight (1921).

2. The process called mental simulation is described by Klein as “the ability to imagine peopleand objects consciously and to transform those people and objects through severaltransitions, finally picturing them in a different way than at the start. This process is not just

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building a static snapshot. Rather, it is building a sequence of snapshots to play out andobserve what occurs” (Klein, 1998).

3. Atonal music is designed to deliberately avoid the traditional musical key or tonal center. Tothe novice’s ear, atonal music sounds as if the instruments are not properly tuned.

4. See Max Wertheimer’s (1959) book Productive Thinking for a full account of the developmentof Einstein’s theory of relativity. Other excellent examples can be found in MihalyCsikszentmihalyi’s (1996) book Creativity.

5. Shackle (1949) was quite adamant that potential surprise is superior to probability theory inaddressing decision problems (see chapter 7 of “Expectations in Economics,” 1949).Unfortunately, his attack focuses on the application of probability techniques to problemsthat would be classified as third-order decisions within the decision alignment framework.This focus is evident in Shackle’s opening remarks: “The frequency-ratio concept ofprobability is suitable and essential for the purposes of mathematical statistics. But as ameans of analyzing those original acts of mind, involving degrees of doubt and beliefassigned to the products of imagination, which are what I mean by expectation, it isessentially and radically inappropriate” (p. ix). Although generally discounted by theestablished literature (Arrow, 1951), Shackle’s theory of potential surprise accuratelyattacked the misapplication of probability theory. In the process Shackle boasted his theory’sgenerality, and positioned it as a replacement for probability theory (the placementillustrated in Figure 4). A better placement and greater acceptance might be gained byrepositioning it to a third-order methodology.

6. Despite the reluctance to recognize quantitative contributions, Heiner’s (1983) qualitativeinsights remain a seminal contribution to the decision-making literature.

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Corresponding authorChristopher M. Scherpereel can be contacted at: chris.scherpereel@nau.edu

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