1-s2.0-S0001879104001125-main

Journal of Vocational Behavior 66 (2005) 273–303

www.elsevier.com/locate/jvb

Vocational choice: A decision making perspective�

Henry Sauermann

Fuqua School of Business, Duke University, Box 90120, Durham, NC 27708, USA

Received 7 June 2004Available online 15 December 2004

Abstract

We propose a model of vocational choice that can be used for analyzing and guiding thedecision processes underlying career and job choices. Our model is based on research in behav-ioral decision making (BDM), in particular the choice goals framework developed by Bettman,Luce, and Payne (1998). The basic model involves two major processes. First, the selection of adecision strategy according to four choice goals: maximizing decision accuracy, minimizingcognitive eVort, minimizing negative emotion, and maximizing justiWability of the decision.Second, the construction of situation-speciWc preferences, which can reXect irrelevant task andcontext factors such as the evaluation mode. This basic model is extended to account for socialinXuences and the long decision time typical of most career and job decisions. We reviewresearch on vocational choice in light of this model, discuss normative implications for coun-seling, and outline a research agenda for studying vocational choice from a behavioral decisionmaking perspective. 2004 Elsevier Inc. All rights reserved.

Keywords: Vocational choice; Decision making processes; Choice goals; Constructed preferences;Decision strategies; Behavioral decision making; Person-environment Wt models; Social inXuences invocational choice; Decision time

� We thank John Payne, Rick Larrick, Gerry DeSanctis, and two anonymous reviewers for commentsand suggestions on earlier versions of this paper.

E-mail address: [email protected].

0001-8791/$ - see front matter 2004 Elsevier Inc. All rights reserved.doi:10.1016/j.jvb.2004.10.001

mailto: [email protected]

mailto: [email protected]

274 H. Sauermann / Journal of Vocational Behavior 66 (2005) 273–303

1. Introduction

The vast literature on career decision making, counseling, recruiting, and search inlabor markets reXects the high importance of vocational choice for individuals andfor society. The dominant approach in studying vocational choice focuses on thecharacteristics of the individual and of the occupations from which the individual hasto choose, whether vocational choice results in some sort of “Wt,” and what the eVectsof Wt are (person-environment Wt paradigm). Holland’s theory of vocational choice(Holland, 1997) is probably the most inXuential theory in this tradition and hasspurred a wealth of interesting insights (Kristof, 1996; Spokane, Meir, & Catalano,2000). Although there remain questions at the level of speciWc constructs, measure-ment, and moderating factors (Tinsley, 2000), there seems to be considerable supportfor the idea that individuals tend to match personal and organizational or task char-acteristics and that Wt has beneWcial eVects at the individual and organizational level(Holland, 1997; Kristof, 1996). The person-environment Wt approach to vocationalchoice is therefore particularly useful from a predictive perspective. But how exactlyis Wt created? Why do we sometimes observe a lack of Wt? How can the quality of adecision, with respect to Wt or some other measure, be improved? To answer thesequestions, a process-focused analysis of vocational choice is necessary.

In developing such a process-focused perspective, we can draw on research in theWeld of behavioral decision making (BDM), which has conducted a large amount ofbasic research on decision processes. Insights from BDM have been successfullyapplied in a variety of areas such as consumer research and managerial decision mak-ing. Findings and concepts from BDM have also been used to develop and testdescriptive models of vocational choice and to develop recommendations for coun-seling (Brooks & Betz, 1990; Gati & Asher, 2001; Gati, Fassa, & Houminer, 1995;Lichtenberg, ShaVer, & Arachtingi, 1993). However, while decision processes havegained considerable attention in the vocational choice literature, most applications ofdecision making research in this Weld involve rather abstract model comparisons orfocus on selected issues. An integrated behavioral perspective on vocational decisionmaking is still lacking. Since scholars in BDM have developed signiWcant insightsover the past 30 years, we are convinced that such a perspective could beneWt the Weldconsiderably by providing a useful basis for studying the processes involved in mak-ing vocational choices.

In this paper, we develop a model of the motivational and cognitive processes thatare involved in vocational choice. Vocational choice (we deWne it broadly as involvingcareers as well as jobs) is a very complex process that can extend over a relatively longperiod of time. One could argue that Wrst aspects of career decision making emergewhen a child dreams of becoming an astronaut, teacher, or “just like Mom.” However,we will consider “vocational choice” more narrowly. The strength of a BDM perspec-tive lies in focusing on the motivational and cognitive issues that arise in the phasewhere the decision maker has a set of alternatives (occupations or jobs) from which achoice has to be made. Not always will such a phase be clearly identiWable; for somepeople vocational choice is of a more “emergent” nature. However, we suggest that inmany cases it is possible and useful to distinguish such an explicit decision phase and

H. Sauermann / Journal of Vocational Behavior 66 (2005) 273–303 275

to study it in more detail. Such a decision phase is of course even more pronouncedwhen professional career counselors get involved who explicitly structure the counsel-ing process as involving the choice between alternatives, sometimes directly buildingon BDM research (e.g., Gati & Asher, 2001; Gati et al., 1995). Our model is applicableto vocational decisions where more controlled, analytical, and deliberative processesdominate. We have chosen not to incorporate the emerging research on intuitive andemotion-driven decision making (e.g., Finucane, Peters, & Slovic, 2003; ShaWr & LeB-oeuf, 2002) since our knowledge about these processes is still very limited, especiallywith respect to their interactions with analytical and conscious processes.

The model presented in this paper is based on research in BDM as well as in careerand job decision making and is primarily descriptive. However, some of its elementshave only been studied by BDM researchers and not in the career choice context;more research is needed to Wll these gaps, and our paper therefore also represents a“research agenda” for scholars interested in studying vocational choice from a BDMperspective.

Throughout the paper, we will illustrate our discussion using the Wctitious case of acollege senior (let’s call him Robert). Robert is a successful college senior with a busi-ness major who is confronted with the decision whether to go into business consulting,management in a private Wrm, or whether to become a business professor. We will seehow BDM research can help us understand how he deals with limited information,conXicting preferences, the demands of his social environment, and his own cognitivelimitations. And we will see how a counselor could help him make a good decision. Ofcourse, the applicability of our model is not restricted to college seniors with a busi-ness major; this mini case is simply meant to make our discussion more tangible.

The remainder of the paper is structured as follows. In Section 2, we present amodel overview. In Section 3, we describe a typical decision task in vocational choiceusing the terminology of behavioral decision making research. In Section 4, we dis-cuss various decision strategies and how they relate to the choice goals of decisionmakers. In Section 5, we discuss the impact of decision strategies as well as of taskand context factors on the construction of preferences in vocational choice. In Sec-tion 6, we brieXy discuss the implications of social factors and of the long decisiontime in vocational choice for our model. In Section 7, we derive normative implica-tions of our model for counseling. A summary and directions for future research fol-low in Section 8.

2. Model overview

Our model is primarily based on the “choice goals framework” developed by Bett-man, Luce, and Payne (1998), which integrates major Wndings in behavioral decisionmaking research over the last decades. We extend that framework in various waysand pay particular attention to research that has been conducted on decision pro-cesses in vocational choice. The model entails two major processes, the selection of adecision strategy in light of certain choice goals of the decision maker and the con-struction of preferences given certain characteristics of the decision task and context.


In the following, we provide a brief overview of these two processes; a detailed dis-cussion follows in the subsequent sections.

When making a decision, a decision maker pursues certain choice goals (Fig. 1).We distinguish four choice goals: maximizing the decision accuracy, minimizing the

Fig. 1. Model overview.


cognitive eVort required to make the decision, minimizing the negative emotionsexperienced during the decision process, and maximizing the justiWability of the deci-sion to signiWcant others. These four goals motivate certain cognitive processes thatultimately lead to the choice outcome. In particular, the choice goals determine whichdecision strategy the decision maker selects to combine and evaluate information onthe alternatives in order to determine the best alternative. DiVerent strategies requirediVerent amounts of information and strategy selection may therefore be constrainedby informational limitations. To some extent, the decision maker can acquire addi-tional information in the course of the decision process; how much and which infor-mation is acquired depends on the choice goals the decision maker pursues. Thesocial context plays an important role in the decision process in that signiWcant othersmay try to inXuence choice goals and often provide informational inputs to thedecision.

The second major process in our model is preference construction. Preferences areused in the decision process to evaluate the attributes of options and to assess theirrelative importance. However, the preferences people express in their choices are notalways stable and well-deWned. Research has shown that they are often inXuenced bysituational variables, in particular the evaluation mode (joint vs. separate evaluationof the options) and its interactions with characteristics of the attributes (context fac-tors). The evaluation mode, in turn, is partially determined by the representation ofinformation and by the decision strategy. In addition to these situational determi-nants of preferences, preferences are often shaped by the social context.

The choice outcome is ultimately a function of the informational inputs the deci-sion maker has, the preferences that are partly constructed in the decision process,and the decision strategy, i.e., the way in which information and preferences are com-bined to select an alternative.

3. Decision tasks in vocational choice

3.1. Alternatives, attributes, and information

The choice options (alternatives) considered in this paper are occupations such asbusiness consultant or industrial engineer and jobs in particular organizations. Inaddition to the characteristics of the occupation itself, job options also entail charac-teristics of the organization such as organizational culture, reputation, or economicviability. Although choices involving these options diVer in a variety of ways, theyshare a number of characteristics that make them similar from a decision makingpoint of view.

We assume some initial set of alternatives from which the decision maker chooses.Which concrete alternatives and how many of them are in the initial choice setdepends, among others, on the breadth of interests of the individual, the decisionmaker’s physical and mental skills, considerations of self-eYcacy (i.e., the individual’sexpectations about his or her ability to perform well in an occupation), and marketconstraints (e.g., availability of jobs in that occupation). These factors have been


discussed extensively in the literature and will not be the focus of our paper (cf. Baltes& Baltes, 1990; Hartung & Blustein, 2002; Lent, Brown, & Hackett, 1994; Morrow,Gore, & Campbell, 1996). Theories on the process that governs the admission ofalternatives to the choice set usually rely on some form of pre-choice screening (e.g.,Beach, 1993). For the remainder of the paper, we will assume that the options thatare in the initial choice set are not completely irrelevant or impossible. Returning toour example of the college senior majoring in business, it is reasonable to assume thathis choice set does not include the career of a concert pianist or of a garbageman;instead he chooses between becoming a business consultant, a manager, or a businessprofessor.

Each alternative in the choice set is characterized by a set of attributes. Attributesinclude, for instance, pay, social status of the occupation, degree of autonomy,amount of social interaction, importance of creativity, and anticipated positive andnegative emotions on the job.1

Occupations have a virtually unlimited number of attributes and many of thesemight be important to the decision maker. This makes vocational decisions poten-tially complex. Extensive research has been conducted on the types of attributes indi-viduals consider in vocational decisions and has revealed the diversity of theseattributes (e.g., Cable & Judge, 1996; Holland, 1997). The literature on work valueshas also contributed considerably to our understanding of what these attributes are(e.g., Elizur, Borg, Hunt, & Beck, 1991; Judge & Bretz, 1992; Sagie, Elizur, & Koslow-sky, 1996). This research shows that the relevant attributes are not only numerousbut also very heterogeneous. The heterogeneity of attributes in vocational choice isexempliWed by the variety of rewards an occupation can entail. One important dis-tinction is that between intrinsic and extrinsic rewards, which emphasizes the sourceof rewards (Ryan & Deci, 2000). A second dimension in which rewards can be distin-guished is what Elizur et al. call modality in the context of work values (Elizur et al.,1991). The three modi they distinguish are instrumental (e.g., pay), aVective (e.g., peerrecognition), and cognitive (e.g., job interest, feeling of self-achievement). ModalityreXects diVerent ways in which rewards are of value to the recipient. The heterogene-ity of attributes such as rewards has important implications for the decision process,as we will discuss below.

The information on the attributes of occupations and jobs is often provided indiVerent forms, such as personal communication with relatives and acquaintances,printed materials, or through television. In addition, individuals who have madeprior vocational choices may draw on their work experience. Information on alterna-tives can be incomplete and the amount of information available for each of thealternatives may diVer.

1 Note that relevant capabilities of the decision maker and certain external constraints can be framed asattributes of the alternative. For instance, required mathematical skills could be considered an attribute ofoccupations and an individual with limited mathematical skills would have a strong preference for a lowlevel of this attribute. Similarly, the general job prospects in a given occupation may be considered an attri-bute of that occupation and decision makers will generally prefer high values of that attribute.


Finally, vocational choice usually involves uncertainty about attributes. Forexample, our college senior might have information on the current average income ofa business consultant. However, this information is only a very crude proxy for hispotential future income as a consultant; a variety of individual and environmentalfactors can lead to a higher or lower actual income should he choose to become aconsultant. The estimation problem is even greater when attributes such as antici-pated emotions are concerned (cf. Kidd, 2004). The estimated “amount” of emotionwill diVer from individual to individual and the same individual may estimate diVer-ent amounts depending on situational factors at the time of decision making (e.g.,general mood) (Anderson, 2003). While we acknowledge the importance of uncer-tainty about future outcomes, we do not incorporate it into our model in order tokeep the discussion manageable.

3.2. Preferences

Preferences are used by the decision maker to assess the attributes and alterna-tives. We will consider two functions of preferences. First, preferences describe thetransformation of the level of an attribute into some measure of value (evaluation).For example, how good or bad is a salary of X dollars? Second, if the evaluations ofattributes are aggregated in the decision process, preferences are used in weightingthe values according to the relative importance of the attributes (weighting). Forinstance, “pay” might receive a larger weight than the expected amount of coVee pro-vided free of charge by the employer. If an attribute is considered irrelevant for thedecision, its implicit weight is zero and it is excluded from further analysis.

Preferences are reXected in concepts such as personality types (e.g., Holland, 1997),interests (Lykken, Bouchard, McGue, & Tellegen, 1993), and work values (Sagie etal., 1996). Both the structure of preferences and the development of preferences overtime have received a great amount of attention in the psychological and vocationalliterature. With respect to changes of preferences over time, several researchersemphasize the importance of social inXuences (e.g., Demo, 1992; Holland, 1997).

When making a decision, his or her preferences are not always known to the deci-sion maker; in fact, a lack of preference information has been identiWed as a majorcause of career indecision in vocational choice (Gati, Krausz, & Osipow, 1996).Research on exploration and feedback from prior choices, identity diVusion, andself-concept crystallization investigates the concrete causes and eVects of a lack ofpreference information, and helping individuals to obtain such information is animportant element of career counseling (Mitchell, Levin, & Krumboltz, 1999; Osi-pow, 1999). However, this task is challenging since research suggests that actual orhypothetical decisions people make do not simply reXect stable, well-deWned andcoherent preferences. Instead, preferences are to some extent constructed and con-text-dependent; they may be formed during the decision process or when they areelicited by means such as preference inventories (Payne, Bettman, & Schkade, 1999).The result of these preference construction processes and thus the decision outcomemay depend on the particular decision strategy used and on the characteristics ofthe choice situation (Payne, Bettman, & Johnson, 1993; Slovic, 1995). Accordingly,


it is useful to conceptualize the preferences people express in choices as consisting ofthree components (cf. Payne et al., 1999). One component, which we call “core pref-erences,” reXects relatively stable values associated with attributes, e.g., that morepay is preferred to less pay or that autonomy is a more important attribute thansocial relations on the job. These core preferences may have genetic origins (Lykkenet al., 1993), are shaped by the social environment (Demo, 1992; Holland, 1997), andcan be thought of as attitudes that are stored in the long-term memory (cf. Hastie,2001; Payne et al., 1999). A second, “situational component ” results from systematiceVects that certain task and context factors in the speciWc decision situation canhave on expressed preferences (“preference construction,” to be discussed in moredetail below). Finally, expressed preferences may reXect “random error” that will notbe considered further (see Fig. 2).

Most research on preferences in vocational choice focuses on the core preferences,implicitly assuming that the situational component can be neglected (e.g., Holland,1997; Lent et al., 1994). In contrast, we will show in Section 5 that preferenceconstruction may play a signiWcant role in vocational choice. For the followingdiscussion of decision strategies and their determinants, however, preferenceconstruction plays no direct role and we will ignore it for simplicity.

4. Decision strategy selection based on choice goals

Decision strategies describe how information on attributes and preferences iscombined in order to select an alternative. For example, one strategy is to carefullyevaluate and weight all the attributes of the various options, to form an overall scorefor each option, and to select the option with the highest score. An alternative, much

Fig. 2. Components of expressed preferences and their determinants.


simpler strategy would be to select the option that is best on the most important attri-bute. Decision makers have been shown to select diVerent decision strategies, depend-ing on their individual goals as well as situational factors (Bettman et al., 1998;Slaughter & Highhouse, 2003). DiVerent strategies, however, can lead to diVerentchoice outcomes.

4.1. General characteristics of decision strategies

In characterizing decision strategies, Payne et al. (1993) consider four generalcharacteristics: the total amount of information processing, the selectivity ofinformation processing, whether the information is processed by alternative or byattribute (across alternatives), and whether the strategy is compensatory or noncom-pensatory.

First, the total amount of information processing depends on the breadth and depthof analysis. Little information processing is required if the decision maker considersthe alternatives only on a broad level and then makes a decision. In contrast, adetailed analysis of each attribute and a sophisticated weighting scheme result inmore intensive processing.

Second, processing can be consistent or selective. Attributes can receive diVerentamounts of attention; a decision maker can consider all available information (con-sistent processing) or selectively process information on a subset of attributes andignore information on other attributes. Our college senior could, for instance, focusonly on the expected monetary payoVs in an occupation and ignore the amount ofsocial interaction or autonomy at work (selective processing).

Third, information can be processed by alternative or by attribute. In alternative-based processing, the attributes of a single option are processed and some aggregateevaluation of the option is made before the decision maker proceeds to the nextoption. For example, our college senior would evaluate the attributes of the consult-ing occupation, arrive at an overall evaluation for consulting and then proceed to thenext occupation (e.g., manager in a private Wrm). In attribute-based processing, incontrast, the same attribute is Wrst evaluated across all options before the next attri-bute is evaluated. The college senior might Wrst compare the expected pay across alloccupations and then compare the expected autonomy for each occupation. Unlikealternative-based processing, attribute-based processing can involve an evaluation ona relative basis.

Finally, processing can be compensatory or noncompensatory. In a compensa-tory strategy, a desirable level of one attribute compensates for a less desirablelevel on another attribute. For instance, the working hours of one job may be eval-uated negatively, but this disadvantage is partially or completely oVset by largerpay. A compensatory strategy thus involves trade-oVs between attributes; the deci-sion maker has to decide how much of one attribute he is willing to give up foranother attribute. In noncompensatory strategies, there is no such compensationacross attributes; for instance, the decision maker would reject an option if it doesnot meet certain minimum standards on important attributes, regardless of thevalues of other attributes.


4.2. SpeciWc decision strategies

A large number of decision strategies have been proposed in the literature andhave been studied in areas as diverse as consumer behavior, strategic decision mak-ing, and vocational choice. In the following, we provide an overview of selected deci-sion strategies and their speciWc characteristics (for more details, see Payne et al.,1993). We do not claim that decision makers use these strategies exactly as describedand in their pure form; the strategies we describe are abstractions or “ideal-types” ofmore complex decision strategies people may use. The primary purpose of this over-view is to illustrate how decision strategies may diVer in fundamental ways, to discusshow diVerent strategies can aVect choice outcomes, and to provide a “vocabulary”that can be used in the later discussion. On that basis, we will discuss in subsequentsections under which circumstances people are likely to use decision processes thatresemble one ideal-type strategy versus another.

In the weighted additive strategy, the decision maker evaluates each attribute andweights its value according to his preferences. The weighted values are summed upfor each alternative and the alternative with the highest overall value is chosen. Thisstrategy requires a large amount of information processing and uses informationconsistently for each attribute. It is alternative-based because the attributes of oneoption are evaluated independently of the attributes of the other options. Due to thesummation for each option, undesirable levels of an attribute can be compensated forby desirable levels of other attributes; the weighted additive strategy is compensa-tory.2 The weighted additive strategy is generally considered the standard for rationaldecision making because it uses all available information and involves making trade-oVs, ideally resulting in the best balance of attributes in the chosen option. Algebrai-cally, it can be represented as Vi D�wk¤vk(xk), where Vi is the value of option i, wk isthe weight for attribute k, vk is the evaluation (value) of attribute k and is a functionof the score or level xk of attribute k.

The equal weight strategy diVers from the weighted additive strategy only in thatthe values of the attributes are weighted equally. The decision maker does not givediVerent weights to the attributes, and therefore, he does not need information abouttheir relative importance.3 Processing is consistent, alternative-based and compensa-tory. Compared to the weighted additive strategy, this strategy involves less informa-tion processing and requires less information on preferences. The equal weightstrategy can be represented algebraically as Vi D �vk (xk).

2 Note that compensatory strategies do not necessarily require trading oV attributes against each otherexplicitly (e.g., “I am willing to accept X more working time in exchange for Y more money”). Rather,trade-oVs can be made implicitly in the choice process. For instance, the weighted additive strategy only re-quires the separate evaluation of each attribute and the summation of the weighted scores. The process ofsummation results in compensation across attributes, but it does not require the explicit trading-oV of at-tributes against each other. However, from a less mechanistic perspective, the decision maker will be awareof the trade-oVs implicit in his choice. Trade-oVs and compromises are an important aspect of vocationalchoice and will be discussed in more detail below.

3 Weights play an indirect role, however, by determining which of the attributes are considered at all bythe decision maker. As discussed above, attributes thought to be irrelevant receive a weight of zero.


Simon (1955) suggested that people often use a strategy called satisWcing, becausetheir cognitive resources are limited (“bounded rationality”) and extensive informa-tion processing such as in the weighted additive strategy is therefore unrealistic. Sat-isWcing is not based on the maximization of value; instead the alternatives areexamined sequentially until the decision maker Wnds an option that exceeds mini-mum standards on all relevant attributes. For instance, our college senior might Wrstlook at the consulting occupation and determine if it exceeds his aspirations withrespect to pay, social recognition, and other relevant attributes. If it does, the searchstops and consulting is chosen. If it does not, the decision maker examines the nextalternative in the same way. In satisWcing, the order in which options are examinedcan thus have an inXuence on the outcome of the choice. This strategy is non-com-pensatory, since a minimum standard has to be met for each attribute, independentof the values of other attributes. It is alternative-based because alternatives are evalu-ated sequentially and no comparison of attributes across alternatives takes place.Information processing in satisWcing is selective in that an option is discarded as soonas it does not meet the standard for one attribute; the remaining attributes of thatoption are then not considered at all. The amount of information processing variesdepending on the number of alternatives considered, but will always be smaller thanin the weighted additive strategy.

When using a lexicographic strategy, the decision maker simply selects the optionthat is best with respect to the most important attribute. For instance, if our collegesenior feels that a high income is the single most important career goal, he mightsimply select the occupation that promises the highest monetary payoVs. Thisstrategy is noncompensatory, attribute-based, and involves very little informationprocessing.

Elimination by aspects (EBA) combines elements of the lexicographic strategy andsatisWcing (Tversky, 1972). In essence, the decision maker Wrst looks at the mostimportant attribute (e.g., pay) and eliminates all options that do not meet a minimumstandard for this attribute. If more than one option remains, the process is repeatedwith the second most important attribute (e.g., autonomy). The process stops whenonly one option remains. Elimination by aspects is attribute-based, noncompensa-tory, and can involve diVerent amounts of information processing, depending on thenumber of cycles needed to reduce the number of options to one. Note that elimina-tion by aspects (and other strategies that evaluate attributes in the order of impor-tance) requires more information on the preferences of the decision maker thanstrategies that put equal weights on all attributes.

Finally, a strategy based on counts of the number of good and bad features has beendescribed by Alba and Marmorstein (1987). On the basis of some cut-oV value foreach attribute, the decision maker rates each alternative’s attributes as either good orbad. For example, if an income of x is the cut-oV value, then all jobs with an income>x have a “good” income, jobs with an income <x have a “bad” income. The optionwith the largest number of positive attribute evaluations is chosen. Note that thisstrategy does not require information on the relative importance of attributes since itimplies equal weights for all attributes that are considered. This strategy is compen-satory, consistent, and alternative-based.


The ideal-type decision strategies discussed and their key characteristics are sum-marized in Table 1. In a given decision situation, the decision maker may use morethan one of these strategies, resulting in a mixed strategy. In particular, a decisionmaker might pre-screen options on the basis of a less cognitively demanding noncom-pensatory strategy (e.g., lexicographic or elimination by aspects) and then make a Wnalchoice on the basis of more thorough processing involving weights and trade-oVs.

4.3. Choice goals and informational constraints

Since there are many possible decision strategies, the decision maker has to make asecond-order choice: he or she has to select a strategy how to decide. Strategies areselected so as to satisfy four choice goals: maximizing decision accuracy, minimizingcognitive eVort, minimizing negative emotion, and maximizing the justiWability of thedecision (Bettman et al., 1998). This metacognitive process of strategy selection canbe conscious and explicit (e.g., the decision maker thinks “There are so many attri-butes for each occupation that considering all information gets too complicated ƒI should focus on the most important attributes”) or relatively unconscious andautomatic (Payne et al., 1993).4 In the following, we discuss the four goals, potentialdeterminants of their relative importance, and how they guide strategy selection.

The goal of decision accuracy is achieved if the option that is chosen maximizes thevalue or utility for the decision maker. A larger amount of information processingwill generally result in more accurate decisions. Similarly, consistent processing willlead to higher accuracy because more information is used. Decision accuracy isgenerally considered to be largest for the weighted additive strategy. The accuracygoal will be more important in decisions that involve high stakes and are hard toreverse. Vocational choice usually has a fundamental impact on the future life of the

4 Some readers will have discovered the problem of “inWnite regress,” i.e., how exactly do people choosehow to choose (ƒ how to choose)? Although we have evidence that people do select decision strategiesconsistent with speciWc choice goals (e.g., Bettman et al., 1998; Lundgren & Prislin, 1998), there is no sys-tematic research on the exact process of strategy selection (i.e., on the second-order choice). Given thecomplexities involved, it seems unlikely that people engage in conscious third- or higher-order choices.

Table 1Characteristics of selected decision strategies (adapted from Bettman et al., 1998, p. 191)

Strategy Cognitive eVort required

Consistent vs. selective processing

Alternative vs. attribute-based

Compensatory vs. non-compensatory

Need for preference information

Order of alternatives matters

Weighted additive

High Consistent AL C High No

Equal weight Medium Consistent AL C Medium NoSatisWcing Variable Selective AL N Low YesLexicographic Low Selective AT N Low NoElimination by

aspectsVariable Selective AT N Medium No

Counts of good/bad features

Medium Consistent AL C Medium No


individual and decisions are costly to reverse (e.g., due to human capital investments).Certain opportunities may not even exist at later points in time. For most people,decision accuracy will therefore be an important goal in vocational decisions.

Cognitive eVort is a function of the amount of information that has to be pro-cessed and of the intensity of processing. In his classic paper on bounded rationality,Simon (1955) pointed out that the cognitive capacity of humans is limited and gener-ally prohibits “rational,” accuracy-maximizing decision making. As outlined previ-ously, decision strategies diVer in the amount of information processing required:required cognitive eVort is highest for the weighted additive strategy and lowest forthe lexicographic strategy. The goal of minimizing cognitive eVort will play a moreimportant role in decisions that stretch the cognitive limits of the decision maker, i.e.,in more complex decisions or when the representation of information makes process-ing diYcult. As discussed above, the number of potentially relevant attributes invocational choice is very large and information often has to be integrated from avariety of sources; the goal of reducing cognitive eVort will be important.

In addition to requiring cognitive eVort, decisions can also induce negative emo-tions, in particular when diYcult trade-oVs between attributes have to be considered.Avoiding negative emotions can then be an important goal in the selection of a deci-sion strategy. Trade-oVs are present in almost all types of decisions, but the trade-oV

diYculty has been found to depend on the objects or attributes involved. Trade-oVsthat involve non-commodities such as objects of sentimental value or that involve“protected values” such as health or honesty have a larger emotional potential thantrade-oVs involving commodities such as money or rebate coupons (Beattie & Barlas,2001; Luce, 1998). A high level of emotional strain can also be expected when individ-uals have to consider accepting attributes that are inconsistent with their self-concept(cf. Festinger, 1957; Sirgy, 1982). For example, a decision maker who considers her-self a trustworthy person might experience great discomfort when thinking about thepossibility to take on a job as sales representative for a company known for high-pressure sales methods. Similarly, a law student considering himself very sociallyresponsible may experience negative emotions when having to decide between pri-vate law with higher expected pay and public interest law with lower expected pay (cf.Erlanger & Epp, 1996). One way to reduce negative emotion is to choose a decisionstrategy that does not require explicit trade-oVs or that frames trade-oVs in a waythat makes them less emotionally stressful. For instance, Luce, Bettman, and Payne(1997) found that decision makers used more attribute-based decision strategies inmore emotion-laden decisions, consistent with an attempt to reduce explicit trade-oVs in emotionally stressful situations. A more extreme way to cope with potentialnegative emotion during a decision is to avoid the decision (Anderson, 2003; Luce,1998). Decision avoidance as a way to cope with negative emotions elicited by a par-ticular set of attributes could be an interesting issue for research on indecision invocational choice; thus far, this research has focused primarily on informational lim-itations, indecisiveness as an individual trait, or general choice anxiety (e.g., Char-trand, Robbins, Morrill, & Boggs, 1990; Osipow, 1999). In sum, vocational choice hasa high potential for emotional strain, particularly when it involves trade-oVs betweenprotected values or values that are relevant for the self-concept. Decision makers can


try to cope with emotional strain by selecting a decision strategy that does notrequire explicit trade-oVs or by avoiding the decision.

The fourth choice goal is justiWability of the decision process or outcome to the selfor to signiWcant others (cf. Lerner & Tetlock, 1999). JustiWcation of the decision pro-cess may involve the decision strategy used, the weights used, the information used,or the trade-oVs made, while outcome justiWability can also be achieved by choosingthe alternative that conforms with the expectations or preferences of signiWcant oth-ers. Importantly, justiWability is not a Wxed property of the decision process or out-come; it depends on the standards of the audience (Brown, 1999). Strategies thatinvolve extensive and consistent information processing will usually be consideredmore accurate and justiWable (e.g., weighted additive strategy); however, the selectionof weights and the resolution of trade-oVs in more complex strategies can be hard tojustify because of their highly subjective nature (Bettman et al., 1998). A simple lexi-cographic strategy based on an attribute that is socially accepted as most importantmay therefore be preferred. The need for justiWability will increase with the impor-tance of the decision and with the impact the decision has on the social environmentof the decision maker. Accountability is therefore partly a function of the decisionoutcome. For instance, the decision to pursue a career in the arts may put higherdemands on parental support than the decision to pursue a career in business con-sulting, so that the former might need more justiWcation. In addition, the pressure forjustiWcation will depend on the extent to which a decision violates expectations orsocial norms. An individual who chooses an occupation that does not meet theexpectations of the social environment or conXicts with certain norms (e.g., withrespect to gender stereotypes) will feel a particularly high pressure for justiWcation.Although the decision maker generally does not know the particular decision out-come when selecting the decision strategy, he or she may anticipate a higher need forjustiWcation if the choice set involves such controversial options.

The relative importance of these four choice goals in making a strategy selectioncan depend on several determinants, including individual, social, and situationalfactors. For example, some persons have a higher need for approval than others,potentially leading them to emphasize the goal of justiWability more. Similarly,some (“collectivist”) cultures value cohesion and conformity highly, potentiallyleading their members to emphasize justiWability (cf. Eby & Dobbins, 1997).Finally, an important situational determinant of the relative importance of choicegoals is the availability of timely and unambiguous feedback on goal achievement(Bettman et al., 1998). In vocational decisions, the availability of feedback is likelyto diVer substantially for the four goals. The decision maker notices the eVortnecessary to process information immediately and experiences emotions resultingfrom trade-oV diYculty at the time of making the decision. The accuracy of thedecision, however, can only be assessed after it is implemented, which will be muchlater. The justiWability of a decision will become apparent either during thedecision process (e.g., if the decision process is followed by the family or profes-sional counselors) or shortly after the decision is made and publicly announced.The usual pattern of feedback on choice goal achievement in vocational choicemay therefore increase the perceived importance of saving cognitive eVort, reduc-


ing emotional strain, and increasing justiWability while reducing the perceivedimportance of decision accuracy.

The selection of a decision strategy does not only depend on the four choice goals,but also on informational constraints. With respect to information, our focus thus farhas been on the reduction of cognitive eVort through mechanisms such as the selec-tive use of information or the simpliWcation of information processing. However, theliterature on indecision in vocational choice suggests that a perceived lack of infor-mation rather than information overload is characteristic of many decision situations(Chartrand et al., 1990; Gati et al., 1996). In such cases, the availability of informa-tion is an important constraint on strategy choice; a particular strategy can only beused if the decision maker has the necessary information on attributes and prefer-ences. For instance, the weighted additive strategy requires complete information onall attributes and preferences. If the decision maker lacks preference informationwith respect to the relative importance of attributes, strategies that require theweighting or ordering of attributes cannot be used. To some extent, the decisionmaker can try to infer missing information on attributes on the basis of availableinformation on other attributes; however, such inferences will be less reliable thanexplicit information and may even be misleading (Huber & McCann, 1982).

Overall, informational constraints in a particular choice task limit the feasibility ofdecision strategies, whereas the four choice goals determine the relative attractivenessof the feasible strategies to the decision maker.

4.4. Research on decision strategies in vocational choice

In the previous sections, we provided an overview of decision strategies that canbe used in vocational choice and brieXy discussed their key characteristics. We thensuggested that decision makers will choose a strategy based on the available informa-tion and on the relative importance of four choice goals: maximizing decision accu-racy, minimizing cognitive eVort, minimizing negative emotion, and maximizing thejustiWability of the decision. We will now review additional research on decision pro-cesses in vocational choice in light of our discussion. Scholars generally agree thatvocational choice does not follow the “ideal” of a weighted additive strategy (Phil-lips, 1997). However, there is only a limited amount of research on how individualsdeviate from this ideal and under what conditions alternative strategies are used.

Soelberg (1967) developed a model of “unprogrammed decision making” on thebasis of interviews with job candidates. He found that decision makers did not useweighted additive models, even though some stated their intention to do so. Accord-ing to his model, a job searcher identiWes primary and secondary attributes (based onattribute importance), and considers these attributes to make implicit choicesbetween known alternatives already during the search for more alternatives. In a so-called conWrmation phase, the decision maker then rationalizes the selection of hisimplicit favorite, which can entail the ex post construction of a seemingly rationaldecision rule (Soelberg, 1967). Soelberg’s model emphasizes informational constraints(the decision is made before all alternatives are identiWed) as well as the goal of justiW-

ability to self and others (post hoc creation of seemingly rational decision rules).


The impact of the social context on career decisions is an important aspect ofresearch on exploration and nontraditional career choices (e.g., Felsman & Blustein,1999; Morrow et al., 1996; Phillips & ImhoV, 1997). Most of these studies focus onsocial inXuences via the formation of preferences or via feelings of self-eYcacy. How-ever, we argue that many of the Wndings are also consistent with social inXuencesduring the actual decision making process. For example, a person who does notchoose a “gender-inconsistent” occupation could have truly internalized gender-related stereotypes and therefore prefer other occupations; alternatively, however,the person might actually like that occupation, but give much weight to the justiW-ability of the decision. In the latter case, the individual may select a decision strategythat leads to the rejection of the gender-inconsistent occupation in order to maximizethe justiWability of the decision outcome. For instance, the individual could ignorethe attributes he or she likes but should not like according to social stereotypes. Morerecent research has begun to investigate social inXuences during the decision makingprocess in more detail (e.g., Phillips, Christopher-Sisk, & Gravino, 2001), and we willreturn to this important issue in Section 6.1.

In their normative decision model for vocational choice, Gati and his colleaguesadvocate the sequential use of diVerent decision strategies, resulting in a mixed strat-egy (Gati & Asher, 2001; Gati et al., 1995). In their model, the number of alternativesis Wrst reduced in a screening phase using elimination by aspects. Once the number ofalternatives is manageable, the remaining options are ranked using strategies thatrequire more intensive information processing.

Gottfredson’s (1981) theory of compromise explains vocational choice from adevelopmental perspective, but is structurally similar to a mixed strategy such as thatproposed by Gati et al. (1995). In a so-called circumscription phase, which can extendover several years, individuals eliminate unacceptable occupations from the range ofcareer possibilities and create a zone of acceptable alternatives. When selecting fromthe acceptable options, individuals become aware of barriers that may inhibit themfrom achieving their goals in certain occupations, and they choose by making trade-oVs between the desirability and the feasibility of options.

5. Preference construction

In the preceding section, we assumed that the decision maker has predeterminedpreferences and that the situational component of preferences is negligible. On thisbasis, we discussed diVerent strategies to integrate information on preferences andattributes and how decision makers may select a strategy. We now turn to the situa-tional component and consider the strong evidence that the preferences that areexpressed in actual or hypothetical choice situations are often constructed at the timeof the decision. They can thus be partly determined by characteristics of the speciWcdecision situation that ideally should not aVect the choice outcome (Hsee, Loewen-stein, Blount, & Bazerman, 1999; Slovic, 1995; Tversky & Simonson, 1993).

Payne et al. (1993) distinguish between two types of situational factors that canlead to preference construction. Task factors are associated with the general structure


of the decision problem, including the number of alternatives, the representation ofinformation, time pressure, and the evaluation mode. Context factors refer to the par-ticular values of the attributes in the speciWc choice set, including the similarity of thealternatives and attributes and the overall attractiveness of the alternatives. Becauseof diVerent constellations of task and context factors, a decision maker may preferalternative A to alternative B in one decision situation, but alternative B to alterna-tive A in another (Payne et al., 1993). The possibility of such “preference reversals” isespecially problematic in high stakes decisions such as vocational choice; it just doesnot seem right that the decision to become a business manager as opposed to ascholar may depend on factors such as the number and types of other occupationsconsidered, how the information on the occupations is presented, or how the coun-selor elicits the client’s preferences. In the following, we discuss a particularly impor-tant task factor, the evaluation mode, and how it interacts with other task andcontext factors in inXuencing the construction of preferences.

5.1. Joint vs. separate evaluation

Two evaluation modes are generally distinguished in the decision making litera-ture (cf. Hsee et al., 1999). In joint evaluation, decision makers are asked to chooseone of the simultaneously presented options. In separate evaluation, individuals areasked to evaluate each alternative separately, e.g., by asking them to rate the alterna-tives successively on an absolute scale. Separate evaluation involves alternative-basedprocessing of information, whereas joint evaluation may be either alternative-basedor attribute-based. The interesting general Wnding is that choice outcomes maydepend on the evaluation mode; for instance, options that receive a higher overallrating in a separate evaluation may be rejected in a direct choice situation. Preferencereversals in joint vs. separate evaluation are contingent on context factors such as theheterogeneity of attributes, the evaluability of attributes, and the comparability ofoptions. In the following, we review research on the interactions between the evalua-tion mode and these context factors and how they aVect preference construction. Indoing so, we focus on eVects that may be particularly relevant to vocational choice.

5.2. Importance and salience of attributes

The attributes of the alternatives in an occupational choice situation are highlyheterogeneous. Rewards, for instance, can range from instrumental (e.g., pay) to cog-nitive (e.g., interesting task) to aVective (e.g., peer recognition). Some of the attributesare more salient or more important to the decision maker than others (Elizur et al.,1991). If such diVerences in the importance or salience of attributes are present, thepreferences that are constructed in a joint evaluation may diVer from those in a sepa-rate evaluation of the options. More speciWcally, research indicates that the weightsassigned to heterogeneous attributes depend on the evaluation mode; more impor-tant and non-commodity attributes tend to receive more weight in joint evaluationthan in separate evaluation (Beattie & Barlas, 2001; Tversky, Sattath, & Slovic, 1988).This eVect has been conWrmed in a variety of studies and has been captured in the


so-called “prominence hypothesis” (Tversky et al., 1988). For vocational choice, thiswould imply that decision makers tend to prefer occupations or jobs that are supe-rior on very important dimensions (e.g., attributes that are relevant to the self-con-cept) more when asked to evaluate the options jointly, but prefer such options lesswhen evaluating the options separately.

5.3. Evaluability

People often cannot evaluate levels of attributes in isolation because well-deWnedpreferences with respect to these attributes do not exist in their memory (Hsee et al.,1999). Instead, evaluations of attributes as well as weights assigned to attributes arebased on a variety of cues. For instance, Ariely, Loewenstein, and Prelec (2003)showed that the monetary value people put on goods and feelings was substantiallyhigher when people had previously been primed to think of a large number andsmaller when they had been primed to think of a small number. The numbers appar-ently served as anchors for the evaluation, even though they did not convey any sub-stantive information. Highhouse, Brooks-Laber, Lin, and Spitzmueller (2003) foundthat people evaluated salary levels based on “what is normal” and used diVerent cuesto estimate the normal pay level. As a result, the same absolute salary was evaluatedpositively when it was at the high end of the range of options but negatively when itwas at the low end. With respect to relative weights, Hsee et al. (1999) found that peo-ple gave less weight to attributes that are hard to evaluate when they were asked toevaluate options separately. However, more weight was given to these attributeswhen the options were evaluated jointly.

Underlying these eVects is the concept of “evaluability,” the ease of evaluating anattribute. If an attribute is hard to assess, considering the alternatives jointly asopposed to separately can signiWcantly increase the evaluability of that attributebecause the decision maker can establish a relative preference based on the distribu-tion of that attribute in the choice set (Hsee et al., 1999). Such an attribute may thenalso receive more weight in the overall assessment of the alternatives because its eval-uation seems more reliable and informative.

The evaluability of an attribute in isolation will be high (and joint and separateevaluation will be similar) if the decision maker has suYcient information about thedistribution of the attribute without knowing the particular choice set. For example,knowing the average salary or the range of salaries in a certain occupation will be veryhelpful to evaluate the salary for a given job option. The appropriate information isoften obtained from career centers, industry surveys, or acquaintances (cf. Tenbrunsel& Diekmann, 2002). In addition, distributional information may reside in the individ-ual’s memory, but may be more readily available for some attributes (e.g., pay) thanfor others (e.g., degree of autonomy). In either case, the better the individual isinformed prior to the decision, the smaller will the impact of the evaluation mode be.

The concept of evaluability not only suggests that joint evaluations may diVerfrom separate evaluations, but also that the structure of the choice set may play arole in the evaluation (cf. Tversky & Simonson, 1993). For example, our collegesenior may reevaluate the relative merits of the options “consultant” and “business


manager” when the third option “professor” is introduced that conveys additionalevaluation information. Supporting this idea, Highhouse et al. (2003) found that thefrequency of alternatives involving salaries above a certain target level in the choiceset aVected the salary expectations and salary evaluations of decision makers. Merelyintroducing new job options can thus change the evaluation and importance of salaryas an attribute, which in turn can result in a change in the relative attractiveness ofdiVerent options.

5.4. Comparability and feature matching

A third situation in which joint and separate evaluations may diVer is when thealternatives are not directly comparable because information is available on a certainset of attributes for one alternative, but on a diVerent set of attributes for anotheralternative. For example, our college senior might have information on the salary,degree of autonomy, and working conditions in business consulting. For managers, hehas information on the salary, social relationships, and working conditions. Whenevaluating the two occupations separately, he will give certain weights to the threeattributes on which he has information. However, these weights will diVer from thosehe would use in joined evaluation. The reason is that people tend to engage in “featurematching” when evaluating alternatives jointly: they give more weight to attributesthat are shared by the alternatives (e.g., salary and working conditions) and less weightto unique attributes (e.g., autonomy and social relationships). As a result, alternativesthat have an advantage in shared attributes are evaluated more positively in joint eval-uation than in a separate evaluation (Houston, Sherril-Mittleman, & Weeks, 2001).

Research has established two important boundary conditions for the featurematching eVect. First, Johnson (1984) suggested that as alternatives become less com-parable, decision makers represent attributes at more abstract levels in order to allowcomparisons. For instance, a decision maker may aggregate the job attributes salary,bonus, and stock options into the more abstract attribute “compensation” that ismore comparable across the alternatives. To the extent that unique attributes arereXected in the weight of the more general attribute, joint evaluation does not resultin a loss of weight for unique attributes. Second, the degree of feature matching maydepend on the complexity of the choice task and on the way in which information isrepresented. Slaughter and Highhouse (2003) conducted an experiment involving thechoice between jobs and found that participants engaged in feature matching wheninformation was presented in a simple way that facilitated attribute-based compari-son (e.g., a matrix) but did not engage in feature matching when information was pre-sented in more complex ways (e.g., in recruitment brochures and site visit summaries).Apparently, the more complex representation of information led to more alternative-based processing, in which feature matching does not occur.

5.5. When are preferences constructed?

The strength of task and context eVects depends on the nature of the decision. Forsome decisions, the relevant core preferences will be clearly deWned in the decision


maker’s memory; in such cases, preference construction may only play a minor rolefor the choice outcome. However, even if clear core preferences are stored in memory,they will only be used when they are perceived as relevant to the new situation andwhen they are easily accessible (Payne et al., 1999). Slaughter and Highhouse (2003)demonstrated the role of accessibility; in their experiment, feature matching did notoccur when subjects had rated the importance of job attributes prior to the choicetask (increasing accessibility), but feature matching did occur when participants hadnot rated the attributes prior to making the choice.

Core preferences will be well-deWned for choices that are familiar and that aredirectly experienced, because experience with a choice situation and with the choiceoutcome gives individuals the opportunity to receive feedback that helps to formcore preferences (Hsee et al., 1999; Payne et al., 1999). For instance, choices involvingcertain consumer products (e.g., washing detergent, soft drinks) will often involve rel-atively stable preferences. Vocational choice, however, seems to Wt the opposite pic-ture. Vocational decisions and even job choices are extremely infrequent for anindividual and there is a long time lag between the decision and the experience of thedecision outcome. Preference construction may therefore play a particularly impor-tant role in vocational choice.

6. Extensions of the model

In the preceding sections, we presented a basic model of vocational choice thatfocused on individual decision making and did not explicitly consider the duration ofthe decision. In the following sections, we discuss how two important characteristicsof vocational decisions—social inXuences and a long decision time—can be incorpo-rated into our model. While this discussion can only scratch the surface of a numberof interesting issues we still think that it is useful to outline some connections, and aswe will see, promising areas of future research.

6.1. Social dimensions of decision making

Phillips et al. (2001) conducted a study on the social dimensions of career choice ina sample of young adults, and we will discuss two of the inXuences they found to beparticularly important. First, many respondents indicated that a very importantaction of others was the provision of information as well as of concrete alternatives(e.g., job oVers). In terms of our model, both the alternatives that are potentiallyincluded in the initial choice set and the information on the alternatives are to a con-siderable extent provided by others. While these contributions of others will in mostcases improve the decision maker’s informational position and enlarge the initialchoice set, the implicit “Wltering process” may reinforce the impact the social envi-ronment has via the formation of preferences and interests, possibly contributing tophenomena such as the reproduction of socio-economic status. Note that we do notrefer to deliberate inXuence attempts of signiWcant others by providing biased infor-mation (see below), but to what could be called “non-random sampling” of jobs and


information. To return to our example of the college senior, if his social environmentconsists to a large degree of people who work in business related occupations (e.g.,peers, faculty, and internship contacts) and who will think that these are goodoptions (many of them have already self-selected into these occupations at an earlierpoint in time), he will be exposed to particularly rich information on certain businessrelated occupations and this information will tend emphasize the advantages of suchoccupations. Similarly, when looking for concrete job oVers, his social contacts aremost likely to have knowledge of or access to opportunities that are related to whatthey do.

According to the research of Phillips et al. (2001), a second major activity of thesocial environment is to “push/nudge” the decision maker in career choices (52% ofthe respondents reported such activities). Relevant individuals such as friends andfamily try to steer the decision maker towards a certain course of action they think isbest. The extensive research on social inXuence and attitude change (cf. Lerner & Tet-lock, 1999; Wood, 2000) suggests that many people are receptive to such social inXu-ences. How does our model capture such inXuences?5 It is useful to distinguish threeforms of inXuence attempts: inXuence via the provision of biased or selective infor-mation (e.g., “Consultants have to work 80 h a week and are much more likely to getheart attacks than average people!”), inXuence attempts via preferences (e.g., “I don’tthink you should pay so much attention to money, what ultimately matters is thatyou love what you do.”), and inXuence by establishing explicit or subtle norms (e.g.,“Our business has always been under family leadership, and you should continuethat tradition.”). Social inXuence via the provision of information aVects the infor-mation the decision maker uses, at least to the extent that the decision maker isunable to verify, de-bias, or ignore the information. Research on “mental contamina-tion” has shown how diYcult it can be to correct or ignore Xawed or irrelevant infor-mation (Wilson & Brekke, 1994). To the extent that inXuence attempts are targeted atpreferences, they are reXected in the core preferences; they would not aVect prefer-ence construction, which, by deWnition, results from the speciWc characteristics of thealternatives and attributes (see Section 3.2). Finally, inXuence via norms is bestaccounted for by considering choice goals and strategy selection. Strong social inXu-ence via norms may aVect the choice goal of high justiWability and therefore indi-rectly inXuence strategy selection and the Wnal choice outcome (see Section 4).

Finally, social inXuences operate not only with respect to preferences, but alsowith respect to the relative importance of choice goals. Instead of trying to steer thedecision maker towards a speciWc choice outcome, signiWcant others may try to raisethe importance of certain choice goals such as decision accuracy. In fact, we will sug-gest below that the key to improving vocational decisions in a counseling situation isto understand and change choice goals.

5 In the extreme case, where the decision is eVectively made by someone else (e.g., the parents), it may benecessary to redeWne the locus of decision making. Instead of studying the decision processes of the aVect-ed individual, one might ask questions such as: How do parents decide what they “tell” their child to do?What are the parents’ goals in such a decision, where do they get the information on available options,what biases might occur in processing the information?


6.2. The role of decision time

The vocational choice processes that are captured in our model can easily extendover several weeks or even months and we will now brieXy discuss two mechanismsthrough which this long decision time may aVect the decision processes and the choiceoutcome. First, a long decision time may change the relative importance of choicegoals. Second, it allows the acquisition of additional information on the alternatives aswell as on individual preferences, a process that may be directed by choice goals.

When discussing the impact of time on choice goals, the BDM literature primarilyfocuses on decisions with time constraints rather than decisions with a particularlylong decision time. When time constraints reduce the processing capacity of individu-als and make the choice goal of reducing cognitive eVort more important, decisionmakers tend to adopt simpler strategies such as the lexicographic strategy or elimina-tion by aspects (Gilliland & Schmitt, 1993; Payne et al., 1993). A long decision timewill increase the cognitive capacity of decision makers, but saving cognitive eVort willstill be an important goal due to the high complexity of vocational choice problems aswell as the cost of eVort. We can only speculate how a longer decision time aVects therelative importance of choice goals since research on this issue is lacking. One interest-ing possibility concerns the choice goal of reducing negative emotion resulting fromdecision diYculty and diYcult trade-oVs. The importance of this goal might increasewith decision time because the negative emotion is experienced or expected over alonger period of time. In other words, repeatedly contemplating a diYcult trade-oV

and every time experiencing emotional discomfort may lead the decision maker toswitch to a decision strategy that does not involve the trade-oV, e.g., by focusing onone important attribute that clearly discriminates the options (lexicographic strategy).

In addition to these potential eVects on choice goals, a longer decision time allowsthe decision maker to engage in the acquisition of additional information about thealternatives. Of particular interest is the question if and when this information acquisi-tion is biased. A large body of research investigates “motivated reasoning,” wherebiases in cognitive processes result from certain underlying motivations of individuals(Brownstein, 2003; Kunda, 1990). For example, decision makers often have an “earlyfavorite” among the alternatives and want to choose that alternative. However, thischoice needs to be justiWable. Decision makers therefore selectively search for infor-mation that further “boosts” the favorite alternative relative to the other alternativesor they Wlter out negative information about the favorite (Kuhl, 1984; Soelberg, 1967).Building on this research, Lundgren and Prislin (1998) studied how diVerent underly-ing motivations may elicit diVerent kinds of biases. In their experiment (a simulateddebate about university politics), Lundgren and Prislin distinguished three types ofmotivation. Accuracy motivation is the motivation to hold attitudes congruent withrelevant information, directional motivation is the motivation to protect existing atti-tudes, and impression motivation is aroused when attitudes are an important elementin social relationships and agreement with others is sought. Lundgren and Prislinfound that individuals in the accuracy motivation condition engaged in objective andunbiased information acquisition, whereas individuals with directional motivationavoided disconWrming information and focused on information that supported their


existing attitude. In addition, these individuals creatively self-generated argumentsand combined external information in new ways to further bolster their position.Finally, individuals with impression motivation engaged in extensive informationsearch but processed information selectively so as to arrive at some compromisebetween their initial position and that of their relevant social environment.

Lundgren and Prislin’s study shows that information acquisition can be directedby diVerent goals concerning the use of the information. Their Wndings resonateextremely well with our model of vocational choice, which considers the choice goalsof high accuracy, low cognitive eVort, low negative emotion, and high justiWability(the latter is similar to Lundgren and Prislin’s impression motivation) as determi-nants of the decision strategy. Synthesizing these two bodies of work, we proposethat choice goals inXuence decisions via the acquisition of information as well as viathe selection of a decision strategy.

Selective information acquisition has also been observed with respect to informa-tion about personality traits and preferences. If individuals are motivated to maintaintheir self-concept, information that disconWrms valued self-beliefs is often discred-ited, rejected as inaccurate, or reinterpreted in such a way as to conWrm self-concep-tions (Swann & Hill, 1982). Individuals also try to obtain feedback that supportsrather than threatens self-conceptions (Swann & Read, 1981). White, Brockett, andOverstreet (1993) found that people judged feedback from personality tests as lessaccurate when it was inconsistent with their self-perception. Finally, Kunda and San-itioso (1989) showed that subjects who were made to believe that certain traits (e.g.,extroversion) were conducive to academic success rated themselves higher on thattrait than other subjects, potentially by selectively accessing their memory in searchof conWrming anecdotal evidence.

The implications of this research on information acquisition (with respect to bothattributes and preferences) for our model of vocational choice are very interesting. Ifthe acquisition of information is directed by choice goals, then the eVects of choicegoals on the decision outcome we have discussed in the previous sections may bereinforced. Consider our college senior as an example. If accuracy is his primarychoice goal, then the senior will not only select an accuracy maximizing decisionstrategy, but will also try to obtain a large amount of accurate information about thethree options as well as about himself. In contrast, if justiWability is the primary con-cern (e.g., his parents want him to become a consultant and he is motivated to con-form), he might not only use an appropriate decision strategy (e.g., EBA starting withsalary) but could also try to boost the consulting alternative by selectively acquiringinformation about the alternatives (e.g., obtain information about bonus sizes inboom years, interview successful consultants) and about himself (e.g., recall howmuch he likes traveling, explore how enjoyable a luxurious life-style can be, etc.).

7. Implications for counseling

Our model of decision processes in vocational choice allows us to derive norma-tive implications for counseling practice. In the following discussion of selected


recommendations, we emphasize the role of career counselors as decision counselors,whose aim it is to facilitate an active decision making process (cf. Gati & Asher,2001). While some of our recommendations will be most eVective when the underly-ing rationale is shared with the client, others may be eVective even if the client doesnot understand the underlying cognitive processes. More speciWcally, we expect thatinterventions that address motivational processes (i.e., choice goals and strategyselection) beneWt from a certain degree of insight on the part of the client becausethey require active participation and commitment. In contrast, most recommenda-tions addressing preference construction processes may be implemented without adetailed explanation to the client since they operate via more automatic cognitiveprocesses.

7.1. Understanding and inXuencing strategy selection and information acquisition

We have discussed how choice goals may direct the selection of a decision strategyand the acquisition of information on alternatives and preferences. Because of thisimportant role of choice goals, the Wrst step in a counseling situation should be tounderstand the relative importance of the choice goals to the client. In a next step, itshould be identiWed how these goals may lead the client to prefer a certain decisionstrategy and to selectively acquire information, and how this might aVect the choiceoutcome. For instance, does the client tend to avoid diYcult trade-oVs? Which alter-natives would beneWt from the use of a trade-oV avoiding decision strategy? Does theclient have an objective picture of the options, where is more information needed?

If potential problems become apparent, reconsidering choice goals may seemdesirable. In particular, to the extent that the client overemphasizes short-term goals(e.g., reduction of cognitive eVort and negative emotion) and underestimates the roleof decision accuracy that is experienced only in the longer term, it might be in the cli-ent’s interest to increase the weight of decision accuracy. We consider understandingand inXuencing choice goals an integral part of the counseling process, but one mayargue that it is easier and less time-consuming to override or ignore seemingly inade-quate choice goals. For example, when a client fails to acknowledge the importanceof the decision and of decision accuracy, prescribing an accuracy-maximizing strat-egy may be in his or her long-term interest. If the client selectively acquires informa-tion, the counselor might just “confront” him with additional information. Whilesuch interventions may be necessary and successful in some cases, we believe thatchoice goals should be consistent with the overall decision process. If they are not, theparticipation of the client in the counseling process as well as his or her commitmentto the choice outcome may be reduced.

Choice goals can be changed by encouraging a more conscious consideration oftheir implications and trade-oVs. Alternatively, the underlying objective and subjec-tive drivers of particular choice goals may be addressed in many situations. Forexample, if justiWability is perceived as very important, the counselor might investi-gate with the client how realistic a perceived need for justiWcation really is; perhaps ajoint session with parents or peers may dispel this concern. Similarly, if an individualis obsessed with (and perhaps paralyzed by) trying to make the perfect decision,


it may help to discuss how irreversible and narrow the choice to be made really is.Finally, the importance of saving cognitive eVort can be reduced by making informa-tion easier to process or by increasing the cognitive capacity of the decision maker(e.g., through training in systematic decision making or through the use of decisionaids).

On the basis of the original or revised set of goals, an appropriate decision strategycan be selected. In doing so, the availability of information on attributes and prefer-ences has to be considered as a constraining factor. Additional information on alter-natives and preferences can be acquired during the counseling process, butinformation acquisition is costly in terms of time and eVort. Accordingly, the follow-ing iterative process seems most adequate: As choice goals and suitable decisionstrategies become clearer, it is determined if the necessary information can beobtained. The availability of the information, in turn, guides the further speciWcationof the decision process. The goal is that the optimal decision process is selected giventhe obtainable information and the choice goals; at the same time, only necessaryinformation should be obtained. Throughout the process, the potential for biases ininformation acquisition should be kept in mind and measures should be taken toobtain information that is as accurate as possible.

In addition to guiding decision makers and their advisors, our model can also beapplied to analyze and evaluate decisions that have already been made. In particular,the explicit analysis of choice goals, decision strategies, and information acquisitionpatterns can help to disentangle several inXuences that may have led to a particularchoice outcome. Based on one’s idea of a “good” decision process, such an analysismay suggest that the decision be reconsidered.

7.2. Understanding and inXuencing preference construction

The evaluation mode (joint vs. separate evaluation) can have a signiWcant impacton the evaluation of attributes and their relative weights in a choice. Unfortunately,when expressed preferences partly reXect the evaluation mode or other task and con-text factors (“situational component”), we do not know which the “true” (core) pref-erences are on which the decision should ideally be based. However, if counselors areaware of the eVects task and context factors can have, they can design and steer thedecision process in a way that may lead to a better achievement of the objectives oftheir clients. One general method to identify and reduce the inXuence of task andcontext factors is triangulation (Payne et al., 1999). Choices should be made in diVer-ent ways (e.g., joint and separate evaluation, diVerent order of options) and the clientshould be confronted with inconsistencies in the choices made. Decision makers areoften able to resolve such inconsistencies (Baron, 2001). A triangulation approachappears particularly suitable for decisions that are made over a longer period of time,since several aspects of the decision (e.g., available information, framing, etc.) willeither vary naturally or can be deliberately manipulated and the individual hasenough time to reXect on hypothetical choices. It is important, however, that counsel-ors facilitate a systematic learning and discovery process; in the same way that a suc-cessful psychological experiment requires a careful setup and analysis of the


experimental conditions, triangulation needs to be systematically conducted in orderto be enlightening and not confusing.

A second recommendation is derived from the fact that the alternatives in voca-tional choice are future occupations or jobs. When decisions involve the prediction offuture preferences or when the decision outcome will be experienced in the future,“context-matching” increases decision accuracy (Hsee et al., 1999; Payne et al., 1999).In context matching, the choice situation is designed to match the context in whichthe decision maker will eventually experience the outcome. In vocational choice, indi-viduals will generally experience the attributes of only the chosen alternative (e.g.,receive the salary, have the social contacts and autonomy in their actual future job);ongoing comparisons with rejected alternatives are rarely the case (cf. PfeVer, 1990).Accordingly, context matching in vocational choice implies a separate evaluation ofoptions.

A third recommendation reXects the important role of hard-to-evaluate attributesin occupational choice: measures should be taken to ensure a high degree of evaluabil-ity for all relevant attributes. Evaluability is usually higher in joint evaluation. How-ever, separate evaluation also has several advantages (e.g., consideration of trade-oVs,no feature-matching eVects, and context-matching beneWts). To combine the informa-tional beneWt of joint evaluation and the beneWts of separate evaluation, we suggestthe following simple two-step procedure. In a Wrst step, distributional information onthe attributes is constructed from the complete choice set. In the second step, thisinformation is then used to facilitate a separate evaluation of the alternatives.

Fourth, a simpliWed representation of information is often advocated but can havemore subtle eVects than just making information processing easier. In particular,matrix representations of alternatives encourage attribute-based processing. This notonly eliminates explicit trade-oVs, but can also lead to a change in the evaluationsand weights of attributes as compared to a more complex representation of the infor-mation (Slaughter & Highhouse, 2003). Therefore, when there are advantages ofalternative-based processing for a given set of choice goals, the beneWts of a simplerepresentation of information and its potential negative eVects via attribute-basedprocessing should be carefully balanced.

Finally, our model may also be helpful to evaluate normative decision strategiesthat have been proposed in the literature. The relative advantages of strategies suchas the “ideal” weighted additive strategy (Broscio & Scherer, 2003), sequential elimi-nation (Gati et al., 1995), or more intuitive approaches (cf. Singh & Greenhaus, 2004)may vary considerably depending on the goals of the decision maker and dependingon the nature of task and context factors.

8. Summary and directions for future research

We presented a model of the motivational and cognitive processes that areinvolved in vocational choice. Our model is based on research in behavioral decisionmaking (e.g., Bettman et al., 1998; Payne et al., 1993; Tversky et al., 1988) andinvolves two major processes:


(1) Decision strategies are selected depending on the relative importance of four choicegoals (maximizing decision accuracy, minimizing cognitive eVort, minimizing neg-ative emotion, and maximizing justiWability of the decision). This strategy selectionis constrained by the availability of information on attributes and preferences.

(2) The evaluation mode (joint vs. separate evaluation of alternatives) and certain char-acteristics of the attributes can lead to preference construction processes andthereby aVect the choice outcome. The evaluation mode is determined in part by thedecision strategy, but can also be inXuenced by the representation of information.

This basic model was extended to account for the role of the social context and fora long decision time, both of which can potentially aVect the decision process and thedecision outcome. The social context can aVect decisions via the provision of informa-tion and alternatives, as well as via changes in preferences. A long decision time allowsthe acquisition of additional information on alternatives as well as on preferences, andthis information acquisition is directed by the choice goals of the decision maker.

Several elements of the model have already been investigated in the particularcontext of vocational decisions; for example, considerable research has been con-ducted on the characteristics of alternatives in vocational choice, on the accuracy ofdiVerent decision strategies, and on selected task and context eVects. However, thereare many opportunities to Wll remaining gaps and to replicate Wndings made inbehavioral decision making research in the context of vocational choice. In particu-lar, there is little descriptive research on decision strategies and how they are aVectedby choice goals. Also, more research is needed on the size of task and context eVectsin more realistic, complex vocational decisions.

A decision making perspective on vocational choice can provide useful recom-mendations for counseling. Our model suggests that the counselor should analyze thechoice goals of the client and their potential eVects on the selection of decision strate-gies and on information acquisition patterns. It may be in the client’s interest toreconsider choice goals if they motivate cognitive processes that are detrimental todecision quality. In addition, task and context eVects should be taken into account.Using approaches such as triangulation and context matching, the counselor can tryto control these situational eVects and to design a decision process that allows themost relevant preferences to emerge.

Our model also oVers an interesting perspective for researchers interested in careertransitions. As an individual’s career progresses, several key constructs in our modelwill undergo systematic changes. For example, the relative importance of choicegoals is likely to change over the course of people’s lives. We also expect that prefer-ence construction will be less pronounced for decision makers that have had theopportunity to form clearer core preferences by actually experiencing job attributes.Finally, work experience will provide people with valuable information on alterna-tives such that informational constraints might be less relevant.

Our discussion has revealed many exciting opportunities for research on voca-tional choice processes and only some elements of such a “research agenda” can behighlighted here. First, an interesting question is if people learn to implement betterdecision processes after experiencing the outcomes from prior vocational choices.


There is some evidence of learning in tasks such as stock trading or inventory plan-ning (e.g., Gibson, 2000; Subbotin, 1996), but we suspect that learning is particularlydiYcult in vocational choice due to the particular nature of the decision problem andof the alternatives. Hopefully, research on this question will point us towards usefultools to improve career and job decisions over time.

Second, the determinants of the relative importance of choice goals are an inter-esting area for future research, especially given the central role of choice goals forstrategy selection and information acquisition. If these determinants are primarilysituational (e.g., time pressure, cognitive load, and salience of social inXuences), coun-selors may be better able to guide the decision process than if they reside on the indi-vidual or cultural level. Individual-level studies could investigate the relationshipsbetween choice goal importance and certain personality traits; cross-cultural studiescould investigate if there are systematic diVerences in the importance of choice goalsin collectivist vs. individualist cultures.

Third, and on a more conceptual level, future work could extend this model toaccount for uncertainty about attributes and alternatives (Tversky & Kahneman, 1974).Research on decision making under risk and uncertainty has advanced considerablyover the last decades and might provide important additional insights into vocationaldecisions (for an excellent review and integration of that literature, see Fox & See, 2003).

Finally, the current model may be of little use for understanding decisions that areprimarily based on intuition and emotional processes. We have a long way to go inunderstanding these processes on a more fundamental level, and we know even lessabout the interaction of emotional and systematic deliberative processes (ShaWr & LeB-oeuf, 2002). Studying these issues is an important task not only for researchers interestedin career decision making but for the Weld of behavioral decision making more generally.

We hope that this paper will contribute to the literature on vocational choice byexposing the Weld to recent research in behavioral decision making and by stimulat-ing further research on decision making processes in the context of vocational choice.To the extent that certain decision processes lead to better outcomes than others, aprocess perspective may help to achieve a signiWcant improvement of vocationaldecisions, be it with respect to person-environment Wt or some other measure of deci-sion quality. Given the high importance of vocational choice for individuals and forsociety, this opportunity should not be neglected.

References

Alba, J. W., & Marmorstein, H. (1987). The eVects of frequency knowledge on consumer decision making.Journal of Consumer Research, 14, 14–26.

Anderson, C. J. (2003). The psychology of doing nothing: Forms of decision avoidance result from reasonand emotion. Psychological Bulletin, 129(1), 139–167.

Ariely, D., Loewenstein, G., & Prelec, D. (2003). “Coherent arbitrariness”: Stable demand curves withoutstable preferences. Quarterly Journal of Economics, 118, 73–105.

Baltes, P. B., & Baltes, M. M. (1990). Psychological perspectives on successful aging: The model of selectiveoptimization with compensation. In P. B. Baltes & M. M. Baltes (Eds.), Successful aging: Perspectivesfrom the behavioral sciences (pp. 1–34). New York: Cambridge University Press.


Baron, J. (2001). Measuring value tradeoVs: Problems and some solutions. In E. U. Weber, J. Baron, & G.Loomes (Eds.), ConXict and tradeoVs in decision making (pp. 231–258). Cambridge: Cambridge Univer-sity Press.

Beach, L. R. (1993). Broadening the deWnition of decision making: The role of prechoice screening ofoptions. Psychological Science, 4, 215–220.

Beattie, J., & Barlas, S. (2001). Predicting perceived diVerences in tradeoV diYculty. In E. U. Weber, J.Baron, & G. Loomes (Eds.), ConXict and tradeoVs in decision making (pp. 24–64). Cambridge: Cam-bridge University Press.

Bettman, J. R., Luce, M. F., & Payne, J. W. (1998). Constructive consumer choice processes. Journal ofConsumer Research, 25, 187–217.

Brooks, L., & Betz, N. E. (1990). Utility of expectancy theory in predicting occupational choices in collegestudents. Journal of Counseling Psychology, 37(1), 57–64.

Broscio, M., & Scherer, J. (2003). Six steps to creating a personal career-decision framework. Journal ofHealthcare Management, 48, 355–361.

Brown, C. L. (1999). “Do the right thing”: Diverging eVects of accountability in a managerial context.Marketing Science, 18(3), 230–246.

Brownstein, A. L. (2003). Biased predecision processing. Psychological Bulletin, 129(4), 545–568.Cable, D. M., & Judge, T. A. (1996). Person-organization Wt, job choice decisions, and organizational entry.

Organizational Behavior and Human Decision Processes, 67(3), 294–311.Chartrand, J. M., Robbins, S. B., Morrill, W. H., & Boggs, K. (1990). Development and validation of the

career factors inventory. Journal of Counseling Psychology, 37(4), 491–501.Demo, D. H. (1992). The self-concept over time: Research issues and directions. Annual Review of Sociol-

ogy, 18, 303–326.Eby, L. T., & Dobbins, G. H. (1997). Collectivist orientation in teams: an individual and group-level analy-

sis. Journal of Organizational Behavior, 18, 275–295.Elizur, D., Borg, I., Hunt, R., & Beck, I. M. (1991). The structure of work values: A cross cultural compari-

son. Journal of Vocational Behavior, 12, 21–38.Erlanger, H. S., & Epp, C. R. (1996). Law student idealism and job choice: Some new data on an old ques-

tion. Law and Society Review, 30, 851–864.Felsman, D. E., & Blustein, D. L. (1999). The role of peer relatedness in late adolescent career development.

Journal of Vocational Behavior, 54, 279–295.Festinger, L. (1957). A theory of cognitive dissonance. Evanston, IL: Row Peterson.Finucane, M. L., Peters, E., & Slovic, P. (2003). Judgment and decision making. The dance of aVect and

reason. In S. Schneider & J. Shanteau (Eds.), Emerging perspectives on judgment and decision research(pp. 327–363). Cambridge: Cambridge University Press.

Fox, C. R., & See, K. E. (2003). Belief and preference in decision under uncertainty. In D. Hardman & L.Macchi (Eds.), Thinking: Psychological perspectives on reasoning, judgment and decision making (pp.273–314). New York: Wiley.

Gati, I., & Asher, I. (2001). Prescreening, in-depth exploration, and choice: From decision theory to careercounseling practice. Career Development Quarterly, 50(2), 140–157.

Gati, I., Fassa, N., & Houminer, F. (1995). Applying decision theory to career counseling practice: Thesequential elimination approach. Career Development Quarterly, 43(3), 211–219.

Gati, I., Krausz, M., & Osipow, S. H. (1996). A taxonomy of diYculties in career decision making. Journalof Counseling Psychology, 43(4), 510–526.

Gibson, F. P. (2000). Feedback delays: How can decision makers learn not to buy a new car every time thegarage is empty? Organizational Behavior and Human Decision Processes, 83, 141–166.

Gilliland, S. W., & Schmitt, N. (1993). Information redundancy and decision behavior: A process tracinginvestigation. Organizational Behavior and Human Decision Processes, 54, 157–180.

Gottfredson, L. (1981). Circumscription and compromise: A developmental theory of occupational goals.Journal of Counseling Psychology, 28, 545–580.

Hartung, P. J., & Blustein, D. L. (2002). Reason, intuition, and social justice: Elaborating on Pearson’scareer decision making model. Journal of Counseling and Development, 80, 41–47.

Hastie, R. (2001). Problems for judgment and decision making. Annual Review of Psychology, 52, 653–683.


Highhouse, S., Brooks-Laber, M. E., Lin, L., & Spitzmueller, C. (2003). What makes a salary seem reason-able. Frequency context eVects on starting-salary expectations. Journal of Occupational and Organiza-tional Psychology, 76, 69–81.

Holland, J. L. (1997). Making vocational choices: A theory of vocational personalities and work environments(3rd ed). Odessa: PAR.

Houston, D. A., Sherril-Mittleman, & Weeks, M. (2001). The enhancement of feature salience in dichoto-mous choice dilemmas. In E. U. Weber, J. Baron, & G. Loomes (Eds.), ConXict and trade oVs in decisionmaking (pp. 65–85). Cambridge: Cambridge University Press.

Hsee, C. K., Loewenstein, G. F., Blount, S., & Bazerman, M. H. (1999). Preference reversals between jointand separate evaluations of options: A review and theoretical analysis. Psychological Bulletin, 125,576–590.

Huber, J., & McCann, J. (1982). The impact of inferential beliefs on product evaluations. Journal of Mar-keting Research, 19, 324–333.

Johnson, M. D. (1984). Consumer choice strategies for comparing noncomparable alternatives. Journal ofConsumer Research, 11, 741–753.

Judge, T. A., & Bretz, R. D. (1992). EVects of work values on job choice decisions. Journal of Applied Psy-chology, 77(3), 261–271.

Kidd, J. M. (2004). Emotion in career contexts: Challenges for theory and research. Journal of VocationalBehavior, 64, 441–454.

Kristof, A. L. (1996). Person-organization Wt: An integrative review of its conceptualizations, measure-ment, and implications. Personnel Psychology, 49(1), 1–49.

Kuhl, J. (1984). Volitional aspects of achievement motivation and learned helplessness: Toward a compre-hensive theory of action control. Progress in Experimental Personality Research, 13, 99–171.

Kunda, Z. (1990). The case for motivated reasoning. Psychological Bulletin, 3, 480–498.Kunda, Z., & Sanitioso, R. (1989). Motivated changes in the self-concept. Journal of Experimental Social

Psychology, 25, 272–285.Lent, R. W., Brown, S. D., & Hackett, G. (1994). Toward a unifying social cognitive theory of career and

academic interest, choice, and performance. Journal of Vocational Behavior, 45, 79–122.Lerner, J. S., & Tetlock, P. E. (1999). Accounting for the eVects of accountability. Psychological Bulletin,

125(2), 255–275.Lichtenberg, J. W., ShaVer, M., & Arachtingi, B. M. (1993). Expected utility and sequential elimination

models of career decision making. Journal of Vocational Behavior, 42, 237–252.Luce, M. F. (1998). Choosing to avoid: Coping with negatively emotion-laden consumer decisions. Journal

of Consumer Research, 24, 409–433.Luce, M. F., Bettman, J. R., & Payne, J. W. (1997). Choice processing in emotionally diYcult decisions.

Journal of Experimental Psychology: Learning, Memory, and Cognition, 23, 384–405.Lundgren, S. R., & Prislin, R. (1998). Motivated cognitive processing and attitude change. Personality and

Social Psychology Bulletin, 24, 715–726.Lykken, D. T., Bouchard, T. J., McGue, M., & Tellegen, A. (1993). Heritability of interests: A twin study.

Journal of Applied Psychology, 78(4), 649–661.Mitchell, K. E., Levin, A. S., & Krumboltz, J. D. (1999). Planned happenstance: Constructing unexpected

career opportunities. Journal of Counseling and Development, 77(2), 115–124.Morrow, S. L., Gore, P. A., & Campbell, B. W. (1996). The application of a sociocognitive framework to

the career development of lesbian women and gay men. Journal of Vocational Behavior, 48, 136–148.Osipow, S. H. (1999). Assessing career indecision. Journal of Vocational Behavior, 55, 147–154.Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The adaptive decision maker. Cambridge: Cambridge

University Press.Payne, J. W., Bettman, J. R., & Schkade, D. A. (1999). Measuring constructed preferences: Towards a

building code. Journal of Risk and Uncertainty, 19, 243–270.PfeVer, J. (1990). Incentives in organizations: The importance of social relations. In O. E. Williamson (Ed.),

Organization theory from Chester Barnard to the present and beyond (pp. 72–97). New York: OxfordUniversity Press.

Phillips, S. D. (1997). Toward an expanded deWnition of adaptive decision making. Career DevelopmentQuarterly, 45(3), 275–287.


Phillips, S. D., Christopher-Sisk, E. K., & Gravino, K. L. (2001). Making career decisions in a relationalcontext. Counseling Psychologist, 29(2), 193–213.

Phillips, S. D., & ImhoV, A. R. (1997). Women and career development: A decade of research. AnnualReview of Psychology, 48, 31–59.

Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic deWnitions and new direc-tions. Contemporary Educational Psychology, 25, 54–67.

Sagie, A., Elizur, D., & Koslowsky, M. (1996). Work values: A theoretical overview and a model of theireVects. Journal of Vocational Behavior, 17, 503–514.

ShaWr, E., & LeBoeuf, R. A. (2002). Rationality. Annual Review of Psychology, 53, 491–517.Simon, H. A. (1955). A behavioral model of rational choice. Quarterly Journal of Economics, 69, 171–191.Singh, R., & Greenhaus, J. H. (2004). The relation between career decision making strategies and person-

job Wt: A study of job changers. Journal of Vocational Behavior, 64, 198–221.Sirgy, M. J. (1982). Self-concept in consumer behavior: A critical review. Journal of Consumer Research, 9,

287–300.Slaughter, J. E., & Highhouse, S. (2003). Does matching up features mess up job choice? Boundary condi-

tions on attribute-salience eVects. Journal of Behavioral Decision Making, 16, 1–15.Slovic, P. (1995). The construction of preferences. American Psychologist, 50(5), 364–371.Soelberg, P. O. (1967). Unprogrammed decision making. Industrial Management Review, 8, 19–29.Spokane, A. R., Meir, E. I., & Catalano, M. (2000). Person-environment congruence and Holland’s theory:

A review and reconsideration. Journal of Vocational Behavior, 57, 137–187.Subbotin, V. (1996). Outcome feedback eVects on under- and overconWdent judgments (general knowledge

tasks). Organizational Behavior and Human Decision Processes, 66, 268–276.Swann, W. B., & Hill, C. A. (1982). When our identities are mistaken: ReaYrming self-conceptions through

social interactions. Journal of Personality and Social Psychology, 43, 59–66.Swann, W. B., & Read, S. J. (1981). Acquiring self-knowledge: The search for feedback that Wts. Journal of

Personality and Social Psychology, 41, 1119–1128.Tenbrunsel, A. E., & Diekmann, K. A. (2002). Job-decision inconsistencies involving social comparison

information: The role of dominating alternatives. Journal of Applied Psychology, 87(6), 1149–1158.Tinsley, H. E. A. (2000). The congruence myth: An analysis of the eYcacy of the person-environment Wt

model. Journal of Vocational Behavior, 56, 147–179.Tversky, A. (1972). Elimination by aspects: A theory of choice. Psychological Review, 79, 281–299.Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185,

1124–1131.Tversky, A., Sattath, S., & Slovic, P. (1988). Contingent weighting in judgment and choice. Psychological

Review, 95, 371–384.Tversky, A., & Simonson, I. (1993). Context-dependent preferences. Management Science, 39(1), 1179–

1189.White, M. J., Brockett, D. R., & Overstreet, B. G. (1993). ConWrmatory bias in evaluating personality test

information: Am I really that kind of person?. Journal of Counseling Psychology, 40, 120–126.Wilson, T. D., & Brekke, N. (1994). Mental contamination and mental correction: Unwanted inXuences on

judgments and evaluations. Psychological Bulletin, 116, 117–142.Wood, W. (2000). Attitude change: Persuasion and social inXuence. Annual Review of Psychology, 51, 539–

570.

Date post:	15-May-2017
Category:	Documents
Upload:	oana-petcu
View:	214 times
Download:	1 times

1-s2.0-S0001879104001125-main

Documents