The Relationship betweenTask Complexity and Information Search:The Role of Self-EfficacyJing Hu Hofstra University

Bruce A. Huhmann New Mexico State University

Michael R. Hyman New Mexico State University

ABSTRACT

Prior research suggests the complexity of a product choice task isinversely related to the extent of consumers’ external informationsearch. The resource-matching perspective holds that cognitive effort(e.g., external information search) is greatest when available cogni-tive resources (e.g., as determined by self-efficacy) match the cogni-tive demands of a task (e.g., perceived task complexity). Within abrand-choice context, the relationship between self-efficacy andextent of information search appears nonmonotonic. In support ofthe resource-matching perspective, consumers conduct the mostextensive information search when their self-efficacy matches per-ceived task difficulty. © 2007 Wiley Periodicals, Inc.

Consumers’ information searches in preparation for choosing brands havelong interested marketing scholars (Katona & Mueller, 1955). Several mar-keting studies have focused on external information search determinants,such as task complexity (often operationalized as the number of brand

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attributes that must be considered (Olshavsky, 1979; Payne, 1976; Simon-son & Tversky, 1992), the product in question (Beatty & Smith, 1987;Katona & Mueller, 1955), and consumer demographics such as gender(Laroche, Saad, Cleveland, & Browne, 2000) and age (L. R. Klein & Ford,2003; Laroche, Cleveland, & Browne, 2004). However, such studies are typ-ically limited to the cognitive domain; for example, product-class knowledge(Brucks, 1985), experience (Srinivasan & Ratchford, 1991), prior productperception (Moorthy, Ratchford, & Talukdar, 1997), expertise (Kuusela,Spence, & Kanto, 1998), time pressure (Kulviwat, Guo, & Engchanil, 2004),and cost–benefit evaluation (Heaney & Goldsmith, 1999).Although severalstudies have drawn from the social domain, such as consumer beliefs (Dun-can & Olshavsky, 1982), consumer enjoyment (Kulviwat et al., 2004) andbuyer uncertainty (Urbany, Dickson, & Wilkie, 1989), no study has focusedon product information search and self-efficacy.

Self-efficacy is a person’s perceived ability to complete a task success-fully.The precursors of self-efficacy—such as amount of experience (Srini-vasan & Ratchford, 1991), prior knowledge (Beatty & Smith, 1987; Bettman& Park, 1980; Brucks, 1985; Urbany et al., 1989), and ability to judgebrands in a product class (Duncan & Olshavsky, 1982; Srinivasan & Ratch-ford, 1991)—have been found to influence consumers’ information search.If these precursors affect information search, then self-efficacy itself shouldalso affect information search; however, there is no empirical evidence tosupport this supposition. Based on this research lacuna, the current studyseeks to examine (1) the relationship between self-efficacy and informationsearch, and (2) the effect on information search of congruence betweenself-efficacy and perceived task complexity. To explain the relationshipsamong self-efficacy, perceived task complexity, and extent of informationsearch when choosing the most suitable alternative in a product class, theresource-matching perspective is used as the theoretical framework. Pre-vious applications of this perspective focused on information-processingtasks (i.e., optimal processing occurs when available resources matchresource requirements); here, it is applied to information search.

The exposition of this study proceeds as follows. First, the extant lit-erature on self-efficacy, task complexity, and external information searchis reviewed. Second, the resource-matching perspective is discussed andhypotheses relating self-efficacy to consumers’ motivation to search forproduct information are developed. Third, an experiment-based test ofthese hypotheses is described. Results show that extensiveness of infor-mation search increases as people’s self-efficacy more closely match theirperceived task difficulty. Finally, implications are discussed.

LITERATURE REVIEW

Self-Efficacy

Self-efficacy is the belief in “one’s capabilities to mobilize the motiva-tion, cognitive resources, and courses of action needed to meet given sit-

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uational demands” (Wood & Bandura, 1989, p. 408); in other words, it isa person’s believed ability to perform a task. Self-efficacy has helpedexplain many phenomena associated with organizational behavior andhuman resource management, especially job performance (Bandura &Cervone, 1986; Cole & Hopkins, 1995; Hunter, 2004) and salesperson’s jobeffort (Chowdhury, 1993; Srivastava, Strutton, & Pelton, 2001). In gen-eral, self-efficacy affects people’s initial task selection and subsequentcoping efforts (Lent, Brown, & Larkin, 1987; Stumpf, Brief, & Hartman,1987). “People process, weigh, and integrate diverse sources of informa-tion concerning their capabilities, and they regulate their behavioralchoices and effort expenditure accordingly” (H. L. Klein, 1989, p.167).

Relative to people low in self-efficacy, people high in self-efficacy aremore motivated to perform a given task because they believe their cur-rent skills are sufficient to achieve positive outcomes (Noe & Wilk, 1993;Tannenbaum et al., 1991). As a result, people high in self-efficacy fore-see their efforts earning a net reward, with greater efforts earning greaterrewards. For example, salespeople’s self-efficacy is significantly and pos-itively associated with their sales effort (Srivastava et al., 2001); stu-dents high in self-efficacy are less likely to procrastinate academic workexcept when preempted by more urgent issues and more likely to devotethemselves quickly and persistently to chosen tasks (Chu & Choi, 2005).Applied to consumer behavior, it follows that consumers use self-efficacyassessments to guide the extent of their search efforts for product infor-mation. Specifically, consumers high in self-efficacy should engage inmore extensive information search than consumers low in self-efficacy.

Although self-efficacy influences consumers’ information search, priorcognition-based research is inconsistent regarding the effect of self-effi-cacy on the extent of that search. In one study, consumers with moder-ate prior knowledge and experience—two precursors of self-efficacy (Ban-dura, 1982)—tended to search for more information than consumers withlow or high prior knowledge and experience (Bettman & Park, 1980),which suggests an inverted-U-shaped relationship between self-efficacyand extent of information search. Other studies showed a negative lin-ear relationship between consumers’ perceived ability to judge productsand brands and the extent of their information search when buying com-plex products like color televisions and automobiles (Duncan & Olshavsky,1982; Srinivasan & Ratchford, 1991). Still other studies showed a posi-tive linear relationship between self-efficacy and information searcheffort; for example, consumers with high prior knowledge conducted moreextensive information searches because their search cost was lower andit was easier for them to process new information (Brucks, 1985; Strebel,Erdem, & Swait, 2004; Urbany et al., 1989).

Information Search and Task Complexity

Information search can be internal or external. Internal search involvesscanning personal memory for information, whereas external search

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involves seeking information from the environment (Schmidt & Spreng,1996). In a marketing context, external search effort has been defined as“the degree of attention, perception and effort directed toward obtain-ing environmental data or information related to the specific purchaseunder consideration” (Beatty & Smith, 1987, p. 85). (Note: As this studyfocuses exclusively on external information search, henceforth it is referredto as information search.)

Most studies of information overload have relied on information dis-play boards (e.g., Jacoby, Speller, & Berning, 1974; Keller & Staelin,1987; Malhotra, 1982) or computer-generated information displays (Lee& Lee, 2004), which present subjects with an attribute-by-brand matrixof information rather than an unorganized mass of brand information.As a result, these studies only showed extent of search in highly struc-tured (i.e., relatively less complex) information environments (cf. Lee& Lee, 2004).

Although previous studies on consumer information overload suggesta relationship between extent of information search and task complex-ity (Jacoby et al., 1974), theories related to information overload cannotfully explain why some consumers facing lower-complexity product choicesmake suboptimal decisions. Obviously, as choice complexity increases—that is, more brands and attributes are considered—consumers mustinvest more time and cognitive effort to gather the information neededto choose the most suitable alternative. Perhaps self-efficacy affects infor-mation gathering and processing efforts. Task complexity is known toaffect peoples’ beliefs about their self-efficacy (Cervone & Peake, 1986);people appear lower in self-efficacy when confronted with the formida-ble aspects of a task and they appear higher in self-efficacy when con-fronted with the doable aspects of the same task (Cervone, 1985). Neither(a) the interrelationships between perceived task complexity, informationsearch, and self-efficacy, nor (b) the effect of perceived brand choice com-plexity on information search have been studied. By implying that bothself-efficacy and task complexity influence extent of information search,the resource-matching perspective provides a framework for addressingthis research gap.

Resource-Matching Perspective

The Resource-Matching Perspective suggests that information process-ing is optimal when the available and required resources to complete acognitive task are matched. If a cognitive task requires unavailableresources, or if available resources exceed required resources, then per-formance on that task suffers (Anand & Sternthal, 1990; Meyers-Levy &Peracchio, 1995). This implies that available processing resources arenonmonotonically (i.e., inverted-U-shaped curve) related to processingoutcomes (e.g., persuasion, recall) when the resources required to com-plete a cognitive task are held constant.

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If cognitive tasks require more resources than are available, then thosetasks cannot be completed successfully. Although theories about infor-mation overload can explain constrained-resource–induced inabilities tocomplete challenging cognitive tasks, the resource-matching perspectivemakes a unique theoretical contribution by explaining why informationprocessing suffers when excess resources are available. When resourcesexceed requirements, performance suffers because people judge a cogni-tive task as insufficiently challenging and thus allocate their excessresources to other tasks; the resulting distraction degrades performanceon the focal task.

Such degraded performance can occur when viewing ads. Relativeto ads that require more processing resources, ads that require fewerprocessing resources trigger fewer (more) cognitive responses frommore (less) motivated consumers (Peracchio & Meyers-Levy, 1997).Similarly, ads that are low in visual complexity are less recalled thanthose that are moderately visually complex (Huhmann, 2003). Manip-ulation of attributes in the ad system, such as viewing angles, iconcuts, and camera movement, will enhance persuasiveness when requiredresources match available resources and reduce persuasiveness whenresource imbalance occurs (Larsen, Luna, & Peracchio, 2004). Thus,processing outcomes worsen when consumers’ available resourcesexceed processing requirements, but improve as required and avail-able resources converge.

Previous studies have relied on the resource-matching perspective toexplain differences in information-processing extensiveness as (a) stim-uli become more complex and thus require more cognitive effort to process,and (b) subjects have fewer cognitive resources to allocate because oftheir ages, needs for cognition, or other individual differences (Anand &Sternthal, 1990; Hahn & Hwang, 1999; Huhmann, 2003; Keller, Anand,& Block, 1997; Larsen et al., 2004; Meyers-Levy & Peracchio, 1995; Per-acchio & Meyers-Levy, 1997). In contrast, for the current study (a) taskcomplexity, rather than stimuli complexity (e.g., executional character-istics of print ads), is manipulated; (b) the resource-matching perspectiveis applied to extent of information search rather than to processing out-comes such as brand attitude, behavioral intentions, or recall; and (c)self-efficacy is used as an individual difference measure.

HYPOTHESES

Self-efficacy for choosing a most suitable alternative in a product classdetermines the believed cognitive resources available for making thischoice, and perceived task complexity determines the resources thoughtnecessary to complete that task successfully. As choice complexity (i.e.,the number of brands and features that must be evaluated) increases, con-sumers high in self-efficacy should grow more confident about their abil-

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ity to interpret and use gathered product information. From a resource-matching perspective, when perceived task complexity matches self-effi-cacy, people maximize their information search. When perceived taskcomplexity exceeds self-efficacy, people should reduce their informationsearch to avoid wasting cognitive resources. When self-efficacy exceedsperceived task complexity, people should reduce their information searchbecause they believe the task does not warrant their full attention oreffort. In other words, the closer the match between perceived task com-plexity and self-efficacy, the more likely a consumer will conduct anextensive information search; that is, the absolute value of the differ-ence between perceived task complexity and self-efficacy should relateinversely to information search. Thus,

H1: When choosing a most suitable alternative in a product class, con-sumers are more likely to plan an extensive information searchthe more closely perceived task complexity matches self-efficacy.

Prior research utilizing the resource-matching perspective hasrepeatedly found a nonmonotonic (i.e., inverted-U-shaped) relation-ship between available resources and processing outcomes whenrequired resources are omitted from the analysis. If self-efficacy rep-resents subjects’ perceptions of available resources to complete thetask, then self-efficacy should be related nonmonotonically to extentof information search when the influence of perceived task complex-ity is ignored. Thus,

H2: The relationship between self-efficacy and extent of informationsearch is non-monotonic rather than linear.

METHOD

Sample

Respondents were 287 undergraduate business majors enrolled in 10classes at a major research university in the southwestern United States.These students, who received copies of the scenarios and questionnairesets (described below) during a regularly scheduled class, earned extraclass credit for participating in the study. The subjects were almostevenly divided between genders (50.7% female), tended to be nontradi-tional students (e.g., roughly two-thirds were more than 25 years old andhad annual incomes greater than $10,000), and as likely to be Cau-casian (54.0%) as non-Caucasian (31.7% Hispanic, 2.2% African Amer-ican, and 12.1% other). Both treatments (also described below) wererandomly distributed to students in each class. Because most collegestudents have participated in the purchase of a personal computer, astudent sample is valid.

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Experimental Design

To avoid relying on recall of past events, which may be inaccurate andtend to cause over- or underreporting (Sudman, Bradburn, & Schwarz,1996), subjects were given the following scenario:

Imagine that you work for a small business, and your boss has putyou in charge of handling the purchase decision for a small office com-puter. Your boss has given you a list of product features that he/she wantsyou to focus on AND a set of brands that he/she wants you to consider.[Brands and features are given here.] Imagine you are starting the processof making the decision now. Based on the scenario above, please indicateyour level of agreement with each of the following statements.

As in prior research (e.g., Olshavsky, 1979; Payne, 1976; Simonson& Tversky, 1992), the complexity of choosing the most suitable alter-native in a product class was manipulated by varying the number ofproduct attributes and choice alternatives. Subjects given the simple(complex) task were asked to evaluate 2 (8) brands and 2 (12) attri-butes of personal computers. They then answered questions about theperceived complexity of this task, their perceived ability to complete itsuccessfully, and their typical extent of information search when buy-ing a personal computer.

Thus, the two independent variables are (a) task complexity, a two-level factor manipulated between subjects, and (b) self-efficacy, a self-report of subjects’ ability to make a good decision when buying a per-sonal computer. The dependent variable is a self-report measure ofsubjects’ extent of information search when buying a personal computer.

Measures

Perceived Task Complexity. Subjects completed a single-item measureof perceived task complexity immediately following their exposure to anexperimental stimulus, which consisted of a list of brands and attrib-utes but not information on how each brand scores on each attribute.Subjects responded from Strongly agree to Strongly disagree on the 5-point, Likert-type item “Making this purchase decision would be a com-plex task.”

Self-Efficacy. Valid measures of self-efficacy require domain-specificscales (Bandura, 1984). The first self-efficacy scale was designed to pre-dict job performance (Bandura, 1982); later scales were used to assess therelationship of self-efficacy to salespersons’ effort and employees’ par-ticipation (Chowdhury, 1993; Noe & Wilk, 1993; Srivastava et al., 2001).No scales have been developed to measure self-efficacy in consumer deci-sion-making; of the extant scales, the self-efficacy measure in Chowd-hury (1993), which was designed for a negotiating context, is most per-tinent. Thus, eight 5-point Likert scale items from Chowdhury (1993)were adapted to a personal computer purchase context (see Table 1).

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An analysis of internal consistency and unidimensionality revealedproblems. Although Cronbach’s � for the eight-item scale was 0.74, inter-nal consistency improved by dropping the third or fourth items, a com-mon factor analysis revealed two factors, and all but the third and fourthitems loaded 0.35 or higher; thus, items three and four were dropped. Acommon factor analysis with six remaining items produced a single-fac-tor solution and the abridged scale was sufficiently reliable (Cronbach’s� � 0.80; Nunnally & Bernstein, 1994).

Extent of External Information Search. External information searchis often operationalized as the sum of the number of contacts within eachsource category multiplied by the category-required effort weight (Duncan& Olshavsky, 1982); for example, Total Search Effort Index � 4*RetailerSearch � 2*Media Search � 1*Interpersonal Search � 2*Neutral SourcesSearch (Beatty & Smith, 1987). As planned rather than actual informa-

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Table 1. Self-Efficacy Items.

Alpha if Loading on Items Mean Std. Dev. Item Deleted Factor 1

I am sure I will make a good purchase decision when it comes to buying a computer. 3.97 0.92 0.701 0.589

When making a decision to buy a computer, I find it relatively easy to refuse an offer to purchase a computer that I don’t like. 3.93 1.17 0.733 0.355

I find it very difficult to refuse an offer to buy a computer if there is a salesperson making a very persistent argument. 3.77 1.14 0.758 0.241

A person has to be a really good computer salesperson to persuade me to accept what he or she is selling. 3.48 1.04 0.766 0.175

I don’t possess the skills that are required to be a prudent computer buyer. 3.56 1.12 0.694 0.628

My friends consider me to be an expert in buying computers. 2.67 1.08 0.681 0.793

If a situation calls for making a purchase decision about a computer, my friends would consider me to be the right person to consult. 2.91 1.09 0.670 0.885

I usually come out better off in situations in which I have the opportunity to buy a computer. 3.28 0.90 0.704 0.635

Cronbach’s � � 0.80.

tion search was assessed (e.g., no visits to retailers or consultations withfriends occurred), Likert-type items about the likely effort spent on dif-ferent information sources are more appropriate than open-ended items.In addition, a weighted measurement scheme causes double weightingwhen used with Likert scales because self-reported source usage con-siders the effort needed for each source (e.g., responses to the item “Didyou get any relevant information about…” in Moorthy et al., 1997, rangefrom “hardly anything” to “quite a bit”). Because an unweighted index ofconsulted information sources eliminates this double-weighting prob-lem, the unweighted Likert scale for clothing information search inLaroche, Saad et al. (2000) was adapted to personal computer purchase(see Table 2). The Cronbach’s � for the adapted 10-item scale was 0.85.

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Table 2. Extent of External Information Search Items.

Alpha if Loading on Items Mean Std. Dev. Item Deleted Factor 1

Before I would start looking for this computer, I would read all the books,magazines, and advertisements that provide information about computers. 3.20 1.20 0.855 0.426

I would go to a store and look at all the items in the computer display area. 3.79 1.01 0.834 0.697

I would search around a store, looking at all the various computer display material. 3.76 1.02 0.835 0.721

I would check all the product feature information carefully. 4.18 0.83 0.843 0.559

I would spend a lot of time comparing the brands in different stores. 3.80 1.00 0.831 0.720

I would read all the signs around a store computer display area. 3.48 1.03 0.839 0.586

I would carefully read manufacturers’ labels. 3.65 1.02 0.837 0.665

I would carefully examine packaging information. 3.50 1.02 0.845 0.538

I would try to get as much information as possible about computers before making a decision. 4.20 0.90 0.837 0.619

I would spend a lot of time talking with salespeople before making a purchase decision. 3.37 1.23 0.848 0.513

Cronbach’s � � 0.85.

ANALYSIS AND RESULTS

Because perceived task complexity, mean score on the self-efficacy scale,and mean score on the planned information search scale are continuousvariables, regression analysis is suitable for hypothesis testing. Assum-ing collinearity between the first two variables is immaterial, regressionanalysis is appropriate whether the relationship between them andplanned information search is linear or nonmonotonic (e.g., quadratic orcubic). Here, collinearity between perceived task complexity and self-efficacy was trivial (i.e., condition index � 15) and the backward-elimi-nation method was used to select the best-fitting model.

H1 entails the main precept behind the resource-matching perspec-tive—that the most cognitive effort is expended when required and avail-able processing resources match—in the previously untested context ofinformation search. To test H1, a new variable, MATCH, was calculated.MATCH is the absolute value of the difference between the mean scoreon the 5-point self-efficacy scale items and the 5-point perceived taskcomplexity measure. Lower values on MATCH represent greater prox-imity between the subjects’ self-efficacy (which represents subjects’ avail-able resources) and perceived task complexity (which represents theresources subjects believe are required to successfully complete the task),with zero as the minimum. A zero score on MATCH means a subject’s levelof self-efficacy exactly matches the level of perceived task complexity(which represents a subject’s available resources equaling the requiredresources to successfully complete the task). The absolute value of the dis-crepancy is used here, rather than the discrepancy itself, because thereis no a priori reason to recognize positive versus negative deviations;both should represent suboptimal information processing of a similarmagnitude. Thus, the absolute value of the discrepancy makes the mostsense theoretically.

If the resource-matching perspective holds, then MATCH and plannedinformation search should be related inversely. Table 3 shows that inthe complete data set an inverse relationship exists between MATCHand planned extent of information search (adj. R2 � .033, F � 10.816, p� .001), which supports H1. For comparison purposes, a multiple regres-sion (with backward elimination) was run with perceived task complex-ity, self-efficacy, and the interaction of perceived task complexity andself-efficacy as predictor variables. The model with MATCH as the pre-dictor variable outperformed any of these models in terms of adjusted R2

and F value.Separate analyses were run on data from the simple and complex

tasks. Results for the complex task data provide the same support for H1,in terms of both F value and adjusted R2, as the complete data set (seeTable 4); in contrast, results for the simple task data provide the samesupport for H1 in terms of F value, but not adjusted R2, as the completedata set (see Table 5). When model fit assessments yield conflicting F

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values and adjusted R2s, the F value tends to be the more conservativeindicator. (Although adjusted R2 compensates for the marginal increasein predictive ability of additional variables, the F test more directly deter-mines if data provide sufficient evidence that independent variables ade-quately predict the dependent variable.) Thus, the data for both tasks ana-lyzed separately also lend support for H1.

Based on the resource-matching perspective, H2 posits that the rela-tionship between self-efficacy (which represents subjects’ available

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Table 3. Regression Results for Self-Efficacy (SE), Perceived Task Complexity(PTC), or the Absolute Value of the Difference between SE and PTC(MATCH), on Planned Extent of Information Search for Both Tasks.

Model Standardized Beta t-value F Adjusted R2

Model 1 10.816a 0.033MATCH –0.191 –3.289a

Model 2 3.796b 0.028PTC –0.331 –1.304SE –0.266 –1.841c

SE*PTC 0.525 1.982b

Model 3 4.831a 0.026SE –0.096 –1.546SE*PTC 0.189 3.058a

Model 4 7.238a 0.021SE*PTC 0.157 2.690a

Note: a p � .01, b p � .05, c p � .10.

Table 4. Regression Results for Self-Efficacy (SE), Perceived Task Complexity(PTC), or the Absolute Value of the Difference between SE and PTC(MATCH), on Planned Extent of Information Search for a Complex Task.

Model Standardized Beta t-value F Adjusted R2

Model 1 6.585b 0.037MATCH –0.208 –2.566b

Model 2 2.381c 0.028PTC –0.326 –1.008SE –0.223 –1.132SE*PTC 0.550 1.597

Model 3 3.063b 0.027SE –0.045 –0.515SE*PTC 0.215 2.438b

Model 4 5.890b 0.032SE*PTC 0.198 2.427b

Note: a p � .01, b p � .05, c p � .10.

resources) and extent of information search is nonmonotonic, ratherthan linear, when perceived task complexity (which represents theresources subjects believe are required to successfully complete thetask) is excluded. Table 6 shows both the quadratic (adj. R2 � .035, F� 6.219, p � .002) and cubic (adj. R2 � .033, F � 4.230, p � .006) mod-els fit better than the linear model (adj. R2 � 0.001, F � 0.302, p �.580). Whereas both the quadratic and cubic models fit the data, the for-mer model is better fitting (higher F value) and has greater explana-tory power (higher adj. R2). Also, both standardized �s are significantfor the quadratic model (p’s � .001), but neither standardized � is sig-nificant for the cubic model (p’s > .05). Thus, the quadratic model ispreferred, which supports H2.

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Table 5. Regression Results for Self-Efficacy (SE), Perceived Task Complexity(PTC), or the Absolute Value of the Difference between SE and PTC(MATCH), on Planned Extent of Information Search for a Simple Task.

Model Standardized Beta t-value F Adjusted R2

Model 1 4.812b 0.027MATCH –0.184 –2.194b

Model 2 2.806b 0.038PTC –0.280 –0.703SE –0.315 –1.473SE*PTC 0.491 1.206

Model 3 3.976b 0.041SE –0.178 –2.050b

SE*PTC 0.211 2.438b

Note: a p � .01, b p � .05, c p � .10.

Table 6. Regression Results for the Relationship between Self-Efficacy (SE)and Planned Extent of Information Search.

Model Standardized Beta t-value F Adjusted R2

Linear 0.303 0.001SE –0.033 –0.550

Quadratic 6.219a 0.035SE 1.417 3.371a

SE*SE –1.464 –3.482a

Cubic 4.230a 0.033SE 0.307 0.144 SE*SE 0.867 0.197 SE*SE*SE –1.241 –0.532

Note: a p � .01, b p � .05, c p � .10.

DISCUSSION

In the context of choosing a most suitable alternative in a product class,the interrelationships among planned information search, perceivedtask complexity, and self-efficacy were studied. In support of theresource-matching perspective, the results show that available cogni-tive resources in the form of self-efficacy are nonmonotonically relatedto cognitive effort in the form of planned extent of information search.More noteworthy, the closer the match between consumers’ self-effi-cacy and perceived task complexity, the more extensive their plannedinformation search.

The resource-matching perspective was applied to a new context—external information search—and to new cognitive resource avail-ability and requirement variables. Rather than external informationsearch, this theory has previously been applied to studies on process-ing tasks such as attitude toward the brand (Anand & Sternthal, 1990;Meyers-Levy & Peracchio, 1995), cognitive responses (Anand & Stern-thal, 1990; Peracchio & Meyers-Levy, 1997), intention to comply withad-recommended behavior (Keller et al., 1997), memory (Hahn &Hwang, 1999; Huhmann, 2003), and persuasiveness (Larsen et al.,2004). Rather than decision complexity, prior studies on required cog-nitive resources have involved audio presentation format (Anand &Sternthal, 1990), color (Meyers-Levy & Peracchio, 1995), music tempo(Hahn & Hwang, 1999), visual complexity (Huhmann, 2003), and vivid-ness (Keller et al., 1997). Rather than self-efficacy, earlier studies onindividual differences in available cognitive resources have involvedage (Huhmann, 2003), familiarity with background music (Hahn &Hwang, 1999), personal relevance (Keller et al., 1997), repeated expo-sures to information (Anand & Sternthal, 1990), and social loafing(Meyers-Levy & Peracchio, 1995).

Consumers higher in self-efficacy—from relevant knowledge or expe-rience—have more cognitive resources for choosing the most suitablealternative. These resources include a pre-existing mental structure tofacilitate information encoding, familiarity with the best informationsources, and knowing the attributes that best differentiate alternatives(Brucks, 1985; Johnson & Russo, 1984). Such consumers plan more exten-sive searches because they know where to find the most useful infor-mation (Brucks, 1985; Johnson & Russo, 1984).

LIMITATIONS AND FUTURE RESEARCH

The generalizability of this study is limited in three ways. First, plannedrather than actual information search was measured. Actual informationsearch may not mirror planned extent of search due to ease of gatheringinformation, comparability of information across brands, time constraints,and the like.

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Second, results based on the chosen measure of external informationsearch—the scale for clothing information search adapted from Laroche,Saad et al. (2000)—may be artifactual if nonscalar differences existamong alternative types of measures, such as observed search behavior,recall of past searches, and self-predicted planned information search. Forexample, social desirability bias may inflate the latter two nonbehav-ioral types of measures.

Third, to achieve better experimental control and reduce idiosyncraticdifferences among subjects, a proposed—rather than actual—purchaseserved as the choice context. This scenario-based approach also permit-ted the manipulation of task complexity. Nonetheless, future researchcould assess whether the results hold for actual as well as hypotheticalpurchase situations.

Future research also could address the following questions about brandattitudes:

1. Does suboptimal information search (e.g., consumers with low self-efficacy making complex product decisions) induce negative brandattitudes and suboptimal choices?

2. In contrast, does optimal information search (e.g., consumers withhigh self-efficacy making complex product decisions) induce positivebrand attitudes and optimal choices?

CONCLUSION

This study, the first to examine the role of self-efficacy in informationsearch, relates the match between self-efficacy and perceived task com-plexity to the extent of information search believed needed to choose themost suitable brand. It is also the first study (a) to apply the resource-matching perspective to information search, (b) to link differences in cog-nitive resource requirements to complexity of product choice (i.e., thenumber of product attributes and brands to evaluate), and (c) to relateindividual differences in cognitive resource available to self-efficacy.

Contrary to extant theory, the relationship between information searchand self-efficacy is nonlinear. It is, in fact, a nonmonotonic relationshipin which the search for information needed to choose the most suitablealternative is greatest when cognitive demands match cognitive resources.When product choice is believed complex, consumers high in self-effi-cacy plan more extensive information searches because they believe suchsearches are a prerequisite for good decisions. When product choice isbelieved simple, these consumers plan limited information searchesbecause they believe choosing the most suitable alternative does notrequire an extensive search. In contrast, consumers low in self-efficacylack the cognitive resources for effectively using information from a moreextensive search; rather than overwhelm cognitive resources, plannedexternal search is decreased when they believe brand choice is exces-

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sively complex. Nonetheless, such consumers plan more extensive infor-mation searches when they believe brand choice is simple.

The results of the present study suggest that managers would benefitfrom controlling the perceptions of targeted consumers. If targeted con-sumers tend to be low in self-efficacy, such as first-time parents needingto buy baby furniture, then a manager with a superior brand should makethe choice of a most suitable alternative seem as simple as possible. In aretail context, this could mean limiting brand options or making com-parisons among brands quick and easy. In an advertising context, thiscould mean presenting fewer options and/or providing information thatfacilitates judging the focal brand as superior to competitors’ brands. Incontrast, a manager with an inferior brand should make the choice of amost suitable alternative seem as complex as possible. In a retail setting,this could mean discouraging external information search as costly andof little benefit. If targeted consumers tend to be high in self-efficacy, suchas frequent buyers, then a manager with a superior brand should makethe purchase choice more complex, whereas a manager with an inferiorbrand should make the purchase choice simpler.

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The authors thank Elise “Pookie” Sautter of New Mexico State University andthe anonymous reviewers for their helpful comments on previous drafts of thismanuscript.

Correspondence regarding this article should be sent to: Jing Hu, Departmentof Marketing and International Business, Frank G. Zarb School of Business,Hofstra University, Hempstead, NY 11549-1000.

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