Decision delegation: A conceptualization and empirical investigation

Decision Delegation:A Conceptualization andEmpirical InvestigationPraveen AggarwalUniversity of Minnesota Duluth

Tridib MazumdarSyracuse University

ABSTRACT

This paper examines a purchase context in which consumers,instead of deciding on their own, delegate either a part of or theentire purchase decision to a surrogate. A path model linking the antecedent variables and delegation is tested in the context ofpersonal computer purchases. It was found that the factors thatensure decision quality emanating from the surrogate’s expertise differentials, trustworthiness, accountability, and willingness to customize increase the likelihood of decision delegation. In additionto its direct positive effect on delegation, trustworthiness mediatesthe effect of expertise difference, surrogate accountability, and customization on delegation. Perceived loss of control inhibits delegation, but only at the stage when the final choice decision ismade. Also, if a decision task is repeatable, the high return on efforthas a negative effect on delegation, but only at attribute set andchoice set delegations. Contributions of the study and directions forfuture research are discussed. © 2008 Wiley Periodicals, Inc.

Consumer decision-making literature has, for the most part, focused onpurchase contexts in which consumers themselves actively acquire andprocess choice-relevant information to make purchase decisions. Con-sumers acquire product information from commercial sources (e.g., media,

Psychology & Marketing, Vol. 25(1): 71–93 (January 2008)Published online in Wiley InterScience (www.interscience.wiley.com)© 2008 Wiley Periodicals, Inc. DOI: 10.1002/mar.20201

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the retail environment, sales persons) and noncommercial sources (e.g.,friends, colleagues, neighbors) (Bearden, Netemeyer, & Teel, 1989; Kiel &Layton, 1981). Using one of several decision strategies available to them,consumers process this information to arrive at a decision (Payne,Bettman, & Johnson, 1993). Regardless of the source of product infor-mation or the decision strategy employed, consumer decision theorists typ-ically place the responsibility for performing the decision task on theconsumers themselves.

An alternative view involves a decision-making process in which con-sumers entrust or delegate the responsibility of performing the decisiontask to someone else. For instance, Price and Feick (1984) report that15% of subjects in their study, faced with searching for product infor-mation for making a choice decision, decided to let experts choose analternative on their behalf. Another field study finds that 75% of new-comers to a community chose a physician primarily on the basis of the recommendation of their neighbors (Feldman & Spencer, 1965).Formisano, Olshavsky, and Tapp (1982) demonstrate that recommenda-tions were used by a majority of the respondents as the sole basis forchoosing a life insurance policy. Other examples of purchase task dele-gation include employing wardrobe consultants or seeking recommen-dations from wine connoisseurs, computer consultants, or stock brokers.Revenues generated by such professions are significant (estimated at$12.8 billion: architectural firms; $34 billion: bridal consultants; $2.3 billion: catering industry; $2.3 billion: interior decorators [Hollander &Rassuli, 1999]).

Despite the widespread prevalence of decision delegation, research onthis topic is limited (Stern, El-Ansary, & Coughlan, 1995). Researchersin marketing have investigated the influence of interpersonal sourcessuch as friends, relatives, salespersons, or experts in consumer decision-making (Crane, 1991; Crane & Lynch, 1988). Interpersonal influenceshave been examined in the contexts of word-of-mouth (Gilly, Graham,Wolfinbarger, & Yale, 1998; Godes & Mayzlin, 2004; Grewal, Cline, &Davies, 2003), opinion leaders (Eliashberg & Shugan, 1997; Feick, Price,& Higie, 1986), and market mavens (Abratt, Nel, & Nezer, 1995; Feick &Price, 1987; Walsh, Gwinner, & Swanson, 2004; Wiedmann, Walsh, &Mitchell, 2001). More recently, researchers have examined the role of the Internet (e.g., discussion groups) as a powerful interpersonal sourceof information and influence (Klein & Ford, 2003; Ratchford, Lee, &Talukdar, 2004).

This research differs from earlier work in at least three ways. First,prior research typically assumes that interpersonal sources are passiveproviders of information or recommendations, serving as input contrib-utors to consumer decision making. The context of this study, on theother hand, is one in which consumers seek out interpersonal sources(e.g., professionals), who actively participate in the decision making asa surrogate of the consumer. Olshavsky and his colleagues identify such

AGGARWAL AND MAZUMDARPsychology & Marketing DOI: 10.1002/mar

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“subcontracting” as a distinct consumer decision-making strategy(Olshavsky & Granbois, 1979; Rosen & Olshavsky, 1987; Olshavsky, 1985).Second, unlike prior research (e.g., Solomon 1986; Chhabra & Olshavsky,1986), where the focus has been to demonstrate the existence of the del-egation phenomenon, this study focuses on identifying conditions inwhich decision delegation may or may not occur. Finally, this study doesnot consider decision delegation as a single binary outcome, as has beenconceptualized in prior research; instead it decomposes the decision del-egation process into three components (attribute identification, choiceset reduction, and final choice decision) and investigates whether theeffect of antecedents varies across different levels of delegation. Thus, thisstudy proposes an expanded conceptualization of decision delegationthat explicitly incorporates delegation at different levels.

In summary, the objective of this research is to identify the factorsthat may influence purchase decision delegation. This study examines thedirect and mediating influences of these factors on the decision delega-tion process. It employs a path analytic modeling approach that helps partial out the interrelationships among the influencing factors and esti-mates the net effects of each factor on decision delegation.

From a theoretical perspective, the study provides a better under-standing of the purchase task delegation process and isolates factorsthat might influence consumers to farm out a portion of or the entirepurchase decision. Marketing practitioners will benefit from the study bylearning when consumers are likely to engage in delegation and howthat process can be influenced. For example, technical salespersons or fieldexperts of high-tech firms often serve as consultants on whom customersrely heavily for product information and for help narrowing down thechoice alternatives. Managers of these companies will therefore be inter-ested in knowing the factors that may influence a consumer’s decision todelegate.

The rest of the paper is structured as follows. The next section providesa conceptual foundation of this study, leading to a path model of deci-sion delegation and development of hypotheses. Following this is a descrip-tion of the methodology for data collection, development of the constructs,discussion of the analysis, and presentation of the results of the empir-ical study. Next, the effects of the antecedent variables on each level ofdelegation are presented. Finally, the contributions and limitations of the study are highlighted, and directions for future research are offered.

MODEL DEVELOPMENT

Solomon (1986) defines a surrogate as “an agent retained by a consumerto guide, direct, and/or transact marketplace activities” (p. 208). Surro-gates differ from opinion leaders in that they are typically more spe-cialized in a purchase category, more rigorous in their research, and more

DECISION DELEGATIONPsychology & Marketing DOI: 10.1002/mar

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formal in their relationship with their clients (Aggarwal & Cha, 1997).In deciding whether to delegate a purchase decision task to an agent or a surrogate, consumers make a trade-off between simplifying an otherwise laborious task by delegating the task to someone else or facing the consequence of reaching a sub-optimal decision outcome (Payne,Bettman, & Johnson, 1993). Thus, factors that lead a consumer to believethat decision quality will be compromised by delegation should hinder thelikelihood of delegation. Conversely, factors that assure the consumer thatthe decision quality will not be seriously compromised (or may actuallybe enhanced through delegation) should promote delegation.

Factors Impeding Delegation

Several factors may limit a consumer’s willingness to delegate. First, bydelegating, the consumer loses control over the process of decision mak-ing and thereby control over the expected decision quality. To the extentthat a person values retaining control over the decision process, s/hewould be less likely to delegate the decision making to someone else.Second, by delegating, the consumer gives up the opportunity to learnchoice-relevant information, which s/he can later use in a similar choicecontext. The greater the perceived loss of opportunity to learn, the lesslikely is the individual to delegate the decision making.

Need for Control. Delegating a decision task involves giving up controlover one’s own decisions and entrusting the decision to someone else.While Namasivayam (2004) has argued that people generally desire con-trol in service transactions, Burger and Cooper (1979) have proposedthat the desire for control over their environment will differ among indi-viduals. De Rijk, Le Blanc, Schaufeli, and De Jonge (1998) have shownthat control over one’s situation is valued more by those who have agreater need for control. The desire to retain control or autonomy maydiscourage a person from seeking recommendation from a surrogate(Steenkamp, Hofstede, & Wedel, 1999). Therefore, consumers with a highneed for control are less likely to delegate than those with a lower needfor control.

H1: The greater the consumer’s need for control over a purchase deci-sion, the lesser the extent of decision delegation.

Return on Effort. By engaging in the decision process, a consumeracquires knowledge from the decision-making experience that can thenbe used for future purchase decisions. When a consumer delegates a deci-sion, s/he gives up this learning opportunity. Thus, the likelihood of del-egation depends on the frequency and repeatability of the purchase task(Punj & Stewart, 1983). Frequent decisions requiring similar informa-tional inputs may discourage delegation because the consumer may find


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the investment of effort in performing a decision task him/herself worth-while. On the other hand, infrequent or one-time purchase decisions, ora decision in which previously acquired knowledge quickly becomes obso-lete, may lead to delegating the decision to a surrogate (Ganesan, 1994).

H2: The greater the expected return on consumer effort invested in making a purchase decision, the lesser the extent of decisiondelegation.

Factors Encouraging Delegation

The likelihood of delegation increases when a consumer is assured of anacceptable level of decision quality. This assessment is positively influ-enced when (1) the surrogate is perceived as possessing the expertisefor the task, (2) the surrogate is willing and capable of customizing therecommendation for the consumer (as opposed to offering a standardsolution for all), (3) the surrogate is perceived as trustworthy, and finally,(4) the surrogate is perceived as accountable for the decision quality.

Perceived Expertise Difference. Witt and Bruce (1972) have demon-strated that the degree of interpersonal influence depends on, amongother factors, the perceived expertise of the other group members. Sim-ilarly, Childers and Rao (1992) suggest that the presumed expertise of thereferent will affect information-based influence for uncertain consumers.Cross and Borgatti (2004) have shown that in a problem-solving context,people tend to seek advice from others who they believe have criticalexpertise relevant to the decision task. In an experiment, Yaniv (2004)has shown that there is a positive relationship between subjects’ ownexpertise and the amount of discounting they do of the advice they receivefrom others. These findings indicate that as the difference in perceivedexpertise between the surrogate and the consumer increases, consumersare likely to delegate more.

H3: The greater the perceived expertise of the surrogate relative to thatof the consumer, the greater the extent of decision delegation.

Customization. A key difference between the recommendation from asurrogate and that from an impersonal source (e.g., Consumer Reportsor Shopper’s Guide) is that the recommendation from an impersonalsource is typically not customized for a specific customer. A surrogate, onthe other hand, can interact with customers, understand their prefer-ences, and make customized recommendations that best satisfy theirneeds and constraints. Through customization, a surrogate can makerecommendations that are “self-relevant” (Bargh, 1984) to consumers,thereby potentially increasing their satisfaction with the recommenda-tion (Suprenant & Solomon, 1987). In the context of real estate dealings,Roulac (1999) argues that it is not just the content-specific attribute


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knowledge that matters to a consumer, but also the customized solutionsto clients’ problems that are valued more than the canned or “off-the-shelf” solutions. Cross and Borgatti (2004) have shown that people tendto seek advice from experts who make an effort to listen to and inquireinto their problems in order to generate unique solutions. As custom-ization helps take into account unique or uncontrollable factors (Huffman & Cain, 2000), it signals to the consumer that a surrogate has engaged in deliberative decision making instead of offering a “cookie-cutter” solution.

H4: The greater the expectation of receiving a customized recom-mendation from a surrogate, the greater the extent of decisiondelegation.

Accountability. Because the reliance on a surrogate makes the con-sumer vulnerable, the consumer would like to protect his/her interestwhen delegating. For instance, the consumer would require some surro-gate accountability that provides a recourse for nonperformance. A sur-rogate may be made accountable by the consumer’s withholding a partof the monetary compensation, discontinuing repeat patronage, or sim-ply by engaging in actions that impair the surrogate’s social or institu-tional reputation. Increased accountability on the part of the surrogateis likely to raise the effort level of the surrogate and/or lower the con-sumer’s cost of monitoring the performance of the surrogate. Either way,greater accountability should increase the extent of delegation.

H5: The greater the perceived accountability of the surrogate for his/herrecommendations, the greater the extent of decision delegation.

Trustworthiness. Researchers have frequently emphasized the impor-tance of trust and trustworthiness in dyadic exchange relationships (e.g.,Homburg & Stock, 2005; Sargeant & Lee, 2004; Shumaila, Pallister, & Foxall, 2005). A consumer may not always be able to monitor whether thesurrogate has performed his decision task with due diligence or whetherthe consumer is being taken advantage of. This is particularly true in sit-uations where the knowledge gap between the two is significant. Becauseof this knowledge gap, a surrogate may be able to take advantage of theconsumer’s ignorance. It has been shown that consumers dislike beingtreated in this fashion (Cialdini, 1999). In order to avoid such conflicts,consumers are likely to seek surrogates they consider trustworthy. Ithas been shown, for example, that software executives are more likely toseek advice from those who are trustworthy (McGrath, Vance, & Gray,2003). Thus, consumers are more likely to delegate to surrogates whothey believe are trustworthy.

H6: The greater the perceived trustworthiness of the surrogate, thegreater the extent of decision delegation


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The Mediating Role of Perceived Trustworthiness. Perceived trust-worthiness has a unique role in the proposed model. As noted above,trustworthiness was expected to have a direct and positive effect on deci-sion delegation. In addition, it was expected that perceived trustworthi-ness would in turn be affected by factors that influence risk perceptionin a decision situation. The reasoning for this expectation is as follows.Trustworthiness is a function of “confident positive expectations” in “sit-uations entailing risk.” Thus, the factors that enhance or mitigate the riskinvolved can also potentially affect trustworthiness. For instance, a rep-utable expert can help reduce the risk involved in making a purchase andwill therefore be perceived as more trustworthy because of that. Thishas been referred to in the literature as competence-based trust (Mayer,Davis, & Schoorman, 1995). A person with greater expertise than theconsumer is viewed as someone with a greater ability to fulfill the needsof the consumer (Doney & Cannon, 1997), which in turn builds trust.Higher confidence in a person’s expertise leads to a higher rating of theirtrustworthiness (Sniezek & Van Swol, 2001). Therefore, one would expectthat the greater the difference in perceived expertise between the con-sumer and the surrogate, the more trustworthy the surrogate will beperceived.1

Likewise, increased accountability of the surrogate may inspire thetrust of the consumer. When a surrogate is contractually or sociallyaccountable, s/he is likely to align his/her goals and objectives with thoseof the consumer. This alignment may in turn increase the perception oftrustworthiness of the surrogate. Just as manufacturer warrantiesenhance consumers’ perceptions of a product’s reliability, surrogates’accountability can enhance their perceived trustworthiness by signal-ing to the market that they stand behind their recommendations, therebyalleviating perceived risk. Accountability creates a disincentive for cheat-ing and may contribute to perceived trustworthiness by enhancing whathas been referred to as “deterrence-based trust.”

Finally, if surrogates custom-design their recommendations and workone-on-one with a customer, they are more likely to be trusted by thecustomer (Moorman, Deshpande, & Zaltman, 1993). Research on trust hasshown that information sharing between two parties can help build trustbetween them (Butler, 1999). Enhanced interaction between the partiescan help increase the transparency of the recommendation-creationprocess. For example, McGrath, Vance, and Gray (2003) have shown that the availability of “cue-rich” communication media (such as a


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1 Although it is possible that expertise difference might lead to mistrust in some unique situations(e.g., where the surrogate has an incentive to work against you), the authors believe that higher lev-els of surrogate expertise (relative to that of the delegator) should, in general, result in highertrustworthiness of the surrogate. This draws partially from the “capability process” view of trustproposed by Doney and Cannon (1997) where expertise can be viewed as contributing to the sur-rogate’s ability to “meet its obligation” of providing quality recommendation. This is also consistentwith Johnson’s (1999) view of “competence” based trust where competence includes “credentials,command of information, the judged entity’s experience (as estimated by the judger of trust), pro-cedural efficiency, and performance” (p. 328).

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face-to-face conversation or video conferencing) allows for greater under-standing and trust building. Customizing requires elaborate exchangesbetween the surrogate and the customer, which convert the consumerfrom a passive recipient of a recommendation to one who has an activerole in the process. Customizing can also make the surrogate more empa-thetic to the consumer’s needs and constraints. Thus, one would expecta positive relationship between recommendation customization and thetrustworthiness of a surrogate.

The mediating role of perceived surrogate trustworthiness is sum-marized in the following process hypotheses (PH):

PH1: The greater the difference in perceived expertise between the con-sumer and the surrogate, the greater the perceived trustworthi-ness of the surrogate.

PH2: The greater the expectation of receiving a customized recom-mendation from a surrogate, the greater the perceived trustwor-thiness of the surrogate.

PH3: The greater the perceived accountability of the surrogate, thegreater the perceived trustworthiness of the surrogate.

A path model depicting the proposed relationships is presented in Figure 1.

METHODOLOGY

Sample Product and Population

For the purposes of this study, it was desirable to choose a product cat-egory that would be conducive to decision delegation. Price and Feick(1984) suggest that the propensity to use an expert is likely to be highfor those decisions for which the “costs of a poor decision are high andsearch costs are also high” (p. 253). Olshavsky and Granbois (1979) havealso suggested that functional products are more relevant than symbolicor hedonic products for search-based choices. Personal computers wereselected as a product category for this study.

Faculty members at a major U.S. university were chosen as respon-dents. All schools and colleges and every department within them wereincluded in the sample. Many faculty members at this university hadpurchased a personal computer in the past several months or were in theprocess of purchasing one at the time of the survey (the time frame fordata collection was 1997–98). Purchases were made using both personaland university funds. Subjects were asked to respond to survey ques-tions for their most recent purchase of a personal computer. Most sub-jects had made their most recent computer purchase within the last two


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years. The faculty members had access to computer consultants whowere responsible for providing assistance and recommendations to anyfaculty member interested in buying personal computers. These con-sultants were paid employees of the university. Besides helping facultybuy computers, these consultants also helped them with their varioustechnology needs. Faculty members didn’t have to pay these consultantsfor their help. Where a faculty member used multiple consultants, s/hewas asked to select the consultant who influenced the purchase decisionthe most.

The “Delegation” Construct and Its Measurement

The existing literature on delegation views the delegation task as abinary outcome task—i.e., consumers either delegate the task or theydo not, and when they delegate, they do not themselves engage in any partof the decision making at all. However, Price, Feick, and Guskey (1995)discuss the role of “market helpers,” who “serve as a comprehensive


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DecisionDelegation

Return on Effort

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Expertise Difference

Customization Accountability

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Figure 1. Model of decision delegation.

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decision aid for the consumer” (pp. 256–257) and can help the consumerseither with the entire decision or with recommendations for differentparts of it. Rosen and Olshavsky (1987) identify two decision strate-gies that state the role of recommendations for two intermediate stepsin the decision-making process: (1) recommendation-forms-standard,where recommendations are made for decision criteria, and (2) recommendation-forms-evoked-set, where recommendations are madeto narrow down a set of alternatives for consideration. Drawing fromthese two studies, the following components of decision delegation are proposed:

Attribute Set Delegation. Consider, for example, the case of a personinterested in buying a personal computer primarily for processing largevideo files. Assume that this person is not very knowledgeable about thefeatures (such as the importance of Video RAM, video card capabilities,etc.) that influence video processing speed and quality of a personal com-puter. Given her needs, the consumer may delegate the task of identify-ing salient attributes to a surrogate. The delegation happens at theattribute set level, and is called attribute set delegation (ASD) in thisstudy. Note here that the consumer retains the option of comparing alter-natives and making the final choice on her own, even though she hasdelegated the “attribute set” part of the decision.

Choice Set Delegation. A consumer may also ask a surrogate to nar-row a choice set for him. Often, a consumer may not be aware of all hisoptions. This is particularly true of those situations where the purchaseis infrequent or the alternatives are not marketed aggressively. Addi-tionally, a consumer may be interested in weeding out the weaker alter-natives to avoid making the mistake of choosing an inferior alternative.The consumer therefore asks others who have experience or expertise inthat product category. This type of delegation is called choice set delegation(CSD) in this study. Again, as with attribute set delegation, the consumerstill retains control over the final choice.

Final Choice Delegation. In the context of product choice, a consumermay ask the surrogate to make the final choice as well. For example, aperson may short-list a set of alternatives based on some predeterminedcriteria, and then seek a surrogate’s help in selecting the best optionfrom the list. Delegation at this level is called final choice delegation(FCD) in this study. Note that delegating the final choice to a surrogatedoes not necessarily mean delegating the other levels of the process aswell. It is possible that a person may do all the groundwork, decide eval-uation criteria, compare alternatives, and narrow a list of acceptablealternatives before seeking an expert’s help in picking the final choice.

To obtain a measure of the dependent variable (i.e., extent of delegation),the respondents were asked one question each for the three components


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of delegation: (1) ASD: extent of using a consultant in obtaining recom-mendations about attributes (using a 1 to 10 scale, where 1 � did notuse at all and 10 � used to a great extent), (2) CSD: extent of using a consultant in narrowing down the number of alternatives (using a 1 to10 scale, where 1 � did not use at all and 10 � used to a great extent),and (3) FCD: extent of using a consultant in choosing the alternativethat you purchased (using a 1 to 10 scale, where 1 � did not use at all and10 � used to a great extent). The inter-item correlations among theseitems were all statistically significant and they all loaded on one factor(factor loadings—ASD: 0.82; CSD: 0.92; FCD: 0.86; variance explained:75.5%). The responses to these three questions were therefore added toarrive at a measure of decision delegation.2 Note that the three measuresare being treated as formative (and not reflective) indicators of delega-tion (Jarvis, MacKenzie, & Podsakoff, 2003).

Measurement of the Antecedents

For all the antecedent variables, multiple-item Likert-type scales wereused. Where available, scale items from existing research were used (e.g.,Comer, 1984; Ohanian, 1990; Urbany, Dickson, & Wilkie, 1989). For theconstructs that did not have readily available scale items, appropriatescale items were developed using guidelines recommended by Nunnally(1978). First, the domain of the relevant construct was specified. Then,items were drafted to map the construct’s conceptual definition, andfinally, the items were pretested on a convenience sample and revisedaccordingly.

The initial measurement model consisted of twenty six items measuringthe six antecedents used in the model. After an initial exploratory fac-tor analysis, “accountability” was reduced to a single item constructbecause the factor analysis failed to reveal any consistency among thethree initial items used for the construct. Of the remaining twenty threeitems representing the remaining five constructs, two items were deleted.These items failed to load significantly on the designated construct and/orhad low item-to-total correlation.

Table 1 presents statistics on the reliability and internal consistencyof the multiple-item constructs. All but one construct meet or exceed therecommended 0.70 level suggested by Nunnally and Bernstein (1994).Cronbach’s alpha for Need for Control is 0.69. Even this is significantlyhigher than the acceptable alpha of 0.60 suggested by Bagozzi and Yi(1988). All factor loadings were significant (average factor loading �0.76). Confirmatory factor analyses were conducted for each of the con-structs separately to assess convergent validity. As noted in Table 2, themodel was insignificant for three of the five constructs. For the remaining


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2 It is possible that the antecedent variables may impact the three levels differently. The authorslater provide additional component level analysis to examine this possibility.

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two constructs (trustworthiness and need for control), the model was sig-nificant and RMSEA (root mean square error of approximation) slightlyhigher than the recommended 0.08. However, the GFI and CFI for allconstructs were significantly higher than 0.90 (Bagozzi & Yi, 1988). Cor-relations among constructs are shown in Table 2.

Discriminant validity was established using a procedure suggestedby Bagozzi and Phillips (1982) and Anderson (1987). Two-factor confir-matory factor analysis of pairs of constructs was conducted. Each modelwas run twice, once constraining the covariance between the two latentconstructs to unity and once freeing the parameter. A comparison betweenthe constrained and unconstrained models shows that chi-square for theunconstrained model was significantly lower (�x2

(1) � 3.85) for all pairsof latent constructs, thereby establishing discriminant validity. Finally,the average variance extracted for each factor was consistently above0.50 (Fornell & Larcker, 1981).


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Table 1. Internal Consistency and Construct Validity.

Factor Fit statistic a A.V.E Mean SD

Customization 0.74 0.56 5.37 1.23GFI 0.99CFI 0.99RMSEA 0.005x2 (p) 2.02 (0.365)

Need for control 0.69 0.52 4.19 1.41GFI 0.98CFI 0.95RMSEA 0.132x2 (p) 11.5 (0.003)

Return on effort 0.71 0.54 4.13 1.40GFI 0.99CFI 0.99RMSEA 0.041x2 (p) 2.94 (0.230)

Trustworthiness 0.75 0.58 5.06 1.26GFI 0.98CFI 0.97RMSEA 0.13x2 (p) 11.92 (0.003)

Expertise difference 0.88 0.69 5.18 1.48GFI 0.98CFI 0.99RMSEA 0.062x2 (p) 10.26 (0.068)

Accountability Single item construct

Note: a � Cronbach’s Alpha; A.V.E � Average Variance Extracted; GFI � Goodness of Fit Index;CFI � Comparative Fit Index; RMSEA � Root Mean Square Error of Approximation.

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Data Collection and Nonresponse Bias

Based on a mailing list obtained from the university’s Office of HumanResources, approximately 900 surveys were distributed, of which 302completed surveys were returned within the first two weeks. At thispoint, a reminder was sent through email. This resulted in an additional87 surveys being returned. In total, 389 responses (for a response rate of43.2%) were received, of which 347 were usable for data analysis.

To test for nonresponse bias, the data were divided into four quartilesbased on the chronological order in which the responses were received.The means of the first and the fourth quartiles were compared (Armstrong & Overton, 1977). Means for all scale items were statisticallyequivalent for the two groups. Given that the fourth quartile consistedprimarily of those subjects who responded after receiving the reminder,this result is indicative of absence of any serious nonresponse bias inthe sample.

DATA ANALYSIS AND RESULTS

The proposed path analytical model represented in Figure 1 was testedusing Amos software. Averaged scales were used to estimate the model.The overall fit of the model was excellent. The GFI and CFI statisticsfor the model were 0.99.The model had a chi-square value of 2.10 (df � 2),which, as required, is nonsignificant (p � 0.35). The RMSEA statistic,which imposes a penalty for model complexity, also is within the accept-able range (�0.08, Cudeck & Browne, 1983). All hypotheses except onewere supported (see Table 3). The results are discussed below.

It was hypothesized that consumers with greater need for control areless prone to delegate the purchase decision. Thus, even though a sur-rogate can potentially simplify the decision process, consumers with astrong desire to retain control over their decisions will allow decision


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Table 2. Construct Correlation Matrix.

1 2 3 4 5 6 7

1. Customization 12. Need for control .214** 13. Trustworthiness .549** �.393** 14. Return on effort �.129* .422** �.376** 15. Expertise

difference .339** �.514** .645** �.551** 16. Accountability �.041 �.180** .139* �.176** .121* 17. Decision

delegation .377** �.409** .509** �.410** .539** .379** 1

* Correlation is significant at the 0.05 level (2-tailed).** Correlation is significant at the 0.01 level (2-tailed).

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delegation to a lesser extent. The coefficient for need for control is neg-ative and significant, as hypothesized. Thus, H1 is supported.

Because consumers are less likely to delegate a purchase decision tosomeone else when they expect a high return on their purchase efforts,return on effort was hypothesized to be negatively related to decision del-egation. Instead of allowing a surrogate to be involved at every stage ofthe decision process, such consumers would prefer investing time andeffort to gain reasonable proficiency in making the decision on theirown. The coefficient for return on effort was found to be negative. How-ever, it is nonsignificant at p � 0.05 level. Thus, H2 is not supported.(However, please see component level analyses reported later in thispaper.)

The difference in perceived expertise between the surrogate and theconsumer was hypothesized to have a positive impact on the decision todelegate. A consumer is more likely to delegate to a surrogate if that sur-rogate is perceived to have greater expertise (relative to the consumer).As hypothesized in H3, the effect of perceived expertise difference ondecision delegation is positive and statistically significant.

One of the major reasons why a consumer would seek a surrogate’s rec-ommendation (rather than a recommendation appearing in a commer-cial publication) is the opportunity to discuss specific requirements orpreferences and to get a “tailor-made” recommendation. Customizationalso indicates to the consumer that the surrogate is processing infor-mation in order to arrive at a recommendation. Building on this expec-tation, H4 projects a positive relationship between customization anddelegation. This hypothesis is supported by the data.


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Table 3. Model Fit Statistics and Standardized Coefficients.

StandardizedRelationship Hypothesis coefficient

Primary HypothesesNeed for Control S Decision Delegation � �0.12**Return on Effort S Decision Delegation � �0.09* Expertise Difference S Decision Delegation � 0.24**Customization S Decision Delegation � 0.21**Trustworthiness S Decision Delegation � 0.12**Accountability S Decision Delegation � 0.31**

Process HypothesesExpertise Difference S Trustworthiness � 0.50**Customization S Trustworthiness � 0.39**Accountability S Trustworthiness � 0.10**

* p � 0.10** p � 0.05Model Fit: x2 � 2.10 (p � 0.35)

GFI � 0.99CFI � 0.99RMSEA � 0.012

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Before discussing the results concerning accountability, it is usefulto note that the faculty members (i.e., the consumers) did not give directmonetary compensation to the computer surrogates, who were univer-sity employees. Because there was no built-in monitoring system tocheck the quality of recommendations given, it was deemed desirable to ensure that the surrogates felt that they were accountable for theirrecommendations. Open-ended interviews with the surrogates revealedthat they did feel accountable for the advice they gave. First, respondentswere very likely to ask for surrogates’ help in the event something wentwrong with their personal computers. This meant additional timedemands on surrogates’ already busy schedules. Second, given the close-knit structure of the university community, word about “bad” recom-mendations had the potential of spreading very rapidly. This also servedas a motivation for the surrogates to make “well-researched recom-mendations.” As postulated in H5, the coefficient for accountability is positive and significant.

Trustworthiness is expected to have not only a direct effect on decisiondelegation, but also to mediate the effect of other variables in its categoryas well. The coefficient for trustworthiness is positive and significant.Thus, there is support for H6. All three process hypotheses concerningpositive relationship between perceived expertise difference and sur-rogate trustworthiness (PH1), between customization and surrogatetrustworthiness (PH2), and between accountability and surrogate trust-worthiness (PH3) are supported at p � 0.05.

Three Levels of Decision Delegation

Because ASD, CSD, and FCD loaded on a single factor, the unweightedsum of ratings on each component was used as a measure of delegation.The authors had previously recognized the possibility that the antecedentvariables might influence these components differently (discussed infootnote 1). In this section, this possibility is examined, and only thoseresults where there was a demonstrable differential impact of the vari-ables on the different components of delegation are reported.

Separate path models were run for each of the three delegation levelsas the dependent variable. The results were nearly identical to thoseobtained for the composite dependent variable. However, two importantdifferences were found. First, the need for control had a significant effecton decision delegation only at the last level (FCD) and not at the first twolevels. Intuitively, consumers feel most loss of control at the final purchasedecision step. Before this, they feel they are not giving up the controlover the decision-making process. As long as the final purchase decisionis in their hands, delegating the first two stages of attribute set andchoice set delegation can be done even by those who exhibit significantlyhigh need for control. Second, the return on effort had a negative effect ondelegation for the first two levels but did not have a significant effect at


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the last level (FCD). A possible explanation of this finding could be that the more laborious parts of decision making are the first two levels(ASD and CSD), where most of the information gathering and process-ing takes place, and this is where the return on effort negatively affecteddecision delegation. The third step is likely the least laborious step andtherefore independent of return on effort.

DISCUSSION

The focus of this study was to examine the phenomenon of purchase taskdelegation where a consumer assigns a part or all of the purchase deci-sion to an agent, referred to as a surrogate. Although decision delegationis aimed at simplifying a purchase task, it was found that consumersare mindful of the benefits and the costs associated with delegation. Fac-tors that ensure decision quality, accruing from the surrogate’s expert-ise, trustworthiness, accountability, and willingness to customize,encourage delegation. Conversely, perceived loss of control and opportu-nity to learn from experience inhibit delegation. Implications of thesefindings are discussed next.

When Consumers Find Delegation Less Desirable

The results of this study strongly suggest that consumers who like toretain control over the decision-making process tend not to delegate thetask to others. Surrogates are less likely to be involved in the decisionprocess of such individuals. An interesting characteristic of the need forcontrol factor is that it is beyond the influence of a surrogate. Need for control is a fairly enduring psychological trait. Nevertheless, it maybe possible for a surrogate to create a perception of control by frequentlyproviding feedback and involving the delegator at different task points.It was postulated that if the consumer expects to face the decision task


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Table 4. Model Estimation with Component Dependent Models.

Relationship ASD CSD CFD

Primary HypothesesNeed for control S Component delegation 0.002ns �0.05ns �0.18**Return on effort S Component delegation �0.11** �0.13** �0.08ns

Expertise difference S Component delegation 0.29** 0.18** 0.15**Customization S Component delegation 0.22** 0.17** 0.16**Trustworthiness S Component delegation 0.08ns 0.15** 0.08ns

Accountability S Component delegation 0.10** 0.28** 0.31**

** p � 0.05 not significant.Coefficients for endogenous equations do not change.Chi-squares for all models not significant at 0.05.GFIs for all models greater than 0.98.

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repeatedly, she may prefer developing the skill set herself to make a gooddecision instead of having to rely on a surrogate every time. In such sit-uations, the economies of scale, as well as the convenience of not havingto depend on a surrogate every time a decision is made, may overridethe desire to simplify through delegation. However, the effect of “returnon effort” was not statistically significant in this study.

When Consumers Find Delegation Desirable

Of all the factors that were hypothesized to have a direct effect on dele-gation, surrogate accountability had the largest standardized coefficient.Viewed from an agency theory perspective, greater accountability of theagent could potentially lower the monitoring cost for the principal, therebyincreasing delegation. Alternatively, it is possible that if the agent’saccountability is linked to higher financial incentives, the agent mayexert greater effort in the purchase decision, thereby making delegationmore attractive to the consumer.

As expected, the positive difference in the surrogate’s and the con-sumer’s expertise has a strong influence on the extent to which con-sumers involve a surrogate in their decision making. The expertise difference signals to the consumer that the surrogate can perform thedecision task better than the consumer can him/herself. It is useful tonote that it is the difference in expertise and not the absolute level ofexpertise that drives delegation. This finding is also consistent with thefinding in the literature that experts themselves often seek advice ofother experts (Feick, Price, & Higie, 1986). The role of perceived exper-tise difference in delegation has important implications for agentsengaged in providing surrogate services. It is important for them tosend cues to the consumer that signal their expertise and professionalqualifications.

To the extent that a surrogate is believed to be providing a recom-mendation customized to the preferences and constraints of a given con-sumer, it encourages delegation. A positive, statistically significant linkwas observed between customization and the extent of decision delega-tion. This finding suggests that the one-size-fits-all recommendationsare unlikely to promote surrogate usage.

Surrogate Trustworthiness

As noted earlier, trustworthiness of the surrogate not only has a directpositive impact on delegation, but it also mediates the effect of threeother variables (expertise difference, surrogate accountability, and cus-tomization) on delegation. These results suggest that surrogates canimprove their perceived trustworthiness among customers in three ways.First, surrogates who are perceived to have more expertise (compared toconsumers) are also regarded as more trustworthy. Thus, any tangible


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cues that a surrogate can use to demonstrate such expertise should helpimprove his/her trustworthiness perception. Second, surrogates who areseen as more accountable for their recommendations are also viewed asmore trustworthy. Finally, the study’s results indicate that surrogatescan improve their trustworthiness perception by customizing the rec-ommendations they make to their customers.

LIMITATIONS

The limitations of the present study provide opportunities for some inter-esting extensions of our understanding of the phenomenon of delega-tion. The authors envisioned a situation where (1) a preassigned pool ofsurrogates was available to assist a consumer’s purchase decision and (2) consumers obtained recommendations from a single surrogate. Thus,this study addressed neither the phenomenon of the choice of a surrogatenor factors affecting that choice. The study also did not address the scenario in which consumers may seek multiple recommendations froma number of surrogates (e.g., a second opinion) before accepting any rec-ommendation. Both scenarios are practically relevant and worth inves-tigating. In an interesting study, Gershoff, Broniarczyk, and West (2001)show that consumers often select inferior agents because of the short-comings in their assessment of agents’ likelihood of “success” given theirprior track records. Also, the model proposed in this paper applies tomore complex and important decisions, and will need to be modified forroutine or low-involvement decisions.

Second, in this study, consumers did not incur a direct cost for utiliz-ing the surrogate. In many commercial settings, customers make directpayment to consultants for their services. Intuitively, the inclusion of acost to consumers would have had a negative effect on their propensityto delegate. On the other hand, however, compensation paid by the con-sumer to a surrogate could raise the accountability of the surrogate orincrease the degree of interaction between the consumer and the surro-gate. Both outcomes would, in turn, increase the likelihood of delega-tion. These agency type issues are very similar to those addressed inprior literature on agent compensation and should be tested empiricallyin the context of purchase decision delegation.

Third, there are situations in which firms pay the consultant for rec-ommending their products to consumers. For instance, airlines have paidcommissions to Web-based travel intermediaries for directing customersto them. In these situations, the agent may straddle the interests of theconsumer and those of the firm whose product s/he recommends. Han-dling this type of a problem will require a modified theoretical framework.

Fourth, this study utilized single item measures for the three levels ofdelegation. Further development of the constructs, as well as their mea-surement scales, is needed to advance research in this area.


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Finally, an interesting area of investigation would be that of the impactof delegation on postpurchase satisfaction. This study provides some pre-liminary evidence that delegated decisions are generally associated withhigher postpurchase satisfaction. The group of respondents that dele-gated more had a mean satisfaction level of 6.16, whereas the low delega-tion (median split) group’s mean satisfaction level was 5.44 (differencesignificant at p � 0.01). Attribution theory could provide some interest-ing hypotheses linking delegation and postpurchase satisfaction.

CONCLUSION

The incidence of delegating buying tasks goes back to the Middle Ages,when feudal lords sent their agents to Europe to buy luxury goods (Dyer,1989). Today, consumers employ bridal consultants, social caterers, imageconsultants, interior designers, and decorators to help them make purchasedecisions. Consumers have been increasingly employing online agentswho act as consumers’ surrogates and assist them in buying a variety ofproducts including automobiles, airline tickets, mortgages, and insurance.

As consumers get overwhelmed with abundantly available productinformation, delegation of purchase decision to a surrogate can help.Public-policy makers can help empower such consumers by providingthem with access to surrogates who are accountable, knowledgeable, andtrustworthy. Moreover, as the consumer environment becomes more information intensive, it is likely that instead of making product-levelchoices, consumers will be choosing decision makers to make product-level choices for them.

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The authors would like to thank Dr. Rajan Nataraajan and anonymous review-ers for their helpful comments on the earlier drafts of this paper.

Correspondence regarding this article should be sent to: Dr. Praveen Aggarwal,412 Library Drive, Duluth, MN 55812 ([email protected]).


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Decision delegation: A conceptualization and empirical investigation

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