Available online at www.sciencedirect.com

Journal of Consumer Psychology 19 (2009) 124–128

Journal ofCONSUMER

PSYCHOLOGY

Research Dialogue

Knowing your costumer betterThe strength of a self-regulatory value approach

Jens Förster ⁎

Department of Social Psychology, Universiteit van Amsterdam, Roetersstraat 15, 1018 WB Amsterdam, The Netherlands

Available online 9 March 2009

Abstract

Regulatory engagement theory's strength over alternative accounts of consumer attitudes is highlighted. In line with the theory, it is argued thatin order to predict behavior, one needs to understand the processes involved in goal pursuit, including the dynamic processes of value creation.The paper also attempts to integrate processes (e.g. multifinality, distance from the goal, inhibition upon goal fulfillment) known from other classicand modern self-regulation models. Furthermore, the role of difficulty as a basis for value creation is discussed. In sum, regulatory engagementtheory seems to be a rich and thought provoking account to predict people's behavior.

As he walks in the door […] we hear a voice say:STORE VOICE: Hello, Mr. Yakamoto! Welcome back to the Gap. How'd those assorted tank tops work out for you?A lady enters the shop:STORE VOICE: Hey Miss Belfor, did you come back for another pair of those chammy lace ups?

© 2009 Society for Consumer Psychology. Published by Elsevier Inc. All rights reserved.

Introduction

In his movie “Minority Report” Steven Spielberg shares withus his vision on the world in 2054, including how we will betreated as costumers. While at the time the movie was screenedvivid discussions about the future of privacy and data theft cameup, already today it seems that companies exploit knowledgethey have about us in order to wake our appetites or make“recommendations”.

To illustrate, some online bookstores present individualizedsuggestions that pop up upon login. They seem to be partlybased on the information customers give before they sign up formembership (their preferences, demographics, etc.) and onformer sales. Apparently, one algorithm to predict consumers'behavior is based on personality theories à la “tell me what youlike ‘in general’ and we assume that you will like it in thefuture”, while another one is “if you ordered 20 opera CDs over

⁎ Fax: +31 20 6391896.E-mail address: J.A.Forster@uva.nl.

1057-7408/$ - see front matter © 2009 Society for Consumer Psychology. Publishdoi:10.1016/j.jcps.2009.02.004

the last month, we will assume that you will order opera CDs inthe future”. Notably, within those models, a person's personalityis conceptualized as an aggregation of experiences, which I willlabel the “accumulation account”. Other recommendations arebased on customer prototypes that sellers and marketers inferfrom “similar” customers (the “prototype account”: if twopeople bought the same opera, recommend to them other itemsthe respective other person ordered). The problem with this typeof (sometimes rather costly) data collection is that it is likely tobe too unspecific to be successful. More generally, the conceptof personality as a stable, inflexible accumulation of experi-ences has been successfully refuted across psychologicaldisciplines (see Mischel, 1969). Nevertheless, psychologistsmade enormous progress in predicting people's behavior bytaking more flexible concepts into account. In their rich andthought provoking article, Higgins and Scholer (2009)introduce Regulatory Engagement Theory (Higgins, 2006;Higgins & Scholer, 2009), a self-regulatory model that involvesgoals as rather dynamic and inherently context-dependentconcepts. The authors do an excellent job by drawing ourattention to the value process itself, a process that has been

ed by Elsevier Inc. All rights reserved.

1 Zhang, Fishbach, & Kruglanski (2007) convincingly showed that addinggoals to one mean can also decrease the value of the mean. In my example,however, I am focusing on the goal, leaving the mean aside.

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under examined so far. I will first focus on the strength of goalmodels in general, then discuss some specific strengths ofregulatory engagement theory, and finally make an attempt tointegrate some process assumptions known from other goalmodels that were not discussed in the paper.

Goal models

From Kurt Lewin (1951) to Tory Higgins and AbigailScholer (2009) we know that, generally, preferences are basedon both characteristics of our personality and the situation.While the psychology of attitudes has been dominated bycognitive accounts, during the last decade goal theoriesenriched the field, attempting to predict behavior in an evenbetter way (see as an example van Harreveld, van der Pligt & deLiver, in press).

Goals, motives and needs can be chronic and thus it maymake sense to learn more about customer's stable desires.Does this person have an achievement goal or does he aim tobecome a more social person? Is the person concerned withsecurity or does she pursue ideal goals? One could for examplereasonably predict that an achievement motivated personwould be more likely to buy a “how to be successful” book,while a person concerned with security might buy moreinsurances (see Werth & Förster, 2007). On the basis ofregulatory engagement theory one may further argue that aperson with a high achievement goal likes to buy a book in anachievement related manner. For example, she may be theperson that waits in front of Barnes and Noble at 5 a.m. toreceive one of the few signed copies of the “How to succeed”book (or in other words is affected by scarcity if it isrepresented as competition). In order to produce such “fit”effects, sellers and marketers may want to learn more about thefactors contributing to fit, such as, for example, people'sunderlying chronic motives (rather than their attitudes).

A goal analysis however, can never stop with merelyidentifying chronic goals and focusing on their content; ratherone needs to take into account the specific underlying processesof goals pursuit and development. Goals and goal-dependentvalue are inherently dynamic and change systematically withinthe given context. By emphasizing fundamental processes anddynamics caused by goals and self-regulation while peoplepursue goals, regulatory engagement theory offers a broadtheoretical account with many ramifications for both basic andapplied research. Rather than asking people what they havebought last time, Higgins and Scholer (2009) argue that sellershave to understand the psychological processes of goal pursuit:“to be effective, the salesclerk needs to predict the effects ofobstacles and challenges, to understand how individualsexperience and respond to recommendations, and to clearlyidentify the value target.” The strength of the theory is theidentification of factors that contribute to value before it is evendefined by the person. In line with many theories on attitudesthe notion of memory as a storehouse for evaluations is rejected.Rather, in order to predict value and subsequent behavior,factors of both the person and the social environment, plus theirinteraction have to be identified.

There are other factors contributing to value creation thatfollow from different models of goal pursuit and attainment.“Goal systems” theory (Kruglanski, Shah, Friedman, Fishbach,Chun & Sleeth-Keppler, 2002), for instance, includes multiplegoal pursuit and goal shielding, whereas Neo-Lewinianaccounts focus on the dynamics of goal fulfillment (Förster,Liberman & Friedman, 2007, 2009; Liberman & Förster, 2005)and distance to the goal (Liberman & Förster, 2008; Förster &Liberman, in press). I will expand a bit on those models in thenext paragraph and then speculate about integration of diverseaspects of goal dynamics.

The dynamics of goals

According to goal systems theory (Kruglanski et al., 2002),people can pursue multiple goals while engaging in one activity.For example, learning that a chef in a three star restaurant onlyuses fair trade and green products may enhance liking of thedinner because one can attain multiple goals at the same time(e.g., attainment of hedonic and moral goals). A personspending €150 for an opera seat in the front row may notonly do so in order to enjoy the music, but also because shethinks it helps support cultural institutions. Whether or not thissecond goal becomes activated, however, depends on processeswe know from Higgins' earlier work on knowledge activationor “priming” (see Higgins, 1996), which indicate that multi-finality is inherently construed and depends on context. Morerelevantly to the present paper, once more than one goal isactive, multifinality increases the instrumental value of a goal-directed event (e.g., having dinner), because it maximizes thegains from that particular action or object (Thompson, Hamilton& Rust, 2005; Tversky & Kahneman, 2002). To use an analogy,one can “kill two birds with one stone”. Sellers may suggest orhighlight second goals in order to create multifinality. In myexamples, the second, more societal or “moral” goal may inaddition salve one's conscience about splurging, in other wordsit may enhance value by reducing conflict.1

Lewinian theories explore value development as a functionof the distance to the goal (the goal looms larger effect, seeFörster, Higgins & Idson, 1998; Liberman & Förster, 2008;Förster & Liberman, in press), and goal attainment (Zeigarnikeffects, see Zeigarnik, 1927; Förster et al., 2005). To give justone example, a gourmet might be less interested in a fancyrestaurant after he has experienced a great meal the day before,whereas a gourmet who was deprived of such events mightcrave one. Thus, value may increase the closer the person gets tothe goal and it may be inhibited upon goal fulfillment. Notably,such dynamics allow predictions that a person who has bought12 opera CDs during the last week might now feel an urge toplay soccer or to read a book instead.

How can one predict whether the person will engage in thesealternative activities? Both goal systems theory and Lewinianaccounts discuss mechanisms of goal shielding (see Shah,

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Friedman & Kruglanski, 2002; Goschke & Dreisbach, 2008).Liberman and Förster (2005) suggested that highly motivatinggoals are more likely than low motivating goals to suppress (orput “on hold”) even important alternative goals. For example, ahigh motivation to prepare for an exam may suppress theimportant goal of socializing, but a weak motivation would notsuffice to produce such suppression. Therefore, as a rule, thegoal one turns to after completion of a previous goal is likely tobe more important if the previously fulfilled goal was highlymotivating: turning to this important goal next is facilitated bystrong inhibition of the previous goal. Thus, the moremotivating the initial goal was, the more functional it is toinhibit its related constructs after goal fulfillment in order to goback to the important alternative goal that was suppressed.

Thus, one needs to know the important goal the consumer islikely to suppress while listening to opera extensively. Dosimilar mechanisms operate when goals temporarily gain value?One may suggest that the rule of “whenever I pursue animportant goal I may suppress other important goals” mightbecome a heuristic over time. People may infer that a goal theysuppress while pursuing another, important goal is highlyimportant: this suppressed goal may then be highly active uponfulfillment of the formerly focal goal. Since regulatoryengagement theory can predict temporary value change whilepeople pursue goals, such ebbs and flows may lead to somecounterintuitive predictions (see below).

Consequently, which advertisement should be sent or whichrecommendation should be given to our excessive CD buyerclearly depends on the underlying psychological model:whereas a pure “accumulation account” would result in arecommendation for just another opera,2 a recommendationbased on a self-regulatory goal model would try—among otherthings—to identify a different personal goal that was suppo-sedly suppressed while the customer listened to operaextensively. For example, if one knows that a second importantgoal of a costumer is staying healthy, one may recommend anew workout book upon extensive orders of opera CDs.Similarly, knowing that the same person just came back from arelaxing vacation would lead to a recommendation for a trip tothe Amsterdam Muziektheater rather than to a suggestion for aWellness hotel in Switzerland. Those ebbs and flows in people'sinterest are important, and much more research is needed tounderstand the when, why and how long people temporarilylose and gain interest in certain events—to the best of myknowledge there are no studies (and no theories) on time curvesor on duration of inhibition effects upon goal fulfillment. Onestrength of regulatory engagement theory is its compatibilitywith such goal process models, since it is inherently built uponthe dynamic notion of goals, incorporating at the same timeprinciples known from motivation and emotion theories andtheories of construct accessibility (see Higgins, 1996). In thefollowing, I want to discuss such dynamics in relation to thetheory.

2 It is less than clear how a prototype account could explain an opera buff'sinterest in soccer.

Combining aspects of goal theories

Let us go back to a gourmet example, where a chef promoteshis meals by advertising fair trade products and, as seems to be atrend for many European chefs, donates money to charity(homeless shelters, endangered herbs, etc.), or hires challengedjuveniles to help out in the kitchen. From a goal systems analysisthis would fit the multifinality principle and the gourmet mayvalue the dinner even more under these circumstances thanunder ones where no such multiple goals are salient. Regulatoryengagement theory could predict increase of value because theadditional information potentially helps oppose an obstacle,namely feeling guilty about spending somuchmoney for a meal.However, on the other hand, providing the information makesthe process of opposing less difficult—and if difficulty is thedriving factor for strength of engagement, value would decreaserelatively. In short: Is difficulty while opposing or overcoming aproblem the driving factor for strength of engagement, and whatis regulatory engagement theory's prediction in case of multiplesupporting goals? Would value increase or decrease?

If difficulty is the driving factor, this situation coulddisentangle a goal systems account from regulatory engagementtheory, since providing information about multiple goalfulfillment (making opposing the goal easier) would then leadto decrease of value. Obviously, my example includes somemoral conflicts a person might experience in a three starrestaurant, so one may as well argue that approaching a goal in a(morally) proper way (by reasoning that at least the pricyproducts are ecologically correct) may increase subjective value.On the other hand, one may argue that moral concerns do not fithedonic goals, so one may as well predict decrease of value bynon-fit. Ironically, the attempts of a chef promoting a luxuriousbut “moral” environment could then backfire because of non-fit.

I agree that it is unfair to come up with such a convolutedexample, but my question would now be: What do we need toknow in order to predict a person's value change in such asituation? Further, I am wondering whether forces producingvalue are additive. Would value increase if both fit by strategicand proper, moral means work in concert?

I can imagine that in some cases, as discussed by Higginsand Scholer (2009), short-term effects should be disentangledfrom long-term effects. I can imagine that in the long run, therestaurant that makes it easy to oppose the potential splurgeproblem will be visited more frequently, simply because onedoes not want to invest so much effort in dealing with potentialconflicts oneself.

Last but not least, I am wondering whether we can clearlydistinguish regulatory engagement theory from the pureoperation of multifinality principles. For example, one mayargue that a process goal, such as “doing things the proper way”,is a goal in itself: in this way, any situation of fit can be reducedto a situation in which two goals are active at the same time,increasing value.

Neo-Lewinian models may add complexity to regulatoryengagement theory by focusing on distance to the goal.Depending on distance to the goal and goal fulfillment, onemay predict when a recommendation for a pricy restaurant sent

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to a gourmet leads to relative increase (or decrease) in the valueof the restaurant (increase when deprivation is strong and thedistance to the goal is short, decrease upon goal fulfillment).Following the logic of Liberman and Förster (2005) thatimportant goals suppress other important goals, especially for ahighly committed gourmet who at the same time has a moralconflict about spending too much, value for new and pricyrestaurants may decrease upon such visits, while more prosocial(or morally accepted) goals would be highly activated.

While I started this example with a rather chronic conflictbetween two opposing goals, regulatory engagement theorycould also predict that the way the dinner is experiencedcontributes to the value of the restaurant, predicting bothdifferential inhibition effects upon goal fulfillment anddifferential activation of the suppressed goal. For example, asregulatory engagement theory argues, if the value of the dinneris increased by situational factors (e.g., obstacles one opposes,fit, higher likelihood, etc.), one may predict that because ofabsorption in such tasks, shielding of alternative goals (e.g.,making a donation) is even stronger. One may further assumethat because of the general rule of “important goals suppressother important goals” post fulfillment inhibition effects will bestronger, thereby leaving room for the suppressed goals whichwill be activated even more strongly. Thus, it may be quiteefficient to ask for donations upon fulfillment of conflicting,hedonic goals, and it might be even more efficient the more thevalue increased due to situational factors increasing strength ofengagement. I need to highlight that predicting differences inthe likelihood of donating after a fancy dinner (which are twosemantically completely different and unconnected activitydomains) is not trivial at all and such predictions cannot bemade on the basis of prototype and accumulation accounts.Furthermore, according to regulatory engagement theory, valueof the focal goal (and the activation of the suppressed goal), canbe created by external circumstances which are againsemantically unrelated to any of the goals. In other words,depending on whether one knows about people's goals, onecould predict that annoying mobile phones during a fancydinner lead to more generous donations to challenged juvenilesupon leaving the restaurant!

It is further interesting to dig deeper into the to and fro ofgoals when obstacles increase strength of engagement, andwhether it matters if people have to make a final decision orwhether the rejected alternative remains relevant. Higgins andScholer (2009) speculate that “if two attractive options are keptin mind during a difficult decision, for example, it is possible forboth options to increase in attractiveness”. The question is whenthis would be the case. Liberman and Förster (2006) recentlyargued that after the decision is made, value depends on whetherthe rejected alternative remains relevant (e.g., because it may bepursued on another occasion) and thus the decision is not final.When people have to decide between two alternatives and thedecision is final, like in typical post-decisional dissonanceparadigms (you can either choose a pen or a mug), spreadingapart of alternatives is expected (i.e., the dissonance effect).However, if the rejected alternative is still relevant for the future(e.g., if one can pursue it at a future occasion), this may not be

the case. For example a person who wants to buy a sweater andchooses A over B may feel an urge to go back to the store thenext day and may also buy B. It is possible that in non-finaldecisions, the rejected alternative will not be discounted. Suchanalysis is even consistent with general dissonance logic.Specifically, one may suggest that if the rejected alternativeremains relevant after the decision is made, dissonance may alsobe reduced by resolving to pursue the rejected alternative onanother occasion. For example, if a student chose to take acourse in social psychology rather than a course in neurobiol-ogy, she can decide to take the latter course the next semester ortry satisfy her curiosity about neurobiology in another way (e.g.,reading a book about the topic). In that case, promising tooneself to return to the rejected alternative is in fact dissonancereducing and is therefore consistent with the logic of cognitivedissonance. In several experiments, Liberman and Förster(2006) manipulated difficulty or (mis)attributions of difficultyshowing that, in fact, perceived difficulty while making adecision between two desirable alternatives increased the valueof both alternatives compared to when the decision wasexperienced to be easy. This is exactly what Higgins andScholer (2009) predict: while difficulty may cause negativearousal, it nevertheless increases value, not only of the chosenbut also of the rejected alternative. However, this was especiallythe case when participants' decision was not final (when theycould consider the other alternative at a later occasion).

The case of a non-final decision may point to self-regulatorymechanisms at work: It is possible to conceptualize the rejectedalternative as an unfulfilled goal (e.g., I might consider it later)and the chosen alternative as a fulfilled goal (e.g., I got it). Uponfulfillment, the value of the rejected alternative is discounted,however, when the goal is still active, its value is still high inorder to keep or increase motivation. I was wondering whethervalue (with respect to chosen and rejected alternatives) producedby strength of engagement (difficulty or other factors) would alsodepend on goal fulfillment, or more specifically, on the questionof whether a decision has been made or is still up in the air.

Furthermore, one may wonder about the specific processincreasing value while people make a difficult decision. In orderto explain their results, Liberman and Förster suggest inferentialself-perception to be at work. In one study that was conceptuallysimilar to Bem's (1972) original studies in providing a puredemonstration of the inference processes with respect todissonance phenomena, participants had to judge to what extentprotagonists of a story wanted a certain dessert from a menu;whereas one protagonist was described as having difficulties inmaking the decision between two options, the other found thedecision to be easy. Participants judged the two alternatives to bemore positive when they learned the protagonist had difficultiesin making the decision compared to when they learned thedecision was easy. This process could also operate when peopleexperience difficulty themselves; they may think “if it is sodifficult for me to decide between two options then they must bevery close to each other” (see Schwarz; 1998; 2004). Inexperiments using misattribution paradigms (Schwarz et al.,1991), the authors provided evidence for such inferentialmechanisms. For example, in one study participants were

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asked to color a copy of a painting so that another participantcould imagine the actual colors of the drawing. Participants hadto decide between a red and a blue pen, which was a difficultdecision because the original was heavily based on these twocolors. In order to establish different misattribution conditions,aromatic oil was wafted into the room and participants wereeither told that the scent would make decisions difficult or easy.Participants decided for either the red or the blue pen, colored thecopy and were then asked to color a different painting for whichthey now could use all colors. The frequency with whichparticipants used the formerly rejected or the chosen color forcoloring the second painting was the dependent measure ofvalue. As predicted, spreading of alternatives was lowest in the“aroma facilitates decision” condition, compared to the “aromacomplicates decision” condition. In other words, when partici-pants thought that the aroma will make the decision easy(inferring that for them personally, the decision was difficult),both the rejected and the accepted pen were frequently used inthe new painting. However, participants that thought the scentwould make the decision difficult (inferring that for them thedecision was easy) did not use the rejected alternative that often.Altogether, Liberman and Förster (2006), like some other goaltheorists (see Fishbach, 2009) assume inferences processes atwork—while it seems that regulatory engagement theoryincludes the operation of inference processes at work (as isreflected in the second last paragraph). I am wondering whetherthey are different to the ones I suggested or whether other, moredirect, alternative mechanisms could produce such effects;furthermore I wondered whether finality of a decision matters.

In sum, the numerous questions raised in this commentaryare testimony to the richness of regulatory engagement theory. Iam convinced that its breadth and scope will trigger many moreresearch questions, one may formerly not have thought about.

So what do we need to know about our costumers?

Obviously we need to know more about people's goals aswell as the processes and dynamics while people pursue them.We need to know people's motives and chronic goals, and howthey change within the social context. We need to understandthe ebbs and flows of increased and decreased interest, and weneed to know which conflicts people may face by consumingproducts. We need to know what triggers certain goals and howpeople react to recommendations and obstacles to goalattainment. In their thought provoking analysis, Higgins andScholer (2009) make very clear that it is simply not enough tolook at a person as an accumulation of experiences or as aconsumer prototype.

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