From Fear to Loathing? HowEmotion Infiuences the Evaluation
and Eariy Use of innovationsInnovation adoption is rarely a short process for consumers; accordingly, recent research has explored adoption asa dynamic process that is characterized by changing patterns, or diffusion, of consumer use of the innovation. Thisresearch suggests that adoption is rarely a neutral process and that consumers can experience strong emotions inthe initial use of innovations. However, given such emotions, two opposing arguments can be made as to whetherthe inclusion of emotional responses increases the predictive power of traditional models of diffusion. On the onehand, experienced emotion may simply be a function of gained benefits and. as such, may already be captured inextant models through cognitive assessments of net benefits. On the other hand, and as data from two empiricaland longitudinal studies demonstrate, the learning process is potentially emotion generating (independent of netbenefits), and this emotion colors product evaluations. The emotional influence is sizable and, importantly, not astraightforward case of "easier is better." In this work, the authors present the E3 (expectation ~> emotion -^evaluation) model, which describes how managers can better predict and influence the successful diffusion ofcomplex technological products.
As technological innovations reach the consumer mar-ket at an unprecedented pace, recent reports havehighlighted Ihe influence of consumers" expecta-
tions of and actual experiences in using a product on a prod-uct's ultimate success. For example, perceptions of usagedifficulty have caused a significant number of consumers todelay purchases (e.g., 48% of potential digital camera buy-ers), and actual usage difficulty has caused them to retumpreviously purchased products (e.g., 30% of all home net-working products: Endt 2004, p. E8). Epitomizing a com-pany caught by these trends. Royal Philips Electronics,which has suffered as a result of consumers' rejection ofcomplex new products, recorded huge losses in 2000 and2001 and reduced its workforce by 55,000 employees (TheEconomi.st 2004). To understand tbe scope of the problem.Chief Executive Officer Gerard Kleisterlee setit 100 Philipsmanagers home one weekend wilb aji assignment to getvarious Philips gadgets lo operate. Many failed and"retumed frustrated and some even angry," wbereas
Stacy L. Wood is Associate Professor of Marketing. Moore School of Busi-ness, University of South Carolina (e-mail: [email protected]). C. PageMoreau is Assistant Professor ot Marketing, Leeds School of Business,University ot Colorado, Boulder (e-mail: [email protected]).The authors thank Bill Bearden, Meg Campbell, Bill Dillon. Paul Herr,Amna Kirmani. Don Lehmann. and John Lynch for their comments andassistance in all phases of this research, and they thank Jen Dale for herresearch assistance. They also gratefully acknowledge research fundingfrom the Leeds School of Business and the Moore School of Businessand the insightful comments of the three anonymous JM reviewers. Bothauthors contributed equally to this work.
To read or contribute to reader and author dialogue on this article, visithttp://www. tnarketingpower.com/jmt^.
"anotber group that succeeded returned quite proud" (Endt2004, p. E8). These managers were struck by tbe strengtb oftbeir own response to product complexity in use. but wenote the emotional nature of the managers' responses.
As tbis example and our own experiences sbow. learn-ing to use a new product can evoke an emotional response,independent of the emotions produced by the attributes orbenefits of tbe product itself. Tbe goal of this researcb is tounderstand the underlying causes of this type of emotionalresponse and study its ramifications lor complex productinnovations.' Altbougb tbe majority of prior innovationresearch has focused on a point of adoption, tbe field basobserved recent calls for more process-oriented research(e.g.. Golder and Tellis 1998; Rogers 1996) to look beyondpurchase and address how early use afiecls ultimate adop-tion (Shih and Venkalesh 2004). This usage focus is increas-ingly important as managers strive to understand why someconsumers fail to keep, or to keep using, new products. Tobetter understand tbis consumer-centric process of innova-tion, we develop the E^ (expectation —> emotion —> evalua-tion) model of emotional influence, test Ibe model in twostudies, and describe how managers can use tbe fmdings tohelp predict and influence new product .success.
lAtthtnigh many of the 4(XX) diffusion articles and books pub-lished to date have demonstrated that perceived produci complex-ity causes consumers to delay or avoid the purchase of a new prod-uct, fewer Ihan \% have examined the influence of perceivedcomplexity on pttstadopiion behavior (Rogers 1996. p. 91). Thissparse attention is .surprising, given that consumers' iniiial usageexperiences are complex, especially for tectinological products(Mick and Foumier 1998). and are likely to influence productretum rate, future brand loyalty, word-of-m<>uth communications,and desire to purchase other technologies.
It has been well established that nev product success can bereliably predicted by an evaluation of the product's benefits,costs, and relative advantage over competing alternatives(Moreau, Lehmann, and Markman 2001; Rogers 1996). InRogers's (1996) classic work on diffusion, he also identifiesthe innovation's "complexity" (i.e., the perceived magni-tude of the learning costs required to achieve its benefits) asan indicator of an innovation's success. Complexity is typi-cally indicative of slower diffusion rates (Rogers 1996) andcan even create disutility through "feature fatigue" (Thomp-son. Hamilton, and Rust 2005). In the formative period oftrial and early use of new. complex products, consumerslearn how to achieve promised benefits. Rogers calls this"how-to" knowledge and acknowledges that it has beenoverlooked to date as a key factor in diffusion success.However, interest in use is growing; to wit, Shih andVenkatesh (2004) recently identified "usage diffusion" as animportant conceptualization to understand better when con-sumers will fully integrate a newly adopted innovation intotheir lives and thus create a strong foundation for the inno-vation's current profitability and future growth.
Simply bringing consumers to the point of trying a newinnovation, let alone establishing regular use, is a difficultprocess (Mcuter et al. 2005). Thus, if firms expend greateffort to bring consumers to the point of trial, it seemsimportant that firms then act to encourage a trial environ-ment that is conducive to positive evaluation. Our researchdemonstrates how consumers' emotions play an influentialrole in early usage environments. We show that a significantproportion of these emotions results from the disconfirma-tion of consumer expectations about ease of use, implyingthat a firm may either predict or influence the consumer'semotional experience. Perhaps the most significant contri-bution of this research is the demonstration that emotionsexperienced in early use of the new product are not simplyreflective of achieved benefits (or risks) but result directlyfrom leaming efforts. These emotions, both immediately
and over time, significantly influence product evaluations,even after we account for the traditional diffusionindicators.
The E3 Model of Emotional InfluenceWe present a five-step conceptual model that describes howemotion intluences the evaluation of complex innovations.This model fits well within the traditional models of diffu-sion but is unique in its focus on the important how-toperiod when consumers first leam to use a new product. Thefive-step E- model appears in Figure I. The core steps thatdescribe consumers' experience of early u.se and the theo-retical process by which emotions may influence diffusion(Steps 2, 3, and 4) are foundational to our theoretical contri-bution. The model also addresses how firms can eitherpredict or influence the process through relevant andactionable antecedents (Step 1) and whether relativelyephemeral emotions can affect more lasting evaluations(Step 5).
Steps 1 and 2: Complexity Expectations and Ttieir(Dis)confirmationIn general, expectations matter. In marketing, the power ofexpectations is demonstrated in satisfaction research (e.g.,Oliver and Swan 1989); thus, we consider the types ofexpectations that may be particularly influential in the earlystages of new product learning. We posit that complexityexpectations (e.g., "How difficult will it be to begin usingthis?") are critical for predicting both emotions and productevaluations.
Defining complexity expectations. We define "complex-ity expectations" as a priori predictions about ease of use,learning time, and learning difficulty. Theoretically, andakin to gap models of satisfaction, this conceptualizationimplies that a new product experience may be challengingbut not necessarily disappointing if difficulty is expected.
This raises two issues that require careful conceptualdistinction. First, although the satisfaction literature empha-sizes the importance of expectations, these are expectations
FIGURE 1The E3 Model of the Influence of Complexity Expectations on Innovation Evaluation and Diffusion
Updating Expectations
Step 1:Complexity
expectations
Step 2:(Dis)confirmed
expectationsH2
Step 3:Experienced
emotionsH3
Step 4:innovationevaluation
H4a. b
Step 5;Usage
diffusionH4C
Awareness Eariy Use of Innovation (Trial) Adoption
Dynamic Innovation Diffusion
Emotion and the Evaiuation and Early Use of Innovations / 45
for the products' promised net benefits (e.g., Cadotte,Woodruff, and Jenkins 1987) and thus are captured in exist-ing diffusion modeis through Rogers*s (1996) long-standinginclusion of nel benefits. This work is not a mere replicationof previous satisfaction research. Here, we address con-sumers' expectations not for the product's ultimate benefitsbut for its complexity in leaming. ln other words, we focuson what consumers expect during their early usage experi-ence (e.g., the rate at which they can achieve those benefits,the ease of leaming, and their own affective response), andwe posii that this is not a trivial construct. Thus, we investi-gate the potential for complexity expectations to influenceproduct evaluation beyond the strong influence of theexpected (and perceived) benefits and costs of the product.As we noted previously, extant diffusion models alreadysuccessfully predict responses to new products (e.g.,Moreau, Lehmann, and Markman 2001; Olshavsky andSpreng 19%), and the demonstration of any additive influ-ence due to consumers' emotional response to initial usagecomplexity would have broad ramifications for new productresearch.
Second, complexity expectations should be moreimpactful when products are innovative than when they arenew additions to well-understood product categories.^ Forsome new products (e.g., a new orange juice), there is littleuncertainty abtiut the difficulty of use (Phillips and Baum-gartner 2002). However, in cases in which the productrequires adaptation or leaming (e.g.. Palm Pilots), usageuncertainty abounds (Oliver and Winer 1987). Thus, com-plexity expectations are likely to be influential only if prod-ucts are both new and relatively complex. This leads towhat is perhaps a marketplace paradox; The emotionalexperience of initial use is more influential for technologi-cal or functional innovations (e.g., a computer program, aGlobal Positioning System in an automobile) than for sim-pie experiential or aesthetic products (e.g.. a book, a mu.sicCD, a candle), which are typically associated with (anddesigned to elicit) an emotional response. However, thisparadox is not so counterintuitive in light of the notion thatthe emotional response we investigate herein is that whicharises from leaming, not that which is tied directly to prod-uct attributes or benefits.
How are complexity expectations formed? For fimis tobe able to predict or influence consumers' complexityexpectations, they must understand how such expectationsare formed. As with attitudes and preferences, we believethat complexity expectations are often constructed ratherIhan retrieved, using both intemal and extemal inputs—namely. (1) consumers" relevant prior knowledge. (2) theobservable experiences of others, and (3) communicationsfrom marketers (Foumier and Mick 1999; Oliver and Winer1987; Shih and Venkatesh 2004). Although complexity
II can also be argued Ihat benefit-oriented expectations becomemore complex with new product categories, and though this topicis still an imponant area for further research, it is addressed in thework of Meyers-Levy and Tybout (1989). Moreau. Lehmann, andMarkman (2(X)I). and Hoeffler and Ariely (1999).
expectations may arise from several different factors, wefocus on two relevant factors: product usage informationprovided by the retailer (a marketer action) and consumers'prior expertise (a latent consumer characteristic). Accordingto Oliver and Winer (1987), both of these types of informa-tion are hypothesized to influence the formation of expecta-tions. Notably, these two factors may also interact so that anexpert consumer may be difterentially affected by marketer/retailer-provided information compared with a noviceconsumer.
Complexity expectations may arise from marketer com-munications. We have discussed various conceptual waysthat expectations can affect product evaluations. In manypractical ways, managers can influence complexity expecta-tions, such as when they show a product being used in com-mercials, describe usage instructions in online formats, oruse other high-involvement sales channels (e.g.. sales repre-sentatives, infomercials). A tactic that marketers increas-ingly employ is the in-store product demonstration (RapheJand Raphel 1994). Product demonstrations reduce the con-sumer's perceived uncertainty before purchase and havebecome an important marketing-mix variable in the auto-mobile and computer industries (Heiman and MuIIer 1996)and in business-to-business domains (Gopalakrishna el al.1995). Product demonstrations can be both benefit oriented(e.g., let consumers gain information about experienceattributes) and process oriented (e.g., teach consumers howto set up or use a product feature). Heiman and MulJer(1996) suggest that the benefit-oriented value of demonstra-tions should be either positive or negative depending onsituational characteristics (e.g.. the type of product [or prod-uct attribute] and its inherent risk). Although Heiman andMuIIer do not address postpurchase evaluation and satisfac-tion, they show strong empirical evidence that the efficacyof demonstrations on purchase probabilities (they focus onbenefit-oriented demonstrations) varies with consumers'product knowledge. Thus, it is easy to argue that theprocess-oriented value of demonstrations similarly dependson situational characteristics: in this case, the value of thedemonstration depends on whether consumers set high orlow expectations for their own difficulty in leaming aboutand using the product.-^ In line with Heiman and Muller'sfmdings, a consumer's expertise may be an important factorin the expectations he or she sets both before and after thedemonstration.
Thus, we posit that consumers' product category exper-tise affects both initial complexity expectations and theextent to which these expectations are influenced by prod-uct demonstrations. Research in expertise suggests thelikely pattem of this influence. Specifically, novices maynot be able to imagine what tbey do not know (Kniger andDunning 1999) and thus may be more positive when there isno demonstration. When a demonstration is provided for anunfamiliar product, the product demonstration may increaseboth the specificity and the negativity of the novices' expec-tations (McGill and Iacobucci 1992). Effectively, tbe
thank an anonymous reviewer for articulating this point.
46 / Journal of Marketing, July 2006
demnnstration may make the technological intricaciesrequired to master the new product highly salient, increas-ing the novices' expectations (perhaps unrealistically) fordifficulty of use. Conversely, expert consumers oftenapproach new product use confidently (Alba and Hutchin-son 2000). Because they rely heavily on their own priorexperiences, experts often pay little attention to any usageinformation the firm provides (Wood and Lynch 2002). For-mally, we predict the following:
H|: Product demonstration (a) increases novice consumers'expectations that the product will be difficult to use but (b)does not affect expert consumers' expectations.
We predict that demonstrations will not influenceexperts' expectations, because experts base their expecta-tions largely on infomiation stored in long-term memory,the ambiguity of which has been discounted (Oliver andWiner 1987). Such discounting diminishes the biasness ofthe expectation, and thus disconfirmation is less likely tooccur Conversely, novices are more likely to hold biasedexpectations, which can be disconfirmed in one of twoways: (I) Those exposed to a demonstration could experi-ence positive disconfirmation ("better than expected") atfirst use, and (2) those not exposed to a demonstration couldexperience negative disconfirmation ("worse thanexpected"). Formally,
H2: (a) Novices who experience a product demonstrationreport more positive disconfirmation of complexityexpectations (better-than-expected ease of use) after firstuse i)f the innovation than novices who do not experiencea demonstration. wherea.s (b) product demonstration doesnot affect expert consumers' disconfirmation of complex-ity expectations.
Step 3: The Influence of Disconfirmation onExperienced EmotionHowever, it would be hasty (and possibly ill-advised) toassume that support for H] and Hi suggests that in-storeproduct demonstrations are a bad idea, having no influenceon experts and a negative influence on novices. Because ofthe relationship between disconfirmation of expectationsand emotion, the potential negative intluence of productdemonstrations on novices may ultimately lead to morepositive results. By depressing expectations of an easyexperience, novices who receive a demonstration may sub-sequently be positively surprised by the actual usage experi-ence, seeing it as "not so bad after all," a situation that cancreate positive etnotions. The E- model suggests that con-sumers who experience different disconfirmation of theirusage expectations have different emotional responses.
Why do consumers experience emotions in using com-plex products? V^hen people are confronted with a learningtask, they develop goals and then monitor progress towardthose goals. Emotions are mechanisms that communicateimportant infomiation relative to expected progress towardgoal achievement (Bagozzi, Gopinath, and Nyer 1999;Luce. Bettman. and Payne 2001); as such, they are likely tooccur spontaneously when consumers first use a new. com-plex product. Specifically, negative emotions occur when
expected progress toward the activated goal is impeded, andpositive emotions occur when expected progress toward theactivated goal is accelerated or when the goal is attained(Carver and Scheier 1990; Luce, Bettman. and Payne 2001).
Although consumption emotions can be driven byactual product performance (Oliver 1993; Phillips andBaumgartner 2002), research also suggests that consump-tion emotions are a function of disconfirmation of expecta-tions (Westbrook and Oliver 1991). In the introduction ofrelatively noninnovative new products (e.g., batteries, hotels(Rust et al. 1999; Voss, Parasuraman. and Grewal 19981) orin domains in which consumers have experience (e.g.. cur-rent subscribers of an interactive television entertainmentservice [Bolton and Lemon 19991), consumers may be ableto generate accurate usage expectations, and disconfirma-tion is not likely to occur. Although uncertainty remainsabout the quality of the consumption experiences in thesecases, the consumer has little uncertainty about the diffi-culty in consuming the product. These situations can becontrasted with those in which consumers are activelylearning the new category (e.g., working with new self-service technologies [Meuter et ai 20051. participating incoproduction |Bendapudi and Leone 2003]) in which realgaps are likely to exist between consumers' expectations ofthe ease/difficulty of goal attainment and their actualexperience. Appraisal of progress, especially given unex-pected difficulty or ease, typically generates an emotionalresponse (Smith and Ellsworth 1985). Thus, we propose thefollowing:
H3: Positive disconfirmation of complexity expectations iscorrelated with positive emotion during use. and negativedisconfirmation is associated with negative emotion dur-ing use.
Step 4: Influence of Emotion on the Evaluation ofinnovative Products
Can emotions affect product evaluations? It could bereasonably argued that emotions that arise from "tempo-rary" learning or trial periods are unlikely to intluence morelong-lasting product evaluations. However, recent researchin emotion shows that affective influence is often strongerand more far-reaching than previously considered (e.g.,Bagozzi, Gopinath, and Nyer 1999). Several recent articleshave shown the significant link between consumption emo-tions and satisfaction (Oliver 1993: Phillips and Baumgart-ner 2002); most empirical findings indicate that both posi-tive and negative emotions influence satisfaction in theirrespective directions (Schwarz and Clore 1983). Yet demon-strations of emotional influence on product evaluation havebeen mixed. In a longitudinal study. Dube and Morgan(1996) demonstrate that retrospective measures of emotiondo not influence product—or, in their case, service—evaluation.
Although we posit that the addition of emotionalresponses benefits traditional models of diffusion, the oppo-site could also be suggested. A reasonable argument may bethat emotions result from the adoption process but that theireffect on evaluations Is negligible compared with traditionalmodels that capture both net benefits and product complex-
Emotion and the Evaluation and Early Use of Innovations / 47
ity. As we noted, net-benefit models have a strong trackrecord of effectively predicting innovation evaluations,sometimes accounting for more than 50% of explained eval-uative variance (e.g., Moreau. Lehmann, and Markman2001).
We hypothesize that because emotions result from dis-conflmiaiion of usage expectations, they act as importantsignals for the consumer regarding his or her ovi'n experi-ence. This hypothesi.s is akin to other examples of "affect asinformation" in which evaluations of products are influ-enced by situational mood states (Pham 1998). This emo-tional influence may operate in addition to other sources ofemotion, such as the utility derived from experienced bene-fits. Because usage expectations are different from con-sumers' expectations about the product's ultimate benefits,focusing instead on early usage experience (e.g., the rate atwhich they can achieve those benefits, the ease of learning,and their own affective response), the emotions derivedfrom usage expectations may be distinct from emotionsexperienced from gaining product benefits.
The changing influence of emotions over time. It isimportant to consider the potentially dynamic influence ofemotion at distinct periods in the adoption process.Whereas the dominant satisfaction paradigm assumes thatconsumers use static standards to make satisfaction judg-ments. Foumier and Mick (1999) find strong evidence thatstandards are often malleable. In the early stages of productlearning, we also expect to observe dynamic changes inusage difficulty expectations as consumers gain directexperience witb the new product and update their priorsaccordingly, in particular, Inexperienced consumers, whomay have been surprised by the difficulty or ease of theirinitial usage experiences, are likely to use their newlyacquired direct experience to adjust their expectations fortheir next use. Although the adjustment may not be accurate(e.g., these consumers may either over- or underadjust),basic tenets of learning hold that over time and experience,usage expectations should stabilize. Over time, we expectemotional influence to be the greatest during the earlylearning stages of product usage, when the disconfirmationgap hetween expectations and actual experience is at itslargest; then, as learning occurs, we expect this gap toshrink, thus decreasing the intensity of the emotions andtheir correlation with evaluations. Formally,
H4: Both positive and negative emotions (a) are significantpredictors ot evaluation at the initial introduction of thenew product {before first use), (b) are significant predic-tors of evaluation at the first use ot the new product, and(c) are not significant predictors of evaluation afterrepeated use of the product.
Shih and Venkatesh (2004) call specifically for experi-mental and longitudinal work to address usage diffusionelements. In Study I, we use a controlled laboratory settingto test our hypotheses. In Study 2, we use a quasi experi-ment over a three-month period to explore these hypothesesfurther in a real-world setting.
Study 1Methods
Choice of innovation stimuli. Three requirementsguided our selection of a new product: (1) It had not beenused by the participant population. (2) there was naturalvariation in prior experience with a previous version, and(3) learning was necessary to achieve basic performancegoals. We selected a new personal digital assistant (PDA),the Paltii Zire—specifically, the use of the handwriting-recognition software, The Palm Zire offers both a calendarfunction and an address function, and to use each of thesefunctions, users must learn the proprietary Graffitihandwriting-recognition software."^ Learning to use theGraffiti software was chosen as a task that was btith com-plex and fundamental to the operation of the new productand that could be viably learned and tested within theparameters of the laboratory setting.
Design and procedure. The study was a 2 (productdemonstration: no versus yes) x 2 (prior PDA experience:no versus yes) hetween-subjects design. Participants were175 undergraduate students at a large midwestem universitywho participated in the hour-long study for course credit.After a brief overview of PDAs and the two required tasks,each participant was given the experimental materials thatdetailed how to use the Graffiti characters. Next, partici-pants in the randomly assigned "demonstration" conditionreceived the demonstration provided by the same experi-menter using the same script.-
All participants tben read additional details about thePalm Zire and answered the preusage dependent measures.Next, the experimenter delivered a Zire to each workstation.Participants were given step-by-step instructions and askedto complete the first task of entering an appointment. Par-ticipants showed completed work to the experimenter andanswered the next set of dependent measures. After tbis, theparticipants proceeded to the second task, namely, enteringan address. Participants then answered a final set of depen-dent measures; before leaving, they were thanked anddebriefed.
Independent and Dependent MeasuresPrior catesoty experience. Participants indicated their
experience level with PDAs on a seven-point scale. Approx-imately half indicated no experience or a very low level(i.e., a 1); thus, we used a median .split to classify partici-pants as inexperienced versus experienced.
•'There i.s an altemate way to enter information using a simu-lated keyboard, but the experimental instructions required partici-pants to use the Graffiti software.
^In the demonstration, the experimenier held up the Zire.showed the participants where the stylus was located, and indi-cated how to tap the main menu buttons to find the appropriatetasks. The experimenter then put a transparency of the sheet detail-ing the Graffiti characters on an overhead projector and brieflydemonstrated how and where to draw the letters on the screen ofthe Zire.
48 / Journal of Marketing, July 2006
Product-related expectation.^. With the exception ofquestions on prior PDA usage and demographics, weemployed the same set of dependent measures at each of thethree points of interest during the experiment. To allow par-ticipants to communicate any type of product-related expec-tation, we employed an open-ended written protocol bothbefore and after product trial ("As you anticipate using thePalm Zire PDA to schedule an appointment, please describethe expectations that you have about your first experienceusing the Palm Zire PDA"). An examination of theresponses indicated that almost all contained information onthe anticipation of and reactions to the Zire's ease/difficultyof use. Two coders, who were blind to the condition of theparticipants, coded the written responses on two seven-point scales that captured expectations of difficulty in learn-ing and using. After each subsequent use, the coder ratedthe written protocols on two additional scales that capturedhow the experience compared with expectations (muchmore difficult to learn, much more difficult to use). Thecoders" reliability was high (correlation on all scales >.74);thus, we averaged their responses. Furthermore, the correla-tion between each pair of items was also high (each pair>.88): thus, we averaged each pair of items to form a mea-sure of usage expectations for Time I and a measure of dis-confirmation of usage expectations for Times 2 and 3. Wecombined the coded measures for Time I with the tradi-tional complexity measures that Rogers (1996) used (wedescribe this further in the next section) to form an overallexpectation score.
Complexity expectations. Following Rogers's (1996)definition of complexity, participants indicated on threeseven-point scales how difficult they expected/perceived theZire to be to use, how long it would take to leam to use, andhow much of a challenge it would be to use (at each period,a > .90); higher numbers indicated greater expected diffi-culty. At Time 1 (preusage), we combined these three self-report measures with the two items derived from the writtenprotocols to create an overall index of expected usage diffi-culty (a = .92).
Disconfirmation of complexity expectations. A standardmethod for assessing disconfirmation is to measure partici-pants' retrospective assessments of whether the product orservice was better or worse than expected (e.g., Oliver1993; Phillips and Baumgartner 2(X)2). Effectively, our cod-ing of the written protocols at Times 2 and 3 captures asimilar appraisal of this comparison in a more open-endedmanner. In addition, we subtracted usage expectations attime t from usage expectations at time t + I. We combinedthis difference with the two coded items.
Emotions. To assess participants' emotions at each stagein the adoption process, we employed a modified version ofIzard's (1977) differential emotions scale (DES-II); we bal-anced the scale's positive and negative items as Richins(1997) suggests. Similar to previous consumption emotionresearch (e.g., Phillips and Baumgartner 2O()2), we found atwo-factor solution at each point in time using the modifiedscale, with the two factors representing the positive andnegative emotions. We combined the seven positive andseven negative items to create a positive and a negativeemotion variable for each point in time (see Figure 2).
Product evaluations. We used a five-item scale withseven-point items to assess participants" evaluations of theZire at each of the three points in time ("As an overallevaluation of this particular PDA, 1 think I will "dislike itvery much/like it very much""; "I think this particular PDAand its features are good"': "strongly disagree/stronglyagree"; "I am very pleased that I will have to try this par-ticular PDA in this study"; "strongly disagree/stronglyagree"; "I would like to use this PDA on a regular basis":"strongly disagree/strongly agree"; "Would you recommendthis PDA to your friends?": "definitely would not/definitelywould"). At each point in time, all five items loaded on asingle factor, and thus we summed the five items to form asingle evaluation measure for each point in time (all threea > .89).
Covariates. We included compatibility and net benefitsas covariates that have been shown to influence new prod-uct success. In line with the work of Rogers (1996), we
FIGURE 2Emotion Response Scale (Modified from Izard's DES II Checklist)
The DES Emotions Checklisttruly feel.
Not at all happyNot at all frustratedNot at all sadNot af all joyfulNot at all angryNot at all confusedNot at all motivatedNot at all determinedNot at all excitedNot at all scaredNot at all boredNot at all irritatedNot accomplishedNot at all proud
Emotion and the Evaluation and Early Use of Innovations / 49
measured compatibility with three summed scale items toindicate how significantly participants would need tochange the way they currently schedule appointments, keeptrack of addresses, and manage their information (at eachperiod, a > .74). Two scale items measured net benefits ofthe Zire (the perceived functional benefits less associatedrisks), and two other scale items measured participants'evaluations of the Zire's appointment and address functions.The four items loaded on the same factor (a > .78), and wesummed them at each of the three points in time.
Results
Tiie factors influencing initial complexity expectations.We used a two-way analysis of variance (ANOVA). withexperience and demonstration as the independent factors, totest H|. The results indicate both a main effect of experi-ence (F(l, 172)= 19.62./7<.(X)01) and the predicted inter-action between the two factors (F(l, 172) = 5.21. p < .02).In support of H]y, inexperienced participants who receivedthe demonstration had significantly higher expectations ofdifficulty than those who did not (F(l, 172) = 3.96. p < .05;contrast; M emo = 20.9 versus M,, , . ,,, = 18.5). However,the demonstration did not significantly influence those withprior experience (F(l, 172) = 1.83, p > .10; M^^^,,^ = 14.8versus Mn,,t|p,T,,)= 16.6), in support of Hn,.
The disconfirmation of complexity expectations at firstuse. The negative expectations ibat the product demonstra-tion created for inexperienced consumers may not be ulti-mately detrimental to product evaluations. Witb the mea-sure of disconfirmation as the dependent variable, we used atwo-way ANOVA to test Hi. which revealed the predictedinteraction (F(L 172)= 14.2'3,/;< .001; see Figure 3, PanelA). Inexperienced participants who received tbe demonstra-tion experienced tbe least negative disconfirmation ofexpectations (i.e., tbe most positive disconfimiation. findingthe Zire easier to use tban expected) conipaied with theircounterparts who did not receive the demonstration(F(l. 172) = 22.53,/?<.OOO1: contrast: Mnojc,m, = 4.5 ver-' "s M teino = -2.6, p < .0001), in support of H y. Again, thedemonstration did not significantly influence tbe degree ofdisconfirmation for those with prior experience (F(l. 172) =1.09. p> AO; Mdemo = 4.2 versus Mnoj n,,, = 2.3), in sup-port of Hjb-
Impact of disconfirmation on experienced emotion andevaluation at first use. If disconfirmation influences emo-tion (and subsequently product evaluation), we wouldexpect to observe an experience x demonstration interactionfor emotion that mirrors the pattern tested in H2. The pre-dicted interaction is significant (F(l. 170) = 4.90, p = .02;Figure 3, Panel B). Experienced participants" emotionswere not influenced by the product demonstration, yet inex-perienced piirticipants were strongly influenced by thedemonstration; those who had a demonstration reportedmore positive emotion (Mjj.n,,, = 32) than those who did nothave a demonstration lM|,,,(Semo= 27).
Another way to test H3 is through regression analysis byexamining the comparative influence of complexity expec-tations on positive and negative emotion at all three periods(before use, at first use, and after repeated use). At each
point in time, we ran two regressions with positive andnegative emotions as tbe dependent variables. Predictorsincluded tbe two independent factors (experience anddemonstration), their interaction, the measure of usageexpectations, and the covariates (compatibility and netbenefits). At Times 2 and 3, we also included as predictorstbe disconfirmation of usage expectations. Perceptions oftbe Zire's usage difficulty and its net benefits significantlypredicted emotions at each point in time. As Table 1 shows,complexity is a significant driver of emotion at Time 1(B[x,.i,ive = -.31, p < .001; Bn,g,ji,, = .20. p < .05). Beforeuse, net benefits influence only positive emotion (B = .18,/J < .01), not negative emotion. Importantly, this pattern alsoholds at Time 2 and Time 3, augmenting tbe support for H3.
Similar to H3, we test H4a_e with regressions at eachperiod. For each period, we ran a regression using evalua-tions of the Zire as the dependent measure. Predictorsincluded the two independent factors, their interaction, tbemeasures of usage expectations, the covariates (compati-bility and net benefits), and positive and negative emotions.At Times 2 and 3. we also included as predictors tbe dis-confirmation of complexity expectations. Before first use,positive emotion was a significant driver of evaluation (B =.41. p < .001), as were net benefits (B = .27, p < .001; seeTable 2). However, negative emotions were not significantpredictors of evaluation (B = -.10, /? > .05). Tbis result isnotable, and we discuss it furtber in Study 2. After firsl use,both positive (B = .33, /? < .001) and negative (B = -.22, p <.01) emotions were significant drivers of product evalua-tions, as were net benefits (B = .25, p < .001). Finally, afterrepeated usage, positive (B = .47. p < .001) and negative(B =-.24./j<.001) emotions were still both significant dri-vers of product evaluation, as were net benefits (B = .25,p< .001). Thus, tbe inlluence of emotion can be observed atall points in time, in support of H4a and H^^, but not H4 ..
Discussion
Tbe results from Study I demonstrate that a consumer'sexpectations about tbe difficulty he or she will experience inusing a new product can be influenced by both marketer-directed communications and by his or her own priorexperience. The.se usage expectations and their (dis)confir-mation were important factors that predicted emotions anddrove iheir cbanges over tbe course of tbe usage experience.In turn, emotions predicted product evaluations at all pointsin time, even after we controlled for tbe other diffusionvariables.
Note that only positive emotions predicted evaluationstoward the Zire before its use, whereas both positive andnegative emotions influenced evaluations after use. Beforeuse, any threats to expected goal progress were only hypo-thetical: perhaps the negative emotions generated before useof the Zire were not substantial enough to influence evalua-tions. Only when the expected progress was actuallyimpeded did negative emotions influence evaluation. Thisphenomenon might be conceptualized as a "suspension ofjudgment." in which the consumer holds doubts or negativefeelings at bay in the initial assessment of tbe product. Inother words, the consumer may experience negative emo-
50 / Journal of Marketing, July 2006
FIGURE 3Expectation Disconfirmation, Experienced Emotions, and Product Evaluation After First Use
A: Disconfirmation of Complexity Expectations AfterFirst Usea B: Positive Emotion After First Use
tions when considering the leaming/usage difficulties butmay not incorporate these initial emotions in evaluating theproduct. This is a notable and somewhat counterintuitivefinding, given that consumers pay disproportionately moreattention to negative attribute performance in postuse prod-uct evaluations (Mittal, Ross, and Baldasare 1998).
Innovation Continuity andComplexity Expectations
In Study 1, the complexity expectations of the inexperi-enced consumers were the most influenced by tbe product
demonstration. As such, these consumers were subject tothe greatest amount of disconfirmation and, consequently,produced greater emotional reactions to the Zire. In thispopulation, inexperienced participants had no experiencewith the PDA category. Discontinuous innovations havebeen defined as those thai do not fit neally into an existingcategory (Lehmann 1994), and for consumers with no priorPDA experience, there is likely to be no substantial PDAcategory representation existing in memory. For consumerswith PDA experience, however, such a representationexists. As such, the Zire could be considered a discontinu-ous innovation for those without PDA experience but a con-
Emotion and the Evaluatioti and Early Use of Innovations / 51
TABLE 1Study 1: Predictors of Emotions (Standardized Estimates)
Predictors
Prior PDA experienceProduct demonstrationExperience x demo
Product-Related ExpectationsNet benefitsCompatibilityUsage (difficulty)Disconfirmation of usage expectations
R2
Before
Positive
.03
.10-.07
.18"-.08- . 3 1 * "
.10
Use
Negative
-.12-.04
.01
.10-.11
.20'
.12
Emotions
After First Use
Positive
.08
.01-.04
.37"*-.11- . 5 2 ' "
.04
.33
Negative
-.10.01
-.04
-.08-.04
.88*"
.19*
.59
After Second Use
Positive
.07
.18*- .t2
.16*-.01.42'**
-.17*
' .31
Negative
-.02-.07.06
-.04-.04
.68"'
.07
.54' < .05.
TABLE 2Studies 1 and 2: Predictors of Evafuations of the innovation (Standardized Estimates)
Predictors
Prior experienceProduct demonstrationExperience x demo
Product-Reiated ExpectationsNet benefitsCompatibilityUsage (difficulty)Disconfirmation of usage expectations
Consumption EmotionsPositiveNegative
R2
Before
Study 1
-.06.04
-.04
.27*"
.08-.16-
.41***-.10
.33
Use
Study 2
.13
.01-.04
.36"*
.02-.18*
.35*"-.08
.52
Evaiuations
After First Use
Study 1
J&l-.01
.03
.25"*
.04-.23*-.16'
.33*"- .22"
.65
Study 2
.13
.21-.28
.13
.07- . 5 1 * "
.36*
.35"*-.24*
.51
After
Study
-.07-.01
Second Use
1 Study 2
-.01.01
-.04
.25"* .38***-.01 .18-.26*** - .21*
.05 -.03
.47"* .22*- .24"- -.11
.71 .46'p < .05."p< .01.•••p< .001.
tinuous innovation for those with it. Because the literaturehas documented the effect of continuity on responses tonew products (e.g., Moreau, Lehmann. and Markman 2001;Rogers 1996), we designed Study 2 to control for the conti-nuity ol" the innovation, white assessing differences in prod-uct experience.
With a discontinuous innovation, the magnitude of thegap between expected and actual goal progress is likely tobe significant because the consumer has no direct categoryexperience on which to base expectations (Oliver and Winer1987). As a result, the emotions that ensue are likely to bemore powertui and predictive of overall evaluations. How-ever, with a continuous innovation, the size of the gap is
likely to be smaller. In these situations, ali consumers arelikely to have some direct experience with existing productsin the category, bui the level may vary across consumers.Although their existing knowledge may not be perfectlycalibrated, it is likely to provide a more accurate predictionof the actual usage experience than if it were unavailable. InStudy 2, we test this boundary condition to determinewhether the E^ model (H1-H4) holds for a continuousinnovation.
Study 1 also involved the typical level of artificialitythat is characteristic of lab studies, and its controlled naturedictated an accelerated usage schedule. As such, partici-pants had no discretion over the timing of the use of the
52 / Journal of Marketing, July 2006
product. In both academic (e.g.. Rogers 1996) andpractitioner-oriented (e.g.. The Economist 2004) literature,there is evidence that the expected usage difficulty of aproduct causes consumers to delay a purchase. Thus, wemight expect differential complexity expectations to affectusage timing even in situations in which the consumer doesnot make a purchase decision, such as with a forced adop-tion. Formally.
H5: Expectations of complexity are positively correlated withthe delay in initial usage.
In Study 2, we use a quasi-experimental methodologyover an extended period to test H]-H5 in the context of acontinuous innovation. Whereas we predict that complexityexpectations are less influential for a continuous innovation,we expect (and demonstrate) sitnilarities between Study 1and Study 2 in the factors that influence overall evaluations.As we observe, the pattern of emotions predicting evalua-tions of the discontinuous innovation at all three times isalmost identical to the pattern predicting evaluations of thecontinuous innovation.
Study 2MethodAs in Study 1, several criteria guided our selection ofstimuli: (1) The product had to be a continuous innovationfor all participants, with natural variation in usage experi-ence among the set, and (2) the product had to require somelearning for all participants to achieve its basic performancegoals. We selected PageOut, a new course managementsoftware program from McGraw-Hill. A pretest confirmedthat there was variance in participants' experience in usingonline course management tools but that all potential (stu-dent) participants were highly familiar with different typesof software, with Web page interfaces, and with computers.As a cover story, students were told that they had been vol-unteered to beta-test the software and would be asked toassess PageOut three times throughout the semester.
Design and ProtocolUndergraduates who were enrolled in three sections of anintroductory marketing course participated in this 2 (highversus low experience) x 2 (demonstration versus nodemonstration) between-subjects study throughout thesemester as part of their curriculum. Although 123 studentswere enrolled in the three sections, 17 failed to complete allaspects of the study and were subsequently deleted fromfurther analyses. The students were not randomly assignedto a section, but the course prerequisites, the professor, theclass format, and the course content were held constantacross all three sections.
On the first day of class, the professor introduced Page-Out to tbe students in all three sections using a consistentscript and provided all students witb the same handout con-taining step-by-step instructions. In one randomly selectedsection, the professor used the computer podium to log onto the Web site and to demonstrate how to create a studentprofile. The other two sections received just the verbal
introduction and the instructional handout for this task. Thisoperationalization of the demonstration differed from Study1 in that it used a different medium (computer), used a dif-ferent instructional material for the Web site's use. and wasconducted in a group rather than individually.
Before use (Time 1). On the first day of class, immedi-ately following the introduction, students reported the emo-tions they experienced while the professor was introducingPageOut. Students then completed demographic and experi-ence measures, followed by the dependent measures.Finally, students were given an assignment to log on toPageOut and register by the next class. A small extra creditincentive was provided. The administrators' softwareenabled tbe professor to record students' log-on and regis-tration times.
Af^er the first use (Time 2). On the second day of class,students reported the emotions they felt while logging onand registering witb PageOut and responded to the depen-dent measures.
After extended usage (Time 3). Over the course of thesemester, students used PageOut to take online quizzes,check exam grades, download class notes, and communi-cate witb the professor. At a mandatory class meetingshortly before the end of the semester, students reportedtheir current emotions and completed the final set of depen-dent measures.
Dependent VariablesExpectations, emotions, and evaluations. Measures of
product-related expectations, emotions, and evaluationswere similar to those used in Study 1, witb one exception.In an effort to minimize the time required to collect thedata, no open-ended protocols for usage expectations wereemployed.
Log-on time. As we noted previously, the administra-tors' software enabled the professor to track the time thatelapsed from the time each student left class to the time thathe or sbe registered on the Web site.
Prior e.xperience. Experience witb online course man-agement tools was measured using two items. A mediansplit identified students as experienced or inexperienced.
ResultsInitial usage expectations and timing of initial use. H5
predicted tbat higher expectations of usage difficulty wouldbe associated with delays in product usage, Tbe simple cor-relation between participants' perceptions of usage diffi-culty and tbe hours they allowed to elapse before their ini-tial registration suggests that such a mechanism was at work(T = .25, p < .01).
The infiuence of experience and demonstration on ini-tial complexity expectations. We tested H| using a two-wayANOVA. As in Study 1, there was a significant main effectof experience (F(K 102) = 7.30, /? < .01). Tbe pattern ofdata was consistent with the first study; participants withmore experience with course management softwareexpected their initial use of the PageOut software to be less
Emotion and the Evaluation and Early Use of Innovations / 53
difficult than did those with les.s experience4.9 versus Mi expcriencol = 6.8).
The accuracy of complexity expectations for a continu-ous innovation. After participants logged on and registered,how did their initial usage expectations compare with theiractual experience? Given the continuous nature of the inno-vation, we expected that H2 would be attenuated in thisstudy. Accordingly, in contrast to the findings in Study 1,there were no significant effects of prior experience ordemonstration on the degree of disconfirmation of complex-ity expectations. The means for all four groups were closeto zero, indicating that participants were relatively well cali-brated in their expectations.
Consumption emotions and product evaiuations. As weexpected, given participants" greater accuracy in complexityexpectations, disconfirmation has a much smaller influenceon experienced emotion at each of the three periods. Wereplicated the experience x demonstration interaction forfelt emotion at first use observed in Study I (using stan-dardized measures, B = - .21, p < .05, r = .19). Less-experienced participants experienced more positive emotionat first use when they received a demonstration (M = .31)than when they did not receive one (M = -.24), whereasmore-experienced participants were not affected similarlyby the demonstration (Mji ,,, = -.12 versus Mp,, de io = .13).However, as we expected with the small degree of" discon-firmation, the experience x demonstration interaction forTime 2 evaluations of the innovation was not significant(F= .bl,p> .2).
Tliere are dramatic similarities between tbe Study 1 andthe Study 2 findings in the factors tbat influence overallevaluations. Tbe pattern of results that predict evaluationsof the Zire across time is almost identical to that of PageOut(Table 2). In botb studies, only positive emotions predictevaiuations before use, which might be interpreted as an ini-tial suspension of judgment during the product introductionsuch that negative emotions, though they are experienced,are held at bay. However, both positive and negative emo-tions significantly influenced evaluations after use in bothstudies, in support of H41,. Finally, H4 predicted that tbeinfluence of emotions would wane over time. Unlike Study1, in which the impact of emotions remained strong duringtbe duration of the one-hour session, the impact of emotionsdeclined over the semester for PageOut, in support of H4C.Taken together, these findings suggest that the diminishinginfluence of emotions can be observed only over longertime horizons.
Study 2 demonstrates that the pattern of results fromStudy 1 holds in a real-world quasi experiment but thatemotional influence is not as extreme when the product isless innovative. However, even when emotional influencewas smaller as a result of more accurate complexity expec-tations, the inclusion of emotion still increased the predic-tive power of the traditional diffusion model.^ Finally, this
'•To assess this contribution, we compared the explanatorypower of the model shown in Table 3, Panel B. with that of areduced model that does not include the two emotion variables(R2 = .38). The resulting F statistic (F(2,95) = 9.13, p < .01)
Study shows that complexity slows consumers' time to trial(H5).
General DiscussionTheoretical Contribution
The £3 model. By demonstrating how affectiverespt)nses during new product learning influence innovationevaluations over time, we extend previous work in both sat-isfaction and diffusion. The E^ model provides a parsimo-nious description of how emotions are created in tbe earlyuse of complex products and how they may influence prod-uct evaluations beyond diffusion's traditional focus on netbenefits. These results emphasize the dynamic nature ofbotb emotion and evaluation in innovation diffusion.
Adding emotion to innovation frameworks. Refiningmodels of innovation is easily justified by the importance ofnew product success to company profitability. Priorresearch on new product adoption focuses largely on cogni-tive processes, despite the increasing knowledge of tbe roleof emotion in product consumption. We demonstrate tbatemotions resulting from early experience with the product(which therefore might be dismissed as ephemeral) canhave a lasting influence on diffusion.^
Exploring and expanding recent innovation research.The use-diffusion model (Shih and Venkatesh 2004)describes four patterns of consumer use of new productsbased on the rate and variety of consumer use. Shih andVenkatesh {2(KM) theorize that usage diffusion depends on(1) characteristics of the individual or household (e.g., inno-vativeness, resources, prior experience). (2) characteristicsof the innovation (e.g., complexity, technological sophisti-cation, existence of complementary products), and (3) char-acteristics of the environment (e.g.. communication inten-sity, technological access, media exposure). The currentresearch expands the knowledge of how these factors mayinteract by examining how the characteristics of the envi-ronment (how the innovation is promoted to the consumer),the product (complexity), and the consumer (prior exper-tise) combine to create a successful or unsuccessful initialproduct experience.
In addition, the importance of ttie phenomena we reportherein is reflected in recent work on feature fatigue.Thompson, Hamilton, and Rust (2005) show tbat con-sumers assign more weight to product capabilities (addi-
demonstrates that the addition of emoti(tns to the standard modelof preference makes a significant contribution.
^Researchers have long advocated the addition of affective ele-ments to primarily utilitarian-based models of product evaluation(Derbaix and Pham 1991). However, many researchers view emo-tion as another viable end-state benefit similar to utilitarian bene-fits. For example. Havlena and Holbrook (1986) note that con-sumers might buy a luxury car lor the pleasure of feeling superior.We do not investigate emotions as independent benefits in ihi.swork but rather as a moderator of product learning and acceptance.Although emotions can color utilitarian attribute evaluation (Smithand Ellsworth 1985), they can also have a more global influenceon product experiences (Cohen and Areni 1991).
54 / Journal of Marketing, July 2006
tional features) in their evaluatiotis before use than after useand le.ss weight to product usability before use than afteruse. Thus, they show that consutners are often miscalibratedin their expectations about product complexity and usabilitybefore use. Our research shows that inaccurate and overcon-fident expectations about usability (e.g., when consumersseek excessive features in prospect) may create situations inwhich negative etnotions have a powerful influence onproduct evaluations and, thus, in retrospect, satisfaction.
Managerial implicationsOur results show that the predictive power of traditionalmodels of innovation adoption can be improved by theaddition of emotional responses. Importantly, a substantialproportion of consumers' emotional responses appear to bea function of disconfirmed complexity expectations, notsimply a result of product benefits. This tells marketers twothings. First, emotional influence can be assessed by mea-suring disconfirmation of usage expectations, and second,emotional responses can be influenced by changing con-sumers" complexity expectations before use. As we notedpreviously, marketers can influence complexity expecta-tions when they show product use in commercials, describeusage instructions in online formats, or use other high-involvement sales channels (e.g., sales reps). We testedproduct demonstration a.s one way to influence expecta-tions. Product demonstrations are believed to be beneficialto both marketers and consumers but expensive to execute,and marketers do not have a clear idea of how they trulyaffect consumers (Ailloni-Charas 2000). Our research sug-gests that training frontline saies personnel is important; thesalesperson may be tempted to insist that a technologicalproduct is easy to leam to close the sale, yet the misman-agement of usage expectations can lead to high rates ofproduct return in the long run. Promising "easy'* use maypromote trial but sabotage product evaluation and diffusion.
Thus, marketers must seek a balance in which con-sumers are encouraged to try new products but not throughpromises of ease that create unrealistic expectations forearly use. For example, we can compare the recent strategicactions of Royal Philips and Best Buy to observe two oppo-site outcomes of influencing consumers' compiexity expec-tations. Best Buy's Geek Squad (currently in 642 stores) isa team of professionals, attired in stereotypically nerdy uni-forms (black polyester pants, white shirts, and clip-on ties),who make house visits to help consumers learn complicatedproduct tasks, such as how to program an iPod or connect aTiVo system (Deam 2004). Rather than downplaying thecomplexity of products sold in the store. Best Buy leveragesthese perceptions by offering (for a fee) a way to shift thelearning costs to a team of prototypically smart people. Notonly does this generate revenue, but our research would pre-dict that the Geek Squad also sends a signal, albeit humor-
ous, to consumers that product learning is not always easy:with such a signal, consumers tnay subsequently experiencelittle negative disconfirmation in early usage and be posi-tively surprised if they successfully achieve product use ontheir own. Conversely, Philips's strategy of promoting itselfas a provider of user-friendly gadgets (The Economist 2004)may be less optimal for the company. By launching a $100million ad campaign to promote its technology as "easy touse" (Endt 2004). Philips may lower consutners' preusecomplexity expectations and subsequently strengthen thenegative emotions that occur in the early learning phase ofuse. Most learning requires .some setbacks, and reducingconsumers' expectations of setbacks may have a negativeimpact on long-term product and brand evaluations.
Limitations and Further ResearchA limitation of the current research is our use of forcedadoption in the two studies. Participants in both studies didnot choose to adopt the new products, though they had anoutlet to report their opinions and evaluations at multiplepoints in the usage period. Although this situation was cho-sen for practical reasons, on reflection, we question whetherforced adoption has a positive side in offering a better andmore conservative test of the influence of complexityexpectations and emotion. When consumers choose to try/buy a product innovation, there are certain emotional andevaluative processes that arise solely from the evaluation ofthe choice, such as cognitive dissonance. Dissonance mayamplify emotions that arise from disconfirmation of usageexpectations, Thus, emotional responses may be heightenedin voluntary adoption, creating even stronger influence ofemotions on innovation evaluations.
There are many other diverse avenues for furtherresearch suggested by our results. First, the E- model advo-cates the inclusion of both cotnplexity expectations andemotions (positive and negative) in existing frameworks ofevaluation, satisfaction, and diffusion. Second, these rcstilts,combined with work on regret (e.g., Tsiros and Mittal 200),begin to build a more complete picture of negative emotionafter a choice is made. Luce, Bettman, and Payne (2001)show the importance of negative emotion as consumerscontemplate choice. The act of making a choice is not theend of emotional influence. Consumer expectations for theusage or learning process may have a surprisingly strongimpact, influencing regret over product choice dissatisfac-tion not only with product benefits but also with the costs toachieve those benefits in a timely manner. Can complexproducts make consumers feel smart because of unexpectedhurdles overcome or stupid because of unexpected slownessin achievement? More important, can such emotional reac-tions influence the evaluation of the innovation beyondmore concrete evaluative criteria? The current research sug-gests that they can.
Emotion and the Evaluation and Early Use ot Innovations / 55
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