Attribute-Level Performance, Satisfaction, and ...

Vikas Mittal, Pankaj Kumar, & Michael Tsiros

Attribute-Level Performance, Satisfaction, and Behavioral Intentions over Time: A Consumption-System Approach Instead of offering products or services alone, increasingly, firms and their partners are offering consumption systems. Consumption systems are offerings characterized by a significant product and service subsystem, as well as a pattern of consumption in which consumption occurs in multiple episodes over time. The authors develop a theoretical model for conceptualizing satisfaction with consumption systems and empirically test it using longitudinal data from 5206 automobile owners. Results show that an intertemporal examination of attribute-level performance, satisfaction, and behavioral intentions can improve an understanding of their relationships because these relationships change as the consumption of the product unfolds. For example, on the basis of their salience, attribute weights in determining satisfaction shift over time. Furthermore, the crossover effect of product and service satisfaction in determining intentions toward the manufacturer and the service provider is asymmetric, and this asymmetry reverses over time. Service satisfaction initially has a much larger impact in determining intentions toward the manufacturer, but later, product satisfaction is more influential in generating intentions toward the service provider and manufacturer. The results show that there is no direct link between satisfaction and behavioral intentions. Rather, satisfaction affeots behavioral intentions in the future through a dual-mediation route.

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The two most successful automotive launches in recent history have been the Lexus and Saturn cars (Reichheld 1996). Executives in the industry attribute their success to the high quality of the products and dealership service. However, the relative contribution of vehicle and service satisfaction in reciprocally generating loyalty to the manufacturer and dealership is not known. Executives wonder how the relative magnitude of satisfaction with the product and service and consequent behavioral intentions toward the dealer or manufacturer change over time. Also. it is not known how the magnitude of the impact of attribute performance on satisfaction changes over time.

T

his scenario highlights several dilemmas faced by firms involved in marketing consumption systems. Such systems contain a significant product and service subsystem, and their consumption unfolds over time as a series o f consumption episodes. For example, the consumption o f a car unfolds over time, and satisfaction is dependent j o i n t l y on not only the product, but also associated services offered by the dealer. In addition to representing managerial concerns (e.g., how to allocate resources between the product and service subsystem), this scenario also

Vikas Mittal is an assistant professor, Katz Graduate School of Business, University of Pittsburgh. Pankai Kumar is an assistant professor, Baruch College, City University of New York. Michael Tsiros is a visiting assistant professor, Olin School of Business, Washington University. The authors thank James Cuthrell, Mike Gerhart, Kristine Gupta, Jerry Katrichis, Vishal Mittal, and Edwina Monsoon for their support; and Frank Forkin of J.D. Power and Associates for providing the data used in this article.

88 / Journal of Marketing, April 1999

highlights gaps in extant satisfaction research. Specifically, i l remains to be examined how satisfaction w i t h the product and the service interact in forming intentions toward ihc product manufacturer and service provider and how the relative magnitude o f these impacts changes over time. Such

an understanding has major implications for coordinating and managing channels lo achieve higher customer value (Anderson and Narus 1995). It also retnains to be understood whether and why, in determining overall satisfaction, attribute weights shift over time. Empirical documentation and clarification of possible causes of such shifts could help the satisfaction literature appreciate the temporal dependence of this link. Finally, it remains to be understood how satisfaction in a given time period affects future behavioral intentions. Is the link between these two constructs direct, or is it a mediated link? Clarification of such mechanisms is important to understand more fully the link between satisfaction and its consequences (Zeitham!, Berry, and Parasuraman 1996). Although much work has been done to understand the processes underlying the antecedents of satisfaction (e.g., Oliver 1980), the direct and mediated processes by which satisfaction translates into its consequences—behavioral intentions—are not well understood. In this article, we investigate the relationships among attribute-level perfonuance, satisfaction, and behavioral intentions to shed light on the preceding issues. In the following sections, we develop the consumption system idea and empirically test it using longitudinal data from 5206 automobile owners. We conclude by discussing implications for marketing research and practice.

Journal of Marketing Vol. 63 (April 1999), 88-101

Consumption System

systems from both the consumer's (Rust and Oliver 1994) and the finn's (Files 1996) perspectives. Aconsumption system can be examined cross-sectionally to gain a structural view of consumption experiences. Thus, we may examine attribute weights or compare the product and service subsystem and understand how these elements are linked together and affect consumption. A consumption system also can be examined longitudinally to gain a process view of the system. For example, we may examine how ihe link between attributc-levcl evaluations and satisfaction changes over time or how satisfaction translates lo intentions over time. Each type of examination affords a diflereni perspective on a consumer's experience. More itiiportant, both types of examination lend different insights into the relationship between attribute performance, satisfaction, and behavioral intentions. Tliese are discussed next.

As we show in Figure I, a consumption system consists of a bundle of goods and services that are consumed over time in niulliptc consumption episodes.' The conceptual foundation o!" this approach can be traced to General Living Systems Theory (for a review, see Reidenhach and Oliva 1981). Complex phenomena are conceptualized as a system that consisis of various subsystems that work in tandem and evolve over time. Conceptually, there are three constitutive elements of a consumption system:- attribute-level evaluations, satisfaction, and behavioral intentions. Furthermore, a consumplion system can be composed of several subsystems, of whieh the product and service systems are key sub-

'|[ musl be emphasized that whether an offering can be deemed as a consumption Nystem is a matter ot degree. 2A consumption ."system should be differentiated from other seemingly similar concepts. For example, il is diflereni than a "durable good" becau.se ihe term "durable good" refers only to the produci clemenl (e.g., a sofa or a table) with no regard to the service clement. Thus, the term "durable goixl" narrows down a concept lo its phy.-iical attributes only {by ignoring the systemic view). Siinilariy. labels such as "service-enhanced" or "augmented" products (used forone-shol items such as a meal in an expensive restaurant) may not capture ihe dynamic aspect in such a system adequately. Nonetheless, it seems beneficial to think of "durable goods" or "service-enhanced products" as special cases of a consumption system, the broader concept.

The Product and Service Subsystems in a Consumption System As per the model shown in Figure I,^ the product and service elements constitute key subsystems of a consumplion

'Although the model is shown for two time periods, it can be extended to multiple peritxls, Furthermore, the model is applicable to situations in which the service and product are provided by the same entity, as well as those in which the service and product are provided by difterent entities.

FIGURE 1 Satisfaction with Consumption Systems: Conceptual Model Attribute,

Attributej...

Attribute^

Attribute,

>r

Attribute 2...

Attribute^

>f

>

Service subsystem satisfaction

\ \

\

/ \

\

c\ Intention toward product manufacturer

Notes: ^

Straight arrows represent effects within a subsystem.

y'—^

Curved arrows represent temporal effects. ^

\

Intention toward service provider

Dotted arrows represent cross-over effects between two subsystems.

Consumption-System Approach / 89

system. For example, the automobile and dealership service are the key subsystems of the automotive consumption system, and a mutual fund and tbc consultant's services are key components of tbe mutual fund investment system. Because of the changing nature of the economy, many firms that traditionally focused only on tbeir product offering increasingly rely on associated services to differentiate and add value to tbeir product offering. As tbe chief executive officer of Caterpillar, a company known for its product offerings, says.

witb tbe car, and Westbrook (1981) finds a significant correlation between satisfaction witb tbe retailer and satisfaction witb the products purchased from the retailer. However, neither study (I) related satisfaction to behavioral intentions, (2) investigated tbe crossover effect between satisfaction and intentions, or (3) studied bow tbis effect varies over time. Consequently, of interest is not only the significance of patbs b and c, but also their relative magnitude and how tbe magnitudes vary over time.

The biggest reason for Caterpillar's success has been our system of distribution and product support. Don't get me wrong. We think we are better engineers and manufacturers than our competitors. But we are convinced that our single greatest advantage over our competition was and still is our system of distribution and product support (Fites 1996, p, 85),

The Dynamic Link Between Attribute Evaluations and Satisfaction

Simultaneously managing tbe product and service subsystem also helps ftrms manage profitability by allocating resources more optimally between tbc product and service subsystems (Anderson, Fomell, and Rust 1997). Tbis is true not only when tbe product or service component is managed by the same firm (cf. Crosby and Stephens 1987), but also wben they are managed by different firms (cf. Anderson and Narus 1995). Especially in ibe latter case, the consumptionsystem approach provides guidance for understanding the complex dynamics of the power balance between manufacturers and distributors (e.g., Fites 1996). Moreover, it enables a melding of academic researcb on product satisfaction and service quality. Despite the acknowledged need to determine bow the product and service elements '^jointly affect satisfaction, how tbey interact, and how tbeir relative influence cbanges" (Rust and Oliver 1994, p. 15, italics in original), research in tbese areas bas proceeded witb little or no overlap. Yet, if academic researcb is lo find welcome applications in firms, sucb a blending is necessary, becau.se firms want to manage tbc product and service aspects of tbeir offerings simultaneously. A consumption-systems perspective specifically argues that botb tbe product and service subsystems influence eacb otber. We call sucb effects "crossover effects," and they are sbown as paths b and c in Figure I. A crossover effect implies that product satisfaction affects bebavioral intentions toward the service provider, and satisfaction witb tbe service influences bebavioral intentions toward tbe product manufacturer. In the case of typical nondurable and convenience goods, for wbicb the service provider and product manufacturer are distinctly different entities (e.g., a convenience store tbat sells branded gtwds). consumers should form bebavioral intentions about tbe product manufacturer and service provider separately. Tbis sbould bappen because satisfaction witb service sbould be attributed to the service provider alone, whereas satisfaction witb tbe product should be attributed to tbe product manufacturer alone. However, for complex systems, this distinction generally is blurred. High-quality service at a dealership may be attributed, in part, to efforts made by tbe product manufacturer Conversely, high-quality service from the dealer may engender positive intentions toward the manufacturer. Prior literature has made some attempt to examine this crossover effect. Oliver and Swan (1989) find tbat satisfaction witb tbe dealer was related significantly to satisfaction

90 / Journal of Marketing, April 1999

Managers historically have been most interested in linking attribute performance to overall satisfaction for making resource allocation decisions (Griffm and Hauser 1993). Among academics, however, an attribute-level conceptualization of tbe antecedents of satisfaction is a recent phenomenon (e.g., Oliver 1993), and most early work focused on global evaluations (cf. Oliver 1980). In an attribute-level approach, overall satisfaction is a function of attribute-level evaluations (LaTour and Peat 1979). Tbese evaluations may be based on performance and/or disconfirmation at tbe attribute level and typically capture a significant amount of variation in overall satisfaction (cf Bolton and Drew 1991; Oliver 1993). Relative to the "global" evaluation approach, tbe multiattribute model bas two key advantages. First, it is consonant with consumers' representations of consumption experiences in memory. For example, Gardial and colleagues (1994) find that, when making postpurchase evaluations and describing consumption outcomes, consumers are almost twice as likely lo use attributes than the overall product. Second, an attributelevel analysis provides bigher specificity and diagnostic usefulness by enabling us to ask specific questions about antecedents of satisfaction. For example, is disconfinnation on certain attributes more critical in dctennining overall satisfaction iban otbcr attributes are? Tbus. previous models of antecedents of customer satisfaction can be extended to tbc attribute level to increase their specificity and actionability. However, virtually all tbe work relating attribute evaluations to overall satisfaction has been performed on a crosssectional basis (for a review, see Oliver 1997). Researcb bas not examined if attribute weigbts in determining overall satisfaction are temporally labile. Consequently, even less is known about the direction and magnitude of tbcsc shifts. Tbese weigbts typically are derived from regression analysis using cross-sectional data. Tbe underlying assumption in such analyses is that attribute weigbts are temporally invariant or tbal sucb temporal variability is tbeoretically or managerially unimportant. We are interested specifically in changes in attribute weights over time. Tbus, we examine links a| ... a^, in Figure I. We argue that attribute weights in determining overall satisfaction sbift over time. One approacb (o understanding these shifts is to examine tbe extent to wbicb attribute performance relates to consumption goals. As tbese goals cbange during the consumption experience, so migbt attribute weigbts. That is, an attribute's relationsbip to consumption goals may drive its salience. For example, color and styling—two important attributes in the purchase decision for a car—contribute a lot to the satisfaction judgment

during the initial consumption period, but their weights decline over time. During later periods, reliability and engine performance are found to contribute more to satisfaction (Mittal ct al. 1993). An attribute's weigbt also may be driven by tbc frequency with which a consumer is exposed to the attribute and the extremeness of the perceived performance during each exposure (Oliver 1997). Moreover, the perceived variability in performance on an attribute may change over time, leading to changes in its weight. Higher variability ensures more occasions for disconfirmation and, therefore, may increase the attribute's salience and weight in the satisfaction judgment. For example, if the miles per gallon (mpg) on a person's car are highly variable from trip to trip,"* then, for tbat customer, mpg may have more weight in the satisfaction judgment than for another customer whose car provides con.sistent mpg. Thus, over time, an attribute's weight may be determined in part by the perceived variability in attribute performance. Over time, consumers using a consumption system are likely to bave different levels of exposure to and perceive different levels of variability of different attributes. Tbus. we expect tbe coefficients for palbs aj ... a,, to cbange over time. In summary, we argue for a temporally situated perspective to examine tbe relationsbip between attribute performance and satisfaction. Sucb an approach is likely to yield interesting insigbls about the patterns in attribute weigh! shifts. Consequently, as a first step, we are interested in understanding the direction and magnitude of such attribute weigbt sbifts over time.

The Dynamics of the Satisfaction-Intention Link Acknowledging tbat consumption tKcurs as a series of encounters between a consumption system and the consumer, researchers distinguish between transaction-specific and cumulative satisfaction (Oliver 1997). However, most research has examined the satisfaction-intention link on a cumulative basis (cf. Anderson and Sullivan 1993). Research relating encounter-specific satisfaction to bebavioral intentions over time is sparse. It is not clear wbetber and how satisfaction judgments and bebavioral intentions formed during one consumption (Kcasion carry over to behavioral intentions at subsequent consumption occasions. In a longitudinal study, Mazursky and Geva (1989) asked respondents to indicate satisfaction and intention ratings toward an antitheft alarm. Two weeks later, tbese same respondents were asked about tbeir bebavioral intentions toward tbe antitheft alarm. Results sbowed tbal satisfaction ratings obtained in tbe first time period were predictive of intentions in tbe first period, but not in tbe second period. Using tbis result as a basis, tbe authors concluded tbat tbe satisfaction-intention link decays rapidly. More important, tbis result implies tbat, over time, satisfaction does not affect intentions directly and casts doubt on tbc result presumed to be true in most crosssectional studies: that satisfaction directly affects intentions in the long run. However, this study did not consider that, •*This variahility may stem from a variety of sources, including changes in driving condition, quality of the engine, and so forth. The issue here is that variation occurs and inlluences attribute weights over time, though further research could examine theorelically why this variation occurs.

over time, satisfaction's impact on intentions might be mediated. Two studies (LaBarbera and Mazursky 1983; Oliver 1980) sbed ligbt on possible mediating mecbanisms. Tbese studies find tbat the impact of intentions in the first time period (Tj) on intentions in tbe second (Ti) was mediated by satisfaction in tbe intervening time period. However, in neitber study was the impact of satisfaction in T] on satisfaction in T i studied in conjunction with intentions in T\. They also were limited to products or services whose consumption did not unfold over time. Tbe dynamic aspect of tbe m(xiel tbat relates satisfaction to bebavioral intentions is termed the "carryover effect." We propose that both satisfaction and bebavioral intentions in tbe intervening periods act as mediating variables. Thus, witb regard to Ihe model in Figure 1, we are interested in links d, e, f, and g as mediating routes. Regarding links f and g, most researcb has been performed on a cross-sectional basis with tbe assumption tbat satisfaction in T| directly affects satisfaction in T2. However, the only study (Mazursky and Geva 1989) to examine such a direct link on a longitudinal basis finds no support for such a link. Regarding the temporal impact of intentions (links d and e). though some evidence exists to show that prepurchase intentions affect postpurchase intentions (LaBarbera and Mazursky 1983; Oliver 1980). no study bas examined the dynamics of postpurchase intentions. Lack of work in this area may be due to tbe difficulty in obtaining longitudinal data. Or. it could be tbat most research to date bas focused on elaborating detailed and alternative models of the antecedents of satisfaction witb virtually no effort devoted to understanding satisfaction and its consequences over time. Conceptually, at least three theoretical perspectives support these intertemporal links, that is. why people may "update" satisfaction judgments and bebavioral intentions rather than construct them anew for each consumption episode. The first is found in adaptation-level theory (Hclson 1964), on which the expectation-disconfirmation paradigm is based (Oliver 1980). According to adaptation-level theory, prior judgments and intentions act as anchors for future judgments and intentions. In other words, intentions and judgments in T2 are not absolute but are made relative to intentions and judgments in T|. The second perspective is found in variants of consistency theory, which suggests that people are predisposed to maintain cognitive and attitudinal consistency (cf. Festinger 1957; Hcider 1958). To reduce dissonance (Festinger 1957) or maintain balance in mental representations of ideas (Heider 1958), people selectively process information that enhances consistency. Thus, ifat time T). a person is satisfied with his or her car. he or she is motivated to perceive subsequent consumption episodes sucb tbat they are consistent witb the initial judgment. The third perspective is based on learning theory (Bagozzi 1981). Thus, repeated encounters with tbc product or service reinforce tbe satisfaction judgment and behavioral intentions.''

^Whereas the consistency theory argument may be interested in motivational issues (when are consumers more or less motivated lo achieve consistency), learning theory arguments would be more interested in repetition, time lag between consumption episodes, and so forth. Untangling ihese sorts of issues is beyond the scope of this research.

Consumption-System Approach / 91

More generally, the three perspectives are consistent with the belief-updating paradigm (Hogartb and Einhorn 1992), whereby consumers form judgments and intentions at a given time by updating prior judgments and intentions. Tbus, satisfaction in T2 sbould not only be a function of attribute-level evaluations in T2, but also of tbe level of

tive of its customer base. In tbe current study, 57% of the subjects were men and 43% women. Furtbermore, 30% had finisbed high school, 56% bad an undergraduate degree, and 14% held a graduate degree. Tbe age distribution was as follows: 9% were less tban 24 years of age, 11% were 25 to 29 years, 37% were 30 to 44 years, 33% were 45 to 64 years,

overall satisfaction experienced iti Tj. Similarly, intentions

and 10% were 65 years of age or older.

in T2 are not only a function of satisfaction in T2, but also intentions in T|. If we combine these arguments with tbe well-establisbed result tbat satisfaction in a given period infiuences intentions in tbe same period, we can establish a dual-mediation mecbanism for linking satisfaction in Tj to intentions in T2. In tbe proposed dual-mediation model, botb satisfaction in T2 and intentions in T| mediate the link between satisfaction in T[ and intentions in T2. Tbat is, satisfaction in T[ affects intentions in T| and satisfaction in T2. In turn, satisfaction in T2 and intentions in T| aftect intentions in T2. To evaluate the proposed dual-mediation model, we compare it with two conceptually "rival" models. Tbe first is a direct model tbat does not consider the possibility of mediation mechanisms. The second is an alternative mediation model, tested in prior research (LaBarbera and Mazursky 1983; Oliver 1980), in wbicb intentions in T| affect intentions in T2 directly, as well as tbrough the satisfaction experienced during the consumption. However, this model does not consider the mediating role of intentions. The current study clarifies the nature of the satisfaction-intention link by empirically comparing the tbree models using longitudinal data. More generally, it extends satisfaction researcb by positing rival models about tbe process by wbich satisfaction translates into its consequences.

The firm provided data on 5206 customers who bad responded to the initial consumption period service survey (T|) and tbe 21-month follow-up survey for tbc laler consumption period (T2). The initial evaluation survey was mailed to owners who visited tbe dealership for a repair associated with the manufacturer warranty. Excluded were customers who incurred repairs associated with emissions, corrosion, recalls, and after-warranty adjustments. This survey measures satisfaction with the vehicle, satisfaction witb the service process, and likelihood of recommending the dealer and manufacturer. The survey also asks respondents to rate their satisfaction with key attributes of tbe product and the service. Tbe second survey is mailed to all owners 21 months after tbeir initial dealersbip visit. Because practically every owner goes to tbe dealership for the initial visit, this survey is essentially a resampling from the original sampling frame. Thus, this survey provides satisfaction and intention data in T2 for the same respondents who completed the survey in T], but witb a 21-montb lag. In other words, these surveys were conducted almost two years apart: tbc first wave in tbe fall of 1993 and the second in the fall of 1995. Therefore, these data provide a conservative test of the temporal aspects of the model.

Study

A key concern with using panel-type data is that customers who filled out both surveys may be systematically different than other respondents. From a theory-testing perspective, this is not a key concern. Although the absolute level of variables might differ for panel members and nonmembers (e.g., more satisfied people may be more responsive), there is no reason to suspect tbat the hypothesized relationships would be different. Nevertbeless, we are interested in knowing if any potential biases exist in the sample. For example. Westbrook (1981) finds tbat panel members were more critical in evaluating tbeir satisfaction witb restaurants than nonmembers were. Therefore, to check for respondent bias, we took the following steps.

Tbis study sougbt to test empirically tbree key conceptual ideas embedded in tbe consumption system perspective: (I) dynamic attribute weigbt.s, (2) crossover effects, and (3) the mediation mechanism in the satisfaction-intention link. Our major interest is in understanding whether and how the key linkages in tbe system change over time. Longitudinal data from the automotive industry related to two specific consumption periods were used in empirical testing. The automotive industry was considered a good setting for testing the model because automobiles are an important consumption system for consumers and entail multiattribute products and services whose consumption occurs over time.

Survey Description Tbe data for the study come from a commercial satisfaction tracking study that was in place before this researcb began. This satisfaction tracking study measures satisfaction throughout the ownership period amon^ automotive customers by surveying customers right after sales, after a service encounter during the initial consumption period (3 to 4 months after the sale), and finally during the later consumption period (another 21 months after completing the second survey). Response rates for eacb wave range from 35% to 45%. The automotive firm also has conducted several pilots over the years and found the respondents were representa-

92 / Journal of Marketing, April 1999

Checks for Respondent Bias

We obtained two additional random samples, of 1000 respondents eacb, for tbe two waves of tbe survey. Tbese samples are representative of the larger database of more than 300,000 completed surveys obtained every year by tbe firm. Tben we compared tbe panel sample of 5206 respondents witb tbe first and second wave samples. These comparisons were made on tbe basis of demographics and tbe overall satisfaction and intentions scales used in tbe study. There were two comparisons: the first between the pane! and the initial consumption survey sample (5206 versus IO(X)) and the second between tbe panel and tbe later consumption survey sample (5206 versus 1000). Botb comparisons showed that the demographic profiles of the members were similar (all ps > .20) and tbat tbe ratings on tbe overall satisfaction and

intention scales were statistically the same (all ps > .20). Thus, we can be reasonably assured that the data set used in our study is not biased.

Measures Tbe key constructs—attribute-level evaluations, product and service satisfaction, and behavioral intentions—were measured on a ten-point scale. For each attribute, respondents were asked "bow satisfied are you with the performance on...," and tbey indicated tbeir response on a ten-point scale for which 10 = completely satisfied and 1 = very dissatisfied. As we show in Figure 2, five attributes for the product and five tor the service were included. Overall satisfaction was measured on a similar ten-point scale, in response to the question, "Overall, bow satisfied are you with the product (service)?" Finally, using a ten-point scale (10 = definitely would, 1 = definitely would not), respondents indicalcd how likely they would be to recommend tbe company's prtxluct and tbe dealcrsbip to others in the future. All of tbe constructs are measured as single-item scales. Altbougb tbe use of single-item measures may attenuate tbe estimated rclationsbips, sucb measures have been employed successfully in large-scale commercial surveys (cf Bolton and Drew 1991; Mittal. Ross, and Baldasare 1998). LaBarbera and Mazursky (1983) point out tbat. in longitudinal surveys, tbe use of multi-item scales can affect the response rate adversely, due to longer survey length, and may decrease, rather than increase, overall reliability. Therefore, single-item measures were considered adequate for this study. Also note that this survey used "likelihood to recommend" to measure bebavioral intentions. Tbis is different iban tbe typical intentions variable (repurcbase intentions) used in most satistaction studies. However, tbere is empirical support for using tbc likelibood to recommend as a dependent variable. Zcitbaml. Berry, and Parasuraman (1996) lest a 13-itcm battery of bebavioral intentit)ns in the context of five industries. Across all industries, they find that intentions to recommend and repurchase were bigbly correlated. In particular, for the automotive industry, intentions to recommend and repurcbase had loadings of .94 and .87 on the same factor. Moreover, intention to recommend is an important dependent variable because, in most product and service categories, word of moutb is one of tbe most important lactors in acquiring new customers. Tbus. the current measure of behavioral intentions is considered appropriate.

Analysis and Results Tbe analytical model can be specified tbrougb the system of equations sbown in Table I. Equations I. 2. 3. and 4 pertain to T]. In Equations I and 2, satisfaction witb the product and service are multiattribute judgments. Equations 3 and 4 specify tbat the level of satisfaction experienced with the product and the service, respectively, influences bebavioral intentions toward tbe manufacturer and tbe service provider. The crossover effect is incorporated by sbowing tbat service satisfaction infiucnce.s intentions toward tbe manufacturer and that vehicle satisfaction inlluences intentions toward the service provider.

Equations 5, 6, 7, and 8 pertain to T2. In Equation 5, vehicle satisfaction in T2 is a function of attribute-level performance and overall vehicle satisfaction evaluation in Tj. In Equation 6. satisfaction witb tbe service experience is a function of attribute-level performance in T2 and the overall satisfaction with the sales experience in T|. Equations 7 and 8 model the formation of intentions toward the manufacturer and dealer. In Equation 7, intention toward the manufacturer is not only a function of vehicle satisfaction in T2, but also of service satisfaction during T2 and intentions toward tbe manufacturer in T|. Finally, in Equation 8, intention toward the dealership is a function of satisfaction with the service in T2, satisfaction with the vehicle in T2. and intention toward the dealership in T]. Thus, Equations 7 and 8 capture the carryover and crossover effects. Tlic system of equations is estimated as a path model. Estimation results are summarized in Figure 2, in wbich it can be seen that all coefficients are in the expected direction and support the conceptual model. Moreover, all but one of the coefficients are statistically significant.

Model Fit The overall X^^legrees of freedom [df] = 156) = 3287.57 {p < .0001) is highly significant. However, because of the large sample size, this may not be a useful diagnostic for evaluating model fit. All tbe other fit indices indicate tbat the model fits tbe data well. Tbe goodness-of-fit index is .96. witb a root mean square residual (RMSR) of .15. Furthermore, Bentler and Bonett's nortned fit index is .97, and Bentler's comparative fit index is .97. These diagnostics indicate excellent model fit.

Attribute Weights Cross-Sectionally and over Time The examination of attribute weights pertains to patbs a| ... a,, in Figure 1. Our main interest is understanding the dynamic nature (i.e., intertemporal variability) of attribute weigbts. However, we first briefly consider cross-sectional variability in attribute weigbts. Cross-sectUmal results. Consistent with previous studies (Bolton and Drew 1991), these results show tbat, for botb periods, satisfaction with the product and service is a multiattribute function, However, for botb T| and T2. attributelevel evaluations explain mucb less of the variation in vehicle satisfaction (55% and 58%) than of the variation in service satisfaction (79% and 83%). This may imply that the survey used here has a more complete attribute list for the service than for the product subsystem. Furthermore, and as was expected, for both the product and service element, difterent attributes have different weights within eacb time period.^ Firms can use tbese differential weigbts for allocating resources among various attributes. Attribute weight shifts over time. Our main interest, bowever, Is to compare tbe weigbt of eacb attribute in T| witb its weight in T2 (21 montbs later). To do tbis, first a constrained model in which the weight of an attribute is set st of these ditTerenees were statistically significant, using the incremental x2 test described subsequently. In the interest of space, a detailed discussion of the significance lesiing i.s excluded. Detailed results can be obtained from the lead author. Consumption-System Approach / 93

FIGURE 2 Model Estimation Results for Automotive Industry

Accessories

Transmission

Wait before write up

Quality of work done Vehicle ready when promised completed

.07

Product satisfaction (T,)

Intention toward manufacturer (T,)

I .17

Service satisfaction (T,)

Intention toward

service provider (T,)

Wait before write up

Quality of work done Vehicle ready when promised completed

.003"^ I .15

Product satisfaction

Intention toward manufacturer (T2)

Service satisfaction

Intention toward service provider

Notes: N = 5206. Time interval between T1 and T2 = 21 montbs. 1 All coefficients (except as indicated) are at least significant at p < .001. Goodness-of-fit index = .96, adjusted goodness-of-fit index = .90, root mean square residual = .16, comparative fit index = .97, B&B normed fit index = .97. The boxes around attributes have been ommitted for clarity. f

^ ^

Curved arrows represent temporal effects. Dotted arrows represent crossover effects.

94 / Journal of Marketing, April 1999

TABLE 1 Analytical Model Time Period 1 Product satisfaction = f(attribute performance)

(1)

Service satisfaction = f(attribute performance)

:•

(8)

Behavioral intentions toward manufacturer = f(product satisfaction, service satisfaction)

(3)

Behavioral intentions toward service provider = f(product satisfaction, service satisfaction)

•

(4)

Time Period 2 Product satisfaction = f(attribute performance, product satisfaction in Ti)

(5)

Service Satisfaction = f(attribute performance, service satisfaction in T^)

(6)

Behavioral intentions toward manufacturer = f(product satisfaction, service satisfaction, behavioral intentions toward manufacturer in T^) Behavioral intentions toward service provider = f(product satisfaction, service satisfaction, bebavioral intentions toward service provider in T,)

(7)

to be equal in botb time periods is estimated. Then, the overall fit of the constrained model is compared with the fit of an unconstrained model, in which the weights are allowed to be ditferent for botb time periods. If the fit of the constrained model is significantly worse than that of the unconstrained model, it is concluded that the attribute's weigbt changed over time. Tbe statistical significance of tbe decrement in fit is evaluated by comparing the X' statistic (df = I) tor the constrained and unconstrained nuxlel. If the x~ for the constrained mtKJel is significantly larger than that for the unconstrained model, the constrained mixlcl can be rejected, and it can be concluded that the weight of an attribute shifted over lime. Results based on this testing strategy are summarized in Table 2. For the product component, the attribute weigbts remain uncbanged for three attributes: "roominess," "accessories." and "handling." For "transmission," however, tbe weigbt drops almost balf, wbereas for "brakes," tbe weight increases from .11 to .15. Tbis could be due to tbe cbanged salience of tbese two attributes. Initially, consumers may be

(8)

more interested in the "transmission" of a new car, but not the "brakes." After two years of ownership, tbeir preoccupation witb "brakes" may increase, wbereas tbe preoccupation with "transmission" may decline. The salience of the other three product attributes may have stayed constant over time. Another interpretation of these results is that the shift in weight occurred for "functional/mechanical" attributes, wbereas the weigbts for "experiential" attributes did not cbange as mucb. Tbis explanation, bowever. is only partially borne out for tbe service subsystem, in which the weigbt for "wait before write up" decreased from .07 to .003 and the weight tor "vehicle ready when promised" decreased from .17 to .09. For arguably the most "experiential" of all attributes, "boncsty and sincerity." tbe weigbt increased from .45 to .55. In tbe case of services, attributelevel salience may belp explain the results. "Wait before write up" and "vebicle ready when promised" are procedural attributes whose already low salience decreases with time (perhaps because tbe dealersbip performs well on them on every successive encounter). However, on the oth-

TABLE 2 Shifts in Attribute Weights over Time: Model Estimates Attribute Roominess Accessories Handling Transmission Brakes Honesty Wait before write up All work completed Quality of work done Vehicle ready at promised time

Weight at T,

Weight at T

Is Shift Significant?

.11 .22 .26 .11 .45 .07 .17 .22

.20 .11 .22 .14 .15 .55 .003 .15 .19 .09

No No No Yes Yes Yes Yes No No Yes

.17

Note: The alpha for significance is set at .005 rather than .05 because of the Bonferroni adjustment. The logic is as follows: Because we have five attributes for the product and five for the service (a total of ten attributes), ten alternative models with the equality constraint are estimated. The Bonferroni correction is applied to the customary alpha level of .05 to control the overall Type 1 error rate to.05. Specifically, the alpha is lowered by a factor of 10 (.05/10 = .005), such that each test is considered significant (i.e.. the constrained model is rejected in favor of the base model) only if it exceeds tbis tfireshotd. With degrees of freedom = 1 and a = .005, the x^ should increase by at least 7.88 to reject the constrained model.

Consumption-System Approach / 95

er three attributes, the salience Increases with time due to performance variability, especially because these three attributes entail a significant amount of human interaction. Thus, over time, their salience and their weight increased or remained the same. Finally, ihese results should be interpreted in light of the large sample size and consequent increase in statistical power that made it possible to detect small fluctuations in attribute weights. Even so, the magnitude of several shifts is substantial. For example, the weight for "transmission" and "vehicle ready when promised" decreased by almost half. These are substantive shifts for a manager interested in allocating resources among attributes to improve overall satisfaction. Thus, the conclusion is that attribute weights determined over a cross-section of time may not generalize to the entire consumption period. However, additional research is needed to understand the causes underlying shifts in attribute weights.

Crossover Effects of Product and Service Satisfaction in Determining Behavioral Intentions Our interest is to understand links b and c in Figure 1. Conceptually, there are two issues: (i) How does satisfaction with the product affect intentions toward the service provider and vice versa? and (2) How does the relative magnitude of these effects change over time? The impact of product satisfaction on intention toward manufacturer is virtually identical in T| (.52) and Ti (.49). However, it shows a sharp decline for service; the impact of service satisfaction on intention toward the dealership declines from .58 in T[ to .39 in TT (p < .01). This may have occurred because, in addition to satisfaction, other aspects of a consumer's experience affect intention toward the dealership. Those aspects were not addressed in this survey. Thus, these results caution against generalizing results on the basis of only a cross-section of the entire consumption experience. More important, they indicate additional research should explain why, over time, the relative importance of product and service satisfaction varies for the same set of consumers. We now examine the crossover effects. The effect of vehicle satisfaction on intentions toward the service provider is significant in T, (.14, p < .0001) and Tj (.25, p < .0001). Similarly, service satisfaction has a significant effect on intentions toward the manufacturer in T| (.17. p < .000!) and T2 (.14, p < .0001). We also investigate whether these crossover effects are asymmetric and if they vary over time. That is, (I) is the impact of product satisfaction on intentions toward the service provider greater than the corresponding impact of service satisfaction on intentions toward the manufacturer? and (2) does the ratio of these impacts change over time? To answer these questions, two additional tests were performed. In the first test, the relative magnitude of the crossover effects in Tj was assessed. Specifically, the impact of product satisfaction on intentions toward the service provider (.14) was constrained to be equal to the corresponding impact of service satisfaction on intentions to-

96 / Journal of Marketing, April 1999

ward the manufacturer (.17). The constrained model provided a worse fit to the data than the unconstrained model (A^- = 5.8. df = \, p .05) and was rejected. Therefore, it can be concluded that, during the initial consumption period, service satisfaction has a larger crossover effect than product satisfaction. In the second test, the same constraint was imposed, but for TT. Specifically, the impact of product satisfaction on intentions toward the service provider (.25) was constrained to be equal to the impact of service satisfaction on intentions toward the manufacturer (.15). The model with the equality constraint was rejected in favor of the model in which these paths were allowed to vary freely (Ax- = 40.8, df = \,p< .0001). Therefore, it is concluded that, during the later consumption period, product satisfaction has a larger crossover effect than service satisfaction. To put these results in perspective, we note that, in a similar analysis conducted to compare satisfaction within one week of ownerships it was found that the link between satisfaction with the dealership sales experience and intention to recommend manufacturer was much stronger (.44,/J < .0001) than the link between satisfaction with the car and intention to recommend the dealership (.04, p < .01). In other words, the crossover effect of service satisfaction was much larger than that of vehicle satisfaction. Collectively, these results show that, in the automotive industry, the crossover effect of product and service satisfaction on intentions toward the dealer and manufacturer is highly asymmetric. During the initial consumption period, service satisfaction has a larger crossover effect than product satisfaction. Over time, however, this asymmetry reverses; after 21 months, product satisfaction has a larger effect than service satisfaction. This pattern of results is consistent with the success of brands such as Lexus and Saturn, which invested heavily in dealership service and generated high levels of positive word of mouth toward the manufacturer during the initial stages of ownership. This also implies thai, during the early stages of building a relationship, manufacturers should focus on the service, and then during the later stages, they may focus on the core product of the consumption system. It is also instructive to compare these results with those found by Crosby and Stephens (1987). In the context of the insurance industry, they test two alternative models: "rational evaluation model or REM," in which satisfaction with the core product drives satisfaction with peripheral services, and "relationship generalization model or RGM." in which peripheral services drive satisfaction with the core product. They find that satisfaction with the core product (i.e., the policy) affected satisfaction with peripheral services (e.g.. contact person) and conclude in support of the REM model. However, this support may have occurred because respondents in their study had owned the policy for extended periods of time (13 months or more). Had these respondents been surveyed at a shorter time interval after purchasing a ''Detailed results can be obtained from the lead author.

policy, the results may have been different and supported Ihe alternative RGM. To a large extent, the automotive data initially support RGM and later REM. That is, the time interval may moderate the extent to which RGM or REM is supported. Again, the importance of considering temporal variahility in understanding the relationship among attribute evaluation, satisfaction, and intentions is highlighted. Satisfaction and Intentions over Time: Carryover Effects We are interested in Ihe mediating role of links d, e, f, and g in Figure I. Specifically, we want to understand how satisfaction influences intentions over titne. Is there a direct link between satisfaction and intentions, or Is their relationship explained through a mediation model? Three alternative models were estimated to investigate these issues (see Table 3 for results). However, because the three models are nonnested, we cannot statistically reject one model in favor of another using the incremental change in X' (Boilen 1989). Therefore, we estimated a full model, also shown in Table 3, in which all three models are nested

and evaluated each of the three rival models in comparison with the full model (e.g.. Cliff 1983; Cudeck and Browne 1983). Compared with the lull model, we expect the fit of each of the models lo deteriorate, but an examination of the relative deterioration enables an evaluation of the competing models. Furthermore, we cannot draw statistical inferences about the relative deterioration in fit for the three competing models. For example, though we can say that, compared with the full model. Model A fits much worse than Model B, we cannot statistically test this hypothesis (i.e., we cannot associate a p value to the differential fit between Models A and B). With this limitation in mind, the results for the models are shown in Table 3 and described next. Direct model (Model I). To establish whether satisfaction in T] has a direct influence on intentions in Ti. a tnodel in which behavioral intentions in Ti are a function only of satisfaction in Ti was estimated. Thus, intentions in Tj are set to be a function only of satisfaction with product and service in T|. Results are shown in the second column of Table 3. Compared with the full model, this model fits the data significantly worse (Ax- = 4917.7, df = 8. p < .(H)01). Note al-

TABLE 3 Test of Alternative Models: Estimation Results Full Model For comparison purpose only

Path Manlntj, a. ProdSatxi b. ProdSatn Srvlntji c. ServSatji d. ServSatTi-> Manlnty, e. ProdSatji -> ProdSatyg f. SrvSatji -^ SrvSatT2 g. Manlntxi -* Manlntjg h. Srvlntji k. m. ProdSatji -^ Manlntj2 n. ProdSatji 0. ServSatji p. ServSatji

ProdSatjg r. Srvlntji -* Df Akaike's Information Criterion (AlC) Schwarz's Bayesian Criterion (SBC) Root mean square residual (RMSR)

Model 1

Model 2

Model 3

Direct model

Dualmediation model (proposed model)

Alternative mediation model

.52 .14 .58

.14

.17

.17

.15 .04 .34 .33 .51 .25 .38 .15 .08' .01 ns. .00" s. .Oins.06 .01 "s. 3230.0 150 2930.0 1933.7 .1548

.52

.€8 — • ^ - ~

.52 .14

.56 .17 .19 .04 .29

.33 .49 .25 .39

_

.15

.36 .24 .33

—

.14 — —

_— —

8147.7 158 7831.7 6782.3 .3700

3287.6 156 2975.6 1939.4 .1559

.52 .14 .58 .17 — — .29 .33 .49 .25 .38 .15 — — — — .16 .03 3440.3 156 3128.3 2092,1 .1574

Note: All paths are significant at p < -01. except those indicated by *(p < .05) or "•s(p > .10). The paths from attribute-level evaluation to satisfaction, though included in each model, are omitted to facilitate interpretation. When paths m. n, o, and p are included in Model 2, their estimates are as follows: m = -.08, p < .05; n = .01. n.s.; o = .00, n.s.; p - .01, n.s. ProdSat,] = Product satisfaction in T^; ServSat,, = Service satisfaction in T,; ProdSat^ = Product satisfaction in Tg; ServSatij = Service satisfaction in Tg; Manlntn = Intention toward manufacturer in Ti; Srvlnt,i = Intention toward service provider in T,; Manlnti2 = Intention toward manufacturer in T j ; and Srvlnt^ = Intention toward service provider in Tg.

Consumption-System Approach / 97

so that, of the three candidate models, this model has the worst fit. Dual-mediation tnodel (Model 2). As we show in column 3 of Table 3, this model posits a dual-mediation path. All the paths are significant and in the expected direction, in support of the dual-mediation model. Although the fit of this model is significantly worse than the full model (Ax^ = 57.6, df = 6. p < .0001). the Akaike's Information Criterion (AIC), Schwartz's Bayesian Criterion (SBC), and the RMSRs show that the relative deterioration in fit is not as large as in the direct model. This suggests that the temporal link between satisfaction and intentions is best understood as a mediated relationship. However, to establish mediation conclusively, we also must include the direct paths (paths m. n. o, and p in Table 3) in Model 2 and compare them with the coefficients obtained in Model 1 (Baron and Kenny 1986). Note that in this model, paths m, n, o, and p are not included because we do not hypothesize a direct link between satisfaction in T| and behavioral intentions in Ti. If mediation exists, then in Model 2, these direct paths should become 0 or nearly 0. Therefore, Model 2 was reestimated after including paths m, n, o, and p. TTieir coefficients were near 0, as follows: m = -.08, p < .05; n = .01. n.s.; o = .00, n.s.; and p = .01. n.s. This also can be determined from the coefficients of these paths in the full model, in which they become almost 0 (see Table 3). We conclude that the effect of satisfaction in Tj on intentions in T2 is mediated and not direct. Alternative mediation model (Model 3), Model 2, the dual-mediation model, also was compared with an alternative mediation model, shown in the last column of Table 3. The alternative mediation model postulates that intentions in T| have a direct impact on intentions in Ti. as well as through satisfaction in Ti. This model does not consider the role of satisfaction in T| at all. To estimate this model, the paths linking satisfaction in T| to satisfaction in TT were dropped, and instead, paths linking intentions in Tj to satisfaction in Ti were introduced. Estimates based on this model are shown in the fourth column of Table 3. Again, as we expected, the fit of this model was worse than the full model (Ax- = 210.3, df = 6, p < .0001). Also, this model is clearly better than the direct model. Model 1. The usual strategy of comparing incremental changes in the x~ value cannot be used to compare Models 2 and 3 because they are not nested models. However, other statistics, such as the AIC and SBC, can be used to compare nonnested models (Bollen 1989; Cudeck and Browne 1983). For both the AIC and SBC, smaller values indicate better fit. On the basis of the AIC and SBC, we can conclude that the dual-mediation model (Model 2) is better than the alternative mediation mode! (Model 3). However, it is not possible to assess the statistical significance of these differences because the distribution of these measures is unknown (Bollen 1989). It is also worth noting that, for the automobile industry, the mediation occurs largely through the product (paths e and q) rather than the service (paths f and r) subsystem. One explanation for this finding is that automotive owners have more structured and frequent exposures to the product

98 / Journal of Marketing, April 1999

than to the serviee. This may lead more opportunities to evaluate the product, which facilitates higher levels of elaboration and recall at T2 and, therefore, more mediation. However, more research is needed to clarify such issues.

Summary, Discussion, and implications of Resuits Using longitudinal data, this article examined the temporal lability in the relationship among attribute-level evaluations, overall satisfaction with product and service subsystems, and behavioral intentions. Results show the following: •The relationship between attribute-level evaluations and overall satisfaciion, which only has been investigated on a crosssectional basis in prior studies, i.s dynamic and changes over time. For five of the ten attributes investigated, there was a shift in weights from one time period to the other. •Both product and service satisfaction have an asymmetric crossover effect in determining behavioral intentions toward the manufacturer and dealer. In addition, the relative magnitude of the crossover effect changes over time, [niiially, satisfaction with the service is more important, but satisfaction with the product becomes more important during later consumption periods. •The results clarify the process by which satisfaction in T| influences intentions in T2. Both satisfaction at T2 and intentions a t T | act as mediators for this link. The model positing only a direct link between satisfaction in T| and behavioral intentions in TT performs poorly. Thus, over time, the satisfaction-intention relationship is best conceptualized as a mediated rather than a direct relationship. Collectively, these results indicate the need to examine the temporal lability in the magnitude of relationships among attribute-level evaluations, satisfaction, and behavioral intentions. Moreover, they indicate that consumer experiences should be examined as a system involving a product and service subsystem. Both notions have implications for academic research and marketing practice.

The Product and Service Subsystems of the Consumption System Many firms are realizing the complementary role of product and service subsystems in structuring consumer experiences. Rucci. Kirn, and Quinn (1998) document the efforts of Sears, whose managers, after decades of reliance on product offerings alone, started emphasizing service during the late 1990s. The marketing research system was redesigned to measure product and service subsystems specifically and link each of them to consumer behaviors and profits. However, these efforts do not account for the shifting emphasis on the product and service subsystems as consumers' experience with the consumption systems evolves. Moreover, it is not known how evaluation of one infiuences the other. For example, it remains to be investigated whether consumers make comparisons between the product and service subsystem and how such comparisons affect consumer judgments of overall satisfaction and behavioral intentions. Similarly, exceptionally good service may cause a customer to have a negative or positive view of the product, depending on whether there is an assimilation or contrast effect (Oliver

1997). Examining such issues and uncovering factors that moderate the outcomes of such comparisons over time are key research areas. The consumption system approach also enables a reconciiialion of disparate notions of ulihty engendered by many researchers. Researchers distinguish hetween (!) utility derived from the transaction versus utility derived trom the product (Frenzen and Davis 1990) or (2) decision utihty versus product utility {Fitzsimons, Greenleaf, and Lehmann 1997). These distinctions may denote satisfaction with various suhsystems of a consumption system. However, the consumption system approach goes one step further hy highlighting that satisfaction with the different suhsystems is time-dependent. Further research should focus on charting and explaining the temporal lahility of satisfaction with diffcrcnl subsystems- More generally, research should determine factors that explain the shifting importance of each subsystem. One such factor may be the frequency of exposure and cxlrenicness uf performance during each exposure to a suhsystem (Oliver 1997).

The Attribute Evaluation and Satisfaction Relationship: Dynamic View We demonstrated the temporal iahility of attribute weights in determining overall satisfaction. However, the reasons lor such shifts were nol ascertained fully. Thus, overlaying a theoretically motivated explanation for the ohserved attribute weight shifts is a key research opportunity. Consider the following pattern of results observed among investors of a mutual fund investment firm: When measured within one year of joining the firm, investors accorded a high weight to attributes such as "feel comfortable talking to advisor" and "advisor is courteous." However, after being with the firm for more than five years, they accorded high weight to attributes such as "advisor provides just the right amount of information" or "advisor can solve problems or answer questions in a single visit." Follow-up qualitative research showed that, early on, investors were looking to build trust and confidence, but at later stages of the relationship, efficiency became a key factor. Data also showed that tbe product component (performance of the mutual fund) was more important during the early stages compared with the later stage, though it was always more important than the service elements.** By understanding that trust and efficieney requirements change over time. Ihe firm was able to manage its investors much better than a static view would have allowed. The point is ihat firms must rethink their satisfaction programs to aeeommodate such temporal shifts. At the least, satisfaction tracking studies should be designed to measure satisfaction across the entire consumption experience rather than a single cross-section of it. Operationally, this may entail using longitudinal research designs to supplement currently exist-

'*Noic that these resulis are different than the auiomotive results. The impodancc of the automobile was lower during the initial consumption periiKl and increased over time. However, for the mutual fund, the exact opposite happened; the product was more important than the service in the initial period.

ing cross-sectional studies. For research, attribute weight variability over time should be Investigated using experimental studies in which various dimensions of attributes (e.g., the variation in attribute performance, the salience of an attribute) are controlled carefully. Tbe goal should be to identify unique antecedents that can predict tbe magnitude and direction of intertemporal change in an attribute's weight. Investigations along these lines could enable us a priori to predict temporal changes in an attribute's weight. It also remains to be examined how attribute-level evaluations affect retrospective evaluations of consumption experiences. Mitchell and colleagues (1997) find that retrospective evaluations of consumption experiences tend to be biased positively. However, tbis may have occurred because they examined experiences such as vacations, for whieh people are motivated to impose a more "rosy view" to reduce dissonance or tninimize regrei. By taking ati attribute-level approach, a more diagnostic model of such evaluations ean be formulated. An attribute's eontribution lo tbe overall evaluation may depend not only on its salience, but also on its temporal distance from tbe final overall evaluation. Thus, attributes tbat are experienced closer to tbe final evaluation may contribute more than those with a larger temporal distance. Conversely, attributes tbat are experienced early in a consumption experience may act as key reference points against wbicb subsequent performances are judged. These ideas ean be explored systematically to better understand wby retrospective evaluations of consumption experiences do not always correspond with "in process" evaluations. At a fundamental level, these results raise a key issue: acknowledging and investigating the temporally situated nature of the relationships involved, in an early study, Calantone and Sawyer (1981, p. 322) observe large temporal sbifts in attribute importance weigbts among consumers and suggest tbat "tbe evidence of individual change migbt signal an opportunity to gain useful insigbt into the dynamic bebavior of consumer markets." Yet within satisfaction literature, progress in this regard has been modest. Perhaps this bappened because of the dominanee of an "effeets" paradigm, in wbich the sole concern is to show ihe existence or absence tbereof of an effect. Only recently (cf. Fomell 1992) has literature begun to address variation in tbe magnitude of key relationsbips empirically. Even so, mucb of the focus is on cross-sectional comparisons, thougb theory indicates the importance of considering temporal variability in these relationsbips (Jobnson and Fornell 1991). However, doing so entails an explicit attempt to demonstrate empirically tbe temporal lability in key relationsbips and then examine the antecedents of such variability. We bope tbat additional researcb will move in tbat direction, that is, acknowledging, investigating, and understanding tbe temporally labile nature of the relationship among attribute pertbrmance. overall satisfaction, and behavioral intentions. Finally, adopting a temporally situated view enables us to investigate tbe role of consumer learning in influeneing these relationships. For example, over time, as consumers learn more about certain attributes, tbe attributes' weight in detennining overall satisfaction may cbange. Such learning is even more important for information-intensive and skill-

Consumptlon-System Approach / 99

based products, such as software and Web sites. As consumers learn more about a particular attribute, they may become more efficient users of it, and that efficiency direelly may affect the level of satisfaction they experience. Moreover, with increased efficiency, these consumers may be reluctant to switch to other brands. Thus, consumer learning (e.g., efficiency gains) may mediate the effect of attributelevel performance on satisfaetion and bebavioral intentions. Such issues can be examined effectively in a consumption systems context.

(e.g., buyers of sports cars). More generally, researchers should investigate the processes by wbich satisfaction translates into its consequences over a series of consumption episodes. Experimental research may be used to test whether this occurs due to learning, need for consi.stency, or adaptation of reference points. There is also a need to outline boundary conditions in which each of the three competing explanations is more likely to hold. Model Extensions

The temporally dynamic and asymmetric crossover effects suggest a need to examine how several firms, acting jointly, try to influence satisfaction with the subsystem that each contributes to the consumption experience and behavioral intentions toward themselves and other firms involved. The inherent asymmetry of crossover effects provides a behavioral foundation for exploring interorganizational conflict and cooperation (Smith, Carroll, and Ashford 1995), whereas the evolutionary nature of these crossover effects provides a basis for researching resource allocation issues related to satisfaction management (Chu and Desai 1995). Although there is anecdotal evidence on how manufacturers and their service partners cocreate customer value (Anderson and Narus 1995). satisfaction research could benefit from a general framework to model conflict and cooperation explicitly within a consumption system setting. For example, is overall satisfaction experienced by the consumer

Finally, the model developed here ean be extended in many ways. First, the asymmetry and nonlinearity found in the attribute performance and overall satisfaction link should be incorporated in the model. For example, it has been shown that negative performance on a utility-preserving attribute has a larger deleterious impact on overall satisfaction than the beneficial impact of positive performance on that same attribute (Mitta!. Ross, and Baldasare 1998). However, research has yet to examine whether and how these asymmetries vary systematically over time. For example, during the initial consumption period, an attribute may be utility enhancing, such that positive performance is more consequential than negative performance. Over time, as consumers' reference points adapt, the same attribute may become utility preserving, such that negative performance is more consequential than positive performance. Thus, for the same consumer, we may fmd a reversal of the asymmetry. Moreover, the magnitude of the asymmetry may change over time. If so, why? These issues should be investigated

higher when the manufacturer exerts greater control over the

in further research.

dealership? What are the normatively desirable incentive structures that simultaneously maximize satisfaetion with the dealer and the manufacturer?

Second, building on the belief updating perspective (Hogarth and Einhorn 1992), the mode! should be refined to explicate the cognitive processes that link attribute-level evaluations to satisfaction judgments. For example, how are reference points about attribute-level performance set and updated during a series of consumption episodes, and what is the influence of such updating on the satisfaction judgment? Although many variants of reference points, such as desires, norms, and standards of comparison (Oliver 1997) have been suggested, it is not known when or why one type of reference point is more important than others. Clarifying the relative importance of various reference points over time would be an important extension of the consumption systems model. Such extensions should be bundled with a replication of the current study in different industries and settings and using different measures to increase their generalizability.

Channels Management

The Satisfaction-Intention Relationship Our results show ihat the process underlying the translation of satisfaction into intention over time is rather complex. Both satisfaction and intentions were found to be mediating routes in the automotive industry, and in general, the product subsystem showed stronger results for the satisfaction-intention linkage. A dynamic model of satisfaction and consequent intentions should explain the relative weight given to each subsystem. For example, one explanation is that a subsystem's contribution to goal-attainment may moderate its role as a mediator. Thus, the service subsystem may be a stronger mediator for service-driven consumers (e.g., a mother) than for product-driven customers

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