Judgment and Decision Making, vol. 6, no. 4, June 2011, pp. 307-313
Maximizing and customer loyalty: Are maximizers less loyal?Linda Lai* |
Despite their efforts to choose the best of all available solutions, maximizers seem to be more inclined than satisficers to regret their choices and to experience post-decisional dissonance. Maximizers may therefore be expected to change their decisions more frequently and hence exhibit lower customer loyalty to providers of products and services compared to satisficers. Findings from the study reported here (N = 1978) support this prediction. Maximizers reported significantly higher intentions to switch to another service provider (television provider) than satisficers. Maximizers’ intentions to switch appear to be intensified and mediated by higher proneness to regret, increased desire to discuss relevant choices with others, higher levels of perceived knowledge of alternatives, and higher ego involvement in the end product, compared to satisficers. Opportunities for future research are suggested.
Keywords: maximizing, satisficing, customer loyalty, regret, ego involvement.
Schwartz et al. (2002) presented new evidence that people differ in their general motivation to invest time and resources in the decision making process. Individuals with a preference for maximizing aspire to find the best possible option and are motivated to seek information about as many alternatives as possible before making a choice. Individuals with a preference for satisficing, in contrast, tend to consider a more limited range of alternatives with the purpose of finding an option that satisfies given criteria or aspirations, i.e., an option that is considered satisfactory or “good enough”. Schwartz et al. (2002) propose that differences in the preference for maximizing (versus satisficing) may be conceptualized as a stable personality trait. Some individuals are chronic maximizers whereas other individuals are habitual satisficers across a wide range of decision making tasks and domains.
A particularly interesting finding from Schwartz et al. (2002) and several subsequent studies is that maximizers, despite their efforts to find the best possible option, appear to regret their decisions more often than satisficers. Subsequent studies also indicate that maximizers also tend to experience more postdecisional dissonance compared to satisficers (Iyengar, Wells & Schwartz, 2006). These seemingly paradoxical findings are supported by other studies that indicate that the maximizing trait may represent a reliable predictor of whether or not a person is prone to change his/her initial decisions. For example, in one study, maximizers were found to change their gift purchase decisions significantly more often than satisficers, if given the opportunity to do so (Chowdhury, Ratneshwar & Mohanty, 2009). Preliminary findings also indicate that, in order be satisfied and engage in repeat purchase, maximizers rely more on high service quality than satisifcers (Carrillat, Edmondson, & Ladik, 2006).
Implications of the maximizing trait for decision stability and change are of high potential significance to a wide array of personal or professional decision making domains, for example job search and career development (Iyengar et al., 2006), negotiations (Hackley, 2006), investments, education, marriage (or divorce), and consumer choice. Many US corporations lose around half of their customer base in a five year period (Sood & Kathuria, 2004), and churn rates exceed 50 per cent for many companies offering subscription services1. In spite of the considerable costs associated with customer churn, determinants of consumers’ decisions to switch brands or service providers are still not well understood. Although there is an increased awareness that individual differences affect customers’ attitudes and behaviors, very little is known about how and why customers differ (Simonson & Nowlis, 2000; Sood & Kathuria, 2004). Schwartz et al.’s (2002) work on the maximizing trait therefore offers valuable potential for advancing research on individual differences in customer behavior and choice. The main purpose of the present study is to investigate whether the maximizing trait is relevant to explaining customer loyalty and switch intentions, i.e., whether or not maximizers are less loyal customers that more frequently intend to switch from one service provider to another.
The hypotheses tested here rest on previous findings that link the maximizing trait to increased inclination for regret (Schwartz et al., 2002), post-decisional dissonance, fixation on realized and unrealized options (Iyengar et al., 2006), dissatisfaction with choices (Schwartz et al., 2002; Iyengar et al., 2006), and increased rates of decision change (Chowdhury et al.,2009). Based on these findings, it seems plausible to expect that maximizers will differ from satisficers in terms of an increase in the propensity for continued search for better, yet unrealized options. Satisficers, in contrast, having made their choices based on given aspirations or standards rather than the quest for the best possible solution, may be expected to be less affected by external triggers such as unfavorable price increases or competing offers. It therefore seems likely that satisficers will exhibit higher (passive) loyalty and correspondingly lower levels of decision change and intentions to switch.
Maximizers will exhibit higher intentions to switch between service providers compared to satisficers.
Four potential mediators of the proposed relationship will be tested in the present study. Findings from Schwartz et al.’s (2002) studies suggest that regret may play a mediational role in the relationship between maximizing and depression and between maximizing and happiness. However, based on the methods used, definite conclusions about causality cannot be drawn. Yet, as argued by Schwartz et al. (2002: 1185), the risk of regret is ever present since maximizers will ask themselves; “Is this the best outcome?” and “Could I have done better?” The experience of regret in turn, represents a likely trigger of continued search for present or future alternatives as well as of prolonged comparison of realized and unrealized options. Maximizers will therefore tend to develop a better knowledge of the range of available alternatives compared to satisficers as well as more in-depth knowledge about each option, which in turn may increase the likelihood of identifying an option that seems better than the realized one. Accordingly, it may be expected that maximizers’ level of knowledge of potential options will be positively associated with intentions to switch.
Maximizers desire for extensive alternatives search also increases the likelihood of initiating and becoming involved in relevant discussions with others, including family and friends. Findings by Parker, Bruin de Bruine and Fischhoff (2007) also suggest that maximizers rely more on others when making decisions. Discussions with others imply not only a possibility for information exchange, but also an arena for social comparison. In view of Schwartz et al.’s (2002) findings that maximizers tend to engage in more (upward) social comparison, it seems likely that discussions with others will enhance the likelihood of developing intentions to switch service provider in order to satisfy needs triggered by social comparison.
When we investigate the relationship between maximizing and intentions to switch, we should also consider consumers’ involvement in the decision making domain. Consumer involvement plays a key role in research on consumer loyalty and switch behavior. Involvement in the purchase (process involvement) and/or the product (ego involvement) has been found to moderate many relationships involving customer satisfaction and brand commitment (Sood & Kathuria, 2004).
The purchase dimension of consumer involvement tends to be conceptualized and measured in a way that closely resembles the extensive alternative search aspect of maximizing. Purchase involvement may therefore be seen as a manifestation of maximizing efforts within the particular domain. The second dimension of involvement, in contrast, typically referred to as “ego involvement” in a product, reflects “the importance of the product to the individual and to the individual’s self concept, values, and ego” (Beatty, Kalhe & Homer, 1988). Ego involvement in a product tends to be a long-term, enduring type of involvement that reflects a stable concern for a particular class of products across different purchase processes and situations. Highly involved customers have been found to exhibit higher levels of maximizing efforts such as greater prepurchase search (Beatty & Smith, 1987) and greater deliberation in choice (Celci & Olson, 1988). These findings may indicate that ego involvement and maximizing are related, although the direction of causality is unclear. On the one hand it seems plausible that high ego involvement in a product tend to trigger efforts to maximize. One the other hand, it seems possible that consumers’ propensity for maximizing represents one of many influences on ego involvement and that increased product involvement may be developed as a function of efforts to maximize. This implies that the relationship between maximizing and product involvement may be reciprocal. Although the theoretical foundation is weak, we wish to explore the possibility that maximizing affects ego involvement in a product and that ego involvement acts as a (partial) mediator of the relationship between maximizing and switch intentions.
However, previous research has demonstrated that highly involved customers tend to engage in higher levels of positive disconfirmation and satisfaction (Richins & Bloch, 1991), which has been associated with higher levels of commitment to a decision and resistance to belief change (Pritchard, Havitz, & Howard, 1999), and highly involved customers have been found to engage less in switching behavior compared to less involved customers (Keaveney & Parthasarathy, 2001). The latter findings gives reason to hypothesize that ego involvement will moderate the effects of maximizing on intentions to switch, and that highly involved maximizers will be less (rather than more) inclined to report switch intentions than less involved maximizers.
The relationship between maximizing and intentions to switch service provider will be mediated by (a) disposition for regret, (b) knowledge of potential providers, (c) desire to discuss (similar types of) decisions with others, and (d) the level of consumer involvement, and moderated by (e) the level of consumer ego involvement.
Data were collected from 1978 subjects from the general population in Norway. Subjects responded anonymously to an electronic questionnaire that was distributed via e-mail. Subjects’ mean age was approximately 50, and approximately 77 per cent were male.
Intentions to switch television provider (i.e., cable or satellite television distributor, corresponding to, for example, Comcast or DirectTV in the US) was chosen as the sample domain. Churn rates within this business are very high, averaging around 20 per cent annually for the major providers in Norway.
Maximizing was measured by the five item scale developed by Lai (2010). This scale encompasses the extensive alternative search and the high standards aspects of maximizing, but not the difficulty aspect included in Schwartz et al.’s (2002) original maximizing scale. Items are: “Whenever I’m faced with a choice, I try to imagine what all the other possibilities are, even ones that aren’t present at the moment”, “My decisions are well though through”, “I am uncomfortable making decisions before I know all of my options”, “Before making a choice, I consider many alternatives thoroughly”, and “No matter what I do, I have the highest standards for myself”.
Regret was measured by the five items presented by Schwartz et al. (2002).
Intentions to switch (television) service provider was measured with four items that were developed for the purpose of this study based on previously well validated scales for measuring customers’ loyalty and switch intentions (e.g., Ganesh, Arnold and Reynolds, 2000; Sood and Kathuria, 2004). Items include: “I often consider changing television provider”, “I am happy to accept an offer from another television provider if I’m given an offer that is better than what I have today”, “I will probably change television provider within a year”, “I often consider offers from other television providers”.
Perceived knowledge of potential providers was measured with two items. The television provider market is divided into cable and satellite distribution services. Cable providers tend to offer television and broadband services, while satellite broadcasters offer one-way television only. Customers of satellite providers therefore need to subscribe to broadband services from another provider, and an increasing number of television viewers prefer to view television via broadband rather than traditional television distribution nets. Items were therefore formulated to refer to television providers as well as broadband providers, and final phrasing was based on pretesting that demonstrated that the two items loaded on the same factor. Items were: ”I have knowledge of all possible television providers to my home” and ”I have knowledge of all possible broadband providers to my home.”
The inclination to discuss decisions with others was also measured two items, of which one item referred to television provider whereas the other item referred to broadband provider: “I discuss the choice of television provider with my family and friends” and “I discuss the choice of broadband provider with my family and friends.”
Consumer involvement was measured by five items that referred to television involvement. End-product (television) involvement was preferred over provider involvement as most people see the provider as only a means to an end (i.e., access to television programs and related services) and hence develop very little if any involvement in the provider per se. The end-product (television), in contrast, is generally considered a high involvement product. Items include: “Television is important to me”, “I watch television a lot if I have the opportunity”, “I regularly check program guides for television”, “I often discuss what I have watched on television with others”, and “It is important to me to watch certain programs or series on television”.
All items that were drawn from English sources were adapted into Norwegian,2 using the recommended translation and back-translation procedure, and all items were measured using a 5-point scale (1 = completely disagree, 5 = completely agree).
Table 1: Descriptive statistics.
Variables Items Mean SD 1 2 3 4 5 6 1 Maximizing 5 3.86 .67 (.77) 2 Intention to switch 4 2.36 1.02 .14** (.82) 3 Regret 5 2.62 1.02 .16** .23** (.85) 4 Knowledge of providers 2 3.47 1.44 .17** .32** .05* (.68) 5 Discussions with others 2 2.93 1.39 .18** .29** .11** .37** (.69) 6 Television involvement 5 3.24 .93 .14** .19** .23** .27** .25** (.82) Notes: N=1978, ** p < 0.01 level, * p < 0.05 level (2-tailed).
Data were analyzed in several steps. First, factor analysis (principal component analysis with promax rotation) was performed in order to determine item retention. In order to avoid problems associated with confounded measures of closely related constructs, relatively stringent rules of thumb were applied and only items with loadings of .50, cross-loadings of less that .35 and a differential of .20 or higher with other factors where retained for further analysis. All scales demonstrated reliability estimates that were satisfactory or considered acceptable for first time measures (Cronbach’s Alpha ranges from .68 to .85). Analyses were also performed to ensure that the assumptions regarding multicollinearity were not violated.
Main effects were tested with regression analysis. To test the four mediation hypotheses, the three-step procedure recommended by Baron and Kenny (1986) was followed. In order to infer mediation, three criteria must be met. First, the independent variable must be significantly associated with the mediator. Second, the independent variable must be significantly associated with the dependent variable. Third, when the mediator is entered into the analysis, the relationship between the independent and the dependent variables should either disappear (full mediation) or significantly diminish (partial mediation), while the mediator should still predict the dependent variable. Correlation analysis was performed to test the first criterion. The second and third criteria were tested by hierarchical regression analysis, i.e., by entering the independent variables and potential mediators into the regression model in separate steps. Finally, hierarchical moderated regression (Cohen and Cohen 1983) was used to test the moderation hypothesis. The interaction term was computed by centering the relevant variables before multiplying them with each other, which is a procedure that reduces potential problems associated with multicollinarity.
Table 1 reports correlation between variables as well as the number of items, mean scores and standard deviations. Significance levels are reported, but should be interpreted with caution due to the large sample sizes and the likelihood of a Type 1 error, i.e., of incorrectly rejecting the null hypothesis. Results indicate that maximizing and switch intention are moderately correlated (r = .14, p = .000).
The results from hierarchical regression analyses, in which the independent variable and the four potential mediators are entered in separate steps, are reproduced in Table 2. The results suggest that maximizing significantly predicts intentions to switch (β = .12, p = .001). Hence, the results offer support for Hypothesis 1, but the magnitude of the differences in the means is quite small (η2 = .01, cf. Cohen, 1988).
Table 2: Regression results: direct and mediated effects on intentions to switch service provider.
Step 1 Step 2 Step 3a Step 3b Step 3c Step 3d Step 3 e Step 4 –.08*** –.08*** –.07*** –.01 –.07** –.08*** –.08*** –.01 –.08*** –.09*** –.06** –.07*** –.08*** –.09*** –.09*** .05 .13*** .09*** .04 .06** .10*** .12*** –.01 .20*** .16*** Knowledge of providers .37*** .28*** Discussions with others .30*** .17*** Television involvement .18*** .06** Maximizing x Television involvement .01 .012 .026 .066 .150 .120 .059 .026 .210 .014 .040 .124 .094 .033 .000 .184 12.997*** 18.991*** 35.079*** 89.472*** 66.740*** 31.944*** 14.195*** 76.988*** Notes: Standardized regression coefficients are reported. *p < .05; **p < .01; ***p < .001. Δ R2 in Steps 3a through 4 are relative to Step 2. Maximizing x Television Involvement refers to the interaction term, based on centering of scores. * Gender: male = 0, female = 1.
Correlation results (Table 1) reveal that the first criterion of mediation, that the independent variable must be associated with the mediator, was met for all four variables (regret: r =.158, p = .000; overview of providers: r = .172, p = .000; discussions with others: r = .177, p = .000; TV involvement: r = .135, p = .000). Regression results (Table 2) reveal that the second criterion of mediation, that the independent variable must be associated with the dependent variable, also was met (maximizing: β = .126, p = .000). Finally, the third criterion of mediation, that the relationship between the independent and the dependent variable should disappear (full mediation) or diminish (partial mediation), was met for all four variables, and each mediator continued to predict the dependent variable when maximizing was included. Results suggest that the relationship between maximizing and intentions to switch was fully mediated by perceived knowledge of providers (β = .037, p = .086) and partially mediated by regret (β = .094, p = .000), discussions with others (β = .064, p = .003), and television involvement (β = .101, p = .000). When considering all mediators simultaneously, the model explains approximately 21 per cent of the observed variance in intentions to switch television provider. Hence, findings offer support to Hypothesis 2a, 2b, 2c, and 2d.
As shown in Table 2, step 3e, regression results for the interaction term between maximizing and television involvement are low and insignificant (β = .005, p = .818) and hence offer no support for the predicted moderator role of ego involvement in the product. Results therefore provide lack of empirical support for Hypothesis 2e.
The study reported here builds on Schwartz et al.’s (2002) seminal work on individual differences in the desire to identify and choose the best possible solution. So far, most studies on the maximizing trait have focused on construct clarification and scale development (e.g., Diab, Gillespie & Highhouse, 2008; Nenkov et al., 2008; Lai, 2010). In view of the attention drawn to Schwartz et al.’s work, relatively few studies have investigated implications of maximizing across different types of decision making domains.
The findings reported here offer empirical support to the predicted association between the maximizing trait and consumer loyalty, i.e., that maximizers report lower loyalty than satisficers. Maximizers in this study were found to exhibit higher intentions to switch to another (television) service provider. Four variables were found to mediate the relationship between maximizing and intentions to switch. Regret, perceived knowledge of alternatives, discussions with others, and ego involvement in the product all acted as partial mediators, whereas perceived knowledge of possible providers acted as a full mediator of the relationship between maximizing and switch intentions. The magnitude of effects of maximizing is quite small in the present sample, however, and the maximizing trait explains a small proportion of observed variance in switch intentions. Compared to maximizing, all of the mediators included account for a larger proportion of the observed variance in intentions to switch service provider. Perceived knowledge of providers and inclination to discuss choices with others seem most important to explain variance in switch decisions pertaining to television provider, followed by proneness to regret and ego involvement in the product. The findings provided here therefore offer not only new insight into the mechanisms by which the maximizing trait influences intentions to switch service provider, but insight into different influences of consumers’ switch intentions. The variables considered mediators here also affect intentions to switch independently of maximizing. Although the mediator variables are influenced by maximizing, they are also influenced by other variables that are not included in the present study. The findings presented here may therefore be of value to practitioners by highlighting five variables that have a unique as well as a joint influence on consumers’ intentions to switch. Nevertheless, the nature and strength of the different relationships need further testing across different domains of decision making in general and across domains with varying involvement in particular.
The findings presented here refer to a low involvement domain (distribution) that entails high involvement end products (television), and consumers’ ego involvement in the product was expected to represent a moderator of the effect of maximizing on switch intentions. More specifically, since highly involved customer have been found to exhibit higher loyalty (Keaveney & Parthasarathy, 2001), ego involvement in the product was expected to counteract the effects of maximizing on switch intentions. The data offer no support to this hypothesis. The results suggest, however, that ego involvement in the product partially mediates the relationship between maximizing and switch intentions, and that maximizing as well as high ego involvement in the product are associated with an increase (rather than a decrease) in the level of switch intentions. The latter results contradict Keaveney and Parthasarathy’s (2001) findings and therefore highlight the need for future research that explores the association between domain involvement and maximizing and its outcomes. For example, it is important to investigate whether maximizers tend to develop higher ego involvement across domains as a function of their maximizing efforts. Insight into the relationship between the maximizing trait and domain involvement is of high potential relevance to marketing practitioners as well as to practitioners in other domains of decision making. High ego involvement has been found to enhance cognitive biases (Greenwald, 1980), which may be exploited by influence agents. The relationship between maximizing and cognitive biases also calls for further inquiry, and previous research indicates that maximizing is associated with several dysfunctional outcomes, including lower decision making competence, less effective and more problematic decision-making styles, less behavioral coping, and greater dependence on others when making decisions (Bruine de Bruin, Parker & Fischhoff, 2007; Parker et al., 2007).
Research on social judgment theory (e.g, Sherif and Sherif, 1969), has also linked ego-involvement to an increase in the space of unacceptable alternatives (“the latitude of rejection”) compared to the space of acceptable alternatives (“the latitude of acceptance”). Based on the latter framework, it would be of interest to examine whether the level of ego-involvement influences efforts to maximize as well as the perceived difficulty of making a decision. Future research should therefore address the direction of influence between maximizing and ego-involvement and whether or not the relationship is reciprocal as well as the relationship between maximizing, involvement and perceived decision difficulty.
The hypothesized relationship between maximizing and desire to discuss relevant decisions with others is consistent with Parker et al.’s (2007) findings that maximizers tend to rely more on others when making decisions and with Iyengar et al.’s (2006) findings that maximizers tend to rely more on external sources of information. Dependence on others when making decisions has been seen to reflect the desire for interpersonal comparisons as well as the quest for information that is associated with maximizers extensive search for options (Parker et al., 2007). The reliance on others as well as on external sources of information may undermine maximizers efforts to avoid regret and lead them to doubt their choices as well as to experience regret (Schwartz et al., 2002). The findings from this study therefore offer additional support to Schwartz et al.’s (2002) line of reasoning by providing empirical evidence that maximizers are more prone to engage in discussions with others and, as a result, develop stronger intentions to change their decision by switching service provider.
The fourth and final mediator included in the present study, which is self-reported knowledge of potential options, refers to a plausible outcome variable of the extensive search for options that is associated with maximizing. The level of knowledge of possible options explains a larger proportion of the observed variance in intentions to switch than any of the other mediator variables consider. The mediation of the relationship between maximizing and switch intentions is also full rather than partial. These findings indicate that maximizers’ extensive alternative search and level of knowledge of possible options are important in explaining why maximizers seem less loyal than satisificers. One reasonable interpretation is that maximizers become more susceptible to experiencing regret as a result of identifying other and potentially more attractive options, but the correlation between the level of knowledge of possible options and proneness to regret is low. It therefore seems likely that knowledge of possible options triggers intentions to switch provider through knowledge of potentially better options rather than proneness to regret. Measures of actual regret are not included however, and, as noted above, actual regret should be included in future studies of the implications of maximizing efforts.
As with many earlier studies on the maximizing trait, this study relies on correlation. The direction of possible causality between trait related variables, such as preference for maximizing and proneness for regret, therefore cannot be firmly determined. However, when considering the variables that reflect self-reported behaviors as well as behavioral intentions, it seems less likely that assumed directions of causality should be reversed. For example, since maximizing represents a general trait, it seems more likely that maximizing has an effect on knowledge of possible providers and the desire to discuss decisions with others than the opposite. As noted previously, the nature and direction of causality between maximizing and domain involvement seems more ambiguous, and inferences about causality must be drawn with caution until this relationship is better understood. However, if the results reported here indeed reflect causal relationships in the directions anticipated, findings may have valuable implications for understanding individual differences in proneness to decision change and loyalty as well as implications for practitioners who strive to improve customer retention rates.
Baron, R. & Kenny, D. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical consideration. Journal of Personality and Social Psychology, 51, 1173–1182.
Beatty, S. E., Kahle, L. R. & Homer, P. (1988). The involvement- commitment model: Theory & implications. Journal of Business Research,16, 147–67.
Beatty, S. E. & Smith, S. M. (1987). External search efforts: An investigation across several product categories. Journal of Consumer Research, 1, 83–95.
Bruine de Bruin, W., Parker, A. M., & Fischhoff, B. (2007). Individual differences in adult decision-making competence. Journal of Personality and Social Psychology, 92, 938–956.
Carrillat, F., Edmondson, D. & Ladik. D. (2006). An integrative view of customer loyalty: Is it different for maximizers versus satisficers? AMA Winter Educators’ Conference Proceedings, 17, 212–213.
Chowdhury, T. G., Ratneshwar, S. & Mohanty, P. (2009). The time-harried shopper: Exploring the differences between maximizers and satisficers. Marketing Letters, 20, 155–167.
Cohen, J. W. (1988). Statistical power analysis for the behavioral sciences (2nd edn.). Hillsdale, NJ: Erlbaum Associates.
Cohen, J., & Cohen, P. 1983. Applied multiple regression/correlation analysis for the behavioural sciences (2nd edn.) Hillsdale, NJ: Lawrence Erlbaum.
Diab, D. L., Gillespie, M. A., & Highhouse, S. (2008). Are maximizers really unhappy? The measurement of maximizing tendency. Judgment and Decision Making, 3, 364–370.
Ganesh, J., Arnold, M. J., & Reynolds, K. E. (2000) Understanding the customer base of service providers: An examination of the differences between switchers and stayers. Journal of Marketing, 64, 65–87.
Greenwald, A. G. (1980). The totalitarian ego: Fabrication and revision of personal history. American Psychologist, 35, 603–618.
Hackley, S. (2006). Focus your negotiations on what really matters: An excess of choices can blind you to a good offer. Negotiation, 9, 9–11.
Iyengar, S ., Wells, R. E. & Schwartz, B. (2006). Doing better but feeling worse: Looking for the “best” job undermines satisfaction. Psychological Science, 17, 143–150.
Keaveney, S. M. & Parthasarathy, M. (2001). Customer switching behavior in online services: An exploratory study of the role of selected attitudinal, behavioral, and demographic factors. Journal of the Academy of Marketing Science, 29, 374–390.
Lai, L. (2010). Maximizing without difficulty: A modified maximizing scale and its correlates. Judgment and Decision Making, 5, 164–175.
Nenkov, G. Y., Morrin, M., Ward, A., Schwartz, B. & Hulland, J. (2008). A short form of maximization scale: Factor structure, reliability and validity studies. Judgment and Decision Making, 3, 371–388.
Parker, A. M., Bruine de Bruin, W. & Fischoff, B. (2007). Maximizers versus satisficers: Decision making styles, competence, and outcomes. Judgment and Decision Making, 2, 342–350.
Pritchard, M. P., Havitz, M. E., and Howard, D. R. (1999). Analyzing the commitment-loyalty link in service contexts. Journal of the Academy of Marketing Science, 27, 333–348.
Richins, M. L. & Bloch, P. H. (1991). Post-purchase product satisfaction: Incorporating the effects of involvement and time. Journal of Business Research, 23, 145–158.
Sherif, M. & Sherif, C. W. (1969). Social Psychology. New York: Harper and Row Publishers.
Simonson, I., & Nowlis, S. M. (2000). The role of explanations and need for uniqueness in consumer decision making: Unconventional choices based on reasons. Journal of Consumer Research, 27, 49–68.
Schwartz, B., Ward, A, Lyubomirsky, S., Monterosso, J., White, K., & Lehman, D. R. (2002). Maximizing versus satisficing: Happiness is a matter of choice. Journal of Personality and Social Psychology, 83, 1178–1197.
Sood, S. & Kathuria, P. ( 2004). Switchers and stayers: An empirical examination of customer base of an automobile wheel care centre. Journal Of Services Research, 4, 75–90.
This document was translated from LATEX by HEVEA.