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The impact of product regret on repurchase intention.

Price discounts have a positive effect in the context of marketing practice (Blattberg, Eppen, & Lieberman, 1981), not only in terms of reducing inventory, improving sales performance, and increasing profit levels but also through acquiring new customers. In contrast, customers who make full-price purchases may consider product alternatives and change their mind about the brand for a number of reasons (Heussler, Huber, Meyer, Vollhardt, & Ahlert, 2009). First, they may regret their choice to support the brand or product. Second, they may express their dissatisfaction through negative word-of-mouth in regard to the promoted product or brand. Third, they may take decisive action, such as decreasing their repurchase intention or switching brands. Therefore, it is important to understand the mechanism behind these potential negative effects, especially repurchase intention.

Previous researchers have focused on the cognitive consequences of price discounts, for example, product attitude, expected value, or brand value (Ailawadi & Neslin, 1998). In addition to cognitive and behavioral outcomes, emotional consequences are associated with price discounts because customers may detect these through self-comparisons and/or other-comparisons (Kim, 2014). On the one hand, customers may perceive that they paid a higher price than they had previously for the same product. Through self-comparison, customers use the discounted price as a reference and, by engaging in counterfactual thinking, experience a sense of regret regarding their product choice (Tykocinski & Pittman, 2001). On the other hand, through other-comparison, consumers experience a strong sense of unfairness (Heussler et al., 2009). If the price of the product is already very high, these consumers may experience anger or aggression or possibly even engage in violent conflict, as is the case for some price discounts in China (see e.g., Xu, 2012). A major problem in understanding this phenomenon is identifying the mechanism of product regret caused by price discounts and how consumers respond to product regret.

When consumers regret a purchase, a cognitive adjustment will occur, then cognitive dissonance may arise before, through attribution, the balance of emotions is restored. Different forms of attribution result in varying behavioral outcomes (Dunn & Dahl, 2012). To extend the results of previous studies, we explored product regret as a form of consumers' emotional outcome in relation to price discounts. We introduced consumers' power state as a moderator in the context of postpurchase price discounts.

Theoretical Background

Regret and Repurchase Intention

Product regret, as a negative emotion, can be triggered by the awareness of a postpurchase price discount. Lee and Cotte (2009) conceptualized product regret as comprising two dimensions: outcome and process. When experiencing product outcome regret, consumers believe the full price to be inferior to the discounted price that could have been paid (Zeelenberg & Pieters, 2007). In contrast, when consumers experience process regret caused by underconsideration (i.e., not taking enough time when making a purchase decision), they are skeptical of their heuristic processing that led to the purchase.

Each type of product regret has an effect on consumers' purchase behavior. Researchers have demonstrated, for example, that when consumers experience product aversion to avoid the feeling of regret, they are less likely to purchase the product later at the regular price (Tykocinski & Pittman, 2001). Further, to minimize product regret, consumers are likely to reduce their purchase intention and behavior (Spears, 2006). Strahilevitz, Odean, and Barber (2011) demonstrated that negative emotions (e.g., regret or disappointment) deterred investors from repurchasing stocks that had gone up in value after they had sold them. Thus, in each case, regret played a role in reducing product satisfaction and repurchase intention. Therefore, we proposed the following hypotheses:

Hypothesis 1a: Outcome regret will have a negative effect on consumers' repurchase intention.

Hypothesis 1b: Process regret will have a negative effect on consumers' repurchase intention.

Product Regret and Regret Attribution

Consumers exhibit different attribution responses to each type of product regret. Jin, He, and Zhang (2014) reported that attribution activities associated with product regret may lead to different levels of self-threat, with internal attribution (i.e., the perception that the regret is caused by one's own behavior; Folkes, 1984) leading to higher levels than does external attribution (i.e., the perception that the regret is caused by others; Folkes, 1984). Regret is related to choice, and the very nature of choice implies that there were other possibilities that could have been chosen over the selected alternative (Zeelenberg & Pieters, 2007). In this situation, consumers shift the blame to the seller to protect and enhance their own self-esteem (Tice, 1991). In contrast, consumers use internal attribution when experiencing process regret because they perceive strong controllability and autonomy in the outcome. As there is little self-justification, these consumers focus on the underconsideration that is associated with internal attribution (Inman & Zeelenberg, 2002). Therefore, we proposed the following hypotheses:

Hypothesis 2a: Outcome regret will be positively related to external attribution.

Hypothesis 2b: Process regret will be positively related to internal attribution.

Regret Attribution and Repurchase Intention

Negative emotions and behaviors often result from consumers externally attributing their product regret, and consumers tend to be dissatisfied with these negative outcomes (Hunt, 1991; Tsiros, Mittal, & Ross, 2004). The more blame that is placed on the seller--for example, marketing institutions--for the dissatisfaction, the greater is the tendency to engage in negative word-of-mouth (Chen, Tsai, & Chuang, 2010). We built on these findings for our proposal that consumers' external attribution can result in reduced repurchase intention.

There are no significant behavioral outcomes when consumers attribute product regret to themselves (Krishnan & Valle, 1979). Internal attribution leads to self-threat, which consumers ultimately aim to avoid in order to protect their self-esteem (Dunn & Dahl, 2012) by not identifying themselves in relation to the experience, or by trivializing the outcomes of consumption (Holland, Meertens, & Vugt, 2002). However, individuals can reduce their sense of discomfort through self-affirmation (McGlone & Aronson, 2006). Thus, we believed that consumers' internal attribution would not significantly affect their repurchase intention. Therefore, we proposed the following hypothesis:

Hypothesis 3: The attribution of product regret will have a negative effect on repurchase intention.

The Moderating Effect of Consumers' Power State

Recent researchers have shown that individuals' power state can dramatically affect their psychological and behavioral intention (Tost, Gino, & Larrick, 2012). A power state involves perceptions of personal control and sense of entitlement as psychological consequences of power (Tost et al., 2012). Consumers' attribution styles may be affected by their level of self-esteem and action orientation as connected to their power state. High self-esteem individuals tend to make external attributions for negative outcomes to achieve self-enhancement (Tice, 1991), whereas low self-esteem individuals are more likely to attribute responsibility to themselves (Brewin & Shapiro, 1984). Although some researchers (e.g., Adler, 1980) have found that level of self-esteem does not significantly affect the attribution of dissatisfaction, many others (e.g., Mitchell, 1988) support the argument that individuals' attribution styles vary according to their level of self-esteem.

With regard to action orientation, elevated power is associated with an increase in traits such as extraversion, charism, and Machiavellianism, thereby activating approach-related tendencies (e.g., making complaints), whereas reduced power is associated with increased threat, punishment, and social constraint, thereby activating inhibition-related tendencies, such as feelings of sadness or termination of a relationship (Keltner, Gruenfeld, & Anderson, 2003). High-power individuals are more likely to be action oriented toward attaining external resources or goals (Galinsky, Gruenfeld, & Magee, 2003), and to connect socially with others (Narayanan, Tai, & Kinias, 2013). As a consequence, high-power consumers tend to adjust their connection levels with the seller to eliminate the negative impact of product regret, thus activating the external attribution process. In contrast, low-power consumers exhibit an internally inhibited action orientation and attain psychological balance through internal attribution.

As regards the relationship between outcome regret and attribution, high-power consumers display external attribution more than low-power consumers do. Conversely, low-power consumers exhibit internal attribution more than high-power consumers do in the context of process regret. Therefore, we proposed the following hypotheses:

Hypothesis 4a: A consumer's power state will have a moderating effect on the relationship between outcome regret and regret attribution.

Hypothesis 4b: A consumer's power state will have a moderating effect on the relationship between process regret and regret attribution.

Method

Participants

Participants were students and staff at the Huazhong University of Science and Technology, all of whom had had many shopping experiences and were, thus, more likely to purchase clothes and other articles on a regular basis. Data were collected using a survey in study rooms and laboratories. Of 230 returned survey forms, 27 invalid forms were excluded, yielding an effective response rate of 88.3%.

Most respondents were aged between 18 and 29 years (96.6%), with the rest aged between 30 and 39 years (2.5%), and 40 years or over (0.9%). There were 145 women (71.4%) and 58 men (28.6%). College students accounted for 91.6% of the participants, with 17 (8.4%) of these having already graduated and the rest being undergraduates. Respondents' monthly income ranges were as follows: RMB 500 (54.7%; approximately US$80), RMB 501-1,000 (21.7%), RMB 1,001-1,500 (13.3%), and more than RMB 1,500 (10.3%; approximately US$250).

Measures

To measure price discounts, we adopted the qualitative interview technique and modified measurement items. Individuals (N = 21) who had experienced price discounts were invited to participate in the interviews. We removed eight items related to regret because participants reported that they had no relevant experience of these. Product regret was measured with eight items from two dimensions of Lee and Cotte's (2009) scale. Three locus of causality items from Russell's (1982) scale were used to measure regret attribution, and we did not use the stability and controllability items because the participants cared only about attributing responsibility internally to the self rather than externally to others in the context of price discounts. We incorporated one item from Spreng, Harrell, and Mackoy (1995) and two items from Zeithaml, Berry, and Parasuraman's (1996) model to measure repurchase intention. Two dimensions of perception power from Brill's (1992) scales yielded 14 items. Responses to all items were made on a 7-point Likert scale ranging from 1 = extremely unlikely to 7 = extremely likely (see Table 1 for items used in the model).

Results

Reliability and Validity of the Constructs

AMOS version 5.0 was used to test the reliability and validity of the constructs in the research model. Reliability was established by Cronbach's a coefficient, which was higher than the recommended value of .70 for all constructs.

To test the convergent validity, we conducted a confirmatory factor analysis, as shown in Table 1. The standardized factor loadings of the items were higher than the recommended value of .60. The average variance extracted (AVE) of latent variables exceeded the acceptable threshold of .50, indicating adequate convergent validity. To test the discriminant validity, we compared the square root of the AVE of each construct and its correlation coefficients with other constructs. The AVE square roots were all greater than the corresponding correlation coefficients of the other constructs, indicating discriminant validity, as shown in Table 2.

Testing the Main Effect

We used structural model analysis to generate the results of the model tests (Figure 1). Initial verification of the model was undertaken using a chi-square test ([chi square] = 151.412, df = 71, [chi square]/df = 2.133). In addition, we used the following goodness-of-fit criteria to establish that the model fit was good: normed fit index (NFI) = .919, incremental fit index (IFI) = .955, Tucker-Lewis index (TLI) = .933, comparative fit index (CFI) = .954, and root mean square error of approximation (RMSEA) = .055.

We tested five hypotheses for the main effect tests (see Table 3). Hypothesis 1a was supported, indicating that outcome regret had a positive effect on repurchase intention. Hypothesis 1b was supported, indicating that process regret had a negative effect on repurchase intention. Hypothesis 2a was supported, but Hypothesis 2b was not; nor was Hypothesis 3.

Testing the Mediating Effect

To test the mediating effect, we adopted a causal steps analysis of the regression coefficients of the independent, mediator, and dependent variables, although we tested the full model and the submodel without the mediator variable. A chi-square test indicated that the submodel without the mediator variable provided an acceptable fit to the data ([chi square] = 100.059, df = 41, [chi square]/df = 2.440; see Table 4). In addition, the goodness-of-fit values indicated that the model fit was acceptable: NFI = .930, IFI = .957, TLI = .930, CFI = .956, RMSEA = .062.

We tested the mediating effect through two path relationships (see Table 4). The path relationships between outcome regret and repurchase intention, and between process regret and repurchase intention were both supported. The path relationship between outcome regret and attribution was significant, as was that between process regret and attribution. Therefore, the mediating effect of regret attribution in the relationships among outcome regret, process regret, and repurchase intention was supported. In the full model, the significance of the path coefficients among outcome regret, process regret, and repurchase intention did not weaken significantly, indicating that attribution had a partial mediating effect. With regard to the path relationship between process regret and repurchase intention, a partial mediating effect was found in the difference between the full model and submodel.

Testing the Moderating Effect

To test the moderating effect, we conducted a multiple-group analysis on highand low-power state subgroups. The structural equation model was tested using data from the two subgroups, and indicated acceptable goodness-of-fit (see Table 5). If the t value is greater than 1.96, it is reasonable to conclude that there are differences between the two groups.

There were significant differences between the high- and low-power state subgroups, with the former's path reflecting a more significant positive valence of process regret, indicating a weaker effect of process regret on attribution. In addition, we found a positive effect of outcome regret on attribution. In the low-power state, the two paths of outcome regret and process regret to attribution were supported. We conducted a comparison test to examine whether or not the strengths of the path coefficient between the two subgroups were significantly different. With regard to the recommended t value of 1.96, Hypothesis 1a was supported, suggesting that the effect of the participants' power state was significantly different for the two groups. In addition, although Hypothesis 4a was supported, Hypothesis 4b was not.

Discussion

Our results showed that Hypothesis 3, regarding the relationship between product regret and repurchase intention, was supported. Loyal customers experienced outcome regret because of their comparison between the full and discounted prices. As the change in price was triggered by the sellers, affected consumers--especially those with high power--were more likely to let their negative emotions affect their purchase behavior. When they experienced process regret, consumers were skeptical of the heuristic processing that led to the purchase decision, and the seller was seen as responsible for the heuristic cues. These results are consistent with the findings of previous researchers, wherein product regret influences consumer purchase behavior (Spears, 2006; Strahilevitz et al., 2011).

The partial mediating role of regret attribution in the relationships between process and outcome regret and repurchase intention was supported. The finding regarding the relationship between outcome regret and attribution was consistent with that of prior researchers (Tice, 1991); however, the unexpected relationship between process regret and attribution indicated that consumers' self-justification did not have an effect on regret attribution. To transfer the self-accusation effect, the consumer may blame the seller for providing temptation cues; therefore, process regret becomes negatively related to internal attribution, a result that differs from that of previous researchers (Inman & Zeelenberg, 2002). Although consumers who have experienced process regret focus on the underconsideration associated with internal attribution, initially they still respond by engaging in internal attribution and then, after noting cues provided by the seller, move on to making an external attribution to the seller. In the absence of such cues, consumers do not progress beyond internal attribution.

Moderation of the relationship between outcome regret and attribution was supported, but the relationship between process regret and attribution was not significant. High-power consumers are associated with external attribution facilitated by an external approach orientation and higher self-esteem. In contrast, low-power consumers exhibit internal attribution that is related to internal inhibition and low self-esteem. However, our finding of the moderation of the relationship between process regret and attribution was contrary to our expectations and previous results (Brewin & Shapiro, 1984; Keltner et al., 2003). An explanation for the mediator effect in power state could be that consumers still self-justify via external attribution, because of heuristic cues provided by sellers in the context of price discounts.

We have contributed to the literature on product regret, attribution in marketing, and consumer identity and behavior. First, we examined product regret in the context of price discounts. Second, we extended product regret and attribution research by showing that consumers responded with different attribution patterns to different types of product regret. The focus in extant research on attribution in marketing has typically been on the role that attribution plays in influencing word-of-mouth or purchase behavior. We extended this to reveal that attribution can play an important role as a mediator in bringing about different levels of repurchase intention. Third, we identified consumers by their power state, showing that an individual characteristic variable influences attribution style and, thereby, different levels of repurchase intention. In sum, we have provided insight into the connections between price discounts and consumer identity.

Management Implications

On the basis of our results, marketers should reevaluate the importance of cultivating their relationship with loyal customers, rather than attracting new customers through price discounts. Therefore, if a price discount is offered, loyal customers must be separated from new customers because the former are aware of previous pricing and may experience product regret upon noticing that a product they had purchased at full price is now discounted. In Chinese real estate, for example, consumers are more likely to attribute depreciation to property developers' marketing strategies; thus, these developers should employ other promotional tools.

As another way of classifying consumers, market practitioners can separate their consumers according to power state (e.g., high vs. low socioeconomic status). If the target consumers of a product are high-power consumers, they will respond to a lower purchase price and market practitioners should, therefore, avoid price discounts; however, price discounts are still a useful strategy for low-power consumers, who are responsive to these. Market practitioners can also use their knowledge of consumers' power states by providing environmental cues that can shift consumers' power state.

There are several limitations in this study. First, we treated product regret as an independent variable, focusing on its subsequent negative outcomes. It would be useful to examine price discount alternatives as the antecedent to outcome or process regret, so that product regret can be taken as a dependent variable. Second, different types of emotions are triggered in the context of product price discounts. For example, house buyers may feel angry because of price reductions, comparing what that they paid with the discounted price available to subsequent homebuyers. Future researchers could focus on the moderating effect of product category on this relationship.

http://dx.doi.org/10.2224/sbp.2015.43.8.1347

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YA PING CHANG, YIN GAO, AND DONG HONG ZHU

Huazhong University of Science and Technology

Ya Ping Chang, Yin Gao, and Dong Hong Zhu, School of Management, Huazhong University of Science and Technology.

This work was supported by the National Natural Science Foundation of China (71372132, 71302093, 71072032).

Correspondence concerning this article should be addressed to: Dong Hong Zhu, School of Management, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, People's Republic of China. Email: jinshang40_80@126.com

Table 1. Confirmatory Factor Analysis for Study Constructs

Variables        Items                  Loadings       CR

Outcome regret   I should have chosen     .726         --
                 something other than
                 the product I
                 bought.

                 I regret the product     .819     11.192 ***
                 choice that I made.

                 I now realize how        .863     11.750 ***
                 much better my other
                 choices were.

                 If I were to go back     .829     11.325 ***
                 in time, I would
                 choose something
                 different to buy.

Process regret   Given more               .706         --
                 information, I feel
                 that I could have
                 made a better
                 decision.

                 I feel that I did        .868     10.916 ***
                 not put enough
                 consideration into
                 buying the product.

                 With more effort, I      .764     9.893 ***
                 feel that I could
                 have made a better
                 decision.

                 I regret not putting     .725     9.427 ***
                 enough thought into
                 my decision.

Attribution      Reflects on              .887         --
                 you-Reflects your
                 situation

                 Outside of               .800     12.525 ***
                 you-Inside of you

                 Something about          .734     11.380 ***
                 you-Something about
                 others

Repurchase       I would select a         .702         --
intention        product from the
                 merchant again.

                 I would consider the     .765     9.268 ***
                 merchant as my first
                 choice to buy.

                 I would decide to        .780     9.383 ***
                 buy more products
                 from the merchant.

Variables        Items                  [alpha]   AVE

Outcome regret   I should have chosen     88      .66
                 something other than
                 the product I
                 bought.

                 I regret the product
                 choice that I made.

                 I now realize how
                 much better my other
                 choices were.

                 If I were to go back
                 in time, I would
                 choose something
                 different to buy.

Process regret   Given more               .85     .59
                 information, I feel
                 that I could have
                 made a better
                 decision.

                 I feel that I did
                 not put enough
                 consideration into
                 buying the product.

                 With more effort, I
                 feel that I could
                 have made a better
                 decision.

                 I regret not putting
                 enough thought into
                 my decision.

Attribution      Reflects on              .85     .66
                 you-Reflects your
                 situation

                 Outside of
                 you-Inside of you

                 Something about
                 you-Something about
                 others

Repurchase       I would select a         .79     .56
intention        product from the
                 merchant again.

                 I would consider the
                 merchant as my first
                 choice to buy.

                 I would decide to
                 buy more products
                 from the merchant.

Note. CR = critical ratio. AVE = average variance extracted.
*** p < .001.

Table 2. Discriminant Validity of Model Constructs

Variables              Outcome   Process   Attribution   Repurchase
                       regret    regret                  intention

Outcome regret          .811#
Process regret          .597      .768#
Attribution             .583      .517        .809#
Repurchase intention    -.684     -.671       -.571         .750

Note. Square root of AVE on diagonal in bold. Other data refer to
the coefficients of other variables.

Note: Square root of AVE on diagonal are indicated with #.

Table 3. Results of Main Effects Testing

Hypothesis   Path relationship      Path         t
                                 coefficient

H1a          Outcome regret       -.395 ***    -4.304
             [right arrow]
             Repurchase
             intention

H1b          Process regret       -.297 ***    -3.415
             [right arrow]
             Repurchase
             intention

H2a          Outcome regret       .339 ***     3.765
             [right arrow]
             Attribution

H2b          Process regret        .242 *      2.723
             [right arrow]
             Attribution

H3           Attribution          - .170 *     -2.304
             [right arrow]
             Repurchase
             intention

Note. * p < .05, ** p < .01, *** p < .001.

Table 4. Results of Mediation Effect Testing

Hypothesis   Path relationship          Path         t
                                     coefficient

H1a          Outcome regret [right    -.450 ***    -4.899
             arrow] Repurchase
             intention

H1b          Process regret [right    -.339 ***    -3.865
             arrow] Repurchase
             intention

Note. *** p < .001.

Table 5. Results of Moderation Effect Testing

Path                 Full model (%)       High-power state
relationship                                   group

                     Path        CR        Path        CR
                  coefficient           coefficient

Outcome regret     .339 ***     3.765    .562 ***     4.356
[right arrow] *
Attribution

Process regret      .242 **     2.723      .221       1.888
[right arrow] *
Attribution

Goodness-of-fit      [chi square] =        [chi square] =
                    151.412 (df = 71)     105.117 (df = 71)

                      [chi square]/       [chi square]/df =
                       df = 2.133              1.481

                    NFI = .919, IFI =     NFI = .883, IFI =
                    .955, TLI = .933,     .959, TLI = .946,
                       CFI = .954,           CFI = .958,
                      RMSEA = .055          RMSEA = .070

Path                Low-power state     t
relationship              group

                     Path        CR
                  coefficient

Outcome regret      .308 *      2.197
[right arrow] *
Attribution

Process regret      .290 *      2.031   11.815-7.783
[right arrow] *
Attribution

Goodness-of-fit      [chi square] =
                    75.896 (df= 71)

                    [chi square]/df =
                          1.069

                    NFI = .901, IFI =
                    .993, TLI = .991,
                       CFI = .993,
                      RMSEA = .026

Note. CR = critical ratio. * p < .05, ** p < .01, *** p < .001.
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Article Details
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Author:Chang, Ya Ping; Gao, Yin; Zhu, Dong Hong
Publication:Social Behavior and Personality: An International Journal
Article Type:Report
Geographic Code:9CHIN
Date:Sep 1, 2015
Words:4784
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