author’s name
Responses to Negative eWOM (Electronic Word-of-Mouth): the Role of eWOM Attribute
Ruirui Zhang1, Jinjin Gao1, Rong Wang2, Yiwen Chen1*
1Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
2Department of Psychology, The Chinese University of Hong Kong, Hong Kong
* Corresponding author.
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Received **** 2014
Copyright © 2014 by author(s) and Scientific Research Publishing Inc.
This work is licensed under the Creative Commons Attribution International License (CC BY).
http://creativecommons.org/licenses/by/4.0/
Abstract
This study aimed at exploring how companies’ responses to negative electronic word-of-mouth (eWOM) influence brand attitude and purchase intention, as well as the moderating role of eWOM attribute (subjective or objective) in their relationships. By using a scenario experiment, two variables were manipulated: responses (response vs. no response) and attribute of negative eWOM (objective vs. subjective). The results showed that: a) responses to negative eWOM positively predicted brand attitude and purchase intention; b) attribute of eWOM moderated the responses-purchase intention relationship but not the responses-brand attitude relationship. Specifically, under objective eWOM condition, responses (compared with no response) were more likely to improve purchase intention, but such effect was not significant under subjective eWOM condition. Finally implications of these findings were discussed.
Keywords
Negative eWOM; eWOM attribute; E-commerce; Company responses
1. Introduction
The last decade has seen an explosive growth of China’s online shopping [1]. A report released by the McKinsey Global Institute has indicated that by holding the worlds’ largest online population, China has been the second-largest e-tail market, coming close to equaling the United States, with sales more than $190 billion in 2012 and a high annual growth rate of 120 percent since 2003 [2]. However, compared with offline purchase environment, online shopping is associated with more uncertainty, triggering consumers to become information seekers for previous consumers’ use experience or evaluations regarding the products they want to buy [3]. Negative electronic word-of-mouth (eWOM), associated with high perceived risk, negative framing effect [4] as well as high credibility [5], has been found to exert tremendous harm on customers' brand evaluations, attitude, purchase intention and behavior [6].
Considering the influence of negative eWOM, satisfactory responses from companies when facing consumers’ complaints are therefore crucial to increase brand equity and reduce purchasing risk perceived by observers of the communication dialogue online [7]. This research first investigated the effect of responses to negative eWOM on brand attitude and purchase intention (i.e., two pivotal antecedents of purchase behavior) [8] among potential consumers.
The effects of responses to negative eWOM are also contigent on other variables, such as eWOM attribute. Generally, eWOM can be differentiated into two types based on its attribute: subjective and objective [5]. Subjective eWOM is more self-related, experience-based, and subject to personal preference, such as personal evaluations regarding purchase and use experience; whereas objective eWOM is characterized as factual search-type information with less individual bias, such as prices, product functions and physical characteristics [5]. It has been shown that consumers prefer searching for objective information online, but tend to search for subjective information offline [9]. The credibility of objective eWOM was also shown to be higher than that of subjective eWOM [5]. Besides, given the risk and uncertainty of online purchasing, consumers tend to rely on objective information more as it produces less misunderstanding and bias than subjective information [9]. The negative eWOM with different attributes (i.e., objective and subjective) should exert different influences on consumers’ purchase behaviors. Therefore, the current study also aimed to test the moderating role of eWOM attribute on the influences of responses on brand attitude and purchase intention. The following hypotheses were posited:
Hypothesis 1. Responses to negative eWOM will improve consumers’ brand attitude and purchase intention.
Hypothesis 2. The attribute of negative eWOM will moderate the influence of responses on brand attitude and purchase intention.
Overall, in this paper we tried to investigate the effect of companies’ responses to negative eWOM on consumers’ brand attitude and purchase intention. Moreover, these effects would be explored across conditions regarding different eWOM attributes. It is hoped that our findings will deepen the understanding about the potential influence of companies’ responses to negative eWOM on consumer behaviors, as well as how and when companies should respond to negative eWOM.
2. Methods
*Special description of the title. (dispensable)
2.1. Materials
First of all, we selected smart phone as the virtual product in experiment for two reasons [5]: it is familiar to our participants (i.e., undergraduates); people are interested in knowing the use experience of previous consumers toward smart phone.
In order to collect the eWOM cases, 45 undergraduates were recruited to rate the valence of 30 eWOM message cases selected from popular e-commerce websites in China (e.g., Jingdong, amazon). We chose 5 cases which were rated as the most negative and then asked participants to rate their attribute from “1 = extremely objective” to “5 = extremely subjective”. Finally, the cases rated as the most objective (M = 1.24; described as “The system at times crashes”) and subjective (M = 4.25; described as “The system didn’t work smoothly”) were adopted. Moreover, considering the authenticity of experiment materials, in addition to one negative message, another two messages (positive and neutral) would also be shown to our participants. The neutral message was “Just received and haven’t used”, and the positive message rated the most positive was “Wonderful use experience after several days’ use”.
The responses to the two negative eWOM cases were also chosen from the same e-commerce websites. Again, we asked participants to rate their valence based on a 5-point scale anchored from “1 = extremely negative” to “5 = extremely positive”, and the two responses used in our experiment were rated almost equally positive (M1 = 4.20 & M2 = 4.22). The response to objective negative eWOM was “Press both Power Key and Volume Key for 3-5 second to start the phone, then choose update the firmware without installing the software; if the problem still exists, please return to the factory for inspection”; and the response to subjective negative eWOM was “Thank you or your feedback, we will try to improve the system smooth in future”.
2.2. Participants
Totally, 149 undergraduates from a university located in Bejing, China were recruited (56 male), their average age was 20.96 (SD = 1.10). Responses (positive response vs. no response) and attribute (subjective vs. objective) were manipulated, so all participants were randomly assigned into the following four conditions: subjective-positive response group (n = 38); subjective-no response group (n = 36); objective-positive response group (n = 37); objective-no response group (n = 38).
2.3. Procedure
Firstly, the participants were required to imagine that they were buying a smart phone of a particular brand online, and the depiction about the technical details of the smart phone was also shown to them. Then, the participants were presented by a set of eWOM messages (differed in attribute of negative eWOM and whether to respond or not). Finally, the participants finished the measures of brand attitude, purchase intention, and several demographic questions.
2.4. Measures
Brand attitude was assessed by 6 items measuring consumers’ attitude towards the brand of this smart phone, which was adopted from a previous study [10] using 6-point semantic differential items from “1 = disagree strongly” to “6 = agree strongly.” Cronbach’s alpha coefficient was 0.84.
Purchase intention was assessed by a 4-item purchase intention scale used in a previous study [11]. The participants were required to rate based on a 6-point Likert scale from “1 = disagree strongly” to “6 = agree strongly.” Cronbach’s alpha coefficient was 0.81.
3. Results
On average, the online time of the participants was more than 4 hours per day and the purchase frequency was about twice per month. The bivariate correlations among the variables were shown in Table 1.
The ANCOVA analysis was conducted to test hypothesis 1 and 2 that proposed the main effect of responses on brand attitude and purchase intention, and the moderation role of attributes in their relationships. Online shopping frequency was treated as a covariate because previous evidence has shown that purchase frequency is associated with perceived shopping convenience and future purchase intention [12].
The results showed that in terms of purchase intention, the main effects of both responses and eWOM attribute were significant (F(1,144) = 7.03, p < .01; F(1,144) = 4.76, p < .05). Moreover, a significant two-way interaction between responses and eWOM attribute was observed (F(1,144) = 3.91, p < .05) (see Table 2). As shown in Figure 1, under the objective condition, response group had higher levels of purchase intention than no response group (M1 = 3.07 ± 0.98, M2 = 2.41 ± 0.83; t = -3.17, p < .01); while under the subjective condition, the difference on purchase intention between response and no response group was not significant (M1 = 3.11 ± 0.84, M2 = 3.00 ± 0.89; p > .05). In terms of brand attitude, the main effect of responses was significant (F(1,144) = 4.30, p < .05), supporting our hypothesis 1, but the interaction effect between responses and eWOM attribute was non-significant. Therefore, our hypothesis 2 was partially supported.
Table 1. Univarate and bivariate statistics for major study variables (N = 149)
Variable / M / SD / 1 / 2 / 3 / 4 / 5 /1. Online shopping frequency per month / 1.95 / .88 / - /
2. Brand attitude / 3.59 / .84 / -.01 / - /
3. Purchase intention / 2.91 / .92 / .02 / .46** / - /
4. Responses / - / - / .01 / .16* / .21** / - /
5. eWOM attribute / - / - / .02 / .08 / .19* / .02** / - /
Note: Response was coded such that 1 = no response and 2 = positive response.
eWOM attribute was coded such that 1 = objective and 2 = subjective. *p .05, **p .01.
Table 2. Interaction effect of responses and eWOM attribute on brand attitude and purchase intention
DV = Brand attitude / DV = Purchase intentionSource / MS / F / Effect Size / MS / F / Effect Size
Online purchase frequency (Control variable) / .01 / .02 / ＜0.000 / .25 / .31 / .002
Responses (A) / 2.94 / 4.30* / .03 / 5.58 / 7.03** / .05
eWOM attribute (B)
A × B / .70
1.36 / 1.03
1.98 / .007
.01 / 3.78
3.10 / 4.76*
3.91* / .03
.03
Error / .69 / - / - / .79 / - / -
Note: MS = Mean squares, effect size = η2. Response was coded such that 1 = no response and 2 = positive response. eWOM attribute was coded such that 1 = objective and 2 = subjective. *p .05, **p .01.
Figure. 1. The effect of responses on purchase intention.
4. Discussion
From the perspective of e-commerce complaints management, the current study hypothesized a causal relationship between companies’ responses to negative eWOM and two consequences (i.e., brand attitude and purchase intention). Moreover, we proposed that their relationships were contingent on eWOM attribute. We manipulated the attribute of negative eWOM to derive two types of eWOM (i.e., objective and subjective), and then explored the differentiated main effects across the two conditions.
Enriching the previous findings, we found that responses to negative eWOM, especially the objective ones, led to increased purchase intention and brand attitude. Althoung scant attention has been directly paid to the influence of eWOM attribute on the effect of complaints management, previous evidence has shown that objective messages exert a more profound effect than subjective messages [5]. Besides, from the eyes of consumers with adequate knowledge about the products, eWOM conveying objective attributes of the products (e.g., technical standards) is more preferred than those reflecting subjective interpretations [13] and consumers tend to give more credibility to objective information [5], because subjective eWOM cannot provide as much information as objective ones in order to help consumers judge the value of products when making purchase decision [14]. Similarly, responses to objective complaints also possess more value and include more useful information compared with those responses to subjective complaints.
We found that there was a positive effect of responses on brand attitude, but such effect did not depend on the attribute of eWOM. Previous study has shown that responding to negative eWOM is an effective way to increase initial trust of potential consumers [15], and then lead to favorable brand attitude. Therefore, regardless of whether the negative eWOM is objective or subjective, appropriate responses from companies are powerful enough to build the good brand image by showing concern and giving good service to consumers.
The findings of the current study highlight the importance of developing and implementing strategic managements to proactively respond to negative eWOM. To improve online consumers’ purchase intention, e-commercees should respond to negative eWOM effectively by providing useful information and suggestions, especially when the consumers’ complaints are about the objective attributions of the product. Moreover, responding to negative eWOM is a vital necessity to improve consumers’ brand attitude though some complaints online make no sense.
Considering the variability and complexity of the eWOM cases in real online shopping, in this study we designed a scenario to test the hypotheses. However, the generalization of the findings to real online shopping is restricted due to the low external validity of scenario experiment. Future research is encouraged to replicate our findings by employing other scenarios with different settings or other methods (e.g., questionnaire survey).
Acknowledgements
This study was supported by the National Natural Science Foundation of China under Grant No. 71171188.
References
[1] Liu, X., He, M., Gao, F. and Xie, P. (2008) An empirical study of online shopping customer satisfaction in China: a holistic perspective. International Journal of Retail & Distribution Management, 36, 919-940. http://dx.doi.org/10.1108/09590550810911683