Electronic Consumer-to-Consumer Interaction (eCCI) Post a Service Failure: The Psychological Power of Need for Approval

Customer–Customer Interactions (CCIs) in Service Failure Contexts

Customer-to-customer interaction (CCI) is defined as any form of customer interaction experienced during the possession or consumption of goods and services (Georgi & Mink, 2013). Fellow customers can affect the service experience in a pleasurable and positive manner resulting in satisfaction, which enhances the overall perception of service quality (e.g., Levy, 2010; Wei et al., 2021). On the other hand, the uncivil behavior of other patrons can lead to the focal customers’ dissatisfaction, decrease their perception of service quality (Bacile, 2020), and jeopardize an organization’s success (Bani-Melhem et al., 2020). While service is traditionally seen as the sole responsibility of the company or firm providing the service, CCI literature posits that the customers within the service environment are an essential component of the service offered (Hanks et al., 2020). This is further supported by Wei and colleagues (2021), who examined theme parks and found that other customers are an essential component of the social servicescape. Firms need to develop and adhere to procedures that better support other guests as customers are increasingly the source of workplace incivility (Baker & Kim, 2020).

Prior research has suggested that it is critical to understand and study CCI in a service failure context (e.g., Bacile, 2020) for several reasons. First, as service technologies grow and become more prevalent—for example, self-service kiosks and better AI capabilities—CCIs increasingly outnumber customer-to-employee interactions (Nicholls, 2020). Second, dissatisfied customers have referred to the strong influence CCIs have had on their overall dissatisfaction (Nicholls, 2010). This suggests that firms should prioritize developing systems to manage customer-to-customer relationships (Colm et al., 2017). Finally, and perhaps most importantly, research has found that the focal customer tends to hold the service establishment or firm responsible for service violations, even when negative CCI (NCCI) is attributed to other customers (Baker & Kim, 2018). An example is if a focal customer has a negative dining experience due to a baby crying at another table, they will not blame the family with the baby but are more likely blame the dining establishment even though the establishment is not directly responsible for the negative CCI. In an unusual or unexpected event such as a service failure, whether directly the firm’s fault or due to other customers, the breakdown in service can prompt customers to warn others about their experience, share their frustrations, and discuss the overall quality of their perceived experience (Harris & Baron, 2004). Since CCIs can be negative (NCCI) or positive (PCCI) in nature (Nicholls, 2020), firms must understand and recognize the varying types of CCI that occur in the service environment to better manage these encounters with the overall goal of influencing customer experiences (Heinonen et al., 2018). Firms benefit from PCCI with increased loyalty of guests and positive word-of-mouth outcomes (Altinayet al., 2019), which benefit the firm overall. Conversely, firms can suffer a loss spiral from NCCI that results in employee burnout and employees engaging in uncivil behavior themselves (Kim & Qu, 2019), leading to poor organizational outcomes.

Modern technologies have extended opportunities for customers to communicate with one another in an online context. Through the combination of social media and e-commerce, social commerce can greatly influence customers’ purchase intentions (Liu et al., 2021). This form of CCI is called virtual CCI, or eCCI (Nicholls, 2010). eCCI is the interaction between two or more customers of a goods or service provider via a computer and an online network (Casaló et al., 2007). The prevalence of eCCI is rapidly growing as customers increasingly employ online tools to contact and reach fellow customers, seek suggestions from friends, and voice their opinions about products and services (Leung et al., 2019). It has been found that customers interact more with other individuals when they have access to Internet platforms and resources (Heinonen et al., 2018). However, due to the anonymity that the Internet provides, customers can engage in questionably toxic behaviors displaying their dissatisfaction, making it difficult for firms to recover effectively during a service failure situation (Bacile, 2020). Under this circumstance, increased customer interactions can lead to the co-destruction of service quality and brand image (Luo et al., 2019). The present research examines eCCI following a service failure and its impact on the focal customer. Specifically, we propose that a negative CCI will lead to a lower level of perceived interaction justice, empathy quality, and intent to engage on social media. However, such an effect is moderated by the customer’s need for approval level.

Need for Approval

The psychological need for approval is the desire to gain other people’s approval in social situations (Cohen, 2018). In other words, people who need approval “are very concerned with social approval and might reflect this concern via both opinions and behaviors that conform to social norms, rather than deliberate attempts at deception” (Johnson et al., 2012, p. 1885). The need for approval might best be thought of as a type of perfectionism regarding the obtainment of social approval rather than the more passive wish to be loved, cared for, nurtured, and protected (Brown & Beck, 2002). As such, a person’s need for approval may play a role in how they engage with CCI during service encounters because they are susceptible to the opinions of others.

Empirical evidence shows that an individual’s need for approval can be extremely motivating in determining personal decisions like career choice, and also deleterious to one’s physical and mental health, leading to serious health complications (Kelly, 2020). Within hospitality research, need for approval was argued to be one of the pull factors of entrepreneurship (Ahmad & Arif, 2016). From a consumer perspective, need for approval helps tourists determine a choice of destinations based on the environmental impact of travel, such that potential tourists will choose destinations based on the recognition they will receive from others and the social desirability of traveling to a specific place (Hindley & Font, 2018). Finally, in a study of hotel guests’ use of social media, Stone (2017) highlighted that hotel guests desire interaction with each other and the hotel through social media in a type of eCCI so as to fulfill their need for approval, which implies the potential interaction between CCI and need for approval in online contexts. Empirical evidence suggests that need for approval determines outcomes like vacation destination selection (Hindley & Font, 2018) and sharing behavior of travel (Stone, 2017). The common thread for all the aforementioned empirical results is that need for approval creates the desire for a person to emphasize how they relate to others (Blaney & Kutcher, 1991). As such, this research proposes an association between interactions in the form of eCCI and the sensitivity to how people perceive their standing with others as defined as their psychological need for approval.

Hypotheses Development

Building upon the stimulus-organism-response (SOR) theory, this research proposes that individuals with a lower level of need for approval are more likely to engage more on social media and perceive a higher level of CCI justice (i.e., a consumer’s fairness perception of interpersonal treatment by others as defined by Blodgett et al., 1997) and greater empathy quality (i.e., how the actions of others make one feel respected and understood as defined by Kim, Choi, & Martin, 2020) when receiving a positive (vs. negative) comment from another customer following a service failure experience. On the other hand, individuals with a high level of need for approval will exhibit a similar level of engagement intention, perceived CCI justice, and empathy quality regardless of the valence of the comment received.

The SOR theory has been widely adopted by prior research to examine the links among inputs (stimulus), processes (organism), and outputs (response; Kim, Lee & Jung, 2020). It suggests that both external (i.e., CCI in the study context) and internal (i.e., need for approval in the study context) cues could be used as sources of information that influence consumer attitudes and behaviors through their cognition and emotion (Kim & Lennon, 2013). The CCI literature indicates that the presence of others as a stimulus makes the focal customer an object of evaluation (van Rompay et al., 2009). It subsequently triggers self-evaluation and impression management behaviors (Leary & Kowalski, 1990). Additionally, as an internal stimulus, individual need for approval will interact with the CCI effect. Specifically, while people high in need for approval strive to exhibit behaviors consistent with social norms, people low in need for approval are less concerned about such consistency (Kallgren et al., 2000). They rely on themselves as a frame of reference rather than using external sources (Witkin et al., 1954). When experiencing a negative situation, people low in need for approval respond more accurately regarding their feelings and behaviors (Dozier et al., 1998). In other words, they could act more angrily after being instigated (Conn, 1964). When they receive a negative comment from a fellow customer, we predict that they will consider the interaction as online incivility and thus have a more negative reaction. It has been established by prior research that people react to online incivility with increased negative emotions such as stress, anxiety, and anger. As a result, they will perceive the online environment as hostile and unjust, which further leads to a decrease in their future communication and engagement intent (Scheff & Schorr, 2017; Vivek et al., 2012), as well as perceived interactional justice (Bacile et al., 2020). In contrast, when customers low in need for approval receive a positive comment from another customer, they tend to evaluate the interaction more positively due to the supportive nature of the comment and the potential social bonding opportunities (Berger, 2014).

Berger (2014) suggests that one of the main motivations for a consumer to write an online review is emotion regulation by generating social support. Particularly when people have experienced a service failure, receiving a supportive comment from another person can provide comfort and consolation (Rimé, 2009), which, in turn, may mitigate the negative feelings that arise from the service failure. A positive CCI experience also encourages social bonding. People have a fundamental desire for social relationships and connections, and interpersonal communication fulfills that need (Berger, 2014). A positive CCI acts like “social glue” that brings people together and makes them feel connected with like-minded others (Muniz & O’Guinn, 2001). Given that people low in need for approval tend to act accordingly with their inner feelings (Dozier et al., 1998) and are less concerned about impression management, we predict that they will react more positively after receiving a positive CCI.

However, for consumers high in need for approval, their reactions are more complicated given the high importance they attach to others’ expectations and judgments and their adaption-oriented behaviors in social interaction (Canlı & Karaşar, 2021). In this study, we argue that people high in need for approval will not react to a positive versus a negative CCI differently for the following reasons. First, Witkin and colleagues (1954) suggest that people high in need for approval often show low self-esteem and lack of self-assurance. They tend to guard against the arousal of anger and hostility through the imposition of repressive defense as a self-protection mechanism (van Rompay et al., 2009). Thus, they would be more likely to avoid further engagement and any negative feelings when facing a frustrating situation such as online incivility. Second, prior research indicates that people high in need for approval tend to anticipate social rejection and believe that they are less favorably evaluated by others (Conn, 1964). Their low self-esteem could lead to a rejection of favorable feedback (e.g., a positive CCI; McFarlin & Blascovich, 1981), which results in a similar reaction toward a positive and a negative CCI experience. Additionally, compared to those with a low need for approval, high need for approval individuals often engage in a self-blame mode. After a negative interaction with the other customer, people high in need for approval are more likely to blame themselves for the unpleasant experience (Cramer, 2009) and avoid attributing the unpleasant experience to the other customer. Their main goal is to create harmony and connectedness with others (Rudolph et al., 2005). Therefore, anything they do that might provoke disapproval would be avoided at all costs. Based on the argument above, we put forth the following hypotheses, which are further illustrated in Figure 1:

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Figure 1.

Research Model.

Research Design and Experimental Stimuli

A pretest was conducted with 123 restaurant consumers recruited from Amazon Mechanical Turk (MTurk) in order to check the effectiveness of the manipulation of CCI and the readability of the selected measurement items. MTurk has been recognized as providing reliable and valid data representing users with high demographic diversity (Kees et al., 2017). Minor changes were made to improve the clarity of the experimental stimuli.

For the main study, a 2 (CCI: positive vs. negative) x 2 (need for approval: high vs. low) quasi-experimental design was employed. A restaurant take-out service failure situation was chosen owing to (1) the prevalence of such a service under the impact of the COVID-19 pandemic and (2) the frequency of failure incidents in this industry (Bacile, 2020; Smith et al., 1999). Participants were first presented with a textual description of the failure and instructed to imagine it had happened to them. Given that it might be challenging to create hypothetical scenarios for well-known brands because participants may hold strong preexisting attitudes (Mattila et al., 2020), a fictitious restaurant (ABC restaurant) was manipulated in the present study. Specifically, participants were shown a detailed complaint posted to ABC restaurant’s Yelp page. The complaint affixed “You” in the area where the complainant’s name appeared on the social media posting. This was intended to increase the participants’ sense that the described situation had truly happened to them (Bacile, 2020). After the posted comment by the complainant, the participants were shown two responses to their initial post. The first response was from another customer Alex (a gender-neutral name to avoid potential gender bias), who was a stranger in the scenario. Participants were randomly assigned to one of the two CCI conditions using the “randomization” survey tool empowered by Qualtrics. In the negative CCI condition, Alex’s response mocked and insulted the complainant. The text used in this response was adapted from Bacile (2020), based on actual online complaint responses. In the positive CCI condition, Alex’s response sympathized with and supported the complainant. The authors developed the text used in this response based on their observations of actual complaint responses online.

The second response was from ABC restaurant. The ABC restaurant’s response only addressed the complainant and his or her service failure without acknowledging Alex’s reply. The text used in the restaurant’s response was adapted from Bacile (2020), which was also built upon a format commonly seen in complaint responses of employees on social media. Each reply was time-stamped to indicate that the first response from customer Alex preceded that of the response from the ABC restaurant (see the Appendix in the online supplemental material). After reading the initial complaint and the following two responses, all participants completed two items for a realism check (Zhang et al., 2021) to ensure that the scenario was read and understood, along with manipulation check questions and all items for the constructs of interest and demographic questions. In addition, two attention check questions were placed in the survey.

In order to assess the external validity of the results, the authors replicated the same research design in Experiment 2, except for the restaurant’s managerial response. As Heinonen and colleagues (2018) suggest, the role of the service provider in CCI is a promising area for future research. Previous studies have shown that specific versus generic manager responses could result in different consumer responses (e.g., Wei et al., 2013). Experiment 2 was designed to test whether the focal consumer’s evaluation of the CCI encounter would change when a general managerial response commonly seen on any given restaurant’s social media page was included (Bacile, 2020) compared to a specific managerial response (Wei et al., 2013) that particularly addressed customer Alex’s comment on the initial online complaint (see the Appendix in the online supplemental material). The same set of independent and dependent variables were used.

Measurement

Need for approval, as one of the independent variables, was measured via six items adapted from Jones (1969; e.g., “It is important to me that others approve of me”; Cronbach’s α = 0.705). Three dependent variables were measured. Consumers’ social media engagement intention was measured via three items adopted from Wei and colleagues (2017a; I will respond to the fellow customer Alex’s response: “very unlikely – very likely,” “inclined not to – inclined to,” and “definitely will not – definitely will”; Cronbach’s α = 0.897). Consumers’ perceived customer–customer interactional justice was captured via four items adapted from Bacile and colleagues (2018; e.g., “I was treated fairly by the fellow consumer Alex”; Cronbach’s α = 0.889). Consumers’ perceived empathy quality was measured via three items adapted from Kim and colleagues (2020; e.g., “I believe the fellow consumer Alex tried to imagine how s/he would feel if s/he were in my situation”; Cronbach’s α = 0.844). Perceived service failure severity and the potential influence of COVID-19 were included as control variables. Perceived service failure was measured via three items adapted from Wei and colleagues (2012; e.g., “Minor problem – major problem”; Cronbach’s α = 0.782). One item was measured for the potential influence of COVID-19 adapted from Cheng and colleagues (2021). All items (see Table 1) were measured using a 7-point Likert-type scale (1 = strongly disagree, 7 = strongly agree). The means of the multi-item measures were used in the analysis.

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Table 1

Measurement Scales

Variables	Source	Items	Reliability
Need for Approval	(Jones, 1969)	1. It is important to me that others approve of me.	.705
		2. I find it hard to go against what others think.
		3. I have considerable concern with what people are feeling about me.
		4. I want everyone to like me.
		5. I often worry about how much people approve of and accept me.
		6. I can like myself even when many others don’t. (R)
Social Media Engagement	(Wei, Lu, Miao et al., 2017)	I will respond to fellow customer Alex’s response:	.897
		1. Very unlikely - Very likely
		2. Inclined not to - Inclined to
		3. Definitely will not - Definitely will
Customer–Customer Interaction Justice	(Bacile, 2020; Bacile et al., 2018)	1. During this complaining episode, the fellow consumer Alex showed a real interest in trying to be fair to me.	.889
		2. During this complaining episode, the fellow consumer Alex treated me in a courteous manner.
		3. The fellow consumer Alex treated me ethically during the encounter.
		4. I was treated fairly by the fellow consumer Alex.
Empathy Quality	(Kim, Choi, & Martin, 2020)	1. The way in which I was treated by fellow consumer Alex made me feel respected.	.844
		2. The actions of fellow consumer Alex demonstrated that he/she understood my situation.
		3. I believe fellow consumer Alex tried to imagine how s/he would feel if s/he were in my situation.

Sample

A total of 239 restaurant consumers recruited from Amazon Mechanical Turk took the survey. The authors undertook several steps to minimize the response bias. First, two attention check questions were placed in the questionnaire. Respondents who did not correctly choose the indicated options were removed. Second, responses with uniform answers were identified and deleted. Third, cases that did not recognize reverse-coded items were removed (e.g., choosing the same value point for two statements with opposite meanings). Finally, items were randomized (Tehseen et al., 2017). To avoid the nonresponse bias, compensation was only given to participants who fully completed the questionnaire through the MTurk platform. Second, the protection of all participants’ confidentiality and anonymity was stressed in the cover letter, following Podsakoff and colleagues’s (2003) guidelines. Third, the questions and items were carefully constructed and revised to reduce ambiguity (Tourangeau et al., 2000). As a result, 201 valid surveys (NpositiveCCI = 99, NnegativeCCI = 102) were included in the main data analysis. These respondents met all four screening criteria: (1) 18 years old or above; (2) have used platforms such as Yelp or TripAdvisor; (3) have prior experience of writing online reviews; and (4) have had a to-go order from a restaurant in the past 6 months. Most of the participants were male (64.7%), between 26 and 40 years old (64.2%), Caucasian (86.1%), had a household income of $50,001 to 100,000 (52.7%), and a bachelor’s degree (65.7%; see Table 1 in the online supplemental material).

Manipulation and Realism Checks

Two manipulation check questions for CCI were employed: “I find Alex’s comment positive” and “I find Alex’s comment negative” (1 = strongly disagree, 7 = strongly agree). ANOVA results indicated that participants in the positive CCI condition responded to the first questions more positively (MpositiveCCI = 5.23, MnegativeCCI = 4.74, p < .05, Cohen’s d = .28) and to the second questions less positively (MpositiveCCI = 4.48, MnegativeCCI = 5.32, p < .001, Cohen’s d = .51) that those in the negative CCI condition. In addition, four questions (Cronbach’s α =.793) adapted from Reynolds and Harris (2009) were asked concerning the perceived dysfunctional behavior of customer Alex (e.g., “The fellow consumer Alex conducted him-/herself in a manner that I do not find appropriate.” ANOVA results revealed that participants in the positive CCI condition responded less positively than their counterparts in the negative CCI condition (MpositiveCCI = 4.72, MnegativeCCI = 5.33, p < .001). Thus, the manipulation of CCI was successful. Moreover, participants in the two experimental conditions both perceived the scenario as realistic (MpositiveCCI = 5.51; MnegativeCCI = 5.53), and the difference was not statistically significant (p = .89); thus, it was not likely to have any materialized effect on the results.

Hypotheses Testing

ANCOVA results revealed a significant effect of CCI on social media engagement, F(1,197) = 6.31, p < .05, perceived customer–customer interaction justice, F(1,197) = 12.08, p < .001, and perceived empathy quality, F(1,197) = 6.77, p < .05. Participants in the positive CCI condition were more likely to engage with Alex on social media further (MnegativeCCI = 5.06 vs. Mpositive = 5.32), and perceive a greater level of customer–customer interaction justice (MnegativeCCI = 4.94 vs. Mpositive = 5.39) and empathy quality (MnegativeCCI = 5.01 vs. Mpositive = 5.35).

Hayes’ (2017) PROCESS model (Model 1) was used with the recommended bias-corrected bootstrapping technique (number of bootstrap samples = 5000) to test the interaction effect of CCI and need for approval on consumers’ social media engagement, customer–customer interaction justice, and perceived empathy quality. Perceived service failure severity and the impact of COVID-19 were included as control variables. Results indicated that the interaction effect was marginally significant on social media engagement (b = -.31, t = -1.77, p = .079) and significant on customer–customer interaction justice (b = -1.00, t = -6.85, p < .001) and perceived empathy quality (b = -.84, t = -5.50, p < .001). Specifically, people with a lower need for approval reported a higher level of social media engagement, greater customer–customer interaction justice, and perceived empathy quality in the condition of positive CCI (vs. negative CCI). People with a higher need for approval exhibited a similar level on all three dependent variables regardless of the CCI conditions (see Figures 2, 3, and 4). The results are presented in Supplement Tables 2, 3, and 4 (see online supplemental material). As such, H1 and H2 were supported.

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Figure 2.

The Interaction Effect of CCI and Need for Approval on Social Media Engagement.

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Figure 3.

The Interaction Effect of CCI and Need for Approval on Customer–Customer Interaction Justice.

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Figure 4.

The Interaction Effect of CCI and Need for Approval on Empathy Quality.

Sensitivity Test—Experiment 2

Data were collected from 204 restaurant consumers recruited from Mturk. The sample demographic profile was similar to that of the main study. The participants were asked to indicate if the restaurant addressed Alex’s response. The result showed that, as intended, participants did perceive that the restaurant had addressed Alex’s response (M = 5.34 on a 7-point Likert scale). Participants in the two experimental conditions perceived the scenario as realistic (MpositiveCCI = 5.68; MnegativeCCI = 5.52), and the difference was not statistically significant (p = .19).

The results of Hayes’ (2017) PROCESS model (Model 1) indicated that the interaction effect was significant on social media engagement (b = -.86, t = -5.20, p = < .001), customer–customer interaction justice (b = -.89, t = -6.24, p < .001) and perceived empathy quality (b = -.78, t = -5.60, p < .001). Specifically, people with a lower need for approval reported a higher level of social media engagement, greater customer–customer interaction justice, and higher empathy quality in the condition of positive CCI (vs. negative CCI). People with a higher need for approval exhibited a similar level on all three dependent variables regardless of the CCI conditions. Thus, the results of hypotheses testing remained consistent in Experiment 2, indicating that the restaurant’s response strategy did not effectively influence how consumers reacted to others’ comments on their dissatisfying experience.

Conclusions

The present research examined the joint effects of CCI and need for approval on consumers. Results indicate that consumers with a lower need for approval showed more favorable responses after a positive CCI experience. Such a finding supports the CCI literature that all elements of the service encounter, including online CCI, are likely to influence consumer experience and future behavioral intentions (e.g., Wu, 2008). The finding that consumers reported more negative perceptions of the interaction encounter following an uncivil comment from a fellow customer is consistent with the research on customer incivility (e.g., Anderson et al., 2014; Bacile, 2020). Our study reveals that consumers are more negatively affected by this type of dysfunctional customer behavior in an online environment. Interestingly, such an effect is only observed among consumers with a low level of need for approval. Although the interaction effect on the intention to further engage with Alex on social media was not as statistically significant as perceived empathy quality and customer–customer interaction justice, the direction of the effect was as intended. Besides, one possible explanation is that the measure of social media engagement captures a future behavioral intention, while the other two outcome variables are more directly related to the interaction encounter itself.

Last but not least, we performed a sensitivity test by examining the role of manager response within a CCI experience. Results indicate that the manager’s response strategy (general vs. specific) does not effectively influence how consumers evaluate an eCCI experience as manifested in their social media engagement intention, perceived customer–customer interaction justice, and perceived empathy quality. Some previous studies have shown that different manager responses could result in different consumer experiences. For instance, Wei and colleagues (2013) found that specific manager responses to negative online reviews gained more trust and delivered higher communication quality than generic ones. Wang and Chaudhry (2018) revealed that manager responses to negative reviews significantly and positively influenced subsequent ratings compared to manager responses to positive reviews. In contrast, our research demonstrates that such differential effects of specific versus negative manager responses in a CCI encounter disappeared as it could be overshadowed by a consumer’s experience during a CCI encounter.

Theoretical Implications

This research yields unique theoretical implications for the literature on customer-to-customer interaction (CCI) and need for approval. First, this study has revisited the conceptual realm of how CCI is defined in virtual engagements, encapsulating individuals not having first-hand experience with the business (e.g., consumption experience). Prior research has called for additional CCI studies in technology-saturated service settings (Heinonen et al., 2018). The rise of social media and virtual communities not only provides platforms for CCIs that are independent of physical service settings or firm-controlled environments (Libai et al., 2010), but also increases the connectivity of consumers and the social nature of consumption (Heinonen et al., 2018), hence expanding the relevance and scope of CCIs. As such, this study expanded the traditionally defined “other customers” in the service environment, who must present in the same physical setting and share the same consumption experience with the focal customer. In this study, the focal customer and other customers are connected by a digital platform rather than the service provider, fostering the development of eCCI. The findings of this research offer empirical evidence for the significant impacts of eCCIs via social media on focal consumers.

The present research extended CCI research to a service failure context. CCI research is often seen in service encounters where interpersonal communications might be intentional, expected, or unavoidable (Wei, Miao, Cai, & Adler, 2017). By addressing a service failure context where the initial consumer comment was intended for a firm, this research uncovers the power of unexpected involvement of other customers in influencing the service experiences.

Third, this research extended the CCI literature by incorporating and comparing cases of both positive and negative CCIs in one study. While favorable CCIs have been widely studied in offline contexts, such as conferences (Wei, Miao, Cai, & Adler, 2017), recreation centers (Jung & Yoo, 2017), and theme parks (Luo et al., 2019), varying balances of both positive CCI and negative CCI exist in different services (Nicholls, 2010). Prior research found a significant impact of negative CCI on customer dissatisfaction (Sreejesh et al., 2017), which could be stronger than that of the service setting, the frontline service providers, and even the overall service performance (Groveet al., 1998). Especially on the Internet, people will most likely misbehave more than they do in face-to-face situations (Suler, 2016), yet CCI research in this regard is lacking. This research filled these voids by addressing the differential impacts of positive and negative CCI in an online setting in one study. It went a step further, too, by incorporating an individual difference relevant in interpersonal communications—need for approval. The identified differential impacts of positive and negative CCI on people with varying levels of need for approval underline CCI’s complex nature and the merit of considering individual differences in future CCI investigations.

Another theoretical implication concerns the boundary conditions for CCI’s influence on consumer perceptions that prior work has yet to examine (Bacile, 2020). As the results reveal, the key to moderating the influence of such behavior resides in an individual difference: need for approval. The seminal work on need for approval suggested that people low in need for approval (vs. high) are more independent and self-directed, and rely on themselves as a reference rather than on external sources (Witkin et al., 1954). In contrast, people high in need for approval attach greater importance to others’ judgments and are more likely to adapt their behaviors accordingly in social interactions (Canlı & Karaşar, 2021). The findings of the present research revealed the opposite in a digital setting post a service failure: consumers low (vs. high) in need for approval are more easily influenced by how others respond to their initial complaint made toward a firm, in that upon receiving an uncivil response from another customer, they are less likely to engage in social media while perceiving lower customer empathy and customer–customer interaction justice. Although counterintuitive, such findings support another line of research, which suggests that in the face of a negative situation, people low in need for approval respond more authentically (Dozier et al., 1998), such that they would act out their negative feelings after being instigated, whereas their high need for approval counterparts are more likely to engage in self-criticism, suppress their negative feelings, and adjust their behaviors to gain social support (e.g., Canlı & Karaşar, 2021; Rudolph et al., 2005). People with high need for approval’s indifference towards a positive CCI is also consistent with prior research findings that they are used to giving negative feedback and can sometimes reject positive feedback (McFarlin & Blascovich, 1981). The present research thus offers empirical support for the broader effects of need for approval in digital customer service and helps to further develop the overall domain and complexity of the need for approval construct.

Managerial Implications

The results of this study indicate that all elements of a service encounter, including online CCI, are likely to influence consumer experience and future behavioral intentions. Managers are advised to include CCI as part of a well-rounded customer service strategy and recovery plan. By fully understanding that CCI is an active component of consumers’ perceived satisfaction and dissatisfaction, managers should consider having social media and online monitoring programs in place to flag, hide, or alert managers about disruptive or uncivil remarks not only about the firm but occurring between customers. Additionally, firms can encourage customers to reach out to them through private messages to resolve any issues that may arise, as Southwest Airlines encourages guests to do (Elliott, 2022). These online monitoring and response programs should complement a detailed communication program pre- and post-service encounter for maximum retention of customers and to increase consumer satisfaction.

The research findings also suggest that consumers with a lower need for approval showed more favorable responses after a positive CCI experience than those consumers with a higher need for approval. This indicates that managers should prepare contingencies for differing consumer communications and not treat all consumers the same. While it is not reasonable for managers at hospitality firms to fully recognize and diagnose levels of need for approval in guests, managers should realize that interacting with customers in personalized ways on social media and during onsite service encounters can mitigate the negative effects derived from need for approval in eCCI situations. Personalizing communications can be accomplished by segmenting social and digital media and personalizing marketing journeys to make customers feel important. Hyatt and Hilton are hospitality firms that have employed the personalized customer response tactic to great effect on social media sites (Time, n.d.).

Since consumers who receive an uncivil comment from a fellow customer tend to feel that it is unjust and show a lower level of future engagement on social media—which can produce negative perceptions about their service experience—managers need to be mindful of how best to re-engage them via strategic follow-up communications post service and post purchase. First, managers can easily identify and recognize these individuals based on social media interactions between consumers and reach out to them personally. An example of this type of strategy in use is for the firm to proactively reach out to and re-engage consumers in private while apologizing for the negative CCI they experienced and ensuring the firm will do its best to maintain a supportive environment in their online brand community moving forward. In addition, managers should be cognizant of using a higher quality of empathy when communicating with customers to ensure that their needs are fully met, expectations are exceeded, and ultimately to ensure the best possible service experience outcomes. As such, having a solid service recovery strategy pertaining to both CCI and eCCI is becoming more important for firms, specifically the management of exogenous factors during consumer-to-consumer interactions concerning service quality (Choi & Kim, 2020). By having a company culture and infrastructure that encourages and allows guest feedback, and strategies to mitigate potentially harmful CCI, firms can be more apt to identify service failures and potentially offset any negative CCI impact.

Limitations and Suggestions for Future Research

This research has some limitations, which imply several avenues for future research. First, this research adopted a hypothetical scenario, a method widely accepted in the service failure literature (e.g., Bacile, 2020), in its exploration of online CCIs. One limitation regarding this design is that the type of CCIs and social media platforms that can be incorporated are quite limited. In real-life scenarios, consumers may post positive and negative reviews, receive multiple comments from others, and use various online platforms (e.g., peer-to-peer services, independent online platforms, the focal firm’s platforms). Future research should examine whether the number, intensity, and valence of the initial post and other customers’ responses would produce different effects on different platforms.

Second, this research adopted the scenario from CCI research published recently (Bacile, 2020), the text of which was built upon actual complaint responses observed on today’s social media. However, the negative CCI manipulation may be perceived as stronger than the positive CCI manipulation. Future research should b alerted to efforts to make the manipulations equivalent in impact. In addition, while we focused on consumers’ evaluations of the interpersonal experience in reaction to other customer’s negative responses to the focal customer’s complaint, such phenomenon of customer incivility could potentially lead to other punitive results on consumers welfare, emotions, attitude toward the business, and behavioral intentions such as reporting customer incivility to the business. We call for future research to consider more consequences of others’ negative eCCI to make even more powerful contributions to the literature and practices. Further, while need for approval, as a personality trait, was measured in the present quasi-experimental study, future research can consider experimentally manipulating need for approval so as to make it more activated or more suppressed at the time of filling out the questionnaire.

Third, as a sensitivity test, this research considered firms’ involvement strategies based on the quality of the manager’s response. Although this study did not detect significant impacts of the firm’s different responses on how consumers evaluate their interpersonal experience, whether it would make a difference in how consumers evaluate the service climate and the firm is worthy of future investigation. As Heinonen and colleagues (2018) suggest, the role of the service provider in CCI, although a promising area for future research, has yet to be explicitly addressed. This research lays the foundations for follow-up studies on an oblivious service provider that chooses not to address CCIs versus an attentive one that actively responds to CCIs.

Additionally, while the present research revealed that low (vs. high) need for approval consumers were more affected by CCI in an online environment, the underlying mechanism explaining such a relationship was not tested. Future research is encouraged to consider potential mediators in a positive versus negative CCI condition. Lastly, the study focused on how CCI impacted customers. As a growing body of research is calling for greater emphasis on the effect of customer incivility on employees (Baker & Kim, 2020), seeing how negative CCI versus outright customer incivility affects employees can also add value to the scholarly conversation.