1. Introduction
Looking at the dining out frequency patterns in China, a global consumer survey conducted in 2017 reported that 43% of the Chinese customers eat lunch out five times and more a week and 78% of them eat dinner out at least once a week [
1]. China’s food service establishments revenue in 2018 touched 4271.6 billion yuan, and the number of restaurants in 2019 reached 7,118,058 [
2]. However, the coronavirus pandemic has significantly damaged the restaurant industry globally [
3] and specifically in China [
4]. Emerging evidence reveals that the risk of spreading COVID-19 increases in the restaurant setting, and restaurants worldwide have been forced to protect public health by adapting to necessary social distancing practices [
5].
In addition, customers pay more attention to food safety and seek restaurants that perform hygiene practices including cleaning, sanitization, and disinfection [
6]. Thus, restaurant guests who are unsure of whether it is truly safe to dine out, put more importance on the entire service consumption experiences from service processes to service outcomes [
7]. Therefore, it has become more critical during the COVID-19 pandemic that restaurants communicate their best food and service practices clearly. In order for customers to feel confident dining out, restaurants must make their best efforts to minimize critical flaws in their service provision process and outcomes.
However, the nature of service is not simple to define and can be highly personalized concerning customer demand and preferences [
8,
9]. Service quality is evaluated based on how satisfied customers are with the service outcome [
10]. When customers perceive that the service provided is less than their expectations, those customers are likely to experience service failures [
11,
12]. In particular, service failures in the hospitality industry are inevitable because services are offered by “human beings” [
13]. An early study by Hoffman et al. [
14] classified service failures into a three-dimensional concept that can occur in the hospitality industry: “(1) employee responses to service delivery failures (e.g., restaurant meal defects and slow or unavailable service), (2) employee responses to customer needs and requests (e.g., failing to cook foods as requested and responding to seating preferences), and (3) unprompted and unsolicited employee actions (e.g., wrong order and mischarging)” (p. 49). This three-dimensional concept of service failures is classified into two stages involving processes and outcomes [
15,
16]. Process failures involve the interpersonal process of service delivery while outcome failures involve the core and impersonal service outcome [
15].
As literature revealed that service failures are critical in determining a business’s survival, many scholars have attempted to identify how customers react to service failures and how their actions affect the business [
17]. Service failures result in customer dissatisfaction, negative word-of-mouth (WOM) behavior and customer defection [
18]. In particular, the impact of WOM has become more powerful in the restaurant industry because today’s consumers seek dining experiences shared by others before consumption. Consumers thus rely on WOM to make inferences about product and service quality [
19]. Due to the pervasive use of online platforms to share their dining experiences, it is easier for WOM to spread more extensively and rapidly [
20]. This situation leads customers who encounter service failures to spread their unpleasant dining experiences and engage in negative WOM more easily and actively [
21]. This can cause a significant damage to the restaurant business, and particularly during the pandemic.
Several studies reported different customer reactions with dissatisfying service encountered in the hotel context. These include “exiting silently”, “spreading a negative word-of-mouth (WOM)”, “directly complaining to the service provider”, and “continuing patronage despite their service failure experiences” [
13,
18,
22]. Accordingly, this current study cross-examines why customers experiencing similar service failures show different reactions. Schutte and Ciarlante [
23] proposed that an understanding of consumer behaviors would be ineffective without taking cultural differences into consideration. Following this proposition, several studies revealed that customers from different cultures perceived service quality differently [
24,
25] and exhibited different responses and behaviors towards the same service failure event [
26,
27]. Although there are existing studies on the effects of cultural differences in the cross-national comparison context, it is still important to expand the academic effort in the examination of cultural values possessed by restaurant diners. During a crisis such as the COVID-19 pandemic, a more accurate understanding of customer perceptions about what matters in a restaurant service failure context is required to identify the differences between the main triggers to switching intentions and revisit intentions. Thus, obtaining an insight into how cultural values perceived by restaurant guests involves the interpretation of service failures would be useful in developing more effective service recovery strategies.
With this information in mind, this study explored the roles that unique cultural-based traits of traditional Chinese relationships representing
Guanxi played in the restaurant service failure setting. Generally,
Guanxi is defined as a deep psychological commitment among Chinese people in an emphasis on mutual empathetic understanding, sharing of feelings and emotional identification rather than responsibilities or obligation [
28,
29]. The hospitality industry is largely human-resource-oriented and inevitably relies on various interpersonal and social interactions between customers and employees (or managers and owners), which can be affected by their culture and society [
30,
31]. Those interactions between Chinese are culturally rooted based on
Guanxi, conceptualizing informal personal connections developed based on social norms (e.g., reciprocity, mutual commitment) and long-term relationships [
32].
Guanxi reflects in a variety of contexts (i.e., customer to business, or business to business). It is common that Chinese tend to use their personal connections developed based on
Guanxi to attain competitive business advantages. Exchange parties connected based on
Guanxi develop trust in their open-ended and long-term transactions and feel morally obliged to favor other parties [
33]. Thus, if they do not do something beneficial for other parties, they feel like they lost face and connections [
34].
Guanxi appears to be an important topic in academic research. As an example, several studies found that hotel managers make put a great deal of effort into developing and leveraging personal connections with important and beneficial accounts [
35,
36].
Applying the Guanxi concept to Chinese customers in the restaurant context, this study explores how customers from a different cultural background would react to restaurant service failures. It is crucial to recognize how customers who have developed personal connections (Guanxi) with a restaurant owner or employee may have a different viewpoint regarding service failures, leading to varying responses. In other words, when restaurant guests having high Guanxi encounter service failures, their responses tend to be directed towards helping the restaurant rather than expressing their anger. This may imply that Guanxi mitigates the negative impact of service failures on consumer negative responses. Thus, this study aimed to identify how Chinese customers respond to restaurant service failures. This study also strived to answer questions such as: Do they directly complain to the service provider? or do they spread negative WOM to others? In addition, this study examined how negative WOM, and direct complaints, were associated with their switching and revisiting intentions. More importantly, this study focused on identifying if Guanxi has vital moderating effects on negative WOM relationships, direct complaints, switching intentions, and revisit intentions.
4. Results
4.1. Reliability and Validity of the Measures
Confirmatory factor analysis was conducted to estimate reliability and validity of the measures used for this study. Among the three items of direct complaints, the factor loading of one item (D1: I will forget the unsatisfactory experience and not complain to anyone) was found to be smaller than 0.5; thus, it was eliminated. CFA was then re-run and its results are presented in
Table 3. The assessment of the measurement model in this study indicates that it is an acceptable model fit (χ
2 = 198.30;
df = 94; χ
2/
df = 2.11; CFI = 0.93; GFI = 0.90; NFI = 0.88; RMSEA = 0.06). All standardized factor loadings exceeded 0.50 at a significance of
p < 0.001. Cronbach’s alpha coefficients of each construct ranging between 0.740 and 0.853 which are all above the reference value of 0.5 (Nunnally 1978). All average variance extracted (AVE) values were above 0.500 (ranging from 0.500 to 0.660). All composite reliability values exceeded the threshold value of 0.70 (ranging from 0.707 to 0.812). Thus, convergence validity of this study measures was supported [
113].
Table 4 shows the means, standard deviations, and correlation coefficients of the study constructs. Correlation analysis found that “negative WOM” was positively related to “switching intention” and also negatively associated with “revisit intention.” “Direct complaints” was positively related to “switching intention”; however, was negatively associated with “revisit intention.”
Guanxi was positively related to “direct complaints” and “revisit intention”; however, it was negatively associated with “negative WOM” and “switching intention.” All values of the square root of the average variance extracted (AVE) were larger than the correlation coefficients among the constructs, which supported discriminant validity of the measures [
113].
4.2. Results of Testing Hypotheses 1 to 4
To test the hypothesized relationships in this study, a structural equation model was developed. In addition, respondents’ age, gender, marital status, and income were included into the SEM to control their potential effects on the relationships. First, we tested hypotheses 1
p (process failures) through 4
p (process failures), expecting the significant relationships between negative WOM, direct complaints, switching intention and revisit intention within the process failures setting. As presented in
Table 5, our structural equation model (SEM) was found to be acceptable with goodness-of-fit-indexes (χ
2/
df = 2.15, CFI = 0.93; NFI = 0.90; GFI = 0.92). Regarding the relationships between “negative WOM,” “switching intention,” and “revisit intention,” results revealed that “negative WOM” increased “switching intention” (
β = 0.782,
p < 0.001), but decreased “revisit intention” (
β = −0.422,
p < 0.001). Thus, hypotheses 1 and 2 were supported. Furthermore, our results found that “direct complaints” had no significant effect on “switching intention” (
β = 0.055,
p > 0.05). However, “direct complaints” significantly and positively influenced “revisit intention” (
β = 0.153,
p < 0.05). Since we expected the negative relationship between “direct complaints” and “revisit intention,” Hypotheses 3 and 4 were not supported.
Second, hypotheses 1
o (outcome failures) through 4
o (outcome failures), expecting the significant relationships between negative WOM, direct complaints, switching intention and revisit intention within the outcome failures setting. As shown in
Table 6, the goodness-of-fit-indexes of SEM were satisfactory (χ
2/
df = 2.43, CFI = 0.93; NFI = 0.92; GFI = 0.93). We found the same effects of “negative WOM” on “switching intention” (
β = 0.718,
p < 0.001), and on “revisit intention” (
β = −0.227,
p < 0.01) as the results found in the process failures condition. Thus, hypotheses 1 and 2 were supported. In the same vein, we found that “direct complaints” had no significant effects on “switching intention” (
β = −0.009,
p > 0.05), but it had the significant and positive effect on “revisit intention.” Hence, hypotheses 3 and 4 were not supported.
4.3. Results of Testing Hypotheses 5 and 6
First, Guanxi was tested to see if it has the moderating roles on the hypothesized relationships among the proposed constructs (i.e., negative WOM, direct complaints, switching intention, and revisit intention) within the process failures setting. Using the mean value of
Guanxi, the respondents were divided into two groups: the high-
Guanxi (
n = 105) and low-
Guanxi (
n = 106) groups (see
Table 7). Then in order to test if there are significant differences in the relationships between the study constructs between the high-
Guanxi and the low-
Guanxi group, a multi-group analysis was conducted.
Results showed that the effect of “negative WOM” upon “switching intention” was found to be significantly positive in both the high-Guanxi group (β = 0.799, p < 0.001) and in the low-Guanxi group (β = 0.714, p < 0.001) under the process failures condition. The difference in the path coefficients was not significant (Δχ2 (1) = 0.507, p > 0.05). Thus, hypothesis 5ap was not supported. Additionally, the effect of “negative WOM” upon “revisit intention” was found to be significantly negative in the high-Guanxi group (β = −0.589, p < 0.001) and in the low-Guanxi group (β = −0.606, p < 0.001), indicating no significant difference in the path coefficients (Δχ2 (1) = 1.036, p > 0.05). Thus, hypothesis 5bp was not supported either.
Results found that the effect of “direct complaints” upon “switching intention” was negative but insignificant in the high-Guanxi group (β = −0.051, p > 0.05). However, such the relationship was significantly positive in the low-Guanxi group (β = 0.228, p < 0.01). This difference was significant (Δχ2 (1) = 4.463, p < 0.05). Thus, hypothesis 6ap was supported. In addition, the effect of “direct complaints” upon “revisit intention” was found to be negative but insignificant in the high-Guanxi group (β = −0.033, p > 0.05) while its relationship was positive and significant in the low-Guanxi group (β = 0.247, p < 0.05). However, this difference was not significant (Δχ2 (1) = 0.808, p > 0.05). Thus, hypothesis 6bp was not supported.
Second, using the same methods, respondents were divided into two groups: the high-
Guanxi (
n = 126) and low-
Guanxi (
n = 102) groups to test the moderating roles of
Guanxi on the hypothesized relationships within the outcome failures setting (see
Table 8).
The effect of “negative WOM” upon “switching intention” was found to be significantly positive in both the high-Guanxi group (β = 0.637, p < 0.001) and the low-Guanxi group (β = 0.821, p < 0.001). This difference was significant (Δχ2 (1) = 6.197, p < 0.05). Thus, hypothesis 5ao was supported. Moreover, the effect of “negative WOM” upon “revisit intention” was negative but insignificant in the high-Guanxi group (β = −0.145, p > 0.05) while the effect was significantly negative in the low-Guanxi group (β = −0.456, p < 0.001). Accordingly, the difference was significant (Δχ2 (1) = 4.142, p < 0.05). Thus, hypothesis 5bo was supported.
Results found the effect of “direct complaints” on “switching intention” was insignificant in either the high-Guanxi group (β = 0.107, p > 0.05) or the low-Guanxi group (β = −0.098, p > 0.05). This difference was not significant (Δχ2 (1) = 2.193, p > 0.05). Thus, hypothesis 6ao was not supported. Furthermore, the effect of “direct complaints” on “revisit intention” was found to be insignificant in the high-Guanxi group (β = 0.093, p > 0.05) but its effect was significant and positive in the low-Guanxi group (β = 0.447, p < 0.01). However, this difference was not significant (Δχ2 (1) = 1.371, p > 0.05). Thus, hypothesis 6bo was not supported.