Expectation and Complaint: Online Consumer Complaint Behavior in COVID-19 Isolation

Abstract

Purpose This study aims to articulate the nature of consumer complaining behavior (CCB) by analyzing the mechanism and characteristics of online CCB in COVID-19 isolated environment. Patients and Methods For the purpose, this study collected data via a web-based questionnaire survey from 408 consumers in Shanghai, China during COVID-19 isolation. Through building and analyzing a structural equation model that consists of six latent variables such as perceived service quality, perceived product quality, customer satisfaction, negative emotion, customer complaint; the study analyzed the basic characteristics of CCB, and focused on the moderation test of consumer expectation to validate its important role in consumer decision-making behavior. Results First, compared to perceived service quality, perceived product quality has a stronger influence on customer satisfaction and has a weaker influence on negative emotions in the COVID-19 isolated environment. Second, the total influence of perceived product quality on customer complaints is stronger than that of perceived service quality. Third, the direct impact of negative emotions on customer complaints was much stronger than the effect of customer satisfaction on customer complaints. Meanwhile, it can also act as a mediating variable to make customer satisfaction have an additional indirect effect on complaints. Finally, the study also found that consumer expectation can reinforce the influences of customer satisfaction on negative emotions and customer complaints, while it weakens the effect of negative emotions on customer complaints. Conclusion This study suggests that the classic CCB factors still exert a stable influence on customer complaints through cognitive and emotional response pathways, but the influence difference is obvious in the COVID-19 isolated environment. And the influence processes are significantly moderated by consumer expectation level. Enterprises should conduct more targeted marketing interactions, according to these CCB characteristics.

How to believe? Building trust in food businesses' consumers based on psychological contracts

“…This means that during the process of online shopping, the quality of service provided by the food sales platform has a significant impact on consumers' expectations and perceptions of benefits and relationships. This is in line with the consensus of previous scholars that service quality is a universal factor that influences online transactions, meeting consumer expectations, promoting purchases and strengthening marketing relations (Wang et al ., 2022a; Koay et al ., 2022; Wang and Kim, 2019).…”

“…Furthermore, 118 respondents (32.9%) reported a monthly income between 3,000 and 6,000 CNY, which represents the highest proportion. These sample characteristics differ slightly from those of Chinese online consumers who are typically younger, have higher levels of education and lower incomes (Wang et al ., 2022a; Wang and Kim, 2019). In this sample, over half of the respondents (50.7%) did not receive higher education, and a significant proportion of the population (18.1%) is over 60 years old.…”

“…The level of service quality on online platforms widely influences consumer behavior. Previous literature has proved that online service quality is closely related to typical consumer behavior elements such as satisfaction, purchase intention and customer loyalty (Wang et al, 2022a;Koay et al, 2022;Rita et al, 2019). However, these findings have limited help in explaining how service quality generates consumer trust.…”

“…Therefore, the service quality issue concerned by this research is also mainly aimed at the service quality level that the APP platform can provide. Furthermore, in the actual transaction process, consumers' shopping experience is often based on the combination of service and product (Wang et al ., 2022a; Szwejczewski et al ., 2015). It's hard to impress consumers with a single product or service; the same is true of the food trade (Grewal et al ., 2009).…”

“…As shown in Table 4, the HTMT values were below 0.85 in all instances (Hair et al ., 2021). The correlation coefficient between the constructs is smaller than the square root of the average variance extracted (AVE) for each construct (Wang et al ., 2022a). The discriminant validity has been verified.…”