Customer Engagement With Online Restaurant Community During COVID-19–Model Inventory

Abstract

The Customer Engagement With Online Restaurant Community During COVID-19–Model (Liu et al., 2023) was developed to investigate the drivers of customer engagement within digital restaurant communities during the COVID-19 pandemic. The core focus of the study was to explore how mutual disclosures between restaurant servers and customers influence critical outcomes, specifically customers’ social influence engagement and knowledge-sharing engagement.

The theoretical framework underpinning this model is grounded in both Social Penetration Theory and Social Exchange Theory. The model posits that the relationship between mutual disclosure and engagement is mediated by customer trust and the rapid formation of relational ties known as swift guanxi. The 25-item instrument was adapted from existing validated scales and was administered to a sample of consumers in China. Rigorous psychometric evaluation, including Structural Equation Modeling (SEM) and Confirmatory Factor Analysis (CFA), confirmed the measure’s reliability and validity.

Keywords

Server Disclosure, Customer Disclosure, Customer Trust, Swift Guanxi, Customers’ Social Influence Engagement, Customers’ Knowledge-Sharing Engagement, COVID-19 Pandemic, Customer Engagement, Online Restaurant Community, Social Penetration Theory, Social Exchange Theory

Authors

Liu, Min, Xu, Jie, Li, Shuhao, Wei, Min

Purpose

The principal objective of this measurement model is to provide a validated instrument capable of accurately assessing the behavioral dimensions of customer engagement in the specific setting of an online restaurant community. This is particularly relevant for understanding consumer behavior shifts that occurred during the COVID-19 pandemic, a period marked by increased reliance on digital interaction for hospitality services.

The scale is designed to quantify the extent to which customer and server openness (mutual disclosure) contributes to the development of relational capital—namely, customer trust and swift guanxi—and how these factors subsequently drive proactive customer behaviors such as social promotion and collaborative knowledge sharing.

Construct

The scale measures six distinct constructs that form the comprehensive framework for online customer engagement. These constructs describe the input, mediating, and output variables of the proposed model:

• Server Disclosure: The extent to which restaurant employees share information or opinions with the customer.

• Customer Disclosure: The extent to which customers share personal information or preferences with the server.

• Customer Trust: The customer’s belief in the reliability and integrity of the restaurant employee.

• Swift Guanxi: The rapid establishment of harmonious, friend-like relationships between the customer and the server in the online community.

• Customers’ Social Influence Engagement: The customer’s active willingness to promote the restaurant and influence others positively.

• Customers’ Knowledge-Sharing Engagement: The customer’s willingness to provide constructive feedback, suggestions, and knowledge to improve the restaurant’s products or services.

Validity

The study established both convergent and discriminant validity for the instrument through rigorous psychometric testing.

Convergent Validity: This was confirmed by ensuring that all item factor loadings were statistically significant at the 0.001 level and exceeded the 0.60 threshold (Anderson & Gerbing, 1988). Furthermore, the Average Variance Extracted (AVE) values for most constructs were above the required 0.5 level, confirming that the latent constructs explained more than half of the variance in their respective indicators (Fornell & Larcker, 1981).

Discriminant Validity: The distinctiveness of the constructs was established by demonstrating that the correlation coefficients between any two constructs were lower than the square root of the AVEs of those related variables (Fornell & Larcker, 1981). This result suggests that each construct measures a unique aspect of the overall engagement model.

Reliability

The internal consistency of the measurement model was assessed using two widely accepted statistical measures: Cronbach’s alpha and Composite Reliability (CR).

The calculated values demonstrated strong reliability across all constructs. The Cronbach’s alpha coefficients ranged from 0.670 to 0.924. The Composite Reliability (CR) values, which are preferred when utilizing SEM, were consistently high, ranging from 0.762 to 0.938. These results confirm the internal consistency and stability of the scale items within their respective constructs.

Factor Analysis

The psychometric evaluation included assessment for potential bias and confirmation of the structural model fit.

Common Method Variance (CMV): Harman’s single-factor method was employed using SPSS 26.0 to assess the threat of CMV. The analysis revealed that the data extracted six distinct factors with eigenvalues greater than 1. Crucially, the first factor accounted for only 40.3% of the total variance, falling below the critical threshold, thereby indicating that CMV was not a significant concern for the study (Podsakoff et al., 2003).

Confirmatory Factor Analysis (CFA): The CFA provided strong evidence of a good model fit. Key goodness-of-fit indices (χ2 = 528.682, df = 215, χ2/df = 2.459, GFI = 0.869, NFI = 0.893, IFI = 0.934, TLI = 0.921, CFI = 0.933, RMSEA = 0.066, RMR = 0.114) met the generally accepted standards for model adequacy (Hu & Bentler, 1999), supporting the hypothesized six-factor structure of the engagement model.

Instrument

Test Type: Original

Format: Items are rated using a seven-point Likert scale.

Language Available: Chinese

Population Group: Human; Male; Female

Age Group: Adulthood (18 yrs & older); Young Adulthood (18-29 yrs); Thirties (30-39 yrs); Middle Age (40-64 yrs)

Population Details: Location: China, Respondents: Consumers

Test Methodology: Test Validity; Convergent Validity; Discriminant Validity; Test Reliability; Internal Consistency; Factor Analysis; Confirmatory Factor Analysis; Measurement Model; Structural Equation Modeling

Keywords

Customer Engagement, COVID-19, Online Restaurant Community, Server Disclosure, Customer Trust, Swift Guanxi

Authors

Author ORCID Identifier:

• Liu, Min: http://orcid.org/0000-0002-5696-5405

• Xu, Jie: http://orcid.org/0000-0002-1964-0820

• Wei, Min: http://orcid.org/0000-0002-8441-4600

Affiliation Email addresses:

• Liu, Min (Xiamen University School of Management): [email protected]

• Xu, Jie (Xiamen University School of Management): [email protected]

• Li, Shuhao (Qingdao University School of Tourism and Geography Science): [email protected]

• Wei, Min (Xiamen University School of Management): [email protected]

Correspondence Address:

Xu, Jie: Xiamen University, School of Management, 422 South Siming Road, Xiamen, China, 361005, [email protected]

Permissions & Fee and Test Year

Permissions: May use for Research/Teaching

Commercial: No

Fee: No

Test Year: 2023

References

Liu, M., Xu, J., Li, S., & Wei, M. (2023). Engaging customers with online restaurant community through mutual disclosure amid the COVID-19 pandemic: The roles of customer trust and swift guanxi. Journal of Hospitality and Tourism Management, 56, 124–134. https://doi.org/10.1016/j.jhtm.2023.06.019

Items of the Customer Engagement With Online Restaurant Community During COVID-19–Model Inventory

This is a 25-item measure. The constructs assessed include Server disclosure; Customer disclosure; Customer trust; Swift guanxi; Customers’ social influence engagement; Customers’ knowledge-sharing engagement. Specific items are available in the source reference: Liu, M., Xu, J., Li, S., & Wei, M. (2023). Engaging customers with online restaurant community through mutual disclosure amid the COVID-19 pandemic: The roles of customer trust and swift guanxi. Journal of Hospitality and Tourism Management, 56, 124–134. https://doi.org/10.1016/j.jhtm.2023.06.019, specifically in Table 2, Page 129.

Server disclosure

• When (If) there are mistakes during the services delivery process, the employee of this restaurant tells (would tell) me in the WeChat Group.

• The employee of this restaurant tells me about his/her personal opinion (e.g., food taste, food price, portion size) in the WeChat Group.

• The employee of this restaurant gives appropriate advice to my menu choice in the WeChat Group.

• The employee of this restaurant shares food information with me in the WeChat Group.

Customer disclosure

• I express thanks to the employee of this restaurant for his/her services on the WeChat Group.

• I tell the employee of this restaurant about my preference (e.g., food taste, food price, portion size) on the WeChat Group.

• I tell the employee of this restaurant that I am a regular customer of this restaurant on the WeChat Group.

• I share personal information with the employee of this restaurant (e.g., food allergy, vegetarian) in the WeChat Group.

Customer trust

• I think the employee of this restaurant is reliable.

• I have confidence in the employee of this restaurant.

• The employee of this restaurant is trustworthy.

• I think the employee of this restaurant has high integrity.

Swift guanxi

• The employee of this restaurant in the WeChat Group and I can understand each other.

• The employee of this restaurant in the WeChat Group and I treat each other as we treat our friends.

• The employee of this restaurant in the WeChat Group and I have harmonious relationships.

Customers’ social influence engagement

• I will talk about my positive experience at this restaurant with others.

• I will discuss the benefits that I get from this restaurant with others.

• I will actively mention this restaurant in my conversations.

• I will actively discuss this restaurant on different media platforms.

Customers’ knowledge-sharing engagement

• I am willing to provide feedback about my experience with this restaurant.

• I am willing to provide suggestions for improving the performance of the restaurant’s products/services.

• I am willing to provide suggestions/feedback about the new product/services to this restaurant.

• I am willing to provide feedback/suggestions for developing new products/services for this restaurant.

Note. Items are rated using a 7-point Likert scale (1 = extremely disagree; 7 = extremely agree).