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Article

Service Quality of Social Media-Based Self-Service Technology in the Food Service Context

by
Chen-Kuo Pai
1,
Ze-Tian Wu
1,
Seunghwan Lee
2,
Jaeseok Lee
3 and
Sangguk Kang
3,*
1
Faculty of Hospitality and Tourism Management, Macau University of Science and Technology, Macao 999078, China
2
Department of Tourism Management, Kongju National University, Gongju 32588, Korea
3
Department of Tourism Management, Gangneung-Wonju National University, Gangneung 25457, Korea
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(20), 13483; https://doi.org/10.3390/su142013483
Submission received: 12 September 2022 / Revised: 4 October 2022 / Accepted: 12 October 2022 / Published: 19 October 2022
(This article belongs to the Special Issue Technology Innovation: Applications in Sustainable Tourism and Hospitality)
Browse Figure
Versions Notes

Abstract

:
Social media connects individual users and corporate bodies on a self-service technology (SST) platform. The food and beverage industry has increasingly adopted the social media-based SST over other online and kiosk types of technologies for their service delivery. The present study sheds light on the dimensionality of service quality on the social media-based SST in the food service delivery context and went further to investigate the impact of the SST service quality on functional value, user satisfaction, and intention to reuse. The analytic results of 410 valid survey data found five salient dimensions (i.e., Functionality, Enjoyment, Assurance, Convenience, and Customization) constituting the service quality. The results also revealed that the perceived quality of social media-based food service is directly and positively associated with consumer’s satisfaction and perceived functional value and is indirectly associated with intention to reuse. The results provide practical suggestions regarding how to take advantage of using social media platforms for food and beverage professionals.

1. Introduction

As the restaurant industry has evolved with the development of self-service technology (SST) over the last decade, substantial academic attention has been paid to understanding how to make the best use of SST to optimize the business operation and management [1,2,3]. From suppliers perspective, SSTs are often adopted to save on operating costs and to improve financial performance and work efficiency during the service delivery process [4,5,6,7]. Existing research shows that customers are in favor of the implementation of SST when it meets customer’s need, expectation, and satisfaction [1,2,8]. Such endeavors came to fruition to a certain extent by identifying some noticeable triggers for the use of SSTs from the customer’s perspective [2,9,10,11]. The adoption of SSTs saves customers’ waiting time and cost, allows them to have a better control over the service delivery, increases location benefits, and enables them to enjoy the technology experience [12,13].
As the way SSTs are applied to the restaurant context rapidly evolves from on-site SSTs (e.g., a tablet attached to the table and a standalone kiosk) to off-site SSTs with a personal and portable device, the way in which customers perceive the service quality in relation with using SSTs also changes. Particularly in the restaurant industry, the SST has evolved the way that consumers are engaged in from assisting food ordering and queuing to assisting payment [9,14]. Recently, the SST has expended its application to a social media app, which is one of the most crucial communication platforms. Therefore, previous findings on the implementation of on-site SSTs in the restaurant context raised the academic concerns on how customers perceive the service quality of off-site SST linked with their personal social media account, and what implications from the previous studies should be retained or updated.
Owing to the advantages of SST, many restaurants have adopted the self-service food ordering system to increase sales performance and to provide better customer service. To reiterate the transit of restaurant SST application, it was mainly employed as on-site technology in its early stage such as a touchscreen menu kiosk or that of a tablet attached to a table, which allows customers to simply place an order and pay. With the advancement and popularity of smart devices, diverse food service applications (e.g., Uber Eats, DoorDash, etc.) changed the way that customers have food consumption experience. More recently, social media applications extend their functions to the food delivery service and are capable of placing an order inside its application. For instance, a national-wide social media application in China, WeChat, launched a food ordering add-on service. The social media-based mini-app provides an integrated food service system incorporating a food ordering, payment, and delivery functions as well as food consumption sharing experience within the social media app. Customers use the mini-app to order food and beverages at a convenient time and location (i.e., to a particular table in the restaurant or to any other off-restaurant places) and to settle the bill. Furthermore, customers can post their food service experience on the sister mini-app embedded in the same social media app. While taking advantage of the familiarity and popularity of social media app, customers are no longer get pressured to download another third-party food app and fill out sensitive, private, and financial information to the app [11,14,15]. Customers do not need to switch to another social media app manually to post their food service experience. Additionally, consumers became familiar with a contactless lifestyle amid the pandemic. As a personal device replaces with a public device during the service delivery, the reduced number of human contacts relieves any concerns and worries related to the spread of COVID-19 [11,16,17].
Meanwhile research on service quality has been conducted in conjunction with the means-end model in the hospitality and tourism field in recent decades [18,19,20]. Proposed by Zeithaml [21], the means-end model posits that the attributes of service affects perceived value and satisfaction of consumers. Literature shows that intrinsic attributes of service is often substituted with service quality [18,19,20], which provides a theoretical framework of the current study. At the same time, social media has changed our lives in many ways. The infiltration of social media into our lives also drew academic findings such as smoking relapse prevention [22] and H1N1 disease activity tracking [23]. The increased use of social media has also changed how visitors travel [24,25]. As social media expanded its functions and applications, the evolved social media also changed the way that we experience food service.
Despite the recent substantial research efforts on the service quality of SST following Lin and Hsieh’s [26] SSTQUAL scale development study, the nature of social media-based SST service quality and its impacts have received insufficient attention in the food and beverage industry, considering the recent dynamics. Therefore, the current study aims to shed more light on the multi-dimensionality of the social media-based SST service quality in the food and beverage industry, especially in the Chinese context, and the structural relationship among the SST service quality, perceived functional value, satisfaction, and intention to reuse from a customer perspective. The findings of the current study provide implications on how and what aspects of service quality dimensions would play a pivotal role in keeping consumers’ perceived value, satisfaction, and reuse intention of social media-based food service SST. With the findings of the study, the practitioners would also find relevant tactics in regard to revisiting their marketing strategy.

2. Literature Review

2.1. Self-Service Technology (SST) and Service Quality

SST enables customers to participate directly in the production of services and obtain the required services with limited engagement with (front-line) employees [13,27]. According to Kotler and Keller [28], convenience is one of the most valued attributes from customer perspective, which catalyzes that SST replaces the traditional customer-employee interactions in every avenue of service delivery. The SST research on the change of customer-employee interaction has mainly discussed the consequences of technology infusion on service customization, service recovery, and customer satisfaction [29], identification of the factors affecting customers’ use of self-service kiosks in hotels [5], technology readiness in the transportation and financial service areas [30,31], technology adoption of financial workers [32], and customer attitudes toward technology in the retail industry [33]. Despite the success and development of SST, only a few studies have explored the effect of SST in the restaurant industry from customers’ perspective [1,2,9], leaving the unsettled answer on the application of SST [34]. For example, Dixon et al. [9] found that people who regularly use technology products frequently visit restaurants that offer SSTs compared with consumers who less frequently use technology products. Kim et al. [2] revealed that extrinsic motivation and previous SST experience affect the likelihood of using restaurant kiosks. Ahn and Seo [1] elucidated the psychological response of consumers to the quality of on-site SST in interactive restaurants and found that how on-site SST affects consumer willingness and their ‘approach and avoidance’ behavior toward the use of SST.
Service quality is defined as the gap between customer expectations and the perception of service experience [35,36,37]. When a service provider uses SSTs to serve consumers in a “customer–technology” manner, the active SST customers participate in the production of the required services [27]. This implies that measuring service quality with SSTs should differ from customer–employee interactions. Parasuraman et al. [35]’s service quality scale (SERVQUAL) is based on the difference between customer expectations and perceived actual performance, where service is delivered in a face-to-face manner. Subsequently, Cronin and Taylor [38] developed a customer perception-based service performance scale (SERVPERF) for measuring the service quality. However, the SERVQUAL and SERVPERF scales are limited to assessing the service quality in the process of service delivery directly from employees to customers, because both scales are not optimized to measure the service quality in the interaction between customers and SSTs. In this respect, scholars have put forward different measurement scales for the scientific and technological service modes. For example, Dabholkar [39] introduced the quality attribute model from his investigation on the customer’s use of touchscreen self-service ordering systems in fast food restaurants. Dabholkar [39] investigated consumer’s decision making process when they use SSTs and validated two service quality models: attribute-based cognitive model and affective model. Subsequently, the quality of online shopping website (SITEQUAL) model and electronic retail quality (eTailQ) scale were developed to measure the perceived quality of internet shopping website and consumers’ online shopping experience, respectively [40,41]. The quality of electronic services (E-S-QUAL) scale was also developed to assess the service quality of online shopping providers [36]. Using the electronic transaction process quality of service, the transaction process-based e-service quality (eTransQual) scale was proposed to measure the service quality delivered by electronic services [42]. Ding et al. [43] proposed the e-SELFQUAL scale to examine the quality of online self-services in e-retailing.
Although the abovementioned studies established the scales of service quality evaluation from the “customer–technology” interaction perspective, most of the scales are limited to the application to the online technology industry. Thus, Lin and Hsieh [26] presented the SST service quality (SSTQUAL) scale for the application to the various industries. SSTQUAL is made up of seven dimensions in a second-order factor structure: functionality, enjoyment, security/privacy, assurance, design, convenience, and customization [26]. First, the functional response to the performance of SSTs in utilitarianism is defined as the efficiency of SSTs in transaction processing [44]. Functionality reflects the specific characteristics of SSTs, and it measures the efficiency of the technology in delivering services in a short period of time, ease of operation, and accuracy of service results [26]. Second, enjoyment is defined as the extent to which customers experience the feeling of enjoyment when using SSTs [26]. If technology-based self-service seems enjoyable, people are more likely to use the self-service options [39,45]. Third, security/privacy means that people do not feel threatened, at risk, or suspicious when they interact with SSTs. It refers to customers’ trust in the use of SSTs, trust that the technology will not misuse their personal information, and belief that there is no risk of fraud [26]. Fourth, assurance is the image of customer service providers in terms of safety and credibility in the service delivery [35]. Lin and Hsieh [26] interpreted the SSTs assurance as the reputation and competence of companies providing SSTs, claiming that the assurance is a key factor of SST evaluation. Fifth, the design of SSTs includes the visual appeal of the color, layout of the interface screen, and the aesthetics of the facilities involved in the latest images [1]. The design has been used to measure (1) customers’ perception of the interface and aesthetics of the SSTs, and (2) the extent to which the technology is considered a novel one [26]. Sixth, convenience refers to the extent to which customers are able to use the SSTs at a convenient time and place [46]. Seventh, customization is the extent to which SSTs meet customer needs and preferences [47]. The addition of customization dimension enables to fulfill customer-specific needs and preferences, resulting in higher service quality [26]. Given that SSTs with a social media application on a personal device provide a more personalized service experience over on-site public SSTs (e.g., favorite products saved, personal preference, etc.), the customization dimension becomes more important in social media-based SSTs using a personal device.

2.2. Influence of Social-Media Based SST Quality

The use of SSTs can bring customers the benefit of convenient and efficient access to services. As customers come to understand that SSTs are functional and useful, they can get an enhanced experience, thereby put a larger value on the SSTs. In this vein, the service quality research has proven its significant influence on perceived functional value of customers, which, in turn, leads to customer loyalty in many settings. For instance, Chen and Hu [48] found that the qualities of coffee, service delivery, and menu choice variety are significantly associated with coffeeshop customers’ perceived functional value, whereas atmosphere and extra benefit qualities are not. In the on-demand entertainment service setting, speed of transaction, exploration, and trust in service provider are found to be significant predictors of perceived value [44]. Although some other studies found that the service quality of SSTs affects customers’ perceived value of SSTs [49,50], the perceived functional value of SSTs in the restaurant industry has not received much attention. For instance, Sweeney and Soutar [51] delved deeper into the multi-item scale of perceived value and found that perceived functional value consists of two dimensions: “quality/performance” and “price/value for money.” While the price/value for money dimension focuses on the comparison between the benefit gained and sacrifices made, the quality/performance dimension refers to the degree to which a consumer perceive usefulness of product quality and expected performance [51]. As the quality/performance dimension puts more emphasis on the most likely utility gained through the consumption experience rather than its cost, the dimension would be more meaningful to understand the perceived functional value when there is no or minimum cost incurred like social media-based food ordering SST. Therefore, the following hypothesis is posited:
H1. 
Customer’s perceived quality of a social media-based food ordering self-service technology is significantly associated with the perceived functional value.
While customer satisfaction is related to the expected performance [52], other scholars interpret satisfaction as an emotional response to the use of a product or service and whether the product or service meets the actual assessment of the criteria that gives consumers pleasure [53,54]. As the academic research on the interpretation of satisfaction develops, satisfaction is defined as the extent that individuals’ sensory state is generated by comparing their feelings about the performance of the product or service actually experienced with their previous expectations, and it is used to assess whether the resulting product or service meets their needs and expectations [55]. Some studies have explained the role of customer satisfaction in electronic services and showed that the effect on customer loyalty and willingness to act would be enhanced by managing and improving customer satisfaction [56,57]. In the retail industry, Marzocchi and Zammit [58] assessed customer satisfaction with supermarket self-service scanning technology and found that both sense of control and the pleasure component have a positive effect on technology satisfaction. They also found that customers’ views on the supermarkets and the decision to make future visits to stores depend on the extent to which the supermarkets’ self-service scanning technology meets the customers’ needs [58]. Wang [59] explained the causes and consequences of consumers’ satisfaction with the use of SSTs in the retail environment. Kim and Qu [5] found that travelers would be content to use technology if they think the use of the hotel kiosk suits with their lifestyle. Collier and Sherrell [44] emphasized that customer satisfaction with the SSTs is closely related to the perceived speed of transaction and trust in service providers. Although people with higher levels of technological anxiety are shown to be less satisfied with their interaction with SSTs [60], other studies found that the customers’ perceived service quality of SSTs affects satisfaction positively in financial service, transportation, and retail industries [31,61]. Therefore, the following hypothesis is proposed:
H2. 
Customer’s perceived quality of a social media-based food ordering self-service technology is significantly associated with satisfaction.
Intention to reuse is positively correlated with the likelihood of the actual use of product and service, and it can bring direct economic benefits to the merchant [62,63]. Additionally, intention to reuse not only reflects the subjective consumer preference for the use of a particular service but also affects recommendation intention for one’s family and friends [14]. Bhattacherjee [64] found that the degree to which a customer uses online banking information system is dependent on (1) whether the system meets the customer expectation and (2) how useful it is. Wang and Lin [65] pointed out that location service application providers should develop more useful user interfaces or deliver timely and personalized services so as to reduce the perceived privacy risk and to make customers loyal to the application. A tourism study found that customers reuse a hotel booking application (1) when they believe the application provides better performance than others, (2) when hotel search is easy to use and fully configured, and (3) when it is recommended by friends and family [66]. Other studies corroborates that perceived service quality is a salient predictor of the intention to (re)use SSTs [33,67]. Therefore, the current study proposes a hypothesis as follow:
H3. 
Customer’s perceived quality of a social media-based food ordering self-service technology is significantly associated with the intention to reuse.

2.3. Distant Consequence of Social-Media Based SST Quality

Consumers’ assessments of the usefulness of services or products affect service or product satisfaction and intention to reuse [49,68,69]. The higher perceived value of customers leads to higher satisfaction; customer satisfaction is directly affected by functional value in particular and is closely related to a company’s customer relationship management performance [70]. Cheng et al. [68] found that the perceived functional value is a key factor for customers to use the internet as a platform for collecting information and placing an order in online retailing setting. Yang and Jolly [71] found that the functional value of the mobile data service motivates consumers to use mobile data services as consumers receive the expected performance and quality of service from mobile data services. These findings were further reaffirmed in the automobile [72], telecommunication [73], and restaurant sectors [74], stating that if service providers offer services and products that enable customers to appreciate the excellent functional value, then customers would build the satisfaction and intention of (re)use the items. In the SST research area, customer perceived value for the use of SSTs in online banking positively affects intention to reuse [50]. Therefore, the current study proposes the following hypotheses:
H4. 
Customer’s perceived functional value of a social media-based food ordering self-service technology is significantly associated with satisfaction.
H5. 
Customer’s perceived functional value of a social media-based food ordering self-service technology is significantly associated with the intention to reuse.
Satisfaction with the SST experience is an important determinant of customers’ future use of the technology [8,68]. In addition, customer satisfaction with SSTs is closely linked to the continued willingness to use SSTs [75]. When customers use SSTs to access services, satisfaction with the SSTs has a positive effect on the willingness to reuse the SSTs [50]. A number of studies have examined the intention to reuse SSTs in different industries, including self-service information technology with retailing [33] and mobile banking services [76]. In the tourism field, customers reuse the hotel booking application when apps provide higher performance than other options [66]. Orel and Kara [61] studied the technical self-checkout service technology of supermarkets and convenience stores and found that consumers’ perception of service quality of the self-checkout service technology affects loyalty through satisfaction. Therefore, the current study postulates that the customer satisfaction and the intention to reuse a social media-based food ordering service in fast food restaurants have the following relationship:
H6. 
Customer’s satisfaction with a social media-based food ordering self-service technology is significantly associated with the intention to reuse.

3. Research Methodology

3.1. Data Collection

A pre-test was conducted with 103 returned questionnaires from 110 distributed questionnaires to check the validity and reliability of the constructs. According to the results of the pre-test, the Cronbach’s alpha value was above 0.70, which indicates that the questionnaire had acceptable reliability [77]. The main data was collected using a convenience sampling in December 2018 in Macao, China. Convenience sampling is one of the most common sampling methods in social science studies [78]. The survey was conducted in the area of famous integrated resorts in Macao. Upon consent, fast food restaurant customers who used a social media-based food ordering system in their trip were invited to the survey. A total of 445 survey questionnaires were collected, but 35 incomplete questionnaires were excluded. Thus, 410 responses were used to test the proposed hypotheses.

3.2. Survey Instrument

The questionnaire was composed of three parts. The first part asked whether the respondents have used a social media application, WeChat, to order fast food on its add-on service in the recent month before the survey firsthand. The consent form was also obtained here. The second part asked questions for each variable in the proposed model. All of the measurement items were adopted from previous research. First, perceived service quality of a social media-based food ordering self-service technology was adopted from SSTQUAL [26] and eTail quality inventories [40]. The service quality was measured with seven dimensions (five functionality items, four enjoyment items, four security/privacy items, four assurance items, four design items, four convenience items, and four customization items). Second, four perceived functional value items were adopted from Walsh et al. [79]. Four satisfaction items were adopted from Orel and Kara [61] and Wang [59]. Four Intention to reuse items were adopted from Wang [59]. All items were measured using a seven-point Likert scale (1= strongly disagree and 7 = strongly agree). The third part asked for demographic information including gender, age, education, monthly income, monthly frequency of fast food restaurant visit, and period of using mobile devices. For the data collection, all measurement items were translated from English to Chinese, and later back translated to English to double check the consistency of the translation.

3.3. Data Analysis

According to the two-step approach [80], the corrected data was first examined on whether the survey items were measured as they were intended. A confirmatory factor analysis was conducted to see if the data fit the proposed hypothetical model and whether the measurement items are valid and reliable. Upon the satisfactory validity and reliability secured, a structural model was tested to verify the proposed hypotheses. For a more succinct modeling, a second-order factor structure was employed.

4. Results

4.1. Demographic Information

Table 1 shows the demographic characteristics of the study sample. The proportion of the respondents’ gender was relatively balanced as 47.3% were male and 52.7% were female. The majority of the respondents were young people aged 21–30 years old (52.2%), followed by those aged 31–40 years old (22.9%). In terms of education level, most of the respondents had a college degree (70.2%). The participants’ average monthly income was below 4000 CNY (32.2%), 4001–8000 CNY (26.3%), and 8001–12,000 CNY (26.1%). Almost half of the respondents visited fast food restaurants 1–2 times per month (47.6%), followed by 3–4 times (30%). Most of the participants had been using mobile devices for more than 8 years (61.7%), followed by those using mobile devices for 4–8 years (35.9%).

4.2. Measurement Model

This study conducted a confirmatory factor analysis with a robust maximum likelihood (MLR) estimator to examine how well the measurement model fit with the data as well as the reliability and validity of the latent constructs employed in the proposed model [80]. MLR estimation uses a maximum likelihood estimator with robust standard errors and the χ2 test statistic using a numerical integration algorithm, which is robust to non-normally distributed multivariate data and is asymptotically equivalent to the Yuan–Bentler T2* test statistic [81]. Table 2 shows that the observed covariance from the data and the model-implied covariance fit well with each other (MLR χ2(df) = 690.657(419) (p < 0.05), CFI = 0.951, TLI = 0.942, RMSEA = 0.040 (90% CI = 0.034–0.045), SRMR = 0.048) [82]. The MLR scaling correction factor is 1.1923, which shows the level of non-normality of the analyzed data technically. That is, MLR scaling correction factor of 1 means that all MLR test statistics are identical with those of ML estimation, which assumes multivariate normal distribution. All the observed items showed an acceptable range of standardized factor loadings from 0.508 to 0.920, and most items exhibited loadings higher than 0.7 [80,83,84]. Furthermore, all latent variables showed an average variance extracted (AVE) value higher than 0.5, thereby confirming the convergent validity of the latent variables [85]. In terms of reliability, construct reliability (CR) was calculated for each latent variable and showed a range of 0.709–0.890, which confirms the internal consistency of the latent variables [85].
Table 3 shows the correlation coefficients on the lower triangular matrix and their 95% confidence interval (CI) on the upper triangular matrix. Discriminant validity was examined by checking whether any upper bound of 95% CI of the correlation coefficient exceeds 1 [80]. The highest correlation was between satisfaction and perceived functional value with a CI of 0.864–0.976, thereby confirming the discriminant validity of the research constructs.

4.3. Structural Model

To investigate the structural relationship among the service quality of social media-based SST, perceived functional value, satisfaction, and intention to reuse, a formative second-order structural equation modeling was conducted (Figure 1). Seven first-order factors for the service quality of social media-based SST were constructed as a second-order factor in a formative manner. The structural model fits the data well: MLR χ2(df) = 733.966(431) (p < 0.05), CFI = 0.945, TLI = 0.937, RMSEA = 0.041 (90% CI = 0.036–0.046), SRMR = 0.051 [82]. Functionality, enjoyment, assurance, convenience, and customization were the significant factors forming the higher-order factor with standardized path coefficients of 0.169~0.274, whereas security/privacy and design were shown to be non-significant factors. The results of the structural model showed that the service quality of social media-based SST is significantly associated with users’ perceived functional value (β = 0.791, p < 0.001) and satisfaction (β = 0.326, p < 0.001) but is not significantly associated with intention to reuse. Moreover, perceived functional value was significantly associated with satisfaction (β = 0.662, p < 0.001) but not with intention to reuse. Satisfaction and intention to reuse were found to be significantly associated (β = 0.578, p < 0.05). The explanatory power for perceived functional value, satisfaction, and intention to reuse was 0.626, 0.886, and 0.705, respectively.
The covariance structure is omitted for the clarity.

5. Conclusions

5.1. Discussion

This study aimed to identify the relationship between the perceived quality of a social media-based food ordering self-service technology, perceived functional value, satisfaction, and intention to reuse. Following the previous studies, the seven dimensions (i.e., functionality, enjoyment, security/privacy, assurance, design, convenience, and customization) were incorporated to measure the perceived quality of a social media-based food ordering service in fast food restaurants. Among the seven dimensions, to begin with, enjoyment was shown as the most salient factor building the service quality. This implies that service providers (i.e., platform operators and restaurants) should apply more hedonic-related attributes to the service delivery process. Assurance is the second most important determinant of the service quality. As assurance refers to the credibility in using a social media-based food ordering service, service providers should ensure that consumers feel secure when they are engaged in transactions such as order placement and payment. As monetary issues are critical for all stakeholders to assure a better service quality, the social media-based food ordering service should have a secured and stable transaction system. Third, convenience is also a significant determinant of the service quality. This implies that customers take into account whether the food ordering service is faster than the traditional approach and whether they can order in advance for pick up or delivery at a convenient time. Fourth, functionality is another significant factor for the service quality. As functionality aims to make SSTs be efficient, service suppliers should employ an accurate and easy operation to the food ordering service so that the customers can experience a better service quality. Fifth, customization is the least but still significant determinant of the perceived quality for the social media-based food ordering service. As customization provides customers with their specific needs and preferences, customers can evaluate the service quality as the degree to which the system is flexible for user customization.
In contrast, the findings show that security/privacy and design are not significant determinants of service quality for the social media-based food ordering SST. The food ordering mini-app is embedded in the social media app, which has been widely accepted as a secured and private mobile application by its users. Taking advantage of the social media app, the mini-app does not request additional personal information for each food order made. This would be a reason that security/privacy is not a significant issue for users. In addition, users can track their cash flow and transactions electronically on the app, lowering users’ security/privacy concerns. Regarding design, as most users are familiar with the interface of the social media app, and the mini-app is embedded in a consistent interface, they may not consider design as an important service quality factor.
Based on the results of the structural modeling, customers perceive the quality of a social media-based food ordering SST, especially in the fast food restaurant industry, as a salient factor in explaining both satisfaction (H1) and perceived functional value (H2) directly. Given the number of users of social media apps and its popularity, the delivery of better service quality is critical for the success of the food and beverage industry, especially amidst the COVID-19 pandemic. Although the perceived quality of a social media-based food ordering service is not significantly associated with intention to reuse (H3), the users may reuse SSTs for ordering food if they are satisfied with the service quality, as indirectly evidenced by H4 (i.e., satisfaction is significantly associated with intention to reuse). Other studies also support the current findings as customers are willing to reuse mobile services when they are satisfied with the high-quality services [31,44]. The perceived functional value is significantly associated with customer satisfaction (H5) but not with intention to reuse (H6). The findings show that customers are more likely to reuse the technology when they are satisfied with the service, and their satisfaction is associated with the service quality and the practical and technical benefits of SSTs. This study confirms that user satisfaction with SSTs is critical for the continuous use of SSTs in terms of service quality and functional value. To sum up, the service quality of social media-based SST is found to be significantly associated with perceived function value, satisfaction, and reuse intention of the SST in the food service area. Yet, the constituents of service quality are found to be different from what Lin and Hsieh [26] proposed.

5.2. Theoretical Implications

This study attempts to extend the theoretical point of view on the use of personalized social media-based SST in the food and beverage industry. The present study adopted SSTQUAL scale to the off-site and personalized technology, thereby identifying two valuable outcomes. First, perceived quality of social media-based food ordering SST in fast food restaurants were examined by seven dimensions (i.e., enjoyment, design) and the results showed that enjoyment was the most important factor for the social media-based food ordering SST quality. This finding echoes other SST research that seeking pleasure and fun while using a gadget is basic human nature [59,86,87]. Second, the study findings reflect the Chinese social perspective on the privacy. Security/privacy was not a significant determinant of service quality in our study context, against other many studies establishing security/privacy as a critical determinant of using SSTs [88,89].

5.3. Practical Implications

As SSTs deeply permeates the daily life of service consumers in an advanced technology society, customers get more familiar with and build a positive attitude toward a social media-based food ordering system. As evidenced by many Chinese restaurant consumers who has widely been using the food ordering min-app embedded in WeChat for a faster and more accurate food service, the tide of a similar system in the global fast food restaurant industry is irreversible. Food and beverage industry leaders should use a social media-based food ordering system for the promotion and publicity of their food service, thus reducing marketing costs and contributing to profits. In this regard, the food and beverage industry need to develop the staff’s capability and competence on the efficient use of social media platforms in order to take advantage of SSTs such as improving work efficiency and enhancing customer satisfaction. Although the current study corroborates the insignificant role of design on the intention to reuse SSTs in restaurants [1], professionals still need to pay cautious attention to this finding. For example, Dixon et al. [9] shows that customers put value on the provision of nutritional information with online reservation and payment functions in restaurant settings. Given that security/privacy is a significant factor to the intention to reuse in hotel setting [66], likewise, security/privacy could also be more important if the transaction amount increased.
Furthermore, fast food restaurant industry managers need to collect customer feedback and monitor the service quality of a social media-based food ordering SST regularly to enhance the user experience in a timely manner. Given that the service is being operated on a third party’s social media platform from the food service provider’s perspective, restaurants may consider assigning service staff to assist customers in using the system and to monitor its quality.

5.4. Limitations and Suggestions for Future Research

With a global proliferation of personal devices and social media use, various food and beverage business entities have employed social media-based food ordering service as an application of SST on personal portable devices. Given that this study is limited to the Chinese fast food market and WeChat, future studies are recommended to investigate the influence of other social media platforms in the extended area of food and beverage industry globally. Additionally, many consumers still choose the traditional and manual ordering method, and they have never used SSTs on social media platform yet. Therefore, future studies may compare the customers with the food ordering SST experience and those without the experience. Lastly, future research should also consider the impact of hashtags drawing commercial attention to a particular website or supplier. The role of hashtags has attracted wide academic interest [90,91], which has not been addressed in depth in the current study. For instance, future study considers investigate how customers react to the hashtag by gender, and/or how to make the visitors be engaged with tourism offerings.

Author Contributions

Conceptualization, C.-K.P. and Z.-T.W.; Formal analysis, Z.-T.W. and J.L.; Funding acquisition, S.K.; Investigation, C.-K.P., S.L. and J.L.; Methodology, J.L.; Project administration, C.-K.P. and S.K.; Resources, C.-K.P., S.L. and S.K.; Supervision, C.-K.P.; Validation, S.L.; Visualization, S.L. and J.L.; Writing—original draft, Z.-T.W. and J.L.; Writing—review & editing, J.L. and S.K. All authors have read and agreed to the published version of the manuscript.

Funding

This paper was supported by research funds for newly appointed professors of Gangneung-Wonju National University in 2020.

Institutional Review Board Statement

Ethical review and approval were waived for this study due to the fact that the study would pose no greater than minimal risk to participants.

Informed Consent Statement

Patient consent was waived due to the fact that the study would pose no greater than minimal risk to participants and does not collect identifiable personal information.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 14 13483 g001 550
Figure 1. Results of the structural modeling. * p < 0.05; ** p < 0.01; *** p < 0.001; n/s p > 0.05. MLR χ2(df) = 733.966(431) (p < 0.05), CFI = 0.945, TLI = 0.937, RMSEA = 0.041 (90% CI = 0.036–0.046), SRMR = 0.051.
Figure 1. Results of the structural modeling. * p < 0.05; ** p < 0.01; *** p < 0.001; n/s p > 0.05. MLR χ2(df) = 733.966(431) (p < 0.05), CFI = 0.945, TLI = 0.937, RMSEA = 0.041 (90% CI = 0.036–0.046), SRMR = 0.051.
Sustainability 14 13483 g001
Table
Table 1. Demographic and behavioral characteristics.
Table 1. Demographic and behavioral characteristics.
CharacteristicsN%CharacteristicsN%
Gender Education
Male19447.30%High School122.90%
Female21652.70%College28870.20%
Graduate School11026.20%
Age Monthly Income
Below 204912.00%Below 4000 CNY13232.20%
21–3021452.20%4001–8000 CNY10826.30%
31–409422.90%8001–12,000 CNY10726.10%
41–504711.50%12,001–16,000 CNY5012.20%
Above 5061.50%Above 16,001 CNY 133.20%
Monthly frequency of fast food restaurant visits Period of using mobile devices
1–2 time(s) Less than 1 year00.00%
3–4 times19547.50%1–3 year102.40%
5 times or more12330.00%4–8 year14735.90%
9222.50%More than 8 years25361.70%
Table
Table 2. Results of the confirmatory factor analysis.
Table 2. Results of the confirmatory factor analysis.
Latent/Observed VariablesStandardized Coefficients *
Functionality (AVE = 0.518; CR = 0.810)
-I can get my service done with the social media-based food ordering system in a short time.0.796
-The service process of the social media-based food ordering system is clear.0.763
-Using the social media-based food ordering system requires little effort.0.584
-I can get service done smoothly using the social media-based food ordering system.0.719
Enjoyment (AVE = 0.553; CR = 0.709)
-The operation of the social media-based food ordering system is interesting.0.651
-I feel good being able to use the social media-based food ordering system.0.826
Security/Privacy (AVE = 0.574; CR = 0.793)
-A clear privacy policy is stated when I use the social media-based food ordering system.0.508
-I trust that the social media-based food ordering system will not share my information with other companies without my permission.0.92
-I trust the social media-based food ordering system will not misuse my private information.
0.785
Assurance (AVE = 0.663; CR = 0.796)
-Transactions using the social media-based food ordering system are reliable and credible.0.745
-I feel relieved when transacting with the social media-based food ordering system.0.878
Design (AVE = 0.571; CR = 0.799)
-The layout of the social media-based food ordering system is aesthetically pleasing.0.69
-The social media-based food ordering system appears to use up-to-date technology.0.778
-The social media-based food ordering system is visually appealing.0.795
Convenience (AVE = 0.633; CR = 0.873)
-The social media-based food ordering system has operating hours convenient to customers.0.79
-It is easy and convenient to reach the social media-based food ordering system.0.84
-I am able to place a food order at a convenient location with the social media-based food ordering system.0.808
-The social media-based food ordering system gives me greater mobility.
0.741
Customization (AVE = 0.523; CR = 0.766)
-The social media-based food ordering system understands my specific needs.0.694
-The social media-based food ordering system has my best interests at heart.0.69
-The social media-based food ordering system has features that are personalized for me.0.782
Satisfaction (AVE = 0.580; CR = 0.804)
-Overall, I am satisfied with the social media-based food ordering system experience.0.679
-Generally, I am very happy with the social media-based food ordering system.0.762
-The social media-based food ordering system is close to my ideal self-service technologies.0.835
Perceived Functional Value (AVE = 0.529; CR = 0.818)
-The social media-based food ordering system has consistent quality.0.709
-The social media-based food ordering system is well designed.0.77
-The social media-based food ordering system has an acceptable standard of quality.0.736
-The social media-based food ordering system is well made.0.692
Intention to Reuse (AVE = 0.670; CR = 0.890)
-I intend to continue using the social media-based food ordering system.0.703
-I will regularly use the social media-based food ordering system in the future.0.789
-I will continue using the social media-based food ordering system.0.898
-I will strongly recommend others to use the social media-based food ordering system.0.871
MLR χ2(df) = 690.657(419) (p < 0.05), CFI = 0.951, TLI = 0.942, RMSEA = 0.040 (90% CI = 0.034–0.045), SRMR = 0.048. AVE: average variance extracted; CR: construct reliability. * All standardized factor loadings are significant at 0.001.
Table
Table 3. Correlations among the latent variables.
Table 3. Correlations among the latent variables.
Func.Enjoy.S/PAssur.DesignConv.Cust.Satis.PFVIR
Functionality [0.523, 0.723][0.060, 0.308][0.314, 0.530][0.127, 0.383][0.524, 0.724][0.160, 0.428][0.490, 0.698][0.397, 0.641][0.535, 0.731]
Enjoyment0.623 [0.090, 0.370][0.477, 0.685][0.326, 0.602][0.576, 0.744][0.139, 0.415][0.624, 0.828][0.526, 0.746][0.545, 0.733]
Security/Privacy0.1840.230 [0.437, 0.629][0.232, 0.496][0.234, 0.434][0.164, 0.460][0.307, 0.531][0.362, 0.582][0.235, 0.443]
Assurance0.4220.5810.533 [0.415, 0.647][0.544, 0.704][0.138, 0.390][0.638, 0.806][0.549, 0.733][0.490, 0.662]
Design0.2550.4640.3640.531 [0.312, 0.528][0.306, 0.550][0.459, 0.667][0.499, 0.707][0.285, 0.541]
Convenience0.6240.6600.3340.6240.420 [0.139, 0.355][0.595, 0.751][0.544, 0.728][0.622, 0.766]
Customization0.2940.2770.3120.2640.4280.247 [0.350, 0.582][0.312, 0.556][0.209, 0.461]
Satisfaction0.5940.7260.4190.7220.5630.6730.466 [0.864, 0.976][0.768, 0.896]
Perceived Functional Value0.5190.6360.4720.6410.6030.6360.4340.920 [0.699, 0.867]
Intention to Reuse0.6330.6390.3390.5760.4130.6940.3350.8320.783
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Pai, C.-K.; Wu, Z.-T.; Lee, S.; Lee, J.; Kang, S. Service Quality of Social Media-Based Self-Service Technology in the Food Service Context. Sustainability 2022, 14, 13483. https://doi.org/10.3390/su142013483

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Pai C-K, Wu Z-T, Lee S, Lee J, Kang S. Service Quality of Social Media-Based Self-Service Technology in the Food Service Context. Sustainability. 2022; 14(20):13483. https://doi.org/10.3390/su142013483

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Pai, Chen-Kuo, Ze-Tian Wu, Seunghwan Lee, Jaeseok Lee, and Sangguk Kang. 2022. "Service Quality of Social Media-Based Self-Service Technology in the Food Service Context" Sustainability 14, no. 20: 13483. https://doi.org/10.3390/su142013483

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