While the economic significance of the Chinese customers is beyond dispute, we lack significant theorizing about how service interactions unfold in this context. In Chinese society, face (miàn-zi) has the potential to explain significant aspects of service interactions. In this (Confucian) relational-oriented society, people understand themselves through their relationships with others. This hierarchically structured network of Chinese social relations forms the norms of reciprocity which underpins social interactions. Judgements about relationships both within the direct interaction (e.g., customer and employee) and others present (e.g., others customers) or even those not even present (e.g., significant others) infer the appropriateness of different behaviours. During interactions, miàn-zi, or reciprocated deference, acts as the means for self-enhancement and to maintain a sense of harmony.
Due to the challenge of construing face, little research explores its significance within Chinese service interactions. In an attempt to address this challenge, we take an identity perspective on face in service interactions. From this standpoint, face becomes integral to the enactment of a customer-employee identity.
In this study, we explore how face integrates the enactment of identity and so shapes behaviors in service interactions. We specifically examine how situational disturbances in service make face a focal part of the interaction. The study design, qualitative, involves with 89 episodes from 44 interviews that yield rich data about face interactions.
We develop three fundamental types of situational disturbances (employee professional role failure, employee specific role failure, and customer role failure) that reveal how face becomes the dominant focus of attention in service interactions. We also explain how employees, under these different situational disturbances, derive meanings from situational cues (customer’s social roles and behaviors) to construct their facework strategies (upgrading facework, downgrading facework, and neutralizing facework). The findings are significant as they advance understanding of how face plays a role in service interactions in the Chinese setting.

Introduction to the research problem
Nowadays, digital transformation is quickly becoming an essential element for business survival. The most crucial goal of digital transformation initiatives in any industry is to improve customer experience. The development of digitization has resulted in the changes of many business models and has had substantial influences on the retail industry, including the fashion industry. Businesses are experimenting innovative ways to suit consumers' growing requirements, such as interactive technology - Augmented Reality (AR). This study aims to examine the impact of AR technology on the intention of Vietnamese youth to use fashion goods online.
Literature review
The rapid growth in the online retail industry is putting pressure on companies to keep up with modern consumer trends, shifting authority away from businesses and toward consumers (Wu et al., 2016). Accordingly, AR emerges as an optimal solution with the potential to change the shopping experience (Ducan et al., 2013; Javornik, 2016; Grewal et al., 2017) by combining real-world environment with virtual objects. Retailers are increasingly employing AR as a marketing technique to engage customers by extending product features through visual interactions (Papagiannidis et al., 2017; Cuomo et al., 2014; Fiore and Fin, 2003; Rese et al., 2014). However, existing literature on commercial applications of AR is still limited. Previous researchers often focused on utility and hedonic value, or self-esteem, rather than technological characteristics. Therefore, to address the research gap on this issue, this paper investigates the impacts of AR’s technological characteristics of AR on consumer behavior, specifically the intention to use fashion products in the Vietnamese online retailing context.
Proposed Framework
We propose a novel framework based on the technology acceptance model - TAM (Davis, 1989) to measure the fashion products’ usage intention. The model preserves the key variables while expanding to investigate the impact of external variables related to technological characteristics. In recent years, TAM relationships have been used for evaluating technological innovations providing a wide range of systems such as smart digital content information technology, digital signage, 3D virtual reality systems, and AR-based systems (Sha et al., 2013; Dennis et al, 2014; Pantano et al., 2016; Rese et al., 2016).
Method/Approach
We conducted an online survey among 200 young Vietnamese people who have been or would experience online shopping services applying AR technology. The data was analyzed using IBM SPSS25 and structural equations modeling on AMOS20.
Preliminary results/Findings
This study identifies the commercial impact of AR technology contributing to the actual knowledge, elucidating factors that can determine the intention to use fashion products.
Discussion and implications
The findings present impacts of AR technology on customers' intention to use fashion products, particularly among young people. By that, this technology assists businesses in the fashion industry and other sectors in grasping new trends. Furthermore, it helps enterprises to implement efficient strategies to improve their competitive position by enhancing customers’ shopping experience, interaction, and usage.

Service robots are gradually replacing human service providers in numerous industries, which profoundly impacts how service is delivered (Bornet, Barkin, and Wirtz, 2021; Wirtz et al., 2018). Accordingly, service robots’ encounters represent a primary research area in service. However, to date, researchers and practitioners have focused on the general application and acceptance of service robots with an increasing stream of research in the service and consumer behavior literature examining consumer responses to various characteristics of robots, consumers, and context (Mustak et al. 2021; Mariani et al. 2022), including characteristics such as the level of human-likeness and the level of automated social presence.

While most of these studies have explored potentially negative consumer responses and other concerns related to service robots, not much attention has been given to potential boundary conditions when human-service robot encounters might be beneficial to consumers and even become their preferred service delivery channel. For example, a service setting where the presence of other individuals (incl. service employees) may reduce the consumer experience is embarrassing service encounters. Consumer embarrassment is a widespread social emotion induced when a transgression is witnessed or perceived to be witnessed by others. For embarrassment to be elicited, individuals must be concerned about what others perceive or think about them. Thus embarrassment is dependent on the presence of others.

Recent studies have shown that interactions with service robots decrease consumer embarrassment via lower perceptions of agency (Mozafari et al., 2021; Pitardi et al., 2021). We build on this work and show through four experimental studies that interactions with service robots positively influence customers’ service satisfaction and repeat purchase intentions due to a decrease in embarrassment. We further demonstrate that this effect is mediated by low level of perceived agency attributed to the robots and consumers’ subsequent metaperceptions (i.e., their assessment of how they may be perceived and judged by robots). Finally, we investigate two potential boundary conditions. Specifically, we show that during embarrassing service encounters, the close presence of other people (e.g., customers or employees) eliminates the positive influence of service robots on consumer responses, while the effect holds whether the robot is a humanoid or nonhumanoid. Theoretical and managerial contributions are discussed.

Service companies have begun to employ Artificial Intelligence (AI) (i.e., a machine that exhibits aspects of human intelligence that can learn, connect, and adapt) in the service provision process. However, these companies have often committed corporate social irresponsibility (CSI) (i.e., violation of the social contract between society and corporation) in using AI. In particular, providing discriminatory services to different groups of customers, such as in the financial services sector, which is considered as societal CSI (i.e., a corporate violation of compliance with human rights), is a common socially harmful outcome that a service company can bring about when applying AI irresponsibly. Although the extant service research has examined the impact of incidental service failure, no research has examined AI CSI that involves a service company systematically committing socially irresponsible deployment of AI. CSI can have a detrimental impact on corporate image, yet little is known when AI CSI, as compared to traditional CSI, can influence corporate image, particularly in terms of the dimensions of warmth and competence. A company’s attempt to develop and maintain an image of warmth on top of competence is increasingly more prevalent nowadays due to the rise of interest in providing sustainable service to meet sustainable development goals. Also, the corporate image of competence including particularly innovativeness has become a key image that more service companies wish to achieve and maintain, particularly through adapting an advanced technology like AI, as it signals an ability to develop and provide unique and rewarding products and service solutions.

Through four experiments designed with different settings, the research explores how AI CSI impacts the corporate image of innovativeness and warmth. We further adapted the attribution theory and involvement theory to investigate the underlying mechanism and boundary conditions of the found effects. The research found that although in general service CSI negatively influences the corporate image of both innovativeness and warmth, AI CSI (compared to traditional service CSI without AI involvement) is less likely to damage the corporate image of innovativeness because the use of AI decreases the blame attribution to the service company. However, in general, there is no difference regarding the impact on the corporate image of warmth between AI CSI and traditional CSI. Yet, when the AI system is externally developed by third parties other than the focal service company, consumers tend to perceive the company warmer compared to when the AI system is developed internally or when such information is not specified. These relationships were also mediated by blame attribution to the company. In addition, this effect is stronger for consumers who are more strongly involved with the focal CSI issue or cause. This research contributes to the service literature by examining the impact of service AI CSI (beyond an incidental service failure) on corporate image. The research further theoretically contributes to this literature by determining the mechanism of blame attribution to the firm and distinguishing the conditions that increase and decrease the blame attribution and its effect on corporate image.