1. INTRODUCTION

1.1 Background of the study

Asian consumers in emerging markets support luxury shopping, in particular, a younger generation has become a powerhouse of luxury consumption (Jain, 2020; Kim et al., 2020). It appears to exist misconceptions and stereotypes emphasising the impact of unique consumer values, such as conspicuous value, collectivism and personalised experience, on individual consumer behaviour (e.g. Jain, 2020; Oe et al., 2018). Recent academic work has focused on young shoppers, who are referred to as ‘label-obsessed’ buyers (Kim et al., 2020). In this case, it must be asked which models and frameworks have been applied to discussions regarding their behaviour.

1.2 Research rationale and aim of the study

The luxury goods purchasing model proposed by the consultancy firm McKinsey and Company argues that China's post-1980s and -1990s generations should be at the heart of research into the generations that will drive the global luxury goods market. In particular, the post-1990s Generation Y (Gen Y), which we refer to as the powerhouse generation, is expected to be the driving force of such consumption, and the younger among this group are more likely to purchase luxury goods based on individual communication with brands and user experience rather than brand adherence (Kim et al., 2020). The challenge is thus to prepare a framework to analyse new behaviours that cannot be analysed using the antecedent factors assumed by the traditional shopping model.

Based on the above research gaps, this study aims to present a research agenda to discuss and validate consumers’ shopping behaviour regarding luxury goods, and it proposes a conceptual model using new measures. A quantitative method based on survey data is used to analyse current shopping behaviour and to develop an analytical model with new antecedent factors that can successfully address current consumer perceptions and attitudes towards luxury shopping.

2. LITERATURE RIVIEW

2.1 Luxury goods shopping behaviour

Value in marketing, also known as customer perceived value, is the difference in a prospective customer's evaluation of the benefits and the costs of one product compared to another. Dhaliwal et al. (2020) identified a number of antecedent factors that define consumer behaviour and argue that these are useful tools in business strategy as they come into play at various stages of the buying process. As the global consumer market shrinks, partly due to COVID-19, emerging power of Asian markets are attracting attention from marketers as the leading retail purchasers of many commodities and products, not only luxury goods. Indeed, a number of researchers are studying how factors previously included in analytical models have impacted and transformed recent consumer purchase behaviour in the Asia-Pacific region (Jain, 2020). However, no valid and practical model to analyse the behaviour of luxury goods buyers in the region has yet been agreed upon. Therefore, this study attempts to reveal current Asian consumers’ perspectives and attitudes regarding purchase behaviour of luxury goods empirically using a dataset collected by the emerging Asia-Pacific region to develop a practical analytical model with novel measures for further study. Based on the academic interests and developed topics, it is also needed to add some more contemporary themes such as new generation perceptions and attitudes as a new power of consumption in luxury markets with an aim to develop useful analytical model and measurements to contribute both to practices and theories.

2.2 New powerhouse group of luxury shoppers in Asia

The World Factbook is a data source owned by the US intelligence agency CIA, and it used gross domestic product for a comparative study of purchasing power parity of global markets. The currency valuation of developing or similar countries is relatively weak, and purchasing power parity is used to evaluate economic power (International Monetary Fund, 2019). Regarding the luxury market, differences among generations have also been identified as important factors to examine (Kim et al., 2020).

Kim et al. (2020) found that purchasing power of Gen Y (i.e. the post-1980s generation) has been fuelling luxury shopping, calling them the vanguard of Asian urban middle-class spenders. Further, the McKinsey model presented by Kim et al. (2020) emphasises that those who were born in the post-1980s and post-1990s time periods are the core powerhouse consumers, and younger consumers tend to enjoy luxury more freely without being loyal to specific brands. Instead, they enjoy luxury goods from a broader perspective that matches their tastes and brand values. Guercini et al. (2020) discussed the new luxury shoppers as brand surfers and status seekers in the digital market, forming a new central customer group leading the luxury fashion market. Both Kim et al. (2020) and Guercini et al. (2020) found that the new brand seekers’ behaviour is different from that of traditional loyal luxury shoppers and that this trend will lead to new luxury marketing strategies to sustain the market. Following the changing Asian shopper population in the current market, this study focuses on consumers’ income and age group as control variables to examine their purchasing behaviour of luxury goods.

2.3 Key antecedent factors for luxury shoppers in the millennial context

The luxury consumption has been said to be more connected with social factors rather than personal ones. Their findings also reflect the unique cultural features of consumers, who are more embedded in a cultural context featuring personalised experience in a more complex way than Western consumers (Oe et al. 2018). These arguments and empirical results were followed up on by Oe and Yamaoka (2020) findings on purchasing behaviour in the Thai luxury goods market, where they focused on consumer behaviour in the Asian market. Conspicuous value was shown not to be a significant factor on the purchasing behaviour of their sample of Thai consumers. The results of their study may indicate that the established model is not universally applicable to Asian consumers, who are influenced by both their traditional culture and modern Western values.

This section conceptualises design extravagance as a social phenomenon by investigating the antecedent factors of luxury shopping, with a special focus on the modern buyers’ beaviour in emerging Asian markets. It questions the presumption that shoppers are inactive adopters of imported Western extravagance-related values and outlines the complexity inalienable within customer involvement, highlighting their potential as proactive esteem makers. shoppers characterise extravagance in monetary, utility-based and hedonic terms (Shukla and Purani, 2012; Shukla et al., 2015).

2.3.1 Personalised experience

In the Asian market context, Eastern cultural philosophies, such as Confucianism, emphasise collectivism and subjective norms stemming from the groups and families that shoppers belong to (Wang et al., 2020). At the same time, the necessity of discussing this theme in a more modernised context has been acknowledged, such as by relating it to the impacts of the ubiquitous connectivity in the environment created by internet platforms and to the fact that younger generations have been boosted as the leading powerhouse of luxury shopping behaviour (Kim et al., 2020). It is well known that this new generation is actively searching for meaning, and these savvy consumers have become a major cohort among luxury shoppers. Along with the theme of ‘ownership’ in luxury shopping studies, this has become common knowledge (Oliveira and Fernandes, 2020). In particular, the empowerment of new identities and lifestyles based on the new generation’s perceived values should be considered closely in luxury studies (Jain, 2020).

The very essence of luxury is based on personal and experiential values rather than the functional value of goods. Various analytical models containing perceived antecedent factors have thus been developed (Oe et al., 2018). However, over the past few years, the consumption of luxury appears to be less oriented towards luxury brands themselves and to follow a mode based more on self-orientation, as argued by Kauppinen-Räisänen et al. (2019).

Kim et al. (2020) also claimed that an analytical model including the empowerment of people and considering them as active agents in their customers’ identity creation could contribute to a revision of the traditional limits of analytical models for luxury shopping behaviour (De Silva et al., 2020; Jhamb et al., 2020).

Hence, the following proposition is put forward:

Hypothesis 1: Personalised experience has an impact on consumers’ luxury purchase intentions

2.3.2 Subjective norms

It is often argued that collectivism is a key behavioural norm when discussing Asian consumer behaviour (Chadha and Husband, 2006). Many studies have demonstrated the tendency of consumers to be highly concerned with how they appear to others and to attach great importance to the material meaning of their possessions and their outward display (Fishbein and Ajzen, 1977; Oe et al., 2020; Shukla and Purani, 2012).

The present study focuses on subjective norms, which is an individual's perception that most people important to him or her believe that he or she should or should not perform the behaviour in question (Fishbein and Ajzen, 1977). To date, subjective norms have been important guiding principles in a number of consumer behaviour studies (Jain, 2021).

The purchasing choices of consumers in collectivist social systems, such as those of Thailand and India, are dependent on family members, peers and colleagues, and social identification and subjective norms are considered important among buyers from these countries (Wong and Ahuvia, 1998). In such a relationship-oriented society, people are generally subjected to social pressure in terms of identity to ‘behave as they presume, in line with the expectations of significant others such as family and peers’ (Jain, 2020; Jain, 2021). According to this hypothesis, the purchasing of luxury goods by youth can be seen as an act of vainglory to meet social standards, avoid disappointing others and gain respect from society (Veblen, 1912).

Social norms are distinguished from internalised personal norms and are positioned as influencing factors stemming from others and the environment. Social norms can be broadly divided into injunctive norms, i.e. what is indicated or sympathetic within a society or culture, and descriptive norms, i.e. what most people do (Dempsey et al., 2018). In addition, subjective norms include expectations from others that are important to individuals (Ajzen and Fishbein, 1975). In other words, in the recent discussions, subjective norms have mostly included injunctive norms (Heath and Gifford, 2002). In this study, to analyse the behavioural principles of young luxury goods buyers and with the aim of paying attention to the cultural and social factors of the market, which is influenced by collectivism and Confucianism, a second hypothesis is put forward to test the impact of each consumer's subjective norms. This will help us to understand the various impacts of social norms, in particular, the expectations of others, on purchasing behaviour.

Hypothesis 2: Subjective norms have an impact on consumers’ luxury purchase intentions

2.3.3 Face-saving value

The impact of face-saving value has not been identified as one of the antecedent factors for analytical models. It has also been suggested that studies of luxury consumption be conducted with a consideration of cultural context (Jia, 2021). It has been found that consumers with a face-saving perspective are ready to pay more for products. In line with this context, Kim et al. (2020) revealed that young consumers prefer to be acknowledged by others for their social status by purchasing and owning luxury goods.

When consumers purchase goods, they use their choice of brand to create identity for themselves (Kim et al., 2020). Successful luxury purchasing contributes to advocacy of the social and cultural values of consumers by presenting their inner essence and central values. Consumers’ perspectives regarding face-saving value should be examined in terms of its catalytic role in cultural transition.

Hypothesis 3: Face-saving value has impact on consumers’ luxury purchase intention

In addition to the above three hypothetical factors (personal experience, subjective norms and face-saving value), a fourth hypothesis was established based on the assumption that luxury purchase intention is determined by a combination of these three factors. The model and hypotheses are presented in the following figure.

Hypothesis 4: Three potential antecedent factors have an impact on purchase intention via interrelation with one another.

2.4 Key takeaways and an analytical framework proposition

As discussed, a generic model with established measures has not yet been agreed upon, and this study attempts to develop practical model with measures based on three antecedent factors that influence consumers’ purchase intentions regarding luxury goods: personalised experience, subjective norms and face-saving value. Figure 1 shows the developed analytical model with the three antecedent factors. As shown in the figure, three sub-hypotheses were also developed to imply that the three factors are interrelated and determine luxury purchase intention (H4.1, H4.2 and H4.3).

Figura 1. Analytical model with hypotheses

3. METHODOLOGY

3.1 Survey design and data collection

A quantitative method was applied in this study to test the developed hypotheses (Martin and Bridgmon, 2012). The questions were designed following the key takeaways from the literature review, which covered the three antecedent factors: personalised experience, subjective norms and face-saving value. Based on the research aims and related hypotheses, the questionnaire consisted of 12 questions designed to assess the factors reflecting the hypotheses. Online survey questionnaire was distributed by a web monitor service company across the emerging Asian market to approach a well-balanced sample of luxury consumers.

3.2 Data analysis

The attained the dataset was cleaned and prepared for the analysis, leading to a final sample size of 703. The profile of the included respondents represented a spectrum of ages and a balance regarding gender, and missing variables were avoided in the final dataset. The data were analysed to develop descriptive statistics, and this was followed by a factor analysis and correlational analysis to check the reliability and adaptability of the generated factors for further analysis. The variables to be used for the hypotheses testing were thus confirmed. Structural equation modelling (SEM) was conducted to investigate the hypotheses and the relationships among the latent factors of the study.

4. FINDINGS AND ANALYSIS

4.1 Data profile

The profile of the dataset used for the analysis is shown in Table 1. The data, including that on gender and age group, were collected from online survey monitors from participants with a balance of attributes to ensure the analysis included consumers born in the 1980s and 1990s, who are considered to be the driving force behind luxury goods purchasing in emerging markets in Asia, as indicated in the literature review. As a result of the cleaning process, a total of 703 samples were used in the final analyses. The descriptive statistics of the respondents' attitudes towards the purchase of luxury goods are shown in Figure 2, which indicates that the question with the highest selection rate (3.77) was that on face-saving value, and the question with the lowest selection rate (3.05) was that that on showing off. This question also had the highest standard deviation (SD) of 1.2 compared with the others.

Table 1. Demographic profile of the respondents

Table 2. Descriptive analysis

4.2 Measurement development

4.2.1 Confirmatory factor analysis

A total of 12 questions, presented in Table 3, were subjected to a deterministic principal component analysis to check the reliability and applicability of the factors obtained. From a total of 12 observables, four factors, including the Purchase Intention (PI), were generated, with three observables comprising each factor. The results of the factor analysis showed the KMO of 0.924, which is sufficiently high (Pallant, 2001) and that the four factors explained approximately 80% of the total data. The Cronbach's alpha values of all four factors exceeded the minimum requirement of 0.7 (Taber, 2018), and the factors obtained were found to be well explaining the data set.

Table 3. Confirmatory factor analaysis

4.2.2 Convergent and discriminant validity tests

The next examination was conducted to identify whether the constructs were valid and reliable. The composite reliability (CR) and average variance extracted (AVE) of the constructs were computed by a statistical procedure, also, the examined values were greater than the lowest acceptable CR and AVE values of 0.7 and 0.5 respectively (Alarcón et al. 2015). Therefore, the final model had to fit with the data. The CFA results demonstrated that the factors were not disused to each other. Reliability can be tested based on several criteria. Hence, even though the AVEs were less than 0.5, which is lower than the ideal level, the developed items were able to explain their own constructs correctly (Chatterjee and Chaudhuri, 2020). The results of the test of discriminant validity are presented in Table 4. If we focus on the average variance (AV), which is the square root of the AVE of each construct, we can conclude that it was greater than the Pearson correlation coefficient between that construct and the others. In other words, discriminant validity was confirmed (see the values in bold on the diagonal in Table 4). Thus, it was confirmed that the CA values of each construct indicated reliability, with each being greater than 0.6, which implies that the constructs identified were consistent and reliable (Chatterjee and Chaudhuri, 2020). A correlation analysis was conducted to detect the existence of covariance among factors generated from the CFA. High correlation results in issues regarding the multicollinearity of variables, and the correlation coefficients should ideally not be higher than 0.7(Alarcón et al. 2015). The results of the test showed that this requirement was met, with the correlation coefficients ranging from 0.423 to 0.528.

Table 4.Convergent and discriminant test results

Based on the preparatory tests carried out so far, it was concluded that all the obtained factors and their components were reasonably established and at a credible level. Therefore, we decided to proceed to the next stage of analysis using these variables.

4.3 Study 1: Comparative observations based on demographics

Before moving to the hypotheses testing, non-parametric tests were conducted to reveal the relationships among the generated factors based on the comparative analyses. This was done to examine the various perceptions of luxury goods purchase intention depending on demographic variables, including income, age, gender and education.

4.3.1 Income

Table 5 shows the test results, which reveal that there were statistically significant differences among the three income classes regarding all four factors.

Table 5. Non-parametric test results for income

The results of the tests show that there were significant differences in four factors considered here in relation to income. Figure 2 visualises a comparison of the four factors for the three income groups. The results demonstrate that, of the three groups, the higher-income group had a higher awareness of all the factors and, in particular, a clearer awareness of personalised experience than the other two groups.

Figure 2. Radar chart for means of four factors by monthly income

4.3.2 Age

We examined the differences in awareness of the four factors based on age group. According to the test results, significant differences were found regarding all four factors when comparing the three age groups of 20s, 30s and older, which are considered as powerhouse groups. Table 6 shows the results, which reveal that the three age groups exhibited a statistically significant difference regarding all four factors.

Table 6. Non-parametric test results for age

Figure 3 shows that the youngest of the three groups, those in their 20s, had a higher level of awareness of all four factors, with their recognition of their personal experiences standing out more than the other three factors.

Figure 3. Radar chart for means of four factors by age

4.3.3 Gender

We next turned our attention to gender and examined the differences in the awareness of the four factors between men and women. The results of the test revealed that there was no difference in awareness between men and women for all four factors. Table 7 and Figure 4 show the test results, which indicate that no statistically significant difference was found for any factor between genders.

Table 7. Radar chart for means of four factors by gender

Figure 4. Radar chart for means of four factors by gender

4.3.4 Education

Next, we examined the differences in perceptions of the factors based on educational background, the last of the investigated personal attributes. The results of the test demonstrated that three of the four factors were significantly different in relation to education level, with subjective norms being the odd one out. This means that subjective norms were perceived regardless of educational background. Table 8 shows the tests results. Personalised experience, face-saving value and purchase intention all exhibited a statistically significant correlation with education, while no significant relation was observed for subjective norms.

Table 8. Non-parametric test results for education

Figure 5 shows that, of the three factors with statistically significant differences, the people with the most education had higher perceptions than the other groups, particularly with regard to personalised experience. In terms of face-saving value, it has been demonstrated that the higher the level of education, the stronger the perception.

Figure 5. Radar chart for means of four factors by education

4.4 Study 2: Hypothesis testing via structural equation modelling

Following obtainment of the convergent and discriminant test results, all the factors generated from the confirmatory factor analysis were validated. Therefore, these factors were used for the next analysis step based on SEM. The SEM results revealed that, of the three antecedent factors, face-saving value had a particularly strong impact on PI, while customised experience did not necessarily have a significant impact on consumer purchasing of luxury goods. Figure 6 shows the SEM results. The results suggest that lower income groups are more likely to purchase luxury goods if they only contribute to face-saving value. Table 7 shows the SEM results on the relationships among the antecedent factors for four different sectors, including all samples and by income.

Figure 6. SEM for research model

Table 9. SEM path coefficient

4.5 Discussion

Overall, the results of this research indicate that, among all factors, face-saving value has the most significant impact on the luxury shopping intention, while personalised experience does not have a significant impact. However, the relations of the coefficients among the three antecedent factors imply that all have a significant impact, suggesting that personalised experience has a moderating impact via other antecedent factors.

In terms of demographics, it was found that the higher-educated, younger and more well-off segments acknowledged the impact of personalised experience on luxury shopping intentions to a greater degree. Oe et al. (2020) demonstrated from Thai consumer data that in Asian markets, conspicuous value, which existing studies have generally agreed upon, does not necessarily significantly affect purchase intentions for luxury goods. Based on consumer behaviour data, it has been suggested that the multiple antecedent factors that define luxury purchase behaviour may exert their influence through a complex network of relationships.

The present study hypothesised that personalised experiences, face-saving value and subjective norms are the determinants of the purchasing intention of millennials, the current driving force behind luxury good purchasing. The results revealed that the impact of these three antecedent factors varies depending on the demographics of the consumer, suggesting that it is essential to plan marketing strategies carefully according to the target group.

Personalised experience is one of the most popular topics in the recent debate on luxury good purchasing behaviour (Kim et al. 2020), but, at present, it appears that these values are not yet mature enough to define the behaviour of consumers. Further, it is necessary to discuss the relationship between face-saving value, which is the focus of this study, and conspicuous value, which has been the subject of many studies, in more detail, and to use other large-scale datasets to examine whether it is a valid factor to consider when defining luxury good purchasing behaviour. It will also be necessary to discuss its relationship with conspicuous value more precisely and to test whether it is an effective factor in determining luxury purchase behaviour.

5. CONCLUSION

5.1 Theoretical contribution

This study aimed to examine the relationship between the factors that determine the purchasing intentions of young emerging markets’ consumers in Asia, especially those in Gen Y, who are expected to be the future drivers of luxury good purchasing behaviour, and to propose a practically valid model and scale for the model. The antecedent factors hypothesised to be of interest in this study were personalised experience, face-saving value and subjective norms.

The results of the data testing demonstrated that the analytical model using these three factors is a meaningful method of examining intentions to purchase luxury goods: personalised experiences related to luxury goods, which have received much attention in Western academic debates, are not always explicitly recognised by young Asian consumers. However, it was found that personalised experience had a greater impact on the luxury shopping intentions on the higher-educated, younger and more well-off segments of the sample.

At the same time, face-saving value was found to influence purchase intentions for luxury goods, particularly among lower-income groups, indicating that the factors determining the purchasing behaviour of young consumers, who are the focus of global attention in the luxury goods market, require further scrutiny in the future as more emerging markets and consumers mature.

5.2 Practical contribution

The results of this study can be used to develop marketing strategies that reflect the factors that determine consumer behaviour. The study provides an argument that will contribute to both the theory and practice of luxury purchase behaviour in the future, and it offers a clear indication that we should pay attention to the attitudes of consumers, who are the major powerhouse of luxury purchasing in the global market. In particular, the study suggests that to stimulate the purchasing intentions of the high-income and well-educated segments of the Gen Y consumers who underpin luxury goods purchasing, it is essential that luxury goods marketing communication strategies are aligned with the lifestyles and personal experiences of this group.

5.3 Limitations and further research opportunities

In this study, we present the results of a pilot study on the relationship between the factors that determine the purchasing intentions of young Asian consumers, especially those in Gen Y, who are expected to be the future leaders of luxury goods purchasing behaviour. We have succeeded in proposing a useful model and measures that will contribute to the development of future discussions, both practically and theoretically. However, it is necessary to further validate the results of this study using other datasets and to verify the validity of the scales used to refine the model. In particular, in luxury goods research, there have been many studies on conspicuous value, and it will be necessary to re-examine and re-define the concept of face-saving value to create more robust analytical models and scales.

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