Young Female Customers’ Luxury Fashion Purchasing in the UK

Abstract

The primary objective of this research study was to present an insight into the motives defining the purchasing and consumption of luxury fashion among young female customers in the UK. The researcher applied quantitative research designed since the study was focused on a single market segment. Through direct interview and focused survey, the researcher interacted with a sample of 6 respondents within the age bracket of 18 and 32 years. The findings indicated that the young female market segment is actively involved in luxury fashion within the UK. These consumers drew inspiration from the media commentary, female celebrities, and information about different trends from the views of friends before making a purchasing decision.

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The aspect of social status associated with fashionable wear motivated them to buy different luxury brands that are above their disposable income. Thus, marketers targeting the young female segment should create messages that appeal to the aspired lifestyle to effectively appeal to this group. The research was focused on the young female customers in the UK from a collectivist cultural orientation and motivations behind their purchasing behaviours. Therefore, the results of the study have added new information in the luxury fashion purchasing mix from the perspective of the young female customers. These results could be integrated into the product advertisements to optimise the outcome of a purchasing decision process within the UK market and beyond.

Introduction

Context and Rationale of the Study

In the last two decades, the UK fashion industry has experienced a paradigm shift as luxury brands flood the market. The current predictions indicate that the UK fashion industry will be worth at least £30 billion by 2030 (Homburg, Jozic & Kuehnl 2017). Despite the global economic recession of 2008, the UK fashion industry has experienced positive growth over the last 10 years. A commercial research survey conducted in the year 2013 indicated that the market penetration of different luxury fashion brands increased by 34% in the UK market between the year 2005 and 2012 (Homburg, Jozic & Kuehnl, 2017).

Despite the fact that young female customers do not have adequate disposable income to indulge in the constant purchase of luxury fashion, their exposure to information about these brands is richer than other population segments (Nawaz, Ashraf & Shaikh, 2014). The national media and different social media platforms have perpetuated a culture of shopping via credit cards as customers can purchase and pay at a later date. Therefore, the proposed study intends to link the young female customer segment to luxury fashion products and factors influencing their purchasing behaviour. There are very few studies that have concentrated on this customer segment. This study aims to fill this gap by exploring the motivation and purchasing behaviour of young female customers with regards to luxury fashion brands from hedonic and utilitarian perspectives.

Aims and Objectives

The proposed study aims at establishing the factors influencing young female customers’ purchasing behaviour in the uptake of luxury fashion in the UK. Therefore, the objectives are;

  1. To establish the unique buying behaviour of the young female customers
  2. To ascertain the impacts of collectivist customs in luxury fashion purchase
  3. To establish the luxury effects of the young female customer with the uptake of fashion brands in the UK

Preliminary Literature Review

Theoretical Review

Several theoretical frameworks have been put forward to explain customer behaviour in the general business environment from hedonic influence and utilitarian perspectives. For instance, the consumer decision theory states that customers are end-users inspired mainly by the actual or perceived benefits before making a decision to purchase a product (Kotler & Keller 2016). This means that the process of decision making is dynamic and dependent on variables such as problem identification, evaluation, information search, purchase decision, and post-purchase response (see figure 1).

The five-stage decision making theoretical model.
Fig. 1. The five-stage decision making theoretical model. (Source: Kotler & Keller 2016).

Kotler and Keller (2016) have put forward the consumer behaviour as angled on the elements of preference formulation and intention as correlated to the attitude and utilitarian values of customers towards a product. This means that the decision process is cyclical as behaviour might be influenced by different sets of hedonic factors. This theory further states that the final decision framework is dynamic and multifaceted on the basis of hedonic orientation personal intentions (see figure 2).

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The final decision model.
Fig. 2. The final decision model. (Source: Kotler & Keller 2016).

The theoretical frameworks indicate that purchasing behaviour functions on satisfaction and dissonance matrices in line with the hedonic performance expectations. For instance, the experimental and emotional decisions are triggered by a multi-sensory process associated with a shopping experience (Kotler & Keller 2016). This means that a typical customer might be influenced by the utilitarian dynamics associated with a product. For instance, items associated with status or trend tend to attract more attention than daily usage products.

Empirical Review

Several studies have been carried on the utilitarian and hedonic influences on the purchasing behaviour of customers. For instance, Nawaz, Ashraf and Shaikh (2014) established that the perception of customers is influenced by the perceived benefits attributed to a product. This means that incorporation of the ideal benefits that appeal to specific needs has the potential of stimulating an inclination of a customer towards the purchasing decision. Another study by Homburg, Jozic and Kuehnl (2017) outlined the status as important determinant factors in the purchasing mix for luxurious products. This is an indication that customer adoption is positively tuned by the status quest associated with a product. A study by Meng-Shan et al. (2015) concurs that status is an instrumental determinant of focused purchasing behaviour, especially from a cultural and individualistic society.

The findings indicated that utilitarian and hedonic factors influence the pursuit of a higher status impacts behavioural intendment for luxury products. These influences have a different level of impacts on the purchasing behaviour in various customer segments. Specifically, Lohdi and Naz (2016) established that utilitarian influences are highest among the youthful population due to their integration of social media as an active interaction platform. For instance, across the globe, the youthful population have created a unique media culture characterised by the exchange of information about trend, status, and style. The findings suggested that self-perception of the benefits associated with a luxury brand is a positive customer engagement stimulant as a hedonic factor. On the same note, Nawaz, Ashraf and Shaikh (2014) associated customer purchasing behaviour to trend in a product brand. The author concluded that there is a positive correlation between product acceptance and perceived trend, especially for the youthful population. The author further indicated that young customers are active in pursuing consumption habits that conform to the trendiness desires among peers.

In exploring the principle of rarity, a study by Meng-Shan et al. (2015) found out that luxury brands are perceived as rare in the markets. Therefore, customers who are in a position to get these rare products are perceived as having a higher status. Thus products that are luxurious are perceived as a determinant of prestige and position. The weight of focused groups on the buying habits of luxury products has been recognised by a series of past studies. For instance, Meng-Shan et al. (2015), Lohdi and Naz (2016), and Nawaz, Ashraf and Shaikh (2014) have suggested that isolation of a focused group is responsible for different ‘buying frenzies’ as a result of the limited edition fad. This means that consumers of luxury brands are motivated to buy the ‘right’ brand that fits in their reference groups. The findings confirmed that young customers are prone to influence of reference group phenomenon.

Relevance to the Research Topic

Most of the empirical studies were based on the youthful population in terms of factors that impact their purchasing behaviour on luxury brands. Thus, the researcher was able to use this general insight to design-focused research to target the UK market in line with the five-stage decision making theoretical model.

Research Methods

Research Design

Since this study is focused and subjective, the researcher chose a mixed-method design to integrate a series of relevant data analysis tools (Bryman & Bell 2015). Moreover, the mixed design is associated with a systematic comparative analysis in linking the independent and dependent variables. The dependent variable is young female customers, while the independent variable is purchasing behaviour. The researcher opted for a deductive approach to gain insight into the current indicators of purchasing behaviour in a focused population segment.

Research Methods

Since a deductive approach is effective in establishing the link between purchasing behaviour and age, the researcher used closed-ended questions to roll out a focused and systematic survey consisting of thirty respondents. Data collection was done through a questionnaire consisting of 5 questions prepared on a 5-item Likert scale. The rationale for selecting a closed-ended question format was informed by the need to guide the framing of responses (Bryman & Bell 2015). The researcher selected the respondents through a random sampling strategy. This was followed by a qualitative interview for a single respondent.

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The researcher selected the analysis of variance (ANOVA) instrument to identify the potential statistical variations on the data collected. The two elements of ANOVA analysis are means of age variation

The two elements of ANOVA analysis

and shopping behaviour

The two elements of ANOVA analysis

(Bryman & Bell 2015). Thus, the null and alternative hypotheses for the ANOVA analysis are;

Null hypothesis

Ho: µ1 = µ2

The null hypothesis implies that the mean of the selected sample population on factors influencing customer shopping behaviour is equivalent to the mean for the entire population segment.

Alternative hypothesis

Ho: µ1 ≠ µ2

The null hypothesis implies that the mean of the selected sample population on factors influencing customer shopping behaviour is not equivalent to the mean for the entire population segment.

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When the F-calculated is bigger than the F-critical, the null hypothesis will be rejected at a confidence interval of 99%.

In order to minimise potential biases, the researcher pretested the questions through a pilot study consisting of five respondents to ensure that the final questions are neutral. Moreover, the respondents were selected within the targeted age groups, which are 18 to 32 years old female customers. The other basic requirements to qualify as a respondent are frequency in consumption of luxurious fashion and basic literacy (Bryman & Bell 2015).

Research Ethics and Risk Assessment

The researcher will integrate the University of the Arts Code of Ethics by including a consent letter to ensure that the respondents are informed of the purpose, aim, and objectives of the study. Moreover, the informed consent letter will highlight legal and steps in place to protect the identity of participants from the beginning to the end of the survey (Bryman & Bell 2015). The respondents will also be given an opportunity to either accept or decline a request to participate in the proposed study. The attached consent form will enable the respondents to understand the expectations and terms of engagement as a basic ethical principle in scientific research. Since the researcher has adequate training on how to conduct a survey study, the aspect of professionalism will be upheld at all the time during research planning, data, collection, analysis, and interpretation (Bryman & Bell 2015). In addition, the researcher will seek permission from relevant authorities within the area of study to ensure that the research is protected legally and ethically throughout the survey.

Findings of Primary Research

Findings and Analysis

The response rate was 100%, that is, the targeted participants all filled and submitted their questionnaires on time (see table 1 and 2).

Table 1. Summary of the response rate against gender.

Respondent by age Number Percentage (%)
18-22 10 33.3
23-26 10 33.3
27-32 10 33.3
Total 30 100

Table 2. Summary of the demographic traits of the respondents.

Respondents Employment Status Preferred method of shopping Attributes associate with the shopping experience Time per week spent shopping for luxury fashion
1 Employed Online Social, convenience, status, rarity Over three hours
2 Employed Traditional store Social, status, rarity Over three hours
3 Employed Traditional store Social, convenience, status, rarity Over three hours
4 Employed Traditional store Social, convenience, rarity Over three hours
5 Unemployed Traditional store Social, convenience, status 2-3 hours
6 Employed Traditional store Social, status Over three hours
7 Unemployed Online Social, convenience, status Over three hours
8 Employed Traditional store Social, convenience, status, rarity Over three hours
9 Unemployed Online Social, status, rarity Over three hours
10 Employed Online Social, convenience, status Over three hours
11 Employed Traditional store Social, convenience, status, rarity Over three hours
12 Employed Traditional store Social, rarity Over three hours
13 Employed Online Social, status, rarity Over three hours
14 Unemployed Traditional store Social, convenience, status 2-3 hours
15 Employed Traditional store Social, convenience, status, rarity Over three hours
16 Employed Traditional store Social, convenience, status Over three hours
17 Unemployed Online Social, convenience, status Over three hours
18 Employed Traditional store Social, status, rarity Over three hours
19 Employed Traditional store Social, convenience, status Over three hours
20 Employed Online Social, convenience, rarity Over three hours
21 Unemployed Online Social, status, rarity Over three hours
22 Employed Traditional store Social, convenience, status Over three hours
23 Employed Online Social, convenience, status Over three hours
24 Unemployed Traditional store Social, convenience, status, rarity Over three hours
25 Unemployed Online Social, status Over three hours
26 Employed Online Social, convenience, status Over three hours
27 Employed Traditional store Social, convenience, status, rarity 2-3 hours
28 Employed Online Social, convenience, status Over three hours
29 Unemployed Traditional store Social, convenience, status Over three hours
30 Employed Traditional store Social, status, rarity Over three hours

Statistical Data Analysis

Transcription and data coding was done by the researcher on attributes of a shopping experience for different shopping platforms for rank, standard deviation, and mean (see table 3). The primary attributes reviewed were social, convenience, status, a rarity.

Table 3. Summary of the rank, mean, and standard deviation for different attributes.

Social Media Site Sample size Mean Standard deviation Rank
Social 30 5.2849 0.8905 1
Convenience 30 4.8349 1.1212 2
Status 30 4.4786 0.8533 3
Rarity 30 4.1643 1.1231 4

The findings, as captured in table 3 suggest that social attributes of a luxury fashion product had the highest mean (5.2849) followed by convenience (4.8349). The third attribute in terms of influence on shopping behaviour in the sample population was status, with a mean score of 4.4786. The attribute of rarity had the least influence on the buying habit at a mean of 4.1643. Since all the results are within a single-digit variance, it is in order to conclude that there was consistency in the generated responses. The same trend was repeated for standard deviation and rank.

In order to correlate these attributes to shopping behaviour among the young female respondents, the researcher carried out a comprehensive correlation analysis. The purpose of this analysis was to establish the trend in the magnitude of the impact of each attribute on purchasing behaviour (see table 4). In order to effectively give an insight into the relevant indicators, the researcher modified the earlier proposed hypotheses to test their validity, as presented below.

The null hypothesis, Ho: There is no correlation between different attributes and purchasing behaviour of young female customers within the UK fashion industry.

The alternative hypothesis, H1: There is a correlation between different attributes and purchasing behaviour of young female customers within the UK fashion industry.

Table 4. Correlation analysis results.

Purchasing Behaviour Social Convenience Status Rarity
Young female customer purchasing behaviour 1
Social 0.667 1
Convenience 0.641 0.532 1
Status 0.446 (0.458) (0.374) 1
Rarity 0.427 (0.448 (0.386) 0.312 1

As summarised in table 4, there is a positive correlation between the four attributes and purchasing behaviour in the sampled population. The social attribute had the highest correlation coefficient of 0.667followed by a convenience indicator at 0.641. The attribute of rarity had the least correlation coefficient of 0.427. Generally, the positive correlation for four attributes is a confirmation that the independent and dependent variables are associated. In order to establish specific behaviour orientations in each of the four attributes, the researcher introduced ANOVA analysis with the original hypotheses suggested in the research design section. The ANOVA analysis was necessary since it permits multivariate tests for each variable (see table 5).

Table 5. Summary of the rank, S.D, and mean of each attribute.

Social media platform In terms of gender In terms of age In terms of social orientation
Mean S.D Rank Mean S.D Rank Mean S.D Rank
Social 5.53 0.63 1 6.56 1.64 1 7.82 1.45 1
Convenience 5.51 0.42 2 4.32 1.05 3 6.74 0.86 2
Status 4.62 1.33 4 3.53 0.86 4 3.08 0.89 4
Rarity 4.81 1.12 3 5.93 1.26 2 4.85 1.07 3

Apparently, the social attribute was the predominant factor influencing young female customers’ purchasing behaviour within the indicators of age, frequency of shopping, and employment status. However, rarity had the least influence in the buying decision process among the young female customers sampled. The results of the ANOVA analysis were then tabulated to identify a trend in the multivariate relationship among the indicators of age, employment status, and duration spent in luxury fashion stores (see table 6). In order to establish the multivariate trend for the employment, time spent, and age indicators, the researcher, created the following hypotheses.

Null hypothesis

Ho: µ1 = µ2 = µ3

The null hypothesis states that dissimilarities do not exist in the decision behaviour influence for the indicators of time spent shopping, employment status, and age of young female customers.

Alternative hypothesis

Ho: µ1 ≠ µ2 ≠ µ3

The alternative hypothesis states that dissimilarities exist in the decision behaviour influence for the indicators of time spent shopping, employment status, and age of young female customers.

The findings of the multivariate analysis were tabulated to establish the F-ratio, P-value and degree of freedom for the three indicators (see table 6).

Table 6. Degree of freedom for each of the three indicators.

Variable F-ratio Degrees of freedom P-value
Social, convenience, status, rarity attributes 4.32 7.211 0.001
Analysis of the indicators
Age 9.31 95 0.002
Frequency and duration of shopping 6.81 95 0.003
Employment status 5.87 95 0.004

Interestingly, the findings indicated suggested that there exist a positive relationship between shopping behaviour and the three indicators. The P-value and F-value for the attributes and their relationship to the three indicators are 0.001 and 4.32, respectively. This means that the null hypothesis is invalid at a 99% confidence interval. The result suggests that the four attributes and their impact on young female customer purchasing behaviour vary with employment, age, and frequency/duration of each shopping process. Therefore, these findings have confirmed that the Social, convenience, status, and rarity attributes are significant in manipulating the young female customers’ purchasing behaviour within the UK luxury fashion industry.

On the other hand, the qualitative data analysis also indicated that there is a positive correlation between the four attributes and purchasing behaviour of the sampled population. As confirmed from the empirical and theoretical framework literature, the research study associated the attributes of social, convenience, status, and rarity as influencing the purchasing decision among the young female customers within the UK restaurant sector. For instance, the respondent noted,

Before I buy a luxury fashion brand, I must look at its physical appeal so that it can affirm my social class and status. I might then look at rarity since it is not fun to see somebody else wearing the same cloth. Convenience is the least factor since shopping for luxury clothes can make me travel even for 200 miles in my free time.

Specifically, social attribute had the strongest influence on purchasing behaviour, while the rarity element was the least influencing in the decision patterns of the sampled population. The quantitative and qualitative findings established similar results.

Conclusion and Limitations

Conclusion and Recommendations

The objective of this study was to establish the cultural attributes of the purchasing behaviour of young female customers within the UK fashion industry. The researcher confirmed that the variations, modifications, and processes involved in making a decision to purchase a luxury brand within the sampled population are influenced by different cultural attributes (social, convenience, status and rarity). Specifically, the results indicated that there is a positive correlation between shopping behaviour and cultural attributes in the sample population. These results conform to the theoretical and empirical studies revealing that the social attributes have contributed to the paradigm shift in the shopping patterns among the youthful population. Specifically, the findings were consistent with the five-stage decision model by explaining the systematic relationship between the dependent and independent variables.

The study has revealed that age, employment status, and frequency/duration in a buying cycle are significant indicators of a purchasing pattern. Therefore, the results of this study, empirical research, and theoretical frameworks have proven that young female customers within the UK luxury fashion sector are influenced by social, convenience, status, and rarity attributes to associate with a brand when making the purchase decision. The findings further clarify that age is a significant influence in the perception associated with each attribute associated with a shopping trend. The most predominant attribute impacting the purchasing behaviour in the sample population is social benefits. This means that marketers of luxury brands should integrate messages that appeal to social benefits to appeal to the young female market segment. Moreover, it is necessary for retailers of luxury fashion brands to institutionalise effective information exposure strategies to conform to the expanded online buying habits in this focused customer segment.

Limitations and Avenues for Further Research

Although the findings of this study conformed to the existing empirical and theoretical literature, the researcher identified several limitations. For instance, the sample size of ten participants was inadequate for a scientific study. This means that the results could not be representational of the general shopping behaviour among the young female customers within the UK luxury fashion sector. In addition, the quantitative nature of the researcher could not provide room for capturing respondents’ personal insight beyond the guided responses.

Since the study was limited in terms of time and scope, the researcher was not able to expound on the theoretical frameworks or empirical results. This means that the literature review was focused on the interpretation of the results and not the process of gathering the same. Therefore, there is a need for further research to establish underlying reasons associated with each attribute as impacting on purchasing behaviour among young female customers.

Reference List

Bryman, A. And Bell, E., 2015. Business research methods, 4th edn, Oxford University Press, Oxford.

Homburg, C., Jozic, D. And Kuehnl, C., 2017. Customer experience management: toward implementing an evolving marketing concept. Journal of the Academy of Marketing Science, 45(3), pp. 377-401.

Kotler, P. And Keller, K., 2016. Marketing management, 15th edn, Pearson Prentice Hall, New York, NY.

Lohdi, S. And Naz, U., 2016. Impact of customer self concept and life style on luxury goods purchases: a case of females of Karachi. Arabian Journal of Business Management Review, 6(192), pp. 56-67.

Meng-Shan, S., Wu, I., Cheng-Hao, C., Chen, S. And Nguyen, M., 2015. Luxury fashion brands: factors influencing young female consumers’ luxury fashion in Taiwan. An International Journal, 18(3), pp. 1-39.

Nawaz A., Ashraf, M. And Shaikh, K., 2014. An empirical investigation to the factors influencing buying decision of luxury goods: a study of Y generation. GMJACS, 4(9), pp. 2219-6145.

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StudyCorgi. (2021, July 6). Young Female Customers’ Luxury Fashion Purchasing in the UK. Retrieved from https://studycorgi.com/young-female-customers-luxury-fashion-purchasing-in-the-uk/

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"Young Female Customers’ Luxury Fashion Purchasing in the UK." StudyCorgi, 6 July 2021, studycorgi.com/young-female-customers-luxury-fashion-purchasing-in-the-uk/.

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StudyCorgi. "Young Female Customers’ Luxury Fashion Purchasing in the UK." July 6, 2021. https://studycorgi.com/young-female-customers-luxury-fashion-purchasing-in-the-uk/.

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StudyCorgi. (2021) 'Young Female Customers’ Luxury Fashion Purchasing in the UK'. 6 July.

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