Chapter 4: Discussion
4.1. Content Types Relevance
The distribution of the sample in terms of age and gender revealed that 67% of the sample was formed by female respondents with the average age of individual respondents being 32 years old. This means that the study primarily explored the standpoint of young adults as the category having sufficient resources and a growing interest in purchasing fashion products according to Shin and Damhorst (2018). The analysis of popularity performed for individual types of content revealed several interesting trends. Such factors as customer testimonials and user-generated content, live streams, product- and company-related images, and social media stories all had the aggregate share of ‘agree’ and ‘strongly agree’ answers exceeding 75%. Since most of these formats were deemed more or less traditional by Cham et al. (2018) and Schwemmer and Ziewiecki (2018), it can be concluded that Thai customers are generally conservative in their reactions to content marketing. However, other traditional methods including promotional videos, guides and manuals, and behind-the-scenes videos demonstrated medium effectiveness with the average aggregate share of ‘agree’ and ‘strongly agree’ responses amounting to 60% approximately.
At the same time, the identified relevance of live streams and social media stories confirms the findings of Romao et al. (2019) and von Wachenfeldt (2019) regarding the high perceived popularity of raw and unprocessed content for creating a positive emotional response. Since these materials are delivered ‘as is’ and demonstrate live persons representing the brand rather than polished marketing materials, consumers may feel a greater attachment as a result and respond to this format of advertising communication more positively. On the contrary, company-related charts and infographics, product- and company-related texts, and VR and 3600 content were poorly appraised by the respondents with the aggregate shares of ‘agree’ and ‘strongly agree’ responses amounting to 35%, 42%, and 40% accordingly. It should be noted that the second and third types also had the shares of ‘neither’ replies exceeding 35%, which may demonstrate the high degree of uncertainty regarding the prospective attractiveness of these instruments.
On the one hand, charts and infographics, as well as textual information, may not be fully applicable to the studied industry according to Casalo et al. (2018) and Zhao and Min (2018). Since fashion products usually emphasise hedonic value and uniqueness, it may not be effective to focus on figures and raw data to demonstrate their advantages or the power of a certain brand. On the other hand, the identified lack of VR and 3600 attractiveness may be explained by the limited spread of supporting technologies. As this format of content delivery is not conventional and may require specialised and expensive equipment, both fashion companies and social media users may be reluctant to accept is as a powerful experiential marketing tool (Park et al., 2018). This situation may change in the future when augmented reality and virtual reality technologies become widely adopted by the general population.
4.2. The Impact of Content Types on Purchase Decision-Making
The performed linear regressions revealed that past purchase decisions and the positive attitudes towards the brand as dependent variables were influenced by different sets of antecedents. The first one depended on customer testimonials and user-generated content, product- and company-related images, and promotional videos. These results may be seen as slightly controversial. On the one hand, the identified relevance of customer testimonials and user-generated content is fully in line with the findings of Colicev (2018) who recognised the greater perceived relevance of the information provided by other users in comparison with traditional marketing materials. On the other hand, official images and videos were equally significant in terms of their statistical power, which suggests that both of these marketing channels provide positive results. According to Patel and Bansal (2018), this effect may be attributed to different reception patterns of various customer segments depending on the demographic, psychosocial or behavioural characteristics of customers. This study did not use these elements as the selection criteria for including candidates into the sample, which means that tracing these correlations between consumer-related elements and content preferences may not be possible. This drawback may be addressed by future studies focused on the fashion industry.
4.3. The Impact of Content Types on Brand Attitudes
Positive attitudes towards the brand were primarily influenced by behind-the-scenes videos, customer testimonials and user-generated content, and social media stories. The first factor had the lowest statistical significance (p-value of 0.047), which means that its impact on the studied dependent variable was moderate. This partially supports the ideas of Voorveld (2019) regarding the limited relevance of this antecedent for the development of emotional connections between customers and companies. The positive impact of social media stories on brand attitudes was also in line with these ideas since this format of pull marketing allows consumers to see ‘brand snapshots’ in a highly condensed format, which is more convenient than traditional promotional videos or textual articles. Finally, the relevance of customer testimonials and user-generated content for the company image was in line with the suggestions voiced by Chiosa and Anastasiei (2017) regarding the need for confirmation of company reputation from regular customers.
4.4. Chapter Summary
It can be summarised that the purchase decisions of Thai customers and the positive attitudes towards the brand are both affected by a single factor, namely customer testimonials and user-generated content. This fact partially confirms the ideas voiced by Eigenraam et al. (2018) and Kim and Sullivan (2019) regarding the changing nature of marketing communication in the recent decades and the need to develop brand attitudes through bilateral communication experiences rather than traditional unilateral signals. However, this dissertation did not analyse the impact of non-significant independent variables as moderators of this effect, which may be seen as a possible future research direction. The well-received formats identified in this study may allow companies to achieve maximal marketing reach. The future potential of virtual reality and augmented reality technologies cited by Jang et al. (2019) should also be taken into account to not miss this disruptive innovation opportunity.
Table 1: Discussion Summary
References
Casalo, L., Flavian, C. and Ibanez-Sanchez, S. (2018) ‘Influencers on Instagram: Antecedents and consequences of opinion leadership’, Journal of Business Research, 1 (1), pp. 1-10.
Cham, T., Ng, C., Lim, Y. and Cheng, B. (2018) ‘Factors influencing clothing interest and purchase intention: a study of Generation Y consumers in Malaysia’, The International Review of Retail, Distribution and Consumer Research, 28 (2), pp. 174-189.
Chiosa, A. and Anastasiei, B. (2017) ‘Negative Word-of-Mouth: Exploring the Impact of Adverse Messages on Consumers’ Reactions on Facebook’, Review of Economic Business Studies, 10 (2), pp. 157-173.
Colicev, A. (2018) ‘Modelling the Relationship between Firm and User Generated Content and the Stages of the Marketing Funnel’, International Journal of Research in Marketing, 1 (1), pp. 1-46.
Eigenraam, A., Eelen, J., van Lin, A. and Verlegh, P. (2018) ‘A Consumer-based Taxonomy of Digital Customer Engagement Practices’, Journal of Interactive Marketing, 44 (1), pp. 102-121.
Jang, J., Hur, H. and Choo, H. (2019) ‘How to evoke consumer approach intention toward VR stores? Sequential mediation through telepresence and experiential value’, Fashion and Textiles, 6 (12), pp. 1-16.
Kim, Y. and Sullivan, P. (2019) ‘Emotional branding speaks to consumers’ heart: the case of fashion brands’, Fashion and Textiles, 6 (2), pp. 1-16.
Park, M., Im, H. and Kim, D. (2018) ‘Feasibility and user experience of virtual reality fashion stores’, Fashion and Textiles, 5 (1), pp. 1-17.
Patel, J. and Bansal, A. (2018) ‘Effect of demographic variables on e-marketing strategies: A review’, International Journal of Academic Research and Development, 3 (1), pp. 311-321.
Romao, M., Moro, S., Rita, P. and Ramos, P. (2019) ‘Leveraging a luxury fashion brand through social media’, European Research on Management and Business Economics, 25 (1), pp. 15-22.
Schwemmer, C. and Ziewiecki, S. (2018) ‘Social media sellout: The increasing role of product promotion on YouTube’, Social Media+ Society, 4 (3), pp. 1-20.
Shin, E. and Damhorst, M. (2018) ‘How young consumers think about clothing fit?’, International Journal of Fashion Design, Technology and Education, 11 (3), pp. 352-361.
von Wachenfeldt, P. (2019) ‘The Mediation of Luxury Brands in Digital Storytelling’, Fashion Theory, 1 (1), pp. 1-20.
Voorveld, H. A. M. (2019) ‘Brand Communication in Social Media: A Research Agenda’, Journal of Advertising, 48 (1), pp. 1-13.
Zhao, L. and Min, C. (2018) ‘The Rise of Fashion Informatics: A Case of Data Mining-Based Social Network Analysis in Fashion’, Clothing and Textiles Research Journal, 1 (1), pp. 1-16.
