ARTICLES

 

Dynamics of social media metrics and fashion sub-culture among young South African TikTok users

 

 

Savanna Gouveia^I; Dr Tatenda T. Chabata^II

^ISchool of Fashion, Department of Marketing, STADIO Higher Education, Centurion, South Africa. Email: savannagouveia@me.com. ORCID: https://orcid.org/0009-0006-1050-364X
^IISchool of Fashion, Department of Marketing, STADIO Higher Education, Centurion, South Africa. Email: tatendac@stadio.ac.za (corresponding author). ORCID: https://orcid.org/0000-0002-1183-0689

 

 

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ABSTRACT

The global effect of the social media platform TikTok has resulted in an increase in literature pertaining to the platform. Despite wide acknowledgment in social media, TikTok metrics and how these formulate fashion sub-cultures remains underexplored. This study explored TikTok metrics that resultantly formulate fashion sub-cultures amongst Generation Z (Gen Z) TikTok users. The proposed conceptual framework was developed to assist marketing communication managers and content strategists to explore fashion sub-culture interest via various TikTok metrics. The study followed a qualitative approach. Data was collected through structured interviews, using convenience sampling among five Gen Z participants. Thematic analysis was adopted and findings revealed that engagement metrics indicate strong potential in enabling fashion sub-culture among TikTok users. Furthermore, metrics enabled Gen Z users to discover fashion sub-culture content through hashtags, engagement in social exchanges through likes and comments, store digital fashion inspiration through saves and spread information through shares.

Keywords: marketing communication, brand communication, Generation Z, social media, social media metrics, fashion sub-culture, TikTok

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INTRODUCTION

Expanding technological advancements in the current milieu have revolutionised both marketing and fashion culture. Consequently, new findings, theories and methods surrounding the respective fields contribute to and renew the existing mass of scholarly literature. The evolution of the digital realm, specifically social media, has altered and predisposed marketing and fashion cultural theory (Arriagaga 2021: 233; Sklar et al. 2021). In addition to this, the inherent nature of digital advancements is that it is ever evolving. The ever-evolving nature of the technology landscape, particularly that of social media metrics, was the main motivation for conducting this research.

Cunningham and Craig (2021) state that social media is an internet-based technology that enables its users to interact with one another through the ability to connect and share information on a global scale. Pencarelli and Mele (2020) define social media metrics as a form of data collection for social media platforms that allow for the measurement of the performance of the platform. According to Conner (2022), a sub-culture is a subsection of people within a larger cultural group who can be defined by a collective identity. To better understand sub-culture in a social media context is important for fashion industry practitioners and marketing communication managers to make informed decisions. Previous studies in the same subject area were conducted in other regions, besides that in which this study is centred, and due to the cultural differences across regions therefore required the researchers to undertake this study.

Metrics have become an important concept in the fashion industry as these enable important decision-making purposes for fashion brands. This was confirmed by Beheshti-Kashi et al. (2018), Klug et al. (2021), as well as Poecze et al. (2018). Analysing and interpreting virtual sub-cultures via metrics has become synonymous with fashion trend analysis. Whilst Sklar et al. (2021) assert that sub-cultures stem from common interests, O'Connor et al. (2017) attest to the fact that social media provides the perfect platform to establish fashion sub-cultures. Moreover, Sayed (2022) emphasised that grouping like-minded people together based on shared interests subsequently encourages the emergence of a sub-culture. Prior studies from Maphathe (2021), Dragos et al. (2020), and Archary and Coetzee (2020) established that metrics obtained from social media platforms are a beneficial tool for subcultural formation, in general.

Kemp (2022a) determined that TikTok's success in marketing avenues has been a helpful source of information amongst users. Furthermore, prior literature framed TikTok's user-generated, metric data collection and distribution methods, whilst confirming TikTok users' acknowledgement and understanding of metric data collection (Klug et al. 2021; Costas et al. 2020). Although these studies provide sufficient background as to why it would be beneficial to explore TikTok's metrics and distribution methods, especially amongst younger generations, it does not enable the research questions or objectives to be addressed in this study. This study's primary research reaffirmed both TikTok's success amongst Gen Z and users' knowledge of certain distribution methods that are applied through metrics in a developing country context. This research refined these concepts and specified that South African Gen Z TikTok users purposefully apply metrics to curate a more satisfactory "for you page" and utilised TikTok as a valuable source of fashion related information.

Through evidence obtained from Costas et al. (2020), Etter and Ablu (2020), and Beer (2017), it can be stated that numerous factors beyond a shared common interest influence the creation of modern-day sub-cultures. Though past studies have considered the value that metrics play in the formulation of sub-cultures, most have concentrated on Instagram and Facebook.

 

THEORETICAL GROUNDING

Understanding fashion sub-culture and social media

Conner (2022) proposes that fashion sub-cultures cannot be defined by demographics but rather by psychographics, as culture is born through the deviations of interest within the structure of society. Vänskä (2018), in turn, states that fashion sub-cultures are rooted in common interests, principles and values that veer from the "norms" of society. The concept of fashion sub-cultures is relevant to this study as it is understood by Winge (2018) as the grouping and communication avenues provided by social media platforms that can assist in the emergence, circulation, or expansion of fashion sub-cultures. Etter and Ablu (2020) state that social networking enables users to translate a private connection into open communication; thus, providing the perfect breeding ground for sub-cultures to congregate without boundaries. According to O'Connor et al. (2017), previously, sub-cultures were limited by time and space. However, social media platforms have enabled sub-cultures to transcend beyond such limitations and ensured that sub-cultures are not confined to a particular space or time. These studies attest to the fact that popular social media platforms provide a digital space to assist in the genesis, distribution, or growth of fashion sub-cultures (Winge 2018; O'Connor et al. 2017). Sayed (2022) confirms this notion by stating that 21^st-century technology advancements in social media networking have promoted the distribution and expansion of Japanese culture, for example. Etter and Ablu (2020:80) theorise that this is due to users adopting social media platforms as centres for community creation, the development of social relationships, and the expression of interest. Thus it can be argued that developments in technology and digital media have advanced fashion sub-culture evolution.

Conner (2022) suggests that there is a direct correlation between sub-culture and youth as younger generations are far more likely to participate in sub-cultural fashion than older generations. This ideology emphasises fashion sub-culture's ability to have widespread reach on TikTok as the platform predominately hosts younger users. Based on a study conducted by Park et al. (2022), 67% of TikTok users are part of Gen Z. Green et al. (2022) and Richards (2022: 4) confirm that, due to Gen Z's perceived short attention span, the visual aid made possible by digital media, specifically TikTok's short-form video, has made it easier for the users to engage with content. As a result, it is proposed that TikTok's format enables sub-fashion cultural social exchange and information distribution through engagement tools, as well as provides gratification avenues through metrics in a way that is appealing to Gen Z.

Prior literature on metrics and fashion sub-culture

Etter and Ablu (2020) write that user-generated metrics are collected by TikTok as data and converted through a cultural algorithm altered by the platform. Reynolds and Kinnaird-Heether (2019) postulate that social media algorithms group homogenous and heterogenous users based on past engagement and interest. The grouping of users based on social media metrics are of interest in this research as the inherent nature of exposing homogenous users to similar content could result in the

culmination of fashion sub-cultures. Reynolds and Kinnaird-Heether (2019) point out that the mechanisms put in place by social media platforms determine how and what information is distributed through a population network. Beer (2017) states that those social media algorithms are known as the "decision-making" parts of social media as the social media code is what filters the metrics on social networking sites and decides what users see as content by preference, thus operationalising network perspectives (Diaz-Faes et al. 2019). Reynolds and Kinnaird-Heether (2019) suggest that this is the beginning of a new "sub-cultured distribution mechanism" in society as people no longer need to seek out like-minded groups or people. The TikTok algorithm does it for them in the format of a "for you page" better known as "FYP". Therefore, it can be presumed that the TikTok algorithm essentially lays the foundation to stimulate the distribution and expansion of online fashion sub-cultures.

Park et al. (2022) explain that hashtag metrics utilised by TikTok enable a user to search for specific content if the metrics and algorithm do not curate a "for you page" that is satisfactory. When a TikTok user is not pleased with the curated fashion content supplied by the algorithm, a user may seek more satisfying sub-culture content through other metrics, such as the hashtag metric. Geyser (2022) states that as of 2022, fashion is ranked 7^th in the most popular categories on TikTok with 27 billion views within the videos posted under fashion-related content. According to Veletsianos and Kimmons (2016), the hashtag metric was found to be a powerful tool to connect users to content on platforms. Resultantly, niche fashion sub-cultures can be explored and discovered through the TikTok hashtag, while users seek gratification. Furthermore, Veletsianos and Kimmons (2016) are of the view that metrics such as followers, likes and other engagement tools can be useful metrics of success for businesses in general. Such findings suggest that the volume of engagement proves to be enticing or authoritative. This article argues that TikTok metrics aid in the distribution and circulation of information regarding the fashion sub-culture among Gen Z and further formulates the growth and sustainability of the fashion sub-culture. Thus, it can be assumed that TikTok hashtag metrics provide important form of metrics that can be employed when conducting trend analyses in the fashion industry.

Diaz-Faes et al. (2019) postulate that social media metrics can be utilised as a unit of measurement, suggesting that a fashion sub-culture's influence or prevalence can be translated into tangible measurable statistics in the form of metrics. TikTok metrics such as likes, comments, shares, followers, saves and hashtags could provide direct instant and important feedback to both creators and/or companies monitoring a fashion trend. Thus, metrics could provide companies in the fashion industry with valuable insights. Beheshti-Kashi et al. (2018) indicate that companies and brands within the fashion industry could monetarily capitalise on online fashion trends by supplying trending merchandise to their consumers. Therefore, conducting trend analyses based on quantifiable and measurable interest made available by TikTok metrics could enable companies within the fashion industry to track interest in products and supply different fashion sub-cultures with personalised fashion merchandise. Beheshti-Kashi et al.'s (2018) viewpoint enables this study to highlight the benefits and importance of tracking online fashion sub-culture metrics on TikTok.

 

THEORETICAL FRAMEWORK

The "Theories-in-Use" (TIU) approach, recommended by Zeithaml et al. (2020a), incorporates and applies participants' responses with academic ideologies and theories as individuals' everyday decisions and actions generally operate below their conscious awareness. Zeithaml et al. (2020a) state that the TIU approach is a characteristic methodology, which creates theories that are specific to marketing-related issues - what has been called "organic" or "home-grown" theories. Hence, the TIU approach empowers specialists to adopt applied systems instead of using structures acquired from other disciplines, such as economics or psychology. This study adopts the TIU approach by integrating the following theories: the uses and gratification theory and the social exchange theory.

The uses and gratification theory suggests that media consumers "play an active role in choosing and using the media which means media users can take an active role in the communication process" (Ozelturkay & Yarimoglu 2017). The active role explored within this research is that of user-generated metrics through TikTok. Leong and Meng (2022) define the social exchange theory as a concept designed to analyse the social behaviour between two parties through a cost-benefit analysis, in a social media context. This can be explored through metrics and online consumer behaviour. The person-to-person relationship analysed within this research is focused on fashion content creators and Gen Z TikTok users' relationship through the prevalent platform metrics. Users actively seeking information regarding a specific fashion sub-culture can be explained by Falgoust et al.'s (2022) social media uses and gratification theory, while utilising social media platforms as a mode of information exchange and socialisation can be explored through Giardino's (2021) social exchange theory.

Falgoust et al. (2022: 1) applied the uses and gratification theory directly to TikTok; this study follows suit by examining the relationship between users' motivations and gratifications and their TikTok usage and engagement. TikTok users have been found to manipulate algorithms with their user-generated metrics with the motivation of curating a content feed that satisfies their personal preferences; thus, fulfilling their unique media consumption needs and achieving gratification (Kang & Lou 2022: 7).

Theoretically, through a cost-benefit analysis, the positive social exchange between content creators and TikTok Gen Z users should result in the direct encouragement of users' opinions on fashion and related topics, which consequently makes them part of a fashion sub-culture. Positive exchange can be measured through metrics such as likes, positive comments, shares, saves, hashtags, incremental followers, profile views, and video views. The engagement metrics in the form of likes, comments, saves, follows, and shares on TikTok are user-generated (Pencarelli & Mele 2020), suggesting that TikTok users can unknowingly apply action metrics "disguised" as engagement tools to engage in a social exchange among themselves, content creators, and other TikTok users. The proposed integrated theories shed light on prior academic scholarship and findings that enabled the researchers to obtain the applicable interview questions for this study.

 

METHODS

This empirical study will collect primary data through semi-structured interviews. Interpretivism is the most appropriate paradigm for this study as it includes the context and environment in which the people of interest within the study reside (Markham & Gammelby 2018). Therefore, Gen Z's idiosyncratic ideologies behind a TikTok phenomenon are exp!ored, identifying the potential impact on users' experiences or interpretations that formolate fashion sub-cultures. This study's qualitative research approach is exploratory - exploring TikTok's metrics impact on Gen Z users in a fashion sub-cultural context. The qualitative approach and exploratory design directly correlate with the research aim as the identified "problem" requires comprehension and interpretatioe (Tracy 2020).

The target population for this study required participants born within the Gen Z era, who make use of TikTok, and are affiliated with a fashion sub-culture. According to Richards (2022), the Gen Z age group consists of anyone within the age bracket of eight to 24 years old. This study only considered a target population that included participants who were above the age 18 but not older than 24 years. Furthermore, the participants were required to have frequented TikTok for over a year and consider themselves as part of a fashion sub-culture. The online data collection methods enabled accessibility; however, accessibility was limited to participants who were available for the duration of the interview process and study as the source of the accessible population (Skinner et al. 2020).

A sample size of five Gen Z TikTok users who had used the social media platform for at least a year and are part of a TikTok fashion sub-culture were selected. The contextual reasoning for this requirement is that a year of use provides the user with enough insight into fashion sub-culture trends and metric groupings. The study adopted the convenience sampling technique as there was no readily available sample frame from which the participants could be derived (Schreier 2018). To contextualise this choice of sampling method: the expert opinions needed for this study were immediately available to the authors; thus, the inclusion and exclusion criteria was chosen.

Zeithaml et al. (2020b) state that data analysis is the process of systematically organising and summarising interview answers, transcripts and observation notes in a manner that facilitates the understanding of the phenomenon in question. Furthermore, a "flag and tag" system was utilised to highlight similar answers within the participant interviews to find the common traits, occurrences, and characteristics (George 2022). Thematic analysis was adopted as it identifies broad themes, topics, ideas, and patterns through coding and close examination (Skinner et al. 2020). The analysis method enabled the researchers to identify critical common themes by transforming raw data into holistic, logical categories that enabled the research problem to be addressed.

 

FINDINGS

The main findings indicate the importance of engagement metrics, the reason for engagement, discovery through hashtag metrics, content preservation, control, personal attire influences, external influence, sense of belonging to a community, and TikTok's relatability and relevance towards fashion sub-culture as the key themes.

Firstly, the theme of "engagement metrics" within this study found that participants utilise TikTok metrics (engagement tools) to engage in a direct or indirect form of social exchange amongst themselves, other users, and content creators. Participant 4 explains that "some people tend to like your comments and even respond to it. So, people are very active, and you can be seen on there"; while Participant 2 said, "There are a lot of different options to engage. You can do it in a way that's not so direct with liking and commenting, but you can also watch some of the videos or share videos". Giardino's (2021) study supports this study's findings by emphasising that a social exchange happens between content creators and users when engagement metrics are utilised. Moreover, Winge (2018) asserts that social media platforms enable those who participate in fashion sub-culture to engage in social behaviour and the distribution of information by interacting with online fashion content. All five participants in this study confirmed the viewpoint of Winge (2018) as true, as the participants acknowledged their application of engagement metrics to socialise, communicate, and circulate information regarding their fashion sub-culture as crucial.

Another key theme was "reason for engagement". The findings of this study confirm that TikTok users utilise different engagement tools for their own reasons. Participant 1 utilised engagement tools such as likes and saves to be able to refer back to the fashion-related content at a later time and stated that they were "too shy to comment".

Participant 2, Participant 3 and Participant 5 utilised engagement tools such as likes and saves to curate a "for you page", which aligns with their fashion interests. Participant 4 established the use of metrics that allow for interaction with humorous content, more than anything else.

Among the main reasons for the utilisation of engagement tools stated by the participants was to curate a personalised feed that caters to their preferences, as well as "save" content to review later (content preservation). This suggests that participants are aware of the proposed algorithm from TikTok and that they will purposefully engage with content to be shown more homogenous content. In a similar study, Kang and Lou (2022) found that users were aware of the functionality of the algorithm and had found ways to use the system to their advantage via engagement. This behaviour is explained by Falgoust et al. (2022) through the uses and gratification theory, as users employ engagement tools on online platforms to open future personal gratification avenues on social media.

Thirdly, the theme of "discovery through hashtags" was prevalent as Participant 1, Participant 2 and Participant 5 reported discovering or exploring niche fashion sub-cultures through their designated hashtags on TikTok. Participant 1 provided further insight by explaining that "the other day I was looking at the #CleanGirl aesthetic cause I was looking for like makeup ideas. I've also looked at #CottageCore". Participant 4 reported not utilising hashtags to explore sub-culture content by stating, "No. It usually just comes up on my explore page", although, later in the interview Participant 4 acknowledged seeing repetitive hashtags within the caption or comment section of the fashion content displayed on their "for you page". A similar study by Veletsianos and Kimmons (2016) found that many users utilised the hashtag metric to share their work with those who were looking or searching for homogenous content to reach a broader audience and obtain a larger following. In the study by Veletsianos and Kimmons's (2016), the designated #aera14 hashtag was explored and was found to be a powerful tool to connect users to the relevant content on X, for example. The emerging of hashtags in connecting users on other social media platforms and scholarly ideologies present a viable argument for TikTok hashtag metrics aiding in the emergence, distribution, and circulation of fashion sub-culture information.

The theme of "boost or popularisation of content preservation" speaks to how a fashion sub-culture could expand through the engagement received from those who interact with the content via metrics. Participant 5 stated that while they do not create content, they "definitely participate in the sense that I'm constantly commenting, liking and saving". The prevalence of TikTok metrics such as likes, comments, shares and saves in sub-fashion cultural movements were evident within this study. Thus, this articles provides evidence that the emerging and expansion of fashion sub-culture is achievable through metric data. This aligns with the findings of Sklar et al. (2021) that developments in social media tracking activities have advanced fashion sub-culture evolution. These findings link the fashion sub-culture' genesis, distribution and growth to social media digital advancements. Park et al. (2022) explored the TikTok hashtag metric and Gen Z users' "trickle-up" effect on the #thriftflip fashion sub-culture phenomenon; thus, confirming this study's finding of participation, engagement, discovery and exploration through a TikTok metric. While Park et al. (2022) confirms sub-fashion cultural exploration via the utilisation of a TikTok metric, it does not speak to other metrics of influence. Evidence from this study contributes to new understanding and presents ideologies surrounding social media metrics' effect on contemporary fashion sub-culture more holistically and comprehensively.

The theme of "sub-culture participation" through user-generated metrics was also raised. The findings of this research conclude that Gen Z participants partake in sub-fashion culture on TikTok via the stimulation of metrics such as likes, hashtags, comments, follows, shares and saves.All the participants confirmed fashion sub-cultural participation through the utilisation of one of the aforementioned metrics. For example, Participant 4 stated, "I do tend to engage with a lot of people in the comment section" when questioned about their participation within fashion sub-culture. As previously discussed, participants and findings in relevant academic discourse found that users utilise metrics for their own reasons. Whilst this does not necessarily enumerate active and physical participation within the fashion sub-culture, it does have a positive effect on the sub-fashion cultural movement as these user-generated metrics boost and popularise fashion content and trends within the sub-culture. Sayed (2022) confirms that social media platforms provide a digital space in which sub-cultural engagement is encouraged and they could assist in the emergence, circulation, or expansion of sub-cultures; thus, translating online engagement and interaction into participation within the users' physical existence. O'Connor et al. (2017) reaffirm the same notion as their findings confirm that popular social media platforms provide a digital space to assist in the genesis, distribution and/or growth of fashion sub-cultures.

Another important theme is "control". This theme relates to the perceived power or management the participants had over the content they view on TikTok. Participant 2, Participant 4 and Participant 5 agreed that TikTok gives them control over the content they see, while Participant 1 and Participant 3 said that it was only "to a certain extent". Participant 2 stated, "Videos that I'm not interested in or doesn't reflect my style, I usually just say don't share this content anymore, which is an option on TikTok", while Participant 5 said, "I think there's an option where you say I don't want to see this". It is assumed that Participant 5 and Participant 2 are both referring to the "not interested" feature on TikTok. Thus, the participants acknowledged some form of control, but agreed that it was only to a certain extent or minimal at best. Kang and Lou (2022) found that TikTok users expressed feelings of ambivalence towards being guided by machinery and would alternatively seek more user controllability. In a similar study, Bhandari and Bimo (2022) found that TikTok's algorithm "is an entity with which users can engage and which they can influence and manipulate, so users have some degree of control over what this algorithm shows them". This offers an explanation behind the participants' feeling of a lack of control.

The theme "external influence on personal attire" is also significant. The majority of the participants confirmed that the fashion sub-cultural content on TikTok had influenced their style of dress or had inspired them. Winge (2018) supports this finding by stating that "online sub-cultures, regardless of the level of emersion, live intertwined existences in both the real and virtual worlds". Participant 1 reported making use of metrics such as likes and saves to accumulate their fashion inspiration for later review when dressing. Participant 3 responded by saying that their approach to fashion sub-cultural participation was "following it on TikTok and then I would try some of the styles that I see". The participants were found to translate digitised fashion sub-culture content and then reveal it within their physical existence.

The other theme that emerged in the same category was the "sense of belonging to a community". Participant 2, Participant 3, Participant 4 and Participant 5 confirmed their acknowledgement of the number of likes, comments, saves, and shares to gauge the popularity and favourable opinions of the sub-culture. Additionally, these metrics were monitored by the participants, as the more engagement the content had, the more it evoked a sense of community for them. Participant 5 further expressed that the share engagement tool "feels like you're creating a community on the platform" through interaction via this metric. Kim (2020) writes that "those who were exposed to high social media engagement metrics showed a higher willingness to read the full news story" and "join the bandwagon"; thus, confirming high engagement influence on community recognition, perceived reliability, and participation. Furthermore, Participant 2 and Participant 4 enjoyed making use of the comment metrics as it enabled a more valuable social experience and fulfilled a stronger sense of community for them. Participant 3 also indicated that the "share" engagement tool provided them with a social avenue and connection to friends, whilst creating a community on the platform. Thus, the social exchange theory is suggested to be relevant to the participants, fashion content creators, and other users (Leong & Meng 2022). Participant 2, Participant 3, Participant 4 and Participant 5 stated that the extent to which they followed a content creator influenced their perception regarding the "authority" of the creator sharing the information. These findings align with those of Veletsianos and Kimmons (2016) who confirmed that metrics such as followers on social media could be a useful metric of success, which enables the volume of engagement to be more enticing or authoritative, which creates a mutual community among online platform users.

Another theme that arose from the data analysis was "TikTok relatability and relevancy". All the participants agreed that TikTok supplies relevant and relatable content regarding their respective fashion sub-cultures. When questioned whether TikTok provides content that is relevant to their respective sub-cultures, Participant 1 replied "Yes, they do", Participant 2 stated "Yes, I think it does", and Participant 5 confirmed "Yes, it does". This study argues that this outcome is due to the user-generated metrics associated with the participants. The participants seeking social fulfilment or gratification through engagement tools provide TikTok with the metric data to distribute complementary or homogenous content to similar users (Poecze et al. 2018). This was proven within this study to be a mutually beneficial exchange. All Ave participants confirmed their enjoyment of viewing homogenous content, while Participant 1, Participant 3 and Participant 5 agreed that the fashion-related content on their "for you page" catered to their tastes. The mutually beneficial exchange transpires as the users consume content that is uniquely satisfactory, whilst the expansion of interest in the fashion sub-culture simultaneously occurs by essentially recruiting new members, whether it be through supporting engagement or influencing the way the users dress and style themselves. Costas et al. (2020), Beer (2017), and Poecze et al. (2018) support this finding by providing evidence that algorithms grouping both homogenous and heterogenous users based on past engagement and interest results in a satisfactory, relevant social media experience for content creators and media consumers. Furthermore, Zhang and Lui (2021) state, "TikTok's recommendation algorithm was selected as one of the Top 10 Global Breakthrough Technologies by MIT Technology Review in 2021, mainly because the algorithm satisfies each user's specific and targeted interests which in this study fulfils users' desire to be part of a mutual fashion sub-culture".

 

MANAGERIAL IMPLICATIONS

There are several social media metrics available to fashion industry managers and marketing communication practitioners, though not all are applicable in formulating fashion sub-cultures among South African Gen Z TikTok users. Based on the findings of this study, fashion practitioners and marketing communication managers should monitor fashion sub-culture interest via engagement metrics. The careful and close observation of metrics that formulate fashion sub-culture could result in more refined trend analysis procedures, as well as effective direct marketing communication strategies. Fashion companies could benefit by pushing their focus towards obtaining higher engagement on fashion-related content on TikTok. Moreso, fashion practitioners should also consider that personalisation is key in enabling fashion adoption and further provides a sense of belonging to a community who emerges with greater sub-culture popularity.

Fashion industry managers and marketing communication practitioners should focus on tracking TikTok engagement metrics, as the metrics enable Gen Z to engage in a social exchange in one form or another, resultantly encouraging current and future fashion sub-cultural information adoption with that of formation. Therefore, it could be posited that engagement through metrics (likes, comments, hashtags, follows, saves and shares) should be utilised by fashion industry managers so as to promote more sub-cultural fashion content circulation and participation.

When metrics that affect users' behaviour were analysed through the curated framework of this article, it resulted in the conclusion of users employing metrics to either engage socially or for future gratification. Fashion managers, content strategists and marketing communicaiton practitioners could take advantage of this by ensuring that the content posted on TikTok allows for users to share it among themselves, as well as others, promoting the building of greater gratification and enabling a sustainable formulation of fashion sub-cultures.

Moreover, fashion practitioners in managerial roles could review or predict product performance on a quantifiable and statistical level via these engagement metrics. Through the given statistics and a careful analysis, a manager could ensure that the demand standard and expectation is met or whether the marketing strategy has created sufficient demand for the supply. This is beneficial for fashion industry managers as it provides a method to evaluate if a fashion product will perform at the expected standard.

 

LIMITATIONS AND RECOMMENDATIONS

The limitations include the use of the non-probability sampling method. The sample of five participants is not a true representation of the total population. The context of South Africa as the base for the research could also raise questions whether the same results from this study could be inferred for other countries' contexts. Social media platforms that are emerging (for example Rumble) and metrics (watch time, attention rate, etc.) were not researched in this study, meaning similar findings may not be guaranteed.

The metric of engagement is important in formulating fashion sub-culture. Further research should be conducted to unearth the reasoning behind social media metric data influencing fashion sub-culture to increase the scope of perceptions and reasoning identified within this study.

The proposed conceptual framework (see Figure 1) could be applied in future research in different sociocultural environments to ascertain if similar or different outcomes could be drawn from future studies. There could be other determining factors that formulate fashion sub-culture, which were not discussed in this study. After such research is conducted, a more holistic framework could be developed and utilised that could aid future researchers in following more avenues in which advertisements and trend forecasting could be done through readily available metrics. Future research might unearth new or contradictory findings if implemented in another geographical region or country, due to cultural or behavioural differences. Finally, the sample group of the study focused on Gen Z and future studies could focus on different sample groups to expand on the research area of interest.

 

 

REFERENCES

Archary, D. & Coetzee, M. 2020. Predicting stock price movement with social media and deep learning. Durban: IEEE Xplore. (3^rd conference of the International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems; 6-7 Aug.). https://doi.org/10.1109/icABCD49160.2020.9183802        [ Links ]

Arriagaga, A. 2021. Content creators and the field of advertising. In: Cunningham, S. & Craig, D. (eds). Creator culture: An introduction to global social media entertainment. New York City, NY: New York University Press. https://doi.org/10.18574/nyu/9781479890118.003.0016        [ Links ]

Beer, D. 2017. The social power of algorithms. Information, Communication & Society 20(1): 1-13. DOI: https://doi.org/10.1080/1369118X.2016.1216147.         [ Links ]

Beheshti-Kashi, S., Lutjen, M. & Thoben, K.D. 2018. Social media analytics for decision support in fashion buying processes. Artificial Intelligence for Fashion Industry in the Big Data Era 71-93. DOI: https://doi.org/10.1371/journal.pone.0216408.         [ Links ]

Bhandari, A. & Bimo, S. 2022. Why's everyone on TikTok now? The algorithmized self and the future of self-making on social media. Social Media + Society 8(1): 1-11. DOI: https://doi.org/10.1177/20563051221086241.         [ Links ]

Conner, C.T. 2022. Situating sub-cultures. Studies in Symbolic Interaction 54: 1-6. DOI: https://doi.org/10.1108/S0163-239620220000054001.         [ Links ]

Costas, R., Rijcke, S.D. & Marres, N. 2020. 'Heterogeneous couplings': Operationalizing network perspectives to study science-society interactions through social media metrics. Journal of the Association for Information Science and Technology 72(5): 595-610. DOI: https://doi.org/10.1002/asi.24427.         [ Links ]

Cunningham, S. & Craig, D. 2021. Creator culture: An introduction to global social media entertainment. New York, NY: New York University Press. https://doi.org/10.18574/nyu/9781479890118.001.0001        [ Links ]

Diaz-Faes, A.A., Bowman, T.D. & Costas, R. 2019. Towards a second generation of 'social media metrics': Characterizing Twitter communities of attention around science. DOI: https://doi.org/10.1371/journal.pone.0216408.         [ Links ]

Dragos, V., Forrester, B. & Rein, K. 2020. Is hybrid AI suited for hybrid threats? Rustenburg: IEEE Xplore. (23^rd International Conference on Information Fusion; 6-9 Jul.         [ Links ])

Etter, M. & Ablu, O.B. 2020. Activists in the dark: Social media algorithms and collective action in two social movement organizations. Organization 28(1): 68-91. DOI: https://doi.org/10.1177/1350508420961532.         [ Links ]

Falgoust, G., Winterlind, E., Moon, P., Parker, A., Zinzow, H. & Madathil, K.C. 2022. Applying the uses and gratifications theory to identify motivational factors behind young adults' participation in viral social media challenges on TikTok. Human Factors in Healthcare 2: 1-13. DOI: https://doi.org/10.1016/j.hfh.2022.100014.         [ Links ]

George, T. 2022. Semi-structured interview. Definition, guide & examples. Available at: https://www.scribbr.com/methodology/semi-structured-interview/ [Accessed on 10 August 2022].         [ Links ]

Geyser, W. 2022. TikTok statistics - 63 TikTok stats you need to know 2022 update. Available at: https://influencermarketinghub.com/tiktok-stats/#toc-2 [Accessed on 11 August 2022].         [ Links ]

Giardino, J. 2021. Social media influencer motivation: Exploring what drives micro-celebrities to produce content using social exchange theory. Rochester: Rochester Institute of Technology.         [ Links ]

Green, D., Xanshunta, L.P., Arnold, J., Chester, C. & Matthews, J. 2022. The rise of TikTok: A case study of the new social media giant. Management and Economics Research Journal (8)1: 1-5. DOI: https://doi.org/10.18639/MERJ.2022.9900063.         [ Links ]

Kang, H. & Lou, C. 2022. AI agency vs. human agency: understanding human-AI interactions on TikTok and their implications for user engagement. Journal of Computer-Mediated Communication 27(5): 1-13. DOI: https://doi.org/10.1093/jcmc/zmac014.         [ Links ]

Kemp, S. 2022. Digital 2022: South Africa. Available at: https://datareportal.com/reports/digital-2022-south-africa [Accessed on 20 August 2022].         [ Links ]

Kemp, S. 2022. TikTok trends and statistics. Available at: https://datareportal.com/essential-tiktok-stats?utm_source=DataReportal&utm_medium=Country_ Article_Hyperlink&utm_campaign=Digital_2022&utm_term=South_Africa&utm_ content=Facebook_Stats_Link [Accessed on 20 August 2022].         [ Links ]

Kim, J. 2020. The meaning of numbers: Effect of social media engagement metrics in risk communication. Communication Studies 72(2): 195-213. DOI: https://doi.org/10.1080/10510974.2020.1819842.         [ Links ]

Klug, D., Qin, Y., Evans, M. & Kaufman, G. 2021. Trick and please. A mixed-method study on user assumptions about the TikTok algorithm. United Kingdom: ACM (13^th ACM Web Science Conference; 21-25 Jun.). https://doi.org/10.1145/3447535.3462512        [ Links ]

Leong, T.K. & Meng, T.P. 2022. Antecedents of trust and its impact on online social shopping purchase intention based on social exchange theory. Journal of Business and Technology Management 4(3): 20-38. DOI: https://doi.org/10.55057/ijbtm.2022.4.3.3.         [ Links ]

Maphathe, T.L. 2021. Social media marketing and customer engagement: A focus on small and medium enterprises (SMEs) in South Africa. Journal of Contemporary Management 18(1): 48-69. DOI: https://doi.org/10.35683/jcm20065.96.         [ Links ]

Markham, A.N. & Gammelby, A.K. 2018. Moving through digital flows: An epistemological and practical approach. In: Flick, U. (ed.) The SAGE Handbook of Qualitative Data Collection. London: SAGE. https://doi.org/10.4135/9781526416070.n29        [ Links ]

O'Connor, A., Shen, L. & Manley, N. 2017. The dilemma of prisoners on TikTok: An analysis of the implications of dark sub-cultures online. Available at: https://mastersofmedia.hum.uva.nl/blog/2020/10/20/the-dilemma-of-prisoners-on-tiktok-an-analysis-of-the-implications-of-dark-sub-cultures-online/ [Accessed on 10 August 2022].         [ Links ]

Ozelturkay, E.Y. & Yarimoglu, E. 2017. How and why consumers use social media: A qualitative study based on user-generated media and uses & gratifications theory. Available at: https://www.researchgate.net/publication/321105049_How_and_why_consumers _use_social_media_A_qualitative_study_based_on_User-Generated_Media_and_Uses_Gratifications_Theory [Accessed on 10 August 2022].         [ Links ]

Park, H., Kim, J. & Lee, K. 2022. Interplay between TikTok & Generation Z: The trickle-up effect of #thriftflip. Virtual: ITAA (78^th International Textile and Apparel Association Annual Conference; 3-6 Nov.). https://doi.org/10.31274/itaa.13735        [ Links ]

Pencarelli, T. & Mele, M.G. 2020. A systematic literature review on social media metrics. Mercati & Competitività1: 16-38. https://doi.org/10.3280/mc1-2019oa7624        [ Links ]

Poecze, F., Ebster, C. & Strauss, C. 2018. Social media metrics and sentiment analysis to evaluate the effectiveness of social media posts. Procedia Computer Science 130: 660-666. DOI: https://doi.org/10.1016/j.procs.2018.04.117.         [ Links ]

Reynolds, R. & Kinnaird-Heether, L. 2019. Diversity as a necessity for sustainability in cultural systems: Collective problem-solving in cultural algorithms. Laguna Hills: IEEE Xplore. (1^st International Conference on Humanized Computing and Communication; 25-27 Sep.). https://doi.org/10.1109/HCC46620.2019.00012        [ Links ]

Richards, T. 2020. Investigating the determinants behind advertisement-avoidance on social networking sites by Generation Z students in Durban, South Africa. Durban: The Independent Institute of Education.         [ Links ]

Sayed, A. 2022. Transnational consumption of Japanese popular culture in Egypt: The case of anime and cosplay sub-culture. Endowment Lands: University of British Columbia.         [ Links ]

Schreier, M. 2018. Sampling and generalization. In: Flick, U. (ed.) The SAGE handbook of qualitative data collection. London: SAGE. https://doi.org/10.4135/9781526416070.n6        [ Links ]

Skinner, J., Edwards, A. & Smith, A. 2020. Qualitative research in sport management. (Second edition). New York, NY: Routledge. https://doi.org/10.4324/9780367854249        [ Links ]

Sklar, M., Strubel, J., Freiberg, K. & Elhabbassi, F. 2021. Beyond sub-culture the meaning of style: Chronicling directions of scholarship on dress since Hebdige and Muggleton. Fashion Theory 26(6): 715-735. DOI: https://doi.org/10.1080/1362704X.2021.1954834.         [ Links ]

Tracy, S. 2020. Qualitative research methods: Collecting evidence, crafting analysis, communicating impact. Hoboken, NJ: John Wiley and Sons.         [ Links ]

Vänskä, A. 2018. Fashion and sub-cultures. From the sidewalk to the catwalk. Available at: https://mycourses.aalto.fl/pluginflle.php/901575/mod_resource/content/1/AMV_ lecture%2023%20January%202019_Fashion%20in%20culture_Fashion%20and%20sub-cultures.pdf [Accessed on 20 August 2022].         [ Links ]

Veletsianos, G. & Kimmons, R. 2016. Scholars in an increasingly open and digital world: How do education professors and students use Twitter? Internet and Higher Education 30: 1-10. DOI: https://doi.org/10.1016/j.iheduc.2016.02.002.         [ Links ]

Winge, T.M. 2018. Do androids dream of electric sheep dressed in street fashions? Investigating virtually constructed fashion sub-cultures. In: Holland, S. & Spracklen, K. (eds). Sub-cultures, bodies and spaces: Essays on alternativity and marginalization. Bingley: Emerald Publishing. https://doi.org/10.1108/978-1-78756-511-120181003        [ Links ]

Zeithaml, V.A., Jaworski, B.J., Kohli, A.K., Tuli, K.R., Ulaga, W. & Zaltman, G. 2020a. A Theories-in-Use Approach to building marketing theory. Journal of Marketing 84(1): 32-51. DOI: https://doi.org/10.1177/0022242919888477.         [ Links ]

Ziethaml, V.A., Verleye, K., Hatak, I,. Koller, M. & Zauner, A. 2020b. Three decades of customer value research: Paradigmatic roots and future research avenues. Journal of Service Research 23(4): 1-18. DOI:https://doi.org/10.1177/1094670520948134.         [ Links ]

Zhang, M. & Lui, Y. 2021. A commentary of TikTok recommendation algorithms in MIT Technology Review 2021. Fundamental Research 1(6): 846-847. DOI: https://doi.org/10.1016/j.fmre.2021.11.015.         [ Links ]

 

 

Date submitted: 6 July 2023
Date accepted: 6 September 2023
Date published: 15 December 2023