The Technological Affordances of TikTok Shaping Usability and Engagement among Young Adults: The Case of University of Nairobi Students

The Technological Affordances of TikTok Shaping Usability and Engagement among Young Adults: The Case of University of Nairobi Students

Mabel Luseka Waliaula., Wilson Ugangu., Isaac Mutunga

Department of Journalism and Media Studies, Multimedia University of Kenya Kenya

DOI: https://dx.doi.org/10.47772/IJRISS.2025.90900070

Received: 15 September 2025; Accepted: 23 September 2025; Published: 29 September 2025

ABSTRACT

TikTok has emerged as an intriguing social media application that attracts usage especially among young users. There has also been growing research in media effects, especially with an increasing scrutiny on emerging technologies such as algorithm-driven social media applications pointing to a need for a critical look at nuanced ways in which technological affordances are enhancing the usability and engagement on social media. This study made use of the Affordance Theory and the Uses and Gratifications 2.0 framework, which is also referred to as the MAIN model, to analyze the technological affordances of TikTok that enhance its usability and engagement among young adults in Kenya using the University of Nairobi as the case study. The study was guided by the question; what are the key technological affordances of TikTok that enhance its usability and engagement among young adults? The study used the explanatory sequential design. An online survey and Key Informant Interviews (KIIs) were used to collect data. Quantitative data was analyzed descriptively using percentages, means and standard deviations while qualitative data was analyzed thematically.  The study found that the key technological affordances that enhance its usability among young adults include: its user-friendly interface characterized by its ease of use and access to content; the easy-to use and appealing video creation tools; the interactivity facilitated by the initiating and the maintaining of direct engagement between users through the FYP; community and trends as well as measurement.

Keywords: TikTok, Algorithm, AI, Technological Affordances, Uses and Gratifications 2.0, MAIN Model

INTRODUCTION

In the dynamic landscape of social media, TikTok has emerged as a captivating platform, drawing the attention of millions of users worldwide. Klug, Qin, Evans and Kaufmann (2021) characterize TikTok as a short video sharing platform that stands apart from its social media counterparts; it facilitates users’ interaction with the content and shapes further interaction around the content. TikTok’s hallmark lies in its brevity; TikTok videos range from 15 seconds to several minutes (Zhao, 2020). Since this app is constantly evolving, the video length allowed on the app has been increasing. Other social media platforms have also been integrating brevity of video content in their features, i.e., YouTube shorts, Instagram reels and Facebook stories (Klug et al., 2021; Bhandari & Bimo, 2022).

Originating from China, TikTok emerged under the name Douyin, developed by the company ByteDance. Omar and Dequan (2020) emphasize that TikTok is purposefully designed for mobile phones. Its interface, interactions, and content consumption align seamlessly with the mobile experience.   TikTok’s allure lies in its non-reliance on interpersonal interactions. Unlike follower-based platforms such as Facebook, Instagram, and X (formerly Twitter), where social connections drive content visibility, TikTok users can access content aligned with their interests without necessarily engaging with others. This unique feature fosters a sense of autonomy and serendipity. While TikTok stands apart, it also shares commonalities with other media. Klug et al. (2021) and Bhandari & Bimo (2022) liken TikTok to a content community, akin to YouTube and Instagram. In this classification, content takes centre stage, transcending individual interactions. TikTok’s vibrant ecosystem thrives on creativity, trends, and user-generated content.

Recent developments have brought TikTok under increased scrutiny, particularly regarding its technological affordances and user behaviour. The history of affordances is derived from ecological psychology where affordances were conceptualized as artefacts, objects or properties of the environment (Norman, 2013; Hafezieh & Eshraghian, 2017). The concept has been adopted and used in Information Technology and Media. As much as there appears to be no singular definition of affordances agreed upon by scholars, a look at literature characterizes affordances as underlying action possibilities that features of technologies offer to users which have the capabilities to shape user interactions and experiences (Karahanna, Xu, Xu & Zhang, 2017; Evans et al., 2016; Zhao, Liu, Tang, & Zhu, 2013).

Background Information

TikTok was initially designed specifically for teenagers and pre-teenagers but the COVID 19 pandemic distributed the usage across different age-groups through presenting TikTok as the ideal app for entertainment (Zheng, Abidin and Schaffer, 2021). Currently, TikTok usage still seems to be skewed towards young audiences (Maddox & Gill, 2023). Globally, 66% of TikTok users are below the age of 30 (Culture Intelligence, 2022). In the United States of America, the social media application has not only witnessed significant adoption in usage, but it has also become a major component making up political discussions in the country’s legislature. According to the Pew Research Centre (2024), 62 % of Americans under 30 years of age use TikTok.  As of March 2024, The Economist reported that TikTok had 170 million users in the USA who spent at least an average of 56 minutes a day using the App. This widespread usage underscores the platform’s appeal and its ability to captivate users with its unique features. The demographic data further highlights TikTok’s popularity among the younger generation.

This suggestion that TikTok is particularly resonant with younger audiences is quite intriguing especially when explored in the context of the findings of the Youth Survey 2022 which showed that 60% of Africa’s population comprises of young people below the age of 25 (AfricaNews, 2022). TikTok’s influence extends significantly into the African continent, with notable usage in countries such as Nigeria, South Africa and Kenya. In 2023, Kenya’s TikTok usage was ranked top globally at 54% by the Reuters Institute Digital News report (Oluwole, 2023). Also, in 2023 there were calls on the Kenyan government to ban TikTok through a petition presented to Kenya’s parliament by Bob Ndolo. The petition cited issues of morality, promotion of violence, hate speech etc. as concerns that accompanied the use of TikTok. In addition, a 2022 report by Mozilla Foundation linked TikTok to the gaslighting of political tensions ahead of the 2022 general election (Madung & Mozilla Foundation, 2022).

The discourse surrounding TikTok is complex, encompassing issues such as the unique affordances of TikTok driven by its AI powered algorithm. This has significant implications for understanding the uses and gratifications of TikTok. It becomes important to seek an understanding of the affordances of TikTok that make its usage popular. This study delved into the technological affordances that drive TikTok’s popularity among young adults. The choice of university students was influenced by the fact that the demographics of the usage of TikTok tend to skew towards younger audiences (Vaterlaus & Winter 2021; Culture Intelligence, 2022). Nwafor (2024) agrees and notes that, universities provide auspicious settings for exposure to different technologies, diversity in terms of cultural exchange and an academic environment that fosters digital literacy. This made university students suited for analysis of technological affordances and the gratifications associated with TikTok usage. This study aspired to uncover the nuances that make TikTok a compelling digital playground for the youth.

Q1 What are the key technological affordances of TikTok that enhance its usability and engagement among young adults?

LITERATURE REVIEW

The Uses and Gratifications Theory (UGT) has been a dominant framework in media studies, characterizing discussions around media usage for several decades. This theory emerged as a pivotal paradigm in media uses and effects research, challenging the traditional notion of immediate, unidirectional, and powerful media effects. UGT posits that media users are not merely passive recipients of media messages that could influence them in a direct and potent manner. Instead, it underscores the active role of media users in the consumption process (Katz & Blumler, 1973). According to the Theory, users’ intrinsic needs significantly shape their media use, influencing their choice of media, and determining their continued use or disuse thereof. For decades, this theoretical perspective has significantly contributed to the general understanding of how individuals interact with media platforms and the gratifications they derive from such interactions.

Nevertheless, technological enhancements have led to the development of digital affordances that were not available during the 50s and 70s when these theoretical aspects were being developed. There has therefore been a need to explain media use in the era of technological advancements that have created new media. The MAIN model or uses and gratifications 2.0, is an affordance-driven framework that attempts to integrate social media affordances into literature on uses and gratification. The model proposes that social media gratifications are not determined by intrinsic needs but are rather based on the structure of the medium. This model categorizes these affordances into four categories namely; mode, agency, interactivity and navigability. These join to form the acronym MAIN, hence the term MAIN model. Sundar and Limperos (2013) argue that affordances are instrumental in shaping users’ media experience, leading to a set of gratifications that are uniquely tailored to the capabilities of new media platforms. The Theory of Uses and Gratifications 2.0 was particularly apt for the study because its emphasis on affordances aligns seamlessly with the study’s focus on the technological affordances of TikTok. The theory helped this study to systematically dissect the specific actions that the affordances of TikTok enable, enhancing its likeability and use among young adults. The theory’s adaptability to new media contexts ensured that the research remained grounded in contemporary media usage patterns.

Rathnayake and Winter (2018) applied this framework in their research on college students examining the gratifications that were derived from use of a range of social media such as Facebook, X (formerly Twitter), YouTube, Tumblr, Instagram and WordPress. Rathnayake and Winter (2018) used the Survey instrument to examine affordances of social media as interactive platforms. In Malaysia, Nordin et al. (2021), made use of the UGT 2.0 Framework to analyze how technological affordances on Facebook shape the adoption of new innovations by Paddy farmers. Nordin et al. (2021) found that modality, agency, navigation and interactivity-based affordances influenced the uptake of new information on farming processes.

As observed by Nordin et al. (2021), there appears to be a shift in concepts around media uses from the innate, intrinsically motivated media choices of users to the idea of affordance-driven choices. Therefore, social media use is not only determined by the users’ needs but also the affordances. It is this shift in both possibilities of usage and the perceived sources of motivation for media use that has brought into the effects research the angle that social media uses and gratifications depend on the design and the structure of the platform. This assertion of medium-related factors that shape media usage can be closely linked to the affordance school of thought. The main idea in the Affordance framework is that affordances that are in existence must be perceived by users and actualized for them to influence and affect the users. According to Pozzi et al. (2014), the Information Technology concept of affordances was conceived as follows, affordance existence, affordance perception, affordance actualization and the affordance effects.

Vaast, Safadi, Lapointe and Negoita (2017) applied the Affordance framework to analyze how twitter (now X) was used to facilitate what they term as connective action during the 2010 oil and gas explosion on the Gulf of Mexico. Vaast et al. (2017) explored how the affordances of X enabled users to share and even generate content at low technical and financial costs yet attaining the collective success of making the event linger in the general consciousness of the public as the most micro-blogged topic of 2010.In addition to that, Sedalo, Boateng & Kosiba (2022) grounded their case study of social media use in small and medium-sized enterprises (SMEs) in Ghana on the Affordance Framework. Through use of thematic analysis, they identified brand visibility, relationship-building and sharing as the possibilities that facilitated the acquisition of customers by SMEs. To provide  context that and prevent the oversimplification that could arise from  the affordance framework, this study made use of both the Affordance framework and the MAIN model which provided concrete categories that could be used to discuss the affordances of TikTok.

Digital Affordances and Features

Zhao et al. (2013)and  Evans (2016) differentiate between features and affordances based on the user perception and affordances. Features are referred to as the specific, inherent functionalities or characteristics of digital technologies which are a representation of what the systems can do. Affordances emerge from technological features, but they are not synonymous with them. They are dependent on user perception and context. Contextual variations could impact affordance perception. The action possibilities(affordances) are shaped by design features of the technology as well as the context in which these technologies are used (Evans et al., 2016). Affordance shapes the user interaction as well as the user experience. Affordances transcend the specific functionalities of features. Affordances include not just the function capabilities of the technologies, but the way users perceive and utilize these capabilities (Zhao, et al., 2013).

Zhao et al. (2013) analyzed the relationship between affordances, features and gratifications by explaining that users draw specific gratifications from social media use due to the unique affordances that shape user needs. This therefore calls for a consideration of platform specific features in understanding social media usage. In other words, users perceive and make use of affordances to meet their needs and therefore obtain gratifications. Karahanna et al. (2018) accept the proposition that affordances are underlying action possibilities emerging from social media. However, they diverged from the idea of affordance-determined gratifications and advanced that it is the innate needs of the users that drive users to use the affordances.

Several studies have adopted the affordance framework to explain various phenomena around technological affordances. Zhao and Wagner (2022) investigated how the affordances of TikTok lead to the continued usage of TikTok by its users; a phenomenon they referred to as the user flow. The user flow  identifies three affordances of perceived effortlessness, perceived accuracy and serendipity as the algorithmic based affordances that lead to continued TikTok use. Lee Sun and Wong (2023) investigated and compared how users interact with the affordances of TikTok and YouTube and concluded that, with content relevance as the mediator, the affordances of both TikTok and YouTube impact user experience on those platforms. Ebrahim and Tanner (2023) used a case study which employed interviews and observation to investigate how the actualization of TikTok’s affordances can be used to challenge societal stands about women and beauty. It was concluded that TikTok affordances provided opportunities for women to associate, edit, share and browse through content which contributes to their sense of self-representation.

Technological Affordances of TikTok

According to Rathnayake and Winter (2018), affordances are features of a platform that enable, restrain or constrain user-behavior. Since users can perceive these affordances, their mere existence can influence the way in which users interact with the platform. Karahanna et al. (2018) acknowledge that even though the psychological needs of individuals are innate, social media provides underlying action possibilities that influence media use. Technological features provide a foundation upon which these underlying action possibilities or affordances are built (Vaast et al., 2017). There is an intricate relationship between features of technologies, technological affordances and ultimately the gratifications of social media use. It is apparent that features of TikTok are evolving. However, features of TikTok such as the algorithmically- powered For You Page (FYP), the predominance of the short video content, the multimedia editing options, duet feature etc. dominate scholarly discussions on the key features of TikTok.

User-Friendly Interface

Zheng et al (2021) identify the FYP as the primary interface that users make use of to interact with content on TikTok. Omar and Dequan (2020) note that TikTok’s mobile app is generally easy to navigate. TikTok can be accessed through smartphones, in portrait mode getting the optimum viewing experience without requiring the tilting of the phone (Montag, Yang and Elhai , 2021). TikTok’s FYP initiates a process that observes and eventually reinforces users’ consumption interests (Schellewald, 2023). Once that is established, the ease with which users access the rest of the recommendations is facilitated through scrolling (Zhao, 2020). These aspects of TikTok converge to offer users action possibilities that are unique and interesting. Navigation on TikTok is easy as it enables participants to easily interact with and make changes to the content on the app. Under UGT 2.0, Navigation refers to affordances that make it easy for users to move within the platform. Nordin et al. (2021), observe that visual platforms such as TikTok present content in video form, a form users find appealing. The concept of social media affordances based on visual appeal is referred to as modality under the MAIN Model by Sundar (2008) and Sundar and Limperos (2013). Vaterlaus and Winter (2021) explain that videos are more appealing to human perceptual organs as compared to text because videos appear to be more real(realism).

Video Creation Tools

Under UGT 2.0, the ability of social media users to be sources of content is known as agency. Features of TikTok that are considered distinct such as the short video-format, soundtrack challenges, duet, stich feature, built-in filters, stickers, popular music etc. create a multimedia context of visuals that not only create an appealing viewing experience but also enables TikTok users to become sources of content (Lu & Lu 2019; Montag et al, 2021). This content, which is mostly in the form of short videos, is sometimes referred to as memes (Vaterlaus & Winter, 2021). Memes are media that are shared, imitated and interpreted across the internet (Stern et al . 2020 cited in Vaterlaus & Winter, 2021).

Brenzil (2022) notes that not everyone on social media is there to be an agent of content sharing as only 1% of social media users are content creators; 90% of social media users are said to be simply observers while 9% are active participants. However, as Vaterlaus and Winter (2021) note, even for those who are on TikTok to just observe or even participate, actions such as viewing videos, liking and scrolling are easy to accomplish on TikTok courtesy of the video- creation tools (Vaterlaus & Winter, 2021).

Interactivity

Affordances that allow users to make real-time changes to the content within a social medium are classified under the interactivity category of affordances (Sundar & Limperos ,2013; Rathnayake & Winter, 2018).On TikTok, the algorithm relies on user profile information and interactions, such as likes and comments, to curate content for the users (Kanthawala, Cotter, Foyle, & DeCook,  2022). As Bhandari and Bimo (2022) and Klug et al. (2021) explain, the Algorithm on TikTok initiates the interactive process between the FYP and the users to determine the content to be curated for them. In other words, TikTok’s algorithm can respond to the users’ demands.

The interaction between users and the algorithm facilitates the creation of personas of the users that are used to recommend content that is likely to please them hence retaining them on the app. Zhao (2021) notes that since these algorithms are driven by artificial (AI), the content orientation aspects create powerful distribution strategies that make users receive personalized content based on their personalities. The implication of the direct engagement between TikTok users and the algorithm through the FYP ensures that the users get what Bhandari and Bimo (2022) refer to as ‘hyper-personalized’ content. That is, content that accurately predicts the personalities and interests of the users. Zhao (2021) underscores that, the more people use TikTok, the more accurate their recommendations become. According to Vaterlaus and Winter (2021), TikTok’s primary function appears to be facilitating interaction with video content. It has been identified that algorithmic recommendations of TikTok are the key feature of TikTok (Schellewald, 2021).

Community and Trends

TikTok is not to be viewed as a networking site. However,  TikTok technologies are networked, creating communities that are networked and imagined leading to formation of collectives of users because of their shared interests (Maddox & Gill, 2023). Krutrök (2021) and Maddox & Gill (2023) agree that the TikTok algorithm functions in such a way that it assigns people to different communities based on their interests. In view of that, Maddox and Gill (2023) show that there are distinct platforms on TikTok such as BookTok, FoodTok, KinkTok, JewTok etc. which have come to be referred to as different sides of TikTok (Maddox & Gill, 2023; Kaye et al., 2022). These communities function as affordances that bring people together around shared interests (the sides).Trends on the other hand refer to creative formats, ideas as well behavior that gain specific attention on a platform (Lee & Abidin, 2023). These ideas and creative formats which are characterized by features like hashtags, audio clips, viral challenges have been found to define the way that users interact with the content on the app (Brenzil, 2022). TikTok relies on trends to not only keep users engaged on the platform but also entertained.

Measurement

On TikTok, algorithmic rankings provide ways for ordering and categorizing content; these ultimately shape the way in which users interact with content on the platform (Abidin, 2021).

Since TikTok gives preference to recommendations based on user interests instead of networks, ranking is key in determining how the algorithm structure content on the FYP of users. (Bhandari & Bimo, 2021)

These algorithmic rankings are not strictly ordered sequentially nor chronologically but are based on an assessment of the numbers that content garners through engagement (Abidin, 2020). Klug et al. (2021) propose the ‘partitioned bucket strategy’, a method thought to be used by TikTok’s algorithm to ascertain the probability of content to trend based on views, viewing completion, likes as well as shares. According to Zhao (2021), views, shares, replays and completion rates are effective indicators that are used to recommend content to users. The recommendation strategies form a closed loop relationship between the algorithm, the recommendations it creates and possible addiction tendencies in the users of TikTok. The more people use TikTok, the more TikTok understands them which will consequently create recommendations that will keep them using TikTok.

However, algorithmic ranking systems on TikTok do not just provide ways of ordering content and influence how users relate to the platforms; the inner workings of the TikTok algorithm shape the way users feel within the different communities of TikTok (Lagerkvist, 2019; Abidin, 2020). As Lagerkvist (2019) expounds, quantifying measures have a way of shaping how individuals feel in their personal lives, for instance the feeling of support during mourning. Maddox and Gill (2023) found that users who were mourning examined engagement on the content shared through assessing the number of views, comments and shares to determine the level of support.

METHODOLOGY

The study used the explanatory sequential design (ESD) mixed method. The study used the case of university of Nairobi students to understand specific instances, the context as well the unique insights about TikTok usage among young adults in Kenya. A survey component was incorporated because this study aimed to make use of data from a large and diverse sample size for generalisability. The study site was Nairobi County. The target population for the study were the undergraduate government sponsored students at the University of Nairobi because most undergraduate university students share the same age range as the established age range of TikTok users. Also, the university of Nairobi has a large and diverse students’ body comprising 27,795 government sponsored students (University of Nairobi, 2024). In addition to that, the location of the university in the city is likely to make it a hub of technology adoption hence making it an ideal location for investigating TikTok usage.

The sampling frame for this study comprised of all the government sponsored students at the university of Nairobi who were registered for the September 2024 to January 2025 session of the 2023/2024 academic year. For the quantitative phase, a sample size of 379 students was selected in reference to the Krejcie & Morgan Table. The sample for the qualitative part of the study was drawn from a population of 180 students of UoN who make up the membership of the IEEE  (Institute of Electrical and Electronic Engineers). Specifically, cluster sampling was used based on the 10 faculties of the University of Nairobi to ensure that the sample selected is representative. Proportionate sampling was then used based on the number of students available in each faculty. After that, purposive sampling was used to identify 5 key informants with a background in Technology to participate as key informants. In addition, 2 TikTok influencers were selected as key informants through snowball sampling. The number of participants was considered ideal because of their knowledge of emerging technologies and the fact that they share the same age demographic as most users of TikTok

With the help of student- research assistants, questionnaires were administered online (online survey) where a link was provided to the respondents to access the questionnaire. Thereafter, key informant interviews were used to collect qualitative data. Questionnaires were used to find out how young adults perceive and interact with the technological affordances of TikTok. Subsequently, key Informant Interviews were conducted with 5 participants from IEEE and 2 influencers to elaborate on the findings from the questionnaire responses. Ethical guidelines were considered by the researcher. For instance, approval was sought from the relevant bodies, identities of respondents were protected and informed consent was sought.

FINDINGS

A link to the online survey was shared with 379 undergraduate students at the university of Nairobi with the help of a research assistant. The response page in the application recorded a total of 379 responses which translated to 100% response rate. The high response rate could be attributed to the researcher maintaining active engagement during the process. The researcher understood that the sampled population is accustomed to short form multimedia content and may be prone to distractions. Therefore, the researcher not only shared the link with the respondents but also maintained an observational presence to mitigate delays and the possibility of respondents failing to submit the filled survey. Holtom et al. (2022) examined trend rates in surveys between 2011 and 2020 and found that researcher presence and participant motivation boast the response rate in surveys. In the qualitative phase of the study, the 7 participants sampled through purposive and snowball sampling participated in the interviews.

Demographic Information

The study assessed demographic information among young adults, including gender, age, and TikTok usage duration, to understand the characteristics of users and how these factors influence the technological affordances and gratifications derived from TikTok use. The sample was almost evenly split between male and female respondents, with males comprising 50.1% (n=190) and females 49.9% (n=189). This balanced gender representation ensured that the study captured diverse perspectives on TikTok use and gratifications across both genders. Most respondents (71.5%) were within the 17-21 age group, followed by 19.3% in the 22-23 age range, 6.9% aged 24-25, and only 2.4% aged over 25 years. The age range is consistent with the typical age bracket of undergraduate university students in Kenya especially for those who transition directly from secondary school to university. The high representation of users aged 17-21 suggests that this demographic finds TikTok’s technological affordances and gratifications particularly appealing. The results also align with those of Brenzil (2022) who found that the dominant demographic on TikTok is composed of young adults.

Regarding TikTok usage experience, 43% of respondents had been using TikTok for a year, while 32.2% had been using it since its introduction in 2019. Additionally, 22.7% had used TikTok for six months, and a small fraction (2.1%) had been on the platform for over two years. These findings indicate that most respondents have substantial experience using TikTok, with a significant portion (75.2%) engaging with the platform for at least a year. This suggests that respondents have had enough exposure to TikTok’s technological affordances to provide meaningful insights into the gratifications they derive from its use.

User-friendly Interface

This study assessed respondents’ perceptions of TikTok’s interface, focusing on ease of access, personalized content recommendations, and the evolving relevance of the “For You Page” (FYP) algorithm. Table 4.1 presents the findings on the user-friendly interface and its impact on usability and engagement.

Table 4.1 User-friendly interface

The overall user-friendly interface was rated positively (3.74, SD = 0.73), with low variability in responses, indicating a generally consistent perception among users. These findings highlight TikTok’s ability to provide an intuitive experience while improving content relevance through algorithmic adjustments, reinforcing its role in sustaining user engagement. This aligns with the proposition by Schellewald (2023) and Zhao (2020) that TikTok initiates a process that observes and eventually reinforces users’ consumption interests facilitating  the ease with which users access recommendations of content which is facilitated through scrolling.

Video Creation Tools

The study assessed video creation tools on TikTok, focusing on aspects such as the availability of soundtracks, filters, multimedia features, ease of creating videos in portrait mode, and the overall motivation derived from using TikTok’s creative tools. Findings are presented in Table 4.2.

Table 4.2 Video Creation Tools

The results indicated in Table 4.2 showed that users generally found TikTok’s soundtracks, filters, and multimedia tools effective in simplifying video creation, as indicated by a high mean score (4.04, SD = 0.80). This suggests that TikTok’s built-in editing features significantly contribute to a seamless content creation experience. Overall, video creation tools were rated positively (3.88, SD = 0.62), with a low standard deviation indicating a generally consistent perception among users. These findings highlight TikTok’s effectiveness in providing accessible and engaging video creation features, reinforcing its appeal among young adults.

The sentiments of R6 regarding the portrait mode of recording appear to be explaining these findings.

“I also find the portrait mode that allows creating videos without tilting the phone impressive. I noticed that the TikTok app appears to have just been created with portrait mode shooting in consideration. When you use landscape mode, the video  does not fit properly on the entire screen, it leaves space. However, I do not use the portrait much because I record comical skits which have more actors and landscape mode is therefore ideal in my case because it has more room for more people.”

Interactivity

The study assessed interactivity on TikTok, focusing on aspects such as access to preferred content without following users, the impact of the For You Page (FYP) on user experience, and users’ understanding of engaging with the algorithm. Findings are presented in Table 4.3.

Table 4.3 Interactivity

Overall, interactivity on TikTok was rated positively (3.60, SD = 0.89), indicating that TikTok’s algorithm and engagement mechanisms contribute to an interactive user experience. However, the moderate standard deviation implies some level of inconsistency in user experiences, possibly influenced by individual familiarity with the platform’s recommendation system. These findings highlight TikTok’s success in creating a personalized, interactive environment while also suggesting potential areas for improvement in user guidance on optimizing content recommendations.

Community and Trends

The study assessed community engagement and trend awareness on TikTok, focusing on users’ sense of connection with others, the role of memes in trend discovery, and the interest generated by interactions such as views, shares, and comments. Findings are presented in Table 4.4.

Table 4.4 Community and Trends

Overall, community and trend engagement on TikTok was rated positively (3.75, SD = 0.87), indicating that users generally perceive TikTok as an effective platform for staying updated on trends and engaging with like-minded individuals. However, the moderate standard deviation suggests variability in user experiences, with some feeling a stronger sense of connection and trend awareness than others. These findings underscore TikTok’s role as a key platform for community building and trend dissemination, while also suggesting potential opportunities to enhance user engagement through more personalized community-driven features.

Measurement

The study assessed how users evaluate TikTok videos based on popularity metrics such as views, shares, and likes. Findings are presented in Table 4.5.

Table 4.5 Measurement

Overall, the measurement of TikTok video popularity was rated moderately (3.22, SD = 0.84), suggesting that while popularity metrics influence content consumption, users do not rely solely on them to assess content quality or creator appeal. These findings highlight the role of algorithm-driven visibility on TikTok as described by Zhao (2020) and Lu and Lu (2021).However, whereas this study suggests room for personalized recommendations that prioritize content relevance over raw engagement statistics, there are studies that suggest that algorithmic recommendations are not just centered on meeting user interests, but they possess capabilities of making users get addicted to the content they are continuously exposed to (Krutrök, 2021; Zhao, 2021 ; Schellewald, 2021). 

DISCUSSION

The findings of the study highlight the important role of the technological affordances of TikTok in shaping the usability and the likeability of the App among young adults. To begin with, the  findings highlight TikTok’s ability to provide an intuitive experience while improving content relevance through algorithmic adjustments, reinforcing its role in sustaining user engagement. Similarly, Schellewald (2023) and Zhao (2020) found that TikTok initiates a process that observes and eventually reinforces users’ consumption interests facilitating  the ease with which users access recommendations of content which is facilitated through scrolling. Furthermore,  TikTok was found to be effective in providing accessible and engaging  video creation tools which reinforce its appeal among young adults. The findings align  with the findings of Vaterlaus and Winter (2021) who note that actions such as viewing videos, liking and scrolling are easy to accomplish on TikTok courtesy of the video- creation tools.

Furthermore, findings indicate that TikTok facilitates direct user engagement, though inconsistencies in user experiences were observed. So, while the algorithm is effective in engaging with users to shape their  recommendations, it differs for some possibly influenced by the perception of users on how the algorithm works. This aligns with the Affordance Theoretical framework that suggests that affordances must be perceived by users before they can be actualized to produce any effect on the users. The pointing of the findings to the fact that users may not be passive partakers of the algorithmic recommendations suggests that even though the FYP tries to dictate and limit the users’ choices by initiating and relentlessly attempting to reinforce control of the content recommendations, it may not always work. As Klug et al. (2021) found, experienced users of TikTok believed they were able to tweak their usage to get the algorithm to work in their favor. This aligns with Scharlach and Hallinan (2023) who argue that even though technological affordances could be a determinant in influencing gratifications, the role of everyday users of technologies in constructing and challenging the significance of these technological affordances cannot be overlooked. However, Bhandari and Bimo (2022) disagree and view algorithmic determinism as a threat to user agency on TikTok. According to Bhandari and Bimo (2022), even what appear as autonomous choices on TikTok could easily be because of pre-determined and algorithmically influenced strategies meant  to impose on users’ algorithmic recommendations disguised as user-influenced personal recommendations. In his ethnographic study on TikTok use among Uk young adults, Schellewald (2021) found that sometimes young TikTok users perceived the FYP’s  recommended content as  an attempt to project personalities on them based on their demographic information.

Moreover, findings suggest that users generally perceive TikTok as an effective platform for staying updated on trends and engaging with like-minded individuals. However, some users experienced a more sense of connection and trend awareness than others. The findings are compatible with those of Cotter et al. (2022) and Jones (2023) who observe that, when it comes to community and identity formation on TikTok, TikTok has established itself as a medium not so keen on fostering social connections between people as it is focused on bringing people closer to their interests, an idea that Schellewald (2023) sides with. The study assessed how users evaluate TikTok videos based on popularity metrics such as views, shares, and likes. The study found that popularity metrics influence content consumption on the App just to a certain extent because TikTok users did not solely depend on the algorithm to determine content quality and creator appeal. However, Zhao (2020) and Lu and Lu (2021) found that algorithm-driven visibility on TikTok is mostly controlled by the FYP through measurement.

CONCLUSION

From the findings discussed, it can be concluded that TikTok as a platform has technological affordances that promote its usability and engagement among young adults. Basically, the user-friendly interface of TikTok provides an intuitive experience while improving content relevance through algorithmic adjustments, reinforcing its role in sustaining user engagement. TikTok’s built-in video creation tools appear to contribute significantly  to a seamless content creation experience through acting as a motivation for content creation. The fact that the algorithm tries to make content alienated from the user means that users can directly engage with the algorithm to curate their personalized recommendations without having to follow other users. This concurs with Schellewald (2021) who found that TikTok users found the  algorithmically- powered FYP relatable in terms of provision of authentic and relatable content that aligned with their preferences.

In addition to that, TikTok’s algorithm gives prominence to  trends which facilitate creativity on the App. However, some users may interpret trends as a call to conform and be a certain way when engaging on the platform. Popularity metrics such as views, shares and likes are important in the algorithm-driven recommendations that shape content consumption on TikTok, but they are not the sole determining factor of how users engage with content

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