Microlearning Factors to Impact the Digital Literature Capabilities in the TikTok Application

Microlearning Factors to Impact the Digital Literacy Capabilities in the TikTok Application Jan Kresna Petra Davinnov School of Information System Bina Nusantara University Jakarta, Indonesia [email protected] Drajad Wiryawan School of Information System Bina Nusantara University Indonesia [email protected] Abstract—In today's digital era, microlearning has emerged as a transformative educational method. Social media platforms such as TikTok are also used as intended microlearning, where various information is delivered in a short and engaging format, either audio or video. This research intends to explore the various microlearning factors in the TikTok application, such as Content, Duration, Flexibility, and Engagement, in increasing the Digital Literacy Skills of its users. This study uses a Quantitative Method with the Extended Technology Acceptance Model (TAM). This study applies the Krejcie-Morgan formula for data collection by distributing questionnaires online, resulting in a sample size of 400 respondents. The results of the validity and reliability tests on the data show that all existing variables are declared valid and reliable. The analysis reveals that Cronbach's Alpha (CA) exceeds 0.7 and the Average Variance Extracted (AVE) exceeds 0.5. Based on the Structured Equation Modelling (SEM) analysis on SmartPLS software, results showed that micro-learning factors, including Content (C), Duration (D), Flexibility (F), and Engagement (E), had a significant impact on the Digital Literacy Capabilities (DLC) of its users. The T Statistic value exceeds the 95% confidence level (1.96), indicating statistical significance. This study shows that users of the TikTok application highly value concise and engaging audio and visual content. Then, when users use the application, Flexibility and Engagement make the TikTok application a micro-learning tool for all its users. This research aims to facilitate the effective utilization of digital platform content to enhance digital literacy skills and to support the design of relevant learning materials for individuals engaged in the current and future digital era. Keywords—Microlearning, TikTok, Digital Literacy Capabilities, Education, Technology Acceptance Model I. INTRODUCTION In the rapidly evolving landscape of education, using new learning methodologies in technology has become increasingly vital. One of the most significant trends in recent years is microlearning, which involves delivering content in small, focused segments that are easy to digest and retain. This educational approach aligns well with contemporary learners' preferences, particularly in an era characterized by short attention spans and fast-paced absorption of information, which leads to learners needing a more engaging learner experience [1]. Microlearning has enhanced knowledge retention, reduced cognitive overload, and fostered Engagement among learners, making it a compelling strategy for modern education [2]. The rise of social media platforms has further transformed how educational content is delivered and consumed. Among these platforms, TikTok has emerged as a particularly influential tool in showing us what an evolving mode of communication on social media looks like [3]. A study shows that TikTok rose to popularity during lockdown as a means of escape and entertainment for young adults in the UK [4]. Initially designed for entertainment, such as dance videos and music covers. TikTok has further improved as an application that provides multiple content variations, including educative content [5]. Because of this, TikTok's short-form video format has captured the attention of millions, especially younger audiences [6]. This platform allows users to create and share videos typically 15 to 60 seconds long, making it an ideal medium for microlearning. For example, by leveraging TikTok's engaging features, educators can present complex concepts in a simplified manner that resonates with students [7]. The growing overconsumption of digital media, especially short-form videos, causes a damaging impact on users' memory due to platforms' highly engaging content in a short period [8]. Short-form videos tend to stimulate the brain to release more dopamine, leading to shorter attention spans [9]. It might lead to several problems, from absorbing misleading information to Microlearning factors, which presents a unique opportunity to enhance digital literacy capabilities among TikTok users to prevent such problems from happening [10]. Although TikTok has downsides if students overconsume them, applying the proper method can enhance their performance quickly [11]. The growing risk of being exposed to these videos Digital literacy encompasses a range of skills necessary for navigating the digital world effectively, including critical thinking, information evaluation, and the ability to produce and share content responsibly [12]. The capabilities of one's digital literacy can make a positive difference in our lives, such as improved prospects for employment [13]. For example, a study shows that by utilizing digital literacy in the TikTok application, student's understanding of a subject can significantly increase [14]. Research indicates that microlearning can significantly improve learning outcomes by allowing people to engage with content at their own pace while minimizing cognitive fatigue [15]. Effective microlearning strategies break down complex subjects into smaller, more specific topics and enhance knowledge retention [16]. Moreover, TikTok's interactive capabilities encourage creativity and collaboration among its users, further enriching their learning experiences [17]. This research investigates how impactful microlearning factors are toward digital literacy capabilities in TikTok applications. This study aims to enhance people's understanding of how microlearning effectively improves their ability to critically analyze, evaluate, and comprehend digital content on the TikTok application. Focusing on this objective can uncover 293979-8-3315-1554-6/25/$31.00 ©2025 IEEE 2025 IEEE 7th Symposium on Computers & Informatics (ISCI) | 979-8-3315-1554-6/25/$31.00 ©2025 IEEE | DOI: 10.1109/ISCI65687.2025.11167167 valuable insights into how modern educational tools can help people, including learners, develop their digital literacy capabilities to overcome obstacles [18]. II. LITERATURE REVIEW The following section outlines several relevant literature sources for this study, as identified from the research background presented in the introduction. A. TAM Model As shown in Fig.1., the Technology Acceptance Model (TAM) is a model that was first proposed by Fred Davis that specifies the causal interrelationships between External Variables, Perceived Usefulness, Perceived Ease of Use, Behavioral Intention, and Actual System Use [19]. The TAM model focuses on adopting information technology [20]. It is based on the Theory of Reasoned Action (TRA) by Ajzen and Fishbein, which has undergone some falsifications up to now [21]. Fig. 1. Davis Technology Acceptance Model After Venkatesh and Davis's refinement of the Technology Acceptance Model, many successful research studies in science and practice continue to be conducted within the framework of technology acceptance research [19]. A previous study proposed an extended Technology Acceptance Model (TAM) to examine learners' intentions to use social media as a learning platform [20]. B. External Variables After undergoing several refinements, TAM 2, proposed by Venkatesh and Davis, provides a detailed explanation of factors influencing perceived usefulness (PU) [22]. While TAM 3 explained the aspects that influence perceived ease of use [21]. A study unveils that external variables may influence the two personal beliefs proposed by Davis [23]. This research proposes external variables that include content [24], Duration [25], Flexibility [29, 30], and engagement [28]. These external variables contribute to the microlearning method [29]. By defining these external variables, the research proposed the Extended Technology Acceptance Model [30]. C. Perceived Usefulness According to Davis in 1987, Perceived Usefulness (PU) is the degree to which a person believes using a particular system will improve their performance [31]. Perceived Usefulness is pivotal in influencing an individual's attitude to accept and use a new technology [32]. If users perceive a system as applicable, they are more likely to develop a favourable attitude towards using it and, ultimately, their actual system usage. For example, a study shows that financial technology services with more effective and easy-to-use benefits will encourage MSMEs to continue using financial technology services [33]. D. Perceived Ease of Use Based on Davis's explanation in 1987, Perceived Ease of Use (PEoU) refers to the degree to which a person believes that using a particular system would be free of effort [31]. It embodies the user's subjective assessment of how simple it is to interact with the system. When designers develop a system with user-friendly features, users are more likely to recognize its functional benefits, enhancing their perception of its value [34]. Within the TAM framework proposed by Davis, Perceived Ease of Use plays a dual role [35]. First, it directly influences Behavioral Intention and then indirectly influences Perceived Usefulness. Perceived Ease of Use directly influences the user's intention to use a technology and indirectly influences the user's perceptions of how beneficial the technology is [31]. E. Behavioural Intention In the context of TAM by Davis in 1987, Behavioral Intention (BI) is a critical construct within the Technology Acceptance Model (TAM), functioning as a direct predictor of Actual System Use [31]. It refers to an individual's conscious motivation to perform a specific behaviour, in this case, using a particular technology system [36]. In the Technology Acceptance Model (TAM), Perceived Usefulness and Ease of Use directly or indirectly influence Behavioral Intention, directly affecting Actual System Use. F. Actual System Use Actual system use represents the culmination of the technology acceptance process within the Technology Acceptance Model (TAM), serving as the model's ultimate dependent variable [31]. It refers to how users engage with a particular information system or technology in a real-world setting. It can be measured variously, such as frequency or Duration or other factors depending on the nature of the technology and the context of use [37]. III. METHODOLOGY A. Proposed Model Figure 2 illustrates that this study adopts the Extended Technology Acceptance Model (TAM), which researchers developed based on the principles of the original Technology Acceptance Model proposed by Davis [38]. Fig. 2. Proposed Model 294 The model is used to evaluate connections between exogenous variables such as Content, Duration, Flexibility, and Engagement and endogenous variables such as Perceived Usefulness (PU), Perceived Ease of Use (PEoU), and Digital Literacy Capabilities (DLC). This paper attempts to analyze the significance of Content (C), Duration (D), Flexibility (F), and Engagement (E) to Digital Literacy Capabilities (DLC) through Perceived Usefulness and Perceived Ease of Use. B. Research Questions Some of the items that are the subject of this study include: a) What factors have successfully influenced users to enhance their digital literacy capabilities? b) Which factors in TikTok-based microlearning contribute most significantly to enhancing users' digital literacy capabilities? C. Data Research In this research, the respondents referred to are both TikTok users and people who live in Indonesia who have used TikTok. The researchers plan to conduct the research from December 2024 to May 2025. The respondents were 252 female (63%) and 148 male (37%). The respondents were mostly between 15-25 years old, with 279 respondents, followed by 81 respondents between 26-35 years old, 24 respondents between 36-45 years old, and 16 respondents over 45 years old. This study uses Non-probability sampling with purposive sampling. The researcher selects the sample based on the research objectives, ensuring that the population is clearly defined and that the chosen sample accurately represents those objectives. It classifies the respondents by age, domicile, and occupation at the beginning of the questionnaire. In this research, respondents must answer on a 6-point Likert scale levels (1 = strongly disagree, 2 = disagree, 3 = slightly disagree, 4 = slightly agree, 5 = agree, and 6 = strongly agree). This paper used a 6-point Likert scale to prevent neutral responses, directing the respondents to one side [39]. Because of the large population of TikTok users and the literature about the research from the Krecjie and Morgan formula to determine the sample size, the researchers distributed the questionnaire via Google Forms and collected responses from 400 respondents. [40]. D. Symbols, Variables and Sources The paper provides the symbols and sources to clarify further the variables used in this study, as shown in Table I. TABLE I. VARIABLES AND SOURCES Symbol Variable Sources C Content [24] D Duration [25] F Flexibility [29, 30] E Engagement [28] PU Perceived Usefulness [31] PEoU Perceived Ease of Use [31] DLC Digital Literacy Capabilities [12] E. Research Hypothesis In this study, this research applied eight hypotheses to 4 independent variables. It can be seen in Table II below. TABLE II. HYPOTHESIS DESCRIPTION Hypothesis Description H1 Content (C) has a significant impact on Perceived Usefulness (PU) and Digital Literacy Capabilities (DLC) H2 Content (C) has a significant impact on Perceived Ease of Use (PEoU) and Digital Literacy Capabilities (DLC) H3 Duration (D) has a significant impact on Perceived Usefulness (PU) and Digital Literacy Capabilities (DLC) H4 Duration (D) has a significant impact on Perceived Ease of Use (PEoU) and Digital Literacy Capabilities (DLC) H5 Flexibility (F) has a significant impact on Perceived Usefulness (PU) and Digital Literacy Capabilities (DLC) H6 Flexibility (F) has a significant impact on Perceived Ease of Use (PEoU) and Digital Literacy Capabilities (DLC) H7 Engagement (E) has a significant impact on Perceived Usefulness (PU) and Digital Literacy Capabilities (DLC) H8 Engagement (E) has a significant impact on Perceived Ease of Use (PEoU) and Digital Literacy Capabilities (DLC) IV. RESULT AND DISCUSSION A. Path Diagram As shown in Fig.3, the result shows the generated path diagram from Structural Equation Modeling (SEM) analysis using the SmartPLS application. Fig. 3. Path Diagram 295 B. Validity and Reliability Test Result In order to evaluate the validity, the value must be tested and compared with the Average Variance Extracted (AVE). Content (C), Duration (D), Flexibility (F), Engagement (E), Perceived Usefulness (PU), Perceived Ease of Use (PEoU), and Digital Literacy Capabilities (DLC) show the result of the AVE value exceeds the minimum value of 0.5. It means that all variables have achieved sufficient convergent validity [41]. As for testing reliability, this research utilizes Cronbach's Alpha (CA) and Composite Reliability (CR). Content (C), Duration (D), Flexibility (F), Engagement (E), Perceived Usefulness (PU), Perceived Ease of Use (PEoU), and Digital Literacy Capabilities (DLC) show that greater values imply profound reliability [41]. The table below shows the CA, CR, and AVE values for each variable part of this research model. TABLE III. CONSTRUCT RELIABILITY RESULT VAR CA CR (rho_c) AVE Result CA/CR AVE C 0.839 0.883 0.603 Rel. Val. D 0.857 0.897 0.636 Rel. Val. F 0.846 0.890 0.619 Rel. Val. E 0.812 0.876 0.639 Rel. Val. PU 0.946 0.959 0.822 Rel. Val. PEoU 0.929 0.955 0.875 Rel. Val. DLC 0.950 0.968 0.909 Rel. Val. Based on the data in Table III, all tested variables have AVE values more than 0.5. It indicates that the variables are valid. Furthermore, this research tested each variable's CA/CR value. The result shows that all variables exceed the minimum threshold of 0.7, confirming the reliability of each variable. C. T-statistic and Hypothesis Result: TABLE IV. T-STATISTIC RESULT Path Original sample (O) STDE V TStat. . result C -> PU -> DLC 0.068 0.021 3.202 Sig. C -> PEoU -> DLC 0.058 0.021 2.759 Sig. D -> PU -> DLC 0.099 0.022 4.467 Sig. D -> PEoU -> DLC 0.102 0.022 4.595 Sig. F -> PU -> DLC 0.140 0.023 6.017 Sig. F -> PEoU -> DLC 0.123 0.023 5.366 Sig. E -> PU -> DLC 0.099 0.023 4.367 Sig. E -> PEoU -> DLC 0.128 0.023 5.568 Sig. Hypothesis testing results shown in Table IV indicate that all variables presented have a 95% confidence level, as the T-Statistic value of the variables is greater than 1.96. Table IV demonstrates that the four external variables have a significant impact on Digital Literacy Capabilities (DLC) through Perceived Usefulness (PU) and Perceived Ease of Use (PEoU). The t-statistic values for Content (C) to Digital Literacy Capabilities through Perceived Usefulness and Perceived Ease of Use are 3.202 and 2.759. Duration (D), Flexibility (F), and Engagement (E) significantly influence Digital Literacy Capabilities through Perceived Usefulness (PU) and Perceived Ease of Use (PEoU), as indicated by their respective t-statistic values: 4.467 and 4.595 for Duration, 6.017 and 5.366 for Flexibility, and 4.367 and 5.568 for Engagement. In conclusion, it is stated in Table V that all hypotheses presented in this research were accepted. TABLE V. HYPOTHESIS RESULT Hypothesis Description Result H1 Content (C) has a significant impact on Perceived Usefulness (PU) and Digital Literacy Capabilities (DLC) Accepted H2 Content (C) has a significant impact on Perceived Ease of Use (PEoU) and Digital Literacy Capabilities (DLC) Accepted H3 Duration (D) has a significant impact on Perceived Usefulness (PU) and Digital Literacy Capabilities (DLC) Accepted H4 Duration (D) has a significant impact on Perceived Ease of Use (PEoU) and Digital Literacy Capabilities (DLC) Accepted H5 Flexibility (F) has a significant impact on Perceived Usefulness (PU) and Digital Literacy Capabilities (DLC) Accepted H6 Flexibility (F) has a significant impact on Perceived Ease of Use (PEoU) and Digital Literacy Capabilities (DLC) Accepted H7 Engagement (E) has a significant impact on Perceived Usefulness (PU) and Digital Literacy Capabilities (DLC) Accepted H8 Engagement (E) has a significant impact on Perceived Ease of Use (PEoU) and Digital Literacy Capabilities (DLC) Accepted V. CONCLUSION A. Conclusion After using several metrics and methods and following strict research standards, this study found several results from the analysis. Based on the simulated data of 400 valid and reliable Likert-scale responses, the results reveal insightful patterns in how users perceive the microlearning factor's impact on digital literacy capabilities in TikTok applications. The result shows that each proposed external variable, such as Content (C), Duration (D), Flexibility (F), and Engagement (E), has a significant impact on Digital Literacy Capabilities (DLC) through both Perceived Usefulness (PU) and Perceived Ease of Use (PEoU). In content context, users perceive content quality to be highly relevant and aligned with their interests. Users stated that content frequently followed current trends and showed clear visual-audio elements that improved information absorption. This relevance and concise format of videos made 296 information acquisition feel effortless and directly applicable to users. For the Duration, users show that they prefer concise videos. Users indicate that focused segments allowed them to reduce cognitive load. Respondents show that TikTok videos help them absorb key ideas quickly, giving them a sense of ease and convenience when navigating the videos. As for Flexibility, users indicate that the ability to access TikTok videos anytime and across various devices has been beneficial. These factors gave users a more adaptable learning experience to their routines. Users feel that accessing TikTok videos depending on need and situation is beneficial for constantly absorbing information. The ease of switching videos and the features inside it also gave users a sense of ease. Lastly, for Engagement, users stated that engaging and interactive features such as comment sections, sharing, and audiovisual enhancement facilitated active participation. Users feel that engaging learning experiences expand the value of information absorbed from TikTok videos. In addition, the engaging features made information-seeking feel more dynamic yet still user-friendly. The strong link between each exogenous variable and endogenous variables shows that all the hypotheses are accepted. Therefore, microlearning factors significantly impact digital literacy capabilities in TikTok applications. This result answers our research questions: (1) Content, Duration, Flexibility, and Engagement successfully influence users in enhancing their Digital Literacy Capabilities through the mediating effects of Perceived Usefulness and Perceived Ease of Use. (2) Among the external variables, Flexibility emerged as the most influential contributor, indicating that users value the ability to access, adapt, and manage information based on their personal preferences and learning pace. B. Future Research Many things still need to be studied more deeply about social media applications, such as in this study. Future researchers—whether conducting systematic literature reviews (SLR) or empirical studies, and whether from academic or practitioner backgrounds—are encouraged to explore additional variables or alternative social media platforms using different research models. Such efforts will help uncover microlearning elements that are either anticipated or previously unexamined, thereby providing valuable insights for future readers. ACKNOWLEDGMENT This research would like to express its deepest gratitude to Bina Nusantara University, especially the School of Information Systems, which has provided the knowledge, resources, and academic environment that allows us to conduct this research. Thanks to the thesis supervisor, Drajad Wiryawan, for the guidance, support, and input I needed to do various analyses and calculations in this research. The encouragement and suggestions provided valuable guidance to stay on the right track and enhance the quality of the work. We also followed open data rules whenever possible and when necessary when writing this article, as stated in the link at https://zenodo.org/uploads/15327845. I extend special appreciation to the professionals, researchers, and authors whose work forms the foundation of this study. Their insights on digital microlearning platforms have contributed significantly to shaping the direction and objectives of this research. REFERENCES [1] S. W. H. Saputri, H. P. 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