Unlocking Insights: The Influence of Age, Education, and Gender on Short Video Users’ Time Allocation and Temporal Preferences ()
1. Introduction
The term “Web 2.0” was popularized in the early 2000s to describe the internet’s transition from a static, one-way medium to a dynamic, collaborative platform, driven primarily by the rise of user-generated content (UGC) and the emergence of social media (National Assembly, 2014). This shift has significantly influenced digital marketing, giving rise to “Web 2.0 marketing,” which emphasizes interactivity, community engagement, and content sharing. This new landscape empowers users to share knowledge and to contribute actively to content creation, ushering in an era of unprecedented interactivity (Murugesan, 2007).
The introduction of 5G technology, offering faster connections, lower latency, and increased bandwidth, has brought about transformative changes in the mobile internet landscape. These advancements have enhanced users’ experiences, including improved access to high-speed internet, faster download speeds, and more reliable connectivity. 5G, as well as the development of smartphones, has greatly influenced the digital environment, influencing how users create, share, and engage with content in an increasingly mobile society. Notably, according to OptinMonster, a software for marketing agencies, bloggers, e-commerce websites, and all small businesses, 72 percent of customers prefer learning about a product or service through video rather than reading text (Taheer, 2024).
As high-speed internet access, smartphone usage, and social media adoption continue to rise, video platforms have emerged as key derivatives of Web 2.0, fostering user participation, communication, openness, content creation, and social interaction through such features as liking, disliking, sharing, and video evaluation. These platforms have become integral to mainstream marketing strategies (Zhang, 2020).
In 2022, over three billion internet users watched or downloaded videos at least once a month, a number projected to reach nearly 3.5 billion by 2023. In the final quarter of 2022, online videos reached an impressive 91.8 percent of internet users worldwide, highlighting their broad appeal. The most popular video categories during this period included music videos, comedies, viral content, and tutorials or how-to videos, underscoring the immense popularity of video content. Ciftci et al. (2021) reported further results underscoring this trend, indicating that in 2022, 82 percent of global internet traffic was attributed to either video streaming or video downloads. During the second quarter (Q2) of 2023, a Statista report indicated that 92.3 percent of internet users worldwide consumed online videos. Music videos, comedies, viral videos, and tutorial or how-to videos were among the most popular video types (Statista, 2023a).
A 2023 Global Internet Phenomena Report, published by Sandvine, a company specializing in the analysis of global application usage and internet traffic trends, revealed a 23 percent increase in global internet traffic volume (Sandvine, 2023). This surge is primarily driven by the rising use of streaming videos and continued growth in traffic across various app categories, including gaming, cloud services, and virtual private networks (VPNs) (Sandvine, 2023).
Research on mobile videos has shown that keeping videos short is crucial to retaining viewers’ attention (O’Hara et al., 2007), which has contributed to the rise of short video platforms. These platforms have emerged in response to users’ evolving preferences for content that is quick and effortless to consume yet remains engaging and entertaining.
Short-form video platforms provide users with videos that are typically recorded in a vertical orientation, lasting for an average duration of 30 to 60 seconds. Through the utilization of diverse filters, background music (BGM), and lip-synching templates, users can communicate with an online community of viewers by creating and sharing videos. These platforms host a variety of popular short-form video categories, including entertainment, comedy, dance, pranks, beauty, and skincare.
Vine was one of the pioneering platforms to revolutionize digital content with a six-second video format. Launched in 2013, Vine was the first social media application (app) to make short videos both mainstream and accessible, becoming a cultural phenomenon and contributing to the success of short video platforms (Morsen, 2022). The app’s simplicity and unique format attracted millions of users, reaching 13 million in just five months after its official launch in January and surpassing over 40 million registered users by August 2013. Although fast-growing competition led to Vine’s decline and eventual shutdown in 2017, the short video entertainment landscape continues to evolve (Morsen, 2022).
Launched in 2017 on the international market, TikTok, a short-form video app, has emerged as a major player, with over 3 billion downloads (McAuliffe, 2022) and availability in more than 150 countries and more than 50 languages (D’Souza, 2025), constituting one of the most popular apps. Thanks to its “For You” page and “Following” feeds and driven by a content-based algorithm and easy-to-use editing tools, as well as engaging user-generated content (UGC), TikTok has gained significant popularity, especially among the younger generations, with nearly 66 percent of users aged below 30 years (Barta et al., 2023). Since TikTok’s launch, other short-form video apps, such as YouTube Shorts, Instagram Reels, and Facebook Reels, have entered the market to mimic TikTok’s success.
Users typically spend approximately on average 30 minutes per day on Instagram, engaging in various activities, such as watching Instagram Reels, posting pictures, consuming Instagram stories, and interacting with communities (Melnick, 2022). In contrast, in 2022, TikTok users spent an average of 95 minutes daily on the app, a substantial increase from the 62 minutes reported in 2020. As of 2024, the average daily time spent on TikTok per day reached an estimated 58 minutes and 24 seconds (Duarte, 2024).
The use of short-form video is experiencing rapid growth, solidifying its status as a primary content format. Short videos, owing to its concise nature, enabling users and brands to create more personal connections. Research highlights the power of UGC, showing that it is 2.4 times more authentic and effective in generating interest and in engaging viewers than longer videos (Brian, 2022). The global market for short-form video platforms is expected to increase from USD 1.1 billion in 2020 to USD 2.3 billion by 2030, with a compound annual growth rate (CAGR) of 10.2 percent from 2021 to 2030. These figures underscore the growing significance of short-form videos in both academic discourse and practical business applications.
The aim of this study is to examine users’ preferences and behaviors on short video platforms, with a specific focus on identifying their usage volume, frequency, and the platform(s) where they spend the most time. By understanding users’ engagement patterns and time allocation when consuming short videos, companies, users, and brands can optimize content strategies and advertising campaigns. This enables content creators and businesses to schedule posts and ads during peak engagement periods, resulting in increased visibility, broader reach, and enhanced user interaction.
Additionally, this study seeks to explore the factors influencing user time allocation and temporal preferences media usage. To guide this investigation, the following research questions have been formulated:
RQ1. How do demographic factors, such as age and gender, influence user time allocation and temporal preferences across short video platforms?
RQ2. What is the relationship between educational attainment and its influence on the amount of time users allocate to and their temporal preferences across short video platforms?
2. Literature Review and Research Questions Development
2.1. Gender- and Age-Differences
The existing literature reveals a discrepancy in identifying the influence of gender variations on internet usage patterns. While some studies suggest that gender differences affect internet usage behaviors (Alnjadat et al., 2019; Lokithasan et al., 2019; Lu et al., 2010), others find no significant variations (Liu, 2017; Nisiforou & Laghos, 2015). Data published in January 2023 revealed a predominance of female users on Snapchat (51%) and Instagram (48.2%), while male users comprised the majority on Twitter (63%) and Facebook (56%) (Statista, 2023b). Meanwhile, TikTok demonstrated a nearly equal split between genders worldwide, with 49.2 percent female and 50.8 percent male users. Similarly, YouTube demonstrated a slight male majority, with 54.4% male users and 45.6% female users (Statista, 2023c).
Additionally, studies have found that young adolescents represent a larger proportion of social media users compared to the general population (Mann & Blumberg, 2022). As of October 2023, women between the ages of 18 and 24 years accounted for 18.2 percent of TikTok’s global audience, whereas men in the same age range constituted approximately 18 percent, and TikTok’s online audience in the 25 to 34 age range comprised 16.3 percent female users and 16.6 percent male users (Statista, 2023c).
A 2023 Pew Research Center survey of American teenagers (ages 13 to 17) revealed significant shifts in social media preferences, with TikTok experiencing a surge in popularity. Among the 1453 teens surveyed, 63% reported using TikTok, with 16% using it almost constantly. In contrast, Facebook’s usage among adolescents has sharply declined from 71% in 2014-2015 to just 33% in 2023, while YouTube remains dominant in the teen online landscape, with 95% usage. Instagram and Snapchat follow, each utilized by approximately six in ten teens. The survey also identified notable demographic differences, with Black and Hispanic teens showing higher usage of TikTok, Instagram, Twitter, and WhatsApp compared to their White peers. Additionally, the study highlighted the frequency of social media use, revealing that 35% of teens use at least one platform “almost constantly,” with TikTok and YouTube leading in this category.
As for the frequency of user activity on social media, Gao and Bai (2014) reveal that peak activity was observed during two primary time slots: 10:00 a.m. to 14:00 p.m. and 18:00 p.m. to 22:00 p.m. In addition, according to Choi et al. (2021), a significant 61.64 percent of 219 users engage with mobile short video platforms daily, with the majority (36.07 percent) spending 10 to 30 minutes daily. TikTok users exhibit varied usage frequencies, with 46.9 percent of 2,301 responders utilizing daily and a substantial portion (23.6 percent) engaging less than once a month (Cuesta-Valiño et al., 2022). Moreover, the study also reveals that 35 percent spend 0 to 15 minutes, 22.1 percent 16 to 30 minutes, 25.3 percent 31 to 60 minutes, and 17.6 percent over an hour on TikTok (Cuesta-Valiño et al., 2022).
In addition, insights from Sprout Social and HubSpot emphasize the significance of understanding audience demographics for effective content scheduling. For instance, peak engagement times on Facebook are Wednesdays between 11:00 a.m. and 13:00 p.m. to 14:00 p.m., while Instagram sees high engagement Monday through Friday from 09:00 a.m. to 16:00 p.m. and Twitter peaks at 09:00 a.m. for engagement and 17:00 p.m. for posting (Bouchrika, 2023).
Given the importance of understanding online user behavior and the existing gap in research on the impact of age and gender on usage patterns, this study aims to explore engagement on short video platforms, with a particular focus on age and gender demographics. The objective is to identify the platforms on which different age and gender groups spend the most time, as well as the time periods during which they engage. The findings from this analysis could assist platform providers and marketers in refining content scheduling and targeting strategies.
2.2. Education Differences
Research conducted over the past several years consistently demonstrates that individuals with a college education show a stronger inclination toward social media usage compared to those with only a high school diploma or lower educational background. This trend, as reported by Perrin (2015), remained constant since 2005, initially showing adoption rates of 4 percent for high school graduates or lower, 8 percent for those with some college experience, and 12 percent for college graduates.
A notable shift occurred in 2013, when over half of individuals with a high school-level education or below began using social media, marking a significant increase in social media adoption among this demographic (Perrin, 2015). By 2015, adoption rates had risen to 76 percent among college or graduate degree holders, 70 percent among those with some college education, and 54 percent among high school graduates or less.
Simultaneous research on students’ social media habits across educational levels indicates substantial time spent on these platforms, with high school students dedicating 6 to 8 hours daily (Tantarangsee et al., 2017), and senior high school students spending approximately 11.31 hours per day (Sriadhi et al., 2018). College students, including undergraduates and postgraduates, average 8.23 hours daily, with platforms like YouTube and Facebook being popular choices (Bhosale & Biswas, 2022). Postgraduates tend to use social media for 1 - 2 hours per day, favoring late-night usage (Gora, 2022), while undergraduates spend around eight hours per day on technology, including social media (Ericson, 2011). Despite these differences, both groups are active on multiple social networks, with Facebook remaining the most prevalent (Nagel et al., 2018).
3. Methodology
A pilot survey was designed on Google Forms and sent to five prospective short video platform users. Based on the feedback received, the survey was revised. As a precaution, a screening question was included prior to the main survey to exclude individuals who have never used a short video application: “Have you ever used or are you currently using a short video platform?”
A convenience sampling method was employed, collecting samples based on internet services or location. The final version of the survey was distributed and posted on Facebook, LinkedIn, and Instagram, as social media users are the most active short video platform users and therefore most relevant to this study. To encourage participation, three USD 50 vouchers were randomly awarded to participants. A sample size of 138 was initially obtained, with six non-users of short video platforms excluded, resulting in a final sample size of 136 short video users. Data analysis was conducted using SPSS 29.0, and a descriptive analysis was performed to summarize the sample and identify emerging patterns from the data.
Since data were collected through a cross-sectional survey (i.e., data collected at a single point in time), potential issues of common method bias (CMB) needed to be addressed (Khan, 2017) using SPSS, as CMB can distort research findings by attributing variance to the measurement method rather than the research constructs (Podsakoff et al., 2003). To assess CMB in this study, the Harman one-factor test (Harman, 1976) was conducted across all variables. A CMB issue is indicated if one factor accounts for more than 50 percent of the total variance; however, the total variance explained by one factor in this analysis was only 12.60 percent. Based on this result, this study concludes that no significant CMB is present in the data, as the variance explained by one factor is well below the 50 percent threshold. The survey questions were presented in a multiple-choice format to investigate participants’ preferences concerning short video platforms, with 8 platforms provided as options, and the respective time allocation, which refers to the minutes/hours spent on the platform, typically devoted to these platforms throughout a typical week. The time preference question diverged from typical other questionnaire questions by affording respondents the opportunity to select one or multiple options from a provided list, encompassing “Morning,” “Afternoon,” “Evening,” “Night,” and “No Preference.” This format enabled a detailed exploration of participants’ temporal preferences, enhancing the flexibility and depth of their responses. Specifically, respondents were asked to select the time frames that most accurately represented when they typically used their primary short video app. By allowing multiple selections, this method provided a deeper insight into temporal preferences and enhanced the richness of the data, facilitating a more nuanced understanding of participants’ digital engagement behaviors throughout the day.
The decision to conduct separate regression analyses for education—distinct from age and gender, stems from the unique theoretical and practical role that education plays in influencing the study’s dependent variables. Unlike age and gender, which are fixed demographic traits, education is a dynamic factor that can be influenced by policy and individual choices. This study hypothesizes that education has a distinct and potentially stronger impact on both the amount of time users allocate to short video platforms and their temporal preferences across these platforms (Shabur & Siddiki, 2024).
The analysis of this time preference question involved employing the “multiple-response” feature in SPSS 29.0, enabling a comprehensive examination of respondents’ varied choices and preferences across different periods.
To compare means across different age groups and education levels, this study employed a frequency table, descriptive statistics, multiple-response cross-tabulation, and regression analysis to identify the proportion of cases within different subgroups. This approach facilitated a deeper understanding of how various demographic factors intersect with specific time slots and usage patterns. The comprehensive analysis provided nuanced insights, such as whether certain demographic groups exhibit higher or lower engagement at specific times of day or varying usage durations.
Abbreviations and Acronyms
For this research, the following measurement tools were used:
Gender: Coded as Female and Male.
Age: Divided into four groups: users aged below 18 years, aged from 18 to 25 years, aged from 26 to 35 years, and aged 36 years or older.
Education: Level was categorized into four groups of highest education grade achieved: Bachelor’s Degree, High School, Master’s degree, and PhD or higher.
Time Allocation: Short video platform duration was divided into five groups: less than 30 minutes, 30 minutes to 1 hour, 1 to 2 hours, 2 to 3 hours, and more than 3 hours.
Temporal Preference: Time usage was divided into morning (06:00 a.m. to 12:00 p.m.), afternoon (12:00 p.m. to 18:00 p.m.), evening (18:00 p.m. to 00:00 a.m.), night (00:00 a.m. to 06:00 a.m.), and no preference.
4. Results
4.1. User Profile and Preferences
This study included 132 respondents (91 female, 41 male) from over 30 countries, with notable representation from American (16.7%), South Korean (8.3%), Indian (7.6%), and German (6.8%) participants. Socio-economic data showed that 62.1% were students, followed by 23.5% employed and 12.9% self-employed. In terms of income, 71.2% earned less than 3 million KRW, while 7.6% fell into higher income brackets (above 3 million KRW). In this study, which involved 132 respondents, the gender distribution was as follows: 91 participants were female, and 41 were male. The results revealed that Instagram Reels was the most popular short video platform among respondents, with 51 users, followed by TikTok, which was used by 38 participants. Among the 51 users of Instagram Reels, 34 were women and 17 were men (Table 1).
Table 1. Gender-platform preference.
Which short video platform do you use the most in a typical week? |
As which gender identity do you most identify? |
Female |
Male |
Total |
Douyin |
1 |
2 |
3 |
Facebook Reels |
6 |
4 |
10 |
Instagram Reels |
34 |
17 |
51 |
Kuaishou |
1 |
1 |
2 |
Little Red Book |
2 |
0 |
2 |
Snapchat Spotlight |
4 |
2 |
6 |
TikTok |
29 |
9 |
38 |
YouTube Shorts |
14 |
6 |
20 |
Total |
91 |
41 |
132 |
a. Totals are based on respondents.
Examining the age distribution, the majority of respondents (76 users) fall within the 18 to 25 years age group. The 26 to 35 years age group comprises 42 users, while the over 36 years age group accounts for 9 users, and the under 18 years age group, the least represented, includes 5 users. Respondents aged 18 to 25 are the primary users of Instagram Reels, TikTok, and Facebook Reels, whereas those aged 26 to 35 predominantly favor Douyin and Snapchat Spotlight. Little Red Book appears to be exclusively popular among the 18 to 25 age group. Interestingly, YouTube Shorts shows a relatively even distribution across age ranges, particularly within the 18 to 25 and 26 to 35 age groups, each representing 45.0 percent of the total (Table 2).
Regarding educational attainment, the highest degree obtained by the participants is a Bachelor’s degree, at 66 users, followed by a Master’s degree, attained by 32 participants. Based on the cross-tabulation findings, out of the 64 Bachelor’s degree holders, 26 users favor Instagram Reels, followed by 18 users opting for TikTok and 10 users for YouTube Shorts. Similarly, among respondents holding a Master’s degree, 15 users lean toward Instagram Reels, while 5 users each opt for TikTok and YouTube Shorts. High School degree holders are mostly active on TikTok followed by Instagram and YouTube Shorts. Meanwhile, the PhD users (seven in total) exhibit an almost equal distribution among Douyin, YouTube Shorts, and TikTok (Table 3).
Table 2. Age-platform collation.
Which short video platform do you use the most in a
typical week? |
What is your age range? |
Under 18 years |
18 to 25 years |
26 to 35 years |
36 years
or older |
Total |
Douyin |
0 |
1 |
2 |
0 |
3 |
Facebook Reels |
0 |
5 |
3 |
2 |
10 |
Instagram Reels |
2 |
33 |
14 |
2 |
51 |
Kuaishou |
0 |
1 |
1 |
0 |
2 |
Little Red Book |
0 |
2 |
0 |
0 |
2 |
Snapchat Spotlight |
0 |
2 |
4 |
0 |
6 |
TikTok |
3 |
23 |
9 |
3 |
38 |
YouTube Shorts |
0 |
9 |
9 |
2 |
20 |
Total |
5 |
76 |
42 |
9 |
132 |
a. Totals are based on respondents.
Table 3. Age-platform collation.
Which short video platform do you use the most in a
typical week? |
What is the highest degree or level of education attained? |
High
School |
Bachelor |
Master |
PhD or higher |
Total |
Douyin |
0 |
0 |
2 |
1 |
3 |
Facebook Reels |
1 |
6 |
3 |
0 |
10 |
Instagram Reels |
8 |
26 |
15 |
2 |
51 |
Kuaishou |
1 |
0 |
1 |
0 |
2 |
Little Red Book |
1 |
1 |
0 |
0 |
2 |
Snapchat Spotlight |
0 |
5 |
1 |
0 |
6 |
TikTok |
13 |
18 |
5 |
2 |
38 |
YouTube Shorts |
3 |
10 |
5 |
2 |
20 |
Total |
27 |
66 |
32 |
7 |
132 |
a. Totals are based on respondents.
4.1.1. Time Allocation - Temporal Preference - Age Collation
Across all age groups, the period from 18:00 p.m. to 00:00 a.m. emerges as the primary period for active engagement with short video apps, represented by a total of 80 participants. Particularly, individuals under the age of 18 years (represented by four respondents) and those within the 18-to-25-year age bracket (comprising 49 respondents) demonstrate a tendency toward prolonged usage, exceeding 3 hours on their primary short video platform preference during the time slot mentioned above. Conversely, respondents aged 26 to 35 years (8 responders) and those aged 36 years and older (3 respondents) generally allocate 1 to 2 hours to app usage during the evening hours. The results also reveal that the late-night hours (00:00 a.m. to 06:00 a.m.) comprised a substantial proportion, with 34 users. Particularly notable is the discernible engagement pattern observed among individuals aged 26 to 35 years, who demonstrated a tendency toward devoting either 1 to 2 hours or 2 to 3 hours during this period. It is also noteworthy that individuals aged 18 to 25 years exhibit a similar engagement pattern, allocating 1 to 2 hours during these late-night hours (Figure 1).
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Figure 1. Time allocation - temporal preference - age collation.
4.1.2. Time Allocation - Temporal Preference - Gender Collation
The findings show that regardless of gender, the evening period (18:00 p.m. to 00:00 a.m.) is the most active time to use short video applications, with users spending over 3 hours on their primary short video platform during this period. Specifically, among female participants, 57 users spend over 3 hours during the evening, representing the largest time category, followed by 26 users engaging during night time, constituting the second-largest timeframe for females. In a similar vein, male respondents also exhibit a significant preference for evening usage, with 13 users, constituting 62.5 percent, allocating over 3 hours during this timeframe, marking it as their predominant segment, in contrast to 31.8 percent of females. Furthermore, among male respondents allocating over 3 hours, 36.4 percent express “no preference,” making it the second largest category, contrasting with 33.3 percent of female participants (Figure 2).
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Figure 2. Time allocation - temporal preference - gender collation.
4.1.3. Time Allocation - Temporal Preference - Education Collation
Bachelor’s degree holders, the largest group among degree holders, allocate either 1 to 2 hours or more than 3 hours of their evening to their primary short video platform, with each category consisting of 12 users. Seven high school graduates dedicate over 3 hours in the evening to their primary short video platform. Similarly, 11 Master’s degree holders spend between 1 to 3 hours in the evening, while 2 PhD holders allocate only 30 minutes to 1 hour on their primary short video platform during the evening. Notably, 4 Master’s degree holders and 4 high school graduates prefer to spend more than 3 hours in the afternoon. In contrast, 7 Bachelor’s degree holders tend to allocate 1 to 2 hours during the night. Regarding time and platform preference, Instagram Reels is predominantly favored by respondents during evening hours, while YouTube Shorts and TikTok are more popular during night time.
Concerning time spent on short video platforms, Instagram Reels and TikTok users spend over 3 hours, while users of Facebook Reels, Snapchat Spotlight, and YouTube Shorts typically spend 1 to 2 hours (Figure 3).
Figure 3. Time allocation - temporal preference - education collation.
4.2. Regression Analysis
To address the first research question, “How do demographic factors, such as age and gender, influence user time allocation and temporal preferences across short video platforms?”, a regression analysis was conducted. The results indicate that neither gender nor age range significantly influences temporal preferences. The significant constant term (1.808, p < 0.001) indicates that there is a statistically significant baseline value for the dependent variable (temporal preference) when all independent variables are zero. However, the coefficients for the independent variables (gender identity and age range) are not statistically significant. The coefficient for gender identity (−0.039, p = 0.786) and age range (−0.131, p = 0.185) both lack statistical significance, indicating that neither variable significantly influences temporal preference. While the model as a whole predicts temporal preference (evidenced by the significant constant (<0.001)), the lack of significance for gender and age could stem from various factors such as omitted variables, weak relationships, measurement error, or limited sample size. Further research is necessary to better understand the determinants of gender and age within temporal preferences (Table 4).
The regression analysis reveals that age range significantly influences the amount of time individuals spend on their primary short video app per week, with older individuals tending to spend less time, as reflected by a negative coefficient of −0.565 (p = 0.009). However, gender identity does not show a statistically significant influence, at the conventional significance level of 0.05, on time allocation, although there appears a positive trend, denoted by a coefficient of 0.526 (p = 0.091). The intercept of 3.555 (p < 0.001) represents the estimated average time spent on the app per week when all other predictors (age and gender) are zero or non-applicable. These findings underscore the importance of considering demographic factors, such as age, in understanding user behavior on short video platforms, while the influence of gender identity requires further investigation (Table 5).
Table 4. Age, gender, and temporal preference regression.
Model |
Coefficientsa |
Unstandardized Coefficients |
Standardized
Coefficients |
t |
Sig. |
B |
Std. Error |
Beta |
(Constant) |
1.808 |
0.286 |
|
6.328 |
<0.001 |
As which gender identity do you most identify? |
−0.039 |
0.144 |
−0.024 |
−0.272 |
0.786 |
What is your age range? |
−0.131 |
0.099 |
−0.118 |
−1.332 |
0.185 |
a. Dependent Variable: Temporal Preference.
Table 5. Age, gender, and duration regression.
Model |
Coefficientsa |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
B |
Std. Error |
Beta |
(Constant) |
3.555 |
0.614 |
|
5.787 |
<0.001 |
What is your age range? |
−0.565 |
0.212 |
−0.230 |
−2.665 |
0.009 |
As which gender identity do you most identify? |
0.526 |
0.309 |
0.147 |
1.702 |
0.091 |
a. Dependent Variable: On average, how much time do you (did you) spend on your primary short video app per week?
In summary, as for the first research question, neither gender identity nor age range significantly influences how users allocate their time across short video platforms. In contrast, while age range significantly influences the amount of time users allocate to their primary short video app, gender identity does not exert a significant influence on temporal preferences across short video platforms. In other words, users of different age groups tend to spend varying amounts of time engaging with their primary app while gender has no impact on time preference or time allocation. To address the second research question, “What is the relationship between educational attainment and its influence on the amount of time users allocate to and their temporal preferences across short video platforms?”, an additional regression analysis was conducted. The results indicate that education does not significantly affect the amount of time individuals spend on their primary short video app per week (p = 0.568 > 0.05) (Table 6) or their temporal preferences (p = 0.295 > 0.05) (Table 7). In essence, education does not exert an influence on either time preference or time allocation.
Table 6. Education and duration regression.
Model |
Coefficientsa |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
B |
Std. Error |
Beta |
(Constant) |
2.720 |
0.313 |
|
8.678 |
<0.001 |
What is the highest
degree or level of
education you follow
or have completed? |
0.086 |
0.150 |
0.050 |
0.572 |
0.568 |
a. Dependent Variable: On average, how much time do you (did you) spend on your primary short video app per week?
Table 7. Education and time regression.
Model |
Coefficientsa |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
B |
Std. Error |
Beta |
(Constant) |
1.307 |
0.142 |
|
9.221 |
<0.001 |
What is the highest
degree or level of
education you follow
or have completed? |
0.071 |
0.068 |
0.092 |
1.051 |
0.295 |
a. Dependent variable: Temporal preference.
5. Discussion
This study examines users’ profiles on their primary short video platforms, along with their time preferences and allocation patterns. The findings are consistent with existing literature, which suggests that younger users tend to engage with short video platforms more frequently than older users. Specifically, individuals aged 18 to 25 years, as well as those with higher levels of education, exhibit a preference for TikTok and Instagram Reels over other platforms, as observed in previous research (Bhosale & Biswas, 2022). This phenomenon can be attributed to the familiarity of Generation Z and Millennials with consuming video content on mobile devices, tablets, and computers, particularly on platforms like YouTube. Their extensive exposure to such platforms has fostered a greater comfort with short video formats compared to older or younger generations. Despite efforts to ban or restrict TikTok in countries such as the United States and India (Are, 2022), both TikTok and Instagram remain popular platforms among users. This aligns with the findings of the current study, which indicate that Instagram Reels and TikTok are the primary platforms for users. Notably, the growing popularity of Instagram may be attributed to the anticipated prohibition of TikTok (Are, 2022). Further research is recommended to investigate into this phenomenon.
In addition, research by Manago (2012) highlights the role of social media, including short video platforms, in maintaining and strengthening social connections among young adults. Given the transitional nature of social networks in younger demographics, social media provides a convenient and accessible way to stay in touch with peers and family members. Additionally, a study by the Pew Research Center (2019) highlights the technological proficiency of younger generations, who are more likely to own smartphones and actively seek out new experiences and content on social media platforms. Consequently, the prominence of younger individuals on social media can be attributed to the significant influence of these platforms, which also explains the higher number of younger users, consistent with the findings of the current study. However, further research into user motivations is warranted. In terms of usage patterns, approximately half (49.8 percent) of 482 participants allocate less than one hour on average daily, whereas 50.2 percent dedicate more than one hour (Gan, 2024). This finding is congruent with the findings elucidated in this study, revealing that users spend an average of 1 to over 3 hours per week on their primary short video platform, underscoring how much time individuals are willing to dedicate to consuming short videos or how easy it is for users to lose track of time as they become deeply engrossed in short video apps. However, this finding may raise concerns about the potential implications for short videos users’ attention spans and the psychological effects of excessive screen time. Interestingly, this study also reveals peak activity during evening and night hours, corresponding with similar findings in other research (Yang & Mu, 2020), suggesting short video platforms serve as a popular form of entertainment after work or school. These findings offer valuable insights for marketers, enabling them to optimize content scheduling and target specific time slots to maximize user engagement.
Limitations and Recommendations
While this study endeavors to unravel the intricate dynamics of user patterns on social media, it is crucial to acknowledge certain limitations that may influence the broader applicability of the findings. One significant limitation arises from potential sampling biases, where certain age groups or geographical locations may be overrepresented in the study’s sample. This challenge impacts the generalizability of the results by not capturing the full diversity of social media users. Future research should adopt more robust sampling techniques to ensure a broader and more representative participant pool, encompassing a wider range of demographics and geographic locations.
Additionally, the study faces a constraint related to the sample size of 132 users, recruited through convenience sampling on different social media platforms. This relatively small sample size may compromise the statistical power and generalizability of the study’s findings. Biases such as the overrepresentation of certain age groups, socio-economic backgrounds, or individuals with specific interests in social media use may arise, which may not fully represent the broader population. To enhance the credibility and applicability of the research, it is crucial to discuss these potential biases and their implications. Future studies should prioritize larger and more diverse samples to improve the reliability of statistical analyses and the external validity of the results, allowing for more confident assertions about the broader population of social media users.
Correspondingly, future research should consider using stratified or random sampling techniques to ensure a more representative sample. Stratified sampling involves dividing the population into subgroups and sampling proportionally from each, which can improve the generalizability of the results. Additionally, increasing the sample size and recruiting participants from a wider range of sources beyond social media would help mitigate these biases and strengthen the overall validity of the study’s conclusions. Given the study’s demographic imbalance—with a higher proportion of female respondents and a concentration in the 18 - 35 age group—future research should aim for a more balanced distribution to enhance generalizability. This imbalance may stem from sampling methods, as online surveys and social media recruitment tend to attract younger, digitally engaged users, particularly women, who are often more active in online discussions. Additionally, the topic of the study itself may have been more relevant or appealing to this demographic. To address this, future studies should employ targeted recruitment strategies, such as offline sampling, broader outreach across diverse platforms, and quota-based sampling, to ensure a more representative sample across age and gender groups.
Interaction effects among age and gender were omitted due to model complexity, prioritization of education’s main effect, and limitations in sample size. Future studies with larger sample sizes and more advanced statistical techniques could address this limitation by investigating these interaction effects.
Examining the significance of specific days of the week, like Monday or Tuesday, is essential for future research. This understanding can help in scheduling content releases, promotions, and events for maximum impact, and aids in targeting advertising efforts when users are most active and receptive. It allows content creators to tailor their content to align with user behavior and preferences, potentially increasing reach and engagement. Focusing efforts on high-traffic days can maximize operational efficiency and return on investment for businesses. Tracking trends over time provides valuable insights into the effectiveness of marketing campaigns and product launches, guiding future strategies for success.
Notably, while this study offers a valuable snapshot of user behavior, it is important to recognize the limitation of capturing the dynamic evolution of user conduct over time. Although the insights provided are significant for understanding user dynamics at a specific point, they may not fully represent the shifts and changes in behavior as they evolve. Future research should consider conducting longitudinal studies to explore the temporal progression of user behavior. By tracking user conduct over extended periods, researchers can gain deeper insights into the evolving trends and preferences, as well as the underlying factors driving these shifts. This approach would facilitate a more comprehensive understanding of the long-term developments in user behavior and the factors influencing these changes.
6. Conclusion
In conclusion, user patterns on short video platforms are characterized by a dynamic landscape, influenced by a variety of factors such as demographics, platform preferences, age, and educational background. In terms of its theoretical contributions, this study offers valuable insights into users’ preferences regarding their primary short video platforms. It provides an in-depth understanding of users’ time of usage, frequency of engagement, and the platforms on which they allocate the most time compared to others. A better understanding of user behaviors and user patterns can help companies and social media managers optimize and design more targeted content or engagement strategies, enabling content creators and businesses to schedule their posts or ads during peak user engagement, resulting in increased visibility, wider reach, and enhanced engagement. This study also proposed new questions and offered novel perspectives for the entire field of media and entertainment, as well as offered valuable insights for marketers and advertisers looking to target specific demographics and enhance their understanding of human decision-making and behavior, particularly regarding generational differences and intertemporal decision-making complexities (time-based choices). Future research could delve deeper into users’ potential motives for engaging with their primary short video platforms. Researchers could also explore the various factors that drive individuals to use short video platforms, such as social connection, entertainment, and self-expression.
The finding of a statistically significant constant in the regression models, while other predictors such as age and gender were not significant, suggests a baseline level of usage and preference independent of the demographic factors considered. This observation implies that there may be other underlying variables—such as psychological, cultural, or behavioral factors—that were not captured in this study but could potentially influence the results. To enhance the understanding of user behavior and preferences on short video platforms, future research should explore these factors further. Investigating these unexamined variables could offer a more comprehensive perspective on the determinants of usage patterns and preferences.
Another area for future research lies in exploring the role of short video platform features, such as augmented reality (AR), in enhancing user engagement. As AR technology continues to evolve, short video platforms are integrating these features to offer more immersive and interactive experiences, thereby fostering deeper user interaction and engagement.