Introduction
Today, technology makes our lives more convenient, enjoyable, and efficient. Over the last two decades, advancements in technology have accelerated, resulting in increased usage across the globe. The initialization of the Internet has significantly contributed to this development. Nowadays, with just one click, people can access information, receive the latest news, and communicate with others by sending messages or sharing video clips of live news almost instantaneously.
Various companies and industries are working to enhance technology and make different services effortlessly available to people worldwide. The introduction of smartphones, iPads, and iPhones has transformed lifestyles, making life convenient. Today, people cannot imagine a day without the Internet, as it provides access to various applications that serve daily needs. With smartphones, individuals can quickly perform various tasks at home such as paying bills, ordering food, playing games, and creating educational content including videos on cooking or learning. In Bangladesh, the use of smartphones has become widespread, with people from all walks of life taking advantage of their ease of use and Internet access. YouTube content creation is rapidly gaining popularity, with people of all ages, literacy levels, and economic statuses engaging with it daily.
Currently, creating a YouTube channel and producing content is a profitable business. A YouTuber can connect with a vast audience from different parts of the world through their channel.
YouTube content creation goes beyond mere entertainment; it also serves as an educational tool. Teachers conduct classes through YouTube, and various seminars and meetings are broadcasted via the platform. Millions of people access a wide range of applications through YouTube, obtaining the latest news on politics, technology, education, research, food item, culinary techniques, fashion, events, and more.
This research paper aims to identify the prime factors influencing YouTube content creation, focusing on gaining popularity and acceptance. To achieve this, the author utilizes personal technology acceptance index (PTAI) model and probability theory for data analysis.
PTAI combines elements of the technology acceptance model (TAM), technology readiness index (TRI), and personal innovativeness in IT (PIIT). The TAM concept explains how people use technology based on their beliefs regarding its perceived usefulness and ease of use of a system. The TRI measures an individual’s propensity to embrace and use new technologies, considering both positive and negative aspects. Furthermore, the PIIT refers to a personal trait construct that helps understand an individual’s innovativeness in the domain of information technology.
In this research, the author adopts the PTAI theorem to explore why people accept or reject YouTube technology, emphasizing the TAM and its related concepts such as TRI and PIIT.
Probability theory is also used, as it helps quantify uncertainty, enabling informed decision-making, risk assessment, and predictions in various fields, from finance and insurance to everyday life. The process of creating YouTube content and managing a channel encompasses multiple dimensions such as entertainment, income generation, communication, social influence, learning, and knowledge development, making probability theory a suitable framework for this research.
Objective of the research
Thus, the present research aims to achieve the following objectives:
i. To recognize the usefulness of YouTube content.
ii. To evaluate the social impact of YouTube content making.
iii. To identify the factors that affect the YouTube content creation decision.
Research questions
1. What is the probability of various personal technology acceptance indices that influenced the YouTube content creator?
2. How various social factors affect the decision of YouTube content creation?
3. To what extent education level and intelligence of respondents are associated with the type of YouTube content making?
Literature review
YouTube is a very popular and free video-sharing website that allows registered users to upload and share video clips online. While registration is not necessary to watch videos, it enables users to interact more with the platform. Launched in 2005 by former PayPal employees, YouTube was later acquired by Google Inc.
As a platform for video sharing, YouTube encourages users to create and share content with anyone who can access the site. These videos can also be embedded and shared on other sites.
The YouTube contents include how-to videos, recipes, and humorous clips, all of which are available for free. Thus, YouTube qualifies as social media since it allows users to share free content with one another (1).
Content creators on YouTube are commonly referred to as YouTubers or YouTube creators. These individuals regularly produce videos to share with their audience on the popular social media platform (2).
There are various types of YouTube contents such as product reviews, blogs, educational videos, unboxing videos, autonomous sensory meridian response (ASMR), walk-through videos, explainer videos, daily vlogs, comedy sketches, challenge videos, video games, behind the scenes, interviews, “ask me anything” (AMA) videos, video podcasts, Q&A videos, video tutorials, music videos, reaction videos, product videos, travel vlogs, education, video testimonials, tutorials, and more.
YouTube channels are diverse, encompassing topics such as entertainment, gaming, education, music, food, beauty, travel, and more. Popular formats include vlogs, tutorials, reviews, and ASMR content.
Here’s a breakdown of common YouTube channel types:
Popular categories
• Entertainment: This broad category includes comedy sketches, web series, movie/show reviews, and general entertainment content.
• Gaming: These channels focus on playing video games, often with commentary and audience interaction.
• Education & Tutorials: These channels provide instruction on various topics, from software tutorials to academic subjects.
• Music: These channels are dedicated to music videos, album reviews, and live performances.
• Food & Cooking: These channels feature recipes, cooking demonstrations, and food reviews.
• Beauty & Fashion: These channels include makeup tutorials, style advice, and fashion reviews.
• Travel & Vlogs: These channels document travel experiences, share daily routines, and offer behind-the-scenes content.
• Science & Technology: These channels explore scientific concepts, tech reviews, and product demonstrations.
• Sports: These channels focus on specific sports, teams, or athletes.
• DIY & Crafts: These channels demonstrate “do-it-yourself” projects and creative crafts.
• Health & Fitness: These channels offer workout routines, fitness tips, and health advice.
• Personal Finance & Investing: These channels provide financial advice and tips on investing.
• Product Reviews & Unboxings: These channels feature reviews of products and unboxing videos.
• ASMR: These channels focus on creating relaxing and calming sounds and visuals.
• Challenge videos: These channels feature challenges that are entertaining to watch.
Other notable types
• Autos & Vehicles: Focuses on cars, trucks, and other vehicles.
• Film & Animation: Features films, animation, and related content.
• News & Politics: Covers current events and political topics.
• Nonprofits & Activism: Supports non-profit organizations and social causes.
• People & Blogs: Features personal stories and vlogs.
• Pets & Animals: Focuses on pets and animal-related content.
Having a YouTube channel offers numerous benefits, including reaching a global audience, building a community, showcasing talent, and the potential to earn income through monetization. It also serves as a platform for creative expression and sharing information.
One common trend across all digital platforms is the rise of video content, which shows no signs of slowing down. YouTube is a major contributor to growth, boasting over 2 billion monthly active users and tens of millions of content creators (3).
The benefits of YouTube content creators for business
• Facilitate the creation of attractive content.
• Build consistent branding.
• Help us emerge as an expert.
• Manage content strategies.
• Ensure our content is easy to find.
• Provide tons of creative ideas.
• Develop good research skills.
• Cultivate mastery of supporting tools.
“YouTube Analytics helps me to understand audience retention, video performance, when is the best time to post, and even which thumbnails are best.” “I started blogging because I could create content about my day-to-day life and culture without expensive equipment or perfect editing skills.”
Social impact
At present, social impact is taken into account as a prime aspect behind the extension and development of YouTube content creation.
As a consequence, on the grounds of the aforesaid statement, the mentioned hypothesis are stated:
Hypothesis 1. (H1) Social impact has a crucial impact on the use context regarding YouTube content makers.
Hypothesis 2. (H2) Social impact has a crucial impact on perceived benefits regarding YouTube content makers.
Use context
Though various circumstantial/contingent agents influence user acceptance of YouTube content, the study identifies two key factors actively connected to this content: the context of use and users’ emotions and passions (4). For example, while traveling on public transportation, carrying a laptop or PC may not be feasible, but people can easily use their smartphones. This allows them to spend quality time watching YouTube content on their devices.
Based on this understanding, the following hypotheses are proposed.
Hypothesis 3. (H3) Use context has a notable impact on perceived benefits regarding YouTube contents.
Hypothesis 4. (H4) Use context has a remarkable impact on perceived ease of use regarding YouTube content.
TAM of YouTube content
The TAM (5) is an influential socio-technical model that aims to explain user acceptance of information systems. According to the TAM, a user’s behavioral intention to use an information system is indicative of their acceptance of that system. The TAM hypothesizes that a user’s behavior, ease of use, intention, and other factors are interconnected (4).
From this model, the following hypotheses are derived:
Hypothesis 5. (H5) Perceived ease of use has a crucial impact on perceived usefulness regarding YouTube content.
Hypothesis 6. (H6) Perceived benefits have a notable impact on perceived usefulness regarding YouTube content.
Hypothesis 7. (H7) Perceived benefits have a notable impact on flow regarding YouTube content.
Hypothesis 8. (H8) Perceived usefulness has a notable impact on attitude regarding YouTube content.
Hypothesis 9. (H9) Perceived benefits have a notable impact on attitude regarding YouTube content.
Hypothesis 10. (H10) Attitude has a crucial impact on behavioral intention regarding YouTube content.
Hypothesis 11. (H11) Flow has a notable influence on behavioral intention regarding YouTube content.
Hypothesis 12. (H12) Perceived usefulness has a crucial impact on behavioral intention regarding YouTube content makers.
Safety Technology
Safety is the concept that everyone should have the right to use technology without facing harm or harassment.
We consider content to be harassment when it targets an individual with prolonged or malicious insults based on intrinsic attributes, including their protected group status or physical traits. This also includes harmful behavior such as threats, bullying, doxing, or encouraging abusive fan behavior (4).
YouTube does not allow content that promotes hate speech, predatory behavior, graphic violence, malicious attacks, and content that is harmful or dangerous. YouTube has established several policies concerning:
• Harmful or dangerous content.
• Violent or graphic content.
• Violent criminal organizations.
• Hate speech.
• Harassment & cyberbullying.
Trust
Trust perceived by technology is a concept, and its implication turns on the circumstances of the mechanism employed. Consumers’ awareness of confidence/faith in mechanisms related to cyberspace and the Internet is envisaged, while the reliability of the combined statistics/facts and the certainty of the method in which the knowledge/instruction is interchanged.
Personal innovativeness
Certain factors contribute to the success of every new innovation. Each successful invention often involves some confidential aspects regarding its development. In this research, a significant shift is examined, particularly in relation to media and small-screen channels (4).
In February 2005, three former PayPal employees launched a video-sharing website, marking the beginning of YouTube. YouTube is a platform where individuals can openly share and view videos, quickly gaining popularity and marketability worldwide due to its simplicity, accessibility, and the rapid rise of mobile phones equipped with standard cameras alongside advancements in Internet speed.
Personal technology acceptance index
The author aims to create a PTAI to help consumers understand how various factors affect each other. Probability theory is employed to construct a systematic PTAI (3), using marginal and conditional probabilities to assess the interconnections among TAM factors and to actualize the PTAI.
Perceived benefit
Perceived benefits from using technology include access to news, entertainment, knowledge, skills, and information. The result indicates that perceived benefits have a positive relationship with technology usage: as perceived benefits increase, consumers are more likely to engage with the technology.
Perceived novelty
The perceived novelty of a technology refers to its new traits, factors, and distinct attributes that ideally assist users. A new technology is more likely to be accepted by its user group if its perceived novelty exceeds that of other technologies. Additionally, perceived novelty influences both perceived ease of use and perceived usefulness, which subsequently affects attitudes and intentions.
Perceived novelty constitutes how individuals perceive specific attributes of an IT innovation, whereas PIIT is a trait that enhances the likelihood of these perceptions (6).
Since its inception, YouTube content and channels have gained significant acclaim and recognition. YouTube has proliferated to the point where nearly everyone has visited its homepage or watched at least one reel or video clip from the platform.
This observation indicates that YouTube’s immense success is due to its unique qualities. This research highlights the beneficial advantages and social values associated with YouTube content creation and channels.
Research methodology
In this study, the author employed a non-probability sampling method for data collection. A total of 312 respondents participated, with the respondents located throughout Bangladesh (i.e., Dhaka, Rajshahi, Sylhet, Rangpur). Questionnaires were distributed either in person or by post. The author utilized a 5-point Likert scale for data collection. Various tools and techniques were used for data analysis including path coefficients, Cronbach’s test, probability theory, SPSS, and PTAI.
A considerable number of innovative insights can be generated when features such as innovativeness, clarity, affordability, trustworthiness, and serviceability are flawlessly customized to the vestige. The heart of the research paper is YouTube, exploring the expected utilities arising from all these aspects.
The author chose non-probability sampling over probability-based approaches because resources are limited and access to a specific population is challenging. Moreover, this research aims for exploratory and qualitative insights rather than statistical generalization.
Data analysis
The study’s population consisted of 312 respondents from various regions in Bangladesh. Participants were selected through purposive random sampling using a 1–5 Likert scale questionnaire. Research data analysis was conducted using Pearson correlation R test, Cronbach’s test, path coefficient test, validity test, goodness-of-fit test, and chi-square test. Participants were guided to complete a questionnaire focusing on their experience with YouTube content.
All questionnaires in this paper were internally managed. The testing process was completed when the participants finished their answers.
The methodology used in this research is a quantitative descriptive method.
The range of interpreting the Likert scale mean score is as follows: 1.0–2.4 (Negative attitude), 2.5–3.4 (Neutral attitude), and 3.5–5.0 (Positive attitude).
Here, the author got mean for the first, second, and third statements is 3.38, 3.47, and 3.49, respectively, which indicates the neutral attitude of the use context of YouTube blogger.
A low standard deviation means there was a lot of agreement about the answers. High SD means there was a wide range of answers, indicating disagreement. Here, the author gets a high standard deviation, indicating disagreement among the answers.
Here, the author got mean for the first, second, and third statements are 3.48, 3.55, and 3.86, respectively, which indicate the neutral attitude of the first statement of the social impact of YouTube bloggers and positive attitude of second and third statements toward the social impact of YouTube content.
The author gets a high standard deviation, which indicates that there was a wide range of answers, indicating disagreement.
The means of the three statements are 3.87, 3.66, and 3.78, respectively, which indicate a positive attitude toward the perceived ease of use of YouTube content. Standard deviations of the three statements are 9.13, 8.82, and 8.91, which indicate high disagreement among the answers.
The means of the three statements are 3.62, 3.61, and 3.77, respectively, which indicate a positive attitude toward the perceived usefulness of YouTube content. Standard deviations of the three statements are 8.47, 8.61, and 8.89, which indicate high disagreement among the answers.
The means of the three statements are 3.63, 3.85, and 3.78, respectively, which indicate a positive attitude toward the perceived benefit of YouTube content. Standard deviations of the three statements are 8.49, 9.03, and 8.92, which indicate high disagreement among the answers.
The means of the three statements are 3.77, 3.82, and 3.72, respectively, which indicate a positive attitude toward YouTube content.
Standard deviations of the three statements are 8.84, 9.00, and 8.69, which indicate high disagreement among the answers.
The means of the three statements are 3.43, 3.46, and 3.50, respectively. The first two statements indicate a neutral attitude, and the third statement indicates a positive attitude toward the flow of YouTube content.
Standard deviations of the three statements are 8.04, 8.13, and 8.23, which indicate high disagreement among the answers.
The means of the three statements are 3.79, 3.76, and 3.71, respectively, which indicate a positive attitude of intention toward YouTube content. Standard deviations of the three statements are 8.97, 8.85, and 8.67, which indicate high disagreement among the answers.
Cronbach’s alpha is a measure of internal consistency, that is, how closely related a set of items is as a group. It is considered to be a measure of scale reliability. A “high” value for alpha does not imply that the measure is unidimensional.
A generally accepted rule is that α of 0.6–0.7 indicates an acceptable level of reliability, and 0.8 or greater is a very good level. However, values higher than 0.95 are not necessarily good, since they might be an indication of redundancy.
However, there is no clear agreement on the specific criteria for interpreting Cronbach’s alpha. A common interpretation of the coefficient is α< 0.5 for low reliability, 0.5 <α< 0.8 for moderate (acceptable) reliability, α> 0.8 for high (good) reliability. Here, the author gets low reliability.
Path coefficients close to + 1 indicate a strong positive relationship (and vice versa for negative values). The important decision should be made after running the significance test.
In this study, path coefficient values close to 0.5 or greater were interpreted as corresponding to large effect sizes, values around 0.3 were interpreted as corresponding to medium effect sizes, and values near and below 0.1 as corresponding to small effect sizes.
The means of the three statements are 3.89, 3.17, and 3.99, respectively. The first and third statements indicate positive agreement with personal innovativeness of YouTube content, but the second statement indicates neutral.
Standard deviations of the three statements are 9.67, 7.32, and 9.69, which indicate high disagreement among the answers.
The means of the three statements are 3.98, 3.86, and 3.94, respectively, which indicate a positive attitude of perceived novelty toward YouTube content. Standard deviations of the three statements are 9.84, 9.58, and 9.70, which indicate high disagreement among the answers.
The means of the three statements are 3.97, 3.36, and 3.96, respectively. Here first and third statements indicate a positive attitude of safety toward YouTube content. The second statement indicates a neutral attitude toward the safety of YouTube content. Standard deviations of the three statements are 9.69, 8.16, and 9.66, which indicate high disagreement among the answers.
The means of the three statements are 3.91, 3.93, and 3.67, respectively, which indicate a positive attitude of trust toward YouTube content makers. Standard deviations of the three statements are 9.38, 9.40, and 8.67, which indicate high disagreement among the answers.
The marginal probability of an event is the probability distribution that describes that single event only, and it is independent of other variables. Table 22 shows that 41% males are innovative and 27% males have no innovativeness upon YouTube content. Again, 19% females are innovative and 13% females are not innovative in making YouTube content.
The conditional probability, on the other hand, is a distribution that represents the likelihood of an event occurring given a particular outcome of another event. Table 23 shows that 61% chance of a male becoming innovative and 40% chance of a male becoming noninnovative to make YouTube content. Again 59% chance of a female becoming innovative and 39% chance of a female becoming noninnovative in making YouTube content.
Table 25 shows that 18 rural people have positive behavior intention and 22% rural people have negative behavior intention toward YouTube content. Again, 41% urban people have positive behavior intention and 19% urban people have negative behavior intention toward YouTube content, and alike for other statements.
The conditional probability, on the other hand, is a distribution that represents the likelihood of an event occurring given a particular outcome of another event. Table 26 shows that 45% chance of rural people adopting positive behavior intention and 55% chance of rural people adopting negative behavior intention to use YouTube content.
Again 68% chance of urban people becoming positive behavior intention and 31% chance of urban people becoming negative behavior intention to use YouTube content.
Table 28 shows that 37 employed people have a positive attitude toward YouTube content and 15% employed person have a negative attitude toward YouTube content. Again, 29% unemployed people have positive attitude toward YouTube content and 20% unemployed negative attitude toward YouTube content.
Table 29 shows that 71% chance of employed people developing a positive attitude and 29% chance of employed people developing a negative attitude to use YouTube content.
Again 60% chance of unemployed people developing a positive attitude and 40% chance of unemployed people developing a negative attitude toward YouTube content.
Table 31 shows that 18% of high school pass people have positive behavior intention toward YouTube content.,4% high school pass have neutral behavior intention toward YouTube content, 11% high school pass people have negative behavior intention toward YouTube content.
Table 32 explains that 55% chance of a high school passed people develop positive behavior intention toward YouTube content. Notably, 12% chance of high school passed people developing neutral behavior intention toward YouTube content and 33% chance of a high school passed people developing negative behavior intention toward YouTube content.
Table 34 shows that 43% male have positive perceived novelty and 24% male have negative perceived novelty upon YouTube content. Again, 22% female have positive perceived novelty and 11% female have negative perceived novelty toward YouTube content, and alike for other statements.
Table 35 shows that 64% chance of a male developing positive perceived novelty and 36% chance of a male developing negative perceived novelty toward YouTube content. Again, 67% chance of a female developing positive perceived novelty and 33% chance of a female developing negative perceived novelty toward YouTube content.
Table 37 shows that never-married people have 12% positive behavior intention toward YouTube content. Never-married people have 3% neutral behavior intention toward YouTube content. Never-married people have 1% negative behavior intention toward YouTube content, and similar content for other statements.
Table 38 shows that 48% chance of never-married people developing positive behavior intention toward YouTube content. Notably, 20% chance of never-married people have developed neutral behavior intention toward YouTube content and 4% chance of never-married people have developed negative behavior intention toward YouTube content, and alike for other statements.
Table 40 shows that the first statement has 14% positive attitude toward YouTube content, first statement has 2% neutral attitude toward YouTube content, first statement has 13% negative attitude toward YouTube content, and alike for other statements.
Table 41 shows that first statement has 48% chance of developing positive attitude toward YouTube content, first statement has 7% chance of developing neutral attitude toward YouTube content, first statement has 45% chance of developing negative attitude toward YouTube content, and alike for other statements.
Table 43 shows that the first statement has 21% positive flow toward YouTube content, first statement has 5% neutral flow toward YouTube content, and first statement has 11% negative flow toward YouTube content, and alike for other statements.
Table 44 shows that the first statement has 57% chance of positive flow toward YouTube content, The first statement has 14% chance of neutral flow toward YouTube content, and the first statement has 29% chance of negative flow toward YouTube content, and alike for other statements.
Goodness-of-fit test
Null Hypothesis 13: There is no significant association between gender and their attitude toward the benefits of YouTube content.
Here, the author used chi-square test, whose calculated value is = 10.7047587.
At 0.05 level of significance and 1 degree of freedom, the table value of chi-square value is = 3.841.
So, chi-square cal > chi-square table.
So in the null hypothesis, there is no significant association between gender and their attitude toward whether the benefit of YouTube content is rejected or not true. There is a significant association between gender and their attitude toward the benefits of YouTube content.
Null Hypothesis 14: There is no significant association between education level and creativity toward YouTube content making.
Here, the author used chi-square test, whose calculated value is = 48.07037547.
At 0.05 level of significance and 8 degrees of freedom, the table value of chi-square value is = 15.507.
So, chi-square cal > chi-square table.
So in the null hypothesis, there is no significant association between education level and creativity toward YouTube content making is rejected or not true. There is a significant association between education level and creativity toward YouTube content making.
Null Hypothesis 15: There is no significant association between employment status and average time spent on you tube content making.
Here, the author used chi-square test, whose calculated value is = 0.806181034.
At 0.05 level of significance and 1 degree of freedom, the table value of chi-square value is = 3.841.
So, chi-square cal < chi-square table.
So in the null hypothesis, there is no significant association between employment status and average time spent on YouTube content making is true or accepted.
Validity test
Content validity
For content validity test purposes, the author gives a questionnaire to customers (Table 48).
where Ne = number of experts voting essential.
N = total number of recruited experts.
Now, content validity rate = 0.076923076.
This formula yields values, which range from +1 to −1; positive values indicate that at least half the respondents rated the item as essential.
Convergent validity
To measure convergent validity, the author used two related scales such as making profit from YouTube content makers and number of creating new content frequently.
Here, the author used Pearson r test, calculated r = 0.0785843399.
Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
Convergent validity is generally considered adequate if >75% of hypotheses are correct, or if a correlation with an instrument measuring the same construct is >0.50.
Discriminant validity
For testing discriminant validity the author asks respondents to fill in a second questionnaire measuring extroversion in order to test the discriminant validity of his questionnaire. Here, the author tries to construct a relationship between the customer’s extroversion and the type of content making creativity of the respondents.
Here, the author used Pearson r test, calculated r = 0.56002764.
The author shows r value as positive. Although there is no standard value for discriminant validity, a result less than 0.70 suggests that discriminant validity likely exists between the two scales.
Discussion and analysis
In this study, the researcher draws a conceptual and empirical picture of YouTube content makers of Bangladesh. The author found some empirical relationships among the ease of use, perceived usefulness, social impact, flow, and behavioral intention toward flow of YouTube content.
The research found that the use context of YouTube channel allows functional components to access and subscribe to data from a context object (Table 1). Maximum of the respondents agreed that YouTube channel is enabling the sharing of data across multiple components without prop drilling. The research showed that there is a high disagreement among the answers.
Table 1. Use context of YouTube content makers.
The author found that the maximum participants had a positive attitude toward personal innovativeness, perceived novelty, safety, and trust of YouTube content in Bangladesh (Tables 2–4).
Table 2. Social impact of YouTube content makers.
Table 3. Perceived ease of use YouTube content.
Table 4. Perceived usefulness of YouTube content.
The research showed that the perceived benefits of YouTube content (Table 5) are very dynamic. The YouTuber earns money, reaches more people easily, and smoothly shares content through YouTube channel. Again, YouTube content offers perceived benefits such as entertainment, education, community building, and access to diverse perspectives, while also enabling creators to build a following and monetize their work.
Table 5. Perceived benefit of YouTube content.
The research found that most of the respondents express a positive attitude toward YouTube contents and channels (Table 6).
Table 6. Attitude toward YouTube content.
The research disclosed that most of the respondents agreed that the flow of YouTube content creation is very consistent (Table 7). Here, the YouTuber begins content creation with perfect ideation and planning, followed by video production, editing, and then re-purposing for different platforms and formats, culminating in the final upload and promotion. The author found that the flow of YouTube content creation is executed smoothly.
Table 7. Flow toward YouTube content.
The author showed that most of the respondents express hopeful intentions toward YouTube content and channels (Table 8).
Table 8. Intention toward YouTube content.
The demographic data of the respondents are showed in Table 9. In this research, 210 male and 102 female participated. Most of the respondents’ age lies between 16 and 20.
Table 9. Descriptive data.
Cronbach’s alpha quantifies the level of agreement on a standardized 0–1 scale. Higher values indicate higher agreement between items. High Cronbach’s alpha values indicate that response values for each participant across a set of questions are consistent; the author found that the Cronbach’s alpha is less than 0.5, which indicates low reliability. So there is an inconsistency in these questionnaires, which is because people’s perceptions toward YouTube contents are varied from time to time and space to space (Table 10).
Table 10. Data authentication evaluation.
Cronbach’s alpha is a measure of internal consistency, that is, how closely related a set of items are as a group. For use context Cronbach’s alpha is 0.3736, for social impact Cronbach’s alpha is 0.2615, for perceived use of use Cronbach’s alpha is 0.17, for perceived usefulness Cronbach’s alpha is 0.2099, for perceived benefit the Cronbach’s alpha is 0.1841, for attitude toward YouTube content the Cronbach’s alpha is 0.1745, for flow the Cronbach’s alpha is 0.3016, for behavior intention the Cronbach’s alpha is 0.1762. So, this set of items has a low Cronbach’s alpha value, and these set values have low internal consistency; if a low alpha is due to poor correlation between items, then some should be revised or discarded (Table 11).
Table 11. Evaluation of the consequence of path coefficient.
Here, the author found that the path coefficient values around 0.3 were interpreted as corresponding to medium effect sizes. Here, the path coefficients close to + 1 indicate a strong positive relationship between the variables (Table 11). So, the variables of YouTube channels such as use context, perceived benefit, flow, usefulness, safety, trust, and attitude are positively correlated with each other.
The research found that employed people view more YouTube contents or channels (Table 12).
Table 12. Descriptive analysis (employment).
The research showed that urban people are more engaged with YouTube content creation than rural people of Bangladesh (Table 13).
Table 13. Descriptive analysis (location).
The research also found that married people are more likely to view YouTube channels than never-married and single people in Bangladesh (Table 14).
Table 14. Descriptive analysis (marital status).
This research showed that highly educated people are not very much interested in YouTube channels. The high school and undergraduate people preferred more YouTube content making (Table 15).
Table 15. Descriptive analysis (education).
The research found that a group of people aged 16–20 is more engaged with YouTube content creation rather than senior people in Bangladesh (Table 16).
Table 16. Descriptive analysis (age).
The research found that most of the respondents agreed to the personal innovativeness of YouTube content creation. Unique and innovative YouTube content attracts the attention of viewers (Table 17).
Table 17. Personal innovativeness.
The research showed that perceived novelty is another important feature of YouTube content (Table 18). Novel channels are attracting the attention of more public.
Table 18. Perceived novelty.
The research showed that the maximum number of respondents admit the safety of YouTube content creation (Table 19).
Table 19. Safety.
The author used PTAI to explain the interrelationship between gender and personal innovativeness, location and behavioral intention, employment, attitude, and so on (Table 20).
Table 20. Trust.
The author also found that 60% male and female are innovative on YouTube content, and 40% male and female are noninnovative upon you tube content. So, the percentage of innovativeness is greater than no innovativeness on YouTube content of Bangladesh. Male person are more innovative than female person (Table 21).
Table 21. Observations (gender & personal innovativeness).
Again, the research shows that 61% chance of male and 58% female have a chance to become more innovative through YouTube content in Bangladesh. Notably, there is 39% chance of male having no innovativeness and 41% chance of female having no innovativeness upon YouTube content making (Table 22).
Table 22. Simple and marginal probabilities.
Again, the author found the relationship between location and behavioral intention toward YouTube content of participants. Urban people have more positive behavioral intentions toward YouTube content than rural people. Rural people have more negative behavioral intentions toward YouTube content. Again, urban people have a greater chance of developing positive behavioral intention toward YouTube content. Rural people have a greater chance of developing negative behavioral intentions toward YouTube content (Table 24).
Table 23. Conditional probability.
Table 24. Observations (location & behavior intention to use YouTube content).
The researcher also found that employed people have a more positive attitude toward YouTube content than unemployed people. Again, employed people have a greater chance of developing a positive attitude toward YouTube content than unemployed people (Tables 27–29).
Table 25. Simple and marginal probabilities.
Table 26. Conditional probability.
Table 27. Observations (employment & attitude toward YouTube content).
Table 28. Simple and marginal probabilities.
Table 29. Conditional probability.
The researcher also found that undergraduate people have more positive behavioral intention toward YouTube content than high school passed and graduate person. Again undergraduate people have a greater chance to develop positive behavioral intention toward YouTube content (Tables 30–32).
Table 30. Observations (education level & behavioral intention toward YouTube content).
Table 31. Simple and marginal probabilities.
Table 32. Conditional probability.
High school passed and graduate students have less chance to develop a positive behavioral intention toward YouTube content (Table 30).
The research also shows that male person have more perceived novelty toward content than female person. Again, the female person has a greater chance of developing more perceived novelty than male (Tables 33–35).
Table 33. Observations (gender & perceived novelty).
Table 34. Simple and marginal probabilities.
Table 35. Conditional probability.
This research also found that married people have more positive behavioral intention toward YouTube contents than never-married person and single person. Again, married people have a higher chance of having more positive behavioral intention toward YouTube content than never-married and single person (Tables 36–38).
Table 36. Observations (marital status & behavioral intention toward YouTube content).
Table 37. Simple and marginal probabilities.
Table 38. Conditional probability.
The third statement of safety, “The application of YouTube content will not consequence in endanger my safety Design,” has a more positive attitude toward YouTube content than the other two statements of safety (Tables 39–44).
Table 39. Observations (safety & attitude toward YouTube content).
Table 40. Simple and marginal probabilities.
Table 41. Conditional probability.
Table 42. Observations (trust & flow toward YouTube content).
Table 43. Simple and marginal probabilities.
Table 44. Conditional probability.
The third statement of safety toward YouTube has become more likely to have of positive attitude than the other two statements of safety.
The second statement of trust, “I believe that this you tube content has my best interests in mind.” has a more positive flow toward YouTube content than the other two statements of trust. Again, the second statement of trust has a higher chance of positive flow than the other two statements of trust (Tables 39–44).
Here, the author also performs goodness-of-fit test and validity test. The author shows that there exists a significant association between gender and their attitude toward the benefits of YouTube content (Table 45). Mainly in Bangladesh, women are closely associated with YouTube content or bloggers. The author also shows that there exists a significant association between education level and creativity toward YouTube content making (Table 46). Highly educated people normally operate educational YouTube content. Average and low educated people operate cooking, recreational, political YouTube channel, others are business YouTubers. The author also shows that there is no significant association between employment status and average time spent on YouTube content making (Table 47). Employed people may spend more time making valuable YouTube content.
Table 45. Gender and attitude.
Table 46. Education level and creativity.
Table 47. Employment and time spent.
For validity test purposes, the author found content validity rate=0.076923076 (Table 48), which is positive, indicating at least half of the respondents rated the YouTube content as essential for learning, recreational, and innovation sectors. For the convergent validity test, the author gets r=0.56002764 (Table 49), the positive value indicates that there exists a positive correlation between earnings through YouTube and the number of YouTube content made by the YouTuber. For discriminant validity, the author gets, r= 0.56002764 (Table 50), which indicates that discriminant validity likely exists between the two scales respondent’s extroversion and type of content making creativity of the respondents’.
Table 48. Content validity table.
Table 49. Profit and frequency.
Table 50. Extroversion and creativity.
Limitations
There are some limitations in the existing research. First, the data was only gathered from only one country, Bangladesh. Second, the emphasis of this research is as little as a YouTube blogger as the research approves the thoughtfulness of online YouTube content. Third, in this research, the data was congregated from a group of 16- to 35-year-old individuals, in spite of the fact that the research recommend taking into account data of those beyond 35 years old since, in a lot of areas in Bangladesh, YouTube content are extremely favored/well-liked of higher than 35-years-old age group.
Conclusions
This research contributes a thorough assessment of YouTube content makers’ admiration in Bangladesh. The hypothetical model of this research is enclosed due to case investigation points influencing YouTube content making and the occupied TAM theory.
This research was conducted on 312 respondents, with eight factors that are paramount in favor of taking into account YouTube content. This research provides a discussion of behavioral intention and assists the YouTube content maker in getting money.
The research focused on the usefulness of YouTube contents or channels. Maximum of the respondents agreed about the usefulness of YouTube content. The research also explains how various social factor such as employment status, location, and marital status influence the YouTube content-making decision.
In compliance with the outcomes derived from the PTAI model and probability theory, it is evident that perceived usefulness, attitude, and benefits are salient components for the YouTube content creation industry. Social impact plays an important role in the decision for YouTube content creation. Various factors such as gender, level of education, marital status, and employment status are directly associated with the novelty and behavioral intentions related to YouTube content creation. The education level of respondents is directly associated with the type of content they create.
It is transparent from the research that perceived usefulness, social impact, and trust are the most important factors for YouTube content creators or bloggers. Hence, this study concludes that successful YouTube content creators are more likely to invest significant effort into providing informative content in an extensively attainable practice/system, as well as offering clear and explicit value to their audience. In the future, we will evaluate our ideas through diverse designs and additional strategies.
Implications & recommendation
Accordingly, this research is beneficial for both the current and future online content creation and online game developers of this gig economy. It will help individuals understand customer preferences and perceptions of the online platform. Thus, creators should incorporate these findings into their strategies to enhance their online presence and ameliorate business growth through customer satisfaction.
The results of this research could also serve as a reference for other countries, especially developing nations like India, Bhutan, Pakistan, various countries in Africa, and Indonesia, suggesting that TAM is an important variable in YouTube content creation.
To generate higher quality research, it is recommended to conduct a similar study with a larger sample size. Future researchers may consider testing the same constructs on other social platforms, conducting studies in different countries, or exposing various variables such as creativity, certitude, sociotechnology platforms, and social influence to investigate their impact on attitude toward YouTube content or the intention to become a YouTuber.
Funding
This research was not funded.
References
1. Aziz MNA, Kamaruddin N. The evolution of the technology acceptance model (TAM). The Interdisciplinary of Management, Economic and Social Research. Tanjung Malim: Kaizentrenovation Sdn Bhd (2020). 242 p.
2. Gupta S, Abbas A. Technology acceptance model (TAM): a bibliometric analysis from inception. J Telecommun Digit Econ (2022) 10(3):77–106.
3. Herlina, Widyaningrum D. Menakar Kecenderungan Perilaku impulse buying Belanja online Generasi Millenial pada Masa Covid-19. Formosa J Multidiscip Res (2022) 1(3):481–92.
4. Hadianto A. Kajian Tentang Perilaku Pengguna Sistem Informasi Dengan Pendekatan Technology Acceptance Model (TAM). Academia (2008).
5. Davis FD. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly (1989) 13(3):319–40.
6. Permana LGP, Astawan PMA, Laskmi KW. Analisis Penggunaan Paylater dalam Transaksi digital dengan Konstruk UTAUT 2. Jurnal Akuntansi Dan Keuangan (2022) 9(2):84–91.
Bibliography
7. Pamoro GJ. The principle of balance E-contracts in paylater features in E commerce. Notaire (2022) 5(3):377–94.
8. Putri FA, Iriani SS. Pengaruh Kepercayaan dan Kemudahan terhadap Keputusan Pembelian Menggunakan Pinjaman online shopee paylater. Jurnal Ilmu Manajemen (2020) 8(3):818.
9. Rahayu SW, Gani IA. Perlindungan data pribadi konsumen pada transaksi paylater. Jurnal Hukum Magnum Opus (2022) 5(1).
10. Rahayu T. Analisis Akad Jual Beli E-commerce shopee paylater dalam Perspektif Ekonomi Islam. Iqtishodiah (2021) 3(2):1–15.
11. Sari R. Pengaruh Penggunaan Paylater Terhadap Perilaku impulse buying Pengguna E-commerce di Indonesia. Jurnal Riset Bisnis Dan Investasi (2021) 7(1):44–57.
12. Solihin AM, Kosasih AD, Fajrussalam H, Rahmawan DT, Azzahra DA, Fitriyani FM. Analisis Transaksi shopee paylater Dalam Perpektif Hukum Islam. Jurnal Pendidikan Guru (2022) 3(4):284–8.
13. Waluyo M. Gambar 1.1 Measurement model menyeluruh. In: Basuki I , editor. Mudah Cepat Tepat Penggunaan Tools Amos Dalam Aplikasi (SEM). UPN Veteran (2016).
© The Author(s). 2024 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.