INTRODUCTION
Background of the research
Since Vietnam's accession to the WTO in 2007, the country has experienced rapid economic growth, particularly in trade and services This has led to an influx of foreign companies, including joint ventures and affiliates, creating numerous job opportunities for Vietnamese workers As a result, both the economy and labor market in Vietnam have been positively impacted According to the Ministry of Labor, Invalids and Social Affairs, there has been a significant increase in career opportunities and employee incomes in the years following Vietnam's WTO membership.
2011), unemployment rate in urban decreased 0.9% from 5.1% in 2006 to 4.2% in
2011 and the average income per employee has two-fold increase from 1.8 million VND in 2006 to 3.84 million VND in 2011 (Hà Anh, 2012).
Current students represent the next generation of high-quality employees in Vietnam, facing both opportunities and challenges in the international labor market While they possess theoretical knowledge gained during their university years, many graduates lack essential skills, particularly in communication and teamwork, necessary to navigate real-world work environments According to Yamashita, the general director of Tokyo Mitsubishi Bank in Ho Chi Minh City, new employees often struggle to apply their knowledge practically, leading to difficulties in problem-solving and reluctance to engage with supervisors These challenges highlight a significant gap in universities' focus on equipping students with practical skills and extracurricular experiences during their studies.
Extracurricular activities, despite concerns about their impact on academic performance, have been shown to enhance self-esteem, foster social connections among students, teachers, and parents, and contribute to positive educational outcomes Additionally, participation in these activities is associated with lower rates of engagement in risky behaviors, highlighting their overall benefits for student development (Broh, 2002; Dole, 2000; Carns et al., 1995).
Participating in extra-curricular activities during university not only impacts students' academic performance but also provides essential opportunities to develop vital skills such as presentation, communication, and teamwork, which are crucial for future careers While the effects of extra-curricular involvement have been widely studied, there is a notable lack of research focusing on the factors influencing participation, particularly in Vietnam Understanding these factors is essential for university managers to encourage student engagement in extra-curricular activities effectively.
Understanding the key factors influencing student participation in extra-curricular activities is essential for universities Engaging in these activities is positively correlated with academic performance, enhancing the institution's reputation in both academics and extracurriculars Consequently, a research study titled "Antecedents of Student's Intention to Participate in Extra-Curricular Activities" will be conducted to investigate these factors within Vietnamese universities.
Research objectives
The main objectives of the research are:
- To identify antecedents of student’s participation in extra-curricular.
- To investigate the moderating effects of “School year”, “gender”, and “job after school” on the relationship between identified factors and decision to participating in extra-curricular.
Research scope
This study confines itself in the investigation of students in the universities in
Ho Chi Minh city, concretely: University of Economics, Open University.
This study focuses on sophomore and junior university students, as freshmen are new to the academic environment and seniors are occupied with their final thesis, leaving them little time for extracurricular activities.
Structure of the study
This research article is structured into five chapters, beginning with an introduction in Chapter 1 that provides an overview of the study Chapter 2 reviews the relevant literature on the theory of planned behavior and signaling theory, introducing the hypotheses and research model Chapter 3 outlines the research methodology, detailing the sample selection, size, research instruments, procedures, and statistical techniques used for data analysis Chapter 4 presents the data analysis and highlights key findings from the study Finally, Chapter 5 discusses the results, draws conclusions based on the findings, and suggests practical implications, along with recommendations for future research.
LITERATURE REVIEW
Human behavior theories
Understanding human behavior is a complex challenge (Ajzen, 1991) Despite this difficulty, numerous researchers have explored this area to gain a comprehensive understanding of human actions Investigating the intention to engage in extracurricular activities can be informed by various theories of human behavior Several established models exist to effectively predict human behavior in this context.
Theory of reasoned action (TRA)
The Theory of Reasoned Action (Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975) aims to elucidate human behavior in relation to voluntary actions This model focuses exclusively on behaviors that are spontaneous, habitual, and voluntary, intentionally excluding those that are forced, constrained, or require special skills and resources (Liska, 1984) Intention serves as the primary predictor of human behavior, with two key components influencing it: an individual's attitude toward performing the behavior and their subjective norms.
(2) one’s subjective norm related to performing behavior (Subjective norm) The attitude factor can be measured by multiplying the one’s beliefs about the
Attitude towards behavior involves assessing the consequences of that behavior, which reflects the strength of one's beliefs This includes evaluating whether the outcomes are perceived as positive or negative, ultimately shaping an individual's feelings about the behavior itself (Fishbein & Ajzen, 1975).
Subjective norm refers to an individual's beliefs about what significant people in their life think they should or should not do, along with their motivation to adhere to these opinions Essentially, it encapsulates the perception that influential individuals or groups expect certain behaviors from the person, as outlined by Fishbein and Ajzen in 1975.
The Theory of Reasoned Action (TRA) has been extensively applied across various fields, including education, smoking, seat-belt use, and voting behavior, demonstrating its effectiveness in understanding decision-making processes Research indicates that approximately 30% of the variance in behavioral intention is attributed to attitudinal factors, while normative influences appear to have a lesser and less stable impact Intention is a crucial predictor of actual behavior, with a review of 12 studies revealing a strong correlation between intention and behavior, averaging 0.55 (Gaston, 1994).
This model's limitation lies in its assumption that behavioral intention is solely within volitional control, focusing only on an individual's contemplation of action It fails to account for non-motivational factors, such as the availability of essential resources and opportunities, including time, money, skills, and cooperation Despite these shortcomings, this model serves as a foundational framework for subsequent theories.
Technology acceptance model (TAM) was originally proposed by Davis in
In 1986, Davis introduced the Technology Acceptance Model (TAM), building upon the Theory of Reasoned Action (TRA) TAM is widely recognized for its effectiveness in predicting user acceptance and utilization of information technology within organizations According to Davis, an individual's attitude toward using new technology serves as a key predictor of their decision to adopt it.
Davis posits that an individual's attitude towards a system significantly predicts its actual usage This attitude is primarily shaped by two key factors: Perceived Usefulness and Perceived Ease of Use Perceived Usefulness, as defined by Fred Davis, refers to the extent to which a person believes that utilizing a specific system will improve their job performance.
Perceived Ease of Use of-use is defined as "the degree to which a person believes that using a particular system would be free from effort".
Figue 2.2: TAM model Many researchers have conducted a number of researches to test TAM such as Selim (2003) investigated TAM with web-based learning; Constance & Naveen
(2006) use TAM test about Internet usage, so on However, this model is suitable for technology field, it is not adequate for social activities as extra-curricular in this study.
Theory of Planned Behavior (TPB)
The Theory of Planned Behavior (TPB), introduced by Ajzen in 1987, expands upon the original Theory of Reasoned Action (TRA) by addressing its limitations While TRA posits that intention is influenced by motivational factors such as attitude toward the behavior and subjective norms, it overlooks the impact of non-motivational factors Individuals may have the desire to engage in certain activities, but if they lack the necessary opportunities or capabilities—such as time, money, skills, or support from others—they may not intend to act The TPB incorporates three key components: (1) Attitude toward the behavior, (2) Subjective norm, and (3) Perceived behavioral control, the latter of which reflects an individual's actual ability to perform a behavior This addition is crucial for understanding human intention and behavior in the context of the theory (Ajzen, 1991).
The theory of planned behavior (TPB) has been widely utilized across various fields to predict human behavior, demonstrating its effectiveness in understanding diverse actions Research by Ajzen and Driver (1992) highlights the TPB's application in forecasting leisure intentions among college students, identifying three key predictors: attitudes toward the behavior, subjective norms, and perceived behavioral control Their findings establish a clear link between intention and actual behavior Similarly, Parker et al (1992) employed the TPB to assess driver responses in situations like drinking and driving, speeding, and risky overtaking maneuvers, confirming that these three independent factors significantly influence intentions.
This model is ideal for examining curricular activities, as a student's intention to engage in extracurricular activities is influenced not only by their personal attitudes but also by relationships with family and friends, as well as their abilities to participate effectively.
Research model and hypothesis
The research model, based on the Theory of Planned Behavior (TPB), is depicted in Figure 2.1 (page 18) and identifies four key factors influencing the intention to participate in extracurricular activities: (1) Attitude toward extracurricular involvement, (2) Subjective norms regarding extracurricular activities, (3) Perceived behavioral control, and (4) Clarity of extracurricular information, which is derived from signaling theory These four independent variables collectively impact the dependent variable in the final model, with further discussions provided in the subsequent sections.
According to Azjen (1991), Attitude toward behavior has an effect on intention to perform an action In addition, many conducted studies have figured out this relationship (Budd, 1986; Netmeyer & Burton, 1990)
This study examines students' attitudes toward extra-curricular activities, highlighting their perceptions of the benefits such as enhanced relationships, knowledge, and skills Students are likely to participate in extra-curricular activities if they believe these engagements will provide valuable advantages.
Schwartz's theory reinforces the connection between values and decision-making, particularly through the Self-direction value outlined in his Theory of Basic Human Values (1992, 2005) This value emphasizes the importance of individuals evaluating and selecting actions based on their ability to exert control and mastery over their circumstances to achieve personal goals Consequently, people assess the outcomes of their actions to determine what is beneficial or detrimental, guiding their choices accordingly.
Hypothesis 1: There is a positive relationship between Attitudes toward extra- curricular and Intention to take part in extra-curricular activities.
Subjective norm toward extra-curricular
Subjective norm refers to the social pressure individuals feel regarding the expectations of significant others about their actions According to Azjen (1991), there is a positive correlation between subjective norm and intention, suggesting that the opinions of important people can influence one’s decision-making Additionally, the conformity value from the Theory of Basic Human Values supports Azjen's perspective by highlighting the tendency of individuals to avoid actions that may upset or harm close relationships, such as those with parents, teachers, and bosses This value encompasses traits like self-discipline, obedience, politeness, and respect for elders (Schwartz, 2006) Consequently, individuals are inclined to align their behaviors with social norms and the expectations of those they value.
A student's participation in extracurricular activities is influenced by the perceptions of significant individuals in their life, such as parents, teachers, and friends This leads to the hypothesis that the encouragement and expectations from these key figures play a crucial role in motivating students to engage in such activities.
Hypothesis 2: There is a positive relationship between Subjective norm toward extra-curricular and Intention to take part in extra-curricular activities.
Perceived behavioral control, the final component of the theory of planned behavior, reflects an individual's ability to execute a specific action This concept parallels Schwartz's theory of basic human values, where he identifies a similar value known as security Security arises from fundamental individual and group needs, as outlined by Kluckhohn (1951) and Maslow (1965), indicating that people are inclined to engage in behaviors that are perceived as safe and easily achievable.
Students are more likely to participate in extra-curricular activities if they perceive them as easily manageable and have the necessary resources, such as time, finances, and transportation.
Hypothesis 3: There is a positive relationship between Perceived behavior control and Intention to take part in extra-curricular activities.
Clarity of extra-curricular information
Information significantly influences decision-making in households, businesses, and government, with individuals relying on both public and private information (Connelly et al., 2011) However, the information available to the receiver often differs from what the sender possesses Signaling theory addresses this issue by aiming to reduce information asymmetry between parties (Spence, 2002), making it essential for understanding interactions when individuals or organizations have access to varying information This theory is particularly relevant in management fields such as entrepreneurship, strategic management, and human resource management (Connelly et al., 2011).
Numerous studies have explored the effectiveness of signaling in various contexts Connelly et al (2011) reviewed earlier findings, highlighting that increased signaling can enhance effectiveness, as demonstrated by Chung and Kalnins (2001) Zhang and Wiersema (2009) found that more visible signals lead to greater impact, while Filatotchev and Bishop (2002) emphasized that a higher frequency of signals improves the chances of accurate interpretation Furthermore, Janney and Folta (2006) identified that effective signals are observable, irreversible, governed, and credible.
In MBA education programs, research by Tho (2009) indicates that the quality of signals significantly influences perceived quality The effectiveness of these signals is crucial for shaping customer perceptions Therefore, this study will examine the connection between the quality of signals related to extracurricular activities and students' attitudes.
A quality signal encompasses three essential components: clarity, credibility, and consistency (Erdem and Swait, 1998) This study focuses specifically on the clarity factor, examining how universities implement extracurricular programs to benefit students rather than for profit, which enhances the credibility of the information provided In an educational context, it is crucial that the information shared is accurate and trustworthy The key issue addressed is whether universities effectively communicate information to students, ensuring it is easily accessible and clearly understood Thus, the clarity of information is the primary focus of this study regarding signal quality.
Clear extracurricular information can enhance students' understanding and appreciation of these activities, positively influencing their attitudes This research aims to examine the relationship between the clarity of extracurricular information and students' attitudes towards participating in such activities Additionally, it will investigate whether clear information impacts students' intentions to engage in extracurriculars Ultimately, if students receive sufficient and relevant information, it may lead to a positive attitude towards these activities and a greater likelihood of participation.
Hypothesis 4a: There is a positive relationship between clarity of extra- curricular information and Attitudes toward extra-activities.
Hypothesis 4b: There is a positive relationship between clarity of extra- curricular information and Intention to take part in extra-curricular activities.
Numerous studies have highlighted the differences between men and women, particularly in physical and psychological aspects Research by Wright et al (2008) indicates variances in brain hemisphere usage, while Chodorov (1990) suggests that women tend to be more relational, whereas men exhibit greater autonomy Gilligan (1982) emphasizes that women prioritize care and responsibility, in contrast to men's focus on justice and fairness Furthermore, Schwartz (2005) notes that women often embody benevolence and universalism, while men are more inclined towards power and achievement Given these distinctions, this study aims to explore the impact of gender on the relationship between three factors and the intention to participate in extracurricular activities.
H5a: Gender plays the role as moderating variable in research model
Education experiences significantly enhance intellectual openness and flexibility, leading to diverse perspectives among individuals (Kohn & Schooler, 1983) Consequently, varying levels of education influence behavior, particularly in relation to achievement values (Schwartz, 2006) This study will examine how a student's year in university serves as a mediating variable that affects their involvement in extracurricular activities.
H5b: School Year variable plays the role as moderating variable in research model
Part-time jobs have become increasingly common among students, with roles such as tutoring, waiting tables, and sales being popular choices These jobs provide students with a source of income to support their expenses during their academic life while also allowing them to gain valuable experience and skills However, working after school can limit students' availability for other activities and may influence their attitudes differently compared to their peers with more free time This study aims to examine the effects of after-school employment as a mediating variable in the lives of students.
H5c: Job after school variable plays the role as moderating variable in research model
Clarity of extra-curriculum information
Intention to participate extra-curricular
Attitudes toward extra- curricular activities
Subjective norm toward extra curricular Gender
RESEARCH METHODOLOGY
Research method
The research was conducted through two main stages: qualitative research and quantitative research.
Due to a lack of research on the intention to participate in extracurricular activities using the Theory of Planned Behavior (TPB), there appears to be no existing scale specifically measuring this intention Additionally, the application of signaling theory in the context of extracurricular activities has not been explored Consequently, this study combines the TPB scale and signaling scale from other fields with qualitative research to adapt these scales for the extracurricular domain For the TPB scale, references were made to the works of Ya-Yueh Shih et al (2004) and Mohammad Reza Jalilvand et al (2012), while the clarity of extracurricular information from signaling theory was informed by Tho (2009) The original scales for these factors are provided in Appendix A.
Qualitative research was carried out using focus groups to identify key factors, refine terminology, and create a standardized scale for subsequent quantitative research Three focus groups, each consisting of 5-6 sophomore and junior students, were organized Additionally, the author conducted two in-depth interviews with experts overseeing student affairs at the University of Economics Ho Chi Minh City.
The key finding of this research highlights the potential link between students' awareness of extracurricular activities and their intention to engage in these activities Additionally, the author adapted the Theory of Planned Behavior (TPB) scale to better fit the educational context in Vietnam, with detailed modifications provided in the appendix.
Quantitative research was used to test whether the hypotheses were confirmed or ignored In this stage, quantitative data was collected by using questionnaires.
A questionnaire is a structured tool used to gather information from respondents (Malhotra, 1996) Following qualitative research, a draft questionnaire was created and tested through a pilot study involving 60 samples to evaluate its effectiveness.
The final questionnaire of the study comprised two key sections: the first focused on demographic variables such as gender, school year, and employment status, while the second section featured 17 items assessing four independent factors and 3 items for a dependent factor Responses were recorded using five-point Likert-type scales, where 1 indicated "strongly disagree" and 5 denoted "strongly agree."
In this study, the scale of TPB were used to modify for measuring four factors:
(1) Attitudes toward extra-activities, (2) Subjective norm, (3) Perceived behavioral control, and (4) Intention Clarity of extra-curricular information was modified from the scale of Tho (2009)
Table 3.1: The official scale Attitudes toward extra-curricular
1 I believe that I can get good activity mark when I take part in extra-curricular.
2 I believe that I can learn many good skills when I take part in extra-curricular.
3 I believe that I can get many benefits when I take part in extra-curricular.
4 I believe that I can expand my social network when I take part in extra- curricular
5 I feel interesting when I take part in extra-curricular.
Subjective norm toward extra-curricular
1 My parents would think that I should take part in extra-curricular.
2 My teachers would think that I should take part in extra-curricular.
3 My classmates would think that I should take part in extra-curricular.
4 My best friends would think that I should take part in extra-curricular.
1 I believe that I can perform extra-curricular well
2 I have capability to take part in extra-curricular.
3 I have enough resources to take part in extra-curricular
4 I believe that I have enough skills to perform extra-curricular easily
Clarity of extra-curricular information
1 This university provides clear information about extra-curricular activities
2 This university always provides sufficient information about extra-curricular activities
3 I have no trouble to find out the information about extra-curricular activities
4 I have no trouble figuring out what this university is trying to provide for students.
Intention to participate in extra-curricular
1 I have intention to take part in extra-curricular
2 I will take part in extra-curricular when I have information
3 I will take part in extra-curricular in next course
Research process
After reviewing literature, the draft scale was developed This scale would be adjusted in qualitative research with three focus groups and two in-depth interviews
Literature Draft Qualitative research (n=3) scale
The study utilized Cronbach's Alpha to assess reliability, followed by exploratory factor analysis to identify underlying relationships among variables Linear regression was employed to analyze the data, and moderator effect testing was conducted to refine the official scale After distributing the final questionnaire to students, raw data was meticulously screened to exclude unsuitable respondents The final dataset underwent tests for reliability and discriminant validity before performing linear regression and evaluating moderator effects.
Research sampling
Hair et al (2003) recommend using a minimum of five observations to accurately estimate a measurement variable Given that this study included 19 items, a minimum of 100 observations was required (20 observations per item) To enhance the validity of the research, a total of 350 questionnaires were distributed, with the expectation that at least 300 would yield suitable responses.
Due to constraints related to time and budget, the author utilized a convenient sampling method for this study Questionnaires were distributed to colleagues, who were asked to share them with their students for data collection.
Methods of data analysis
The study utilized the Statistic Package for Social Science (SPSS) software for data analysis and presentation Initially, raw data was imported into the SPSS spreadsheet, where it was coded according to the study's objectives Subsequently, various statistical tools were employed, including descriptive statistics, reliability analysis, and exploratory factor analysis (EFA), to ensure the scales were appropriate for the subsequent regression analysis.
Descriptive statistic was actually used to report the characteristics of the data such as mean, frequency, standard deviations, percentage and range (Sekara, 2006).
This study employed descriptive statistics to analyze the data, focusing on the balance of samples concerning gender, school year, and employment status by examining the frequency and percentage of each variable This approach effectively facilitates comparisons among the demographic groups of respondents.
Reliability testing was conducted to assess the measurement items of the variables in the questionnaires, focusing on the internal stability and consistency of each question This evaluation is quantified using Cronbach’s alpha coefficient, as outlined by Nunnally and Bernstein.
1994) According to Hair et.al (2003), the Rules of Thumb about Cronbach Alpha Coefficient as follows:
Table 3.2: Rules of Thumb about Cronbach Alpha Coefficient Size
Alpha Coefficient Range (α) Strength of Association
When conducting research in a new environment or with a completely new concept, a Cronbach’s Alpha index of 0.6 is considered acceptable (Trọng & Ngọc, 2008) Additionally, Nunnally and Bernstein (1994) recommend eliminating any items with an item-total correlation index below 0.3.
Research on the intention to participate in extra-curricular activities based on the Theory of Planned Behavior (TPB) is a novel area of study in Vietnam The study establishes that a Cronbach’s Alpha of 0.6 is the accepted standard for the scale, while an item-total correlation index of 0.3 is deemed acceptable for individual items.
Factor Analysis (FA) is a key technique used to uncover the underlying structure within data by assessing the correlations among numerous variables This method, characterized by its interdependence approach, effectively condenses information from a wide array of variables into distinct factors based on their relationships (Hair et al., 2007).
Exploratory Factor Analysis (EFA) is a valuable technique for data reduction and summarization, enabling researchers to identify essential variable groups relevant to their research problems and uncover the interrelationships among these variables When conducting EFA, researchers typically consider specific criteria to ensure accurate and meaningful results.
The Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s Test are essential tools for evaluating the suitability of the Exploratory Factor Analysis (EFA) method A KMO value between 0.50 and 1 indicates that EFA is applicable, as established by Kaiser in 1974 Additionally, Bartlett’s Test assesses the null hypothesis that variables within the population correlation matrix are uncorrelated; a significance level below 0.05 suggests a strong relationship among the variables, allowing for the rejection of the null hypothesis.
It is a good idea to proceed a factor analysis for the data.
The Kaiser criterion is a valuable tool for identifying significant factors extracted from scales by eliminating less important ones It focuses on Eigenvalues, which indicate the amount of variation explained by each factor According to Anderson and Gerbing (1988), only factors with Eigenvalues equal to or greater than 1 should be retained in the model.
Variance explained criteria: the total variance explained should not be lower than 50% (Anderson & Gerbing, 1988).
To satisfy discriminant validity of the scale: the differences of factor loading values between variables in different factors should be equal or bigger than 0.30 (Jabnoun et al., 2003).
Factor loading is a crucial indicator of the practical significance of the Exploratory Factor Analysis (EFA) method, as outlined by Hair et al (1998) A factor loading of 0.30 is the minimum threshold, while 0.40 is necessary, and 0.50 indicates practical significance Additionally, Hair et al recommend a minimum sample size of 350 for loadings of 0.30, while a sample size of around 100 requires a loading of at least 0.55, and a sample size of about 50 necessitates a loading of 0.75 In this research, with a sample size of 302 items, any observed variables with a factor loading of 0.50 or lower will be excluded.
In this study, the author employs principal component analysis with Varimax rotation to assess the unidimensionality of the intention to use scale, effectively reducing the number of variables involved.
This study employed Multiple Linear Regression analysis to investigate the simultaneous impact of various independent variables—Attitudes toward extra-curricular activities, Subjective norm toward extra-curricular, Perceived behavioral control, and Clarity of extra-curricular information—on the dependent variable, Intention to participate in extra-curricular activities (Cavana et al., 2001) Additionally, Simple Linear Regression was utilized to explore the relationship between the Quality of extra-curricular information and Attitudes toward extra-activities Five hypotheses were formulated and analyzed through regression analysis to identify potential relationships between the independent and dependent variables within the study's context.
To examine the impact of moderator variables, author had used Chow test. The null hypothesis of Chow test is: “there is not a structural break in the model”.
If the Chow statistic is lower than the critical value from the F-Tables, we accept the null hypothesis, indicating that the testing variable does not serve as a moderator However, this conclusion may be reconsidered if we accept the moderator function of the testing variable.
This chapter outlines the research methodology utilized in this study, detailing the research framework, sample design, and data collection procedures Additionally, it highlights the statistical tools employed for data analysis.
DATA ANALYSIS & RESULTS
Descriptions of sample
This study focused on sophomore and junior university students, distributing a total of 350 questionnaires Out of these, 304 students responded, resulting in 278 valid responses after data verification Only questionnaires with complete answers were included in the accepted sample for the research.
- Gender : Among the 278 qualified respondents, 137 respondents (49.3%) were male, and 141 (50.7%) were female.
- In terms of educational level : 125 respondents (45%) were sophomores, and
- Working: In the research sample, there were 115 students work after school
(41.4%) and 163 students did not have any job (58.6%).
Figure 4.3: Working Table 4.1: Demographic characteristics
School year Gender Job after school
Measurement scale
In this study, the measurement scales were evaluated by two methods: Cronbach’s Alpha coefficient and Exploratory Factor Analysis (EFA).
To test reliability of scales, the Cronbach’s Alpha statistic was used in this study The Cronbach alpha coefficients in Table 4-2 showed that measurement scales were reliable.
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted Attitudes toward extra-curricular (ATT) : Cronbach alpha = 0.724
Subjective norm toward extra-curricular (NORM) : Cronbach alpha = 0.689
Perceived behavioral control (PBC): Cronbach alpha = 0.812
Clarity of extra-curricular information (INFO):Cronbach alpha = 0.720
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
Table 4.2 presents five items assessing attitudes toward extracurricular activities (labeled ATT1 to ATT5) The Cronbach’s Alpha value for this scale was 0.724, indicating good internal consistency Additionally, all items demonstrated strong Corrected Item-Total Correlation, with the lowest value being 0.414 for ATT1, confirming the acceptance of all items in the scale.
There were 04 items measuring Subjective norm toward extra-curricular
The analysis revealed a Cronbach’s Alpha value of 0.689 for the scale from NORM1 to NORM4, indicating acceptable reliability, with all items demonstrating strong Corrected Item-Total Correlation, the lowest being 0.322 for NORM4 Notably, removing NORM4 increased the Cronbach’s Alpha to 0.712, suggesting its elimination in future analyses, while the remaining items were deemed acceptable.
The study assessed four items related to Perceived Behavioral Control (PBC1 to PBC4), achieving a Cronbach’s Alpha value of 0.812, indicating good internal consistency All items demonstrated strong Corrected Item-Total Correlation, with PBC4 having the lowest value at 0.452 Notably, if PBC4 were removed, the Cronbach’s Alpha would rise slightly to 0.846, suggesting minimal impact on overall reliability.
The Corrected Item-Total Correlation for PBC4 was notably high at 0.452, exceeding the threshold of 0.4, which justifies its inclusion in the scale Consequently, all items within this scale were approved for subsequent analysis.
There were 04 items for Clarity of extra-curricular information (from
The study demonstrated a Cronbach’s Alpha value of 0.720, indicating good internal consistency among the items Furthermore, all items exhibited high Corrected Item-Total Correlation, with the lowest value being 0.397 for INFO3, confirming the acceptance of all scale items.
The study assessed three items related to Intention (INT1 to INT3), achieving a Cronbach’s Alpha value of 0.694 Each item demonstrated strong Corrected Item-Total Correlation, with the lowest value being 0.482 for INT1, confirming the acceptance of all items in the scale.
After reliability testing, all of items (excepted NORM4) were accepted in next analysis In factor analysis, hence, there were 16 items for independent variables and 03 items for dependent variable.
Exploratory factor analysis (EFA)
4.3.1 Assessing the scales measuring four independent factors
In the initial Exploratory Factor Analysis (EFA) involving 16 items across four factors, item PBC4 was removed due to its loading on two factors, with values of 0.443 and 0.499 Similarly, in the subsequent EFA, item INFO3 was deleted for the same reason, exhibiting factor loadings of 0.450 and 0.448 Further details can be found in the appendix.
The final Exploratory Factor Analysis (EFA) retained 14 items, demonstrating a high KMO indicator of 0.730, exceeding the acceptable threshold of 0.50 Additionally, the Chi-Square value from Bartlett’s test was 1380.263, with a significant p-value of 0.000, which is less than 0.05 Consequently, the null hypothesis stating that the variables in the population correlation matrix are uncorrelated was rejected, confirming the applicability of the EFA method in this research.
Table 4.3: KMO and Bartlett's Test for independent variables Kaiser-Meyer-Olkin Measure of Sampling
In order to identify how many factors could be extracted from 14 items, extraction method Principal Component Analysis (PCA) with Varimax Rotation had been run to discover the result.
Extraction Method: Principal Component Analysis.
The exploratory factor analysis (EFA) conducted on 14 items of the independent variable revealed four distinct factors, each with an Eigenvalue greater than 1 The final Eigenvalue was recorded at 1.177, indicating that these four factors collectively accounted for 63.613% of the total variance in the data.
Table 4.5: Rotated Component Matrix for independent variables
Extraction Method: Principal Component Analysis
Rotation Method: Varimax with Kaiser Normalization a Rotation converged in 5 iterations.
The final results of the Exploratory Factor Analysis (EFA) indicate that all items performed well, as they distinctly loaded onto a single factor Notably, the items ATT4 and NORM1 exhibited dual factor loadings, with the difference in loading values between the factors being less than 0.30; specifically, the difference for ATT4 was 0.268.
1.298 for NORM1 (near 0.3) so these two items would be considered to keep in next regression analysis.
As above, all items were related to four factors Two rejected items were INFO3 and PBC4 The result leaded to the conclusion as follows:
Factor 1 would consist of five items ATT1, ATT2, ATT3, ATT4, ATT5 and was defined to be Attitudes toward extra-curricular (ATT)
Factor 2 would consist of three items PBC1, PBC2, PBC3 and was defined to be Perceived behavioral control (PBC)
Factor 3 would consist of three items NORM1, NORM2, NORM3, and was defined to be Subjective norm (NORM).
Factor 4 would consist of three items INFO1, INFO2, INFO4, and was defined to be Clarity of extra-curricular information (INFO).
In conclusion, all four factors had convergent, discriminative validity.
4.3.2 Assessing the scale measuring dependent variable
Having the same method to analyze, the dependent variable “Intention” (INT) was combined from INT1, INT2, INT3 with KMO= 668 and its significant value equaled 0.000 (p