Research on factors affecting the living and learning habits of gen Z generation: The case of students of the school of economics - Hanoi university of industry

 Corresponding author: Nguyen Thi Thuy Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0. Research on factors affecting the living and learning habits of gen Z generation: The case of students of the school of economics - Hanoi university of industry Thuy Thi Nguyen *, Tuyet Thi Duong, Trang Huyen Thi Hoang, Ha Van Thi Nguyen and Ha Thu Thi Mai Faculty of Management, School of Economics - Hanoi University of Industry World Journal of Advanced Research and Reviews, 2025, 26(02), 702-715 Publication history: Received on 14 March 2025; revised on 03 May 2025; accepted on 06 May 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.26.2.1718 Abstract This study aims to identify and analyze the factors affecting the living and learning habits of Gen Z, with the survey subjects being students of the School of Economics – Hanoi University of Industry. Through quantitative research methods combined with qualitative, data were collected from 401 students using survey questionnaires and group interviews. The results of the analysis show that the main factors affecting Gen Z's living and learning habits include: learning environment, friends around them, lecturers, family – society, the influence of technology and their own cognitive methods. In particular, methods and self-awareness have the strongest impact on the organization of personal activities and learning efficiency of Gen Z. Next is the influence of technology. The study also showed differences in habits between groups of students by school year and gender. From the results achieved, the article proposes a number of solutions to help Gen Z students form living and learning habits. Keywords: Gen Z generation; Living habits; Study habits; Students; Hanoi University of Industry 1. Introduction In the context of increasingly deep digital transformation and globalization, the Gen Z generation – a group of people born between the late 1990s and early 2010s – is becoming the main learning and labor force in society. Unlike previous generations, Gen Z has matured in a thriving technology environment, with the ubiquitous presence of the internet, smartphones, and social networks. These factors not only change the way they access information, but also profoundly affect their living and study habits. In Vietnam, Gen Z currently accounts for a large proportion of universities and colleges, especially among students studying at higher education institutions. Understanding the factors affecting the living and learning habits of Gen Z not only helps schools, lecturers and parents better understand the characteristics of this generation, but also creates a basis for building appropriate educational policies and methods, improve training efficiency. Although there have been many studies in the world and in Vietnam mentioning the relationship between technology, social networks, educational environment and student learning behavior, a comprehensive and systematic analysis of factors affecting Gen Z's living and learning habits in a specific context is still limited. Therefore, this study was conducted to identify and evaluate the factors affecting the living and learning habits of Gen Z students, with the survey subjects being students of the School of Economics – Hanoi University of Industry. Theoretical basis and research methods. 1.1. Theoretical basis Research by the Pew Research Center (2018) has determined that the popularity of social media platforms among young people, especially YouTube, Instagram, and Snapchat. The results show that 95% of teens own smartphones, and 45% of them use the internet almost constantly. World Journal of Advanced Research and Reviews, 2025, 26(02), 702-715 703 Jean Twenge (2017) in his book "iGen" analyzed the impact of family, education, and technology on the behavior and lifestyle habits of Gen Z. Twenge points out that Gen Z, or iGen, is the first generation to grow up entirely in the digital age, with the constant presence of smartphones and social networks. The results of a study by Ralph B. McNeal Jr. (2014) with the aim of analyzing the relationship between parental involvement and student learning achievement, and examining the role of learning attitudes and behaviors show that parental involvement can be divided into two main categories: parent-child involvement, including discussion and monitoring of learning, and parent-school involvement, such as educational support and participation in parent-teacher organizations. The results of a study by Vu Van Tuan et al. (2021) on "The Impact of Social Networking Sites on Study Habits and Interpersonal Relationships among Vietnamese Students" on the analysis of the influence of social networks on the study habits and social relationships of Vietnamese students have shown that the use of social networks can lead to a decrease in concentration in studying, negatively impact academic outcomes and change the way students interact socially. The study "Family - An important factor in moral education for students and students" by Tran Van Phuc and Nguyen Kim Tuyen (2014) emphasizes the essential role of the family in the formation and development of personality, as well as the study habits of students and students. Research on factors affecting Gen Z's living and learning habits has become an important topic in research on education and society. Domestic and international studies have proven that factors such as technology, social networks, the environment and family have had a profound influence on living habits, thereby affecting the learning outcomes of Gen Z. 1.2. Research hypothesis • Hypothesis 1: The learning environment has a positive effect on the living and learning habits of Gen Z • Hypothesis 2: Friends around you have a positive influence on the living and learning habits of Gen Z • Hypothesis 3: Lecturers have a positive influence on the living and learning habits of Gen Z • Hypothesis 4: Family – society has a positive influence on the living and learning habits of Gen Z • Hypothesis 5: Self-awareness has a positive effect on Gen Z's living and learning habits. • Hypothesis 6: Learning methods have a positive effect on the living and learning habits of Gen Z • Hypothesis 7: Technology has a negative impact on Gen Z's living and learning habits 1.3. Recommended research model: Based on the theory and results of the process of conducting an overview of domestic and foreign studies, the team proposed a research model on factors affecting the living and learning habits of Generation Z: The case of students of the School of Economics - Hanoi University of Industry with 7 influencing factors including: (1) Learning environment; (2) Friends around; (3) Lecturers; (4) Family – society; (5) Self-awareness; (6) Learning methods; (7) Technology. These factors are synthesized, building a research model and hypotheses as below: World Journal of Advanced Research and Reviews, 2025, 26(02), 702-715 704 Source: Recommended authors Figure 1 Research model 2. Data and research methodology The main method for collecting survey data on a large scale using questionnaires. During a period of 6 months (from 9/2024 to 3/2025), the authors sent questionnaires to first-year, 2nd, 3rd-year students, 4th-year students, students who are doing internships, students who have completed internships and are participating in studying at the School of Economics - Hanoi University of Industry. This is a group of students who have been studying and researching at the School of Economics, Hanoi University of Industry. Receipts are cleaned and invalid votes are removed. The total number of votes after cleaning and putting into the data analysis was 401 votes.The analytical techniques used in this report include: Cronbach's Alpha scale validation, exploratory factor analysis (EFA), multiple regression analysis. The software used in the analysis is SPSS 26. 3. Result and discussion 3.1. EFA Discovery Factor Analysis 3.1.1. For independent variables The authors conducted an exploratory factor analysis of EFA to reduce the observed variables that correlate with each other into a smaller set and the synthetic variables were more meaningful but still ensured the information content of the original data set; at the same time, eliminate interfering variables, which are not suitable for the research model. Barlett test results and KMO index = 0.866; SIG <0.05 proves that the factor analysis model proposed by the authors is appropriate and the observed variables are closely correlated with each other. Table 1 KMO test results of independent variables KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.866 Bartlett's Test of Sphericity Approx. Chi-Square 8078.799 df 351 Sig. <.001 (Source: SPSS data running results) World Journal of Advanced Research and Reviews, 2025, 26(02), 702-715 705 Next is the analysis of the rotation matrix, after the process of research, verification and elimination of inappropriate variables, the initial rotation matrix with 7 elements has been adjusted, combining elements with similarities to ensure a more reasonable research model. According to Hair & et al. (2009), factors with a load factor greater than 0.5 are satisfactory, and in the final result table, many of the observed variables have a high load factor above ±0.7, demonstrating very good statistical significance. The final results of the factor analysis show 5 main groups of factors, which are presented in the table below: Table 2 Matrix rotates the factors of the independent variable Rotated Component Matrixa Component 1 2 3 4 5 NT3 0.724 NT1 0.722 PP1 0.721 PP3 0.708 NT4 0.697 PP2 0.696 NT2 0.678 PP4 0.574 MT2 0.834 BB2 0.828 GDXH4 0.536 GDXH3 0.510 MT4 0.822 BB4 0.818 GDXH2 0.601 CN3 0.849 CN1 0.791 CN2 0.789 CN4 0.714 GV1 0.809 GV3 0.789 GV2 0.665 MT3 0.892 BB3 0.803 MT1 0.919 BB1 0.810 (Source: SPSS data running results) World Journal of Advanced Research and Reviews, 2025, 26(02), 702-715 706 3.1.2. For dependent variables Table 3 KMO test results of dependent variables KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.788 Bartlett's Test of Sphericity Approx. Chi-Square 599.035 df 6 Sig. <.001 (Source: SPSS data running results) The results of the factor analysis show that the KMO index is 0.788 > 0.5, which proves that the data used for factor analysis is completely appropriate. Barlett's test results with Sig significance level = 0.000 < 0.05 observed variables are not correlated with each other in the whole, variables are correlated with each other and meet the conditions for factor analysis Table 4 Results of extractive variance analysis of dependent variables Total Variance Explained Component Initial Eigenvalues Extraction Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % 1 2.646 66.154 66.154 2.646 66.154 66.154 2 0.597 14.914 81.068 3 0.432 10.797 91.864 4 0.325 8.136 100.000 Extraction Method: Principal Component Analysis. (Source: SPSS data running results) Perform factor analysis by Principal components with Varimax rotation. The results show that the total value of the extracted variance = 66.154% > 50%, then it can be said that this factor explains 66.154% of the variability of the data. Table 5 Component matrix of dependent variables Component Matrixa Component 1 TQ4 0.847 TQ2 0.836 TQ1 0.831 TQ3 0.735 Extraction Method: Principal Component Analysis. a. 1 components extracted. (Source: SPSS data running results) World Journal of Advanced Research and Reviews, 2025, 26(02), 702-715 707 3.2. Pearson Correlation Analysis To perform a polyupile linear regression analysis, we need to consider the correlation between independent and dependent variables and between independent variables. Correlation analysis is a measure of the strength/weakness of the association between the research variables in the model expressed by the Pearson coefficient. From the results of the correlation analysis coefficient, the values show that the variables are correlated with each other, specifically shown in the table below: Table 6 Correlation matrix between factors Correlations Study and living habits Perceptions and Methods Technology Lecturer Environment and friends Family and Society Study and living habits Pearson Correlation 1 0.693** 0.410** 0.439** 0.383** 0.594** Sig. (2tailed) <.001 <.001 <.001 <.001 <.001 N 401 401 401 401 401 401 Perceptions and Methods Pearson Correlation 0.693** 1 0.294** 0.458** 0.559** 0.731** Sig. (2tailed) <.001 <.001 <.001 <.001 <.001 N 401 401 401 401 401 401 Technology Pearson Correlation 0.410** 0.294** 1 0.204** 0.191** 0.232** Sig. (2tailed) <.001 <.001 <.001 <.001 <.001 N 401 401 401 401 401 401 Lecturer Pearson Correlation 0.439** 0.458** 0.204** 1 0.343** 0.397** Sig. (2tailed) <.001 <.001 <.001 <.001 <.001 N 401 401 401 401 401 401 Environment and friends Pearson Correlation 0.383** 0.559** 0.191** 0.343** 1 0.646** Sig. (2tailed) <.001 <.001 <.001 <.001 <.001 N 401 401 401 401 401 401 Family and Society Pearson Correlation 0.594** 0.731** 0.232** 0.397** 0.646** 1 Sig. (2tailed) <.001 <.001 <.001 <.001 <.001 N 401 401 401 401 401 401 **. Correlation is significant at the 0.01 level (2-tailed). (Source: SPSS data running results) World Journal of Advanced Research and Reviews, 2025, 26(02), 702-715 708 Based on the correlations table provided, we can comment as follows: The results of the Pearson correlation analysis show that all independent factors have a relationship with study and living habits at a meaningful level of 0.01 (p < 0.01). This suggests that these factors have a significant influence on students' study habits. In particular, cognition and learning methods had the highest correlation with study and living habits (r = 0.693), indicating that learning methods and personal cognition play an important role in the formation of learning habits. In addition, family and society (r = 0.594), faculty (r = 0.439), environment and friends (r = 0.383), and technology (r = 0.410) also had a significant correlation with study habits, proving that these factors all impact how students organize and maintain their study habits. Notably, the relationship between cognition and learning methods with family and society has a fairly high correlation coefficient (r = 0.731), indicating that the family environment has a great influence on how students receive and develop learning methods. In addition, independent factors are also correlated with each other, such as the association between environment and friends with cognition and learning methods (r = 0.559), suggesting that a good friend environment can help students improve their cognition and learning methods. The correlation between technology and cognition and learning methods (r = 0.294) also suggests that the use of technology may support students' learning but the impact is not so strong. 3.3. Results of Regression Analysis In order to identify, measure and evaluate the influence of factors on the learning habits of Gen Z in the School of Economics - Hanoi University of Industry, the authors used the multiplex linear regression method to analyze the impact of independent variables (05 variables) obtained from the exploratory factor analysis above to the depend. Table 7 Linear regression model synthesis results Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson 1 0.748a 0.559 0.554 0.51690 1.652 a. Predictors: (Constant), Perceptions and Learning Methods, Technology, Family and Society, Environment and Friends, Lecturers b. Dependent Variable: Study and living habits (Source: SPSS data running results) – The R2 value corrected by 0.554 shows that the independent variable introduced into the regression run affects 55.4% of the change of the dependent variable, the remaining 45.3 is due to extra-model variables and random errors. – The Durbin – Watson coefficient = 1.652 is in the range of 1.5 – 2.5, so there is no first-order chain autocorrelation phenomenon. Table 8 ANOVA test results of the regression model ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 133.867 5 26.773 100.204 0.000b Residual 105.539 395 0.267 Total 239.407 400 a. Dependent Variable: Study and living habits b. Predictors: (Constant), Perceptions and Learning Methods, Technology, Family and Society, Environment and Friends, Lecturers (Source: SPSS data running results) – Test sig F = 000b < 0.05 so the multiple linear regression model is consistent with the dataset and can be used. World Journal of Advanced Research and Reviews, 2025, 26(02), 702-715 709 Table 9 Regression coefficients of independent variables Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics B Std. Error Beta Tolerance VIF 1 (Constant) -0.154 0.217 - 0.708 0.479 Perceptions and Methods 0.477 0.053 0.471 9.059 0.000 0.413 2.421 Technology 0.247 0.040 0.215 6.135 0.000 0.907 1.102 Lecturer 0.143 0.042 0.130 3.408 0.001 0.772 1.296 Environment and friends -0.119 0.051 -0.104 - 2.346 0.019 0.563 1.777 Family and Society 0.232 0.058 0.215 3.985 0.000 0.382 2.618 a. Dependent Variable: Study and living habits (Source: SPSS data running results) • Sig tests that the regression coefficients of the independent variables NTPP, CN, GV, MTBB, and GDSH are all less than 0.05, so these independent variables are all meaningful to explain the dependent variable, not excluded from the model. • The VIF coefficients of independent variables are all less than 3, so no multi-collinear phenomenon occurs. • The regression coefficients of the NTPP, CN, GV and GDSH variables are all greater than 0, so these variables when included in the regression analysis all act in the same direction as the Chinese dependent variable. Based on the magnitude of the Beta normalized regression coefficient, the order of impact from strongest to weakest of independent variables to dependent variables is: NTPP (0.477) > CN (0.247) > Social Education (0.232) > GV (0.143). And the MTBB variable has a regression coefficient = -0.119 < 0, so this variable, when included in the regression analysis, will have the opposite effect on the Chinese dependent variable. Corresponds to: • + Transforming Awareness and methods have the first strong influence on the living and learning habits of students of the School of Economics - Hanoi University of Industry. • + Turning technology has the second strongest impact on the living and learning habits of students of the School of Economics - Hanoi University of Industry. • + The third strongest influence on the living and learning habits of students of the School of Economics - Hanoi University of Industry. • + The fourth strongest influence on the living and learning habits of students of the School of Economics - Hanoi University of Industry. • + And finally, the environment and friends have the weakest influence on the living and learning habits of students of the School of Economics - Hanoi University of Industry. World Journal of Advanced Research and Reviews, 2025, 26(02), 702-715 710 (Source: SPSS data running results) Figure 2 Normalized Residual Frequency Chart Histogram – The mean value of MEAN = 1.96E– 15 is close to 0, the standard deviation is 0.994 which is close to 1. Thus, it can be said that the distribution of the surplus is approximately standard. Therefore, it can be concluded that: The assumption of the standard distribution of the remainder is not violated. The regression model used is statistically relevant. (Source: SPSS data running results) Figure 3 Normal P-P Plot Normalized Residual Chart For the Normal P-P Plot chart, we also see that the data points in the distribution of the remainder are close to the diagonal, thus, assuming that the standard distribution of the remainder is not violated.