Keywords
Digital nativity, Digital addiction, Internet addiction, University students, Ghana
This article is included in the Addiction and Related Behaviors gateway.
This research explores the relationship between digital nativity and internet addiction among university students at the Kwame Nkrumah University of Science and Technology (KNUST) in Ghana. The Digital Native Assessment Scale (DNAS) and Internet Addiction Test (IAT) were administered to 411 undergraduate students from various academic disciplines, with 49.15% (n=202) males and 50.85% (n=209) females. College of Arts and Built Environment (21.90%, n=90), College of Engineering (15.33%, n=63), College of Science (14.35%, n=59), College of Humanities and Social Sciences (22.63%, n=93), College of Health Sciences (15.82%, n=65), and College of Agriculture and Natural Resources (9.98%, n=41). Results demonstrated moderate to high levels of digital nativity based on their DNAS scores, with significant positive correlations between DNAS and IAT (r = 0.569, p < 0.001). Suggesting that students with higher scores in digital nativity may be at risk of developing addictive internet behaviours. Interestingly, academic level and college affiliation emerged as factors influencing internet addiction, with students at a higher level and those in the College of Science showing intensified vulnerabilities.
Digital nativity, Digital addiction, Internet addiction, University students, Ghana
Technological advancements have improved our environment, and most people agree that understanding and applying information technology (IT) is now widely considered a vital skill. Those who engage in digital technology usage, accessibility, and advancement more are known to be “digital natives.” Prensky (2001) coined the term “digital native” as individuals born when technology was prevalent. Defining this concept solely based on age is too simplistic, as it contains factors. In his representation, he separated individuals into two groups: those who speak the digital language fluently, have little trouble adjusting to new technologies and are tech-savvy. The second group is the generation that preceded the IT boom, the “Digital Immigrants,” who have difficulty adjusting to the technology environment since they do not speak the language. Digital natives work best when networked and appreciate random access. They prefer games over work and enjoy parallel processes. Many scholars have been drawn to the idea of digital nativity, including Wang, Sigerson and Cheng (2019), which has helped to advance our knowledge of how people are impacted by the digital era (Gupta et al., 2023). Some studies suggest that digital natives report higher usage and acceptance levels than digital immigrants (Hoffmann, Lutz and Meckel 2014). However, (Calvani et al., 2012) indicate that many younger users exhibit lower levels of IT proficiency than initially assumed. Scholars doubt that age is the primary variable separating digital natives from immigrants, Bennett et al. (2008). Individuals’ interest in IT differs, and the current generation’s access to and competence with digital technology is inconsistent, with these differences influenced by demographic factors (Teo, 2013). Teo (2013) proposed a method that focuses on the differences in individuals’ digital nativity to address the inconsistencies surrounding the definition of the term. The method emphasises the critical role of individual characteristics, such as psychological traits and behavioural tendencies, in defining the essential characteristics of a digital native rather than depending solely on age. Digital nativity is not a common trait within an entire generation but rather a common characteristic that defines the individuals of that generation and varies among them (Bagdi and Bulsara, 2023).
Socio-demographic characters influence the use of digital technology and the time spent on a specific device (Kwon et al., 2013). This results from being in a technologically advanced environment where digital natives’ neuroplasticity has altered the structure of their brains. This is shaped by the frequent interactions with technologies, which cause younger individuals to process information differently than their older counterparts. Research confirms that digital literacy includes how the digital age impacts people’s cognitive processes, habits, and learning styles (Bennett and Corrin, 2019; Margaryan, Littlejohn and Vojt, 2011). Wang, Sigerson and Cheng (2019) defined digital nativity based on the person growing up with technology, with factors such as age and personal attitudes towards technology. Huang, Teo and He (2019) posit that proficiency in engaging with digital technology constitutes a vital aspect of digital nativity. Digital nativity is a multidimensional construct that embraces highly skilled technology users’ psychological traits and behavioural patterns (Hui et al., 2022). Teo (2013) developed the Digital Native Assessment Scale (DNAS) to measure individuals’ digital nativity. This framework divides the usual behaviours of digital natives into four categories: (1) grew up with technology, (2) Comfortable with multitasking, (3) Reliant on graphics for communication, and (4) thrives on instant gratifications and rewards.
The strenuous demands of university life in the context of academic and social often expose students to digital technology and the Internet. Digital addiction has become a primary global concern across various forms, such as internet addiction, gaming addiction, and obsessive use of digital devices. It has gained significant attention due to its effects on mental health, academic performance, and overall well-being (Kuss, Griffiths and Binder, 2013; Essel et al., 2022). Internet addiction is characterised by futile time management, an overpowering need for online activities, and the inability to regulate usage, resulting in social issues. (Kolaib, Alhazmi and Kulaib, 2020; Sharma, Hallford and Anand, 2022). Immoderate internet use affects individuals’ mental health, impacting the cognitive control systems in the brain. (Dossi and Pesce, 2023). Stress-coping strategies, anxiety, depression, and impulsivity (Zhang et al., 2021; Xie, Cheng and Chen, 2023), and personality traits have been associated with internet addiction, particularly among students (Jacob, 2020; Ikeda et al., 2022). Psychosocial consequences affect mental health and the overall well-being of the individual (AlHeneidi and Smith, 2021; Memon et al., 2021; Naseem et al., 2021). Studies report different rates of internet addiction among student populations in Malaysia and India, highlighting the necessity for awareness initiatives and intervention strategies due to the impact on academic performance and social relationships. Neuroticism and introversion correlate with internet addiction among university students (Pujitha et al., 2022). The COVID-19 pandemic caused an increase in internet addiction due to more time spent on online activities like gaming. (Li et al., 2021). The impact of internet addiction is different across regions and populations, such as students, adolescents, and adults. (Yaunin et al., 2021; Zhang et al., 2021; Özarıcı and Cangöl Sögüt, 2022). This has been a great concern among university students, highlighting its impact on academic performance, behaviour, and (Mboya et al., 2020; Acharya et al., 2023; Salehi et al., 2023).
The Internet Addiction Test (IAT), by Dr Kimberly Young, is a valid instrument to access Internet addiction (Acharya et al., 2023; Ariyadasa et al., 2023). The IAT is a 20-item, 5-point Likert scale that has scores ranging from 1 (strongly disagree) to 5 (strongly agree). The s-IAT is a shorter version of the IAT to evaluate internet addiction efficiently. (Fernando and Rahardjo, no date; Baltaci, Yilmaz and Tras, 2021; Tateno et al., 2023). In Poland (Rosliza et al., 2020), a study found an increase in internet addiction among nursing, midwifery, and medical rescue students, with prevalence rates of 10.3%, 9.9%, and 9.1%, respectively. In Malaysia, a survey showed that 7.8% of undergraduate students are internet addicts, whereas 56.5% were identified as problematic internet users (Lukić, Ranković and Ranković, 2017).
It is vital to recognise the continent’s various digital landscapes, where nations have made significant paces in technology adoption while others face hefty challenges (Pick and Sarkar, 2015; Essel et al., 2022; Adarkwah and Huang, 2023). While internet addiction has been extensively studied, the relationship between digital nativity and internet addiction remains largely unexplored. Digital natives face challenges in disconnecting from the internet and may possess different biases towards internet addiction than digital immigrants. However, there is a lack of comprehensive research investigating the specific role of digital nativity in influencing behaviour tendencies of internet addiction, especially within the African context (Beard, 2005; Pezoa-Jares, 2012; Salicetia, 2015; Lai et al., 2017; Pujitha et al., 2022).
The study aims to explore these research questions regarding students at Kwame Nkrumah University of Science and Technology (KNUST):
1. What is the level of digital proficiency exhibited by the students?
2. What is the extent of internet addiction among students?
3. Is there any correlation between digital nativity and student internet addiction?
4. Are there any differences in the relationship between digital nativity, internet addiction, and socio-demographic traits such as gender, age, and educational level?
The study employed quantitative, descriptive correlational research to peruse the relationships between digital nativity and internet addiction at Kwame Nkrumah University of Science and Technology (KNUST), an African institution tailored towards science and technology. The survey was created using Microsoft Forms and sent to participants via email and WhatsApp. In-person recruitment was used as a supplementary measure. The sample included 411 participants, with an almost equal representation across genders, 49.15% (n = 202) males and 50.85% (n = 209) females. The majority of the participants were aged 20 years or below, 44.28% (n = 182), 24.57% (n = 101) in the 21–25-year bracket, and 31.14% (n = 128), 26–30 years. Proportional stratified sampling was used to select participants, where the various colleges in the universities formed the strata; later, a random sampling was used to get the required proportion number of respondents for each college. The College of Humanities and Social Sciences accounted for 22.63% of the sample (n = 93), followed by Arts and Built Environment (21.90%, n = 90), Science (14.35%, n = 59), Health Sciences (15.82%, n = 65), Engineering (15.33%, n = 63), and Agriculture and Natural Resources (9.98%, n = 41). The academic level of the participants provided a balanced representation, with 26.04% in 1st year (n = 107), 25.06% in 2nd year (n = 103), 24.57% in 3rd year (n = 101), 21.65% in 4th year (n = 89), and 2.68% in 5th year (n = 11). The slightly lower percentage of fifth-year students is consistent with the typical four-year duration of undergraduate programs. Table 1 illustrates the descriptive characteristics of the participants.
2.2.1 Digital Nativity
The Digital Nativity Assessment Scale (DNAS), created by Teo, Huang and He (2022), was used to measure the participants’ digital nativity level. To use employ the DNAS in this study, permission was sought from the original authors. The DNAS has 21 items divided into four distinct constructs: (1) grew up with technology, (2) Comfortable with multitasking, (3) Reliant on graphics for communication, and (4) thrives on instant gratifications and rewards. Teo, Kabakçı Yurdakul and Ursavaş (2016) conducted a study to investigate and validate the Turkish version of the DNAS and the presence of digital natives among pre-service teachers in Turkey. He also examined the invariance and latent mean differences of the DNAS across the Chinese mainland, Macau, and Taiwan (Teo, Huang and He, 2022). The outcomes demonstrated the effectiveness and accuracy of the Scale of digital nativity for evaluation within different cultural settings. Participants rated their agreement or disagreement with items of the scale on a 5-point Likert scale from “strongly agree” to “strongly disagree”. The level of digital nativity among the participants was indicated by a mean score of 78.5, with a standard deviation of 14.29. The Cronbach’s alpha of the scale was α = 0.940 with coefficients for each of the four traits as α = 0.835 α = 0.870, α = 0.849, α = 0.856 for growing up with technology, comfort with multitasking, reliance on graphics for communication, and thriving on instant gratification and rewards, respectively.
2.2.2 Internet Addiction
Young (1998) introduced the Internet Addiction Test (IAT) to assess internet addiction among students. To use employ the IAT in this study, permission was sought from the original authors. The IAT is a 20-item, 5-point Likert scale with scores ranging from 1 (strongly disagree) to 5 (strongly agree). The short version has 12 items instead of 20 items. The IAT has a Cronbach’s α of 0.937 in our research. The abbreviated version of the IAT (s-IAT) has been proven effective in detecting internet addiction among autistic adolescents (Tateno et al., 2023) and was also used to assess problematic internet use among university students in Malaysia (Černja, Vejmelka and Rajter, 2019). In a study of Croatian adolescents, the IAT was found to have a three-factor structure: cognitive internet preoccupation, neglecting work and lack of self-control, and social problems. The IAT has also been validated in the French population, showing a strong association between smartphone addiction and internet addiction (Barrault et al., 2019). In the Indian population, the IAT demonstrated a one-factor structure, with the condensed form of the IAT showing construct and convergent validity in measuring symptoms related to internet addiction (Sharma, Hallford and Anand, 2022).
The data was entered and evaluated using Jamovi software, version 2.4.11. The mean (X) and standard deviation (SD) of digital nativity and internet addiction are 78.5; 14.29 and 39.5; 10.80 respectively. The independent samples t-test was used to compare numerical data between gender groups. One-way ANOVA (Welch’s) tests also to determine significant differences in scores on the Digital Native Assessment Scale across various colleges (p < 0.001). However, there were no significant differences in age (p = 0.428) or level of education (p = 0.862). The Tukey HSD post hoc test measured groups that reported differences in statistical significance. Multiple regression analyses were carried out between the items of DNAS as the independent variable on internet addiction (dependent variable). Pearson’s correlation coefficient analyses (r = 0.569, p < 0.001) indicated significant positive correlations between the Digital Native Assessment Scale and internet addiction. The skewness and kurtosis estimates were calculated for normality assumptions, which were acceptable for normally distributed data.
This study was approved by the Ethical Committee of the Department of Educational Innovations in Science and Technology (Approval date: 10 January, Approval number: EIST-EC/REF No.: 101/01/2023). The study adhered to the principles of the Helsinki Declaration (1964) and its later amendments. All participants were 18 years old or older, and informed consent was obtained. Participant privacy and confidentiality were strictly maintained, and measures were in place to minimise any potential harm or discomfort, including the option to withdraw from the study at any time.
Table 2 highlights the scores of participants on the Digital Native Assessment Scale (DNAS) and Internet Addiction Test (IAT). The mean scores and standard deviation (±) for DNAS and IAT were 78.5 (± 14.29) and 39.5 (± 10.80) respectively. The DNAS mean score was higher than that of the IAT, indicating that the participants had higher levels of digital nativity and less internet addiction. The descriptive statistics suggest that the participants were digitally native with moderately low levels of internet addiction (See Figures 1 and 2). The standard deviation for DNAS was higher at 14.29, which indicates more variability in the DNAS scores. The DNAS had negative skewness (-0.4399) and kurtosis (-0.0736) values, a left-skewed distribution with scores clustering at the upper end and a flatter, less peaked curve than a normal distribution. On the other hand, IAT had positive skewness (0.1797) and negative kurtosis (-1.0413), a right-skewed distribution with more clustering around the lower scores.
In assessing participants’ digital nativity and Internet addiction tendencies, the results showed no significant differences in DNAS scores across genders. Both males (M = 78.4, SD = 14.3) and females (M = 78.7, SD = 14.3) had a similar level of digital nativity. A student’s t-test (t (409) = 0.203, p =.839) confirmed no significant difference between the two groups. The p-value further supports this, indicating that the observed difference could have occurred due to chance, making it suitable to accept the null hypothesis that the groups have no significant difference. However, with the colleges, there was a significant difference in the DNAS scores (F (5, 167) = 9.9, p <.001), with the College of Science having the highest mean score (86.2; 11.8), while the College of Agriculture had a lower mean score (70.4; 15.6). Year 3 students had higher scores on digital addiction scales than other years (F (4, 35.8) = 0.984, p =.428). Table 3 illustrates comparison in Sociodemographic Variables and DNAS and IAT scores.
Table 4 illustrates the regression analysis results of Internet Addiction Test (IAT) scores and Digital Nativity factors with IAT as the dependent variable and the DNAS as the independent variable. The overall model evinces a statistical significance (R = 0.578, R2 = 0.334, F = 50.9, df1 = 4, df2 = 406, p < 0.001), indicating that the combined predictors of DNAS significantly explains 33.4% of the variance in IAT scores. The model coefficients reveal specific contributions of each construct with an intercept of 6.437 (SE = 2.481, t = 2.595, p = 0.010). Among the digital nativity factors, “growing up with technology” (b = 0.168, SE = 0.172, t = 0.979, p = 0.328) and “comfortable with multitasking” (b = 0.294, SE = 0.150, t = 1.955, p = 0.051) did not reach statistical significance but “Reliant on graphics for communication” (b = 0.647, SE = 0.154, t = 4.198, p < 0.001) and “Thrive on instant gratification and rewards” (b = 0.621, SE = 0.189, t = 3.294, p = 0.001) surfaced as significant predictors. These results suggest that individuals who rely more on graphics for communication and seek instant gratification and rewards tend to have higher Internet Addiction Test scores, contributing to the understanding of the relationship between digital nativity factors and Internet addiction.
Model Fit Measures | ||||||
---|---|---|---|---|---|---|
Overall Model test | ||||||
Model | R | R2 | F | df1 | df2 | P |
IAT | 0.578 | 0.334 | 50.9 | 4 | 406 | <.001 |
The correlation analysis provides insight into how Digital Nativity and its specific traits, as measured by the Digital Native Assessment Scale (DNAS), relate towards internet addiction as the Internet Addiction Test (IAT) displayed moderate to strong positive correlations with DNAS constructs, including comfort with multitasking (r = 0.512, p < 0.001), reliance on graphics (r = 0.528, p < 0.001), desire for instant gratification (r = 0.735, p < 0.001), and comfort with technology (r = 0.486, p < 0.001). Individuals with high scores in DNAS constructs are more likely to show general internet addiction behaviours. Table 5 illustrates the correlation coefficients between the constructs of DNAS and IAT.
1 | 2 | 3 | 4 | 5 | ||
---|---|---|---|---|---|---|
1. Internet Addiction | Pearson’s r | — | ||||
p-value | — | |||||
2. Grow with technology | Pearson’s r | 0.432*** | — | |||
p-value | <.001 | — | ||||
3. Comfort with multitasking | Pearson’s r | 0.486*** | 0.675*** | — | ||
p-value | <.001 | <.001 | — | |||
4. Reliance on graphical communication | Pearson’s r | 0.512*** | 0.500*** | 0.643*** | — | |
p-value | <.001 | <.001 | <.001 | — | ||
5. Preference for instant gratification and rewards | Pearson’s r | 0.528 | 0.712*** | 0.705*** | 0.677*** | — |
p-value | <.001 | <.001 | <.001 | <.001 | — |
In recent years, the study of digital nativity and its correlation with digital addiction has increased, offering insights into the two variables among students. The rate of digital nativity was equal to moderate to high internet users (Gupta et al., 2023).
The study adds to research on the relationship between digital nativity and internet addiction, particularly in the context of a Ghanaian university. Consistent with Prensky’s (2001) conceptualisation, the findings of this study reveal that today’s students show moderate to high levels of digital nativity, occupied by technology from a young age. Also, aligning with previous findings shows the current generation’s vast access to digital technologies with variances based on demographics (Teo, 2013). A significant positive correlation (r = 0.569, p < 0.001) between scores on the Digital Native Assessment Scale (DNAS) and the Internet Addiction Test (IAT) supports previous research suggesting that digital natives’ comfort and reliance on technology increases their exposure to developing addictive internet use behaviours (Kuss, Griffiths and Binder, 2013; Kwon et al., 2013). While DNAS positively predicted internet addiction overall, the correlation with specific forms of internet addiction was less pronounced (Gupta et al., 2023), making it that different types of internet addiction could likely have distinct causes and not all addictive behaviours are equally associated with that of digital nativity (de Palo et al., 2019). The regression analysis further explained that specific digital native traits like reliance on graphics for communication and desire for instant gratification significantly influenced higher IAT scores. This corroborates Teo’s (2013) multidimensional framework, which encompasses psychological and behavioural characteristics showing digital nativity beyond just age. While there was scepticism about age being the sole defining factor for digital nativity (Bennett, Maton and Kervin, 2008), the present study found no significant differences in DNAS scores across age groups; academic level and college affiliation emerged as notable factors. Students at higher levels and those in scientific colleges like Engineering and the College of Science exhibited an increase in digital nativity alongside more significant internet addiction behaviours. This varies from the view that the characteristics of digital natives are uniformly distributed across a generation (Prensky, 2001) Instead, it acknowledges the heterogeneity influenced by contextual factors like academic environments in digital literacy and engagement (Margaryan, Littlejohn and Vojt, 2011; Teo, 2013). The positive correlations between DNAS constructs and IAT support the premise that digital natives’ neuroplasticity and ducking in technology correspond to cognitive and behavioural patterns, increasing the addiction risk (Prensky, 2001; Bagdi and Bulsara, 2023; Wang, 2023).
As prior research (de Palo et al., 2019; Bagdi and Bulsara, 2023) suggested that distinct types of internet addiction may be driven by different factors beyond global digital native traits. The disciplinary differences observed further show the influence of contextual factors in shaping addictive digital behaviours.
This study provides valuable insights into the relationship between being a digital native and experiencing tremendous internet addiction among university students in Ghana. Its findings support previous research that has linked higher levels of digital native traits with a higher probability of engaging in addictive internet usage. The study explicitly found a strong positive correlation between scores on the Digital Native Assessment Scale (DNAS) and the Internet Addiction Test (IAT), providing empirical evidence of this association within the context of an African university. The research also reveals necessary complexities and shades. While digital nativity emerged as a significant predictor of general internet addiction behaviours, its connections with specific forms of internet addiction (e.g., gaming addiction) were less pronounced, suggesting different types of addictive online behaviours driven by clear factors beyond global digital native characteristics.
The study challenges the assumptions that digital nativity and internet addiction are uniformly distributed across student populations or generations. Factors such as academic discipline and level of education played a crucial role in the exposure to internet addiction. This aligns with studies recognising variations in digital literacy and technology engagement influenced by environmental and demographic factors.
The findings have important implications for digital literacy initiatives, educational policies, and mental health interventions to promote healthy internet usage and mitigate addiction risk. Educators and institutions must recognise that digital native students face higher exposure to internet addiction due to their ducking in technology from a young age (Kwon et al., 2013). Developing impulse control, self-regulatory skills, and purposeful internet use should be key in training programmes. Tailored intercessions for varied forms of internet addiction are necessary based on their distinct drivers (de Palo et al., 2019). The sample was limited to students from one university in Ghana (KNUST), which may limit the generalisability of the findings to broader populations of African youth. Replicating the study across diverse educational and cultural settings could improve generalisability.
Akuteye A. D.: Analysis, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing; Essel H. B.: Supervision of Methods and Analysis, Writing – Review & Editing, Conceptualization; Nunoo F.: Literature Review, Drafting, Distribution; Tachie-Menson A. (Corresponding Author): Conceptualization, Data Curation, Writing – Review & Editing; Ato Essuman M.: Project Administration, Data Collection, Writing – Review & Editing; Appau E.: Investigation, Data Management, Writing – Review & Editing; Achiamaa Boadi E.: Validation, Writing – Review & Editing; Tetteh Quaye N.: Resources, Writing – Review & Editing.
This study was conducted under the Helsinki Declaration (1964) and equivalent ethical criteria, or its later amendments. The participants in this study were 18 years old and above. Participants’ informed consent was obtained, their privacy and confidentiality were strictly sustained, and all efforts were made to minimise any potential harm or discomfort, including the option to withdraw at any time. The Ethical Committee of the Department of Educational Innovations in Science and Technology approved the study (Approved on January 10, EIST-EC/REF No.: 101/01/2023).
The EIST-EC has thoroughly reviewed your ethical considerations and has noted the precautions taken to ensure participant protection. We believe you have met all ethical research standards and that these will be upheld throughout the research. The Committee reserves the right to withdraw its approval if it is adequately informed that ethical standards are not being adhered to.
Chairman, EIST-EC
Written informed consent was obtained from the participants to publish this paper.
Mendeley Data: Digital Nativity and Internet Addiction, 10.17632/nn3fwy4h4f.1 (Akuteye et al., 2024).
This project contains the following underlying data:
Data are available under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license and in adherence to the STROBE guideline.
Mendeley Data: Digital Nativity and Internet Addiction, https://doi.org/10.17632/nn3fwy4h4f.1 (Akuteye et al., 2024).
This project contains the following extended data:
Data are available under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license and in adherence to the STROBE guideline.
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Is the work clearly and accurately presented and does it cite the current literature?
No
Is the study design appropriate and is the work technically sound?
No
Are sufficient details of methods and analysis provided to allow replication by others?
No
If applicable, is the statistical analysis and its interpretation appropriate?
I cannot comment. A qualified statistician is required.
Are all the source data underlying the results available to ensure full reproducibility?
Partly
Are the conclusions drawn adequately supported by the results?
No
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Scale development, adaptation and validation, digital addiction, educational counselling.
Is the work clearly and accurately presented and does it cite the current literature?
Partly
Is the study design appropriate and is the work technically sound?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
No
If applicable, is the statistical analysis and its interpretation appropriate?
No
Are all the source data underlying the results available to ensure full reproducibility?
Partly
Are the conclusions drawn adequately supported by the results?
Partly
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: My research focuses on leisure studies, recreation management, and tourism, with an emphasis on behavioral, psychological, and social aspects. I explore topics such as leisure competence, recreational service quality, digital leisure, and sustainable consumption behaviors. My work examines how leisure activities, including digital and interactive experiences, influence well-being, resilience, and environmental behavior. My studies incorporate statistical analysis, survey research, and qualitative methods to analyze user experiences, attitudes, and motivations across different leisure contexts.
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