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Abstract

South African undergraduate university students face many unique challenges that put them at risk of developing mental health concerns such as depression, anxiety, stress, suicidal ideation, and posttraumatic stress disorder. However, psychological well-being has been found to play an essential role when it comes to effectively coping with and adjusting to university life. This study aimed to identify the predictor variable(s) or combination(s) of predictor variables that explained a significant percentage of the variance in psychological well-being amongst undergraduate university students. A total of 1,191 students between the ages of 18 and 30 participated in the study. This was a non-experimental study using a cross-sectional correlational research design. The results from the hierarchical regression analyses indicated that adjustment to university life statistically and practically significantly contributed to the explanation of the variance of the following dimensions of psychological well-being: Positive Relations, Self-acceptance, and Purpose in Life. These findings suggest that interventions that target adjustment could be used to improve the psychological well-being of undergraduate university students.

Keywords

psychological well-being adjustment media and technology usage emotional intelligence gender undergraduate university students predictors South Africa

Background

South Africa is known for its rich cultural diversity and racial history (Braathen et al., 2013; Das-Munshi et al., 2016; Ramdass, 2009), and despite the ending of Apartheid over 25 years ago, various racial and material inequalities remain present (Beaubien, 2018; Braathen et al., 2013; Das-Munshi et al., 2016; Pillay, 2021; Ramdass, 2009). South Africa faces many socio-political issues (e.g., high levels of violence, crime, and unemployment) (Banda Chitsamatanga & Malinga, 2021; Braathen et al., 2013; Das-Munshi et al., 2016; Pillay, 2021; Ramdass, 2009) and South African university students face many unique challenges, such as unequal access to resources and political unrest (Chetty & Pather, 2015; Du Plessis, 2021; Edwards et al., 2004; LaBrie et al., 2012; Makgahlela et al., 2021). Students are now faced with greater academic demands than ever before and a greater sense of responsibility (Banda Chitsamatanga & Malinga, 2021; Credé & Niehorster, 2012; Makgahlela et al., 2021; Stoklosa, 2015; van Breda, 2017). They also face adjusting to a new social environment and taking on new roles and responsibilities, such as managing their time and finances (Credé & Niehorster, 2012; Stoklosa, 2015). Many university students also face separation from their friends and family due to moving to another city for tertiary education purposes (Credé & Niehorster, 2012; Stoklosa, 2015). International research identified various stressors faced by university students, such as personal inadequacy, fear of failure, interpersonal difficulties with lecturers, poor time management, peer competition, financial burdens, inadequate study facilities, and managing their personal and academic life (Chernomas et al., 2023; Jayasankara Reddy et al., 2018; Ramachandiran & Dhanapal, 2018; Sreeramareddy et al., 2007). These challenges put university students at risk of developing mental health concerns such as depression, anxiety, stress, suicidal ideation, sleeping problems, and posttraumatic stress disorder (PTSD) as they often do not have the coping skills needed to deal with these challenges (J. Bantjes et al., 2019; Olasupo et al., 2018; Ramachandiran & Dhanapal, 2018; van Breda, 2017). These challenges and further mental health concerns can result in poor academic performance, increased dropout rates, and reduced graduation rates (Essop, 2020; Mokgele & Rothmann, 2014; Pather & Dorasamy, 2018). In South Africa, the first-year university dropout rate is 40% to 55%, with only 20.3% of university students reaching graduation (Essop, 2020; Mokgele & Rothmann, 2014) compared to for example 61% of students in the United States (Hussar et al., 2020). Compared to other countries within Africa, Ghana has a 50% drop out rate while Nigeria has a 33% drop out rate (Ankeli, 2023; PASGR, 2019). In South Africa, university students’ psychological well-being (PWB) is significantly lower than university students in the United States (Edwards et al., 2004). This may be attributed to the various socio-cultural and political factors faced in South Africa (Banda Chitsamatanga & Malinga, 2021; Braathen et al., 2013; Das-Munshi et al., 2016; Edwards et al., 2004; Pillay, 2021). University students in low to middle income countries are more likely to be exposed to trauma and crime and less likely to access affordable mental health care. Between 2016 and 2019, depression rates in South African university students increased yearly (Rousseau et al., 2021; Twenge, 2019). Approximately 33.2% of South African university students experience mild to moderate symptoms of depression, while 15.8% of students experience mild to moderate symptoms of anxiety (J. R. Bantjes et al., 2016; Makhubela, 2021). Not only does this highlight problems in the higher education system, but it also raises concerns about the competency of the South African labor market (Cilliers & Flotman, 2016).

However, studies have found that increased levels of PWB in university students tend to lead to lower rates of depression, anxiety, and stress, resulting in better academic performance, goal attainment, mindfulness, physical health, resilience, and coping strategies (Chow et al., 2018; Das-Munshi et al., 2016; Freire et al., 2016; Harding et al., 2019; Olasupo et al., 2018; Ramdass, 2009; Siddiqui, 2015; Smith & Yang, 2017; Turashvili & Japaridze, 2012; Udhayakumar & Illango, 2018). This research highlights the importance of researching PWB among university students and indicates how PWB can increase university students’ quality of life, subsequently lowering the dropout rate (J. Bantjes et al., 2019; Freire et al., 2016; Olasupo et al., 2018).

PWB has been conceptualized in two different ways, namely hedonic and eudaimonic well-being (Dodge et al., 2012; Ryan & Deci, 2001; Vázquez et al., 2009). The hedonic conceptualization views well-being as the pursuit of happiness and pleasure (Bhullar et al., 2013; Dodge et al., 2012; Keyes et al., 2008; I. P. Khumalo et al., 2012; I. Khumalo et al., 2013; Ryan & Deci, 2001; Ryff, 2014; Vázquez et al., 2009; Wissing et al., 2011; Wissing & van Eeden, 2002). However, not all researchers agreed with this conceptualization of well-being and argued that happiness is not the main criterion of well-being (Bhullar et al., 2013; Dodge et al., 2012; Ryan & Deci, 2001; Vázquez et al., 2009). Eudaimonic theorists argued that not all human desires that are pleasure producing are beneficial for individuals and do not promote wellness (Dodge et al., 2012; Ryan & Deci, 2001; Vázquez et al., 2009). This research study used the eudaimonic conceptualization of well-being. Eudaimonic psychological well-being is described as self-actualization or striving to one’s full potential (Dodge et al., 2012; Ryan & Deci, 2001; Vázquez et al., 2009). Ryff elaborated on this argument to describe well-being as “striving for perfection that represents the realization of one’s true potential” (Ryff & Keyes, 1995, p. 100). For Ryff, PWB encompasses an individual’s positive functioning, dividing PWB into six core dimensions, namely (i) autonomy, (ii) environmental mastery, (iii) personal growth, (iv) positive relations with others, (v) purpose in life, and (vi) self-acceptance (Ryff, 1989, 2014; Ryff & Keyes, 1995; Ryff & Singer, 2008). Within these dimensions, Autonomy refers to one’s ability and desire for independence as well as an internal locus of control (Gustems-Carnicer et al., 2019; Opree et al., 2018). Environmental Mastery refers to one’s ability to manage and control one’s environment to suit one’s needs (Gustems-Carnicer et al., 2019; Opree et al., 2018). Personal Growth refers to one’s need for continued personal development or self-actualization (Gustems-Carnicer et al., 2019; Opree et al., 2018). Positive Relations refers to one’s ability to establish deep and satisfying relationships with others (Gustems-Carnicer et al., 2019; Opree et al., 2018). Purpose in Life refers to one’s sense of direction and goals in life (Gustems-Carnicer et al., 2019; Opree et al., 2018). Lastly, Self-acceptance refers to positive attitudes toward oneself (Gustems-Carnicer et al., 2019; Opree et al., 2018). Based on eudaimonic well-being, PWB can be defined as an ongoing state of optimal functioning characterized by high levels of Autonomy, Environmental Mastery, Personal Growth, Positive Relations with Others, Purpose in Life and Self-acceptance (Joshanloo, 2016; Ryff, 1989, 2014, 2017). The Scales of Psychological Wellbeing (SPWB) developed by Ryff in order to measure these six core dimensions (Henn et al., 2016; Ryff, 1989, 2014) remains a prominent measure of PWB in research and has reported multiple sources of evidence for the validity of the SPWB (Ryff, 2014).While there is debate over the structure of SPWB, using the components of SPWB rather than a global score provides a more detailed understanding of SPWB in students and allows us to target specific dimensions to provide more detailed support for university students (Blasco-Belled & Alsinet, 2022; Ryff, 2014).

Variables such as emotional intelligence, adjustment, media and technology usage, and gender can be studied as possible predictor variables of PWB. Studies done on South African university students show that higher levels of emotional intelligence tend to result in increased levels of PWB (Beckmann & Minnaert, 2018; Carmeli et al., 2009; Cronje, 2019; Lawal et al., 2018). Lack of adjustment to university has been shown to significantly predict anxiety, depression, and social dysfunction amongst South African undergraduate university students (Olasupo et al., 2018), with media and technology usage having adverse effects on the PWB of university students (Çardak, 2013; Twenge, 2019). Differences have been found between male and female university students in PWB; each sex scored higher on specific aspects of PWB (Chraif & Dumitru, 2015; Roothman et al., 2003).

PWB and Emotional Intelligence

Emotional intelligence is defined as the ability to accurately perceive, understand, utilize and manage emotions in oneself and others (Schutte et al., 2013). Emotional intelligence has been found to have a positive effect on PWB (Asad Ali Shah et al., 2018; Balluerka et al., 2016; Görgens-Ekermans et al., 2015; Lawal et al., 2018; Yusoff et al., 2013). Studies on university students have shown that higher levels of emotional intelligence tend to result in decreased levels of somatic complaints, depression, and anxiety, and improved PWB, success, self-esteem, and mental and physical health (Beckmann & Minnaert, 2018; Cronje, 2019; Lawal et al., 2018; Malinauskas & Malinauskiene, 2020; Moeller et al., 2020; Rehman & Sohail, 2018; Singh & Kaur, 2019). Further South African studies have shown that as emotional intelligence encompasses the ability to deal with negative moods, students with higher emotional intelligence are less likely to experience negative moods, and more likely to experience positive affect that is associated with higher levels of PWB (Görgens-Ekermans et al., 2015).

PWB and Adjustment

University students tend to face many challenges and their adjustment to university life is essential to their PWB, as positive adjustment tends to lead to increased levels of PWB (LaBrie et al., 2012; Olasupo et al., 2018). Adjustment refers to how well university students cope with university life (Feldt et al., 2011). University students who can adjust to university life are less inclined to experience anxiety, depression, and social dysfunction and more prone to experience increased levels of PWB (Olasupo et al., 2018). In addition, both South African and international studies found that successful adjustment is associated with better academic performance (Petersen et al., 2009; Wintre & Yaffe, 2000). It is important to note that COVID-19 is increasing adjustment problems amongst university students, but further research is still required to clarify its severity (Arslan et al., 2022).

PWB and Media and Technology Usage

Media and Technology usage encompasses using a wide range of technological equipment such as cell phones, televisions, and computers (Rosen et al., 2013). It also includes a range of activities that can be performed on this equipment, such as texting, watching TV, and utilizing social media (Rosen et al., 2013). Media and technology usage is very prevalent amongst university students (Çardak, 2013; Twenge, 2019), with up to 95% of university students currently utilizing media and technology, with social media being the primary media used (Anand et al., 2018; Mese & Aydin, 2019; Nagel et al., 2018). Some studies show that media and technology usage can have adverse effects on the PWB of university students (Çardak, 2013; Twenge, 2019). During the COVID-19 pandemic, many university students have had to complete their studies online, leading to increased media and technology usage, resulting in increased anxiety levels due to the spread of false information and the inability to connect in real-time life (Jiang, 2021). Other studies, however, show the opposite, emphasizing the potential uses of media and technology in measuring and intervening in well-being (Magsamen-Conrad et al., 2014; Yaden et al., 2018). Regardless, media and technology overuse amongst university students tends to lead to increased mental health concerns and lower levels of PWB in terms of diminished impulse control, loneliness, depression, anxiety, lower social comfort, and distraction (Anand et al., 2018; Becker et al., 2013; Çardak, 2013; Tangmunkongvorakul et al., 2019).

PWB and Gender

Differences have been found between men and women in terms of PWB, with each gender scoring higher on specific aspects of PWB and with women having an overall lower sense of PWB (Gómez-Baya et al., 2018). Statistically significant differences have been found on four of the dimensions of PWB between males and females (Chraif & Dumitru, 2015; Matud et al., 2019). Men score significantly higher in self-acceptance and autonomy, while women score higher on personal growth and positive relations with others (Chraif & Dumitru, 2015; Matud et al., 2019). Similar differences have also been found to occur between male and female university students (Chraif & Dumitru, 2015; Roothman et al., 2003). This could be attributed to societal stereotypes around gender roles. Men are encouraged to be more independent and self-reliant, leading to men having a higher sense of autonomy and self-acceptance (Maroof & Khan, 2016; Matud et al., 2019). In comparison, women are encouraged to build relationships and be social, leading to women having a higher sense of personal growth and positive relations with others (Matud et al., 2019).

Purpose of This Study

University students tend to fall into the developmental stage known as emerging adulthood, marked by identity exploration and a variation in pathways to adulthood (Arnett, 2000). This developmental stage encompasses those aged between 18 and 29 years and is distinctly different from adulthood (Arnett, 2000, 2006, 2007). However, this developmental stage is not universal and is culturally determined (Arnett, 2000). This stage consists of endless possibilities in which attending university is only one option. During this developmental stage, university students have left the dependency associated with adolescence, but they have not yet taken on all of the responsibilities related to adulthood (Arnett, 2000, 2006). Although university life presents students with many options and choices, it also raises many challenges for undergraduate students (Credé & Niehorster, 2012; Feldt et al., 2011; Olasupo et al., 2018; Stoklosa, 2015; van Breda, 2017). Some of the challenges that students face include greater academic demands and a greater sense of responsibility (Credé & Niehorster, 2012; Stoklosa, 2015; van Breda, 2017). They need to adjust to a new social environment and take on new roles and responsibilities (Credé & Niehorster, 2012; Stoklosa, 2015) while facing separation from their friends and family (Credé & Niehorster, 2012; Stoklosa, 2015). PWB can benefit university students’ academic, emotional, and mental well-being (Chow et al., 2018; Harding et al., 2019; Olasupo et al., 2018; Panahi et al., 2016; Smith & Yang, 2017; Turashvili & Japaridze, 2012). Therefore, identifying possible predictors of PWB and studying the PWB of university students can provide valuable information for intervention programs to improve the PWB of university students (Morales-Rodríguez et al., 2020).

Methodology

Research Aim and Questions

This research study aimed to determine which variable(s) or combination(s) of variables explain a significant percentage of the variance in PWB amongst undergraduate university students at the University of the Free State. In this study, PWB was the criterion (dependent) variable, while Adjustment, Emotional Intelligence, Media and Technology Usage, and Sex were the predictor (independent) variables. In order to address the research aim of the study, the following research questions were explored:

Does the combination of Media and Technology Usage, Adjustment, Emotional Intelligence, and Sex explain a significant percentage of variance in psychological well-being amongst undergraduate university students?

Do any of the individual variables significantly contribute to the variance in psychological well-being amongst undergraduate university students?

Research Design

The study was quantitative and non-experimental in nature and used a cross-sectional correlational research design (Stangor, 2015).

Participants and Sampling

A non-probability sampling technique, more specifically, convenience sampling (Stangor, 2015), was used, and data was collected voluntarily from university students. The sample consisted of 1,191 South African undergraduate university students aged between 18 and 30. The sample consisted mostly of female students, comprising 77.5% of the sample (n = 923), while the remaining 22.5% of the sample consisted of male students (n = 268). The average age of the participants was 22.12 years (SD = 2.65). This sample consisted of a diverse range of ethnicities, with the vast majority of the sample identifying as Black (n = 961, 80.7%). The remaining 19.3% consisted of participants who identify as Colored (n = 49, 4.1%), White (n = 153, 12.8%), Asian (n = 1, 0.1%), and Indian (n = 4, 0.3%). The majority of the undergraduate students were in their second year of study (n = 596, 50.0%). The rest of the students were in the following years: the first year of study (n = 29, 2.4%), the third year of study (n = 439, 36.9%), the fourth year of study (n = 72, 6.0%), and other (n = 55, 4.6%). With regards to first-generation students, the sample was more or less evenly divided between first-generation students (n = 539, 45.3%) and non-first-generation students (n = 652, 54.7%), with there being more non-first-generation students in the sample.

Measuring Instruments

Data was collected using a biographical questionnaire to obtain demographic information and four formal measuring instruments to measure the various variables.

Ryff’s Scales of Psychological Well-Being (SPWB; Ryff & Singer, 2008) was used to measure the PWB of the participants. The SPWB consists of 42 items across six dimensions, namely (i) Autonomy, (ii) Environmental Mastery, (iii) Personal Growth, (iv) Positive Relations, (v) Purpose in Life, and (vi) Self-acceptance (Henn et al., 2016; Ryff, 1989, 2014). The SPWB uses a six-point Likert-type scale with response options that range from 1 (“strongly disagree”) to 6 (“strongly agree”) (Henn et al., 2016; Ryff, 1989, 2014). Internal consistencies identified in previous studies for the core dimensions ranged between 0.40 to 0.88 (Chan et al., 2019; Gao & McLellan, 2018; Li, 2014; van Dierendonck et al., 2008). Higher scores imply higher levels of PWB in terms of the specific PWB dimensions (Ryff, 1989).

The Student Adaption to College Questionnaire (SACQ; Credé & Niehorster, 2012) was used to measure the adjustment of the participants. The SACQ consists of 55 items covering two subscales, namely (i) Positive Adjustment and (ii) Negative Adjustment (Credé & Niehorster, 2012; LaBrie et al., 2012). The responses are indicated using a nine-point Likert-type scale ranging from 1 (“doesn’t apply to me at all”) to 9 (“applies very closely to me”) (LaBrie et al., 2012). Exceptional internal consistencies for the subscales have been identified in a previous study as 0.92 for Negative Adjustment and 0.93 for Positive Adjustment (LaBrie et al., 2012). A higher score for Positive Adjustment implies that a person is positively or well adjusted to university. In contrast, a higher score for Negative Adjustment suggests that a person is negatively or poorly adjusted to university (LaBrie et al., 2012).

The Schutte Emotional Intelligence Questionnaire (SEIS; Schutte et al., 1998) was used to measure the emotional intelligence of the participants. The SEIS consists of 33 items on a five-point Likert-type scale with response options that range from 1 (“strongly disagree”) to 5 (“strongly agree”) (Gardner & Qualter, 2010; Jonker & Vosloo, 2008; Schutte et al., 1998, 2009). The SEIS measures global trait emotional intelligence (Schutte et al., 1998, 2009). A high score is indicative of higher levels of emotional intelligence (Schutte et al., 2009). Exceptional internal consistencies, ranging between 0.90 and 0.93, have been reported in previous studies (Cronje, 2019; Gardner & Qualter, 2010; Jonker & Vosloo, 2008; Schutte et al., 1998, 2009).

The Media and Technology Usage and Attitudes Scale (MTUAS; Rosen et al., 2013) was used to measure the media and technology usage of the participants. The MTUAS is a 60-item scale that includes 11 usage subscales: Smartphone Usage, General Social Media Usage, Internet Searching, E-Mailing, Media Sharing, Text Messaging, Video Gaming, Online Friendships, Facebook Friendships, Phone Calling, and TV Viewing (Rosen et al., 2013). The MTUAS also includes four attitudes subscales (Rosen et al., 2013). However, for this study, only the usage subscales were used. The usage subscales use a 10-point Likert-type scale with response options that range from 1 (“never”) to 10 (“all the time”) (Rosen et al., 2013). Adequate to good internal consistencies of the subscales, ranging between 0.71 and 0.89, have been reported in previous studies (Cronje, 2019; Oäzguär, 2016; Van Tonder, 2020). Higher scores indicate more regular use of media and technology (Rosen et al., 2013). Previous studies have grouped the 11 subscales into three dimensions: (i) Media usage for social engagement (Online Friendships, Facebook Friendships), (ii) Media usage for communication (E-Mailing, Text Messaging, Phone Calling, Smartphone Usage, Media Sharing), and (iii) Media usage for leisure (TV Viewing, Internet Searching, Video Gaming, General Social Media Usage) (Cronje, 2019; Van Tonder, 2017, 2020). Good to exceptional internal consistencies for the three respective dimensions, ranging between 0.89 and 0.96, were reported in previous studies (Cronje, 2019; Van Tonder, 2017, 2020). These three dimensions were used in this research study.

Procedure

This study was approved by the Institutional Review Board (IRB) at the authors’ affiliated university before data collection. The research project was advertised during undergraduate Psychology lectures, and students could voluntarily participate in the study. The questionnaires were administered in English and completed in the students’ own time through the online educational platform, Blackboard. The students had 3 months to complete the questionnaires. A coding system was used to ensure the anonymity of the participants. During data collection, the principles of confidentiality, beneficence, and non-maleficence were upheld (Allan, 2015). All the participants had to provide written informed consent before they could participate in the study. The research information leaflet explained that the study was anonymous and voluntary, and permission to report and store their data was obtained. The safety and confidentiality of the data was ensured. Participants were allowed to withdraw from the study at any point. Counseling services were available should any participants experience any distress due to the study. However, no students reported any need for counseling services.

Data Analysis

Descriptive statistics were calculated for the sample and the measuring instruments. Cronbach alpha coefficients were calculated to investigate the internal consistencies of the measuring instruments. Pearson correlations were then calculated to investigate the relationships between the variables, namely PWB, Emotional Intelligence, Adjustment, Media and Technology Usage, and Gender. In order to determine the contribution of the various predictor variable(s) or combination of predictor variables to the variance of PWB, hierarchical regression analyses were conducted. According to Steyn (2005), the effect size can be defined as small (0.10), medium (0.30), and large (0.50) regarding correlations. With regards to hierarchical regression, Cohen (1992) stated that effect size can be defined as small (0.02), medium (0.15), and large (0.35). In this study, the focus was only on results that were statistically and practically significant. Statistical significance demonstrates the existence of an effect in a study based on levels of significance, while practical significance demonstrates the magnitude of the effect and whether the effect is significant enough to have practical implications in the real world. Both the 1% and 5% level of significance was used in the analyses of the data.

Results

Descriptive Analyses

The means, standard deviations, skewness, kurtosis, and internal consistencies of the various measuring instruments are reported in Table 1.

Table 1.

Descriptive Statistics and Reliability Coefficients for the PWB, SEIS, SACQ, and MTUAS Dimensions.

Measures	N	M	SD	$α$	Skewness	Kurtosis
SPWB
Autonomy	1,191	29.63	5.398	.60	−.058	−.152
Environmental mastery	1,191	27.38	4.668	.40	−.089	0.041
Personal growth	1,191	32.41	5.242	.61	−.234	−.777
Positive relations	1,191	30.17	5.867	.63	−.095	−.463
Purpose in life	1,191	30.77	5.259	.60	−.368	−.124
Self-acceptance	1,191	29.97	6.270	.71	−.383	−.051
SACQ
Positive adjustment	1,191	143.65	29.621	.89	−.121	0.253
Negative adjustment	1,191	137.25	39.027	.90	−.088	−.181
SEIS	1,191	120.40	16.654	.93	−.290	0.154
MTUAS
Media usage for social engagement	1,191	21.1788	11.022	.88	0.886	0.053
Media usage for communication	1,191	139.3006	42.714	.95	0.642	−.465
Media usage for leisure	1,191	100.9009	41.250	.95	0.687	−.397

It is evident in Table 1 that five of the SPWB subscales, the SEIS, SACQ subscales, and the three dimensions of the MTUAS all have adequate to exceptional internal consistencies ranging from 0.60 to 0.95. However, the subscale Environmental Mastery was not included in further statistical analyses due to an unsatisfactory Cronbach alpha of .40. With regards to skewness and kurtosis, according to Kahane (2008), the cut-off point for skewness is $> | 2 |$ and kurtosis is $> | 4 |$ . From Table 2, it is evident that all of the subscales are within the cut-off ranges and therefore do not deviate from normality.

Table 2.

Correlations Between the PWB Scales and Gender, Positive and Negative Adjustment, Emotional Intelligence, and the MTUAS Dimensions (N = 1,191).

	1	2	3	4	5	6	7	8	9	10	11	12
1. Gender	-	.36	.116**	.118**	.097**	.084**	−.171	−.160	.095**	−.138**	−.056	−.100**
2. Autonomy		-	.439**	.379**	.395**	.493**	.346**	−.292**	.344**	−.80**	−.067*	−.113*
3. Personal Growth			-	.487**	.505**	.496**	.410**	−.384**	.458**	−.147**	−.101**	−.171**
4. Positive relations				-	.459**	.522**	.474**	−.391**	.406**	−.113**	−.104**	−.140**
5. Purpose in life					-	.612**	.475**	−.357**	.397**	−.138**	−.081**	−.133**
6. Self-acceptance						-	.528**	−.480**	.393**	−.073**	−.082**	−.130**
7. PA							-	−.465**	.507**	−.135**	−.103**	−.136**
8. NA								-	−.326**	.127**	.132**	.188**
9. Emotional intelligence									-	−.335**	−.224**	−.286**
10. M1										-	.687**	.743**
11. M2											-	.856**
12. M3												-

Note. PA = positive adjustment; NA = negative adjustment; M1 = media usage for social engagement; M2 = media usage for communication; M3 = media usage for leisure.

p ≤ .01. *p ≤ .05.

In order to investigate the correlations between the variables, Pearson correlations were calculated for the variables. In order to use Gender as a demographic predictor variable, the researchers created two categories, namely Male (coded 1) and Female (coded 2). The correlation coefficients are illustrated in Table 2.

Table 2 shows that Autonomy has statistically significant positive correlations with Positive Adjustment and Emotional Intelligence. These correlations are statistically significant at the 1% level with respective medium effect sizes of .35 and .34. These findings seem to suggest that students with higher levels of autonomy tend to adjust better to university and have higher levels of emotional intelligence. Similarly, these findings may also suggest that well-adjusted students and students with higher levels of emotional intelligence tend to have higher levels of autonomy.

Personal Growth has statistically significant positive correlations with Positive Adjustment and Emotional Intelligence. These correlations are statistically significant at the 1% level with respective medium effect sizes of .41 and .46. Personal Growth also has a statistically significant negative correlation with Negative Adjustment. This correlation is statistically significant on the 1% level with a medium effect size of .38. These findings seem to suggest that students with higher levels of personal growth tend to be better adjusted to university and have higher levels of emotional intelligence. Similarly, these findings may suggest that well-adjusted students and students with higher levels of emotional intelligence tend to have higher levels of personal growth.

Table 2 indicates that Positive Relations has statistically significant positive correlations with Positive Adjustment and Emotional Intelligence. These correlations are statistically significant at the 1% level with respective medium effect sizes of .47 and .41. Positive Relations also has a statistically significant negative correlation with Negative Adjustment. This correlation is statistically significant on the 1% level with a medium effect size of .39. These findings seem to suggest that students with higher levels of positive relations tend to be better adjusted to university and have higher levels of emotional intelligence. Similarly, these findings may suggest that well-adjusted students and students with higher levels of emotional intelligence tend to have higher levels of positive relations.

In addition, Table 2 indicates that Purpose in Life has statistically significant positive correlations with Positive Adjustment and Emotional Intelligence. These correlations are statistically significant at the 1% level with respective medium effect sizes of .48 and .40. Purpose in Life also has a statistically significant negative correlation with Negative Adjustment. This correlation is statistically significant on the 1% level with a medium effect size of .36. These findings seem to suggest that students with higher levels of purpose in life tend to be better adjusted to university and have higher levels of emotional intelligence. Similarly these findings suggest that well-adjusted students and students with higher levels of emotional intelligence tend to have higher levels of purpose in life.

Lastly, Table 2 indicates that Self-acceptance has statistically significant positive correlations with Positive Adjustment and Emotional Intelligence. These correlations are statistically significant at the 1% level with respective large and medium effect sizes of .53 and .39. Self-acceptance also has a statistically significant negative correlation with Negative Adjustment. This correlation is statistically significant on the 1% level with a medium effect size of .48. These findings seem to suggest that students with higher levels of self-acceptance tend to be better adjusted to university and have higher levels of emotional intelligence. Similarly, these findings suggest that well-adjusted students and students with higher levels of emotional intelligence tend to have higher levels of self-acceptance.

Hierarchical Regression Results

Hierarchical regression analyses were conducted to identify the contribution of the combination of predictor variables (i.e., Gender, Adjustment, Emotional Intelligence, and Media and Technology Usage) and the contribution of the individual predictor variables to the percentage of the variance in PWB.

The results of the hierarchical regression analyses with Autonomy as criterion variable are reported in Table 3.

Table 3.

Contributions of Gender, Adjustment, Emotional Intelligence, and MTUAS Dimensions to R² With Autonomy as Criterion Variable.

Variables in equation	R ²	Contribution to R²: full minus reduced model	F	f ²
1. [Gender] + [PA + NA] + [EI] + [M1 + M2 + M3]	0.178	1-5 = 0.004	1.919	0.01
2. [Gender] + [PA + NA] + [EI] + M1	0.176	2-5 = 0.002	2.876	-
3. [Gender] + [PA + NA] + [EI] + M2	0.175	3-5 = 0.001	1.436	-
4. [Gender] + [PA + NA] + [EI] + M3	0.174	4-5 = 0.00	-	-
5. [Gender] + [PA + NA] + [EI]	0.174
6. [Gender] + [PA + NA] + [M1 + M2 + M3] + [EI]	0.178	6-7 = 0.031	44.610**	0.04
7. [Gender] + [PA + NA] + [M1 + M2 + M3]	0.147
8. [Gender] + [EI] + [M1 + M2 + M3] + [PA + NA]	0.178	8-11 = 0.053	38.138**	0.06
9. [Gender] + [EI] + [M1 + M2 + M3] + PA	0.164	9-11 = 0.039	55.234**	0.05
10. [Gender] + [EI] + [M1 + M2 + M3] + NA	0.158	10-11 = 0.033	46.404**	0.04
11. [Gender] + [EI] + [M1 + M2 + M3]	0.125
12. [EI] + [M1 + M2 + M3] + [PA + NA] + [Gender]	0.178	12-13 = 0.001	1.439	-
13. [EI] + [M1 + M2 + M3] + [PA + NA]	0.177

Note. PA = positive adjustment; NA = negative adjustment; M1 = media usage for social engagement; M2 = media usage for communication; M3 = media usage for leisure.

p ≤ .01.

As illustrated in Table 3, the combination of the independent variables is responsible for 17.8% ( $F_{7; 1183}$ = 36.559; p ≤ .01) of the variance in the Autonomy scores of the sample. No practically significant results were found with this hierarchical regression analyses.

The results of the hierarchical regression analyses with Personal Growth as a criterion variable are reported in Table 4.

Table 4.

Contributions of Gender, Adjustment, Emotional Intelligence, and MTUAS Dimensions to R² With Personal Growth as Criterion Variable.

Variables in equation	R ²	Contribution to R²: full minus reduced model	F	f ²
1. [Gender] + [PA + NA] + [EI] + [M1 + M2 + M3]	0.298	1-5 = 0.004	2.247*	-
2. [Gender] + [PA + NA] + [EI] + M1	0.294	2-5 = 0.000	-	-
3. [Gender] + [PA + NA] + [EI] + M2	0.294	3-5 = 0.00	-	-
4. [Gender] + [PA + NA] + [EI] + M3	0.294	4-5 = 0.00	-	-
5. [Gender] + [PA + NA] + [EI]	0.294
6. [Gender] + [PA + NA] + [M1 + M2 + M3] + [EI]	0.298	6-7 = 0.059	99.26**	0.08
7. [Gender] + [PA + NA] + [M1 + M2 + M3]	0.239
8. [Gender] + [EI] + [M1 + M2 + M3] + [PA + NA]	0.298	8-11 = 0.075	63.194**	0.11
9. [Gender]+ [EI] + [M1 + M2 + M3] + PA	0.267	9-11 = 0.044	71.072**	0.06
10. [Gender] + [EI] + [M1 + M2 + M3] + NA	0.281	10-11 = 0.058	95.510**	0.08
11. [Gender] + [EI] + [M1 + M2 + M3]	0.223
12. [EI] + [M1 + M2 + M3] + [PA + NA] + [Gender]	0.298	12-13 = 0.007	11.796**	-
13. [EI] + [M1 + M2 + M3] + [PA + NA]	0.291

Note. PA = positive adjustment; NA = negative adjustment; M1 = media usage for social engagement; M2 = media usage for communication; M3 = media usage for leisure.

p ≤ .01. *p ≤ .05.

As illustrated in Table 4, the combination of the independent variables is responsible for 29.8% ( $F_{7; 1183}$ = 71.755; p ≤ .01) of the variance in the Personal Growth scores of the sample. No practically significant results were found with this hierarchical regression analyses.

The results of the hierarchical regression analyses with Positive Relations as criterion variable are reported in Table 5.

Table 5.

Contributions of Gender, Adjustment, Emotional Intelligence, and MTUAS Dimensions to R² With Positive Relations as Criterion Variable.

Variables in equation	R ²	Contribution to R²: full minus reduced model	F	f ²
1. [Gender] + [PA + NA] + [EI] + [M1 + M2 + M3]	0.303	1-5 = 0.002	1.31	-
2. [Gender] + [PA + NA] + [EI] + M1	0.302	2-5 = 0.001	1.700	-
3. [Gender] + [PA + NA] + [EI] + M2	0.301	3-5 = 0.000	-	-
4. [Gender] + [PA + NA] + [EI] + M3	0.301	4-5 = 0.000	-	-
5. [Gender] + [PA + NA] + [EI]	0.301
6. [Gender] + [PA + NA] + [M1 + M2 + M3] + [EI]	0.303	6-7 = 0.025	42.432**	0.04
7. [Gender] + [PA + NA] + [M1 + M2 + M3]	0.278
8. [Gender] + [EI] + [M1 + M2 + M3] + [PA + NA]	0.303	8-11 = 0.127	107.77**	0.18
9. [Gender] + [EI] + [M1 + M2 + M3] + PA	0.275	9-11 = 0.099	161.678**	0.14
10. [Gender] + [EI] + [M1 + M2 + M3] + NA	0.249	10-11 = 0.073	115.089**	0.10
11. [Gender] + [EI] + [M1 + M2 + M3]	0.176
12. [EI] + [M1 + M2 + M3] + [PA + NA] + [Gender]	0.303	12-13 = 0.011	18.670**	0.02
13. [EI] + [M1 + M2 + M3] + [PA + NA]	0.292

Note. PA = positive adjustment; NA = negative adjustment; M1 = media usage for social engagement; M2 = media usage for communication; M3 = media usage for leisure.

p ≤ .01.

As illustrated in Table 5, the combination of the independent variables is responsible for 30.3% ( $F_{7; 1183}$ = 73.420; p ≤ .01) of the variance in the Positive Relations scores of the sample. The Adjustment subscales as a combination were found to account for 12.7% of the variance in the Positive Relations scores. This finding is statistically significant at the 1% level, with the corresponding effect size ( $f^{2}$ = .18) suggesting that this is of medium practical significance. Positive Adjustment accounts for 9.9% of the variance in the Positive Relations scores. This finding is statistically significant at the 1% level and the corresponding effect size ( $f^{2}$ = .14) suggests that this is of medium practical significance.

The results of the hierarchical regression analysis with Self-acceptance as a criterion variable are reported in Table 6.

Table 6.

Contributions of Gender, Adjustment, Emotional Intelligence, and MTUAS Dimensions to R² With Self-acceptance as Criterion Variable.

Variables in equation	R ²	Contribution to R²: full minus reduced model	F	f ²
1. [Gender] + [PA + NA] + [EI] + [M1 + M2 + M3]	0.374	1-5 = 0.006	3.78**	0.01
2. [Gender] + [PA + NA] + [EI] + M1	0.372	2-5 = 0.004	7.548**	0.01
3. [Gender] + [PA + NA] + [EI] + M2	0.368	3-5 = 0.000	-	-
4. [Gender] + [PA + NA] + [EI] + M3	0.368	4-5 = 0.000	-	-
5. [Gender] + [PA + NA] + [EI]	0.368
6. [Gender] + [PA + NA] + [M1 + M2 + M3] + [EI]	0.374	6-7 = 0.014	26.457**	0.02
7. [Gender] + [PA + NA] + [M1 + M2 + M3]	0.360
8. [Gender] + [EI] + [M1 + M2 + M3] + [PA + NA]	0.374	8-11 = 0.204	192.757**	0.33
9. [Gender] + [EI] + [M1 + M2 + M3] + PA	0.316	9-11 = 0.146	252.725**	0.21
10. [Gender] + [EI] + [M1 + M2 + M3] + NA	0.303	10-11 = 0.133	225.928**	0.19
11. [Gender] + [EI] + [M1 + M2 + M3]	0.170
12. [EI] + [M1 + M2 + M3] + [PA + NA] + [Gender]	0.374	12-13 = 0.006	11.339**	-
13. [EI] + [M1 + M2 + M3] + [PA + NA]	0.368

Note. PA = positive adjustment; NA = negative adjustment; M1 = media usage for social engagement; M2 = media usage for communication; M3 = media usage for leisure.

p ≤ .01.

As illustrated in Table 6, the combination of the independent variables is responsible for 37.4% ( $F_{7; 1183}$ = 100.955; p ≤ .01) of the variance in the Self-acceptance scores of the sample. The Adjustment subscales as a combination were found to account for 20.4% of the variance in the Self-acceptance scores. This finding is statistically significant at the 1% level and the corresponding effect size ( $f^{2}$ = .33) suggests that this is of large practical significance. Negative Adjustment accounts for 13.3% of the variance in the Self-acceptance scores. This finding is statistically significant at the 1% level and the corresponding effect size ( $f^{2}$ = .19) suggests that this is of medium practical significance. Positive Adjustment accounts for 14.6% of the variance in the Self-acceptance scores. This finding is statistically significant at the 1% level, with the corresponding effect size ( $f^{2}$ = .21) suggesting that this is of medium practical significance.

The results of the hierarchical regression analysis with Purpose in Life as criterion variable are reported in Table 7.

Table 7.

Contributions of Gender, Adjustment, Emotional Intelligence, and MTUAS Dimensions to R² With Purpose in Life as Criterion Variable.

Variables in equation	R ²	Contribution to R²: full minus reduced model	F	f ²
1. [Gender] + [PA + NA] + [EI] + [M1 + M2 + M3]	0.285	1-5 = 0.002	1.103	-
2. [Gender] + [PA + NA] + [EI] + M1	0.283	2-5 = 0.000	-	-
3. [Gender] + [PA + NA] + [EI] + M2	0.284	3-5 = 0.001	1.660	-
4. [Gender] + [PA + NA] + [EI] + M3	0.283	4-5 = 0.000	-	-
5. [Gender] + [PA + NA] + [EI]	0.283
6. [Gender] + [PA + NA] + [M1 + M2 + M3] + [EI]	0.285	6-7 = 0.021	34.745**	0.03
7. [Gender] + [PA + NA] + [M1 + M2 + M3]	0.264
8. [Gender] + [EI] + [M1 + M2 + M3] + [PA + NA]	0.285	8-11 = 0.121	100.1**	0.17
9. [Gender] + [EI] + [M1 + M2 + M3] + PA	0.268	9-11 = 0.104	168.248**	0.14
10. [Gender] + [EI] + [M1 + M2 + M3] + NA	0.221	10-11 = 0.057	36.634**	0.07
11. [Gender] + [EI] + [M1 + M2 + M3]	0.164
12. [EI] + [M1 + M2 + M3] + [PA + NA] + [Gender]	0.285	12-13 = 0.007	11.581**	-
13. [EI] + [M1 + M2 + M3] + [PA + NA]	0.278

Note. PA = positive adjustment; NA = negative adjustment; M1 = media usage for social engagement; M2 = media usage for communication; M3 = media usage for leisure.

p ≤ .01.

As illustrated in Table 7, the combination of the independent variables is responsible for 28.5% ( $F_{7; 1183}$ = 67.243; p ≤ .01) of the variance in the Purpose in Life scores of the sample. The Adjustment subscales as a combination were found to account for 12.1% of the variance in the Purpose in Life scores. This finding is statistically significant at the 1% level and the corresponding effect size (${f^2}$ = .17) suggests that this is of medium practical significance. Positive Adjustment accounts for 10.4% of the variance in the Purpose in Life scores. This finding is statistically significant at the 1% level and the corresponding effect size (${f^2}$ = .14) suggests that this is of medium practical significance.

Discussion

Discussion of the Significant Correlations With Adjustment in This Study

Correlations were determined between the predictor variables and the criterion variables. This included correlations between the five subscales of SPWB and Adjustment, Emotional Intelligence, Media and Technology Usage, and Sex. The discussion will focus on the correlations with adjustment due to adjustment being the only individual predictor variable that statistically and practically significantly contributed to the explanation of the variance of PWB.

This study found that improved adjustment (i.e., higher levels of positive adjustment and lower levels of negative adjustment) was statistically and practically significantly associated with increased levels of Personal Growth, Positive Relations, Purpose in Life, Self-acceptance, and Autonomy. Well-adjusted university students tend to have higher levels of autonomy (Girelli et al., 2018; Lamborn & Groh, 2009), as explained by the Self-Determination Theory of Ryan and Deci (2000), indicating that intrinsic or autonomous motivated individuals engage in activities for their own pleasure, interest, and satisfaction. This motivation is often found in self-determined students and is related to greater academic performance and adjustment to university (Girelli et al., 2018; Ryan & Deci, 2000, 2017). Similarly, individuals with higher levels of autonomy tend to be better adjusted to university (Girelli et al., 2018; Lamborn & Groh, 2009).

Well-adjusted university students tend to have deeper relationships that provide a sense of belonging, fostering an ideal environment for individual or personal growth (Ball et al., 2020; Young et al., 2013). This is also in line with Alfred Adler’s theory of Social Interest which states that individuals depend on social interactions to find meaning in life and engage in personal growth (Ball et al., 2020; Crandall, 1980). Personal Growth has furthermore been linked to better adjustment and acts as a buffer against psychological distress (Dweck et al., 1995; Schroder et al., 2017; Staudinger & Kessler, 2009; Yeager et al., 2014). Personal Growth also has a statistically significant negative correlation with Negative Adjustment. Increased levels of maladjustment and increased psychological distress tend to lead to decreased levels of personal growth (Schroder et al., 2017).

According to Erikson’s theory of psychosocial development, individuals in their early 20s face the critical developmental task of establishing intimate relationships with others (Fleming & Erikson, 2004; Goguen et al., 2008; Knight, 2017). Developing mature relations with others is considered one of Chickering’s developmental tasks for undergraduate university students (Chickering, 1969). Therefore, relationships with others are considered vital to our development (Fleming & Erikson, 2004; Goguen et al., 2008; Knight, 2017). Close or positive relations with others can be seen as a marker of adjustment in university students (Baker, 2006; Fass & Tubman, 2002; Laursen & Mooney, 2008). Positive Relations with parents and university peers are associated with improved adjustment amongst university students (Fass & Tubman, 2002; Goguen et al., 2008; Laursen & Mooney, 2008; Wintre & Yaffe, 2000).

Finding one’s purpose in life is considered the driving force in life (Frankl, 1969; Wong, 2014). The university environment provides opportunities for self-exploration, which is important for developing one’s purpose in life (Pfund et al., 2020). The literature shows that well-adjusted university students tend to have a higher sense of purpose in life (DeWitz et al., 2009; Liter, 1987; Wesley & Booker, 2021). Well-adjusted individuals can shift their mindset from what they cannot control onto what they can control and thus has better control over their meaning or purpose in life. Therefore, better adjustment can increase life purpose (Dollinger, 1986; Park & Folkman, 1997; Thompson et al., 2003).

According to Maslow’s hierarchy of needs, self-acceptance is crucial for all self-actualizing individuals (Compton, 2018). The university environment and opportunities that the university provides, such as establishing close relationships and developing self-awareness, tend to contribute to the development of self-acceptance (Ceyhan & Ceyhan, 2011). Ceyhan and Ceyhan (2011) found that self-acceptance increases throughout students’ undergraduate degrees. Well-adjusted university students are more likely to accept the more unflattering aspects of themselves and thus have higher levels of self-acceptance (Taylor & Combs, 1952; Williams & Lynn, 2010).

Discussion on the Predictors of Psychological Well-Being

The combination of the predictor variables (MTUAS dimensions, Emotional Intelligence, Adjustment, and Gender) statistically significantly contributed to the explanation of the variance of the subscales of PWB (Autonomy, Personal Growth, Positive Relations, Purpose in Life, and Self-acceptance). Positive Adjustment as an individual predictor variable statistically and practically significantly contributed to the explanation of the variance of Positive Relations, Self-acceptance, and Purpose in Life. Negative Adjustment as an individual predictor variable statistically and practically significantly contributed to the explanation of the variance of Self-acceptance. Positive Adjustment and Negative Adjustment as a combination statistically and practically significantly contributed to the explanation of the variance of Positive Relations, Self-acceptance, and Purpose in Life.

Positive relations with parents and university peers are associated with improved adjustment amongst university students (Fass & Tubman, 2002; Goguen et al., 2008; Laursen & Mooney, 2008; Wintre & Yaffe, 2000). Well-adjusted university students tend to use social support provided by their friends and tend to have more quality friendships or positive relations (Buote et al., 2007). When students leave high school and start attending university, their social network decreases, especially if they leave their home to study at university (Buote et al., 2007). Therefore, university students need to build up their social network by making new friends at university. Well-adjusted first-year students, therefore, tend to have more quality friendships or positive relations (Buote et al., 2007).

Developing a sense of purpose is another vector of development identified by Chickering (1969). Purpose in life has been linked to the activities that university students partake in Molasso (2006). Well-adjusted university students tend to study more, spend more time with friends, exercise, and socialize at parties, ultimately leading to a higher sense of purpose in life (Molasso, 2006; Terenzini & Pascarella, 1991).The literature shows that well-adjusted university students tend to have a higher sense of purpose in life (DeWitz et al., 2009; Liter, 1987; Wesley & Booker, 2021). Well-adjusted university students tend to study more, spend more time with friends, exercise, and socialize, ultimately leading to a higher sense of purpose in life (Molasso, 2006; Terenzini & Pascarella, 1991). Therefore, better adjustment can increase life purpose (Dollinger, 1986; Park & Folkman, 1997; Thompson et al., 2003).

Self-acceptance entails unconditional acceptance of oneself as an infallible human being who makes mistakes (MacInnes, 2006). Self-acceptance has been linked to lower levels of depression and anxiety and higher levels of psychological health (MacInnes, 2006). Individuals who have higher levels of self-acceptance are more likely to know their strengths and weaknesses, possess personal resources, and have better adjustment to university (Balakrishnan, 2019; Raines & Lewandowski, 2009; Williams & Lynn, 2010).

The COVID-19 pandemic brought many new challenges for university students who had to adjust to working online and losing many of the aspects of university that aid adjustment such as Positive Relations with peers (Adedoyin & Soykan, 2023; Kaisara & Bwalya, 2020). While more research is still needed to clarify the effects of the pandemic on university student’s adjustment, the pandemic highlighted to the world the importance of adjustment and how challenging it can be to one’s mental health (Adedoyin & Soykan, 2023; Arslan et al., 2022).

Psychological well-being continues to be a variable of increased interest as it’s importance in society, especially within the university student population, is evident. The significant finding of adjustment as a predictor of various subscales of PWB can be practically applied in order to better the PWB of university students. This study and previous research have highlighted the need to support South African university students due to the unique challenges they face. As such, the findings of this study and future research should be used to inform workshops, interventions, and psycho-education programs for university students to improve their PWB. This can improve university intervention strategies and ultimately improve the mental health of university students in South Africa. On a larger scale, the improvement of PWB in university students could ultimately lead to decreased drop-out and failure rates amongst university students in South Africa (Essop, 2020; Mokgele & Rothmann, 2014). This could improve the economy of South Africa and improve the high unemployment rates, currently sitting at 35.3% in 2022 (Statistics South Africa, 2022).

Limitations and Recommendations

There are limitations that may have impacted the results obtained within the study, and the results of this study should, therefore, be interpreted against these limitations. Firstly, the results cannot be generalized to different university student populations across South Africa as a result of using non-probability sampling. Secondly, this study used self-reported questionnaires, which are by nature subjective to the participant. This can lead to the limitation of participants not answering truthfully, especially to more sensitive questions.

In order to better the understanding of PWB, it is recommended that further research is conducted at various universities across South Africa as well as in other tertiary education contexts such as FET colleges. This will increase the generalizability of the results across students in South Africa. Furthermore, research should also be conducted on postgraduate university students as they may have different experiences. It is recommended that further studies make use of probability sampling to ensure the generalizability of the results. In addition, a longitudinal or qualitative approach would be beneficial in contributing to a more in-depth understanding of PWB.

Contribution of This Study

The main contribution of this study is the identification of Adjustment as a statistically and practically significant predictor of PWB in university students. Furthermore, this study contributes to the internationally growing body of research dedicated to PWB. In addition, it contributes more specifically to the understanding of PWB in a South African context and within a university student population. This study also contributes to a better understanding of Emotional Intelligence, Adjustment to University and Media and Technology Usage in South African undergraduate university students as predictors of psychological well-being.

Lastly, as previously discussed, PWB influences the academic, emotional and mental well-being of university students. It is, therefore, worth measuring and intervening with the PWB of university students. The findings of this study can be used to inform workshops, interventions and psycho-educational programs aimed at improving the psychological well-being of university students.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethical Statement

This research study was conducted in accordance with the ethical standards of the institutional research committee. The ethical clearance number is: UFS-HSD2020/1134/0510.

ORCID iD

Jacques Jordaan

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

Adedoyin

O. B.

Soykan

(2023). Covid-19 pandemic and online learning: The challenges and opportunities. Interactive Learning Environments, 31(2), 863–875. https://doi.org/10.1080/10494820.2020.1813180

Allan

(Ed.). (2015). Law and ethics in psychology: An international perspective (3rd ed.). Inter-Ed.

Anand

Jain

P. A.

Prabhu

Thomas

Bhat

Prathyusha

P. V.

Bhat

S. U.

Young

Cherian

A. V.

(2018). Prevalence of excessive internet use and its association with psychological distress among university students in South India. Industrial Psychiatry Journal, 27(1), 131–140. https://doi.org/10.4103/ipj.ipj_28_18

Ankeli

(2023). Impact of school dropot rate on Nigeria’s national development aspirations: Implications for university education in Nigeria. International Journal of Educational Policy Research and Review, 9(2), 1–8. https://doi.org/10.36348/sb.2023.v09i02.00X

Arnett

(2000). Emerging adulthood: A theory of the development from the late teens through the twenties. American Psychologist, 55(5), 468–480. https://doi.org/10.1037/0003-066X.55.5.469

Arnett

. (2006). The psychology of emerging adulthood: What is known, and what remains to be known? In Arnett

J. J.

Tanner

J. L.

(Eds.), Emerging adults in America: Coming of age in the 21st century (pp. 303–330). American Psychological Association. https://doi.org/10.1037/11381-013

Arnett

. (2007). Emerging adulthood: What is it, and what is it good for? Child development perspectives, 1(2), 68–73. https://doi.org/10.1111/j.1750-8606.2007.00016.x

Arslan

Yıldırım

Zangeneh

(2022). Coronavirus anxiety and psychological adjustment in college students: Exploring the role of college belongingness and social media addiction. International Journal of Mental Health and Addiction, 20, 1546–1559. https://doi.org/10.1007/s11469-020-00460-4

Asad Ali Shah

Yezhuang

Muhammad Shah

Khan Durrani

Jamal Shah

(2018). Fear of terror and psychological well-being: The moderating role of emotional intelligence. International Journal of Environmental Research and Public Health, 15, 2554–2567. https://doi.org/10.3390/ijerph15112554

10.

Baker

J. A.

(2006). Contributions of teacher–child relationships to positive school adjustment during elementary school. Journal of School Psychology, 44(3), 211–229. https://doi.org/10.1016/j.jsp.2006.02.002

11.

Balakrishnan

(2019). Who I am is shaped by my past and impacts my future: Exploring antecedents and outcomes of self-views across international, immigrant and Canadian born undergraduate students [Doctoral dissertation, The University of Western Ontario]. Electronic Thesis and Dissertation Repository.

12.

Ball

C. S.

McGowan

M. G.

Predvil

D. S.

(2020). Personal growth groups and college adjustment [Doctoral dissertation, Brenau University]. ProQuest Dissertations Publishing.

13.

Balluerka

Gorostiaga

Alonso-Arbiol

Aritzeta

(2016). Peer attachment and class emotional intelligence as predictors of adolescents’ psychological well-being: A multilevel approach. Journal of Adolescence, 53(1), 1–9. https://doi.org/10.1016/j.adolescence.2016.08.009

14.

Banda Chitsamatanga

Malinga

(2021). A tale of two paradoxes in response to COVID-19’: Public health system and socio-economic implications of the pandemic in South Africa and Zimbabwe. Cogent Social Sciences, 7(1), 1–19. https://doi.org/10.1080/23311886.2020.1869368

15.

Bantjes

Lochner

Saal

Roos

Taljaard

Page

Auerbach

R. P.

Mortier

Bruffaerts

Kessler

R. C.

Stein

D. J.

(2019). Prevalence and sociodemographic correlates of common mental disorders among first-year university students in post-apartheid South Africa: Implications for a public mental health approach to student wellness. BMC Public Health, 19(1), 922–1012. https://doi.org/10.1186/s12889-019-7218-y

16.

Bantjes

J. R.

Kagee

McGowan

Steel

(2016). Symptoms of posttraumatic stress, depression, and anxiety as predictors of suicidal ideation among South African university students. Journal of American College Health, 64(6), 429–437. https://doi.org/10.1080/07448481.2016.1178120

17.

Beaubien

(2018). The country with the world’s worst inequality is .... NPR. https://www.npr.org/sections/goatsandsoda/2018/04/02/598864666/the-country-with-the-worlds-worst-inequality-is

18.

Becker

M. W.

Alzahabi

Hopwood

C. J.

(2013). Media multitasking is associated with symptoms of depression and social anxiety. Cyberpsychology Behavior and Social Networking, 16(2), 132–135. https://doi.org/10.1089/cyber.2012.0291

19.

Beckmann

Minnaert

(2018). Non-cognitive characteristics of gifted students with learning disabilities: An in-depth systematic review. Frontiers in Psychology, 9(504), 504–520. https://doi.org/10.3389/fpsyg.2018.00504

20.

Bhullar

Schutte

N. S.

Malouff

J. M.

(2013). The nature of well-being: The roles of hedonic and eudaimonic processes and trait emotional intelligence. Journal of Psychology, 147(1), 1–16. https://doi.org/10.1080/00223980.2012.667016

21.

Blasco-Belled

Alsinet

(2022). The architecture of psychological well-being: A network analysis study of the ryff psychological well-being scale. Scandinavian Journal of Psychology, 63(3), 199–207. https://doi.org/10.1111/sjop.12795

22.

Braathen

S. H.

Vergunst

Mji

Mannan

Swartz

(2013). Understanding the local context for the application of global mental health: A rural South African experience. International Health, 5(1), 38–42. https://doi.org/10.1093/inthealth/ihs016

23.

Buote

V. M.

Pancer

S. M.

Pratt

M. W.

Adams

Birnie-Lefcovitch

Polivy

Wintre

M. G.

(2007). The improtance of friends: Friendship and adjustment among 1st year university students. Journal of Adolescent Research, 22(6), 665–689. https://doi.org/10.1177/0743558407306344

24.

Çardak

(2013). Psychological well-being and internet addiction amongst university students. Turkish Online Journal of Educational Technology, 12(3), 134–141.

25.

Carmeli

Yitzhak-Halevy

Weisberg

(2009). The relationship between emotional intelligence and psychological wellbeing. Journal of Managerial Psychology, 24(1), 66–78. https://doi.org/10.1108/02683940910922546

26.

Ceyhan

(2011). Investigation of university students’ self-acceptance and learned resourcefulness: A longitudinal study. Higher Education, 61(6), 649–661. https://doi.org/10.1007/s10734-010-9354-2

27.

Chan

D. W.

Chan

L. K.

Sun

(2019). Developing a brief version of Ryff’s scale to assess the psychological well-being of adolescents in Hong Kong. European Journal of Psychological Assessment, 35(3), 414–422. https://doi.org/10.1027/1015-5759/a000403

28.

Chernomas

W. M.

Shapiro

C. R.

Plohman

J. C.

Rabbani

(2023). Stress, depression, and anxiety among undergraduate nursing students in the time of a pandemic. International Journal of Nursing Education Scholarship, 20(1), 1–12. https://doi.org/10.1515/ijnes-2022-0096

29.

Chetty

Pather

(2015). Challenges in higher education in South Africa. In Condy

(Ed.), Telling stories differently: Engaging 21st century students through digital storytelling (pp. 1–6). African Sun Media.

30.

Chickering

(1969). Education and identity. Jossey-Bass.

31.

Chow

K. M.

Tang

W. K. F.

Chan

W. H. C.

Sit

W. H. J.

Choi

K. C.

Chan

(2018). Resilience and well-being of university nursing students in Hong Kong: A cross-sectional study. BMC Medical Education, 18(1), 1–8. https://doi.org/10.1186/s12909-018-1119-0

32.

Chraif

Dumitru

(2015). Gender differences on well-being and quality of life at young students at psychology. Procedia - Social and Behavioral Sciences, 180, 1579–1583. https://doi.org/10.1016/j.sbspro.2015.02.310

33.

Cilliers

Flotman

A. P.

(2016). The psychological well-being manifesting among master’s students in industrial and organisational psychology. South Africa Journal of Industrial Psychology, 42(1), a1323. https://doi.org/10.4102/sajip.v42i1.1323

34.

Cohen

(1992). A power primer. Psychological Bulletin, 112(1), 155–159. https://doi.org/10.1037//0033-2909.112.1.155

35.

Compton

W. C.

(2018). Self-actualization myths: What did Maslow really say? Journal of Humanistic Psychology, Advance Online Publication. https://doi.org/10.1177/0022167818761929

36.

Crandall

J. E.

(1980). Adler's concept of social interest: Theory, measurement, and implications for adjustment. Journal of Personality and Social Psychology, 39(3), 481–495. https://doi.org/10.1037//0022-3514.39.3.481

37.

Credé

Niehorster

(2012). Adjustment to college as measured by the student adaptation to college questionnaire: A quantitative review of its structure and relationships with correlates and consequences. Educational Psychology Review, 24(1), 133–165. https://doi.org/10.1007/s10648-011-9184-5

38.

Cronje

(2019). Predictors of emotional intelligence amongst university students [Master’s thesis, University of the Free State]. KovsieScholar.

39.

Das-Munshi

Lund

Mathews

Clark

Rothon

Stansfeld

(2016). Mental health inequalities in adolescents growing up in postapartheid South Africa: Cross-sectional survey, ShaW study. PLoS One, 11(5), e0154478. https://doi.org/10.1371/journal.pone.0154478

40.

DeWitz

S. J.

Woolsey

M. L.

Walsh

W. B.

(2009). College student retention: An exploration of the relationship between self-efficacy beliefs and purpose in life among college students. Journal of College Student Development, 50(1), 19–34. https://doi.org/10.1353/csd.0.0049

41.

Dodge

Daly

Huyton

Sanders

(2012). The challenge of defining well-being. International Journal of Wellbeing, 2(3), 222–235. https://doi.org/10.5502/ijw.v2i3.4

42.

Dollinger

S. J.

(1986). The need for meaning following disaster: Attributions and emotional upset. Personality and Social Psychology Bulletin, 12(3), 300–310. https://doi.org/10.1177%2F0146167286123006

43.

Du Plessis

(2021). Decolonisation of education in South Africa: Challenges to decolonise the university curriculum. South African Journal of Higher Education, 35(1), 54–69. https://doi.org/10.20853/35-1-4426

44.

Dweck

C. S.

Chiu

C. Y.

Hong

Y. Y.

(1995). Implicit theories and their role in judgments and reactions: A word from two perspectives. Psychological Inquiry, 6(4), 267–285. https://doi.org/10.1207/s15327965pli0604_1

45.

Edwards

S. D.

Ngcobo

H. S.

Pillay

A. L.

(2004). Psychological well-being in South African university students. Psychological Reports, 95(3 Pt 2), 1279–1282. https://doi.org/10.2466/pr0.95.3f.1279-1282

46.

Essop

(2020). The changing size and shape of the higher education system in South Africa, 2005–2017. Ali Mazrui Centre for Higher Education Studies.

47.

Fass

M. E.

Tubman

J. G.

(2002). The influence of parental and peer attachment on college students’ academic achievement. Psychology in the Schools, 39(5), 561–573. https://doi.org/10.1002/pits.10050

48.

Feldt

R. C.

Graham

Dew

(2011). Measuring adjustment to college: Construct validity of the student adaptation to college questionnaire. Measurement and Evaluation in Counseling and Development, 44(2), 92–104. https://doi.org/10.1177/0748175611400291

49.

Fleming

Erikson

(2004). Erikson and personal identity: A biographical profile. https://www.semanticscholar.org/paper/Erikson-and-Personal-Identity%3A-a-Biographical-Fleming-Erikson/ab44d6a2d3178e4e159108ab5333504d23cc9508

50.

Frankl

V. E.

(1969). The will to meaning: Principles and application of logotherapy. World Publishing.

51.

Freire

Ferradás

M. D.

Valle

Núñez

J. C.

Vallejo

(2016). Profiles of psychological well-being and coping strategies among university students. Frontiers in Psychology, 7, 1554–1611. https://doi.org/10.3389/fpsyg.2016.01554

52.

Gao

McLellan

(2018). Using Ryff’s scales of psychological well-being in adolescents in mainland China. BMC Psychology, 6(1), 17–18. https://doi.org/10.1186/s40359-018-0231-6

53.

Gardner

K. J.

Qualter

(2010). Concurrent and incremental validity of three trait emotional intelligence measures. Australian Journal of Psychology, 62(1), 5–13. https://doi.org/10.1080/00049530903312857

54.

Girelli

Alivernini

Lucidi

Cozzolino

Savarese

Sibilio

Salvatore

(2018). Autonomy supportive contexts, autonomous motivation, and self-efficacy predict academic adjustment of first-year university students. Frontiers in Education, 3(95), 1–11. https://doi.org/10.3389/feduc.2018.00095

55.

Goguen

Nordstroom

Hiester

(2008). The role of peer relationships in adjustment to college. Journal of College Student Development, 49(6), 551–567. https://doi.org/10.1353/csd.0.0038

56.

Gómez-Baya

Lucia-Casademunt

A. M.

Salinas-Pérez

J. A.

(2018). Gender differences in psychological well-being and health problems among European health professionals: Analysis of psychological basic needs and job satisfaction. International Journal of Environmental Research and Public Health, 15(7), 1474–1490. https://doi.org/10.3390/ijerph15071474

57.

Görgens-Ekermans

Delport

Du Preez

(2015). Developing emotional intelligence as a key psychological resource reservoir for sustained student success. South Africa Journal of Industrial Psychology, 41(1), 1–13. https://doi.org/10.4102/sajip.v41i1.1251

58.

Gustems-Carnicer

Calderon

Batalla-Flores

Esteban-Bara

(2019). Role of coping responses in the relationship between perceived stress and psychological well-being in a sample of Spanish educational teacher students. Psychological Reports, 122(2), 380–397. https://doi.org/10.1177/0033294118758904

59.

Harding

Lopez

Klainin-Yobas

(2019). Predictors of psychological well-being among higher education students. Psychology, 10, 578–594. https://doi.org/10.4236/psych.2019.104037

60.

Henn

C. M.

Hill

Jorgensen

L. I.

(2016). An investigation into the factor structure of the Ryff scales of psychological well-being. South Africa Journal of Industrial Psychology, 42(1), a1275. https://doi.org/10.4102/sajip.v42i1.1275

61.

Hussar

Zhang

Hein

Wang

Roberts

Cui

Smith

Bulllock Mann

Barmer

Dilig

(2020). The condition of education 2020 (pp. 119–201). National Center for Education Statistics. https://nces.ed.gov/pubs2020/2020144.pdf

62.

Jayasankara Reddy

Rajan Menon

Thattil

(2018). Academic stress and its sources among university students. Biomedical & Pharmacology Journal, 11(1), 531–537. https://doi.org/10.13005/bpj/1404

63.

Jiang

(2021). Problematic social media usage and anxiety among university students during the COVID-19 pandemic: The mediating role of psychological capital and the moderating role of academic burnout. Frontiers in Psychology, 12, 612007–612012. https://doi.org/10.3389/fpsyg.2021.612007

64.

Jonker

C. S.

Vosloo

(2008). The psychometric properties of the Schutte emotional intelligence scale. South Africa Journal of Industrial Psychology, 34(2), 21–30. https://doi.org/10.4102/sajip.v34i2.689

65.

Joshanloo

(2016). Revisiting the empirical distinction between hedonic and eudaimonic aspects of well-being using exploratory structural equation modeling. Journal of Happiness Studies: An Interdisciplinary Forum on Subjective Well-Being, 17(5), 2023–2036. https://doi.org/10.1007/s10902-015-9683-z

66.

Kahane

(Ed.). (2008). Regression basics (2nd ed.). Sage.

67.

Kaisara

Bwalya

K. J.

(2020). Investigating the e-learning challenges faced by students during covid-19 in Namibia. International Journal of Higher Education, 10(1), 308–318. https://doi.org/10.5430/ijhe.v10n1p308

68.

Keyes

C. L. M.

Wissing

Potgieter

J. P.

Temane

Kruger

van Rooy

(2008). Evaluation of the mental health continuum–short form (MHC–SF) in setswana-speaking South Africans. Clinical Psychology & Psychotherapy, 15(3), 181–192. https://doi.org/10.1002/cpp.572

69.

Khumalo

Termane

Wissing

(2013). Further validation of the general psychological well-being scale among a Setswana-speaking population. In Wissing

(Ed.), Well-being research in south africa, cross cultural advancements in positive psychology (pp. 199–224). Springer.

70.

Khumalo

I. P.

Temane

Q. M.

Wissing

M. P.

(2012). Socio-demographic variables, general psychological well-being and the mental health continuum in an African context. Social Indicators Research, 105(3), 419–442. https://doi.org/10.1007/s11205-010-9777-2

71.

Knight

Z. G.

(2017). A proposed model of psychodynamic psychotherapy linked to Erik Erikson’s eight stages of psychosocial development. Clinical Psychology & Psychotherapy, 24(5), 1047–1058. https://doi.org/10.1002/cpp.2066

72.

LaBrie

J. W.

Ehret

P. J.

Hummer

J. F.

Prenovost

(2012). Poor adjustment to college life mediates the relationship between drinking motives and alcohol consequences: A look at college adjustment, drinking motives, and drinking outcomes. Addictive Behaviors, 37(4), 379–386. https://doi.org/10.1016/j.addbeh.2011.11.018

73.

Lamborn

S. D.

Groh

(2009). A four-part model of autonomy during emerging adulthood: Associations with adjustment. International Journal of Behavioral Development, 33(5), 393–401. https://doi.org/10.1177/0165025409338440

74.

Laursen

Mooney

K. S.

(2008). Relationship network quality: Adolescent adjustment and perceptions of relationships with parents and friends. American Journal of Orthopsychiatry, 78(1), 47–53. https://doi.org/10.1037/0002-9432.78.1.47

75.

Lawal

A. M.

Idemudia

E. S.

Senyatsi

(2018). Emotional intelligence and mental health: An exploratory study with South African university students. South African Journal of Psychology, 28(6), 492–497. https://doi.org/10.1080/14330237.2018.1540229

76.

R. H.

(2014). Reliability and validity of a shorter Chinese version for Ryff’s psychological well-being scale. Health Education Journal, 73(4), 446–452. https://doi.org/10.1177/0017896913485743

77.

Liter

(1987). The existential factors of death anxiety, purpose in life, and alienation and their relationship to adjustment during the freshman year of college [Doctoral dissertation, University of Denver]. ProQuest Dissertations Publishing.

78.

MacInnes

D. L.

(2006). Self-esteem and self-acceptance: An examination into their relationship and their effect on psychological health. Journal of Psychiatric and Mental Health Nursing, 13(5), 483–489. https://doi.org/10.1111/j.1365-2850.2006.00959.x

79.

Magsamen-Conrad

Billotte-Verhoff

Greene

(2014). Technology addiction’s contribution to mental wellbeing: The positive effect of online social capital. Computers in Human Behavior, 40, 23–30. https://doi.org/10.1016/j.chb.2014.07.014

80.

Makgahlela

Mothiba

T. M.

Mokwena

J. P.

Mphekgwana

(2021). Measures to enhance student learning and well-being during the COVID-19 pandemic: Perspectives of students from a historically disadvantaged university. Education Sciences, 11(5), 212–227. https://doi.org/10.3390/educsci11050212

81.

Makhubela

(2021). Suicide and depression in university students: A possible epidemic. South African Journal of Psychology, 51(1), 3–5. https://doi.org/10.1177/0081246321992179

82.

Malinauskas

Malinauskiene

(2020). The relationship between emotional intelligence and psychological well-being among male university students: The mediating role of perceived social support and perceived stress. International Journal of Environmental Research and Public Health, 17(5), 1605–1616. https://doi.org/10.3390/ijerph17051605

83.

Maroof

Khan

(2016). Age and gender as predictors of psychological well-being. Journal of Humanities and Social Sciences, 24(1), 43–58.

84.

Matud

M. P.

López-Curbelo

Fortes

(2019). Gender and psychological well-being. International Journal of Environmental Research and Public Health, 16, 3531. https://doi.org/10.3390/ijerph16193531

85.

Mese

Aydin

(2019). The use of social networks among university students. Educational Research and Reviews, 14(6), 190–199. https://doi.org/10.5897/ERR2018.3654

86.

Moeller

R. W.

Seehuus

Peisch

(2020). Emotional intelligence, belongingness, and mental health in college students. Frontiers in Psychology, 11(93), 93–10. https://doi.org/10.3389/fpsyg.2020.00093

87.

Mokgele

K. R.

Rothmann

(2014). A structural model of student well-being. South African Journal of Psychology, 44(4), 514–527. https://doi.org/10.1177/0081246314541589

88.

Molasso

W. R.

(2006). Exploring Frankl’s purpose in life with college students. Journal of College and Character, 7(1), 1–9. https://doi.org/10.2202/1940-1639.1502

89.

Morales-Rodríguez

F. M.

Espigares-López

Brown

Pérez-Mármol

J. M.

(2020). The relationship between psychological well-being and psychosocial factors in university students. International Journal of Environmental Research and Public Health, 17(13), 4778. https://doi.org/10.3390/ijerph17134778

90.

Nagel

T. W. S.

Remillard

Aucoin

Takenishi

(2018). Finding on student use of social media at the collegiate, undergraduate, and graduate levels: Implications for post-secondary educators. Journal of University Teaching and Learning Practice, 15(1), 98–114.

91.

Olasupo

Idemudia

Dimatkakso

(2018). Adjustment, psychological well-being and mental health of first year students in a South African University. North American Journal, 20(1), 55–68.

92.

Opree

Buijzen

van Reijmersdal

(2018). Development and validation of the psychological well-being scale for children (PWB-C). Societies, 8(1), 18–14. https://doi.org/10.3390/soc8010018

93.

Oäzguär

(2016). Adapting the media and technology usage and attitudes scale to Turkish. Educational Sciences Theory & Practice, 16(5), 1711–1735. https://doi.org/10.12738/estp.2016.5.0085

94.

Panahi

Yunus

A. S. M.

Roslan

Jaafar

R. A. K.

Jaafar

W. M. W.

Panahi

M. S.

(2016). Predictors of psychological well-being among Malaysian graduates. European Journal of Social & Behavioural Sciences, 16(2), 122–137. https://doi.org/10.15405/ejsbs.186

95.

Park

C. L.

Folkman

(1997). Meaning in the context of stress and coping. Review of General Psychology, 1(2), 115–144. https://doi.org/10.1037//1089-2680.1.2.115

96.

PASGR. (2019). 50% Attition rates in Africa’s tertiary institutions. Author. https://www.pasgr.org/blog/50-attrition-rates-in-africas-tertiary-institutions-ghana-education-minister/

97.

Pather

Dorasamy

(2018). The mismatch between first-year students’ expectations and experience alongside university access and success: A South African university case study. Journal of Student Affairs in Africa, 6(1), 49–64. https://doi.org/10.24085/jsaa.v6i1.3065

98.

Petersen

Louw

Dumont

(2009). Adjustment to university and academic performance among disadvantaged students in South Africa. Educational Psychologist, 29(1), 99–115. https://doi.org/10.1080/01443410802521066

99.

Pfund

G. N.

Bono

T. J.

Hill

P. L.

(2020). A higher goal during higher education: The power of purpose in life during university. Translational Issues in Psychological Science, 6(2), 97–106. https://doi.org/10.1037/tps0000231

100.

Pillay

(2021). The impact of inequality and COVID-19 on education and career planning for South African children of rural and low-socioeconomic backgrounds. African Journal of Career Development, 3(1), 1–7. https://doi.org/10.4102/ajcd.v3i1.36

101.

Raines

Lewandowski

(2009). The role of self views in college adjustment. Graduate Student Journal of Psychology, 11, 46–51.

102.

Ramachandiran

Dhanapal

(2018). Academic stress among university students: A quantitative study of generation Y and Z’s perception. Pertanika Journal of Social Sciences and Humanities, 26(3), 2115–2118.

103.

Ramdass

(2009). The challenges facing education in South Africa. HSRC Press. https://ujcontent.uj.ac.za/vital/access/services/Download/uj:6232/CONTENT1

104.

Rehman

Sohail

(2018). Perceived emotional intelligence and psychological well-being among medical students. International Journal of Scientific and Engineering Research, 9(1), 232–242.

105.

Roothman

Kirsten

D. K.

Wissing

M. P.

(2003). Gender differences in aspects of psychological well-being. South African Journal of Psychology, 33(4), 212–218. https://doi.org/10.1177%2F008124630303300403

106.

Rosen

L. D.

Whaling

Carrier

L. M.

Cheever

N. A.

Rokkum

(2013). The media and technology usage and attitudes scale: An empirical investigation. Computers in Human Behavior, 29(6), 2501–2511. https://doi.org/10.1016/j.chb.2013.06.006

107.

Rousseau

K. L.

Thompson

Pileggi

L. A.

Henry

Thomas

K. G.

(2021). Trends in the prevalence and severity of depressive symptoms among undergraduate students at a South African University, 2016–2019. South African Journal of Psychology, 51(1), 67–80. https://doi.org/10.1177/0081246320977759

108.

Ryan

R. M.

Deci

E. L.

(2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.1037/0003-066X.55.1.68

109.

Ryan

R. M.

Deci

E. L.

(2001). On happiness and human potentials: A review of research on hedonic and eudaimonic well-being. Annual Review of Psychology, 52(1), 141–166. https://doi.org/10.1146/annurev.psych.52.1.141

110.

Ryan

R. M.

Deci

E. L.

(2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Publications.

111.

Ryff

C. D.

(1989). Happiness is everything, or is it? Explorations on the meaning of psychological well-being. Journal of Personality and Social Psychology, 57(6), 1069–1081. http://dx.doi.org/0022-3514/89/SOO.75

112.

Ryff

C. D.

(2014). Psychological well-being revisited: Advances in the science and practice of eudaimonia. Journal of Psychotherapy and Psychosomatics, 83(1), 10–28. https://doi.org/10.1159/000353263

113.

Ryff

C. D

. (2017). Eudaimonic well-being, inequality, and health: Recent findings and future directions. International Review of Economics, 64(2), 159–178. https://doi.org/10.1007/s12232-017-0277-4

114.

Ryff

C. D.

Keyes

C. L.

(1995). The structure of psychological well-being revisited. Journal of Personality and Social Psychology, 69(4), 719–727. https://doi.org/10.1037/0022-3514.69.4.719

115.

Ryff

C. D.

Singer

B. H.

(2008). Know thyself and become what you are: A eudaimonic approach to psychological well-being. Journal of Happiness Studies, 9(1), 13–39. https://doi.org/10.1007/s10902-006-9019-0

116.

Schroder

H. S.

Yalch

M. M.

Dawood

Callahan

C. P.

Brent Donnellan

Moser

J. S.

(2017). Growth mindset of anxiety buffers the link between stressful life events and psychological distress and coping strategies. Personality and Individual Differences, 110, 23–26. https://doi.org/10.1016/j.paid.2017.01.016

117.

Schutte

N. S.

Malouff

J. M.

Bhullar

(2009). The assessing emotions scale. In Stough

Saklofske

D. H.

Parker

J. D. A.

(Eds.), The Springer series on human exceptionality. Assessing emotional intelligence: Theory, research, and applications (pp. 119–134). Springer Science.

118.

Schutte

N. S.

Malouff

J. M.

Thorsteinsson

E. B

. (2013). Increasing emotional intelligence through training: Current status and future directions. The International Journal of Emotional Education, 5(1), 56–72.

119.

Schutte

N. S.

Malouff

J. M.

Hall

L. E.

Haggerty

D. J.

Cooper

J. T.

Golden

C. J.

Dornheim

(1998). Development and validation of a measure of emotional intelligence. Personality and Individual Differences, 25(2), 167–177. https://doi.org/10.1016/s0191-8869(98)00001-4

120.

Siddiqui

(2015). Impact of self-efficacy on psychological well-being among undergraduate students. International Journal of Indian Psychology, 2(3), 5–16.

121.

Singh

Kaur

(2019). A study to understand the relationship between emotional intelligence and psychological well-being among adolescents of Chandigarh and Punjab college students. International Journal of Innovative Technology and Exploring Engineering, 8(7), 21–23.

122.

Smith

G. D.

Yang

(2017). Stress, resilience and psychological well-being in Chinese undergraduate nursing students. Nurse Education Today, 49, 90–95. https://doi.org/10.1016/j.nedt.2016.10.004

123.

Sreeramareddy

C. T.

Shankar

P. R.

Binu

V. S.

Mukhopadhyay

Ray

Menezes

R. G.

(2007). Psychological morbidity, sources of stress and coping strategies among undergraduate medical students of Nepal. BMC Medical Education, 7(1), 26–28. https://doi.org/10.1186/1472-6920-7-26

124.

Stangor

(Ed.). (2015). Research methods for the behavioural sciences (5th ed.). Cengage.

125.

Statistics South Africa. (2022). Quarterly labour force survey (QLFS). Republic of South Africa, Department of Statistics. http://www.statssa.gov.za/publications/P0211/Media%20release%20QLFS%20Q4%202021.pdf

126.

Staudinger

Kessler

(2009). Adjustment and growth: Two trajectories of positive personality development across adulthood. In Smith

DeFrates-Densch

(Eds.), Handbook of research on adult learning and development (pp. 241–268). Routledge.

127.

Steyn

(2005). Guide to determining effect size indices and practical significance. https://natural-sciences.nwu.ac.za/scs/effect

128.

Stoklosa

(2015). College student adjustment: Examination of personal and environmental characteristics [Doctoral dissertation, Wayne State University]. Digital Commons. https://digitalcommons.wayne.edu/cgi/viewcontent.cgi?article=2296&context=oa_dissertations

129.

Tangmunkongvorakul

Musumari

P. M.

Thongpibul

Srithanaviboonchai

Techasrivichien

Suguimoto

S. P.

Ono-Kihara

Kihara

(2019). Association of excessive smartphone use with psychological well-being among university students in Chiang Mai, Thailand. PLoS One, 14(1), e0210294. https://doi.org/10.1371/journalPone.0210294.

130.

Taylor

Combs

A. W.

(1952). Self-acceptance and adjustment. Journal of Consulting Psychology, 16(2), 89–91. https://doi.org/10.1037/h0054142

131.

Terenzini

P. T.

Pascarella

E. T.

(1991). Twenty years of research on college students: Lessons for future research. Research in Higher Education, 32(1), 83–92.

132.

Thompson

N. J.

Coker

Krause

J. S.

Henry

(2003). Purpose in life as a mediator of adjustment after spinal cord injury. Rehabilitation Psychology, 48(2), 100–108. https://doi.org/10.1037/0090-5550.48.2.100

133.

Turashvili

Japaridze

(2012). Psychological well-being and its relation to academic performance of students in Georgian context. Problems of Education in the 21st Century, 49, 73–80.

134.

Twenge

J. M.

(2019). More time on technology, less happiness? Associations between digital-media use and psychological well-being. Current Directions in Psychological Science, 28(4), 372–379. https://doi.org/10.1177/0963721419838244

135.

Udhayakumar

Illango

(2018). Psychological well-being among college students. Journal of Social Work Education and Practice, 3(2), 79–89.

136.

van Breda

A. D.

(2017). Students are humans too: Psychosocial vulnerability of first-year students at the University of Johannesburg. South African Journal of Higher Education, 31(5), 246–262. https://doi.org/10.20853/31-5-1567

137.

van Dierendonck

Díaz

Rodríguez-Carvajal

Blanco

Moreno-Jiménez

(2008). Ryff’s six-factor model of psychological well-being: A Spanish exploration. Social Indicators Research, 87(3), 473–479. https://doi.org/10.1007/s11205-007-9174-7

138.

Van Tonder

J. I.

(2017). The relationship between media and technology usage and interpersonal communication in emerging adults [Unpublished honours manuscript, University of the Free State].

139.

Van Tonder

J. I.

(2020). Predictors of loneliness amongst university students [Master’s thesis, University of the Free State]. KovsieScholar.

140.

Vázquez

Hervásm

Rahona

Gómez

(2009). Psychological well-being and health. Contributions of positive psychology. Annuary of Clinical and Health Psychology, 5, 15–27. https://doi.org/10.4236/psych.2019.104037

141.

Wesley

Booker

J. A.

(2021). Social support and psychological adjustment among college adults. Journal of Social and Clinical Psychology, 40(1), 69–95. https://doi.org/10.1521/jscp.2021.40.1.69

142.

Williams

J. C.

Lynn

S. J.

(2010). Acceptance: An historical and conceptual review. Imagination Cognition and Personality, 30(1), 5–56. https://doi.org/10.2190/ic.30.1.c

143.

Wintre

M. G.

Yaffe

(2000). First-year students’ adjustment to university life as a function of relationships with parents. Journal of Adolescent Research, 15(1), 9–37. https://doi.org/10.1177/0743558400151002

144.

Wissing

M. P.

Khumalo

I. P.

Oosthuizen

T. M.

Nienaber

Kruger

Potgieter

J. C.

Temane

Q. M.

(2011). Coping self-efficacy as mediator in the dynamics of psychological well-being in various contexts. Journal of Psychology in Africa, 21(2), 165–172. https://doi.org/10.1080/14330237.2011.10820445

145.

Wissing

M. P.

van Eeden

(2002). Empirical clarification of the nature of psychological well-being. South African Journal of Psychology, 32(1), 32–44. https://doi.org/10.1177%2F008124630203200105

146.

Wong

(2014). Viktor Frankl’s meaning-seeking model and positive psychology. In Batthyany

Russo-Netzer

(Eds.), Meaning in existential and positive psychology (pp. 149–184). Springer.

147.

Yaden

D. B.

Eichstaedt

J. C.

Medaglia

J. D.

(2018). The future of technology in positive psychology: Methodological advances in the science of well-being. Frontiers in Psychology, 9, 962. https://doi.org/10.3389/fpsyg.2018.00962

148.

Yeager

D. S.

Johnson

Spitzer

B. J.

Trzesniewski

K. H.

Powers

Dweck

C. S.

(2014). The far-reaching effects of believing people can change: Implicit theories of personality shape stress, health, and achievement during adolescence. Journal of Personality and Social Psychology, 106(6), 867–884. https://doi.org/10.1037/a0036335

149.

Young

T. L.

Reysen

Eskridge

Ohrt

J. H.

(2013). Personal growth groups: Measuring outcome and evaluating impact. Journal for Specialists in Group Work, 38(1), 52–67. https://doi.org/10.1080/01933922.2012.745915

150.

Yusoff

M. S.

Esa

A. R.

Mat Pa

M. N.

Mey

S. C.

Aziz

R. A.

Abdul Rahim

A. F.

(2013). A longitudinal study of relationships between previous academic achievement, emotional intelligence and personality traits with psychological health of medical students during stressful periods. Education for Health, 26(1), 39–47. https://doi.org/10.4103/1357-6283.112800

Emotional Intelligence,Adjustment,Media and Technology Usage,and Gender as Predictors of Psychological Well-being Amongst Undergraduate University Students

Abstract

Keywords

Background

PWB and Emotional Intelligence

PWB and Adjustment

PWB and Media and Technology Usage

PWB and Gender

Purpose of This Study

Methodology

Research Aim and Questions

Research Design

Participants and Sampling

Measuring Instruments

Procedure

Data Analysis

Results

Descriptive Analyses

Hierarchical Regression Results

Discussion

Discussion of the Significant Correlations With Adjustment in This Study

Discussion on the Predictors of Psychological Well-Being

Limitations and Recommendations

Contribution of This Study

Footnotes

Declaration of Conflicting Interests

Funding

Ethical Statement

ORCID iD

Data Availability Statement

References