Proactive behaviour is a form of extra-role behaviour that enables individuals to challenge the status quo and improve their current circumstances (Den Hartog & Belschak, 2012). Proactive individuals take self-initiated, anticipatory action to intentionally impact themselves or their situation (Den Hartog & Belschak, 2012) and persist until their intended initiatives occur (Lin et al., 2014).

Proactivity has become particularly important in the university context for first-year students as they adjust to university life. Kadhiravan and Kumar (2012) asserted that students who engage in proactive behaviour can better tap into their personal and psychological resources to reduce environmental stressors and promote their well-being. Specifically, Van Woerkom et al. (2016) defined PBSU as the self-starting behaviour one uses to utilise one’s strengths at work. In comparison, PBDI refers to improving one’s deficits at work (Van Woerkom et al., 2016). Strengths are defined as a person’s ‘natural capacity for behaving, thinking, or feeling in a way that allows optimal functioning and performance in the pursuit of valued outcomes’ (Linley & Harrington, 2006, p. 39). In contrast to this, deficits are defined as less enjoyable patterns of behaviour, thoughts and feelings that do not occur naturally for an individual but that can be mastered by individuals when facilitated and developed (Meyers et al., 2015).

Strengths-based development has positively impacted the educational setting, such as lower absenteeism, and improved grades (Hodges & Clifton, 2004). Furthermore, Hodges and Clifton (2004) reported that strengths interventions may elicit hope and confidence in students, which may, in turn, enable students to feel more optimistic about their studies. The active use of one’s strengths has also been linked to higher subjective well-being, vitality (Park et al., 2004; Peterson & Seligman, 2004), self-confidence and social interaction (Mostek, 2010). Indeed, Steurer et al. (2022) recently emphasised Forrier et al.’s (2015) definition of employability, arguing that it is ‘the presentation of personal strengths that increase employment potential’ (p. 2), which is a critical characteristic that HEIs must embody in their students.

While strengths use may enable students to succeed, students with a mastery or goal-oriented mindset may emphasise developing their weaknesses. Indeed, Van Niekerk et al. (2016) showed that students are more engaged when participating in activities to improve their deficits than when using their strengths – supporting the basic assumptions of goal-orientation theory (Ames, 1992). During their first year, mastery or goal-oriented students will take the initiative to learn new skills and improve their competence to address challenges effectively and become self-sufficient (Yeung et al., 2014).

This study employed the Job Demands-Resources (JD-R) model as a theoretical framework to examine the relationships between PBSU, PBDI, student characteristics as antecedents and students’ well-being as an outcome. The JD-R model has been widely used to investigate the antecedents and outcomes of work-related performance (Bakker et al., 2023) and has demonstrated its effectiveness in higher education contexts (Cilliers et al., 2018; Lesener et al., 2020). Therefore, it is an appropriate framework for exploring and explaining the relationships among the variables included in our study.

According to the JD-R model, job-related characteristics can be divided into two broad categories, namely job demands and job resources, which impact employees’ work-related well-being and outcomes, such as performance, regardless of their occupational context (Bakker et al., 2023). Job demands encompass a job’s physical, social, psychological or organisational aspects that require sustained cognitive or physical effort and often de-energising employees (Bakker et al., 2023). Conversely, job resources are defined as a job’s physical, psychological and organisational aspects that empower individuals to achieve objectives, reduce job demands and promote learning and growth (Bakker et al., 2023). Moreover, job resources seem to trigger a motivational process in employees resulting in favourable outcomes such as work engagement (Bakker et al., 2023). Similarly, characteristics in the student environment can be classified as student demands and student resources.

In the university context, students face many academic demands, including cognitive challenges, pace and amount of work (Cilliers et al., 2018), and pressure to achieve academically (Offstein et al., 2014). However, it can be argued that the demands faced by students are not limited to the academic domain alone. Recent studies by Bakker et al. (2023) suggest that demands faced by individuals are not limited to the workplace but also extend to other life domains. Students, for instance, may encounter various demands that can affect their well-being, such as relationship demands, conflicting roles and deadlines set by lecturers (Offstein et al., 2014). Indeed, student demands have been shown to negatively impact students’ well-being and engagement by increasing stress and depleting energy levels (Cilliers et al., 2018; Lesener et al., 2020; Mokgele & Rothmann, 2014).

In contrast, student resources can stimulate the motivational process, leading to improved well-being, life satisfaction and academic success, as evidenced by Mokgele and Rothmann’s (2014) study. Access to appropriate student resources is crucial for students to manage their demands effectively. These resources may come from various sources such as family, friends, lecturers, personal development opportunities (Cilliers et al., 2018) and autonomy (Lesener et al., 2020).

Recent research by Bakker et al. (2023) further suggests that employees can optimise their job demands and resources through proactive behaviour, leading to positive outcomes and directly impacting stress-provoking and motivational processes. In addition, job demands can motivate employees to be more proactive, increasing job resources, satisfaction and engagement (Tims et al., 2013). Notably, Botha and Mostert (2014) found a positive correlation between PBSU and PBDI and increased employee engagement, a critical outcome of the JD-R model’s motivational process. Likewise, students can employ PBSU and PBDI to effectively self-regulate and influence stress-provoking and motivational processes, improving well-being and academic success.

While the challenges of studying are inevitable, these demands may provide students with opportunities to develop their strengths and improve their ability to cope with demands more constructively through proactive behaviours. Indeed, Bakker et al. (2023) contended that proactive behaviours can help employees to optimise job demands and resources. The latter can be applied similarly to students regarding student demands and resources. Bowers and Lopez (2010) further contended that utilising individual strengths as a resource can help students navigate university demands effectively. Furthermore, Mostert et al. (2017) found student resources to increase student engagement, potentially promoting their inclination to engage in PBSU and PBDI. Indeed, the findings of Mostert et al. (2017) align with Bakker et al.’s (2023) argument that engaged employees tend to act proactively. Therefore, it is argued that sufficient student resources can enhance students’ PBSU and PBDI, even in the face of student demands.

This study investigated student demands and resources as antecedents of PBSU and PBDI. Student demands included academic demands, personal relationship demands, personal problems and lecturer demands, whereas student resources included family support, lecturer support, friend support and autonomy.

Peterson and Seligman (2004) were the first scholars to highlight the importance of strengths in general well-being. Deliberate use and development positively impact individuals’ psychological and subjective well-being (Linley et al., 2010). Developing strengths through intentional use requires personal goal setting, which may influence students’ well-being when progress is made or such goals are attained (Linley et al., 2010).

In the student context specifically, strengths use has been shown to positively influence students’ well-being (Rust et al., 2009). In turn, increased students’ well-being may directly affect student outcomes such as academic aspirations, engagement and dropout (Ratelle et al., 2013). Stander et al. (2015) confirmed this by emphasising that PBSU among students is a strong predictor of feelings of hope and increased efficacy, which may enable them to achieve academic success.

While using one’s strengths has been shown to have a consistently positive impact on well-being, developing one’s weaknesses has also been shown to enhance students’ well-being (Rust et al., 2009). In contrast, when students overfocusses on deficit development and ruminate about their weaknesses, it can negatively influence their well-being (Stander et al., 2015). Nonetheless, PBDI has strongly predicted feelings of hope and efficacy among first-year students (Stander et al., 2015). Therefore, it can be argued that both strengths use and deficit improvement may influence important student outcomes at university. Outcomes related to PBSU and PBDI included in the study were emotional, social and psychological well-being.

This study’s population consisted of first-year university students (N = 773) across different South African tertiary institution campuses. After obtaining formal permission from the respective university, data collection took place. Two platforms were utilised for data collection: (1) posting web-based survey links on the respective university’s electronic information system and (2) emailing web-based survey links to participants. The study’s purpose, objectives and potential value to students and university life were presented to participants on both platforms, ensuring informed consent. Participation in the survey was voluntary, with confidentiality and anonymity guaranteed throughout. The survey was estimated to take 25 min to 30 min to complete, and a reminder email was sent to participants two weeks after initial access to the survey link.

The sample consisted of 773 research participants, of which 43 (6%) were 18 years of age, 186 (24%) were 19 years of age, 296 (35%) were 20 years of age and 201 (26%) were between 21 years and 23 years of age. In terms of race, 449 (61%) students were black, 243 (31%) white, 47 (6.1%) mixed race and 11 (1.4%) Indian. Furthermore, the sample accurately reflected the distribution of first-year enrolments across various campuses, including 246 students from campus 1, which had 3104 enrolments at the time; 384 from campus 2, which had 4750 enrolments; and 134 from campus 3, which had 1724 enrolments first-year enrolments. In terms of gender, the majority of participants (66%) were female participants, while 258 (33%) were male participants.

The statistical modelling program Mplus 8.6 (Muthén & Muthén, 2021) was used to examine the measurement model in this study. With the covariance matrix as input, the maximum likelihood (ML) estimator was used (Muthén & Muthén, 2021). Several fit indices were used to evaluate the model’s goodness-of-fit, including the traditional chi-square (χ²) statistic, the Comparative Fit Index (CFI), the Tucker–Lewis Index (TLI), the Root Mean Square Error of Approximation (RMSEA) and the Standardised Root Mean Square Residual (SRMR) (Hu & Bentler, 1999). The model fit was deemed suitable when the CFI and TLI values were more significant than 0.90 (Byrne, 2010) and well-fitting when they were more significant than 0.95 (Hu & Bentler, 1999). Furthermore, RMSEA values of 0.05 or less indicate a close/good fit, whereas values between 0.05 and 0.08 indicate a good model fit (Byrne, 2010). This study’s cut-off point for SRMR was set at 0.05. (Hu & Bentler, 1999). However, given the lack of agreement among scholars on the values of good fit, the indicated cut-off points should be regarded as mere guidelines in this study.

Furthermore, the reliability of the variables was determined using Cronbach’s alpha coefficients, with values larger than 0.70 indicating satisfactory internal consistency (Bryman, 2012). Pearson’s product-moment correlation was used further to examine the strength and directions of relationships between variables. The statistical significance cut-off points were set at the 95% confidence interval (p ≤ 0.05), at r ≥ 0.30 (medium effect) and r ≥ 0.50 (large effect) for the practical significance of correlation coefficients. A structural model was tested, which included the antecedents (student demands and student resources) and PBSU and PBDI, as well as the student outcomes (see Figure 1).

Permission was granted to collect data from first-year students by the Ethics Committee of the Faculty of Economic and Management Sciences (EC-EMS); reference number NWU – HS – 2014 – 0165-A4.

For this study, the descriptive statistics, Cronbach’s alpha coefficients and correlations between the latent variables are presented in Table 1, while Table 2 reports the regression results of the structural model.

From the results presented in Table 1, it is evident that all antecedents and outcomes were statistically significant and correlated with strengths use and deficit improvement, with the most substantial relationship being between strengths use and autonomy (r = 0.66). Moreover, all scales’ Cronbach’s alpha coefficients were equal to or greater than 0.75, indicating that the measuring instruments were reliable.

The fit of the structural model was found to be satisfactory (CFI = 0.90; TLI = 0.89; RMSEA = 0.04; SRMR = 0.07). As can be seen in Table 2, there were only a few statistically significant relationships between PBSU and PBDI and antecedents. However, most relationships between PBSU and PBDI and well-being were significant, except for deficit improvement, which did not significantly predict emotional well-being. Overall, strengths use was stronger related to autonomy, emotional well-being, and psychological well-being, with the most substantial relationship being between strengths use and autonomy (r = 0.58). Deficit improvement was stronger related to personal problems and social well-being.

Considering the persistently low response rates observed among students in higher education research (Chapman & Joines, 2017; Luo, 2020), it is important to acknowledge that the findings of this study may have limited generalisability. This limitation arises from the relatively small sample size and the challenge of low response rates encountered among first-year university students from a single South African public HEI. To address this limitation, future researchers should broaden the scope of the study to include a more diverse range of participants from various tertiary institutions, both nationally and internationally. In addition, the structural model tested in this study only had a limited number of student demands and resources, which may not fully capture the scope and complexity of factors affecting individual well-being, as Bakker et al. (2023) suggested. Thus, it is recommended that future research expand the range of variables considered to obtain a more comprehensive understanding of the aspects influencing student PBSU and PBDI and other significant outcomes. Furthermore, the study’s cross-sectional design precluded establishing causal relationships among antecedents, PBSU, PBDI and student well-being. To address this issue, future researchers may consider utilising a longitudinal research design to understand better the relationship between PBSU, PBDI and other variables. Lastly, the study relied solely on self-report online questionnaires, which may be susceptible to social desirability and response biases. Future studies should consider implementing multiple data collection methods beyond self-report questionnaires to minimise these potential biases.

The practical implications of this study hold significant value for both institutions and practitioners looking to enhance the quality of higher education and prepare graduates for the labour market. The findings related to PBSU and PBDI’s impact on important student antecedents and outcomes provide valuable insights that can inform the development of evidence-based strategies aimed at student success. Universities can utilise this knowledge to optimise students’ demands and resources and encourage PBSU and deficit improvement, which can improve student well-being and enhance the work readiness of graduates. Indeed, proactivity has become a critical skill for employees and graduates to ensure employability (Tymon & Batistic, 2016), and universities are responsible for fostering such behaviours in students (Abugre, 2018). Therefore, utilising the expertise of practitioners, including industrial psychologists, can help inform practices around developing PBSU and PBDI. Industrial psychologists can guide universities in improving efficiency by designing and implementing training programmes that enhance organisational functioning at individual, group and organisational levels. However, practitioners must fully understand these constructs and their contributions to student success to effectively design initiatives that foster proactive behaviour within the student community. Such initiatives can contribute to student success and the holistic development of graduates well-suited for the demands of the workforce.