Transformational leadership is increasingly recognised as a key management style in contemporary organisations, associated with numerous favourable outcomes, including higher employee engagement and retention, as well as reduced burnout and social loafing (Khan et al., 2020b; Lai et al., 2020; Tian et al., 2020). It involves inspiring followers through vision and action to prioritise organisational goals (Alessa, 2021). According to Meng (2022), such leaders articulate a strategic vision that motivates learning. Similarly, Islam et al. (2025) and Ibrahim et al. (2022) highlight that transformational leaders enhance an organisation’s human capital.

Marie (2023) explains this leadership approach as having four primary elements:

According to Rožman and Štrukelj (2020) and Alshaabani et al. (2020), organisational climate represents employees’ shared understanding of how their organisation operates, including its procedures and everyday work setting. It means how individuals collectively experience fairness, support, communication, innovation, autonomy and recognition within the organisation. Scholars have identified several dimensions of organisational climate, including leadership and supervisory support, prevalent cultural dynamics, reward systems, communication processes, innovation and workgroup cohesion (Belay, 2023; Mutonyi et al., 2022; Nilasari et al., 2023).

These factors are very important because they help determine how employees are motivated, engaged and whether or not they are willing to exceed formal job requirements. For the present study, the focus was on certain dimensions from the Organisational Climate Questionnaire (OCQ) suggested by Litwin and Stringer (1968) (namely autonomy, involvement and supervisory support):

These aspects were selected because they closely align with the conditions under which OCBs are most likely to emerge. Thus, the general objective of the study was to assess the effects of the approaches taken by transformational leaders and the climate in their organisation on the OCB of the support staff in the university.

Despite the existence of literature indicating the negative correlation between employees’ OCB and the transformational leadership approach (Qurtubi, 2022), the majority of recent studies confirm a positive relationship (Davis et al., 2022; Han et al., 2023; Pedregosa et al., 2025; Triningsih et al., 2023; Udin, 2023). While Udin (2023) found the relationship significant only when mediated by knowledge-oriented work passion, other scholars, such as Triningsih et al. (2023) and Pedregosa et al. (2025), emphasise its direct influence. Davis et al. (2022) linked the leadership’s tenets to improved OCB among teachers, while Al-Mamary (2020) and Han et al. (2023) found the relationship strengthened when mediated by organisational commitment and public service motivation.

Similarly, research does support a strong correlation between organisational climate and OCB (Prabowo, 2020; Tata & Joy, 2023). Organisational climate, or the shared perceptions by employees of norms in an organisation, affects motivation and behaviour (Kao et al., 2023). Positive climates lead to engagement, and the behaviour has an effect on discretionary behaviour (Tata & Joy, 2023). Research shows that this relationship is frequently mediated by factors such as job satisfaction (Kharismasyah et al., 2020) and work engagement (Sofwati & Claudia, 2023). Research findings have consistently proved that it is the combination of supportive environments and leadership styles that play an important role in enhancing OCB (Almaqableha & Omarb, 2024; Ismail et al., 2024).

Building on the empirical literature, this study is based on two hypotheses that align with the study’s objectives. Objective 1 in this study is focused on identifying how transformational leadership influences OCB. The subsequent true and null hypotheses are:

Objective 2 in this study focuses on revealing the organisational climate’s influence on OCB. The corresponding true and null hypotheses that are built from the empirical literature are:

The conceptual model of this study displays the inter-relationship between the independent variables (organisational climate and transformational leadership) and dependent variable, orgainsatinal citizenship behaviour (see Figure 1).

The quantitative-based analysis was carried out using SPSS 27. The validity and reliability of the findings were evaluated using exploratory factor analysis (EFA) and coefficient alpha. Structural equation modelling was used to analyse the data (Dafe, 2022), which makes it possible to use confirmatory factor analysis to assess the scales’ measurement qualities. Using the characteristics of LISREL 8.80, a confirmatory factor analysis was performed on the measurement model to evaluate goodness-of-fit (Dafe, 2022). The measurement model for this investigation is shown in Figure 2, and the structural model is shown in Figure 3. The Robust Maximum Likelihood technique was used to obtain estimates (Dafe, 2022).

Ethical clearance to conduct this study was obtained from the Human and Social Sciences Ethics Committee of the University of the Western Cape (No. HS20/5/9), before undertaking fieldwork and data gathering activities (Dafe, 2022). As the researcher could not directly approach the institutional email IDs, an authorised gatekeeper distributed the link to the survey on behalf of the researcher. This protected privacy and enabled the staff to receive the invitation. Every email included an information and consent letter stating the purpose of the study, the voluntary aspect of participation and participants’ right to withdraw from the study without any associated penalties (Dafe, 2022).

Even though the gatekeeper in this study used a staff email list to distribute the invitations, the researcher did not access or store any participant names or participant contact information, ensuring the confidentiality of participants (Dafe, 2022). It should be noted, however, that as names are collected, no real anonymity could have been ensured. The work of the gatekeeper was over after the invitation was sent, and survey answers were gathered via a separate online platform with no identifying fields. All information was stored on an external hard drive that was password-protected, stored in a locked cabinet, and only accessed by the researcher, solely, to enhance participants’ confidentiality through preventing unauthorised access to the study’s data. The researcher enhanced the study’s transparency by detailing and adhering to all the procedures that were utilised in the study. In addition, the researcher protected the study’s participants from psychological harm by refraining from questions that could trigger their anxiety.

By evaluating the effects of transformational leadership and organisational climate on OCB among support staff in university work contexts, the study explores the predictors of OCB (Dafe, 2022). Factor analysis (item and dimension analysis) was performed using SPSS Version 27, and SEM was performed using LISREL 8.8 in order to evaluate the measurement model, goodness of fit and hypothesis test.

The statistical findings from the EFA showed that all transformational leadership subscales had acceptable Kaiser-Meyer-Olkin (KMO) values (above 0.6), hence supporting factor analysis. The KMOs values for the transformational leadership subscales were as follows: idealised influence (0.92), inspirational motivation (0.875), individualised consideration (0.859), and intellectual stimulation (0.852) (Table 4) (Dafe, 2022).

Similarly, all the organisational climate constructs’ subscales were also found to have valid KMO values: supervisory support (0.910), integration (0.741), and training had the least (0.663). However, the involvement subscale was also sub-dimensional with two factors below 0.6 (Table 5) (Dafe, 2022).

Moreover, KMO values of all OCB subscales met the threshold value, which resulted in the following values: conscientiousness (0.90), sportsmanship (0.885), civic virtue (0.874), courtesy (0.835) and altruism (0.917) (Table 6) (Dafe, 2022).

Various indices have been used to determine the model fit shown in Table 7. For example, the root mean square error of approximation (RMSEA) derived from the analysis is 0.0693 (Dafe, 2022). A value of RMSEA of 0.06 is a good fit as it is within the range of 0.05–0.08 (Sathyanarayana & Mohanasundaram, 2024). Furthermore, the root mean squared residual (RMR) and the standardised RMR are 0.044 and 0.048, which indicate a good model fit (Dafe, 2022). The Goodness of Fit Index (GFI) value obtained was 0.877, whereas the Adjusted Goodness of Fit Index (AGFI) value was 0.813 (Dafe, 2022). These values of GFI and AGFI are less than 0.90 and therefore imply good model fit, and hence this is categorised as a reasonable model fit. Furthermore, the Normed Fit Index (NFI), Non-Normed Fit Index (NNFI), Comparative Fit Index (CFI), Incremental Fit Index (IFI) and Relative Fit Index (RFI) values have been computed as 0.978, 0.985, 0.989, 0.989 and 0.970, respectively; values that suggest the model have a reasonable fit and assure the independence-based model considering they exceeded the desired 0.9 value (Dafe, 2022).

Just as with the measurement model, the goodness-of-fit for the structural model is evaluated using a number of different indices. The findings of indices are provided in Table 8. The value of the RMSEA, which is 0.0781, represents a reasonable fit of the structural model. In addition, the RMR and the standardised RMR values are 0.0834 and 0.0595. The values of RMR and standardised RMR are greater than the 0.05 value, which indicates that there exists no closeness of fit in the structural model (Sathyanarayana & Mohanasundaram, 2024). The NFI, NNFI, CFI, IFI and RFI are 0.950, 0.967, 0.972, 0.972 and 0.941, respectively. These values are greater than 0.9, indicating a reasonable fit of the structural model.

Parameter estimates are derived in order to conduct a hypothesis test. This includes analysing the structural model that has the matrices of gamma and beta. Parameters are said to be significant if the p-value is lower than 0.05, and the t-value is greater than 1.96 (Dafe, 2022). Table 9 shows the output of the Beta matrices.

The results of this study showed that transformational leadership approaches combined with encouraging organisational climates are pertinent in the improvement of OCB displayed by non-teaching staff (support staff) in universities. These findings indicated that leaders can support the development of OCB by developing supportive working environments and engaging in transformational behaviours (e.g. effective communication, training and creating and conveying clear leadership expectations to subordinates).

Given OCB’s value in enhancing creativity, engagement and institutional success, university leaders should incorporate transformational practices into staff development. Promoting trust-based leader-follower relationships may also further increase staff motivation and discretionary effort.

Although autonomy was relatively lower in this study in terms of Cronbach’s alpha, it is a conceptually important one, along with supervisory support, welfare and participation, which are recognised in the literature as critical organisational climate drivers of OCB. Supportive environments make employees more confident, which helps them in enhancing their workplace productivity. Organisational leaders should involve support staff in decision-making processes and prioritise their empowerment and well-being. Institutions can thus enhance their efficacy and OCB through aligning leadership styles and organisational climate with the above principles.

This study is not without limitations. Although the close-ended design of survey questions enabled the collection of support staff’s views, attitudes and perspectives over a period of time (Dafe, 2022), the approach made it difficult to collect data that can be used to fully explain the factors that influenced participants’ experiences and the identified correlations between transformational leadership, OCB, and organisational climate. Another limitation of the research is its restricted generalisability, as the findings cannot be confidently applied to support staff across all South African provinces. This constraint reduced the study’s scope because variations in the measured variables may not have been adequately represented. Data collection was also delayed because it was done remotely using Google Forms due to coronavirus disease 2019 (COVID-19) restrictions.

Future investigations should involve a larger sample (greater than 220 in this study) of support staff from universities to ensure their findings better represent the population of interest. Future studies should attempt to gather data from support staff at other universities within the Western Cape as well as South Africa. Sampling participants from multiple institutions would increase the demographic representation of support staff from universities across South Africa. Further studies must also be carried out in accordance with the mixed-methods approach, which will be helpful in extracting more insightful experiences of the participants. The accuracy of the analysis of the study will also be easier if the data collection process of similar studies has instruments like interviews and observation that might be collaboratively used with a questionnaire.