1. Introduction
The construction industry is one of the most significant contributors to the country’s economy regarding infrastructure production and fixed capital assets [1]. The industry plays a unique role in the country’s labour market by employing young people, making it the most sought-after sector, and providing a cutting edge of sustainable economic growth, development, and innovation [2,3].
Given the high unemployment level amongst young people compared with the high labour demand in the industry, it would be assumed that graduates may consider undertaking a career in construction. Instead, the construction industry reportedly faces a skills shortage and declining fortunes, with evidence of shortages in both skilled trades and professional roles [4,5].
Studies have found a decline in the preference for the construction industry as a career option for young people, suggesting that despite the opportunity prospects in the industry and numerous initiatives aimed at attracting young talent, construction is not a preferred career choice [6,7,8,9]. The ‘image’ of the industry, which makes men and women reluctant to enter the industry, is widely documented, and measures geared towards increasing representation have focused on increasing the numbers and filling the pipeline rather than changing the industry’s culture [10,11,12].
Research examining career development and aspirations also highlights that various social and cultural factors, such as family, could affect children’s career choices [13,14]. Children’s aspirations are shaped by the social and cultural context in which they grow up. Factors such as personality interests, family, school, media, socio-economic status, and geographic location all play a role in influencing children’s career goals [15,16]. Due to South Africa’s socio-political history, the career development process is challenging [15]. With a current Gini coefficient of 0.63, South Africa has the highest measurement of income inequality in the world, and salaries, wages, and other social grants vary widely [9].
The aspect of education is no exception. While South Africa offers a high quality of education compared to other countries on the African continent and is favourably ranked internationally, there is an extremely uneven distribution within the country’s dominant population, and progress continues to be impeded by the change-resistant effects of the apartheid education system which legitimises discrimination and racial superiority [11,15]. Despite efforts to diversify this flourishing education system to the total population post-apartheid, educational disparities are still lingering [9]. Similarly, employment levels remain grossly inequitable.
Although limited, studies where inter-group differences have been examined suggest that inbred inequalities related to education and employment influence career development research, theories, and practice [17,18,19]. The relationship between ethnicity, gender, socio-economic background, and career outcomes of South African children remains under-researched.
Against this backdrop, this study examines how social and cultural factors, including personal, contextual, and socio-cognitive factors, predict students’ career outcomes in construction (e.g., outcome expectations, self-efficacy, interests, and gender role stereotypes: see Figure 1).
2. Theoretical Framework
This study examines the role of the Social Cognitive Career Theory (SCCT) in understanding women’s career decisions within the construction industry. The SCCT, developed by Lent et al. [20] and based on Bandura’s social cognitive theory, posits that individuals are not solely shaped by their environments or driven by complete free will. Rather, their behaviour and thoughts both influence and are influenced by the social environment and person factors [21,22]. By applying the SCCT framework, we can gain a comprehensive understanding of the factors that impact women’s career choices in this specific industry. Through rigorous analysis and examination, we aim to provide valuable insights and recommendations to promote diversity and inclusivity within the construction sector. Bandura [21] introduced the concept of “triadic reciprocity”, which encompasses the interplay between personal factors, external behaviour, and the environment. This framework recognises that both the individual and the environment are not static entities [23]. Bandura further emphasised that the relative influence of these three factors varies depending on the specific circumstances. Given the dynamic nature of the person and contextual variables, Bandura’s social cognitive theory provides a solid foundation for the development of a theory of career development. Lent et al. [20] have successfully applied Bandura’s theory in their work on the Social Cognitive Career Theory (SCCT). Therefore, we assert that Bandura’s social cognitive theory is a reasonable framework from which to build a theory of career development.
The SCCT is a direct application of Bandura’s social cognitive theory. It focuses specifically on the formation of educational interests, career development, performance, and persistence of individuals in their career pursuits. Lent et al. [20] sought to create a comprehensive model of individual career choice by combining elements from various theories developed by different theorists. These theories include person–environment correspondence [24], personality typology [25], social learning [26], lifespan, life-space [27], and developmental theory [28]. The result was an inclusive and comprehensive model for understanding individual career choices. The SCCT outlines the processes through which individuals develop their academic and career interests, the effect of these interests and other socio-cognitive mechanisms on career decisions, and the achievement of different levels of career performance and persistence [29,30]. This model provides a framework for understanding and analysing the complex dynamics of career development.
Previous studies have demonstrated the function of the Social Cognitive Career Theory (SCCT) in shaping career outcomes [30,31,32]. These studies have highlighted the adaptability of the SCCT in capturing the social characteristics of diverse environments, making it an ideal framework for understanding the social and cultural factors that influence the vocational choices, interests, and aspirations of women and girls [31,33]. The SCCT, rooted in the social cognitive theory, applies its principles to academic experiences and career development, emphasising the role of cognitive processes and environmental influences in career decision making [23,30,34]. The social cognitive theoretical framework encompasses interconnected processes of choice, motivation, interests, and performance [30,35]. This comprehensive approach allows for a deeper understanding of the complex dynamics that shape individuals’ career trajectories. Social cognitive theory, as described by Hackett and Lent [36], adopts an agency approach to human behaviour. In the process of making formative decisions, students have the capacity to intentionally produce desired outcomes through their actions within their social environments [21,34,37].
This theory places emphasis on cognitive factors, such as choice action, self-efficacy, goal representations, interests, outcomes, and expectations, and their role in the career development of individuals. Moreover, it explores how these cognitive factors interact with internal and individual variables, including gender, ethnicity, religion, and social support, to influence the career behaviour of young people [23,30,37]. Additionally, biological, situational, and contextual factors, such as race, sex, intelligence, personality, and gender role socialisation, act as moderators in the formulation of choice goals and significantly impact career development [23,30].
Hypothesis Development
Hypothesis development details the rationale behind the proposed hypotheses. In the original SCCT model, Lent et al. [20] outlined 12 propositions, which developed 32 hypotheses. The hypotheses tested in the current study were adapted from Lent et al. [20] career choice model, integrating personal, contextual, and socio-cognitive variables.
As shown in Figure 1, this study presents nine hypotheses, whereby each construct has hypothesised relationships between the different variables in the conceptual framework.
-
H1:. Self-efficacy beliefs have a direct influence on career choice.
-
H2:. Outcome expectations have a direct influence on career choice.
-
H3:. Goal representations have a direct influence on career choice.
-
H4:. Interests have a direct influence on career choice.
-
H5:. Social support has a direct influence on career choice.
-
H6:. Learning experiences have a direct influence on career choice.
-
H7:. Perceived barriers have a direct influence on career choice.
-
H8:. Gender stereotypes have a direct influence on career choice.
-
H9:. Access to opportunity structures has a direct influence on career choice.
3. Features of the Model
Of interest to this study is the cognitive process of self-efficacy, interests, goal representations, outcome expectations, learning experiences, and social supports. This study draws on the SCCT and integrates person, contextual, and socio-cognitive factors such as gender stereotypes, perceived barriers, and access to opportunity structures. We apply structural equation modelling to examine causality relationships among independent and dependent variables in the hypothesised theoretical model in Figure 1.
3.1. Self-Efficacy
Self-efficacy, as viewed from the social cognitive perspective, refers to an individual’s beliefs of their own capabilities and abilities to undertake a task in a specific domain. This perception has a significant impact on various events that shape their lives [23,30]. The formation of self-efficacy beliefs is primarily influenced by four sources: performance accomplishments, vicarious learning, social persuasion, and emotional arousal [23]. These elements of self-efficacy play a crucial role in helping individuals undertake an activity, level of persistence, and emotional response to different situations [38].
3.2. Goal Representations
Goals are a crucial factor in the mechanism of career behaviour [20]. They represent the determination to undertake a specific action or achieve a particular future outcome [21]. It is expected that firmly held goals will have a greater influence on career entry choice behaviours [20]. Moreover, goals are perceived to have a significant motivational impact on career choice behaviour, particularly when they are specific, clear, and challenging yet attainable and proximal [39]. Therefore, it is imperative for individuals to set goals that are specific, clear, and challenging yet attainable and proximal to effectively guide their career decisions and actions.
3.3. Outcome Expectations
Outcome expectations are a person’s beliefs about the probable outcomes and consequences of certain actions [40]. In the context of career choice behaviour, these expectations play a significant role in determining one’s actions and decisions [41,42]. According to Bandura [21], individuals base their actions on their judgements of what they can do and their beliefs about the likely consequences of those actions. Career development theories also highlight the importance of outcome expectations in decision making [38]. Locke et al. [41] specifically emphasise the impact of the likelihood of certain actions producing desired outcomes on career choice behaviour.
3.4. Social Supports
Significant people, such as family, teachers, and peers, play a crucial role in shaping students’ occupational aspirations, career decision making, and persistence [38]. These individuals provide key support mechanisms, including exposure to role models, networking contacts, and emotional and financial support [18,20]. Among the various types of social support, parental support is considered.
3.5. Interests
A person’s career interests, defined as patterns of preferences, dislikes, and indifferences towards career-related activities, play an important role in shaping their career choices [31]. These interests are developed through the process of socialisation and ideally guide individuals toward suitable career paths. However, it is important to acknowledge that external factors, such as social and environmental influences, can impact the level of career aspirations and choices [43]. Therefore, it is crucial for individuals to consider their career interests seriously and be mindful of the various factors that may influence their decision-making process. By doing so, individuals can make informed career choices that align with their interests and maximise their potential for success.
3.6. Perceived Barriers
Individuals often encounter numerous barriers when considering a career path. These obstacles encompass discriminatory attitudes, conflicts between work and personal life, wage gap, workplace cultures that hinder progress, limited access to opportunities, challenges in career advancement, unfavourable working conditions, excessive work hours, the glass ceiling effect, gender stereotypes, insufficient knowledge and information about career options, a lack of role models, instances of sexual harassment, inadequate education and training, and limited availability of opportunities [19,44,45]. It is crucial for individuals and society as a whole to acknowledge and address these barriers in order to create a more inclusive and equitable professional landscape. By doing so, we can foster an environment that promotes equal access to opportunities and supports the career aspirations of all individuals.
3.7. Learning Experience
Previous learning experiences play a crucial role in shaping future career behaviours. It has been observed that a person’s career choices are influenced by a combination of various reinforcements [31,37]. These reinforcements can be both positive and negative and contribute to the decision-making process. Career choice behaviour is not solely determined by learning experiences; rather, it is influenced by a complex interplay of personal and contextual factors [32]. Individuals are exposed to different vocational activities through direct and indirect means, such as observing others in their environment. These experiences expose individuals to diverse activities and reinforce their aspirations to pursue certain career paths. It is important to recognise the significance of these learning experiences and their impact on career decision making [23]. People refine their career choices through repetitive engagement in specific activities, the influence of role models, and feedback from models [12]. The acquisition of values through learning experiences is a result of socialisation and fundamental social learning processes, including vicarious learning and self-evaluative experiences [46,47]. Interactions with various individuals and institutions, such as family members, teachers, peers, role models, cultural and religious institutions, and media sources, play a significant role in shaping personal values and standards, which in turn may impact career choice behaviour [22]. It is crucial to recognise and understand the impact of these factors in order to make informed career decisions.
3.8. Gender Role Stereotypes
Gender role stereotypes are deeply ingrained in society and shape our expectations of how individuals should behave based on their gender [48,49,50,51]. These stereotypes encompass various aspects of life, including occupation, cognitive abilities, and skills. Numerous studies have examined the impact of gender-stereotyped beliefs and attitudes on career choices [52,53,54]. It has been found that these beliefs can hinder individuals from pursuing certain careers, as they may be seen as violating traditional gender norms [52]. Consequently, individuals often feel compelled to compromise their career aspirations and opt for more “realistic” choices [55,56,57]. This phenomenon reflects the influence of socio-cultural and stereotypical beliefs on career decision making. As researchers, we must continue to investigate and challenge these gender role stereotypes to create a more inclusive and equitable society.
3.9. Access to Opportunity Structures
Insufficient information regarding career opportunities can significantly impact individuals’ career advancement and their perception of the value associated with different educational and career paths [30]. This lack of access to educational and vocational job-training opportunities has notable consequences for women considering careers in the construction industry [18]. Numerous studies have highlighted the unequal distribution of training and development programs, networking opportunities, and educational initiatives, resulting in an uneven awareness of various career options that could expand women’s choices, including construction as a viable pathway [22]. To address this issue, we must prioritise the dissemination of comprehensive information on career prospects, ensuring equal access to training and development initiatives, networking opportunities, and educational resources for all individuals, regardless of gender. By doing so, we can empower individuals to make informed decisions about their career paths and foster greater diversity and inclusivity within industries such as construction.
4. Materials and Methods
4.1. Participants
In this study, a sample size of 229 individuals was used. 113 were female students, accounting for 49.3% of the sample. Notably, first-year students constituted the largest group, with 94 participants representing 41% of the sample. This high participation rate can be attributed to the fact that the initial year cohort at South African Universities tends to be larger compared to subsequent years or higher levels of study.
Regarding the distribution across disciplines, the majority of respondents, 110 individuals (48%), were enrolled in Construction Management. This program attracted the largest number of participants due to its availability at both participating universities. Conversely, Architecture had the lowest representation, with only one student (0.4%) in the sample. This can be attributed to the fact that only one of the universities offered the Architecture program, which typically has fewer students compared to other disciplines and programs.
4.2. Instrument and Scale Measures
The questionnaire utilised in this study consisted of scales to measure the variables on a 5-point Likert scale were between 1 = strongly disagree and 5 = strongly agree. Respondents were required to indicate their level of agreement with statements about their career choices. Self-efficacy (confidence in abilities to effectively perform construction-related tasks) was measured using the short version of the career choice self-efficacy scale derived and modified from Betz et al. [58].
Using Everhart and Chelladurai’s [59] 15-item scale and Sainz’s scale [54], seven variables focused on occupation attributes specific to the construction field were used to measure goal representations and were adopted in this study. The statements presented to the participants were worded so that their responses would provide information about their personal goals. The Ali and Saunders [30] 32-item measure was adopted to measure social support. For this study, seven items specifically assessing the extent to which students received support from their mother, father, significant other, teachers, and peers were presented to the respondents. Betz and Voyten’s [60] 9-item career decision outcome expectations scale was used to assess the personal beliefs of the students towards the accomplishment of their career choices.
The 15-item scale developed by Betz and Schifano [61] was adopted for interests. The measures were developed to evaluate students’ interests in career-related activities. The scale used in this study included 6 items to which participants were asked to respond regarding their interest in performing the specific tasks.
To measure students’ perceptions of career barriers, the perceived barriers scale developed by McWhirter [62] was adopted and modified in this study. Items presented addressed barriers such as discrimination, environmental support, and role models in the respondent’s future career.
The Schaub [63] learning experiences questionnaire derived from Bandura’s four sources of self-efficacy beliefs, namely, performance accomplishments, vicarious learning, verbal persuasion, and emotional arousal, was adopted to measure learning experiences in this study. Access to Opportunity Structures was measured using Furlong et al.’s opportunity structures scale [64]. To measure gender stereotypes, the study adopted the implicit association test developed by Fakunmoju et al. [65].
5. Data Analysis
The scales were replicated by using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) in SPSS v27 and AMOS v27.
To determine the factor structure of the measurement scales, a Maximum Likelihood analysis and Promax with Kaiser Normalization rotation was conducted. This approach identified the underlying factors that contribute to the observed variables. In order to assess the suitability of the data for factor analysis, the Bartlett’s Test of Sphericity and the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy test were deployed. These tests provided valuable information about the appropriateness of the data for factor analysis.
The results of the exploratory factor analyses demonstrated strong reliability, convergent validity, and discriminant validity of the sub-scales. This indicates that the measurement scales effectively capture the intended constructs and exhibit consistent patterns of relationships among the observed variables. The reliability of the sub-scales suggests that the measurement items are internally consistent and measure the same underlying construct.
Moreover, the convergent validity of the sub-scales indicates that the measurement items within each sub-scale are strongly related to each other, providing evidence of their ability to measure the same construct. The discriminant validity of the sub-scales suggests that the measurement items within different sub-scales are less strongly related to each other, supporting the distinctiveness of the constructs they represent.
Dimensionality and significance of factors were determined by calculating the population values for each factor loading that maximises the likelihood of sampling the observed correlation matrix. Kaiser’s criterion, the eigenvalue rule, was adopted to determine the number of factors to retain. The eigenvalue was used to explain the extent of variance by a factor. Eigenvalues greater than 1 were considered significant and retained and were used to explain the variance obtained by a factor, while eigenvalues less than 1 were excluded. Before performing the Maximum Likelihood analysis, commonalities extracted from each factor variable were assessed and presented. Values with high communalities above or equal to 1 and low communalities (<0.030) indicating problems with the solutions were excluded. The factors were rotated using Promax with Kaiser Normalization rotation to aid the data interpretation process. The selected items in each scale and their factor loadings are presented in Table 1.
All the items on the scale met the criteria for factor analysis. The factor loadings for the interest items were all acceptable. A Bartlett’s test of Sphericity with p < 0.000 was obtained for all the scales. The scale items were, therefore, considered unidimensional, and adequate evidence of convergent and discriminant validity was provided.
In the CFA, the reliability and validity statistics showed that the corrected item–total correlation for all the items in the scales was above the recommended cut-off value of 0.30, indicating internal consistency and that the items measure what they are intended to measure. All the subscales were above the Cronbach alpha cut-off value of 0.70, showing acceptable internal reliability. The factor loadings ranged from 0.556 to 0.943. All items were above the cut-off value of 0.50. In addition, all the constructs’ AVE and CR fell within the acceptable threshold of 0.50 and 0.60, respectively.
The relationships between self-efficacy, outcome expectations, goal representations, interests, social supports, perceived barriers, learning experiences, access to opportunity structures, and gender role stereotypes with the preference for a career choice in construction were assessed using multiple regression analysis. To identify the unique effects of the career choice predictors, separate multiple regression analyses were carried out for the career choice predictors (independent variables, where career choice in construction was the dependent variable). The parameter estimate is significant at p ≤ 0.05.
To test the model’s goodness of fit, the Relative Normed Chi-square, Root-Mean-Square-Error of Approximation (RMSEA), Comparative Fit Index (CFI), Standardised Root Mean Square Residual (SRMR), Normed Fit Index (NFI), Tucker–Lewis Index (TLI), Parsimony Adjusted Normed Fit Index (PNFI), Relative Fix Index (RFI), and Parsimony Adjusted Comparative Fit Index (PCFI) were the fit indices considered in this study, using the following criteria: RMSEA < 0.05, CFI > 0.95, TLI > 0.95, SRMR ≤ 0.05, TLI > 0.95, NFI > 0.95, RFI > 0.95, PNFI > 0.90, and PCFI > 0.90. No cut-off value was set for the Chi-square values as the fit static varies according to the model’s design complexity, amount of data, and sample size. The final model, including its significant paths, is shown in Figure 2.
6. Results
The assessment of the validity of the hypothesised model in Figure 1 produced a chi-square value of 2905.017 and 1997 degrees of freedom. The associated p-value was less than 0.005 (p = 0.00). The CMIN/df = 1.455 and, based on the cut-off criteria, indicated a good fit. The value of RMSEA = 0.045 and SRMR (0.046) showed a good fit. Therefore, the measurement model fulfilled the requirements for absolute fitness.
Although the NFI (0.765) indicated a poor model fit, the CFI (0.953), RFI (0.952), and TLI (0.954) suggested a good fit of the model, therefore exhibiting acceptance of comparative fit. The PNFI (0.712) and PCFI (0.848) indices were above 0.50, indicating an acceptable fit. Although the model met the threshold for parsimonious fit suggested by Hooper et al. (2008), the model is not so parsimonious.
The results of the standardised regression relationships between variables are summarised in Table 2. All estimates for the model presented in Figure 2 were significant for the influence of the exogenous variables on endogenous at the p ≤ 0.05 level, except for learning experiences (LEX). Self-efficacy, outcome expectations, goal representations, interests, social supports, perceived barriers, access to opportunity structures, and gender role stereotypes had significant direct associations with a career choice in construction. Although learning experiences were hypothesised to influence career choice directly, no empirical findings supported the hypothesis, as no statistical significance was found (R2 = −0.500, p = 0.088). Therefore, the hypothesis was rejected.
7. Discussion
The result of the structural model revealed that the general hypotheses, which state that self-efficacy, outcome expectations, goal representations, interests, social supports, perceived barriers, gender stereotypes, and access to opportunity structures jointly influence career choice in the construction industry, could not be rejected. However, the hypothesis that learning experiences influence career choices in construction was rejected.
Results of the structural model revealed no significant direct relationship between learning experiences and career choice. However, findings from past studies have suggested that individuals’ career choices are generally influenced by some parts of their external environment, which they learn from by observing the demographic features of that profession [20,66]. This is contrary to the anticipated outcome of learning experiences as a determinant of career choice. Developing relevant information and skills through learning experiences fosters a strong career self-efficacy belief, which could result in choosing a career in a specific profession [60]. The lack of influence of learning experiences on the career choices of this particular population can be understood through various explanations. One possible reason for the findings in this study is the socio-economic homogeneity of the sample, with a majority of respondents belonging to low socio-economic status (SES) categories. It is plausible that individuals in such circumstances may not have access to positive learning experiences or any learning experiences at all. This aligns with Betz’s [58] argument that an environment lacking information and experiences about certain careers for young adults can be considered a null environment, neither encouraging nor discouraging their participation in those careers. Moreover, an environment characterised by deep poverty, which significantly impacts learning experiences, is unlikely to foster career aspirations and choices. These factors collectively contribute to the non-influence of learning experiences on career decisions within this population.
Another possible explanation for the findings can be attributed to Layton’s theory of locus of control [67]. According to Trice et al. [68], locus of control is the belief individuals have regarding their control over choices and shaping future career plans. It is a measure of the extent to which individuals perceive themselves as having control over their own lives and the ability to determine their own career outcomes. Research suggests that career choices are closely tied to one’s willingness to take control and responsibility for significant life events [69,70]. Consequently, individuals with a high internal locus of control firmly believe that their career choices are shaped by their own skills, abilities, and internal factors that are under their control. This perspective highlights the importance of personal agency and self-determination in career decision making. By recognising and embracing their own capabilities, these individuals are more likely to take ownership of their career paths and actively pursue opportunities that align with their goals and aspirations. In the context of the current study, it is plausible that the respondents perceive their career choices as being minimally influenced by their past experiences.
Implications of Findings
Findings from the study have meaningful implications for practice in career choice and development in male-dominated environments and occupations. Overall, the most significant of the present study is that results suggest clear pathways to making a career choice in construction for people who want to enter and remain in construction work. The SCCT developed by Lent et al. [20] was applied in this study and tested on the study’s sample. The SCCT career choice model was developed by Lent et al. [20] to identify some of the differential issues facing academic and career development. In support of this philosophy, some of the implications stated in this study may be useful to academia and the industry.
Results were consistent with the SCCT-relevant suppositions. The generalisability and applicability of the SCCT in the South African context and culture are supported. The SCCT’s incorporation of important academic and career development factors, such as personal inputs and contextual factors, makes it a sound theoretical framework to examine women’s career choices in non-traditional professions such as construction.
Although the SCCT has been applied to study career choices and persistence in several occupations, little empirical support exists for applying the theory to career choice in construction, particularly women in the construction profession [23,30,34,38,39,46,71,72]. An increasing amount of empirical support for the SCCT’s postulated influence of self-efficacy and outcome expectations on career choice has also accumulated [59,70].
8. Limitations
We conducted a study in the KwaZulu-Natal province of South Africa, sampling men and women from diverse ethnic and socio-economic backgrounds. However, it is important to exercise caution when generalising the findings to the entire South African population. The diversity of the population in South Africa, both in terms of ethnicity and socio-economic status, necessitates careful consideration when extrapolating our results. Further research is needed to explore the nuances and variations within different regions and populations of South Africa to obtain a comprehensive understanding of the entire country. Researchers should take into account the specific characteristics and circumstances of different regions and populations when drawing conclusions and making generalisations.
9. Conclusions
The study findings demonstrate that the structural model exhibits a commendable fit, as evidenced by the analysis conducted. Our examination of the sample data reveals that the model adequately captures the underlying relationships. Additionally, the model fit statistics derived from the measurement models indicate that the constructs incorporated in the structural model meet the necessary criteria for inclusion. Furthermore, it is important to note that the covariance between the constructs in the model and the exogenous variables is statistically significant. These results provide robust evidence for the validity and reliability of the proposed model. This revealed that the career choice model for the construction industry, through a review of the extant literature and questionnaire survey, is validated. Likewise, the study justifies that the identified factors predict career choices in construction-related professions, i.e., self-efficacy, social supports, goal representations, interests, perceived discriminatory barriers, outcome expectations, perceived barriers to success and progression, gender stereotypes, and access to opportunity structures.
Although much needed within the career development literature, there is a dearth of research on career choice and influencing factors in male-dominated fields such as construction. This study presented an in-depth examination of career choice development for a subset of these groups. This study also represents a concrete attempt to examine their career choices from an existing theoretical perspective (SCCT), and the results reveal the applicability of the SCCT to the population in this study. Further, findings from this study suggest that the proposed career choice model could give insight into how members of this population make career decisions and persist in their academic endeavours. Additional variables such as personality, career aspirations, sex role, and biological sex should be considered for inclusion in the model and tested to further investigate career choice predictors in future research.
Future research may investigate the intersectionality between gender, technology, and factors such as sex, class, and market demand to examine the determinants of students’ career choices in non-Western contexts. Undoubtedly, future research can be conducted on an intersectional analysis of diverse groups. Nevertheless, the current study is one of the first to apply the SCCT—a major career theory in the South African context. The current study is anticipated to motivate further cross-cultural studies in this area.
Conceptualization, M.A. and O.J.E.; methodology, M.A.; software, M.A.; validation, T.O., D.E. and M.A.; formal analysis, T.O.; investigation, O.J.E.; resources, M.A.; data curation, D.E.; writing—original draft preparation, M.A.; writing—review and editing, T.O.; visualization, O.J.E.; supervision, T.O.; project administration, D.E. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
Data used in this study are available upon request.
The authors declare no conflict of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Enlarge this image.Figure 1. Hypothesised paths linking the effect of combined SCCT factors and integrate personal, contextual, and socio-cognitive variables on career choice behaviour.
Selected items and their factor loadings in the study scales.
| Item | Element | Factor Loading |
|---|---|---|
| Self-Efficacy | ||
| SEF1 | I have confidence in my ability to identify resources, limitations, and personal characteristics that might influence my career choices | 0.693 |
| SEF2 | I am confident about being able to collect information about training and employment opportunities for myself and manage them effectively | 0.602 |
| SEF3 | I am confident about being able to develop lists of priorities on the effective actions to successfully manage my own personal professional development | 0.622 |
| SEF4 | I am confident about being able to plan the steps needed to realise a project related to my profession | 0.857 |
| SEF5 | I am confident about being able to address any difficulties related to my career | 0.773 |
| Goal Representations | ||
| GRP1 | I will obtain technical/functional skills in my chosen career | 0.724 |
| GRP2 | I will have opportunities for training and development in my chosen career | 0.868 |
| GRP3 | I will have the opportunities for interesting work in my chosen career | 0.878 |
| GRP4 | My chosen career will allow me to meet my financial obligations | 0.784 |
| GRP5 | I will be successful in my chosen career | 0.797 |
| GRP6 | I will occupy leadership positions in my chosen career | 0.775 |
| GRP7 | My chosen career will make my family, friends and society have a good and positive opinion of me | 0.769 |
| Social Supports | ||
| SSP2 | I receive support from my teachers | 0.671 |
| SSP3 | I receive support from my family members | 0.659 |
| SSP4 | I receive support from my peers (e.g., friends, colleagues) | 0.732 |
| Interests | ||
| INT1 | I enjoy performing tasks and activities related to my choice of profession | 0.773 |
| INT2 | I would like to make a lot of money | 0.769 |
| INT3 | I would like to receive recognition in the society | 0.684 |
| INT4 | I would like to perform well at my job. | 0.886 |
| INT5 | I enjoy thinking and solving problems | 0.805 |
| INT6 | I like highly challenging activities and taking risk | 0.699 |
| Perceived Barriers | ||
| PRB1 | Discriminatory behaviors | 0.611 |
| PRB2 | Work–life conflict | 0.605 |
| PRB3 | Wage gap | 0.587 |
| PRB4 | Masculine workplace culture | 0.532 |
| PRB5 | Lack of access to opportunities | 0.755 |
| PRB6 | Poor working conditions | 0.715 |
| PRB9 | Gender stereotypes | 0.648 |
| PRB10 | Glass ceiling (Invisible barrier to career advancement) | 0.655 |
| PRB11 | Lack of knowledge and career information | 0.832 |
| PRB12 | Lack of role models in my chosen career | 0.650 |
| PRB13 | Lack of education and training | 0.811 |
| PRB14 | Lack of opportunities in my chosen career | 0.791 |
Parameter estimation (standardised regression weights) for the modified model.
| Proposed Hypothesis | Path | Regression Estimate | p Label |
|---|---|---|---|
| SEF | <--- | 0.395 | *** |
| OTX | <--- | 0.154 | 0.002 |
| GRP | <--- | 0.617 | 0.030 |
| SSP | <--- | 0.126 | 0.028 |
| LEX | <--- | −0.500 | 0.088 |
| INT | <--- | 0.796 | *** |
| PRBD | <--- | 0.161 | 0.014 |
| PRBSP | <--- | 0.631 | *** |
| GSP | <--- | 0.518 | *** |
| AOP | <--- | 0.344 | 0.011 |
Note: *** p < 0.001.
References
1. English, J.; Hay, P. Black South African Women in Construction: Cues for Success. J. Eng. Des. Technol.; 2015; 13, pp. 144-164. [DOI: https://dx.doi.org/10.1108/JEDT-06-2013-0043]
2. Du Plessis, S.; Venter, C. The Home Team Scores! A First Assessment of the Economic Impact of World Cup 2010; Stellenbosch University, Department of Economics: Stellenbosch, South Africa, 2010.
3. Sangweni, N. Women in Construction: Hindrances That Shorten the Professional Working Life of Female Site Engineers on Construction Sites in South Africa. Ph.D. Thesis; University of the Witwatersrand: Johannesburg, South Africa, 2015.
4. Construction and Hospitality Shortage Review. Available online: https://www.gov.uk/government/publications/construction-and-hospitality-shortage-review/construction-and-hospitality-shortage-review-accessible (accessed on 8 August 2023).
5. Ratcheva, V.S. Conclusions, Insight and Recommendations from the World Economic Forum Global Gender Gap Report 2016. STEM Gend. Equal. Congr. Proc.; 2017; 1, pp. 603-647. [DOI: https://dx.doi.org/10.21820/25150774.2017.1.27]
6. McAlpine, L.W.R. Construction as a Career Choice for Young People; Chartered Institute of Building: Ascot, UK, 2017.
7. Male, S.; Bennett, D.; Figueroa, E.; Gardner, A.; Khan, N. Gender Inclusivity of Engineering Students’ Experiences of Workplace Learning At Three Australian Universities. J. Eng. Educ.; 2018; 43, 360. [DOI: https://dx.doi.org/10.1080/03043797.2017.1397604]
8. Wynn, A.; Correll, S. Gendered Perceptions of Cultural and Skill Alignment in Technology Companies. Soc. Sci.; 2017; 6, 45. [DOI: https://dx.doi.org/10.3390/socsci6020045]
9. Akinlolu, M.; Haupt, T.C. Investigating a Male-Dominated Space: Female Students’ Perceptions of Gendered Cultures in Construction Workplaces. The Construction Industry in the Fourth Industrial Revolution; Springer International Publishing: Cham, Switzerland, 2020; pp. 43-55.
10. English, J.; Le Jeune, K. Do Professional Women and Tradeswomen in the South African Construction Industry Share Common Employment Barriers despite Progressive Government Legislation?. J. Prof. Issues Eng. Educ. Pract.; 2012; 138, pp. 145-152. [DOI: https://dx.doi.org/10.1061/(ASCE)EI.1943-5541.0000095]
11. Watts, J. Leaders of Men: Women ‘Managing’in Construction. Work Employ. Soc.; 2009; 23, 512. [DOI: https://dx.doi.org/10.1177/0950017009337074]
12. Wright, T. Gender, Sexuality and Male-Dominated Work: The Intersection of Long-Hours Working and Domestic Life. Work Employ. Soc.; 2014; 28, pp. 985-1002. [DOI: https://dx.doi.org/10.1177/0950017013512713]
13. Schultheiss, D.E.P. A Relational Approach to Career Counseling: Theoretical Integration and Practical Application. J. Couns. Dev.; 2003; 81, pp. 301-310. [DOI: https://dx.doi.org/10.1002/j.1556-6678.2003.tb00257.x]
14. Alderfer, C. A Family Therapist’s Reaction to “the Influences of the Family of Origin on Career Development: A Review and Analysis”. Couns. Psychol.; 2004; 32, pp. 569-577. [DOI: https://dx.doi.org/10.1177/0011000004265963]
15. Watson, M.; McMahon, M.; Longe, P. Occupational Interests and Aspirations of Rural Black South African Children: Considerations for Theory, Research and Practice. J. Psychol. Afr.; 2011; 21, pp. 413-420. [DOI: https://dx.doi.org/10.1080/14330237.2011.10820475]
16. Porfeli, E.J.; Hartung, P.J.; Vondracek, F.W. Children’s Vocational Development: A Research Rationale. Career Dev. Q.; 2008; 57, pp. 25-37. [DOI: https://dx.doi.org/10.1002/j.2161-0045.2008.tb00163.x]
17. Louw-Harmse, Y.A.S. Gender Imbalance: A Focus on Senior Management Positions in the It Sector in South Africa. Ph.D. Thesis; Cape Peninsula University of Technology: Cape Town, South Africa, 2015.
18. Vainikolo, K. Women’s Career Paths in the Construction Industry in New Zealand. Ph.D. Thesis; Auckland University of Technology: Auckland, NewZealand, 2017.
19. Alves, S.; English, J. Female Students’ Preparedness for a Male-Dominated Workplace. J. Eng. Des. Technol.; 2018; 16, pp. 581-595. [DOI: https://dx.doi.org/10.1108/JEDT-03-2018-0039]
20. Lent, R.W.; Brown, S.D.; Hackett, G. Toward a Unifying Social Cognitive Theory of Career and Academic Interest, Choice, and Performance. J. Vocat. Behav.; 1994; 45, pp. 79-122. [DOI: https://dx.doi.org/10.1006/jvbe.1994.1027]
21. Bandura, A. Human Agency in Social Cognitive Theory. Am. Psychol.; 1989; 44, pp. 1175-1184. [DOI: https://dx.doi.org/10.1037/0003-066X.44.9.1175] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/2782727]
22. Charity-Leeke, P. Women in Engineering: A Phenomenological Analysis of Sociocultural Contextual Meaning of Gender Roles. Ph.D. Thesis; Cleveland State University: Cleveland, OH, USA, 2012.
23. Kelly, M.E. Social Cognitive Career Theory as Applied to the School-to-Work Transition. Ph.D. Thesis; Seton Hall University: South Orange, NJ, USA, 2009.
24. Dawis, R.V.; Lofquist, L.H. A Psychological Theory of Work Adjustment: An Individual-Differences Model and Its Applications; University of Minnesota Press: Minneapolis, MN, USA, 1984.
25. Walsh, B.W.; Holland, J.L. A Theory of Personality Types and Work Environments. Person–Environment Psychology: Models and Perspectives; Walsh, W.B.; Craik, K.H.; Price, R.H. Lawrence Erlbaum Associates, Inc.: London, UK, 1992; pp. 35-69.
26. Krumboltz, J.D.; Mitchell, A.M.; Jones, G.B. A Social Learning Theory of Career Selection. Couns. Psychol.; 1976; 6, pp. 71-81. [DOI: https://dx.doi.org/10.1177/001100007600600117]
27. Super, D.E. A Lifespan, Life-Space Approach to Career Development; Elsevier: Amsterdam, The Netherlands, 1980.
28. Vondracek, F.W.; Schulenberg, J.E. Career Development in Adolescence: Some Conceptual and Intervention Issues. Vocat. Guid. Q.; 1986; 34, pp. 247-254. [DOI: https://dx.doi.org/10.1002/j.2164-585X.1986.tb00860.x]
29. Lent, R.W.; Brown, S.D. Social Cognitive Career Theory in a Diverse World: Guest Editors’ Introduction. J. Career Assess.; 2017; 25, pp. 3-5. [DOI: https://dx.doi.org/10.1177/1069072716657811]
30. Ali, S.R.; Mcwhirter, E.H. Rural Appalachian Youth’s Vocational/Educational Postsecondary Aspirations: Applying Social Cognitive Career Theory. J. Career Dev.; 2006; 33, 87. [DOI: https://dx.doi.org/10.1177/0894845306293347]
31. Saifuddin, S.M.; Dyke, L.S.; Rasouli, M. Gender and Careers: A Study of Persistence in Engineering Education in Bangladesh. Gend. Manag. Int. J.; 2013; 28, pp. 188-209. [DOI: https://dx.doi.org/10.1108/GM-01-2013-0009]
32. Hunt, J.M.; Langowitz, N.; Rollag, K.; Hebert-Maccaro, K. Helping Students Make Progress in Their Careers: An Attribute Analysis of Effective vs Ineffective Student Development Plans. Int. J. Manag. Educ.; 2017; 15, pp. 397-408. [DOI: https://dx.doi.org/10.1016/j.ijme.2017.03.017]
33. Mau, W.-C.; Bikos, L.H. Educational and Vocational Aspirations of Minority and Female Students: A Longitudinal Study. J. Couns. Dev.; 2000; 78, pp. 186-194. [DOI: https://dx.doi.org/10.1002/j.1556-6676.2000.tb02577.x]
34. Chronister, K.M.; McWhirter, E.H. Applying Social Cognitive Career Theory to the Empowerment of Battered Women. J. Couns. Dev.; 2003; 81, pp. 418-425. [DOI: https://dx.doi.org/10.1002/j.1556-6678.2003.tb00268.x]
35. Taasoobshirazi, G.; Carr, M. Gender Differences in Science: An Expertise Perspective. Educ. Psychol. Rev.; 2008; 20, pp. 149-169. [DOI: https://dx.doi.org/10.1007/s10648-007-9067-y]
36. Hackett, G.; Lent, R.W.; Greenhaus, J.H. Advances in Vocational Theory and Research: A 20-Year Retrospective. J. Vocat. Behav.; 1991; 38, 38. [DOI: https://dx.doi.org/10.1016/0001-8791(91)90015-E]
37. Lent, R.W.; Sheu, H.-B.; Singley, D.; Schmidt, J.A.; Schmidt, L.C.; Gloster, C.S. Longitudinal Relations of Self-Efficacy to Outcome Expectations, Interests, and Major Choice Goals in Engineering Students. J. Vocat. Behav.; 2008; 73, pp. 328-335. [DOI: https://dx.doi.org/10.1016/j.jvb.2008.07.005]
38. Peña-Calvo, J.-V.; Inda-Caro, M.; Rodríguez-Menéndez, C.; Fernández-García, C.-M. Perceived Supports and Barriers for Career Development for Second-year STEM Students: Supports and Barriers for Career Development in STEM Students. J. Eng. Educ.; 2016; 105, pp. 341-365. [DOI: https://dx.doi.org/10.1002/jee.20115]
39. Serra, P.; Soler, S.; Camacho-Miñano, M.J.; Rey-Cao, A.; Vilanova, A. Gendered Career Choices: Paths Towards Studying A Degree in Physical Activity and Sport Science. Front. Psychol.; 2019; 10, 1986. [DOI: https://dx.doi.org/10.3389/fpsyg.2019.01986]
40. Lent, R.W.; Brown, S.D. Integrating Person and Situation Perspectives on Work Satisfaction: A Social-Cognitive View. J. Vocat. Behav.; 2006; 69, pp. 236-247. [DOI: https://dx.doi.org/10.1016/j.jvb.2006.02.006]
41. Locke, E.A.; Motowidlo, S.J.; Bobko, P. Using Self-Efficacy Theory to Resolve the Conflict between Goal-Setting Theory and Expectancy Theory in Organizational Behavior and Industrial/Organizational Psychology. J. Soc. Clin. Psychol.; 1986; 4, pp. 328-338. [DOI: https://dx.doi.org/10.1521/jscp.1986.4.3.328]
42. Wanous, J.P.; Keon, T.L.; Latack, J.C. Expectancy Theory and Occupational/Organizational Choices: A Review and Test. Organ. Behav. Hum. Perform.; 1983; 32, pp. 66-86. [DOI: https://dx.doi.org/10.1016/0030-5073(83)90140-X]
43. Bécares, L.; Priest, N. Understanding the Influence of Race/Ethnicity, Gender, and Class on Inequalities in Academic and Non-Academic Outcomes among Eighth-Grade Students: Findings from an Intersectionality Approach. PLoS ONE; 2015; 10, e0141363. [DOI: https://dx.doi.org/10.1371/journal.pone.0141363]
44. Amaratunga, D.; Haigh, R.; Shanmugam, M.; Lee, A.; Elvitigala, G. Construction Industry and Women: A Review of The Barriers. Proceedings of the 3rd International Scri Research Symposium; Delft, The Netherlands, 3–4 April 2006.
45. Chileshe, N.; Haupt, T.C. An Empirical Analysis of Factors Impacting Career Decisions in South African Construction Industry: Male and Female High School Students’ Perspectives. J. Eng. Des. Technol.; 2010; 8, pp. 221-239. [DOI: https://dx.doi.org/10.1108/17260531011062573]
46. Alexander, P.M.; Holmner, M.; Lotriet, H.H.; Matthee, M.C.; Pieterse, H.V.; Naidoo, S.; Twinomurinzi, H.; Jordaan, D. Factors Affecting Career Choice: Comparison between Students from Computer and Other Disciplines. J. Sci. Educ. Technol.; 2011; 20, pp. 300-315. [DOI: https://dx.doi.org/10.1007/s10956-010-9254-3]
47. Kessels, U.; Taconis, R. Alien or Alike? How the Perceived Similarity between the Typical Science Teacher and a Student’s Self-Image Correlates with Choosing Science at School?. Res. Sci. Educ.; 2012; 42, pp. 1049-1071. [DOI: https://dx.doi.org/10.1007/s11165-011-9230-9]
48. Brown, M.J.; Henriquez, E. Socio-Demographic Predictors of Attitudes towards Gays and Lesbians. Individ. Differ. Res.; 2008; 6, pp. 193-202.
49. Nassar, C.; Nastacă, C.C.; Nastaseanu, A. The Leadership Styles of Men and Women—Has the Perception Regarding Female Leaders Changed?. Mei Zhong Gong Gong Guan Li; 2021; 18, pp. 68-80. [DOI: https://dx.doi.org/10.17265/1548-6591/2021.02.002]
50. Holton, S.; Fisher, J.; Rowe, H. Attitudes toward Women and Motherhood: Their Role in Australian Women’s Childbearing Behaviour. Sex Roles; 2009; 61, pp. 677-687. [DOI: https://dx.doi.org/10.1007/s11199-009-9659-8]
51. Kalin, R.; Tilby, P. Development and Validation of a Sex-Role Ideology Scale. Psychol. Rep.; 1978; 42, pp. 731-738. [DOI: https://dx.doi.org/10.2466/pr0.1978.42.3.731] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/674496]
52. Blakemore JE, O.; Hill, C.A. The Child Gender Socialization Scale: A Measure to Compare Traditional and Feminist Parents. Sex Roles; 2008; 58, pp. 192-207. [DOI: https://dx.doi.org/10.1007/s11199-007-9333-y]
53. Deemer, E.D.; Lin, C.; Soto, C. Stereotype Threat and Women’s Science Motivation: Examining the Disidentification. Effect. J. Career Assess.; 2016; 24, pp. 637-650. [DOI: https://dx.doi.org/10.1177/1069072715616064]
54. Sáinz, M.; Meneses, J.; López, B.-S.; Fàbregues, S. Gender Stereotypes and Attitudes towards Information and Communication Technology Professionals in a Sample of Spanish Secondary Students. Sex Roles; 2016; 74, pp. 154-168. [DOI: https://dx.doi.org/10.1007/s11199-014-0424-2]
55. Blanchard, C.A.; Lichtenberg, J.W. Compromise in Career Decision Making: A Test of Gottfredson’s Theory. J. Vocat. Behav.; 2003; 62, pp. 250-271. [DOI: https://dx.doi.org/10.1016/S0001-8791(02)00026-X]
56. Junk, K.E.; Armstrong, P.I. Stability of Career Aspirations: A Longitudinal Test of Gottfredson’s Theory. J. Career Dev.; 2010; 37, pp. 579-598. [DOI: https://dx.doi.org/10.1177/0894845309350921]
57. Tsaousides, T.; Jome, L. Perceived Career Compromise, Affect and Work-Related Satisfaction in College Students. J. Vocat. Behav.; 2008; 73, pp. 185-194. [DOI: https://dx.doi.org/10.1016/j.jvb.2008.04.003]
58. Betz, N.E. Implications of the Null Environment Hypothesis for Women’s Career Development and for Counseling Psychology. Couns. Psychol.; 1989; 17, pp. 136-144. [DOI: https://dx.doi.org/10.1177/0011000089171008]
59. Everhart, C.B.; Chelladurai, P. Gender Differences in Preferences for Coaching as an Occupation: The Role of Self-Efficacy, Valence, and Perceived Barriers. Res. Q. Exerc. Sport; 1998; 69, pp. 188-200. [DOI: https://dx.doi.org/10.1080/02701367.1998.10607683]
60. Betz, N.E.; Voyten, K.K. Efficacy and Outcome Expectations Influence Career Exploration and Decidedness. Career Dev. Q.; 1997; 46, pp. 179-189. [DOI: https://dx.doi.org/10.1002/j.2161-0045.1997.tb01004.x]
61. Bonitz, V.S.; Larson, L.M.; Armstrong, P.I. Interests, Self-Efficacy, and Choice Goals: An Experimental Manipulation. J. Vocat. Behav.; 2010; 76, pp. 223-233. [DOI: https://dx.doi.org/10.1016/j.jvb.2009.09.003]
62. McWhirter, E.H. Perceived Barriers to Education and Career: Ethnic and Gender Differences. J. Vocat. Behav.; 1997; 50, pp. 124-140. [DOI: https://dx.doi.org/10.1006/jvbe.1995.1536]
63. Schaub, M.; Tokar, D.M. The Role of Personality and Learning Experiences in Social Cognitive Career Theory. J. Vocat. Behav.; 2005; 66, pp. 304-325. [DOI: https://dx.doi.org/10.1016/j.jvb.2004.09.005]
64. Furlong, A.; Cartmel, F. Risk and Uncertainty in the Youth Transition. Young; 1997; 5, pp. 3-20. [DOI: https://dx.doi.org/10.1177/110330889700500102]
65. Fakunmoju, S.B.; Bammeke, F.O.; Oyekanmi FA, D.; Temilola, S.; George, B. Psychometric Properties of Beliefs about Relationship Violence against Women and Gender Stereotypes Scale. J. Psychol. Afr.; 2016; 26, pp. 246-258. [DOI: https://dx.doi.org/10.1080/14330237.2016.1185905]
66. Ericksen, J.A. Women Pursuing Nontraditional Careers: A Social Cognitive Career Theory Perspective; Cleveland State University ProQuest Dissertations Publishing: Cleveland, OH, USA, 2013.
67. Layton, C. Externality and Unemployment: Change Score Analyses on Rotter’s Locus of Control Scale for Male School-Leavers and Men Facing Redundancy. Pers. Individ. Differ.; 1987; 8, pp. 149-152. [DOI: https://dx.doi.org/10.1016/0191-8869(87)90025-0]
68. Trice, A.D.; Haire, J.R.; Elliott, K.A. A Career Locus of Control Scale for Undergraduate Students. Percept. Mot. Ski.; 1989; 69, pp. 555-561. [DOI: https://dx.doi.org/10.2466/pms.1989.69.2.555]
69. Luzzo, D.A.; Jenkins-Smith, A. Development and Initial Validation of the Assessment of Attributions for Career Decision-Making. J. Vocat. Behav.; 1998; 52, pp. 224-245. [DOI: https://dx.doi.org/10.1006/jvbe.1997.1582]
70. Abdinoor, N. Socio-economic status, career decision-making self-efficacy, career maturity, and gender with secondary school students in northern Kenya. Int. J. Multidiscip. Curr. Educ. Res.; 2020; 2, pp. 160-167.
71. Daniels, L.F. Career Decision-Making Patterns of Undecided African American Male Transfer Students: A Qualitative Approach; University of Central Florida: Orlando, FL, USA, 2012.
72. Wang, X.; Wickersham, K. Women, Community Colleges, and STEM Careers: Women, Community Colleges, and STEM Careers. New Dir. Inst. Res.; 2018; 2018, pp. 73-90. [DOI: https://dx.doi.org/10.1002/ir.20276]
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Omotayo, Temitope 2
; Obas John Ebohon 3
; Ekundayo, Damilola 4
1 School of the Built Environment, London Metropolitan University, London N7 8DB, UK;
2 School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds LS1 3HE, UK
3 Academy for Sustainable Futures, Canterbury Christ Church University, Canterbury CT1 1QU, UK;
4 School of Engineering and the Built Environment, Birmingham City University, Birmingham B4 7XG, UK;