Navigating the technopreneurial odyssey: determining how technopreneurial self-efficacy, technopreneurial education and technological optimism cultivate tech-driven entrepreneurial intentions

Given the global popularity of the Fourth Industrial Revolution (4IR) and the rise of artificial intelligence and a knowledge-based society over the past decade, the concept of technopreneurship has become increasingly widespread (Yingi et al., 2022; Bui and Lo, 2022). The Fourth Industrial Revolution (4IR) can be viewed as the transformation of industries through the integration of new technologies, which has led to the development of innovations such as Artificial Intelligence (AI), robotics, Internet of Things (IoT), and Quantum Computing among others (Nyagadza et al., 2022). The COVID-19 pandemic has significantly accelerated the technology industry’s remarkable growth from 2020 to 2022, driven by the adoption of social distancing and stay-at-home measures. This has led to significant developments in metaverse marketing, where consumers are increasingly drawn to virtual shopping experiences (Cheung et al., 2024a, b; Ligaraba et al., 2023). Virtual Reality (AR), Blockchain, and Artificial Intelligence (AI) enhanced technologies have been very instrumental during the COVID-19 era through various platforms such as social media and corporate websites, as they engaged made users of products to be more engaged and/or connected with specific brands for goods and services (Wasiq et al., 2024a, b; Bashar et al., 2024a, b). The security and integrity of these transactions were improved due to the developments, resulting in memorable brand experiences that brought delight through associated entertainment, which in turn enhanced digital tech-driven entrepreneurship (Cheung et al., 2024a, b; Leung et al., 2023). Therefore, it is impossible to underestimate the importance of technopreneurship in terms of economic development and modernisation. In this regard, there is a consensus that technological advancements support both economic growth and development (Bomani et al., 2021). It is evident that technology-based industries are supplanting traditional sectors due to technopreneurship (Nikraftar et al., 2022). This implies that, particularly in developing nations, it is pivotal to support technopreneurs who can create innovative technological solutions. Technopreneurship, as defined by Soomro and Shah (2021), is the convergence of high technology, innovation, and entrepreneurship. It encompasses tech-savvy individuals who exhibit creativity and innovation and are willing to take calculated risks. The developing world is lagging behind in advancing cutting-edge technology across all fields, a situation that hinders swift progress in economic development. In line with this, in the current study, technopreneurship self-efficacy can be seen as one’s belief in successfully performing the tasks and roles necessary to start and manage a tech-based business (Maziriri et al., 2024a, b).

Despite the current digital age, Africa is only at the initial stages of technology development and implementation. Given this context, nurturing a technopreneurship culture emerges as a holistic strategy to address the sluggish pace of technological growth and integration in African nations. It is widely recognised that technopreneurship offers diverse benefits, encompassing the capacity to generate income, alleviate poverty and generate employment opportunities (Nikraftar et al., 2022). Numerous governments are instituting various programs to promote commercialisation, knowledge transfer, and the creation of new products to capitalise on these advantages. Greater emphasis must be placed on technopreneurship within this governmental context, particularly among university students. Nonetheless, technopreneurship is a multifaceted and intricate concept (Linton and Xu, 2021). It is evident that technology-based industries are experiencing exponential growth, unveiling fresh prospects. Consequently, it seems imperative to foster the development of technopreneurship (Groen et al., 2015; Nyagadza, 2021). Technopreneurship, which combines technology and entrepreneurship, provides a sustainable competitive advantage by developing and advancing information communication tools and technologies to meet consumer needs in this digital era (Soomro and Shar, 2021).

The imperative to cultivate technopreneurs within university students is a focal point for numerous universities. Business opportunities have now permeated all academic disciplines, including information technology, computer science, chemistry, nursing, engineering, pharmacy and agriculture. This transformation has been observed in numerous African nations, where a paradigm shift toward science, technology, engineering, and mathematics (STEM) is evident in various governmental strategies (Bomani et al., 2021). This underscores the notion that comparative advantage among nations is now entrenched in knowledge-driven policies rather than resource-centric approaches. Consequently, establishing innovation hubs for high-growth start-ups has become a strategic direction that many countries adopt in nurturing technopreneurs (Amante and Ronquillo, 2017).

However, the technological and entrepreneurial intentions of South Africans have not yet received a comprehensive investigation, despite technopreneurship currently propelling the advancement of progressive nations worldwide. It is indisputable that South Africans confront an array of economic and social challenges, including the contraction of the manufacturing sector, which correlates with elevated poverty and unemployment rates. Considering these difficulties, it is disheartening to note that the inclination towards technopreneurship among South Africans remains notably low.

It is documented that there is an increasing media focus on technopreneurship within the Middle East and North Africa (MENA) region as a strategy for fostering technological advancement and industrialisation (Nyagadza et al., 2022). Despite the burgeoning global interest in technopreneurship, limited research in this field is evident due to its relatively recent emergence (Najjari et al., 2021; Nikraftar et al., 2022). Alarmingly, most previous investigations into entrepreneurial intentions among university students have been confined to business students (Shah and Soomro, 2017). To exacerbate the situation, among non-business university students in South Africa, the inclination towards technopreneurship is still nascent. Notably, the government has encapsulated the significance of technology and innovation. Technopreneurship contributes to SDG 9 by fostering technological innovation, enhancing productivity, and promoting sustainable development through the creation of new products, services, and business models. However, there is an absence of a distinct roadmap for instilling a technopreneurship ethos within the country.

In light of limited knowledge on technopreneurship, this study aims to investigate how technopreneurial self-efficacy, technopreneurial education, and technological optimism influence technopreneurial intention among Generation Z students in South Africa. Furthermore, there is little research on the role of technopreneurial education and technological optimism as moderators between technopreneurial self-efficacy and technopreneurship intention. As a result, this study will significantly contribute to closing this gap. A critical literature review methodological approach will be used to gather and organise the literature. The aim of this approach is to achieve a thorough degree of analysis focused on hypothesis development, conceptual model formulation, and subsequent testing (Grant and Booth, 2009). The research will adopt a quantitative methodology, using a cross-sectional survey method to collect data from Generation Z sampled university students. Due to the paucity of convincing technopreneurship studies in Africa’s emerging economies, this study contributes to bridging the knowledge gap as it investigates to determine how technopreneurial self-efficacy, technopreneurial education, and technological optimism would stimulate technopreneurship intention among generation Z students, with technopreneurial education and technological optimism as the moderators, as a pioneering study in South Africa. In connection to the above, what motivated the study is to answer the main research question (RQ):

The research paper is organised as follows: First, the research context is provided, and then a theoretical basis for the analysis is presented. Thereafter, a theoretical model is presented, and the hypothesis is established. The study design and methodology are then discussed, followed by the presentation of the findings and their discussion. The article concludes with a discussion of the implications, limitations, and future research directions.

Based on the rationale that the adoption of technopreneurship by Generation Z students presents a distinct way of contributing to the job market, the present research employs a sample from this age group. Under normal circumstances, Generation Z faces challenges related to independence, self-actualisation, and self-employment (Soomro and Shah, 2021). As a consequence, the domain available literature (Abdulgani et al., 2016; Yordanova et al., 2020; Sharma, 2018; Shah and Soomoro, 2017) and/or bodies of knowledge mainly show empirical enquiries on the antecedents impacting technopreneurship (Abdulgani et al., 2016), whereas among Generation Z students (in countries such as South Africa), the determination of factors such as how technopreneurial self-efficacy, technopreneurial education, and technological optimism in cultivating tech-driven entrepreneurial intentions is still at its emerging levels. The rapid global technological developments have pushed the agenda for innovation development and signalled competitiveness of the business incubation through technopreneurship among Generation Z students in knowledge-driven economies. Generation Z students are often referred to as “digital natives,” having experienced the dynamic technological and economic developments brought about by the Fourth Industrial Revolution (4IR), which has led to a significant reliance on social connections (Kumar and Kaushik, 2020). The embarkation of Generation Z on technological activities is worth investigating, as this, in turn, would develop strengthening strategies aimed at reducing the government’s burden on public sector job opportunities (Singhry, 2015), especially in economic situations following the pandemic outbreak (Koe et al., 2021). This has been well articulated in the Generation Cohort Theory (GCT), which discusses the different values and beliefs systems among various generations (Srivastava et al., 2022; Cheung et al., 2021). Within this context, Ernst and Young (2021) observed that 53% of survey respondents expressed a desire to run their own businesses, focusing on technology. Moreover, the proportion increased for Generation Z respondents who were currently in the workforce, with 65% optimistic that they will be running their own businesses in 2030. Notably, Generation Z students demonstrated a high level of entrepreneurial aspiration, aiming to utilise opportunities to address complex problems (Ernst and Young, 2021). This implies that Generation Z students possess entrepreneurial traits essential for technopreneurship (Schwieger and Ladwig, 2018).

Various models and theories exist to predict and explain human behaviour. This study will be based on the Theory of Planned Behaviour, the Generation Cohort Theory (GCT), and the Technology Acceptance Model (TAM). The theories are discussed in the following sections section.

Ajzen (2002) proposed the Theory of Planned Behaviour. The TPB model has been one of the most widely utilised frameworks for studying environmental behaviours (Fielding et al., 2008). Many researchers assert that the TPB model effectively explains behavioural intentions and predicts future behaviours (Mannetti et al., 2004). The TPB model illustrates that three predictors influence human intention: attitude towards behaviour, subjective norm, and perceived behavioural control. However, despite the extensive application of the TPB model in examining the motivation behind behavioural intentions, researchers have observed that domain-specific factors have not been included in the model (Armitage and Conner, 2001; Donald et al., 2014). Numerous studies have built upon the TPB model by incorporating new constructs (Jang et al., 2015; Maichum et al., 2016; Read et al., 2013). This study expands the TPB by integrating technopreneurial self-efficacy, technopreneurial education, and technological optimism to enhance technopreneurship intention (behavioural intention according to the TPB). The application of this model in this study is that the TPB can clarify how technopreneurial self-efficacy, education, and technological optimism collectively influence individuals’ intentions to engage in tech-driven entrepreneurship.

The Generation Cohort Theory (GCT) demonstrates the different values and beliefs systems amongst various generations (Kumar and Kaushik, 2020; Nayak et al., 2022), with homogenous memories and experiences during their formative years. Application of Generation Cohort Theory (GCT) to this study assists in explaining the ways in which technopreneurs self-efficacy, education, and technological optimism have an influence on individuals’ intentions to engage in tech-driven entrepreneurship. While some studies have investigated the moderating role of general entrepreneurial education (Rauch and Hulsink, 2015; Obschonka et al., 2019), they have not specifically delved into the specialised aspects of entrepreneurship education relevant to technology ventures in line with Generation Z in South Africa. Commonly known for their techno-savvy attitudes as digital natives, the development of the Generation Cohort Theory (GCT) assists in accounting for such relationships between ages and general experiences as well as value systems. The prediction of historical events and social changes in a society because of individuals’ values, beliefs, attitudes and inclinations assists in explaining how people tend to share similar life paths because of broad values and traits. Each generation has its own special spending patterns, consuming behavioural patterns, and purchase behaviours, like the current study, which focuses on how technopreneurial self-efficacy, education, and technological optimism collectively affect Generation Z in South Africa’s intentions to engage in technology-driven entrepreneurship.

The technology acceptance model (TAM) was initially introduced by Davis (1989). This theory provides a comprehensive foundation for demonstrating that specific beliefs, particularly perceived ease of use (PEOU) and perceived usefulness (PU), determine an individual’s behavioural intention to utilise technology (Davis et al., 1989; Surendran, 2012; Partala and Saari, 2015; Dwivedi et al., 2019). In a technopreneurial context, these dimensions (PEOU and PU) are essential for understanding how aspiring Generation Z entrepreneurs may adopt technology as a fundamental tool for innovation and business creation. Furthermore, technological optimism and technopreneurial education enhance Generation Z’s positive perception of technology, aligning with TAM’s assertion that attitudes towards technology are critical for its acceptance. The researchers contend that technological optimism can amplify Generation Z’s intention to employ technology in entrepreneurial ventures. Lastly, TAM’s flexibility across various disciplines establishes it as a robust theoretical foundation for integrating psychological, educational, and entrepreneurial constructs. Thus, evidence from previous studies indicates that this is a valid model for justifying the use of technology in diverse environments and has a close relationship with technological innovation (Almaiah et al., 2022; Kamal et al., 2020). The predictive power of this model arises from its ability to facilitate the relationship between various context-specific factors that could influence the acceptance of a particular technology, including technopreneurial self-efficacy, technopreneurial education, and technological optimism (Lutfi et al., 2022; Almaiah and Al-Khasawneh, 2020).

The research model for the study is illustrated (see Figure 1). The theoretical model showcases the proposed relationships between the study’s four constructs.

Technopreneurial self-efficacy reflects individuals’ beliefs in terms of whether they possess the required technological and entrepreneurial skills needed to start a technology-based venture (Salheieh and Al-Abdallat, 2022). On the hand, technopreneurship intention is state of mind which directs and guides the actions of the individual toward the development and the implementation of new technology business concepts (Hoque et al., 2017). The relationship between technopreneurial self-efficacy and technopreneurship intention is an important area of study that sheds light on the motivations and behaviours of individuals involved technopreneurship. Recent studies on technopreneurship investigated the influence of technopreneurial self-efficacy on technopreneurship intention. For instance, Koe et al. (2021) found out that ICT self-efficacy positively and significantly influenced technopreneurial intention. In addition, Salhieh and Al-Abdallat (2022) discovered that technopreneurial self-efficacy has a positive and significant effect on technopreneurial intentions. Moreover, Hoque et al. (2017) also found out that technopreneurial self-efficacy has a positive and significant effect on technopreneurial intention. Therefore, it is hypothesised that:

Technopreneurship is a learnable discipline, and as noted by Minniti and Bygrave (2001), education serves as the cornerstone of entrepreneurship. According to Kolb’s (1984) theory, technopreneurial learning is an experiential process wherein technopreneurs acquire information through four separate learning capabilities: experiencing, reflecting, thinking, and acting. Extant literature posits the importance of technopreneurial self-efficacy in affecting technopreneurial education. Koe et al. (2021) noted that as one’s self-efficacy or perceived capability can be learned through education, higher learning institutions (HLIs) are important agents in imparting ICT knowledge to future technopreneurs. Entrepreneurship education has been critical in enhancing the development of entrepreneurship skills amongst the Gen Z of the communities (Hasan et al., 2024). This means that there is a positive relationship between entrepreneurship education and entrepreneurial readiness amongst the students and young generations, with the support of relevant materials and equipment. In addition, entrepreneurial education enhances innovation and creativity, and put learnt knowledge into practice (Hasan et al., 2024). According to Baumol and William (2002), technopreneurial self-efficacy (TSE) can be learned from the development of cognitive and social processes as well as skills gained from technical experiences which can be achieved from formal technological education or experiences, and also from practices or training. Although this might be the case, the Theory of Planned Behaviour (TPB) used to support the current study has been widely used to examine the motivation of behavioural intentions, researchers have noticed that domain-specific factors have not been included in the model (Armitage and Conner, 2001; Donald et al., 2014). Studies in Malaysia and Taiwan showed that students’ technopreneurship self-efficacy has a significant effect on technopreneurial education behaviour (Ainul et al., 2016; Chou et al., 2011). Hoque et al. (2017) also found out that technopreneurial Self-Efficacy (TSE) has a positive and significant effect on technopreneurial Learning or education. Based on these findings, the following hypothesis is proposed:

It is imperative to elucidate on the association between technopreneurial education and technopreneurship intention. Sudarwati (2022) elucidated that the acquisition of more entrepreneurial knowledge has the ability to change the mindset of students towards having the intention to become technopreneurs. Furthermore, Sudarwati (2022) discovered that when students have a high technopreneurship intention and this means their desire to become technopreneurs has become stronger through their participation in entrepreneurship classes. There is a need to have the ability to combine entrepreneurial knowledge with technology to become a technopreneur (Blanka et al., 2019; Wardana et al., 2020). Also, it was found out that technopreneurship education significantly affected students’ career intention to be technopreneurs (Harsono, 2013). The Technology Acceptance Model (TAM) provides an extensive foundation for demonstrating that specific beliefs; perceived ease of use (PEOU) and perceived usefulness (PU) determine one’s behavioural intention to use a technology (Davis et al., 1989; Surendran, 2012; Partala and Saari, 2015; Dwivedi et al., 2019). Grounded in the literature and empirical findings outlined earlier, the ensuing hypothesis is posited:

Technopreneurial education plays a critical role in shaping entrepreneurial intentions within the technology sector by equipping individuals with specialised skills and knowledge pertinent to technology-driven ventures (Linan and Chen, 2009). Specifically, individuals with higher levels of technopreneurial self-efficacy, which reflects their belief in their ability to successfully undertake technopreneurial activities (Baron, 2006; Krueger et al., 2000), are likely to exhibit stronger intentions to engage in technopreneurship when they have undergone comprehensive technopreneurial education. However, existing research has underscored the need to explore technopreneurial education not merely as a direct influencer but also as a potential mediator in the relationship between technopreneurial self-efficacy and entrepreneurial intentions. The application of the models to this study is that TPB and TAM can help elucidate how technopreneurial self-efficacy, education, and technological optimism collectively affect individuals’ intentions to engage in tech-driven entrepreneurship. While some studies have investigated the moderating role of general entrepreneurial education (Rauch and Hulsink, 2015; Obschonka et al., 2019), they have not specifically delved into the specialised aspects of entrepreneurship education relevant to technology ventures, which are fully supported by the Theory of Planned Behaviour (TPB) in close link to the Technology Acceptance Model (TAM). While studies have examined general entrepreneurship education as a mediator (Liñán et al., 2011; Zhao et al., 2005), there remains a gap in understanding how the techno-specific aspects of education facilitate the translation of self-belief into concrete entrepreneurial actions within technology contexts. Therefore, it is hypothesised that:

Technopreneurial education refers to structured learning experiences aimed at developing entrepreneurial skills tailored for technology-driven ventures (Linan and Chen, 2009). Individuals with higher levels of technopreneurial self-efficacy are likely to exhibit stronger intentions to pursue technopreneurship when they have received sufficient technopreneurial education, compared to those with lower levels of self-efficacy or education. However, it is crucial to note the lack of exploration regarding technopreneurial education as a moderator variable in the context of technopreneurship. Addressing this research lacuna by examining how technopreneurial education moderates the relationship between technopreneurial self-efficacy and intention to engage in technopreneurship can provide deeper insights into the factors shaping entrepreneurial ambitions within the technology sector. Hence, it can be hypothesised that:

Technological optimism, specifically, reflects a positive outlook on technology, encompassing perceptions of control and agency (Cadenas et al., 2020). Similarly, Danaher (2022) defines technological optimism as the stance that holds that technology plays a key role in ensuring that good does or will prevail over the bad. Optimism, is often likened to an Archimedean point, not only shapes individual entrepreneurial decisions but also contributes to societal well-being and national development (Seligman, 1991). Technological optimism has been found to significantly influence technopreneurship intention in different contexts. For instance, Puga and Garcia (2010), discovered that technological optimism significantly influences the intention to engage in technopreneurship. Moreover, students nowadays are considered digital natives, and most of them have positive opinions towards technology usage (Lewis and Mayes, 2014; Musah et al., 2015). This notion is echoed by Adeoti (2019), who emphasises the importance of a technology-savvy approach in technopreneur development. Furthermore, it is crucial to recognise that the relationship between technology optimism and intention for technopreneurship among Generation Z students requires a nuanced understanding of the contemporary entrepreneurial landscape. In an era marked by rapid technological advancement and digital innovation, the mindset of Generation Z—characterised by their inherent comfort and optimism towards technology—plays a pivotal role in shaping their aspirations for technopreneurship. Therefore, it is plausible to hypothesise that:

Technology optimism suggests that technology enhances control, flexibility, and efficiency (Borrero et al., 2014). Today’s students, often called digital natives, generally maintain positive views on technology (Lewis and Mayes, 2014; Musah et al., 2015). However, there is a gap in research regarding how technology optimism influences the relationship between technopreneurship education and intentions for technopreneurship, as well as technopreneurial self-efficacy and intentions for technopreneurship. While some studies have explored how technology optimism moderates relationships in various contexts, such as Gardezi and Arbuckle (2020) on the link between risk perception and support for adaptation, Chao and Yu (2019) on the relationship between perceived behavioural control and behavioural intention, and Rachman and Hendayani (2023) on the connection between e-service quality and customer satisfaction, further investigation is required to understand its specific role in the context of technopreneurship. Therefore, drawing from the aforementioned studies, the following hypothesis can be proposed:

This research was grounded in positivist philosophy, which asserts that phenomena can be understood by collecting and analysing quantitative data, resulting in verifiable outcomes (Dzomonda and Neneh, 2023; Saunders et al., 2019). Positivism promotes objectivity and the generalisability of findings (Bell et al., 2022; Saunders et al., 2019). A quantitative research approach was adopted, emphasising collecting and analysing numerical data to gain insights into the research problem (Bell et al., 2022). A causal research design was employed to examine the relationships between technopreneurial self-efficacy, technopreneurship education, and technopreneurship intention, with technological optimism acting as a moderating variable. This design is appropriate as it prioritises objectivity and allows for extracting insights from a structured survey through comprehensive statistical analysis (Taherdoost, 2022).

This research focused on Generation Z (Gen Z) students enrolled at the University of the Western Cape in Bellville within South Africa’s Western Cape Province. The University of the Western Cape aims to establish itself as an entrepreneurial university, offering entrepreneurial education through its Department of Management and Entrepreneurship. Students can study entrepreneurship modules, such as Technopreneurship, encompassing topics like innovation management, business model development, digital technologies, and drop shipping, including variations such as formal academic instruction, practical training through incubators, and experiential learning through industry collaborations. Focusing on students aligns with the prevalent use of student samples in entrepreneurial research across various contexts (Neneh and Dzomonda, 2024; Gieure et al., 2020; Kong et al., 2020; Syed et al., 2020). In line with previous studies, university students were selected for this research as they are more inclined to demonstrate interest and participation in entrepreneurial activities (Neneh and Dzomonda, 2024; Cui and Bell, 2022; Maheshwari and Kha, 2023).

Data collection was done using a self-administered survey questionnaire. To participate, students needed to be actively registered at the university during the data collection period, with their student cards—displaying their name and year of enrolment—used for verification. The university’s database, which contained the complete list of registered students, served as the sampling frame. A probability sampling approach was adopted to ensure the findings could be generalised. Specifically, a simple random sampling method was employed, guaranteeing that each student had an equal chance of selection. Quota sampling was also implemented to achieve a balanced distribution of characteristics within the sample, given the study’s focus on Gen Z.

The respondents’ socio-economic background reveals that Generation Z students reside in private or university accommodation. They hail from various provinces, as detailed in Table 2, which presents the sample’s demographic characteristics. The sole screening criterion was employment status, as the study specifically targeted respondents who were not in full-time permanent employment. Economically, these students depend on bursaries, scholarships, and parental allowances. They perceive technopreneurship as a route to success, exploring opportunities in technological ventures such as metaverse-enabled entrepreneurship and drop shipping.

Goh and Lee (2018) defined Generation Z as individuals born between 1995 and 2009, thus limiting the study’s age range to 18–29 years. The sampling frame comprised the university’s database of registered students, and simple random sampling ensured fair representation. The questionnaires emphasised respondent anonymity and outlined the educational purpose of the research. The required sample size was calculated using the Raosoft calculator (Raosoft Inc., 2004), based on the total student population of approximately 23,000. Assuming a 5% margin of error, a 90% confidence level, and a 50% response distribution, the minimum sample size was determined to be 378 participants. Of the 378 questionnaires distributed, 304 usable responses were received, resulting in a response rate of 80.4% for usable data.

The variables under investigation were operationalised based on previous studies. The scales were adjusted to reflect the context of technopreneurship among students. Table 1 presents the measurement scales, the items used, their sources, and Cronbach’s alpha values for the scales. The scale indicators ranged from strongly disagree (1) to strongly agree (5) on a Likert scale.

Table 2 presents an overview of the study participants, who were asked to provide demographic information, including gender, age, year of study, and province of origin. The demographic profile of the sample reflects substantial diversity across several dimensions.

The gender composition reveals a higher proportion of males (61.5%) compared to females (32.6%), alongside a small percentage of respondents who opted not to disclose their gender (5.9%). In terms of age, most participants are young adults from the Generation Z cohort, with significant representation in the 21 to 23 age bracket (31.9%) and the 27 to 29 age range (37.5%). Regarding academic standing, most participants are in their second or third year of study (25.3% and 29.2%, respectively). Additionally, a notable number of postgraduate students are included, spanning Honours (13.4%), Master’s (11.0%), and PhD programmes (5.0%).

Geographically, the respondents come from a wide range of provinces, with the Western Cape (20.07%), KwaZulu-Natal (18.42%), and the Free State (12.50%) being the most represented.

This heterogeneous sample, encompassing variations in gender, age, academic level, and geographical origin, ensures a well-rounded and representative perspective. Such diversity enhances the validity of the study by allowing for a more nuanced examination of how technopreneurial self-efficacy, education, and technological optimism influence the technopreneurial intentions of Generation Z students.

In this study, the researchers will employ a two-step approach to data analysis using Partial Least Squares Structural Equation Modelling (PLS-SEM), as outlined by Hair et al. (2017). The first step will involve validating the measurement model, where the researchers will assess internal consistency reliability through composite reliability (CR), indicator reliability using outer loadings, and convergent validity via average variance extracted (AVE). Discriminant validity will also be evaluated through the Heterotrait–Monotrait (HTMT) ratio and the Fornell–Larcker criterion. In the second step, the structural model will be examined, focusing on the significance and magnitude of path coefficients, the coefficient of determination (R²) to assess model explanatory power, predictive relevance (Q²) to evaluate the model’s ability to predict the endogenous constructs, and effect size (f²) to determine the strength of the relationships between constructs. Additionally, common method bias (CMB) will be tested using variance inflation factor (VIF) values, and model fit will be evaluated using the Standardised Root Mean Square Residual (SRMR) and other fit indices.

Table 3 presents the measures employed to assess the reliability and validity of the study’s constructs. The outer model was initially evaluated using composite reliability (CR) to determine internal consistency, outer loadings for indicator reliability, and average variance extracted (AVE) for convergent validity. CR is preferred for internal consistency reliability as it accounts for the varying outer loadings of indicator variables, unlike Cronbach’s alpha, which assumes equal reliability for all indicators (Hair et al., 2017). All item loadings for the research constructs were above 0.710, with items scoring below 0.5 being excluded for failing to meet convergent validity thresholds (Anderson and Gerbing, 1988). The remaining item loadings exceeded the recommended value of 0.5 (Anderson and Gerbing, 1988), indicating reliable measurement instruments with satisfactory convergent validity. Cronbach’s alpha values ranged from 0.790 to 0.845, surpassing the acceptable threshold of 0.70 for internal consistency reliability (Field, 2013). The lowest CR value was 0.863, above the suggested 0.6 threshold (Hulland, 1999), and the lowest AVE value was 0.603, exceeding the recommended 0.4 (Anderson and Gerbing, 1988), confirming convergent validity and excellent internal consistency and reliability of the measures. The results also demonstrated adequate discriminant validity for all variables, supporting the reliability of the research scale (Chinomona and Chinomona, 2013).

Field (2013) states that discriminant validity refers to items measuring distinct concepts. Table 4 presents the results of the discriminant validity analysis. Discriminant validity was evaluated using the heterotrait–monotrait (HTMT) ratio (Table 4). From a more conservative standpoint, discriminant validity is considered achieved when the HTMT value is below 0.9 or 0.85 (Abaddi, 2025; Verkijika and De Wet, 2018; Neneh, 2019a, b). Table 4 indicates that the highest HTMT value obtained is 0.819, which is below the conservative threshold of 0.85. Therefore, all constructs meet the criteria for discriminant validity.

For PLS-SEM, common method bias (CMB) is detected using a full collinearity assessment approach (Kock, 2015). In this study, the researchers employed the variance inflation factor (VIF) values to evaluate collinearity, with a threshold of 3.3 established. VIF values below 3.3 indicated an absence of CMB, whereas values above 3.3 indicated its presence. Rather than reporting the collinearity issues directly, the researchers computed the VIF values, adhering to standard procedures in business research. Table 2 presents the results of the multicollinearity evaluation using VIF values. The findings reveal that all constructs had VIF values below 3.3 (Kock and Lynn, 2012), suggesting that CMB was not an issue in the investigation.

The researchers also evaluated the model fit using the Standardised Root Mean Square Residual (SRMR), which measures the average standardised residuals between the observed and hypothesised covariance matrices (Chen, 2007). A good fit for the study model is indicated by an SRMR value of less than 0.08, with a lower value signifying a better fit (Hu and Bentler, 1998). In this instance, the theoretical model’s SRMR was 0.06, indicating a good fit. Furthermore, the Chi-Square value was reported as 1,919.037, and the Normed Fit Index (NFI) was measured at 0.900, which met the recommended threshold for NFI (Afthanorhan, 2013). Table 5 presents the results for model fit.

Examining the coefficient of determination (R²) values of the endogenous constructs was conducted as part of the analysis in the study. These values further supported the model’s adequacy. The researchers examined the coefficient of determination (R²) values of the endogenous constructs as part of the analysis in the study. According to Schumacher et al. (2016), the R² value represents the percentage of variance in a variable that the independent variable groups can explain. Hair et al. (2019) suggest that R² values of 0.75, 0.5, and 0.25 can be considered substantial, moderate, and weak, respectively. The researchers reported the R² values for two constructs in the study: technopreneurship education and technopreneurship intention. The R² values for these constructs were 0.587 and 0.731, respectively. These values indicate that the developed model has moderate to substantial explanatory power, according to Hair et al. (2019).

In addition to R2 as a predictive criterion, Hair et al. (2019) suggest that researchers should also examine Q2 to evaluate the predictive relevance of the structural model. The predictive applicability of constructs should be positive and have values greater than zero (Hair et al., 2019). Q2, presented in Table 6, measures how an exogenous construct contributes to an endogenous latent construct. Q2 values can be small (0.02), medium (0.15), or large (0.35) to assess the size of the Q2 effect, as explained in Table 5. In the study, the Q2 values obtained were 0.446 for technopreneurship education and 0.415 for technopreneurship intention. These values fall within the required limit, indicating that the path model has adequate predictive relevance for the endogenous constructs.

According to Habtemaryam et al. (2025), the F-squared (f²) measure of effect size indicates the degree of correlation between a predictor and an endogenous variable in PLS-SEM. Cohen (1988) recommended using the F-squared statistic to assess the magnitude of impact in exploratory and predictive studies. An effect size f² ≥ 0.30, 0.30 < f² ≤ 0.50, and f² > 0.50 are regarded as representing weak, moderate, and strong effects, respectively (Bliwise, 2006). According to Table 6, the f² values for technopreneurship education and technopreneurship intention are deemed strong.

The PLS estimation path coefficient values and the item loadings for the research construct are shown in Figure 2.

Table 7 presents the proposed hypotheses, path coefficients, t-statistics, and the decision to reject or support each hypothesis. Chin (1998) indicates that t > 1.96 signifies a significant relationship and that higher path coefficients reflect stronger relationships among latent variables. Based on the results in Table 6, H1 (β = 0.428; t = 5.841), H2 (β = 0.376; t = 4.276), H3 (β = 0.638; t = 11.180), H4 (β = 0.477; t = 5.982), H5 (β = 0.485; t = 6.058), H6 (β = 0.325; t = 2.947), H7 (β = 0.612; t = 9.971), and H8 (β = 0.525; t = 7.932) are significantly supported, as the t-statistics exceed 1.96. Figure 2 presents the structural model, including path coefficients, p values, and R² values.

The statistical analysis indicated a significant positive impact of technopreneurial self-efficacy on technopreneurial education, aligning with previous research by Bateman and Crant (1993) and Hoque et al. (2017). Studies conducted in Malaysia and Taiwan have similarly demonstrated that students’ technopreneurial self-efficacy significantly influences their learning behaviours (Ainul et al., 2016; Chou et al., 2011). Technopreneurs often lack a background in business or entrepreneurial skills; however, individuals with high self-efficacy tend to adopt a more proactive approach to tasks and exhibit greater confidence in their ability to succeed (Bandura, 1997). This is consistent with the Theory of Planned Behaviour (TPB), which emphasises the role of individual motivational factors in shaping behaviour.

Enhancing technopreneurial self-efficacy among students could encourage their engagement with technopreneurial education and strengthen their entrepreneurial competencies. By fostering self-confidence and entrepreneurial skills within educational programmes, particularly for those lacking prior business experience, students can be better prepared for successful ventures.

Investigating key underlying factors, such as “knowledge stickiness” and “Gen Z literacy,” is essential. “Knowledge stickiness” refers to the retention, sharing, and application of knowledge within a specific context, while “Gen Z literacy” encompasses integrating technological expertise into real-world scenarios. Gen Z students are skilled at quickly acquiring and applying new information, and when technopreneurial education supports the retention of entrepreneurial skills and technological knowledge, it boosts their self-efficacy. A higher level of Gen Z literacy enables students to take greater ownership of their learning, further reinforcing their self-efficacy in technopreneurial ventures. Therefore, the positive effect of education on self-efficacy is not solely due to the provision of technical knowledge but also the effective transfer and retention of knowledge within the Gen Z cohort.

Research has shown that technopreneurial education significantly shapes technopreneurial intentions, suggesting that exposure to education focused on technology and entrepreneurship positively influences individuals’ aspirations to become technopreneurs. These findings are in line with previous studies. For instance, Machmud et al. (2020), in their study “Effect of self-efficacy ICT on technopreneurship intention of technopreneurial learning mediation: The case of the young generation in Indonesia”, emphasised that technopreneurial learning strongly impacts technopreneurship intention. Similarly, Rafiana (2024), in “Technopreneurship strategy to expand entrepreneurship career options for students in higher education”, found that incorporating entrepreneurship education into higher education curricula, such as through business incubators, significantly shapes students’ attitudes towards pursuing careers as technopreneurs. These findings align with existing theories, indicating that internal, external, and contextual factors (Rosdi et al., 2018; Nathalie et al., 2010) all influence technopreneurial intentions. Consequently, acquiring knowledge related to entrepreneurship and technology can profoundly affect an individual’s mindset, motivating them to pursue a technopreneurial career. This supports earlier research suggesting that contextual factors, such as educational programmes, enhance entrepreneurial intentions (Salhieh and Al-Abdallat, 2022).

Nonetheless, further investigation is necessary to examine the impact of “knowledge stickiness” on Gen Z literacy. For Gen Z, technological tools and platforms are integral to their daily lives, and they actively seek information that resonates with their interests. Therefore, the design of technopreneurial education must effectively utilise these tools to shape intentions. The learning process should be dynamic and immersive, ensuring that acquired knowledge is retained and applied meaningfully in entrepreneurial contexts. The application of knowledge is crucial for sustaining strong technopreneurial intentions. When technopreneurial education empowers students to connect theoretical knowledge with practical applications—such as through case studies, internships, or incubators—their intention to pursue technopreneurship is enhanced. Students with high levels of Gen Z literacy quickly recognise the relevance of technology in entrepreneurship, further bolstering their aspirations with a solid foundation in both business and technology.

These findings underscore the significance of technopreneurial education in cultivating an entrepreneurial mindset, particularly within technology-based enterprises. Engagement in programmes that combine entrepreneurship with technology enhances business acumen and promotes entrepreneurial ventures. Higher education curricula ought to integrate more technopreneurial learning opportunities, such as business incubators and mentoring schemes. Developing self-efficacy—the belief in one’s capacity to succeed in entrepreneurship—is essential for shaping technopreneurial intentions. Courses that bolster technical and entrepreneurial skills can elevate students’ confidence to embark on a technopreneurial career. External factors, including institutional support, access to resources, and exposure to role models, also significantly shape entrepreneurial intentions. Institutions can offer support and practical experiences to enhance students’ entrepreneurial confidence and aspirations. Technopreneurial intentions are influenced by individual ambitions as well as by educational and contextual elements. Establishing a supportive environment for learning and networking in technopreneurship can steer students towards innovative and entrepreneurial trajectories in technology. This outcome merits further investigation through the lens of “knowledge stickiness” and its impact on Gen Z literacy. For Gen Z, technological tools and platforms are integral to their daily lives, and they are predisposed to seek information that aligns with their interests. Hence, the manner in which technopreneurial education is structured to harness these tools will affect its efficacy in shaping intentions. The learning process needs to be dynamic and immersive, ensuring that the knowledge acquired is retained and can be applied meaningfully in entrepreneurial contexts. The practical application of knowledge is crucial for maintaining high technopreneurial intentions. The more technopreneurial education allows students to link theoretical concepts with practical applications (through case studies, internships, or incubators), the greater their inclination to pursue technopreneurship. As students with strong Gen Z literacy can swiftly understand the relevance of technology in entrepreneurship, their intentions are further enhanced by a robust foundation in both business and technology.

The analysis reveals that technopreneurship education significantly and positively mediates the relationship between technopreneurial self-efficacy and the intention to pursue technopreneurship. While prior studies on the role of technopreneurial education as a mediator in shaping technopreneurial intentions have been sparse (Hoque et al., 2017), our results align with their findings, demonstrating that technopreneurial education partially mediates the link between self-efficacy and entrepreneurial intention. This suggests that such educational experiences enhance individuals’ understanding of technopreneurship, boosting their self-confidence and skills, which in turn strengthens their intention to embark on a technopreneurial venture (Kakava and Fields, 2017; Liu et al., 2019; Suleiman, 2021). These findings imply that providing robust technopreneurial education can significantly influence individuals’ decisions to pursue entrepreneurial ventures. By improving self-efficacy and competence, education increases confidence and encourages action, ultimately driving higher technopreneurship intentions. This underscores the importance of integrating educational programmes that foster both practical and theoretical knowledge to nurture future technopreneurs. For Gen Z students, these results emphasise the importance of engaging with technopreneurial education to develop competence and confidence. The findings suggest that structured learning experiences, particularly those that integrate technology and entrepreneurship, can encourage students to start their own ventures. Thus, investing time in technopreneurial education could be a game-changer for Gen Z individuals aspiring to become successful technopreneurs.

Technological optimism has been found to positively and significantly impact technopreneurial intention. This suggests that optimistic individuals about technology tend to have favourable attitudes towards technopreneurship. This could stem from their familiarity with technology and their willingness to embrace new and innovative experiences (Maziriri et al., 2023). Such individuals view technology as a transformative force capable of creating ground-breaking products and services, thereby driving the success of technopreneurial ventures. Furthermore, the significant impact of technological optimism highlights that attitudes towards technology play a crucial role in shaping technopreneurial intention. Individuals with more positive attitudes are more likely to express an intention to engage in technopreneurship. This underscores the importance of understanding and promoting technological optimism in fostering the adoption of technopreneurship within a specific group.

This study makes a valuable and unique contribution to the field by extending the well-established Theory of Planned Behaviour (TPB) and the Generation Cohort Theory (GCT). By exploring the moderating effects of technological optimism and technopreneurial education, the study ventures into uncharted territory regarding technopreneurship intentions among Generation Z students in South Africa. Including technological optimism as a moderating factor provides insights into technopreneurial intentions influenced by technopreneurial self-efficacy. Similarly, the investigation of technopreneurial education as a moderator examines the technopreneurial knowledge and skills of Generation Z students in embracing technopreneurship. These distinct moderating roles illuminate the intricate dynamics of Generation Z students in South Africa, enriching our understanding of the factors shaping the adoption of technopreneurship among students.

This study examined the moderating role of technopreneurial education in the relationship between technopreneurial self-efficacy and the intention to engage in technopreneurship. The analysis revealed that technopreneurial education significantly and positively influences the connection between technopreneurial self-efficacy and technopreneurship intention. Given the existing research gaps regarding this moderating effect (Anggraini and Handayati, 2023; Maziriri et al., 2024a, b; Shah et al., 2020; Kee et al., 2017), this study offers new theoretical insights by enhancing our understanding of how technopreneurial education can shape intentions in technopreneurship. It is crucial to comprehend the interaction between the moderating variable (technopreneurial education) and the relationship between technopreneurial self-efficacy and technopreneurship intention. The practical implications of these findings suggest that technopreneurs who enhance their knowledge and skills through education or training are likely to boost their self-efficacy and cultivate a stronger intention to engage in technopreneurship. Consequently, their aspiration to pursue a career in technopreneurship is likely to be reinforced.

This study found that technological optimism significantly moderates the relationship between technopreneurial education and the intention to pursue technopreneurship. Empirical studies support these findings. For example, Kraus et al. (2019) argue that technological optimism enhances entrepreneurs’ ability to overcome barriers and leverage digital opportunities, thereby facilitating the execution of entrepreneurial intentions. Similarly, Venkatesh et al. (2003) highlight that positive perceptions of technology contribute to greater acceptance and utilisation, which are essential for entrepreneurial success in the digital sphere. The results indicate that technological optimism promotes a culture of continuous learning and adaptability, which is vital for technopreneurs navigating a volatile environment. Given the gaps in research regarding the moderating role of technological optimism in the connection between technopreneurial education and intention, this study offers new theoretical insights by identifying technological optimism as a crucial factor influencing Generation Z students’ inclination towards technopreneurship. When Generation Z students are optimistic about the future of technology, they are more likely to stay informed about the latest trends and developments, integrating these into their entrepreneurial endeavours. This positive outlook complements technopreneurial education by encouraging students to rely on the knowledge they have gained and remain flexible and innovative. Consequently, their intention to pursue technopreneurship is strengthened, as they feel equipped to continuously improve and adapt their strategies in response to technological progress.

The statistical analysis revealed that technological optimism either moderates or reinforces the relationship between technopreneurial self-efficacy and the intention to engage in technopreneurship. These findings suggest that technological optimism enhances the belief that technology can provide the necessary tools and platforms to turn innovative ideas into reality. Essentially, individuals with high technopreneurial self-efficacy – confidence in their ability to excel in entrepreneurial tasks – find their belief bolstered by technological optimism, which underscores the potential and availability of technology. Given the existing gaps in research regarding the moderating impact of technological optimism on the relationship between technopreneurial self-efficacy and the intention to pursue technopreneurship, this study contributes fresh insights to the literature. For instance, technological optimism acts as a catalyst, boosting the confidence of potential Generation Z technopreneurs that ongoing technological advancements will support their technological skills and innovative ideas. Consequently, Generation Z students are more likely to pursue technopreneurship, as a positive outlook on the technological landscape reinforces their enhanced self-efficacy.

The study makes significant contributions to the academic literature on technopreneurship, particularly focusing on Generation Z. Firstly, it highlights the positive and substantial impact of technopreneurial self-efficacy (TSE) on technopreneurial education (TE) and technopreneurship intention (TI), emphasising the essential role of self-efficacy in entrepreneurial studies. This is consistent with Bandura’s (1977) theory of self-efficacy, which suggests that individuals with higher confidence in their abilities are more likely to engage in and succeed in entrepreneurial activities.

This study extends the Theory of Planned Behaviour (TPB) and Technology Acceptance Model (TAM) by incorporating three variables: technopreneurial self-efficacy, technopreneurial education, and technological optimism, to enhance technopreneurship intention (behavioural intention as per the TPB). The application of these theories reveals that the TPB and TAM can elucidate how technopreneurial self-efficacy, education, and technological optimism collectively influence individuals’ intentions to engage in tech-driven entrepreneurship. Adopting the TAM perspective, this study presents a distinctive model through which technopreneurial self-efficacy, technopreneurial education, and technological optimism impact technopreneurship intention among Generation Z students in South Africa. Additionally, it examines the moderating effect of technological optimism on the relationship between technopreneurial self-efficacy, education, and technopreneurship intention. The research underscores the importance of self-belief in one’s technopreneurial abilities as a determinant of success in technopreneurial education and the fostering of technopreneurial intentions. Aligning with the TAM, which posits that perceived ease of use and usefulness drive technology adoption, technopreneurial self-efficacy reinforces the argument that confidence in one’s ability to navigate technological and entrepreneurial challenges directly influences outcomes in technopreneurship. Moreover, the study bridges a critical gap by emphasising the psychological construct of technopreneurial self-efficacy within the broader framework of entrepreneurial studies, particularly in the technology-driven domain. Furthermore, the study bolsters the Theory of Planned Behaviour (TPB) and Technology Acceptance Model (TAM) by incorporating a behavioural and attitudinal perspective, emphasising that self-efficacy facilitates technology acceptance and propels entrepreneurial endeavours. Future research could further investigate how varying levels of technological optimism among different cohorts affect entrepreneurial intentions and outcomes, potentially leading to more targeted educational interventions.

The study enhances the perspectives of Generation Cohort Theory (GCT), which considers the relationships between age, general experiences, and value systems across various age groups. Applying Generation Cohort Theory (GCT) to this study elucidates how technopreneurs’ self-efficacy, education, and technological optimism influence individuals’ intentions to engage in tech-driven entrepreneurship. Predicting historical events and societal social changes based on individuals’ values, beliefs, attitudes, and inclinations helps explain why people share similar life paths due to overarching values and traits (Cheung et al., 2023). Each generation exhibits distinct spending patterns, consuming behaviours, and purchasing habits, as demonstrated in the current study, which examines how technopreneurial self-efficacy, education, and technological optimism collectively impact Generation Z’s intentions in South Africa towards engaging in tech-driven entrepreneurship. The study also substantiates the mediating and moderating roles of technopreneurship education (TE) between technopreneurial self-efficacy (TSE) and intentions (TI), resonating with prior research from scholars like Fayolle and Gailly (2015) and Fayolle et al. (2006) that emphasises the significance of education in shaping entrepreneurial intentions.

This study has significant practical and managerial implications for educational institutions and policymakers aiming to foster technopreneurship among Generation Z. The findings emphasise critical managerial and educational implications, which entail the integration of technologies such as AI, VR, metaverse and live streaming into the curriculum. These tools can enhance learning experiences and align with the demonstrated positive impact of technopreneurial self-efficacy on technopreneurial education. For example, VR and Metaverse facilitate immersive simulations for business planning and prototyping while, AI uses intelligent tools for personalised learning and decision-making support. In addition, livestreaming enables real-time engagement with industry experts and technopreneurial mentors. Business and educational institutions should emphasise the opportunities in emerging technological domains, such as starting ventures in metaverse-based economies or developing AI-driven solutions. Highlighting such possibilities can inspire students and potential entrepreneurs to pursue careers in technopreneurship. Moreover, businesses should identify and nurture leaders with high technopreneurial self-efficacy. These individuals will likely pioneer innovative projects in fields like AI, VR, and live streaming, driving technological development within the business. Educational institutions should ensure equitable access to technologies that foster technopreneurial self-efficacy. This includes subsidised tools, free training resources and accessible platforms for underprivileged learners, ensuring that technological development benefits a diverse range of aspiring technopreneurs. By aligning technopreneurial self-efficacy with advancements in AI, VR, metaverse, and live streaming, managers can capitalise on the intersection of education, innovation, and entrepreneurship to drive sustainable growth and competitive advantage in the digital economy.

In conclusion, this study provides a comprehensive examination of the key factors that influence technology-driven entrepreneurial intentions, particularly focusing on the roles of technopreneurial self-efficacy, technopreneurial education, and technological optimism. The findings underscore the significance of technopreneurial self-efficacy in fostering confidence and resilience among aspiring technopreneurs, suggesting that individuals who believe in their ability to succeed are more likely to pursue entrepreneurial ventures in the tech sector. Furthermore, the study highlights the essential role of technopreneurship education in equipping Generation Z with the necessary knowledge, skills, and practical tools to thrive in the evolving digital economy. By providing a structured learning environment, education helps bridge the gap between self-efficacy and entrepreneurial intentions. Additionally, the study reveals how technological optimism, or a positive outlook on emerging technologies, plays a crucial role in shaping students’ perceptions of technological opportunities, motivating them to view tech entrepreneurship as a viable and promising career path. Collectively, these interconnected factors create a robust framework that offers deeper insights into how the Generation Z cohort navigates the technopreneurial journey, with implications for educational practices and policy initiatives to foster a generation of innovative, technology-driven entrepreneurs.

Despite the study’s contributions, the study’s limitations should not be understated. First, the research relies on self-reported data, which may introduce biases such as social desirability or overestimating entrepreneurial intentions. Second, the study is context-dependent, focusing on a specific geographical or institutional setting, which may limit the generalisability of the findings to broader populations. Third, the cross-sectional nature of the research does not account for the dynamic evolution of technopreneurial intentions over time, restricting insights into the long-term impact of education and self-efficacy. Lastly, while the study examines key predictors of technology-driven entrepreneurial intentions, other potential factors, such as access to funding and market conditions, were not included in the analysis.

Future research can address these limitations by adopting a longitudinal approach to track changes in technopreneurial intentions over time, offering deeper insights into how education, self-efficacy, and optimism shape entrepreneurial behaviour. Expanding the study across diverse cultural and economic contexts can enhance generalisability and uncover region-specific drivers of technopreneurial success. Additionally, integrating qualitative methods, such as interviews and case studies, can provide richer narratives on the lived experiences of technopreneurs. Further exploring external factors, including policy interventions, investment climate, and industry collaborations, can offer a more holistic understanding of the technopreneurial environment. Finally, incorporating experimental or intervention-based studies to assess the efficacy of tailored educational programs and mentorship initiatives can refine strategies for cultivating tech-driven entrepreneurship.

The lead author, Professor Eugine Tafadzwa Maziriri, would like to express his heartfelt appreciation to the Humanities and Social Science Research Ethics Committee at the University of the Western Cape for granting ethical clearance and approving the research methodology (HSSREC Reference Number: HS24/10/48). We are truly thankful to all the respondents who generously dedicated their time to complete the questionnaire. A special word of thanks goes to Loveness Kandawasvika for her exceptional work in language editing. We are also deeply grateful for the insightful feedback and valuable suggestions provided by the anonymous reviewers, as well as the Editor-in-Chief of Management & Sustainability: An Arab Review, whose guidance greatly enhanced the quality of this paper.