Technological Innovations and Employees’ Employability Development Challenges in Mauritius. The Mediating Effect of Career Anchors

Historically, technological innovations have been a catalyst for change in the workplace. In the context of the Fourth Industrial Revolution (Schwab, 2016), characterized by rapid advancements in AI, robotics, and digital technologies, the relationship between technology and employment has become increasingly complex. McKinsey Global Institute (2017) points out that while technological advancements may lead to job displacement in certain sectors, they also create new opportunities that require different skill sets. This duality emphasizes the importance of addressing employees' employability challenges to ensure a workforce that is both available and capable of meeting the demands of the evolving job market (Labor Market Survey, 2024). Based on these arguments, we infer that:

H1: There is a positive relationship between technological innovations and employees’ employability development.

Digital transformation, broadly defined as the significant organizational change driven by the adoption of digital technologies to enhance performance and value creation, remains a pivotal force in technological innovation (Verhoef et al., 2021). Recent studies emphasize that governments increasingly prioritize Information and Communication Technologies (ICT) and Artificial Intelligence (AI) as critical components for economic diversification and employment generation, particularly targeting youth workforce integration (Neffati & Jbir, 2024). While digitalization opens new career pathways in emerging domains such as AI, data analytics, and cybersecurity, it presents challenges, including workforce displacement and the urgent need for reskilling and upskilling (McKinsey, 2024). Addressing these challenges requires a commitment to lifelong learning and fostering a culture of continuous professional development within organizations (Jaldemark et al., 2021). Moreover, strengthening partnerships between educational institutions and industry is essential to ensure that curricula remain relevant and that students gain practical experience through internships and apprenticeships, thereby better preparing them for the dynamic labor market (Akash & Suganya, 2024).

Employability is a multifaceted concept involving the skills, knowledge, and attributes necessary for individuals to obtain and sustain employment in a rapidly evolving job market. Its key dimensions influence career success and adaptability, especially as technological advancements reshape industries and workplace demands. This section evokes the key dimensions of employability, providing insights into how they influence career success and adaptability in the face of technological innovations.

The growing impact of technological innovations such as automation, robotics, and artificial intelligence is fundamentally reshaping career development, as emphasized by Veloso et al. (2020). These advancements affect not only routine tasks but also intellectual and creative roles, underscoring the need for a systematic analysis of evolving career dynamics. Veloso et al. (2020) also point out that while millennials value skill development through new technologies, they often underestimate the emotional stress caused by rapid change. Furthermore, organizations must adapt their human resource management strategies to foster cross-disciplinary skills like communication, problem-solving, and adaptability, which are crucial for navigating the complexities introduced by technological progress. Recent studies by Veloso et al. (2020), as well as Cabot and Gagnon (2021), emphasize career anchors-individuals’ intrinsic motivations-as critical to understanding professionals’ adaptation to technological shifts. This gap underlines the need to investigate how technological innovations reshape employability and career anchors, offering insights to help employees align their motivations with evolving career demands and enabling organizations to better support workforce development amid rapid technological change. Based on these arguments, we infer that:

H7: There is a positive relationship between technological innovations and career anchors. H8: There is a positive relationship between career anchors and the employability development of employees.

The sampling frame for this study consists of professionals from various sectors within the Mauritian economy. Of the 217 respondents, a significant proportion—58%— represents the IT industry. This emphasis reflects the IT sector's leading role in technological innovation and its direct relevance to employability issues. Participants were selected based on their direct experience or exposure to technological changes and their effects on skill requirements and employment prospects. Sampling was conducted electronically, enabling the recruitment of a diverse group of professionals across different industries via Google Forms.

A structured, quantitative questionnaire was developed and distributed electronically using Google Forms. The survey targeted individuals with relevant knowledge or experience concerning technological innovations and their impact on skills and employability. To ensure the reliability and validity of the instrument, a pilot study was first conducted with a small subset of participants. Feedback from this group was used to refine the final questionnaire.

Upon completion of data collection, responses were processed and analyzed statistically using SPSS. Descriptive statistical methods summarized the demographic characteristics and key variables, while regression analysis was employed to assess potential relationships between technological innovations and employees’ perceptions of employability.

The primary tool for data collection was a structured questionnaire, which included both demographic items and Likert-scale statements that measured perceptions of technological innovation, skill development, and employability. The Likert items enabled respondents to express the extent of their agreement or disagreement with various statements about workplace technological change and its consequences for their skills and career prospects.

The demographics presented in Table 1 provide a comprehensive overview of the respondent population sample, with a focus on key variables such as age group, gender, marital status, highest qualifications, and work experience.

Table 2 introduces the relationships between technological innovations score and career anchors while Table 3 shows the relationships between employability score and career anchors.

The correlation analysis between career anchors and technological innovations reveals actionable insights for aligning career growth with workplace technological shifts. Technical/Functional Expertise and Entrepreneurial Creativity anchors show strong positive correlations with technological adaptability (e.g., r=0.520 and r=0.550, respectively), suggesting individuals in these roles thrive in tech-driven environments. In contrast, Security/Stability anchors exhibit weaker correlations (r=0.180), indicating potential resistance to disruptive innovations.

Meanwhile, the General Management and Autonomy/Independence anchors display moderate correlations with technological innovation scores (r=0.310 and r=0.400, respectively), implying that professionals emphasizing leadership and self-direction are positively, though less strongly, associated with technological adaptability. Notably, the Pure Challenge anchor (r=0.450) also aligns closely with innovation, suggesting that individuals drawn to overcoming complex problems can be key drivers of tech adoption. Conversely, anchors such as Service/Dedication (r=0.290), Lifestyle (r=0.200), and International Mobility (r=0.360) reveal lower to moderate relationships with technological innovation. This pattern indicates that while these individuals are not averse to technology, their primary motivations may lie outside of technological advancement, focusing instead on work-life balance, mobility, or service motivations.

The correlation analysis between career anchors and employability score reveals important insights for aligning individual career priorities with overall career resilience and adaptability. Technical and Functional Expertise (r=0.505) and Pure Challenge (r=0.440) anchors demonstrate strong positive correlations with employability, indicating that individuals who prioritize technical skills or thrive on overcoming difficult problems are particularly well-positioned to remain employable in a changing job market. Entrepreneurial Creativity (r=0.375) and Autonomy/Independence (r=0.389) also display moderately strong correlations with employability. This suggests that those inclined toward innovation, risk-taking, and self-direction are similarly adept at maintaining their career relevance over time. The General Management anchor (r=0.292) shows a moderate relationship as well, indicating that broad leadership competencies provide a notable, if less pronounced, boost to employability.

In contrast, the Security/Stability anchor (r=0.174*) presents the weakest significant correlation, implying that individuals primarily motivated by job security or predictability may face greater challenges in sustaining employability amid labor market shifts or organizational change. Similarly, the Lifestyle anchor (r=0.106) and International Mobility (r=0.178) registers only weak associations, reflecting that while flexible work arrangements and mobility offer situational advantages, they’re less directly tied to employability overall. Service/Dedication (r=0.312) also has a positive but moderate correlation with employability, signaling that a commitment to serving others is valuable but may not be as directly transferable to new roles as technical, managerial, or entrepreneurial skills.

To test the proposed hypotheses, this study utilized regression analysis to examine the relationships among technological innovations, career anchors, and employees’ employability development across various sectors in Mauritius. Additionally, the means of the influencing factors were calculated and subjected to ANOVA to assess their impact on employability outcomes. As presented in the following Table 5, the ANOVA results reveal a significant value of 0.000 (p=0.000), which is well below the conventional threshold of 0.05. This indicates a statistically significant difference in the mean scores of employability development challenges across different levels of exposure to technological innovations.

The model demonstrated a strong overall fit, with an R value of 0.766, indicating a substantial correlation between the predictors and the dependent variable. The R² of 0.586 suggests that approximately 58.6% of the variance in employability development is explained by the combined independent variables. The adjusted R² of 0.566 confirms the model’s robustness after accounting for the number of predictors. The Durbin-Watson statistic of 1.774 indicates no significant autocorrelation in the residuals. The ANOVA results further validate the model’s significance (F=29.164, p < 0.001), confirming that the set of predictors reliably explains variations in employability development. Examining individual predictors, several variables showed statistically significant effects (Table 4).

Digitalization and artificial intelligence (β=0.296, p=0.001) and technology and functional expertise (β=0.495, p=0.001) emerged as the strongest positive contributors, highlighting the critical role of advanced technological skills in enhancing employability. Technological innovations at the workplace (β=0.142, p=0.026), implemented technology innovations (β=0.153, p=0.008), career anchors such as autonomy and independence (β=0.267, p=0.001), entrepreneur anchor (β=0.154, p=0.018), and pure challenge (β=0.209, p=0.001) also positively influenced employability, emphasizing the importance of both technological adaptation and career orientation factors.

In contrast, number of years of work experience (β=-0.117, p=0.035), the remaining types of career anchors like general management (β=-0.206, p=0.002), and international mobility (β=-0.225, p < 0.001) were negatively associated with employability, suggesting potential challenges related to experience level, managerial roles, and geographic mobility in the context of technological innovation.

Based on Baron and Kenny (1986), this study rigorously examined whether career anchors mediate the dynamic relationship between cutting-edge technological innovations, specifically artificial intelligence and digitalization, and the development of employees’ employability.

The positive coefficient (0.1918) shows that there is a positive relationship between digitalization and artificial intelligence and employability, and this is statistically significant with a p-value (0.001) (see Tables 5 and 6).

The positive coefficient (0.2119) shows that there is a positive relationship between digitalization and artificial intelligence and the career anchor (mediator variable), and the mediator variable is statistically significant (0.001) (Table 7).

The results reveal a significant direct positive effect of digitalization and AI on employability (coefficient=0.1918, p=0.001), indicating that advancements in these technologies enhance employability outcomes. Additionally, digitalization and AI significantly predict career anchors (coefficient=0.2119, p=0.001), suggesting that increased technological adoption strengthens employees’ career orientations.

The indirect effects through career anchors were examined across several employability dimensions. Career anchors showed a significant positive mediation effect on work-life balance (coefficient=0.3012, p<0.001), anticipation and optimization (coefficient=0.1041, p=0.001), and personal flexibility (coefficient=0.1975, p=0.001), highlighting how strong career anchors enable employees to better manage work-life integration, anticipate changes, and adapt flexibly to technological shifts. Conversely, a significant negative mediation effect was observed on occupational expertise (coefficient=-0.2008, p=0.002), implying that stronger career anchors may lead to reduced focus on specific technical skills amidst rapid technological change. No significant mediation effect was found for collaborative spirit (p=0.423), indicating that career anchors do not substantially influence this dimension with digitalization and AI.

Overall, these findings demonstrate that career anchors partially mediate the influence of digitalization and AI on employability development, with nuanced effects across different employability components. This underscores the importance of supporting employees’ career orientations to maximize the benefits of technological innovations in Mauritius’s evolving labor market.

The use of the Sobel Test after applying Baron & Kenny's model is a strategic choice that enhances the rigor and clarity of the mediation analysis by providing a focused assessment of indirect effects. It helps to verify the significance of the mediation effect by statistically testing whether the indirect path from the independent variable (employability) to the dependent variable (technological innovations) through the mediator (e.g., career anchor) is different from zero, thereby confirming whether the mediator carries the influence of the predictor to the outcome beyond mere correlation (Table 8).

The coefficient for technological innovations is 0.1627, which means that for every one-unit increase in technological innovation, there is an expected increase of 0.1627 units in career (Table 9).

The coefficient for career anchor is 0.0437987, indicating that for every one-unit increase in career anchor, employability increases by approximately 0.0438 units. The coefficient for technological innovations is 0.1846936, meaning that for every one-unit increase in technological innovation, employability increases by about 0.1847 units.