A Study of the Relationship Between Servant Leadership, Micro-Aggression and Intention to Leave in IT Sector: Employee Resilience as Mediating Variable

A Study of the Relationship Between Servant Leadership, Micro-Aggression and Intention to Leave in IT Sector: Employee Resilience as Mediating Variable

Mohamed Nazhif bin Ramlan, Nursaadatun Nisak Ahmad, Murni Zarina Mohamed Razali, Azlina Hanif

Universiti Teknologi MARA (UiTM), Malaysia

DOI: https://dx.doi.org/10.47772/IJRISS.2024.8110079

Received: 17 November 2024; Accepted: 21 November 2024; Published: 04 December 2024

ABSTRACT

In Malaysia’s rapidly evolving Information Technology (IT) industry, marked by increasing cyber threats and a scarcity of skilled cybersecurity professionals, this study explores the intention to leave among IT professionals. At the intersection of servant leadership, microaggressions, and employee resilience, it aims to unravel these factors’ relationships and their collective impact on turnover intentions. Emphasizing the lack of studies on race- based microaggressions, particular attention is given to microaggressions as workplace adversity. The research examines servant leadership, microaggressions, and employee resilience, focusing on cybersecurity companies in the Klang Valley region, capturing IT industry dynamics in Malaysia. Using Partial Least Square – Structural Equation Modeling (PLS-SEM), survey data from 385 IT professionals in Klang Valley was analyzed, revealing significant relationships: a positive relationship between servant leadership and employee resilience, a negative relationship between employee resilience and intention to leave, a negative relationship between servant leadership and intention to leave, and a positive link between microaggressions and intention to leave. Additionally, employee resilience was found to complementarily mediate the relationship between servant leadership and intention to leave. However, the hypothesized negative relationship between microaggressions and employee resilience, and the mediation effect of employee resilience between microaggressions and intention to leave, were not supported. The study also utilized PLS Predict to show that the variables can predict future research outcomes. Theoretically, this study enriches understanding by affirming servant leadership’s significance in explaining the nexus between employee resilience and intention to leave. Practically, by unraveling these associations, the research aims to provide organizations with insights to mitigate high turnover rates, potentially reducing hiring costs, training expenses, and overall operational outlays in the IT industry.

Keywords: Servant Leadership, Microaggression, Employee Resilience, Intention to Leave

INTRODUCTION

In the modern era, the term “Information Technology” (IT) has become widely used to refer to the utilization of computers and networks within the context of business operations [1]. It encompasses various applications such as data generation, manipulation, storage, retrieval, transmission, handling, exchange, analysis, and security in electronic formats. Furthermore, IT serves as an umbrella term encompassing telecommunication equipment, software, cyber security, the internet, and television [1].

The significance of IT extends beyond business environments and extends to personal and private spheres as well. Particularly with the increasing sophistication of cybercrime, ensuring the safety of personal and business data is of paramount importance while browsing the internet or engaging with email communications. IT support plays a vital role in addressing technical challenges that may arise, ensuring the use of up-to-date software, and identifying the most effective tools for completing tasks efficiently. In the IT business, turnover rates are reported to be high, leading to a skills deficit caused by the imbalanced rise of technology and skill development [2]. This disparity between technological advancement and human capital capabilities may result in a scarcity of talent capable of supporting future technology development, particularly in the context of Industrial Revolution 4.0 (IR 4.0) [3]. Employee retention, specifically intention to leave, has garnered significant attention from researchers due to its potential consequences, such as increased absenteeism and decreased job engagement [4]. The pronounced turnover rates characteristic of the IT sector not only pose inherent risks to organizational stability but also underscore the industry’s perpetual demand for updated skills. As employees grapple with the dynamic nature of their roles, the intention to leave manifests as a nuanced response, indicative of the pursuit of opportunities aligned with emerging technologies and conducive work environments.

In the domain of leadership within the IT industry, the ascendancy of servant leadership emerges as a noteworthy phenomenon [5]. Defined by a distinct focus on serving others and cultivating a supportive work milieu, servant leadership is posited as a potential mitigator of intentions to leave. Organizations where in leaders prioritize empathetic engagement, support mechanisms, and foster employee growth witness a correlated reduction in the propensity of individuals to explore alternative career paths [6].

Further complicating this intricate landscape, microaggressions contribute to the nuanced understanding of intention to leave in the IT sector. Manifested as subtle yet impactful discriminatory actions or remarks, microaggressions amplify challenges within the workplace. Individuals subjected to these subtle forms of discrimination often experience a sense of alienation and diminished well- being, exerting a discernible influence on their career decisions. Consequently, a nuanced comprehension of the prevalence and consequences of microaggressions becomes imperative in addressing the underlying triggers that potentially catalyse the intention to leave.

Employee resilience serves as a buffer against the potential negative outcomes associated with workplace challenges [8]. For instance, when confronted with microaggressions, resilience individuals are more adept at managing the emotional toll and maintaining a sense of self-worth. Moreover, resilience employees are better positioned to cope with the uncertainty and demands inherent in the IT industry, fostering a work environment where challenges become opportunities for growth rather than impediments to success.

In summation, the intention to leave within the IT sector emerges as a complex phenomenon, intricately woven into the interplay of leadership styles, workplace dynamics, the ever-evolving technological landscape, and the resilience of employees. While servant leadership surfaces as a potential mitigating force, the omnipresent specters of microaggressions underscore the imperative for organizations to systematically address these challenges and cultivate environments that holistically promote employee well-being and retention.

Hypothesis Development

Servant Leadership and Employee Resilience

In the evolving landscape of organizational leadership, the concept of servant leadership has garnered considerable attention and recognition. Servant leadership is characterized by leaders who prioritize the growth and well-being of their team members, foster a culture of collaboration, and exhibit empathy and ethical behavior [9]. This leadership philosophy represents a shift from traditional hierarchical models toward a more supportive and empowering approach to leadership [10].

Employee resilience, on the other hand, is a vital psychological resource that enables individuals to navigate adversity, adapt to challenges, and maintain their psychological well-being in the workplace [11]. It is crucial in helping employees cope with stress, overcome obstacles, and thrive in their roles. In this context, the researcher seeks to explore the relationship between servant leadership and employee resilience. Understanding how servant leadership practices impact employee resilience is integral to comprehending the dynamics of leadership in contemporary organizations. Thus, the following hypothesis is suggested:

H1: There is a positive relationship between servant leadership and employee resilience.

Microaggression and Employee Resilience

Organizations are increasingly recognizing the significance of retaining talented employees and nurturing their overall well-being. One critical aspect of an employee’s capacity to thrive in such environments is their ability to withstand adversity, adapt to challenges, and sustain their psychological well-being qualities collectively referred to as employee resilience [11]. In this context, microaggressions, which are subtle, often unintentional, discriminatory acts directed towards individuals based on their personal characteristics, have gained attention. Microaggressions can manifest as subtle insults, dismissive behavior, or belittling actions, creating an emotionally taxing environment for those who experience them [12]. These experiences can influence an employee’s overall well-being and potentially affect their intention to leave the organization [13].

This research seeks to delve into the complex relationship between microaggression and employee resilience. Understanding how microaggressions impact employee resilience is integral to comprehending the challenges posed by subtle forms of discrimination in the workplace. Thus, the following hypothesis is suggested:

H2: There is a negative relationship between microaggression and employee resilience.

Servant Leadership and Intention to Leave

Servant leadership was a leadership philosophy that emphasizes the well-being and development of employees. It was characterized by leaders who prioritize the needs of their followers, foster a collaborative and supportive work environment, and empower employees to reach their full potential [14]. Servant leadership was often associated with ethical and transformational leadership, and it has gained recognition for its potential to enhance employee satisfaction and commitment [15 – 16].

The reasoning behind this hypothesis was that when employees perceive their leaders as caring, supportive, and focused on their development, they were more likely to feel valued and committed to the organization, thus reducing their intention to leave. Thus, the following hypothesis was suggested:

H3: Servant Leadership was negatively related to employee intention to leave.

Microaggression and Intention to Leave

In today’s diverse and inclusive workplaces, the dynamics of interpersonal interactions and their impact on employees’ intentions to stay or leave have garnered increasing attention. One aspect of these interactions that has come under scrutiny was microaggression, which refers to subtle, often unintentional, discriminatory behaviours or comments that convey bias or insensitivity based on an individual’s characteristics such as race, gender, or ethnicity [17].

The ensuing data analysis will offer insights into the strength and statistical significance of this relationship and its implications for both employees and organizations aiming to create more inclusive work environments. Thus, the following hypothesis was suggested:

H4: Microaggression was positively related to employee intention to leave.

Employee Resilience and Intention to Leave

In today’s fast-paced and ever-changing business landscape, employee retention has become a top priority for organizations across industries. With the constant threat of talent shortages, skill gaps, and economic uncertainty, employers must focus on creating a work environment that fosters engagement, productivity, and long-term loyalty among their workforce [18]. One key factor that can significantly impact employee retention was resilience – the ability of individuals to bounce back from setbacks, adapt to new situations, and maintain their well-being in the face of adversity [19].

This research will explore the relationship between employee resilience and intention to leave. Understanding how employee resilience impact intention to leave was integral to comprehending the challenges in the current workplace. Thus, the following hypothesis was suggested:

H5: There is a negative relationship between employee resilience and intention to leave.

Employee Resilience mediates relationship between Servant Leadership and Intention to Leave

In the contemporary landscape of the workplace, characterized by its dynamism and ever-evolving challenges, the retention of talented employees and the enhancement of their overall well-being were of paramount importance to organizations [20]. In this context, the ability of employees to endure stress, adapt to adversity, and maintain their psychological well-being, known as employee resilience, has garnered increasing significance [19]. Employee resilience was a key factor that enables individuals to navigate the multifaceted demands of the modern work environment [21].

Within this context, this research endeavours to scrutinize the intricate relationships among employee resilience, servant leadership, and the intention to leave. It was important to explore how servant leadership influences employees’ intentions to leave and how employee resilience may mediate this relationship. Thus, the following hypothesis was suggested:

H6: Employee resilience mediates the relationship between servant leadership and the intention to leave.

Employee Resilience mediates relationship between Microaggression and Intention to Leave

In today’s diverse and inclusive work environments, organizations were increasingly recognizing the significance of retaining talented employees and nurturing their overall well-being. One critical aspect of an employee’s capacity to thrive in such environments was their ability to withstand adversity, adapt to challenges, and sustain their psychological well-being qualities collectively referred to as employee resilience [21]. In this context, microaggressions, which were subtle, often unintentional, discriminatory acts directed towards individuals based on their personal characteristics, have gained attention [22 – 23]. Microaggressions can manifest as subtle insults, dismissive behaviour, or belittling actions, creating an emotionally taxing environment for those who experience them [24]. These experiences can influence an employee’s overall well-being and potentially affect their intention to leave the organization [25].

This research seeks to delve into the complex relationship among employee resilience, microaggression, and the intention to leave, with a focus on understanding how employee resilience may mediate the impact of microaggressions on employees’ intentions to leave their organizations. Thus, the following hypothesis was suggested:

H7: Employee resilience mediates the relationship between microaggression and the intention to leave.

RESEARCH METHODOLOGY

Research Design

In this study, the research setting is characterized by a selection of diverse cybersecurity businesses, encompassing a rich tapestry of organizations within the cybersecurity sector. These organizations specialize in various facets of cybersecurity, including cybersecurity firms, IT service providers, technology consulting companies, and software development enterprises. This careful selection of multiple cybersecurity businesses is deliberate and strategic, aiming to provide a multifaceted and comprehensive perspective on the research objectives within the dynamic and ever-evolving field of cybersecurity.

The research design serves as the methodological framework that underpins the empirical exploration [26]. It dictates the approach to data collection, analysis, and the interpretation of this research questions and hypotheses. In the pursuit of understanding the relationships between servant leadership, microaggression, employee resilience and intention to leave. The researcher employs a quantitative cross-sectional survey design, which has been shown to be particularly efficacious in capturing a singular point in time. This quantitative cross-sectional approach enables the researcher to examine a comprehensive snapshot of the dynamics in the chosen organizational contexts [27]. It offers a glimpse into how these dynamics are manifested at a given moment, allowing the researcher to assess the interplay of variables while considering their mediating effects on intention to leave. The cross-sectional design facilitates the simultaneous examination of multiple variables and provides a foundation for statistical analysis [28].

The utilization of surveys as a data collection tool is informed by the advantages they confer in quantifying and measuring the variables under investigation. Surveys have been established as valuable instruments in social science research [29]. Through structured questionnaires, the researcher captures the perceptions and experiences of employees in the context of servant leadership (28 Items) by [30], microaggression (7-items) by [31], employee resilience (9-items) by [32], and their intentions to leave (3-items) by [33]. The structured nature of surveys enables the researcher to gather standardized data that can be analyzed systematically, thus ensuring robust and comparable findings. Given the complexity of the relationships under investigation and the need for rigorous statistical analysis, this sample size was estimated at 377 participants. This sample size provides an appropriate balance between data comprehensiveness and research practicality, enabling robust analysis.

The selection of participants involves a purposive sampling strategy. The participants, representing a diverse cross-section of employees across various organizations, are chosen for their relevance to the study’s objectives. This approach aligns with the principles of purposive sampling, which advocate the selection of participants based on their specific characteristics or attributes that bear significance to the research [34]. In essence, the cross-sectional survey design and purposive sampling strategy collectively furnish the study with a methodological scaffold characterized by precision, efficiency, and the capacity to generate data conducive to statistical analysis. This design choice is meticulously aligned with the research objectives, enabling the researcher to empirically investigate the complex relationships and mediations posited in this research framework.

Data Analysis

The proposed framework can be assessed using Structural Equation Modelling (SEM). There are three main advantages of using structural equation modelling compared to traditional methods [35]. First, it helps to test multiple regressions simultaneously. Second, structural equation modelling program could examine more complex relationships and models which include mediating or moderating variables. Third, it provides individual parameter estimate test and overall model fit simultaneously. Structural equation modelling was chosen in the present study due to its advantages over other multivariate techniques and its applicability in testing the hypotheses with mediating constructs. The common method that was used in assessing mediation is through Baron and Kenny’s procedures [35]. This procedure analyses the regression equations in sequence which is relatively complex for the present study. However, structural equation modelling enables the researcher to test the hypotheses with mediating effect simultaneously.

The two main approaches of structural equation modelling are covariance-based structural equation modelling (i.e., AMOS, LISREL, MPLUS), and variance-based structural equation modelling (i.e., SmartPLS, PLS Graph, WarpPLS). In deciding which to use, the researcher needs to look at their own research objectives (theory testing oriented vs. prediction-oriented), behaviour of the data (normal distribution of data), types of constructs (reflective vs. formative) and sample size. Before conducting the main analysis, the assumptions of covariance-based structural equation modelling specifically normality test was carried out on the dataset. The results showed the constructs and data in the study were not normally distributed. In order to normalize the non-normal data distribution, the data transformation process was conducted by using SPSS 29.0. Once done, the dataset was tested again for normality. However, similar results were produced and it was concluded dataset did not sufficiently meet the assumption of normality.

Thus, covariance-based structural equation modelling (CB-SEM) with AMOS was not used to analyse the data since the assumption of normality was violated. Therefore, the researcher decided to use partial least square (PLS) path modelling, using Smart PLS 4.0 [36] that did not require such assumptions. PLS path modelling involves the two-step approach [37 – 38]. First is evaluation of the measurement model (i.e., outer model), and second is evaluation of the structural model (i.e., inner model). Measurement model focuses on the relationship between construct and its indicators, whereas, structural model focuses on the relationship between constructs.

RESULT AND DISCUSSION

Measurement Model

According to [39], the measurement model was generally referred to as the outer model, which depicts the relationship between constructs and the variables that act as indicators. From the measurement model, this study assesses the reliability and validity of the constructs. This study employs multi-item constructs that were conceptualised as reflective.

Composite reliability was the preferred reliability procedure due to its capacity to apply weights to individual indications based on their loadings [40]. Internal Consistency Reliability was assessed by using two different methods which were Composite Reliability (CR) [39] and Cronbach’s Alpha [41]. The Cronbach Alpha values in this study vary from 0.889 to 0.967, which satisfy the acceptable criterion of 0.7 as suggested by [40]. According to the data, the coefficient of determination (CR) for each construct falls within the range of 0.902 to 0.971, surpassing the recommended threshold of 0.70. The results suggest that all of the constructs exhibit acceptability and reliability. This indicates that the items used to represent the constructions exhibit satisfactory levels of internal consistency

The concept of indicator reliability pertains to the size of outer loadings, as described by [39]. According to [42], it was generally recommended that the factor loadings should have a minimum value of 0.50, as suggested by [40]. Loadings that fall below 0.50 were excluded from the scale in order to enhance convergent validity. It can be observed that all items exhibit a reasonable level of indicator reliability, ranging from 0.715 to 0.937, thereby satisfying the established threshold of 0.5.

Another component to consider when evaluating a reflective measurement model was its convergent validity. This criterion assesses the degree to which the indicators of a construct align with each other and effectively account for the variance observed in the items [39]. According to [42], the Average Variance Extracted (AVE) can be employed as a means to evaluate convergent validity. A commonly accepted AVE was 0.50 or higher [40] [42] [43]. Based on the result of this analysis it was evident that the AVE values for all constructs range from 0.592 to 0.816, surpassing the proposed threshold value of 0.50.

Discriminant validity refers to the extent to which indicators effectively distinguish across constructs or assess separate concepts by analysing correlations between possibly overlapping measures [44]. According to [45], it was recommended that the HTMT values should not surpass 0.90 when the path model has constructs that were conceptually very similar. The HTMT values for all constructs were below 0.90.

Common method variance (CMV) or single source bias may be present in a sample because the dependent and independent variables were measured from the same people. This study employs the Harman’s single factor analysis using SPSS to produce a method factor [46]. The Harman’s single factor analysis using exploratory factor analysis (EFA) was also conducted to identify whether or not any single constructs accounts for the majority of the explained variance. The main purpose of this analysis was to detect the presence of common method variance [47 – 48]. The common method variance exists if any single construct accounts for the majority of the covariance, for example, the first factor in the extraction sum of squared loadings were more than 50 percent. Therefore, based on the results the variance explained by the first factor was 29 percent. Hence, the result revealed that the common method variance was not an issue in these data.

Structural Model

According to [39], the first stage in evaluating the structural model involves analysing the variance inflation factor (VIF) values of all sets of predictor components in order to identify any potential collinearity concerns. Collinearity issues arise when there was a strong correlation between two or more independent variables in a multiple regression model [49]. Multicollinearity can be identified through variance inflation factor (VIF) and the value must be higher than 0.20 but lower than 5.00 [50]. In the present study, the variance inflation factor was between 1.08 and 1.63, which was below five. Thus, the results provide evidence on the absence of multicollinearity among predictor constructs.

This study formulates three direct hypotheses that establish a relationship between the components. According to the evaluation of the path coefficients in this study all direct hypotheses were found to be accepted. All three relationships have t-values over 1.65 (t-value > 1.65), indicating statistical significance at a significance level of 0.05. More precisely, the predictors of servant leadership (SL) (𝛽 = -0.370, p = 0.000, t = 7.418), employee resilience (ER) (𝛽 = 0.180, p = 0.000, t = 3.855), microaggression (MA) (𝛽 = 0.252, p = 0.000, t = 5.634) towards intention to leave (IL) was significant. Therefore, the hypotheses H3, H4, and H5 was supported. Additionally, servant leadership (SL) (𝛽 = 0.441, p = 0.000, t = 11.493) towards employee resilience (ER) was also found significant. Hence, the hypotheses H1 was also supported. However, the findings indicate that there was no significant relationship between microaggression (MA) towards employee resilience (ER), as evidenced by a p-value greater than 0.1 at a significance threshold of 10%. Consequently, hypothesis H2 was not supported (rejected).

This study formulates three indirect hypotheses that establish a relationship between the components. The analysis for H6 indicate that employee resilience serves as partial mediating variable of complementary type in the relationship between servant leadership and intention to leave, indicating statistical significance at a significance level of 0.05. More precisely, the predictors of employee resilience (ER) mediate the relationship between servant leadership (SL) and intention to leave (IL) (𝛽 = 0.083, p = 0.000, t = 3.541). Therefore, only hypothesis H6 was supported. However, the findings indicate that there was no significant mediating relationship between employee resilience (ER), microaggression (MA) and intention to leave (IL), as evidenced by a p-value greater than 0.1 at a significance threshold of 10%. Consequently, hypothesis H7 was not supported (rejected).

Furthermore, the coefficient of determination (R²) was examined. All variables directed towards employee resilience (R² = 0.203) which achieved moderate level and all variables directed towards intention to leave (R² = 0.256) which also achieved a moderate level. The coefficient of determination referred to the model’s predictive accuracy and was a combined effect of the constructs. In business research, the R² value of 0.26, 0.13 and 0.02 for endogenous latent constructs were described as substantial, moderate and weak [51].

A general rule of thumb for the Q²predict was 0 (small), 0.25 (medium), and 0.50 (large), as stated by [52] and [53]. For all of the indicators, it would appear that the Q²predict values were greater than zero (Q²predict > 0), specifically the Q² predicts were 0.191 to 0.213. Therefore, the prediction power of this structural model was sufficient.

FINDINGS AND DISCUSSION

To recap, the primary objective of this study was to explore the complex interactions between servant leadership, microaggressions, and employees’ intentions to leave within the Malaysian IT industry, with a specific focus on the role of employee resilience. The research aimed to address critical gaps in the existing literature by investigating how these organizational factors influence employees’ decisions to stay or leave, particularly considering the mediating role of employee resilience [54].

The results supported Hypothesis 1 (H1), which proposed a positive relationship between servant leadership and employee resilience. This finding aligns with the work of [55], who demonstrated that servant leadership positively correlates with employees’ work resilience. However, Hypothesis 2 (H2), which hypothesized a significant relationship between microaggressions and employee resilience, was not supported. The study found no significant link between microaggressions and resilience, suggesting that resilience alone is insufficient to counteract the negative impact of microaggressions in the workplace. This conclusion is consistent with [56], who found that microaggressions are negatively associated with psychological well-being and physical health, irrespective of an individual’s resilience.

Hypothesis 3 (H3) was supported, revealing a significant negative relationship between servant leadership and employee turnover intentions. This finding is consistent with the research conducted by [57], who observed that servant leadership significantly reduces turnover intentions. Hypothesis 4 (H4) was also supported, indicating that microaggressions significantly predict turnover intentions. This finding echoes the meta-analysis by [58] and [59], which emphasized that the cumulative effects of microaggressions can lead to a sense of exclusion and diminished organizational commitment.

Hypothesis 5 (H5) was similarly supported, showing that employee resilience significantly impacts turnover intentions. This is further corroborated by [60] and [61], who found that resilience, particularly the component of hardiness, is negatively related to turnover intentions. Their studies suggest that resilient employees are more likely to perceive challenges as opportunities, thereby reinforcing their commitment to their roles.

The study also investigated the indirect effects of employee resilience on the relationship between leadership and turnover intentions. Hypothesis 6 (H6) was partially supported, indicating that employee resilience partially mediates the relationship between servant leadership and intention to leave. This finding aligns with [62], who highlighted that perceived organizational support, often fostered by servant leadership, significantly reduces turnover intentions. Additionally, the protective role of resilience against turnover is supported by [63] and [64], who discussed how both organizational and psychological resilience contribute to employee retention.

However, Hypothesis 7 (H7), which proposed that employee resilience mediates the relationship between microaggressions and turnover intentions, was not supported. This suggests that resilience alone may not be sufficient to mitigate the impact of microaggressions on turnover intentions. While studies by [65] and [66] suggest that resilient employees can maintain job satisfaction despite experiencing microaggressions, [67] argue that the psychological toll of microaggressions requires more than just resilience. Specific support mechanisms tailored to address the unique challenges faced by marginalized employees may be necessary, as the stress and resource expenditure associated with microaggressions can overwhelm even resilient individuals.

Despite the rigorous methodology employed, this study has certain limitations that should be acknowledged. Firstly, the research sample was drawn from a specific sector within the IT cybersecurity industry, which may limit the generalizability of the findings to other organizational contexts or industries. Future research should replicate this study across diverse organizational settings to validate the robustness and applicability of the findings.

In conclusion, the findings of this research have significant practical implications for the IT cybersecurity field, offering valuable insights for organizational leaders, cybersecurity professionals, and policymakers. By elucidating the relationships between servant leadership, microaggressions, employee resilience, and turnover intentions, this study provides a foundation for strategic decision- making and intervention strategies aimed at enhancing organizational resilience and mitigating cybersecurity risks.

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