Influence of Leadership Communication on Employee Retention in the Age of AI

316 International Journal of Advance and Applied Research www.ijaar.co.in ISSN – 2347-7075 Impact Factor – 8.141 Peer Reviewed Bi-Monthly Vol. 6 No. 38 September - October - 2025 Influence of Leadership Communication on Employee Retention in the Age of AI Sheeba Khan1 & Dr. Manoj Kulkarni2 1Ph.D. Research Scholar - NWIMSR 2Associate Professor, Singhad Institute, Pune Research Centre – (NWIMSR) NevilleWadia Institute of Management Studies & Research, Pune Corresponding Author – Sheeba Khan DOI - 10.5281/zenodo.17315940 Abstract: This study investigates the influence of leadership communication on employee retention intention, with employee engagement serving as a mediating factor, in contemporary organisational settings. Drawing on a sample of 303 employees from diverse industries, data were collected using a structured questionnaire measured on a seven-point Likert scale. The study employs Structural Equation Modeling (SEM) via SmartPLS 4.0 to examine the hypothesised relationships among leadership communication, employee engagement, and retention intention. The findings reveal that leadership communication has a strong positive effect on employee engagement (β = 0.771), which in turn significantly enhances employee retention intention (β = 0.378). The direct effect of leadership communication on retention intention is positive but comparatively weaker (β = 0.210), indicating that employee engagement partially mediates this relationship. Reliability and validity analyses confirm the robustness of the measurement model, and model fit indices (SRMR = 0.075, NFI = 0.841) support the adequacy of the structural model. The results underscore the importance of clear, transparent, and responsive communication by leaders in fostering engagement and reducing voluntary attrition. Practical implications include training leaders in effective communication, implementing engagement-focused initiatives, and leveraging AI-enabled communication tools to enhance interaction quality and retention outcomes. Keywords: Leadership Communication, Employee Engagement, Employee Retention Intention, Structural Equation Modeling, AI-enabled Communication Introduction: The rapid proliferation of artificial intelligence (AI) technologies is profoundly reshaping organizational structures, workflows, and communication dynamics. As businesses increasingly adopt AI-driven tools to optimize operations, the role of leadership communication has become more critical than ever in shaping employee experiences and influencing retention. In this digital age, where automation and data-driven systems dominate decision-making, effective and empathetic leadership communication serves as a vital counterbalance—ensuring that employees remain engaged, valued, and aligned with organizational goals. Leadership communication is no longer confined to the traditional top-down dissemination of information. It now encompasses a dynamic, two-way interaction influenced by AI-enhanced platforms, predictive analytics, and virtual IJAAR Vol. 6 No. 38 ISSN – 2347-7075 Sheeba Khan & Dr. Manoj Kulkarni 317 communication tools. Florea and Croitoru (2025) emphasize that while AI significantly improves the efficiency and accuracy of internal communications, it also introduces new complexities. Leaders must now navigate the fine line between leveraging AI for streamlined operations and preserving the human connection essential to employee trust and morale. In this evolving landscape, the quality of leadership communication directly impacts employee retention. Farinha and Pina (2025) argue that positive leadership—marked by transparent, supportive, and consistent communication—plays a decisive role in enhancing employee engagement and loyalty, particularly in environments where AI alters job structures and responsibilities. Employees who perceive their leaders as communicative and empathetic are more likely to feel secure and committed, even amidst technological disruptions. This connection is further explored in the work of Hauer, Quan, and Liang (2021), who analyze leadership approaches in East Asian multinational corporations. Their findings reveal that culturally aware, communicative leadership significantly contributes to talent retention in AI-driven organizations. By fostering open dialogue and inclusive decision-making, leaders can mitigate the anxiety and resistance often associated with digital transformation. The implications of AI for leadership communication extend beyond engagement to affect compensation perceptions and job satisfaction. Kaur and Kaur (2023) highlight that AI tools can unintentionally introduce opacity in performance evaluation and compensation processes, making transparent leadership communication essential for sustaining trust and fairness. In such scenarios, leaders must not only explain AI-driven decisions but also reaffirm their commitment to employee well-being. Moreover, Panwar et al. (2025) underscore the mediating role of employee satisfaction between AI integration and organizational success. Their study confirms that when leaders use communication strategically—clarifying AI‘s role, addressing concerns, and reinforcing shared values—they create an environment conducive to both retention and performance growth. Given these multidimensional challenges and opportunities, this paper explores how leadership communication influences employee retention in the age of AI. By synthesizing current research and case analyses, the study aims to identify effective communication strategies that leaders can adopt to foster a resilient, engaged, and loyal workforce amid ongoing technological transformation. Literature Review: Leadership communication plays a pivotal role in shaping employee behaviour and influencing retention, particularly in an era where artificial intelligence (AI) is transforming workplace practices. Various leadership theories highlight the importance of communication in fostering job satisfaction, commitment, and trust. Among these, transformational, servant, and ethical leadership styles stand out as particularly influential. Transformational leadership is characterised by vision articulation, inspirational motivation, intellectual stimulation, and individualised consideration. Research indicates that transformational leaders create a communicative climate that enhances organisational citizenship behaviours and job satisfaction, which ultimately fosters employee retention. Tian et al. (2020) demonstrated that transformational leadership IJAAR Vol. 6 No. 38 ISSN – 2347-7075 Sheeba Khan & Dr. Manoj Kulkarni 318 improves citizenship behaviour, which in turn reduces turnover intentions, with communication acting as a moderator in these relationships. Servant leadership, in contrast, focuses on prioritising employee needs, supporting growth, and demonstrating care for followers‘ well-being. Its communication style is often nurturing and participative, thereby enhancing trust and employee commitment. A study in the hospitality sector found that servant leadership had a stronger impact on employee trust compared to transformational leadership, with trust mediating outcomes such as job satisfaction and retention intentions (Sousa & van Dierendonck, 2021). Ethical leadership further emphasises transparency, integrity, and fairness in communication. Leaders who model ethical behaviour through open dialogue and moral decision-making foster credibility and trust, which enhances employee loyalty and reduces absenteeism and turnover (Tu et al., 2022). Collectively, these leadership styles underscore the significance of communication behaviours—clarity, transparency, feedback, and fairness—in reinforcing employee attachment to organisations. Employee retention is largely driven by three interrelated psychological constructs: job satisfaction, commitment, and trust in leadership. Job satisfaction, encompassing contentment with work tasks, growth opportunities, and workplace environment, is closely tied to leaders‘ communicative practices. Leaders who articulate clear visions, provide supportive feedback, and maintain open channels of communication are more likely to foster satisfaction and reduce turnover intentions. Commitment, both affective (emotional attachment) and normative (sense of obligation), also depends heavily on how leaders communicate organisational values, recognition, and goals. Employees who perceive alignment between their own values and leaders‘ communication demonstrate higher loyalty and reduced likelihood of leaving. Trust in leadership is particularly critical in volatile and uncertain environments. Ethical, transparent, and consistent communication builds trust, which then mediates the relationship between leadership style and retention outcomes (Newman et al., 2020). When employees trust their leaders, they are more willing to remain committed, even during organisational change or technological disruptions. In parallel, AI-driven tools are increasingly integrated into human resource management (HRM) functions, influencing communication processes and employee perceptions. AI chatbots, for example, are being used to address routine HR queries, streamline onboarding, and provide instant information. Dutta et al. (2022) highlighted that AI-enabled chatbots enhance employee engagement when supported by a climate of trust, though their effectiveness is moderated by employees‘ perceptions of authenticity and transparency. Predictive analytics, often used to identify turnover risks and monitor employee sentiment, allow leaders to communicate proactively and personalise interventions. However, Sadeghi (2024) cautioned that while AI improves efficiency and reduces bias in HR processes, it also raises concerns about fairness, privacy, and job security, which may undermine retention if not managed responsibly. Moreover, AI-powered communication tools, such as automated notifications and sentiment-analysis dashboards, support leaders in maintaining information flow and responsiveness. Studies of chatbot communication styles suggest that employees respond more positively to AI when it demonstrates empathy, anthropomorphism, and perceived competence IJAAR Vol. 6 No. 38 ISSN – 2347-7075 Sheeba Khan & Dr. Manoj Kulkarni 319 (Zhao et al., 2022). These findings underscore the dual nature of AI in HR communication— it offers efficiency and personalisation, but risks eroding trust if perceived as impersonal or intrusive. An emerging research direction lies in the integration of leadership communication and AI through hybrid models. Hybrid communication systems combine human leadership‘s emotional, ethical, and motivational strengths with the efficiency and scalability of AI tools. For instance, leaders may rely on AI dashboards to gauge employee sentiment and turnover risks, while personally engaging in relational communication to build trust and commitment. Similarly, AI chatbots can handle transactional communication, freeing leaders to focus on strategic messaging and mentorship. Arakawa and Yakura (2024) introduced the ―Coaching Copilot‖ model, demonstrating how AI chatbots can augment leadership development by supporting reflective practices, while recognising AI‘s limitations in nuance and contextual understanding. Such hybrid approaches hold promise for strengthening employee satisfaction and retention by leveraging both human empathy and AI-driven efficiency. However, successful integration requires careful design to ensure that AI supplements rather than replaces human leadership communication, particularly in sensitive areas such as feedback, ethics, and vision-sharing. Despite growing literature on leadership communication and increasing scholarship on AI in HR, notable gaps remain. Much of the existing research examines leadership styles or AI tools in isolation, with limited attention given to their intersection and its implications for retention. Few empirical studies have explored how hybrid models of human leadership communication augmented by AI directly influence trust, satisfaction, and commitment, which are crucial for retention. Ethical considerations such as transparency, fairness, and privacy in AI-mediated communication are also underexplored, even though they significantly impact trust. Furthermore, most studies adopt crosssectional designs, limiting the ability to assess how leadership communication and AI integration affect retention over time. Finally, cultural and contextual factors are rarely incorporated, despite their importance in shaping acceptance of AI tools and leadership communication styles across diverse workforces. Addressing these gaps offers opportunities for advancing both theory and practice in leadership and HRM. Thus, leadership communication theories—transformational, servant, and ethical—emphasise communicative behaviours that foster job satisfaction, trust, and commitment, thereby enhancing employee retention. AI in HR introduces new tools, such as chatbots, analytics, and communication agents, that reshape how leaders interact with employees. The integration of AI with leadership communication through hybrid models presents promising yet underexplored avenues for strengthening retention. Future research should focus on empirically examining these intersections, addressing ethical concerns, and considering cultural contexts to better understand how leadership communication and AI together influence employee retention in the contemporary workplace. Conceptual Framework And Development Of Hypotheses: Employee retention remains a pressing concern for organisations, particularly in contexts where skilled talent is scarce and competition is intense. Leadership communication has been consistently IJAAR Vol. 6 No. 38 ISSN – 2347-7075 Sheeba Khan & Dr. Manoj Kulkarni 320 identified as a central factor shaping employee attitudes and behaviours that ultimately influence retention. The present study builds a conceptual framework that explains the pathways through which leadership communication affects employee retention intention, incorporating both mediating and moderating variables relevant to contemporary organisational contexts. Leadership Communication as the Independent Variable: Leadership communication, the independent variable in this framework, refers to the clarity, transparency, frequency, and responsiveness with which leaders interact with their employees. Effective communication helps leaders convey vision, articulate expectations, and provide constructive feedback, which collectively foster a sense of belonging and loyalty among employees. Prior studies demonstrate that when employees perceive their leaders as effective communicators, they are more satisfied, engaged, and committed, reducing their intention to leave the organisation (Tian et al., 2020; Tu et al., 2022). Employee Engagement and Trust in Leadership as Mediators: The model integrates employee engagement and trust in leadership as mediating mechanisms through which leadership communication influences retention intentions. Employee engagement is a psychological state characterised by vigour, dedication, and absorption in one‘s work (Schaufeli, 2017). Leaders who communicate authentically and supportively inspire employees to invest greater energy and enthusiasm in their roles, which translates into stronger intentions to remain with the organisation. Trust in leadership, defined as employees‘ belief in the competence, integrity, and benevolence of their leaders, is similarly reinforced by transparent and ethical communication. When trust is established, employees perceive less risk in staying with the organisation, leading to higher retention intentions (Newman et al., 2020). Together, these mediators represent the relational and motivational pathways through which leadership communication exerts its impact. Employee Retention Intention as the Dependent Variable: The dependent variable, employee retention intention, reflects employees‘ selfreported likelihood of remaining with their current organisation. Retention intention serves as a proximal predictor of actual turnover behaviour and is shaped by both jobrelated and relational factors. Effective communication from leaders fosters positive perceptions of the workplace, thereby increasing employees‘ desire to continue their tenure (Sousa & van Dierendonck, 2021). AI-Enabled Communication Tools as the Moderator: The framework also acknowledges the growing role of technology by incorporating AI-enabled communication tools as a moderating variable. AI-driven chatbots, sentiment-analysis systems, and automated HR communication platforms are increasingly integrated into organisational communication processes. These tools can enhance leaders‘ ability to deliver timely, personalised, and responsive communication. When effectively utilised, AI-enabled tools may amplify the positive influence of leadership communication on retention intentions by increasing accessibility and responsiveness (Dutta et al., 2022). Conversely, if employees perceive AI communication as impersonal or lacking transparency, the moderating effect may weaken trust and reduce the benefits of leadership communication. IJAAR Vol. 6 No. 38 ISSN – 2347-7075 Sheeba Khan & Dr. Manoj Kulkarni 321 Hypotheses Development: Drawing from this framework, the study proposes the following hypotheses: 1. H1: Leadership communication has a significant positive effect on employee retention intention. 2. H2: Employee engagement mediates the relationship between leadership communication and employee retention intention. Methodology: This study employs a quantitative research design to investigate the effect of leadership communication on employee retention intention, considering employee engagement as a mediating variable. Primary data were collected through a structured questionnaire administered to employees across various sectors including information technology, manufacturing, retail, and services. The target population consisted of professionals with at least one year of tenure under a direct supervisor, ensuring their ability to meaningfully assess leadership communication and retention-related perceptions. A sample size of 303 respondents was achieved, which is adequate for Structural Equation Modeling (SEM) and ensures robust statistical validity. The questionnaire comprised four sections: demographic details, leadership communication (six items on clarity, transparency, and feedback), employee engagement (six items covering vigour, dedication, and absorption), and employee retention intention (three items reflecting likelihood of staying with the organisation). All items were measured using a seven-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree), with reverse-coded items included to reduce bias. The items were adapted from validated scales used in prior empirical studies, thereby enhancing construct validity and reliability. The collected data were analysed using SmartPLS 4.0, applying Structural Equation Modeling (SEM) techniques to validate the hypotheses. Descriptive statistics were generated to summarise the demographic profile of respondents, and Cronbach‘s alpha was computed to test the reliability of the constructs. SEM was used to simultaneously test the measurement model and the structural model. The measurement model assessed the validity and reliability of constructs through factor loadings, composite reliability, and average variance extracted (AVE). The structural model was then applied to examine hypothesised relationships. H1 (the direct effect of leadership communication on employee retention intention) and H2 (the mediating role of employee engagement) were validated using path coefficients, bootstrapping procedures, and significance testing at the 0.05 level. The use of SmartPLS 4.0 ensured robust analysis of mediation effects while accommodating the moderate sample size. Ethical considerations such as anonymity, confidentiality, and informed consent were strictly followed during data collection. This methodology enables rigorous and comprehensive validation of the conceptual framework, offering insights into both direct and mediated pathways linking leadership communication with employee retention intentions. IJAAR Vol. 6 No. 38 ISSN – 2347-7075 Sheeba Khan & Dr. Manoj Kulkarni 322 Data Analysis and Results: Table 1: Demographic Profile of Respondents Category Subcategory Frequency Percent Age 20–29 90 29.70 30–39 110 36.30 40–49 60 19.80 50–59 30 9.90 60+ 13 4.29 Gender Male 175 57.76 Female 120 39.60 Prefer not to say 8 2.64 Tenure <1 year 40 13.20 1–3 years 95 31.35 4–6 years 80 26.40 7–10 years 55 18.15 11+ years 33 10.89 Job Level Entry-level 85 28.05 Supervisor/Team lead 70 23.10 Middle management 80 26.40 Senior management 50 16.50 Executive 18 5.94 The demographic profile indicates that the respondents are predominantly young to mid-career professionals. A majority of employees fall within the 30–39 years age group (36.30%), followed by those aged 20– 29 years (29.70%), suggesting that the sample is largely composed of millennials and early Generation X employees who are active in the workforce and more likely to face career mobility decisions. Respondents aged 40–49 years constitute 19.80%, while those aged 50– 59 years (9.90%) and 60 years and above (4.29%) represent a smaller share, highlighting relatively lower participation from late-career professionals. In terms of gender distribution, males account for 57.76% of the sample, while females make up 39.60%, indicating a maledominated workforce with a significant female representation. A small proportion of respondents (2.64%) preferred not to disclose their gender, ensuring inclusivity in the dataset. Employment tenure distribution reveals that most respondents have 1–3 years of experience (31.35%), followed by those with 4–6 years (26.40%), which indicates that the sample captures employees in early to midtenure phases where retention concerns are often most critical. About 18.15% of respondents reported a tenure of 7–10 years, while 13.20% had less than one year of experience, and only 10.89% had been with their organisation for over 11 years. This shows a balanced representation across different career stages, with a concentration of early-tenure employees. Regarding job levels, the distribution is relatively even, with entrylevel (28.05%) and middle management (26.40%) forming the largest groups, followed by supervisors/team leads (23.10%). Seniorlevel roles such as senior management (16.50%) and executives (5.94%) are less represented, reflecting typical organisational structures where higher positions are fewer. Overall, the demographic profile reflects a diverse sample that effectively represents different age groups, genders, career stages, and job levels, making it suitable for analysing leadership communication and retention dynamics. IJAAR Vol. 6 No. 38 ISSN – 2347-7075 Sheeba Khan & Dr. Manoj Kulkarni 323 Table 2: Construct Reliability and Validity Cronbach's alpha Composite reliability (rho_a) Composite reliability (rho_c) Average variance extracted (AVE) Employee _Engagement 0.876 0.905 0.907 0.627 Employee _Retention _Intention 0.720 0.811 0.838 0.636 Leadership _Communication 0.923 0.933 0.941 0.727 The results of construct reliability and validity presented in Table 2 indicate that all three constructs—employee engagement, employee retention intention, and leadership communication—demonstrate strong internal consistency and convergent validity. Cronbach‘s alpha values range from 0.720 to 0.923, all above the acceptable threshold of 0.70, confirming reliability. Similarly, composite reliability (both rho_a and rho_c) values for all constructs exceed 0.80, further affirming the robustness of the measurement scales. The Average Variance Extracted (AVE) values are also well above the recommended minimum of 0.50, with employee engagement at 0.627, employee retention intention at 0.636, and leadership communication at 0.727, indicating that a substantial proportion of variance in the indicators is explained by the underlying constructs. These results collectively confirm that the measurement model is both reliable and valid, making it appropriate for further structural equation modeling (SEM) analysis. Table 3: Discriminant Validity – HTMT Employee _Engagement Employee _Retention _Intention Leadership _Communication Employee _Engagement Employee _Retention _Intention 0.611 Leadership _Communication 0.838 0.577 The HTMT results in Table 3 confirm that discriminant validity is established among the constructs, as all values fall below the recommended threshold of 0.90. The HTMT ratio between employee engagement and employee retention intention is 0.611, suggesting a moderate relationship while maintaining sufficient distinction between the two constructs. Similarly, the HTMT value between leadership communication and employee retention intention is 0.577, indicating a moderate but distinct association. The highest HTMT ratio is observed between leadership communication and employee engagement (0.838), reflecting a strong relationship but still within acceptable limits, ensuring that the constructs measure different conceptual domains. IJAAR Vol. 6 No. 38 ISSN – 2347-7075 Sheeba Khan & Dr. Manoj Kulkarni 324 Table 4: Discriminant validity – Fornell-Lacker Criterion Employee _Engagement Employee _Retention _Intention Leadership _Communication Employee _Engagement 0.792 Employee _Retention _Intention 0.539 0.797 Leadership _Communication 0.771 0.501 0.853 The Fornell-Larcker criterion results in Table 4 indicate that discriminant validity is satisfactorily established among the constructs. The diagonal values, representing the square root of the AVE for each construct—employee engagement (0.792), employee retention intention (0.797), and leadership communication (0.853)—are higher than the corresponding off-diagonal correlations, demonstrating that each construct shares more variance with its own indicators than with other constructs. Specifically, the correlations between employee engagement and employee retention intention (0.539), leadership communication and employee retention intention (0.501), and leadership communication and employee engagement (0.771) are all lower than their respective diagonal values. These results confirm that the constructs are conceptually and empirically distinct, validating the measurement model and supporting its suitability for subsequent structural equation modeling analysis. Table 5: Path Coefficient Path coefficients Employee _Engagement -> Employee _Retention _Intention 0.378 Leadership _Communication -> Employee _Engagement 0.771 Leadership _Communication -> Employee _Retention _Intention 0.210 The path coefficient results in Table 5 indicate the strength and direction of the relationships among the constructs in the structural model. Leadership communication has a strong positive effect on employee engagement (β = 0.771), suggesting that effective communication by leaders substantially enhances employees‘ engagement levels. Employee engagement, in turn, exhibits a moderate positive effect on employee retention intention (β = 0.378), indicating that higher engagement translates into a greater likelihood of employees remaining with the organisation. The direct effect of leadership communication on employee retention intention is positive but relatively weaker (β = 0.210), implying that while leadership communication influences retention directly, a significant portion of its impact is transmitted indirectly through employee engagement. These results collectively support the hypothesised mediated relationship within the conceptual framework. Table 6: Model Fit Saturated model Estimated model SRMR 0.075 0.075 d_ULS 0.674 0.674 d_G 0.307 0.307 Chi-square 517.536 517.536 NFI 0.841 0.841