Keywords

Strategic Human Resource Management, Electric Vehicle, Employee Empowerment, Employee Engagement, Customer Satisfaction.

Introduction

The electric vehicle (EV) industry is undergoing a significant transformation driven by rapid technological innovations, increased environmental consciousness, and shifting consumer preferences (Iyer & Dutta, 2017). Recent research underscores the pivotal role of employee empowerment (EM) and engagement (EN) in boosting customer satisfaction (CS), which is increasingly acknowledged as a key differentiator in this highly competitive market. For instance, Prebeg's research demonstrates a strong positive link between employee motivation through empowerment and its impact on customer satisfaction and sales performance, highlighting the crucial role of employee-related factors in achieving customer-focused results in the EV sector. (Prebeg, 2023).

Moreover, the integration of advanced technologies such as artificial intelligence (AI) is reshaping employee roles and customer interactions. AI-driven tools enhance employee efficiency and customer engagement, which in turn fosters higher levels of CS (Ashima Joshi et al., 2023). This aligns with findings from Khwaja and Yang, who emphasize that employee engagement directly influences customer satisfaction and retention, particularly in service-oriented industries like car rentals, which share similarities with the EV market (Khwaja & Yang, 2022). The dynamic interplay between employee engagement and customer satisfaction is further supported by Auh et al., 2019 who illustrate that customer participation, facilitated by empowered employees, can enhance overall performance and satisfaction levels (Auh et al., 2019).

Despite these insights, there remains a notable gap in the literature regarding the specific mechanisms through which EM and EN influence CS within the EV industry. Existing studies highlight this research void, suggesting that further exploration is necessary to understand how these employee-related factors can be leveraged to improve customer satisfaction in this rapidly evolving sector (Prebeg, 2023). Employee engagement plays a crucial mediating role in this context, as it not only boosts employee motivation but also cultivates a culture of service excellence that resonates with customers.

Hence, the EV industry's transformation is intricately linked to the empowerment and engagement of its workforce, which plays a crucial role in enhancing customer satisfaction. As the industry continues to evolve, understanding and optimizing these employee-related factors will be essential for businesses aiming to thrive in a competitive landscape marked by technological innovation and increasing consumer expectations.

The objective of this study is to provide a deeper understanding of the intricate relationships between EM, EN, and CS in the EV industry. By addressing this objective, the study aims to offer valuable insights for businesses looking to enhance their customer experience and achieve sustainable success.

The findings of this study carry substantial implications for businesses and society. Enhanced CS in the EV sector can drive market success, foster brand loyalty, and accelerate the adoption of electric vehicles, thereby advancing sustainability. Additionally, comprehending the intricate connections between EM, EN, and CS is vital for organizational success and market sustainability in this rapidly evolving industry.

Literature Review and Hypothesis Development

Theoretical Framework

In exploring the complex interplay between EM, EN, and CS, Self-Determination Theory (SDT) provides a solid theoretical foundation (Deci & Ryan, 2013). SDT proposes that individuals possess inherent psychological requirements for autonomy, competence, and relatedness. Meeting these needs results in increased motivation and overall well-being. Within the context of EM, SDT becomes particularly pertinent. Autonomy, one of the fundamental components of SDT, aligns with the empowerment of employees in decision-making processes. When employees are granted autonomy in their work, allowing them a degree of control and self-direction, SDT suggests that their innate need for autonomy is satisfied (Deci & Ryan, 2013). Empowerment plays a crucial role in fostering a work environment where employees experience a sense of ownership and autonomy in managing their responsibilities. Competence, another critical component of SDT, is highly relevant to the engagement of employees. When employees perceive themselves as competent in their roles, and having the necessary skills and abilities, their psychological need for competence is met, according to SDT (Deci & Ryan, 2013). Employees who feel empowered and capable are more likely to engage deeply in their work, displaying higher levels of enthusiasm, dedication, and focus on their tasks (Evans et al., 2024).

The link between SDT, EM, and EN becomes evident as employees empowered with autonomy and a sense of competence are poised to exhibit higher levels of engagement (Deci & Ryan, 2013). According to SDT, engaged employees are more inclined to exceed expectations in order to fulfill customer needs. The autonomy and competence experienced through empowerment contribute to employees' intrinsic motivation, fostering a work environment where customer interactions are transactional and driven by a genuine commitment to delivering exceptional service (Deci & Ryan, 2013). This alignment with customer-centric goals will likely result in heightened CS (Lee et al., 2017). The fulfillment of innate psychological needs for autonomy and competence, as proposed by SDT, establishes a foundation for empowered and engaged employees who, in turn, contribute positively to CS in the unique dynamics of the contemporary workplace.

Employee Empowerment and Customer Satisfaction:

EM is widely recognized as a critical factor influencing organizational performance and customer satisfaction (Evans et al., 2024). The concept of empowerment, grounded in SDT, highlights the significance of fostering autonomy, competence, and relatedness in the workplace, enabling employees to thrive and perform at their best (Deci & Ryan, 2013). Studies by Nwachukwu et al. (2021) and Al Zeer et al. (2023) consistently found that granting employees decision-making authority and control over their tasks leads to increased engagement, job satisfaction, and, ultimately, higher customer satisfaction.

Moreover, research by Barnes et al. (2014) and Myrden & Kelloway (2015) has demonstrated a direct link between employee engagement and customer satisfaction. Engaged employees are more likely to demonstrate proactive behaviors, such as anticipating and meeting customer needs, providing personalized service, and resolving issues promptly and effectively (Dike et al., 2024). By empowering employees with the autonomy and responsibility to make decisions that directly impact customer interactions, organizations create an environment conducive to fostering positive customer experiences and building long-term loyalty.

Further, organizations that prioritize EM tend to have a culture that values continuous improvement and innovation (Evans et al., 2024). Empowered employees are more likely to identify opportunities for process improvement, suggest innovative solutions, and take ownership of customer satisfaction metrics. This proactive approach to customer service not only enhances CS levels but also contributes to organizational competitiveness and market differentiation.

Hence, drawing from existing literature it is hypothesized that:

H₁: Employee Empowerment Positively Influences Employee Engagement in the EV Industry.

Mediating Role of Employee Engagement

According to SDT, empowered employees who experience a sense of autonomy and competence are more likely to be engaged with their work (Deci & Ryan, 2013). This engagement manifests in behaviors such as enthusiasm, commitment, and initiative, which are essential for delivering high-quality customer service and creating positive customer experiences (Teixeira et al., 2024). Studies by Lee et al. (2017) and Appelbaum et al. (2010) have provided empirical support for the mediating role of employee engagement in the relationship between employee empowerment and customer satisfaction. Lee et al. (2017) found that empowered employees in the restaurant industry were more likely to be actively engaged in their roles, which in turn led to higher levels of CS. Similarly, Appelbaum et al. (2010) identified a positive association between organizational culture, EM, and CS, suggesting that engaged employees play a crucial role in driving positive customer experiences.

Empowered and engaged employees demonstrate higher levels of commitment and motivation in serving customers (Myrden & Kelloway, 2015). They take the initiative to understand customer needs, provide personalized service, and address issues proactively, leading to increased customer loyalty and satisfaction. Moreover, engaged employees are more likely to exhibit positive attitudes and behaviors, such as empathy, responsiveness, and problem-solving, which are essential for building strong customer relationships and enhancing overall satisfaction levels.

Hence, drawing from existing literature the following hypothesis is proposed:

H₂: EM has an indirect positive impact on CS through EN in the EV Industry.

Conceptual Framework: Based on the hypothesized relationship, the framework is presented below Figure 1:

Research Methodology

This study seeks to investigate the influence of EM on CS, with EN serving as a mediator, in the context of the Indian EV sector. In light of this aim, a positivist approach was considered most suitable, as it facilitates the examination of causal relationships and the effects of one factor on another (Malhotra, Birks, & Inc., 2000). Consequently, deductive reasoning and quantitative methods were employed to collect research data, using a questionnaire as the primary instrument for data gathering (Sekaran & Bougie, 2016). This approach facilitated the use of suitable statistical analyses to test research hypotheses and draw generalizable conclusions from the results.

Sample size: Although SEM lacks a universally defined formula for determining the appropriate sample size, various authors have proposed guidelines to assist researchers in this regard. For instance, Boomsma (1985) recommended sample sizes of approximately 100 to 200 participants, while Bentler and Chou (1987) and Bollen (1989) suggested a ratio of 5 to 10 observations for each estimated parameter. Furthermore, Nunnally (1967) advised a guideline of 10 cases for every variable. Despite the availability of these thumb rules, there is no consensus on the ideal sample size for SEM. In this study, a sample of 260 existing car responded out of the 450 car owners initially approached. Further, a ratio of 2 customers to 1 sales executive was implemented to mitigate any potential bias and ensure robustness in the sample size. Hence total sample include 390 responses including 130 sales executives and 260 customers. This sample size exceeds the minimum recommendations provided by Boomsma (1985), Bentler and Chou (1987), Bollen (1989), and Nunnally (1967), thus providing sufficient data for conducting SEM analysis.

Measures

Employee Empowerment: The Employee Empowerment Scale comprised nine items adapted from (Menon, 2001). Cronbach's alpha for the scale was calculated to be 0.945, indicating high internal consistency reliability.

Employee Engagement: The Employee Engagement Scale consisted of nine items adapted from (Schaufeli et al., 2006). Cronbach's alpha for the scale was calculated to be 0.94, indicating high internal consistency reliability.

Customer Satisfaction: The measurement scale for customer satisfaction was adapted from (Tran & Nguyen, 2022) and comprised four items. The Cronbach's alpha coefficient for the customer satisfaction scale was determined to be 0.866, indicating high internal consistency and reliability. All constructs were measured using a 5-point Likert scale.

Data Analysis

Data analysis was performed using Structural Equation Modeling (SEM) with SmartPLS 4.0 software. SEM is chosen for its capability to simultaneously evaluate both measurement and structural models, enabling a comprehensive exploration of the relationships between latent constructs and observed variables. The selection of SmartPLS 4.0 is driven by its user-friendly interface and robust features, making it particularly suitable for studies with small to medium-sized samples, as Ringle et al. (2016) highlighted.

Convergent validity

As illustrated in Figure 2, the measurement models depict various latent variables, loading values, and path coefficients between constructs. Hair et al. (2017) suggest that a measurement model aids researchers in comprehending how latent variables are gauged through pertinent indicators. According to the guideline proposed by Hair Jr et al. (2019), individual factors/items are considered reliable if the outer loading value is 0.708 or higher. Based on this criterion, the latent variables analyzed in this study are considered valid and reliable Table 1.

Table 1 Reliability and Validity
Variables	Item codes	Items	Loading	CA	CR	AVE
Employee Empowerment	EM1	Perceived Control	0.827	0.945	0.952	0.695
	EM2	Influence work	0.819
	EM3	Influence decision	0.878
	EM4	Perceived Authority	0.840
	EM5	Perceived Capabilities	0.866
	EM6	Perceived Skills and abilities	0.798
	EM7	Inspired of work	0.835
	EM8	Inspired by organizational goals	0.835
	EM9	Enthusiastic for work	0.801
Employee Engagement	EN1	Feel bursting with energy	0.821	0.940	0.946	0.705
	EN2	Feel strong and vigorous	0.835
	EN3	Like going to work	0.855
	EN4	Passionate about my job	0.854
	EN5	Job inspires me	0.896
	EN6	Proud of my job	0.841
	EN7	Feel happy to work intensely	0.802
	EN8	Immersed in work	0.808
	EN9	Highly focused	0.821
Customer Satisfaction	CS1	Feelings of pleasure	0.797	0.866	0.878	0.714
	CS2	Met expectation	0.889
	CS3	Good experience	0.896
	CS4	Right choice	0.792

Table 1 also provides indicators of convergent validity Cronbach’s Alpha (CA), Composite Reliability (CR) and Average Variance Extracted (AVE). Composite Reliability (CR) and Cronbach’s Alpha assess the internal validity of the construct, while AVE gauges the external validity of the constructs. Following the requirements outlined by Hair Jr et al. (2019), a CR value exceeding 0.60, a CA value surpassing 0.70 indicates internal consistency reliability, and an AVE value above 0.50 signifies external consistency reliability. Consequently, it is deduced that all constructs considered in this study exhibit internal reliability.

Discriminant Validity

Discriminant analysis indicates the uniqueness of individual constructs with all others (Hair et al., 2017). Discriminant validity is established when the shared variance of a construct surpasses the rest of the constructs. As per the rule of thumb, discriminant validity is established when the square root of the AVE for each construct exceeds that of the other constructs. Thus, in line with the criteria established by Fornell and Larcker (1981), the findings support the establishment of discriminant validity Table 2.

Table 2 Fornell- Larcker Criterion
	CS	EM	EN
CS	0.845
EM	0.426	0.833
EN	0.684	0.538	0.839

Model fit

In PLS-SEM, the absence of a universal goodness-of-fit index poses challenges. To address this, researchers often turn to bootstrapping and blindfolding techniques (Hair, Ringle, & Sarstedt, 2013). Prior to this, reliability and validity tests were conducted to assess the measurement models. While goodness-of-fit indices like SRMR and NFI are not always reported, they offer insights into model fit. For instance, an SRMR below 0.08 and an NFI close to 1 suggest a good fit. In our study, the SRMR value is 0.079, indicating a good fit and the NFI value is 0.79, representing an acceptable fit. These metrics help ensure the model accurately reflects real-world observations.

Path coefficients significance

In PLS-SEM, the significance of path coefficients is evaluated using bootstrapping, a method that does not rely on the assumption of normal data distributions. This method involves generating numerous subsamples (typically around 5000) from the original dataset. Each subsample is utilized to estimate the PLS-SEM model, yielding path coefficients, indirect effects, outer loadings, and outer weights. From these values, distributions of the PLS-SEM results are generated, allowing for the calculation of T-statistics to assess significance. Table 3 displays the T-statistics for path coefficients obtained through bootstrapping and the final SEM is presented in Figure 3. The SEM illustrates the connection between latent variables or constructs. Following the guideline Hair et al. (2017) provided, a link between two variables is considered significant if the t-value is 1.96 or greater. In line with this criterion, the t-values of 8.081, 2.191, and 15.487 in the structural model indicate the significance of the relationships between the variables.

Table 3 Bootstrapping Results for the Outer Weights
	Original sample (O)	Sample mean (M)	Standard deviation (STDEV)	T statistics (|O/STDEV|)	P values
EM -> CS	0.081	0.083	0.037	2.191	0.028
EM -> EN	0.538	0.546	0.067	8.081	0.000
EN -> CS	0.640	0.637	0.041	15.487	0.000

Path coefficients interpretation

This section examines path coefficients in PLS-SEM, which represent standardized regression coefficients. These coefficients enable meaningful comparisons of the direct effects of exogenous latent variables on endogenous latent variables. The interpretation relies on the standard deviation of each explanatory variable. Path coefficient values range from +1 to -1, where values closer to +1 indicate a stronger positive relationship, closer to -1 indicate a stronger negative relationship, and zero signifies no relationship between the constructs. Accordingly, the path coefficients can be interpreted as follows:

EM -> CS: According to the results EM practices account for 8.1% of the variability in customer satisfaction.

EM -> EN: This suggests that EM practices account for 53.8% of the variability in employee engagement.

EN -> CS: This suggests that EN practices account for 64% of the variability in customer satisfaction.

The mediation test

To explore the mediating role of EN between the relationship of EM (exogenous) and CS (endogenous), this study employed bootstrapping analysis with a 95% confidence interval and 10,000 subsamples, following the approach outlined by Hair et al. (2013). The mediation effect can result in three outcomes: no mediation, full mediation, or partial mediation, determined by the significance of t-statistics and p-values. Table 4 here outlines these mediation outcomes, while Table 5 provides the t-statistics and p-values for both the direct and indirect effects.

Table 4 T-Test Results for Direct and Indirect Significance Effects between Exogenous and Endogenous Variables
Presence of a Direct Relationship	Presence of an indirect relationship	Mediation effect
Significant	Significant	Partial
Significant	Not	No mediating
Not	Significant	Fully Mediating

Table 5 P-Values and T-Statistics
	O	M	STDEV	T statistics	P values
Indirect Effect
EM -> EN -> CS	0.345	0.347	0.040	8.669	0.000
Direct Effect
EM -> CS	0.081	0.083	0.037	2.191	0.028
EM -> EN	0.538	0.546	0.067	8.081	0.000
EN -> CS	0.640	0.637	0.041	15.487	0.000

The findings presented in Table 5 demonstrate significant direct and indirect relationships between EM and CS, as indicated by the t-statistics and p-values. In summary, there exists a partial mediating effect of EN between EM and CS, supporting the second hypothesis. Further, the analysis reveals that EN has a standardized indirect effect of 34.7% between EM and CS, with a p-value of 0.00. This suggests that a one-standard-deviation increase in the effect of EM on CS through EN results in a 0.347 standard deviation increase in CS.

The results shown in Table 6 indicate that EM has a direct effect of 0.081 on CS and an indirect effect of 0.345 on CS.

Table 6 Total Effect (Breakdown)
Exogenous variable	Endogenous variable	Direct Effect	Indirect Effect	Total Effect
EM	CS	0.081	0.345	0.425
EM	EN	0.538	0.000	0.538
EN	CS	0.640	0.000	0.640

The multicollinearity test examines whether there are significant inter-correlations among the independent constructs within the structural model. Its purpose is to ensure that problematic multicollinearity, which can inflate standard deviations and render T-statistic tests unreliable, is absent. This test can also detect issues arising from noteworthy correlations between exogenous variables and endogenous variables. Hair et al. (2009) suggest that variance inflation factor (VIF) values should remain below 10, with values ranging from 5 to 10 considered high and potentially problematic. As presented in Table 7, all VIF values for the independent latent variables are under 5, indicating that multicollinearity is not a concern within the research data.

Table 7 The VIF Values
	CS	EM	EN
CS
EM	1.408		1.000
EN	1.408

The analysis successfully addressed the research questions and effectively tested the hypotheses. Multiple regressions were conducted to evaluate the impact of EM practices on CS in the Indian EV industry. Furthermore, SmartPLS software was employed to validate the SEM of the study.

Discussion

This section provides an interpretation of the study's results concerning the hypotheses. The first research question sought to explore the connection between EM and CS. The analysis demonstrated a statistically significant relationship between EM and CS (β = 0.081, p < 0.028), thereby validating the first hypothesis that increased levels of EM correlate with enhanced CS. The findings regarding the impact of EM on CS align closely with several previous studies. For example, Al-Omari et al. (2020) found a significant positive relationship between EM and CS in their predictive-descriptive study at Safeway Company, Jordan. Similarly, Ojo. et al. (2017) reported similar results in their quantitative study of employees of 20 registered five-star hotels in Benin City, Nigeria.

Additionally, the second hypothesis investigated the mediating role of EN in the relationship between EM and CS. The results demonstrate a significant indirect effect of EM on CS through EN (β = 0.345, p < 0.000), indicating that EN partially mediates the relationship between EM and CS. This finding supports the hypothesis that EN acts as a mediator in the EM-CS relationship. Additionally, the mediating role of EN gains support from a recent study conducted by Evans et al. (2023) across diverse industries. Although not focused on the electric vehicle sector, their meta-analysis validates the intermediary function of engaged employees in driving customer-focused outcomes. This broader perspective strengthens this study's contribution by highlighting the robustness of the mediation model across different organizational contexts. As the EV industry evolves, understanding these underlying dynamics becomes crucial for businesses seeking to strategically position themselves in a competitive market driven by technological advancements and customer expectations.

Practical Implications

In an industry characterized by sustainable practices and technological innovation, empowering employees to contribute to environmental consciousness and innovation can enhance their engagement and effectiveness. Strategically focusing on fostering an engaged workforce emerges as a key recommendation. Training programs, recognition initiatives, and cultivating a supportive organizational culture can be prioritized to elevate engagement levels, thus positively impacting CS. The mediating role of engagement emphasizes that empowered employees should be engaged and act as catalysts for customer-centric outcomes. Creating a symbiotic relationship between empowerment and engagement becomes integral to organizational success. As the EV industry undergoes rapid technological advancements and shifts in consumer preferences, businesses should recognize the dynamic nature of these relationships. Continuous adaptation of empowerment and engagement strategies ensures organizational practices remain relevant and effective amid evolving industry dynamics.

Managerial Implications

From a managerial perspective, the implications guide specific actions that businesses in the EV sector can implement to navigate the challenges and opportunities unique to their industry. Investing in sustainable empowerment aligns with the industry's emphasis on environmental consciousness. Businesses can empower employees to contribute to sustainable practices and innovation, resonating with the ethos of the EV sector. Acknowledging the technological focus of the EV industry, businesses can implement engagement strategies that leverage technology. This might include digital platforms for employee recognition, virtual collaboration tools, and innovative training programs aligning with the industry's tech-driven landscape. Recognizing the pivotal role of engaged employees in enhancing CS, businesses should invest in customer-centric employee training programs. These programs can equip employees with the skills and knowledge necessary to meet the unique demands of the electric vehicle market.

Limitations and Future Directions

In considering the limitations of the study on EM and CS in the EV industry, several factors warrant careful examination. First, sample biases may arise due to the demographic characteristics of participants, sample size, or recruitment methods, potentially limiting the generalizability of the findings. Coenen & Gureckis discuss how biased sampling can distort research outcomes, emphasizing that even natural sampling can lead to flawed inferences (Coenen & Gureckis, 2021). This highlights the importance of ensuring a representative sample to enhance the validity of the study's conclusions.

Methodological constraints also pose significant challenges. Limitations in data collection techniques or measurement instruments can impact the reliability and validity of the results. Cuypers et al., 2022 note that methodological advancements are crucial for addressing these constraints, particularly in the context of international business research, where evolving technological trends necessitate robust methodologies (Cuypers et al., 2022). Future studies could benefit from employing more sophisticated measurement tools or experimental designs to mitigate these limitations and yield more nuanced insights.

Additionally, the presence of potential confounding variables that were not accounted for in the study could introduce bias and affect the interpretation of results. Mir et al., 2018 emphasize the importance of considering various factors that may influence the outcomes in supply chain management research, suggesting that overlooking such variables can lead to an incomplete understanding of the phenomena under investigation (Mir et al., 2018). Future research should investigate the impact of additional variables or moderators not examined in this study, thereby enhancing the understanding of the relationship between EM and CS within the EV sector.

Acknowledging these limitations is essential for advancing knowledge in the field. Future research should focus on employing larger and more diverse samples, utilizing robust measurement tools, and exploring additional variables to address the gaps identified in the current study. By doing so, researchers can contribute to a more thorough understanding of the dynamics between employee empowerment, engagement, and customer satisfaction in the rapidly evolving electric vehicle industry.

Conclusion

In conclusion, this research provides in-depth insights into the interconnections among EM, EN, and CS within the EV industry. This research, guided by specific objectives, has provided empirical evidence to support key hypotheses, contributing to the academic literature and practical implications for EV businesses.

The first key finding underscores the positive influence of EM on CS within EV businesses. Aligning with prior research, the results highlight the significance of granting decision-making authority, skill development opportunities, and access to information in empowering employees (Ojo et al., 2017). These insights carry practical implications for EV companies, emphasizing the importance of cultivating an empowered workforce to enhance overall organizational performance.

Secondly, the findings affirm the mediating role of engagement between EM on CS within the EV industry. Engaged employees, characterized by enthusiasm, motivation, and a strong sense of belonging, are shown to play a crucial role in meeting customer needs and delivering exceptional service. This underscores the strategic importance of fostering a work environment that nurtures EN for EV businesses looking to enhance CS and build brand loyalty. This mediation model provides a nuanced understanding of how empowered employees contribute to improved customer-centric outcomes through heightened engagement.

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Received: 01-Sep-2025, Manuscript No. AMSJ-26-16939; Editor assigned: 03-Oct-2025, PreQC No. AMSJ-26-16939(PQ); Reviewed: 11-Jan-2026, QC No. AMSJ-26-16939; Revised: 17-Mar-2026, Manuscript No. AMSJ-26-16939(R); Published: 24-Mar-2026