The Impact of Generative AI and Hybrid Work on Employee and Customer Experience: A Comprehensive Approach

Abstract

This paper examines the impact of employee experience with Generative AI (GenAI) on employee experience, employee engagement, and their effects on customer experience, such as satisfaction, loyalty, and engagement in the service sector. Following a mixed-methods approach, this research comprises two studies: Study 1 uses survey data from 578 frontline employees in the UK and Vietnam to examine the research model, and Study 2 involves qualitative interviews to further elaborate on the findings. Results show that GenAI experience enhances employee experience and engagement, which in turn improves customer experience. The paper also highlights the mediating effect of employee experience and the context-dependent moderating effect of hybrid work. This research comprehensively explores the link between employee and customer experiences, while integrating employee experience with GenAI and hybrid work as timely constructs that reflect the complexities at the frontline. These findings contribute to the literature on human–technology interaction and organizational change, extending the service climate framework with empirical evidence on the evolving dynamics between technology, employees, and customers.

Keywords

GenAI employee engagement employee experience customer experience hybrid work

Introduction

As a universal phenomenon, work-from-home arrangements accelerated further after the COVID-19 pandemic, as employees and organizations became acquainted with them (Appel-Meulenbroek et al., 2022). Combining remote and in-office work, hybrid work environments present both flexibility and challenges for employee productivity and satisfaction, requiring behavioral adjustments and adaptation to new technologies for remote collaboration (Allen et al., 2024). The adoption of such arrangements is unavoidable in the long run, as employee demand has increased considerably and it is practicable in more roles than previously believed (Gifford, 2022). Simultaneously, business operations have been transformed by the growth of artificial intelligence (AI), which in the service sector optimizes decisions, streamlines operations, and enhances customer experience (CX), though leveraging it for maximum efficiency (Xu et al., 2020) and understanding changes in the position of human staff (Noor et al., 2022) remain open questions.

Advancements in technology, such as AI, can reshape the dynamics of organizational frontlines, changing how services are provided as well as both frontline staff and customers’ experiences (Y. Chen & Prentice, 2025). Generative AI (GenAI), a prominent subset of AI, is increasingly being adopted in the workplace. GenAI, when utilized effectively, can offer tailored support in real time, responding to complex inquiries with creative solutions and providing assistance across a broad range of tasks (Manresa et al., 2025). By complementing human employee capabilities, especially in service settings, these technological tools can contribute to improved performance outcomes (Wang et al., 2024; Wirtz & Stock-Homburg, 2024). These advanced competencies of GenAI can both enhance motivation and pose challenges to workers, as the fear of displacement may demotivate them (Manresa et al., 2025). Despite these opposing effects, there is a lack of studies examining the impact of those particular tools on frontline employees’ overall experiences and engagement at work, which are crucial for CX, especially in a service organization context (Bowen, 2024). Moreover, scholars and practitioners face difficulties when integrating EX and CX approaches, as these factors encompass a range of interrelated variables.

To address this gap, this paper aims to explore how implementing GenAI in the service sector, along with hybrid work arrangements, can be an effective way to transform internal employee dynamics into superior CX. With this objective, three research questions are offered as follows:

RQ1: How does employee experience with GenAI affect employee and customer outcomes?

RQ2: In what ways does EX serve as a mediating mechanism between employees’ use of GenAI and outcomes such as employee engagement and CX?

RQ3: How does a hybrid working arrangement moderate the relationships among employee experience with GenAI, EX, and employee engagement?

Built on the service climate framework, this research advances the emerging literature on human–AI collaboration in services. It integrates employee experience with GenAI as a timely construct, contributing to both employee experience and technology adoption domains. Examining EX as a mediator and hybrid work as a moderator captures the complex dynamics of contemporary organizations and offers a more integrated model of organizational behavior in the service sector. Using a comparative, cross-national mixed-methods approach focused on frontline employees, this paper provides empirical evidence and significant insights to guide strategic GenAI adoption and hybrid work practices for better employee and customer outcomes.

Literature review

Service Climate Framework

The Service Climate Framework (SCF) by Bowen and Schneider (2014) offers a strong theoretical basis for connecting internal organizational elements, particularly employee perceptions and behaviors, with external customer outcomes. A positive and strong service climate reflects the shared belief among employees that service-related practices and policies are consistently promoted and supported (Bowen & Schneider, 2014). Schneider and Bowen (2019) address the changing nature of the service climate as technology and organizational structures evolve at the frontline, raising questions about its cohesiveness as frontlines increasingly integrate human and AI elements.

In service encounters, frontline employees play a critical role in shaping CX (S.-C. Chen & Quester, 2015; de Villiers et al., 2023). These employees deliver the best service when their expectations and needs are met through organizational support (Morgan, 2017). Bowen and Schneider (2022) further emphasize the reciprocal influence between employee and customer experiences, where better support and treatment of employees improve customer outcomes, which in turn reinforce employee motivation and engagement. Building on this, Bowen (2024) extended the SCF by incorporating human experience and emphasized the crucial role of EX in enhancing CX. These insights highlight the importance of aligning management practices with frontline employee experiences to strengthen employee–organization relationships and enhance service quality.

This paper draws on the SCF because it integrates perspectives from organizational behavior, human resource management, and marketing by examining how internal experiences influence customer-facing performance. With the support of GenAI in service processes, organizations can not only improve EX but also foster deeper engagement. This enhancement translates into higher-quality service delivery, driving better customer satisfaction, loyalty, and engagement.

Employee experience with GenAI

According to Feuerriegel et al. (2023), GenAI encompasses “computational techniques that are capable of generating seemingly new, meaningful content such as text, images, or audio from training data.” In addition, this advanced technology has the power to enhance efficiency and productivity by automating numerous tasks that were once carried out by humans, including content creation, customer service, code generation, and more (Feuerriegel et al., 2023; Gupta et al., 2024). Moreover, it has the potential to not only reduce costs and unlock exciting prospects for growth and innovation (Eloundou et al., 2023). With these useful features, GenAI can offer employees flexibility and adaptability in their work tasks, delivering reliable and timely support that ensures accurate outcomes (Prentice & Nguyen, 2020; Wirtz & Stock-Homburg, 2025). When employees perceive these capabilities in GenAI, it contributes to a more positive experience in using this technology in their work.

Employee experience

Plaskoff (2017) defines EX as employees’ perceptions of their relationship with the organization, while Gustafsson et al. (2024) describe EX as how employees internalize and interpret their interactions with their organization, including the surrounding context. Morgan (2017) defines EX, which includes physical, technological, and cultural experiences, as the convergence of employees’ expectations, demands, and needs with organizational design. The physical dimension pertains to the work environment, such as workspace and facilities (Lee & Kim, 2023). The technological dimension refers to the tools and technology available for work (McCarthy & Wright, 2004). Cultural experience refers to organizational values, beliefs, and norms that shape the work environment, reflected in interactions with colleagues and managers, as well as the recognition and appreciation employees receive (Lee & Kim, 2023).

With the aid of technology, employees can better excel in their roles, offering personalized services that would be impossible otherwise (Grewal et al., 2023). For instance, through the use of tablets connected to GenAI systems, employees can receive support and suggestions as they interact with customers (Grewal et al., 2023). A robust GenAI system enables efficient work with minimal effort, signaling organizational investment in employees’ success and fulfilling their expectations (Morgan, 2017). Organizational practices that promote broad access to information, knowledge, and skills can help employees feel empowered, leading to a positive EX (Bowen, 2024). Consequently, the following hypothesis is proposed:

H1a: Experience with GenAI is positively and significantly related to EX.

Employee engagement

The pioneering research by Kahn (1990) defines employee engagement as the psychological state of an individual’s meaningfulness, safety, and availability at work, whereas Boccoli et al. (2023) argue this should also be reconsidered as a social and relational concept. Soane et al. (2012) comprehensively conceptualize engagement as consisting of three facets. Specifically, intellectual engagement refers to being intellectually absorbed in one’s work beyond simply fulfilling job responsibilities, while affective engagement relates to positive emotions experienced in their roles. Social engagement reflects feeling socially connected to the work environment and sharing values with colleagues (Soane et al., 2012).

Previous research has demonstrated the potential of technology to enhance employee engagement. For instance, Prentice et al. (2023) indicate that investing in AI technology can effectively improve employee engagement. In Dutta et al.’s (2022) study, AI-enabled chatbots give employees the opportunity to express opinions, concerns, and receive individualized attention, thereby increasing their engagement. Highly engaged employees often take on additional tasks, learn new technologies, and seek opportunities to contribute when they feel they have sufficient resources to handle challenges (Sharma & Nambudiri, 2020). In this regard, GenAI serves as a valuable job resource that simplifies tasks, reduces workloads, and provides innovative tools to boost productivity and creativity, ultimately enhancing engagement. Thus, we propose the following hypotheses:

H1b: Experience with GenAI is positively and significantly related to employee engagement.

The previous perspective sees employee engagement as a series of actions and presents the idea that EX has an impact on employee engagement (Malik et al., 2023). The current HR approach presents EX as the modern employee value proposition (Panneerselvam & Balaraman, 2022), indicating that a positive EX results in increased employee engagement, potentially setting off a cycle of positive culture, enhanced engagement, and improved organizational performance. EX can be context-specific as employees form perceptions through various encounters that shape their experience in the workplace (Batat, 2022; Morgan, 2017). When employees feel well-supported and provided with adequate resources, it creates the foundation for engagement, boosting their energy and motivation at work (Bowen, 2024). Therefore, the third hypothesis is offered:

H2: EX is positively and significantly related to employee engagement.

Customer experience

CX refers to customers’ spontaneous and non-deliberate responses to a company’s offerings (Becker & Jaakkola, 2020). CX involves interconnected constructs such as quality, satisfaction, and loyalty, as seen in prior service climate research (Bowen & Schneider, 2014). Moreover, engagement serves as an indicator of customer involvement and connection with a company’s products, services, and activities (Verhoef et al., 2010). An engaged customer results from feeling sufficiently satisfied and interacting with the company itself (Hollebeek, 2011). Thus, in this study, customer engagement is viewed as an aspect of CX, along with customer satisfaction and loyalty.

While the literature suggests that there is a connection between EX and CX (Batat, 2022), the results are inconclusive partly because researchers collect data at the organizational level to examine the relationship between these variables (Gauthier et al., 2022). Another challenge faced by scholars and practitioners who wish to adopt the EX and CX approaches is that these factors encompass various variables. Zeithaml et al. (2002) indicated that the behaviors of all employees, especially those who interact directly with customers, significantly impact the perceived experience of the customer. Additionally, the study by Pine (2020) also presented that a better EX results in the creation of a superior CX, which, in turn, enhances the overall EX. This discussion leads to the following hypothesis:

H3: EX is positively and significantly related to CX.

According to Chidley and Pritchard (2014), CX generated by employees often determines if customers feel prioritized. They also assert that this environment highlights the importance of individual employees feeling engaged, satisfied, and aware of their value to customers. When customers sense a higher level of value and connection with engaged employees, it enhances their gratitude, ultimately resulting in more positive CX (Qi et al., 2023). Pimpakorn and Patterson (2010) also indicated that engaged and joyful employees are more productive, leading to higher customer satisfaction and firm performance. In this regard, we propose the fifth hypothesis:

H4: Employee engagement is positively and significantly related to CX.

Mediating effect

Previous research as mentioned earlier has mostly focused on the direct effects of these factors. A positive interaction with GenAI can enhance EX which helps them feel more engaged (Panneerselvam & Balaraman, 2022) as they see the advantages of incorporating AI into their daily tasks. Similarly, experience with GenAI can enhance EX by offering advanced benefits that can improve employee performance and the quality of service provided to customers (Grewal et al., 2023), ultimately leading to an improved CX (Pine, 2020; Zeithaml et al., 2002). Hence, we offer the following hypotheses to clarify the mediating role of EX:

H5: EX has a significant mediating effect between employee experience with GenAI and (a) employee engagement, as well as (b) CX.

Moderating effect

Ipsen et al. (2021) indicated that many employees reported feeling more in control, more productive, and having a better work-life balance as a result of this working setting. Recent research by Teng-Calleja et al. (2024) highlighting useful team- and organizational-level actions related to work tools and technology integration in hybrid work also indicated higher productivity, savings, and work-life balance of employees. From McKinsey and Company’s (2020) survey of over 800 employees, remote workers experience higher engagement, better well-being, and more positive impacts on daily work compared to those without remote work flexibility. By employing a hybrid working level as a moderator, this study attempts to provide a more nuanced view of the linkages among EX, employee engagement, and CX in service sector especially in this emerging working mode, with the proposed hypotheses as follows:

H6: Hybrid work level moderates the relationship between EX and (a) employee engagement, as well as (b) CX.

Based on the proposed hypotheses, Figure 1 shows an illustration of the research model.

Figure 1.

Research model.

Methods

The present research applied an explanatory sequential mixed methods design (QUAN→qual), where the quantitative phase was conducted first, followed by a qualitative phase aimed at deepening the interpretation of the quantitative findings (Creswell & Plano Clark, 2018). In the quantitative stage, survey data were collected and analyzed to test the hypothesized relationships in the proposed model. To enhance the generalizability of the results, data were obtained from two different national contexts. Once the measurement model’s reliability and validity were confirmed, the structural relationships were evaluated. The subsequent qualitative phase involved conducting in-depth interviews, with the goal of validating and enriching the quantitative insights. Participants in both research phases were frontline service employees who had experience using GenAI in their work.

All participants were informed about the research objectives, and their consent was obtained to confirm voluntary participation. Their personal information was anonymized to ensure privacy. It was made clear to them that there were no right or wrong answers, thus encouraging them to provide honest and accurate responses. More than a single study, this mixed-methods approach enables the paper to provide a more comprehensive understanding of how GenAI and hybrid work levels influence employee experience, ultimately shaping customer outcomes.

Study 1

The study adopted a comparative design using two data samples collected from distinct contexts to examine the proposed model: the United Kingdom (UK) as a developed country and Vietnam as a developing country. This aims to provide comparative insights and strengthen the contribution to the literature by addressing gaps in understanding how GenAI and hybrid work influence EX and CX throughout diverse settings. We employed PLS-SEM to evaluate our measurement and structural models, suitable for exploratory research and higher-order constructs (Hair et al., 2022).

Measure

Employee experience with Generative AI was measured by four aspects: reliability, assurance, empathy, and responsiveness from Parasuraman et al.’s (1991) SERVQUAL scale, adapted by Prentice and Nguyen (2020). EX was measured using Lee and Kim’s (2023) scale with three dimensions: physical, technical, and cultural experience. Employee engagement, consisting of intellectual, social, and affective engagement, was measured using the scale from Soane et al. (2012). CX included customer loyalty measured by Tokman et al.’s (2012) scale, customer satisfaction by Proenca et al.’s (2017), and customer engagement by Cambra-Fierro et al.’s (2014). This study adopted a 5-point Likert scale from 1 (Strongly disagree) to 5 (Strongly agree) for all measurement items.

Prior to the official data collection, the questionnaire was subject to a pilot test with three scholars. This test aimed to guarantee adequate response time and clarity in the wording of the questionnaire. As a result, certain items were modified to improve face validity.

Procedure and sample

In July 2024, participants were recruited via Prolific, using pre-screening filters to target frontline employees in the service sector. Over 400 UK respondents completed an online Qualtrics survey within a week. After filtering to exclude those failing the attention check question, 304 responses were retained for subsequent analysis. Of these, 53% were male, 43.4% female, and the rest identified as third gender or preferred not to disclose. Most participants were aged 26 to 35 years (44.7%), followed by 36 to 45 years (24%). Over half held a Bachelor’s degree (52.3%), and 27% had postgraduate qualifications. Many participation were from education (13.5%), healthcare (12.8%), and public services (12.8%) sectors. Nearly half of the respondents were employed as professionals (47.7%) and reported an annual income of £20,000 to £39,999 (48.4%).

For data collection in Vietnam, an online survey was conducted via Google Forms, using a snowball sampling technique to reach eligible participants. Around 300 responses were gathered within 2 weeks in December 2024. Following the same filtration process, 274 valid responses were used for analysis. Of the participants, 62.8% were female, 36.1% male, and the remaining identified as third gender. Most were aged 18 to 25 years (59.9%), followed by 26 to 35 years (36.1%). A majority held a Bachelor’s degree (82.8%), with 13.9% holding postgraduate qualifications. Finance and banking (16.1%) and retail (8.6%) were the main sectors. Nearly half worked as clerical support staff (44.2%), and 60.6% earned 10 to 25 million VND (approximately £321–£802) per month.

Common method bias

First, Harman’s single-factor test involved restricting all variables to a single factor in an exploratory factor analysis, revealing that the constrained factor explained 30.6% of the variance, below the 50% threshold (Podsakoff et al., 2003). Second, the marker-variable technique used job competence as a marker variable, showing a mean change of less than .01 in construct correlations when its effect was partialled out (Lindell & Whitney, 2001). Finally, the factor-level variance inflation factor (VIF) from the PLS algorithm analysis confirmed no bias, as all VIFs in the inner model consistently remained below the 3.33 threshold across six iterations, with each iteration designating a distinct construct as the dependent variable and the others as predictors (Kock, 2015, 2019).

Measurement model assessment

The constructions Customer engagement (CEN), Customer loyalty (CLO), and Customer satisfaction (CSA) were evaluated using a first-order reflective method, whereas the Employee experience with GenAI (EGenAI), Employee experience (EX), Employee engagement (EE) components were evaluated using a second-order reflecting-reflective measuring model.

One item (GARP_1) was eliminated because of their insufficient outer loadings while all other items were above the minimum threshold of 0.4 (Hair et al., 2022), indicating adequate levels of indicator reliability. All constructs had Cronbach’s alpha and rhoA values exceeding the minimum acceptable threshold of .6 (Hair et al., 2022). Convergent validity was evaluated using the Average Variance Extracted (AVE), and all constructs met the minimal criterion of .5 (Fornell & Larcker, 1981; see Table 1). The heterotrait-monotrait (HTMT) ratio of correlations was used to evaluate discriminant validity. As the HTMT ratio should not exceed one and should be below 0.90 (Henseler et al., 2015), results in Table 2 confirm that the conditions for discriminant validity are satisfied.

Table 1.

Internal Reliability and Convergent Validity.

	Construct	UK (N = 304)					Vietnam (N = 274)					Total (N = 578)
	Construct	Loading	Alpha	rhoA	AVE	VIF	Loading	Alpha	rhoA	AVE	VIF	Loading	Alpha	rhoA	AVE	VIF
Customer experience (CX)	Customer engagement (CEN)	[0.833;0.859]	0.801	0.801	0.715	1.675	[0.802;0.861]	0.776	0.783	0.690	1.606	[0.813;0.855]	0.788	0.790	0.702	1.627
	Customer loyalty (CLO)	[0.849;0.889]	0.832	0.833	0.749	1.675	[0.849;0.888]	0.840	0.841	0.758	1.606	[0.851;0.889]	0.837	0.838	0.755	1.627
	Customer satisfaction (CSA)	[0.829;0.880]	0.827	0.837	0.742	1.675	[0.823;0.895]	0.840	0.847	0.757	1.606	[0.853;0.893]	0.844	0.845	0.762	1.627
Second-order	First-order
Experience with GenAI (EGenAI)	GenAI assurance (GAAS)	[0.513;0.872]	0.650	0.744	0.594	1	[0.864;0.878]	0.839	0.839	0.756	1	[0.751;0.872]	0.772	0.787	0.688	1
	GenAI empathy (GAEM)	[0.822;0.882]	0.814	0.816	0.730	1	[0.766;0.859]	0.772	0.780	0.687	1	[0.792;0.872]	0.793	0.799	0.708	1
	GenAI reliability (GARL)	[0.780;0.917]	0.836	0.848	0.756	1	[0.743;0.910]	0.803	0.818	0.721	1	[0.747;0.916]	0.816	0.833	0.735	1
	GenAI responsiveness (GARP)	[0.919;0.921]	0.819	0.819	0.847	1	[0.792;0.899]	0.616	0.661	0.718	1	[0.849;0.910]	0.711	0.740	0.774	1
Employee experience (EX)	Physical experience (EPX)	[0.731;0.835]	0.674	0.697	0.603	1	[0.809;0.817]	0.744	0.746	0.661	1	[0.767;0.823]	0.706	0.716	0.628	1
	Technical experience (ETX)	[0.857;0.872]	0.834	0.834	0.751	1	[0.863;0.884]	0.843	0.844	0.761	1	[0.866;0.879]	0.841	0.841	0.759	1
	Cultural experience (ECX)	[0.784;0.899]	0.858	0.861	0.703	1	[0.858;0.882]	0.895	0.896	0.761	1	[0.827;0.888]	0.880	0.881	0.736	1
Employee Engagement (EE)	Intellectual engagement (EIE)	[0.928;0.943]	0.926	0.926	0.871	1	[0.840;0.876]	0.819	0.820	0.734	1	[0.889;0.912]	0.881	0.882	0.808	1
	Social engagement (ESE)	[0.904;0.926]	0.906	0.908	0.842	1	[0.809;0.881]	0.792	0.794	0.707	1	[0.872;0.891]	0.854	0.855	0.774	1
	Affective engagement (EAE)	[0.941;0.957]	0.945	0.945	0.902	1	[0.890;0.935]	0.891	0.891	0.821	1	[0.920;0.948]	0.923	0.923	0.866	1

Table 2.

Heterotrait-Monotrait Ratio (HTMT).

	CEN	CLO	CSA	GAAS	GAEM	GARL	GARP	EPX	ETX	ECX	EIE	ESE
CEN
CLO	0.862
CSA	0.783	0.766
GAAS	0.369	0.368	0.361
GAEM	0.344	0.360	0.358	0.458
GARL	0.378	0.369	0.374	0.824	0.478
GARP	0.428	0.320	0.419	0.424	0.553	0.434
EPX	0.551	0.483	0.497	0.248	0.320	0.292	0.297
ETX	0.525	0.525	0.584	0.359	0.405	0.414	0.273	0.777
ECX	0.583	0.510	0.609	0.278	0.310	0.338	0.331	0.766	0.763
EIE	0.492	0.454	0.572	0.327	0.324	0.282	0.333	0.413	0.512	0.501
ESE	0.470	0.440	0.433	0.317	0.222	0.297	0.264	0.420	0.450	0.486	0.482
EAE	0.503	0.507	0.588	0.313	0.307	0.304	0.268	0.574	0.591	0.627	0.698	0.590

Structural model assessment

The structural model was evaluated using bootstrapping with 10,000 samples (Hair et al., 2022). All predictors in the inner model had VIF values below 5, confirming the absence of multicollinearity (Hair et al., 2022). The model also demonstrated good fit with an SRMR value of 0.086, falling below the .1 threshold (Hu & Bentler, 1999). Moreover, all R² values in this model exceeded .1, suggesting acceptable in-sample explanatory power (Ozili, 2023). The Q² values are all above 0, indicating acceptable predictive relevance for the endogenous constructs, according to Hair et al. (2022).

The results with all hypotheses being supported, were consistent across both country samples (see Table 3). There was a significant impact of EGenAI on EX (β = .400, p < .001) and EE (β = .178, p < .001), providing support for H1a and H1b. The result indicated that EX had a favorable and substantial effect on EE (β = .550, p < .001), supporting H2. The analysis confirms significant effects of EX on CX, with beta values of .331, .297, and .342 (p < .001) for three aspects of CX, including CSA, CLO, and CEN, respectively. Thus, hypotheses H3a, H3b, and H3c were supported. Similarly, the effects of EE on CX are significant, with beta values of .357, .305, and .281 (p < .001) for CSA, CLO, and CEN, respectively, supporting hypotheses H4a, H4b, and H4c. Furthermore, the effect sizes (f²) for these relationships, ranging from small to large (Cohen, 1988), are also illustrated in Table 3. In addition to these rule-of-thumb benchmarks, we conducted formal significance testing by converting f² values into F-statistics and evaluating them against the F-distribution (Cohen, 1988). This distribution-based approach provides additional statistical support for the meaningfulness of the reported effect sizes. Moreover, the indirect effects from EGenAI on EE (β = .220, p < .001) and three subdimensions of CX (β_CSA = .132, β_CLO = .119, β_CEN = .137, p < .001) through EX were all substantial and favorable. Hence, hypotheses H5a and H5b were supported.

Table 3.

Results of Hypotheses Testing.

Hypotheses	Relationship	UK (N = 304)			Vietnam (N = 274)			Total (N = 578)			Result
	Relationship	β	f ²	F	β	f ²	F	β	f ²	F
H1a	Employee experience with GenAI→Employee Experience	.386***	0.175	52.5***	.398***	0.188	50.8***	.400***	0.190	109.1***	Supported
H1b	Employee experience with GenAI→Employee Engagement	.219***	0.073	21.9***	.130*	0.024	6.5*	.178***	0.045	25.8***	Supported
H2	Employee Experience→Employee Engagement	.550***	0.463	139***	.562***	0.438	118.3***	.550***	0.432	248.0***	Supported
H3a	Employee Experience→Customer Satisfaction	.231**	0.044	13.2***	.367***	0.166	44.8***	.331***	0.109	62.6***	Supported
H3b	Employee Experience→Customer Loyalty	.261***	0.055	16.5***	.313***	0.090	24.3***	.297***	0.077	44.2***	Supported
H3c	Employee Experience→Customer Engagement	.252***	0.053	15.9***	.409***	0.160	43.2***	.342***	0.105	60.3***	Supported
H4a	Employee Engagement→Customer Satisfaction	.355***	0.105	31.5***	.414***	0.211	57.0***	.357***	0.127	72.9***	Supported
H4b	Employee Engagement→Customer Loyalty	.302***	0.074	22.2***	.316***	0.092	24.8***	.305***	0.081	46.5***	Supported
H4c	Employee Engagement→Customer Engagement	.333***	0.092	27.6***	.244***	0.057	15.4***	.281***	0.071	40.8***	Supported
H5a	Employee experience with GenAI→Employee Experience→Employee Engagement	.213***			.224***			.220***			Supported
H5b-1	Employee experience with GenAI→Employee Experience→Customer Satisfaction	.089**			.146***			.132***			Supported
H5b-2	Employee experience with GenAI→Employee Experience→Customer Loyalty	.101**			.125***			.119***			Supported
H5b-3	Employee experience with GenAI→Employee Experience→Customer Engagement	.097**			.163***			.137***			Supported
		R ²	Q ²		R ²	Q ²		R ²	Q ²
	Employee experience	.149	0.142		.158	0.150		.160	0.156
	Employee engagement	.443	0.179		.392	0.116		.412	0.153
	Customer engagement	.281	0.122		.349	0.123		.315	0.129
	Customer loyalty	.260	0.115		.320	0.117		.293	0.124
	Customer satisfaction	.283	0.097		.494	0.147		.383	0.142

p < .05. **p < .01. ***p < .001.

While the results were broadly consistent between the UK and Vietnam, the strength of specific relationships varied. Notably, the effect of EGenAI on EE (H1b) was stronger in the UK, whereas the impact of EX on CX (H3a, H3b, and H3c), as well as its mediating effect between EGenAI and CX (H5b), was found to be greater in Vietnam. However, when examining the significance of differences in group-specific parameter estimates, no substantial cross-country variation was detected, suggesting that the model’s structural relationships are robust and generalizable across both contexts.

Moderation analysis

This study used the multigroup analysis to examine the moderating role of hybrid working levels in the research model (Sarstedt et al., 2011). The data were divided into two groups: 393 employees working from home 0 to 2 days/week were classified as the low hybrid work group, while 185 employees working three or more days remotely were assigned to the high group. Specifically, in the UK, 162 participants fell into the low group and 142 into the high group. In Vietnam, 231 employees were categorized as low hybrid, and 43 as high hybrid. Table 4 presents the differential analysis for each path.

Table 4.

Results of Moderation Analysis.

Hypotheses	Relationship	UK (N = 304)	Vietnam (N = 274)	Total (N = 578)
	Relationship	Path coefficients difference (hybrid high-low)	Path coefficients difference (hybrid high-low)	Path coefficients difference (hybrid high-low)
H6a	Employee Experience→Employee Engagement	−0.190*	−0.160	−0.174*
H6b-1	Employee Experience→Customer Satisfaction	0.141	0.024	0.107
H6b-2	Employee Experience→Customer Loyalty	−0.077	0.284*	0.086
H6b-3	Employee Experience→Customer Engagement	0.024	0.159	0.053

p < .05.

In the pooled sample, hybrid working levels significantly and negatively moderated the association between EX and EE (β = −.174, p < .05), while showing no moderating effect on the relationship between EX and CX. These results are consistent with those observed in the UK sample (β = −.190, p < .05). In contrast, in the Vietnam sample, high levels of hybrid work significantly strengthened the effect of EX on customer loyalty (β = .284, p < .05), an effect not evident in either the UK or the total sample. The moderating role of this variable was not found in other relationships in the Vietnam context.

Study 2

In the second study, a qualitative approach with in-depth interviews was utilized to elaborate on the quantitative findings regarding how experience with GenAI influences employee experience within the organization and their engagement, ultimately contributing to enhanced customer experience. The study offers additional contextual insights from frontline employees within service settings.

Procedure and sample

In this qualitative phase, we conducted semi-structured interviews. Participants were frontline employees with experience using GenAI tools in their work, recruited from service-oriented organizations in Vietnam. Each session lasted between 15 and 30 min and followed a semi-structured guide covering: (1) personal experience with GenAI tools, (2) perceived impact on employee experience and engagement, (3) perceived impact on CX, and (4) the role of hybrid working conditions. Open-ended questions encouraged participants to share examples and reflections based on real work scenarios. To protect confidentiality, personal identifiers were removed and pseudonyms assigned.

The sample of 21 interview participants was relatively balanced in gender (52.4% male, 47.6% female), with most participants holding officer-level positions (81.0%). Experience levels were varied, with 28.6% having less than 3 years, 38.1% having 3 to 5 years, and 33.3% having more than 5 years in their respective industries. Participants came from diverse service sectors, including education (23.8%), banking and finance (23.8%), technical consulting (19.1%), sales/marketing (14.3%), HR/recruitment (9.5%), and public service (9.5%).

Findings

Benefits of positive experience with GenAI at work

Across interviews, participants consistently reported that using GenAI tools significantly improved their day-to-day service work. These tools with their helpful features were especially valuable for handling repetitive, language-heavy, and information-driven tasks that otherwise consumed time and mental energy, leading to more improved employee outcomes. GenAI allowed participants to complete these tasks more efficiently and with greater precision, which in turn contributed to a smoother and more satisfying work process.

Participant 5 (Banking officer) shared, “GenAI helps me draft documents and emails for clients. It makes my communication clearer and more professional, which reduces pressure during peak periods.”

Moreover, positive experiences with GenAI contributed to a more positive employee experience, as participants felt supported and valued by organizations that provided the resources needed to perform their roles effectively. The sense of being supported led participants to perceive their companies as more attentive, responsive, and committed to improving work conditions. As a result, this reinforces a more favorable employee experience.

Participant 6 (Sales representative) said, “GenAI tools are very helpful. I am happy that the company has made a good effort to provide the necessary equipment and encourage the use of GenAI to support our work.”

Additionally, many participants reported that GenAI allowed them to shift from routine execution to more strategic and creative thinking. This shift not only enhanced their effectiveness but also deepened their intellectual and affective engagement with the work itself. Particularly for employees in consultative and knowledge-intensive service roles, GenAI was seen not as a replacement but as an enabler of more meaningful contributions.

As Participant 11 (Marketing consultant) noted, “I am definitely more engaged, because when I can solve problems faster with GenAI, I have more time to explore deeper industry issues.”

While a few respondents acknowledged the potential downside of reduced interpersonal interaction, such as asking GenAI instead of colleagues, many others highlighted how GenAI actually enabled more human connection by enhancing internal communication, freeing up time, and energy for social and collaborative engagement. Participants reported that, with routine tasks handled more easily, they were more available, both mentally and practically, for meaningful peer interactions.

Participant 10 (Student services manager) shared, “GenAI frees up time. I can have more meaningful conversations with my colleagues.”

The importance of EX: Internal fulfillment, external impact

With the relief and assistance provided by GenAI tools at work, participants consistently described a connection between feeling supported by their organizations and the energy, motivation, and care they brought to their roles and customer interactions. Regarding employee engagement, many frontline workers emphasized how their experience with the organization shaped their commitment to the job. This alignment between organizational support and individual expectations generated a sense of reciprocity. When employees felt that the company was investing in their success, they reciprocated with stronger emotional and cognitive involvement. The convergence of physical comfort, technological enablement, and cultural positivity fueled stronger belonging and motivation. These aspects of employee experience helped shape a mindset that views the workplace as a space of mutual respect and empowerment.

Participant 4 (Translator) said, “A supportive culture and facilities help work flow more smoothly and improve my mood, so that helps me stay more engaged with work and deliver better results”

When it came to CX, participants clearly articulated how the quality of their work environment influenced the energy and attention they could offer clients. This connection between internal positivity and external service quality recurred across multiple interviews. These insights point to how EX is not merely an internal outcome but a foundational layer that influences how service is delivered. A positive and well-supported work setting enabled employees to bring their full attention, empathy, and professionalism to customer interactions. In other words, EX became a channel through which the organization’s values were communicated to clients, subtly but powerfully.

Participant 16 (Software support officer) explained, “The work environment is now more dynamic and convenient, helping me complete tasks more efficiently. When I have a good work environment, I’m in a better mood and more motivated to serve customers attentively and thoroughly. Customers are more satisfied and willing to come back because they receive better service.”

These reflections are more than momentary mood changes, they point to a reinforcing cycle where EX fuels CX, which in turn contributes to a deeper sense of fulfillment in one’s role. Others emphasized the role of EX in enabling them to align with broader organizational goals. This insight illustrates how EX not only energizes service behaviors but also reinforces a shared sense of purpose between employees and their organization.

Participant 2 (Education Consultant) added a relational dimension: “When I have a positive experience with my work environment and organizational culture, I am more likely to reflect and communicate that positivity to customers, leading to better customer experiences.”

Together, these insights confirm that when employees perceive their organizational environment as supportive and responsive, they not only engage more deeply in their work but also elevate the service experience for customers. The positive cycle between EX and both internal and external outcomes becomes evident: empowered employees build stronger relationships with both their organization and the clients they serve.

The moderating role of hybrid work: Divergent patterns

The quantitative results revealed hybrid work as a double-edged contextual factor in the relationship between EX and employee engagement and CX. Interview insights add nuance to these findings, highlighting how frontline employees experienced both the benefits and trade-offs of hybrid work. While it enabled greater flexibility for employees, it also posed challenges related to interpersonal cohesion, spontaneous collaboration, and team dynamics.

Participants reflected on how hybrid setups influenced their ability to deliver quality service and remain emotionally connected to their work. For some, the autonomy and comfort of working from home, particularly when using GenAI tools, enhanced their productivity. In these cases, hybrid models strengthened the effect of EX by allowing employees to engage more deeply in value-generating work within supportive, well-equipped environments. Some emphasized that with adequate planning and supportive policies, remote work did not hinder customer-facing outcomes.

Participant 10 observed, “Full in-office can waste resources and time. If the company starts with a 20-50% remote policy, staff can plan accordingly and still show up when needed. If planned properly, I don’t think it would badly affect customer outcomes. We usually schedule appointments with customers anyway.”

Meanwhile, some other participants reported no significant differences in their work mode that affected their output as long as the companies treated and equipped them well, resulting in no changes to the services they provided to customers. Therefore, customers were likely to have the same experience. Furthermore, some respondents expressed concerns about more frequent distractions and the weakening of social bonds and emotional connection due to reduced face-to-face interaction in hybrid models. These reflections suggest that the use of GenAI in hybrid settings may unintentionally replace certain interpersonal touchpoints that foster organizational cohesion. Additionally, emotional engagement with work was sometimes seen as tied to the in-office environment.

Participant 2 stated, “Even at home, I can handle inquiries smoothly with provided tools. But it’s the office vibe that keeps me emotionally engaged.”

Discussion

The rapid adoption of GenAI technologies and hybrid work models in the service sector has significantly transformed EX, employee engagement, and CX. A comparative analysis of Study 1 across different national contexts, combined with the qualitative insights from Study 2, provides empirical evidence of these relationships and the dynamics in organizations leveraging GenAI to enhance EX and CX.

GenAI integration with EX and employee engagement

Consistent findings from both the quantitative phase across developed and developing countries, and the qualitative phase affirm that employee experience with GenAI significantly enhances overall EX. This reinforces the powerful role of technology in shaping workplace perceptions, as also noted in previous studies. Quantitative results showed a positive link between GenAI use and EX, which was reinforced by interview insights. Participants highlighted how GenAI reduced routine burdens, enabled more meaningful work, and reflected organizational support, contributing to a more efficient and empowered work environment. By simplifying tasks and automating repetitive processes, technology enables employees to perform their roles more efficiently, thereby increasing their satisfaction (Bowen, 2024). These technological advancements help employees to excel by enabling them to offer personalized services to customers—capabilities that would be unattainable without such technology (Grewal et al., 2023). In this way, GenAI can elevate the technological aspect of EX. Moreover, this grants employees greater freedom and flexibility, significantly enhancing their physical work experience (Lee & Kim, 2023; Morgan, 2017). By increasing opportunities and resources for employee growth and reducing their burnout, positive experience with GenAI also substantially enriches the cultural experience, leading to an overall improvement in their experience within the organization (Batat, 2022).

Similarly, employee experience with GenAI significantly enhances their work engagement, as revealed in both the quantitative and qualitative results. This finding aligns with prior studies (e.g. Dutta et al., 2022; Prentice et al., 2023). Employees might feel not only more capable and intellectually stimulated but also more enthusiastic, with GenAI enhancing their capabilities and allowing them to shift focus from repetitive, time-consuming tasks to more meaningful, value-added activities. Such conditions help create a work environment that cultivates deeper engagement.

Notably, the statistical correlation between EX and employee engagement was the strongest in both contexts. This link was also consistently reinforced by frontline employees in the interviews, many of whom described it as a reciprocal dynamic. Positive experiences, whether they be cultural, technological, or physical, can boost employee engagement at work as employees feel valued and well-supported by their organizations (Bowen, 2024; Bowen & Schneider, 2014). Prior studies by Panneerselvam and Balaraman (2022) and Malik et al. (2023) similarly demonstrated that high EX levels considerably increase employee engagement, especially when supported by AI-assisted applications.

EX and employee engagement with CX

The findings highlight the EX-CX relationship, as also demonstrated by the SCF, which suggests that workers with positive experiences are more likely to provide outstanding service, leading to enhanced customer satisfaction and loyalty (Bowen, 2024). Pine’s (2020) findings are similar, indicating that better EX generates outstanding CX, which in turn enhances overall EX. As Pimpakorn and Patterson (2010) emphasized, delivering excellent customer service depends on employees being both engaged and capable, which can now be further enhanced through technological advancements (e.g. GenAI). Positive customer outcomes are also considerably improved by engaged employees, who also tend to display better levels of empathy, responsiveness, and service orientation.

More specifically, both studies confirm that improved EX and engagement lead to higher customer satisfaction. When feeling supported, frontline staff’s improved mood and confidence allow them to serve customers more attentively and thoroughly. Additionally, engaged employees, who are motivated by positive experiences, are more likely to build trusting bonds with clients and, therefore, promote their loyalty. Customer engagement, a result of both satisfaction and interaction with the company (Hollebeek, 2011), is further fueled by EX and employee engagement. Engaged employees tend to go the extra mile in their interactions with customers, thus establishing a stronger connection between customers and the company. For example, to enhance customer engagement, traditional customer relationship management (CRM) is shifting to social CRM, demanding more from employees to interact with both customers and their extended networks (Ng et al., 2020).

Mediating role of EX

The mediating role of EX in the relationship between their experience with GenAI and customer outcomes was also highlighted in both studies. Specifically, employees’ positive experience with GenAI fosters higher customer satisfaction, loyalty, and engagement through enhanced EX. Although there were prior studies emphasizing the direct effects between these factors (Grewal et al., 2023, Pine, 2020; Zeithaml et al., 2002) yet this mediating role has not been officially confirmed. Therefore, the findings of this study underscore the importance of focusing on EX when implementing GenAI technologies. It suggests that the benefits of GenAI extend beyond operational efficiency to include a positive impact on CX, driven by the improved EX. Similarly, findings also indicate that employees tend to be more committed and enthusiastic when their wants and needs are met, particularly through GenAI-enabled support that allowed them to concentrate more on tasks they enjoy. By improving EX, positive experiences with GenAI can indirectly boost employee engagement.

Moderating role of hybrid working level

The quantitative findings revealed notable contextual differences in how hybrid work moderates the relationship between EX, employee engagement, and CX. In general, high levels of hybrid work significantly weakened the positive relationship between EX and employee engagement, a pattern also observed specifically in the UK sample. This means even when employees perceive strong organizational support, frequent remote work may dilute some of the interpersonal, cultural, or environmental reinforcements that typically drive engagement. In contrast, this moderating effect was not statistically significant in the Vietnam sample. This suggests that hybrid models may be experienced differently depending on organizational maturity or infrastructure. In developing countries, where remote work in the service sector is less common, the opportunity to work from home may be perceived as a privileged policy. This perception of organizational support can enhance employees’ sense of agency and well-being; however, employees may still get distracted easily or feel distant from their colleagues and the company, as confirmed in Study 2. These offsetting pros and cons may ultimately result in a nonsignificant moderation of hybrid work in the relationship between EX and engagement.

In terms of CX, hybrid work did not statistically significantly moderate the EX–CX relationship, suggesting customer-facing outcomes remained stable regardless of work arrangements, according to Study 1. However, in the Vietnam sample, a significant moderation emerged: higher levels of hybrid work amplified the positive effect of EX on customer loyalty. This may be because hybrid work improves employees’ mood and work-life balance, enabling them to offer more attentive service. As several frontline employees indicated in Study 2, the ability to manage their time more independently allowed them to deliver more thoughtful, personalized service, which in turn fostered stronger customer bonds. These variations likely reflect underlying differences in hybrid work structures and lived experiences across contexts. In developed countries, where technological and organizational support systems are more established, hybrid work may no longer add incremental benefits to EX. Employees can still deliver standardized services no matter of where they are working, leading to no significant differences in CX.

Implications

Theoretical implications

Through a mixed-methods approach, the exploratory findings of this research offer significant theoretical implications by extending the SCF through the integration of emerging technologies and complex work arrangements in advancing employee and customer outcomes in service organizations. While exploratory, these findings offer promising theoretical extensions that may apply across service contexts, subject to further validation. By positioning employee experience with GenAI as a timely and vital antecedent of EX and employee engagement, while also demonstrating its significant favorable indirect effects on employee engagement and CX through EX, this research contributes to extending existing models of human–technology collaboration in services. It further sheds light on continuing argument about the ways AI-enabled, technology-mediated environments influence the relationship between EX and employee engagement (Malik et al., 2023).

This research also enriches the SCF by validating EX as a mediating mechanism through which positive organizational support structures, now inclusive of GenAI, are transmitted into enhanced frontline performance and service outcomes. As the integration of advanced technologies accelerates, frontline staff serve not only as differentiators who offer emotional depth and empathy, but also as enablers who guide customers through complex service journeys (Bowen, 2024). This research provides an on-time response to the concern of addressing how evolving technology can be embedded within complex organizational design in ways that support but not replace human service delivery (Schneider & Bowen, 2019), and by offering a more integrated model of how EX bridges technology adoption and both internal and external outcomes.

The complex moderating effects of hybrid work add further nuance to the existing literature. While prior research suggests that the distribution of autonomy, information, and resources across the organization, particularly to frontline employees, fosters an environment where employees feel empowered, thereby enhancing both employee and customer outcomes (Bowen, 2024; Gustafsson et al., 2024), this paper reveals diverse insights. Despite the flexibility of hybrid work, combined with workplace technologies that empower employees to perform effectively regardless of location (Panneerselvam & Balaraman, 2022), such arrangements may also dilute the cohesive climate needed to fully translate EX into better performance outcomes.

Practical implications

First, service firms need to acknowledge the necessity of investing in GenAI technologies and closely monitoring employee experience with these tools. This investment not only enhances internal organizational dynamics but also positively influences customer outcomes. To facilitate a positive GenAI experience for frontline positions, capacity building is essential. Companies should implement proper training programs tailored to specific job roles, ensuring that frontline employees can use GenAI effectively and confidently. Given the task-specific requirements across different departments, this should include expert-led training on AI fundamentals, prompt creation, and applied case scenarios (e.g. GenAI for sales, cost estimation, or communication). These interventions help frontline staff deliver faster, higher-quality services while also improving clarity, accuracy, and responsiveness in their communication, ultimately strengthening long-term customer relationships (Singh & Bridge, 2023).

Furthermore, the mediating role of EX in the relationship between GenAI experience and CX highlights the need for holistic integration. When adopting technology, organizations must go beyond the technical aspect; they should also consider the physical and cultural dimensions of the workplace to ensure that GenAI tools are fully leveraged. This includes providing adequate infrastructure and resources that reduce friction in employee workflows. A climate of respect, nurture, and inclusion fostered by organizations can promote both individual and organizational results.

Finally, the moderating role of hybrid working arrangements underscores the importance of adapting to the evolving nature of work. While better EX may enhance customer outcomes, a high level of hybrid working should be carefully managed or maintained at an appropriate level to avoid diminishing employee engagement. Leaders in organizations should adopt change management practices, supportive and collaborative communication strategies, and ensure the effective use of technology, particularly in virtual settings, to enhance employee well-being and experience (Shambi, 2021). Service companies can provide clear performance expectations, recognize employee contributions, and promote social interactions to maintain team cohesion in remote environments. These practices may also need to be adapted to different cultural and economic contexts to yield better results.

Limitations and future research directions

While this paper provides valuable insights, there are still certain limitations. One limitation is the focus on frontline positions in the service sector, which may limit the generalizability of the findings. Future research could expand the scope to explore these correlations in different roles and industries. Additionally, this research evaluated customer outcomes primarily from the employee’s perspective, which might not fully capture CX. Further studies could incorporate feedback from customers to provide a more holistic and validated view of service outcomes. Furthermore, this paper explored hybrid working levels as a moderator by categorizing them into two groups: low and high. As this approach may have oversimplified its complex moderating mechanisms, future studies may consider hybrid work as a continuous moderator to yield richer insights. The context-dependent effects of this moderator also suggest that future research should examine it in other national contexts to better understand its role in different cultural and economic settings.

Another limitation of our quantitative study lies in the use of non-random sampling methods, which may affect the generalizability of the findings due to potential sampling bias and mode effects related to different survey platforms or digital interfaces. Further research could consider applying quota sampling or weighting techniques to ensure more representative coverage across key demographic and occupational groups within the complex service sector, as these characteristics may also serve as significant control variables. Moreover, future research could also explore the role of individual differences, such as employee attitudes toward technology or digital literacy, as moderators. Understanding how these factors influence the effectiveness of GenAI could help organizations tailor their implementation strategies to meet the needs of diverse employee groups.

Footnotes

Author’s Note

Nhung Trinh is now affiliated with Griffith University, Nathan, QLD, Australia.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Nhung Trinh

Mai Nguyen

Liem Viet Ngo

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