In knowledge-intensive occupational contexts such as the information technology (IT) sector, employees are often required to sustain high levels of performance while managing demands across work and non-work domains. In such environments, work–life balance has become an increasingly relevant construct for understanding employee functioning, well-being, and effectiveness. Previous research has shown that difficulties in balancing professional and personal roles may be associated with impaired work outcomes, whereas more favorable balance conditions tend to relate to stronger motivation, commitment, and performance (Roopavathi & Kishore, 2021; Wiradendi Wolor, 2020). At the same time, emotional intelligence and psychological well-being have received growing attention as personal resources that may help explain variability in occupational performance (Lyons & Schneider, 2005; Udayar et al., 2020).

Work‒life balance to an effective alignment of professional and personal roles such that neither domain disproportionately overwhelms the other. It involves managing work responsibilities while preserving time and psychological space for personal life, thereby supporting adaptation to shifting demands across both domains (Byrne, 2005). In occupational settings characterized by sustained workload and role interference, work–life balance is especially relevant because it reflects how employees negotiate the interface between competing demands.

Emotional intelligence is conceptualized as a set of emotion-related abilities involving the perception, understanding, regulation, and strategic use of emotions in oneself and others (Salovey & Mayer, 1990, p. 189). In occupational settings, emotional intelligence has been associated with better performance, more effective conflict management, and stronger interpersonal functioning. Conversely, lower emotional intelligence has been linked to greater difficulty in emotion regulation and expression, which may contribute to misunderstanding and conflict at work (Cherniss et al., 2006).

Psychological well-being is commonly conceptualized as eudaimonic terms as positive psychological functioning across dimensions such as self-acceptance, autonomy, positive relations with others, environmental mastery, personal growth, and purpose in life (Ryff, 1989; Ryff & Singer, 2008). In the present study, particular attention is given to autonomy, positive relations, and environmental mastery, given their conceptual relevance to functioning in occupational contexts that require self-direction, interpersonal coordination, and effective management of everyday demands. Psychological well-being is also related to broader indicators of subjective functioning, including life satisfaction, with both constructs reflecting positive functioning across personal and work domains and capturing complementary facets of employee well-being (Linley et al., 2009; Ryff, 1989).

Job performance is commonly conceptualized as a multidimensional construct comprising task, adaptive, and contextual performance (Pradhan & Jena, 2017). This multidimensional perspective is particularly relevant in occupational settings such as the IT sector, where employee effectiveness depends not only on core task execution but also on adaptability and collaboration in dynamic work environments.

Empirical evidence has consistently linked work–life balance to job performance across sectors and occupational contexts. Poor work–life balance has been associated with lower performance, whereas supportive balance conditions and policies have been linked to stronger motivation, commitment, and work outcomes (Roopavathi & Kishore, 2021; Wiradendi Wolor, 2020). Related studies further suggest that work–life balance contributes positively to job satisfaction and performance in IT-related settings (Kanwar et al., 2019), and that work–family conflict may undermine performance indirectly through its detrimental effect on balance processes (Isa & Indrayati, 2023). Additional evidence indicates that long working hours and excessive workload may adversely affect health and functioning, with downstream consequences for performance (Colten & Altevogt, 2006; Mulyadi et al., 2024).

Emotional intelligence has likewise been associated with greater effectiveness in demanding work contexts. Prior research has linked emotional intelligence to more adaptive appraisal patterns and better performance under stress, including evidence from both ability- and trait-based approaches (Lyons & Schneider, 2005; Udayar et al., 2020). Additional studies have shown that emotional intelligence is associated with higher performance and lower burnout, with professional efficacy and exhaustion identified as relevant pathways (Sanchez-Gomez & Breso, 2020). In organizational settings, emotional intelligence has also been reported as a significant correlate of performance-related outcomes, including job effectiveness and related attitudinal variables (Francis & Milton, 2024; Serafim et al., 2024). Collectively, these findings support emotional intelligence as a robust correlate of job performance and as a theoretically plausible mechanism in occupational models (Rajesh, 2019).

Evidence on psychological well-being further indicates that specific dimensions may be relevant to job performance. Research has reported positive links between autonomy and performance-related indicators in organizational contexts (Tentama et al., 2020), while workplace arrangements that increase discretionary control have been associated with more favorable returns (Viete & Erdsiek, 2020). Environmental mastery and positive relations have likewise been associated with stronger performance patterns in employee samples, including IT-related contexts (Obrenovic et al., 2020; Prasad et al., 2024). More broadly, psychological well-being has been positively related to work outcomes and may operate as a mechanism linking contextual resources to performance (Anwar & Sarfraz, 2023; David et al., 2024; Fateh et al., 2021; Kara & Çetinel, 2023; Nika & Bashir, 2023).Taken together, these findings suggest that the relationship between work–life balance and job performance is unlikely to be purely direct. Instead, it may depend on personal resources and well-being-related processes. This rationale is consistent with resource-based and role-boundary perspectives frequently used in occupational research.

Conservation of Resources theory posits that personal resources help prevent loss spirals and support resource gains under stress, which is compatible with the proposed role of emotional intelligence as a protective personal resource. Job Demands–Resources models likewise conceptualize personal resources as factors that may mitigate demand-related strain and facilitate adaptive functioning. Boundary and role perspectives further suggest that work–life balance reflects how individuals manage role permeability and role interference across life domains (Edwards & Rothbard, 1999; Frone et al., 1992; Greenhaus & Beutell, 1985; Voydanoff, 2005).

Within this framework, emotional intelligence may be relevant not only as a direct correlate of performance but also as a variable that helps explain and condition the association between work–life balance and performance. Through self-regulation, interpersonal effectiveness, and coping processes, emotional intelligence may help account for how balance-related experiences translate into work outcomes and may also shape the strength of this association under different levels of personal emotional resources (Sun & Lyu, 2022). Emerging evidence supports both indirect and conditional roles of emotional intelligence in occupational settings, indicating that it may function as a mediating and moderating mechanism in pathways linking contextual demands to employee outcomes (Laulié et al., 2023; Sanz-García et al., 2024; Wang et al., 2024). Related research has also shown positive associations between emotional intelligence and psychological well-being (Ahmadi et al., 2014; Malinauskas & Malinauskiene, 2020), supporting the broader relevance of emotional intelligence within models of employee adjustment and functioning.

Although prior studies have linked work–life balance, psychological well-being, emotional intelligence, and job performance, these associations have often been tested in partial or fragmented models. Fewer studies have integrated all four constructs within a single framework while simultaneously examining multidimensional job performance and testing both mediating and moderating roles of emotional intelligence in the same model. This gap is particularly relevant in the IT sector, where employees face sustained performance demands alongside ongoing work–nonwork boundary management.

Accordingly, the present study examined the associations among work–life balance, psychological well-being, emotional intelligence, and job performance among IT employees in and around Hyderabad. More specifically, the study addressed two research questions: whether work–life balance and selected dimensions of psychological well-being were associated with job performance, and whether emotional intelligence functioned as a mediator and moderator in the work–life balance–job performance association. The objectives were to examine the association between work–life balance and job performance, assess whether autonomy, positive relations, and environmental mastery were associated with job performance, and evaluate the mediating and moderating roles of emotional intelligence in the relationship between work–life balance and job performance. Based on the reviewed literature, the following hypotheses were tested: H1: Work–life balance is significantly associated with job performance among IT employees. H2: Emotional intelligence is significantly associated with job performance among IT employees. H3: The autonomy dimension of psychological well-being is significantly associated with job performance among IT employees. H4: Environmental mastery is significantly associated with job performance among IT employees. H5: Positive relations are significantly associated with job performance among IT employees. H6: Emotional intelligence mediates the relationship between work–life balance and job performance among information technology employees. H7: Emotional intelligence moderates the relationship between work–life balance and job performance among information technology employees. Figure 1 summarizes the hypothesized conceptual model guiding the present study, in which work–life balance, psychological well-being dimensions, and emotional intelligence were examined in relation to multidimensional job performance, with emotional intelligence additionally specified as a potential mediating and moderating mechanism in the work–life balance–job performance association.

Figure 1 summarizes the hypothesized conceptual model guiding the present study, in which work–life balance, psychological well-being dimensions, and emotional intelligence were examined in relation to multidimensional job performance, with emotional intelligence additionally specified as a potential mediating and moderating mechanism in the work–life balance–job performance association.

Methods

Study Design

The study used a cross-sectional, nonexperimental survey design to examine associations among work–life balance, psychological well-being, emotional intelligence, and job performance among employees working in the IT sector in and around Hyderabad. The study further tested the mediating and moderating roles of emotional intelligence in the association between work–life balance and job performance.

Participants and Procedure

Participants were recruited using convenience sampling strategy aimed at maximizing heterogeneity across multiple IT organizations in and around Hyderabad and from varied cultural and educational backgrounds. Because the population size of information technology employees in Hyderabad was unknown, the minimum target sample size was estimated using Cochran (1977) formula, which yielded a target of 385 participants. The final analytic sample comprised 389 participants, exceeding this threshold and meeting commonly cited sample-size recommendations for structural equation modeling (Wolf et al., 2013).

Eligible participants were adults (18 years or older) who were currently employed in the IT sector, able to read and understand the questionnaire language, and willing to complete the survey voluntarily. No additional eligibility criteria were applied. Data were collected between August and December 2024 using a cross-sectional online survey administered via Google Forms. The survey link was distributed to 500 IT-sector employees via email and WhatsApp, yielding 420 responses (response rate: 84%).

The online form presented the questionnaires in two parts: Part 1 collected demographic information; Part 2 measured emotional intelligence, work–life balance, psychological well-being, and job performance. Items measuring different constructs in Part 2 were presented in systematically intermingled order to minimize response bias. All item responses were set as optional to preserve voluntary participation. The form allowed participants to review and change prior responses before final submission. Google Forms settings restricted participation to one submission per respondent. The survey required approximately 10–15 minutes to complete.

Thirty-one cases were excluded from the 420 responses: 14 due to incomplete responses (>20% missing items) and 17 due to straightlining (identical responses across all items), resulting in a final analytic sample of 389 participants. Participant characteristics are presented in Table 1.

The study complied with the Declaration of Helsinki and was reviewed by the Institute Research Committee, Symbiosis Institute of Business Management. The committee issued a formal exemption statement on 25 April 2024 (No. 08/2024). Before participating, respondents were informed about the purpose of the study, the voluntary nature of participation, the intended use of the data, and their right to withdraw at any time prior to submitting the questionnaire without penalty. Informed consent was obtained electronically via a mandatory checkbox before participants accessed the survey.

Measures

Emotional Intelligence

Work‒life Balance

Variables	Category	N	Percent
Gender	Male	222	57.1
	Female	167	42.9
Age Group (Years)	20–30	180	46.3
	31–40	126	32.4
	41–50	41	10.5
	> 50	42	10.8
Marital status	Married	250	64.3
	Not married	139	35.7
Educational Qualifications	High school	40	10.3
	Undergraduate	180	46.3
	Postgraduate	120	30.8
	Others	49	12.6
Children	Yes	210	54.0
	No	179	46.0
Experience (in years)	1–5	102	26.2
	6–10	125	32.1
	11–20	101	26.0
	> 20	61	15.7

Emotional Intelligence Scale, a 30-item measure developed by Srivastava et al. (2011) that spans four domains—Self-Awareness, Self-Management, Social-Awareness, and Social-Skills. Responses are recorded on a five-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree), with lower scores indicating higher emotional intelligence. Because the study used an eight-item brief set, the scale is referred to here as an adapted measure of emotional intelligence. The eight-item version included two items from each domain based on their relative factor loadings and conceptual representativeness as reported in the original validation study (Srivastava et al., 2011).

Job Performance

Work‒life Balance Work–life balance was assessed using the four-item Work–Life Balance Scale developed by Brough et al. (2014), a unidimensional self-report measure of individuals’ perceived balance between work and non-work domains. Items are rated on a five-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). After reverse scoring negatively worded items, higher scores indicated better perceived work–life balance.

Psychological Well-Being

Job Performance Job performance was assessed using an 11-item self-report measure adapted from the Employee Performance Scale validated by Pradhan and Jena (2017), which conceptualizes employee performance as comprising three dimensions: Task performance, Adaptive performance, and Contextual performance. In the present study, items were selected to index these three domains (Task Performance: 4 items; Adaptive Performance: 4 items; Contextual Performance: 3 items). Items were rated on a five-point Likert response scale (anchors to be specified), with higher scores indicating better self-reported performance.

Data Analysis

Data analyses were conducted using IBM SPSS Version 29 and IBM AMOS Version 28. The analytic strategy proceeded in stages.

Prior to substantive analyses, the data were screened for missing values, response-quality problems, outliers, and distributional assumptions.

First, an exploratory factor analysis (EFA) was conducted using principal component extraction to examine the dimensional structure of the initial item pool and to identify poorly performing indicators. Factors were retained based on the eigenvalue criterion (eigenvalues > 1). Sampling adequacy was assessed using the Kaiser–Meyer–Olkin (KMO) index. Items were retained when they showed primary loadings ≥ .50 on a single factor and no problematic cross-loadings. Items with low loadings or problematic cross-loadings were iteratively removed to achieve a clear and reliable factor structure (Hair et al., 2013).

Second, a confirmatory factor analysis (CFA) was performed using maximum likelihood (ML) estimation, with all indicators treated as continuous variables, to test the measurement model for the retained items and constructs. Internal consistency was evaluated using Cronbach’s alpha and composite reliability (CR). Convergent validity was assessed based on standardized factor loadings and average variance extracted (AVE). Discriminant validity was evaluated using the Fornell–Larcker criterion and the heterotrait–monotrait ratio (HTMT); was computed outside AMOS. HTMT values < .90 were interpreted as evidence of discriminant validity (Henseler et al., 2015).

Third, structural equation modeling (SEM) was used to test the hypothesized associations among work–life balance, emotional intelligence, selected psychological well-being dimensions, and the three job-performance dimensions. The structural paths were estimated in a single simultaneous model, with all exogenous constructs allowed to covary. ML estimation was used. Model fit was evaluated using χ²/df, goodness-of-fit index (GFI), comparative fit index (CFI), Tucker–Lewis index (TLI), incremental fit index (IFI), normed fit index (NFI), standardized root mean square residual (SRMR), and root mean square error of approximation (RMSEA), following commonly used SEM reporting conventions (Bentler, 1990; Hair et al., 2011; Tucker & Lewis, 1973). Squared multiple correlations (R²) were examined to estimate the proportion of variance explained in adaptive, task, and contextual performance.

Mediation was examined by testing the indirect effect of Work–Life Balance (X) on each job-performance dimension—Adaptive Performance, Contextual Performance, and Task Performance (Y)—through Emotional Intelligence (M). This corresponded to a set of single-mediator models (one model per outcome) specified within the structural equation modeling framework. Following Hayes (2018), indirect effects were quantified as a × b, while estimating the residual direct effect (c′). Indirect effects were estimated using bootstrap 95% confidence intervals based on 5,000 resamples, and evidence for mediation was inferred when the confidence interval did not include zero (Hayes, 2018). Partial mediation was concluded when both the indirect effect (a × b) and the direct effect (c′) remained statistically significant (Hayes, 2018).

Moderation was examined by testing whether Emotional Intelligence moderated the association between Work–Life Balance and each job-performance dimension (Adaptive, Task, and Contextual Performance). Moderation was operationalized by including the product (interaction) term between Work–Life Balance and Emotional Intelligence in regression-type models specified using mean-centered composite variables (Aiken & West, 1991; Dawson, 2014). Composite scores were computed as the arithmetic mean of scale item for Work–Life Balance and Emotional Intelligence, and each performance dimension. Prior to computing the interaction term, Work–Life Balance and Emotional Intelligence composites were mean-centered to reduce nonessential multicollinearity and to facilitate interpretation of the lower-order effects (Aiken & West, 1991). For each outcome, the model included the main effects of Work–Life Balance and Emotional Intelligence and the centered interaction term (Work–Life Balance x Emotional Intelligence). A significant coefficient for the interaction term was interpreted as evidence of moderation. Interaction effects were probed using simple-slope analysis and by visual inspection of two-way interaction plots (Aiken & West, 1991; Dawson, 2014; Park & Yi, 2023).

Given the cross-sectional, self-report design, common method bias was assessed using Harman’s single-factor test as a preliminary diagnostic (Podsakoff et al., 2003). Specifically, all measured items were entered into an exploratory factor analysis with an unrotated factor solution; the proportion of variance explained by the first factor was examined, with values substantially below 50% interpreted as providing limited evidence against a dominant single-factor account. Because Harman’s test has recognized limitations and does not definitively rule out method effects, results were interpreted cautiously and in conjunction with study design considerations (Podsakoff et al., 2003).

Results

Factor Analyses and Measurement Model

The initial pool of 32 items was subjected to EFA. Three emotional-intelligence items and one task-performance item were removed due to low loadings (< .50). The final EFA solution retained 28 items without salient cross-loadings (> .30), and supported an eight-factor structure. Sampling adequacy was acceptable (KMO = .89), and the eight factors accounted for 80.3% of the total variance.

The CFA evaluated the eight-construct measurement model with 28 retained items. The model showed excellent fit to the data (CMIN/df = 1.17, GFI = .94, CFI = .99, TLI = .99, IFI = .99, NFI = .95, SRMR = .03; RMSEA = .02, pclose = 1.00). Standardized factor loadings were generally strong, ranging from .66 to .94 (Table 2).

Structural Model

After establishing adequate measurement-model fit and construct validity, a SEM was estimated to test the hypothesized associations between Work–Life Balance, Emotional Intelligence, Autonomy, Environmental Mastery, Positive Relations, and the three job-performance dimensions (Adaptive, Task, and Contextual Performance). The structural model showed excellent fit to the data (CMIN/df = 1.1, GFI = .92, CFI = .97, TLI = .98, IFI = .98, NFI = .95, SRMR = .03, RMSEA = .02, pclose = .998). Squared multiple correlations (R²) indicated that the predictors accounted for 27% of the variance in Adaptive Performance, 19% in Task Performance, and 47% in Contextual Performance (standardized direct effects and p values are reported in Table 5 and Figure 2).

Testing of Hypotheses

Consistent with the structural model, Work–Life Balance showed positive associations with Adaptive (β = .24, p < .001), Task (β = .32, p < .001), and Contextual Performance (β = .11, p = .002), supporting H1. Emotional Intelligence was also positively associated with Adaptive (β = .37, p < .001), Task (β = .45, p < .001), and Contextual Performance (β = .40, p < .001), supporting H2. Autonomy and Environmental Mastery were not significantly associated with any Job-Performance dimension (all p > .05), not supporting H3 and H4. Positive relations were positively associated with Adaptive (β = .15, p = .008), Task (β = .10, p = .005), and Contextual Performance (β = .29, p < .001), supporting H5 (Table 5).

Mediation Analysis

Mediation was tested by estimating the indirect effect of Work–Life Balance on each job-performance dimension through Emotional Intelligence. Indirect effects were statistically significant for Adaptive Performance, Contextual Performance, and Task Performance (Table 6). A partial mediation interpretation is consistent with statistically significant indirect effects and statistically significant residual direct effects (Tables 5–6), supporting H6. Figures 3–5 display the estimated mediation models for Adaptive, Contextual, and Task Performance, respectively.

Moderation Analysis

A moderation was examined by testing the interaction between Work–Life Balance and Emotional Intelligence in predicting Adaptive, Task, and Contextual Performance. Composite variables were mean-centered prior to creating the interaction term to reduce nonessential multicollinearity. The moderation models included the corresponding main effects of Work–Life Balance and Emotional Intelligence; main effects for Work–Life Balance are reported in Table 5, whereas Table 7 reports Emotional Intelligence main effects and the interaction terms. The Work–Life Balance × Emotional Intelligence interaction was positive and statistically significant for all three performance dimensions (Table 7), supporting H7. Figures 6–8 show that the association between Work–Life Balance and each performance dimension was positive at both low and high Emotional Intelligence, and the slope was steeper at higher Emotional Intelligence, consistent with a positive moderation effect. WLB main effects are reported in Table 5.

Robustness Checks: Common Method Bias

Common method bias was examined using Harman’s single-factor test. The first unrotated factor accounted for 34.9% of the variance, which is below the conventional 50% threshold, providing limited evidence against a dominant single-factor account of the observed covariance.

Discussion

This study examined the associations among work–life balance, selected psychological well-being dimensions, emotional intelligence, and job performance in a sample of information technology employees in Hyderabad. Consistent with the hypothesized model, work–life balance and emotional intelligence were positively associated with adaptive, task, and contextual performance, and emotional intelligence showed evidence of partial mediation and positive moderation in the work–life balance–performance association. In contrast, autonomy and environmental mastery were not significantly associated with any performance dimension, whereas positive relations showed small-to-moderate positive associations across outcomes. Given the cross-sectional design and exclusive reliance on self-report measures, the findings should be interpreted as correlational and not as evidence of temporal or causal effects.

Item	Description	Loading	CR	AVE
EI1	I seek out activities that make me happy	.89	.95	.78
EI2	By looking at their facial expressions, I recognize the emotions people are experiencing	.92
EI3	I know why my emotions change	.87
EI4	I easily recognize my emotions as I experience them	.87
EI5	I can tell how people are feeling by listening to the tone of their voice	.86
WLB1	I currently have a good balance between the time I spend at work and the time I have available for non-work activities	.88	.91	.71
WLB2	I have difficulty balancing my work and non-work activities	.87
WLB3	I feel that the balance between my work demands and non-work activities is currently about right	.93
WLB4	Overall, I believe that my work and non-work life are balanced	.82
TPER1	I use to maintain high standard work	.90	.91	.76
TPER2	I am capable of handling my assignment without much supervision	.89
TPER3	I am very passionate about my work	.83
APER1	I can handle effectively my work team in the face of change	.94	.95	.83
APER2	I am very comfortable with job flexibility	.87
APER3	I could manage change in my job very well whenever the situation demands	.93
APER4	I use to cope well with organizational changes from my team members	.90
CPER1	I use to praise my coworkers for their good work	.85	.90	.75
CPER2	I derive lot of satisfaction nurturing other in organization	.94
CPER3	I use to maintain good coordination among fellow workers	.81
AUT1	I tend to be influenced by people with strong opinions	.87	.86	.68
AUT2	I have confidence in my own opinions, even if they are different from way most other people think	.86
AUT3	I judge myself by what I think is important, not by the values of what others think is important	.73
PR1	Maintaining close relationships has been difficult and frustrating for me	.86	.77	.53
PR2	People would describe me as a giving person, willing to share my time with others	.66
PR3	I have not experienced many warm and trusting relationships with others	.68
EM1	The demands of everyday life often get me down	.83	.82	.61
EM2	In general, I feel I am in charge of the situation in which I live	.67
EM3	I am good at managing the responsibilities of daily life	.83

Construct	EI	APER	WLB	TPER	AUTN	ENVM	CPER	POSR
EI	.88
APER	.44***	.91
WLB	.38***	.33***	.84
TPER	.40***	.41***	.36***	.88
AUTN	−.06	−.06	−.08	−.11†	.82
ENVM	.01	−.04	.01	−.03	.50***	.78
CPER	.65***	.55***	.34***	.47***	.06	.02	.87
POSR	.09	.11†	−.13*	−.05	.44***	.34***	.19**	.73

Construct	EI	APER	WLB	TPER	AUTN	ENVM	CPER	POSR
EI	—
APER	.41	—
WLB	.36	.30	—
TPER	.37	.38	.33	—
AUTN	.05	.06	.07	.10	—
ENVM	.00	.04	.01	.03	.41	—
CPER	.62	.51	.30	.43	.04	.01	—
POSR	.06	.07	.12	.06	.38	.27	.13	—

Structural path (Exogenous → Endogenous construct)	β	SE	t value	p value
Emotional intelligence → Adaptive Performance	.37	.06	6.52	< .001
Emotional intelligence → Task Performance	.45	.04	11.43	< .001
Emotional intelligence → Contextual Performance	.40	.07	5.62	< .001
Work–life balance → Adaptive Performance	.24	.06	4.11	< .001
Work–life balance → Task Performance	.32	.07	4.34	< .001
Work–life balance → Contextual Performance	.11	.04	3.06	.002
Autonomy → Adaptive Performance	-.05	.06	-0.88	.379
Autonomy → Task Performance	-.08	.07	-1.06	.290
Autonomy → Contextual Performance	.05	.04	1.30	.192
Environmental Mastery → Adaptive Performance	-.06	.05	-1.20	.229
Environmental Mastery → Task Performance	-.00	.06	-0.01	.989
Environmental Mastery → Contextual Performance	-.04	.03	-1.41	.160
Positive Relations → Adaptive Performance	.15	.06	2.67	.008
Positive Relations → Task Performance	.10	.04	2.83	< .001
Positive Relations → Contextual Performance	.29	.07	4.28	.005

Outcome	Direct effect (c′)	Indirect effect (ab)	LL	UL	p (ab)	Mediation
Adaptive performance	0.27	0.16	0.11	0.21	.001	Partial
Contextual performance	0.15	0.14	0.13	0.24	.001	Partial
Task performance	0.15	0.15	0.10	0.22	< .001	Partial

Outcome	EI (β)	t	p	WLB × EI (β)	t	p
Adaptive performance	.39	6.43	< .001	.10	2.88	.020
Task performance	.32	10.01	< .001	.09	5.72	< .001
Contextual performance	.42	9.38	< .001	.09	3.10	.010

The pattern of direct effects is broadly consistent with organizational research linking work–life balance to performance-relevant functioning and highlighting emotional intelligence as a salient personal resource for work effectiveness. The positive associations of emotional intelligence with all three performance dimensions align with prior evidence that emotional competencies are related to performance under demanding conditions (Nag et al., 2023; Vasumathi & Sagaya, 2017). Similarly, the positive associations between work–life balance and performance outcomes support the view that better work–nonwork alignment is linked to more effective task execution and adaptation at work. At the same time, the null findings for autonomy and environmental mastery suggest that, in this sample, these psychological well-being dimensions may not translate directly into performance differences once work–life balance and emotional intelligence are modeled simultaneously, whereas positive relations retained a significant association across outcomes.

The mediation results suggest that emotional intelligence may represent one pathway through which work–life balance is related to job performance, as the indirect effects via emotional intelligence were statistically significant across outcomes while the direct effects of work–life balance remained significant. This pattern is consistent with partial mediation (Hayes & Preacher, 2014), indicating that work–life balance may be associated with performance both through emotional intelligence–related processes and through other unmodeled mechanisms. In parallel, the significant interaction terms indicate that the strength of the work–life balance–performance association differed as a function of emotional intelligence, which is the defining feature of moderation (Aiken & West, 1991; Dawson, 2014). The interaction plots further suggest a steeper positive association at higher emotional intelligence, consistent with interpreting emotional intelligence as a boundary condition of the work–life balance–performance link in this sample. These interpretations remain subject to the limits of cross-sectional inference.

The null associations for autonomy and environmental mastery warrant careful interpretation. Although these dimensions are often theorized as beneficial for functioning, they were not significantly associated with adaptive, task, or contextual performance once work–life balance and emotional intelligence were included in the model. One possibility is that these well-being dimensions are more distal correlates of performance and may operate indirectly (e.g., through motivation, engagement, or health-related functioning) rather than as direct predictors in the present specification. Another possibility is that, in this occupational context, variance in performance is more strongly captured by work–life balance and emotional intelligence than by these particular well-being dimensions, especially given that all constructs were assessed via self-report at a single time point. By contrast, positive relations showed consistent positive associations with all performance dimensions, suggesting that interpersonal functioning may be more proximally aligned with contextual and adaptive aspects of performance in this sample.

Implications

The findings extend prior work by jointly modeling work–life balance, emotional intelligence, selected psychological well-being dimensions, and multidimensional job performance within a single framework in an information technology context. Conceptually, the results support treating emotional intelligence as relevant both to indirect processes linking work–life balance to performance and to conditional effects that vary as a function of emotional intelligence, consistent with interaction-based accounts in organizational research. From an applied perspective, the results suggest that organizations may benefit from interventions that support work–life balance (e.g., boundary-supportive practices and manageable workload expectations) alongside initiatives that develop emotional competencies. Given the consistent association of positive relations with all performance dimensions, practices that strengthen collegial support and constructive interpersonal functioning may be particularly relevant for contextual and adaptive performance.

Limitations

Several limitations should be considered when interpreting these findings. First, the cross-sectional design precludes temporal ordering; therefore, the reported associations should not be interpreted as evidence that work–life balance or emotional intelligence causes changes in job performance. Second, the use of a convenience sample of information technology employees from a single metropolitan context may limit generalizability to other occupational groups, regions, or organizational settings. Third, all constructs were assessed via self-report at a single time point, which may inflate associations due to shared method variance; although Harman’s single-factor test did not indicate a dominant single-factor structure, this diagnostic is limited and does not rule out common method bias. Following prior practice (Cao et al., 2024), alternative factor solutions were compared, and the multi-factor measurement model provided better fit than a single-factor model; however, this provides only limited, indirect evidence against a dominant common-method factor and should not be interpreted as ruling out common method bias. Future work should use more rigorous approaches (e.g., a common latent factor test or a marker-variable approach) and, where feasible, multisource measurement to reduce shared method variance. Finally, the measurement workflow involved item removal and includes unresolved reporting ambiguities (e.g., inconsistent item counts across sections and unclear labeling of some table values), which should be resolved to ensure full transparency and reproducibility.

Future Directions

Future research should replicate these findings using longitudinal designs to evaluate temporal dynamics and potential reciprocal relations among work–life balance, emotional intelligence, psychological well-being, and job performance. Multiwave designs with temporally separated measurement occasions would strengthen inference about ordering and reduce shared method variance. Where feasible, future studies should incorporate multisource performance indicators (e.g., supervisor ratings or objective metrics) and more rigorous common method variance diagnostics (e.g., common latent factor tests or marker-variable approaches). Experimental or quasi-experimental studies of work–life balance interventions and emotional intelligence training could further test whether changes in these factors are followed by changes in well-being and performance outcomes. Finally, studies across multiple organizations and regions, including multigroup analyses, would clarify generalizability and potential subgroup differences in the observed mediation and moderation patterns.

Conclusion

In summary, the study provides evidence that work–life balance and emotional intelligence are meaningfully associated with multidimensional job performance among information technology employees, and that emotional intelligence shows both indirect and conditional roles in the work–life balance–performance association. These findings support an integrative perspective in which work–nonwork functioning and emotional competencies jointly relate to performance-relevant outcomes, while underscoring the need for longitudinal and multisource research to clarify temporal ordering and reduce method-related bias.

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Introduction