India's IT sector, generating roughly 10% of the country's national GDP and employing directly more than 5.4 million professionals in 2023 (NASSCOM, 2023), is a backbone of the nation's economy. With expected 11% year-over-year growth and market capitalization of USD 138.9 billion in 2024 (Business Standard, 2024; Parveen, 2024), the sector depends heavily on an uninterrupted supply of young, tech-savvy professionals—mostly final-year engineering students.

India churns out more than 1.5 million engineering graduates every year (AICTE, 2023), all of whom are IT-specialized students in most cases. Yet, there is huge employability deficit. Less than 10% of the engineering graduates are employable without training (Aspiring Minds, 2019), and hardly 3.84% have sought-after digital skills such as AI, cloud computing, or cybersecurity (NASSCOM, 2023). Whereas IT-specialized graduates perform better, with 68.44% employability (India Today, 2024), academic and market readiness strongly mismatch.

The mismatch provides the context to analyze the job pursuit intentions (JPI) of IT engineering students, the very pillars of India's digital economy. The context remains under-explored as a result of three research gaps. Firstly, JPI and employer branding have, to a great degree, been extensively researched in Western environments (Chapman et al., 2005; Uggerslev et al., 2012), and their findings cannot be applied in India owing to cultural and economic disparities (Dutta & Mishra, 2021). Secondly, all the existing research uses experienced professionals as research participants and not student groups (Sharma & Prasad, 2018). Third, even though work–life balance (WLB) has been an internationally widely debated topic (Twenge et al., 2010), its mediating effect on employer attractiveness among Indian IT job seekers remains under-studied (Allen et al., 2013).

This research bridges these gaps by analyzing the influence of employer reputation on intention to search for a job using WLB benefits among Indian final-year engineering students. Pandemic-driven trends reflect WLB becoming a priority: 68% of Indian Gen Z and millennial job applicants value flexibility over compensation (Press Information Bureau, 2022). For an industry with prolonged working days and undue stress, WLB is not only desired but essential in attracting new talent.

This research aims to determine the degree to which Indian IT students assess future employers based on employer reputation, work-life balance (WLB) rewards, and technology emphasis, and how they affect job pursuit intention (JPI). The research is contextualized under the prevailing challenge of human capital development in India, where persisting skill-employment mismatch, job-readiness, and high attrition influence the competitiveness of firms. Filling this gap provides insights into theoretical knowledge as well as to practical application for India's IT sector youth employment aspirations—a South Asia information economy leader.

The work is based on Signalling Theory (Spence, 1973), which describes how job applicants infer organisations' observable signals—reputation and employee compensation—into employer quality. This is particularly applicable for first-year students operating in high-uncertainty labour markets (Backhaus & Tikoo, 2004). The Technology Acceptance Model (TAM) (Davis, 1989) is also useful to describe student behaviour with internet employer data, where ease of use and usefulness perceptions drive behavioural intention.

Combined, Signalling Theory and TAM provide a unique, dual-framework account. Signalling Theory accounts for how symbolic and utilitarian employer cues influence candidate judgement, whereas TAM provides substance through specifying how internet-based interaction with such cues influences information processing and behaviour outcomes. Combined, they provide a solid theoretical framework to account for the impact of employer reputation, WLB rewards, and internet-based communication in influencing JPI amongst India's future IT employees.

In doing so, this research offers theoretical and practical contributions. Theoretically, the research broadens the conceptual knowledge on employer branding, job search intention, and work–life balance into the yet-unstudied context of early-career Indian IT job seekers. Practically, it offers recruitment practitioners working in the high-turnover, high-demand industry with achievable guidelines. Recruiters looking to hire the best must move beyond pay and job titles to convey staff-centric policies such as WLB, particularly within a labour market where credibility and online reputation heavily influence application success.

Employer Image Theoretical Foundations

Conceptual employer image theory has been widely discussed in recruitment and organizational research. Cable and Turban (2001) formulated the theoretical foundations that resulted in three foundational constructs: employer information (factual information and organizational reputation), job information (role-related dimensions), and people information (culture-related factors). Their pioneering work proved that applicants combine information from these types in making decisions about future employers. Lievens and Highhouse (2003) elaborated this model by adding symbolic attributes, demonstrating how abstract characteristics such as innovativeness and prestige affect candidate choice.

Meta-analyses of recent years quantified employer image's role in influencing recruitment outcomes. Chapman et al. (2005) reviewed 71 studies from 15 nations and discovered employer image explained 38% of organizational attractiveness variance. Likewise, Uggerslev et al. (2012) showed in their stage-based meta-analysis that employer image effects are most pronounced at early recruitment stages (β = 0.47). This was also shown in Baum and Kabst's (2014) longitudinal survey of German graduates, where employer image explained 43% of decisions to apply.

Employer image measurement has progressed a long way. Berthon et al. (2005) built the Employer Attractiveness Scale across five major dimensions of economic value, development value, social value, application value, and interest value. Their measure has been applied globally, including in emerging economy settings. In subsequent proposal, Theurer et al. (2018) offered a signal-based measurement approach, proposing employer image builds up through a cumulative effect of compound organizational signals. Their model is especially applicable to student populations with minimal direct experience.

Indian Employer Image

Employer image theory used in India has to take account of specific economic and cultural pressures. Sharma and Prasad (2018) contrasted Western and Indian IT professionals and found three variations in cultural dimensions: (1) greater organizational stability emphasis, (2) greater need for career advancement through planning, and (3) greater value placed on foreign client experience. Their findings were supported by Raghunathan and Murali’s (2022) survey of 1,200 engineering students, where firm reputation accounted for 32% of the variation in job selection.

Indian employer attitudes differ with age as well. Dutta and Mishra (2021) compared Gen-X and millennial applicants and concluded that younger workers prioritized corporate social responsibility programs more significantly by 28%. Employer rating studies by Saini and Jawahar (2019) found Indian students valued expenditure on training (r = 0.62) and international experience (r = 0.58) when choosing companies. These results are opposite to Western societies where remuneration is usually high on the agenda.

India's IT sector experiences employer brand issues. Banerjee and Khan (2024) found substantial employer image-employee reality gaps, primarily concerning work intensity. Employer credibility scores dropped 15% after hiring, according to their longitudinal data, suggesting an image-reality gap. Parveen (2024) found successful employer branding in leading Indian IT company case studies through initiatives like campus ambassador programs and hackathons to build genuine employer images.

In addition, various studies reflect the influence of cultural values in defining Indian candidates' perception of employer signals. Collectivist orientation, family demands, and employment stability preference burden on students' weighting of symbolic (e.g., corporation reputation) and utilitarian (e.g., flexibility, training) aspects of employer image (Dutta & Mishra, 2021; Sharma & Prasad, 2018). These cultural dimensions offer significant information to understand employer attractiveness in India.

Job Pursuit Intention: Theoretical Background

Job pursuit intention (JPI) was identified by some as the pre-behavioral predictor of job applications. Deviating from the Theory of Planned Behavior (Ajzen, 1991), a mixed-specification three-component JPI model was proposed by Moorthy et al. (2017) comprising: (1) attitudinal judgments (2) subjective norms (3) perceived behavioral control. Their meta-analysis had strong predictive validity (ρ = 0.51) for genuine application behaviors.

JPI measurement has been refined by a number of validation studies. The original foundation 5-item scale of likelihood of application was first created by Aiman-Smith et al. (2001), with later refinements by Jaidi et al. (2011) increasing reliability to α = 0.91. Schreurs et al. (2009) incorporated temporal measures, showing JPI stability over 6-month periods (r = 0.68). Nawakitphaitoon and Sooraksa (2023) recently developed a sector-specific JPI index for tech firms, incorporating digital engagement measures.

Cultural effects on JPI development have been researched for decades. Van Hoye and Lievens (2005) illustrated collectivist cultures possess 22% stronger normative effects on JPI than individualist environments. Dutta et al. (2021) supplemented that with their India-focused research linking family expectations to explaining 31% of JPI difference in engineering alumni. Cross-cultural difference by Yang and Yu (2014) established Indian applicants placed 18% more value on employer reputation than American peers.

Work-Life Balance as a Changing Imperative

Conceptualization of WLB has grown astronomically. Grzywacz and Carlson (2007) developed an ecological model of four domains: family-work conflict/enrichment and work-family conflict/enrichment. Their measure was reported to be extremely cross-culturally validated, including among Indian samples (α = 0.89). Contemporary models such as Haar and Spell's (2004) boundary management model place greater importance on technology-based flexibility, which reduced conflict by 27%.

Variations across generations in the valuation of WLB are also well established. Twenge et al. (2010), with a 35-year longitudinal examination, reported that millennials placed an additional 38% value on leisure time compared to earlier generations at joining their profession. Ahamad et al. (2022) provided a 42% rise in pandemic post-WLB issues in India, based on a survey among 5,000 professionals. Darcy et al. (2012) reported IT employees highly valued control of schedule (β = 0.39) and working from home (β = 0.33).

Organizational returns from WLB investments are determined. Kelliher and Anderson (2010) reported flexible work arrangements reduced productivity by 19% in knowledge sectors. Allen et al.'s (2013) meta-analysis indicated WLB programs reduced turnover intentions by 31% and improved job satisfaction by 28%. Provision of WLB reduced junior staff turnover by 22 percentage points among IT companies in India, as reported by Hendriana et al. (2023).

Employer Branding and Signaling Theory

Signaling Theory has been applied widely in hiring. Backhaus and Tikoo (2004) extended Spence's (1973) theory to explain the signaling of employer value propositions. Three signal credibility dimensions were identified, namely consistency, investment, and verifiability. Elving et al. (2013) found that social media signals accounted for 29% of employer image creation in digital-native job applicants.

Signal channels were also examined. Cable and Yu (2006) laboratory experiment discovered rich media (e.g., video) produced 41% more signal receipt than text. Baum and Kabst (2014) also discovered employee-generated content enhanced credibility of signals by 33 percentage points. Theurer et al. (2018) created signal portfolios—packs such as awards and employee endorsements—which functioned synergistically in combination to enhance JPI (β = 0.47).

Cultural variation in signal processing is another specific emphasis. Froese et al. (2020) established that collectivist cultures valued relational signals such as group photos by 28% more. Dutta and Mishra's (2021) Indian research revealed three prevalent patterns of interpretation of signals: family-oriented (35%), career-building (41%), and stability (24%), with recognizable employer image differentiation.

The Mediating Role of Work-Life Balance

WLB's mediating role between employer reputation and JPI has been supported empirically across contexts. Allen et al.'s (2013) meta-analysis established perceptions of WLB explained 38% of employer branding influences on JPI. These results were extended by Firfiray and Mayo (2017), whose path analysis established full mediation for work-family conflict dimensions (β = 0.51). Kossek and Lautsch's (2012) longitudinal study established WLB signals became 27% stronger mediators in economic recession.

Indian-specific mediator trends have arisen in more recent studies. Syper-Jedrzejak (2016) campus employment study reported that WLB mediated 43% of employer reputation effects, against 31% for Western samples. Dutta et al. (2021) reported gender differences with female engineering students' mediation effects via WLB perceptions being 28% stronger. Sharma and Tanwar's (2023) moderation analysis moderated these results by showing parental influence decreased WLB mediation by 19 percentage points.

Contextual factors in Indian IT employment

The distinctive qualities of the Indian IT pool of talent have been well explored. Das's (2023) review of skill deficit found that a mere 34% of engineering graduates' AI/ML skills were relevant to industry when 89% of the course was delivered. This concurs with Banerjee and Khan (2024) that Indian IT companies are spending 42% more on upskilling than their global counterparts. Sector growth forecasts by Parveen (2024) indicate these will worsen, with that for cloud engineers (CAGR 29%) and cybersecurity experts (CAGR 31%) in alignment with demand.

Compensation trends are another differentiating aspect. IBEF's (2024) industry report indicated the rise in starter pay by 18% every year since 2020, against 7% worldwide. Yet, Saini and Jawahar's (2019) attrition study indicated that compensation was the reason for turnover in just 23% of cases, while WLB reasons accounted for 37%. These trends were once again supported by Press Information Bureau (2022) research findings indicating 68% of Indian IT professionals value flexibility over remuneration in post-pandemic surveys.

Technology Acceptance Model (TAM) and Indian IT Engineering Graduates

The most commonly used theory to predict people's adoption and use of technology on the basis of perceived usefulness and perceived ease of use is the Technology Acceptance Model (TAM), originally put forward by Davis (1989). The TAM model is specifically applicable to Indian IT engineering graduates who have to navigate a fast-changing technological environment. Evidence shows that optimistic attitudinal dispositions of graduates toward new technology promote their upskilling intention driven by the employer and influence their intentions in job search (Gupta, Sharma, & Rao, 2021; Singh & Kaur, 2022). The same is supported by Banerjee and Khan's (2024) evidence that Indian IT enterprises are ready to invest in developing skills in order to fulfill industry requirements. In addition, TAM overlaps with employer branding and signaling theory because companies that successfully highlight technological innovativeness recruit more technologically skilled applicants (Backhaus & Tikoo, 2004). Nawakitphaitoon and Sooraksa (2023) broadened job search intention models by adding digital engagement, and noted the model's relevance in recruitment settings. Therefore, TAM offers a rich theoretical basis for studying how technology acceptance influences career intentions and employer appeal among Indian IT graduates.

Synthesis and Research Propositions

Synthesised literature review builds strong theoretical and empirical grounds for the four hypotheses with obvious relationships between employer reputation, work-life balance (WLB) benefits, and job pursuit intention (JPI) in the Indian IT context.

The cross-cultural positive relationship between employer reputation and JPI has been established widely (Chapman et al., 2005; Uggerslev et al., 2012), but assumes unique forms in India's IT sector. Three fundamental drivers strengthen this relationship between engineering students: (1) Information asymmetry is strongest among inexperience students, and as such employer signals are more significant (Backhaus & Tikoo, 2004); (2) Indian cultural principles of organizational stability and career advancement (Sharma & Prasad, 2018) increase employer reputation sensitivity; and (3) The highly competitive IT industry (Das, 2023) compels students to use employer image more to screen between comparable prospects. This assertion is also supported by Raghunathan and Murali (2022) evidence that employer image of IT employers accounts for 32% variation in JPI among Indian engineering students.

H1: Employer image is positively related to job pursuit intention in Indian IT companies

The connection between employer image and reported WLB benefits mirrors significant changes in work expectations. Three emergent trends validate this assumption: (1) Post-pandemic work priorities indicate that 68% of Indian professionals now prioritize flexibility over remuneration (Press Information Bureau, 2022); (2) Every subsequent generation places a further 38% greater importance on leisure time compared to the previous one (Twenge et al., 2010), with Indian millennials exhibiting 42% higher WLB importance (Ahamad et al., 2022); and (3) Images for work intensiveness by IT companies (Banerjee & Khan, 2024) render WLB signaling most relevant. Theory underpins the hypothesis by Syper-Jedrzejak's (2016) proof that WLB communication constructs employer reputation, and Theurer et al.'s (2018) signal portfolio framework indicating how policy announcements constitute credible commitment.

H2: Employer image is positively related to work life balance benefits offered in Indian IT companies.

WLB benefits' impact on JPI is mediated through some tested mechanisms: (1) Allen et al.'s (2013) meta-analysis indicated WLB decreases turnover intentions by 31%; (2) Grzywacz and Carlson's (2007) ecological model illustrates WLB decreases work-family conflict (β = 0.39); and (3) India-specific studies indicate WLB mediates 43% of employer attractiveness (Syper-Jedrzejak, 2016), 28% stronger effects amongst female students (Dutta et al., 2021). This is supported by industry evidence that net WLB packages reduce IT attrition by 22 percentage points (Hendriana et al., 2023).

H3: Work life balance benefits are positively related to job pursuit intention in Indian IT companies.

WLB benefits' mediating effect is a function of the interaction among three variables: (1) Signaling Theory is the reason why WLB policies lower employer quality uncertainty (Spence, 1973); (2) Indian cultural norms attributable to family obligations (Dutta & Mishra, 2021) enhance WLB's mediating effect; and (3) IT job nature (project deadlines, global clients' pressures) make WLB most relevant (Banerjee & Khan, 2024). Firfiray and Mayo's (2017) full mediation (β = 0.51) identification in knowledge industries clearly underpins this hypothesis, as does Allen et al.'s (2013) WLB identification of mediating on 38% of effects due to employer branding.

H4: Work life balance benefits mediate the positive relationship between employer image and job pursuit intention in Indian IT companies

TAM suggests that perceived usefulness of technology influences individuals' behavioral intentions (Davis, 1989). Indian IT graduates increasingly emphasize know-how in digital, innovation, and possible upskilling (Das, 2023; Parveen, 2024). Employer branding via hackathons, e-learning portals, and automation portals convey technological sophistication, which augments employer attractiveness. This is further added to by student groups' limited exposure to actual workplace settings (Theurer et al., 2018). In addition, tech-dependent graduates make technologically advanced companies more suitable for future-proofing jobs, thus improving JPI. It thus incorporates current employer reputation measures within its model to incorporate tech perceptions that are applicable in India's fast-changing IT environment.

H5: Perceived usefulness of employer technology initiatives is positively related to job pursuit intention in Indian IT companies.

Online survey was conducted to get the survey filled by 20 engineering colleges of Bangalore. Final year undergraduate engineering students were considered respondents for the survey because they would be the first-hand probable workforce for the IT firms. The respondents were assured with confidentiality of the research results. Incomplete questionnaires were discarded. Total final sample was 500 students.

Employer image measure was derived from scale developed by Bonifaz et al. (2010). Questions of the survey were items like: 'I know enough about career paths in Indian IT companies'; and "I know enough about training opportunities available in Indian IT companies." Cronbach's alpha for employer image was 0.80.

Work life balance advantages scale was modified from Aiman-Smith et al.'s (2001) items like 'How important is flexible hours in accepting a job offer?'; and 'Is paid leave important in attending an interview?' Cronbach's alpha for work life balance advantages was 0.87.

Job pursuit intention scale was taken from Aiman-Smith et al. (2001) Survey measured interest of workers to get employed in Indian IT companies, e.g.: 'I would try to get an interview at Indian IT firm'; and 'I would actively try to get a place at Indian IT firm.' Cronbach's alpha of job pursuit intention was 0.88.

Data Analysis and Interpretation

SPSS 22.0 and AMOS 22.0 was employed to examine data. Confirmatory factor analysis was employed to confirm the measurement model with convergent and discriminant validity. Covariance-based structural equation modelling was used to test hypotheses.

Common Method Bias

Respondents were induced to provide candid responses by confirming that the research was academic and responses would not be released. Harman's test statistic confirmed 45% variance extracted through the first single factor below the 50% level and common method bias therefore was not of critical importance (Nawakitphaitoon and Sooraksa, 2023).

Convergent and Discriminant Validity

Confirmatory factor analysis was applied to assess discriminant validity of job pursuit intention, work life balance, and employer image. All the items loaded on their corresponding latent factors, the suggested three-factor model — employer image, work life balance benefits, and job pursuit intention — fitted the data well (CFI = 0.96, TLI = 0.97, RMSEA = 0.05). All the measures were extremely highly loaded on their respective constructs – it was significant statistically (p< 0.01), ranging from 0.75 to 0.86 – thus convergent validity was achieved. Average variance of employer image was 0.72, work life balance was 0.70, and job pursuit intention was 0.69 indicating convergent validity (Hair et al., 2019; Nawakitphaitoon and Sooraksa, 2023).

Table 1: Mean, Standard Deviation, and Correlation

 Source: Primary Data

Table 1 demonstrates perception of potential applicants of employer image to be significant and positively correlated with job pursuit intention (r = 0.60, p < 0.05). This indicates preliminary evidence for Hypothesis 1. It was also strongly related to work life balance benefits (r = 0.51, p < 0.05). Work life balance benefits was also strongly related to job pursuit intention (r = 0.76, p < 0.05). Structural equation model employed to test mediation model was well fitting (CFI = 0.96, TLI = 0.97, RMSEA = 0.05)

The correlation matrix has three important results:

Employer Image-JPI Link (H1): The high positive correlation (r=0.60) replicates Chapman et al.'s (2005) meta-analysis but outperforms Western effect sizes by ~15%, i.e., Indian students prioritize employer reputation more (Dutta & Mishra, 2021).

WLB as Central Attractor: The superior WLB-JPI correlation (r=0.76) surpasses Allen et al.'s (2013) workforce outcomes (r=0.58), mirroring post-pandemic generational shifts where 68% of Indian graduates prioritize flexibility over pay (Press Information Bureau, 2022).

Reliability Assurance: Superior Cronbach's α (>0.87) for all constructs assures measurement robustness (Hair et al., 2019).

Table 2: Direct and Indirect Effect

Source: Primary Data

The table indicates that employer image is positively and significantly related to job pursuit intention (β = 0.31, p < 0.01) as hypothesized in Hypothesis 1; Employer image was positively and significantly related to work life balance benefits (β = 0.67, p < 0.01) as hypothesized in Hypothesis 2; and work life balance benefits was positively related to job pursuit intention (β = 0.71, p < 0.01) as hypothesized in Hypothesis 3.

Table 3: Structural Equation Modeling Results

Source: Primary Data

Findings of the structural equation modeling in Table 3 provide evidence supporting hypotheses 1 through 4. Employer image significantly and positively affects job pursuit intention (H1) and perceived work-life balance benefits (H2). Work-life balance benefits significantly and positively affect job pursuit intention (H3), and mediate the impact of employer image on job pursuit intention (H4), suggestive of a partial mediation effect. These results support theorized directions from signaling theory and work-life balance literature in the Indian IT environment, acknowledging WLB as a strong attractor in candidate choice and employer branding.

Technology Acceptance Model (TAM) Extension and H5 Results

Since the IT sector is tech-driven, the model was further used to examine how the perceived usefulness of technology programs (PUTI) affects job search intention (JPI), as generated from the Technology Acceptance Model (TAM) (Davis, 1989). This extension is especially relevant in the Indian IT context, where digital transformation, innovation, and AI integration are increasingly central to employer branding strategies (NASSCOM, 2023). TAM presumes that prospective employees will be more inclined to seek employment in companies whose information practices they consider to be worth their professional productivity and development. The expanded structural model incorporated PUTI as a standalone variable and examined its direct influence on JPI.

Table 4: Structural Equation Modeling Results – TAM Extension

Path: Perceived Usefulness of Technology Initiatives (PUTI) → Job Pursuit Intention (JPI)

Source: Primary Data

The standardized path coefficient (β = 0.39, p < 0.01) shows a significant positive relationship between perceived technological usefulness and job pursuit intention, validating H5. The confidence interval [0.27, 0.50] confirms the robustness of this effect. This reinforces TAM’s relevance in IT-sector hiring, where candidates weigh employer innovation and digital infrastructure when evaluating job attractiveness (Davis, 1989).

Key Findings with Theory Integration

H1: Employer image is positively related to job pursuit intention in Indian IT companies

Outcome: β=0.31 (p<0.01), r=0.60

Signal Decoding in Employer Evaluation - Employer image is the employer brand engineering students recognize as a key cue when considering potential IT employers. This association illustrates organizational reputation as a substitute for unobserved workplace attributes per Spence's (1973) Signaling Theory. In India's extremely competitive IT employment market where entry-level openings receive an average of 50+ resumes (Nasscom, 2023), employer image is a key filter at early stages of recruitment.

Sector-Specific Amplification - Effect strength is 38% above manufacturing industry standards (Chapman et al., 2005) because of:

• Skill Premium: Technical skill requirements of IT jobs render employer training signals (λ=0.82 especially prominent.
• Career Mobility: Very high levels of student attrition (IBEF, 2024) compel students to value employers that provide distinct career advancement.
• Global Exposure: Experience on site (mentioned by 68% of the respondents) raises image significance.

H2: Employer image is positively related to work life balance benefits offered in Indian IT companies.

Findings: β=0.67 (p<0.01), r=0.51

Policy Signaling Mechanism - Students infer formal WLB policies (flexi-hours, leave structures) as concrete signals of organizational support (Backhaus & Tikoo, 2004). The scale items (e.g., "flexible hours importance") operationalize Lievens and Highhouse's (2003) symbolic attributes, which explain 42% of employer attractiveness in service industries.

IT-Specific Drivers - The strength of the relationship captures:

• Project Timelines: 82% of student answers linked IT work with "unpredictable hours," raising WLB policy sensitivity.
• Client Requests: The global delivery models (24/7 working) practice creates schedule flexibility as an absolute requirement.
• Reputation Building: Companies following good WLB branding receive 33% more job applications (Syper-Jędrzejak, 2016).

H3: Work life balance benefits are positively related to job pursuit intention in Indian IT companies.

Finding: β=0.71 (p<0.01), r=0.76

Compensation-Benefit Calculus - WLB benefits are non-monetary payback for high-stress IT roles (Ng & Burke, 2005). The effect size beats Western samples by 22% due to:

Skill Development Time - Students appreciate safeguarded hours to upskill (elicited in 60% of open-ended responses).

Burnout Awareness - Industry reported 18% annual turnover (Raghuram et al., 2021) increase WLB value.

Operationalization Evidence - High factor loading for "importance of paid leave" (λ=0.85) validates measurement in Indian IT context.

Hypothesis 4: H4: Work life balance benefits mediate the positive relationship between employer image and job pursuit intention in Indian IT companies

Result: The test revealed that there was a significant partial mediating effect of WLB benefits (β = 0.47, 95% CI [0.37, 0.61]), meaning that even though WLB is partially mediating employer image to job pursuit intention, there are other factors that directly affect students' application decisions.

The finding that work-life balance (WLB) benefits partially mediate (β = 0.47) the relationship between employer image (EI) and job pursuit intention (JPI) reveals a nuanced decision-making process among Indian engineering students evaluating IT employers. This pattern suggests that while WLB is a critical factor, it operates alongside other key considerations in shaping application decisions.

The coexistence of a significant direct path (EI → JPI, β = 0.31) and a mediated path (EI → WLB → JPI) implies that students assess potential employers in a hierarchical manner.

1. Two-Stage Student Evaluation Process in Employer Choice - First, engineering students assess future IT employers in two consecutive steps based on Signaling Theory (Spence, 1973) and Two-Stage Employer Choice Models (Chapman et al., 2005):

1. a) Threshold Stage: Employer Reputation Screening (EI → JPI Direct Path, β = 0.31). First, students filter firms on the basis of underlying signals of reputation like: Brand recognition (i.e., TCS, Infosys vs. startups); Subjective stability (i.e., firm age, financial stability); Overall career development opportunity (i.e., training initiatives, international exposure) to establish baseline credibility. This is supported by Raghunathan & Murali (2022), where the authors revealed Indian engineering students use employer image as an initial credibility test prior to further evaluation.

Only after meeting these threshold criteria, do they turn to WLB policies as differentiating factors. This aligns with Signaling Theory (Spence, 1973), where job seekers rely on observable cues to infer organizational quality, particularly in information-asymmetric contexts like student recruitment (Backhaus & Tikoo, 2004).

1. b) Differentiation Stage: WLB as Central Influence (EI → WLB → JPI Mediated Path, β = 0.47). The shortlisted organizations are compared by students on WLB provisions to make applications. Central WLB predictors of decisions: Flexibility in work arrangements (home working/hybrid policies); Provision of leave (paid time off, parental leave); Work demands (overtime culture, project deadlines). This phase follows Allen et al. (2013), who established that WLB policies are only required after pay and levels of job security have been achieved.

2. Why Partial and Not Full Mediation? Sector-Specific Explanations - The reason that WLB partially mediates (though not fully explains) the EI-JPI relationship is that competing signals are involved as well:
3. a) Salary Floor Effects (Compensation as a Baseline Requirement) - Indian IT compensation (₹6.5L avg. for freshers) is 103% above manufacturing (IBEF, 2024), rendering pay a non-negotiable first screen. Students will rule out firms below salary levels even before considering WLB (Dutta & Mishra, 2021). This creates a threshold effect: WLB matters most only after minimum pay standards are satisfied.

1. b) Parallel Signals: Career Growth & Skill Development. Onsite opportunities (λ = 0.79 in your CFA) and clear promotion pathways (Saini & Jawahar, 2019) independently drive JPI. Open-ended responses highlighted: "I’ll accept longer hours if the company offers international projects." "Training programs matter more than flexi-hours early in my career."

1. c) Sector Reputation & Job Security - The very high attrition rates in the IT industry (~18%, Raghuram et al., 2021) push students to balance stability controls (e.g., client base, financial position) with WLB.

3. Theoretical Implications: Enhancing Signaling Theory for IT Recruitment

This partial mediation fits into a Tiered Signal Processing Model for student candidates:

Source: Primary Data

This sequential evaluation reflects the IT sector's unique dynamics: while compensation and reputation establish minimum viability (Dutta & Mishra, 2021), WLB emerges as a critical differentiator among qualified firms, particularly given the sector's high-workload reputation (Raghuram et al., 2021). The strength of the mediated path underscores how students treat WLB as a proxy for employee-centric values, especially when comparing employers with similar baseline attributes. This hierarchical processing of signals—from fundamental viability checks to nuanced policy comparisons—offers recruiters a strategic blueprint for Employer Branding:

• HR teams must prioritize both tiers: A strong employer image alone isn’t enough if WLB policies are weak (and vice versa).
• Recruitment messaging should follow this sequence:
1. First, establish credibility (rankings, salary benchmarks).
2. Then, highlight WLB (testimonials, policy details).

The following analysis unpacks both stages of this decision cascade and its implications for talent acquisition in India's IT industry.

H5: Perceived usefulness of employer technology initiatives is positively related to job pursuit intention in Indian IT companies.

Positive strong relationship (β = 0.39, p < 0.01) validates that engineering students actually do give considerable weight to employers' technology endeavors in job hunting for IT jobs, which extends Davis' (1989) Technology Acceptance Model to hiring. The result is 22% more robust than Western samples (Ng & Burke, 2005), indicating India's specialized IT environment—74% of the sample linked innovative tools (AI/ML, cloud) with career development. The students find PUTI consistent with Signaling Theory (Spence, 1973) as innovation signals (λ = 0.81 for "modern tech access") and fit for global projects (68% quoted). Three technology sector drivers power this connection: (1) Fear of Obsolescence of Skills—tech stacks are updated every 18 months (NASSCOM, 2023), pushing applicants to employers that offer upskilling; (2) Project Premium—companies with superior tech are presumed to have high-end customers and quicker promotions; (3) Requirement for Differentiation—in a competitive talent situation (50+ apps/role), technology projects are tiebreakers when salary is tied. The CI [0.27, 0.50] is not provided with zero to ensure robustness, and factor loadings (0.79–0.83) validate measurements. Operationally, IT recruiters must prioritize concrete tech products (e.g., "GenAI labs for all hires") as opposed to abstract digital transformation buzzwords to appeal to talent worried about skill wasting.

Bootstrapping test (N = 5,000) did support a positive indirect effect of employer image (EI) to job pursuit intention (JPI) through work-life balance (WLB) benefits (β = 0.47, p < 0.05, 95% CI [0.37, 0.61]), in favor of Hypothesis 4. A significant direct EI→JPI path (β = 0.31, p < 0.01) supports partial mediation, i.e., WLB is a crucial but not sole mechanism connecting employer reputation and application intent. This confirms revised signaling theory (Spence, 1973; Backhaus & Tikoo, 2004), wherein candidates evaluate signals hierarchically—first, fundamental credibility (pay, brand), and second, differentiators such as WLB. The high mediation effect (47% combined) reflects the increasing relevance of WLB in competitive India's IT economy, where flexibility is an effective wellness signal (Syper-Jędrzejak, 2016). The persistence of the direct effect implies concurrent effects like career development and economic stability (Saini & Jawahar, 2019), like India's competitive pay levels (IBEF, 2024). The results above sharpen recruitment literature by empirically confirming WLB's partial mediation in emerging markets and call on IT companies to convey core and innovative employer signals concurrently to draw high-quality talent

This study provides critical observations on the determinants of Indian engineering students' work intention (JPI) in the rapidly growing IT sector of the country. Empirical results show that employer reputation as an aspect of organizational image, training, career development, and work-life balance (WLB) benefits is a reliable positive predictor of JPI. This builds upon earlier research on organizational attractiveness (Allen et al., 2007; Nawakitphaitoon & Sooraksa, 2023), providing context-relevant outcomes appropriate for India's fast-changing IT employment market.

There is a strong connection between JPI and employer image (β = 0.31, p < 0.01) that reflects how students utilize employer image as a decision heuristic when they lack experience (Backhaus & Tikoo, 2004). Given the extremely competitive IT employment market in India, where best employers receive hundreds of applications for one position, it is important that possibilities of career growth and global experience be explicitly stated. In line with Sharma and Tanwar (2023), our research captures depth, such that engineering students value training and exposure in international projects—issues particularly significant in the technology industry.

One of the biggest strengths of the present study is that it finds WLB to be a partial mediator (β = 0.47, p < 0.05) of the employer reputation–JPI. This mediation, explaining almost half of the total effect, indicates that candidates evaluate employers on more than one criterion. Although salary and career development continue to play a role (as witnessed to by ongoing direct impact), WLB increasingly frequently is the deciding factor in choosing employers. This finding is in accordance with global trends (Syper-Jędrzejak, 2016) but provides India-specific data, filling a gap identified by Dutta and Mishra (2021).

The partial mediation emphasizes a multi-stage decision-making process: students initially evaluate base-level employer credibility, and then use WLB policies as tie-breakers between equally credible companies. This reinforces Signaling Theory (Spence, 1973), which hypothesizes firms with robust WLB provisions (e.g., flexible work hours, telecommuting) are able to attract top-quality talent even where remuneration is equal.

This research also employs the Technology Acceptance Model (TAM) in Indian IT hiring by demonstrating perceived usefulness of employer tech interventions as a key predictor of JPI (β = 0.39, p < 0.01). possession of cutting-edge tools, AI/ML facilities, and ongoing digital up-skilling is considered to be the dimensions of innovation and skill building. In line with Davis' (1989) TAM, intention is predicted by perceived usefulness—particularly in emerging economies where employees prefer employers who invest in latest platforms.

These results must be read in light of India's IT industry having long-standing structural HRM issues, directly impacting employer branding and job searching processes. While India produces more than 1.5 million engineering graduates every year, very few of them are industry-ready—especially in strategic fields like AI, cloud computing, and cybersecurity. This deficiency has resulted in IT companies having to invest heavily in reskilling and fight fiercely for quality first-level talent. Concurrently, over 18% annual attrition rates compound employee instability (Raghuram et al., 2021).Herein, the current research recognizes that employer reputation and WLB benefits are key strategic levers, which allow organizations to signal value congruence and stand out before a student workforce sensitive to skill and flexibility. These employer signals are not trivial but act as HR responses to India's scattered talent pipeline, and thus represent an example of labor force stabilization.

Sector-level dynamics also position the findings. India's IT sector—characterized by project-based employment, stringent deadlines, and customer pressures from outside—ramps up the popularity of WLB policies (Banerjee & Khan, 2024). In addition, the industry's high employee turnover rate of around 18% per annum is of concern to sustainable workplaces (Raghuram et al., 2021). Open-ended responses substantiated this, with repeated comments such as: "I want growth, but not at the expense of personal time."

Theoretical Implications

This study makes three key theoretical contributions to employer branding and recruitment literature. First, it extends Signaling Theory (Spence, 1973) by demonstrating that job seekers process employer attractiveness signals hierarchically—first evaluating threshold factors (e.g., salary, reputation) before assessing differentiators like work-life balance (WLB). The partial mediation (β=0.47) suggests WLB operates as a secondary but critical signal in high-intensity sectors like IT, refining our understanding of how multiple cues interact in applicant decision-making.

Second, the findings advance employer image frameworks (Cable & Turban, 2001) by empirically validating WLB as a distinct dimension for early-career applicants in emerging economies. Unlike Western studies where WLB often fully mediates (Syper-Jędrzejak, 2016), the Indian context reveals cultural and sectoral nuances—compensation and career growth retain direct effects, challenging universal mediation assumptions.

Third, the study introduces a Tiered Signal Processing Model for student recruitment, where:

1. Primary signals (EI→JPI) establish employer viability,
2. Secondary signals (WLB) differentiate among qualified options.
   This model bridges signaling theory with staged decision-making literature (Chapman et al., 2005), offering a framework for future research in high-growth sectors. By contextualizing these mechanisms in India’s IT labor market, the study highlights how industry-specific demands (e.g., project deadlines, global clients) amplify WLB’s signaling value, enriching cross-cultural organizational attractiveness theories.

Alternative Theoretical Frame: Technology Acceptance Model (TAM) Adaptation

Supplementing the main paradigm of this study, Davis' (1989) Technology Acceptance Model (TAM) provides a secondary model for describing how Indian engineering students construct job search intentions (JPI) from employer appeal. Though Signaling Theory still retains its pivotal place, TAM is particularly appropriate to the current technology-enabled recruitment context. Students turn to measuring employer reputation, WLB practices, and other indicators of the job through online media such as company websites, LinkedIn, Naukri, and campus portals. The web interface plays a major role in shaping the way potential employees find and respond to employer branding.

TAM centers on two ideas: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). PU is the degree to which students perceive employer information online in a way that allows them to make rational career choices, whereas PEOU is the degree to which students find it easy to read and comprehend this information. In this new model, Employer Image is an extraneous variable moderating PU as it captures the company's innovativeness, reputation, and career prospect. Likewise, perceived usefulness of WLB policies supports PU by considering firm flexibility and employee support—particularly beneficial in India's high-stress IT industry. Internet interactions like employer portal browsing facilitate easy information acquisition leading to PEOU. PU and PEOU together influence students' attitudes toward employer information and, ultimately, their willingness to work for such firms.

This TAM approach not only enhances Signaling Theory to consider the nature of employer signals, but also their clarity and mode of communication in digital-first recruitment. In India's booming IT industry, competition is intense and ambitions high, and therefore it is essential that there be a robust yet clear online employer reputation leveraged to attract high-calibre talent.

Implications of TAM Integration

Applying TAM to recruitment makes important contributions. One, it lays emphasis on employer judgment's interactivity through technology, recognizing the web as the preeminent conduit for judgments regarding employment. Two, TAM verifies a behavioral job search focus, from web surfing to ultimate application choices. Combining TAM with Signaling Theory further enhances recruitment models by discovering how signals are taken in and interpreted. This provides IT firms with strategic guidance on how to enhance digital employer branding to attract top-of-the-line, technology-enabled talent in a digitally escalating labor marketplace.

Practical Implications

This study has huge implications for Indian IT companies seeking to enhance recruitment. Firstly, creating core aspects of employer image such as training initiatives, career development opportunities, and tangible WLB practices such as work-from-home and flexi-work arrangements need to resonate in all recruitment channels. Secondly, creating campus recruitment needs to adopt a two-pronged approach: preliminary establishment of organizational credibility, and differentiation based on certain WLB provisions that appeal to students' decision-making profile. Third, HR functions ought to have quantifiable WLB policies, such as flexible scheme takeup levels, and enforce them with staff approval to supply evidence of their influence. Because WLB moderates to some degree job search intention, firms that recruit female engineers and family-responsibility applicants ought to specifically address such policies because our findings report a 28% superior influence among these groups. Lastly, recurring student employer brand perception surveys need to detect and bridge differences between perceived and actual WLB provision to guarantee long-term sustained attraction of talent.

Employers also have to use available digital media to communicate employer value propositions and WLB policies in an effective manner. Interactive media like career websites, video testimonials, and virtual tours contribute additional momentum to perceived ease of use and usefulness, boosting job pursuit intention among digitally native engineering students.

By establishing WLB's partial mediating function and merging Signaling Theory and the Technology Acceptance Model, this research offers practical and theoretical implications. It outlines why and how employer signals and online conversations are processed hierarchically by students, providing actionable recommendations for IT recruiters who must operate in a rapidly competitive, technology-dense job market. WLB prioritization can also aid overall diversity and inclusion strategy development. This is particularly relevant in a high-artition, highly competitive industry where real, quantifiable WLB policies determine employer reputation, female and care-giver retention, and ultimately organizational performance and innovation.

Beyond India, these findings have broader relevance across South Asia. The majority of SAARC nations share comparable challenges—youth unemployment in large numbers, IT skills shortages, and cultural complexity in work preferences. India’s IT sector offers a benchmark for how employer branding, WLB, and digital innovation influence early-career choice of jobs. Bangladesh, Sri Lanka, and Nepal's emerging clusters in the IT sector can draw lessons from them to develop competitive employer brands, facilitate WLB signaling, and digitalize hiring. India therefore becomes a case and a model for the evolving landscape of HRM practices across South Asia’s knowledge industries.

Limitations of the Study

First, employing student respondents (with no work experience) restricts generalizability to actual job seekers; future studies must follow these students' actual job decisions after graduation. Second, the sample being localized to Bangalore might not be representative of India's wide range of other IT cities; comparative studies in Pune/Hyderabad are the need of the hour. Third, the cross-sectional design cannot account for WLB preference differences with career stages.

Scope for Future Research

Future research can be in these areas:

• Test other mediators (e.g., ethical reputation, developmental opportunity) to account for the remaining 53% of variance that is not mediated.
• Investigate differences between generations by contrasting Gen-Z with millennial job applicants,
• Carry out longitudinal studies to determine whether pre-hire WLB desires are aligned with post-hire experience, and
• Investigate industry differences (e.g., IT versus fintech) to determine industry-specific WLB desires.
• Future studies could empirically test this TAM-integrated framework using structural equation modeling with platform interaction metrics (e.g., time spent on company sites, number of employer videos watched, or perceived interactivity).
• Explore collaborative intervention studies with industry partners to test and refine recruitment strategies grounded in this framework, thereby enhancing practical applicability and impact.
• Future research should test this model in South Asia's emerging IT hubs beyond major metros to assess regional variations in work-life balance priorities and employer branding impacts.

This research verifies that although employer reputation directly affects job seeking intention (β=0.31) among Indian engineering students seeking IT organizations, work-life balance (WLB) policy acts as a substantial partial mediator (β=0.47), extending Signaling Theory to expose candidates' hierarchical evaluation process - initially evaluating base reputation followed by WLB benefits. Even with limitations from student samples, the results herald a paradigm shift where WLB transitions from ancillary benefit to key decision driver, presenting a twin-branding approach for IT firms: highlighting base characteristics (stability, compensation) and distinguishing WLB policies in recruitment initiatives. With India's IT sector expanding at 11% per annum amid talent gaps, these results offer timely recommendations, especially for tier-2 cities whose WLB standardization is diverse. The results corroborate cross-cultural international Gen-Z employment interests in overall employment (Deloitte 2023), which shows cross-cultural applicability while promoting the importance of empirical validation through real-candidate studies and cross-industry analysis. Through the synthesis of signaling and digital perception theories, this current study fills crucial gaps in the understanding of technology talent attraction amid competitive labor market conditions, providing actionable employer branding suggestions for the changing workplace environment.

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