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  • The Effectiveness and Challenges of Online Platforms for Talent Sourcing: A Perception Study of Recruiters in the IT Sector

  • Department of Management Studies, Bishop Heber College, Trichy -17

Abstract

This research examines the role of online recruitment platforms in sourcing talent in the IT industry, using a perception-based study among recruiters at VDart, a global staffing and technology solutions company. Through descriptive and inferential statistical methods, the study explores the effectiveness, challenges, platform preferences, and candidate engagement strategies associated with online talent acquisition. Findings indicate that LinkedIn is the most dominant and effective platform in terms of cost, time, and quality, but high costs and profile limitations hinder optimal usage. The paper concludes with strategic recommendations for enhancing digital recruitment and improving platform functionalities.

Keywords

Online recruitment, Talent acquisition, LinkedIn, AI in hiring, Digital platforms

Introduction

Digital transformation has radically altered the way companies attract and hire talent. Online recruitment platforms such as LinkedIn, Naukri, Indeed, and AI-powered tools have emerged as key channels in the modern hiring ecosystem. This study focuses on the recruiters' perspective to analyze the effectiveness and limitations of such platforms, using a case study of VDart, an IT staffing company with global reach.

OBJECTIVES:

  • To evaluate the effectiveness of online platforms in sourcing IT candidates
  • To identify challenges faced by recruiters in digital hiring
  • To assess the return on investment (ROI) of these platforms in terms of cost, time, and quality
  • To provide recommendations for improving the online recruitment process

LITERATURE REVIEW

The literature on digital recruitment reveals both strategic advantages and limitations:

  • Stone et al. (2015) emphasize technology's role in enhancing recruitment efficiency.
  • Nikos & Rachel (2018) identify cost-effectiveness and access to wider talent pools as key benefits.
  • Smith & Patel (2020) and Ramanath et al. (2018) explore the growing role of AI in automating and improving job matching.
  • Williams & Carter (2019) and Peng (2022) focus on LinkedIn’s evolving role in passive hiring.
  • Challenges such as data privacy, algorithmic bias, impersonal candidate experiences, and profile misrepresentation are highlighted by Miller & Anderson (2021), Thompson & Roberts (2020), and Garcia & Kim (2022).

This body of work provides a foundation to evaluate how these global trends apply in the context of Indian IT recruitment.

METHODOLOGY

The study adopted a descriptive quantitative approach, using structured questionnaires distributed to 110 recruiters at VDart.

  • Sampling Method: Convenience sampling
  • Tools Used: Descriptive statistics, Chi-square, and ANOVA (via SPSS)
  • Scope: Recruiters engaged in sourcing IT talent via online platforms
  • Data Source: Primary (survey) data

4. Data Analysis and Results

The data analysis reveals several critical trends related to recruiters' perceptions, usage, and challenges associated with online recruitment platforms. A structured table summarizing the quantitative insights is provided, followed by a narrative elaboration to contextualize these findings.

Category

Metric

Result

Demographics

Age group 20-30

50%

 

Female respondents

56.4%

 

0-2 years of experience

53.6%

Platform Preferences

LinkedIn preference

28.2%

 

Naukri preference

19.1%

 

Foundit/Ceipal preference

~18%

Effectiveness Perception

Agree or strongly agree on platform effectiveness

93.6%

 

Believe online > traditional recruitment methods

87.3%

Engagement Strategies

Use of personalized messaging

42.7%

 

Assess via LinkedIn profiles

38.2%

 

Use of skill tests

9.1%

Challenges Identified

High platform cost

34.5%

 

Limited free features

40%

 

Duplicate/outdated profiles

72.7%

 

Low candidate response, high competition

Not quantified

AI in Recruitment

Find AI-based recommendations effective

58.2%

 

Find AI-based recommendations very effective

16.4%

Statistical Tests

ANOVA (effectiveness vs. experience)

p = 0.689 (not significant)

 

Chi-square (platform effectiveness vs. experience)

p = 0.815 (not significant)

Detailed Interpretation

The demographic profile shows that the majority of respondents are young (20–30 years), with over half being women and relatively new to the recruitment field. This demographic trend reflects the growing influx of younger HR professionals in the tech-driven recruitment industry, particularly in the Indian IT context. Regarding platform preferences, LinkedIn clearly stands out as the most preferred online tool due to its comprehensive candidate database, professional interface, and recruitment features. Naukri and Foundit/Ceipal also enjoy moderate popularity, indicating a diversified sourcing strategy. Despite the dominance of LinkedIn, cost concerns and limited free functionality often compel recruiters to explore alternatives. The perceived effectiveness of online recruitment is overwhelmingly positive. A striking 93.6% of respondents agree or strongly agree with the effectiveness of online platforms, and 87.3% acknowledge that these platforms outperform traditional hiring methods. This underscores a shift in perception and reliance on digital hiring tools as primary recruitment channels. Candidate engagement remains a critical part of digital recruitment. The most commonly employed strategy is personalized messaging (42.7%), which indicates a recruiter preference for tailored communication to increase candidate interest. LinkedIn profile assessments are widely used (38.2%), while only 9.1% use skill tests, indicating an area for process enhancement. The minimal use of structured assessment tools may lead to inefficiencies in evaluating candidate capabilities, suggesting a reliance on resumes and profiles over data-driven testing. Key challenges hindering the optimal use of online platforms include high subscription costs (reported by 34.5%), restricted access to features (40%), and a significant issue with outdated or duplicate candidate profiles (72.7%). These issues impact the efficiency of sourcing, screening, and hiring, despite the platforms’ strong potential. AI is increasingly shaping recruitment workflows. More than half (58.2%) find AI-powered candidate recommendations effective, and 16.4% rate them as very effective. While this shows a promising outlook for AI integration, it also indicates room for growth in user trust and functionality of AI tools. The effective deployment of AI remains limited by recruiter experience, platform design, and the interpretability of machine learning outputs. Inferential statistical analysis through ANOVA revealed no significant variation in platform effectiveness perceptions based on recruiter experience (p = 0.689), and Chi-square results also confirmed that platform effectiveness is not significantly associated with years of experience (p = 0.815). These findings imply that regardless of experience, recruiters across levels view online platforms with similar appreciation and criticism. Overall, the findings of this study provide a comprehensive understanding of the landscape of online talent sourcing in the IT industry. The data underscores the dominance of LinkedIn as a recruitment tool, high receptivity toward AI features, and the prevalence of digital-first approaches among new-age recruiters. At the same time, they point to systemic challenges in cost structures, candidate verification, and underutilization of structured assessment tools, which need to be addressed for optimal outcomes.

DISCUSSION

Online recruitment platforms, especially LinkedIn, have become the dominant channel in the IT hiring landscape. Recruiters benefit from broader access to candidates, faster screening, and AI-assisted matching. However, challenges such as premium pricing, limited access, and unreliable candidate data impact recruiter satisfaction. Interestingly, the study reveals that despite varying experience levels among recruiters, the perception of online platforms remains consistent. This indicates a widespread adoption and reliance on digital tools, regardless of experience. The underutilization of candidate assessments like skill tests and background verification suggests an area of improvement. Similarly, overreliance on AI without adequate human judgment can compromise hiring quality.

RECOMMENDATIONS

  1. LinkedIn Optimization: Use premium analytics, targeted ads, and engagement content
  2. Cost Reduction: Negotiate subscriptions or diversify platforms to include lower-cost alternatives
  3. Candidate Verification: Integrate automated verification and encourage user-updated profiles
  4. Expand Assessment Tools: Incorporate pre-screening skill tests and video interviews
  5. Platform Diversification: Combine job boards, niche platforms, and internal referrals
  6. Improve Candidate Experience: Balance automation with personalization in communication

CONCLUSION

The study confirms the rising importance of online platforms in IT talent sourcing. LinkedIn emerges as the top choice, offering the best ROI in terms of candidate quality and time-to-hire. While these platforms offer speed and reach, recruiters face limitations like high costs, restricted access, and candidate authenticity issues. By adopting a balanced strategy that includes technology, personalization, and multi-platform integration, companies like VDart can significantly improve their recruitment outcomes.

REFERENCE

  1. Anagnostopoulos et al. (2020). Algorithms for Hiring and Outsourcing in the Online Labor Market.
  2. Garcia & Kim (2022). Diversity and Inclusion in Digital Recruitment.
  3. Miller & Anderson (2021). Recruitment Analytics.
  4. Nikos & Rachel (2018). E-Recruitment and its Impact on Organizational Hiring.
  5. Peng (2022). Talent Recommendation on LinkedIn User Profiles.
  6. Smith & Patel (2020). The Role of AI in Modern Talent Acquisition.
  7. Stone et al. (2015). The Influence of Technology on the Future of HRM.
  8. Williams & Carter (2019). LinkedIn and Professional Networking in Recruitment.

Reference

  1. Anagnostopoulos et al. (2020). Algorithms for Hiring and Outsourcing in the Online Labor Market.
  2. Garcia & Kim (2022). Diversity and Inclusion in Digital Recruitment.
  3. Miller & Anderson (2021). Recruitment Analytics.
  4. Nikos & Rachel (2018). E-Recruitment and its Impact on Organizational Hiring.
  5. Peng (2022). Talent Recommendation on LinkedIn User Profiles.
  6. Smith & Patel (2020). The Role of AI in Modern Talent Acquisition.
  7. Stone et al. (2015). The Influence of Technology on the Future of HRM.
  8. Williams & Carter (2019). LinkedIn and Professional Networking in Recruitment.

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Glenny Jocelyn G.
Corresponding author

Department of Management Studies, Bishop Heber College, Trichy -17

Photo
Judy Grace Nitta J.
Co-author

Department of Management Studies, Bishop Heber College, Trichy -17

Glenny Jocelyn G.*, Judy Grace Nitta J., The Effectiveness and Challenges of Online Platforms for Talent Sourcing: A Perception Study of Recruiters in the IT Sector, Int. J. Sci. R. Tech., 2025, 2 (7), 318-321. https://doi.org/10.5281/zenodo.16016340

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