Research

Talent acquisition has become one of the most technology-intensive organisational functions, yet its underlying logic has remained largely unchanged since the widespread adoption of Applicant Tracking Systems (ATS) in the early 2000s. ATS platforms operate on a principle of systematic elimination: automated résumé parsing, keyword matching, and Boolean filtering reduce large applicant volumes to manageable shortlists, optimising for speed and cost-efficiency (Cappelli, 2023). This logic, while operationally effective at managing scale, embeds a mechanistic, linear worldview into the heart of talent strategy — one that treats applicants as discrete data points to be sorted rather than as participants in a dynamic organisational relationship. The consequences of this paradigm are increasingly well documented. Up to 88% of employers acknowledge that qualified candidates are routinely filtered out because they do not match exact job description criteria (Fuller et al., 2021). Applicant experiences are degraded through opaque processes, absent feedback, and one-directional communication, eroding employer brand and organisational reputation (Greenhouse, 2024). The emergence of AI-powered résumé optimisation tools has further destabilised the system, creating an adversarial dynamic between applicants gaming algorithmic filters and organisations tightening them — a reinforcing loop that degrades signal quality on both sides. Applicant Relationship Management (ARM) was introduced by Specchia (2026a, 2026b) as a comprehensive, relationship-centric paradigm for talent acquisition. ARM's theoretical foundations trace back to relationship marketing (Carlzon, 1987; Parasuraman, Zeithaml & Berry, 1985) and its subsequent formalisation into Customer Relationship Management (Berry, 1983; Grönroos, 1994; Payne & Frow, 2005; Specchia, 2022), extending this relational tradition into Human Resource Management. ARM replaces transactional elimination with relational cultivation, repositioning applicants as stakeholders in a reciprocal value exchange. Its distinctive features include co-creative assessment — where applicants actively participate in reviewing and enriching the AI-generated evaluation of their application — and the systematic re-engagement of Silver Medallists, past applicants who demonstrated genuine potential but were not selected for a specific role. Crucially, Silver Medallist re-engagement in ARM is not a downstream marketing campaign as practised by Candidate CRM platforms; it is grounded in the quality of the assessment interaction itself — each moment of truth during co-creative assessment builds the relational equity that sustains meaningful future engagement.

Contemporary recruitment practices remain dominated by Applicant Tracking Systems (ATS) that operate on elimination logic, sequentially filtering candidates through keyword gates and multiple-hurdle screenings. While efficient at reducing applicant volume, this paradigm systematically discards potentially suitable candidates and degrades the quality of information available to hiring decisions. This paper introduces Applicant Relationship Management (ARM), a relational paradigm for talent acquisition derived from Customer Relationship Management (CRM) theory. ARM reconceptualises applicants as stakeholders in a co-creative assessment process, replacing sequential elimination with compensatory multi-dimensional evaluation, dialogue-based interaction, and sustained relationship management including silver medallist re-engagement. Drawing on stakeholder theory, relationship marketing, and compensatory assessment science, we develop the ARM framework and its lifecycle model. We then examine its implications for emerging regulatory and sustainability reporting frameworks, including the EU Artificial Intelligence Act (which classifies recruitment AI as high-risk from August 2026), the UN Sustainable Development Goals 8 and 10, and the CSRD/ESRS S1 workforce disclosure standards.

The agentic economy presents organizations with a fundamental choice: deploy AI to accelerate existing transactions, or orchestrate human-AI collaboration to transform stakeholder relationships. Across sectors—insurance claims, banking operations, healthcare delivery, legal services, and talent acquisition—a common pattern is emerging. Organizations that treat AI as an efficiency tool replicate transactional logic at machine speed. Those that position AI as a collaboration partner are discovering new paradigms for stakeholder engagement. This paper examines the emergence of what we term the “orchestration imperative”, the strategic necessity to design human-AI systems that enhance relationship quality rather than merely transaction velocity. We focus on recruitment as a critical demonstration domain, introducing Applicant Relationship Management (ARM) as a framework that exemplifies orchestration principles applicable across the agentic economy.

The paper identifies two interconnected problems that the existing literature has treated as separate concerns. The first is compensatory talent exclusion. The multiple hurdle model, which dominates personnel selection practice, assesses candidates sequentially against independent thresholds: a candidate who falls below the cut-off on any single criterion is eliminated regardless of their standing on all others. Ock and Oswald (2018), in a direct comparison of selection architectures, demonstrated that compensatory models — in which a candidate's full profile is evaluated as an integrated composite — yield superior predictive validity and better workforce diversity outcomes. De Corte, Lievens, and Sackett (2007) reached compatible conclusions through a different analytical route. The evidence is unambiguous: the most widely adopted selection architecture systematically excludes candidates whose integrated profiles would, under compensatory evaluation, be among the strongest in the pool.

Frame of the research: This study contributes to the intersection of talent acquisition, corporate governance, and sustainability scholarship. It extends Customer Relationship Management (CRM) theory into Human Resource Management, positioning recruitment as a governance function with direct implications for ESG performance and stakeholder stewardship. Purpose of the paper: This paper introduces Applicant Relationship Management (ARM) as a comprehensive, relationship-centric paradigm for talent acquisition. ARM is contrasted with traditional Applicant Tracking Systems (ATS) and Candidate Relationship Management (Candidate CRM) platforms, proposing a fundamental shift from transactional elimination to relational cultivation of applicants as stakeholders. Managerial implications: HR leaders should reconceptualise recruitment as relationship stewardship rather than process execution. Organisational leadership should recognise talent acquisition as a governance responsibility with brand, ESG, and regulatory implications.