PURPLE HIRING: MATRIX OF INCLUSIVE ACQUISITION (MIA) FRAMEWORK FOR DIVERSITY AND INCLUSION IN RECRUITMENT PRACTICES

Abstract

Background and Purpose: Biases embedded in conventional recruitment processes continue to impede equitable representation of diverse talent, particularly Persons with Disabilities (PWDs), gender minorities, and marginalized ethnic communities. This study introduces and empirically examines "Purple Hiring," a structured, bias-attenuated recruitment paradigm designed to enhance organizational diversity and inclusion (D&I) at systemic rather than merely symbolic levels. Grounded in Social Identity Theory, Organizational Justice Theory, and Signaling Theory, the Purple Hiring model integrates structured assessment protocols, blind recruitment techniques, and inclusive talent pipeline development to overcome ingrained hiring biases (Shore et al., 2011; Nishii, 2013).
Methodology: A stratified random sample of 225 HR professionals and recruiters drawn from IT and ITES firms in Bengaluru, India, completed a structured questionnaire. Data were analysed via descriptive statistics, chi-square tests of independence, Pearson correlation analysis, binary logistic regression, and a Random Forest machine learning classification model evaluated using accuracy, precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC).
Findings: Purple Hiring demonstrably reduces recruitment bias: 97.3% of respondents endorsed equal employment opportunity principles; 92.9% were aware of Purple Hiring practices; and 99.1% expressed willingness to hire PWDs (45.3% actively; 53.8% conditionally). Binary logistic regression revealed that employment practice perception (OR = 1.864, p < .001), organisational inclusion climate (OR = 1.718, p = .002), and bias/discrimination awareness (OR = 1.489, p = .005) are the strongest predictors of active PWD hiring (Nagelkerke R² = .322). The Random Forest classifier achieved 64.4% accuracy (AUC = 0.73), identifying employment practices and instances of favouritism as the top predictive features.
Originality: This study provides a validated conceptual framework and the first machine learning-based quantitative evaluation of Purple Hiring in an emerging-market context, offering actionable implications for HR policy design and inclusive organisational culture.