http://hdl.handle.net/123456789/11985 2026-10-17T01:49:39Z http://hdl.handle.net/123456789/21843 The Influence of Managerial Attitudes and Perceptions on Artificial Intelligence Adoption in Pakistan's Banking Industry: The Moderating Role of Technological Infrastructure Fatima Saleem, 01-221251-002 Artificial Intelligence (AI) has emerged as a transformative technology in the banking industry, improving operational efficiency, decision-making, fraud detection, and customer service. adoption in Pakistan's banking industry while investigating the moderating effect of technological infrastructure. A quantitative research approach was adopted using a structured questionnaire administered to 199 managers working in commercial and Islamic banks in Islamabad, Lahore, and Karachi. Data were analyzed using SPSS Version 26, employing descriptive statistics, reliability analysis, correlation analysis, multiple regression, hierarchical regression, and an independent-samples t-test. The findings revealed that Managerial Attitudes and Perceptions positively and significantly influenced AI Adoption (β = 0.430, p < .001), while Technological Infrastructure also had a significant positive effect on AI Adoption (β = 0.297, p < .001). The regression model explained 47.4% of the variance in AI Adoption (R² = 0.474; Adjusted R² = 0.469; F = 88.31; p < .001). Furthermore, technological infrastructure significantly moderated the relationship between managerial attitudes and AI adoption, indicating that stronger technological capabilities enhance the effectiveness of managerial support in implementing AI. Comparative analysis also showed that commercial banks possessed stronger managerial support and technological infrastructure than Islamic banks. The study concludes that successful AI adoption in Pakistan's banking industry requires both positive managerial attitudes and robust technological infrastructure. The findings contribute to the Technology Acceptance Model (TAM) and the Technology–Organization–Environment (TOE) framework while providing practical recommendations and an implementation roadmap to support AI adoption in the banking sector. Supervised by Dr. Asif Khattak 2026-01-01T00:00:00Z http://hdl.handle.net/123456789/21844 Determining the Influence of Generative AI on Recruitment and Talent Acquisition Ali Shaiyan Saqlain Bukhari, 01-321251-003 This dissertation critically reviews the recruitment and talent acquisition role of Generative Artificial Intelligence (GAI), comprehensively addressing how operational efficiency, screening fairness, workforce diversity, ethical transparency, and candidate experiences evolve under AI-assisted hiring paradigms. Driven by strict qualitative feedback from the viva voce panel, this study transitions fully away from quantitative assumptions to adopt a rigorous, qualitative exploratory research design rooted in the interpretive paradigm. Guided by the Technology Acceptance Model (TAM) and Stakeholder Theory, this study operationalizes how HR professionals, talent acquisition leads, and AI software engineering specialists experience and interpret the deployment of GAI across localized professional ecosystems. Primary data gathered via in-depth semi-structured interviews with 12 purposively and snowball-sampled participants across three leading, private software development and IT infrastructure firms operating within the Rawalpindi-Islamabad tech cluster. The target industry strictly rationalized due to its rapid, tech-driven talent requirements and early-stage adoption of algorithmic automation. Data processing conducted via programmatic open coding and rigorous thematic analysis aligned with Braun and Clarke’s (2006) six-stage framework. The empirical findings reveal that while GAI serves as a powerful administrative tool—drastically reducing time-to-hire, minimizing operational costs, and automating monotonous tasks like high-volume resume parsing—it introduces significant systemic vulnerabilities. These include algorithmic amplification of historical data biases, opaque black-box architecture, data privacy non-compliance, and severe limitations in evaluating complex interpersonal soft skills and cultural alignment. The study establishes five distinct qualitative propositions demonstrating that GAI must act exclusively as an augmented decision support mechanism rather than an autonomous proxy for human judgment. Ethical governance frameworks, continuous representative dataset auditing, upfront transparent disclosure to candidates, and proactive HR capability building presented as mandatory pre-conditions for responsible algorithmic integration within contemporary strategic human resource management. Supervised by Ms. Sumeela Amin 2026-01-01T00:00:00Z http://hdl.handle.net/123456789/21852 When Caregivers Feel Dehumanized: Exploring Political Behavior through Perceived Injustice in Pakistani Healthcare Settings Muhammad Ali, 01-321251-017 Pakistan’s healthcare sector operates under persistent structural and managerial pressures, including resource scarcity, workload intensity, professional hierarchy, limited employee voice, and unequal decision-making power. In such conditions, perceived injustice becomes more than a routine workplace concern; it can trigger political and deviant employee responses that weaken teamwork, service quality, employee dignity, and organizational trust. Drawing on Social Exchange Theory, this study examined whether perceived injustice leads to deviant workplace behavior through political behavior and whether Machiavellianism strengthens this relationship in Pakistan’s healthcare sector. The study also connects this issue with the Sustainable Development Goals, particularly SDG 3: Good Health and Well-being, SDG 8: Decent Work and Economic Growth, and SDG 16: Peace, Justice and Strong Institutions, because fair and accountable healthcare workplaces are essential for employee well-being, ethical service delivery, and institutional trust. The study adopted a positivist, deductive, quantitative, and cross-sectional research design. Data were collected through personally administered questionnaires from doctors, nurses, paramedical staff, technicians, and administrative employees working in Pakistan’s healthcare sector. Out of 384 distributed questionnaires, 202 valid responses were retained after removing incomplete, missing, and biased responses. Established scales were used to measure perceived injustice, political behavior, deviant workplace behavior, and Machiavellianism. The findings supported the proposed moderated-mediation model. Perceived injustice significantly predicted political behavior (β = 0.43, p < .001), indicating that unfair treatment encouraged employees to engage in informal and self-protective workplace politics. Political behavior significantly predicted deviant workplace behavior (β = 0.51, p < .001), confirming that workplace politics can become a pathway toward harmful conduct. The indirect effect of perceived injustice on deviant workplace behavior through political behavior was significant (effect = 0.22, Boot SE = 0.05, LLCI = 0.13, ULCI = 0.33). Machiavellianism also significantly moderated the relationship between perceived injustice and political behavior (β = 0.19, p < .001). The conditional indirect effect increased from 0.11 at low Machiavellianism to 0.36 at high Machiavellianism, and the index of moderated mediation was significant. Supervised by Ms. Madiha Ali 2026-01-01T00:00:00Z http://hdl.handle.net/123456789/21846 Determinants of HR Analytics Adoption: Examining the Roles of Performance Expectancy, Effort Expectancy, Facilitating Conditions and Social Influence with the Mediation of Behavioral Intention Ammar Sarwar, 01-221251-015 The concept of Human Resource Analytics has become one of the strategic solutions that can allow organizations to enhance workforce planning, talent management, and evidence-based decision-making by leveraging the utilization of employee data. Although the telecom industry in Pakistan is fast adopting digital transformation, the uptake of HR analytics has been low because of organizational, technological, and behavioral factors. A lot of telecom organizations still use traditional HR practices, and this limits their capacity to maximize workforce performance and enable them to make strategic decisions. This research therefore explored the factors affecting the uptake of HR analytics by exploring the impact of performance, effort and facilitating conditions and social influence whilst considering the mediating effect of behavioral intention. The research sampled employees of the large telecommunication companies in Pakistan, such as Jazz, Zong, Telenor, Ufone, and PTCL. The conceptual framework proposed was tested empirically to identify both the direct and indirect impacts of the independent variables on the adoption of HR analytics and the indirect impacts via the behavioral intention.The results showed that the performance expectancy, effort expectancy, facilitating conditions, and social influence were all significant and positively related to HR analytics adoption. Among them, performance expectancy stood out as the most predictive factor that showed that employees were more ready to embrace HR analytics when they believed their performance and the quality of decision-making would improve. Moreover, the behavioral intention played an important role in mediating all the independent variables with HR adoption, which proved that behavior of intention formation translated perceptions of employees into real adoption behavior. The results are useful in advising telecom organizations to speed up the process of digital HR transformation by improving organizational support, easy-to-use analytics systems, building employee capabilities, and managing change. Finally, the research will help in enhancing the evidence-based human resource management, workforce productivity, and long-term organizational competitiveness in the Pakistani telecom industry. Supervised by Dr. Muhammad Arif Khattak 2026-01-01T00:00:00Z