Book Chapters

Permanent URI for this collection https://rda.sliit.lk/handle/123456789/4200

The book chapters authored by SLIIT researchers are included in this collection. Access to full texts may be restricted depending on the access and licensing terms.

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Author: search.filters.author.Rajapakshe, WStart date: 2010

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Now showing 1 - 4 of 4
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    Introduction to Compensation Management
    (IGI Global, 2025-01-01) Rajapakshe, W
    This chapter examines compensation management, emphasizing its vital role in modern organizations. It defines compensation management, distinguishing between direct (salaries, bonuses) and indirect (benefits, non- monetary rewards) forms. The chapter highlights its importance in attracting, retaining, and motivating talent, linking these outcomes to performance and sustainability. It explores how compensation reflects and reinforces culture and values, shaping behavior and commitment. The evolution from an operational to a strategic HR function is outlined, showing alignment with goals such as innovation and efficiency. The role of compensation in managing change, supporting globalization, and embracing diversity is discussed, illustrating adaptability to complex environments. Key principles of effective compensation management and a six- step process—from job analysis to wage and salary implementation—are presented, underscoring its contribution to a motivated, engaged, high- performing workforce aligned with strategy.
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    Designing a Compensation Strategy
    (IGI Global, 2025-01-01) Rajapakshe, W
    This chapter provides a comprehensive analysis of compensation design as a strategic lever for organizational success. It examines strategic compensation planning, highlighting the alignment of pay systems with business objectives to motivate desired behaviors, enhance productivity, and support long- term goals. Factors affecting employee compensation—including internal equity, external competitiveness, performance, skills, experience, and market benchmarks—are explored to inform effective pay decisions. The chapter distinguishes job evaluation from performance appraisal, emphasizing its role in establishing fair, transparent pay structures. Key features and methods of job evaluation—ranking, classification, point- factor, and factor comparison—are discussed, offering practical guidance for designing compensation systems that attract and retain talent, reinforce performance, and sustain competitive advantage in evolving business environments.
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    The Impact of Artificial Intelligence on Modern Hiring
    (IGI Global, 2025-08-07) Rajapakshe, W
    This study explores the transformative impact of AI on talent acquisition, highlighting its evolution within HRM and redefining recruitment in the digital era. It examines AI's integration across key recruitment functions, including resume screening, chatbots, interview scheduling, and predictive analytics for candidate suitability. The research reviews core technologies such as Natural Language Processing (NLP), Machine Learning (ML), Robotic Process Automation (RPA), and facial recognition. While AI enhances efficiency, reduces bias, and supports data-driven decisions, it also introduces ethical and regulatory challenges related to fairness, privacy, and transparency. The study draws upon theoretical models like the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and Diffusion of Innovation (DOI) to contextualize AI adoption. Finally, it presents a comparative analysis of global trends and Sri Lanka's local context, offering insights into the factors influencing AI adoption in recruitment and the future of digital HR practices.
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    Learning Style Preferences of Business Students in Saudi Arabia
    (IGI GLOBLE Scientific, 2024) Rajapakshe, W
    This chapter describes different types of learning style models It further explores Saudi Arabian business students' behavior regarding their preferences for different learning styles, as defined by the VAK learning styles model. 138 respondents who registered for theoretical courses and 128 for mathematical courses participated in the study. The data was gathered using a VAK modality questionnaire. The analysis indicated an apparent inclination towards multimodal learning compared to unimodal learning, with visual learning being the most favored unimodal mode. Moreover, the results of the independent sample t-test reveal no statistically significant difference in preferences among students enrolled in theoretical modules and those enrolled in mathematical modules.