Research Publications Authored by SLIIT Staff

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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.

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2017 - 20204

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Subject: search.filters.subject.Human Resource Management

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Now showing 1 - 4 of 4
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    PublicationOpen Access
    Factors Affecting Human Resources Management Policy Implementation in Small and Medium Enterprises (SMEs) in Sri Lanka
    (Human Resource Management Academic Research Society, 2017-12) Rajapakshe, W
    Policy implementation in SMEs related to Human Resource Management in Sri Lanka is often ignored because the issues are indirect and hidden. The main objective of this paper to review and understand theories and models related to the policy implementation and develop conceptual framework describing the factors affecting successful policy implementation in SMEs in Sri Lanka. Literature related to policy implementation as well as policy implementation models were analyzed and illustrated a conceptual framework. This framework directs attention to eight major variables that affect implementation of public policy. The three intervening variables showed that without implementing agency capacity, negotiation ability, and sector awareness, policy implementation might be weakened. Further, this conceptual model offers a blueprint for the successful policy implementation to solve the HRM issues in the SMEs in Sri Lanka.
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    PublicationEmbargo
    The Role of Human Resource Information System (HRIS) in Human Resource Planning in Banking Sector of Sri Lanka.
    (SLIIT Business School, 2019-12-10) Tharushika, G.D.R.; Withana, L.K.N.; Jayasinghe, P.D.P.; Kumari, P.D.S.C.; Dissanayake, L.D.A.D.
    Despite its wide variety of uses, the management of the banks in Sri Lanka is using HRIS (HRIS) for fundermental operations. In the study, it focuses on the utilization of HRIS in the Human Resource Planning (HRP), with special focus on the banking sector in Sri Lanka. The study focuses on one sub function in HRP: recruitment and selection. The problem arises as how HRIS contributes towards the planning of recruitment and selection in the banking sector. Thus, the aim of the research is to analyse how recruitment and selection sub function efficiently affected by HRIS towards the achievement of HRP. There are three constructs, developed through the literature survey to achieve the main objective of analysing the impact of recruitment and selection sub system done through HRIS on the HRP. Result of the analysis determine, “Job Analysis” and “E-Recruitment” have positive impact for HRP in Banking sector while “Skill inventory” result in negative impact. Authors suggest to use this study as a guide or a base for future researchers who were willing to research related to HRIS, HRP and banking industry.
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    PublicationEmbargo
    Business Intelligence Assistant for Human Resource Management for IT Companies
    (IEEE, 2020-11-04) Athukorala, C; Kumarasinghe, H; Dabare, K; Ujithangana, P; Thelijjagoda, S; Liyanage, P
    The advancement in technology is exponential. Moore's law supports this argument, by stating that the computing power doubles every two years. In such a premise, many IT companies have risen to meet the challenges. These companies provide various solutions in various fields of enterprises, pushing the limits of technology. Human resource is considered the most important asset in any organization. In order to utilize this asset beneficially, an organization must have great Human Resource Management practices. This includes practices from recruitment until employee termination. One great employee can offset the work of several regular employees. IT companies strive to acquire and retain such talent. But this is not a simple task. It requires resources including manpower and time. There should be knowledgeable individuals to handle important human resource processes, and many organizations lack these. They do not have enough time or labor to invest in good human resource processes. This research proposes a solution to this problem by creating a Business Intelligence Assistant for Human Resource Management Targeting Information Technology companies. It delves into Human Resource Management practices revolving around employee recruitment, job placing, employee engagement and human resource decision making. The solution consists of four solutions, namely; Structured Resume Analyzer, Smart Candidate Ranker, Employee Engagement Survey Generator and Business Intelligence Processor. Each component will enable the organization to streamline certain processes helping them save both time and labor. The individual components will make use of various applications of artificial intelligence to aid in the decision-making of an organization.
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    PublicationEmbargo
    Smart Human Resource Management System to Maximize Productivity
    (IEEE, 2020-12-17) Hewage, H. A. S. S; Hettiarachchi, K. U; Jayarathna, K. M. J. B; Hasintha, K. P. C; Senarathne, A. N; Wijekoon, J
    Human resource is one of the most valuable assets in an organization. They are bounded to develop the unique and dynamic aspects that strengthen their competitive advantage to persist in an always changing market environment. In order to recruit a quality candidate for an organization, reducing human involvement and verifying details of the candidate is important in recruitment process. Furthermore, having an idea about how well or poor the employees perform, and how likely the employee attrition can occur is vital in human resource management process. This paper is an attempt to introduce smart human resource management system that can maximize the productivity of an organizational environment using machine learning and blockchain technologies. The end goal of this research is a smart human resource management system that reduces human judgment, time in the candidate selection process and predicts employee performance and attrition to motivate current employers to maximize productivity with minimal financial loss in the workplace environment. Skill assessment and resume classification have been done using unsupervised learning algorithms and natural language processing after extracting raw data from employee resumes using Object Character Recognition. Candidate details verification is done by comparing the hashes of the records which are stored in the blockchain. Employee performance and attrition are predicted using supervised machine learning classification techniques with high accuracy and the result of the final performance is generated as a score for each employee considering the multiple attributes that has been standardized and regulated by some specifically considered e-competence frameworks.