Recommendation System for Human Resource Department

The objective is to find suitable skilled employees for the job among different departments within the organization. For finding the quality of an applicant or even the already employed employee, the HRs of companies goes through a lot of hectic schedule, time consuming processes, decision making, etc. In this case, Recommendation System, which is a part of Machine Learning, proves to be effective in making decisions on behalf of the HRs if an employee or an applicant is suitable enough for the job. The aim of the project is to predict whether the already employed employees, who belong to different department within the organization can perform well or not if assigned to a different department. Keywords: Recommendation system, Collaborative Learning, K-NN, Similarity, Similarity Correlation, Cosine etc.

The study of knowledge employee voice among the knowledge-based companies: the case of an emerging economy

PurposeA review of previous studies on the voices of employees and knowledge workers clarifies that paying attention to employees' voice is critical in human resource management. However, limited studies have been conducted on it, and much less emphasis has been placed compared to other human resource management activities such as human resource planning. Therefore, the voice of knowledge employees has been one of the critical issues that have attracted a great deal of attention recently. Nonetheless, there is no evidence of various comprehensive and integrated voice mechanisms. As a result, this study aims to design knowledge workers' voice patterns in knowledge-based companies specialising in information and communication technology (ICT) in Iran in May and June 2020.Design/methodology/approachThis study is a qualitative grounded theory research. We collected the data from a target sample of 15 experts in knowledge-based ICT companies using in-depth semi-structured interviews. Since all the participants had practised the employee voice process, they were regarded as useful data sources. Data analysis was also performed using three-step coding (open, axial and selective) by Atlas T8, which eventually led to identifying 14 components and 38 selected codes. We placed identified components in a paradigm model, including Personality Characteristics, Job Factors, Economic Factors, Cultural Factors, Organisational Policies, Organisational Structure, Climate Of Voice in the Organisation, Management Factors, Emotional Events, Communications and Networking, Contrast and Conflict and, etc. Then, the voice pattern of the knowledge staff was drawn.FindingsThe results showed that constructive knowledge voice influences the recognition of environmental opportunities and, additionally, it helps the competitive advantages among the employees. By forming the concept of knowledge staff voice, it can be concluded that paying attention to knowledge staff voice leads to presenting creative solutions to do affairs in critical situations. The presentation of these solutions by knowledge workers results in the acceptance of environmental changes, recognition and exploitation of new chances and ideas, and sharing experiences in Iranian knowledge-based companies.Practical implicationsStrengthening and expanding the voice of employees in knowledge-oriented companies can pave the way to growth and development towards a higher future that prevents the waste of tangible and intangible assets.Originality/valueCompanies' ability to engage in knowledge workers is a vital factor in human resource management and strategic management. However, the employee voice has not been involved integrally in the context of corporate.

Recruitment Through AI in Selected Indian Companies

Artificial intelligence (AI) is being used very pervasively with the ever-evolving and competitive business world and has become the 21st-century buzzword. Countless innovations in technology have pushed businesses to make their value creation processes more effective and customer friendly. Digitization has played a significant role in reshaping the different human resource functions and processes. This study aims to elucidate the acceptance of automation in human resource management by employers and the degree to which recruiters can use AI to hire people. The study incorporates a thematic analysis approach, and the data is collected from primary sources by conducting semi-structured interviews with four experts working in IT organizations. This research would be useful for recruiters and HR managers to consider the fields of AI implementation and management to take advantage of cost-cutting technical developments.

Chain Affiliation and Human Resource Investments: Evidence from the Restaurant Industry

Drawing on organizational theory, agency theory, and research in strategic human resource management, this study explores how chain affiliation influences human resource (HR) investments using data from a nationally random survey of restaurant establishments. We propose that chain-affiliated units will make different investments in those areas of the HR system where chains establish superior organizational routines compared with nonaffiliated units. By contrast, we argue that in the absence of chain routines, ownership incentives will drive differences in human resource investments. Specifically, we find that franchisee-owned units focus more on cost reduction by underinvesting in human resource practices compared with company-owned units and independently owned units when organizational routines are not provided by the chain. We provide further support for our theoretical arguments using additional data on multiunit ownership and franchisor influence. Finally, we conduct supplemental analyses to explore the relationship between different human resource investments and two important organizational outcomes: employee turnover and customer satisfaction ratings from Yelp. Our results highlight the types of human resource practices that are important for service work and suggest that the provision of organizational routines can have important implications for the long-run success of chains and their units.

Achieving Human Resource Management Sustainability in Universities

The sustainability of human resource management (HRM) is the basis for an organization’s future growth and success. This study aims to investigate achieving HRM sustainability in universities. We use a quantitative research method design to investigate the factors that affect HRM sustainability at universities. The study was conducted during the spring and summer of 2020 at Iranian state universities. As the study’s statistical population included 2543 employees, a sample size of 334 employees was calculated using the Cochran formula. A questionnaire with 32 statements based on a 5-point Likert scale was used to collect the data, which were analyzed using PLS3 software. The findings show that human resource practices, social factors, psychological factors, employer branding, and economic factors have positive and significant effects on HRM sustainability at universities. Findings indicate that it is essential to consider the implementation of adequate HRM practices and related socio-economic and psychological supports for HRM sustainability in universities that can lead to the competitiveness of the higher education institutions such as universities.