An Effective Solution For Issues In Human Resource Management – Phd Journey During Beginning Of Your Business And Management Research.

Sharing is caring!

An Effective Solution For Issues In Human Resource Management   – Phd Journey During Beginning Of Your Business And Management Research.


In brief

  • You will find the best dissertation research areas / topics for future researchers enrolled in Business & Management.
  • In order to identify the future research topics, we have reviewed the Business & Management literature (recent peer-reviewed studies).
  • There are issues in managing the employees and recruiting new candidates to large organisations.
  • These organisations require effective optimisation techniques for effective solutions
  • This article reviews the related work and gives recommendations for future work.


Large organizations have difficulties managing their vast man power since they operate in many places at the same time. They may operate in different countries where the labour laws vary from place to place. Human resource management (HRM) is one of the most important organisation functions which foster employee performance like workforce planning, recruitment, selection, training and development, compensation and legal issues (Batarlienė, Čižiūnienė, Vaičiūtė, Šapalaitė, & Jarašūnienė, 2017). The way that the department is managed is continuously changing with the rise of computer technology. The traditional methods collect all the data, but do not have any meaningful way of analysing the work and making decision. Hence, it is necessary to develop an efficient methodology to create meaningful information from the raw data. This will help the organisation in better decision making (Kappelides & Spoor, 2019).

The work efficiency of employees is an important factor to the organisation. It can be calculated in small scale by individual teams however, gathering and analysing details for the entire organisation is a tedious process. Some of the problems faced in HRM are Stress and Tension, agility, interviews, Employee satisfaction and turnover (Fındıklı & Bayarçelik, 2015).
Employee satisfaction can be achieved by providing career opportunities and performance based incentives. Better satisfaction among the employees will lead to positive productivity which is ultimately good for the company. Handling stress is difficult and wears out employees. This can be avoided by placing recreational activities which can be used during break hours. This will reduce the tension and stress. Maintaining agility of the organisation is important and it gets difficult as the number of employees grow (Ahammad, Glaister, & Gomes, 2019). The clarity and transparency among the work must be improved to improve the agility. Conducting interviews and keeping track of repeated candidates is the main issue for the organisation. It is time consuming and also difficult to track the quality of their work when move internally.

Qu, (2015) has analysed the turnover flow of the production line staff from a Chinese company. Around 5277 stores with staffs have been considered for collecting the data. Using questionnaire, personal information like name, age, nationality, marital status, staff salary and shift timings were asked. Correlation analysis and decision tree were used for pre-processing and classification respectively. The staff turnover of the enterprise has been explored and it has been said that data mining techniques can be used for efficient analysis of data.
Papoutsoglou, Mittas, and Angelis,(2017) has extracted the skills and competencies (hard and soft skills) from job advertisements and people profile using Stack Overflow and multivariate statistical data analysis. R coding and JavaScript were used to obtain the data. Pre-processing was performed and then Principal Component Analysis was used for identifying the number of factors. Spearman correlation was used to measure sample adequacy. Skills were then obtained from social networking sites.
Zhang,(2017) has analysed factors that influence the relieved staff based on China Construction Bank Hangzhou. In this work, both correlation and regression analyses were used and it is seen that the expected annual salary of the staff were higher than the average annual salary in the company. However, only one-year data was used for this work.
Hernández-Chacín, (2018) has performed data mining using WEKA for predicting employee performance on the basis of attributes like age, gender, and years of experience. Three different data mining algorithms ID3, C4.5 and K-nearest neighbour were used for identifying the most suitable algorithm and it is seen that C4.5 classifier algorithm worked better than the other classifiers with an accuracy of 92.69%. These results indicated the potential classification algorithm for human talent data.

De Mauro, Greco, Grimaldi, and Ritala,(2018) has applied text mining and classification algorithm to recognize big data skills required for each job type and to which extent. A 4-step methodology has been created where the downloaded number of online job posts through web-scrapping techniques followed by analyses of words in the titles through expert judgment. Then, the topic modelling algorithm is applied on the content of the job posts to review the current job offer and to identify the skill set within the job, adopted mixed membership model Latent Dirichlet Algorithm (LDA) and finally assess the relative importance of skill set.

Future Scope:

Hence, data mining and optimisation algorithms can be used for optimum extraction and analysis of data from social media and other data sources.
• A framework can be built that automatically extracts the necessary details of the candidate or employee and stores them in the cloud database.
• Artificial intelligence based techniques can be proposed for effective analysis and optimisation in the future.

  1. Ahammad, M. F., Glaister, K. W., & Gomes, E. (2019). Strategic agility and human resource management. Human Resource Management Review, 100700.
  2. Batarlienė, N., Čižiūnienė, K., Vaičiūtė, K., Šapalaitė, I., & Jarašūnienė, A. (2017). The Impact of Human Resource Management on the Competitiveness of Transport Companies.
  3. De Mauro, A., Greco, M., Grimaldi, M., & Ritala, P. (2018). Human resources for Big Data professions: A systematic classification of job roles and required skill sets. Information Processing and Management.
  4. Fındıklı, M. A., & Bayarçelik, E. beyza. (2015). Exploring the Outcomes of Electronic Human Resource Management (E-HRM)? Procedia – Social and Behavioral Sciences, 207, 424–431.
  5. Hernández-Chacín, S. J. K. authorAmarapali T. V. (2018). Application of Classification Technique of Data Mining for Employee Management System. In T. Q. (eds) Tan Y., Shi Y. (Ed.), International Conference on Data Mining and Big Data (pp. 434–444). Springer, Cham.
  6. Kappelides, P., & Spoor, J. (2019). Managing sport volunteers with a disability: Human resource management implications. Sport Management Review, 22(5), 694–707.
  7. Papoutsoglou, M., Mittas, N., & Angelis, L. (2017). Mining people analytics from stackoverflow job advertisements. Proceedings – 43rd Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2017.
  8. Qu, X. L. (2015). A decision tree applied to the grass-roots staffs’ turnover problem – Take C-R Group as an example. Proceedings of IEEE International Conference on Grey Systems and Intelligent Services, GSIS.
  9. Zhang, L. J. ; H. (2017). Improving Managerial Efficiency Through Analyzing and Mining Resigned Staff Data. 13th International Conference on Computational Intelligence and Security (CIS).

Related Topics Bussiness and Management

Related Services

Sharing is caring!

Leave a Reply

Your email address will not be published. Required fields are marked *