Dissertation Proposal
Introduction
Performance is the goal of an organization that every employee must exhibit to be productive for it. Individual performance leads to organizational performance, and organizations develop strategies and policies to manage it. Performance management is the process that considers different actions and strategies to ensure productivity and desired outcomes for employees. Organizations implement performance management systems, which consist of different factors and actions for the management and advancement of desired outcomes and goals for employees (Darmawan, et al., 2020). Evaluation of individual performance is possible through it, and it must be effective. Effective performance management system contributes to work performance through employees’ work and job outcomes.
Problem statement
The work performance of employees is the target of any organization. It is more important for industries facing a competitive landscape. In this respect, the semiconductor industry is one such industry where the level of competition is fierce (Ishak et al., 2023). Therefore, it is worthwhile to consider the work performance of employees for which the desired performance management system should be there. It calls for evaluating the role of an effective performance management system, and this research studies its impact on this system (Zhao & Burke, 2021). Five factors of the independent variable have been studied to determine their effect on the dependent variable i.e. effective performance management system.
Research Aim
The research aims to study the effectiveness of performance management systems on the work performance of employees. It involves five factors or the independent variables, as all of them are independent variables. The study explains how these independent variables affect effective performance management systems. It also studies work performance of employees. Ultimately, the research will find how to affect the effective performance management system and the role of work performance of employees in the chosen industry, which is the semiconductor industry. It will show how effective a performance management system does this job.
Research Objectives
The research is going to serve the following objectives:
- To investigate which factors and variables affect performance management systems for employees in the semiconductor industry.
- To measure and determine the impact of different variables and the role of work performance of employees.
- To recommend how to improve the effectiveness of performance management systems in the semiconductor industry considering the work performance of employees.
Literature Review
Performance management systems contribute to the performance of individuals and organizations. The effectiveness of a performance management system lies in its ability to achieve desired outcomes precisely and realistically. Research studies have studied different variables related to these systems.
Independent Variables
Fairness (Sharma et al., 2016)
Research has noted that an effective performance management system occurs when there is accuracy and fairness in the system. Justice and fairness develop a climate where employees perceive confidence in putting their efforts into the organization (Sharma et al., 2016). Employees perform well, and their work performance improves when the implemented performance management system is effective due to justice and fairness.
Prompt performance management (Franco‐Santos & Otley, 2018)
Performance management systems should be reflective of employees’ performance and expectations. Every employee wants the system to be aligned with his performance so that every aspect of performance is measured (Franco‐Santos & Otley, 2018). Efficiency implies that performance management is prompt in that it directly and perfectly measures and evaluates the performance of employees.
Elevation of Employees through career development (Mone et al., 2018)
Performance management system affects employees and brings positive outcomes for an organization if it elevates performance. Measurement of performance is one of the purposes of performance management systems. Broadly and comprehensively, these systems should elevate their performance. Research has noted that employees are concerned about their careers, and performance management should enhance these prospects by elevating their work performance (Mone et al., 2018). Career development is one of the primary desires of employees so that they can ensure their bright careers.
Compensation Management (Candradewi & Dewi, 2019)
Performance management system is based on the performance of employees. Compensation is also based on performance management, and it helps organizations to enhance performance. Compensation based on performance motivates employees, and monetary benefits contribute to their performance (Candradewi & Dewi, 2019). Non-monetary compensation also helps motivate employees, and as a result, they perform better. Performance management system needs to contribute to compensation-based decisions.
Multiple data sources (DeNisi & Murphy, 2017)
Performance management systems should be objective, and different data sources should contribute to the system. It increases trust in the system, and the resulting decisions are also trustworthy. It results in better and improved performance of employees in their work. This is an important variable contributing to the work performance of employees and is part of the performance management system (DeNisi & Murphy, 2017). It also ensures the effectiveness of the performance management system.
Dependent Variable: Effective Performance Management System
The performance of employees is subject to different variables. Internal and external environmental factors contribute to employees’ work performance. Organizational culture and management policies are vital to the performance of employees. Performance management systems and efforts from the management can make the work climate better for employees (Pradhan & Jena, 2017). Performance for an organization has two components. First, it is individual performance, and the second is organizational performance. Both levels of performance are integrated and connected (Kamble & Gunasekaran, 2020). Performance management system considers them and carries out strategies for their improvement and betterment.
Conceptual Model
Literature has developed the following conceptual model after reviewing studies. The conceptual model shows that there are five variables affecting effective performance management system.
Research Methodology
The research proposal is going to investigate how effective performance management system works on the work performance of employees. The dependent variable is the effective performance management system, and it has further found five variables affecting the dependent variable. It is quantitative research and will collect data from respondents. More details about its methods are in the following.
Research methods.
The research is based on primary responses and is quantitative. It is the deductive paradigm that is wise for its generalized results. The research design is explanatory and investigates the impact of one variable on the other (Rahi, 2017). It also explores how the effect takes place. It will help to investigate what and how performance management system is affected (Hollway, 2018). So far as the philosophical foundation is concerned, it is an ontology that wants to know about the variables and their relationships (Al-Ababneh, 2020). As the research is going to collect data from the semiconductor industry, it will investigate how the variable works in its context.
Data collection strategy and sample
As noted above, it is quantitative and primary research. A sample of one hundred employees from the semiconductor industry will be selected. The reason for choosing the industry is the convenience as the researcher works there (Basias & Pollalis, 2018). It also decides about the sampling strategy, which is convenient random sampling. However, sample participants have strict inclusion criteria (Parker et al., 2019). Participants will be working in the semiconductor industry for at least two years and above. They will be full-time employees and have gone through two performance appraisals in the company. They are employees with no executive position held, and their age should be between 25 and 55. To gain access to the sample, an online data collection tool, i.e., Google Forms, will be used, and a generated link will be sent to them.
Data analysis methods
Data analysis will be crucial as the findings will help to meet objectives. First, the test of correlation will be applied to know the relationship between each independent variable and the dependent variable. It will also help to find the impact of each independent variable on the dependent variable (Rahi, 2017). A regression test will also be applied along with the descriptive statistics. This is how findings will be obtained, which will be analyzed in the end.
Ethical considerations
It will be primary research, so it must abide by ethical considerations. First, all respondents will be part of the research after giving their informed consent. The information given to them will be confidential and anonymous. They can withdraw from research at any time, even after giving their informed consent (Bryman, 2016). Their given data will be kept responsibly and will be lost once the study is completed. Along with this, the research will follow all ethical considerations set by the university.
References
Al-Ababneh, M. M. (2020). Linking ontology, epistemology and research methodology. Science & Philosophy, 8(1), 75-91. doi:10.23756/sp.v8i1.500
Basias, N., & Pollalis, Y. (2018). Quantitative and qualitative research in business & technology: Justifying a suitable research methodology. Review of Integrative Business and Economics Research, 7, 91-105. https://sibresearch.org/uploads/3/4/0/9/34097180/riber_7-s1_sp_h17-083_91-105.pdf
Bryman, A. (2016). Social research methods. Oxford university press.
Candradewi, I., & Dewi, I. G. (2019). Effect of compensation on employee performance towards motivation as mediation variable. International research journal of management, IT and social sciences, 6(5), 134-143. https://core.ac.uk/download/pdf/230599807.pdf
Darmawan, D., Mardikaningsih, R., Sinambela, E. A., Arifin, S., Putra, A. R., Hariani, M., . . . Issalillah, F. (2020). The quality of human resources, job performance and employee loyalty. International Journal of Psychosocial Rehabilitation, 24(3), 2580-2592. https://www.researchgate.net/profile/Didit-Darmawan/publication/340986809_The_Quality_of_Human_Resources_Job_Performance_and_Employee_Loyalty/links/610226361ca20f6f86e61257/The-Quality-of-Human-Resources-Job-Performance-and-Employee-Loyalty.pdf
DeNisi, A. S., & Murphy, K. R. (2017). Performance appraisal and performance management: 100 years of progress? Journal of applied psychology, 102(3), 421. doi:https://doi.org/10.1037/apl0000085
Franco‐Santos, M., & Otley, D. (2018). Reviewing and theorizing the unintended consequences of performance management systems. International Journal of Management Reviews, 20(3), 696-730. doi:https://doi.org/10.1111/ijmr.12183
Hollway, W. (2018). The importance of relational thinking in the practice of psycho-social research: Ontology, epistemology, methodology, and ethics. Object relations and social relations, 137-161. https://oro.open.ac.uk/15404/4/114D4931.pdf
Ishak, S., Shaharudin, M. R., Salim, N. A., Zainoddin, A. I., & Deng, Z. (2023). The effect of supply chain adaptive strategies during the COVID-19 pandemic on firm performance in Malaysia’s semiconductor industries. Global Journal of Flexible Systems Management, 24(3), 439-458. doi:https://doi.org/10.1007/s40171-023-00347-y
Kamble, S. S., & Gunasekaran, A. (2020). Big data-driven supply chain performance measurement system: a review and framework for implementation. International journal of production research, 58(1), 65-86. doi:https://doi.org/10.1080/00207543.2019.1630770
Mone, E., London, M., & Mone, E. M. (2018). Employee engagement through effective performance management: A practical guide for managers. Routledge.
Parker, C., Scott, S., & Geddes, A. (2019). Snowball sampling. SAGE research methods foundations.
Pradhan, R. K., & Jena, L. K. (2017). Employee performance at workplace: Conceptual model and empirical validation. Business Perspectives and Research, 5(1), 69-85. doi:https://doi.org/10.1177/2278533716671630
Rahi, S. (2017). Research design and methods: A systematic review of research paradigms, sampling issues and instruments development. International Journal of Economics & Management Sciences, 6(2), 1-5. doi:10.4172/2162-6359.1000403
Sharma, N. P., Sharma, T., & Agarwal, M. N. (2016). Measuring employee perception of performance management system effectiveness: Conceptualization and scale development. Employee Relations, 38(2), 224-247. doi:https://doi.org/10.1108/ER-01-2015-0006
Zhao, J., & Burke, A. F. (2021). Review on supercapacitors: Technologies and performance evaluation. Journal of Energy Chemistry, 59, 276-291. doi:https://doi.org/10.1016/j.jechem.2020.11.013
