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Problem Statement
The high turnover rate among retail sales employees is a considerable bother of modern human resource (HR) management. According to the United States Department of Labor Statistics (2015), the retail turnover rate increased from 26.8% in 2011 to 33.5% in 2014. Avanzi, Fraccaroli, Sarchielli, Ullrich, and van Dick (2014) mention that employee turnover is associated with considerable financial losses, job satisfaction issues, and performance problems. Therefore, one of the primary tasks of HR managers is to employ efficient techniques for preventing high employee turnover. However, HR managers may be unaware of a useful method to address and prevent high turnover rates.
Literature Review
The problem of high turnover rates of retail employees is a widely discussed topic in scholar and professional literature due to its considerable implications for a company’s performance. Avanzi et al. (2014) identify turnover as a permanent exit from the organization, which is often preceded by other withdrawal behaviours, such as lateness or absenteeism. According to Lee (2018), a considerable body of research supports the idea that there is a negative linear correlation between turnover rates and a company’s performance. Low retention is associated with both pecuniary, which is recruiting and training costs for new employees, and nonpecuniary costs, such as low employee morale (Lee, 2018). In other words, companies lose money when they need to substitute a more experienced employee, which possesses valuable knowledge for the company. The money is lost due to training costs and differences in performances between the two employees. The recruiting costs also include the time when a job position remains vacant. In short, the implications of high turnover rates are considerable and efficient retention strategies need to be employed to reduce the possible damage.
The reasons for turnover intentions may vary depending on the situation; however, there are certain aspects, upon which all the researchers agree. First, Wilson (2018) mentions that the compensation level is one of the leading causes of a person quitting his or her job. The matter is confirmed by Pryce (2016) since salary disparities often mean a higher rate of employee turnover and job dissatisfaction. Second, Onsardi, Asmawi, and Abdullah (2019) state that empowerment is also critical for job loyalty. Wilson (2018) and Pryce (2016) also mention empowerment as one of the ways to improve retention rates. Finally, Leder, Newsham, Veitch, Mancini, and Charles (2016) claim that office environment and positive relationships with peers and authorities are vital for improved retention rates. All authors of the reviewed literature support the idea that job satisfaction is the key to low turnover. Job satisfaction is influenced by all the mentioned factors, including compensation, peer relationships, empowerment, and relationships with the managers.
There are multiple strategies for reducing employee turnover, as discussed in scholarly literature. All of them address the reasons for turnover intentions improving job satisfaction in different spheres. Wilson (2018) states that among one of the primary ways to improve retention is to offer sufficient compensation and provide the employees with professional growth opportunities. Pryce (2016) and Leder et al. (2016) emphasize the importance of workplace culture and positive relationships in the workplace and describe strategies to address the matter. Gupta et al. (2014) propose the employment of principles of spirituality to improve the loyalty of the staff. Gilani and Cunningham (2017) demonstrate a positive correlation between companies’ branding strategies and employee retention. While all the described measures seem adequate, they provide insufficient information about what specific steps should an HR manager take, to improve the current retention rates.
A revolutionary way of addressing the problem is gaining its popularity among scholars. Pryce (2016) briefly mentions that among other reasons for high employee turnover is the unwillingness of companies’ authorities to spend additional money on hiring. According to Pryce (2016), in-depth assessment of candidates can lead to improved job retention rates. Predictive hiring using analysis of Big Data is a growing trend among researchers. Bongard (2019) states that using algorithms for decision-making in HR is associated with improved outcomes and decreased bias in employee selection. Even though the model is an effective way to address low retention rates, it is a complicated method that requires complex computations. Therefore, a more comprehendible strategy needs to be introduced for employee selection.
The purpose of the present paper is to introduce data-driven criteria for predictive hiring. It contributes to the overviewed body of literature by assessing demographical and professional factors, which may influence the performance of an employee in retail. The present research may help HR managers to understand which criteria are vital for choosing employees with lower turnover intentions.
Analysis Methodologies
Variables
As identified by the literature review, one of the methods of preventing high turnover rates is a careful choice of candidates. Since financial performance was recognised as one of the leading causes of employee satisfaction, HR managers can benefit from predicting the parameter to ensure high retention rates. Therefore, the primary dependent variable was identified as income or annual salaries of retail workers. The independent variables are age, experience in retail, years of higher education, gender, and marital status. The aim of the analysis was to understand if there is a correlation between independent and dependent variables.
Sample and Data Collection
In order to answer the research question, a sample of 50 random retail employees in one organisation was surveyed online. The retail workers were given a link to the survey, and they had to complete it within one week after the link was issued. The surveys did not include any personal information, such as names, addresses, and phone numbers, to ensure the anonymity of the survey. The data was stored on a private personal computer protected by a password. The data can be viewed in Appendix A.
Identified Test
Two types of test were performed using the collected data. Since the aim of the study is to determine if there are any interrelationships between the variables, correlation analysis was performed. Pearson’s correlation coefficient is a measure of the linear correlation between two variables. It helps to quantify the association and interpret the degree of interrelationship between all the variables. The second type of performed tests is equal variances t-test, which was needed to compare the means of annual income between people of different genders and marital statuses. The test was chosen as a result of F-test that helped to identify the variances of the samples.
Procedures
In order to answer the research question, three tests were accomplished. First, a correlation analysis was performed using financial performance, age, experience in retail, and years of higher education. Second, the sample was divided into two depending on gender, and equal variances t-test was performed to compare the means of female and male retail workers. Finally, equal variances t-test was also performed to understand if there was a significant difference in salaries of married and single employees. All the tests were performed using Excel’s standard statistical package.
Results of Analysis
Test Results
The correlation analysis demonstrated that there is a strong positive correlation between financial performance (annual salary) and experience in retail. Table 1 shows the results of Pearson’s correlation analysis. According to the table, there are no other strong correlations between any of the other identified variables. The study also demonstrated that there is a weak interconnection between age and experience in retail, which is foreseeable because older people have lived more years and they have a higher chance of having more experience in the area. The t-tests demonstrated that there are no differences in annual salary means between males and females or married and single employees. The results of these tests are shown in Table 2 and Table 3 correspondingly.
Table 1. Pearson’ r Analysis Results.
Table 2. Results of t-Test for Males and Females.
Table 3. Results of t-Test for Single and Married.
Implications
The findings suggest that HR managers should pay attention to the years of experience in retail while hiring employees. The results demonstrated that there is a linear correlation between years of working in retail and annual salary. High financial performance is expected to lead to increased retention intentions, as suggested by the literature review. The notion, however, is intuitive, and there is little value in confirming the matter. While positive results are not informative, negative results have greater importance for HR practice.
The common belief that there is a direct link between formal education and employee performance was not confirmed. In fact, the results demonstrate that there is a slight negative correlation (which is not statistically significant) between years of formal education and performance in retail. This may mean that professional education is not needed for being a successful retail worker. Therefore, HR managers may consider paying less attention to education level while hiring retail employees. However, it needs to be emphasised that the findings are relevant only to retail workers, and they cannot be applied to any other sphere.
The results also demonstrate that marital status does not influence job performance in retail. It was initially hypothesised that married employees would have a higher degree of responsibility, which may lead to being more industrious and motivated to make more money. The hypothesis was not confirmed meaning that there is no link between annual salary and marital status. However, it should be considered that marital status may still have a positive effect on retention intentions due to intentions of taking fewer risks.
It is also worth mentioning that the research confirmed that gender does not have any influence on employees’ performance. The finding supports gender diversity in the workplace and demonstrates that gender bias cannot be justified in retail. Therefore, HR managers should avoid including sex as a criterion for hiring a person for a retail position. The notion, however, applies only to retail and gender may be a criterion for hiring in other industries.
Conclusion
High employee turnover is a considerable bother for HR managers since it negatively affects the performance of the company. While there are many methods for employee retention discussed by scholars, little of them provide HR managers with specific steps to follow for reducing employee turnover. The present research aimed at identifying the criteria for hiring decisions to prevent high turnover. The analysis of annual salary, age, experience in retail, years of higher education, gender, and marital status demonstrated that there is a linear correlation between financial performance and experience in retail. The finding suggests that HR managers should consider working experience as the primary criteria for selecting candidates with high retention intentions. Moreover, the results demonstrate that marital status, gender, and years of formal education do not influence the performance of retail employees.
The research is limited due to the sample characteristics, which negatively influences the generalisability of the results. The sample size is small, and it represents information only about retail employees in one geographical area. Additionally, the results apply only to retail workers. Future research should focus on extending the sample size and considering other variables for predicting turnover. For instance, scholars may consider analysing the correlation between age, marital status, or a number of children and retention intentions.
References
Avanzi, L., Fraccaroli, F., Sarchielli, G., Ullrich, J., & van Dick, R. (2014). Staying or leaving: A combined social identity and social exchange approach to predicting employee turnover intentions. International Journal of Productivity and Performance Management, 5(3), 272. Web.
Bongard, A. (2019). Automating talent acquisition: Smart recruitment, predictive hiring algorithms, and the data-driven nature of Artificial Intelligence. Psychosociological Issues in Human Resource Management, 7(1), 36-41.
Gilani, H., & Cunningham, L. (2017). Employer branding and its influence on employee retention: A literature review. The Marketing Review, 17(2), 239-256.
Gupta, M., Kumar, V., & Singh, M. (2014). Creating satisfied employees through workplace spirituality: A study of the private insurance sector in Punjab (India). Journal of Business Ethics, 122(1), 79-88. Web.
Lee, S. (2018). Employee turnover and organizational performance in US federal agencies. The American Review of Public Administration, 48(6), 522-534.
Leder, S., Newsham, G. R., Veitch, J. A., Mancini, S., & Charles, K. E. (2016). Effects of office environment on employee satisfaction: A new analysis. Building research & information, 44(1), 34-50.
Onsardi, A., Asmawi, M., & Abdullah, T. (2017). The effect of compensation, empowerment, and job satisfaction on employee loyalty. International Journal of Scientific Research and Management, 5(12), 7590-7599.
Pryce, A. C. (2016).Strategies to reduce employee turnover in small retail businesses. Web.
The United States Department of Labor Statistics. (2015). Web.
Wilson, D. (2018). Strategies for reducing employee turnover in retail outlets.Web.
Appendix A. Data under Analysis
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