An analytical approach for a research on employee retention

Research Problem

Most employers are concerned about retaining their employees due to the current market competition (some employers offer more to their employees than others) and the mobility of highly skilled employees (employees are always motivated by businesses/organizations that have the ability to value, support, and recognize their skills). Retaining competent employees has become a struggle for the human resource section since competent employees have the ability to move around the global market. Competent employees are no longer required to spend their entire career working for the same company or employer. Moreover, when employers are committed to their employees; employees feel secure and are motivated to pay back to the organization they work for. The specific business problems can be resumed as such: What are the challenges face by the retail industry when employee retention is compromised by employees’ skills and mobility? Due to these challenges, what are the consequences face by the retail sector when employers are not taking adequate measures to maintain a low employee retention rate?

Employee retention is characterized by several determinants such as career stability, career development opportunities, benefits, rewards, training, development training, performance assessment through feedback, accountability, and responsibility through empowerment, equity of compensation by remuneration, and business coaching program utilization, and supervisors support. Low retention rates remain a big challenge for employers although employees will remain for a long time in companies with great retention strategies. Moreover, research showed that businesses/organizations lose considerably when they replace old employees by new ones because of the cost of recruiting, hiring, training, and keeping new employees. When organizational stability is diminished, it becomes hard for any business to remain competitive in the marketplace; managers become under pressure, conflict is inevitable, and productivity is affected.

Data Analysis

Researchers, especially school psychology researchers, agree to say that data analysis is the most complicated step in the research process. It starts with the collection of unstructured data followed by their conversion of these data into textual form (WORD). With the help of technology, it is easier to transcribe data effectively and at a faster rate by choosing one of the several computer-assisted qualitative data analysis software (CAQDAS) using tools, like ATLAS. TI, NVIVO, and EVASYS. The analysis of a study is difficult because of the various types of research problems, research questions, and data. Moreover, the implication of qualitative data increases data analysis complexity since researchers in the retail sector do not have much training in qualitative methods; they are often unaware of new qualitative data analysis techniques.

Content analysis procedure

The researcher will pick a number of companies in the retail sector with low employee retention rate. The researcher of this study will utilize a qualitative content analysis procedure to examine employees and employers’ responses. At this stage of the study, researchers usually begin to gather lots of information; they start to figure out the meanings of data. They proceed by simplifying responses, organizing, and reducing data categories (words and phrases that have the same meaning can be grouped together). The researcher (of this study) will adopt the same inductive content analysis used by Bezyak, Umucu, Wu, et al. (2018) to separate and classify data. This inductive content analysis is constituted of five steps: (a) open coding, (b) coding sheets (c) grouping, (d) categorization, and (e) abstraction. The data from selective companies within the retail sector (clothing) will be gathered in different formats through in-person interviews, transcripts from interviews, audio/video recordings, and observation. Managers and associates (full time or part time) will be interviewed. Interviews will be guided by a semi-structured interview schedule which will give participants more time to properly understand, answer and bring up issues they are facing when they are at work. For each company, several data will be collected such as employee retention strategy/policy of the company, participants’ full understanding of retention, the demographics of employers and employees, the education level of employers and employees, information on when the rate of employee retention started to touch its lower point, the reason(s) why these companies cannot maintain employee retention, the differences and similarities between employee retention rate within companies, interviewees’ recommendations based on future strategies and promising retention actions.

Manual analysis

Many researchers still use manual analysis techniques and manual methods of data analysis although software packages such as NUD. IST, NVIVO, QualPro have helped social scientists to analyze and store data. This method of storage and analysis is messy and tends to be time-consuming because it is easy to forget or overlook hidden data. Nevertheless, researchers suggested that nothing can replace complex processes of reading, understanding, and interpretation. Researchers added that using computerized software does not provide systematic solutions to problems related to representation and analysis of data. Handling data While handling data, researchers must ensure that it is reliable and valid. Data validation justifies research success. It must be valid (accuracy of design/methods) and reliable (extent to which researchers’ procedures produced consistent and dependable results). The purpose of inquiring and handling data is to bring a solution by gathering large amounts of rich, descriptive data using qualitative data collection techniques via in-depth interviews, focus groups, observations, video recording, transcription (audio recording).

Computer Aided Qualitative Data Analysis

Software (CAQDAS)

CAQDAS is a software that creates organized modes of qualitative data analysis. According to Oliveira, Bitencourt, Zanardo dos Santos, and Teixeira (2016), computer software could be one of the easiest solutions for effective, quick, and valid analysis of data collection. These authors believe that the CAQDAS accelerates the process, intensifies the rigor, supplies more adjustable data analysis from distinct perspectives, speeds up the exchange and reproduction of data, and gives researchers the ability to reflect in greater depth by reducing the operational activities.

Software packages

Today, an increasing number of disciplines are carrying out qualitative research. Producers in favor of the qualitative data analysis software packages support their advantages and uses. It is therefore important that researchers become aware of the possibilities of using software packages. It takes time to analyze data and software package is a tool that can decrease the time spent when conducting a research. Moreover, due to the growth of these various packages, it becomes time-consuming and overwhelming for researchers to pick the most suitable one for their research approach. Therefore, due to this issue, researchers are tempted to depend on mentorship or recommendations from more knowledgeable colleagues, or to choose what is quickly available. Furthermore, researchers do not always choose the most appropriate software package for data analysis; they may use a package that they believe they already know and understand instead of using one specifically designed to suit their current research approach.

To facilitate qualitative data application and a better selection for novice researchers, it is paramount to continuously disseminate information on the effectiveness of the packages. It is certain that choosing the most appropriate software package is difficult and challenging but they (software packages) offer real benefits when using them.