The ‘customer is always right’ is a slogan popularized by the management to encourage staff to take customer complaints seriously. This view assumes that customers are always “rational and functional” in their encounters with employees (Reynolds & Harris, 2003, p. 145).
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While honest customer feedback is vital in improving business services or products and growth, addressing unrealistic expectations and requests can affect employee morale, leading to high turnover. Therefore, business owners should protect staff from deviant customers to improve employees’ satisfaction and confidence in their work and reduce turnover.
The research question for this study is; does customer deviance influence turnover intentions in the service industry?
Research shows that unruly customers cause psychological and emotional stress to staff through verbal abuse, unreasonable requests, and disrespect for company policies (Harry & Reynolds, 2003). Psychological stress in the workplace lowers staff morale and job satisfaction, which increase turnover. In this view, support from the management can help staff deal with challenging customers.
Ben-Zur and Yagil (2005) state that without organizational support, customer deviance can cause “burnout, emotional exhaustion, and low self-esteem” in staff, which affect productivity and retention (p. 91). Thus, customer deviance coupled with a lack of organizational support can increase turnover.
The study will test the null hypothesis that there is no difference in turnover intentions between bank employees (front-desk staff) and factory workers.
The study design will involve a descriptive research design. The key study variables will be turnover intention, job satisfaction, customer incivility, and organizational/management support. The study will draw its participants from the front-desk and management staff.
Interviews will be used to collect data from the participants. Each will be interviewed in a 15-minute session using semi-structured questions. The key focus will be on customer incivility, management support, and turnover intentions. Data analysis will involve thematic analysis method.
A convenient sample of 25 participants will be sampled from the staff of a bank and a factory. It will consist of 10 front-desk staff (bank), 10 factory workers, and 5 managers/supervisors. A preliminary request for participation will be sent to the institutions to obtain approval and informed consent. Upon approval, the researcher will schedule the interview dates to collect the data.
Possible Types of Secondary Data
Secondary data for hypothesis testing are obtained from existing sources when primary data are unavailable. Zikmund and Babin (2006) outline four different forms of secondary data, namely, published data, personnel records, government reports, public sector reports, and electronic records. Journals, books, and periodicals archived in libraries are the major sources of published data (Zikmund & Babin, 2006).
Data published in periodicals and journals are often reliable and current. Personnel records encompass personal communications that can be used as sources of secondary data. Personal letters and diaries can provide information, but efforts must be taken to eliminate any bias they may contain.
According to Zikmund and Babin (2006), government reports, particularly “surveys, tax records, and census data”, can also provide secondary data for hypothesis testing (p. 37). They are widely available in official government sites and databases. In addition, public/private sector reports published by various institutions contain information that can be useful in research.
Documentaries and films provide electronic data that can be useful in research. To test the study’s hypothesis, the researcher will use more than one type of secondary data. Government reports, newspaper/magazine articles, and private sector reports will be useful sources of secondary data.
These sources will provide useful statistics on turnover rates in the banking and manufacturing industries. This will allow the researcher to compare turnover rates between factory and banking staff.
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Possible Measurement Benchmarks and Scales
A benchmark indicates the critical point at which the difference between the sample mean and the expected value becomes significant, i.e., it supports the null hypothesis. A p-value indicates the acceptable level of significance of a test (Zikmund & Babin, 2006). In most studies, the p-values of 0.1, 0.05, and 0.01 are used as benchmarks for acceptable levels of type I error.
When the value obtained from statistical tests, such as t-test or Z-test, is lower than the benchmark value, it indicates that the difference is significant, i.e., the null hypothesis is not supported. According to Zikmund and Babin (2006), the main scales used to measure variables include “nominal, ordinal, ratio, and interval” scales (p. 155). The nominal scale classifies variables into mutually exclusive groups while the ordinal scale organizes data in a ranking order or hierarchy. In an interval scale, the difference between any two values is fixed.
On the other hand, a ratio scale is similar to an interval one, but contains “a true zero point” (Zikmund & Babin, 2006, p. 157). The type of measurement scale to be used in research depends on the nature of the study variables. The proposed research will use a single measurement benchmark, namely, p = 0.05.
Higher values than 0.05 will indicate an acceptable level of significance, i.e., the null hypothesis will be accepted. To measure turnover intentions, job satisfaction, customer incivility perceptions, and organizational support, the study will use the Likert (interval) scale. This scale will provide quantitative differences between the participants’ responses with respect to the four study variables.
Ben-Zur, H., & Yagil, D. (2005). The Relationship between Empowerment, Aggressive Behaviours of Customers, Coping, and Burnout. European Journal of Work and Organizational Psychology, 14, 81–99.
Harris, L. C., & Reynolds, K. L. (2003). The Consequences of Dysfunctional Customer Behaviour. Journal of Service Research, 6, 144–161.
Zikmund, W., & Babin, B. (2006). Essentials of Marketing Research. Mason, OH: Cengage Learning.