Relationship Between Opportunity for Advancement, Salary/Pay, and Retail Salesperson Turnover Intentions

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Relationship Between Opportunity for Advancement, Salary/Pay, Relationship Between Opportunity for Advancement, Salary/Pay,

and Retail Salesperson Turnover Intentions and Retail Salesperson Turnover Intentions

Edith Mae Thompson

Walden University

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This is to certify that the doctoral study by

Edith M. Thompson

has been found to be complete and satisfactory in all respects,

and that any and all revisions required by

the review committee have been made.

Review Committee

Dr. Laura Thompson, Committee Chairperson, Doctor of Business Administration

Faculty

Dr. Irene Williams, Committee Member, Doctor of Business Administration Faculty

Dr. Brenda Jack, University Reviewer, Doctor of Business Administration Faculty

Chief Academic Officer and Provost

Sue Subocz, Ph.D.

Walden University

2023

Abstract

Relationship Between Opportunity for Advancement, Salary/Pay, and Retail Salesperson

Turnover Intentions

Edith M. Thompson

MHRM, Walden University, 2018

BBA, Austin Peay State University, 2016

Doctoral Study Submitted in Partial Fulfillment

of the Requirements for the Degree of

Doctor of Business Administration

Walden University

March 2023

Abstract

Employee turnover intention is the principal antecedent and predictor of employee

turnover behavior, which is a substantial threat to automotive retail dealerships’ current

and future organizational performance. Understanding employee turnover intentions is

vital for dealership general managers to reduce salesperson turnover, manage dealership

costs, and maintain dealership competitive advantages. Grounded in Herzberg’s two-

factor theory, this quantitative correlational study examined the relationship between

opportunity for advancement, salary/pay, and employee turnover intention among

automobile salespeople. Using an online survey administered by Survey Monkey, data

were collected from 76 retail salespeople in Tennessee, Kentucky, and Alabama. The

survey questions were drawn from the Minnesota Satisfaction Questionnaire (Short

Form), the Pay Satisfaction Questionnaire, and Cohen’s Turnover Intention Scale. The

responses were analyzed using multiple linear regression analysis. The model was able to

predict employee turnover intentions significantly, F (2, 73) = 25.897, p <.001, R

= .415.

The recommendation for action is for dealership general managers to use the findings of

this study to form collaborations with dealership human resource partners to design and

implement transparent succession planning processes that promote pay and advancement

opportunities to reduce employee turnover intentions. The implication for positive social

change is the implementation of pay and succession structures that improve the

salesperson’s work experience, thus contributing to ongoing, lucrative employment

opportunities that contribute to and enhance the relationship between the dealership, retail

salespersons, and the community that they serve.

Relationship Between Opportunity for Advancement, Salary/Pay, and Retail Salesperson

Turnover Intentions

Edith M. Thompson

MHRM, Walden University, 2018

BBA, Austin Peay State University, 2016

Doctoral Study Submitted in Partial Fulfillment

of the Requirements for the Degree of

Doctor of Business Administration

Walden University

March 2023

Dedication

I want to dedicate this work to my parents, James and Mattie. I want to thank

them for always believing in me and encouraging me to use my head for something other

than a hat rack.

Acknowledgments

I want to acknowledge and thank everyone who was instrumental in completing

this journey. First, I would like to thank my biggest cheerleader, my husband, Adam.

Thank you for understanding late-night writing, missed family functions, and boxes of

articles and papers all over the house. Secondly, I want to acknowledge and thank my

chair, Dr. Laura Thompson. Thank you for your academic prowess and guidance. I would

not have completed this journey without our frequent conversations, our celebratory

wins, our step back and punt again sessions and the gentle encouragement of eating an ice

cream cone when we experienced success! You will forever be an instrumental part of

my life. I also want to thank my second committee member, Dr. Irene Williams. Thank

you for being the second set of eyes and ears of this study. Your advice and

encouragement were the touches needed to complete this journey. Lastly, I want to thank

all the Walden University faculty and staff who provided me with the tools, seminars,

residencies, and academic nuggets that made this journey less daunting. Thanks to all of

you, I am equipped to be a proud Walden University social change agent!

Table of Contents

List of Tables .......................................................................................................................v

List of Figures .................................................................................................................... vi

Section 1: Foundation of the Study ......................................................................................1

Background of the Problem ...........................................................................................1

Problem and Purpose .....................................................................................................3

Population and Sampling ...............................................................................................4

Population ............................................................................................................... 4

Sampling and Sample Size...................................................................................... 5

Figure 1 G *Power 3.1.9.2 Power Analysis for Study Sample Size ........................ 6

Nature of the Study ........................................................................................................6

Research Question and Hypotheses ...............................................................................7

Theoretical or Conceptual Framework ..........................................................................8

Operational Definitions ................................................................................................10

Assumptions, Limitations, and Delimitations ..............................................................11

Assumptions .......................................................................................................... 11

Limitations ............................................................................................................ 12

Delimitations ......................................................................................................... 13

Significance of the Study .............................................................................................13

Contribution to Business Practice ......................................................................... 13

Implications for Social Change ............................................................................. 14

A Review of the Professional and Academic Literature ..............................................15

Herzberg et al.'s Two-Factor Theory (Motivation-Hygiene Theory) ..........................17

Contrasting or Rival Theories ............................................................................... 21

Intrinsic Independent Variable: Opportunity for Advancement ........................... 30

Extrinsic Independent Variable: Salary/Pay ......................................................... 33

Dependent Variable: Employee Turnover Intentions ........................................... 35

Independent Variable Measurements .................................................................... 40

Dependent Variable Measurement ........................................................................ 43

Transition .....................................................................................................................44

Section 2: The Project ........................................................................................................47

Purpose Statement ........................................................................................................47

Role of the Researcher .................................................................................................48

Participants ...................................................................................................................49

Research Method and Design ......................................................................................50

Research Method .................................................................................................. 50

Research Design.................................................................................................... 51

Population and Sampling .............................................................................................53

Population ............................................................................................................. 53

Sampling Method .................................................................................................. 53

Sample size ........................................................................................................... 55

G*Power Analysis ................................................................................................ 55

Ethical Research...........................................................................................................56

Data Collection Instruments ........................................................................................58

iii

Demographic Survey ............................................................................................ 58

Minnesota Satisfaction Questionnaire (Short form) ............................................. 59

PSQ 60

Turnover Intention Scale....................................................................................... 62

Data Collection Technique ..........................................................................................63

Data Analysis ...............................................................................................................66

Assumptions .......................................................................................................... 67

Data Cleaning and Missing Information ............................................................... 69

Inferential Statistics and Interpretation ................................................................. 69

Study Validity ..............................................................................................................71

Internal Validity ...........................................................................................................71

Statistical Conclusion Validity ............................................................................. 71

Validity Threats .................................................................................................... 72

Instrument Reliability ........................................................................................... 72

Data Assumptions ................................................................................................. 73

Parametric assumption testing .............................................................................. 74

Sample Size ........................................................................................................... 74

External Validity ................................................................................................... 75

Transition and Summary ..............................................................................................76

Section 3: Application to Professional Practice and Implications for Change ..................78

Introduction ..................................................................................................................78

Presentation of the Findings.........................................................................................78

Test of Assumptions ............................................................................................. 78

Demographic Statistics ......................................................................................... 83

Descriptive Statistics ............................................................................................. 83

Inferential Results ................................................................................................. 84

Opportunity for Advancement ............................................................................... 85

Salary/Pay ............................................................................................................. 85

Analysis Summary ................................................................................................ 86

Theoretical Conversation on Findings .................................................................. 87

Applications to Professional Practice ..........................................................................91

Implications for Social Change ....................................................................................92

Recommendations for Action ......................................................................................94

Recommendations for Further Research ......................................................................95

Reflections ...................................................................................................................96

Conclusion ...................................................................................................................98

References ..........................................................................................................................99

Appendix A: Permission to Use Minnesota Satisfaction Questionnaire Short-Form

(1967) ...................................................................................................................137

Appendix B: Permission from H. G. Heneman to use Pay Satisfaction

Questionnaire (PSQ) 1985 ...................................................................................137

Appendix C: Permission to use Cohen (1999) Turnover Intention Scale ........................138

List of Tables

Table 1. Numerical Count and Percentage Values for Cited Sources .............................. 17

Table 2. Study Variables and Measurement Instruments with Permission to Use Status 63

Table 3. Collinearity Statistics for the Independent Variables ......................................... 80

Table 4. Correlation Coefficients Among Study Predictor Variables .............................. 80

List of Figures

Figure 1. G *Power 3.1.9.2 Power Analysis for Study Sample Size .................................. 6

Figure 2. Herzberg et al.’s Two-Factor Theory of Motivation as it Applies to Employee

Turnover .................................................................................................................... 10

Figure 3. Illustration of Herzberg et al.’s (1959) Two-Factor Theory of Motivation ...... 21

Figure 4. Illustration of Maslow’s (1943) Hierarchy of Needs Theory ............................ 26

Figure 5. Illustration of Vroom’s (1964) Expectancy Theory .......................................... 28

Figure 6. Illustration of Deci and Ryan’s (1985) Self-determination Theory .................. 30

Figure 7. G* Power Priori Analysis for Sample Size ....................................................... 56

Figure 8. Scatterplot of Standardized Residuals ............................................................... 81

Figure 9. Normal P-P of the Regression Standardized Residuals ..................................... 82

Section 1: Foundation of the Study

Background of the Problem

Over the years, scholars have identified the intent to turnover and employee

turnover as recurring threats in retail sales-related positions. Scholars have suggested that

salesperson turnover substantially impacts organizational success and profitability

(Badrinarayanan et al., 2021; Fleming et al., 2022; Lai & Gelb, 2019; Lucas et al., 1987).

Lee et al. (2018) stated that employee turnover is a primary contributor to declined

business continuity and revenue loss, costing businesses nearly 200% in annual salary per

worker in worker replacement costs. Lai and Gelb (2019) further posited that the average

cost of sales turnover linked to forfeiture of recruitment funds, training costs, and direct

sales loss is $97,960 per salesperson, with it taking approximately 4 months to replace a

sales hire. Additionally, statisticians suggested that over 50% of currently employed retail

sales associates will leave their organizations in conjunction with the data presented in

the U.S. Bureau of Labor Statistics (BLS; 2018) annual report of total separations by

industry.

Retail sales organizations, such as automotive dealerships, heavily depend upon

their sales force. Fu et al. (2017) indicated that the retail sales employee is critical to

developing ongoing customer relationships, significantly enriching product loyalty and

future business growth. General managers generally compensate and advance high-

performing sales employees into dealership leadership positions that pay industry-leading

salaries. The BLS (2020) reported that 6% of the 4.5 million retail sales personnel in the

United States worked in automotive sales in 2018, with automotive dealers paying the

highest median hourly wage in the retail sales industry segment.

However, like other retail selling entities, the automotive sales industry is highly

cyclical and precipitously impacted by many internal and external threats that affect job

satisfaction and turnover intentions (Jaura, 2020; Lai & Gleb, 2019; McNeilly & Russ,

1992). High turnover among automotive retail sales employees leads to loss of vehicle

sales, diminished consumer confidence in the dealership and brand, reduction in the talent

pool, and inflated dealership costs associated with recruiting, hiring, and training (Al

Mamun & Hasan, 2017; Fu et al., 2017). Competent and talented sales employees may

consider leaving the dealership when specific intrinsic (e.g., opportunity for

advancement) and extrinsic (e.g., salary/pay) motivational job satisfaction factors are not

met. Nonetheless, the BLS (2018) predicted that between 2018 and 2028, retail sales

management jobs would experience a 5% growth, statistically demonstrating the

importance of employee retention and advancement initiatives in retail sales. In

conjunction with the BLS calculations, industry statisticians forecasted that new vehicle

revenue would reach $945.9 billion over the next 5 years, with recent vehicle sales

accounting for 50.8% of total industry revenue.

Automotive dealership general managers need to understand the concept of

intrinsic and extrinsic job motivators that contribute to job satisfaction or dissatisfaction

in the automotive retail sales work environment. General managers who understand and

analyze the constructs that may contribute to the retail sales employee’s intent to turnover

and any possible relationships between these characteristics can prove vital to dealership

success and protect the dealership’s competitive advantage (Agarwal & Sajid, 2017).

Conversely, many dealership leaders and managers need to understand or recognize the

relationship between intrinsic motivators, such as the opportunity for advancement, and

extrinsic hygiene factors, such as salary or pay, and whether these constructs significantly

predict employee turnover intention among automotive retail salespeople. Automotive

retail sales are a substantial contributor to the U.S. economy, and the retail sales

employee is the most consumer-trusted facilitator for the sales transaction (Friend et al.,

2018). Therefore, automotive dealership general managers must develop processes that

appeal to sales personnel’s intrinsic job satisfiers (i.e., motivators) and address extrinsic

job dissatisfiers (i.e., hygiene) to reduce turnover intentions and encourage sales

employee retention. In this study, I examined to what degree the intrinsic job satisfier of

opportunity for advancement and extrinsic job dissatisfier of salary/pay predict retail

sales employee turnover intention in the automotive sales industry. I expected this study

to contribute confidently to the literature’s broad gap regarding employee turnover

intention in the automotive sales industry.

Problem and Purpose

Organizational leaders across various industries recognized employee turnover

intention as the principal antecedent and predictor of employee turnover behavior, a

substantial threat to current and future organizational performance (Aburumman et al.,

2020; Belete, 2018; Lee et al., 2018; Sanjeev, 2017). The retail sale employee’s intention

to turnover endangers the retail sales organization’s profitability, with employee

replacement costs as high as $10,000 per replaced employee, almost equaling double the

annual salary of the replaced employee (Belete, 2018; Chhabra, 2018; Olubiyi et al.,

2019; Oruh et al., 2020). The general business problem was that employer turnover

intention among retail sales employees in the automotive sales industry could become a

costly problem that affects dealership profitability, performance, and competitive

advantage. The specific business problem was that many automotive dealership general

managers need to understand the relationship between the intrinsic job satisfier of the

opportunity for advancement, the extrinsic job dissatisfier of salary/pay, and employee

turnover intention.

The purpose of this quantitative correlational study was to examine the

relationship between the intrinsic job satisfier of the opportunity for advancement, the

extrinsic job dissatisfier of salary/pay, and employee turnover intention. The independent

or predictor variables were an opportunity for advancement and salary/pay. The

dependent or criterion variable was employee turnover intention. The implications for

positive social change include developing a mutually constructive relationship between

the dealership and the community it serves. This reciprocally valuable relationship could

contribute to employee stability, new job opportunities, and local community economic

growth associated with increased vehicle sales and registrations, such as local tax revenue

and charitable contributions to local community organizations.

Population and Sampling

Population

The population for this study were franchised new and used car salespersons in

dealerships located in Tennessee, Kentucky, and Alabama. The participants were retail

sales employees who fit all-gender specifications and were at least 21 years of age, with 1

year or more of retail selling experience in the automobile dealership environment. The

participants were full-time retail sales employees who sell vehicles only. The retail sales

employees did not comprise other sales-related positions within the dealership structure,

such as parts salespeople, service salespeople, or financial products salespeople.

Sampling and Sample Size

I used the nonprobabilistic convenience sampling method to select retail sales

employees who matched the inclusion criteria of this study. This sampling method

allowed me to target participants via online surveys and was both time-saving and cost-

effective (Etikan et al., 2016; Rahi, 2017). The G*Power 3.1 power analysis program was

the most applicable test for determining this study’s minimum and maximum sample

size. I conducted an a priori analysis with an effect size f = 0.15, an a = 0.05, and a power

level of 0.80 to determine the minimum sample size of 68 participants and a power level

of 0.99 to determine the maximum sample size of 146 participants. Therefore, the

appropriate sample size for this study ranged from 68 to 146 participants. Figure 1

illustrates the G *Power 3.1.9.2 power analysis results for the sample size calculations.

Figure 1

G *Power 3.1.9.2 Power Analysis for Study Sample Size

Nature of the Study

I chose a quantitative research methodology to examine the business phenomenon

in this study. There are several methodologies researchers can employ to examine or

explore their phenomenon; however, I considered the three primary methods to examine

or explore this business phenomenon (see Ellis & Levy, 2009; Saunders et al., 2016).

Researchers use qualitative methods to explore the business process’s philosophical,

subjective, and perceptive nature (Ellis & Levy, 2009; Murshed & Zhang, 2016;

Saunders et al., 2016). Researchers seeking to examine or test hypotheses employ the

quantitative method to numerically represent the generalizability, causality, or scale of

effects between the independent and dependent variables (O’Leary, 2017; Sligo et al.,

2018). Researchers who cannot collect additional insight from quantitative or qualitative

findings may implement a combination of quantitative and qualitative attributes or a

mixed-methods methodology (Saunders et al., 2016; Sligo et al., 2018). Since the

philosophical and explorative qualitative feature was absent from this study and the

0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95

100

Total sample size

= 0.15

Effect size f²

F tests - Linear multiple regression: Fixed model. R² deviation from zero

Number of predictors = 2. α err prob = 0.05. Effect size f² = 0.15

Power (1-β err prob)

mixed-methods methodology was not congruent with my research question (see Sligo et

al., 2018), I did not implement a qualitative or mixed-methods approach. In this study, I

tested hypotheses and conducted numerical calculations to determine to what degree an

intrinsic job satisfier (i.e., opportunity for advancement) and an extrinsic job dissatisfier

(i.e., salary/pay) significantly predicted employee turnover intention; therefore, a

quantitative methodology was the most appropriate method for this study.

The three quantitative research designs are descriptive, experimental, and

correlational (Saunders et al., 2016). Researchers use the descriptive design to explore or

describe the phenomenon without statistical inference (Siedlecki, 2020). Researchers

interested in the causal relationship of the variables in a controlled environment apply an

experimental design (Armstrong & Kepler, 2018; Saunders et al., 2016; Zellmer-Bruhn et

al., 2016). Researchers employ the correlational design to ascertain the degree of

noncausal strength to predict a relationship between two or more variables (Curtis et al.,

2016; Seeram, 2019). I chose the correlational design because the objective was to

investigate the extent to which a statistically negative or positive linear correlation exists

between the independent and dependent variables. The descriptive and experimental

designs were inappropriate for this study because I was not observing, describing, or

drawing a causal inference.

Research Question and Hypotheses

The research question for this study was: Within small to medium franchise new

car dealerships, what is the relationship between the intrinsic job satisfier of the

opportunity for advancement, the extrinsic job dissatisfier of salary/pay, and employee

turnover intention?

: There is no statistically significant relationship between the intrinsic job

satisfier of the opportunity for advancement, the extrinsic job dissatisfier of

salary/pay, and employee turnover intentions.

: There is a statistically significant relationship between the intrinsic job

satisfier of the opportunity for advancement, the extrinsic job dissatisfier of

salary/pay, and employee turnover intentions.

Theoretical or Conceptual Framework

I selected Herzberg et al.’s (1959) two-factor theory of motivation or motivation-

hygiene theory as the theoretical framework for this research study (see Gardner, 1977;

Herzberg et al., 2017; Tan & Waheed, 2011). Herzberg et al. (2017) theorized that work

motivation encompassed two sets of characteristics associated with job satisfaction or

dissatisfaction (Alshmemri et al., 2017; Gardner, 1977). Herzberg et al. (2017) identified

job satisfiers with intrinsic motivators, such as recognition, advancement, and

responsibility, while classifying job dissatisfiers with extrinsic hygiene work factors, such

as salary, pay, compensation, benefits, and job security (Hur, 2018). Herzberg et al.

(2017) postulated that intrinsic motivators stimulated positive job satisfaction produced

by the natural conditions resulting from the job itself (Hur, 2018; Tan & Waheed, 2011).

Similarly, Herzberg et al. postulated that the shortage or absence of extrinsic factors

linked to working conditions, such as salary, compensation, benefits, and work

conditions, contribute to job dissatisfaction (Hur, 2018). In contrast, in the hierarchy of

needs theory, Maslow (1943) argued that basic human needs, such as safety or the need

to eat, are some of the driving forces behind job motivation. In expectancy theory, Vroom

(1964) interjected the construct of motivational force generated by expectancy (i.e., effort

will equal outcome), valance (i.e., desire for expected result), and instrumentality (i.e.,

achieved outcome will equal the desired reward) as a job motivator (Baciu, 2017; Lloyd

& Mertens, 2018).

I chose to use Herzberg et al.’s (1959) two-factor theory as the theoretical lens to

examine the independent variables, intrinsic job satisfier (i.e., opportunity for

advancement) and extrinsic job dissatisfier (i.e., salary/pay), measured by the Minnesota

Satisfaction Questionnaire-Short Form (MSQ-SF), and the Pay Satisfaction

Questionnaire (PSQ; see Heneman & Schwab, 1985; Morgeson et al., 2001; Weiss et al.,

2010). Cohen (1999) developed an adaptive employee turnover decision process model

based in Mobley’s (1978) intent to leave scale, that was applicable to any work

environment and measured employee turnover intention using intrinsic and extrinsic job

satisfaction and dissatisfaction experiences. Cohen’s turnover intention scale is the

chosen instrument for measuring this study’s dependent variable. Sager et al. (1988)

employed Mobley’s model to measure turnover intentions among retail salespeople from

different industries. Therefore, as applied to this study, I expected the independent

variables to predict turnover intention. In recent studies, researchers revealed that job

satisfaction is critical to employee turnover across various industries (Guha &

Chakrabarti, 2015; Hur, 2018; Tan & Waheed, 2011; Voight & Hirst, 2015).

Furthermore, these researchers also concluded that the multiple constructs, singularly or

in correlation with each other, predicted either a negative or positive relationship with

employee turnover intention. Figure 2 illustrates Herzberg et al.’s two-factor theory of

motivation as it applies to employee turnover intentions.

Figure 2

Herzberg et al.’s Two-Factor Theory of Motivation as it Applies to Employee Turnover

Operational Definitions

Employee turnover: The act of an employee leaving or exiting an organization

(Fang et al., 2018).

Extrinsic job satisfaction: How an employee feels about their work situation

includes supervision, work conditions, coworkers, company policies, job security,

worker’s personal life, and salary/pay (Hur, 2018; Tepayakul & Rinthaisong, 2018).

Job satisfaction: The positive or negative attitudes or feelings an employee has

towards their job or place of employment (Tepayakul & Rinthaisong, 2018).

Intrinsic job satisfaction: The way an employee feels about and is motivated by

the nature of the job itself, including achievement, recognition, responsibility, growth,

and the opportunity for advancement (Hur, 2018; Tepayakul & Rinthaisong, 2018).

Retail sales employee: The employee who assists retail customers with finding

and selecting their desired product by answering product questions, demonstrating

Employee

turnover

intention

Opportunity

for

Advancement

Intrinsic Job

Satisfier

Salary / Pay

Extrinsic Job

Dissatisfier

product features and benefits, and processing customer payments for the selected product

(BLS, 2018).

Separation rate: The total annual separations as a percentage of annual average

employment (BLS, 2018).

Turnover intention: An employee’s conscious decision to begin the process of

thinking about leaving the job, looking for a new job, or leaving the job (Aburumman et

al., 2020).

Assumptions, Limitations, and Delimitations

The assumptions, limitations, and delimitations are the research elements that are

either controlled or not controlled by the researcher (Saunders et al., 2016). These

elements are essential to establishing research relevance, credibility, and reliability

(Saunders et al., 2016). Ellis and Levy (2009) emphasized that researchers who need to

articulate these vital research elements clearly could generate uncertainty and reduce

credibility within their research; however, researchers can use the assumptions,

limitations, and delimitations to clarify vague presuppositions and identify future

research opportunities.

Assumptions

The researcher utilizes the assumptions section of the research study to

communicate what they assume and accept to be true without any formidable evidence

from the limited knowledge and probabilities of the theory and practices (Ellis & Levy,

2009; Waller et al., 2017). I assumed that I received the required number of surveys to

produce reliable data analysis results and that there were no missing responses to survey

questions. I further assumed that the study participants provided open and honest

feedback to all survey questions. I alleviated any apprehensions with these two

assumptions by emphasizing that the survey was strictly voluntary, anonymous, and

designed to protect participants from the perceived repercussions of the dealership

management personnel. I, therefore, created an atmosphere for open and honest responses

from all qualified participants.

Another assumption was that all participants were automotive retail sales

employees. The study did not include (a) automotive parts sales managers; (b)

automotive parts sales employees; (c) automotive accessory sales employees; (d)

automotive finance managers; (e) automotive service department managers; or (f)

automotive services department employees, such as mechanics, porters, and service

writers. Participants from other dealership departments would alter research findings and

render the study invalid and unreliable (Cerniglia et al., 2016). To mitigate any issues

associated with surveying the wrong participants, I clearly and concisely emphasized the

research study’s purpose and the dealership role characteristics of the participants.

Limitations

Limitations represent the circumstances that are not under the researcher’s

control, which may introduce potential weaknesses and compromise research methods

and analysis (Waller et al., 2017). The first limitation was the inherently limited nature of

the correlational design. The correlational design cannot be used to extrapolate or

conclude the cause and effect between the variables (Rugg, 2007; Saunders et al., 2016).

Therefore, I only employed the correlational design to predict the level of interaction

between the variables but not to assess causation.

The second limitation of the research study was achieving a sufficient sample size

for reduced biases, validity, and generalization of the findings (see Cerniglia et al., 2016).

The use of an online survey produced the level of participation required to satisfy the

large sample size needed to conduct quantitative research that generated valid, reliable,

and unbiased findings.

Delimitations

The researcher uses delimitations to indicate the study’s boundaries and scope

(Waller et al., 2017). The delimitations of this study included the geographical locations

of the dealerships in only three states within the continental United States: (a) Tennessee,

(b) Kentucky, and (c) Alabama. The scope of the study was set to examine small, strictly-

to medium-sized, privately owned automotive dealerships, excluding all (a) large,

publicly traded automotive dealer groups; (b) large (i.e., having more than 200

employees), privately owned dealerships; and (c) online automotive vendors.

Significance of the Study

Contribution to Business Practice

The relationship between the customer and the retail sales employee is the most

influential element in the customer’s purchasing decision (Edmondson et al., 2019). As

the most trusted member of the selling organization, the retail sales employee is a

fundamental link to organizational performance and profitability (Friend et al., 2018).

U.S. businesses spend approximately $800 billion on various incentives designed to

retain retail sales force employees and lessen turnover (Sunder et al., 2017). Automotive

dealership general managers can apply the findings of this study to identify, design, and

implement positive organizational practices that may reduce employee turnover

intentions. These practices can include initiatives such as (a) performance-based pay

plans, (b) monetary incentives for elevated customer service indexes, and (c) a future

leader succession plan for top performers. Implementing these strategies may assist

dealership general managers with controlling lost sales revenue, regaining competitive

advantage, and solidifying consumer trust. Therefore, this study contributes to business

practice by providing new information on retail sales employee turnover intention and

helping dealership general managers better understand a possible relationship between

the opportunity for advancement, salary/pay, and employee turnover intentions.

Implications for Social Change

Automotive dealership general managers operate small to large businesses that

service various communities and can be significant community stakeholders. The BLS

(2019) reported that automobile dealers are the fifth top-paying industry for retail sales

employees. Dealership general managers can utilize their lucrative role as community

stakeholders to promote community programs and local social enterprises (Park &

Campbell, 2018). Dealership general managers that embrace the role of a viable

community conscience partner can enable the community stakeholder culture and use this

position to develop jobs and training opportunities that encourage amplified employment

and work opportunities for the community workforce. Dealership general managers can

engage their stakeholder position to create community opportunities that promote and

demonstrate corporate social responsibility, including charitable donations to programs

that support employee and community well-being. Dealership general managers can

utilize the findings of this study to improve their current knowledge concerning employee

retention benefits. They can use the results to develop programs and processes to

decrease employee turnover intention, lower community unemployment rates, generate

steady wages, and create a constant influx of tax revenue (see Park & Campbell, 2018).

Dealership general managers can also use the findings of this study to validate the

business need for supporting mentorship programs that offer community-based talent

from local high schools, technical colleges, 4-year colleges, and vocational rehabilitation

centers exclusive access to job openings within the dealership. The dealership’s general

manager can use these programs to secure top-level local talent, which boosts the

dealership’s community stakeholder position and promotes the overall future success of

the dealership and the community it serves.

A Review of the Professional and Academic Literature

The review of the professional and academic literature encompassed current and

seminal research from peer-reviewed journals, scholarly books, government agency

publications, and industry-specific publications for the examination of the relationship

between the independent variables of intrinsic job satisfaction (i.e., the opportunity for

advancement) and extrinsic job dissatisfaction (i.e., salary/pay) and the dependent

variable of retail employee turnover intention. In the review, I also incorporated current

and seminal literature that examined the strengths, weaknesses, and limitations of

Herzberg et al.’s (1959) two-factor theory of motivation (i.e., Herzberg, 1965; Hur, 2018;

Tan & Waheed, 2011), which was the primary theoretical lens for the study. The review

includes comprehensive research of divergent or rival theories, such as Deci and Ryan’s

(1985) self-determination theory, Vroom’s (1964) expectancy theory (Baciu, 2017; Lloyd

& Mertens, 2018), and Maslow’s (1943) hierarchy of needs theory. Additional

components of the literature review include literature that establishes the reliability and

validity of the psychometrical scales employed to measure the chosen independent

variables, (a) opportunity for advancement (i.e., the MSQ-SF; see Weiss et al., 2010), and

(b) salary/pay (i.e., the PSQ; see Heneman & Schwab, 1985; Morgeson et al., 2001), as

well as Mobley et al.’s (1979) turnover intentions model (see Sager et al., 1988), that

measures the dependent variable of (c) employee turnover intention.

I searched the following databases in Walden University’s library for peer-

reviewed articles and books published within 5 years of expected chief academic officer

approval, with the frequency of those sources listed in Table 1: ABI/Inform Complete,

Academic Search Complete, Business Source Complete, eBook Collection (EBSCOhost);

Emerald Management, ERIC, IBISWorld, PsycINFO, PsycTESTS, SAGE Journals,

ScholarWorks, Science Direct, Taylor and Francis Online, and Walden Library Books. I

used the Boolean identifiers and/or, which assisted in achieving the optimal results using

the following key search words or terms: employee turnover intention, employee

turnover, turnover intentions, automotive retail sales, retail sales, salesperson,

automotive salesperson, Herzberg's theory, two-factor theory, job satisfaction, job

dissatisfaction, pay, promotion, career advancement, advancement, extrinsic motivation,

intrinsic motivation, retail turnover, sales turnover, employee advancement, employee

salary, employee pay, employee promotion, involuntary turnover intention, and voluntary

turnover intention.

Table 1

Numerical Count and Percentage Values for Cited Sources

Source

Older than

6 years

Within 5

years or less

Total

Percentage

Books

5.2%

Peer-reviewed articles

159

228

91.5%

Government

2.2%

Other

Total

171

248

100%

Note: This table denoted compliance with university guidelines for reference and cited

sources.

Herzberg et al.'s Two-Factor Theory (Motivation-Hygiene Theory)

Herzberg et al.’s (1959, 2017) two-factor or motivation-hygiene theory served as

this study’s theoretical framework or lens. In 1959, Frederick Herzberg utilized the

critical-incident method to interview engineers and accountants in Pittsburg,

Pennsylvania. Herzberg et al. (1959) asked the participants to recount incidences where

they felt extremely good or bad about their jobs. The researchers then posited that worker

satisfaction is primarily generated from several crucial constructs that promote and

produce either job satisfaction or dissatisfaction (Alshmemri et al., 2017; Gardner, 1977;

Herzberg et al., 2017; Hur, 2018; Tan & Waheed, 2011; Ward, 2019). Zhang et al. (2020)

discussed that Herzberg et al.’s research was based on the construct of work, the

employee’s attitude towards work, and the effect the work perspective produced

regarding job satisfaction or dissatisfaction. The various seminal and recent examinations

and explorations of this theory prove that Herzberg et al.’s motivation-hygiene theory is

relevant in today’s workplace.

Herzberg et al. (1959, 2017) identified job satisfaction or intrinsic motivation

factors as (a) achievement, (b) recognition, (c) advancement, (d) growth, (e) work-itself,

and (f) responsibility (Hur, 2018; Tan & Waheed, 2011; Ward, 2019). Whereas the

fundamental factors of job dissatisfaction or extrinsic hygiene factors, Herzberg et al. and

Ward (2019) identified as (a) supervision, (b) salary/pay, (c) work conditions, (d)

interpersonal relationships, and (e) company policies. Herzberg et al. (1959), Ann and

Blum (2020), and Zámečník and Kožíšek (2021) postulated that the absence or shortage

of extrinsic hygiene factors influenced job dissatisfaction but that those factors were not

the primary creators of dissatisfaction. Employees could feel job dissatisfaction even

when extrinsic or hygiene constructs were in place; however, hygiene-oriented

constructs, such as stressful interpersonal relationships with fellow workers or

management and company policies, produced dissatisfaction and decreased motivation

(Alrawahi et al., 2020). Herzberg et al. (1959) further added that intrinsic motivators

inspired job satisfaction and that the experience of job dissatisfaction and job satisfaction

were two different phenomena or feelings (Zhang et al., 2020). Herzberg (1965) stated

that job satisfaction and dissatisfaction are not opposite entities. Zhang et al. (2020)

postulated that the contrasting position to job satisfaction is no job satisfaction, not

dissatisfaction, which generated different performance outcomes. When job

dissatisfaction is not present, that does not denote that job satisfaction is present; it

suggests the employee is not satisfied with the job (Oluwatayo, 2015; Wilson, 2015).

Organizational leaders can use this valuable data to evaluate intrinsic and extrinsic factors

influencing employee turnover intentions and increasing retention.

Herzberg et al. (1959) found that intrinsic motivators, such as the opportunity for

advancement, are components of the theory that provide employees with long-term

performance results. The achievement factor proved to be an essential satisfier with the

highest frequency in long-range (38%) and short-range (54%) high attitude sequences

(Herzberg et al., 1959). Herzberg et al. further explained that extrinsic hygienic factors,

such as salary/pay, produce short-term effects that affect overall employee job

perceptions, attitudes, and performance (Herzberg, 1965). In a study surveying Malaysian

retail salespeople, Tan and Waheed (2011) reported similar results, discovering a

statistically significant relationship between job satisfaction and extrinsic hygienic job

motivators, such as working conditions, company policy, and salary/pay.

Herzberg et al. (1959) posited that intrinsic motivators encouraged job

satisfaction, resulting from the job itself. Herzberg et al. postulate that job content

generated job satisfaction indicate that employees can accomplish elevated job

satisfaction levels when achieving their goals. This attitude toward job content can

provide employers with employees who demonstrate positive work behaviors, which

leads to enhanced productivity and employee retention (Herzberg et al., 1959). However,

Herzberg (1965) theorized that extrinsic factors associated with job contexts, such as

salary/pay, working conditions, and security, are directly controlled by the employer,

directly related to job dissatisfaction, and indirectly related to the employee’s overall job

performance.

Herzberg et al. (1959) found that positive customer relationships were critical to

providing motivation and job satisfaction among those studied. The participants indicated

having the ability to control their time via scheduling and having open communication

lines with the customer-generated feelings of job satisfaction (Herzberg et al., 1959).

Additional researchers applying Herzberg et al.’s two-factor theory found that retail

salespersons are highly motivated by intrinsic motivating factors, such as achievement

and advancement; however, the extrinsic hygiene factor of salary pay was the second

highest-rated motivator among retail salespersons (Tan & Waheed, 2011; Winer &

Schiff, 1980). Tan and Waheed (2011) further posited that retail store managers should

design and implement salesperson reward initiatives that endorse extrinsic and intrinsic

elements, such as work conditions, recognition, salary/pay, and company policies, to

generate high levels of job satisfaction. Researchers indicated that sales managers should

strive to ensure salesperson satisfaction and happiness and postulated that salespeople

satisfied with their job would communicate their satisfaction to potential new employees

and customers and perform better in their roles (Lai & Gelb, 2019; Prasad Kotni &

Karumuri, 2018; Tan & Waheed, 2011). Furthermore, since the customer is the primary

focus of the retail salesperson and the sole source of income in most cases, initiatives

designed to enhance the retail sales employee’s satisfaction would boost their satisfaction

and the satisfaction of the consumer and workplace morale.

Prasad Kotni and Karumuri (2018) tested retail outlet employee provocation using

Herzberg et al.’s (1959) two-factor theory, finding that extrinsic rewards were

significantly preferred over intrinsic rewards, increasing productivity and job satisfaction.

Ziar and Ahmadi (2017) hypothesized that motivational factors differed according to the

employee’s age. Ziar and Ahmadi concluded that intrinsic factors, such as opportunities

for advancement, significantly influenced younger employees. In contrast, older

participants were prone to extrinsic hygienic factors, such as salary/pay.

Figure 3

Illustration of Herzberg et al.’s (1959) Two-Factor Theory of Motivation

Note. This figure illustrates how the constructs of the two-factor theory of motivation

interact to create job satisfaction or dissatisfaction.

Contrasting or Rival Theories

Herzberg et al.’s (1959) two-factor theory aims to identify what process or

content will generate job satisfaction or dissatisfaction in conjunction with other

motivational-based views. Herzberg et al. theorized that the intrinsic factors of (a) career

advancement, (b) achievement, (c) recognition, (d) work itself, (e) responsibility, and (f)

Extrinsic Dissatisfiers

• Pay/salary

• Working conditions

• Company policies

• Supervision

• Job security

• Work life balance

Intrinsic Satisfiers

• Opportunity for advancement

• Recognition

• Growth

• Status

• Challanging work

• Promotion

• Sense of personal achievement

growth possibilities increase job satisfaction. However, the extrinsic or hygiene factors of

(a) salary/pay, (b) organizational policies and procedures, (c) relationship with

supervisor, (d) interpersonal relationships, and (e) working conditions can reduce job

satisfaction. Herzberg (1965) opined that these extrinsic and intrinsic factors share an

inverse relationship where the absence of hygiene factors decreases motivation, but

inherent or intrinsic factors will stimulate motivation when present.

Herzberg et al.’s (1959) two-factor theory and Maslow’s (1943) hierarchy of

needs theory are similar in that both are examples of content theories. Content theories

investigate the internal qualities a person uses or possesses to stimulate personal

motivation (Issac et al., 2001). Contrastingly, Vroom’s (1964) expectancy theory is a

process theory. Vroom’s expectancy theory investigates an individual’s logical process to

make conscious choices that provide them with a specific expected outcome: pleasure or

pain. Vroom's expectancy theory is contrary to Herzberg et al. and Maslow’s hierarchy of

needs theory in that it is not based on the satisfaction of individual needs. However,

Harris et al. (2017) opined that Vroom’s expectancy theory is based on the direct

outcomes of individual behaviors resulting from their decision expectations. In their self-

determination theory, Deci and Ryan (1985) suggested that an employee’s inherent

tendencies of growth and psychological needs dictated intrinsically or extrinsically

motivating attributes toward autonomy, competence, and relatedness. Szulawski et al.

(2021) and Wingrove et al. (2020) posited that intrinsic motivation is encouraged by

internal rewards and linked to performance prediction or enhancement.

Maslow’s Hierarchy of Needs Theory

Maslow (1943) identified five immediate human needs as motivators (Güss et al.,

2017) and categorized those needs as (a) physiological, (b) safety, (c) self-esteem, (d)

growth or self-actualization, and (e) love. Unlike Maslow, Herzberg et al. (1959) centered

their theory around the fundamental thesis that employee motivation is based on extrinsic

and intrinsic rewards or recognition. No sequence of extrinsic or intrinsic rewards leads

to the principal cause. However, Maslow postulated that motivation was established on

personal needs and satisfaction and opined that these needs followed a sequenced

hierarchal path that led to the highest form of motivation fueled by self-actualization.

In the hierarchy of needs theory, Maslow (1943) posited that physiological needs

essential for basic human survival, such as air, water, and food, were the most basic

conditions and formed the foundation for higher-level needs satisfaction. Maslow (1970)

stated that once a human’s basic needs were met, safety and security, or the need to avoid

physical or psychological danger, were next in the hierarchy. The need for safety and

security is followed by love or longing for affection and support. Individuals who feel

valued and supported become confident and robust, satisfying their sense of belonging

and need for admiration or love (Güss et al., 2017; Maslow, 1943). Once these lower

needs are met, then the higher conditions become achievable (Güss et al., 2017; Maslow,

1943), giving way to a person’s need to be the best they can be or, as Maslow (1943) and

Guss et al. (2017) labeled it, the desire for self-fulfillment or self-actualization.

Maslow (1943) opined that the five needs correlated with each other. The higher-

level needs could not be fulfilled if the lower conditions remained not satisfied, and

recent research supported this hypothesis (Kanfer et al., 2017; Siahaan, 2017). Kanfer et

al. (2017) postulated that managers or business leaders who develop workplace initiatives

that meet the needs Maslow identified in the theory position their organizations for high

levels of employee job satisfaction and low employee turnover. Kanfer et al. and Siahaan

(2017) suggested that job dedication decreases turnover intentions. Therefore,

organizational leaders can use Maslow’s hierarchy of needs theory to deduce that

employees who achieve self-actualization will also experience elevated job satisfaction

levels and diminished turnover intentions.

Various researchers have challenged the physiological need hypothesis posited by

Maslow. These researchers presented theories and cases demonstrating that more than

bare human essentials were needed to propel human motivation. Alam et al. (2020)

opined that wages were the most significant contributor to employee work motivation

versus trust and safe work environments. In contrast, Criscione-Naylor and Marsh (2021)

found that since the onset of the COVID-19 pandemic, employees have been significantly

motivated by the organization’s ability to provide a secure and safe workplace. The

researchers showed that motivational needs differ from one employee to the next.

Organizational size and location are vital in establishing a workplace hierarchy (Jonas et

al., 2016). Stewart et al. (2018) further posited from data generated by studies conducted

with Southwest Airlines, Valve Software, and Google that companies that satisfy the

upper levels of the hierarchy, self-actualization, and self-esteem create a workplace

environment that encourages job security while meeting an employee’s need for

emotional compensation and satisfying physiological and safety requirements with

monetary compensation.

Recent researchers demonstrated how Maslow's theory applied to higher

education institutes students. Abbas (2020) posited that higher education institutes

provide students with basic education needs, such as a safe campus, state-of-the-art

facilities, teaching quality, employability, and extracurricular activities, which support

Maslow's hierarchy of needs theory. Abbas opined that students are motivated to

participate in activities that promote personal development and enhance leadership skills.

Therefore, it demonstrates that when students' basic needs are satisfied, this satisfaction

encourages them, boosts self-esteem, and creates self-actualization. However, Maslow

(1943) viewed self-actualization from a personal perspective since this attribute could

possess varying characteristics instituted in the individual's personality (Compton, 2018).

Some researchers further proposed that some individuals do not have the drive or want to

reach self-actualization (Kaufman, 2018). In contrast to Herzberg et al. (1959), Alrawahi

et al. (2020) suggested that intrinsic motivating factors prompt individual progress and

create a desire for career advancement and recognition.

Figure 4

Illustration of Maslow’s (1943) Hierarchy of Needs Theory

Vroom's Expectancy Theory

Vroom (1964) theorized that employees would choose specific actions expecting

a particular result or outcome, pleasure or pain. Vroom viewed motivation as a force and

defined motivational force as the resultant product of (a) expectancy, (b) instrumentality,

and (c) valence (Baciu, 2017; Lloyd & Mertens, 2018; Kumar & Prabhakar, 2018).

According to Vroom, expectancy is an employee's anticipation of the performance results

produced by a conscious, deliberate action on their part. (Baciu, 2017; Lloyd & Mertens,

2018; Pereira & Mohiya, 2021)) Furthermore, Baciu (2017), Lloyd and Mertens (2018),

and Pereira and Mohiya (2021) postulated that Vroom viewed expectancy as an action-

outcome association that takes on the values of 0 (no expectation) to 1 (full expectation),

which correlated with the employee's belief that their efforts would generate the expected

result. Vroom defines the construct of instrumentality as the perception that an

anticipated reward will result from the performance. Vroom described this as an

Self Actualization

Esteem Needs

Social Needs

Safety Needs

Basic Needs

outcome-outcome association with the same outcome designation values; 0 (no

probability of reward delivery) to 1(reasonable probability of reward delivery); (Baciu,

2017; Lloyd & Mertens, 2018). Vroom described valance as indicating the individual's

degree of preference toward the subsequent outcome or reward (Lloyd & Mertens, 2018).

These rewards could be positive, such as an increase in pay, or negative income, such as

penalties or sanctions (Baciu, 2017). This view contrasts with Herzberg et al.'s (1959)

theory, which focused on employees' intrinsic and extrinsic individual needs that

influence job satisfaction or dissatisfaction, not just the individual motivational results

and actions spurred by a personal decision and the ensuing outcome.

Researchers inferred a significant association between employee personality,

career development, work motivation, and employee retention. Kumar and Prabhakar

(2018) discovered that workplace motivation strategy centered on initiatives that promote

career development and rewards based on employee career planning and development

increased employee motivation levels, providing organizational leaders with a preface for

implementing personality analysis as motivational human resource management policies.

Waltz et al. (2020) revealed the motivational value professional development initiatives

had in improving retention and performance among millennial nurses. Organizational

leaders who embrace employee initiatives or rewards that yield positive results and

reduce adverse effects could increase employee motivation and promote retention.

Motivated employees who are productive and have specific career goals or

outcomes are easy to retain. Kanfer et al. (2017) investigated goal choice and how it

influences employee motivation, applying Vroom's expectancy theory as their theoretical

lens. The researchers argued that work outcomes could impact employee job satisfaction

and their decisions not to stay with an organization. Kanfer et al. demonstrated that

motivation is a direct result of goal-orientated resources, and when employees achieve

goal accomplishment, retention increases, and employees readily accept organizational

work goals. Vroom (1964) hypothesized that the results of motivational force influenced

employee behavioral outcomes. Corporate leaders can implement employee initiatives or

rewards into career development and advancement opportunities that encourage

individuals to engage in behaviors that generate the expected positive results associated

with increased employee motivation (Baciu, 2017). Employee productivity and job

effectiveness are crucial links to overall job fulfillment and organizational effectiveness

(Kumar & Prabhakar, 2018; Prentice & Thaichon, 2019), supporting a portion of

Herzberg et al.’s (1959) two-factor theory regarding intrinsic motivators.

Figure 5

Illustration of Vroom’s (1964) Expectancy Theory

Self-determination Theory

Deci and Ryan (1985) postulated that extrinsic motivation could be internalized,

and as extrinsic rewards increased over a prolonged period, extrinsic motivation became

• Effort

Expectancy

• Performance

Instrumentality

• Reward

• Outcome

Valance

Motivation

autonomous. According to Deci and Ryan, intrinsic motivation is the individual’s natural

tendency to learn and to search for new challenges based on their interests and passions,

whereas extrinsic motivators are viewed as external regulations generated by external

rewards, punishment, or amotivation (Grabowski et al., 2021; Szulawski et al., 2021;

Wait & Stiehler, 2021). Amotivation is the influence of extrinsic or external factors

utterly independent of the individual, creating a lack of effort or desire (Grabowski et al.,

2021; Wait & Stiehler, 2021). The self-determination theory (SDT) argues that people are

motivated by activities they initiate and possess a strong psychological need to belong or

relatedness. SDT is grounded in a person's need for growth, which is the product of

competence, relatedness, and autonomy (Wait & Stiehler, 2021). Employees focus on the

outside or extrinsic factors anticipated outcomes and their ability to dictate and control

their positive results, defined as autonomy (Good et al., 2020; Wait & Stiehler, 2021). To

further this point, Wingrove et al. (2020) postulated that SDT and the fundamental

constructs of this theory were most appropriate for coaching supervision within coaching

ranks. SDT was identified as directly impacting a coach’s inherent growth tendencies and

regulated self-behaviors (Wingrove et al., 2020). Competence and relatedness are

produced when employees feel their decisions will influence a specific result or make a

difference. They identify and join others they trust and share common values (Good et

al., 2020; Wait & Stiehler, 2021). Good et al. (2020) hypothesized in their qualitative

findings, using self-determination theory as their conceptual framework, that when

salespeople determine they are making a difference, they experience the highest level of

motivation.

In contrast, Herzberg et al. (1959) would argue that Deci and Ryan’s (1985)

findings demonstrated extrinsic factors' influence on employee motivation. Still, those

outside factors are not experienced at their highest level without a counterbalance of

intrinsic factors. Therefore, Herzberg et al. concluded that extrinsic hygiene factors are

absent without an intrinsic motivating factor to produce an ideal environment for job

satisfaction.

Figure 6

Illustration of Deci and Ryan’s (1985) Self-determination Theory

Intrinsic Independent Variable: Opportunity for Advancement

Researchers identified intrinsic factors as motivators or high-level growth

constructs that promote job satisfaction. Herzberg et al. (1959) defined intrinsic

motivators as factors that contribute directly to an employee or employees' motivation.

Herzberg et al. identified several attributes that stimulate intrinsic motivation, such as (a)

achievement, (b) responsibility, (c) recognition, (d) work itself, (e) opportunity for

advancement, and (f) growth (Herzberg, 1974; Hur, 2018: Tan & Waheed, 2011).

Intrinsic Motivation and Self-

Determined Extrinsic Motivation

Satisfaction of

Psyhcological Needs

Autonomy

Competence

Relatedness

Intrinsic work motivation that is challenging and meaningful and offers an employee

recognition for accomplishments and opportunities for growth and advancement

influences an employee's satisfaction level (Barrick et al., 2015; Basinska & Dåderman,

2019), generating positive work attitudes that impact turnover intentions.

Opportunities for developmental career progression or advancement within an

organization are principal determinants of whether employees stay or leave (Carter &

Tourangeau, 2012; Crafts et al., 2018). Carter and Tourangeau (2012) supported these

findings in their research. The authors posited that the lack of inside opportunities for

advancement increased employee turnover versus the accessibility of ample

opportunities. Pediatric physicians further indicated in Crafts et al.'s (2018) study that the

perceived lack of opportunity for career advancement (p = <0.001) was a significant

influencer in pediatric physicians deciding to change their place of employment early in

their careers.

Personal growth and job satisfaction stem from the employee's desire for career

advancement opportunities (Lee et al., 2017; Lester, 2013). Tan and Waheed (2011)

posited that career advancement was essential to employees' life fulfillment goals and

was necessary to achieve job satisfaction. Parsa et al. (2014) concluded that career

advancement opportunities among academic employees positively correlated with work-

life quality. Lack of advancement opportunities increases turnover intentions and

eventually leads to elevated employee turnover, whereas increased opportunities for

advancement can positively influence commitment, reducing employee turnover levels

(Carter & Tourangeau, 2012; Crafts et al., 2018; Erasmus, 2020). Therefore, effective

dealership succession plans and promotion initiatives could slow turnover intentions and

eventual turnover.

Researchers present evidence that career advancement opportunities that motivate

employees to perform increase organizational commitment, job contentment, and job

satisfaction, reducing turnover intentions. Andrews and Mohammed (2020), in

conjunction with Xie et al. (2016), posited that career advancement opportunities directly

influenced employee job performance. However, employees who experience career

plateau experience decreased job satisfaction levels, and their preferences for turnover

increase (Andrews & Mohammed, 2020; Xie et al., 2016). Wang et al. (2016) suggested

that opportunities for advancement can bolster an employee's willingness to demonstrate

their advancement potential or merit, cultivating better performance.

Career advancement opportunities and promotability lessen turnover intentions

(Chan et al., 2016). However, in conjunction with management recognition of sales

performance, work-life balance and sales incentives proved to be primary motivators for

Indian retail salespeople (Prasad Kotni & Karumuri, 2018). Gunn et al. (2017)

demonstrated that opportunity for advancement was a prevailing theme in retail sales

management career profiles. Opportunities for advancement, earnings, and career paths

were vital contributors to the retail industry's negative career perception (Gunn et al.,

2017). Shaju and Subhashini's (2017) study of the automobile industry hypothesized that

extrinsic job factors, such as the opportunity for advancement, strongly correlated to job

satisfaction. This correlation was higher among those at the supervisory level. Additional

research shows that career management processes are designed to promote advancement

that reduces turnover intentions (de Oliveira et al., 2019). This relationship varied

depending on the amount of mediation from organizational management.

Extrinsic Independent Variable: Salary/Pay

In contrast to intrinsic satisfying motivators, researchers suggested that extrinsic

hygiene factors, such as pay/salary, are primarily lower-order environmental needs that

fulfill the basic requirements inherent to the job. Herzberg et al. (1959) theorized that in

conjunction with intrinsic job satisfaction factors, employees were affected by extrinsic

job factors that contributed to job dissatisfaction. Herzberg et al. identified salary/pay, or

compensation, as an extrinsic factor that adversely affects overall employee satisfaction.

Employee salary, sales force compensation, pay, or pay satisfaction is recognized as a

critical influencer of job satisfaction, performance, employee retention, motivation, and

turnover intentions (Call et al., 2015; Chan & Ao, 2019; Fatima, 2017; Tan & Waheed,

2011). Mburu (2017) opined that pay significantly influences work performance, and

organizations implementing strategies that promote compensation and advancement

experience decreased turnover and higher retention. Chan and Ao (2019) discovered that

highly paid casino employees were dissatisfied with their pay. Employees who expect

their jobs to pay well need additional pay incentives such as bonus packages. Božović et

al. (2019) concluded that human resource management practices that encompass

consistent compensation systems would positively increase banking employees' job

satisfaction levels. However, Chan and Ao revealed that dissatisfaction with pay did not

decrease employee commitment levels, indicating that well-thought-out pay plans can

seal between employee organizational commitments and decreased turnover intentions.

Pay incentives or rewards proved influential among salespersons and sales

managers if these pay programs are not perceived as methods to control behaviors

(Mallin & Pullins, 2009). Mallin and Pullins (2009) postulated that pay could be

detrimental to intrinsic motivation, and unattractive earning potential negatively impacts

job satisfaction and motivation. Holmberg et al. (2017) further posited that the

nonexistence of extrinsic factors adversely affects job satisfaction. Managed salary/pay

strategies help organizational managers attract, retain, and motivate employees to achieve

organizational goals and maintain competitive advantage (Arocas et al., 2019; Sarmad et

al., 2016). Therefore, dealership leaders could achieve collaborative effort and support by

designing and implementing pay structures that promote and fulfill higher-order needs.

Dealership managers who organizationally control extrinsic rewards can also

implement pay/salary initiatives that generate job satisfaction and lessens dissatisfaction

by offering rewards or incentives not based on monetary measures. Good et al. (2020)

discovered that intrinsic motivators were positively associated with increased salesperson

effort and that younger salespeople were not as motivated by the desire for money.

Therefore, it challenges managers and business owners to switch from outcome-based

monetary controls to more purpose-driven employment opportunities and initiatives.

Automobile salesperson compensation or pay is highly variable and depends on

individual dealership structure (Habel et al., 2021; Joetan & Kleiner, 2004). Most

salary/pay plans for automotive salespeople are commissions-based, highly competitive,

pressure-driven, and directly connected to a small percentage or commission of variable

profit generated from individual automobile and automobile product sales (Habel et al.,

2021; Joetan & Kleiner, 2004). This pay/salary system contributes to high job stress,

emotional exhaustion, low extrinsic job satisfaction, and high turnover among automotive

salespeople (Habel et al., 2021; Joetan & Kleiner, 2004). Hung et al. (2018) posited that

satisfaction with salary/pay structures generated elevated organizational commitment

levels, even in high-pressure operations, and turnover intentions remained low.

Researchers consistently demonstrated that pay/salary dissatisfaction negatively

correlated with turnover intention (Mohamed et al., 2017). Mohamed et al. (2017) tested

a hypothesis to examine if this relationship could be non-linear. The researchers

concluded that pay satisfaction and turnover intention shared a nonlinear relationship,

positing that pay dissatisfaction did not always increase employee turnover intentions

(Mohamed et al., 2017). Furthermore, Alshmemri et al. (2017) affirmed Herzberg et al.'s

(1959) position, concluding and further positing that reducing or eliminating concepts

that contribute to turnover or turnover intentions could improve the employee experience,

therefore contributing to job satisfaction and lessening extrinsic job dissatisfaction.

Chinyio et al. (2018) further concluded that turnover rates were low when employees

experienced high job satisfaction, crediting Sankar (2015), who hypothesized that once

hygiene or extrinsic factors such as pay/salary are met, elevated employee retention is

attainable.

Dependent Variable: Employee Turnover Intentions

Researchers defined turnover intention as an employee's conscious decision to

start contemplating leaving the job, actively looking for a new job, or the active voluntary

plan to quit their job (Aburumman et al., 2020; Ikatrinasari et al., 2018; Tastan, 2014).

Turnover intention is a direct predictor of actual turnover, which threatens overall

organizational performance, due to the high costs associated with turnover and produces

adverse effects on employee morale, employee engagement, performance, and job

satisfaction (Jones et al., 2007; Kang & Sung, 2019; Kim, 2018; Lin & Liu, 2017). Oruh

et al. (2020), in conjunction with other supporting researchers, contented that employee

turnover intention is not about the employee leaving the job put; it is the thought process

leading up to the employee’s decision not to keep the job. Oruh et al. further suggested

that lack of interest in the position, lack of engagement, and lack of voice are distinct

characteristics of employee turnover intention.

Researchers discussed engagement and the correlation employee engagement has

with turnover intentions and actual turnover. Kang and Sung (2019) concluded that

employee organizational relationships that foster employee engagement are statistically

significant predictors of turnover intention, suggesting that highly engaged employees are

less likely to contemplate or leave their organization. Employees reflect their level of

engagement through the energy they exert in representing the organization and their

feelings about their work and work environment (Eldor & Vigoda-Gadot, 2017). To

increase job involvement and lessen turnover intentions, Li et al. (2019) suggested that

sales managers concentrate on programs designed to produce elevated levels of internal

service quality for their salespeople that deliver empathy, responsiveness, tangibles,

assurance, and reliability.

Workplace climate and the emotions these climates produce play a vital role in

developing turnover intention (Carter et al., 2016; Joe et al., 2018). Employees who work

in a hospitable working environment that encourages overall organizational concern or

concern for humanity as a whole experience a heightened identification with their

organization, and their intent to turnover decreases (Joe et al., 2018). Turnover intentions

increase when employees lack pride in their performance (Kraemer et al., 2016).

However, Kraemer et al. (2016) suggested that employee pride in performance can create

a sense of self-efficacy, contributing to increased turnover intentions. When the employee

experiences high levels of job satisfaction, the relationship between self-efficacy and

turnover intention becomes inverted and becomes negative. Thakur (2017) investigated

abusive supervision and the outcomes in relation to employee turnover intentions.

Thakur implemented Cohen’s (1999) turnover intention scale to analyze the data and

determined that turnover intentions were significantly positive concerning abusive

supervision and negatively correlated with positive organizational support. These

outcomes demonstrate the requisite positive and sociable work environments for

corporate leaders and managers.

Indian front-end retail sales workers identified several antecedents to turnover in a

qualitative study conducted by Pandey et al. (2018). The employees revealed abusive

supervision, favoritism, perceived job image, work exhaustion, perceived unethical

climate, organizational culture shock, staff shortage, and job dissatisfaction as the most

common reasons for developing turnover intention. Pandey et al. discovered recurring

themes related to the need for career growth opportunities, dead-end jobs, salary

concerning workload, and compensation not reflective of cost-of-living responsibilities.

Retail sales participants indicated that prolonged exposure and the tolerance of these

behaviors influenced employee turnover intentions and directly affected their decision to

turnover or quit (Pandey et al., 2018). Career progression and advancement significantly

contributed to the intention to turnover among the Indian retail sales employees in this

study. Therefore, retail sales managers and their human resource partners should develop

retention programs centered on initiatives that support career advancement opportunities,

like role succession planning, and promote from within policies.

Scholars determined that job satisfaction had a significantly negative effect on

early-career employee turnover intentions. Lee et al. (2017) supported the hypothesis that

personal employee growth played a significant role in employee job satisfaction,

specifically among early-career employees. Lee et al. opined that personal growth and

development were the critical elements that could effectively control this population's

turnover intentions. The researcher’s findings profoundly impact dealership leaders’

decisions to offer new salespersons training programs and personal growth programs

designed to clarify role succession and increase salesperson retention.

Effective HRM practices, such as employee development programs, mentorship

opportunities, career advancement strategies, succession planning, and training programs,

are effective retention methods organizational leaders can implement to reduce turnover

intentions (Ali & Mehreen, 2019; Almaaitah et al., 2017; de Oliveira et al., 2019). HRM

practices that do not address perceived career barriers that substantially affect employee

turnover intentions can produce adverse emotional reactions and increase turnover (Nie et

al., 2018; Rasheed et al., 2018). Flaherty and Papas (2002) implemented the career stage

theory to examine turnover intentions among automotive salespeople. The researchers

presented in their findings that occupational tenure, career stage, and education were

significant influencers of salesperson turnover intentions.

Researchers support that leadership styles and employee commitment levels

influence employee turnover intention. Li et al. (2018) opined that leader-member

exchange (LMX) leadership indirectly influenced turnover intention when mediated by

salesperson performance, job satisfaction, and organizational commitment. Li et al.

hypothesized that LMX reduced salesperson turnover intentions and significantly

impacted salesperson performance and job satisfaction using previous research findings.

Li et al. (2018) further posited that organizational commitment mediated the

relationship between LMX and turnover intention, suggesting that HRM practices should

educate sales managers on LMX qualities such as practical interpersonal communication

skills, sales skills, and emotional intelligence. Li et al. suggested that improving

salesperson performance was critical to reducing salesperson turnover intentions.

Mathieu et al. (2016) indicated that employee commitment consists of several

components and organizations that understand what fuels these components could

minimize turnover intentions. Entrepreneurial leaders can lessen turnover intentions

through high-level person-job fit (Yang et al., 2019). Therefore, providing leaders and

entrepreneurs with evidence supporting putting the right person in the correct position,

training the right person in organizational culture, and specific professional skills to

reduce the intent to turnover.

Seminal and current researchers hypothesized that insufficient wages, lack of

career advancement opportunities, and dissatisfaction could influence employee turnover

intentions within organizations that demonstrate truncated organizational values, poor

performance, and dysfunctional culture (Dusterhoff et al., 2014; Olubiyi et al., 2019;

Stanolampros et al., 2019). However, Jinnett et al. (2017) opined that employee

commitment, retention, and engagement lessens turnover intent. Therefore, organizations

that develop and implement career succession and marked salary and pay initiatives into

their overall culture position themselves for increased job satisfaction and employee

retention.

Independent Variable Measurements

Herzberg et al. (1959) identified specific constructs associated with job

satisfaction. Herzberg et al. asserted that these intrinsic (motivation) and extrinsic

(hygiene) characteristics were directly associated with employee job satisfaction or job

dissatisfaction (Alshmemri et al., 2017). Herzberg et al. labeled intrinsic (motivation)

constructs as (a) recognition, (b) opportunity for advancement, (c) achievement, and (d)

appreciation, while (e) work itself, (f) pay/salary, (g) job security, and (h) supervisions

are hygiene or extrinsic constructs related to job dissatisfaction (Hur, 2018). The

independent variables of opportunity for advancement (intrinsic) and pay/salary

(extrinsic) were measured using the Minnesota Satisfaction Questionnaire -Short Form

(MSQ-SF); (Weiss et al., 1968) and the Pay Satisfaction Questionnaire (PSQ); (Heneman

& Schwab, 1985).

Minnesota Satisfaction Questionnaire Short Form (MSQ-SF)

The MSQ-SF is a shortened derivative of the Minnesota Satisfaction

Questionnaire (MSQ) developed by Weiss et al. in 1968 to measure work adjustment

theory. The MSQ (Weiss et al, 1968) comprises 100 items and measures employee

satisfaction levels. The MSQ-SF contains 20 items and is considered a primary

measurement tool for job satisfaction (Lakatamitou et al., 2020; Weiss et al., 2010; Weiss

et al., 1968; Worsfold et al., 2016). Seminal and recent researchers have implemented this

measurement tool to understand the relationship between employees and their work

environment.

The MSQ-SF is a 5-point Likert scale that measures job satisfaction using the top

questions from each category, with two subscales for intrinsic and extrinsic satisfaction

elements (Lakatamitou et al., 2020). The MSQ-SF contains 20 items that focus

specifically on the inherent (motivation) and outside (hygiene) aspects of job satisfaction

(Lakatamitou et al., 2020; Purohit et al., 2016). The 20 items measured are achievement,

advancement, activity, authority, ability, company policies and procedures,

compensation, coworkers, creativity, moral values, independence, recognition,

responsibility, security, social service, social status, supervision relations, supervision

technical, variety, working conditions, and cumulative job satisfaction (Purohit et al.,

2016). The time required to complete the MSQ-SF in conjunction with its reliability and

validity as a proven instrument for measuring job satisfaction (Heneman & Schwab,

1985; Lakatamitou et al., 2020; Purohit et al., 2016) and is the reason this instrument was

selected to measure the intrinsic (motivator) constructs.

Pay Satisfaction Questionnaire (PSQ)

Heneman and Schwab (1985) designed the 18-item pay satisfaction questionnaire

to address five pay constructs absent in generalized unidimensional instruments, such as

the MSQ and the Job Descriptive pay scales (Heneman & Schwab, 1985). Those five

constructs are (a) pay level, (b) benefits, (c) pay raises, (d) pay structure, and (e)

administration. PSQ measures the constructs using a 5-point Likert scale ranging from 1-

strongly agreed to 5- strongly disagree (Heneman & Schwab, 1985; Olasupo et al., 2019).

Current and seminal researchers have implemented the PSQ to analyze pay satisfaction in

various sectors and industries.

Athamneh (2020) implemented the PSQ as the conceptual framework for a study

on pay satisfaction among Jordanian public sector workers. Lawal et al. (2019) measured

pay satisfaction as one of the constructs of counterproductive work behavior (CWB) and

intent to leave among university support staff. Zaky et al. (2018) further expounded upon

the effects of pay satisfaction, pay level, and positive affect among 207 postgraduate

students. These recent studies further indicate that pay satisfaction is still a viable

construct to consider in research that examines or explores employee turnover intentions.

Athamneh (2020) hypothesized that Jordanian public sector workers were

satisfied with pay level, benefits, pay raises, structure, and administration. Athamneh also

theorized that age and gender would influence the outcome of the PSQ survey conducted

among these workers. Athamneh rejected the hypothesis that Jordanian public sector

workers were satisfied with pay levels, benefits, pay raises, structure, and administration.

Athamneh found that salary structure and administration averages were closer to

dissatisfaction than pay level and benefits averages. Lawal et al. (2019) posited that using

the PSQ to measure pay satisfaction is a significant predictor of CWB. When combined

with age, there was a 1.3% increase in variation. Lawal et al. further discovered that

intention to leave was a significant contributor to CWB; there was no significant impact

when gender was introduced to the data set. Overall, Lawal et al. reported a coefficient

alpha reliability estimate of 0.89 with a Cronbach alpha of 0.97, providing further

evidence of the tested reliability of this questionnaire as a measurement instrument for

pay satisfaction. Zaky et al. (2018) designed hypotheses to analyze the bearing actual pay

and pay affected on the four dimensions of the PSQ, which are (a) pay level satisfaction,

(b) benefit satisfaction, (c) pay raise satisfaction, (c) pay raise satisfaction, and (d) pay

administration satisfaction. Zaky et al. measured the pay satisfaction factors using the

PSQ and determined that all dimensions had good reliability with a Cronbach alpha

greater than 0.80. Zaky et al. further posited that the hypotheses testing revealed that pay

level positively affected every extent of pay satisfaction. The PSQ provides this study

with another measurement tool with high internal consistency and multidimensional data

in conjunction with MSQ-SF. For this reason, I selected the PSQ as the measurement tool

for intrinsic (motivator) pay/salary.

Dependent Variable Measurement

Mobley et al. (1978) created an employee turnover model that measures the

general aspects of job satisfaction, thoughts about quitting, the intention to leave, and the

seeming opportunity to find another job. Mobley et al. concluded that the choice to quit

was significantly correlated to actual turnover, and this simplified model could be

implemented across various industries. Sager et al. (1988) used Mobley et al.’s model to

partially evaluate salesperson turnover, using multiple regression models to test different

hypotheses using the constructs of pay, promotion, supervisor, work, co-workers, and

thinking of quitting, attitude towards searching, and quitting, and intention to leave.

Skelton et al. (2018) examined turnover intentions among workers in the manufacturing

industry by developing a questionnaire using three questions grounded in Mobley et al.’s

model. Thakur (2017) reviewed the relationships between abusive supervision and

psychological contract breach, perceived organizational support, organizational

citizenship behavior, and turnover intentions among 41 Indian employees. Using Cohen’s

(1999) turnover intention scale, Thakur determined that abusive supervision shared a

significantly positive correlation with turnover intention (r =. -50; p <.01).

In this study, I elected to use the turnover intention scale developed by Cohen

(1999), founded upon the fundamental constructs of Mobley et al.’s (1978) turnover

intention model. Cohen’s turnover intention scale is a nine-item, 5-point Likert scale

(1=strongly disagree; 2= disagree; 3=neutral; 4= agree; 5= strongly agree) that inquiries

into turnover intentions using the same sentence structure, however changing the

verbiage from the word organization to the word job, to the word occupation. The

Cronbach’s alpha coefficient for this scale is 0.94 for intentions to leave the organization,

0.89 for intentions to leave the job, and 0.92 for intentions to flee the occupation. Lonial

and Carter (2015) posited that a Cronbach’s alpha coefficient of 0.80 or higher

demonstrates high data reliability. Therefore, I will use this survey instrument to measure

the dependent variable, employee turnover intention.

Transition

Section 1 comprehensively summarizes the study's business phenomena I

examined. In Section 1, I discussed the background of the problem, identified the

business problem, and stated my study’s purpose. I identified the population as retail

salespeople currently employed by automotive dealerships in Tennessee, Kentucky, and

Alabama who have been in the dealership retail selling profession for at least 1 year. In

this section, I further discussed the quantitative research method and correlational design

I implemented to determine if there was a statistical relationship created by the linear

combination of the independent variables (opportunity for advancement and salary/pay)

and did this relationship significantly impact employee turnover intentions among retail

salespeople in the automotive industry. I discussed, in Section 1, the theoretical

framework used to examine the business phenomenon as Herzberg’s two-factor theory of

motivation and discussed the implications for social change and practical business

application of the findings. Section 1 also included the overarching research question and

the null and alternative hypothesis and concluded with a comprehensive and robust

review of seminal and current literature applied to substantiate the research's data.

Section 2 includes the (a) restatement of the purpose statement, (b) a description

of the researcher’s role, (c) an explanation of the eligibility criteria of the participants, (d)

a discussion of the strategy implemented to gain access to the participants, (e) the chosen

research method and design, (f) a description of the population, (g) an explanation of the

instrumentation, (h) the data collection methods and analysis, (i) study validity, and (j)

research ethics. Section 3 presents the findings using descriptive statistics, a description

of the statistical tests, and their relationship to the hypothesis. This section will also

implement the appropriate tables, figures, and illustrations to evaluate the statistical

assumptions. This section concludes with a detailed discussion of the applicability of the

findings in actual business practices, the tangible social change implications, the

recommendation for action, reflection, and further research.

Section 2: The Project

In this section, I discuss and restate the purpose of this quantitative correlational

study. I also address my role as the researcher and the ethical requirements all researchers

need to follow to present research data that are reliable and valid. The chosen research

methodology and design are discussed, and a brief justification of why other standard

methods and techniques were unsuitable for researching the business phenomena in this

study is provided. This section also includes details about the research participants, such

as their role in the automotive retail sales industry and other demographic characteristics.

I justify and describe the sampling method employed in this study and establish

mathematical justification for the effect size, alpha, and power levels. Lastly, the

instrumentation, data collection techniques, and statistical tests used to analyze the

sample and test the research hypotheses are described.

Purpose Statement

The purpose of this quantitative correlational study was to examine the

relationship between an intrinsic job satisfier (i.e., the opportunity for advancement), an

extrinsic job dissatisfier (i.e., salary/pay), and employee turnover intention. The

independent or predictor variables were intrinsic job satisfaction and extrinsic job

dissatisfaction. The dependent or criterion variable was employee turnover intention. The

targeted population encompassed automotive retail salespersons in small- to medium-

sized franchise new car dealerships in Tennessee, Kentucky, and Alabama. Positive

social change implies developing a mutually constructive relationship between the

dealership and the community it serves. This mutually valuable relationship could

contribute to employee stability, new job opportunities, and local economic growth

associated with increased vehicle sales and registrations, such as local tax revenue and

charitable contributions to local community organizations.

Role of the Researcher

The researcher’s primary role is to develop an ethical strategy to collect, analyze,

and validate reliable data from the study’s participants (Dragga & Voss, 2017; Saunders

et al., 2016). Researchers must maintain academic responsibility and honesty, ensure

research accuracy, and protect their human participants’ confidentiality and anonymity

(Dragga & Voss, 2017; Yin, 2018). In this study, I tested hypotheses with analyses of

data collected via an online survey administered to retail salespeople in the automotive

sales industry in Tennessee, Kentucky, and Alabama.

Researchers who elect to employ data collected from human participants must

strive to attain the highest research ethics level. The research protocol outlined in the

Belmont Report provides the researcher with an ethical roadmap that protects and

respects the research participants (National Commission for the Protection of Human

Subjects of Biomedical and Behavioral Research, 1978). The Belmont Report requires the

researcher to draw a clear line between research and practice and to adhere to these three

guiding ethical doctrines: (a) respect for persons, (b) beneficence, and (c) justice (Anabo

et al., 2019; Lantos, 2020). First, researchers implement the respect for person principle

as the guiding principle that requires the researcher to treat human participants as

autonomous agents and protect those agents with diminished autonomy (Lantos, 2020;

National Commission for the Protection of Human Subjects of Biomedical and

Behavioral Research, 1978). Second, researchers must minimize harm and maximize

possible benefits for their research participants or exercise beneficence (Anabo et al.,

2019; Lantos, 2020). Finally, to achieve the principle of justice, the researcher must

ensure fairness and equality in disseminating the research benefits (Anabo et al., 2019;

Lantos, 2020).

As a 36-year veteran in retail sales management, I am highly familiar with the

business phenomena, and the financial ramifications employee turnover intentions and

turnover have on automotive retail sales at the dealership level. I depended on the

consistency and reliability of the selected instruments to ensure unbiased, valid, and

reliable inferences (see Gardiner et al., 2020). As a current employer in the automotive

industry, my familiarity with this industry afforded me access to the study’s participants

and population. The participants knew that their input was voluntary and confidential in

this study. I solicited participants in Tennessee, Kentucky, and Alabama via the online

survey service, Survey Monkey, to ensure confidentiality, quality, fairness, and legality.

Participants

The participants in this study were retail salespersons who sell new and preowned

vehicles at franchised automotive dealerships with at least 1 year of selling experience in

an automotive retail dealership environment. The salespeople were not fixed operations

sales personnel, such as parts, service, and finance sales. The participants were all 21

years or older and included all gender specifications.

I gained access to participants by contacting them via social media outlets, such as

Facebook, LinkedIn, and Instagram; automotive sales-specific online forums; and focus

groups. Salespeople were asked to participate in the research and informed that all their

responses were anonymous. I developed a respectful relationship by identifying my

automotive retail sales experience. Developing a participant-researcher relationship is

fundamental to research success (Murphy et al., 2016; Yin, 2018). Saunders et al. (2016)

stated that gaining participant access by identifying similar experiences in common

industries is the most successful technique among management and organizational

researchers. Once an individual agreed to participate, they were provided a hyperlink to

an on-screen consent statement connecting them to the survey. In the on-screen consent

form, I explained the purpose of the study in detail and the methods used to maintain

confidentiality, and asked that the participants agree to privacy and confidentiality

disclosures before they entered the survey.

Research Method and Design

Research Method

In this study, I tested hypotheses and implemented numerical calculations to

examine what relationship may or may not exist between salary/pay, the opportunity for

advancement, and employer turnover intentions among retail salespeople in the

automotive sales industry. Yin (2018) posited that the research question would determine

the appropriate research method to use in a study, either qualitative, quantitative, or

mixed methods. I sought to examine what relationship the independent variables may

share with the dependent variable, not why or how, which are research attributes

associated with qualitative research methods (see Lau, 2017; Yin, 2018). Therefore, using

this information from the literature, I decided that the quantitative method was most

appropriate for answering the research question (see Sligo et al., 2018).

Researchers who chose to examine the phenomena using mathematical

calculations use the quantitative method to collect data and to test hypotheses to

numerically express the statistical generalizability, causality, or scale of effects between

dependent and independent variables (O’Leary, 2017; Sligo et al., 2018). Quantitative

research requires the researcher to gather data from a population through surveys,

perform statistical tests, and conduct analyses to examine the relationship between

variables (Edmonds & Kennedy, 2017; Saunders et al., 2016; Yin, 2018). I did not

complete the research using natural setting observations to explore the subjective,

perceptive, or philosophical aspect of the business phenomena, which are characteristics

associated with qualitative and mixed-methods research approaches (see Edmonds &

Kennedy, 2017, Ellis & Levy, 2009; Murshed & Zhang, 2016: Saunders et al., 2016; Yin,

2018). The mixed-methods approach combines attributes from both quantitative and

qualitative methods. The researcher uses mixed methods when they cannot collect

additional insight from quantitative or qualitative data or take a holistic view of the

research problem (Saunders et al., 2016; Sligo et al., 2018; Strijker et al., 2020). Since the

explorative and philosophical factors were not present in this study, the mixed methods

approach was not conducive to answering the research question.

Research Design

In conjunction with population appropriateness, the researcher’s chosen research

methodology determines the research design (Saunders et al., 2016; Yin, 2018). There are

four types of quantitative research designs: (a) descriptive, (b) experimental, (c) quasi-

experimental, and (d) correlational (Saunders et al., 2016). Researchers use a descriptive

research design to explore or describe the phenomenon without statistical inference

(Siedlecki, 2020). Researchers implement experimental techniques in controlled

environments to explore the causal relationship of the variables (Armstrong & Kepler,

2018; Edmonds & Kennedy, 2017; Saunders et al., 2016; Zellmer-Bruhn et al., 2016).

Quasi-experimental designs are observational and implemented by researchers who want

to enhance causal effect evidence and lack solid internal validity (Maciejewski, 2020).

Therefore, these designs were not appropriate for this study.

Correlational research design is the most common design choice for researchers to

ascertain the degree of noncausal strength or relationship between two or more variables

(Curtis et al., 2016). The researcher uses the correlational design to analyze patterns in

the data and reduce the variables’ manipulation (Lau, 2017; Saunders et al., 2016).

Furthermore, researchers implement the correlational research design to ascertain the

degree or level of noncausal strength or relationship between the variables in the same

population, not the multiple populations generally associated with experimental designs

(Curtis et al., 2016; Maciejewski, 2020; Seeram, 2019). Using the correlational design in

this study allowed me to clarify whether the independent variables of salary/pay and

opportunity for advancement were significantly correlated with the dependent variable of

employee turnover intention. I used this data to determine the relationship’s statistically

negative or positive linear correlation and statistically predict a result using established

hypotheses testing (see Curtis et al., 2016; Lau, 2017). Therefore, the correlational

research design was appropriate for this study since I was not observing, describing,

exploring, or drawing causal inferences.

Population and Sampling

Population

The population for this study included retail salespersons in the automotive retail

sales industry who worked for franchised new and used car dealerships in Tennessee,

Kentucky, and Alabama. The participants were drawn from all-gender specifications and

were at least 21 years of age, with 1 year or more of retail selling experience in the

automobile dealership environment. The participants were full-time employees who sold

vehicles only. They did not consist of other sales-related positions within the dealership

structure, such as parts salespeople, service salespeople, or financial products

salespeople.

Sampling Method

Researchers employ sampling when surveying an entire population is not feasible,

financially or timely (Saunders et al., 2016). Probabilistic or representative sampling and

nonprobabilistic sampling are the two research sampling methods researchers use to

achieve the most appropriate data set to correctly answer their research question

(Saunders et al., 2016). Probability sampling is commonly used when the researcher

wants to reduce biases and randomly sample their targeted population (Pace, 2021;

Saunders et al., 2016). Nonprobability sampling allows the researcher to select

participants from the targeted population, making the results more generalizable to the

overall population (Saunders et al., 2016). Scholarly researchers can implement

purposive sampling when they seek to select the most appropriate sample for their

research topic (Campbell et al., 2020). The strengths of this sampling technique are the

cost-effectiveness, ease of data collection, and appropriateness for online surveys

(Bullard, 2022, Saunders et al., 2016). However, probability sampling presents

weaknesses, such as redundancy, being time-consuming, and the possibility of only

choosing specific classes within the sample (Bullard, 2022; Saunders et al., 2016).

Therefore, I implemented a nonprobability purposive sampling technique for this study

due to the time- and cost-saving attributes and the voluntary nature of participation.

Researchers conduct nonprobability purposive sampling when seeking cost-

effectiveness and limited resources or when they are time bound (Campbell et al., 2020;

Guest & Namey, 2015; Saunders et al., 2016). Nonprobability sampling requires that the

participants meet a predefined criterion for study participation versus the random

sampling process associated with probability sampling (El-Masri, 2017; Saunders et al.,

2016). Researchers use purposive and convenience sampling techniques to target

accessible and geographically situated populations to ease availability (Etikan et al.,

2016). The strengths associated with this sampling technique are that it is (a)

advantageous to online surveying, (b) inexpensive, (c) provides ease of gaining access to

respondents, and (d) its time effectiveness (Rahi, 2017). I implemented a nonprobabilistic

purposive sampling technique to assist with choosing only retail salespersons who

satisfied the study’s inclusion criteria.

Sample size

Applying the appropriate sample size allows the researcher to align research

designs, interpret the issues, and determine accurate power levels (Fugard & Potts, 2015;

Serdar et al., 2021). The sample size notably affects the study design and hypotheses

while ensuring the accuracy of statistical results and controlling bias (Kaliyadan &

Kulkarni, 2019; Schoemann et al., 2017; Serdar et al., 2021). Statistically, incorrect

sample sizes can lead to inaccurate or inadequate results, increasing organizational costs

associated with ethical considerations, time, and research and development (Serdar et al.,

2021). An appropriate sample size controls the probabilities of Type I and Type II errors.

Researchers have suggested that controlling the sample size helps reduce Type II errors;

however, this method can increase costs and delay research activities (Serdar, 2021).

Therefore, study efficiency relies heavily on adequate sample size.

Researchers utilize various tools and methods to calculate the appropriate sample

size for their studies. For correlation and regression analyses, Faul et al. (2009) posited

that G*Power 3.1 is the power analysis program primarily implemented to test social,

behavioral, and biomedical research sciences. Therefore, I employed this power analysis

program to determine the appropriate sample size for this study.

G*Power Analysis

I conducted an a priori analysis using G*Power 3.1.9.2 with an effect size of f

=.15, a = 0.05, and a power level of 0.80 to determine the appropriate sample size for this

study. Cohen (1992) suggested that these values represented effect size, alpha level, and

power level endorsing a balance of Type I and Type II errors (Hickey et al., 2018). This

study’s G*power a priori analysis results indicated that a minimum sample size of 68

participants achieved a power of .80. To reach a power of 0.99, a maximum sample size

of 146 participants was necessary. Therefore, the study’s power range is .80 to .99, a =

0.05, with a participant sample size between 68 and 146 participants. Figure 7 reflects the

G* Power priori analysis results for the appropriate sample size for this study.

Figure 7

G* Power Priori Analysis for Sample Size

Ethical Research

As the researcher, I was responsible for applying research strategies that satisfied

the requirements posited by The National Commission for the Protection of Human

Subjects of Biomedical and Behavioral Research in the Belmont Report and Walden

University’s Institutional Review Board (IRB). To satisfy the three basic principles of the

Belmont Report, (a) respect for persons, (b) beneficence (not harm), and (c) justice, I

applied the mandated policies and provided study participants with the tools necessary to

protect their confidentiality and anonymity (Anabo et al., 2019; Lantos, 2020; National

Commission for the Protection of Human Subjects of Biomedical and Behavioral

0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95

100

120

140

Total sample size

= 0.15

Effect size f²

F tests - Linear multiple regression: Fixed model. R² deviation from zero

Number of predictors = 2. α err prob = 0.05. Effect size f² = 0.15

Power (1-β err prob)

Research, 1979). I used instruments that ensured ethical compliance for all subjects to

disclose the benefits and risks of the research and to ensure that all moral and social

requirements surrounding fairness were met.

Ethical research practices guided by IRB protocol require all student researchers

to obtain approval before collecting data from study participants (Ritchie, 2021). Ritchie

(2021) postulated that research students who receive intensive instruction and guidance in

IRB protocol demonstrated a healthy appreciation for ethical research principles and

increased their knowledge of ethical practices. I received Walden University's IRB

approval to conduct this study, and the IRB approval number is 05-24-22-0672659. I sent

potential participants an email invitation asking for volunteers. The participation

invitation provided the potential participant with the purpose of the research and the

processes deployed to secure and maintain confidentiality while protecting their privacy

and anonymity. The participants elected to go further in the process by selecting the

affirmative response in the online invitation. Once the participant chose to proceed with

the survey, they selected the provided hyperlink that redirected them to a new screen that

contained the online informed consent form and the survey questions. Walden University

supplied an informed consent template that met the university's IRB requirements. That

was the form I used for this study, with some modifications specific to the study's

constructs.

Researchers implement the informed consent form to assure participants of their

right to choose what will happen with their responses and the information they provide

(Gesualdo et al., 2021). The consent process encourages voluntariness and informs the

participant of the potential benefits or risks associated with the research study, which

further promotes the participant's ability to choose freely and voluntarily to participate

(Gesualdo et al., 2021; National Commission for the Protection of Human Subjects of

Biomedical and Behavioral Research, 1979). All participants were anonymous since all

identifying information other than gender and age was not collected or surveyed by the

online survey service during data collection. Participants could terminate the study

prematurely at any time during the survey process. Participants were not offered any

incentives for participation or a complete survey.

Data Collection Instruments

The appropriate collection of data is one of the many responsibilities of the

researcher. Therefore, the researcher must gather and analyze the data using the

instruments to provide the most reliable and valid findings that answer the overarching

research question (Saunders et al., 2016). I designed a demographic survey that identified

the appropriate participant pool for this study. I implemented seminal scientific

instruments that other scholars and researchers posited as the most suitable and successful

collection instruments for evaluating this study's independent and dependent variables. I

obtained permission to use the selected tools from all parties that required this process.

Demographic Survey

The demographic survey is a self-constructed survey that consisted of five list

questions that will facilitate inquiry about (a) gender, (b) age, (c) dealership tenure, (d)

department of work, and (e) management status. Researchers use list questions to ensure

the respondent meets the desired participant profile and addresses all possible responses

(Saunders et al., 2016). I used the demographic survey for the descriptive statistical

analysis to quantify the percentages associated with the demographic composition of the

participants.

Minnesota Satisfaction Questionnaire (Short form)

The MSQ-SF is a scientifically proven instrument developed by Weiss, Dawis,

England, and Lofquist in 1967 to measure intrinsic and extrinsic constructs associated

with the theory of work adjustment and employee job satisfaction (Brief et al., 1988;

Erdoğan et al., 2020; Inayat & Jahanzeb Khan, 2021; Weiss et al., 2010). The MSQ-SF is

a condensed version of the original 100 questions long form of the MSQ. Senter et al.

(2010) used the MSQ-SF to measure how employees feel about their current job, whether

they exhibit satisfactoriness or gratification from the external factors of the job,

satisfaction (internal) from the inherent aspects of the job, or a combination of both,

identified as general or overall job satisfaction.

There are three versions of the MSQ, two extended versions and the 20-question

short version. The version implemented in this study is the short-form version, consisting

of 20 questions, 14 questions that measure intrinsic items, and six questions that measure

extrinsic items. The responses were 5-point Likert scale reactions ranging from 1= (very

satisfied) to 5 = (very dissatisfied); (Brief et al., 1988; Lakatamitou et al., 2020; Weiss et

al., 1967; Weiss et al., 2010). The participants indicated to what degree they agreed or

disagreed with the statements postulated in the MSQ-SF, with individual scores

representing their extrinsic, intrinsic, or general satisfaction (Senter et al., 2010; Weiss et

al., 1967). Researchers use the summation of all item scores to determine the total test

score, with the highest score indicating a high level of job satisfaction (Erdoğan et al.,

2020; Lakatamitou et al., 2020). I utilized this same summation process to statistically

analyze the collected responses for these constructs in this study.

Researchers used Cronbach's alpha statistic to measure response and internal

consistency (Saunders et al., 2016). Values of 0.70 or higher indicate that the questions in

the scale measure the same construct (Saunders et al., 2016). The Cronbach alpha value

of the MSQ scale reported by Weiss et al. (1967) is 0.77 (Erdoğan, 2020). Senter et al.

(2010) demonstrated the instrument's internal consistency with Hoyt reliability

coefficients of 0.86 for the intrinsic scale, 0.80 for the extrinsic scale, and 0.90 for the

general overall satisfaction scale. Baykara and Orhan (2020) postulated on the job

satisfaction levels of physical education teachers in Turkey that the reliability of the

MSQ-SF proved suitable with a Cronbach alpha of 0.93. The high internal and external

validity properties associated with the MSQ-SF, along with its ease of use and seminal

track record, are why I chose this data collection instrument for this study.

PSQ

In this study, I used Heneman and Schwab's (1985) PSQ as the data collection

instrument for the independent variable, pay/salary. Heneman and Schwab designed the

multidimensional PSQ to measure five hypothesized aspects of pay satisfaction, (a) the

level of pay, (b) benefits, (c) raises, (d) pay structure, and (e) administration (Athamneh,

2020). The PSQ consisted of 18 questions that participants used to designate their

satisfaction with their respective compensation (Heneman & Schwab, 1985; Judge, 1993;

Lawal et al., 2019). The participant responses were collected via a 5-point Likert scale

that ranges from 1= very dissatisfied to 5 = very satisfied.

Heneman and Schwab's (1985) determination of the reliability of the PSQ is

shown in coefficient alpha reliability estimates or Cronbach alpha of four measured

dimensions with a range of 0.80-0.90. Judge (1993) investigated the validity of the

dimensions of the PSQ and concluded that the overall scale was a reliable measure of pay

satisfaction with a coefficient alpha of 0.89. Lam (1998) implemented the PSQ to

measure pay satisfaction among 171 Chinese workers in Hong Kong. The researcher

found the Pearson correlation between the test and retest was 0.78, p =.01 for all 18

questions. Lam further estimated that the instrument's internal consistency was reliable,

with a Cronbach alpha of .77 and .80 for the factors measured. In recent research, Yao et

al. (2018) applied the PSQ to combine theories about the impact and interaction of

reported pay and pay discrepancy. Yao et al. added two more points to their measurement

scale and determined the reliability of the PSQ for their sample to be .94. Lawal et al.

(2019) researched counterproductive work behaviors, pay, age, and intent to leave among

university support workers. Lawal et al. used the PSQ to measure the pay satisfaction

variable using the 5-point rating scale designed by Henneman and Schwabb and reported

a coefficient alpha reliability estimate of 0.89 with a 0.97 reliability coefficient of

Cronbach's alpha in their study. The consistent high-reliability scores and the

scientifically proven validity of this data collection instrument by seminal and recent

research are why I chose this instrument to measure the independent variable, pay/salary,

for this study.

Turnover Intention Scale

The data collection tool I employed for the dependent variable, employee turnover

intention, will be Cohen's (1999) turnover intention scale. Cohen hypothesized that

various forms of employee commitment generated diverse work outcomes in his study.

Cohen deployed Mobley et al.'s (1978) intent to leave scale that measured three items

based on turnover intentions using a 5- point Likert scale that measures the following

items, leaving the organization, searching for an alternative organization, or immediately

leaving the organization. Cohen expounded on Mobley et al. survey using the same

measurement items. However, Cohen substituted the term organization with job and

occupation. Cohen's scale ranges from 1(strongly agree) to 5 (strongly disagree), with a

higher score indicating weaker turnover intentions.

Researchers find Mobley et al.'s (1978) turnover intention scale reliable and valid

across various industries and phenomena. Choi and Chiu (2017) found Mobley et al.'s

statistically accurate with dependable measures proving highly significant (p <.01) and

composite reliability and Cronbach's alpha at C.R. = .933 and a = .892, respectively.

Olawale and Olanrewaju's (2016) investigation of staff turnover intentions among Lagos

State University personnel revealed a Cronbach alpha of 0.83, further demonstrating the

reliability of this data collection tool. Cohen (1999) reported his resultant Cronbach's

alpha as 0.94 for intentions to leave the organization, 0.89 for intentions to leave the job,

and 0.92 for intentions to leave the occupation providing me with additional evidence,

based on the literature, for selecting Cohen turnover intention scale instrument as the

appropriate one to collect the data about employee turnover intention. I will include

copies of the measuring instruments along with permissions to use these instruments in

Appendices B, C, and D of this study.

Table 2

Study Variables and Measurement Instruments with Permission to Use Status

Variables

Measurement

instrument

Designers

Permission to use

Location in

study

contents

Independent

variable:

opportunity for

advancement

Minnesota

satisfaction

questionnaire

short form MSQ-SF

Weiss, Dawis,

England, and

Lofquist, (1967)

Permission is no

longer needed if used

for academic

research.

Appendix

Independent

variable:

salary/pay

Pay satisfaction

questionnaire (PSQ)

Heneman and

Schwab (1985)

Permission granted

via e-mail dated.

Appendix

Dependent

variable:

employee turnover

intention

Turnover intention

scale

Cohen (1999)

Test contents can be

reproduced and used

for non-commercial

research and

educational purposes

without written

permission.

Appendix

Data Collection Technique

The research question for this study: Within small to medium franchise new car

dealerships, what is the relationship between intrinsic job satisfier, the opportunity for

advancement, extrinsic job dissatisfier, salary/pay, and employee turnover intention?

After receiving permission from Walden University’s Institutional Review Board (IRB), I

began collecting data. The IRB number for this study that identifies the board’s approval

for data collection is 05-24-22-0672659. I used an online survey data collection technique

for this study to collect reliable and valid data for the quantitative examination of this

question.

Researchers can implement varying methods of data collection. Those methods

can include (a) live operators, (b) automated systems, such as interactive voice

recognition, (c) online, (d) direct mail, and (e) face-to-face (Kimball, 2019). Online

survey and questionnaire methods allow the researcher to manage costs and provide the

researcher with the affordability to access populations and data samples that may be

inaccessible under varying circumstances (Gomez et al., 2017; Kimball, 2019).

Researchers benefit from online surveys due to design flexibility, faster response rates,

lower error rate, and the ease of randomizing questions (Gomez et al., 2017). I collected

research data utilizing an online survey form designed and distributed using the online

survey platform Survey Monkey.

Survey Monkey enables the researcher to customize the design and structure of

their questionnaire, allowing for various responses that logically guide the survey

participant through the questionnaire (Waclawski, 2012). Survey Monkey is a proven

survey tool applied by researchers across different industries and research genres

(Kimball, 2019). The Survey Monkey platform allows the researcher to distribute surveys

via social platforms such as Facebook and LinkedIn. Researchers can track survey

completion progress using the progress dashboard supplied by Survey Monkey. Survey

Monkey stores data until the researcher terminates or deletes the account or data (Halim

et al., 2018; Waclawski, 2012). Users can also import collected data into a Statistical

Package for Social Sciences (SPSS) file and customize the report design to improve the

data's visualization (Halim et al., 2018). Where internet access is limited, Survey Monkey

allows for a postal questionnaire for participants (Waclawski, 2012), and their responses

are added to those collected via the online version of the questionnaire.

Survey length plays a significant role in completing the research study's online

survey. Brosnan et al. (2021) postulated 10 critical drivers linked to online survey

participation and completion. Brosnan et al. concluded that knowledge or interest in the

topic was an influential driver of survey completion. Other drivers, such as incentive

payments, ease of completion, speed of completion, and benefits to others, were

significantly instrumental in successful online survey completion and participation, with

incentive payments being the most significant contributing factor influencing survey

participation and completion (Brosnan et al., 2021). However, I did not offer survey

participants any incentive for survey participation and completion. The survey flowed

from one subject to the other and was designed to be executed in less than 6 minutes.

Researchers have experienced challenges with online surveys. Sample bias and

low response rate are inherent attributes that indicate the instability of online

communities and virtual groups (Buchanan & Hvizdak, 2009; Wright, 2005). I contacted

potential participants via e-mail and social media to explain the study's objective,

confidentiality and anonymity measures, and a straightforward process for completing the

survey thoroughly and promptly. The participants received an online electronic consent

form after selecting the hyperlink embedded in the online survey invitation sent via

Facebook, Instagram, and LinkedIn. When the participants finalized their survey, they

received a thank-you message for their participation and contribution to this valuable

research. All data collected were transferred to IBM SPSS data analysis software for

quantification. The raw data frequency tables, multiple regression results, descriptive,

demographic statistics, and survey questions are provided in the appendix section of this

study.

Data Analysis

Data analysis is the process researchers use to organize and interpret data or

information collected via the data collection tools executed in their study. For this study,

the research question: Within small to medium franchise new car dealerships, what is the

relationship between intrinsic job satisfier, the opportunity for advancement, extrinsic job

dissatisfier, salary/pay, and employee turnover intention?

To address this research question, I hypothesize the following:

: There is no statistically significant relationship between the intrinsic job

satisfier of the opportunity for advancement, the extrinsic job dissatisfier of

salary/pay, and employee turnover intentions.

: There is a statistically significant relationship between the intrinsic job

satisfier of the opportunity for advancement, the extrinsic job dissatisfier of

salary/pay, and employee turnover intentions.

I quantitatively examined the possible linear combinations using the statistical analysis

software IBM SPSS to perform multiple linear regression calculations to analyze the

collected data.

Researchers use multiple linear regression analysis to estimate the correlation or

relationship between multiple variables (Abdullah & Leong, 2018; Green & Salkind,

2017; Oguntunde et al., 2018). Multiple linear regression analysis is instrumental in

examining relationship strengths among large populations (Brooks & Barcikowski,

2012). Green and Salkind (2017) posited that the multiple correlations (R) are a strength-

of-relationship index indicative of the degree of the predicted scores and their correlation

with the criterion or Y variable. R values range between 0 and 1, with 0 indicating no

linear relationship between the predictor variables (independent) and the criterion

variable (dependent). In contrast, a value of 1 indicates a perfect prediction of the

criterion variable, and a value between 0-1 indicates a less-than-perfect linear

relationship.

Assumptions

The assumptions associated with multiple linear regression are based on whether

the model is fixed or random (Green & Salkind, 2017). In a fixed-effects model, which is

generally appropriate for experimental studies, the assumption is that the dependent

variable is usually distributed within the population at each level of combinations with

the independent variable (Green & Salkind, 2017). In nonexperimental studies, the

assumption is based on a random-effects model. The variables are multivariate and

normally distributed, which indicates that every variable is normally distributed, ignoring

the other variables. The variables are normally distributed within every combination,

signifying a statistically linear relationship between the variables (Green & Salkind,

2017). Green and Salkind (2017) further posited that multiple linear regression analysis

required additional crucial assumptions to lessen the probability of Type I and Type II

errors. Green and Salkind, in conjunction with other scholars, identified those

assumptions as (a) multicollinearity or the existence of a correlation between two or more

independent variables, which is extant when the correlation coefficient ranges between -1

to +1, with -1 reflecting the stronger negative relationship between values, 0 equaling no

relationship, and +1 equaling a robust positive relationship, as measured by Pearson's

product-moment correlation coefficient; (b) the normality of error, which indicates that

the data will follow a normally distributed bell-shaped; (c) homoscedasticity, which

assumes that the variance on the regression line is similar for all predictor variables and

the residual values are equal; (d) linearity, which assumes there is a straight line or linear

relationship between the predictor and criterion variables. Desalegn et al. (2020)

postulated that linear regression analysis requires the researcher to test for (a) normality,

(b) multicollinearity, and (c) homoscedasticity. Uyanik and Güler (2013) stated that

additional assumptions related to multivariate linear regression analysis are (a) freedom

from extreme values (outliers), (b) independence of errors, and (c) linearity. After I

exported all verified data into the IBM SPSS software, I transformed the data to test for

all assumptions. I have included the various scatter plots, histograms, regression plots,

and descriptive tables in the appendix. Khosravi et al. (2021), Lai (2021), and Williams

and Bornmann (2016) discussed bootstrapping as a method researchers can use to

identify unbiased point estimators within the population parameters based on the sample

statistics and renders an impartial estimation of resampled standard deviation and mean

when data normality assumptions and validity are insufficient. I further addressed

multiple linear regression assumption violations by employing bootstrapping techniques.

Data Cleaning and Missing Information

One of the disadvantages of gathering data via online surveys is the researcher's

inability to observe participant actions and interpretations of the questions in the survey

(Evans & Mathur, 2018; Nayak & Narayan, 2019). The prerequisite informed consent

form addresses the need for more understanding and change in participant status. I

ensured the participant met the criteria established for the population by setting

questionnaire parameters that ended the survey immediately if the participant did not

satisfy the descriptive boundaries established in the demographic study. The participant

was offered a selection to voluntarily opt out of the questionnaire if they did not consent

or agree to the survey terms. However, inaccurate and misinformation are unavoidable.

Therefore, I identified and extracted any incomplete surveys to eliminate erroneous data

to improve the quality of the information retained in the dataset (Leeman et al., 2016). To

ensure statistical data are clean, in conjunction with the research findings of Cai and Zhu

(2015), I removed participant results that created extremely skewed results or outliers to

safeguard research findings from data discrepancy and confirm data validity and

reliability.

Inferential Statistics and Interpretation

When testing hypotheses, researchers make inferences about how the statistics

apply to the population. A probability value (p-value) and effect sizes (f) are the

customary inferential statistics researchers associate with hypothesis testing and multiple

regression analysis. The p value reflects the research design's strengths, the measures'

reliability, and the sample size's quality (Kraemer et al., 2019). The p value indicates the

test's significance or the probability of occurrence of the stated event. A p value greater

than 0.05 indicates that the result is not statistically significant, and the null hypothesis is

not rejected. A p value less than 0.05 indicates a statistically significant effect in which

the researchers reject the null hypothesis in favor of the alternative hypothesis. The result

is highly significant if the p value is less than 0.01. The p value ranges from 0 to 1, with 0

representing a higher probability of rejecting the null hypothesis and 1 representing a low

probability of rejecting the null hypothesis (Reito, 2020). Therefore, since probability

values are synonymous with hypothesis testing and multiple regression analysis, I

employed this process to reject or accept the null hypothesis with a significance level of p

< .05.

Researchers use effect size statistics to infer the standardized mean difference

(Cohen’s d) and the coefficient of determination (r

) to quantify the magnitude of

research findings regarding the research question (Lai, 2021; Liu & Yuan, 2021). Effect

size (f) in statistic reference measures the impact of the research treatment /independent

variable on the dependent or criterion variable and the extent the sample mean moves

away from the population mean (Abbott, 2017). However, in multiple linear regression

models, effect size (f) measures the contribution of a set of independent variables or

predictors on the explained variance of the dependent variable (Abbott, 2017). In

quantitative studies, the appropriate effect size is critical to interpreting and reporting

inferential results (Green & Salkind, 2017; Liu & Yuan, 2021). Confidence interval (CI)

is an important statistic because it plays a vital role in sample size planning or power

analysis (Lai, 2021). The CI serves as the population probability factor that indicates the

range of values that will most likely contain the population values within a certain level

of certainty (Abbott, 2017; Green & Salkind, 2017). Current and seminal researchers

recommend a safe power analysis of a lower CI of 80% to 95% of the effect size (Abbott,

2017; Gorard, 2019). Therefore, I employed a CI of 95% as the population probability to

draw inferences for my sample size for this study.

Study Validity

Internal Validity

Quantitative researchers seek study validity and reliability to ensure rigor or

enhanced study quality (Reio, 2021). Internal validity designates whether the study's

design appropriately assesses the hypotheses and research question (Andrade, 2018; Reio,

2021). Internal validity further establishes whether research measures are relevant,

homogenous, stable, and reliable (Mohajan, 2017; Reio, 2021). Internal validity is

imperative in experimental and quasi-experimental research designs. Researchers

implement experimental and quasi-experimental research designs to examine or test

cause and effect. I did not use experimental and quasi-experimental research strategies in

this study, and internal validity is unnecessary since I did not investigate causality.

Therefore, establishing internal validity was not a factor in this correlational design study.

However, I used statistical conclusions to interpret and validate the inferences.

Statistical Conclusion Validity

Statistical conclusion validity (SCV) refers to the degree or magnitude of research

study data that provides adequate and accurate conclusions or findings regarding the

study’s research question (Fabrigar et al., 2020; García-Pérez, 2012; Levine, 2011). SCV

is critical to determining whether the data analysis inferences reveal a significant or

insignificant relationship between the independent and dependent variables (Fabrigar et

al., 2020; García-Pérez, 2012; Levine, 2011). In addition to providing valid inferences,

researchers achieve SCV by appropriately applying the statistical tests and results to the

research to reduce Type I and Type II misinterpretations and invalid outcomes (García-

Pérez, 2012). Therefore, any condition contributing to inflated or skewed Type I or Type

II error rates threatens SCV.

Validity Threats

Threats to SCV include insufficient sample size, instrument reliability, the use of

repeated testing, outliers, and data assumptions (García-Pérez, 2012; Guetterman, 2019).

Using a poor research design or sample size can lead to Type II errors, leading the

researcher to accept the alternative hypothesis in error or inferring that a relationship is

nonexistent when a relationship exists (Fabrigar et al., 2020; García-Pérez, 2012).

Additionally, researchers threaten SCV by not selecting the appropriate significance level

(p-value). Selecting an unacceptable level of significance increases the researcher’s risk

of Type I errors or concluding that a relationship exists when it does not (Fabrigar et al.,

2020; Green & Salkind, 2017). I used the conventional alpha level of p =.05, in

conjunction with statistical software IBM SPSS and the appropriate data collection

instruments, to quantitatively test the data to reduce threats to SCV in this study.

Instrument Reliability

Researchers utilize various instrument reliability checks to validate the

dependability of their data collection instruments. The most applied test for instrument

internal consistency and reliability is Cronbach’s alpha (a) (Heale & Twycross, 2015;

Matkar, 2012; Olvera Astivia et al., 2020). Seminal and current researchers indicated that

a Cronbach’s alpha designation between 0 and 1 with a reliability coefficient score of

0.70 or higher indicates robust instrument reliability (Heale & Twycross, 2015). I

implemented testing procedures in IBM SPSS to calculate Cronbach alpha value and

instrument reliability for the data collection instruments I selected for this study. I

employed Weiss et al.’s (1968) MSQ-SF to measure intrinsic and extrinsic job

satisfaction. The MSQ-SF is a proven instrument for measuring job satisfaction

constructs with reliability coefficients of 0.77 to 0.93 (Baykara & Orhan, 2020; Weiss et

al., 1968). The PSQ (Heneman & Schwab, 1985) is the instrument I chose to measure the

independent variable, salary/pay. Heneman and Schwab (1985) reported the reliability

coefficient for salary/pay between 0.80 and 0.90. I used Cohen’s (1999) turnover

intention scale for the dependent variable, employee turnover intention. The Cronbach

alpha (a) results for this instrument range between 0.83 and 0.94, demonstrating the

instrument's consistency and reliability (Cohen, 1999; Olawale & Olanrewaju, 2016). I

discussed the reliability analysis results for this study’s instrumentation in the

presentation of findings portion of Section 3.

Data Assumptions

The statistical test executed in this quantitative study was a multiple linear

regression test of the study’s variables. The assumptions associated with this testing

method are (a) normality, (b) linearity, (c) homoscedasticity, (e) independence, (f)

multicollinearity, and (g) outliers (Green & Salkind, 2016). Violations of these

assumptions can lead to invalid p values and inaccurate data analyses and research

inferences (Flatt & Jacobs, 2019; Green & Salkind, 2016). I have included scatterplots,

histograms, and coefficient tables in the appendices and in the body of the findings to

demonstrate the absence of potential data assumption violations associated with outliers,

correlations, normal distribution, and independence.

Parametric assumption testing

Testing the normality assumption requires the researcher to examine distribution

(Orcan, 2020). With parametric assumption testing, the statistics depend on normal

population distribution, and the premise is equal to variance and normality (Anderson et

al., 2014; Orcan, 2020). In nonparametric assumption testing, the population distribution

is assumed to be not normally distributed, and inferences can be made without a

distribution designation (Anderson et al., 2014). Parametric testing assumptions require

quantitative data, whereas non-parametric testing or computations can be done with

categorical data. Because I am utilizing a quantitative research methodology for this

study and the study’s population is normally distributed, parametric assumption testing

was appropriate for testing the normality requirement of my research.

Sample Size

An appropriate sample size is a catalyst for study validity. Researchers need to

employ a sample size reflective of the study’s population and large enough to produce

generalizable statistical inferences free of bias and error due to lack of size (Kaliyadan &

Kulkarni, 2019; Schoemann et al., 2017; Serdar et al., 2020). Statisticians suggest a

sample size > 50 if the population distribution is highly skewed or there is a presence of

outliers (Anderson et al., 2014). An adequate sample size reduces mean standard

deviation errors and is vital to achieving confidence levels that alleviate the possibilities

of Type I and Type II errors (Anderson et al., 2014). In conjunction with the G* Power

priori analysis for sample size conducted for this study, an appropriate sample size

between 68-146 participants achieved confidence levels between 0.80-0.99, which

reduced erroneous interpretations of the study’s hypotheses.

External Validity

External validity addresses the generalization and replication of the research

findings (Andrade, 2018; Fabrigar et al., 2020). Researchers can ensure external validity

through proper sampling strategies, such as random or purposive sampling (Saunders et

al., 2016). Random sampling involves the researcher selecting samples from the

population until the appropriate sample size is reached. Random sampling requires an

accessible population and can be costly if not computerized since it does require a

substantial sample (Saunders et al., 2016). However, systematic random sampling

accommodates all sample sizes and is successful when sampling geographically

disseminated populations (Saunders et al., 2016). Nonrandom or nonprobability sampling

depends on the logical relationship shared by the study’s purpose and generalizations

related to the theory (Saunders et al., 2016). Nonprobabilistic sampling strategies are

suitable for quantitative studies and depend on the research questions (Saunders et al.,

2016). Validity in nonprobability sampling is solely based on the data collection and

analysis versus sample size, which drives random sampling validity.

Researchers must ensure that research samples represent their population and that

study results can be generalized to the population from which it is drawn (Andrade, 2018;

Saunders et al., 2016). The researcher demonstrates, through external validity, that the

functionality of the study’s operations appropriately represents the study’s constructs

when the same constructs are introduced to new participants, and the results replicate

(Fabrigar et al., 2020). Population and ecological validity are identifiable threats to

external validity (Devroe & Wauters, 2019). Ecological validity is associated with

experimental research since it tests real-life generalizations (Andrade, 2018). Since this

study is non-experimental, the environmental threat to external validity is eliminated

(Devroe & Wauters, 2019). Population validity is associated with the heterogeneous

nature of the study’s population. Researchers validate heterogeneity by including

participants from diverse backgrounds and workgroups (Ardito & Petruzzelli, 2017). The

participants in this study were selected using a nonprobabilistic purposive sampling

technique that will assist me with choosing only retail salespersons who satisfied the

study's criteria based on work experience, age, and gender from several dealerships in

different states.

Transition and Summary

Section 2's discussion included the restatement of the study’s purpose. This

section elaborated on and substantiated the researcher’s role and commitment to ethical

and unbiased research. I provided additional insight into participant eligibility strategies

for gaining and retaining participants. The nature of the study is revisited in this section,

along with an in-depth observation of the research design. The section concluded with a

detailed population description, appropriate sampling method, and size to secure accurate

analysis and inferences. The instruments and techniques implemented to collect and

analyze research data emphasize the need for tools that satisfy external and statistical

conclusion validity.

In Section 3, I familiarize the reader with the study’s purpose and presented the

findings after data collection and statistical tests were conducted. I discuss in Section 3

the application of the results to professional practice, the implications for social change,

the recommendations for action, and any further research opportunities.

Section 3: Application to Professional Practice and Implications for Change

Introduction

In this quantitative correlation study, I examined the relationship between

opportunity for advancement, salary/pay, and employee turnover intention. The predictor

or independent variables were an opportunity for advancement and salary/pay. The

criterion or dependent variable was employee turnover intention. The null hypothesis was

rejected; therefore, the analyses demonstrated a statistical relationship between

opportunity for advancement, salary/pay, and retail salesperson employee turnover

intention.

Presentation of the Findings

In the presentation of findings, I provide a relevant analysis of the testing of

assumptions and present descriptive and inferential statistics. I explain the theoretical

conversation concerning the conclusions drawn from the data and discuss the quantitative

statistical tests conducted. This segment is concluded with a theoretical summary that

includes a discussion of the study findings.

Test of Assumptions

Using SPSS statistical software, I analyzed the data for assumption violations

associated with multiple linear regression, including (a) sample size, (b) multicollinearity,

residuals (see Green & Salkind, 2017). I employed bootstrapping using 1,000 samples

with a confidence interval of 95% (see Green & Salkind, 2017) to evaluate and establish

the potential influence of assumption violations. Therefore, bootstrap 95% confidence

intervals are presented where appropriate. I used scatterplots, histograms, and coefficient

tables to illustrate the data associated with testing for assumption violations.

Sample Size

I conducted a priori analysis in G*Power 3.1.9.2. software with an effect size of f

=.15, a =0.05, and a power level of 0.80 to calculate the appropriate sample size for this

study. The values implemented for sample size determination were drawn from Cohen

(1992), who posited that these values supported a balance and lessened the probabilities

of Type I and Type II errors (see Hickey et al., 2018; Serdar, 2020). The G*Power priori

analysis showed that the minimum sample size for this study was 68 participants,

achieving a power of .80, and a maximum sample size of 146 participants to achieve a

power of 0.99. The sample size of 76 participants satisfied the minimum sample size

requirement for the study. Figure 6 reflects the G*Power priori analysis that determined

the appropriate sample size for the study.

Multicollinearity

Multicollinearity refers to the correlation among the predictor or independent

variables (Green & Salkind, 2017). Multicollinearity occurs when predictor or

independent variables in a regression model are correlated (Goodhue et al., 2018; Kim,

2019; Lindner et al., 2022). Kim (2020), in alignment with Goodhue et al. (2018), posited

that the variance inflation factor (VIF) is the most common identifier of multicollinearity

in regression models. The presence of multicollinearity is indicated by a VIF value more

significant than three and a tolerance value smaller than 0.10. As the VIF increases, the

degree of dependence between the independent or predictor values becomes stronger

(Kim, 2019). This study’s VIF is 1.060 for the predictor or independent variables. The

VIF value of 1.060 indicates no multicollinearity violations in the study. For further

confirmation of the absence of multicollinearity violations, the larger than 0.10 tolerance

statistic of 0.943 and small bivariate correlations provide sufficient evidence of the

absence of multicollinearity assumption violations. Tables 3 and 4 illustrate the

collinearity statistics and the bivariate correlation coefficient of the predictor or

independent variables.

Table 3

Collinearity Statistics for the Independent Variables

Variable

Tolerance

VIF

Advancement

.942

1.061

Salary/pay

.942

1.061

Note. N = 76.

Table 4

Correlation Coefficients Among Study Predictor Variables

Variable

Advancement

Salary/Pay

Advancement

-.241

Salary/pay

-.241

Note. N = 76.

Homoscedasticity, Normality, Linearity, Outliers, and Independence of Residuals

I conducted various analyses using scatterplots, residual plots, and statistical

computations to evaluate potential assumption violations associated with

homoscedasticity, normality, linearity, outliers, and independence of residuals. To

support the independence of residuals, the scatterplot of the standardized residuals shows

a randomly scattered plot that is absent of any patterns, demonstrating no autocorrelation.

The randomly scattered points on the scatterplot also indicate that the homoscedasticity

assumption was not violated, and no outliers fell to the left or right of -3 to 3 (see

Osbourne & Waters, 2002). To identify potential outliers, I analyzed Cook’s distance,

which was less than 1.0, indicating the absence of influential outliers. Figure 8 is an

illustration of the standardized residual scatterplots.

Figure 8

Scatterplot of Standardized Residuals

To support this evidence, I checked the Durbin-Watson statistic to measure any

dependent or autocorrelations among the residuals. The statistical range for this test is 0 –

4, with values between 1.5 and 2.5, indicating no dependency or autocorrelations among

the residuals (Green & Salkind, 2017; Osbourne & Waters, 2002; Saunders et al., 2016).

The Durbin-Watson statistic for this study was calculated at 1.906, indicating that no

dependency or autocorrelation exists among the residuals.

I evaluated the assumption of normality and linearity using the standardized

residual regression scatterplot and the standardized residual’s normal probability plot (P-

P). The standardized residual scatterplot (Figure 7) illustrates that the distance of

residuals is not markedly greater above or below the 0 lines. If this were the case, it

would be ascertained that the distribution is abnormal. If the line were curved, the curved

line would indicate a violation of the linearity assumption. The normal P-P (Figure 8) for

this study shows the points are distributed along a straight line from bottom left to top

right in a diagonal fashion, indicating that there are no violations of the normality or

linearity assumptions and that all assumptions have been met. Figure 8 shows the

regression standardized residuals' normal P-P.

Figure 9

Normal P-P of the Regression Standardized Residuals

Demographic Statistics

I received 89 surveys from participants. I discarded 4 surveys due to missing or

incomplete data and 9 because they were completed by individuals who identified

themselves as sales managers. Therefore, I had 76 completed surveys (N = 76) that were

sufficient for analysis. The demographic data revealed that 63.2% or 48 of the 76

participants identified as male, 36.8% or 28 as female, and 0% as nonbinary (see Table

5). Over 50% of the respondents were between the ages of 31 and 50, with 17.1% of all

respondents possessing 19 years or more tenure at their respective dealerships. However,

40.8% of the respondents worked at least 1 year but less than 3 years in the salesperson

role.

Descriptive Statistics

Researchers employ descriptive statistics to provide a macro and micro view of

their data. Descriptive statistics assist the researcher with identifying errors or anomalies

in the data and are designed to describe the details of the specific variable set. This

study’s descriptive statistics provide the mean and standard deviation for the dependent

and independent variables based on a population of 76 salespeople (N = 76) and

bootstrapping 1,000 samples at 95% CI.

The dependent variables considered in this study were an opportunity for

advancement and salary/pay. The mean advancement score was 2.84 (SD =1.24), and the

mean score for salary/pay was 3.27 (SD = 0.93). The mean score for the dependent

variable, turnover intentions, was 3.43 (SD =1.15). Table 6 comprises the descriptive

statistics for this study's variables.

Table 5

Means and Standard Deviations for Study Variables

Variable

Bootstrapped 95%

CI (M)

Advancement

2.84

1.24

[ 2.565, 3.131]

Salary/pay

3.27

0.93

[ 3.068, 3.477]

Turnover

intention

3.43

1.15

[ 3.166, 3.698]

Note. N = 76.

Inferential Results

I employed standard multiple linear regression, a =.05(two-tailed), to examine the

opportunity for advancement and salary/pay in predicting employee turnover intentions.

The predictor or independent variables were an opportunity for advancement and

salary/pay. The criterion or dependent variable was employee turnover intention. The null

hypothesis was that opportunity for advancement and salary/pay would not significantly

predict employee turnover intention. The alternative hypothesis was that opportunity for

advancement and salary/pay would significantly predict employee turnover intention. I

conducted initial analyses to check for assumption violations pertaining to sample size,

multicollinearity, outliers, homoscedasticity, normality, linearity, and independence of

residuals. These assumptions were tested using scatterplots, histograms, and other

statistical formulas. All assumptions were met, and no severe violations were notable.

The overall regression model proved significant and could predict turnover

intention with F (2, 73) = 25.897, p <.001, R

= .415. The R

value of .415 indicated that

41% of the variation in the dependent variable of turnover intention can be attributed to

the linear combination of the independent variables of the opportunity for advancement

and salary/pay. However, the B value, or unstandardized coefficient, indicates the degree

to which the predictor or independent variable affects the dependent or criterion variable

when one or the other predictor value or independent variable is held constant.

In the conclusive model, the independent variable of advancement (t = -1.873, p = .065)

indicated that there was not a significant relationship with turnover intention since the p -

value is greater than .05. The independent variable of salary/pay (t = 6.29, p <.001)

proved to have a significant relationship with the dependent variable of turnover intention

with a p value that was less than .05. The final predictive equation was:

turnover intention = 1.54 + -.160 (advancement) + .714 (salary/pay)

Opportunity for Advancement

The negative slope for advancement (-.160) as a predictor of turnover intentions

indicated a .160 decrease in turnover intentions for each unit of increase in advancement.

This statistic shows that turnover intention tends to decrease as advancement

opportunities increase. The squared semi partial coefficient that estimates how much

variance in turnover intention was uniquely predictable from advancement was -0.028,

indicating that -2.8% of the variance in turnover intention is uniquely accounted for by

advancement when salary/pay is controlled.

Salary/Pay

The positive slope for salary/pay as a predictor of turnover intention indicated a

.714 for each unit of increase in salary/pay, indicating turnover intention tends to increase

when salary/pay increases. The squared semi partial coefficient that estimated how much

variance in turnover intention was uniquely predictable from salary/pay was 0.316,

indicating that 31.6% of the variance in turnover intention was uniquely accounted for by

salary/pay when advancement is controlled. The regression summary is depicted in Table

Table 6

Regression Analysis Summary for Predictor or Independent Variables

Variable

SE B

B 95%

Bootstrap CI

Constant

1.549

.501

3.094

.003

[ .551, 2.548]

Advancement

-.160

.085

-.173

-1.873

.065

[-.330, .010]

Salary/pay

.714

.113

.580

6.29

<.001*

[ .488, .940]

Note. N = 76.

Analysis Summary

The purpose of this study was to examine the relationship between opportunity for

advancement, salary/pay, and turnover intention. I implemented the standard multiple

linear regression to analyze this relationship. The assumptions associated with standard

multiple linear regression were assessed with no serious violations noted. The model, in

its entirety, demonstrated a significant relationship between opportunity for advancement,

salary/pay, and turnover intention, F = F (2, 73) = 25.897, p <.001, R

= .415. Based on

these results, I conclude that salary/pay (p = <.001) shared a statistically significant

relationship with turnover intention as a unique standalone variable. However,

advancement (p = .065) did not independently share a statistically significant association

with the criterion variable of turnover intention since its p value was more significant

than a =.05. Therefore, it can be inferred that salary/pay (p = <.001) was a crucial

element in the relationship with turnover intention among retail salespeople in the

automotive sales industry. In contrast, the opportunity for advancement (p = .065) was

not statistically significant with turnover intention among retail salespeople in automotive

sales.

The inferential results led me to reject the null hypothesis that there is no

statistically significant relationship between the intrinsic job satisfier of the opportunity

for advancement, the extrinsic job dissatisfier of salary/pay, and employee turnover

intentions.

Theoretical Conversation on Findings

This study's results indicated a statistically significant relationship between the

independent (opportunity for advancement and salary/pay) and the dependent variable

employee turnover intention, F (2, 73) = 25.897, p <.001, R

= .415. This study's findings

are consistent with seminal and current literature on turnover intentions and job

satisfaction, or dissatisfaction attributes identified by Herzberg's two-factor theory of

motivation. Herzberg et al. (1959) theory of motivation served as the theoretical

framework for this study. Herzberg et al. posited that intrinsic factors such as the

opportunity for advancement increased the satisfaction level of employees, which in turn

positively motivated them. However, Herzberg et al. also conjectured that the absence of

extrinsic or hygienic motivators such as salary/pay leads to job dissatisfaction.

Recent research on turnover intention and motivating factors lends creditability to

Herzberg et al.'s (1959) statistical analysis of the effects of motivation. Herzberg et al.

posited that information was collected where situations in their study did not lead to

participants quitting their jobs but played a significant role in thoughts of leaving, but

with no action. As a result of positive work attitudes, employees changed their minds

about previous decisions to quit or decline other job offers. Lee et al. (2017) described

the lack of work satisfaction as a primary influence on the desire of employees to leave

their job, while Alam and Asim (2019) found that high job satisfaction reduced the level

of an employee wanting to leave their job. Al Jamil et al. (2022) posited that job

satisfaction significantly influenced employee turnover intention. These outcomes

coincide with this study's findings in that both intrinsic and extrinsic job satisfaction

motivation factors combined shared a positive statistical relationship with employee

turnover intentions.

This study's findings disclosed that the stand-alone relationships of the

independent variables signified that salary/pay (p = <.001) shared a statistically

significant association with turnover intention. This data corresponds to Herzberg et al.

(1959) examination of the salary factor. Herzberg et al. postulated that salary/pay was the

sixth highest frequency factor (0.15) to produce high points in job satisfaction. Herzberg

et al. further posited that salary/pay was more potent as a job dissatisfier than satisfier.

Higher-scoring sequences were associated most frequently with advancement (.32). This

postulation can be correlated to Aiyebelehin et al. (2020). These researchers inferred that

wage (salary/pay) was related to turnover intention and that low wages promoted

turnover. Chan and Ao (2019) postulated that satisfaction with pay was positively

correlated with job satisfaction (r =.57, p <.01), and in their model, pay satisfaction (B =

-.329, p <.01) was a significant contributor to turnover intention and that higher job

satisfaction (B = -.366, p < .01) decreased turnover intention. Prasad Kotni and Karumuri

(2018) concluded that workers in the retail sales sector were more satisfied with extrinsic

factors, such as salary/pay, than with intrinsic factors, such as advancement. Prasad Kotni

and Karumuri's outcome supports the findings of this study in that salary/pay (p = .001)

proved to have a statistically significant relationship with employee turnover intentions.

Therefore, the results of this study are substantiated by the data from the literature that

the extrinsic job dissatisfier salary/pay shares a statistically significant relationship with

employee turnover intention.

The second independent variable, opportunity for advancement (p = .069), did not

share a statistically significant relationship with turnover intention as a stand-alone

variable. Herzberg et al. (1959) identified advancement (.20) as the fifth highest

frequency factor for job satisfaction. Advancement was most influential in creating

belongingness in the organization when the advancement was unexpected, and this strong

bond generated a sense of contentment. The participant would not consider leaving when

presented with other job opportunities (Herzberg et al., 1959). Deri et al. (2021)

postulated, using binary logistic regression, that the higher the opportunity for promotion

(p < 0.00) with a probability factor of 0.37 that employees were 37 % least likely to leave

the job. Crafts et al. (2018) posited that the lack of opportunities for advancement (p =

<.001) was a significantly influential factor in turnover intentions. Andrews and

Mohammed (2020) stated that career advancement opportunities encouraged employee

performance and increased organizational commitment and job satisfaction. The

employee's heightened responsibility and job satisfaction decreased turnover intentions

(Andrews & Mohammed, 2020; Erasmus, 2020). Therefore, in alignment with Herzberg

et al. and other recent research, I concluded that opportunity for advancement (p = .068)

was not statistically significant with turnover intentions.

The overall results of this study indicated a statistically significant relationship

between the independent variables, the opportunity for advancement and salary/pay, and

the dependent variable, employee turnover intentions, F (2,73) = 25.897, p <.001, R

.415. The discovery of this relationship is congruent with the findings of the theoretical

framework of Herzberg's two-factor theory of motivation. Herzberg et al. (1959)

postulated that an increased level of job satisfiers, such as advancement, and a decreased

level of dissatisfaction associated with extrinsic factors, such as salary/pay boosted

employee job satisfaction which in turn lessened the intention to turnover. Employing the

data from this study and the literature, I concluded that the linear combination of

opportunity for advancement and salary/pay was statistically significant in relation to

employee turnover intentions among retail salespeople in the automotive sales industry.

Applications to Professional Practice

This study's results directly impact the automotive sales industry and the problem

of high retail salesperson turnover within this industry. Automotive dealership general

managers can use data from this study to understand the relationship between

advancement opportunities, salary/ pay initiatives, and the relationship these attributes

share with employee turnover intentions. General managers can assist human resource

partners with developing human resource initiatives such as collaborative hiring, training,

and advancement strategies that offset antecedents of turnover by encouraging enhanced

pay initiatives connected to retention outcomes. Dealership general managers who work

closely with human resource partners can assist with mitigating costs for succession

planning and assist with the design and formation of dealership policies and processes

that clearly define advancement opportunities and respective salary packages that reflect

pay/salary linked to increased responsibility associated with expanded job roles due to

advancement or promotions. Decreased intentions to turnover give the dealership a

competitive advantage over dealerships that need a solid program to address

advancement and salary/pay or other intrinsic and extrinsic factors associated with job

satisfaction.

Dealership general managers should pay particular attention to frontline

management and their execution of retention initiatives. General managers should

educate their direct reports on the significance of advancement opportunities and

salary/pay and how the propensity for retail salesperson turnover is affected by the

absence or dissatisfaction that sets in when these factors are unmet. Frontline managers

must know the direct cost to their department and the dealership. When frontline

managers and upper-level management navigate job satisfaction together, employee job

satisfaction increases, and employee turnover decreases. The heightened level of

satisfaction increases sales since the retail salesperson is the most trusted person in the

retail transaction, promoting customer retention and increasing revenue associated with

dealership reputation and community trust.

Implications for Social Change

Local dealers establish themselves as community stakeholders by creating job and

career advancement opportunities that, in turn, offer dealership employees good-paying

jobs while boosting community economies and contributing immense amounts of revenue

via state and local tax payments. With over 16,000 new car dealerships operating in the

United States, the social and economic impact created by the auto dealer is highly

influential, especially in smaller communities where one or two dealerships make up a

substantial portion of local employers. The BLS (2019, 2021) reported that automobile

dealers are among the highest-paying entities for retail sales employees, with the average

weekly earnings of dealership employees equaling $1,600 in 2021. Therefore, the

viability of locally owned dealerships is highly dependent upon their locally sourced

workforce and how dealership general managers engineer initiatives that promote pay

and career advancement and increase retention of their locally sourced retail sales force.

Dealership general managers that embrace the role of a community conscience

strategic partner can take the lead in promoting the community stakeholder culture.

Dealership general managers can utilize their lucrative role as community stakeholders to

promote community programs and local social enterprises (Park & Campbell, 2017).

Dealership owners and general managers can develop jobs and training opportunities that

encourage amplified employment and work opportunities for the local community

workforce. Dealership general managers can engage their stakeholder position to create

community opportunities that promote and demonstrate corporate social responsibility,

including charitable donations to programs that support employee and community well-

being.

Dealership general managers can use the findings of this study to enhance their

awareness concerning retail salesperson employee turnover retention and how to develop

hiring initiatives, training programs, pay plans, and succession plans that encourage high

levels of job satisfaction and lessen the intent to turnover among this dealership

demographic. Furthermore, dealership general managers can use the findings of this

study to validate the business need for supporting mentorship programs that offer

community-based talent from local high schools, technical colleges, 4-year colleges, and

vocational rehabilitation centers exclusive access to well-paying careers that provide

advancement opportunities designed to create high retention levels within retail sales

ranks. The development of dealership processes that are designed to decrease employee

turnover intention, lower community unemployment rates, generate steady wages, offer

advancement opportunities, and create a constant influx of tax revenue proves effective

and sustainable in successful dealership operations and induces feelings of partnership

and goodwill within the dealership and the community. The dealership's general manager

can implement these measures to secure top-level local talent, at this moment boosting

the dealership’s community stakeholder position and promoting the overall future success

of the dealership and the community it serves.

Recommendations for Action

The findings of this study indicated a significant statistical relationship between

opportunity for advancement, pay/salary, and employee turnover intention. These

findings benefit automotive dealerships and other retail organizations that experience a

high level of turnover among retail salespersons. This study concentrated primarily on

retail salespersons who sell automobiles. However, the endorsements for action can be

applied across various retail sales industries. The first recommendation would be that

dealership owners and general managers develop a strategic partnership with dealership

HRM to design a succession plan that outlines a clear path of advancement from sales to

sales management or other departments/dealership leadership positions. The

recommendation to dealership owners, general managers, and human resource partners

would be to develop comprehensive programs to link advancement opportunities and

pay/salary to incentives that promote employee retention. Combining the intrinsic and

extrinsic factors of job satisfaction reduces the intentions to turnover and decreases

overall employee turnover (Bhatt et al., 2022; Chan et al., 2016; Deri et al., 2021;

Herzberg, 1965; Ozsoy, 2019). The second recommendation would entail the

implementation of a standardized pay plan that reflects current industry trends that align

with economic and community needs following the COVID-19 pandemic (Morales,

2021), such as online and offsite sales. Therefore, in conjunction with this study's

findings, the construction, design, and execution of dealership processes that

communicate the road to success for every employee from the first hire date will increase

employee retention and diminish thoughts of leaving the dealership.

The intention is to share this study's findings with reputable automotive industry

groups and publications, such as the National Automotive Dealer Association and

Automotive News. I further intend to publish the study's results in the ProQuest database

and other peer-reviewed journals related to management, sales, and human resources. I

plan to present an abbreviated version of the study's findings at automotive-related

meetings, seminars, business events, and online forums to discuss current issues in the

automotive selling sector. As a Walden University Fulbright scholar, I intend to present

the study's social change impact and explore pre- and postpandemic effects on

automotive retail sales and the communities they serve.

Recommendations for Further Research

I recommend that future research into an opportunity for advancement, pay/salary,

and employee turnover intentions among automotive retail salespeople be expanded to

study this business phenomenon regarding gender and race. Only one specific attribute of

each extrinsic and intrinsic factor identified by Herzberg et al. (1959) was implemented

in this research. Therefore, I recommend adopting this study to include all intrinsic and

extrinsic elements to see if overall job satisfaction or dissatisfaction shares a statistically

significant relationship with employee turnover intentions among the same population.

This research study focused on populations in Tennessee, Kentucky, and

Alabama. I propose a nationwide survey with a larger sample size of retail automotive

salespersons. Another recommendation would be to change the population to frontline

retail sales managers in the same industry to determine if being in frontline management

would reflect a different relationship among the variables.

The correlational quantitative research method was implemented in this study to

examine the relationship between the variables to determine whether to accept or reject

the hypothesis. The following recommendation would be to conduct a qualitative or

mixed methods study to determine if personal interviews would allow the participants to

freely express opinions that could not be expressed using Likert-type scale responses

about their turnover intentions.

Reflections

Initially, I was highly interested in alleviating the high turnover rates experienced

in the industry where I have worked for over 30 years. As an employee who has worked

in all facets of dealership operations, I came to the table with a preconceived bias

regarding the role varying pay structures and unclear or meager advancement

opportunities played in the automotive retail salesperson's intentions to turnover. I have

worked in dealerships where turnover rates among salespeople have exceeded over 100%

annually, which drains dealership revenue and contributes to customer attrition. My goal

was to quantitatively examine the relationship between advancement, pay/salary, and

turnover intentions to discover how this relationship needed to be the driver for processes

and procedures that lessen turnover intentions. Because these variables are immediately

controllable and the methods to control these elements are easily adaptable at the

dealership general manager's level of responsibility, the data needed to be free of

participant and researcher biases. These quantitative findings of this research study

eliminated biases that can be introduced through researcher interpretations of the

qualitative data and the possible intimidation of participants since I am an industry

insider. The anonymity and privacy elements of quantitative research allowed me to

collect data through online surveys containing no identifying information, eliminating

participant bias. Therefore, my beliefs or preferences are not present in this study's

findings, and only the collected data are used to examine this study's hypotheses and to

answer the research question.

The study's findings impressed upon me that, as a combination, the predictor

variables did share a relationship with employee turnover intentions, R

= .415, indicating

that 41% of the change in employee turnover intention is attributed to the linear

combination of these variables. However, when the predictor variables were analyzed as

stand-alone predictors, only salary/pay, p = <.001, was a significant predictor of

employee turnover intentions among automotive retail salespersons. As a result of this

study, I developed a perceptiveness and a value-added approach to the significance of

pay/salary to employee turnover intentions among this demographic.

The journey along the doctoral path has made me a prolific researcher, a scholar-

practitioner, and an advocate for social change. I am charged to be a change agent within

the automotive sales industry. The disciplines required to complete this journey and the

information I have gathered from the numerous articles, previous research, and the

Walden University academic community reflect my management style and are assisting

me with coaching and mentoring the members of my management and sales team.

Conclusion

In the automotive retail sales sector, the linear combination of opportunity for

advancement and salary/pay significantly predicted employee turnover intentions among

retail salespeople. This direct relationship could contribute to job satisfaction or

dissatisfaction, as discussed throughout this study. The seminal theoretical framework of

this study indicated a relationship between the proclivity and causation of employee

turnover. Dealership general managers are the first line of defense for the advancement of

succession and pay structures that influence the advancement and pay of dealership

salespeople. Therefore, dealership general managers and other organizational leaders can

adapt the data presented in this study to offset actual turnover by addressing the predictor

variables through the design, development, implementation, and execution of dealership

programs that address employee turnover intentions attached to advancement and

pay/salary.

As a primary contributor to local economies and various community initiatives, a

healthy dealership benefits the employee and the community the dealership serves.

Therefore, it is imperative that dealership leaders initiate the discussion of employee

turnover intentions, gather the data to support their position on costs associated with new

strategic planning initiatives, and implement a retention strategy that disseminates

turnover intentions and generates growth opportunities that increase and solidify

competitive advantage.

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Appendix A: Permission to Use Minnesota Satisfaction Questionnaire Short-Form (1967)

Appendix B: Permission from H. G. Heneman to use Pay Satisfaction Questionnaire

(PSQ) 1985

138

Appendix C: Permission to use Cohen (1999) Turnover Intention Scale