1. Introduction
Human resource managers consistently face pressure to enhance employee performance across various industries. To effectively increase performance, it is essential to understand the factors that drive employee motivation. Research indicates that employees are primarily motivated by internal factors rather than external ones. Specifically, beyond a certain threshold of salary compensation, financial incentives such as pay raises and bonuses offer limited motivational value. The literature does not define a specific threshold amount, likely because the threshold can vary across individuals. In contrast, intrinsic motivators, such as autonomy and a sense of purpose, are more effective in fostering enhanced job performance. This study primarily examines the relationships between salary, autonomy, and job performance.
First, the author will examine the relationship between salary and job performance. Salary is commonly defined as a form of periodic compensation provided by an employer to an employee, typically outlined in an employment contract. When employees are asked about their primary motivators, many are likely to identify money as their most significant incentive. It is reasonable to assume that the traditional practice of offering raises or bonuses for good performance would be universally effective in motivating employees. However, existing research does not consistently support this assumption. If employees claim that money is their primary motivator, why does the research fail to fully validate this claim? Could financial compensation be less influential on job performance than it appears? The answer likely depends on the nature of the job. To explore this further, we will consider how salary impacts performance in three distinct contexts: sales positions, non-sales roles, and professional athletics.
An employee in a sales-based role might be assumed to be highly motivated by financial incentives. However, this is not always the case. Research indicates that employees in sales positions who are eligible for incentive pay, such as bonuses or commissions, tend to perform better than those who are not eligible for such incentives. The correlation between incentive pay and performance is notably stronger than the correlation between fixed salary and performance in sales roles, with a coefficient of (r = .62) for employees receiving incentive pay, compared to (r = .10) for those receiving only a fixed salary (Joshi, Liao, & Jackson, 2006). Human resource managers, drawing on these findings, would likely be inclined to implement bonus or commission structures to enhance employee performance.
It is generally assumed that employees in non-sales-based roles, such as government positions, are less motivated by financial incentives. Historically, individuals have pursued government jobs primarily for their security rather than for high compensation. Research supports this assumption, revealing a very weak correlation (r = .02) between salary and job performance for employees in these roles (Harris, Gilbreath, & Sunday, 1998). Human resource managers examining this data would need to explore alternative methods for motivating their employees to perform effectively.
What about professional athletes? Does their salary have an impact on their performance on the field or court? For the purposes of this study, I will focus on professional baseball players. Existing research indicates a modest correlation (r = .23) between salary and performance, with player ratings used as the measure of performance (Bloom, 1999). Based on these findings, a human resource manager might conclude that professional athletes are primarily motivated by intrinsic factors rather than extrinsic ones, and would therefore consider alternative motivators, such as mastery and purpose, to enhance performance.
What are the advantages of utilizing salary as a predictor of job performance? In sales-based roles, enhanced employee performance directly contributes to greater organizational profitability. Increased profits, in turn, benefit executives and shareholders. For sales professionals, using financial incentives to drive higher sales results not only motivates employees but also fosters job satisfaction, thereby reducing the likelihood of turnover and employees seeking opportunities with competitors. Thus, two key benefits for human resource managers in linking salary to job performance are boosting company revenues and reducing employee turnover.
Are there drawbacks to using salary as a predictor of job performance? While salary can influence performance, other factors may serve as more reliable predictors. Motivation is a key determinant of how effectively an employee performs their duties. There are several challenges associated with relying on salary as a predictor of job performance. For instance, obtaining accurate performance ratings can be difficult due to supervisor bias or ambiguous performance criteria. Additionally, performance-based pay plans often undermine group collaboration, as they encourage a “self-interested” mentality focused on individual performance. The merit differences between high and low performers are often minimal, due to standard cost-of-living adjustments and employers’ reluctance to offer merit increases beyond these adjustments. Furthermore, unions typically oppose performance-based pay plans, as they do not consider seniority or length of service when determining pay increases (Parnell & Sullivan, 1992). If human resource managers use salary to predict job performance for employees who do not receive financial incentives for strong performance, the results may not align with expectations. In such cases, human resource managers should consider alternative predictors of job performance, such as autonomy.
Next, the author will examine the relationship between autonomy and job performance. Job autonomy refers to the degree of freedom and discretion an individual has in performing job tasks (Zhou, 1998). The level of autonomy granted to an employee is shaped by various organizational and external factors. For instance, autonomy preferences are individual psychological traits that influence the extent of autonomy an individual needs (Langfred & Moye, 2004).
Individual employee autonomy is also significantly shaped by the organizational culture of the firm. Autonomy is more common in organizations that foster innovation without the fear of failure, and in those where management promotes confidence and self-sufficiency (Wallace, Johnson, Mathe, & Paul, 2011). One advantage of using autonomy as a predictor of job performance is that it is relatively low-cost for an organization to enhance autonomy when necessary. Furthermore, the ability to maintain independence in one’s work is highly valued by many employees, making it a relevant area of study for strategic human resource managers.
Having discussed salary and autonomy separately, the author will now compare and contrast their effectiveness as predictors of job performance. First, the author will examine how salary and autonomy align in their ability to predict job performance. Next, the author will explore the differences between salary and autonomy as predictors of job performance.
How can two seemingly unrelated variables, such as salary and autonomy, exhibit similarities in their ability to predict job performance? First, employers possess the capacity to adjust both salary and autonomy within an organization. Similar to salary, autonomy is often considered a key factor in enhancing employee motivation and satisfaction. Both increased salary and autonomy are viewed as attractive incentives by employees. Furthermore, providing opportunities for higher levels of salary and autonomy can lead to reduced turnover, thereby decreasing recruitment and training expenses. These cost savings can subsequently be reinvested into the organization’s budget, potentially facilitating further salary adjustments.
How do salary and autonomy differ in their capacity to predict job performance? Several key distinctions can be made between the two variables. Firstly, autonomy is not directly observable, and its definition is frequently open to subjective interpretation. Autonomy is identified as the most effective predictor of performance within high-performance work systems (Aryee, Otaye, Seidu, & Walumbwa, 2012), a criterion that does not appear to be a requisite for the salary-performance relationship. Moreover, restructuring job roles to enhance autonomy is typically a more cost-effective strategy than increasing salary levels within an organization.
2. Method
To ensure the studies included in this meta-analysis were comparable and representative, and took into account the variability in organizational culture, samples were drawn from peer-reviewed journals covering a range of industries and counties. As a result of the diversity of the samples, the findings would be generalizable across various industries and cultures. To control for potential external factors that could impact the relationship between salary, autonomy, and job performance, this study only considered those components directly related to on-the-job factors.
2.1. Salary
To determine the average correlation between salary and job performance, the author reviewed fifteen empirical studies examining this relationship. The first predictor, salary, was assessed using variables such as fixed annual salary, base pay, workers’ pay, salary, and salary grade. Individual objective job performance was measured using parameters like sales goal achievement, performance rating, emails per hour, overall performance, and sales performance.
The author restricted the scope of the research to base salary for employees and individual job performance. For the salary predictor, the author excluded data that involved incentive pay or bonuses as variables. Regarding job performance, the author focused solely on individual performance and excluded data related to corporate performance or perceived/self-rated job performance.
Three of the samples in the studies had exceptionally large sample sizes (n > 2000). To assess their impact, the author analyzed the data with and without these “outlier” samples. Removing these samples resulted in a decrease in statistical significance but provided a more balanced comparison between salary and autonomy, which clarified the variance between the two predictors in relation to job performance. Consequently, the author decided to exclude these three samples from the final analysis.
Once the data was compiled, it was entered into the Hunter/Schmidt meta-analysis spreadsheet to calculate the average correlation between salary and job performance. The author extracted the sample sizes and correlations from each of the twelve referenced articles (see Table 1) and input them individually into the database to determine the overall average correlation.
Table 1. Samples for salary variable.
Referenced Article |
Sample Size (n) |
r (obs) |
St. Onge, 2003 |
174 |
0.11 |
Rode et al., 2008 |
62 |
0.13 |
Colvin et al., 2001 |
242 |
0.38 |
Miceli et al., 1991 |
1029 |
0.07 |
Greenberg, 2003 |
276 |
0.38 |
Ferris & Witt, 2001 |
106 |
0.38 |
Pazy & Ganzach, 2009 |
259 |
0.13 |
Campbell et al., 1998 |
201 |
0.19 |
Harris et al., 1998 |
218 |
0.03 |
Gomez-Mejia et al., 1987 |
71 |
0.21 |
Kessleman et al., 1974 |
76 |
0.39 |
Trevor et al., 1997 |
265 |
0.19 |
2.2. Autonomy
To assess the correlation between job autonomy and job performance, the author reviewed fifteen empirical studies exploring this relationship. The second predictor in my study, autonomy, was measured using variables such as task freedom, task discretion, and observable self-management by employees through the Job Diagnostic Survey (Hackman & Oldham, 1976). Individual objective job performance was evaluated using parameters like creative performance, manufacturing production performance, sales performance, and employee performance reviews conducted by management.
The author confined the scope of my research to objective measures of autonomy and individual job performance. Additionally, the author excluded data related to organizational performance or perceived individual autonomy that was not directly observable. After collecting the data, it was entered into the Hunter/Schmidt meta-analysis spreadsheet to calculate the correlation between autonomy and job performance. The author extracted the sample sizes and correlations from the referenced articles and input them individually into the database to determine the average correlation reflecting the overall relationship between autonomy and job performance.
One article (Langfred, 2004) regarding autonomy and performance was excluded from the meta-analysis due to an anomalous correlation between autonomy and performance, resulting in fourteen usable samples (see Table 2). The correlation (r = −.52) was deemed an outlier and not representative of the typical correlations found in other studies on autonomy and job performance. Removing this data point resulted in overall correlations that were less statistically significant but led to a narrower range of confidence levels for the autonomy predictor.
Table 2. Samples for autonomy variable.
Referenced Article |
Sample Size (n) |
r (obs) |
Chen et al., 2007 |
538 |
0.21 |
Dieffendorf et al., 2006 |
317 |
0.12 |
Dodd & Ganster, 1996 |
258 |
0.42 |
Wallace et al., 2011 |
539 |
0.18 |
Langfred, 2007 |
100 |
0.19 |
Zhou, 1998 |
210 |
0.41 |
Parker, 2003 |
368 |
0.47 |
Greguras & Dieffendorf, 2009 |
163 |
0.14 |
Man & Lam, 2003 |
197 |
0.26 |
Colarelli et al., 1987 |
280 |
0.20 |
Barrick & Mount, 1993 |
146 |
0.04 |
Hackman & Lawler, 1971 |
208 |
0.26 |
Kim et al., 2009 |
196 |
0.10 |
Morgeson et al., 2005 |
132 |
0.17 |
3. Results
The results of this study reveal a relatively weak correlation between salary and job performance (r = .171), and a slightly stronger correlation between autonomy and job performance (r = .237). Descriptive statistics for both predictors are presented in Table 3.
Table 3. Descriptive statistics.
Hypothesis Frame |
k |
N |
Mean r |
95% Conf LO |
95% Conf HI |
Qb |
Qb p |
All samples |
26 |
6,631 |
.208 |
.158 |
.257 |
|
|
Salary v Autonomy |
|
|
|
|
|
7.185 |
0.007 |
Salary |
12 |
2,979 |
.171 |
.100 |
.242 |
|
|
Autonomy |
14 |
3,652 |
.237 |
.174 |
.301 |
|
|
Note: K: number of samples; N: number of observations; mean r: sample size-weighted correlation; CONF: confidence interval for r; Qb: χ2 based test for significance of moderation.
The weak correlation (r = .171) between salary and job performance aligns with the belief among many managers that the non-contingent nature of base pay makes it an ineffective motivator for job performance. This also suggests that the perceived popularity of performance-based pay systems among employees may be overstated (Kuvaas, 2006). One reason for the limited effectiveness of some performance-based pay systems can be attributed to individual differences. Some individuals prefer that their outcome/input ratios align with those of their comparison others, while others may favor inequity and, therefore, be less motivated or satisfied by pay-for-performance systems (Parnell & Sullivan, 1992). The individual correlations used to determine the average correlation between salary and job performance varied in strength, ranging from r = .07 to r = .39, with the majority falling between r = .21 and r = .39. A slightly stronger correlation (r = .237) was found between autonomy and job performance. Autonomy was influenced by a range of situational factors that contributed to the relationship between autonomy and performance, making it challenging to isolate the performance benefits directly attributable to autonomy from those resulting from other factors, such as psychological empowerment and organizational culture (Barrick & Mount, 1993). Figure 1 shows the range (minimum, mean, and maximum) of correlations for all samples combined, as well as individually for salary and autonomy.
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Figure 1. Range of correlations for all samples, including salary and autonomy individually.
It was also found that high levels of employee autonomy occasionally led to situations where performance was negatively impacted. This was particularly evident in competitive environments or when task conflict arose due to high levels of individual autonomy (Langfred, 2007). Additionally, cognitive ability was found to be closely linked to autonomy, raising the question of whether cognitive ability is a necessary condition for autonomy to have a positive effect on employee performance (Colarelli, Dean, & Konstans, 1987).
4. Conclusions, Recommendations, and Future Research
Based on the findings of this study, I recommend that human resource managers consider both the relationship between autonomy and job performance and the relationship between salary and job performance when attempting to identify what motivates their employees. Many organizations implement a combination of performance-based systems alongside other reward structures that recognize behaviors beyond job performance. These organizations also tend to offer non-monetary rewards, such as fringe benefits, promotions, job security, and favorable working conditions. Additionally, it is important to consider other forms of motivation, such as increased job autonomy, job challenges, and task significance (Parnell & Sullivan, 1992).
I also recommend that human resource managers apply this information during the hiring process by evaluating the specific job in question to determine which of the two predictors should be prioritized. For instance, when hiring a team of salespeople, salary should be given more weight as a predictor of job performance, particularly if the organization offers incentive or bonus pay for good performance. In contrast, government jobs, often chosen for their job security, should emphasize the value of security and the benefits provided to potential employees. For roles in creative industries, such as a video game development company hiring an “inventor,” the relationship between autonomy and job performance should be given greater consideration, as these positions typically require independent work and creative freedom.
The responsibilities of human resource managers are continuously evolving, and it is essential for the success of any organization to hire individuals who are well-suited for their roles and maintain their motivation to ensure optimal performance. By understanding the relationship between salary, autonomy, and job performance, human resource managers can make more informed hiring decisions, which will, in turn, increase company revenues and reduce recruitment and training costs. Furthermore, with this knowledge, human resource managers will be better equipped to motivate their employees, fostering improved job performance and, consequently, enhancing company performance and profitability.
Future research could explore the influence different roles have on the relationship between autonomy and job performance, including creative roles and administrative positions. Additionally, categorizing job types for individual analysis on the impact of salary and autonomy on job performance is another avenue for future research.