4.2%

22.9%

45.8%

27.1%

100.0%

Processing plant 2

1.2%

24.7%

39.5%

34.6%

100.0%

Open pit mining

3.1%

31.3%

21.9%

43.8%

100.0%

Energy supply

16.7%

33.3%

16.7%

33.3%

100.0%

Laboratory

8.1%

40.3%

35.5%

16.1%

100.0%

Quality control

19.0%

52.4%

28.6%

100.0%

Sample preparation

9.8%

31.7%

43.9%

14.6%

100.0%

Procurement

18.2%

27.3%

54.5%

100.0%

Geology

10.5%

21.1%

31.6%

36.8%

100.0%

Surveying

15.4%

23.1%

46.2%

15.4%

100.0%

Exploration

33.3%

46.7%

13.3%

6.7%

100.0%

Automatization

14.3%

85.7%

100.0%

Total

7.0%

26.2%

42.3%

24.5%

100.0%

Table 6

Division and Age Cross Tabulation

	Age (% within each Division )	Total
	18-24 y.o.	25-34 y.o.	35-44 y.o.	45-54 y.o.	55-64 y.o.	65 and more
Division	Processing plant 1	8.9%	54.4%	29.1%	5.1%	2.5%		100.0%
	Transport and mobile machinery	3.4%	24.6%	28.8%	30.5%	11.0%	1.7%	100.0%
	Processing plant 2	7.4%	34.6%	38.3%	14.8%	3.7%	1.2%	100.0%
	Open pit mining	3.1%	21.9%	34.4%	18.8%	18.8%	3.1%	100.0%
	Energy supply	16.7%		33.3%	16.7%	33.3%		100.0%
	Laboratory	9.7%	32.3%	33.9%	19.4%	4.8%		100.0%
	Quality control	4.8%	52.4%	9.5%	33.3%			100.0%
	Sample preparation	19.5%	46.3%	31.7%	2.4%			100.0%
	Procurement		27.3%	45.5%	9.1%	9.1%	9.1%	100.0%
	Geology	15.8%	31.6%	31.6%	21.1%			100.0%
	Surveying	7.7%	38.5%	23.1%	30.8%			100.0%
	Exploration	40.0%	33.3%	10.0%	6.7%	10.0%		100.0%
	Automatization		85.7%	7.1%		7.1%		100.0%
Total	9.1%	9.5%	36.6%	29.4%	17.1%	6.5%	100.0%



3.2 Comparing the current satisfaction level with its perceived change

Since the first two questions in the questionnaire sound very similar - “How would you evaluate your overall job satisfaction” and “How has your overall job satisfaction changed, as compared to the previous year?”, it would be useful to know if respondents tend to answer them differently and, consequently, if they should be analyzed separately. To answer this research question, one-sample t-test was conducted to compare the means of the overall satisfaction and perceived change in satisfaction and see if the answers to these questions differ significantly. The t-test showed a statistically significant mean difference with the overall satisfaction (3.99 ± 0.799 min, p < 0.001) and a lower perceived change in satisfaction ( 3.61 ± 0.878 min, p < 0.001) for each of the cases, which allows rejecting the null hypotheses that overall satisfaction score does not significantly differ from the perceived change in satisfaction.

Table 7

One-Sample t-test statistics and results for overall satisfaction and| perceived change in satisfaction

	Mean	Std. Deviation	Std. Error Mean	T	Sig. (2-tailed)	95% Confidence Interval of the Difference
						Lower	Upper
Overall satisfaction	3.99	.799	.035	114.576	.000	3.92	4.06
Perceived change in satisfaction	3.61	.878	.038	94.217	.000	3.53	3.68

3.3 Comparing means of satisfaction with motivating and hygiene factors

As it was revealed above, employees do not evaluate their change of satisfaction as positively as they evaluate their current satisfaction. To look deeper into the nature of satisfaction and dissatisfaction in the given company, it was questioned whether the level of satisfaction with motivating factors is as high as satisfaction with hygiene factors. The questionnaire itself did not distinguish motivating and hygienic factors, but this classification was applied at the methodology stage of this research for exploratory purposes.

New variables were introduced - Motiv_mean and Hyg_mean, which were calculated for each respondent as their mean score of motivating factors and mean score of hygiene factors, respectively. Then, one-sample t-test was run to compare the means of these two new variables (see Table 8). The results demonstrated a statistically significant mean difference between the Motiv_mean (3.74 ± 0.748 min, p < 0.001) and a higher Hyg_mean (3.98 ± 0.599 min, p < 0.001). This rejects the null hypothesis that the means of Motiv_mean and Hyg_mean do not differ significantly.

Table 8

One-Sample t-test statistics and results for Motiv_mean and Hyg_mean

	Mean	Std. Deviation	Std. Error Mean	T	Sig. (2-tailed)	95% Confidence Interval of the Difference
						Lower	Upper
Motiv_mean	3.74	.748	.0326	114.786	.000	3.68	3.81
Hyg_mean	3.98	.599	.0261	152.339	.000	3.93	4.03

3.4 Comparing means for different groups of respondents

To answer the second research question, ANOVA test was carried out with Age, Tenure and division being factors and overall satisfaction and perceived change in satisfaction being the dependent variables.

The ANOVA test results showed that neither overall nor the perceived change in satisfaction is significantly influenced by age or tenure (see Table 9). However, the means significantly differ for different divisions: the means comparison test showed the division's significance for overall satisfaction (F(12, 514) = 4.772, p < 0.000) and for perceived change in satisfaction (F(12, 630) = 2.290, p < 0.007).

Table 9 ANOVA test results for Age, Tenure and Division

	Sum of Squares	df	Mean Square	F	Sig.
Age
Overall satisfaction	Between Groups	4.507	5	.901	1.417	.216
	Within Groups	331.424	521	.636
	Total	335.932	526
Perceived change in satisfaction	Between Groups	3.595	5	.719	.883	.492
	Within Groups	509.513	626	.814
	Total	513.108	631
Tenure
Overall satisfaction	Between Groups	.076	3	.025	.039	.990
	Within Groups	335.856	523	.642
	Total	335.932	526
Perceived change in satisfaction	Between Groups	1.006	3	.335	.409	.746
	Within Groups	519.032	634	.819
	Total	520.038	637
Division
Overall satisfaction	Between Groups	33.673	12	2.806	4.772	.000
	Within Groups	302.259	514	.588
	Total	335.932	526
Perceived change in satisfaction	Between Groups	21.868	12	1.822	2.290	.007
	Within Groups	501.370	630	.796
	Total	523.238	642

To see which particular divisions differ from the others, ANOVA was be followed by a single-step multiple comparison procedure called Tukey's test.

According to the results of Tukey's test, the overall satisfaction score of Sample Preparation unit is significantly lower than for eight of the other 12 divisions. It means that employees of this division are less satisfied with their job than employees in most of the other divisions. The biggest gap in overall satisfaction is observed between Procurement and Sample preparation - 1.251, and between Geology and Sample preparation - 1.067.

The highest satisfaction scores were reported by Procurement. Its mean of overall satisfaction is significantly higher than that of five other divisions. It goes in line with the descriptive statistics evidence that employees from the Procurement division are the most satisfied with the job, with the overall satisfaction mean equaling to 4.71, with no respondent having evaluated their job satisfaction lower than 4 out of 5.

However, if the similar analysis is run taking the second questionnaire question as a dependent variable - employees' perceived change in satisfaction - the Tukey's test results do not show significant differences between any pairs of the divisions, despite the fact that the ANOVA test above showed that perceived change in satisfaction means are significantly different by divisions (with F(636,12)=, 630)=2.290, p<0.007.). This could mean that the respondents' answers are not consistent across the sample, as the question “How has your overall job satisfaction changed, as compared to the previous year?” is very subjective and people are not sure how to answer it.

3.5 Regression analysis

To investigate the relationship among motivating and hygiene factors and the overall job satisfaction, stepwise multiple regression was conducted. The choice of the stepwise method was justified by its exploratory nature which allows seeing all the predictors, which could have a significant impact on the dependent variables.

The stepwise regression results provided us with five models (Table 10), which were built by step-by-step selection of the factor with the highest impact on the dependent variable and inclusion it into the model. As the aim of this study is to reveal as many significant job satisfaction factors as possible, the closer look should be taken at the last model, which includes five predictors. This model also has the highest explanatory power, with R Square equal to 0.382.



Table 10

Stepwise regression results: models

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
a. Predictors: (constant), the work itself	.540	.291	.290	.673
b. Predictors: (constant), the work itself, communication with the supervisor	.576	.332	.329	.655
c. Predictors: (constant), the work itself, communication with the supervisor, salary	.599	.359	.355	.642
d. Predictors: (constant), the work itself, communication with the supervisor, salary, communication with other divisions	.611	.373	.369	.635
e. Predictors: (constant), the work itself, communication with the supervisor, salary, communication with other divisions, working tools	.618	.382	.376	.631

The results indicate that, among the 25 explanatory variables used in the regression analysis, five variables statistically significantly (p < 0.05) impact on employees' overall satisfaction (R=0.618), with one motivating factor which has the strongest impact on the response variable - the work itself (в=0.316, p=0.000), and four hygiene factors - communication with the supervisor (в=0.081, p=0.007), salary (в=0.147, p=0.000), communication with other divisions (в=0.115, p=0.003), Working_tools (в=0.083, p=0.008). See the detailed stepwise regression results with significant coefficients in Appendix 2. As one could expect, all the significant variables have positive в coefficients in the regression, as all the questions in Part 3 of the questionnaire were formulated in a way that higher score meant higher satisfaction with the aspect of the job.

4. Discussion and managerial implications

This research takes advantage of the data collected during the company's regular satisfaction survey to provide guidance for further managerial improvements and contribute human resource management theory in the context of the industry. This chapter will discuss the results of the quantitative research, compare and contrast it with findings of the similar studies and suggest managerial implications.

First of all, the means comparison test showed that employees evaluate their current level of satisfaction higher than they evaluate its improvement from the previous year, as we have observed that the mean of the answers to the question “How has your overall job satisfaction changed, as compared to the previous year?” is significantly lower than for the question “How would you evaluate your overall job satisfaction?”. This could mean that the employees of the given company already feel quite satisfied with their job, however, little has changed since the previous year. It could signalize, on the one hand, that the working conditions were already at a decent level in the previous year, but on the other hand, it could also mean that there are employees who think that the company has not taken enough action to improve those conditions during the last year. To get a more reliable picture of year-on-year changes in employees satisfaction, it is better to consider not respondents' perception of change in the level of satisfaction comparing to the previous year, but conduct yearly surveys and compare overall satisfaction results for each year.

Another means comparison test revealed significant differences between the mean score for motivating factors and hygiene ones. In other words, employees of the studied company are less satisfied with the aspects related directly to the work they do (the work itself, promotion opportunities, professional recognition, etc.), than by the aspects related by the work environment (remuneration, benefits, working conditions, equipment, etc.). As motivating factors, according to Herzberg, account for long-term employee motivation to perform well at this particular company, this finding means that the company is exposed to a risk of low employee loyalty and growth of employee turnover. The negative correlation between employee satisfaction and turnover has been proved in the studies of Stamolampros et al. (2019), Zhang et al. (2019). Studies showed that even a small increase in satisfaction with work-related factors might significantly reduce turnover intentions of employees.

These results may be connected with the specifics of the mining industry, where the work is often very physically demanding, especially in remote regions with harsh climate conditions where mining operations are often situated. This is why the management of mining companies put physical work conditions and decent infrastructure first, and more attention should be paid to aspects that intrinsically motivate people to work harder and stay with the company. This means providing and communicating professional growth and development opportunities, as well as ensuring challenging and reasonably autonomous work that fits the employee's interests and competency.

It is also important to note that in the company's questionnaire most items refer to hygiene factors, with just a few motivating ones. However, most of the reviewed literature showed that motivating factors are often more significant for job satisfaction, that is why it would be beneficial for further research to include such motivators as responsibility level according to Spanjol, Tam and Tam (2014), recognition of education, professional recognition, industry recognition, and self-taught recognition (Usman et al., 2018). These factors will be particularly important for such a technically complex and risky industry as gold mining and processing. To manage the risk of employees being insufficiently motivated and increase employee loyalty, it would be beneficial for the company to add more questions related to motivating factors. These could be, for example, sense of achievement at work, satisfaction with the level of responsibility and accountability, satisfaction with recognition coming from supervisor and other colleagues, satisfaction with opportunities to be initiative and innovative at work. Questions like these will bring more insights on the long-term loyalty and motivation of employees.

Age, tenure and division. Interestingly, no impact of age or tenure on overall job satisfaction was revealed by ANOVA test. These findings correspond with results of Tso, Liu and Li (2015), Stamolampros et al. (2019), Spanjol, Tam and Tam (2014). However, job satisfaction significantly varies across the company's divisions. Similar results were obtained by Zhang et al. (2019), Karamanis, Arnis and Pappa (2019). According to the results of Tukey's test, the “overall satisfaction” score of Sample Preparation department is significantly lower than for eight of the other 12 divisions. It means that employees of this division are less satisfied with their job than employees in most of the other divisions. Procurement unit showed the highest level of satisfaction. Its mean of overall satisfaction is significantly higher than that of the five other divisions. It corresponds with the descriptive statistics evidence that employees from the Procurement division are the most satisfied with the job, with the mean overall satisfaction equal to 4.71. Surprisingly, no one has evaluated the job satisfaction lower than 4 out of 5. Such variance of results across different departments could mean that there are units where working conditions and atmosphere need to be improved more than in the others. That is why the company should pay much attention to those ones, improving those areas that have gained the lowest scores. In our case, Sample preparation unit has turned out to be less satisfied, which poses a risk that chemists and mineralogists, who have the qualifications which are highly demanded in the mining industry, will choose not to stay with this company if that have a similar offer from another potential employer. This will lead to high turnover among employees in this division. To figure out what motivational and hygiene factors play significant role in job satisfaction, several stepwise regressions were done. The findings show that both motivating and hygiene factors affect job satisfaction, with the job the work itself having the strongest influence and communication with the supervisor and colleagues, salary and availability of working tools and equipment having significant influence as well.

The work itself. The significance of the employees' satisfaction with job the work itself goes in line with other studies that look into this factor, such as Kaiser (2014) and Castillo and Cano (2004). The latter found that the factor “work itself” was the most motivating aspect in developing employees' satisfaction. These findings imply that the content of the work itself is crucial for people. That is why the management should seek to improve the challenging nature of the work process: employees who highly appreciate the contribution and significance are more satisfied with the job. What is even more important, the employer should match employee abilities with responsibilities, making sure that the right people are chosen to do the right work. To manage the situation where an employee is dissatisfied with his or her job responsibilities and day-to-day tasks, it is necessary to provide them with an opportunity to safely communicate their concern, for example, through regular meetings with supervisors, mentorship and sponsorship programs. Finally, so-called job enrichment interventions can be used to design jobs that motivate people to do their best (Mallikarjuna, 2012). It is important for heads of every department to learn how to delegate tasks and provide employees with higher level of responsibilities and autonomy. Delegating not only empowers the team and assists with professional development but also helps leaders to manage their teams more efficiently by finding out their subordinates' strengths.

Salary. Unsurprisingly, salary turned out to be significant for employees' satisfaction at the company. These results correspond with findings of Tessema, Ready and Embaye (2013), Daud (2016), and Kwon, Byun and Park (2020). These authors proved that good salary has a positive effect on employees' satisfaction. As long as employees are rewarded decently for their efforts, they are satisfied with their job. The studies above conclude that financial rewards, as measured by pay, benefits and bonuses, have a role in influencing job satisfaction. On the other hand, research of Tshivhase and Vilakazi (2018) showed salary is not critical for employee satisfaction.

There could be several reasons for such contrasting results on salary's significance. Firstly, salary significance can differ depending on the research methodology. Diriwaechter and Shvartsman (2018) analyzed data covering the years 1990-2013, and proved that the increase of wage positively affects job satisfaction in the short-run. However, these positive satisfaction effects appear not to last long, since people partially adapt to the increased wage quickly. This idea suggests that wage increases only exhibit their full effect on employee job satisfaction in the short-run. Secondly, different findings concerning the impact of salary on job satisfaction can also be explained by the sampling techniques. The study of Tshivhase and Vilakazi (2018) mentioned earlier used a much smaller sample - only 66 respondents. This limitation means that the results of the mentioned study can be inconsistent with the results of studies with larger samples. What is more, Tessema, Ready and Embaye (2013) admit that level of salary's importance for satisfaction of employees can be explained by cultural differences of analyzed respondents. These authors confirmed that samples from different countries demonstrate different attitude to monetary rewards.

To provide a higher level of employees' satisfaction in terms of salary, managers should integrate more flexible remuneration system benchmarked to industry average when developing recruitment and retention policies. A performance-based wage system should be based both on operational results and safety performance, as the mining industry in is known for its high health and safety risks, and employees' health and safety should not be compromised in attempts to achieve operational goals. Other meaningful implication for remuneration practices is that the frequency of salary increase should be taken into account. Sliwka and Werner (2017) show that frequent wage increases in small increments enhance employee performance as compared to the same total of wage raises distributed in bigger, but less frequent steps. Thus, implementation of adequate remuneration systems might become a very powerful tool of employee motivation and performance.

Communication with the supervisor and colleagues. Interestingly, two of the most significant variables are connected with communication among employees, namely communication with the supervisor and communication with colleagues. The results obtained by Varma, Patil and Ulle (2018) go in line with our findings in terms of the significance of communication. So, to improve engagement and satisfaction of employees it is crucial to work on communication improvement among employees. For management, this proves that even in an industry where hard skills are of utmost importance, employees' ability to communicate well - both vertically and horizontally - is the factor that significantly contributes to their level of satisfaction. To provide effective vertical and horizontal communication, it is important to offer different tools for it. Firstly, tools for interpersonal communication should be simplified and digitalized where possible. The company should ensure employees' fast access to phone numbers of colleagues and to a dedicated phone line for grievances. Secondly, the development of continuous improvement programs based on employees' suggestions can help people communicate their ideas up to management. These programs should be designed to bring the potential of the company's best innovators to improve operational processes. Finally, the employer should consider offering training on soft skills for employees at all levels, where they could learn how to convey their ideas and communicate in a respectful and efficient way.

Working tools. Availability of working tools was found to be significant to the overall satisfaction, which could be explained by the industrial context. In an industry such as mining where productive work highly depends on physical equipment, the absence of low quality of the tools which are needed to perform the job (e.g. poor maintenance of trucks or other mining machinery) can cause irritation among employees, low quality of work done or, what's worse, can compromise the work safety. The results of Raziq and Maulabakhsh (2015) correspond with the findings of this study. Solving a problem of working tools requires prompt cooperation with administrative and procurement department and clear procedures of tools supply. Employees should be able to inform their supervisors and procurement departments in case of any lack of equipment, for example, via sending an e-mail or making a phone call to the relevant administrative staff. Improving tools and equipment supply may involve not only administrative improvements but also the implementation of innovative technologies. For instance, Russian oil and gas companies are already applying 3D printing to supply remote operation sites with the details which need frequent replacement. This technology saves time and logistic expenses because employees do not have to wait for the detail to be delivered, which can sometimes take months in remote regions of Russia.



Conclusion

Today it is becoming more and more evident for management that even in such asset-intensive industry as mining the fight for talents is one of the key business issues. Timely understanding employees' satisfaction drivers and efficiently responding to them is crucial for the company to succeed. There is no one single approach to the problem - each company should build a trustful dialogue with its employees and make the most of their feedback.

The main goal of the current study was to determine what factors tend to have the most significant influence on job satisfaction of employees in a gold mining company, on the example of a Russian subsidiary owned by a large international mining company group. Herzberg's Two Factor Theory combined with the appropriate tools of statistical analysis was applied to the secondary survey data which had already been collected by the company for managerial purposes. The need for this research is justified by, firstly, the lack of job satisfaction studies in the gold mining in general, and secondly, by an opportunity for managerial implications which could be provided based on the deep analysis of the survey data.

The evidence from this study suggests that both motivating and hygiene factors impact job satisfaction, however, as the questionnaire included mostly hygiene factors, it is difficult to judge which type of factors has stronger influence. It was revealed that even though the employees in the studied company seem to be quite satisfied in general, they do not evaluate the change in satisfaction since last year very high. What is more, it was found that their satisfaction with factors defined by Herzberg as motivating (responsibilities, professional grown and development opportunities and recognition) is significantly lower than that of hygiene factors (salary, benefits, equipment, job safety, communication, etc.). This means the company is probably exposed to the risk of turnover growth and lack of long-term employee loyalty, because, according to Herzberg's theory, focusing primarily on hygiene factors will make employees less unsatisfied instead of positively satisfied. To tackle this risk, the company should provide employees with appropriate challenges and reasonable autonomy for employees, while communicating to them professional growth and development opportunities.

With neither age nor tenure influencing job satisfaction, the divisions of the company show significant differences in their satisfaction level. As for the impact of various job aspects, five of the 25 factors mentioned in the questionnaire were found to be significant.

In line with the majority of the studies in the field of job satisfaction, this research found salary satisfaction to have a significant influence on the overall satisfaction. For the company, it means that performance-based remuneration system should be regularly reviewed and, what is even more important, clearly and promptly communicated to the employees. Another significant factor in this study is communication - both with the supervisor and with other departments. The management should take steps to simplify and digitalize the communication tools for communication - corporate mail, phone, meetings and corporate media, also raising the quality and culture of internal communication. As for availability of working tools which is often an issue for remote operation sites, effective supply procedures should be ensured to minimize work delays related to the lack of tools and equipment. Innovative technologies that reduce logistics costs and time, such as 3D printing, can also be considered a solution, depending on what particular tools are frequently needed.

In addition to HR management improvements, several suggestions for further job satisfaction surveying practices can be made based on this research. First of all, if we look at the questionnaire through the lens of Herzberg's Two Factor Theory, we can see that it consists mostly of hygiene factors, with just a few questions that would reveal motivators of employees. Adding more questions referring to motivating factors such as recognition, opportunities, the work itself etc. will help management to identify the gaps in long-term employees' satisfaction.

The company can also consider gaining more information on the employee's profile to find out which demographic groups suffer most from dissatisfaction. Of course, with every personal data point collected the anonymity of the questionnaire is threatened, but having the information on at least respondent's gender could bring additional insights, especially in the light of gender diversity issues which have been recently dominant in the industry (Has mining discovered its next great resource?, 2017). Another recommendation regarding employees surveying approach is to monitor aspects such as work atmosphere more frequently through so-called pulse surveys. This will allow reacting more promptly to the dissatisfaction of employees, especially in departments and divisions that show a low level of job satisfaction.

Finally, to ensure efficient managerial decisions it is crucial to understand how employees' satisfaction changes from year to year. While the current survey used a question “How has your overall job satisfaction changed, as compared to the previous year?”, it is better to consider not the subjective perception of change to the previous year, but consistently conduct and analyze job satisfaction every year. The company's management can also go further and analyze not only satisfaction, but also employees' engagement through a system of quantitative metrics and qualitative interviews tailored to the company.

As any empirical study, this research has a number of limitations, which can at the same time be considered as scopes for further research. The main limitation of the study is that the aspects of job analyzed in this research were limited to the questions in the survey which had already been conducted before this research was designed. The recommendations for questionnaire improvement have been given in the discussion chapter above. Secondly, the results of the study cannot be generalized to other industries - as it can be seen from the literature review, the findings are not consistent across various industries. Another limitation is the sample size -the initial data included the responses of just in one of the subsidiaries of the mining group and it would be beneficial to analyze the questionnaires on a group-wide level to see how the group's HR policies and strategies are reflected at each particular operation site.

To finalize the results and recommendation, it should be said that today, when human resources analytics has a lot to offer for effective management yet still underused by business, the studied gold mining company should not only reactively analyze the collected data, but try to proactively manage job satisfaction by looking at its trend and factors (Bersin, 2016). To do so, an appropriate environment for data collection, consolidation, storage and analysis should be created. While advanced data analytics in the mining sector is already used for exploration (e.g. via Geobank geological data software) and even for optimizing ore processing, it is highly underused in less technical field such as talent management. As the growing technical complexity of geological, mine planning, ore processing and ore recovery works requires more talented and highly qualified employees, the company can set itself apart from competitors by promptly responding to employees' needs and carefully planning its human resource management strategy, making the most of data analysis.



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Appendix 1 Literature review of job satisfaction factors

Job satisfaction factor	Reference	Paper's title	Impact on job satisfaction	Industry
Salary	Tessema, Ready and Embaye (2013)	The Effects of Employee Recognition, Pay, and Benefits on Job Satisfaction: Cross Country Evidence	Significant	Education
	Daud (2016)	Determinants of Job Satisfaction: How Satisfied are the New Generation Employees in Malaysia?	Significant	Various
	Diriwaechter and Shvartsman (2018)	The anticipation and adaptation effects of intra- and interpersonal wage changes on job satisfaction	Significant	Various
	Kwon, Byun and Park (2020)	Exploring the determinants of bus drivers job satisfaction: Evidence from South Korea	Significant	Transportation
	Tshivhase and Vilakazi (2018)	Job Satisfaction: What factors in the Coal Mining Industry will lead to Higher Satisfaction?	Not significant	Mining (coal)
Bonuses	Varma, Patil and Ulle (2018)	An Empirical Study on Job Satisfaction and Employee Loyalty	Significant	Various
	Tessema, Ready and Embaye (2013)	The Effects of Employee Recognition, Pay, and Benefits on Job Satisfaction: Cross Country Evidence	Significant	Education
	Stamolampros et al. (2019)	Job satisfaction and employee turnover determinants in high contact services: insights from employees' online reviews	Not significant	Tourism
Benefits	Tessema, Ready and Embaye (2013)	The Effects of Employee Recognition, Pay, and Benefits on Job Satisfaction: Cross Country Evidence	Significant	Education
	Stamolampros et al. (2019)	Job satisfaction and employee turnover determinants in high contact services: insights from employees' online reviews	not significant	Tourism
The work itself	Lee et al. (2013)	The impact of CSR on casino employees' organizational trust, job satisfaction, and customer orientation: An empirical examination of responsible gambling strategies.	significant	Gambling
	Kaiser (2014)	What factors influence employee satisfaction?	significant	Public Services
	Karamanis, Arnis and Pappa (2019)	Impact of working environment on job satisfaction: evidence from Greek public sector	significant	Public Services
Promotion and development opportunities	Daud (2016)	Determinants of job satisfaction: how satisfied are the new generation employees in Malaysia?	significant	Various
	Stamolampros et al. (2019)	Job satisfaction and employee turnover determinants in high contact services: insights from employees' online reviews	significant	Tourism
	Varma, Patil and Ulle (2018)	An empirical study on job satisfaction and employee loyalty	significant	Various
Working conditions and equipment	Raziq and Maulabakhsh (2015)	Impact of working environment on job satisfaction	significant	Education, Banking, Telecommunication
	Karamanis, Arnis, Pappa (2019)	Impact of working environment on job satisfaction: evidence from Greek public sector	less significant than endogenous factors.	Public services
	Varma, Patil and Ulle (2018)	An empirical study on job satisfaction and employee loyalty	significant	Various
	Kwon, Byun and Park (2020)	Exploring the determinants of bus drivers job satisfaction: Evidence from South Korea	significant	Transportation
	Hoboubi et al. (2017)	The impact of job stress and job satisfaction on workforce productivity in an Iranian petrochemical industry	significant	Petrochemistry
Stress	Sell and Cleal (2011)	Job satisfaction, work environment, and rewards: motivational theory revisited	significant	Transportation
	Hoboubi et al. (2017)	The impact of job stress and job satisfaction on workforce productivity in an Iranian petrochemical industry	significant	Petrochemistry
	Tshivhase and Vilakazi (2018)	Job Satisfaction: What factors in the Coal Mining Industry will lead to Higher Satisfaction?	significant	Mining (coal)
	Kwon, Byun and Park (2020)	Exploring the determinants of bus drivers job satisfaction: Evidence from South Korea	significant	Mining (coal)
Promotion and development opportunities	Stamolampros et al. (2019)	Job satisfaction and employee turnover determinants in high contact services: insights from employees' online reviews	significant	Transportation
	Daud (2016)	Determinants of job satisfaction: how satisfied are the new generation employees in Malaysia?	significant	Various
	Varma, Patil and Ulle (2018)	An empirical study on job satisfaction and employee loyalty	significant	Various
Communication with colleagues	Varma, Patil and Ulle (2018)	An empirical study on job satisfaction and employee loyalty	significant	Various
	Karamanis, Arnis and Pappa (2019)	Impact of working environment on job satisfaction: evidence from Greek public sector	less significant than endogenous factors	Public Services
Age	Stamolampros et al. (2019)	Job satisfaction and employee turnover determinants in high contact services: insights from employees' online reviews	not significant	Tourism
	Spanjol, Tam and Tam (2014)	Employer-Employee Congruence in Environmental Values: An Exploration of Effects on Job Satisfaction and creativity	not significant	Engineering
Tenure	Tso, Liu and Li (2015)	"Identifying factors of employee satisfaction: a case study of Chinese resource-based state-owned enterprises"	not significant	Resource-based State-owned Enterprises



Appendix 2 Stepwise regression results: significance coefficients

Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.
	B	Std. Error	Beta
a	(Constant)	1.985	.140		14.229	.000
	The work itself	.527	.036	.540	14.690	.000
b	(Constant)	1.443	.166		8.675	.000
	The work itself	.438	.038	.449	11.444	.000
	Communication with the supervisor	.208	.037	.220	5.617	.000
c	(Constant)	1.097	.179		6.125	.000
	The work itself	.380	.039	.390	9.641	.000
	Communication with the supervisor	.178	.037	.188	4.826	.000
	Salary	.168	.036	.182	4.708	.000
d	(Constant)	.950	.182		5.217	.000
	The work itself	.337	.041	.345	8.224	.000
	Communication with the supervisor	.141	.038	.149	3.699	.000
	Salary	.157	.035	.170	4.430	.000
	Communication with other divisions	.134	.038	.143	3.486	.001
e	(Constant)	.964	.181		5.323	.000
	The work itself	.316	.042	.324	7.609	.000
	Communication with the supervisor	.108	.040	.115	2.722	.007
	Salary	.147	.035	.159	4.129	.000
	Communication with other divisions	.115	.039	.123	2.965	.003
	Working_tools	.083	.032	.112	2.643	.008

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