How Job Stress Influences Organisational Commitment: Do Positive Thinking and Job Satisfaction Matter?

Abstract

This study was conducted to examine the moderating effects of positive thinking and job satisfaction on the connection between job stress and organizational commitment. This study surveyed 201 salespeople in the Thai food business to establish the links between moderating influences using hierarchical regression analysis. Job satisfaction had a negative connection with organizational commitment; positive thinking had a moderating effect on the relationship between job satisfaction and organizational commitment; and job satisfaction had a statistically significant moderating influence on the relationship between job stress and organizational commitment. Assessing the moderating effects of positive thinking and job satisfaction on 201 salesmen in the Thai food business, the results suggested that employees should engage in positive thinking while management should create resources that best meet the needs of employees in order to lower job stress levels and increase organizational commitment. Managers and human resource departments should be aware of the detrimental effects of job stress on the positive thinking and job satisfaction of their employees, which reduces their organizational commitment. This study contributes to the existing knowledge on the effects of job satisfaction on organizational commitment by elucidating the effects of positive thinking and job satisfaction on the link between job satisfaction and organizational commitment.

1. Introduction

The negative influence of job stress (JST) on organizational commitment (OC) is of interested to us in the fields of organizational behavior, industrial psychology, and human resource development [1,2,3]. JST refers to a person’s feelings or views about environmental variables, including heavy workload, role uncertainty or conflict, an unsupportive work environment, job over-expectations, or unpleasant relationships with coworkers or superiors [4]. All these variables produce stress or mental tension that might lead to physical or mental illness. JST can produce eustress, leading to task success. According to Nappo [5], JST is a difficulty that many employees face. Narsa and Wijayanti [6] deemed JST a global epidemic caused by firm downsizing, technology change, work redesign, mergers, and restructuring. Unexpected effects can raise JST. Many psychologists, managers, and HRD experts have studied JST. Some research has used JST as an independent variable [7,8] or a moderating variable [9,10] to predict OC [11,12] and contribute to sustainable OC [13,14], JST may moderate OC-related psychological factors. This research revealed that raising OC through employees’ PT should minimize JST [15,16,17].

Kapikiran [15], Khan and Husain [16], and Pukkeeree, Na-Nan, and Wongsuwan [17] all found that PT (psychological state) moderated the link between JST and OC. Similarly, Ngirande [18], Shin and Jung [19], and Soomro et al. [20] came to the conclusion that environmental happiness and diversified job duties decreased employees’ stress levels, hence increasing OC. These results indicated that the effects of JST on OC were contingent on both PT and JS. In addition, they noted that the impacts of JST on OC was dependent on the levels of PT and JS and indicated that enhancing the psychological state of employees through PT and developing their JS were crucial for reducing JST. When less stressed, employees exhibit greater OC. Therefore, this study will provide significant information for executives and related agencies to improve the PT and JS of their employees in order to reduce JST.

The food industry is one of five industries driving the Thailand 4.0 model. The food industry is regarded as one of Thailand’s potential industries due to the country’s abundant natural resources, which are conducive to its status as an agricultural nation with an abundance of agricultural products. Currently, the Thai food industry exports products to more than 200 countries throughout the world, accounting for approximately 800,000 million baht per year [21]. As the annual income of the food industry grows, so does the industry’s level of competition, which causes employees to experience JST as they strive to meet their organizations’ goals. According to the research of Eidnut and Klanbut [22], employees in the Thai food manufacturing industry are exposed to job-related stress, resulting in a decline in mental health. Some employees switch jobs, others leave their organizations, and others perform poorly on the job. Due to the significance of this issue, research on the effects of JST on OC is essential because it provides crucial information for planning to reduce JST or appropriately addressing problems. According to empirical evidence, salespeople in the food industry are inevitably subjected to occupational stress. This is congruent with the concept and theory of stress, which states that stress is something no one can escape, but that everyone can control or reduce. Therefore, businesses must assist workers in managing or reducing JST. Previous research has demonstrated that JST negatively impacts employee health, causing some to leave their firms. This suggests that certain businesses lack efficient methods for managing JST, and it also reveals a research vacuum that must be filled in order to successfully solve and manage the difficulties. This is a difficult task for studies in Thai contexts, particularly in the food industry.

For the advancement of knowledge in Thai contexts, notably in the food business, it is crucial and urgent to conduct research on JST. Therefore, precise evidence or testing is useful for organizations and executives in resolving these issues, as well as for researchers, scholars, and others interested in the effects of JST on OC. Based on the aforementioned theories and issue statements, this study examined the direct effects of JST on OC, as well as the indirect effects of JST on OC via moderators of PT and JS. In accordance with the aforementioned aims, the study questions asked whether JST was a factor influencing OC and how PT and JS moderated JST and influenced OC. Researchers, scholars, educators, human resource officers, and others interested in investigating and resolving problems of JST with consequences on OC will find the study’s findings interesting.

This work is structured as follows: the next section of the literature review will discuss organizational commitment, job stress, positive thinking as a moderating variable, and job satisfaction as a moderating variable. Next, the study’s methods, including samples, nonresponse bias and common method bias, instrument validity and reliability, and data analysis, are discussed. Third, the results include sample characteristics, descriptive statistics, and an examination of direct and moderating effects. The discussion portion follows. The paper concludes with a discussion of theoretical and practical implementation, research limitations, and suggestions for further research.

2. Literature Review

To comprehend the mechanism for job stress and its effects, the job demand–resource (JD–R) model is recommended [23], as this model is all-inclusive for explaining the phenomenon of sales employees’ stress [24]. Job characteristics in the JD-R model can be classified as either a job demand or a job resource. In this model, it is postulated that there are two distinct fundamental psychological processes: a health disorder process leading to burnout, and a motivation-building process leading to work participation and employee engagement [24,25]. Job demand and resource availability have a variety of effects on employees’ work operations [26], including JS [27] and OC [28]. The relationship between JST and psychological outcomes is somewhat moderated by resource variables. For instance, resources can negate the effect of job demand on JST [24]. Using the suggestions of the JD-R model, we incorporated a JST variable into the theoretical framework along with the basic mechanism of JST that influences OC. However, we questioned whether using JS and PT as moderators could explain OC more effectively or not.

2.1. Organisational Commitment

OC refers to the mental bond between employees and organizations [29]. Employees with high OC positively strive to attain company goals and values and put full effort into working to achieve success [30]. Meyer and Allen [31] divided OC into three aspects: affective commitment, continuance commitment, and normative commitment. Firstly, affective commitment is an employee’s emotional expression as part of the organization. This also relates to personal characteristics, organizational structures, work experience, benefits, supervision, a clear role, and different skills [32]. Secondly, continuance commitment reflects employees’ belief that resignation will have a negative financial impact. Long-term employees perceive resignation as a loss of working investment with reduced retirement benefits. Continuance commitment ensures that employees do not suffer the social and economic costs of resignation [33]. Finally, normative commitment refers to employees’ strong commitment to company methods and values. Employees with normative commitment are strictly compliant with company guidelines. Normative commitment develops from the experience of socialization, focusing on the appropriateness of acceptance and loyalty [34].

OC has many positive company benefits. Steers [35] stated that OC can be used as a better predictor of staff turnover than JS. OC drives employees to perform well and remain with the company on a long-term basis. OC also encourages and promotes employees to put full physical and mental effort into performing work assignments. Moreover, OC can be used as a performance indicator and also enhances employees’ motivation and work attitudes.

Previous literature suggests that OC influences both employees and society. A strong company commitment reduces inappropriate work behavior, and good personnel can create valuable innovations for the community and society. Therefore, the promotion and support of employees’ JS or a reduction of JST can increase OC and also reduce staff turnover, thereby reducing the costs of recruiting new employees.

2.2. Job Stress

JST refers to “physical and emotional responses that are harmful when job requirements do not conform to or respond to the employee’s needs” [12]. JST is a psychological state perceived when faced with needs, limitations, and important chances with uncertain outcomes [36]. According to Aruldoss et al. [37], JST refers to different characteristics of the work environment that employees find threatening, including overdemand for, or insufficiency of, resources or materials. In a convenience store, a salesperson gets pressured by dealing with various duties such as building customer satisfaction and making sales to reach targets set by the management [38,39]. JST may also result from the constant change in working conditions, regular short and long distance travel, and information preparation during weekends or days off [40] as well as having the latitude to close a sale that is beyond the scope of their authority [41]. These stress situations were explained by Karasek [42] using a model of general adaptation syndrome. In this model, job demand consists of operational speed, work quantity, and conflicts as a result of job requirements. Job demand is a psychological, not a physical, demand. Internal job demand may be high and cause fatigue, but psychological demand results in stress. A person may become tired due to the need to maintain operational speed and worry that he/she will not complete the work in time. The second characteristic in the model is job decision latitude in the two aspects of decision-making authority and different employee operational skills. Decision-making authority may change from a low to a high level when a person has work freedom. Moreover, work-skill requirements differ among jobs. A person with a high level of work skills usually chooses to use suitable skills and time periods. For example, a salesperson chooses a time period to present a product and uses his or her skill to present a product, solve a customer’s problem, or close the sale [43]. These matters are regarded as stimuli that directly impact JST.

JST affects employees’ daily lives in ways such as a lack of concentration, sadness, boredom, a reduction in life satisfaction, life imbalance, and family problems [37,44,45]. Similarly, JST has direct effects on work life, such as decreased JS, burnout syndrome, decreased performance, antisocial behavior, and poor corporate citizenship [46,47]. The effects of JST are usually negative. However, JST can cause stress as a result of unexpected work success or completion [48]. Eustress may occur if a person can adapt himself or herself and overcome JST or when a person perceives a stress stimulus as a life challenge and is ready to face it. However, according to previous studies and related literature, JST usually has negative effects both in daily life and work life.

When considering the stimuli of JST, its stimuli typically result in negative outcomes, such as increased demands from the workplace, supervisors, coworkers, and customers. These stimuli also have negative effects, such as a decrease in work performance and OC. As a result, a study on the JST index in a negative dimension can more accurately reflect employees’ work lives.

The direct negative effects of JST on employees’ work behaviors inevitably impact OC. Employees with JST show a reduced effort to devote knowledge and abilities [49]; they do not accept or comply with values, goals, guidelines, methods, rules, and company regulations [14,50], often desiring to change jobs [51]. JST directly and negatively affects OC. Saadeh and Suifan [12] identified factors increasing JST as job difficulty, increasing work quantity, higher targets, or a bad working environment, resulting in decreasing levels of OC. Antón [52], Chiang and Liu [53], and Li et al. [54] also found that JST had a direct negative effect on OC. Accordingly, the first hypothesis was posited as follows:

H1: 

JST has negative effects on OC.

2.3. Positive Thinking as a Moderating Variable

PT is a thinking process to perceive and interpret something in a good way with good attitudes and subconscious tendencies towards oneself, other people, objects, or situations. PT can be used to accept an emerging problematic condition with strength and to promote success in various aspects [55,56]. PT leads to good actions and positive results [57]. Moreover, PT results in motivation to solve different problems. A person with PT perceives stimuli such as animals, objects, people, and places as good life opportunities [58]. In the dimension of working people, employees with PT perceive surroundings or stimuli such as colleagues, supervisors, work, or the working environment as challenges. Employees with PT perceive negative effects as challenges that can be managed and solved [59]. When a salesperson gets JST, he or she will not accept organizational goals, values, or cultures and will seek change. In contrast, if a salesperson has PT, he or she will perceive JST stimuli as a challenge to deal with and overcome. Kooshalshah et al. [60] likened PT to an intervention tool for solving different problems and supporting and promoting employees to feel good. Positive attitudes lead to positive behaviors. Chang and Bridewell [61] found that PT was helpful for employees to reduce anxiety and JST, while Ong et al. [62] mentioned that PT acted as a moderator of stress and successful adaptation. A person who employs PT usually has lower stress levels and better adaptation [63]. PT also encourages a person experiencing emotions or feelings to bounce back to normal with reduced stress [64]. Tavakoli [65] stated that PT can support a person to manage stress effectively. Kapikiran [15], Khan and Husain [16], and Pukkeeree, Na-Nan, and Wongsuwan [17] used PT as a moderating variable. All agreed that PT was a psychological state that diverted other variables or stimuli in a better direction. For employees, PT and JST occur in work situations. If employees use PT, their psychological state will return to normal quickly and effectively because they perceive JST as a challenge that they have to overcome.

According to the above concepts and empirical data, PT accurately represents a person’s psychological state in perceiving various matters. PT can be used to manage JST and consequently increase OC. Therefore, the second hypothesis was posited as follows:

H2: 

PT acts as a moderating effect between JST and OC.

2.4. Job Satisfaction as a Moderating Variable

JS refers to a state of pleasant emotion [66] as an important predictor of OC [67]. Similarly, Bateman and Organ [68] and Organ [69] pointed out that JS had two components: an affective component referring to the emotional state of an employee and a cognitive component referring to satisfaction relating to performance appraisal. Ahsan et al. [70], Chawla and Guda [71], Zhu [72], Haijuan et al. [73], and Wen et al. [74] explained that JS was a hot issue in the literature on industrial psychology, organizational behavior, human resource management, human resource development, and social psychology. JS is an important factor when evaluating how the working environment impacts employees. If employees are satisfied with their working environment, this will positively impact their work performance and other behaviors, including OC. JS influences the emotional OC of salespeople in relation to their responsibilities and operational contexts. The two-factor theory of Herzberg states that the hygiene factor is a stimulus or extrinsic reward that is helpful in reducing job dissatisfaction. For example, if employees receive suitable benefits, have good colleagues or supervisors, clear organizational policies and goals, and a good working environment, they will be satisfied with their jobs and feel relieved from tension or working conditions [75]. Fletcher and Payne [76] mentioned that dissatisfaction is a cause of JST if employees resent their work responsibilities. Negative feelings will make them not want to perform their responsibilities, and consequently, they will resent organizational values or cultures. If such negative feelings are not resolved, they may seek jobs elsewhere.

According to the aforementioned literature on JS, JS is a psychological state involving the perception and evaluation of the work environment. If employees have a favorable perception of their work environment, their JS will increase. In contrast, if they perceive the work environment negatively, they will experience job dissatisfaction. The relationship between JS and work responsibility is functional. According to Woo et al. [77], JS is a psychological condition related to various things or situations involving a person. According to their research, JS acts as a moderator and aids in reducing job burnout significantly. Allan et al. [78] consistently suggest that JS can be used as a moderator to explain JST. Negative stimuli will reduce the JST experienced by an individual. As a result, we chose to investigate moderator JS.

Many studies have explored the relationship between JS and JST. Shin and Jung [19] found that JS had a significant relationship with JST. One study of workers in the oil industry in Iran found that stress from the working environment influenced JS and OC with statistical significance. Soomro, Breitenecker, and Shah [20] used JS as a moderating variable. Findings showed that if employees lacked JS or had low JS, this increased JST and reduced OC, while Terry et al. [79] found that high levels of JST negatively impacted JS. Thus, employees with high job satisfaction will have reduced JST. Furthermore, Abraham [80] pointed out that employees without JS tended to have less OC. Ngirande [18] studied history scholars in South Africa and used JS as a moderating variable between occupational stress and OC. Although occupational stress was high, with high JS as a moderator, OC could also be high.

According to the above concepts and empirical data, JS can be defined as the emotional state of a person towards work responsibility and the working environment. JS is considered by scholars and researchers to be a moderator of JST on OC with statistical significance [18,20,79,80]. Therefore, to verify whether JS was a moderator between JST and OC for salespeople in convenience stores in Thailand, the third hypothesis was posited as follows:

H3: 

JS is a moderating variable between JST and OC.

According to theories of JST, a person experiencing JST has fewer OC behaviors. According to some academics and researchers, OC will increase if PT and JS variables moderate JST. The study questioned whether JST theories can predict the occurrence of OC behaviors if PT and JS serve as moderators of co-prediction.

Figure 1 presents the research framework to explain the developed hypotheses as mentioned earlier. The basic model examined the direct effects of JST on OC (H1) by considering personal factors of gender, age, experience, education, status, and position as the moderating variables. The study model was expanded by adding PT and JS and their interactions with JST to examine the effects of PT (H2) and JS (H3).

3. Research Methodology

3.1. Populations and Samples

In this particular research project, the units of analysis were 400 salespersons working in the Thai food business. We chose this population and sample size because salespeople in the Thai food industry have obvious sales-related key performance indicators, thus they are typically anxious about acquiring consumers. Furthermore, the majority of Thais have a distinct identity and pleasant views, as evidenced by the phrase “Land of Smile”. The idea presented by Yamane [81] was utilized in order to calculate the size of the sample group based on the formula of an undetermined population. The number that was arrived at after the calculation was 400 units. Regarding sample selection, convenience sampling was employed by sending only one set of questionnaires to human resource departments and requesting that they select a qualified sales employee to serve as a representative of sales employees in their organization. In this study, convenience sampling was chosen since the researchers were constrained in a variety of ways, including the inability to determine the true number of samples, communication, study duration, and expenses, among others. As a result, the researchers decided to employ convenience sampling to collect the required amount and period of data. Questionnaires were sent to the human resource departments of 400 food companies, requesting they select one salesperson as a questionnaire respondent. After 4 weeks, completed questionnaires were received from 86 respondents. Questionnaires were then sent to another 314 organizations, with 72 responding after 4 weeks. Respondents from the two rounds totaled 158 (39.5%). Therefore, a further 242 questionnaires were sent out, and after 4 weeks, 48 were completed and returned, making a total of 201 respondents. This was 50.25% of the original estimated required sample size of 400 and the decision was made to send out no further questionnaires. While Baruch and Holtom [82] claimed that the response rate in business and management research might be anywhere between 50% and 80%, Berdie et al. [83] stated that a response rate of 50% on a questionnaire could be regraded as acceptable at a very good level. The waiting period of 4 weeks or 30 days is appropriate, as the website SurveyMonkey.com [84] indicates that 7 days is the optimal waiting period with a high probability of receiving responses. The researchers increased the waiting time to one month. At the end of the waiting period, however, the response rate was extremely low. As a result, we resent the questionnaires three times before deciding that the response rate of more than 50 percent was sufficient for the analysis and stopping the process. Another reason why the researchers ceased collecting data was that the amount of data returned with each iteration decreased significantly. In addition, a 4-week waiting period was imposed between each phase in order to collect data for three rounds (about 120 days for three rounds of data collecting). In the final wave, more than fifty percent of questionnaires were returned, which was adequate for the primary data analysis, therefore the researchers decided to cease data collection (Appendix A).

3.2. Non-Response Bias and Common Method Bias

Non-response bias was tested according to the concept of Armstrong and Overton [85]. They suggested analyzing differences between characteristics of the quick-response and slow-response groups using the t-test to determine personal characteristics of gender, age, status, educational level, and position of the respondents in the first and final rounds, with slow responses regarded as representatives of a non-response group. Results showed that the response group and the non-response group were not significantly different, with no problem of non-response bias.

The social response bias of the respondents was also tested using the Harman single-factor through exploratory factor analysis [86]. In this analysis, all questions were determined as only one factor and the covariance was determined as not over 50%. The test results showed variance at 41.905%, indicating that the common method bias did not have any impact on the data. Confirmatory factor analysis determined χ² = 2626.969 with df = 527, root mean square error of approximation (RMSEA) = 0.141, standardised root mean square residual (SRMR) = 0.110, non-normed fit index (NNFI) = 0.548, and comparative fit index (CFI) = 0.600. The fit indices did not pass the analysis criteria with poorer values than the measurement models. Sanchez and Brock [87], Verhagen and Van Dolen [88], and Yang et al. [89] suggested that different model indices not passing the criteria indicated no common method bias. As all the analysis results from the 1-factor model did not pass the criteria and were rejected, it was concluded that the studied variables had no common method bias.

3.3. Validity and Reliability of the Instrument

All scales of the studied factors showed improvement from previous studies and were suitable for the study contexts in the conceptual model. The JST scale used 2 items in the scale of Park et al. [90], the PT scale used 10 items in the scale of Watson et al. [91], the JS scale followed Tsui et al. [92], and the OC scale was adjusted from 16 items in the scale of Na-Nan and Saribut [93]. All scales were validated for content validity by five experts in organizational behavior, industrial psychology, human resource management, human resource development, and testing and evaluation. Results showed that values of content validity ranged between 0.8 and 1. The scales that passed content validation were then tested for questionnaire reliability. The results of the reliability tests were 0.954 for OC, 0.969 for PT, 0.914 for JST, and 0.922 for JS, whereas the reliability of the whole questionnaire was 0.960. The questionnaire was then utilized to collect data from the target group after these exams.

To determine whether the questionnaire items adhered to the concepts, theories, and empirical evidence, the construct validity of the questionnaire items was evaluated using confirmatory factor analysis in accordance with the concept of [94]. Construct validity was determined from the index of item-objective congruence as Chi-square (χ²), relative Chi-square (χ²/df), goodness of fit (GFI), adjusted goodness of fit index (AGFI), comparative fit index (CFI), standard root mean square residual (SRMR), and root mean square error of approximation (RMSEA) [95]. Results of convergent validity were χ² = 482.490, df = 457, p-value = 0.198, GIF = 0.902, AGIF = 0.946, CFI = 0.995, NFI = 0.917, RMSEA = 0.046, and RMR = 0.046. Standardised factor loading was significant at p < 0.001 (all t-values were more than 3) and all questionnaire items had significant relationships according to the theoretical structure.

The composite reliability (CR) and average variance extracted (AVE) were calculated to test the construct validity [94] to consider the scales and structures of the measurement scale at the final stage. As presented in

Table 1. Confirmatory factor analysis results of the test questions.

Latent Factors/Questions	Factor Loading	Construct Validity
		CR	AVE
OC		0.979	0.590
oc1	0.728
oc2	0.748
oc3	0.775
oc4	0.801
oc5	0.754
oc6	0.802
oc7	0.692
oc8	0.737
oc9	0.530
oc10	0.820
oc11	0.834
oc12	0.827
oc13	0.378
oc14	0.351
oc15	0.557
oc16	0.643
JST
jst1	0.897
jst2	0.885
PT
pt1	0.775
pt2	0.796
pt3	0.775
pt4	0.871
pt5	0.837
pt6	0.839
pt7	0.853
pt8	0.846
pt9	0.798
pt10	0.871
JS
js1	0.752
js2	0.762
js3	0.776
js4	0.857
js5	0.780
js6	0.819

, the composite reliability of every variable was 0.979, which is higher than the criteria (AVE > 0.50). Therefore, all theoretical structures possessed acceptable psychological properties.

3.4. Control Variables

Gender, age, status, education, work experience, and position are the six control variables used to assess OC in this analysis. To begin, we dummy coded males as 0 and females as 1 according to gender. Second, ages were categorized using an ordinal scale, with 21–25 years old being 1, 26–30 years old being 2, 31–39 years old being 3, and 40 years old and above being 4. Thirdly, there was an ordinal scale for status, with “single” being 1, “married,” and “divorced” each being 2. As a fourth point, education was recorded using an ordinal scale, with a bachelor’s degree being recorded as a 2, and a master’s degree as a 3. Fifthly, work experience was categorized using an ordinal scale, with 1 representing less than three years of experience, 2 representing four to six years, 3 representing seven to nine years, and 4 representing more than ten years. Finally, positions were dummy coded so that all staff members were assigned the value 0 and all managers were assigned the value 1. In this study, personal factors were employed as control variables in order to limit the amount of inaccuracy that the study results contained.

3.5. Data Analysis

Descriptive statistics were used to analyze the characteristics of the participants and the variable levels, whereas inferential statistics were employed to examine the direct effects and moderating effects of the variables using the SPSS program. Analyses of low, moderate, and high situations for the moderating effects and the independent variables were performed using Process Macro 3.1 with Model 3 [96].

4. Results

4.1. Sample Characteristics

Table 2. Biographical profile of respondents.

Variable	Frequency	%
Gender
Male	85	42.3
Female	116	57.7
Age
21–25 years	172	61.2
26–30 years	22	10.9
31–39 years	49	24.4
Over 40 years	7	3.5
Status
Single	149	74.1
Married	47	23.4
Divorced	5	2.5
Education
Under bachelor’s degree	54	26.8
Bachelor’s degree	131	65.2
Master’s degree	16	8
Work experience
1–3 years	72	35.8
4–6 years	71	35.3
7–9 years	27	15.4
Over 10 years	31	13.4
Position
Employees	144	71.6
Senior employees	57	28.4

reveals that 57.7% of the study’s samples were female, while the remaining 42.3% were male. Nearly two-thirds of the respondents were between the ages of 21 and 25, followed by those aged 26 to 30, 31 to 39, and those over 40 (24.4%, 10.9%, and 3.5%, respectively). Nearly three-quarters of the respondents (74.1%) were single, while 23.4% were married, and 2.5% were divorced. In total, 65.2% of the respondents graduated with a bachelor’s degree, followed by those with less than a bachelor’s degree (26.8%) and those with a master’s degree (8%). Approximately two-thirds of the respondents had job experiences ranging from 1 to 3 years and 4 to 6 years at the same rate of 35%, followed by 7 to 9 years (15.4%) and over 10 years (13.4%). Nearly three-quarters of respondents were employees (71.6%), while the remaining respondents were senior employees.

4.2. Descriptive Statistics

The results in

Table 3. Mean, standard deviation, and correlation coefficients among the study variables.

	Mean	SD.	1.	2.	3.	4.	5.	6.	7.	8.	9.	10.
1. Gender			1.000
2. Age			−0.287 **	1.000
3. Status			−0.239 **	0.567 **	1.000
4. Education			0.054	−0.017	−0.030	1.000
5. Experience			−0.246 **	0.648 **	0.456 **	−0.078	1.000
6. Position			−0.217 **	0.444 **	0.403 **	0.184 **	0.520 **	1.000
7. JST	2.871	0.993	0.043	0.061	0.045	−0.039	−0.005	−0.061	1.000
8. OC	3.759	0.716	0.008	−0.019	−0.010	0.256 **	0.068	0.168 *	0.028	1.000
9. PT	3.829	0.796	−0.089	−0.026	0.143*	0.146 *	0.025	0.179 *	−0.012	0.572 **	1.000
10. JS	3.576	0.841	0.110	−0.119	0.050	0.095	−0.075	0.061	0.076	0.341 **	0.312 **	1.000

Note: * indicates correlation is significant at the 0.05 level. ** indicates correlation is significant at the 0.01 level.

show the highest mean as PT (3.829), followed by OC, JS, and JST (3.759, 3.576, and 2.871, respectively). JST had the highest standard deviation (0.993), which was followed by JS, PT, and OC (0.841, 0.796, and 0.716, respectively). For correlation, all the studied variables were continuous. Correlation analysis between the independent and dependent variables revealed negative and positive relationships ranging from 0.287 to 0.648. No variable pairs were found above 0.80, indicating multicollinearity.

4.3. Direct and Moderating Effects Analysis

Table 4. Moderating effects of PT and JS on JST with consequent effects on OC.

	Dependent Variable: OC
	Step 1	Step 2	Step 3	Step 4	Step 5	Step 6
Constant	3.411 *** (12.313)	3.650 *** (11.248)	1.735 *** (5.225)	1.314 *** (3.803)	2.836 *** (3.792)	3.696 *** (4.356)
Gender	0.044 (0.502)	0.039 (0.440)	0.082 (1.129)	0.045 (0.635)	0.036 (0.509)	0.017 (0.236)
Age	−0.048 (−0.551)	−0.059 (−0.666)	0.039 (0.534)	0.051 (0.720)	0.040 (0.564)	0.047 (0.674)
Status	−0.026 (−0.215)	−0.031 (−0.256)	−0.156 (−1.534)	−0.172 (−1.737)	−0.176 (−1.799)	−0.194 (−1.985)
Education	0.075 (0.954)	0.077 (0.973)	0.049 (0.753)	0.048 (0.763)	0.053 (0.849)	0.041 (0.665)
Experience	0.054 (0.786)	0.059 (0.860)	0.064 (1.137)	0.072 (1.309)	0.073 (1.329)	0.076 (1.396)
Position	0.085 (1.287)	0.089 (1.341)	0.001 (0.027)	−0.009 (−0.164)	−0.012 (−0.230)	−0.022 (−0.418)
JST		−0.073 (−1.406)	−0.068 (−1.594)	−0.061 (−1.469)	−0.547 * (−2.528)	−0.802 ** (−3.247)
PT			0.524 *** (9.676)	0.483 *** (8.949)	0.111 (0.651)	0.166 (0.967)
JS				0.173 ** (3.438)	0.175 ** (3.513)	−0.088 (−0.650)
PT × JST					0.121 * (2.287)	0.106 * (1.999)
JS × JST						0.082 * (2.084)
R square	0.026	0.036	0.352	0.390	0.406	0.419
Adjusted R square	−0.004	0.001	0.325	0.361	0.375	0.385
R square change	0.026	0.010	0.316	0.038	0.016	0.013

Note: * indicates correlation is significant at the 0.05 level. ** indicates correlation is significant at the 0.01 level. *** indicates correlation is significant at the 0.001 level.

shows the mean and correlation coefficients of JST, PT, JS, and OC. Hierarchical regression analysis was used to test the three hypotheses. Initially, the regression model was developed to test the direct effect of JST on OC (H1). The regression results summarised in Table 4 showed that the coefficient of JST was not statistically significant on OC (p < 0.05) so the findings did not support H1. Next, we examined the effect of personal factors as control variables. Results showed that personal factors had no effect on the relationship with OC.

As presented in Table 3, we included PT (the moderating variable) to examine the interaction between JST and OC in the regression equation model (H2). We also included JS to examine the interaction between JST and OC (H3) and analyzed the proportion of covariance when including each variable following Licht [97]. To reduce multicollinearity, the raw scores of the independent variables and moderating variables were deducted from the mean-centered before developing the interaction conditions [98].

As shown in Steps 3 and 4 of Table 3, we included PT and JS in the regression and saw a significant change in R² (p < 0.1), indicating an increase in the regression model’s predicting power. The findings revealed that PT and JS were statistically significant (p < 0.001). In Step 5 and Step 6, we evaluated the moderacting effects of PT and JST in the regression model, resulting in an R² change with increasing model predicting power. The results showed that PT interacted with JST and had significant effects on OC (p < 0.05), so H2 was supported, while JS interacted with JST and had significant effects on OC (p < 0.05), so H3 was also supported. The results suggested that the effect of JST on OC depended on the PT and JS of employees (PT and JS moderated JST). To obtain a clear picture, see the results in Figure 2.

Following Hayes [96], the PROCESS macro for SPSS was introduced to place the moderating effects and explain the predictions at different levels of the moderators. Pick-a-point analysis was used to consider details at three points of the relationship of the moderators (PT and JS) with the independent variables and their effects on the dependent variables. How did the low, medium, and high values of PT (3.033, 3.829, and 4.625) and the low, medium, and high values of JS (2.735, 3.577, and 4418) moderate OC? Interestingly, PT in the low situation and JS in the low situation significantly moderated JST on OC (p < 0.001), while PT in the low situation and JS in the medium situation significantly moderated JST on OC (p < 0.01).

Similarly, PT in the medium situation and JS in the low situation significantly moderated JST on OC (p < 0.001), whereas PT in the medium situation and JS in the medium situation significantly moderated JST on OC (p < 0.05). The other situations showed no statistical significance, as shown in

Table 5. Simple slope values of the moderating variables with 3-way interaction.

MPS	MJS	Effect	se	t	p	LLCI	ULCI
3.033 (−SD)	2.735 (−SD)	−0.254	0.075	−3.401	0.001	−0.401	−0.107
3.033 (−SD)	3.577 (−SD)	−0.184	0.066	−2.800	0.006	−0.314	−0.054
3.033 (−SD)	4.418 (−SD)	−0.114	0.073	−1.569	0.118	−0.258	0.029
3.829	2.735	−0.169	0.059	−2.880	0.004	−0.285	−0.053
3.829	3.577	−0.100	0.043	−2.335	0.021	−0.184	−0.016
3.829	4.418	−0.030	0.049	−0.606	0.545	−0.127	0.067
4.625 (+SD)	2.735 (+SD)	−0.085	0.070	−1.211	0.228	−0.223	0.053
4.625 (+SD)	3.577 (+SD)	−0.015	0.054	−0.281	0.779	−0.121	0.091
4.625 (+SD)	4.418 (+SD)	0.055	0.056	0.983	0.327	−0.055	0.165
STR × MPS, R2-chng = 0.012, F = 3.998, df1 = 1.0000, df2 = 189.000, p = 0.047 STR × MJS, R2-chng = 0.013, F = 4.346, df1 = 1.0000, df2 = 189.000, p = 0.038

.

The conditional analysis results of the moderating effects according to the pick-a-point method showed statistical significance at some points, as observed from the slope values in three situations (Table 4). In the first situation, PT in the low situation and JS in the low situation had significant moderating effects on the relationship between JST and OC (p < 0.001). Meanwhile, PT in the low situation and JS in the medium situation had significant moderating effects on the relationship between JST and OC (p < 0.05). However, PT in the low situation and JS in the high situation did not show statistical significance of moderating effects on the relationship between JST and OC (p < 0.05), as shown in Figure 3.

In the second situation, PT in the medium situation and JS in the low situation had significant moderating effects on the relationship between JST and OC (p < 0.01). Meanwhile, PT in the medium situation and JS in the medium situation had significant moderating effects on the relationship between JST and OC (p < 0.05). However, PT in the medium situation and JS in the high situation did not show statistical significance of moderating effects on the relationship between JST and OC (p < 0.05), as shown in Figure 4.

In the third situation, PT in the high situation and JS in the low situation did not show statistical significance in the moderating effects on the relationship between JST and OC (p < 0.05). Meanwhile, PT in the high situation and JS in the medium situation did not have statistical significance of the moderating effects on the relationship between JST and OC (p < 0.05). Similarly, PT in the high situation and JS in the high situation did not show statistical significance of moderating effects on the relationship between JST and OC (p < 0.05), as shown in Figure 5.

5. Discussion

The results of this study indicated that genders, ages, statuses, educations, work experience, and job positions had insignificant effects on OC. This can be explained by the fact that personal elements are physical characteristics that are currently regarded as identical and subject to the same organizational assignment standards. Moreover, equality is currently the issue that civilizations prioritize and adhere to. The significant negative relationship between JST and OC found in this study suggests that JST reduces the OC of employees. As a result, human resource departments recognized that JST could interfere with OC and employee work [99]. JST may originate from work overload, decision latitude, the environment, colleagues, or supervisors. All these factors can generate abnormal psychological stress that hinders work operations, negatively impacts physical and mental health, and reduces the commitment to company values. Employees lose interest in work and may even seek alternative employment [1]. The findings of this study provide evidence that the concepts and theories underlying the employment demand–resource model are accurate. Our results suggested that employees with JST had reduced OC, supporting empirical data from previous studies [1,12].

The results also suggested that PT had a moderating effect on the relationship between JST and OC and was helpful in increasing OC, thereby supporting the concept and theory of PT as an emotional state to positively perceive surroundings. Therefore, employees with PT will perceive problems or stress in a positive way as challenges to overcome [100], resulting in eustress [95,101], by putting knowledge and effort into good effect to enhance company prosperity [102,103,104]. A stressful environment and rapidly changing situation can also cause JST in salespeople. Adopting an optimistic approach through PT towards problems can reduce JST. This finding concurs with Kapikiran [15], Khan and Husain [16], Pukkeeree, Na-Nan, and Wongsuwan [17], Tugade, Fredrickson, and Feldman Barrett [63], Tavakoli [65], and Tully and Tao [64], who argue that PT is beneficial for lowering occupational stress and weariness among employees. PT assists a person to repair and normalize a negative disposition. According to some experts, PT is a psychological state that manages or regulates JST. PT is advantageous for stress management since it is creative and resists negative thinking (a source of workplace stress). With less JST, employees are more commited with their organizations because they have a favorable perception of the organization’s goals, values, and work environment, and so want to continue working for their organizations.

Our findings suggested that JS had a positive moderating effect on the relationship between JST and OC, similarly to PT as presented earlier. The results showed that JS was helpful in reducing JST while increasing OC. This finding supports the concept and theory that JS is a psychological state whereby employees take pleasure in their working environment [105,106,107]. JST in salespeople is caused primarily by job demand, decision latitude, or different environments. If these stimuli are managed properly, JS will reduce, leading to positive behaviors or feelings towards the sources of dissatisfaction or stress. When employees’ JST decreases, their OC increases. Therefore, creating JS can reduce JST. Moreover, salespeople usually work in environments with high demand and rapid change that can easily cause JST. Companies should support or build JS to enhance employees’ pleasure when dealing with their responsibilities in the working environment. JST can be reduced by building and supporting JS to increase OC. This finding concurs with those of Ngirande [18], Shin and Jung [19], Soomro, Breitenecker, and Shah [20], Terry, Nielsen, and Perchard [79], and Abraham [80] in that the emotional component of job satisfaction reflects the employee’s feelings about their work and the work environment. When an employee states that he or she has a high level of job satisfaction, it means that he or she places a high value on his or her job dignity, enjoys his or her work, and has good thoughts about it. Individuals’ levels of performance and the organization’s overall productivity benefit from a good attitude about their workplace. Nonetheless, insufficient job satisfaction might result in demotivation, which in turn reduces organizational commitment. Those with a high degree of job happiness will have a lower JST, whereas those with less job satisfaction will have a lower OC.

6. Conclusions

JST is a factor that has a negative influence on the OC. The findings of this study are in line with the findings of other investigations. On the other hand, the findings of this study indicated that a good attitude and feeling satisfied with one’s work were important mediators in the relationship between JST and OC. According to the findings of this study, JST has a negative effect on OC. PT and JS were studied together, which led to an increase in OC. Moreover, help with resources, job design, or atmosphere could lessen JST, resulting in higher OC.

6.1. Theoretical Implications

Our results had three important implications. The first is that JST has an influence on reducing OC, while JS and PT can moderate JST, resulting in a higher OC. OC stems from various factors, regardless of internal or external stimuli. Our findings suggested that JST caused by internal or external stimuli can reduce OC. By contrast, PT and JS can lower JST and raise OC. The academics and researchers who are studying the topic of OC need to consider employees’ JST as the primary causal variable with an effect on lower OC. Research on OC should also take into account a JST variable because it is an important factor that employees cannot escape. It is vital to investigate JST in order to explain the phenomenon of OC if one wishes to acquire a high level of reliability from research conducted on such a problem.

The second implication of this study is that PT in low and medium situations, together with JS in low and medium situations, can help to increase OC when employees become stressed. Despite the prevalence of JST, PT in the high situation and JS in the high situation increased OC. Therefore, in order to adequately understand the phenomenon of OC, researchers and academics who investigate the topic need to incorporate the variables of PT and JS as moderators into their studies. According to the findings of the study, low levels of PT and low to moderate levels of JS may be able to alleviate stress on the job and boost OC. Because of this, the study or explanation of the OC phenomenon should incorporate these elements into the study in order to gain a better understanding of OC and to ensure that the study’s conclusions are as accurate as possible.

The third implication from this study is that PT and JS can lower JST and effectively raise OC. As a theoretical application, academics and researchers can use the findings of this study to explain JST since the findings of this study suggest that having a positive attitude and being satisfied with one’s job can minimize the amount of JST experienced by employees. As a result, making use of these two variables in conjunction with JST in an effort to co-explain or investigate it can result in increased accuracy and dependability. In a similar vein, PT refers to a mental state that can help a person become more optimistic in the face of challenges or unfavorable occurrences. It is permissible to use these two factors in the study or explanation in order to obtain answers for describing ways to lessen JST while studying OC. Therefore, the management, promotion, and development of PT and JS among employees must be considered.

6.2. Practical Implications

According to the findings, PT and JS are the moderating variables that contribute to a lower level of JST and a higher level of OC. Managers should pay special attention to staff and offer them direction in both their professional and personal lives to help them cope with or mitigate the consequences of JST. Concurrently, businesses need to set JST management policies that apply to all staff members. Activities like employee relations and sports might be organized, supervisors’ abilities to care for employees’ wellbeing could be enhanced, psychologists could be brought in to advise businesses, jobs could be designed to maximize efficiency, and happiness at work could be fostered, and so on. For a fuller understanding of the variations in OC, modifiers like JS and PT should be added into study design.

Human resources departments can increase workers’ happiness by giving them what they need to do their jobs well in the form of benefits, welfare, working resources, the arrangement of a supportive environment, the promotion of collaboration, and the creation and support of a good atmosphere, as well as by providing necessary feedback and setting policies to support the work operation. In addition, if you take care of your employees, offer them opportunities to learn and grow, and set up a counseling service, you can expect them to approach obstacles with optimism and confidence.

6.3. Research Limitations and Suggestions for Future Study

According to our findings, JST, PT, and JS all have an effect on OC. Therefore, future research attempting to explain OC in other occupational situations should include the effects of these three variables. This may limit the generalizability of the results because the study’s samples consisted of Thai food salespeople. Future researchers should collect data from various professions, such as those in engineering, accounting, information technology, human resource management, etc. In addition, all of the employees in the sample worked in the food industry. This industry has job characteristics that are distinct from those of other industries. To increase the rigidity of the produced model, the study’s contexts should also be broadened to encompass a variety of jobs, languages, societies, and cultures.