Table test the statistical association between seven occupational

 

Table
2. Cronbach’s alpha values for factors used in this study

Sl.
No

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Factor

Cronbach’s alpha

 

Women

Men

 

Overall independent Stress Factors (1-8)      

0.79

0.83

1

Shift
work

0.64*

0.67

2

Working Hours

0.71

0.81

3

Lack
of control

0.72

0.83

4

Social
Support

0.82

0.71

5

Job-insecurity

0.80

0.73

6

Lack
of reward

0.78

0.74

7

Social
support

0.84

0.70

8

Performance

0.76

0.77

Overall:
C-alpha: 0.83; Women: 0.79; Men: 0.83(*very few women work in shifts)
Source:
Primary Data

 

Statistical analysis

 

The chi-squared test was
applied to study the if any statistically significant association between
occupational stress factors, general characteristics and its effect on
performance. Univariate and multivariate logistic regression analysis carried
out to test the statistical association between seven occupational stress
causing factors – Shift work, Demanding job (excessive work demand), Working
Hours, Lack of Control, Social Support, Job Insecurity, Lack of reward and
their effect of employee performance. The univariate logistic regression
analysis was carried out to determine the influence of occupational stress
factors and employee performance, and the multivariate logistic regression
analysis was carried out to adjust the variables showing statistically significant
association in the univariate analysis. All the statistical analyses were
conducted using Statistical Analysis System (SAS) ver. 9.4.

Results

 

Influence of general characteristics and health behaviours on
employee performance: The association between general characteristics, physiological
and psychological factors are presented in Table 3. In this study Women (50.4%)
and men 49.6%) participants more or less equal. There was a high correlation
between gender parity (p=0.028), Age
group (p<0.001), Marital status (p=0.009), having children (p<0.01), diabetes (p<0.01), physiological factors (p<0.001), psychological factors (p=0.003) and experience (tenure of employment, p<0.01) shown significant association with employee performance. The employee health factor hypertension shown no statistically significant association with employee performance (p=0.612). Therefore except, hypertension all other factors effecting the employee performance, however the association between the general characteristic age group, having children, health factor diabetes and tenure of employment more significant effect on employee performance.   Table 3: The association between general characteristics, occupational stress factors physiological and psychological factors, and its effect of employee performance   Effect on Performance Gender N2 % Numbers % p-value1 Women 381 50.4 236 61.9 0.028 Men 375 49.6 208 55.5   Age (Years)           20-29 226 29.9 26 11.5 <0.01 30-35 265 35 157 59.2   36-40 173 22.9 87 50.2   >40

92

12.2

57

62.0

 

Marital status

 

 

 

 

0.009

Married

623

82.4

444

71.3

 

Unmarried

123

17.6

56

45.5

 

Children

 

 

 

 

<0.001 Yes 542 71.7 431 79.5   No 224 29.3 13 5.8   Diabetic           Yes 348 46.0 296 85.0 <0.001 No 408 54.0 148 36.2   Hypertension           Yes 287 38 165 57.5 0.612 No 469 62 287 61.2   Physiological         <0.01 Yes 375 49.6 221 58.9   No 381 50.4 112 29.4   Psychological         0.003 Yes 175 23.1 81 46.3   No 581 76.9 164 28.22   Experience (Years)         <0.01 0-5 188 24.9 16 8.5   6-10 318 42.1 196 62.2   11-15 159 21.0 85 53.5   >15

91

12.0

56

61.5

 

1Comparison
by chi-square test
2All
numbers are based on weighed frequencies

 

The univariate analysis
of occupational stress factors and employee performance are presented in Table.
The performance of the employees who work in shifts slightly more effected than
non-shift workers as the shift workers were more prone to occupational stress
than the non-shift employees, however the differences were not statistically
significant (p=0.55). The performance
of the group whose job is more demanding (64.0%) significantly affected when
compared with low demand of job (29.1%) and the differences are statistically
different (p=<0.01). The group of employees who worked >60 workers per
week (53.8%) showed high occupational stress so more effect on their
performance when com compared with <40 hrs per week (13.8%), 41-50 hours per week (19.1%), 51-60 hrs (17.1%) and the differences are statistically different (p=<0.01). Occupational stressors like lack of control, social support, job insecurity and lack of reward have statistically significant association with performance. The stress factor lack of control (p<0.01) effects performance significantly when compared with other factors (social support p=0.029, job insecurity p=0.024 and lack of reward (p=0.042) Table 4. Table 4: The association between occupational stress factors and performance   Effect on Performance   N2 % Numbers % p-value1 Shift work           No 671 88.75 135 20.1 0.55 Yes 85 11.25 20 23.5   Demand Job           Low 423 55.5 123 29.1 <0.001 High 333 44.5 213 64.0   Working hours/week           <40 362 47.9 50 13.8 <0.01 41-50 220 29.2 42 19.1 51-60 105 14.1 18 17.1 >60

69

8.8

35

53.8

Lack of control

 

 

 

 

 

Low

521

68.9

123

23.6

<0.01 High 235 31.1 111 47.2 Social Support           Low 587 77.65 233 39.7 0.029 High 169 22.35 45 26.6 Job insecurity           Low 616 81.48 400 64.9 0.024 High 140 18.52 67 47.85 Lack of reward           Low 456 60.32 245 53.7 0.042 High 300 39.68 123 41.0 1Comparison by chi-square test 2All numbers are based on weighed frequencies   The multivariate analysis of occupational stress factors and their association with employee performance at 95% CI is shown in Table. Working in shifts (p=0.39, OR) demanding job (p=0.54, OR), lack of control (p=0.67, OR) and lack of reward (p=0.29, OR) has no statistically significant relationship on its association with performance. However, working for long hours has significant risk factor and effect employee performance. Compared with the reference group of <40 hours/week, the ORs for working >60 hours 1.94 (95% CI 1.65-2.44), 51-60 hrs 1.41 (95% CI 1.09-1.71); and
41-50 hours 1.12 (95% CI 1.02-1.41) and P<0.01 for unadjusted model, whereas 1.87 (95% CI 1.54-1.62), 1.52 (95% CI 0.97-1.76), and 1.17 (95% 1.03-1.61) (p<0.01) in adjusted model respectively, indicating statistically significant association between working hours and effect on employee performance.   We also found statistically significant association between occupational stress factors and employee performance were significant for Job insecurity and social support. The odds ratios for job insecurity 3.42 (95% CI 2.86-6.83) social support 4.47 (95% CI 4.38-5.98) for unadjusted model and 4.27 (95% CI 2.86-6.87) and 4.47 (95% CI 4.38-5.98) respectively. Lack of inadequate support and high job insecurity significantly influence on the occupational stress and employee performance (Table 5.)   Discussion   In this study we identified the association between occupational tress factors and its effect on employee performance using the large representative data collected through a survey questionnaire circulated among the agricultural research sector employee in and around the Hyderabad. We have observed a strong association between employee performance and factors like gender, age, health factor, diabetes, marital status. The women experience more stress than the men because of their dual roles as employee and handling the family.  The physiological factors like back/neck pain, short of breathlessness, nervousness, relaxing issues, psychological factors like anxiety, sleep disorders, health conditions and depression will cause the stress and effect the performance. This hypothesis was supported by recent studies (de Graaf et al. 2012). We also found significant relationship between working hours and effect on performance. Those who worked more than 60 overs per week had nearly a 2-fold higher odds ration than those who worked 40 or less hours per week. Respondents working 51-60 higher odds ration followed by 41-50 hours. This explains the adverse health effects of long working hours and this is in line with the studies carried out by (Kivimaki et al. 2011 and Karasek R, 1981).   Table 5: Univariate and multivariate logistic regression analysis of occupational stress factors and their association with employee performance (the odds ratios and 95% CI levels)   Unadjusted OR   Adjusted OR1   OR (95% CI) p OR (95% CI) p Shift work No 1.00 1.00 Yes 1.21 0.92-1.36 0.45 1.10 0.81-1.41 0.39   Demanding Job Low 1.00 High 1.76 1.54-2.25 0.68 1.62 1.47-2.92 0.54   Working hours/week <40 1.00 1.00 41-50 1.12 1.02-1.41 1.17 1.03-1.61 51-60 1.41 1.09-1.71 <0.01 1.52 0.97-1.76 <0.01 >60

1.94

1.65-2.44

1.87

1.54-2.67

 
Lack of control

Low

1.00

1.00

High

0.97

0.87-1.14

0.55

0.98

0.84-1.31

0.67

 
Social support

Low

1.00

 

 

1.00

 

 

High

4.42

2.76-6.74

<0.01 4.47 4.38-5.98 <0.01 Job insecurity Low 1.00 1.00     High 3.42 2.86-6.83 <0.01 4.27 2.86-6.87 <0.01   Lack of reward Low 1.00 1.00 High 1.72 1.67-3.86 0.34 1.67 1.32-2.94 0.29 1Adjusted model for all listed variables ; OR: odds ratio; CI: Confidence interval   The other stress causing factors which have strong association with the employee performance are job insecurity and social support. There was an increase in odds ration with increase in social support indicating the adequate social support will reduce the stress and improves the performance. The similar results were reported in a study carried on CBSE teacher in and around Hyderabad (Prasad et al. 2016). The insecurity was studied the factors of job displacement, change of work place in the same organization, unable to code the new technologies. The job insecurity is one of the major factors of occupation stress in our study and this confirm the studies carried out by (Prasad et al. 2015).  Most of our results are in confirmatory with the results of the several studies of the recent past published in referred journal articles.   Conclusions and limitations   The main reason for conducting this study is that authors are unable to find sufficient literature for finding the association between occupational stress factors and its relationship with performance. We made a sincere attempt to use multivariate logistic regression analysis and univariate logistic regression to analyse dichotomous data and like odd ratios using adjusted and unadjusted models. The limitation of the study is that the authors are not aware that the responses are submitted are top of the mind decision and are own, not influenced by other factors. We have used the Cronbach Alpha reliability statistic to measure the internal consistencies of the research instrument. However, survey research other limitation like the authors have no idea the environment and mood of the respondent when submit the questionnaire. We are surprised by the active participation of the women employees and receipt of the responses were quick and frank compared with men employees. We recommend this type of studies considering gender-related parity on each and every occupational stress factor and its effect on performance.

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