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43I6 IJAET0612656

  1. 1. International Journal of Advances in Engineering & Technology, Jan 2012.©IJAET ISSN: 2231-1963 ANALYSIS AND MULTINOMIAL LOGISTIC REGRESSION MODELLING OF WORK STRESS IN MANUFACTURING INDUSTRIES IN KERALA, INDIA K. Satheesh Kumar1 and G. Madhu21 Assistant Professor (Sr. Grade), Department of Mechanical Engineering, Federal Institute of Science and Technology, Ernakulam, Kerala, India.2 Professor and Head, Safety and Fire Engineering Division, Cochin University of Science and Technology, Ernakulam, Kerala, India.ABSTRACTThis study examines the influence of factors responsible for work stress among the employees in themanufacturing industries in Kerala, India. The sample size of the subjects selected for the study consists of 75Engineers, 110 Supervisors and 675 Workers in the selected manufacturing industries in Kerala ,India. Sevenfactors were identified with the existing literatures, and in consultation with safety experts for the evaluation ofwork stress. The instrument developed by using these factors had validity, unidimensionality and reliability. Theresponse rate was 81.3%. It is observed that existence the factors responsible for work stress among all thecategories of employees in these industries. The multinomial logistic regression model developed is found goodin predicting the work stress in manufacturing industries.KEYWORDS: Work stress, multinomial logistic regression model, manufacturing industries I. INTRODUCTIONOccupational stress is becoming a major problem in both corporate and social sectors .Inindustrialized countries, there have been quite dramatic changes in the conditions of work, during thelast decade due to the economic, social and technical development. As a consequence the peopletoday at work are exposed to high quantitative and qualitative demands at the work place. Inmultinational companies, lean production, and down sizing has raised stress level of employees [1-3].The national institute of occupational safety and health (NIOSH-USA) defines stress as “the harmfulphysical and emotional responses that occur when the requirements of the job does not match with thecapabilities, resources of the workers.” [4,5 ]The cost associated with work place stress indicates an international trend among industrializedcountries. A recent report says that work related ailments due to work related stress are likely to costIndia’s exchequer around 72000 Crores in 2009-15 [6]. Though India is a fast developing country itis yet to create facilities to mitigate the adverse effects of work stress. The study of work stress in themember states of European Union (EU) points out that an average of 22% of the working Europeansexperience work stress [7-9].It is noted that work stress occurs among the employees at the context of work and at the content ofwork [10, 11]. The potential stressors for these hazards in the context of work are organizationalculture and function, role in the organization, career development, decision latitude and control,interpersonal relationship at work, work-home interface and change [11-13].Studies on the employees perceptions and descriptions of their organizations, suggest three distinctaspects of organizational function and culture: organization as a task environment, as a problemsolving environment and as a development environment [11, 14]. The available evidence suggests that 410 Vol. 2, Issue 1, pp. 410-418
  2. 2. International Journal of Advances in Engineering & Technology, Jan 2012.©IJAET ISSN: 2231-1963the organization is perceived to be poor in respect to these environments, will likely to be associatedwith higher stress [11, 14, 15].Another major source of stress is associated with person’s role at work. A great deal of research isdone on role ambiguity and role conflict .It is found that role conflict and role ambiguity areinstrumental in developing physiological disorders and says that the above factors can also lead toorganizational dysfunction and decreased productivity [16-18]. Lack of expected career growth is oneof main sources of work stress. The factors connected with this are poor promotion polices, jobinsecurity and poor pay in the organization [19-21].Decision latitude and control are important aspects of work stress. These shows the extent which theemployees are participating in the decision making process, and also shows the freedom given to theemployees for choosing their work [22-24].The number of research works points out the need of good relationship with superiors , support fromthe superiors and support from the colleagues at work for the elimination of work related stresshazards [25-28].Many literature points out the work related stress hazards due to work-family conflict .It is foundthat that work-family conflict is a form of inter role conflict ,in which the role pressures from thework family domains are mutually non compatible in same respect [29-31].Change is one of the mostcommonly found stressor at the context of work[11]. It is observed that changes in the modern workenvironment as result of technological advances, organizational restructuring and various redesignoptions can elevate the work stress [32, 33].Like context of work, content of work are also leads to work stress. These factors arise due toimproper design of the task ,work load and workplace, and work schedule [11,13,34,35].There areseveral aspect of job content ,which are found hazardous and these include low value of work ,lowuse of skills ,repetitive work , uncertainty , lack of opportunity to learn, high attention demand ,conflicting demand , insufficient resources [11].The research work shows that ,work related stresshazards arise due to meaning less task and lack of variety etc….It is also noted that most stressfultype of work are those which have excessive demand and pressures that do not match with theworkers knowledge and abilities [11,36-38]The studies on the effect of work stress among men and women working groups in USA and foundthat due to high psychological work demands like excessive work load and time pressures leads towork stress and cause depression and anxiety in young working adults[11,39-41]Two major factors responsible for work stress due to the improper work schedule are shift workand long working hours .The studies conducted in Italy among the shift workers observed that shiftwork leads to poor sleep and health related problems [42-46].Studies conducted among white collarworkers in Sweden, points out that work stress is associated with men subjected to long workinghours (75 hours/week) and it is shown that this leads to wide range of ill health in men and women[42-46 ].Several models have been proposed to explain the causes of work related stress[47-49].Frankenhaeuser have described a model where stress is defined in terms of imbalance between theperceived demands from the environment and individuals perceived resources to meet those demands[50].This imbalance can be caused by quantitative overload (A very high work pace, too much workto do etc…) or qualitative overload (too much responsibility, problems too complex to solve, conflictsetc…).A well known model describing work stress or strain is the demand control model proposed byKaresek and Theorell and developed and expanded by others. According to this model, thecombination of high demands and lack of control and influence (low job discretion) over the worksituation causes high work strain [51, 52].Johannas Siergrist proposed a new model for stress at the work called the effort-reward imbalancemodel. According to this model, lack of adequate reward in response to the individual’s achievementefforts is considered to contribute to high stress levels and elevated health risks .Reward could beobtained in terms of economic benefits, such as higher income [53,54].Recently multinomial logisticregression models are used to establish relationship between psychosocial work stress factors likework content ,work load and social support and job burn out [55-57]. 411 Vol. 2, Issue 1, pp. 410-418
  3. 3. International Journal of Advances in Engineering & Technology, Jan 2012.©IJAET ISSN: 2231-1963 II. SUBJECTSTotal number of subjects selected for this study is 830 and the resulted sample consists of Engineers(75 Nos.), Supervisors (110 Nos.) and workers (675 Nos.). Participants selected for this study consistsof both male and female employees of age between 25 to 55 and had sufficient educational background for their job. All employees are permanent and working in shifts in rotation and each shiftconsists of 8 hour duration per day. However the majority of the employees, in these industries weremales and number of woman participants is about 10% of the male participants. All the industries arelarge scale and profit making for the last five years and located at different districts of Kerala, India. .III. METHODSFrom the literature review and with the consultation of safety experts seven factors were identifiedfor the evaluation of work stress in the absence of well defined factors for the evaluation of workstress in Kerala, India. They are demand, control, manger support, peer support relationship, role andchange. The final draft of the questionnaire had 35 items with seven subscales .All the questions werelikert type with five fixed alternatives (always, often, sometimes, rarely, never). In addition to this 10demographic questions are also included in the questionnaire. This questionnaire was refined andvalidated further by means of confirmatory factor analysis (CFA)[58].This resulted in removal of fiveitems from the questionnaire. The number of retained items in the questionnaire were demand (7items), control (4 items), manager support (4 items), peer support (4 items), relationship (4 items),role (5 items) and change (2 items). The values of Comparative Fit Index (CFI), Tucker Lewis Index(TLI), and Cronbach alpha shows that the refined scale has good validity and unidimensionality inaddition to reliability [58-60]. The analysis was performed by using the software AMOS-7 [61]. Thefilled up schedules are then carefully edited for completeness, consistency and accuracy. The overallresponse rate was 81.3%.On the basis of data so collected, the influence of factors on works stress analysis is performed usingone-way ANOVA. Multinomial logistic regression modelling was done further to find the associationof factors responsible for work stress in manufacturing industries.IV. RESULTS4.1. Correlation MatrixA correlation analysis between the variables /factors so identified was performed and the result ofthe analysis is given in the Table-1.It is noted that all the correlations were positive , but nosignificant correlation was found between the variable /factors (<0.5) .Therefore the variable selectedfor the study can be treated as independent variables for the purpose of research. The correlationanalysis were carried out by means of SPSS-15. Table 1 : Correlation between the factors 412 Vol. 2, Issue 1, pp. 410-418
  4. 4. International Journal of Advances in Engineering & Technology, Jan 2012.©IJAET ISSN: 2231-19634.2 .Influence of factors on different categories of employeesThe influences of these factors are analyzed among different categories of employees by means ofone-way ANOVA. The result of the test is given in the Table -2 .The tests are conducted for 0.5 levelsignificance. Table 2 : Mean Score of FactorsThe mean score of the factors /variables points out that existence of factors responsible for work stressamong all the categories of the employees in these industries.It is noted that , significant difference in the factors , control, manager support, and peersupport(p<0.05) among different categories of employees To identify which among the categories hassignificant difference , Tukey’s multiple comparison test for each of the factors and the results aregiven in the Table -3 Table -3. Significant difference between different categories of employees Factors/Variables Difference between different designation levels Control Engineer and worker Supervisor and worker Manager support Supervisor and worker Peer support Supervisor and workerThe post- hoc analysis, reveals that considerable difference in the mean score of the factor “control”exists between engineers and worker. Further a noted difference is observed for this factor betweensupervisor and worker .While analyzing the variables manger support and peer support considerabledifference is observed only between supervisors and workers4.3. Multinomial logistic regression modeling of work stressMultinomial logistic regression is the extension of (binary) logistic regression [62], when categoricaldependent outcome has more than one level. An attempt is made to establish multinomial logisticregression model by developing the relationship with the factors/predictor variables .Thefactors/predictor variables are demand, control, manager support, peer support, relationship, role and 413 Vol. 2, Issue 1, pp. 410-418
  5. 5. International Journal of Advances in Engineering & Technology, Jan 2012.©IJAET ISSN: 2231-1963change. The dependent variables for this study are different designation levels namely, engineers,supervisors and workers in the selected manufacturing industries.For this modelling the reference category is chosen as worker and the multinomial odds ofimprovement in work stress due to unit increase in the predictor variables/ factors among engineersover the workers is analyzed by this model [see equation----4.3(1)]. Similarly the multinomial oddsof improvement in work stress due to unit increase in the predictor variables among supervisors overthe workers is analyzed [see equation----- 4.3.(2)].This relative improvement in work stress for onegroup over the reference group is explained by means of odds ratio (OR), which is shown under thecolumn Exp(B) (See Table -5).The odds ratio for the event at 95% confidence level is also evaluated.The model fitting information (Table-4), reveals that the initial log likelihood value obtained for themodel with no independent variables ( intercept only model) is 1076.51.The final log likelihood valueobtained for the model by considering all independent variables is 1003.57 .The chi-square valueobtained is72.95. As the p-value obtained is below 0.05, we can conclude that the final model is betterthan the intercept only model. Table-4. Model fitting information –different designation levels Model Fitting Likelihood Ratio Tests Criteria Model -2 Log Likelihood Chi-Square df p-value Intercept Only 1076.51 Final 1003.57 72.947 14 <0.01The associations of predictor variables are found with different designation level of the employees.For this analysis, we choose base category as workers Table-5 . Parameter estimates 414 Vol. 2, Issue 1, pp. 410-418
  6. 6. International Journal of Advances in Engineering & Technology, Jan 2012.©IJAET ISSN: 2231-1963The variable shown in the Table-5, has two parts labelled with out come variable designation .Thiscorrespond to two equations shown belowLog(p(designation=engineers)/p(designation=workers)=-4.002+.068De+0.240Cl+0.074Ms+0.073Ps-0.124Re+0.064Rl-0.215Ch (1)Log(p(designation=supervisors/p(designation=workers)= -4.548+0.016De+0.076Cl+0.104Ms+0.063Ps+0.011Re+0.046Rl -0.230Ch (2) V. DISCUSSIONThe main aim of the study is to develop and analyze the factors responsible for work stress amongthe employees in the public sector manufacturing industries in Kerala, India. Accordingly sevenfactors were developed and the validity, and unidimensionality of the questionnaire was analyzed bymeans of CFA and the overall reliability of the questionnaire was found satisfactory (>0.70). .Interestingly it is found that the factors responsible for work stress is prominent in differentcategories of employees namely engineers, supervisors and workers these industries. It is also notedthat lack of control among lower categories of employees particularly among workers compared toother categories of employees. The results of many earlier research supports the finding [52].The multinomial logistic regression models can be interpreted by means of odds ratio asOne unit increase in the variable- demand the multinomial odds of improvement of work stressamong engineers over workers is expected to increase by a factor 1.070.Similar trend is obtained onsupervisors over worker (OR >1),while keeping all other variables constant[62]. Similarly one unit increase in variables –control, manager support, and peer support ,the multinomialodds of improvement of work stress expected to increase among engineers over workers by thefactors 1.272,1.076and 1.075 respectively. Similar trend is noticed for supervisors over workers(OR>1).The multinomial odds of improvement in work stress, for the unit change in the variable –relationshipis expected to increase among supervisors over workers is expected to increase by a factor of 1.012and reverse trend is noticed for the same variable among engineers over workers (OR<1).One unit increase in the variable – role the multinomial improvement in work stress is expected toincrease among engineers over workers by a factor 1.066and similar trend is noticed for this variableamong supervisors over the reference groupThe multinomial odds of improvement in work stress for the unit change in the variable – change isexpected to decrease among engineers over the workers by a factor 0.807 and similar trend is noticedfor this variable among supervisors over workers(OR<1). This model can be used to predict workstress among the employees in manufacturing industries.Like any other research, the study also not free from limitations. The present study is limited only topublic sector industries in Kerala, India, where majority of employees are males. Therefore it wouldbe inappropriate to draw conclusions about male and female workers based on this result. Theconclusion is drawn based on the data obtained by means of self reported measures. A comparativestudy was not carried out because of lack of literature or study of work stress in the context of Indianpublic sector industries.VI. CONCLUSIONConsistent with the literature, the results indicate that existence of factors responsible for work stressamong all the categories of the employees working in manufacturing industries in Kerala, India andthe instrument developed for the evaluation of work stress by using the variables / factors ,namelydemand ,control, manager support, peer support, relationship, role and change had validity,unidimensionality and reliability and the instrument can be effectively used for the evaluation of workstress in different type of industries in addition to manufacturing industries . Low level of job controlwas noticed among lower designation level particularly among workers than engineers and 415 Vol. 2, Issue 1, pp. 410-418
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  9. 9. International Journal of Advances in Engineering & Technology, Jan 2012.©IJAET ISSN: 2231-1963[55]. R.M. Leontaridi and R.M. Ward, (2002) “Work related stress, quitting intensions and absenteeism,” IZA discussion paper No. 493 Germany, pp 1-23.[56]. A. Kouvonen, M. Kivimaki, M. Virtman, J. Penti, and J. Vahtera, (2005) “Work stress smoking status and smoking intensity: An Observed study of 46190 employees,” Journal of Epidemiology and Community Health, Vol. 59, pp 63-69.[57]. K.M. Lindblom, S.J Linton, C. Fedli , and I.L Bryngelssm (2006)”Burn out in the working population :Relations to the Psychosocial work factors,” International Journal of Behavior Medicine ,Vol.13, No.1,pp51-59.[58]. G. Natemeyer, W.O. Bearden, and S. Sharma, (2003) “Scaling Procedures –Issues and Applications,” New Delhi ,Sage.[59]. L .J.Cronbach, and P. E.Meehl, (1955) “Construct validity in psychological tests,” Psychological Bulletin, Vol. 52, pp 281-302.[60]. S. L Ahire, D.Y.Golhar and M. A .Waller (1996) “Development and validation of TQM implementation construct,” Decision Science, Vol. 27,No.1,pp 23-56.[61]. J. L.Arbuckle(2006)”AMOS 7.0”AMOS Users Guide .Chickago,IL:Small waters corporation.[62]. Hosmer. D.W and Lameshow .S (2000) “Applied logistic regression,” second edition, John wiely & sons, Canada, pp1-369.AuthorsK. Satheesh Kumar is working as Assistant Professor (Senior Grade), in the Department ofMechanical Engineering, Federal Institute of Science and Technology, Kerala, India. He hasgot about 20 years of experience in teaching and published research papers in national andinternational journals.G. Madhu is working as Professor and Head in the Division of Safety and Fire Engineering,Cochin University of Science and Technology, Kerala, India. He has got about 25 years ofexperience in teaching and published research papers in national and international journals. Hehas guided many M.Tech and Ph.D students. 418 Vol. 2, Issue 1, pp. 410-418