Correlation Network Analysis on Worker s Behaviour and Safety Culture: An Experience in Manufacturing Industry

Transcription

1 Internatonal Journal of Basc & Appled Scences IJBAS-IJENS Vol:12 No:05 1 Correlaton Network Analyss on Worker s Behavour and Safety Culture: An Experence n Manufacturng Industry Shamshurtawat Sharf 1, Maman A. Djauhar 2 and Harza Djauhar 3 1,2 Department of Mathematcs, Faculty of Scence, UTM, UTM Skuda, Johor, Malaysa 1 College of Arts and Scences, UUM, UUM Sntok, Kedah, Malaysa 3 Coordnatng Mnstry for Economc Affars, Indonesa 1 shamshurta@uum.edu.my, 2 maman@utm.my, 1 harza.djauhar@yahoo.com Abstract A manufacturng ndustry contrbutes around 10% Malaysan economy. It provdes economc opportuntes for related ndustres and busness. However, the number of accdents n manufacturng sector, ncludng fatal accdents, has been ncreased from tme to tme. To understand the real stuaton, n ths paper, we used a correlaton network analyss to analyze 43 characterstcs of worker s behavour and ther safety culture. The method developed n econophyscs has been used to transform the correlaton structure nto dstance structure. Its correspondng mnmum spannng tree and the centralty measure such as degree centralty, betweenness centralty, closeness centralty and egenvector centralty are performed to dentfy the most nfluental characterstcs. A case study on Malaysan manufacturng ndustry has been presented to llustrate the advantage of the proposed approach. Some of mportant results and recommendatons for the Government of Malaysa wll be delvered. Index Term adjacency matrx, complex system, correlaton matrx, dstance matrx. I. INTRODUCTION Department of Occupatonal Safety and Health (DOSH) of Malaysa has to provde a safety and healthy work envronment for all employees and protect those who may be affected by ndustry actvtes. DOSH actvtes are to guarantee employers and employees n the country pay more attenton to safety and health at work [1]. On the other hand, however, the number of accdents n manufacturng sector, ncludng fatal accdents, has been ncreased from tme to tme. In the last three years, Malaysa s manufacturng sector has been contrbutng the hghest number of accdents whch result n non-permanent dsabltes (NPD), permanent dsabltes (PD) and death (D). Ths sector becomes the second sector where accdent occurrences causng death s placed on the top behnd the constructon sector as can be seen n Table 1. The occurrences of occupatonal accdents tself are beleved due to drect, ndrect and basc causes. Human factor s beleved as the most contrbutors to the accdent occurrences. In ths regards [2] and [3] have mentoned that unsafe behavour of the workers s the drect source of accdent along wth unsafe condton. TABLE 1 The occupatonal accdent by sector n Ths paper ams to have a better understandng to what extent the current practce of safety culture and worker s behavour ntolerably dffers from DOSH strategy. The remander of the paper s desgned as follows. Secton II wll be devoted to research desgn and mplementaton on safety culture and worker s behavour followed by data analyss methodology n Secton III. Later, n Secton IV we dscuss the research results. A concluson wll be delvered n the last secton. II. RESEARCH METHODOLOGY In ths study, safety culture and worker s behavour are consdered as a complex system consstng of 43 characterstcs as nodes connected by (43-1)*43/2 = 903 lnks each of whch s related to the correlaton coeffcent between the two nodes adjacent to t. The nodes and lnks consttute a socal network n the form of a weghted undrected graph [4]. Ths pont of vew s useful n order to smplfy, vsualze, and summarze the most mportant nformaton contaned n that complex system. In what follows we dscuss frst the research desgn, data collecton, and data analyss based on the so-called mnmum spannng tree (MST) and centralty measures. A. Survey Desgn and Data Collecton There are 136 workers that have been partcpated n ths survey. Our focus s on the front lne workers only,.e., operators and techncans because they are the man target of DOSH polcy. The questonnare conssts of 43 questons (characterstcs). Among them, the frst 18 are related to safety culture and the rest to worker s behavour. The safety culture characterstcs are classfed nto nne factors, namely; management commtment, communcaton, prorty of safety, safety procedure and polcy, supportve envronment, nvolvement, personal prorty and need of safety, personal apprecaton towards rsk, and work envronment.

2 Internatonal Journal of Basc & Appled Scences IJBAS-IJENS Vol:12 No:05 2 On the other hand, worker s behavour s classfed nto seven factors; reactng behavour, personal protectve equpment, specfc job rsk, tools and equpments, safe work practce, ergonomcs, and communcaton. See [5] for the detals of the questonnare. B. Methodology Those 43 nodes and 903 lnks can be consdered as a socal network,.e., a network representaton of socal phenomenon vewed as a complex system. The essence of a network s ts nodes and the way how they are lnked. Network analyss was orgnally developed n computer scence. However, nowadays, t has been used n varous felds of study. See, for example, [6] n socology, [7] and [8] n fnance, and [9] n transportaton. In practce, network analyss mght start wth a correlaton matrx. Then, we transform t nto a dstance matrx [10]. From that matrx we construct the mnmum spannng tree (MST). For ths purpose, as suggested n [7] and [10], Kruskal algorthm can be used. MST wll then be used to flter the orgnal network and summarze the most mportant nformaton. Furthermore, to nterpret the MST we use dot plot matrx, and centralty measures such as degree, betweenness, closeness, and egenvector centraltes. To make the MST more attractvely and effcently useful, we use the Kamada Kawa procedure provded n Pajek [11]. III. DATA ANALYSIS A. Correlaton matrx We denote X s the -th characterstc under study where = 1, 2,, 43. The correlaton matrx among those characterstcs, ssued from a sample, s a symmetrc matrx of sze where the element n the -th row and j-th column s, X X X X j j j X X X j X j representng the correlaton coeffcent between -th and j-th characterstcs [10]. That correlaton coeffcent quantfes the degree of lnear relatonshp between -th and j-th varables. By defnton, 1 for all and j can vary from 1 to 1 for all j where, j (1) 1 means perfectly postve lnear relatonshp 0 means no lnear relatonshp 1 means perfectly negatve lnear relatonshp () dj dk dkj. The frst property tells us that two characterstcs that are perfectly correlated (ether postve or negatve), 1, wll be represented by a sngle pont n j Eucldean space ( dj 0 ). More over, 0d j 2. The second property s symmetrc property; the dstance between the -th and j-th characterstcs s equal to the dstance between the j-th and -th characterstcs. In other words, the correlaton between the -th and j-th characterstcs s equal to the correlaton between the j-th and -th characterstcs ( j j dj dj ). The last property s well known as trangular property. From (2), we conclude that, n general, the hgher the correlaton coeffcent the smaller the dstance. By usng equaton (2), we obtan a dstance matrx D of sze wth d j as the element n the -th row and j-th column. It s ths matrx that we analyze n the rest of the paper. C. Informaton Summarzaton To vsualze, smplfy and summarze the mportant nformaton contaned n the network represented by D, we use the noton MST [12] [13]. Then, we determne MST by usng Kruskal algorthm [14]. Furthermore, to nterpret the MST, dot plot matrx and centralty measures are used. These measures are very helpful to understand the mportance and or nfluence of each node relatve to the others [15], [16], and [17]. The role of each measure n detals and ts formula are dscussed n [18]. IV. RESULT AND DISCUSSION A. Mnmum Spannng Tree In Fg. 1 we present the dot plot matrx of the adjacency matrx A that corresponds to the MST of dstance matrx D gven by Kruskal s algorthm. The element of A s a j = 1 f the -th and j-th nodes are lnked and 0 otherwse. Ths matrx s symmetrc and all dagonal elements are 0. In Fg. 1, blank cell represents 0 and black cell 1. That fgure shows the hghest correlatons among safety culture characterstcs (the frst eghteen rows and columns) are concentrated along dagonal whle worker s behavour characterstcs (the remanng rows and columns) are more dspersed around dagonal. Moreover, some of worker s behavour characterstcs are also hghly correlated wth some safety culture characterstcs. Ths ndcates that managng worker s behavour s more complcated compared to safety culture. B. Dstance matrx To analyze the network, we transform the correlaton matrx nto a dstance matrx by usng the followng formula [10]. d 2(1 ) (2) j j Ths d j s a dstance between the -th and j-th characterstcs snce t satsfes the followng three propertes; () d 0 and dj 0 X X j, () dj dj, and j

3 Internatonal Journal of Basc & Appled Scences IJBAS-IJENS Vol:12 No:05 3 Furthermore, accordng to Fg. 1, BE2 (supportve envronment) n the yellow group s more smlar to red group than yellow group. We conclude that Fg. 1 and Fg. 2 gve us a general relatonshp among characterstcs of the two groups. In what follows we analyze the partcularty of each characterstcs by usng the noton of centralty measure. Fg. 1. Dot plot matrx From that fgure we also learn that: () There s hgh correlaton among characterstcs wthn factors n safety culture but low correlaton between factors. () There s hgh correlaton among characterstcs wthn and also between factors n worker s behavour. () The followng safety culture characterstcs BE2, BH1, and BI1 (supportve envronment, personal apprecaton towards rsk, and work envronment factors) are hghly correlated wth the followng worker s behavour characterstcs CD1, CC2, and CA3, (reactng behavour, specfc job rsk, and tools and equpments factors), respectvely. To elaborate the above fndngs more clearly, we use Pajek software to represent n Fg. 2 the correspondng MST. See, [19] for consultng the open source. From ths fgure we see the nterconnectvty among all characterstcs of both groups. Yellow ponts represent safety culture characterstcs and red ponts are for worker s behavour characterstcs. In general, the two groups are clearly separated except the followng two factors of safety culture that are more smlar wth factors of worker s behavour than wthn ts own group, () () personal apprecaton towards rsk (BH1 and BH2), work envronment (BI1 and BI2). Fg. 2 MST of safety culture and worker s behavour B. Centralty Measures From socal network vew pont, each partcular node can be analyzed by usng ts centralty measures such as degree, betweenness, closeness and egenvector centraltes to fnd the most mportant nodes n the network structure. Those measures are computed based on the MST n Fg. 2. See [9], [18], and [20]. () () () Degree centralty of node s d = 1 n aj. n 1 j1 Betweenness centralty of node s b, the rato of the number of path passng through between two dfferent nodes and the number of all possble paths from j to k for all j and k where j and k. Closeness centralty of node, c s the rato of the number of lnks n the MST (n-1) and the number of lnks n the path from to j for all j. (v) Egenvector centralty of node s, ev = n 1 j1 ae t where e1, e2,..., e n s the egenvector of A that corresponds to the largest egenvalue. In Table 2, we present the value of those measures of each node. TABLE 2 Centralty Measures No. d b c ev 1 BA BA BB BB BC BC BD BD BE BE BF BF BG BG BH BH BI BI CA CA CA CA CA j j

4 Internatonal Journal of Basc & Appled Scences IJBAS-IJENS Vol:12 No: CA CB CB CB CB CB CB CC CC CD CD CD CD CE CE CF CF CF CG CG To vsualze the result n Table 2, n Fg. 3 6 we present the MST where the sze and colour of the node represent the score of centralty measure and the rank of mportance, respectvely. Based on degree centralty, see Fg. 3, BD1-Rules and Procedures on Health and Safety Programme and CA6- Serousness n Work (red ponts) have the hghest number of lnks (4) n the network. Each of the followngs has 3 lnks: BA2-Management Responsveness, BC2-Concern on Safety, BE2-Health and Safety Performance, CA5-Safe Envronment, CB3-Hearng Protecton, CB4-Respratory Protecton, CD2- Tools and Equpments Used Correctly, CD3-Tools and Equpment Well Mantan, CD4-Workplace n Good Housekeepng, CE1-Understand Work Safely, and CF3-Zero Awkward Posture (yellow ponts). Programme, BF1-Partcpate n Management Safety Issues, CG2-Concern on Work Envronment (purple ponts or the fourth most mportant). Ths means that f those varables are well managed, then the others wll be well nfluenced. Accordng to the closeness centralty, see Fg. 5, CD2- Tools and Equpments Used Correctly (red pont) has an excellent poston compared to the others where the nformaton flow n the network can easly reach others. Ths node s the closest node to the others. The second (thrd, and fourth, respectvely) closest node to the others are CD3-Tools and Equpment Well Mantan (yellow pont) (CD4- Workplace n Good Housekeepng (blue pont), and all purple ponts, respectvely). In term of egenvector centralty, see Fg. 6, BD1-Rules and Procedures on Health and Safety Programme (red pont) s the node that lnks to the other hgh scored nodes. It s the most nfluental characterstc to the second most nfluental characterstcs CD2-Tools and Equpments Used Correctly, CD3-Tools and Equpment Well Mantan, and CD4- Workplace n Good Housekeepng (yellow ponts). The thrd most nfluental are the blue ponts. Fg. 4. Betweenness centralty Fg. 3 Degree centralty The rests are of 1 and 2 lnks only. The hgher the number of lnks, the hgher the nfluence of a partcular characterstc. For example, BD1-Rules and Procedures on Health and Safety Programme s the most nfluental characterstc to the larger number of other characterstcs of safety culture. In terms of betweenness, see Fg. 4, the four most mportant nodes are CD2-Tools and Equpments Used Correctly (red pont) plays the most mportant role n the network followed by, n order of mportance: CD4- Workplace n Good Housekeepng, and CD3-Tools and Equpment Well Mantan (yellow ponts or the second most mportant), BE2-Health and Safety Performance (blue pont or the thrd most mportant), BF2-Partcpate n Work Safety Issues, BD1-Rules and Procedures on Health and Safety Fg. 5. Closeness centralty

5 Internatonal Journal of Basc & Appled Scences IJBAS-IJENS Vol:12 No:05 5 () (v) communcaton reactng behavour Those fve factors of safety culture as well as those four factors of worker s behavour should be of hgh prorty n reducng the number of fatalty and pad more attenton by DOSH as well as Malaysan ndustral management Fg. 6. Egenvector centralty V. CONCLUSION Dot plot matrx analyss shows that managng worker s behavour s more dffcult to manage than safety culture because, () the characterstcs of the latter s hghly correlated wthn factors but not between them, and () the characterstcs of the former s hghly correlated wthn and between factors and also wth the factors of the former. From Fg. 2 we learn that the factors of safety culture are clearly separated from those of worker behavour except Personal apprecaton toward rsk and Work envronment that are more smlar to the second group of factors rather than ther own group. Accordng to the four centralty measures, after usng the Pareto analyss, the followng characterstcs of safety culture are the vtal few; BE2- Health and Safety Performance, BD1-Rules and Procedures on Health and Safety Programme, BF2- Partcpate n Work Safety Issues, BA2-Management Responsveness, and BC2- Concern on Safety. These characterstcs represent the followng factors n order of mportance, () () () (v) (v) supportve envronment safety procedure and polcy nvolvement management commtment prorty of safety On the other hand, the vtal few of worker s behavour characterstcs are CD2-Tools And Equpments Used Correctly, CD3-Tools and Equpment Well Mantan, CD4- Workplace n Good Housekeepng, CE1-Understand Work Safely, CD1-Rght Tools and Equpments, CG2-Concern on Work Envronment, CA2- Follow a Regulaton that cover the factors, () () tools and equpment safe work practse I. CONCLUDING REMARKS In ths paper, we show that correlaton network analyss can be used n dentfyng the most nfluental varables, n the case of several nterrelated varables. For that purpose, t s not an easy task when nvolved large data set wth hgh dmenson. To close ths paper, we hghlght three potental topcs from ths study. Frst, t s stll n our nvestgaton to defne the new measure of centralty due to some lmtaton of degree centralty. Second, t wll be more frutful to explore the lmtaton of the algorthm (.e Kruskal algorthm) n order to determne a mnmum spannng tree snce that algorthm wll only gve us a unque soluton. Thrd, we can also use the noton of MST n order to see the separaton of suspected outlers as well as outler testng. More studes can be carred out to nvestgate the lmtaton of ths topc. ACKNOWLEDGMENT The authors gratefully acknowledge Goverment of Malaysa for those sponsorshps, Unverst Teknolog Malaysa and Unverst Utara Malaysa for the facltes. Specal thanks go to the anonymous referees for ther constructve comments and suggestons. REFERENCES [1] (2010) Malaysa Economc Report. [Onlne]. Avalable: [2] D. Heberle, Constructon Safety Manual. New York: McGraw-Hll Professonal, [3] J. Short, The role of safety culture n preventng commercal motor vehcle crashes. Volume 14 of Synthess (Commercal Truck and Bus Safety Synthess Program (U.S.)). Washngton D. C. Transportaton Research Board, [4] P. Jayawant and K. Glavn, Mnmum spannng trees, Involve a journal of mathematcs, vol.2, no.4, pp , [5] H. Djauhar, Safety Culture at a manufacturng Industry n Johor Malaysa, Master Dssertaton. Unverst Teknolog Malaysa, Johor, Malaysa, [6] T. Krchel, and N. Bakkalbas, A Socal Network Analyss of Research Collaboraton n the Economcs Communty, The Internatonal Workshop on Webometrcs, Informetrcs and Scentometrcs & Seventh COLLNET Meetng, France, [7] R. N. Mantegna, Herarchcal Structure n Fnancal Markets, European Physcal Journal B, vol. 11, pp , [8] S. Mccchè, G. Bonanno, F. Lllo, and R.N. Mantegna, Degree stablty of a mnmum spannng tree of prce return and volatlty, Physca A, vol. 324, pp.66 73, [9] K. Park, and A. Ylmaz, A Socal Network Analyss Approach to Analyze Road Networks, ASPRS Annual Conference. San Dego, CA, [10] R. N. Mantegna, and H.E. Stanley, An Introducton to Econophyscs: Correlatons and Complexty n Fnance. Cambrdge Unversty Press, Cambrdge UK, [11] T. Kamada, S. Kawa, An algorthm for drawng general undrected graphs. Informaton Processng Letters, vol. 31, no.1, pp. 7 15, [12] J. P. Benzécr Hstore et préhstore de l'analyse des Données, DUNOD: Pars, [13] B. M. Tabak, T. R. Serra, and D. O. Cajuero. Topologcal propertes of stock market networks: The case of Brazl. Physca A, vol. 389, pp , 2010.

6 Internatonal Journal of Basc & Appled Scences IJBAS-IJENS Vol:12 No:05 6 [14] J.B. Kruskal, On the shortest spannng subtree and the travellng salesman problem. Proceedngs of the Amercan Mathematcal Socety, vol. 7, no. 1, pp , [15] Y. Xu, J. Ma, Y. Sun, J. Hao, Sun, Y. And Y. Zhao, Usng Socal Network Analyss As A Strategy For E-Commerce Recommendaton, Pacfc Asa Conference on Informaton Systems (PACIS). Inda, [16] A. Abbas, and J. Altmann, On the Correlaton between Research Performance and Socal Network Analyss Measures Appled to Research Collaboraton Networks. TEMEP Dscusson Paper, No.2010, Seoul Natonal Unversty, Korea, [17] J. Monárrez-Espno, and J. R. Caballero-Hoyos. Stablty of Centralty Measures n Socal Network Analyses to Identfy Long- Lastng Leaders from an Indgenous Boardng School of Northern Mexco, Estudos sobre las Culturas Contemporaneas, vol. 16, no. 32, pp , [18] S. P. Borgatt, Centralty and AIDS, Connectons, vol. 18, no. 1, pp , [19] V. Batagelj, and A. Mrvar, (2011) PAJEK: Program for Analyss and Vsualzaton of Large Networks, verson [onlne]. Avalable: [20] P. Seczka, and J. A. Holyst, Correlatons n commodty markets, Physca A, vol. 388, pp , Shamshurtawat Sharf was born n Kedah, Malaysa, n She s currently a PhD student n Mathematcs at Unverst Teknolog Malaysa, Malaysa. Her dploma and Bachelor of Scence n Statstcs were obtaned from MARA Insttute of Technology (ITM). Her Master degrees n Decson Scence were from Unverst Utara Malaysa n She s nterested n multvarate analyss, ndustral statstcs, and socal network analyss.