In Malaysia, the production of activated carbons is still coconut-based although Malaysia has long shifted from coconut into palm oil plantation. Huge amount of waste Palm Kernel Shells (PKS) are being generated and disposed off into the landfill with little known of their usage on large scale. In this study, the potential of production of activated carbon from raw palm kernel shells are studied. Activated carbon was prepared from raw palm kernel shells using chemical activation with potassium hydroxide (KOH) as an activating agent. The effects of different process parameters: KOH concentration, activation temperature and time on physicochemical properties of the prepared activated carbon were investigated. The activated carbon was analyzed using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), Fourier Transform Infrared (FTIR) spectroscopy, proximate analysis and methylene blue adsorption study. FTIR analysis indicates that raw palm kernel shell has successfully been converted into activated carbon. SEM photograph revealed that prepared activated carbons have numerous burn-off pores with extensive surface area for adsorption. Activated carbon sample prepared at 700 ºC and 1 hour activation with 30 wt % KOH impregnation showed greatest extend of methylene blue removal of 6.932 mg/g equivalent to 69.324 %RE with largest specific surface area of 21.137 x 10-3 km2kg-1 have been reported. This study shows that palm kernel shells can be used as a good source for the production of activated carbon

Che Pa, Nasrul Fikry

Chew, Yi Zhong

English

UTHM Institutional Repository

17th International Conference on Industrial Engineering Theory, Applications and Practice (IJIE 2013)  MEASURING HUMAN PERFORMANCE OF MALAYSIAN TRAIN DRIVERS: DEVELOPING MODEL USING PLS APPROACH  Jalil Azlis-Sani1,2, Siti Zawiah Md Dawal2, Norhayati Mohmad Zakwan3 and Mohd Faizal Mohideen Batcha1  1Faculty of Mechanical & Manufacturing Engineering,  Universiti Tun Hussein Onn Malaysia (UTHM) 86400 Batu Pahat, Johor, MALAYSIA Corresponding author’s e-mail: azlis@uthm.edu.my   2Centre for Product Design & Manufacturing (CPDM), Faculty of Engineering, University of Malaya Lembah Pantai, Kuala Lumpur, MALAYSIA  3Faculty of Management and Human Resource Development,  Universiti Teknologi Malaysia,  Skudai, Johor, MALAYSIA  Abstract: The purpose of this paper is to address the assessment of validity and reliability of measured items used in survey research of train driver performance. This paper is a continuation from the first part of the research, which identified number of factors to be used on measuring human performance of train drivers and measured the impact of occupational stress and job satisfaction on train driver performance. The structural equation modelling (SEM) techniques and Partial Least Square (PLS) were adopted to assess the goodness of measures of constructs used in the model to examine the performance of the Malaysian train drivers. The measurement process involved assessment of construct validity of the items and followed by convergent validity. Then, the composite reliability was assessed with internal consistency measure of Cronbach’s alpha and discriminant validity was tested to assess the validity of the measurement. Statistical results confirmed that occupational stress was an influential factor of performance; however, job satisfaction did not affect the performance of the train driver. The findings of this research are useful for policy makers, train operating company (TOC), and practitioners to improve human performance and safety of railway system.   1. INTRODUCTION  Job performance is an important dependent variable in achieving high-quality output and services (Kahya, 2009). In order to remain competitive with high job performance level, human operator is the main focus to be considered (Layer, Karwowski, & Furr, 2009). In previous studies, job performance is only indicated through assessing employees’ workload where determination of workload plays an important role in designing and evaluating an existing man-machine system (Chang & Chen, 2006; Jung & Jung, 2001). Chang & Chen (2006) state that long-term heavy workload can affect an employee’s physical or mental health, performance or productivity, as reported by Ivenson and Pullman (2000). However, job performance does not only rely on workload, but there are other influential factors. Therefore, human who performs the task and who faces and feels the effect of the interaction in man-machine system needs to be investigated.  It is important to understand human as a major component in any relationship with machine and environment (Branton, 1987). Human performance model provides complete understanding on relationship among human, machine, and environment (Wilson, 1990). Awareness and better understanding on the importance of human performance are increasing and consideration on the influential factors comes earlier. In railway industry, there are many studies on how train accident happens, new design of cab, fatigue and sleep behaviour on existing design and operating system (Darwent, Lamond, & Dawson, 2008; Dorrian, Roach, Fletcher, & Dawson, 2006, 2007; Edkins & Pollock, 1997; Farrington-Darby, Wilson, & Norris, 2005; Jay, Dawson, Ferguson, & Lamond, 2008), but studies on human performance as an integrated understanding of the factors are very limited.  This research generally aimed to study human performance of train driver. It is essential to understand the capability a local train drivers who operate the locomotive. By understanding human factors and performance of the train drivers, it will improve quality of service, reduce degree of risk, and avoid accidents from occurring. Therefore, as interest grows in understanding factors affecting human performance in railway industry, it becomes equally important to understand the influence of human performance on safety outcomes.    79017th International Conference on Industrial Engineering Theory, Applications and Practice (IJIE 2013)  Therefore, this paper empirically measures the factors influencing train driver performance in Malaysia. In the research, partial least square (PLS) and structural equation modelling (SEM) were used to examine the factors influencing the performance of the train drivers.    2. RESEARCH CONTEXT AND RESEARCH MODEL  2.1 Research Context  A self-administered survey, which was completed by the locomotive drivers and junior drivers, was conducted among train drivers from a local train operating company (TOC) in Malaysia. The survey was conducted in five railway depots across Peninsular Malaysia, namely Perai, Ipoh, Kuala Lumpur, Gemas and Kuala Lipis. Off-duty respondents from the depots were picked randomly. Upon receiving the questionnaire, they filled it immediately. Researcher was present at the survey location to provide assistance. A total of 229 responses were returned.    2.2 Survey Instrument  An adapted questionnaire using 5-point Likert scale was used to collect the data for each of the constructs from the respondents. The questionnaire was adapted from previous literatures namely Ryan, Wilson, Sharples, Morrisroe, and Clarke (2009) and Strahan, Watson, and Lennonb (2008).    2.3 Research Model  Factor analysis technique was performed in previous paper, proposing five-factor solution of train driver performance model. The proposed model consisted of five independent constructs namely (a) job satisfaction – JS, (b) job satisfaction 2 – JS2, (c) job-related tension – JRT, (d) job-related tension 2 – JRT2, and (e) occupational stress – STR, as depicted in Figure 1.   Figure 1. Human performance model of train drivers   From the factor analysis, five hypotheses were formed and they need to be tested:  H1 Job satisfaction (JS) has a direct positive effect on the performance of train drivers in Malaysia  H2 Job satisfaction (JS2 - working time) has a direct positive effect on the performance of train drivers in Malaysia   79117th International Conference on Industrial Engineering Theory, Applications and Practice (IJIE 2013)   H3 Occupational stress (STR) has a direct positive effect on the performance of train drivers in Malaysia  H4 Job-related tension (JRT - people perception) has a direct positive effect on the performance of train drivers in Malaysia  H5 Job-related tension (JRT2 - responsibility) has a direct positive effect on the performance of train drivers in Malaysia   3. RESEARCH METHOD  Co-variance based-SEM (CB-SEM) approach, in particular PLS analysis, was suggested to test the goodness of measures in two steps (Anderson & Gerbing, 1991; Chin, 2010). The first step was to analyse the measurement model by examining validity and reliability of the survey items, while the second step was to create and analyse the structural model.    3.1 Measurement Model Analysis  3.1.1 Construct validity  Individual reliability of the items to their respective constructs should be determined in order to measure construct validity. Hair, Black, Babin, and Anderson (2010) indicate that significant cut-off value for assessed items is 0.5. Therefore, any item below that particular cut-off value should be deleted. In this research, step-by-step procedure of individual reliability was conducted and cross-loadings were computed. Table 1 presents the item loadings and cross loadings of the constructs for the final algorithm.    Table 1. Results of construct validity   Items Job-related tension Job-related tension (2) Job satisfaction Job satisfaction (2) Performance Occupational stress JRT11 0.671 0.345 -0.228 -0.090 0.157 -0.211 JRT15 0.561 0.366 -0.209 -0.026 0.137 -0.325 JRT3 0.688 0.319 -0.172 -0.034 0.181 -0.262 JRT5 0.643 0.236 -0.180 -0.074 0.120 -0.330 JRT7 0.725 0.247 -0.260 -0.093 0.196 -0.222 JRT9 0.506 0.180 -0.203 0.018 0.055 -0.222 JRT1 0.342 0.549 -0.208 -0.058 0.051 -0.160 JRT14 0.374 0.928 -0.108 -0.033 0.114 -0.217 JS1 -0.155 -0.167 0.646 0.084 -0.065 0.162 JS2 -0.243 -0.152 0.817 0.117 -0.157 0.255 JS3 -0.221 -0.054 0.642 0.034 -0.106 0.159 JS4 -0.317 -0.186 0.841 0.065 -0.146 0.220 JS5 -0.236 -0.060 0.774 0.041 -0.078 0.155 JS6 -0.024 -0.024 0.100 0.895 -0.056 0.188 JS7 -0.137 -0.070 0.075 0.901 -0.057 0.164 JS8 -0.063 0.014 0.003 0.624 -0.005 0.058 PMS2008 0.177 0.038 -0.175 -0.032 0.803 -0.105 PMS2009 0.228 0.140 -0.115 -0.062 0.889 -0.231 PMS2010 0.149 0.087 -0.093 -0.052 0.664 -0.069 STR14 -0.284 -0.220 0.177 0.120 -0.176 0.826 STR16 -0.331 -0.201 0.229 0.163 -0.128 0.805 STR18 -0.336 -0.149 0.232 0.186 -0.123 0.713   3.1.2 Convergent validity  Convergent validity was assessed using factor loadings, composite reliability, and average variance extracted (AVE) (Hair et al., 2010). Loading of the items should be more than 0.5 with composite reliability (CR) value of 0.7 (Hair et al., 2010). The   79217th International Conference on Industrial Engineering Theory, Applications and Practice (IJIE 2013)  average variance extracted (AVE) proposed by Farnell and Lacker (1981) was to measure variance amount by the indicators relative to measurement error. The AVE should be more than 0.5 (Chin, 2010). However, the value of AVE for construct JRT was 0.406, i.e., below 0.5 as required. Therefore, two items with the lowest loadings, namely JRT15 and JRT9 (0.561 and 0.506, respectively), were deleted. The new computed value of AVE for JRT was 0.500. At the same time, construct JRT2 was also deleted as the reliability of the construct, measured using Cronbach’s alpha, was 0.330. Table 2 presents the result of final measurement model with four-factor solution.    Table 2. Result of measurement model  Model construct Measurement item Loading Composite Reliability (CR) Average Variance Extracted (AVE) Cronbach’s alpha Job-related tension JRT JRT11 0.690 0.800 0.500 0.672 JRT3 0.720 JRT5 0.665 JRT7 0.750 Job satisfaction JS JS1 0.646 0.863 0.561 0.806 JS2 0.816 JS3 0.645 JS4 0.841 JS5 0.774 Occupational stress STR STR14 0.828 0.825 0.612 0.688 STR16 0.803 STR18 0.711 Job satisfaction (2) JS2 JS6 0.894 0.856 0.669 0.794 JS7 0.902 JS8 0.629 Performance PMS2008 0.803 0.833 0.627 0.701 PMS2009 0.881 PMS2010 0.678   3.1.3 Discriminant validity  Discriminant validity is a test to assess the validity of the measurement. The constructs are not supposed to measure other constructs or overlapping constructs. The cross-construct correlations should be very low but it measures strongly the construct it attempts to reflect (Chin, 2010). As shown in Table 3, the average variance extracted (AVE) of the construct was much higher than the squared correlations for each construct thus indicating adequate discriminant validity. Therefore, the measurement model demonstrated adequate convergent validity and discriminant validity.    Table 3. Discriminant validity of constructs    Job-related tension Job satisfaction Job satisfaction 2 Performance Occupational stress Job-related tension 0.500         Job satisfaction 0.090 0.561       Job satisfaction 2 0.010 0.009 0.669     Performance 0.056 0.026 0.004 0.627   Occupational stress 0.124 0.070 0.037 0.034 0.612 *diagonals (in bold) represent the AVE while the off diagonals represent the squared correlations   3.2 Structural Model Analysis  The second step was to create and analyse the structural model by testing the hypothesis (Anderson & Gerbing, 1991; Chin, 2010). Path loadings, β-value between constructs of inner model were examined through hypothesis testing. Bootstrapping   79317th International Conference on Industrial Engineering Theory, Applications and Practice (IJIE 2013)  technique was run and t-statistics were used to test for significance. Table 4 shows the path coefficients and results of the hypothesis testing.  Four hypotheses were drawn from the modified model. Job-related tension was positively related (t = 2.873, p<0.010) with the performance of the driver. The hypothesis testing proved that occupational stress had positive relationship with performance as the hypothesis was supported (t = 1.411, p<0.10). However, based on hypothesis testing conducted, both job satisfaction constructs were not significant factors of train driver performance (t = 1.218 and t = 0.216, respectively). Therefore, H3 and H4 of this model were supported but H1 and H2 were not. Job-related tension was the most significant factor in influencing train driver performance followed by occupational stress.    Table 4. Path coefficients and hypothesis testing    Relationship Beta Standard Error t-value Decision H1 Job satisfaction → Performance -0.080 0.066 1.218 Not supported H2 Job satisfaction (2) → Performance -0.017 0.078 0.216 Not supported H3 Occupational stress → Performance -0.097 0.069 1.411* Supported H4 Job-related tension → Performance 0.177 0.062 2.873** Supported                        Figure 2. Result of the path analysis   4. DISCUSSION AND CONCLUSION  This paper explains how PLS could be used to assess goodness of measure by looking at the validity and reliability of the measure and by testing the hypotheses. In this research, construct validity, which consists of convergent and discriminant validity, was measured. Cronbach’s alpha values and composite reliability were also identified to assess the reliability of the measures. From the analysis, the measures were shown to be reliable.  The findings of this study support conventional thoughts that occupational stress and job-related tension influence performance of the train drivers in Malaysia. During the survey, items for occupational stress covered several types of stressors including shift work, amount of work, emotion, and morale. These particular factors were found to have significant impact to the performance of the train drivers. Thus, this occupational stress also leads to job-related tension, which is a factor that focuses more on external emotional factors such as perception of other employees towards the respondent, expectation of the supervisor towards subordinates, and ability to satisfy most of the stakeholders. These causes of tension were determined as influential factors towards the performance of the train driver.    79417th International Conference on Industrial Engineering Theory, Applications and Practice (IJIE 2013)  However, Malaysia train drivers decided that job satisfaction was not a significant factor influencing their performance. This was proved with their loyalty to the company as majority of the drivers had worked for more than ten years with the same company. The TOC operated as a government-liaised company (GLC) with sufficient benefits such as health and compensation, making them satisfied with their working environment and organisation. Therefore, job satisfaction was not significant towards performance of the train driver in Malaysia.  Future study should aim to investigate other influential factors with regard to the norm of work of train drivers, which contributes to their performance.    5. REFERENCES  1. Anderson, J. C., & Gerbing, D. W. (1991). Predicting the Performance of Measures in a Confirmatory Factor Analysis With a Pretest Assessment of Their Substantive Validities. Journal of Applied Psychology, Vol. 76(No. 5), 732 - 740.    2. Branton, P. (1987). In praise of ergonomics - a personal perspective International Reviews of Ergonomics, 1, 1-20   3. Chang, S.-Y., & Chen, T.-H. (2006). Discriminating relative workload level by data envelopment analysis. International Journal of Industrial Ergonomics, 36(9), 773-778.    4. Chin, W. W. (2010). How to Write Up and Report PLS Analyses: Handbook of Partial Least Squares. In V. Esposito Vinzi, W. W. Chin, J. Henseler & H. Wang (Eds.), (pp. 655-690): Springer Berlin Heidelberg.   5. Darwent, D., Lamond, N., & Dawson, D. (2008). The sleep and performance of train drivers during an extended freight-haul operation. [doi: DOI: 10.1016/j.apergo.2008.02.012]. Applied Ergonomics, 39(5), 614-622.   6. Dorrian, J., Roach, G. D., Fletcher, A., & Dawson, D. (2006). The effects of fatigue on train handling during speed restrictions. Transportation Research Part F: Traffic Psychology and Behaviour, 9(4), 243-257.    7. Dorrian, J., Roach, G. D., Fletcher, A., & Dawson, D. (2007). Simulated train driving: Fatigue, self-awareness and cognitive disengagement. Applied Ergonomics, 38(2), 155-166.    8. Edkins, G. D., & Pollock, C. M. (1997). The influence of sustained attention on Railway accidents. [doi: DOI: 10.1016/S0001-4575(97)00033-X]. Accident Analysis & Prevention, 29(4), 533-539.    9. Farrington-Darby, T., Wilson, J. R., & Norris, B. J. (2005). Investigating Train Driver Behaviour: The Use of Lineside Information When Regulating Speed. In J. R. Wilson, B. Norris, T. Clarke & A. Mills (Eds.), Rail Human Factors: Supporting the Integrated Railway (pp. 60 - 69): Ashgate.   10. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (7th ed.): Prentice Hall.   11. Jay, S. M., Dawson, D., Ferguson, S. A., & Lamond, N. (2008). Driver fatigue during extended rail operations. [doi: DOI: 10.1016/j.apergo.2008.01.011]. Applied Ergonomics, 39(5), 623-629.    12. Jung, H. S., & Jung, H.-S. (2001). Establishment of overall workload assessment technique for various tasks and workplaces. International Journal of Industrial Ergonomics, 28(6), 341-353.    13. Kahya, E. (2009). The effects of job performance on effectiveness. International Journal of Industrial Ergonomics, 39, 96-104.    14. Layer, J. K., Karwowski, W., & Furr, A. (2009). The effect of cognitive demands and perceived quality of work life on human performance in manufacturing environments. International Journal of Industrial Ergonomics, 39(2), 413-421. doi: DOI 10.1016/j.ergon.2008.10.015   15. Ryan, B., Wilson, J. R., Sharples, S., Morrisroe, G., & Clarke, T. (2009). Developing a Rail Ergonomics Questionnaire (REQUEST). Applied Ergonomics, 40(2), 216-229.      79517th International Conference on Industrial Engineering Theory, Applications and Practice (IJIE 2013)  16. Strahan, C., Watson, B., & Lennonb, A. (2008). Can organisational safety climate and occupational stress predict work-related driver fatigue? [doi: DOI: 10.1016/j.trf.2008.04.002]. Transportation Research Part F: Traffic Psychology and Behaviour, 11(6), 418-426.    17. Wilson, J. R. (1990). A framework and a context for ergonomics methodology. In J. R. Wilson & E. N. Corlett (Eds.), Evaluation fo Human Work (2nd edition ed., pp. 1-39): Taylor & Francis.       796

A framework and a context for ergonomics methodology.

Can organisational safety climate and occupational stress predict work-related driver fatigue? [doi:

Developing a Rail Ergonomics Questionnaire (REQUEST).

Discriminating relative workload level by data envelopment analysis.

Driver fatigue during extended rail operations. [doi: DOI: 10.1016/j.apergo.2008.01.011].

Establishment of overall workload assessment technique for various tasks and workplaces.

How to Write Up and Report PLS Analyses: Handbook of Partial Least Squares. In

In praise of ergonomics - a personal perspective

Investigating Train Driver Behaviour: The Use of Lineside Information When Regulating Speed. In

Predicting the Performance of Measures in a Confirmatory Factor Analysis With a Pretest Assessment of Their Substantive Validities.

Simulated train driving: Fatigue, self-awareness and cognitive disengagement.

The effect of cognitive demands and perceived quality of work life on human performance in manufacturing environments.

The effects of fatigue on train handling during speed restrictions. Transportation Research Part F: Traffic Psychology and Behaviour,

The effects of job performance on effectiveness.

The influence of sustained attention on Railway accidents.

The sleep and performance of train drivers during an extended freight-haul operation. [doi: DOI: 10.1016/j.apergo.2008.02.012].

Preparation and characterization of activated carbon from palm kernel shell

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Preparation and characterization of activated carbon from palm kernel shell

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