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The roles of com m unication process for an effect ive lean m anufacturing im plem entat ion 136
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continuous im provem ent, zero defects, j ust - in- t im e, pull instead of push, m ultifunctional team s, decentralized responsibilities, integrated functions and
vert ical inform at ion funct ions. The response scale is 1 through 5, representing the range of no adopt ion, less adopt ion, part ial adopt ion, do adopt ion and t ot ally
adoption. The second part of the questionnaire was used to m easure the m anagerial
com m itm ent and their support in the m anufacturing infrastructures. The supporting m anufacturing infrastructure m easured is Worker Em powerm ent, Training, Group
Problem Solving and Qualit y Leadership, which was stated by Boyer 1996 . The first variable is rated by score range from 1 to 5 representing no em phasis, less
em phasis, m oderat e em phasis, do em phasis and ext rem e em phasis. The last t hree variables rated with sam e score range but with different headings - are strongly
disagree, disagree, m oderat e, agree and st rongly agree. I n t his part , t he respondents have to rate the m anagem ent com m itm ents to six lean practices
m entioned in first chapter. The score range is from 1 to 5, representing no com m itm ent, less com m itm ent, partial com m it m ent, do com m itm ent and total
com m itm ent.
3.2 Pilot test
A pilot test was conducted to ensure the results of the questionnaire are valid and m eet the obj ectives of this proj ect. This is done by distributing questionnaire to
two lean expertise of the com pany. A discussion was held with the respondents regarding the questionnaire and the feedback given by the respondents helped the
researcher to edit and m ake changes to the questionnaire. Besides, based on the pre- test, total tim e spent to answer the questionnaire is ascertained.
3.3 Questionnaire modification
I f the responses from the pilot test do not show the validit y, m odifications on the questionnaire will be necessary. This m ay happen when the respondents m ay not
be aware of certain inform ation which is not their field Grandzol et al., 1998 . Modification of the questionnaire was deem ed necessary. Based on Boyer’s 1996
m odel, respondents were asked to rate m anagem ent com m itm ent for two lean m anufacturing practices, which was j ust- in- tim e and total qualit y m aintenance.
Hence, the researcher changed the two practices to six practices as those practices had been carried out in the subj ect of research.
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3.4 Sending and receiving questionnaire
The questionnaire is directed to three levels of t he com pany, which are, t op m anagem ent, m iddle m anagem ent, and shop floor workers. For the top
m anagem ent level respondents, the questionnaires were answered by all heads of departm ents of the com pany. However, the questionnaire was lim ited to the last
two levels of kitting departm ent. The feedback was received wit hin two weeks from a total of 53 respondents.
3.5 Data analyzing
I n the first part of the questionnaire, the m ean and standard deviation were com puted with the scores of the nine variables. The m ean is t he value of t he
degree of adoption DOA . Degree of leanness DOL was m easured as the m ean value of the nine separate variables in the m odel. Degree of com m itm ent DOC
was m easured by the level of invest m ent in support ing m anufact uring infrastructure, as m easured by Worker Em powerm ent, Training, Group Problem
Solving and Qualit y Leadership. There are a num ber of different reliability coefficients. One of the m ost com m only
used is Cronbach’s alpha Coakes, 2005 . I t is used to assess the internal consist ency reliabilit y of several it em s or scores that the researcher wants to add
together to get a sum m ary or sum m ated scale score Morgan et al., 2004 . Cronbach’s alpha is based on the average correlat ion of it em s wit hin a t est if the
item s are st andardized Coakes, 2005 . The alpha value should be posit ive and usually great er t han 0.70 in order t o provide good support for int ernal consist ency
reliabilit y Morgan et al., 2004 . Correlat ions m easure how variables are relat ed. Before calculat ing a correlat ion
coefficient, data is screened for outliers and evidence of a linear relat ionship. Pearson’s correlat ion coefficient is a m easure of linear association. I f the
relat ionship is not linear, Spearm an’s rho will be used to m easure the correlation between the variables Morgan et al., 2004 . A Pearson’ correlation coefficient
describes t he relat ionship bet ween t wo cont inuous variables. A correlat ion bet ween two dichotom ous or categorical variables is called a phi- coefficient Coakes, 2005 .
Correlation is m easured between each of the supporting m anufacturing infrastructure variables and the m anagem ent com m itm ent. Correlat ion also has
been m easured between the degree of adoption and the nine variables.
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Linear Regression estim ates the coefficient of the linear equat ion, involving one or m ore independent variables, which best predicted the value of the dependent
variable. For each value of the independent variables, the distribution of the dependent variable m ust be norm al. The variance of the distribution of the
dependent variable should be constant for all values of the independent variable. The relationship between the dependent variable and each independent variable
should be linear, and all observat ions should be independent Morgan et al., 2004 . There are three m aj or regression m odels, which are sim ultaneous regression,
hierarchical regression and stepwise regression. These m odels differ in t wo ways: first, in the treatm ent of overlapping variability due to correlation of the
independent variables, and second, in term s of the order of entry of the independent variables into the equation. Regression analysis was used to
determ ine the strength of the relationship between the supporting m anufacturing infrastructure variables and the com m itm ents to lean m anufacturing. Regression
between the degree of adoption and the nine variables was also m easured. With these correlation and regression analyses, the two hypotheses of the proj ect will be
proved or rej ected.
4 Results
The response showed that all the workers and m anagem ent are happy to take part in the st udy. The num ber of em ployees from kiting departm ent that are willing t o
part icipat e were only 35 people, which is 77.78 as som e of them were busy wit h their own work and others have resigned, on leave, out station, or at t ending
courses. On top of that, three feedbacks were rej ected by researcher because the respondents rated the entire questions with the sam e answer or called patterned
response bias, which is in line wit h Grandzol et al. 1998 opinion that this kind of feedback will affect t he analysis of the result .
Feedback Number of employees
Percentage Accepted
40 75.47
Rejected
3 5.66
Excluded
10 18.87
Tot a l
53 100
Table 1. “ Statistics of type of feedbacks” .
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P. Puvanasvar an; H. Megat ; T. S. Hong; M. M. Razali
Based on the statistics in Table 1, there were 40 feedbacks accepted with 75.47 . Num ber of feedbacks rej ected is 3, which is 5.66 . Tot al num ber of em ployees
who were unwilling part icipant s were 10 person or 18.87 . Therefore, t he analysis of this study was based on 40 em ployees of t he com pany. The higher num ber of
people who want t o take part in t he study m eans that they are very positive towards the new changes and really understand what is happening in the
departm ent. This could happen only if there is a very good com m unication and int eract ion bet ween the various levels of em ployees. A good com m unication
process ensures that the em ployees were m ore recept ive to changes and were willing to accept and carry out the new ideas.
Figure 2 indicates the percentage of the respondents based on their levels in t he com pany. From the figure, the highest percentage of respondents is from bottom
level, which is 57.5 , followed by m iddle level wit h 22.5 . The lowest percentage of respondent s is from t op level, which is only 20 .
Figure 2. “ Respondent ’s Status in Percentage” .
4.1 Reliability
