European Journal of Scientific Research
ISSN 1450-216X / 1450-202X Vol. 157 No 3 September, 2020, pp.234 - 246 http://www. europeanjournalofscientificresearch.com
Modeling the Impact of Total Quality Management on Costs and Customer Satisfactions of Kuwaiti Courier Service
Companies Using Information Index
Raed A. Al-Husain
Corresponding Author, College of Business Administration Kuwait University
Tel: +965-2498-8432 E-mail: raed.husain@ku.edu.kw
Basel M. Al-Eideh
College of Business Administration Kuwait University
Tel: +965-2498-8451 E-mail: basel.aleideh@ku.edu.kw
Abstract
The main objective of this paper is to study the impact of total quality management (TQM) on the cost and customer satisfaction of Kuwaiti courier service companies. An information index (II) and autoregressive integrated moving averages (ARIMA) models have been developed to estimate the future propensity and measure the impact of applying TQM on the cost and customer satisfaction of Kuwaiti courier service companies. Data from 23 courier service companies in Kuwait were used to estimate the models. The proposed models of the TQM factors information index shows that ARIMA (2,0,1) is found to be an appropriate model to fit CTQMII for the costs group, whereas the ARIMA (1,1,1) model is found to be an appropriate model to fit STQMII for the customer satisfaction group. Also, the ARIMA (0,1,0) model is found to be an appropriate model to fit TQMII for both groups. This study is motivated predominately by the growing attention of the worldwide public to various factors affecting the TQM.
Keywords: Statistical analysis, information index, total quality management, ARIMA models.
1. Introduction
In today's competitive environment, organizations across all industries are more than ever compelled to offer better, faster, and cheaper products and services to their customers to flourish, and courier service providers are no exception. An essential approach to win this race is through the adaptation of the TQM philosophy (Dubey, 2015; Yala et al., 2018; Sadik, 2018; Tashmukhamedova, 2019). TQM is a management philosophy that is aimed to improve business processes continuously across an organization. Although the development of TQM initially aimed to improve manufacturing, its effect on improving the performance of service industries, such as hotel, banking, and healthcare, to name a few, is evident (Nazar et al., 2018; and Badhurudheen, 2018; Jabbarzare and Shafighi, 2019).
Modeling the Impact of Total Quality Management on Costs and Customer
Satisfactions of Kuwaiti Courier Service Companies Using Information Index 235 However, there is limited research on the impact of TQM practices on courier service providers. Among the earliest studies conducted in this area is the one provided by Varey and Hamblett (1997) on the Royal Mail in the United Kingdom. A survey was developed using the European Foundation for Quality Management model, established in 1989, and found that applying quality standards highly improved business performance. Litifi (2012) conducted a study on the impact of quality in the courier service industry in Tunisia and showed that higher quality standards could improve customer satisfaction and business performance. Azizzadeh et al. (2013) carried a similar investigation on the Iranian Postal Service. They found that the quality of service provided, namely, factors that include credibility, security, responsiveness, empathy, and costs, did not meet customer expectations. Hence, customer loyalty was in jeopardy. Sweis et al. (2016) surveyed courier service providers’ firms in Jordan to investigate the effect of TQM practices on performance. They found that a positive correlation exists between TQM practices and organizational performance, specifically the
“continuous improvement” factor of TQM.
In contrary to previous studies, an investigation of the expectation of the impact of TQM practices on business performance and customer satisfaction in courier service companies in Kuwait, conducted by Al-Husain and Al-Sayegh (2016), surprisingly suggest that courier service providers in Kuwait believe that TQM practices neither affects business performance nor customer satisfaction. In their study, the Mail Preparation Total Quality Management (MPTQM) model, developed in 1995 by United States Postal Services, is used to create a questionnaire to survey courier service providers in Kuwait. The Mann-Whitney and Kruskall-Wallis tests were used in their analysis. The unexpected results, however, were suggested to be due to governmental intervention.
More recently, Buchunde and Sangode (2019) performed empirical research comparing the effect of TQM practices on three types of service sectors, namely retail, courier services, and restaurants. They found that all three sectors can significantly benefit from implementing TQM. Also, their results showed that the impact of TQM on all three sectors are mostly related to customer satisfaction, customer feedback, and organizational performance.
In general, TQM focus on the following eight dimensions that concerns: customer focus, continuous improvement, employee involvement, supplier involvement, management support, quality information, process management, and product design (Lewis et al., 2006; Hietschold et al.,2014;
Neyestani and Juanzon, 2016). However, the MPTQM model, which is used in this study and developed explicitly for the courier industry, uses 11 dimensions of quality consisting of 56 attributes.
Table 1 below shows the dimensions (factors) of the MPTQM and their explanations (Coverings).
Table 1: The Total Quality Management Factors and their Coverings
No. Factors Attributes Explanations
1 Quality management standards 10 Construction quality management manual
Applying quality assurance checks on business performance Applying quality assurance checks on customer satisfaction Taking corrective and preventive actions of customer satisfaction 2 Organizational management
standards
8 Communication with customers and getting their feedback Setting goals and mission statement
3 Human resources training standards
4 Employees involvements Written job description 4 Program management
standards
5 Measuring the level of preventive procedures Customer involvement
5 Customer satisfaction standards 3 Handling customer feedback 6 Maintenance and calibration
standards
3 Postage payments methods Software performance
7 Mailpiece elements standards 5 Providing on-site assistance to customers
Describing printing procedure and preventive procedure 8 Data preparation standards 5 Mail data preparation procedures
Standardization of communication processes
236 Raed A. Al-Husain and Basel M. Al-Eideh
No. Factors Attributes Explanations
9 Collecting and receiving standards
3 Communication and handling procedures with customer and suppliers
10 Mail production standards 7 Mail sorting procedures Mail item tracking Types of equipment used Quality checks
11 Presentation standards 3 Mail handling procedures
In this paper, a total quality management information index (TQMII) is used to model the impact of TQM on the cost and customer satisfaction of Kuwaiti courier service companies. An autoregressive integrated moving average (ARIMA) model is used to fit the TQMII model to measure the impact of applying the total quality management on decreasing cost and increasing the customer satisfaction of the courier service companies in Kuwait.
2. Methodology
2.1. Study Sample and Procedures
The study population is the courier service companies in Kuwait. Whereas, the study sample consisted of a simple random sample of 23 companies from the courier service companies in Kuwait. The study questionnaire was distributed at (23) companies of the study population, where all of the companies are included making the response rate up to 100%.
As the study tool, a questionnaire consists of two parts is used. The first part of the questionnaire includes a set of questions that deal with TQM factors, which may increase costs. The second part consists of a set of questions that deal with TQM factors, which may increase customer satisfaction.
To ensure the validity of the study tool, the questionnaire was initially distributed to three expert faculty members, where they were asked to express their opinion on the suitability of the tool, the suitability of study questions, and the safety of the appropriate language. Then, the tool was amended according to the faculty members' remarks and notes, which some were positive, and some were negative ones.
To ascertain the external validity of the tool, the amended questionnaire was then distributed to 5 companies of the population as a pilot sample to ensure its clarity and its extent to respond to it.
Some have made remarks on the lack of clarity of some questions, and accordingly, the tool was amended again.
To ensure the stability of the tool, the reliability coefficient (Cronbach Alpha) was calculated and found to be close to 0.83. The calculated coefficient indicates high stability and refers to a remarkable degree of the consistency of the questions.
2.2 Total Quality Management Information Index (TQMII)
The total quality management information index (TQMII) of courier service companies is developed to measure the personal intention rates of TQM through their factors. Therefore, TQMII is defined as the average of all respondent indices of information of TQM regarding their factors for both cost and customer satisfaction.
The TQMII index measures the average of the personal intention rates to all items offered by the sample. This gives quantitatively the propensity a courier service company is loyal to the TQM factors (C.f. Al-Hussainan and Gharraph (1999) and Al-Ansari and Al-Eideh (2005)).
Let Yibe the individual information index for the courier service companyi, =i 1,2,...,n, then
=
=
p
j ij
i I
Y p
1
1
, i=1,2,...,n. (2.1)
Modeling the Impact of Total Quality Management on Costs and Customer
Satisfactions of Kuwaiti Courier Service Companies Using Information Index 237 Where Iij is defined as the respondent response such that if Iij is 0, it means the respondent has no intention of total quality management factors of personal intension with probability 1. If the response (Iij) is 1, it means the respondent has an intention of total quality factors with probability 1.
p j
n i
Iij ; 1,2,..., ; 1,2,...,
factor TQM to intention an
has
; 1
factor TQM
to intention no
;
0 = =
= (2.2)
Where p is the number of categorical variables (elven factors) related to the model under study. Thus, the TQMII will be given by
=
=
n
i
Yi
TQMII n
1
1 (2.3) Now we define CTQMII to be the TQMII of the number of categorical variables related to cost, STQMII to be the TQMII of the number of categorical variables related to customer satisfaction. Then
=
=
n
i
Yi
CTQMII n
1
1 (2.4) and
=
=
n
i
Yi
STQMII n
1
1 (2.5) respectively.
Note that 0≤ TQMII≤1 for the above case. As such, if TQMII turned out to have a value of 0.6, then it means on average respondents have a 60% propensity of the intention of the TQM factors.
Also, TQMII value increases as more respondents state their intention to the TQM factors for both cost and customer satisfaction. Also, if CTQMII turned out to have a value of 0.7, then it means on average respondents have a 70% propensity of the intention of the TQM factors on increasing the cost.
Whereas, if STQMII turned out to have a value of 0.8, then it means on average respondents have an 80% propensity of the intention of the TQM factors on increasing customer satisfaction.
3. Estimation Results and Analysis
3.1. Total Quality Management Information Indices TQMII
The SPSS statistical package was used in the statistical analysis to calculate the appropriate statistics, such as the descriptive statistics for CTQMII, STQMII, and TQMII. Table 2 below shows the mean TQM factors information indices of intended outcomes for all courier service companies in the study sample.
Table 2: The Mean Total Quality Management Information Index of the Study Sample
Category N Minimum Maximum Mean Std. Deviation
CTQMII 23 .00 1.00 .5889 .39897
STQMII 23 .00 1.00 .6719 .36952
TQMII 23 .00 1.00 .6304 .35562
The table shows that the mean TQM information index CTQMII for the costs’ group is 0.5889 with a standard deviation of 0.39897, and the mean TQM information index STQMII for the customer satisfaction group is 0.6719 with a standard deviation of 0.36952. The mean TQM information index TQMII for both costs and customer satisfaction groups is 0.6304, with a standard deviation of 0.35562.
This means that respondents have more intention propensity for the customer satisfaction group than the cost group respondents.
238 3.2
This section will be devoted to model the total quality management information
respondents’ intention on costs’ group, which is defined as the average of all courier service companies indices of information of respondents’ intention regarding the TQM factors assuming
individual info
observed 238
3.2. Modeling the Total Quality Management
This section will be devoted to model the total quality management information
respondents’ intention on costs’ group, which is defined as the average of all courier service companies indices of information of respondents’ intention regarding the TQM factors assuming
individual info
Figure 1 below shows the original data of CTQMII obtained for all companies in the sample study.
Figures 2 and 3 observed CTQM
Modeling the Total Quality Management
This section will be devoted to model the total quality management information
respondents’ intention on costs’ group, which is defined as the average of all courier service companies indices of information of respondents’ intention regarding the TQM factors assuming
individual information index for company
Figure 1 below shows the original data of CTQMII obtained for all companies in the sample study.
ures 2 and 3 below
CTQMII obtained from the survey for Figure 2
Modeling the Total Quality Management
This section will be devoted to model the total quality management information
respondents’ intention on costs’ group, which is defined as the average of all courier service companies indices of information of respondents’ intention regarding the TQM factors assuming
rmation index for company
Figure 1 below shows the original data of CTQMII obtained for all companies in the sample study.
Figure 1: The Original CTQMII of the Study Sample
below show the autocorrelation and partial autocorrelation functions of the II obtained from the survey for
2: The Autocorrelation Function (ACF) for the Observed CTQMII Modeling the Total Quality Management I
This section will be devoted to model the total quality management information
respondents’ intention on costs’ group, which is defined as the average of all courier service companies indices of information of respondents’ intention regarding the TQM factors assuming
rmation index for companyi, =i 1,2,...,
Figure 1 below shows the original data of CTQMII obtained for all companies in the sample study.
The Original CTQMII of the Study Sample
show the autocorrelation and partial autocorrelation functions of the II obtained from the survey for companies
The Autocorrelation Function (ACF) for the Observed CTQMII Raed A. Al
Information
This section will be devoted to model the total quality management information
respondents’ intention on costs’ group, which is defined as the average of all courier service companies indices of information of respondents’ intention regarding the TQM factors assuming
n
,..., as defined earlier in the methodology in section 2.
Figure 1 below shows the original data of CTQMII obtained for all companies in the sample study.
The Original CTQMII of the Study Sample
show the autocorrelation and partial autocorrelation functions of the companies.
The Autocorrelation Function (ACF) for the Observed CTQMII
Raed A. Al-Husain and Basel M. Al nformation Index for Costs CTQMII
This section will be devoted to model the total quality management information
respondents’ intention on costs’ group, which is defined as the average of all courier service companies indices of information of respondents’ intention regarding the TQM factors assuming
as defined earlier in the methodology in section 2.
Figure 1 below shows the original data of CTQMII obtained for all companies in the sample study.
The Original CTQMII of the Study Sample
show the autocorrelation and partial autocorrelation functions of the
The Autocorrelation Function (ACF) for the Observed CTQMII
Husain and Basel M. Al ndex for Costs CTQMII
This section will be devoted to model the total quality management information index (CTQMII) of respondents’ intention on costs’ group, which is defined as the average of all courier service companies indices of information of respondents’ intention regarding the TQM factors assuming
as defined earlier in the methodology in section 2.
Figure 1 below shows the original data of CTQMII obtained for all companies in the sample study.
The Original CTQMII of the Study Sample
show the autocorrelation and partial autocorrelation functions of the
The Autocorrelation Function (ACF) for the Observed CTQMII
Husain and Basel M. Al- ndex for Costs CTQMII
index (CTQMII) of respondents’ intention on costs’ group, which is defined as the average of all courier service companies indices of information of respondents’ intention regarding the TQM factors assuming Yi to be the as defined earlier in the methodology in section 2.
Figure 1 below shows the original data of CTQMII obtained for all companies in the sample study.
show the autocorrelation and partial autocorrelation functions of the
The Autocorrelation Function (ACF) for the Observed CTQMII
-Eideh
index (CTQMII) of respondents’ intention on costs’ group, which is defined as the average of all courier service companies to be the as defined earlier in the methodology in section 2.
Figure 1 below shows the original data of CTQMII obtained for all companies in the sample study.
show the autocorrelation and partial autocorrelation functions of the
Modeling the Impact of Total Quality Management on
Satisfactions of Kuwaiti Courier Service Companies Using Information
For t
autoregressive integrated moving average of order, CTQMII data.
The
Using SPSS Software, the model parameter estimates were as follows:
254 . ˆ 1
1 = φ
Thus, the predicted model will be as follows:
For the compatibility of the fitted model with original observations, we use the autocorrelation function ACF and the partial autocorre
model. Figure 4
Modeling the Impact of Total Quality Management on
Satisfactions of Kuwaiti Courier Service Companies Using Information Figure 3:
For the ACF and PACF
autoregressive integrated moving average of order, II data.
The ARIMA (2 CTQMII
Using SPSS Software, the model parameter estimates were as follows:
254, ˆ 0.412
2 =− φ
CTQMII
Thus, the predicted model will be as follows:
_ Pred
For the compatibility of the fitted model with original observations, we use the autocorrelation function ACF and the partial autocorre
Figure 4 below Figure
Modeling the Impact of Total Quality Management on
Satisfactions of Kuwaiti Courier Service Companies Using Information ure 3: The Partial Autocorrelation
he ACF and PACF for the observed CTQMII autoregressive integrated moving average of order,
2,0,1) model is given by CTQMIIt =
φ
0 +φ
Using SPSS Software, the model parameter estimates were as follows:
412, and ˆθ1 = CTQMIIt = 6060.
Thus, the predicted model will be as follows:
_CTQMIIt
For the compatibility of the fitted model with original observations, we use the autocorrelation function ACF and the partial autocorre
below shows these plots.
Figure 4: The ACF and PACF of the residual of the CTQMII model Modeling the Impact of Total Quality Management on
Satisfactions of Kuwaiti Courier Service Companies Using Information The Partial Autocorrelation
for the observed CTQMII autoregressive integrated moving average of order,
) model is given by CTQMIIt
φ
1 −Using SPSS Software, the model parameter estimates were as follows:
370 .
0 . The fitted model is th CTQMII
+ 2541. 606
Thus, the predicted model will be as follows:
. 1 606 .
0 +
=
For the compatibility of the fitted model with original observations, we use the autocorrelation function ACF and the partial autocorrelation function PACF for the residuals of the above
se plots.
: The ACF and PACF of the residual of the CTQMII model Modeling the Impact of Total Quality Management on Costs and Customer
Satisfactions of Kuwaiti Courier Service Companies Using Information
The Partial Autocorrelation Function (PACF) for the Observed CTQMII
for the observed CTQMII
autoregressive integrated moving average of order, ARIMA (2,0,1 ) model is given by
CTQMII
φ
−1+ 2
Using SPSS Software, the model parameter estimates were as follows:
. The fitted model is th CTQMIIt−1−0 Thus, the predicted model will be as follows:
254
. CTQMII
For the compatibility of the fitted model with original observations, we use the autocorrelation lation function PACF for the residuals of the above
: The ACF and PACF of the residual of the CTQMII model Costs and Customer
Satisfactions of Kuwaiti Courier Service Companies Using Information
Function (PACF) for the Observed CTQMII
for the observed CTQMII shown in Figures 2 and 3 ARIMA (2,0,1),
CTQMIIt−2 −
θ
1ε
Using SPSS Software, the model parameter estimates were as follows:
. The fitted model is then given by CTQMII 412
. 0
1 0.412
− − CTQMIIt
For the compatibility of the fitted model with original observations, we use the autocorrelation lation function PACF for the residuals of the above
: The ACF and PACF of the residual of the CTQMII model Costs and Customer Satisfactions of Kuwaiti Courier Service Companies Using Information Index
Function (PACF) for the Observed CTQMII
shown in Figures 2 and 3
, model was suggested to fit the
t
t
ε
ε
−1− . Using SPSS Software, the model parameter estimates were as follows:en given by
CTQMIIt−2 −0.370
412CTQMIIt−
For the compatibility of the fitted model with original observations, we use the autocorrelation lation function PACF for the residuals of the above
: The ACF and PACF of the residual of the CTQMII model
Function (PACF) for the Observed CTQMII
shown in Figures 2 and 3, respectively model was suggested to fit the
Using SPSS Software, the model parameter estimates were as follows: ˆφ0
t
t
ε
ε
−1− 3702 0.370 −
− −
ε
tFor the compatibility of the fitted model with original observations, we use the autocorrelation lation function PACF for the residuals of the above
: The ACF and PACF of the residual of the CTQMII model
239
respectively, an model was suggested to fit the
(3.1) 606 .
0 =0 ,
(3.2)
1
− (3.3) For the compatibility of the fitted model with original observations, we use the autocorrelation lation function PACF for the residuals of the above-fitted
240
(2
information index Figure 5:
3.2.
This section will be devoted to model the total quality manag respondents’ intention on customer satisfaction group, which service companies
assuming
methodology in section 2.
study.
240
The ACF and PACF plots (Figure 4) 2,0,1) model is
Figure information index Figure 5: The Obser
CTQMII for costs
3.2. Modeling the Total Quality Management Information Index for Customer Satisfactions STQMII
This section will be devoted to model the total quality manag respondents’ intention on customer satisfaction group, which service companies
assuming Yi
methodology in section 2.
Figure 6 below shows the original data of STQMII obtained for all companies in the sample study.
The ACF and PACF plots (Figure 4)
model is a statistically adequate representation of the
Figure 5 below shows the observed and the predicted values of the information index CTQMII
The Observed and the Predicted Values CTQMII for costs
Modeling the Total Quality Management Information Index for Customer Satisfactions STQMII
This section will be devoted to model the total quality manag respondents’ intention on customer satisfaction group, which service companies indices of information
to be the individual information index for methodology in section 2.
Figure 6 below shows the original data of STQMII obtained for all companies in the sample The ACF and PACF plots (Figure 4)
statistically adequate representation of the
shows the observed and the predicted values of the CTQMII of companies’ intention for the costs group
ved and the Predicted Values CTQMII for costs
Modeling the Total Quality Management Information Index for Customer Satisfactions
This section will be devoted to model the total quality manag respondents’ intention on customer satisfaction group, which
indices of information
be the individual information index for methodology in section 2.
Figure 6 below shows the original data of STQMII obtained for all companies in the sample
Figure 6: The Original ST The ACF and PACF plots (Figure 4) above
statistically adequate representation of the
shows the observed and the predicted values of the of companies’ intention for the costs group ved and the Predicted Values
Modeling the Total Quality Management Information Index for Customer Satisfactions
This section will be devoted to model the total quality manag respondents’ intention on customer satisfaction group, which
indices of information of
be the individual information index for
Figure 6 below shows the original data of STQMII obtained for all companies in the sample
The Original ST
Raed A. Al
above clearly support the fact that the fitted statistically adequate representation of the CTQM
shows the observed and the predicted values of the of companies’ intention for the costs group
ved and the Predicted Values of the Total Quality Management Information Index
Modeling the Total Quality Management Information Index for Customer Satisfactions
This section will be devoted to model the total quality manag respondents’ intention on customer satisfaction group, which
of respondents’ intention be the individual information index for company
Figure 6 below shows the original data of STQMII obtained for all companies in the sample
The Original STQMII of the Study Sample
Raed A. Al-Husain and Basel M. Al clearly support the fact that the fitted
CTQMII data.
shows the observed and the predicted values of the of companies’ intention for the costs group.
Total Quality Management Information Index
Modeling the Total Quality Management Information Index for Customer Satisfactions
This section will be devoted to model the total quality management information
respondents’ intention on customer satisfaction group, which is defined as the average of all s’ intention
companyi, =i 1,2
Figure 6 below shows the original data of STQMII obtained for all companies in the sample
QMII of the Study Sample
Husain and Basel M. Al clearly support the fact that the fitted
II data.
shows the observed and the predicted values of the total quality management
Total Quality Management Information Index
Modeling the Total Quality Management Information Index for Customer Satisfactions
nformation index ( is defined as the average of all s’ intention regarding the
n ,...,
2 as defined earlier in the Figure 6 below shows the original data of STQMII obtained for all companies in the sample
QMII of the Study Sample
Husain and Basel M. Al- clearly support the fact that the fitted ARIMA
total quality management
Total Quality Management Information Index
Modeling the Total Quality Management Information Index for Customer Satisfactions
ndex (STQM is defined as the average of all courier
regarding the TQM factors as defined earlier in the Figure 6 below shows the original data of STQMII obtained for all companies in the sample -Eideh ARIMA total quality management
Total Quality Management Information Index
Modeling the Total Quality Management Information Index for Customer Satisfactions
STQMII) of courier TQM factors as defined earlier in the Figure 6 below shows the original data of STQMII obtained for all companies in the sample
Modeling the Impact of Total Quality Management on Costs and Customer
Satisfactions of Kuwaiti Courier Service Companies Using Information Index 241 Figures 7 and 8 below show the autocorrelation and partial autocorrelation functions of the observed STQMII obtained from the survey for companies.
Figure 7: The Autocorrelation Function (ACF) for the Observed STQMII
Figure 8: The Partial Autocorrelation Function (PACF) for the Observed STQMII
For the ACF and PACF for the observed STQMII shown in Figures 7 and 8, respectively, an autoregressive integrated moving average of order, ARIMA (1,1,1), model was suggested to fit the STQMII data.
The ARIMA (1,1,1) model is given by
( )
t t tt STQMII
STQMII =
φ
0 + 1+φ
1 −1−θ
1ε
−1−ε
. (3.4) Using SPSS Software, the model parameter estimates were as follows: ˆ 0.0400 =−
φ , ˆ 0.519
1 = φ and ˆ 1
1 =
θ . The fitted model is then given by
t t t
t STQMII
STQMII =−0.040+1.519 −1−
ε
−1−ε
(3.5) Thus, the predicted model will be as follows:1
519 1
. 1 040 . 0 _
Pred STQMIIt =− + CTQMIIt− −
ε
t− (3.6)242
function ACF and the partial autocorrelation function PACF for the residuals of the above model.
(1
informatio Figure
3.3
This section will be devoted to model the total quality management respondents’ intention for both groups, which
indices of
individual information index for section 2.
242
For the compatibility of the fitted model with origi
function ACF and the partial autocorrelation function PACF for the residuals of the above model. Figure 4
The AC 1,1,1) model is
Figure information index
Figure 10: The Observed and the Predicted Values STQMII for customer satisfactions
3.3. Modeling the Total Quality Management
This section will be devoted to model the total quality management respondents’ intention for both groups, which
indices of information
individual information index for section 2.
For the compatibility of the fitted model with origi
function ACF and the partial autocorrelation function PACF for the residuals of the above Figure 4 below show
Figure
The ACF and PACF plots (Figure
model is a statistically adequate representation of the Figure 10 below
n index STQMII
The Observed and the Predicted Values STQMII for customer satisfactions
Modeling the Total Quality Management
This section will be devoted to model the total quality management respondents’ intention for both groups, which
information of
individual information index for
For the compatibility of the fitted model with origi
function ACF and the partial autocorrelation function PACF for the residuals of the above shows these plots
Figure 9: The ACF and PACF of the residual of the STQMII model
F and PACF plots (Figure
statistically adequate representation of the
shows the observed and predicted values of the
STQMII of companies’ intention for the customer satisfaction group The Observed and the Predicted Values
STQMII for customer satisfactions
Modeling the Total Quality Management
This section will be devoted to model the total quality management respondents’ intention for both groups, which
of respondent individual information index for company
For the compatibility of the fitted model with origi
function ACF and the partial autocorrelation function PACF for the residuals of the above se plots
The ACF and PACF of the residual of the STQMII model
F and PACF plots (Figure 9) above
statistically adequate representation of the
shows the observed and predicted values of the
of companies’ intention for the customer satisfaction group The Observed and the Predicted Values
STQMII for customer satisfactions
Modeling the Total Quality Management I
This section will be devoted to model the total quality management
respondents’ intention for both groups, which is defined as the average of all respondents’ intention
companyi, =i
Raed A. Al
For the compatibility of the fitted model with original observations, we use the autocorrelation function ACF and the partial autocorrelation function PACF for the residuals of the above
The ACF and PACF of the residual of the STQMII model
above clearly support the fact that the fitted statistically adequate representation of the STQM
shows the observed and predicted values of the
of companies’ intention for the customer satisfaction group The Observed and the Predicted Values of the Total Quality
Information
This section will be devoted to model the total quality management
is defined as the average of all s’ intention regarding the
n ,..., 2 , 1
= as defined earlier in the methodology in Raed A. Al-Husain and Basel M. Al
nal observations, we use the autocorrelation function ACF and the partial autocorrelation function PACF for the residuals of the above
The ACF and PACF of the residual of the STQMII model
clearly support the fact that the fitted STQMII data.
shows the observed and predicted values of the
of companies’ intention for the customer satisfaction group Total Quality
nformation Index for a
This section will be devoted to model the total quality management information is defined as the average of all
regarding the TQM factors assuming
as defined earlier in the methodology in Husain and Basel M. Al
nal observations, we use the autocorrelation function ACF and the partial autocorrelation function PACF for the residuals of the above
The ACF and PACF of the residual of the STQMII model
clearly support the fact that the fitted II data.
shows the observed and predicted values of the total quality management of companies’ intention for the customer satisfaction group
Total Quality Management Information Index
all Groups TQMII nformation index (
is defined as the average of all courier service companies TQM factors assuming
as defined earlier in the methodology in Husain and Basel M. Al-
nal observations, we use the autocorrelation function ACF and the partial autocorrelation function PACF for the residuals of the above
clearly support the fact that the fitted ARIMA total quality management of companies’ intention for the customer satisfaction group.
Management Information Index
ll Groups TQMII ndex (TQMII) courier service companies TQM factors assuming Yi to
as defined earlier in the methodology in -Eideh nal observations, we use the autocorrelation function ACF and the partial autocorrelation function PACF for the residuals of the above-fitted
ARIMA total quality management
Management Information Index
II) of all courier service companies to be the as defined earlier in the methodology in
Modeling the Impact of Total Quality Management on
Satisfactions of Kuwaiti Courier Service Companies Using Information Figure 11 below shows the original
study.
Figures observed TQM
Modeling the Impact of Total Quality Management on
Satisfactions of Kuwaiti Courier Service Companies Using Information Figure 11 below shows the original
Figures 12 and
TQMII obtained from the survey for Figure
Figure 1
Modeling the Impact of Total Quality Management on
Satisfactions of Kuwaiti Courier Service Companies Using Information Figure 11 below shows the original
Figure
2 and 13 below show the autocorrelation and partial autocorrelation functions of the II obtained from the survey for
ure 12: The Autocorrelation Function (ACF) for the Observed TQMII
3: The Partial Autocorrelation Function (PACF) for the Observed TQMII Modeling the Impact of Total Quality Management on
Satisfactions of Kuwaiti Courier Service Companies Using Information
Figure 11 below shows the original data of TQMII obtained for all companies in the sample
11: The Original TQMII of the Study Sample
show the autocorrelation and partial autocorrelation functions of the II obtained from the survey for
The Autocorrelation Function (ACF) for the Observed TQMII
The Partial Autocorrelation Function (PACF) for the Observed TQMII Modeling the Impact of Total Quality Management on Costs and Customer
Satisfactions of Kuwaiti Courier Service Companies Using Information
data of TQMII obtained for all companies in the sample
The Original TQMII of the Study Sample
show the autocorrelation and partial autocorrelation functions of the II obtained from the survey for companies.
The Autocorrelation Function (ACF) for the Observed TQMII
The Partial Autocorrelation Function (PACF) for the Observed TQMII Costs and Customer
Satisfactions of Kuwaiti Courier Service Companies Using Information
data of TQMII obtained for all companies in the sample
The Original TQMII of the Study Sample
show the autocorrelation and partial autocorrelation functions of the .
The Autocorrelation Function (ACF) for the Observed TQMII
The Partial Autocorrelation Function (PACF) for the Observed TQMII Costs and Customer
Satisfactions of Kuwaiti Courier Service Companies Using Information Index
data of TQMII obtained for all companies in the sample
The Original TQMII of the Study Sample
show the autocorrelation and partial autocorrelation functions of the
The Autocorrelation Function (ACF) for the Observed TQMII
The Partial Autocorrelation Function (PACF) for the Observed TQMII
data of TQMII obtained for all companies in the sample
show the autocorrelation and partial autocorrelation functions of the
The Autocorrelation Function (ACF) for the Observed TQMII
The Partial Autocorrelation Function (PACF) for the Observed TQMII
243 data of TQMII obtained for all companies in the sample
show the autocorrelation and partial autocorrelation functions of the
244
autoregressive integrated moving average of order, TQM
model is then given by
function ACF and the partial autocorrelation function PACF for the residuals of the above model.
(0
information index Figure
244
For the ACF and PACF
utoregressive integrated moving average of order, TQMII data.
The ARI
Using SPSS Software, the model parameter model is then given by
Thus, the predicted model will be as follows:
For the compatibility of the fitted model with original observations, we use the autocorrelation function ACF and the partial autocorrelation function PACF for the residuals of the above model. Figure 4
The ACF and PACF plots (Figure 0,1,0) model is
Figure information index
Figure 15: The Observed and the Predicted Values for All Groups
he ACF and PACF
utoregressive integrated moving average of order, ARIMA (0,1,0
TQMIIt =
Using SPSS Software, the model parameter model is then given by
TQMIIt =
Thus, the predicted model will be as follows:
_
Pred TQMII
For the compatibility of the fitted model with original observations, we use the autocorrelation function ACF and the partial autocorrelation function PACF for the residuals of the above
Figure 4 below show Figure
The ACF and PACF plots (Figure
model is a statistically adequate representation of the Figure 15 below shows the observed and the
information index TQMII
The Observed and the Predicted Values for All Groups
he ACF and PACF for the observed TQMII utoregressive integrated moving average of order,
0,1,0) model is given by TQMII
φ
+= 0
Using SPSS Software, the model parameter TQMII
+
−
= 0.031
Thus, the predicted model will be as follows:
. 0
−
t = TQMII
For the compatibility of the fitted model with original observations, we use the autocorrelation function ACF and the partial autocorrelation function PACF for the residuals of the above
shows these plots Figure 14: The AC
The ACF and PACF plots (Figure
statistically adequate representation of the shows the observed and the
TQMII of companies’ intention for all groups The Observed and the Predicted Values
for the observed TQMII utoregressive integrated moving average of order,
) model is given by
t
TQMIIt−1−
ε
. Using SPSS Software, the model parameterTQMIIt−1−
ε
Thus, the predicted model will be as follows:
031
. +TQMII
For the compatibility of the fitted model with original observations, we use the autocorrelation function ACF and the partial autocorrelation function PACF for the residuals of the above
se plots
The ACF and PACF of the residual of the TQMII model
The ACF and PACF plots (Figure 14) above statistically adequate representation of the
shows the observed and the
of companies’ intention for all groups The Observed and the Predicted Values of the
Raed A. Al for the observed TQMII shown in Figures utoregressive integrated moving average of order, ARIMA (0,1,0
Using SPSS Software, the model parameter estimates were as follows:
ε
t Thus, the predicted model will be as follows:1
−
TQMIIt
For the compatibility of the fitted model with original observations, we use the autocorrelation function ACF and the partial autocorrelation function PACF for the residuals of the above
F and PACF of the residual of the TQMII model
above clearly support the fact that the fitted statistically adequate representation of the TQM
shows the observed and the predicted values of the of companies’ intention for all groups
of the Total Quality Management Information Index Raed A. Al-Husain and Basel M. Al shown in Figures
ARIMA (0,1,0), model was suggested to fit the
estimates were as follows:
For the compatibility of the fitted model with original observations, we use the autocorrelation function ACF and the partial autocorrelation function PACF for the residuals of the above
F and PACF of the residual of the TQMII model
clearly support the fact that the fitted TQMII data.
predicted values of the of companies’ intention for all groups.
Total Quality Management Information Index Husain and Basel M. Al shown in Figures 12 and 1
model was suggested to fit the
estimates were as follows: ˆφ0 =
For the compatibility of the fitted model with original observations, we use the autocorrelation function ACF and the partial autocorrelation function PACF for the residuals of the above
F and PACF of the residual of the TQMII model
clearly support the fact that the fitted II data.
predicted values of the total quality management
Total Quality Management Information Index Husain and Basel M. Al-
13, respectively model was suggested to fit the
031 . 0
− . The fitted
For the compatibility of the fitted model with original observations, we use the autocorrelation function ACF and the partial autocorrelation function PACF for the residuals of the above
clearly support the fact that the fitted ARIMA total quality management
Total Quality Management Information Index -Eideh respectively, an model was suggested to fit the
(3.7) . The fitted (3.8) (3.9) For the compatibility of the fitted model with original observations, we use the autocorrelation function ACF and the partial autocorrelation function PACF for the residuals of the above-fitted
ARIMA total quality management
TQMII
Modeling the Impact of Total Quality Management on Costs and Customer
Satisfactions of Kuwaiti Courier Service Companies Using Information Index 245 4. Concluding Remarks and Recommendations
Analyses conducted in this paper provide some insights into a group of TQM factors that play an important role in the courier service companies' intention in Kuwait. In this paper, we estimated the information index model for the TQM factors of the intention of CTQMII for the cost group and STQMII for the customer satisfaction group as well as TQMII for both groups. We provided forecasts of the intention propensity for Kuwaiti courier service companies using the predicted models of CTQMII, STQMII, and TQMII.
The presented models of the TQM factors information index are fundamentally based on the stated intentions of respondents. They do not account for explanatory variables in predicting the intention probabilities of respondents. As such, we have estimates of the CTQMII, STQMII, and TQMII of the respondents’ intentions. It turns out to fit the stated CTQMII, STQMII, and TQMII data very nicely.
An ARIMA (2,0,1) is found to be an appropriate model to fit these CTQMII for the costs group, whereas the ARIMA (1,1,1) model is found to be an appropriate model to fit STQMII for the customer satisfaction group. Also, the ARIMA (0,1,0) model is found to be an appropriate model to fit TQMII for both groups.
As a recommendation, courier service providers in Kuwait should not be deterred from applying TQM practices. Although there is a consensus among courier service providers in Kuwait that following TQM practices might increase operational costs, there is a great benefit to be gained from an increase in customer satisfaction. As shown earlier, based on most studies conducted on the effect of TQM practices on service industries, an improvement of business performance can be realized when applying TQM.
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