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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).

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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

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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)

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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.

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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

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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),

CTQMIIt2

θ

1

ε

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− 370

2 0.370

ε

t

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

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

(7)

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

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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 t

t STQMII

STQMII =

φ

0 + 1+

φ

1 1

θ

1

ε

1

ε

. (3.4) Using SPSS Software, the model parameter estimates were as follows: ˆ 0.040

0 =−

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)

(9)

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

(10)

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

(11)

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 parameter

TQMIIt−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

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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.

References

[1] Al-Ansari, E. M. & Al-Eideh, B. M. (2005). Information Indices for the Kuwait University Community Service and Continuing Education center, European Journal of Social Sciences, 1(2), 37-47.

[2] Al-Hussainan, A. & Gharraph, M. K. (1999). Information Indices for the Touristic Enterprises Company in Kuwait, 6(3), 451-457.

[3] Al-Husain, R., and Al-Sayegh, W. (2016). Analysis of the Explanation of TQM Practices in Courier Service Providers in Kuwait: An Empirical Study. Arab Journal of Administrative Sciences, 23(1), 43 – 66.

[4] Azizzadeh, F., Khalili, K., and Soltani, I. (2013). Service Quality Measurement in the Public Sector (Ilam Province Post Office Case Studies). International Journal of Economics, Finance and Management, 2(1), 114- 121.

[5] Badhurudheen, A. (2018). Total Quality Management Practices and Organizational Performance: Case of Private Healthcare Sector in Sri Lanka. European Journal of Business and Management, 10(3), 76 – 85.

[6] Buchunde, P., and Sangode, P. (2019). TQM: A tool for Sustainable Competitive Advantage for Small Enterprises. IOSR Journal of Engineering, 9(5), 11 – 21.

[7] Dubey, R. (2015). An insight on soft TQM practices and their impact on cement manufacturing firm’s performance: does size of the cement manufacturing firm matter? Business Process Management Journal, 21(1), 2–24.

[8] Hietschold, N., Reinhardt, R., and Gurtner, S. (2014). Measuring critical success factors of TQM implementation successfully–a systematic literature review. International Journal of Production Research, 52(21), 6254 - 6272.

[9] Jabbarzare, E., and Shafighi, N. (2019). Total Quality Management Practices and Organizational Performance. Open Science Journal of Statistics and Application, 6(1), 6 – 12.

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246 Raed A. Al-Husain and Basel M. Al-Eideh [10] Lewis, W.G., Pun K.F., and Lalla T.R.M. (2006). Exploring soft versus hard factors for TQM

implementation in small and medium-sized enterprises. International Journal of Productivity and Performance Management, 55(7), 539-554.

[11] Litifi, M., and Gharbi, J. (2012). Satisfaction and loyalty with the Tunisian Postal Service.

International Journal of Humanities and Social Sciences, 2(7), 178-191.

[12] Nazar, N., Ramzani, S., Anjum, T., and Shahzad, I. (2018). Organizational Performance: The Role of TQM Practices in Banking Sector of Pakistan. European Scientific Journal, 14(31), 278 – 302.

[13] Neyestani, B., and Juanzon, J. B. P. (2016). Identification of A Set of Appropriate Critical Success Factors for Successful TQM Implementation in Construction, and Other Industries.

International Journal of Advanced Research, 4(11), 1581-1591.

[14] Sadik, R. (2018). Impact of Total Quality Management on Customer Satisfaction (E-Services).

International Journal of Advances in Computer Science and Technology, 7(6), 31 – 35.

[15] Sweis, R., Saleh, R., Al-Etayyem, R., Qasrawi, B., and Al Mahmoud, A. (2016). Total quality management practices and organisational performance in Jordanian courier services.

International Journal of Productivity and Quality Management, 19(2), 258 – 276.

[16] Tashmukhamedova, G. (2019). The impact of Total Quality Management on organisational performance. International Journal of Research, 6(2), 763 – 779.

[17] Varey, R., and Hamblett, R. (1997). Business excellence review at Royal Mail (NW/NW): a case of strategic communication management. Managing Service Quality, 7(6), 281-289.

[18] Yala, H., Ododa, H., and James. C. (2018). The impact of total quality management (TQM) policy on customer satisfaction at Kenya power and lighting company (KPLC) in Uasin Gishu County, Kenya (2010-2012). International Journal of Academic Research and Development, 3(2), 187 – 193.

References

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