The Impacts of Implementing Total Quality Management on Organization Performance of Construction Industry in Malaysia

The Impacts of Implementing Total Quality Management on Organization Performance of Construction Industry in Malaysia

Alnamrani Abdulhameed, Wang Zhong

College of Business Administration, Hunan University, China

Abstract:- The adoption and application of Total Quality Management (TQM) is likely to enhance the performance of a business. In Malaysia, implementing effective comprehensive quality management has become a problem since it affects both corporate performance and the finished products of construction. The study's findings highlight any potential obstacles that businesses may have while trying to develop high-quality goods that will best satisfy their clients. The primary goal of the study is to pinpoint the reasons why Total Quality Management (TQM) initiatives in Malaysia's construction sector have failed, as well asto assess how serious they are from the contractor's perspective. Therefore, in order to address the issue faced by Malaysian construction enterprises, it is essential to identify the challenges with TQM adoption. On the other side, there are some issues that fall under the economic situation and building environment of the nation, such as low bidder mentality, quality culture, and attitude. The Malaysian construction sector has come to understand the value of early identification of organizational reasons of TQM issues. Additionally, the majority ofbusinesses are happy to get assistance in identifying their vulnerabilities. Future use ofthe industry will greatly benefit from the generated model from this research.

INTRODUCTION

Total Quality Management (TQM) has been shown to excel in the areas of Construction Firm (Kakkad et al.2014), organizational performance (Mehralian et al. 2017), business performance (Miyagawa and Yoshida 2010), and improving quality performance (Zehir et al. 2012). TQM was developed in the manufacturing sector, where it quickly gained worldwide recognition for its impressive results, including greater output, decreased costs, and improved product dependability. The manufacturing sector's innovative implementation of TQM has inspired businesses in other sectors, such as the building trade, to adopt and implement the same methodology.

The construction industry contributes significantly to the nation's Gross Domestic Product (GDP) (Razak, Ibrahim,

& Roy, 2010). In a large way, the Gross Domestic Product determines a country's prosperity. More than 11% of global GDP between 2010 and 2020 came from the building sector (Shahbazi, Akbarnezhad, Rey, Ahmadian & Loosemore, 2019).

Although the literature on TQM has improved throughout time, there is still a lackof research and implementation in the construction business. Most TQM research hasbeen conducted in more developed countries, with few studies conducted in underdeveloped ones. The present research has primarily focused on TQM implementation, critical success factors, and impediments, but the impact of TQM practices on project performance has received less attention. It is necessary to conducta more updated study on the current situation of the Malaysian building industry. A more empirical study is needed to turn the TQM ideology into practical principles incorporated into Malaysian construction operations.

Considering the construction industry's importance to the Malaysian economy, it is crucial to successfully manage TQM procedures and produce a satisfactory conclusion if the country is to maintain a competitive edge. The purpose of this research was to assess the prevalence of TQM techniques in the construction industry in Malaysia and to determine whether or not these methods may be used to boost output. Professionals in the field of project management will use the information gleaned from this study to make the Malaysian construction industry more resilient in the face of current challenges.

BACKGROUND OF THE STUDY

During 2014, 2015, and 2016, the Malaysian construction industry was a primary economic sector, generating RM 1,012.5 billion, RM 1,062.8 billion, and RM 1,016.1 billion. Malaysia's highest construction work value in 2019, at approximately RM 146.37 billion.

Malaysia, like other ASEAN emerging countries, is now showing signs of recession. Small and medium-sized businesses (SMEs) have reported a slowdown, which is indicative of a deteriorating economy and rising unemployment. As a result, policymakers and bureaucrats came to appreciate the value of small and medium-sized enterprises (SMEs) and devised initiatives to boost their effectiveness (Khan & Khalique, 2014).

2013	955.1	38.6	3.73	10.6
2014	1,012.5	43.1	3.9	11.7
2015	1,062.8	46.6	8.2	8.2
2016	1,106.1	50.4	4.4	7.9

Table 1: Sector-wise Quarterly Gross Domestic Product (GDP) of Malaysia

In 2019, the value of all construction projects in Malaysia topped RM 146.37 billion. The yearly value of construction activities increased by close to RM 10 billion between 2012 and 2018. Following the World Health Organization's (WHO) declaration that the global spread of COVID-19 constitutes a pandemic, most countries have prepared for a nationwide lockdown, effectively shutting down their economies. Financial losses, cost increases, project delays, and job losses occurred in the construction industry and other industries as a result. With development suspended by the Malaysian government in an effort to reduce debt, the value of building projects in 2018 and 2019 is expected to be around the same.

MALAYSIAN CONSTRUCTION INDUSTRY

A high multiplier effect is created by the building industry in Malaysia due to its extensive recursive and transitive ties to other parts of the economy (Ibrahim et al. 2010). Backward links support production of raw, semi-processed, and processed materials like steel and cement, whereas forward linkages promote financial and professional services. Regulations have been put in place by the Malaysian government because of the vital role the building industry plays in the country's economy.

The construction industry in Malaysia plays an important role in the growth of other industries, including manufacturing, finance, and professional services, because to the extensive supply and demand networks it has established.

Year	GDP (RM billion)	Construction sector output at 2010 constant prices (RM billion)	Construction sector contributionto GDP (%)	Construction sector growth (%)
2012	912.3	34.9	3.53	18.1

Table 2: Malaysian construction sector contribution

Source: Bank Negara Malaysia and Department of Statistics Malaysia

However, because of current research deficit, it is unable to provide clear guidance for managers on how to apply TQM, which may cause uncertainty. Professional managers and academic researchers are required. Quality management professionals, who have a firm grasp on which TQM techniques to deploy in real-world contexts, continue to show a keen interest in TQM and the performance links between them.

REVIEW OF LITERATURE

TQM in other Countries

TQM in Turkey Construction Industry

TQM has been widely used in the Japanese construction industry since the 1970s and in the United States since the 1990s, it has yet to be broadly and successfully applied in the Turkish construction sector (Atilla Damci & Gul Polat, 2011). However, there are significant impediments to widespread TQM adoption in the building industry. The three most essential impediments are a lack of top management support, commitment, and leadership (Atilla Damci & Gul Polat, 2011).

TQM Current Practice in the Nigerian construction industry

Osuagwu (2012) mentions failed attempts to apply TQM in Nigeria. According to Osuagwu, early organizational efforts that are only simplistically grasped are implicated in such failures. Significantly, Kanji and Asher (1993) focused on the barriers that prevent firms from successfully implementing TQM. According to their study, many hurdles that inhibit TQM initiatives could be linked to how well quality transformation is managed, which this research could investigate. The researcher noted a dearth of

empirical effort to explore the barriers to Total Quality Management implementation in developing nations like Nigeria. Total quality is a comprehensive idea that demands all employees to be motivated towards a single objective. Most construction companies offer high-quality project execution but fall short of the required standards and client expectations. Numerous hours, dollars, and assets are wasted annually due to ineffective or nonexistent quality management systems (both human and material). This exemplifies the widespread problem of poor quality in the Nigerian construction industry (Oyedele, 2013).

The construction industry in Kenya

Construction in Kenya is regulated by the Ministry of Public Works and the National Construction Authority. According to the Kenya Private Developers Association, Kenyan construction enterprises' key obstacles are capital, quality control, and changing client demands. Previously, local governments were responsible for quality assurance. As a result, most of these authorities delegated to Public Health Officers. Most contractors have adopted a cautious approach to the market to deal with quality issues, preferring to engage with clients that share their outlook. The Ministry of Public Works, on the other hand, has been hosting workshops to discuss the issues. They envisioned that every construction company should be able to reach organizational level quality knowledge.

TQM IN Zimbabwe

The main challenges faced by construction companies in Zimbabwe are political and economic. Zimbabwe is still plagued by adversarial government authorities working for their own families. These issues harm the country's GDP and have led to high inflation rates (Pollin & Zhu, 2006). However, the construction industry has survived the continuing economic calamity (Wong & Fung, 2009). Foreign direct investment (FDI) in the construction sector has been severely impeded as investors weigh investing in Zimbabwe. The country's leading businesses have been hit hard by the economic downturn. Excessive inflation has driven several construction firms to shut down due to uncontrollable operational costs (Pollin & Zhu, 2006).

Methodology

The most frequent means of gathering qualitative data, a questionnaire was used in this study to determine how widespread the adoption of TQM procedures are in the Malaysian construction industry.

There are seven different categories that construction firms fall into according to the Construction Industry Development Board (CIDB). Due to the vast size of the study population, it was determined to implement physical barriers to limit the population's growth. Figure 3.2 depicts 13 of Malaysia's states. The area around Klang Valley will serve as the focus of the survey and its subsequent data analysis. The term "Klang Valley" refers to the metropolitan region that surrounds the Malaysian capital of Kuala Lumpur and the state of Selangor. For starters, most projects in Klang Valley adhere to quality standards, therefore there

is more readily available, up-to-date quality data there than in other districts.

In order to reflect the current state of the construction industry, the Construction Industry Development Board (CIDB) ranks construction firms on a scale from one to seven based on their financial, technical, human, and plant and machinery capabilities and years of experience. Table 3 illustrates this.

Table 2: Financial Limit and Grade [source CIDB]

Grade	Financial Limit (RM)
G1	X 200,000
G2	200,000 <X 500,000
G3	500,000 <X 1,000,000
G4	1,000,000 <X 3,000,000
G5	3,000,000 <X 5,000,000
G6	5,000,000 <X 10,000,000
G7	X > 10,000,000

Research into the various company categories is evaluated in light of the CIDB-imposed budgetary constraints to determine its relevance and usefulness. Finally, only seventh graders will do as they are the designated audience. The capacity to answer the questionnaire is also a determining factor during the primary screening phase.

According to CIDB, there are 722 active Grade G7 construction enterprises in Klang Valley. 488 in Selangor, 242 in KL and Putrajaya.

Table 3: Number of Companies in Klang Valley [source CIDB]

State (Klang Valley)	Number of Companies (G7)
Selangor	480
Kuala Lumpur & Putrajaya	242
Total target	722

The Sampling Process

Several factors go into the sampling procedure used in this investigation. The sample procedure consisted of (1) Sampling, (2) Determining the Sample Size, and (3) Choosing the meth Method.

Sampling and Sample Size

It was decided to use a random sampling strategy in order to achieve the desired sample size and increase the validity of the results. As a result, the sampling size for this study is based on the lists of construction enterprises in the Klang Valley district and classified by CIDB as active Grade G7. The target group was used to determine the sample frame.

In addition, it has been mentioned that a statistical method in quantitative research was employed to determine the size of the sample in a comparable study (Alhallaq and Mohammed, S. 2006). The procedure can serve as a basis for estimating the size of a study's sample, and it can improve confidence in statistical inferences and the validity of the study's findings. The following formula was used to determine how many people needed to be surveyed to achieve a 90% confidence level and a confidence interval of (+/-10) for a population size of 722.

SS = Z ² * P (1 – P) / C²

[Source Creative Research System] [Source Paul Mathews. (2010)] Where;

SS = Sample Size

Z = Z value (1.65 for Confidence Level of 90%) P = percentage Picking a Choice,

Expressed as Decimal (0.5 used for sample size needed)C = Confidence Interval, Expressed as Decimal (+/- 10% = 0.1)

SS = 1.65 ² * 0.5 (1 0.5) / 0.1² = 68

Correction for Finite Population;

Corrected SS = SS/ [1+ (SS-1)/Population] Therefore,

Corrected SS = 62/ [1+ (62-1) / 722 ] = 62

A high sample size typically yields favorable results regardless of the research approach. On the other hand, the researcher must appropriately determine the sample size to grasp all of these factors. I emailed the questionnaire to

62 construction companies afte considering all of the above information and accounting for the non- response.

Research Instrument

Likert scales were used because they accurately reflect the frequency and correctness of respondents' responses (Burns, Bush, and Sinha, 2014; Babin and Zikmund, 2015) and because they produce greater reliability coefficients with fewer questions (Hayes, 1998). In the study conducted by Likert (1932), the overall scale's reliability is best when the respondent uses a 5-point scale. As a result, a 5-point Likert scale was utilized in this investigation. Each questionnaire item was evaluated using a 5-point Likert scale. The frequency levels of responses to TQM techniques were graded as follows: (1) extremely low, (2) low, (3) medium, (4) moderate, and (5) very high.

On a 5-point Likert scale with levels of agreement including

1. strongly disagree,

2. agree, (3) neutral, (4) agree, and (5) strongly agree, responses for measures measuring the project performance

of construction enterprises that had embraced TQM techniques were also collected.

Data Gathering Procedure (Data Collection)

A questionnaire with closed and open questions is utilized as a data collection tool Closed questions use a 5-point Likert Scale. It simplifies the participants responses and statistically measures their ideas and experiences. Also, the Likert Scale is a popular approach for scaling questions. It is suitable for this research because all variables, or Likert items, are similar. The questionnaire will also be distributed physically by visiting each organization to ensure adequate feedback. Easy for both parties. Questionnaire Development

Rating Scale

A five- point scale can be well balanced on both sides, as seen below:

• 5 = Very High Effect

• 4 = High Effect

• 3 = Moderate Effect

• 2 = Low Effect

• 1 = Very Low Effect

In this study, the variables or Likert items represent the potential issues with applying total quality management (TQM) in a construction company.

Formulation of Questions

Table 5 shows the research objectives for which prepared the questions. For objective 1, questions 1 and 2 are designed to collect the data needed to determine the causes of failure in implementing TQM in the Malaysian construction industry. The respondents can express their views on the causes of TQM concerns through question

1. The answer to question 3 has been incorporated into the answer to question 4. To achieve target 3, the researcher relied on question 4 for guidance (To develop a model to detect the probable cause of TQM problems in the construction company and assign a score for the company)

   Table 4: Question and Purpose

   No#	Question	Type of Question	Purpose
   1	The list has 17 problems that can consider as barriers to TQM implementation in a construction company; please rate each situation according to its seriousness. Use X to select your opinion.	Closed question with the scaling system	Objective 1
   2	Please specify if you think there are other probable problems besides the problems listed above.	Open question	Objective 1
   3	Will it be helpful if there is an easy methodto identify the potential problems of TQM implementation in your company? Use an X to select your opinion.	Closed question	Objective 3
   4	If yes, for Question 3, please explain whatsort of method you think will be most appropriate and convenient?	Open question	Objective 3

   Analysis

   The questions use a Likert scale scoring method, as indicated under "Data Collection." In statistical analysis, the Likert scale is regarded as equal intervals. In the distance, the difference between each response is believed to be similar. As a result, parametric tests were used to assess the data. One of the most frequent parametric statistical methods for determining the importance level of each variable is to calculate the Mean Item Score (MIS). The factors can then be ordered to decrease MIS to produce the rankings. Mofokeng, T.G., utilized this strategy to rank the variables in a similar study (2012). Meanwhile, each variable's Standard Deviation (SD) is generated better to understand the distribution of responders across each Likert scale. The MIS and SD of the variables are calculated using the equation below.

   Where;

   MIS = ()+ ()+ ()+ ( )+ ()

   N

   n1 number of respondents selected 1n2 Number of respondents selected 2n3 Number of respondents selected 3 n4 Number of respondents selected 4n5 Number of respondents selected 5N Number of total respondents

   .

   SD = [( )²].

   Where;

   n = average number of respondent = N / 5

   ni = number of respondents for each scale = n1, n2, n3, n4, n5

   The frequency of responses to each question is used to examine the data for the open-ended questions. This is done manually using an excel spreadsheet and a frequency table. Each response will be thoroughly examined and grouped based on similarities, with the number of responders for each category being counted. The percentage of each kind is then determined using the usual procedure, which gives a more accurate picture of the results.

   Excel spreadsheets are used to summarize the responses to the "Yes," "No," and "DO NOT KNOW" questions. The majority opinion of respondents is then identified for each question independently.

   Validation and Reliability of Data

   Before conducting more in-depth analysis and discussion, this study eliminated items with poor reliability and validity. Validity is the amount of systematic or built-in error in measurement (Radha krishna, R. B. 2007). The validity of the questionnaire is established by answering the following questions: Is the questionnaire measuring what it intended to measure?

   1. Does it accurately reflect the content?

   2. Is it suitable for the population and sample size?

   3. Is the questionnaire adequate to elicit the information necessary to address the study's purposeand objectives?

   4. Is the instrument designed in the manner of a questionnaire?

The questionnaire development process addresses the following questions, improving the questionnaire's and data's validity. The reliability indicates the accuracy or precision of the measurement device. First, the questionnaire's dependability is tested during the pilot test to improve the questionnaire. After that, the survey data's dependability is assessed similarly. Because of the nature of the questionnaire and the research, it was decided to utilize the Internal Consistency and Inter-rater reliability (IRR) testing methods.

Internal Consistency Reliability assesses whether the ratings of numerous items intended to measure the same broad product are comparable. In other words, numerous questions about the same construction will be asked in the same questionnaire. The instrument and the data can be regarded as consistent and dependable if the respondent expresses the same responses for similar questions. The inter-item correlation between two questions on an instrument designed to assess the same construct should be equal or close to one to view the results consistently. The questionnaire will be designed to assess the data's internal

consistency.

In IRR, the same test is administered to a similar group of people, and the results are compared. The premise of this test is that in the same trial, equal participants must provide the same results or scores. IRR can be calulated in various ways; the most frequent method is to use the Spearman's Rho formula (Excel Master Series, 2014)

= [()]

()

Table 5: Calculation of Company Score

No#	Problem	MIS	NO=0 Or YES=1	Score
1	X1	m1	0/1	m1 x (0 or 1)
2	X2	m2	0/1	m2x (0 or 1)
3	X3	m3	0/1	m3x (0 or 1)
.	.	.	.	.
.	.	.	.	.
.	.	.	.	.
N	Xn	mn	0/1	mn x (0 or 1)
Summation				SUM

Where;

= Spearman Correlation Coefficient xi = Mean Value of Variable

yi = Rank by a participant n = Number of Variables

The two data series are perfectly and positively connected if the correlation coefficient equals +1. As a result, the data can be deemed highly dependable. Values ranging from

1. to 0.75 suggest moderate to good correlation, whereas values ranging from 0.75 to 1 indicate very good to outstanding correlation. If less than 0.5, the data obtained cannot be trusted for further investigation.

   Development of a Model

   The overall program philosophy is to work toward being a world-class organization with an effective complete quality management system adopted and maintained by construction enterprises. Furthermore, to strengthen the Malaysian construction industry. Only critical high- impact TQM implementation difficulties are examined when building the program; the model will be designed to detect whether these problems exist in the regarded organization and provide a score to the company. This score is used to estimate the likelihood of TQM problems arising.

   Behind screening the 17 factors for the issues with the most significant impact, a procedure is built to determine whether those issues exist in the company under consideration. The identification method takes advantage of readily accessible data within the organization. The software's users must enter data into the appropriate fields as directed by the program. The computer will then use the input data to identify future problems in the particular business. The program will draw attention to the issues that the organization must address in future initiatives. In other words, the program will identify the company's shortcomings that could result in total quality management TQM issues.

   Next, the program will use the below format to calculate and assign a score to the company. This score is used to indicate the tendency of TQM problems occurring. The program will give one for the issues identified and zero for the other issues in the particular company (Yes=1 and No=0)

   Company Score (CS) = [1-(VALUE/65.16)]*100%

   If the CS is 100%, it can be considered that the tendency of occurring total quality management TQM is minimal. The CS 0% means a very high direction of facing TQM problems soon.

   Results

   According to the methods mentioned in Methodology, the questionnaire was given out to 62 construction companies to collect their thoughts. There was a good response rate, with 62 returned questionnaires. The responses of participants for the rating and closed questions are better compared to the open-ended questions of the questionnaire. The fundamental cause for this can be considerably greater time demanding to answer open-ended questions than closed-ended ones. However, the data acquired through the survey can be deemed adequate to fulfill the needs of the research objectives. The responses of 2 out of 62 had to be eliminated from the study owing to the inconsistency of the answers, which made the total legitimate responses to 60. Hence, two new respondents substituted the omitted one to satisfy the minimal sample size of the data obtained 62 respondents are acceptable for the statistical analysis of the research as mentioned under methodology section (sampling and sample size). A more considerable percentage of the respondents are the engineers of certain building enterprises. The following table 7 indicates the responders' ratio and designation inside the firm.

   Designation of the Respondent	Number of Respondents	(%)
   Project Manager	21	34
   Site Manager	5	8
   Project Executive / Site Agent	10	16
   Quality Assurance/Quality Control	5	8
   Quantity Surveyor	1	1.67
   Civil & Structural Coordinator	2	3
   Architectural Coordinator	1	1.67

   Table 6: Designation and Percentage of Respondents

   Mechanical & Electrical Coordinator	1	1.67
   Engineer	5	8
   Senior Site Supervisor	11	18
   Total	62	100

   The data collected by the survey have been analyzed per the process specified in methodology.

   Internal Consistency and Reliability Analysis

   The Internal Consistency of the questionnaire responses was determined by analyzing the scores supplied by the respondent to variables with the same broad construct. The ratings awarded to the subsequent set variable have been compared to examine their consistency.

   • Lack of knowledgeable personnel

   • Lack of understanding in the TQM

     Responses with differences of two or more Likert scores between these two factors were deemed inconsistent. As a result, these responses were removed and not included in the study. Two responses out of 62 were deemed inconsistent based on this criterion and had to be removed from the data. The following table 8 depicts the responses of the two respondents to factors that were excluded from the analysis.

     Table 7: Scores Assigned to Considered Variables by Eliminated Respondents

     Variable	Score
     	Respondent A	Respondent B
     Lack of knowledgeable personnel	2	4
     Lack of understanding in the TQM	5	1

     The following variable was included in the questionnaire to generate the same construct with another variable and to assess the internal consistency, but it was not included in the analysis. Consequently, the total number of variables decreased from 18 to 17.

   • Lack of knowledgeable personnel

     The data's dependability was evaluated using the approach described in the methodology section. Each respondent's Spearman Correlation Coefficient () was calculated using equation 3. Table 8 depicts the correlation coefficients of the data from 62 respondents.

     Table 8: Spearman Correlation Coefficient ( ) for each Respondent

     Responden t		Responden t		Respondent
     1	0.875	22	0.854	43	0.817
     2	0.777	23	0.695	44	0.808
     3	0.892	24	0.745	45	0.803
     4	0.767	25	0.814	46	0.897
     5	0.773	26	0.792	47	0.756
     6	0.882	27	0.789	48	0.866
     7	0.752	28	0.859	49	0.859
     8	0.862	29	0.899	50	0.821
     9	0.868	30	0.752	51	0.773
     10	0.865	31	0.841	52	0.888
     11	0.773	32	0.766	53	0.748
     12	0.886	33	0.776	54	0.850
     13	0.810	34	0.794	55	0.826
     14	0.733	35	0.862	56	0.727
     15	0.803	36	0.792	57	0.815
     16	0.886	37	0.752	58	0.848
     17	0.783	38	0.832	59	0.858
     18	0.767	39	0.843	60	0.949
     19	0.825	40	0.727	61	0.803
     20	0.762	41	0.759	62	0.797
     21	0.968	42	0.727

     Table 9 shows that the correlation coefficients vary from 0.695 to 0.968, more than the baseline value of 0.5. Hence the data can be considered reliable and acceptable. Further, 89% of the coefficient values lie between 0.75 to 1.0, where the data's reliability can be considered very good. In the meantime, the overall correlation coefficient of the data series is an average of 0.814. Hence the overall data gathered through the research is considered to be reliable.

     Ranking the Problems of TQM

     Equation 4 from the methodology section was used to compute the mean item score (MIS) of the variable, which was then sorted according to each variable. In addition, as described under methodology, variables with MIS scores of greater than 2.5 out of 5 are considered severe concerns with TQM implementation. They will be referred to as "top- ranked" issues. The MIS of the TQM problems and their corresponding ranking are presented in Table 10.

     Table 9: MIS and Ranking of TQM Problems

     Problem	MIS	Rank
     Lack of management commitment and support	4.193	1
     Extra time consuming	4.048	2
     Project supervision	3.984	3
     Lack of knowledgeable personnel	3.984	4
     Poor workmanship skills	3.952	5
     Quality leadership	3.952	6
     Extra cost	3.935	7
     Lack of effective communication	3.871	8
     Quality Culture and Attitude	3.871	9
     Economic of construction environment	3.807	10
     Defective building materials	3.790	11

     Project Information, Specification and Documentation	3.774	12
     Low bid mindset	3.645	13
     The human resource management	3.629	14
     Supplier relationship	3.597	15
     Understandable and applicable design	3.597	16
     Too much paperwork	3.532	17

     Problem	STD	Rank
     Lack of management commitment and support	0.846	1
     Extra time consuming	0.931	2
     Project supervision	0.967	3
     Lack of knowledgeable personnel	0.949	4
     Poor workmanship skills	0.931	5
     Quality leadership	1.093	6
     Extra cost	1.006	7
     Lack of effective communication	0.896	8
     Quality Culture and Attitude	1.094	9
     Economic of construction environment	1.022	10
     Defective building materials	0.943	11
     Project Information, Specification and Documentation	0.818	12
     Low bid mindset	1.118	13
     The human resource management	0.996	14
     Supplier relationship	0.914	15
     Understandable and applicable design	1.093	16
     Too much paperwork	0.970	17

     Table 10: Illustrates the Standard Deviation of Top-ranked Problems

     Table 10 shows that all of the problems have MIS scores of at least 2.5, making them all among the top problems. According to MIS 4.193, the most significant issue overall is a lack of management commitment and support. The critical component in implementing TQM is top management support. The crucial resources for implementing a new system are frequently under the authority of top management. It is impossible to obtain the necessary resources to adopt TQM without the backing of top management. Furthermore, senior management frequently serves as an example for other employees. Employees won't be motivated or exert effort to aid the company in achieving its goal if senior management is not dedicated to implementing TQM.

     The application of TQM has been hampered by a low bid mentality or the conventional bidding procedure. The understandable and applicable design also impacts the quality of the construction projects. Problems with building plans and construction detail were found, such as not being transparent and drawing mistakes, so they also become big problems in construction. The amount of documentation associated with a building project is usually rather substantial to begin with. Material safety data sheets, forms to record the request, order, delivery, and movement of material, plant, and labour, and lengthy contract documents and amendment records and architects' instructions and steel bending schedules. Implementing TQM principles results in excessive paperwork, which is problematic yet received the lowest MIS score.

     Standard Deviation of Results

     The distribution of respondents across the five Likert scales is measured by the standard deviation (SD). The SD values of each variable were calculated using Eqn. 2. There are 62 total genuine respondents in the study. Consequently, the average number of respondents on each Likert scale is 62/5, or 12.4. The SD of the given variable will be close to 0 if the respondents are uniformly distributed throughout the five Likert scales. On the other hand, if every respondent assigned the same score to a specific variable, the SD of that variable will be at its maximum. For instance, the SD of a particular item will equal 24.8 if all 62 respondents choose the same score for that factor, which is the highest potential SD value in this survey. As a result, each problem's SD ranges from 0.0 to 24.8. It is excellent in this survey if every responder selects the same score for each TQM (total quality management) issue. Therefore, the perfect outcome may occur when the SD value is greater than zero.

     Other Possible Problems of TQM

     The second question in the questionnaire sought other potential total quality management TQM issues that had not been found in the literature review. However, the percentage of participants that responded to the question was low; it was just 15% of the total responders. One key factor contributing to the low response rate may be the additional time needed to respond to the open- ended questions. On the other hand, it can be said that the study has discovered and resolved the majority of the TQM's issues.

     However, the other probable problems identified by the respondents are as follows:

   • Lack of guidelines towards TQM.

   • No teamwork.

   • Absence of internship.

   • Safety.

   • Discipline.

   • Stakeholders involvements

Suggestions by Respondents

In order to decrease total quality management TQM issues in construction organizations, questions 3 and 4 were developed to grasp better the expectations of the construction companies from the researcher. The data

collected from these inquiries help the researcher create more useful study outcomes.

Table 11: Question 3 Answer by the Respondents

Answer	Percentage Based on Total Respondents
Yes	45%
No	32%
Do Not Know	23%

45 percent of respondents felt that it would be beneficial if there was a technique or aid to identify the possible TQM problems in their companies, encouraging researchers to continue their work in this field. The construction businesses' opinions and recommendations are described in table 13.

Table 12: Recommendations and Suggestions Made by the Respondents

Suggestions	Percentage Based on Total Suggestions
Defect management system or software	39%
Guideline	36%
Strategy/model/work flow	14%
To minimize double handling work	4 %
Experienced QA/QC at site	7%

According to the suggestions forwarded by the respondents, software or computer program will be the most convenient to identify the problems of total quality management TQM in a company.

Development of the Model

The model has been developed according to the inception explained in methodology. The following flow chart illustrates the basic steps of the development process.

Figure 1: Flow Chart for the Development of the Model

The causes scored MIS more than 2.5 have been considered as the critical factors of TQM problems. Then the model illustrated in table 14 has been used to screen the causes that can be existent in the considered company.

Rank	Problem	Criteria	Baseline
1	Lack of management commitment and support	CoursesTraining Seminars	Yes
2	Extra time consuming	Periods of the last 5 yearsprojects	More than 1
3	Project supervision	meetings to control siteactivities	WeeklyDaily
4	Lack of knowledgeable personnel	Personnel experience	Yes
5	Poor workmanship skills	Skilled/unskilled ratio	More than 1

Table 13: Model of Screening the Cause of TQM Problems

6	What are the most factors that your management considers during bidding process?	Open
7	Does your company have a policy or system of effective communication either within the company (internal) or among the company and the client or suppliers (external)?	Closed
8	How many projects did your company finish without any additional cost on the last 5 years?	Open
9	How many projects did your company finish without any delay on the last 5 years?	Open
10	How often does your organization conduct site meeting?	Open
11	How many skilled workers does the company have?	Open
12	How many unskilled workers does the company have?	Open
13	At what stage of construction phases does your company considerthe selection of the construction materials?	Open
14	Does client likes to give lowest specification of certain materials or products?	Closed
15	Why does your company implement TQM?	Open
16	Does your top management ever communicate the importance of meeting customer requirements to the subordinate?	Closed
17	Does your top management lead in setting quality policies?	Closed

td>

Stage of selection the materials

6	Quality leadership	Quality policies	Yes
7	Extra cost	costs of last 5 years projects	More than 1
8	Lack of effective communication	Effective internal and external communication	Yes
9	Quality Culture and Attitude	To improve the company business performance and corporate image	Yes
		fulfill the standard quality obligation and requirements	No
10	Economic of construction environment	Status of the last 5 years projects of the company in terms of cost and time	Yes
11	Defective building materials	Early stage
12	Project Information, Specification and Documentation	Specifications given bythe client	Lowest
13	Low bid mindset	Price factor	Yes
14	The human resource management	human resources allocation	Yes
15	Supplier relationship	Low quotation cost	No
		Quality of the supplier	Yes
		Partnership approach	Yes
16	Understandable and applicable design	Personnel experience	Yes

Once the user inputs the required data, the program uses a platform shown in table 16 to process the results.

Table 15: Result Processing Platform of the Program

No	Problem	MIS	NO=0 and YES=1	Score
1	Lack of management commitment and support	4.193	0/1	4.193 x (0 or 1)
2	Extra time consuming	4.048	0/1	4.048 x (0 or 1)
3	Project supervision	3.984	0/1	3.984 x (0 or 1)
4	Lack of knowledgeable personnel	3.984	0/1	3.984 x (0 or 1)
5	Poor workmanship skills	3.952	0/1	3.952 x (0 or 1)
6	Quality leadership	3.952	0/1	3.952 x (0 or 1)
7	Extra cost	3.935	0/1	3.935 x (0 or 1)
8	Lack of effective communication	3.871	0/1	3.871 x (0 or 1)
9	Quality Culture and Attitude	3.871	0/1	3.871 x (0 or 1)

Table 15 shows the set of question that programmer will ask to gather the required data for the analysis.

Table 14: Set of Questions ask by the Program

No.	Question	Type
1	What is your organizations perception of total qualitymanagement?	Open
2	Does the company provide courses and training or any other activity about TQM for the company personnel?	Closed
3	What are the factors that your organization takes into consideration to choose its employees?	Open
4	Do you recommend the allocation for human resources to be increased?	Closed
5	What are the factors that your management considers in selecting the suppliers?	Open

10	Economic of construction environment	3.807	0/1	3.807 x (0 or 1)
11	Defective building materials	3.790	0/1	3.790 x (0 or 1)
12	Project Information, Specification and Documentation	3.774	0/1	3.774 x (0 or 1)
13	Low bid mindset	3.645	0/1	3.645 x (0 or 1)
14	The human resource management	3.629	0/1	3.645 x (0 or 1)
15	Supplier relationship	3.597	0/1	3.597 x (0 or 1)
16	Understandable and applicable design	3.597	0/1	3.597x (0 or 1)
17	Too much paperwork	3.532	0/1	3.532x (0 or 1)
	Sum	65.16		VALUE

The score of the company will be calculated using equation 4 of the methodology as follows:

Company Score (CS) = [1-(VALUE/65.16)]*100%

If the CS is closer to 100%, it can be considered that the tendency of occurring total quality management TQM problems is minimal. While If the CS is closer to 0% means that a very high tendency to face TQM problems and need to concentrate on the highlighted factors by the program. A CD consisting of the computer program is attached to the report.

Application of the Program

The model has been run using data from the listed construction companies in Malaysia. Figure 4.2 to figure 4.3 shows the data input windows of the program.

Figure 2: Data Entry Window Number 1

Figure 3: Data Entry Window Number 2

Figure 4: Result From the Program

The model highlights seven problems to which the company must pay attention. The seven problems are as follows:

1. Poor workmanship

2. Quality leadership

3. Quality culture and attitude.

4. Project information, specification and documentation.

5. The human resource management.

6. Supplier relationship

7. Too much paperwork

In the meantime, the model has assigned a score of 64.29% for the company. This provides an idea for the company how critical their situation is regarding total quality management TQM. A score of 64.29% can be considered comparatively safe.

CONCLUSION

The major objective of this research is to examine how severely contractors see roadblocks to Total Quality Management (TQM) being implemented in the Malaysian building sector. This investigation covered seventeen distinct issues. The following concerns have been identified by construction firms as having a high potential for causing difficulties in total quality management (TQM):

1. Lack of management commitment and support

2. Extra time consuming

3. Project supervision

4. Lack of knowledgeable personnel

5. Poor workmanship skills

6. Quality leadership

7. Extra cost

8. Lack of effective communication

9. Quality Culture and Attitude

10. Economic of construction environment

11. Defective building materials

12. Project Information, Specification and Documentation

13. Low bid mindset

14. The human resource management

15. Supplier relationship

16. Understandable and applicable design

17. Too much paperwork

The detection model that has been developed using the finding of the research will be helpful for construction companies. This model has been converted into a simple computer application, which is very versatile and user- friendly. This program works like a self-assessment; hence the users or the companies do not need to reveal their secured information to a third party. The program will assist the management of the companies in identifying the probable causes or problems of TQM within the organization. Further, the model's score assigned for the company will provide an ideato the management of their current organization situation in terms of total quality management TQM.

Moreover, the most pressing issues with total quality management (TQM) stem fro administrative causes. The construction sector in Malaysia has come to appreciate the value of root-cause analysis in addressing TQM issues. Further, most of companies are contented to get assistance in the process of identifying their weaknesses. The developed model through this research will be beneficial for the industry in the future.

Limitations and Future Perspective

• Due to limitations, the study was restricted to one administrative district of Malaysia (Klang Valley). The data survey can be carried out in the other districts and compare the results

• Due to limitations, the study was restricted to one administrative district of Malaysia (Klang Valley). The data survey can be carried out in the other districts and compare the results.

• The implementation of the detection model can be tested and improved by Malaysian construction companies.

• The next step would be to find the most effective remedies that construction companies can implement to avoid these causes and problems.

• To develop a full detailed model and program that can detect the problems of TQM, at thesame time provide some effective remedies for the construction company

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