Transactions Template


 

   

JOURNAL OF ENGINEERING RESEARCH AND TECHNOLOGY, VOLUME 6, ISSUE 2, OCTOBER 2019 

  

  

 

10  
 

Critical Factors Causing Contractor's Business Failure in Gaza 
Strip 

Khalid Al Hallaq 
 

Abstract— The construction industry remains a major player of the Palestinian economy. Business failure is an 
important issue for companies in the Gaza Strip uncertain environment. This paper aimed to explore the critical 
factors that have the potential to cause contractor's business failure and to determine their level of severity from 
contractor's viewpoint. This paper has considered. Critical factors were listed under five groups: financial and 
political, contractual, managerial, organizational, and economical causes. Contractors have been advised to 
consider the most critical factors that have the potential to cause business failure. The most critical factors 
include: cost of materials, lack of resources, delay in collecting dibs from clients, monopoly, changing funding 
sources, dealing with suppliers and traders and Israeli attacks. 

Index Terms— Business failures, Contractors, Construction industry, Contracting failure, Construction in 
Palestine, Gaza Strip, Factor analysis.  

1- Introduction: 
The pattern of Palestinian economic activity in Palestine 

is uncertain and unusual. While economic activity and 

growth stimulators in conventional economies are largely 

related to internal economic variables and policies, the 

Palestinian economy operates in an environment rife with 

different internal and external risks and challenges, which 

significantly affect and change the economic situation. 

The most external challenges facing the Palestinian econ-

omy include the Israeli occupation and closure (Econom-

ic Forecast Report, 2018). Organizations need to be well 

prepared, organized, and plan appropriate strategies to 

stay relevant, competent, and active, in the industry ( Abu 

Bakar et al., 2016). 

In Gaza Strip, construction has a positively affecting on 

economic, social, educational and vocational sectors and 

other sectors. It has a large contribution to the gross do-

mestic product directly, in addition to the indirect contri-

bution through the related activities such as manufactur-

ing, electricity, water and other economic activities. It 

used to employ an average of 14.4 % of Palestinian labor 

force volume (PCU, 2017). Present contribution to real 

GDP by construction at the end of year 2018 was 6% and 

6.7% in Gaza Strip and the West Bank respectively. The 

rate of change of real value added by construction be-

tween year 2017 and 2018 recorded a significant contrac-

tion  by (-22.5%) in the Gaza Strip. During the same pe-

riod, a significant expansion was noticed by (+6.0%) in 
the West Bank (UNSCO Socio-Economic Report, 2019).  

Among other unique characteristics of life in Palestine, 

the construction industry stands out from other parts of 

the economy. Construction is heavily affected by eco-

nomic cycles and political environment, which change 

frequently and dramatically in Palestine in general and in 

Gaza Strip in particular. The construction industry has a 

significantly high rate of business failure due to high op-

erating risks and uncertain conditions. All over the world, 

contractors compete fiercely in the marketplace, exposing 

themselves to risk of failure, as well as the prospect for 

success. Palestine is not an exception.   

In the last two years, more than 50 contracting companies 

exposed to failure as a result of unnatural environment in 

Gaza Strip. Currently, there are about 70 contractors fac-

ing business failure due to their inability to cope with 

environmental, subjective and competitive conditions. 

This research will identify the most important and critical 

factors that lead to the failure of contractors in order to 

enhance the ability of these companies to survive, com-

pete,  and overcome the abnormal conditions.  
2- Literature Review 
2.1 Definition of Failure 
There are many definitions of business failure. Altman 

(1968) defined the failure from an economic viewpoint 

and said that a company is considered to have failed if the 

realized rate of return on invested capital, with allowanc-

es for risk considerations, is significantly and continually 

lower than prevailing rates on similar investments. Ber-

ryman (1893) recognized it as the condition of the firm 

when it is unable to meet its financial obligations to its 

creditors in full. It is deemed to be legally bankrupt and is 

usually forced into insolvency liquidation. Another defi-

nition of failure denoted by Watson & Everett (1883) as 

attributed business failure to four different situations: 

discontinuance for any reason; ceasing to trade and credi-

tor loss; sale to prevent further losses; and failure to make 

a go of it. Shepherd (2003) show that, the failure occurs 

when a fall in revenues and/or a rise in expenses are of 

such a magnitude that the firm becomes insolvent and is 

unable to attract new debt or equity funding; consequent-

ly, it cannot continue to operate under the current owner-

ship and management. Ucbasaran et al. (2010) said that 

the failure as not only the sale or closure of a business 

due to bankruptcy, liquidation, or receivership but also 

the sale or closure of a business because it has failed to 

meet the entrepreneur‟s expectations, which reflects the 

varying personal thresholds of performance among entre-



KHALID AL HALLAQ/ CRITICAL FACTORS CAUSING CONTRACTOR'S BUSINESS FAILURE IN GAZA STRIP 
 

11  
 

preneurs.. David O. Mbat & Eyo (2013)  said that the 

failure could be seen in terms of the inability of a corpo-

rate organization to conform itself with its strategic path 

of growth and development to attain its economic and 

financial objectives as well as legal obligations.  

2.2 Causes of Failure 
A number of researchers had studied the causes of con-

tracting business failure. Dun and Bradstreet Corporation 

(1986) had identified the major causes of business fail-

ures in the construction industry as; economic factors, 

inexperience, poor sales, expense, customer, fraud and 

neglect, asset and capital, and disaster. They found the 

most significant failure cause as economic factors. Within 

the economic factors category, there were five subcatego-

ries that were bad profit, high interest rates, loss of mar-

ket, no customer spending and no future. Schleifer (1989) 

also identified ten causes as the bane of the construction 

industry. The first five of the identified factors are related 

to business strategies and the second five are related to 

accounting considerations. The factors were; increasing 

project size, expanding into unfamiliar locations, replac-

ing key personnel, moving into new construction, not 

maturing in management as business expands, using poor 

accounting systems, evaluating project profit incorrectly 

or not in time, not controlling equipment costs, not billing 

or collecting effectively and jumping between computer-

ized accounting systems. 

 

The findings indicated that over 80% of the failures were 

caused by five factors, namely insufficient profits (27%), 

industry weakness (23%), heavy operating expenses 

(18%), insufficient capital (8%) and burdensome institu-

tional debt (6%). All these factors, except for industry 

weakness, are budgetary issues and should therefore be 

handled by companies that are cognizant of the effects of 

these factors on their survivability ( Donkor, 2011). 

 

Argenti (1976) in his book 'corporate collapse' summa-

rized what was written in failure. He concluded six main 

causes as a result of what written about the subject of 

company failure follows; top management, accounting 

information, change, accounting manipulation, rapid ex-

pansion, economic cycle. 

Hartigan (1973) listed seven main causes of failure were 

as follows: Lack of capital, Under costing, lack of con-

trol, lack of advice, the government, trade fluctuations 

and fraud.  

Jannadi (1997) had previously presented a study of the 

factors that contribute to the failure of construction con-

tractors in Saudi Arabia and found that the most im-

portant factors were: difficulty in acquiring work, bad 

judgment, lack of experience in the firm‟s line of work, 

difficulty with cash flow, lack of managerial experience, 

and low profit margins. 

 

Davidson and Maguire (2003), based on their accountan-

cy experience, identified ten most common causes for 

contractor failures as: growing too fast, obtaining work in 

a new geographic region, dramatic increase in single job 

size, obtaining new types of work, high employee turno-

ver, inadequate capitalization, poor estimating and job 

costing, poor accounting system, poor cash flow, and 

buying useless stuff. 

 

Enshassi, et al. (2006) identified the main factors that 

cause business failure based on contractors‟ view point in 

Palestine. The research identified delay in collecting debt 

from clients (donors), border closure, heavy dependence 

on bank loans and payment of high interest on these 

loans, lack of capital, absence of industry regulations, 

low profit margin due to high competition, awarding con-

tracts by client to the lowest bidder, and lack of experi-

ence in contract management. 

 

Kivrak and Arslan (2008) examined the critical factors 

causing the failure of construction companies through a 

survey conducted among 40 small to medium-sized Turk-

ish construction companies. A lack of business experience 

and the country‟s economic conditions were found to be 

the most influential factors in company failure. 

 

Mahamid (2011) ranked the factors as highly influential 

with huge potential to cause contractor‟s business failure 

based on contractors‟ view point in Palestine: fluctuation 

in construction material costs; delay in collecting dibs 

from clients; lack of experience in contracts; low margin 

of profit due to competition; and closure and limitation of 

movement between West Bank areas. 

 

Mbat and Eyo (2013) concludes that there are a lot of 

factors, internal and external, to the firm could be respon-

sible for corporate failure. The corporate should consider 

the relative influence of management, board of directors, 

employees, external auditors, regulatory bodies, and gov-

ernment to avert failure. Holt (2013) aimed to synthesize 

published knowledge in construction business failure to 

explore the failure agents. He concluded that the broad 

practical propositions to help negate the potential nega-

tive effects are the managerial, financial, company char-

acteristics, and macroeconomic environment. 

 

Wang and Wu (2017) adopted modified two-stage learn-

ing algorithm to predict business failure. The modified 

learning model can utilize geometric feature of the data to 

discover the low-dimensional manifold embedding in the 

high-dimensional space by coordinate representations. It 

is more suitable to select feature values for financial data. 

The first stage, the stepwise forward selection approach is 

easy to understand and implement, and can enhance the 

performance of the selective ensemble model efficiency. 

In the second stage, different selective ensemble models 

are integrated according to normal or failed firms, which 

can exert the respective advantage of ensemble models to 

process the suitable firms. Doumpos et al. (2017) exam-

ined the development of corporate failure prediction 

models for European firms in the energy sector, using a 



KHALID AL HALLAQ/ CRITICAL FACTORS CAUSING CONTRACTOR'S BUSINESS FAILURE IN GAZA STRIP 
 

12  
 

large dataset from 18 countries. The construction of mod-

els is based on multiple criteria decision aid approach 

taking into consideration both ordinal criteria and nomi-

nal country-sector effects. The results confirmed the im-

portance of incorporating energy-related data to the anal-

ysis of the distress risk for firms in the energy sector. It 

was found that data related to the quality and reliability of 

energy networks, energy sustainability factors, as well as 

the size and openness of a country's internal energy mar-

ket, can provide valuable additional information com-

pared to firm-specific attributes and economic/business 

environment. 

 

Venugopal (2018) explained the persistence discourse of 

failure in development as a point of departure to under-

stand what is signifies, how it is structured, and what 

consequences it bears. He framed failure as a socially 

constructed category. He also concluded that changing 

sets of beneficiaries, definitions, goals, and indicators of 

success, and outcomes that are multi-layered, evolve over 

time, hard to measure, and generate unpredictable exter-

nalities, every successful project can also be reinterpreted 

as a failure. Cui et al. (2018) concluded that: the compa-

ny's business capacity cannot adapt to the company's de-

velopment is the most primary factor in the green busi-

ness failure. While the "short-term investor mind-set and 

less investment" had the strongest effect on green busi-

ness failure.                   

 

3- Methodology 
A total of 73 factors that might affect contractors‟ busi-

ness failure were defined through a detailed literature 

review of relevant research studies (Hartigan, 1973; Ross 

& Rami, 1973; Cohen, 1973; Argenti, 1976; Dun and 

Bradsteet, 1986; Kangari, 1988; Schleifer, 1989; Abidali 

and Harris, 1995; Osama, 1997; Assaf, S., 2004; Peter-

son, 2005; Enshassi et al., 2006; Strischek and Mclntyre, 

2008; Donkor, S., 2011). The factors were tabulated in a 

questionnaire form and the questionnaire was reviewed 

by three groups of experts to test its content validity.  

The target population in this study is all contractors of the 

first, second and third categories for building works that 

have valid registration by the Palestinian Contractors 

Union with a total of 203 contractors. The following statis-
tical equation was used to determine the sample size:  

)1(
2

  ZX  

))1((
2

XEN

NX
n


     Where: 

Z: (1.96 for 95% C.I)    P: (0.50)   n: Sample size   N: Popu-

lation size =186 

E: Maximum Error of estimation (0.07)  

133

))9604.0)05.0)(1202((

9604.0203

))1((

9604.0)5.01(5.096.1

2

2

2













XEN

NX
n

X

 

 

Therefore, the calculated sample size is 133 contractors 

based on a 95% confidence level. The questionnaire was 

sent out to a total of 133 contractors asking their contri-

bution in ranking the identified 73 factors in terms of 

severity using an ordinal scale. The ordinal scale that was 

used are 1 = very low influence, 2 = low influence, 3 = 

moderate influence, 4 = high influence, and 5 = very high 

influence. Only a total of 101 completed questionnaires 

were returned representing a good response rate of 

75.93%. 

 

Factor analysis was employed to reduce a large number 

of variables (factors of business failure) to a smaller set 

of underlying factors that summarize the essential infor-

mation contained in these variables. Using SPSS v.22, 

Principle Component Analysis with Varimax rotation 

were performed to set up which items could capture the 

aspects of same dimension of the proposed determinants 

causes of business failure and examine the underlying 

structure or structure of interrelationships among these 

causes. In order to perform the factor analysis for pro-

posed items, all appropriate checks, requirements and 

procedures were fulfilled, as mentioned in Table (1). 

Three main three phases were proceeded to accomplish 

factor analysis, as follows: preliminary analysis, factors 

extraction, and factors naming and interpretation. 

 

Table (1): Factor analysis process requirements and 

criteria 
Factor 

analysis 

phase 

Requirement 
Acceptation cri-

teria 
References 

Preliminary 

analysis 

(First phase) 

Type of the study 

data (variables) 

Subjective varia-

bles 

(Yong and 

Pearce, 

2013) 

Distribution of the 

data 

Normal distribu-

tion (Sample size 

of the study larger 

than 30) 

(Hair et al., 

2010) 

(Field, 

2009) 

Sample size More than 50 

(Winter et 

al., 2009) 

(Sapnas and 

Zeller, 

2002) 

Data reliability test 

(Internal consisten-

cy) 

Cronbach coeffi-

cient alpha > 0.7 

(Pallant, 

2005) 

Factorability of the 

correlation matrix 

(Visual inspection 

of the correlation 

matrix) 

 

Each item (variable) 

correlated with 

several other varia-

bles with correlation 
coefficients greater 

than 0.30 and none 

of the correlation 

coefficients has a 

(Field, 

2009) 

(Tabachnick 

and Fidell, 

2007) 



KHALID AL HALLAQ/ CRITICAL FACTORS CAUSING CONTRACTOR'S BUSINESS FAILURE IN GAZA STRIP 
 

13  
 

Factor 

analysis 

phase 

Requirement 
Acceptation cri-

teria 
References 

value greater than 

0.9. 

Anti-image corre-

lation matrix 

The diagonals on 

the anti-image 

correlation matrix 

should have an 

overall measure of 

sampling adequa-

cy (MSA) of 0.50 

or above 

(Hair et al., 

2010) 

Items Correlation 

Matrix Adequacy 

“Kaiser-Meyer-

Olkin (KMO) 

Measure of Sam-

pling Adequa-

cy/Bartlett's Test of 

Sphericity 

the Bartlett‟s test 

of sphericity is 

significant when 

(p-value <0.05), 

and when the 

value of the KMO 

index is above 

0.5. 

(Mane and 

Nagesha, 

2014) 

(Hair et al., 

2010) 

 

Factors ex-

traction 

(Second 

phase) 

Communality val-

ues 

 

Each item com-

munality value 

more than 0.5 

(Field, 

2009) 

Cumulative per-

centage of variance 

explained by the 

extracted factor 

solution 

The cumulative 

variance ex-

plained by the 

extracted factors 

should be greater 

than 50% of total 

variance ex-

plained. 

(Meyers et 

al., 2006) 

(Mane and 

Nagesha, 

2014) 

Loaded items and 

extracted factors 

properties 

Each item should 

has at least one 

factor loading 

value equal or 

more than (0.5). 

(Pallant, 

2005) 

(Mane and 

Nagesha, 

2014) 

Each one of the 

extracted factors 

should include at 

least three items 

to be acceptable. 

(Costello 

and Os-

borne, 

2005) 

Any item loaded 

on more than one 

factors with factor 

loading greater 

than 0.5 should be 

removed “no 

cross-loading 

items”. 

(Henson 

and Rob-

erts, 2006) 

(Hair et al., 

2010) 

Reliability measure 

of the extracted 

factors 

The variables 

formed each fac-

tor explains the 

measure within 

this factor based 

on Cronbach‟s 

Alpha (Cα) value, 

which should be 

more than 0.7 

(Pallant, 

2005) 

Factors nam-

ing and in-

terpretation 

(Second 

phase) 

Arrangement of 

extracted factors 

Extracted  factors 

should be ar-

ranged and num-

bered in a de-

scending order on 

the basis of the 

amount of vari-

ance explained by 

each one. 

(Hart, 

2008) 

(Henson 

and Rob-

erts, 2006) 

(Williams 

et al., 2010) 

Factor 

analysis 

phase 

Requirement 
Acceptation cri-

teria 
References 

Factor naming 

Each factor sub-

jectively labeled 

in accordance 

with the factor 

loading values and 

the correlation 

between the indi-

vidual items load-

ed on it. 

 

Interpretation of 

the principal fac-

tors 

Interpretation of 

each factor should 

be provided based 

on the labeling 

and items includ-

ed in each factor. 

 



KHALID AL HALLAQ/ CRITICAL FACTORS CAUSING CONTRACTOR'S BUSINESS FAILURE IN GAZA STRIP 
 

14  
 

4- Result and Discussion 
Factor analysis was used to examine the pattern of inter-

correlations between the 73 items/ variables of success 

factors for the application of EMS in an attempt to reduce 

the number of them. It also used to group items/ variables 

with similar characteristics together. In other words, it 

identified subsets of items/ variables that correlate highly 

with each other, which called factors or components. Fac-

tor analysis was conducted for this study using the Prin-

cipal Component Analysis (PCA). 

Appropriateness of factor analysis 

The data was first assessed for its suitability to the factor 

analysis application. There were many stages of that as-

sessment: 

1. Distribution of the data: With the base of Central 
Limit Theorem, the data collected can be considered 

normally distributed because sample size for this 

study was 101 and it was larger than 30 as proposed 

by Hair et al. (2010). Therefore, the normal distribu-

tion requirement for factor analysis application for 

this part of study has been satisfied as stipulated by 

Field  (2009). 

2. Validity of sample size: the reliability of factor 
analysis is dependent on sample size. Factor analy-

sis/ PCA can be conducted on a sample that has few-

er than 100 respondents, but more than 50 respond-

ents. The sample size for this study was 101. 

3. Data reliability test: The first stage of the quantita-
tive analysis was related to the reliability test where 

the reliability of the questionnaire was tested accord-

ing to the Cronbach‟s alpha measurement. Through 

the analysis that has been done, the alpha reliability 

of the scale of 73 items (factors) in this study was 

0.94 for the items indicating that 94% of the variance 

of the total scores of all factors can be attributed to 

systematic variance. Since the result was achieved 

above 0.7, it showed that all items have indicated in-

ternal consistency and achieved high reliability as 

proposed by Pallant (2005).  

4. Kaiser-Meyer-Olkin (KMO) and Bartlett's test: 
the Kaiser-Meyer-Olkin (KMO) sampling adequacy 

test and Bartlett's test of Sphericity were carried out. 

The results of these tests are reported in Table (1). 

The value of the KMO measure of sampling adequa-

cy was 0.792 (close to 1) and was considered ac-

ceptable and marvelous because it exceeds the mini-

mum requirement of 0.50 and it is above 0.90 („su-

perb„ according to Kaiser, 1974; Field, 2009; 

Zaiontz, 2014). Moreover, the Bartlett test of sphe-

ricity was another indication of the strength of the re-

lationship among items/ variables. The Bartlett test 

of sphericity was 1417.778, and the associated sig-

nificance level was 0.000. The probability value 

(Sig.) associated with the Bartlett test is less than 

0.05, which satisfies the PCA requirement. This re-

sult indicated that the correlation matrix was not an 

identity matrix and all of the items/ variables are cor-

related (Field, 2009; Zaiontz, 2014). 

 

 

Table (1): KMO and Bartlett's test for business 

failure factors 

KMO and Bartlett's test  

Kaiser-Meyer-Olkin Measure of Sampling 

Adequacy. 
0.792 

Bartlett's Test of Sphericity Approx. Chi-

Square 
1417.778 

DF 378 

P-value 0.000 

Cronbach's Alpha (Cα) 0.90 

 

After all the appropriate checks were performed and indi-

cated that all the 73 variables should be retained in an 

initial capture of factors, using the principal component 

analysis approach with exploratory factor analysis 

through SPSS v.22. Several criteria should be achieved in 

order to accept the extracted solution obtained in any 

phase and to consider this solution as a suitable final so-

lution for the involved variables. The following sections 

explains these criteria and process of investigation for the 

final solution (after sixteenth run).  

 

1. Communalities (common variance) 
Communality is the first criteria to be checked in the ex-

tracted solution. It reveals the percentage of variance in a 

particular variable that is explained by the factor (Wil-

liams et al. 2010). Larose (2006) has also claimed that 

communalities less than 0.5 were considered too low, 

since this would meant that the variable shares less than 

half of its variability with other variables. Higher com-

munality value means higher importance of the variable. 

After (sixteenth run) of factor analysis, we get (28) fac-

tors that  communality values confirms with this assump-

tion as their values larger than 0.5.  

Table (2) : Communality values of business failure 

factors “Final run” 

Item 
Communality values of final run 

“Sixteenth run” 

A4 0.676 

A5 0.576 

A6 0.626 

A7 0.667 

A8 0.598 

A11 0.620 

A18 0.568 

A28 0.601 

A30 0.651 

A32 0.509 

A43 0.577 

A44 0.632 

A45 0.701 

A46 0.686 

A49 0.649 

A50 0.560 



Khalid Al Hallaq/ Critical Factors Causing Contractor's Business Failure in Gaza Strip  
  

15  
 

Item 
Communality values of final run 

“Sixteenth run” 

A54 0.547 

A55 0.619 

A59 0.680 

A63 0.598 

A64 0.667 

A65 0.856 

A66 0.813 

A67 0.672 

A68 0.575 

A70 0.673 

A71 0.730 

A73 0.732 

     Extraction Method: Principal Component Analysis. 

 

1. Total Variance Explained 
By using the output from iteration 1, there were five ei-

genvalues greater than 1 (Figure 1). The eigenvalue crite-

rion stated that each component explained at least one 

item's/ variable's worth of the variability, and therefore 

only components with eigenvalues greater than one 

should be retained (Larose, 2006; Field, 2009). The latent 

root criterion for some factors to be derived would indi-

cate that there were five components (factors) to be ex-

tracted for these items/ variables. Results were tabulated 

in Table (3). The five components solution explained a 

sum of the variance with component 1 contributing 

28.894%, component 2 contributing 11.105%, component 

3 contributing 8.597%, component 4 contributing 

7.010%, and component 5 contributing 5.306%.  All the 

remaining factors are not significant. 

The five components were then rotated via varimax (or-

thogonal) rotation approach. This approach does not 

change the underlying solution or the relationships 

among the items/ variables. Rather, it presents the pattern 

of loadings in a manner that is easier to interpret factors 

(components) (Reinard, 2006; Field, 2009; Zaiontz, 

2014). The rotated solution revealed that the five compo-

nents solution explained a sum of the variance with com-

ponent 1 contributing 14.405%, component 2 contrib-

uting 14.047%, component 3 contributing 11.436%, 

component 4 contributing 11.325%, and component 5 

contributing 9.522%.  These five components (factors) 

explained 60.734% of total variance for the varimax rota-

tion. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table (3): Total variance explained by factor analysis 

for the final run of business failure factors 

C
o

m
p

o
n

e
n

t 

Initial Eigen-

values 

Extraction 

Sums of 

Squared Load-

ings 

Rotation Sums 

of Squared 

Loadings 

T
o

ta
l 

%
 o

f 
V

a
r
i-

a
n

c
e
 

C
u

m
u

la
ti

v
e
 

%
 

T
o

ta
l 

%
 o

f 
V

a
r
i-

a
n

c
e
 

C
u

m
u

la
ti

v
e
 

%
 

T
o

ta
l 

%
 o

f 
V

a
r
i-

a
n

c
e
 

C
u

m
u

la
ti

v
e
 

%
 

1 

8.0

90 

28.

89

4 

28.

894 

8.0

90 

28.

89

4 

28.8

94 

4.

03

3 

14.

405 

14.

40

5 

2 

3.1

09 

11.

10

5 

39.

999 

3.1

09 

11.

10

5 

39.9

99 

3.

93

3 

14.

047 

28.

45

1 

3 

2.4

07 

8.5

97 

48.

596 

2.4

07 

8.5

97 

48.5

96 

3.

20

2 

11.

436 

39.

88

7 

4 

1.9

63 

7.0

10 

55.

607 

1.9

63 

7.0

10 

55.6

07 

3.

17

1 

11.

325 

51.

21

2 

5 

1.4

86 

5.3

06 

60.

912 

1.4

86 

5.3

06 

60.9

12 

2.

66

6 

9.5

22 

60.

73

4 

6 
0.9

98 

3.5

77 

64.

489 

 
     

7 . 

. 

. 

. 

. 

. 

. 

. 

. 

. 

. 

. 

       

8        

9        

1

0 

  
     

2

8 

0.0

68 

0.2

43 

100

.00 

  
     

Extraction Method: Principal Component Analysis 

1. Scree Plot 
The scree plot below in Figure (1) is a graph of the ei-

genvalues against all the factors. This graph can also be 

used to decide on some factors that can be derived. The 

point of interest is where the curve starts to flatten. It can 

be seen that the curve begins to flatten between factors 1 

and 5. Note also that factor 6 has an eigenvalue of less 

than 1, so only five factors have been retained to be ex-

tracted. 

 

 
Figure (1): Scree plot of business failure factors 



Khalid Al Hallaq/ Critical Factors Causing Contractor's Business Failure in Gaza Strip  
  

16  
 

 

 

 

2. Rotated Component (Factor) Matrix 
Table (4) shows the factor loadings after rotation of 28 

items/ variables on the five factors extracted and rotated. 

The pattern of factor loadings should be examined to 

identify items/ variables that have complex structures 

(Complex structure occurs when one item/ variable has 

high loadings or correlations (0.50 or greater) onto more 

than one factor/ component). If an item/ a variable has a 

complex structure, it should be removed from the analy-

sis (Reinard, 2006; Field, 2009; Zaiontz, 2014). It was 

loading onto five components. 

On the basis of such restriction, seven items loaded on 

the first factor, six items loaded on the second factor, five 

items loaded on the third factor, five items loaded on the 

fourth factor, five items loaded on the fifth factor “Table 

(5)”. It is worth noting here, that rotated component ma-

trix table should be checked only after satisfying all re-

quirements mentioned above such as MSA values, com-

munalities, KMO, p-value for Bartlett‟s test of sphericity 

and etc.,. However, three conditions should be satisfied in 

this table to consider the solution acceptable. 

Table (4): Rotated component matrix for the final run of 

business failure factors 

No. 

Factors/ Compo-

nents of business 

failure factors 

F
a
c
to

r 
lo

a
d

-

in
g
 

E
ig

e
n

v
a
lu

e
s 

%
 v

a
ri

a
n

c
e
 

e
x

p
la

in
e
d
 

C
ro

n
b

a
c
h

's
 

A
lp

h
a
 (

C
α

) 

Component/ Factor One: Financial and political causes 

A66 
High cost of ma-

terials 
0.754 

8.10 14.41 0.87 

A67 Lack of resources 0.734 

A70 

Delay in collect-

ing dibs from cli-

ents 

0.729 

A65 Monopoly 0.718 

A73 
Changing funding 

sources 
0.713 

A68 
Dealing with sup-

pliers and traders 
0.688 

A66 Israeli attacks 0.604 

Component/ Factor two: Contractual causes 

A11 
Owner absence 

from the company 
0.760 

3.11 14.05 0.82 

A28 

Low margin of 

profit due to com-

petition 

0.723 

A55 

Owner involve-

ment in construc-

tion phase 

0.712 

A30 
Estimating prac-

tices 
0.706 

A54 
Award contracts 

to lowest price 
0.651 

No. 

Factors/ Compo-

nents of business 

failure factors 

F
a
c
to

r 
lo

a
d

-

in
g
 

E
ig

e
n

v
a
lu

e
s 

%
 v

a
ri

a
n

c
e
 

e
x

p
la

in
e
d

 

C
ro

n
b

a
c
h

's
 

A
lp

h
a
 (

C
α

) 

A59 

Monopoly of 

some important 

material for con-

struction 

0.649 

Component/ Factor three: Managerial causes 

A6 

Use of project 

management 

techniques 

0.741 

2.41 11.44 0.79 

A4 

Bad decisions in 

regulating com-

pany policy 

0.733 

A7 
Company organi-

zation 
0.710 

A5 

Labor productivi-

ty and improve-

ment 

0.656 

A8 
Procurement prac-

tices 
0.654 

Component/ Factor four: Organizational causes 

A45 
Increase number 

of projects 
0.792 

1.96 11.33 0.82 

A46 
Increase size of 

projects 
0.750 

A49 
Increase number 

of employees 
0.721 

A44 

Contractor's diffi-

culties in achiev-

ing bank facilities 

0.589 

A43 

Problem rising 

due to temporary 

items in the con-

tract 

0.573 

Component/ Factor five: Economical causes 

A64 Banks policy 0.754 

1.49 9.52 0.75 

A50 

Change work 

from private to 

public or vice 

versa 

0.635 

A63 
General govern-

ment restriction 
0.635 

A18 Inflation 0.610 

A32 
Bill and collecting 

effectively 
0.558 

Extraction Method: Principal Component Analysis. 
Rotation Method: Varimax with Kaiser Normalization. 

 

Once factors have been extracted and rotated, it was nec-

essary to cross checking if the items/ variables in each 

factor formed collectively explain the same measure 

within target dimensions (Doloi, 2009). If items/ varia-

bles indeed form the identified factor (component), it is 

understood that they should reasonably correlate with one 



Khalid Al Hallaq/ Critical Factors Causing Contractor's Business Failure in Gaza Strip  
  

17  
 

another, but not the perfect correlation though. 

Cronbach's alpha (Cα) test was conducted for each com-

ponent (factor). 

The higher value of Cα denotes the greater internal con-

sistency and vice versa. An alpha of 0.60 or higher is the 

minimum acceptable level. Preferably, alpha will be 0.70 

or higher (Field, 2009; Weiers, 2011; Garson, 2013). Ac-

cording to the results which were tabulated in Table (4), 

Cα for each factor higher than 0.7, they are considered to 

be excellent. 

Financial and political factors 

It  is  clear  that  the  seven items that  loaded  on  this 

group are  related  to financial and political factors that 

can cause business failure according to the view point of 

local contractors.  This group accounts for 14.41% of the 

total variance explained and the reliability score 

(Cronbach‟s α) of 0.87. According to factor analysis theo-

ry, the first factor  accounts  for  the  largest  part  of  total  

variance  of  the  data.  Hence, it implies that high cost of 

materials considered as the most important factors cause 

business failure in Gaza Strip. It is closely followed by 

lack of resources, delay in collecting dibs from clients, 

monopoly, changing funding sources and dealing with 

suppliers and traders. Most of these factors are financial 

factors but associate with political conditions.  

According to the statistical analysis, there is no weighted 

difference between the financial and political factors ex-

cept the lowest ranking factor, which was Israeli attacks. 

It may be interpreted as most of companies in Gaza strip 

are not exposed to Israeli attacks directly, and if exposed 

to attacks, they compensate by local authorities.  

Contractual factors 

It can been seen from Table (6) that  there are  six  

items\variables  that  loaded  on  this group. The total 

variance was 14.04% and the reliability score 

(Cronbach‟s α) of 0.82. Table (6) illustrates that the own-

er absence from the company, low margin of profit due to 

competition, owner involvement in construction phase, 

estimating practices are the top ranked four factors. These 

are closely followed by award contracts to lowest price 

and monopoly of some important material for construc-

tion. 

The owner absence from the company is the most factor 

affecting to the failure of company because the loss of 

experience in the stuff  and not good  following the work 

result the failure, the second factor more competitive lead 

to less profit in the contract from the contractor side, the 

monopoly  is important factor in failure in Gaza because 

the closure of ports.   

Managerial factors 

There are five factors listed under this group as shown in 

Table (6). The highest three business failure causes are 

the use of project management techniques, bad decisions 

in regulating company policy, and company organization. 

 It is closely followed by two factors which were labor 

productivity and improvement and procurement practices. 

It is quite interesting to note that the use of the project 

management techniques  is heavily affecting in failure 

because the very good techniques lead to good manage-

ment at the project less the failure, the bad decision at 

company affect more on the work and make problem at 

the sites that is a failure, company organization to the 

employee in the company is good less failure and choice 

bad engineer or any employee do the failure, good 

productivity for the employee is important factor to less 

the failure, less factor affecting on the failure in this 

group procurement practices. 

Organization factors 

Table (6) illustrates the ranking of five factors under this 

group. The top-ranked factors are increase number of 

projects, increase size of projects and increase number of 

employees. That more affecting three factor because more 

skilled employees are needed to sequence the project 

without problem, increase successful project less failure.  

According to the contractors, there is significance differ-

ence between the three top factors and the two lowest 

factors which were contractor's difficulties in achieving 

bank facilities and problem rising due to temporary items 

in the contract, this factor lead to failure because no bank 

facilities is stopping the project that may lead to financial 

failure, the lowest factor in the items of the contract lead 

to more problem between the  owner stuff and contractor 

stuff may lead to failure. 

 Economic factors 
It is obvious that the five factors that loaded on this group 

are related to economic factors that can cause business 

failure according to the viewpoint of local contractors.  

The first factor  accounts  for  the  largest  part  of  total  

variance  of  the  data.  Hence, it implies that banks poli-

cy considered as the most important in  the economic 

factors in Gaza Strip and it is heavily affecting factor. The 

other factors respectively are change work from private to 

public or vice versa, general government restriction, in-

flation, bill and collecting effectively. The change of the 

type of work that need to skilled employee and more ex-

perience project manager to do best in the project new 

type without failure, inflation is a worldly reason from 

financial viewpoint and contractor.  
 Conclusion  
Business failure has become an increasingly important 

issue in the Gaza Strip construction industry due to ongo-

ing closure that cause business instability. The failure of a 

company may cause considerable losses to all parties in 

the construction industry. In particular, it may affect vari-

ous stakeholders, such as clients, contractors, subcontrac-

tor, suppliers, consultants, investors, or employees.  

There are many factors that could be responsible for the 

contractors failure which impact negatively the local eco-

nomic environment. The main objective of this paper is to 

identify the critical factors that have the potential to cause 

contractor's business failure in the Gaza Strip and to de-

termine their level of severity from contractor's view-

point. Seventy-three factors were considered in this re-

search paper, and then reduced to twenty-eight factors 

using factor analysis. They were listed under the follow-

ing five groups: (1) financial and political, (2) contractu-



Khalid Al Hallaq/ Critical Factors Causing Contractor's Business Failure in Gaza Strip  
  

18  
 

al, (3) managerial, (4) organization, and (5) economical. 

The most critical factors that highly affect contractor's 

business failure are: (1) high cost of materials, (2) Lack 

of resources, (3) Delay in collecting dibs from clients, (4) 

Monopoly, (5) Changing funding sources, (6) Dealing 

with suppliers and traders, (7) Israeli attacks. 

 

It is recommended that contracting companies should 

consider the influence of the previous factors to avert 

failure. They should also focus on the remedies of failure 

by using a blend of managerial and organizational actions 

to overcome the impacts of financial and political impli-

cations of failure.     

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