Sheraz Khan, Head of Department Managemnet Sciences,
University of Haripur. Email: shirazkhan@uoh.edu.pk
Ghulam
Murtaza, PhD Scholar, NUML University, Islamabad.
Email: gmurtazaaa@gamil.com
Raja
Ahmed Jamil, PhD Scholar, Riphah International University Islamabad Email: raja.ahmed@uoh.edu.pk
Imran
Qadir, Lecturer, University of Haripur. Email: iqadir808@gmail.com
Abstract. This paper is focused on confirmatory
analysis of key determinants of
competitiveness in the textile sector, with special reference to Pakistan.
Researchers have conducted a survey that is designed to use financial side,
productivity side, supply side, and demand side determinants to measure firms’
level Competitiveness. Primary data was collected at a seven point scale from
354 respondents representing145 Listed Textile Companies at KSE. Collected data
was then analyzed by using the confirmatory factor analysis to find out the
rate of interdependency between the dependent and independent variables. The
result shows that highest impact is being shown by the demand side determinants
followed by financial side determinants. While the impact of productivity side
and supply side determinants remained comparatively low.
Key words: Pakistan, competitiveness, textile industry
Introduction
Globalization and
liberalization of economies at very fast speed, and also due to a great
development in the means of transportation and communication whole world is
becoming accessible market for large as well as medium and small business
organizations. But developing countries are facing challenges, how to
strengthen their capacity to benefit from emerging trade and investment
opportunities.
It is now an axiom that
acceleration in the movement of capital and goods globally, termed
conventionally ‘globalization’ carries both serious potential threats and
immense opportunities. Eventually, it will be the international competitiveness
of firms, in particular economies, that will determine how far opportunities
are converted into lasting national benefits or alternatively how far the loss
by the potential threats been suffered. In the new and more liberal
international and domestic environment industrial competitiveness will have a
critical bearing on economic prospects for the probable future. Due to inefficiency
and poor competition at domestic level and relying heavily on imports which
could lead to worse economic conditions for Pakistan (Choudhry & Amin, 2012).
Ricardo (1817 ) first time formulated the theory of comparative
advantage. It is totally different from the competitiveness. Comparative
advantage just means unique abilities of a country to produce cheaper goods
which others cannot do. By competitiveness we mean the best and maximum
utilization of the available resources at macro as well as a micro level. A
country can only be competitive when its firms are competitive. It is firms
that compete not the nations (Krugman, 1996).
Competitiveness has been
studied extensively through-out the world. But comprehensive study regarding
competitiveness at an enterprise level from the point of view of the critical
factors of competitiveness in Pakistan textile sector is lacking. This study is
an attempt to explore the rate of interdependency among the critical factors of
competitiveness of textile sector of Pakistan at a micro level.
Literature
Review
Whenever a reader tries
to make him/her more clear towards competitiveness, always gets confused with
the three tiers of competitiveness; that is enterprise, industry and nation.
Enterprise competitiveness leads to national level competitiveness or national
level competitiveness give birth to the enterprise level competitiveness. So the competitiveness can also be measured
at three levels. Competitiveness should only be regarded as a domestic
productivity problem because when it is applied to national economies becomes
meaningless, and also “obsession with competitiveness is both wrong and dangerous”
(Krugman, 1994). Therefore, in the present study we are only concerned with the
competitiveness of industries and enterprises in Pakistan’s textile sector.
The conservative view of
enterprises’ competitiveness focuses on costs: those enterprises that are able
to deliver the lowest product prices to markets are likely the most competitive
and viable. Total factor productivity (TFP), labor productivity (LP) and unit
labor cost (ULC) are the most widely adopted approaches for measuring
industrial competitiveness. Measuring TFP and ULC growth measurement is
probably the simplest, most convenient methods, as enterprises and industries
cost of production can be compared by these.
Competitiveness is
traditionally considered modeled as possessing the abundant natural and well as
human resources. But it is not true in case of many countries like Switzerland
and Sweden having highest per capita nominal wages but also ranked in the first
tire of the world. Therefore ULC alone cannot exactly measure the competitiveness
of a concern. We see that Italy in 2007 having the higher labor cost as of
India, China other developing countries but is number one in the world of
textile and apparel. Fashion industry of Italy is also considered as the pillar
of the Italian economy.
Hu (2004) studies the Chinese industries and examine the
contributions of internal R&D, technology transfers and FDI to their
productivity. They find that the internal R&D of an enterprise could significantly
replace the effect of a technology transfer of FDI using enterprise data for 29
two-digit manufacturing industries and over 400 four-digit industries over the
period of 1995–1999.
Porter (1979) of Harvard
Business School presented a framework “Porter’s five forces” for the industry
analysis and business strategy development. Developing Industrial Organization
(IO) is the main concept behind this framework, to determine the competitive
intensity and therefore attractiveness of a market. Here overall profitability
of the industry means attractiveness. An “unattractive” industry is one where
the combination of forces acts to drive down overall profitability. Porter’s
five force include three forces from ‘horizontal’ competition: threat of
substitute products, the threat of established rivals, and the threat of new
entrants; and two forces from ‘vertical’ competition: the bargaining power of
suppliers, bargaining power of customers.
Markus (2008) used the
theoretical framework of Porter’s Diamond Model to measure the company level
competitiveness with 8 variables but by ignoring the larger business
organizations. He used varimax rotation resulting in four factors. The
variables which he selected worked were, (i) Knowledge base, (ii) Financial
prospects, (iii) Lack of qualified experts, (iv) Cooperation with other
organizations, (v) Demand Index, (vi) Past tendencies of sales revenue growth
and expected future tendency (sales revenue trend), (vii) Past tendencies of
headcount growth and expected future tendency.(headcount trend) and (viii)
Innovation activities. He selected his variables according to the Porter’s
Diamond model factors: (a) Factor Conditions, (b) Related and Supporting
industries Clusters, (c) Demand Conditions, (d) Firm strategy, structure and
rivalry, and also added one additional factor i.e., (e) Innovation.
A study made by Narayana
(2004) for determinants of competitiveness of small scale
industries in India, taking a sample of 373 SSIs (Small Scale Industries)
looking for the impact of quality and cost of infrastructure and business
environment on the competitiveness for the SSIs. Infrastructure includes
transport, market information, credit, power, water, telecom and technology up
gradation facilities while business environment indicated by Government
permissions ad clearances. The result showed that poor quality and high cost of
infrastructure effects are less server in Bangalore region than in the regions,
whereas, getting credit sanctioned from banks, tax and duty-drawbacks,
temporary and permanent registration, clearances for export, permission for
expansion and diversification, power and water connections, and clearance form
pollution control board reduce the competitiveness of the SSIs by adding costs.
Lau (2009) while finding out the determinants of the
competitiveness in the textile and apparel industries of China divided the
determinants of competitiveness of an economic entity into three groups:
productivity, supply side determinants and demand side determinants. Questions
have been asked from the respondents about each determinant by dividing it into
various dimensions.
Methodology
The research at hand is
quantitative in nature. The researchers have been guided by the Diamond Model
by Porter (1985) and the same used by Lau et.al in China (2009) with some
modification and addition of a variables and detailed variables. This part of
the study will enable us to see the relationship between the competitiveness
(Dependent variable) and its determinants (independent variables) also how
these are impacting the performance of the industry. The sample companies are
listed at Karachi Stock exchange in the year 2012. Structural Equation Model
(SEM) has been used to find out the key contributing factors to the competitiveness
of the textile industry of Pakistan.
Data Set and Sample
Multidimensional
approach of Sectoral analysis was adopted in order to conduct a thorough
analysis across the Textile Value-added Products. Textile Sector consists of
numerous sectors and sub sectors based on inputs and finished products. Each
sector has its own characteristics. Variety and diversity of sectors starts
from cotton ginning till Garments and Made-Ups. For a comprehensive study
Primary and Secondary sources were used to collect required information and
data.
Competitiveness is the
key to productivity growth (Porter, 1990), the leading competitiveness
theorist, defines competitiveness as sustainable increases in productivity that
the lead to increases in prosperity. The World Economic Forum (WEF) defines
competitiveness as the “set of institutions, policies, and factors that
determine the level of productivity of a country.” Competitiveness is
simultaneously driven by a combination of macroeconomic policies, a sound
institutional framework, modern infrastructure and an efficient financial
system to ensure an enabling business and investment climate.
In the light of Porter’s
diamond model following are the four major determinants of the competitiveness
used in the paper and the detailed aspects that provided the base for the
questions asked in questionnaire.
|
Financing |
Equity Markets efficiency. Raising funds Issuing of Bonds Fair cost and time efficient |
|
Financial Markets: Timely availability of funds timely Available at low cost Short as well as long term loans. |
|
|
Productivity |
Labor Productivity Material utilization Material Wastages Reuse of wastages Production technology up gradation New brands New structures |
|
Supply
Side Determinants |
Availability of material inputs Cost of material inputs Availability of labor inputs Cost of labor inputs Supplies inputs Government support Supply chain management |
|
Demand
Side Determinants |
Access and volume of local demand Customers’ demand specific Average demand throughout the year Customers’ complaints R & D/innovation for improvements. |
|
Competitiveness |
Financial competitiveness level Productivity competitiveness level Supply side competitiveness level Demand Side competitiveness level |
(1)
Financing (Independent variable):
This determinant includes two
dimensions: (1) capital markets efficiency and (2) financial markets
efficiency.
(2)
Productivity (Independent variable)
Productivity
of a business unit can be measured through the ability of that organization to
produce maxim but most economically. Existing of policies for the maximum
utilization of material labor and machine hours ensures the best utilization of
resources. New technologies tend to reduce the wastages and produce
efficiently, so the how frequent is the organization to adopt the new
technologies also add to its productivity. Introducing new brands and new
structures confirms the productivity competitiveness.
(3)
Supply-side Determinants (Independent Variable)
Availability
of the cheapest input resources enhances the supply side competitiveness.
Locally available inputs are less in cost and need comparatively less space for
storage. Organizations adopting the supply chain management techniques are
considered to be more competitive. Role of government for the provision of
uninterrupted power supply, quality road access, gas and other infrastructure
enable the local organizations to survive and compete nationally and
internationally.
(4)
Demand-side Determinants (Independent Variable)
Greater the
demand from local markets lessor the dependency on foreign markets. Local
demand enhances the production, quality and marketing skills of the local
manufactures. Secondly greater local demand also attracts the FDI and larger
foreign manufacturers (Lau, 2009). Caring attitude of the organizations for
their customers develops the customers’ loyalty. Average demand throughout the
year can be achieved managerial and marketing skills. Organization successful
in managing the demand evenly proves to be more competitive. Bulk of demand at
one part of the year demands lager capacity to store and also enhances the risk
of obsolescence and expiring. Amount and efforts invested in research and
innovation to increase the demand reflects the organizational commitment to
achieve the goal.
5)
Competitiveness (Dependent Variable)
Existing
level of certain indicators of competitiveness reflects the rank of the firm to
compete. Respondents have been asked at a scale, represents the dependent
variable in the study.
Data
relating to 2014-2015 formed the basis of our calculations. The Sample is based
on 352 respondents of 145 listed textile companies at Karachi Stock Exchange.
Source of other relevant information used in this study are as: some selected
non-listed textile firms at Faisal Abad industrial Estates, data available at
the website of different Firms, different state Departments, Organizations and
Regulatory Authorities.
Survey was
conducted through a close ended questionnaire. The questionnaire is divided into
two main parts. First part, a very short one, has been designed to collect
demographic information: organization name, age, level of product, and
designation/job title. The second part contained 42 questions for four
independent and one dependent variable. These questions have been worded in the
first person and applied to the real situation. Respondents have been provided
with the options to rate their responses on a 7-point Likert scale (1= Strongly
disagree, 7 = Strongly agree). Besides this, respondents have been provided
with space to offer their comments/opinion if they like to say something about
the competitiveness. Statistical technique of SEM has been applied to confirm
the volume of impact of the determinants on the firm level competitiveness.
The
conceptual model figure 2 depicts the picture of the independent and dependent
variables along with their theoretical impact.
Figure 2. Theoretical Model
Demographics of the data collected:
In this study detail of the companies
having total assets in billions (Pak Rupees):
Table 1 Total
Assets of the Sample.
|
Assets in Billion (Rs) |
Companies |
Percentage |
|
Rs.1----2 Rs.2---4 Rs.
Above 5 |
25 16 16 |
44 28 28 |
Out of the total sample of 57 companies 45
organizations are ISO (International Standard Organization) certified while 12
are not certified. Analysis of the age of the total sample of 57 companies
reveals that 22 were established before 1974, which shows the most experienced
management and production skill, 25 were established in between 1975 to 1991,
highest number established from the sample in this period. Which also reveals
the mentioned period was helpful and very much friendly for the establishment
of the new industrial units? Companies established between 1992 and 2002 were
8, companies established after 2002 were only 2 (table 1).
Surveyed units divided as spinning,
weaving and composite are engaged in producing Yarn, Cloth, Garments and some
are making only household thing like towel etc. Of the total sample 32 are
spinning, 19 are composite and 4 weaving (table 2).
Table 2 Product-wise Division
of the Firms
|
Spinning |
Composite |
Weaving |
|
32 |
21 |
4 |
|
56% |
37% |
7% |
Exports of textile sector contain a major
portion of yarn export. What Pakistan’s international trade is lacking is the
export of value added goods. In the previous two to three years government took
few measures to stop the extraordinary export of yarn, because of the shortage
for domestic industries. But even then most of the surveyed spinning units more
than 90% business is for exporting yarn. Which on the one hand showing a great
rise in the total exports of the textile sector but on the other hand damaging
the domestic industry.
Of the total companies 95% are engaged in
the mass production, only 5% of the sample engaged in producing specialized
goods for specialized/targeted customers see figure 4.4.
All the selected companies are engaged in
satisfying the general customers, except
Figure 3 Production Pattern
Of the total sample 49 were operational
for 100% capacity available. Only few pointed out certain hurdles. 5 out of
total 57 claimed for lack of market for their less capacity utilization, 4
claimed government rules, 2 for the unavailability of raw material, 1 each for
unavailability of spare parts and machinery breakage, See table 8. Only two out
of 57 called electricity shortage a major reason for being not utilizing the
100% capacity. While responding to another question 100% of the sample pointed
out the shortage of Gas and Power for the basic infrastructure deficiency. It’s
may be due to shifting towards own power generation. While surveying the
textile units it was found that most of the big units have shifted towards the
business of power generation.
Figure 4 Reasons
for being non-operational
Of the total surveyed units 30 having a
future plane to make it operational while 12 do not have any such plane.
Descriptive
Analysis of all Items
Details of descriptive analysis of all
items of questionnaire are given in the Annexure 1. Questionnaire was based on
7-points liker scale (1 strongly disagree, 7 strongly agree). Mean values of
all indicators show that none of the item had very high or very low mean score.
Items in each construct had mean score just above the midpoint of the scale.
This provided support that all data were normally distributed.
Contribution
of Determinants to Competitiveness
Annexure 1 at the end indicates the
reliability analysis quite satisfactory. “There are different reports regarding
the acceptable value of Alfa ranging from 0.70 to 0.95. A low value of Alfa
could be due to low number of questions, poor interrelatedness between items or
heterogeneous constructs” (Tavakol
& Dennick, 2011). Alpha values for the construct 0.786, .843, 0.851,
0.881, and 0.886 are well in the range of acceptable slots.
Component
factor analysis (annexure 2) technique has been used to find out the
uni-dimensionality of the data as suggested by Droge and Daugherty (as cited in
Hoe, 2008, p. 80). Annexure 3 shows all the constructs hold the first Eigen values
greater than 1 this provided support for the uni-dimensionality of these
scales. Sekaran,
(2003) is of the view that reliability can also be achieved if the respondents
attach the same meaning to each of the item while measuring the same concept
and that the items should “hang together as a set” (Akhtar, 2009).
It has been intended to measure the impact
of an individual facto having on the competitiveness; confirmatory factor
analysis (SEM) confirms the role of the contributing factor of the
competitiveness in the textile industry of Pakistan. An exploratory study has
been conducted by Lau. et.al (2009) in China for the textile and apparel sector
through exploratory factor analysis technique (Factor analysis). The same model
with some additions has been used over here in Pakistan, with a view that the
adjacent neighbor, having almost same climatic and environmental effects.
Results
Analysis
Confirmatory
Factor Analysis (CFA) has been employed to analyze the appropriateness of the
measurement model for each construct separately. For parameter estimation
several goodness of fit statistics, including Chi-square, Comparative Fit Index
(CFI), Root mean square residual (RMR), Root Mean Square Error of Approximation
(RMSEA), Goodness-of-fit Index (GFI), and Root Mean Square Residual (RMSR),
were employed.
The confirmatory factor analysis technique
provides the theoretical model fit in three steps: (i) individual model fit for
all contributing factors, (ii) overall measurement model fit for all the
factors used, and (iii) the theoretical model fit, (see figure 2). For a model
fit the critical value of RMR (Root mean residual) < .1, GFI (Goodness of
fit index) > 0.85 , CFI (Comparative fit index)> 0.90 and the most important RMSEA (Root mean square
error of approximation) < .08 are
ideal (Hair, Black, Babin, Anderson, &
Tatham, 2006).
Measurement
Model
Financial Side: Financial side determinants have been investigated
through 8 dimensions. The values for financial model were within the critical
limit only for CFI, GFI, RMSEA and CMIN. Looking at the modification indices
the element 1-3 and 5-6 were showing the highest I.M values so these were
correlated with covariance double arrow. The individual model for financial
side determinants showed the CMIN/DF1.730, GFI at 0.980, CFI at 0.983, RMR at
.030, and RMSEA at 0.045 makes the individual model fit for financial side
determinants.
Productivity Side: Productivity side determinants have divided into 9
dimensions. The individual model fit for productivity side determinants showing
the values of CMIN/DF at 2.520 significant at P 0.000, GFI at 0.972, CFI at
0.974, RMR at .053, and RMSEA at 0.066 makes the determinants fit for
productivity side.
Supply Side: Supply side asked through 8 dimensions. Showing
CMIN/DF at 2.5, GFI at 0.971, CFI at 0.974, RMR at .049, and RMSEA at 0.066
make the model fit.
Demand Side: Values for the nine sub-dimensions of demand side
showing CMIN/DF at 2.540, GFI at .970, CFI at 0.978, RMR at .053, and RMSEA at
0.066 make the model fit.
Competitiveness: Values for the dependent variable i.e.
competitiveness are CMIN/DF at 2.457, GFI at .976, CFI at 0.980, RMR at .048,
and RMSEA at 0.064 make the model fit.
Structural
Model
The results
for the overall model fit are also within the desired parameter i.e. CMIN/DF at
2.041, RMR at 0.085, GFI at 0.917, CFI at 0.966 and RMSEA at 0.054.
Figure 5 Structural model
Discussions
The main objective of this paper was to
validate some previous research regarding the determinants of competitiveness
in the textile industry of Pakistan. The results of the current research have
revealed that:
·
The highest coefficient is shown by the demand side i.e. 0.37.
·
The second highest coefficient is shown by finance side i.e. 0.20.
·
The lowest coefficients 0.13 and 0.04 are shown by productivity and
supply side respectively.
Findings
indicate that the competitiveness level currently availed by the textile
industry of Pakistan is mainly due to the strong demand side determinants and
easy availability and low cost of the finance facilities. Productivity side and
supply side are contributing a very low towards the competitiveness of the
textile industry of Pakistan.
Conclusion
In the current study among the four
determinants of the competitiveness of the textile industry of Pakistan,
financial side, productivity side, supply side, and demand side determinants.
Study revealed that the demand side determinants are having the highest impact
on the competitiveness, followed by financial side. While the productivity side
and supply sides are showing a very meager impact on the competitiveness. The
study further suggests that main focus of the industrialist and government must
be on demand side and financial side determinants to strengthen the prevailing
level of competitiveness. Access to local markets by better communicational and
road transport system can enhance the impact. Similarly maintaining the
prevailing conditions in financial and capital markets by state bank
regularities and state relevant policies to make the availability of finance
more easily shall add to the overall competitiveness of the textile sector.
It could be worth exploring factors of
competitiveness in relation with other countries and regions, for example in
China, India and in other Asian countries; they are Pakistan’s textiles and
clothing enterprises’ main potential competitors.
References
Akhtar, (2009). The
Relationship of Organizational Learning and Competitive Advantage: A Case Study
of Petroleum Companies of Pakistan. PhD Dissertation, National University
of Modern Languages, Islamabad.
Balassa, B. (1965).
Trade liberalization and revealed comparative advantage. Manchester School
of Economic and Social Studies, 33(2), 99-124.
Bartlett, A., &
Ghoshal, S. (1989). Managing Across Borders. Boston, MA: Harvard
Business School Press.
Barney, J. (1986).
Strategic factor markets: expectations, luck and business strategy. Management
Science 21, 1231- 1241.
Chaudhary, M. A.,
& Amin, B. (2012). Impact of trade openness on exports growth, imports
growth and trade balance of Pakistan. Forman Journal of Economic Studies 8,
63-81.
Lau, C. K. T., Zhang,
Z. K., & Chen, J. (2009). Determinants of competitiveness: Observations in
China's textile and apparel industries. China & World Economy, 17(2),
45-64.
Porter, M., E.
(1990). The Competitive Advantage of Nations. Free Press (New York, 1990).
Sekaran, U. (2003).
Research methods for business (4th ed.): Hoboken, NJ: John Wiley & Sons.