Developing DEMATEL-CCA Hybrid Algorithm Approach to Analyze the Causal Relations on Global Competitiveness Pillars
Prof. of Operational Research, University of Tehran, Tehran, Iran
Ph.D. of Production and Operations Management, University of Tehran, Tehran, Iran
Mphil. Student of Operations Research, Tilburg University, Tilburg, Netherlands
M.Sc. in Corporate Entrepreneurship, University of Tehran, Tehran, Iran
The concept of competitiveness has attracted abundant attentions of both scholars and governments during the past decade. The World Economic Forum (WEF) has published the Global Competitiveness Index (GCI) in order to measure national competitiveness and the GCI is based on 12 pillars. The main purpose of the present study is to investigate the cause–effect relationships between the pillars. Therefore, a hybrid method (CCA-DEMATEL) is used to delve into the internal dimensions of GCI. This hybrid method provides a strong support for DEMATEL technique. In this regard, first, the cause and effect relations between global competitiveness pillars are investigated by CCA. As the next step, the output of CCA is used as DEMATEL’s input; in this way, the most important pillars identified with regard to measures taken by global competitiveness pillars are determined in order of priority. The amount of effect each pillar has on another is calculated. Based on our findings, “Institutions” and “Goods market efficiency” seem to have relatively bigger effects when compared to the other pillars of GCI. On the other hand, “business sophistication”, “Innovation”, “Higher education and training (HET)” and “Technological readiness” appear to be affected more than the other pillars.
Keywords: CCA-DEMATEL Hybrid, Global Competitiveness Index (GCI), Global Competitiveness Pillars, International Competition, World Economic Forum (WEF).
In our globalizing world, the economic competition among countries and their firms has been expanded on an unprecedented scale (Batey and Friedrich, 2013). The novel thoughts and products are intensifying this expansion by introducing new opportunities and threats caused by transmutation in the structure of these firms and the nature of competition (Porter and Heppelmann, 2014). This situation has resulted in a fierce competition between companies — as country’s representatives — and their foreign rivals; they are vying for an increased customer satisfaction and a furthered business process (Rugman, Oh, & Lim, 2012).
As a consequence of the described volatile conditions, the concept of competitiveness has gained global credence which increases tendency toward applying the newly proposed notions of this concept among decision – and policy–makers throughout the world (Martin and Sunley, 2003). Not only is competitiveness appealing to firms, – because it involves the most critical fields related to a firm’s function, including international business, economics, marketing, and strategy (Rugman et al., 2012) – but also it is of particular importance to governments due to its considerable impact on a nation’s exports and growth rate (Naceur, Bakardzhieva & Kamar, 2012).
The prospective benefits of being a competitive nation are not restricted to exports and growth rate; since Porter and Rivkin (2012a) establish a strong association between competitiveness, productivity, and people’s living standards. The aforementioned implications of becoming a competitive nation – improvements in exports, growth rate, productivity, and advanced living standards – provide ample rationale for policy–makers to strive to obtain higher levels of competitiveness; nonetheless, they must refrain from dealing with this delicate concept in a superficial way. As Porter and Heppelmann (2014) contend, although the past decades are distinguished for considerable cost reduction, prudent investment, increased corporate profits, and widespread innovation across all industries, the support for business and capitalism has been diminishing due to the slower job growth, decreased pace of improvements in wages and living standards, and metamorphosed nature of economic opportunity.
Whilst policy–makers do not share similar views regarding the avenues to achieve competitiveness, there is a strong consensus that it is a chief target for national economic policy (Delgado, Ketels, Porter, & Stern, 2012). The surging attentions to nations’ competitiveness aroused scholars’ interest and compelled them to separate it from the main concept. In this regard, Porter and Rivkin (2012b) described national competitiveness as the level of domestic firms’ capability to compete and thrive globally while improving living standards, while others define it as a complex of institutes, factors and policies representing the extent to which a country is productive (Porter and Schwab, 2008).
In accordance with the special consideration given to competitiveness, the World Economic Forum (WEF) has published the Global Competitiveness Report (GCR), in which it studies and benchmarks the factors buttressing national competitiveness, for more than three decades (Schwab, 2014). From the onset, the report aimed to provide countries with perspicacious strategies and policies in order to help them to address the issues surrounding improvement of competitiveness; moreover, since 2005, this report is based on the Global Competitiveness Index (GCI), an exhaustive tool that includes 12 pillars and assesses the macroeconomic and microeconomic foundations of national competitiveness (Schwab, 2014).
There is ample evidence underpinning the fact that the pillars of global competitiveness are rather interrelated than discrete. Schwab (2014, P.9) argues that the pillars are not independent and actually tend to reinforce each other. Consider high levels of innovation (pillar 12) which is impracticable to achieve in the absence of a deft, well-educated workforce (pillar 4 and 5), or an efficient market (pillar 6) and sufficient financing (pillar8) which help innovation levels to escalate (Delgado, Porter, & Stern, 2014; Schwab, 2014, P.9). In this regard, this paper aims to investigate the relations between pillars of GCR in order that it may shed light on these questions: What is the nature of the relations between pillars? Which pillars are mostly affected by other pillars? Which pillars are capable of effecting a major change on other ones? The answers to these questions provide policy–makers with valuable insights into the global competitiveness issue.
The Forum’s GCR divides countries into three main classes and two transitional stages, regarding the stages of countries’ development. First, there are factor–driven, then efficiency–driven, and ultimately innovation–driven economies. These three stages are segmented by two transition stages. For a given economy to improve its competitiveness, it is crucial to first realize its stages and prioritize its objectives, and then to commence benchmarking and developing roadmaps (Önsel et al., 2008; Schwab, 2014; Vares, Parvandi, Ghasemi, & Abdullahi, 2011). There are myriads of studies on national competitiveness, avenues to achieve it, and its relation to other aspects of competitiveness and economics. However, they are not highly responsive to the multifarious demands of the policy–makers in today’s radically changing world. Because of the following gaps, conducting the current research is highly required, since;
Although there is an excess of attempts to study competitiveness pillars, most of them are far from perfect, as they study no more than a small number of pillars and fail to adopt a comprehensive approach (e.g. see Razavi et al., 2012; Vares et al., 2011). Along the same line, Vares and Parvandi (2011) exclusively studied the relationship between infrastructures and efficiency enhancers and ignored the internal correlations. Hence, based on the abovementioned reasons, a novelty of this paper is that it extensively studies the relationships between the pillars and investigates their interrelations. For achieving the research goals, the cause–effect relationships between the pillars of global competitiveness reported in the GCR are investigated and the mutual impact of pillars are quantitatively explored, in 2014-2015, which provides policy–makers with a functional framework and a quantitative tool, in order to improve national competitiveness.
The remainder of the paper is organized as follows: first, based on the 2014-2015 version of GCR, the concepts of competitiveness and global competitiveness are defined. Then, the steps to develop a hybrid DEMATEL-CCA model are expounded, and finally, the causal diagram of competitiveness pillars is drawn and analyzed.
The Entity of Competitiveness
Both policy–makers and scholars use the term competitiveness in a confusing manner; while some equate this concept with the ability to achieve overall outcomes, e.g. economic growth, others consider it as the ability to achieve specific economic outcomes, e.g. foreign direct investment (Delgado et al., 2012). By considering the variety of definitions of competitiveness, Ketels (2013) alleges that there is no correct or incorrect definition for competitiveness; the only determinant factor is the extent to which a definition enables policy–makers to address a particular problem. Although national competitiveness is an oft–repeated term in recent business terminology, there is a lack of consensus on its definition (Ketels, 2013). Along the same line, here are some allusions to previous attempts, in chronological order: Porter (1990) believed that “the only meaningful definition of competitiveness at the national level is national productivity”, while Porter and Schwab (2008) defined national competitiveness as a complex of institutes, factors and policies representing the extent to which a country is productive. On the other hand, Dijkstra et al. (2011) described national competitiveness as a tendency and proficiency to vie for a position in the international market and obtain market share and profitability and ultimately mount thriving mercantile strategies in order to provide a sustainable environment for firms and people. A year later, Delgado et al. (2012), in their analysis for National Bureau of Economic Research, interpreted competitiveness as “the level of output per working-age individual given the overall quality of a country as a place to do business.” Also it has been argued that national competitiveness is the level of domestic firms’ capability to compete and thrive globally while improving people’s living standards; for a given country to succeed globally and raise living standards, there is no way, other than enhancing productivity – maximizing the value of goods and services produced per unit of consumed resources, e.g. capital, human, and natural (Porter & Rivkin, 2012b).
Finally, probably the most recent definition of national competitiveness is presented by Schuller and Lidbom (2015), in which they maintain that national competitiveness can be described as a comparative measure which determines the level of capability of a countries’ firm to sell goods and services in a specific market.
Measuring Nation’s Competitiveness
There is considerable debate over measuring the Nation’s Competitiveness. In this regard, since 1979, GCR published by WEF, has annually examined the factors enabling national economies to achieve sustainable economic growth and long–term prosperity. In these reports, competitiveness has been defined as a set of institutions, policies, and factors that determine the level of productivity of a country (Porter and Schwab, 2008; Schwab, 2014, P.64). Furthermore, since 2005, the WEF has developed the GCI: as a thorough index, GCI captures the micro and macroeconomic foundations of national competitiveness. According to GCI reports, “a nation’s level of competitiveness reflects the extent to which it is able to provide rising prosperity to its citizens” (Schwab, 2009).
The GCI provides a weighted average of the multitudinous components of pillars, each of which reflects one dimension of the complex concept of competitiveness (Schwab, 2014, p.93). GCI contains 12 pillars in three main sub–indexes which are classified as shown in the following Figure:
Figure 1: The Global Competitiveness Index framework (Schwab, 2014, P.9)
Based on Figure 1, GCI reports consider three different stages of development in which each country falls.
The Pillars of Global Competitiveness
In this section, we briefly introduce pillars of global competitiveness which are shown above in Figure 1. Each pillar is constituted from some components.
1st pillar: Institutions (INS)
Individuals, firms, and governments cooperate within the legal and administrative framework to generate wealth. This framework determines institutional environment of a given country (Schwab, 2014, P.4).
2nd pillar: Infrastructure (INF)
Exhaustive and efficacious infrastructure is vital for securing the effectual functioning of the economy, as well as determining the variety of activities or sectors that can evolve within a country (Schwab, 2014, P.6).
3rd pillar: Macroeconomic environment (ME)
Consistency of the macroeconomic environment is particularly important in order to do business; thus, it is significant for the overall competitiveness of a country (Schwab, 2014, P.6).
4th pillar: Health and primary education (HPE)
A robust workforce is requisite for a country in order to improve competitiveness and boost productivity. Ill workers are not capable of working to their utmost; therefore, productivity is reduced. Moreover, this pillar considers the quality and quantity of rudimentary education received by population; as it positively affects the efficiency of workforce (Schwab, 2014, P.6).
5th pillar: Higher education and training (HET)
Decent higher education and training is of particular significance to economies aiming to transcend simple processes and products through moving up the value chain (Schwab, 2014, P.7).
6th pillar: Goods market efficiency (GME)
There are two noteworthy advantages for an economy which benefits from an efficient goods market. Firstly, such an economy is able to provide the proper mix of products and services according to its specific supply-and-demand situations. And secondly, it guarantees that the abovementioned products and services are effectively traded in the economy (Schwab, 2014, P.7).
7th pillar: Labor market efficiency (LME)
Since the efficiency and flexibility of labor market play a crucial role in assigning workers to their most productive task in the economy and ensuring that they are provided with incentives to give their best efforts, labor markets must possess the adaptability to transfer workers swiftly from one part of the economy to another at the lowest possible cost (Schwab, 2014, P.7).
8th pillar: Financial market development (FMD)
The recent economic crisis has accentuated the pivotal role of a well-founded and functional financial sector in an economy. The pecuniary resources saved by population and those injecting from abroad can be efficiently appropriated to their most productive use by a robust financial sector (Schwab, 2014, P.7).
9th pillar: Technological readiness (TR)
Today, technology is becoming more and more indispensable to firms. The technological readiness pillar measures the pace of economies in utilizing available technologies to raise productivity. This pillar specifically considers to the capability of economies to fully leverage information and communication technologies (ICT) in diurnal tasks and activities, and manufacturing processes to first, enhance efficiency and then enable innovation for competitiveness (Schwab, 2014, P.7).
10th pillar: Market size (MS)
Since larger markets provide more opportunities for firms to benefit from economy of scale, size of a market –which includes both domestic and foreign markets–hugely affects the productivity (Schwab, 2014, P.8).
11st pillar: Business sophistication (BS)
There is no misgiving regarding the fact that sophisticated business practices lead to improved levels of efficiency in producing goods and services. Business sophistication deals with two factors which are elaborately associated: the excellence of firms’ operations and strategies, and the efficacy of a given country’s overall business network (Schwab, 2014, P.8).
12st pillar: Innovation (IN)
Innovation may emerge from non-technological knowledge, called non-technological innovation, or novel technological knowledge, namely technological knowledge. The former and the know-how, skills, and working conditions are closely intertwined, thus, they are largely covered by 11th pillar. While this pillar particularly focuses on technological innovation (Schwab, 2014, P.8).
The Relationship Between Some Pillars of GCI
There is a plethora of studies stipulating the fact that the pillars of global competitiveness are rather interrelated than discrete. Schwab (2014, P.9) argues that pillars are not independent and actually tend to reinforce each other. Consider high levels of innovation (pillar 12) which is impracticable to achieve in the absence of a deft, well–educated workforce (pillar 4 and 5), or an efficient market (pillar 6) and sufficient financing (pillar 8) which helps innovation levels to escalate (Delgado et al., 2014; Schwab, 2014, P.9).
Delgado et al. (2014) developed a structured empirical framework to investigate the role of regional clusters. Their results suggest that policies bolstering infrastructures and institutions that ease the access to demand, professional workers and suppliers, are important facilitators of development (Delgado et al., 2014). In their analysis, infrastructures, institutions, and development can be respectively considered as the 2nd pillar of GCR, 1st pillar of GCR.
Tellis, Prabhu and Chandy (2009) investigated the relation between renowned country–level elements and innovation. They argued that capital, labor, culture, and government regulations are not capable of effeting a drastic change on radical innovation. Their investigated elements, namely, capital, labor, government regulations, and innovation are respectively represented in GCR as 8th, 7th, 1st, and 12th pillar (Schwab, 2014, PP.49-51).
Porter (2011) points out that macroeconomic competitiveness depends on microeconomic policies, social infrastructure, and political institutions. In his analysis, one can establish an association between macroeconomic environment and 3rd pillar, microeconomic policies and 6th and 11th pillars, and political institutions and 1st pillar (Schwab, 2014, PP.49-51).
Delgado et al. (2012) developed a framework which defines the distinction between the contribution of microeconomic and macroeconomic effects on competitiveness, to delineate competitiveness throughout the world. In their research, they investigated three interrelated factors as chief drivers of competitiveness: “social infrastructure and political institutions (SIPI), monetary and fiscal policy (MFP), and the microeconomic environment”. Ultimately they reported that SIPI and MFP are heavily influenced by policy–makers and public institutions such as government; which can be equated with the correlation between 1st, 6th, and 8th pillar of GCR (Delgado et al., 2012; Schwab, 2014, PP. 49–51). Furthermore, they found that SIPI has positive influences on output per potential worker; which can be considered as a causal relationship between 1st and 7th pillars of GCR (Delgado et al., 2012; Schwab, 2014, PP.49-51).
In addition, Vares and Parvandi (2011) showed a meaningful, positive correlation in the significance level of 0.05 between “basic requirements” and “efficiency enhancers” utilizing data gathered from GCR 2010-2011 version (139 countries). Their results indicated that “Technological readiness” and “Infrastructure” have the strongest correlation and “Market size” and “Institutions” the weakest (Vares and Parvandi, 2011).
Likewise, Vares et al. (2011) found a meaningful positive correlation in the significance level of 0.05 between “efficiency enhancers” and “innovation and sophistication factors” based on GCR published in 2010-2011 (139 countries). According to their results, “Technological readiness” and “Business sophistication” have the strongest correlation and “Labor market efficiency” and “Business sophistication” the weakest.
It is important to note, however, that the last two studies – Vares et al. (2011), and Vares and Parvandi (2011) – are imperfect; the authors of both papers used Pearson correlation coefficient in order to measure the extent to which the factors are related; therefore, they could only compute the scale of the relationship between pillars and were unable to report the direction of influence. Furthermore, the researchers in the abovementioned studies did not utilize the full dataset of GCR and overlooked sub-indexes and sub-pillars; it illuminates the major contribution of this paper to fully cover the sub-pillars and sub-indexes of Global Competitiveness.
There are even more studies exploring GCI pillars’ correlation.
Table 1 summarizes some endeavors to clarify the relationships among global competitiveness pillars.
Table1: Some studies on the relationships among global competitiveness’ pillars
|They investigated the relationship between “Infrastructure” and “Technological readiness” using CCA for GCI 2012–2013 data (144 countries). They revealed that there is a meaningful relationship between them. Based on their findings, 65.39% of changes in “Technological readiness” are predictable by changes in “Infrastructure”. Also 60.40% of changes in “Infrastructure” are predictable by changes in “Technological readiness”.||Ghasemi, Hashemi–Petroudi, Mahbanooei and Mousavi–Kiasari (2013)|
|They investigated the relationship between “Labor Market Efficiency” and “Business Sophistication” using CCA for GCI 2011-2012 data (142 countries). They found that there is a meaningful relationship between them. According to their findings, 64.01% of changes in “Business Sophistication” are predictable by changes in “Labor Market Efficiency”. Also 25.89% of changes in “Labor Market Efficiency” are predictable by are predictable by changes in “Business Sophistication” (p.87).||Vesal et al. (2013)|
|They investigated the relationship between “Business sophistication” and “innovation” using CCA for GCI 2011-2012 data (142 countries). They understood that there is a meaningful relationship between them. In accordance with their findings, 70.68% of changes in “Business sophistication” by changes in “Innovation”. Also 70.75% of changes in “Innovation” are predictable by changes in “Business sophistication” (p.35).||Razavi et al. (2012)|
|They investigated the relationship between “Technological readiness” and “Labor market efficiency” using CCA for GCI 2011-2012 data (142 countries). They reported that there is a meaningful relationship between them. In line with their findings, 25.88% of changes in “Labor market efficiency” by changes in “Technological readiness”. Also 57.21% of changes in “Technological readiness” are predictable by changes in “Labor market efficiency”.||Rastegar, Mahbanooei and Ghasemi (2012)|
|They investigated the relationship between “Higher education and training” and “Technological readiness” using CCA for GCI 2010-2011 data (139 countries). They declared that there is a meaningful relationship between them. In conformity with their findings, 79.38% of changes in “Technological readiness” by changes in “Higher education and training”. Also 66.97% of changes in “Higher education and training” are predictable by changes in “Technological readiness” (p.144).||Safari, Ghasemi, Elahi Gol and Mirzahossein Kashani (2012:b)|
|They investigated the relationship between “Technological readiness” and “Innovation” using CCA for GCI 2010-2011 data (139 countries). They specified that there is a meaningful relationship between them. Following their findings, 64.31% of changes in “Innovation” are predictable by changes in “Technological readiness”. Also 68.78% of changes in “Technological readiness” are predictable by changes in “Innovation” (p.324).||Razavi, Ghasemi, Abdullahi, & Kashani (2011)|
|They investigated the relationship between “Financial market Development” and “Technological readiness” using CCA for GCI 2010-2011 data (139 countries). They maintained that there is a meaningful relationship between them. According to their findings, 59.62% of changes in “Technological readiness” are predictable by changes in “Financial market Development”. Also 50.40% of changes in “Financial market Development” are predictable by changes in “Technological readiness”.||Jafarnejad, Ghasemi and Abdullahi (2011)|
There has been little research into GCR 2014–2015. Also, relationships between all pillars and interrelationships between most of the pillars have remained untouched, and to deal with this a holistic view is required. In this regard, DEMATEL is one of the most commonly used techniques for investigating interrelations (Tzeng and Huang, 2011). Nonetheless, since this technique is based on a limited number of experts’ opinions, a hybrid technique CCA-DEMATEL is used in this study to address this problem by changing DEMATEL into a stochastic one via redundancy index in CCA. According to the expanded literature review the research’s sub-questions are:
This research uses descriptive–correlation method. So, first, we studied literature of competitiveness, GCI, DEMATEL, and Canonical Correlation Analysis (CCA); and previous studies on global competitiveness’ pillars were reviewed.
The Statistical population in this study was 144 countries whose data was reported by WEF in GCI report in 2014-2015 (Schwab, 2014). This study utilizes secondary source – library and other documented observations – data. Along the same line, secondary analysis method was used for analyzing secondary data source. According to De Vaus (2002), it would be appropriate to use data collected by other people or agencies to address the relevant research questions. Such data is called secondary data resource. So we utilized the data published by WEF (GCI report in 2014-2015) as our secondary data resource. Then, CCA was conducted by SAS 9 and STATISTICA 7 software and analysis output was published as the next step. In this study, CCA was used to measure interrelationships between “global competitiveness’ pillars”, by the paired comparison of each pillar with others. Finally, DEMATEL–CCA hybrid method was utilized to analyze the cause–and–effect relations among “global competitiveness’ pillars” in 2014-2015.
Canonical Correlation Analysis (CCA)
CCA as a multi–variable statistical approach for quantifying linear relationships between different groups of variables is crucial; also it is essential in exploratory mean, especially at the time when, multi–attribute variables are related to an analytical category. CCA results in the linear composition of predicting variables which have the highest correlation with linear combination of criteria variables. These combinations are (Safari, Abdollahi and Ghasemi, 2012a):
The number of dependent variables or the number of independent variables, whichever is smaller, determines the maximum number of canonical functions. Thus, the analysis is based upon the derivation of four canonical functions (Mai and Ness, 1999). For illustrating the appropriateness of this method, Table 2 shows some studiesin CCA field.
Table 2: Some studies in CCA field
|They demonstrated a meaningful relationship between people criterion and people results criterion in EFQM model by utilizing CCA.||Safari et al. (2012a)|
|They demonstrated a meaningful relationship between Job satisfaction and EFQM by utilizing CCA.||Tutuncu and Kucukusta (2010)|
|They used CCA to study relationships between TQM and organizational performance.||Macinati (2008)|
|Using canonical correlation analysis, this study examined the interdependencies in investing And financing decisions of restaurant firms.||Jang and Ryu (2006)|
|They used CCA to study relationships between enablers and results in EFQM.||Bou-Llusar, Escrig-Tena, Roca-Puig and Beltrán-Martín (2005)|
|This study utilized a canonical correlation approach to segment the senior pleasure traveler market.||Baloglu et.al (1998)|
The Decision Making Trial and Evaluation Laboratory (DEMATEL) method is used for researching and solving the complicated and interwoven problems. DEMATEL methodology, according to the concrete characteristics of objective affairs, can confirm the interdependence among the variables/attributes and restrict the relation that reflects the characteristic with an essential system and development trend. Through the DEMATEL process, the final product of the analysis is a visual representation according to which the respondents organize their own action in the world, if they are to remain internally coherent to respect their tacit priorities and to achieve their implicit goals (Tzeng and Huang, 2011). The procedure of DEMATEL method is presented below (Tzeng and Huang, 2011):
Step 1: Calculate the Average Matrix (Z)
Step 2: Calculate the normalized Initial Direct Influence Matrix (N).
Step 3: Derive the Full Direct/Indirect Influence Matrix (T).
Step 4: Set the Threshold Value and build a cause and effect relationship diagram.
Data Analysis: Developing DEMATEL–CCA Hybrid Algorithm
In DEMATEL method, researchers gather experts’ opinions and calculate the average matrix (Z) (Tzeng and Huang, 2011), but in our DEMATEL–CCA hybrid algorithm, the result of canonical correlation analysis will be used for filling the average matrix (Z).
Gathering experts’ subjective opinions in DEMATEL is not a suitable method when gathering required data for average matrix (Z), as the expert’s opinions are gathered in a fully subjective manner, without conducting any statistical analysis on the issue.
In the hybrid of DEMATEL–CCA, GCI report data were utilized instead of experts’ opinions. Considering the global competitiveness’ pillars (12 pillars), their relations were investigated by CCA. For these 12 pillars, 66 comparisons were drawn. Each of these comparisons had two important results. Table 3 shows total redundancy, p–value, chi–square, and canonical R, in CCA between paired comparisons of each pillar with others.
Step 1: Calculating the Direct Relations Matrix (Instead of the Average Matrix) Between Pillars Using CCA Outputs
Since CCA investigates the mutual relations between two sets, the degree of change in one variable due to change in the other one with which it has a causal relation would be measurable. Table 4 shows the direct relations matrix (matrix Z).
: Redundancy Index in CCA outputs shows the effect of pillar i on pillar j (i≠j).
Step 2: Calculating the Normalized Initial Direct Influence Matrix (N)
On the basis of the overall crisp direct-relation matrix Z, the normalized direct-relation matrix N can be obtained through expressions (3) and (4) (Fu, Zhu and Sarkis, 2012, P: 360).
Table 5 is acquired using the formulas above. To arrive at matrix N, the normalization value is “s = 0.1345”.
Table 5: The normalized direct-relation matrix (N)
Step 3: Derive the Full Direct/Indirect Influence Matrix (T).
The total relation matrix (T) is determined by expression (5) where I represents an n×n identity matrix (Fu et al., 2012, P.360). Table 6 is acquired through the above formula.
Table 6: The total-relation matrix (T)
Step 4: Set the Threshold Value and Build a Cause and Effect Relationship Diagram
To develop the causal influence and digraph diagram in DEMATEL-CCA three sub-steps are taken.
Step (4-1): Determining row (Ri) and column (Dj) sums for each row i and column j from the total relation matrix (T). That is:
The row values Ri are the overall direct and indirect effects of a GCI pillar i on other GCI pillars. In a similar way, the column values Dj represent the overall direct and indirect effects of all the GCI pillars on GCI pillar j (Fu et al., 2012, PP.360-361).
Step (4-2): Determining the overall importance or prominence (Pi) of a GCI pillar i and net effect (Ei) of GCI pillar i using formulas (Fu et al., 2012, P.361).
The larger the value of Pi the greater the overall prominence (visibility/importance/influence) of GCI pillars i in terms of overall relationships with other GCI pillars. If Ei>0 then GCI pillar is a net cause (foundation) for other GCI pillars. If Ei<0, then GCI pillar i depends on (net effect of) implementation or operation of other GCI pillars (Tzeng, Chiang and Li, 2007). These values may then be plotted onto a two–dimensional axis for each GCI pillar.
Step (4-3): A digraph relationship can be determined for each GCI pillar with respect to other GCI pillars using the total relation matrix T. To complete this step a threshold value θ should be determined by the evaluators, experts or the analysts (Liou, Tzeng and Chang 2007). If tij≥θ, then GCI pillar i influences or causes GCI pillar j and a directed arrow is incorporated into the analysis (Fu et al., 2012, P.361). Using formulas (8) and (9), the degree of the inﬂuence was obtained for each GCI pillar (Table 7).
Table 7: The degree of prominence and net cause/effect values for each GCI pillar
|pillars GCI||Ri sum||Dj sum||Pi||Ei|
|Macroeconomic Environment (ME)||2.4353||1.6318||4.0671||0.8035|
|Health and primary education (HPE)||2.5256||2.2320||4.7576||0.2936|
|Higher education and training (HET)||3.1243||3.4642||6.5885||-0.3398|
|Goods market efficiency (GME)||3.4856||2.5027||5.9883||0.9829|
|Labor market efficiency (LME)||3.0218||2.2028||5.2245||0.8190|
|Financial market development (FMD)||2.8411||3.1554||5.9965||-0.3143|
|Technological readiness (TR)||2.9157||3.2651||6.1809||-0.3494|
|Market size (MS)||1.4905||2.0078||3.4983||-0.5173|
|business sophistication (BS)||2.9789||3.8213||6.8002||-0.8424|
According to Table7, Goods Market Efficiency, Institutions, and Higher Education and Training are the most influential pillars. Moreover, Business Sophistication, Innovation, Higher Education and Training, and Technological Readiness are under the strongest influences of other pillars. This analysis provides an answer to the third question of the paper.
Taking these into account, two types of rankings in order of importance (Table 8) and net influence (Table 9) are possible.
Table 8: Ranking GCI pillars in order of importance
|1||Business sophistication (BS)||6.800|
|3||Higher education and training (HET)||6.588|
|5||Technological readiness (TR)||6.181|
|6||Financial market development (FMD)||5.997|
|7||Goods market efficiency (GME)||5.988|
|9||Labor market efficiency (LME)||5.225|
|10||Health and primary education (HPE)||4.758|
|11||Macroeconomic Environment (ME)||4.067|
|12||Market size (MS)||3.498|
Based on Table 8, the most important pillars are Business Sophistication, Institutions, Higher Education and Training, and Innovation. This table provides further details to the abovementioned explanation of the 3rd question of the paper.
Table 9: Ranking GCI pillars in order of net influence
|1||Goods market efficiency (GME)||0.983|
|2||Labor market efficiency (LME)||0.819|
|3||Macroeconomic Environment (ME)||0.804|
|4||Health and primary education (HPE)||0.294|
|7||Financial market development (FMD)||-0.314|
|8||Higher education and training (HET)||-0.340|
|9||Technological readiness (TR)||-0.349|
|10||Market size (MS)||-0.517|
|12||Business sophistication (BS)||-0.842|
According to Table 9, the most influential pillars, based on their net influence, are GME, LME, ME, and HPE. Pi and Ei for each GCI pillar can be demonstrated in Figure 2.
Figure 2: Importance and net influence for each pillar
The development of the digraphs (arrows) in Figure 3 shows the interrelationships amongst each of the individual GCI pillars. Since the number of relationships can include all the possibilities, the relationships are only mapped over a threshold Ө (Fu et al., 2012, p: 364). Because of the large number of GCI pillars, it was decided to maintain a high threshold value.
This value is calculated by taking the mean standard deviation of the values tij from the T matrix, then adding the standard deviation to the mean (Fu et al., 2012, p: 364). Thus, we get (Ө = 0.3092). All the relationships that meet or exceed the threshold are bolded in the T matrix (Table 6). Then these relationships are plotted.
Figure 3: The prominence–causal DEMATEL-CCA graph with digraph
relationships for most strong relationships.
It is apparent from the diagram that “Institutions (INS)” and “Goods market efficiency (GME)” pillars are more influential when compared to other pillars, while “business sophistication (BS)”, “Innovation (IN)”, “Higher education and training” and “Technological readiness (TR)” have higher effects on them. This diagram only shows important relations.
To delve deeper, the overall relation matrix in Table 6 should be seen. According to GCR, the innovation–driven economies are more capable of meeting the nation’s needs and people are benefiting from a more GDP per capita in such countries (Schwab, 2014, P.10). The most critical feature of these economies is their pillars of developing business sophistication and an innovative environment, insofar as all other countries are striving to follow their lead and establish their economic structures innovatively (Razavi et al., 2012). The findings of this paper are comletely concurrent with the aforementioned desire of countries; Innovation and business sophistication are two of the most prominent pillars which have noticeable impacts on other pillars and are under the influence of them. Therefore, it seems impracticable to achieve business sophistication or innovation without paying enough attention to other pillars of competitiveness. For example, our findings suggest that Institutions has an enormous impact on Higher education and training, Financial market development, and Technological readiness. Thus, the direct effect of Institutions on Business sophistication, observed in the graph, may be due to these mediators. Furthermore, Health and primary education and Higher education and training were of meager influence on other pillars; for example, “Health and primary education (HPE)” has a weak relationship with other pillars in Ө = 0.3092, and it has its highest relationship with “Higher education and training (HET)” (tij = 0.2848). It is shown with a red arrow in Figure 3.
Conclusion and Discussion
The history of competitiveness is entwined with the history of the ways we have learned to manipulate economic knowledge in our efforts. In order to develop our knowledge in this area, the current research has been conducted. The main purpose of this study was to investigate the causal relations between pillars of Global Competiveness Index (GCI) in the period of 2014–2015. According to World Economic Forum (WEF) report, 12 pillars of GCI (with 114 sub–indexes) are: Institutions, Infrastructure, Macroeconomic environment, Health and primary education, Higher education and training, Goods market efficiency, Labor market efficiency, Financial market development, Technological readiness, Market size, Business sophistication, and Innovation.
Afterwards, a hybrid model of CCA and DEMATEL was developed to study the interrelations of GCI pillars. This hybrid technique offers a strong support for DEMATEL. First, the causal relations between pillars were investigated by CCA. Next, the output of CCA was used as the input of DEMATEM; doing so, the most important pillars of GCI in 2014-2015 were determined in order of priority. Moreover, the degree of effect each pillar has on another and the causal relations among pillars were calculated
It was demonstrated that “Institutions (INS)” and “Goods market efficiency (GME)” had relatively more significant effects compared to the other two pillars of GCI and that “business sophistication (BS)”, “Innovation (IN)”, “Higher education and training” and “Technological readiness (TR)” had been affected more than the previous ones. The results of the present study were concordant to the findings of the study conducted by Vesal et al. (2012), Razavi et al. (2012), Safari et al. (2012b), and Razavi et al. (2011), in that the same results were found to be at work.
The main defect of the previous studies is that they merely investigated the interrelations between a maximum of two pillars (e.g. Razavi et al., 2011; Safari et al., 2012a; Razavi et al., 2012). However, there were a number of studies such as Vares et al. (2011), and Vares and Parvandi (2011) which studied the correlations between more than two pillars; nevertheless, they did not include all pillars in their works and totally ignored sub-indices. Hence, an innovative result of this paper is a comprehensive model of the interrelations between competitiveness pillars which has been mentioned by Schwab and Porter (2008) and Schwab (2015) from the viewpoint of concept and without presenting the degree of effects. This novel model –propounded by this paper- provides policy makers with an extensive approach which enables them to perceive the causal relations among competitiveness pillars and apply this approach to operational strategies in order to enhance their competitiveness level.
Although this study was an attempt to shed light on the relationships among pillars of GCI through proposing a comprehensive approach, there are some minor flaws and limitations in the results of this paper; firstly, the data used are confined to the 144 countries reported in GCI and there is a lack of information on other countries; and secondly, this study is based on the data of a one-year timespan. Therefore, it is suggested that further research be conducted using data from different time periods.
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