Evaluating the best outsourcing service country in the southeast asian region: A fuzzy-ahp approach

Nội dung text: Evaluating the best outsourcing service country in the southeast asian region: A fuzzy-ahp approach

1. EVALUATING THE BEST OUTSOURCING SERVICE COUNTRY IN THE SOUTHEAST ASIAN REGION: A FUZZY-AHP APPROACH Pham Van Kien (PhD)1 and Truong Hoang Anh Tho (MBA)2 1Faculty of International Economics - Banking University HCMC 2Faculty of Economics and International Business - Foreign Trade University Abstract Recently, outsourcing has also been seen as a key source of innovation. In line with this trend, an increasing number of researches have been carried out to aid firms in figuring out the optimal outsourcing service destinations. Unfortunately, most of these studies only emphasized a specific field at the company level, so a complete framework in this field has never been done before. This study therefore attempts to address this research gap by constructing an outsourcing hierarchy model with four levels, namely overall goal, criteria, sub-criteria, and alternative. In here, criteria would include the most concern attributes such as cost competitiveness, human resources, business environment, and government policies. Each criterion itself would be supported by several-sub criteria. With respect to these criteria, seven typical countries in the Southeast Asian region were selected to serve as alternatives. This paper applied the fuzzy analytic hierarchy process approach to help decision-makers identify the most important dimensions and the optimal outsourcing service country. As a result, the higher the priorities weights, the more important the criterion or the alternative will be. In this study, cost competitivenessvis considered the most important element, followed by human resources and business environment. Corresponding with this, China is the best destination among seven typical outsourcing service countries. Besides that, the rise of Vietnam, Thailand and the Philippines are creating new choices for decision makers. Generally, this study not only enriches the existing literature, but also provides readers and decision makers a comprehensive view toward the given field. Keyword:Outsourcing, fuzzy analytic hierarchy process (FAHP), multi-criteria decision making, decision maker, Asian region. 1. INTRODUCTION To compete in the global market, companies in developed countries tend to transfer parts of their business to partners in emerging countries in Asia or Latin America (e.g., India, China, Brazil) where they could use external resources at lower prices (Donahoe & Pecht, 2003; Kotabe et al., 2008; Kotabe & Zhao, 2002; Nahar & Kuivanen, 2010). Research on the world’s top outsourcing service countries shows that six out of ten countries which have the highest overall outsourcing index are located in Southeast Asia as presented in Figure 1(SourcingLine, 2012) and there is an upward movement of key service delivery locations in this area because of the improvement in macro-economic environment and the expansion of domestic market of members in this region (Tholons, 2013). Over the last decade, China and India, as the major traditional outsourcing service locations in Asia, have become the ideal destinations for huge corporations to transfer their business (Palugod & Palugod, 2011). However, recently, the combination of infrastructure development, human capital development programs and incentive policies of government to attract outsourcing contracts in other emerging economies such as Vietnam, 507
2. Indonesia, and Thailand have provided firms with more options(Tholons, 2013). Therefore, which criteria should be evaluated to identify the best outsourcing service destination among Southeast Asian countries is the key research question that should be investigated conscientiously. Regarding important criteria for choosing an outsourcing destination, prior researchers have used various approaches to develop models to assist managers in making decision. For instance, for individual approaches, there are Scoring (Lucas & Moore, 1976), Ranking (Buss, 1983), Multi-Criteria Linear Goal-Programing Model (Buffa & Jackson, 1983), Goal Programing (Hoffman et al., 2007), Analytic Hieratical Process/AHP (Ghodsypour & O'Brien, 1998; Liu et al., 2000), Analytic Network Process/ANP (Bayazit, 2006; Liao et al., 2011; Weber et al., 1991), Fuzzy Logic (Bevilacqua & Petroni, 2002). Because these approaches could not reflect all aspects of the problem sufficiently, other integrated approaches such as Fuzzy AHP (Kahraman et al., 2003), Fuzzy ANP (Attari et al., 2012), combining AHP and TOPSIS (Bỹyỹkửzkan & ầifỗi, 2012), Fuzzy and Multi-Segment Goal Programing (Chin-Nung Liao et al., 2012) were introduced. However, the studies used these methods only ranked and valuated outsourcing providers in different specific fields at the company level(e.g., Attari et al., 2012; Bỹyỹkửzkan & ầifỗi, 2012; Chin-Nung Liao et al., 2012; Kahraman et al., 2003), ignoring the complicated process of outsourcing which involves a great number of tangible and intangible elementsat both company and national level(Ghodsypour & O'Brien, 1998). To fill this gap, this study uses the combination of the Analytic Hierarchy Process and the fuzzy theory(labeled as FAHP) to develop a hierarchy model which presents all possible factors affectingmanagers’ decision on selecting the optimaloutsourcing countries. Based on experts’ opinionson the given field,this study compares twopopular outsourcing service countries namely China, Singapore with other five most typical nations in the Southeast Asian region including Indonesia, Malaysia, Thailand, The Philippines, Vietnam. 2. LITERATURE REVIEW 2.1. Outsourcing and its pros and cons The term “outsourcing” was first used in 1989 to describe the transfer of information technology activities of Kodak Company to subsidiary of IBM (Applegate & Montealegre, 1991; Lonsdale & Cox, 2000). Generally, it is defined as “an arrangement in which one company (the client) hires another company (the service provider) to perform a particular function on its behalf” (Mohr et al., 2011). It is a business strategy in which managers have to decide whether business functions should be performed in-house or by another firm (McIvor, 2000; Palugod & Palugod, 2011). Since the early 1990s, there have been two waves of outsourcing: the first wave focused on manufacturing activities in the 80s and 90s and the second wave emphasized on information technology and business processes after 90s (Lee & Kim, 2010; Shahin & Rostamian, 2011). In recent years, outsourcing has become more intensive in its scope, involving more business functions such as research and development (R&D), product innovation activities (Mohr et al., 2011; Weeks & Feeny, 2008). This fact reveals that firms are apt to outsource any parts of their supply chain to operate more economically and effectively.Prior research has exclusively emphasized the significant of cost saving when firms outsource their business functions (Anderson & Parker, 2002; Canez et al., 2000), yet in these years it is not considered the dominant reason for outsourcing (Edgell et al., 2008) due to the complex process of outsourcing which 508
3. include both tangible and intangible factors (Kilincci & Onal, 2011). Besides cost saving, previous research have indicated many other benefits, for instance,accessing to world-class capabilities, sharing risks, accelerating advantages from reengineering (Deavers, 1997), acquiring better management, focusing on core competencies (Brown & Wilson, 2005), gaining flexibility, rationalizing rent, avoidingbureaucratic cost (Kotabe et al., 2008), accessing to innovation (Costa et al., 2012). These benefits could be gained if firms decide and manage outsourcing activities appropriately. However, since outsourcing is a complex strategy, a poorly made decision could potentially cause problems such as loss of confidential information, supplier failure, massive job loss associated with outsourcing function (Bragg, 2006). In addition, more serious issues (e.g., business failure) may occur if firms fail to outsource their business functions to external partners(Cross, 1995). Thus, insight understandings of outsourcing as well as careful evaluations are vital for every firm before performing any outsourcing practice. 2.2. Factors affecting the selection of outsourcing country Because outsourcing plays a crucial role in the survival of firms and the implementation of this business strategy is complicated, many studies have examined factors affecting the selection of outsourcing destinations in an attempt to help managers make the proper decisions. According to Ghodsypour and O'Brien (1998), firms could choose the best outsourcing vendors by making a tradeoff between tangible and intangible factors. These factors could relate to every aspect of destination countries (e.g., economic and business environment, infrastructure, politics and government policies, culture language) (Apte & Mason, 1995; Beaumont & Sohal, 2004; Carmel, 2003; King, 2005; Venkatraman, 2004; Vestring et al., 2005). Smith et al. (1996) developed a framework which introduces business environment and location resources as main criteria for selecting outsourcing vendors. Vestring et al. (2005) added some critical determinants such as langue, skilled labors, infrastructure, costs, political stability, and government regulatory. Technical readiness and cultural differences are other important factors that should be considered when deciding outsourcing destinations (Balakrishnan et al., 2008; Collier, 1985). However, most of prior research investigate outsourcing service providers at the company level(Carter et al., 2008; Kremic et al., 2006; McKeon, 1991; Trunick, 1989) for certain specific fields such as human resources (Prajogo & Ahmed, 2006), information technology (Yang & Peng, 2012), although the process of selecting the ideal destination involves lots of conflicting criteria at both company and national level. The following partsof this study examine critical factors affecting the selection of outsourcing destination at the national level. 2.3. Criteria and sub-criteria for evaluating and selecting an outsourcing service country According to the existing literature, there are four most popular reasons for implementing outsourcing, namely cost competiveness, human resources,businessenvironment, and government policies. Therefore this study divides all possible factors affecting the evaluation and selection of optimal outsourcing service countryinto four main groups with sub-criteria (see Table 1). Table 1.Criteria and sub-criteria for evaluating and selecting an outsourcing service country Criteria Sub-criteria Explanations SC11 Transportation cost to deliver cargo from one point to Cost Freight costs another, inventory cost, and insurance cost competitiveness SC12 Labor costs Amount of money paid to employees SC13 Taxes/Tariffs Profit or corporate income tax, social contributions and 509
4. labor taxes paid by the employer, property taxes, turnover/ revenue taxes, and other taxes SC14 Production Costs incurred by a business when manufacturing or costs producing a product or service SC21 Workforce Size of labor force, rigidity of employment, and cost of size laying off employee efficiency SC22 Education Total annual university graduates Human level resources SC23 Technology Technology capabilities of labor forces readiness SC24 English Adult literacy and familiarity with English ability SC25 Culture Differences of culture, habits and customs Politic stability, economic stability, safe business SC31 Stability environment, and rare natural disasters The state of electricity, road, water, and communication SC32 Infrastructure infrastructure as well as transportation system. Business Amount of “Bribes” necessary to enforce an outsourcing environment SC33 Corruption contract Full Ability to provide a “full-service outsourcing” from raw SC34 outsourcing materials to finished products service Overall ease of doing business covers construction permits, SC41 Regulations registering property “Fair trade is a trading partnership, based on dialogue, transparency, and respect, that seeks greater equity in Fair-trade international trade. It contributes to sustainable Government SC42 protection development by offering better trading conditions to, and policies securing the rights of, marginalized producers and workers”- EFTA SC43 IP protection Copyright protection, software piracy SC44 Tax/tariff Including tax cuts, tax exclusion or exemption incentives Sources: Apte and Mason (1995), Bahli and Rivard (2005), Nahar and Kuivanen (2010), Ghodsypour and O'Brien (1998), Carmel (2003), King (2005), Venkatraman (2004), Vestring et al. (2005), Beaumont and Sohal (2004), Brown and Wilson (2005), Collier (1985), Bahli and Rivard (2005), Jain and Song (2002), Jennex (2003), Kumar and Palvia (2002), Rajkumar and Mani (2001), Gattiker et al. (2000), Raval (1999), Tan and Leewongcharoen (2005), Dedrick and Kraemer (2001), WFTO (2013), Cloete et al. (2002) 3. APPLICATION OF FAHP 3.1. Structuring the Hierarchy Model In order to apply FAHP, the first task is to construct a hierarchy model. In this study, the hierarchy model that was built relied on the wide review of literature in the given field. Normally, a hierarchy should include four basic levels arranged in descendent order. Therefore, the study’s model also consists of four levels as indicated in Figure 1. The first level located in the top of the hierarchy indicates the overall goal which is selection of the best outsourcing service destination in Southeast Asia. Moving down to the second level, four main criteria selected from the previous studies were inserted into the model and arranged in equal positions. These criteria are cost 510
5. competiveness, human resources, business environment, and government policies. Each criterion itself includes several sub-criteria located in the third level of the hierarchy. For more detail, costs criterion consists of four major sub-criteria as: freight costs, labor costs, taxes/tariffs, and production costs. The other sub-criteria are shown clearly in Figure 2. Finally, in proportion to the overall goal, seven typical countries in the Southeast Asian region including China, Indonesia, Malaysia, Singapore, Thailand, The Philippines, and Vietnam. Selecting the Best Outsourcing Service Cost Business Government Human Competitiveness resources environment Polies C3 C4 C21. Workforce SC11. Freight costs SC31. Stability Efficiency SC32. Infrastructure SC41. Regulations SC12. Labor costs SC22. Education SC33. Corruption SC42. Fair-trade SC13. Taxes/tariffs Level SC34. Full protection SC14. Production SC23. Technology outsourcing SC43. IP protection Costs Readiness service SC44. Tax incentives SC24. English ability SC25 C lt China Indonesia Malaysia The Singapore Thailand Vietnam Philippines G1 G2 G3 G4 G5 G6 G7 Figure 2. The hierarchy model for selecting the best outsourcing service country 3.2. Questionnaire Design and Data Collection The FAHP questionnaire was constructed by making a total of 103 pair-wise comparisons among the main criteria, sub-criteria, and alternatives. The study designed the questionnaire based on the typical nine-point scale combined with fuzzy numbers as shown in Table 2. After completing and testing the valid of the questionnaire, the study distributed a total of 102 questionnaires to 102 different experts to obtain their individual opinions about pair-wise comparisons. These experts came from different nations such as French, England, Korean, Taiwan, India, America etc. In addition, they were working in different fields for different companies which outsource some parts of their business to the Southeast Asian countries such as Vietnam, China and so forth. Therefore, they deeply understand what are going on in these Southeast Asian countries. The questionnaires were administered by using both email and direct interview. 3.3. Computing the Weights of Criteria, Sub-Criteria, and Alternatives 511
6. Once the FAHP hieratical model was carefully constructed and the FAHP questionnaire was successfully collected, the next step is to deal with calculating the priority weights of criteria, sub-criteria, and alternatives by adopting FAHP approach that has been introduced previously. The idea of calculating the priority weights of attributes is based on the pair-wise comparisons given in the questionnaire. In doing so, a set of comparison questions were made to ask the experts for their valuations of one over another. The higher the evaluation, the greater the importance of a criterion will be. Corresponding to four level of the hierarchical model, the experts first evaluated the four main criteria in the second level with respect to the overall goal. Then, they compared the sub- criteria in the third level with respect to the main criteria. Eventually, in the fourth level, pair-wise comparisons of alternatives were made with respect to the overall goal. The linguistic variables were used to direct experts giving their rates, and then these rates in turn were translated into triangular fuzzy numbers as shown in Table 2. As mentioned early, a total 102 questionnaires were administrated to 102 different experts from 15 different countries in order to gather their opinions about outsourcing service country selection problem. As a consequence, 102 questionnaires returned accounting for 100% of total sample. Table 2. Fuzzy AHP Scale Intensity of Linguistic variable Positive TFN Positively reciprocal TFN the AHP scale 1 Equally important (1, 1, 1) (1, 1, 1) 3 Weakly more important (2, 3, 4) (1/4, 1/3, 1/2) 5 Fairly more important (4, 5, 6) (1/6, 1/5, 1/4) 7 Strongly more important (6, 7, 8) (1/8, 1/7, 1/6) 9 Absolutely more important (8, 9, 10) (1/10, 1/9, 1/8) 3.4. Aggregation of Decision Makers’ Evaluations After all pair-wise comparisons were rated by the evaluators, it is crucial to aggregate the decision makers’ evaluations. To complete this task, there are some methods that have been done overtime. For example, Bỹyỹkửzkan et al., (2008) and Chang et al., (2009) suggested using the following algorithm to combine fuzzy pair-wise comparison: K kK1/ k , mw []; k lwkKjj min { | 1, 2, , }; jjuwkKjj max { | 1, 2, , }. k k 1 k This approach seems to be not efficient when using min max values if the sample has a wide range of lower and upper bandwidths. For this reason, this study applied geographic mean for both l j and uj. A geographic mean approach was suggested by Saaty (1990), Dyer & Forman (1992), and Davies (1994) to integrate the individual judgments. The geographic mean is able to deliver satisfying fuzzy group weighting. The geographic average is applied to combine the fuzzy weight of decision makers. , k = 1, 2 k Where: ∏ : combined fuzzy weight of decision element i of K decision makers w : Fuzzy weight of decision element i of decision maker k. w 512
7. k: number of decision makers. 3.5. Approximation of fuzzy priorities After the evaluations of 102 decision makers were combined and had already passed the consistency test in the previous section, the study then estimates the fuzzy priorities adopting the most common method proposed by Chang (1996). This method is known as the extend analysis method which is defined as follows: In an outsourcing destination evaluation and selection problem, let X = {x1, x2, x3 ,xn} indicates an objective goal set, and G = {g1, g2, g3 ,gn} be a goal set. According to the method of Chang’s extent analysis, each object is taken and extent analysis for each goal performed respectively. Therefore, m extent analysis values for each object can be obtained, with the following signs: i = 1, 2 n (1) Where , , , The followingM j ,2, ,m steps are used to apply Chang’s extends analysis to fuzzy priority estimations: Step 1: the value of fuzzy synthetic extent with respect to ith object is defined as: (2) To obtain∑ ,∑∑ perform the fuzzy addition operation of m extent analysis values for a particular matrix such that: ∑ M (3) ∑And to obtain ∑ ; ∑ ; ∑, perform the fuzzy addition operation of values such as that ∑∑ ,2, , (4) ∑∑And then calculate ∑the inverse; ∑ of the; vector∑ in equation (4) such that (5) Step∑∑ 2: The degree of possibility∑ ; ∑ of M; ; 2∑ = (l2, m2, u2) ≥ M1 = (l1, m1, u1) is as defined as: (6) And can be equivalently Sup expressed as ,follows: (7) 1, Where d is the ordinate of∩ the highest intersection point0, D . Figure 3 below illustrates the intersection between M1 and M2. In order to compare ,, M1 and M2, both the between and values of V (M1 ≥ M2) and V (M2 ≥ M1) are used. Step 3: The degree possibility for a convex fuzzy number to be greater than k convex fuzzy numbers Mi(i=1, 2 k) can be defined by: ,, , (8) , 1,2,3 ,. 513
8. Assume that d’ (Ai) = min V (Si ≥ SK) (9) For k = 1, 2 n; k ≠ i. Then the weight vector is given by W’ = (d’ (A1), d’ (A2) d’ T (An)) (10), where Ai (i= 1, 2, , n) are n elements. Step 4: Via normalization, the normalized weight vectors are: T W = ((d (A1), d (A2) d (An)) , (11)Where W is a non-fuzzy number which is seen as the priority weights of one criterion, one sub-criterion, and one alternative over another. à l M2 M1 à(d) 0 l2 m2 l1 u2 m1 u1 Figure3. The interaction between M1 and M2 (Kahraman, 2004; Chang, 1996) 4. THE FAHP RESULT 4.1. The fuzzy comparison matrix and the priority weights with respect to the overall goal By using the geographic mean, we obtained the fuzzy comparison matrix as indicated in Table 3. Table 3 also shows that the consistency ratio is 0.03 which is less than the suggested value of 0.1. This means that the matrix can be considered as having an acceptable consistency in the first level of the hierarchy. Accordingly, in order to find the priority weights of main criteria, equation (2) was utilized to calculate the fuzzy synthesis values. The different values of four different criteria were labeled as SC1, SC2,SC3and SC4. Table 3. The fuzzy comparison matrix of criteria with respect to the overall goal C C1 C2 C3 C4 Sum C1 (1,1,1) (0.7,1.1,1.5) (0.5,0.83,1.17) (0.74,1.07,1.42) (2.94,4.0,5.09) C2 (0.39,0.48,0.59) (1,1,1) (1.09,1.321.55) (0.79,0.92,1.05) (3.27,3.72,4.19) C3 (0.35,0.39,0.45) (0.64,0.76,0.92) (1,1,1) (1.15,1.34,1.53) (3.14,3.49,3.9) C4 (0.33,0.39,0.47) (0.95,1.09,1.27) (0.65,0.74,0.87) (1,1,1) (2.93,3.22,3.61) Sum (2.07,2.26,2.51) (3.29,3.95,4.69) (3.24,3.89,4.59) (3.68,4.33,5) (12.28,14.43,16.79) Consistency ratio: 0.03 SC1 = (2.94, 4.0, 5.09) ì (1/16.79, 1/14.43, 1/12.28) = (0.18, 0.28, 0.41) SC2 = (3.27, 3.72, 4.19) ì (1/16.79, 1/14.43, 1/12.28) = (0.19, 0.26, 0.34) SC3 = (3.14, 3.49, 3.9) ì (1/16.79, 1/14.43, 1/12.28) = (0.19, 0.24, 0.32) SC4 = (2.93, 3.22, 3.61) ì (1/16.79, 1/14.43, 1/12.28) = (0.17, 0.22, 0.29) 514
9. Then, the equation (6) and (7) were adopted to ascertain the possibility degree of SCi over SCj (i, j = {1, 2, 3, 4, 5}; i ≠j) as below: V (SC1 ≥ SC2) = 1; V (SC1 ≥ SC3) = 1; V (SC1 ≥ SC4) = 1;  0.89 ; V (SC2 ≥ SC3) = 1; V (SC2 ≥ SC4) = 1 .. VSC2 SC1 .... 0.58; .. V SC3 SC1 .... 0.87; V (SC3 ≥ SC4) = 1 .. V V (SC4SC3 ≥ SC1) SC2 .... 0.65; V (SC4 ≥ SC2) = 0.71; V .. .. (SC4 ≥ SC3) ....0.83 .... .. .... After comparing those above fuzzy numbers, the minimum degree of possibility or the priority weight was given by using the equation (9) as follows: d' (Sc1) = min (1, 1, 1) = 1 d' (Sc2) = min (0.89, 1, 1) = 0.89 d' (Sc3) = min (0.58, 0.87, 1, ) = 0.58 d' (Sc4) = min (0.65, 0.71. 0.83) = 0.65 Finally, the weight vector is determined as W’ = (1, 0.89, 0.58, 0.65) T. This weight vector was normalized with the aim to determine the priority weights (i.e. Eigen values) of the main criteria with respect to the overall goal. As a result, the weight vector of the main criteria which include cost competitiveness, human resources, business environment, and government policies was calculated as (0.32, 0.29, 0.19, 0.21).The final results shows that among five main criteria, decision makers rank cost competitiveness are the most important factor accounting for 32% of their decision, followed by human resources (29%), government policies (21%), and business environment (19%), respectively. Additionally, the same process using equations (2), (6), (7) and (9) was carried out in the next sections for the other pair-wise comparison matrices and the priority weights of each sub-criterion and alternative with respect to each main criterion. 4.2. The fuzzy comparison matrix and the priority weights with respect to costs (C1) Table 4 shows that the consistency ratio is 0.01 smaller than the standardized value of 0.1. This means that the matrix can be considered as having a good consistency in the second level of the hierarchy. The different values of four different sub-criteria were denoted as SSC11, SSC12, SSC13, and SSC14. Table 4. The fuzzy comparison matrix of sub-criteria with respect to costs C1 SC11 SC12 SC13 SC14 Sum SC11 (1,1,1) (0.62,0.72,0.84) (0.59,0.69,0.81) (0.97,1.16,1.36) (3.18,3.57,4.01) SC12 (1.19,1.39,1.6) (1,1,1) (1.06,1.26,1.44) (1.68,1.95,2.18) (4.93,5.6,6.22) SC13 (1.23,1.44,1.7) (0.7,0.79,0.94) (1,1,1) (1.24,1.52,1.89) (4.17,4.75,5.53) SC14 (1.03,1.15,1.32) (0.85,0.98,1.13) (1.09,1.32,1.67) (1,1,1) (3.97,4.45,5.12) 515
10. Sum (4.45,4.98,5.62) (3.17,3.49,3.91) (3.74,4.27,4.92) (4.89,5.63,6.43) (16.25,18.37,20.88) Consistency ratio: 0.01 After comparing those above fuzzy numbers, the minimum degree of possibility or the priority weight was given by using the equation (9) as follows: d' (SSC11) = min (0.08, 0.42, 0.55) = 0.08 d' (SSC12) = min (1, 1,1) = 1 d' (SSC13) = min ((1, 0.71, 1)) = 0.71 d' (SSC14) = min (1, 0.57, 0.86) = 0.57 As a result, the weight vector was given as W’ = (0.08, 1, 0.71, 0.57)T. After Normalizing, the weight vector of sub-criteria (set-up costs, freight costs, administrative costs, labor costs, taxes/tariffs, production costs) was calculated as (0.03, 0.43, 0.30, 0.24). Based on these findings, we can see that freight costs account for only 3% in decision maker’s evaluations, while labor costs become the most important factor which account for 43%. The second most important factor is taxes/tariffs accounting for 30%, followed by production costs. Therefore, it can be concluded that when considering costs in an outsourcing service location, the most influential dimension is labor costs. Besides that, taxes/tariffs and production costs also play significant roles, while freight costs seem to be a small problem in making an outsourcing performance. 4.3. The fuzzy comparison matrix and the priority weights with respect to Human resources (C2) As indicated in Table 5, the consistency ratio is 0.03 less than 0.1, which means that the matrix can be considered as having an acceptable consistency in the second level of the hierarchy. The different values of five different sub-criteria (workforce size efficiency, English ability, culture differences, education level, skilled workers, and technology readiness) with respect to human resources were represented as SSC21, SSC22, SSC23, SSC24, and SSC25. 516
11. Table 5. The fuzzy comparison matrix of sub-criteria with respect to human resources C2 SC21 SC22 SC23 SC24 SC25 Sum SC21 (1,1,1) (0.87,1.08,1.35) (0.82,1.15,1.5) (0.6,0.93,1.28) (0.7,1.03,1.38) (3.99,5.19, 6.51) SC22 (0.74,0.93,1.15) (1,1,1) (0.95,1.26,1.5) (0.48,0.9,1.3) (0.44,0.85,1.37) (3.61,4.94,6.32) SC23 (0.46,0.5,0.55) (0.4,0.44,0.51) (1,1,1) (1.5,1.85,2.21) (1.19,1.48,1.74) (4.55,5.27, 6.01) SC24 (0.5,0.75,0.9) (0.7,0.98,1.23) (0.45,0.54,0.67) (1,1,1) (0.85,1.02,1.23) (3.5,4.29, 5.03) SC25 (0.44,0.62,1.06) (0.47,0.88,1.35) (0.84,1.51,2.35) (0.98,1.79,2.08) (1,1,1) (3.73,5.8, 7.84) Sum (3.14,3.8,4.66) (3.44,4.38,5.44) (4.06,5.46,7.02) (4.56,6.47,7.87) (4.18,5.38,6.72) (19.38,25.49,31.71) Consistency ratio: 0.03 After comparing those above fuzzy numbers, the minimum degree of possibility and the priority weight was given by using the equation (9) as follows: d' (SC21) = min (1, 0.95, 1, 0.88) = 0.88 d' (SC22) = min (0.95, 0.9, 1, 0.85) = 0.85 d' (SC23) = min (1, 1, 1, 0.91) = 0.91 d' (SC24) = min (0.81, 0.88, 0.75, 0.71) = 0.71 d' (SC25) = min (1, 1, 1, 1, 1) = 1 From the above calculations, the weight vector was given as W’= (0.88, 0.85, 0.91, 0.71, 1) T. This weight vector after normalization becomes as (0.2, 0.2, 0.21, 0.16, 0.23). Thus, it can be concluded that among five sub-criteria under human resources, culture differences play the most remarkable role in an outsourcing decision, which accounts for 23%. The second most important sub-criterion belongs to technology readiness, followed by workforce efficiency and educational level with 20%. Finally, English ability shows the less important role with 16%. In general, there are small differences among five sub-criteria, which mean that decision makers attach much importance to every aspect of human resources, especially culture differences. 4.4. The fuzzy comparison matrix and the priority weights with respect to business environment (C3) The fuzzy comparison matrix is presented in Table 6. In here, the consistency ratio is 0.01 which is less than the suggested value of 0.1. This means that the matrix can be considered as having an acceptable consistency in the second level of the hierarchy. The four different sub- criteria of business environment attribute were labeled as SSC31, SSC32, SSC33, and SSC34. Table 6. The fuzzy comparison matrix of sub-criteria with respect to business environment C3 SC31 SC32 SC33 SC34 Sum SC31 (1,1,1) (1.02,1.23,1.44) (1.27,1.47,1.7) (1.23,1.48,1.77) (4.52,5.18,5.91) SC32 (0.69,0.81,0.98) (1,1,1) (1.16,1.52,1.87) (1.68,1.98,2.18) (4.53,5.31,6.03) SC33 (0.7,0.93,1.21) (0.84,1.05,1.36) (1,1,1) (1.3,1.39,1.66) (3.84,4.37,5.23) SC34 (0.81,1.03,1.37) (0.76,0.95,1.15) (0.96,0.31,1.5) (1,1,1) (3.53,3.29,5.02) Sum (3.2,3.77,4.56) (3.62,4.23,4.95) (4.39,4.3,6.07) (5.21,5.85,6.61) (16.42,18.15,22.19) Consistency ratio: 0.01 517
12. After comparing those above fuzzy numbers, the minimum degree of possibility and the priority weight was given by using the equation (9) as follows: d' (SSC31) = min (1, 1, 1) = 1 d' (SSC32) = min (1, 1, 1) = 1 d' (SSC33) = min (0.71, 0.71, 1) = 0.71 d' (SSC34) = min (0.5, 0.5, 0.71) = 0.5 From the above calculations, the weight vector of the business environment attribute, which includes stability, infrastructure, corruption, and full outsourcing service, was determined as W’ = (1, 1, 0.71, 0.5) T. Then, this weight vector was converted to the normalized weight vector as (0.31, 0.31, 0.22, 0.16). As a consequence, when considering the business environment of an outsourcing service country, the stability and state of infrastructure are the most important elements accounting for 31%, followed by the corruption situation (22%) and full outsourcing service (16%) respectively (Figure 7). 4.5. The fuzzy comparison matrix and the priority weights with respect to government policies (C4) Table 7 below illustrates that the matrix consistency ratio of the fuzzy comparison is 0.01 lower than 0.1. This means that the matrix has a good consistency. The different values of four different sub-criteria were denoted as SSC41, SSC42, SSC43, and SSC44. Table 7. The fuzzy comparison matrix of sub-criteria with respect to government policies C4 SC41 SC42 SC43 SC44 Sum SC41 (1,1,1) (1.03,1.23,1.67) (0.99,1.47,1.58) (0.81,1.48,1.71) (3.83,5.18,5.96) SC42 (0.6,0.81,0.97) (1,1,1) (1.16,1.52,2.26) (0.97,1.98,2.81) (3.73,5.31,7.04) SC43 (0.63,0.68,1.01) (0.44,0.66,0.86) (1,1,1) (0.68,1.39,1.53) (2.75,3.73,4.4) SC44 (0.59,0.67,1.23) (0.36,0.51.03) (0.65,0.72,1.47) (1,1,1) (2.6,2.89,4.73) Sum (2.82,3.16,4.21) (2.83,3.39,4.56) (3.8,4.71,6.31) (3.46,5.85,7.05) (12.91,17.11,22.13) Consistency ratio: 0.01 After comparing those above fuzzy numbers, the minimum degree of possibility and the priority weight was given by using the equation (9) as follows: d' (SSC41) = min (1, 1, 1) = 1 d' (SSC42) = min (1, 1, 1) = 1 d' (SSC43) = min (0.68, 0.68, 1) = 0.68 d' (SSC44) = min (0.61, 0.61, 0.83) = 0.61 Based on the results above, the weight vector was given as W’ = (1, 1, 0.68, 0.61) T. This weight vector then is normalized to draw the priority weights of the main criteria with respect to government policies. Eventually, the weight vector of the main criteria, which includes quality of roads, quality of electric supply, transport system, the communication system, and quality of water supply, was calculated as (0.3, 0.3, 0.21, 0.19). Therefore, we can conclude that the most 518
13. important element with respect to government policies in outsourcing evaluation and selection process is government regulations and the protection of fair trade which accounts for 30%. The second important element is IP protection accounting for 21%, followed by taxes/tariffs incentives (19%). 4.6. The fuzzy comparison matrix and the priority weights of alternatives At last,the fuzzy comparison matrix and the priority weights of alternatives with respect to the main criteria were obtained in this section. Table 8 displays the consistency ratio is 0.03, which is smaller than 0.1. Therefore, the matrix can be considered as having a good consistency in the fourth level of the hierarchy. Alternatives in this study consist of eight representatives in the Southeast Asian region, which are China, India, Indonesia, Malaysia, Singapore, Thailand, The Philippines, and Vietnam. The different values of different alternatives were designated as SG1, SG2, SG3, SG4, SG5, SG6, and SG7, respectively. 519
14. Table 8. The fuzzy comparison matrix of alternatives with respect to the main criteria G G1 G2 G3 G4 G5 G6 G7 Sum G1 (1,1,1) (0.8,1.22,1.67) (0.98,1.3,1.33) (1.39,1.72,2.04) (0.5,0.97,1.47) (0.75,1.06,1.41) (0.74,0.95,1.3) (6.16,8.25,10.22) G2 (0.33,0.39,0.46) (1,1,1) (1.02,1.17,1.34) (0.71,0.91,1.2) (0.54,0.69,0.91) (0.74,0.92,1.15) (0.57,0.67,0.83) (4.91,5.75,6.89) G3 (0.35,0.42,0.51) (0.75,0.86,0.98) (1,1,1) (0.69,0.91,1.27) (0.54,0.68,0.88) (0.78,1.02,1.32) (0.5,0.61,0.79) (4.61,5.5,6.75) G4 (0.49,0.58,0.72) (0.83,1.1,1.41) (0.79,1.09,1.45) (1,1,1) (1.07,1.33,1.74) (1,1.25,1.68) (1.07,1.21,1.53) (6.25,7.56,9.53) G5 (0.33,0.4,0.49) (1.1,1.46,1.85) (1.14,1.47,1.85) (0.57,0.75,0.93) (1,1,1) (1.05,1.24,1.5) (0.87,1.03,1.23) (6.06,7.35,8.85) G6 (0.45,0.54,0.65) (0.87,1.09,1.35) (0.76,0.98,1.28) (0.6,0.8,1) (0.67,0.81,0.95) (1,1,1) (0.8,0.95,1.17) (5.15,6.17,7.4) G7 (0.56,0.64,0.75) (1.21,1.5,1.75) (1.26,1.64,2) (0.66,0.83,0.93) (0.81,0.97,1.15) (0.86,1.05,1.25) (1,1,1) (6.36,7.63,8.83) Sum (3.51,3.97,4.58) (6.56,8.23,10.01) (6.95,8.68,10.25) (5.62,6.92,8.37) (5.13,6.45,8.1) (6.18,7.54,9.31) (5.55,6.42,7.85) (39.5,48.21,58.47) Consistency ratio: 0.03 After comparing those above fuzzy numbers, the minimum degree of possibility and the priority weight was given by using the equation (9) as follows: d' (G1) = min (1, 1, 1, 1, 1, 1) = 1 d' (G2) = min (0.55, 1, 0.15, 0.7, 0.3, 0.6) = 0.15 d' (G3) = min (0.5, 0.35, 0.5, 0.64, 0.8, 0.55) = 0.35 d' (G4) = min (0.93, 1, 1, 1, 1, 1) = 0.93 d' (G5) = min (0.85, 1, 1, 0.91, 1, 0.92) = 0.85 d' (G6) = min (0.67, 1, 1, 0.7, 0.82, 0.73) = 0.67 d' (G7) = min (0.92, 1, 1, 1,1,1) = 0.92 As a consequence, the weight vector was determined as W’ = (1, 0.15, 0.35, 0.93, 0.85, 0.67, 0.92) T. after normalizing, the priority weights of the alternative was found as (0.21, 0.03, 0.07, 0.19, 0.17, 0.14, 0.19). The final results show thatChina is considered the most important country with highest the priority weight (21%), followed by The Philippines and Vietnam with the same percentage share of 19%. Singapore comes third with 17%, followed by Thailand with 14%. The last two countries, Malaysia and Indonesia only account for 7% and 4% respectively. We therefore can conclude that China is the optimal country in providing outsourcing services to outsourcers. Besides that, Vietnam and The Philippines are emerging to be attractive destinations for outsourcing services nowadays. 520
15. 5. CONCLUSION This study’s main purpose is to identify the best outsourcing destination in the Asia region. By using the FAHP approach to combine and rank the decision makers’ evaluations, among four main criteria, cost competitiveness are the most important criterion for DMs when carrying out an outsourcing contract. This finding is correspondingwith previous studies which often consider costs are the leading reason for an outsourcing action(Cỏnez et al., 2000; Anderson and Parker, 2002; Edgell et al., 2008).However, everything is done by manpower, whether companies can lower cost or not, it really depends on how people can work affectively. Additionally, most outsourcing companies come from developed countries, so they have advanced technologies, whereas, outsourcing service providers come from developing countries with lower- level human resources. Thus,it would be understandable when evaluatorsnowadays consider human resources are the second important attribute. This finding contribute an important part in order to strengthen existing arguments which state that human resources must be one of the most worth-concerning factors (Prajogo & Ahmed, 2008). Besides that, the business environment and government policies of a country are also very important because they are concerned with the risks that an enterprise may face. With respect to the study’s four criteria, China is the leading outsourcing service destinationfor decision makers. It hardly surprises because the rise of “the factory of the world” has recently been recognized as a miracle that any countries in the world desire to emulate. Besides this huge player, Thailand, Vietnam, and The Philippines are emerging to be three attractive destinations due to the positive change of government policies as well as the improvements of human resources and the national infrastructure. Conversely, Indonesia and Malaysia are the least attractive nations for the reason of the complex culture system. Finally, Singapore, if in the last decades were ideal locations for software outsourcing, IT outsourcing or semiconductor outsourcing, in today’s global economy, with the outsourcing rises of the other countries in the region, do not present themselves as the two most attractive countries anymore. In conclusion, the study successfully converted the outsourcing country selection problem to the MCDM problem and solved it based on the comprehensive model by using the advanced methodology to calculate the priority of criteria and alternative. Thus, the study has brought the more comprehensive view of the give field to readers and practitioners whereby they could easily and quickly make outsourcing decisions. Furthermore, the study also contributes to improve the existing literature by filling the research gap. 6. FUTURE RESEARCH Although the research model was constructed based on the extensive review of literature, it is not yet a perfect solution for the problem of outsourcing vendor selection. In addition, although the advantages and usefulness of the FAHP approach have been extensively recognized by both researchers and practitioners over the last decades, it still shows many limitations so far. Thus, it is still valuable for future researcher’s to improve and identify the best MCDM model for evaluating and selecting the best outsourcing destination. 521
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