FACTA UNIVERSITATIS Series: Mechanical Engineering Vol. 19, No 3, Special Issue, 2021, pp. 579 - 600 https://doi.org/10.22190/FUME210711062Z © 2021 by University of Niš, Serbia | Creative Commons License: CC BY-NC-ND Original scientific paper COMBINING THE SUITABILITY-FEASIBILITY- ACCEPTABILITY (SFA) STRATEGY WITH THE MCDM APPROACH Sarfaraz Hashemkhani Zolfani1, Ramin Bazrafshan2, Parnian Akaberi2, Morteza Yazdani3, Fatih Ecer4 1School of Engineering, Catholic University of the North, Larrondo, Coquimbo, Chile 2Department of Industrial Engineering and Management Systems, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran 3ESIC University, Madrid, Spain 4Department of Business Administration, Afyon Kocatepe University, Afyonkarahisar, Turkey Abstract. Suitability-Feasibility-Acceptability (SFA) is a fundamental tool for the development and selection of strategy. Any type of decision-making problem can be resolved by Multiple Criteria Decision Making (MCDM) methods. In this research, we explore the complexity of determining the proper goal market for the Chilean fish market. This study proposed a combined approach of SFA with MCDM methods in a real case study. The proposed structure helps to assign the best market for Chilean export fish to West Asia. Three countries (Saudi Arabia, the United Arab Emirates, and Oman) are selected as a target market in this region, and then related criteria are obtained from various sources. In order to develop a new market for the Chilean fishery industry, five major criteria, including the potential of a target market, region's economic attractiveness, consumption of the seafood, location, cost of transportation, and country risks, were selected based on the SFA framework. Calculating the criteria weights is performed by the Best-Worst (BWM) method, and ordering the alternatives is operated by Measurement Alternatives and Ranking according to compromise Solution (MARCOS) methods. The results showed that Oman is the best destination (importer) for the Chilean fish market (Salmon fish as the case). Key Words: International Markets, Multiple Criteria Decision Making (MCDM), Suitability-Feasibility-Acceptability (SFA) Method, Export Received July 11, 2021 / Accepted September 23, 2021 Corresponding author: Morteza Yazdani ESIC University, Cam. Valdenigriales, S/N, 28223 Pozuelo de Alarcón, Madrid, Spain E-mail: morteza.yazdani@esic.university mailto:morteza.yazdani@esic.university 580 S. H. ZOLFANI, R. BAZRAFSHAN, P. AKABERI, M. YAZDANI, F. ECER 1. INTRODUCTION In recent years, worldwide, some concepts such as globalization, fast technological changes, the appearance of new markets, and changing customer expectations cause fierce emerging competition. This phenomenon forced stakeholders to think strategically and make strategic plans for their business [1]. Strategic planning is the art of creating strategies and aligning a business’s vision for the future of industries or markets. In order to select the proper strategy, many strategic options should be surveyed. The strategic planning process usually consists of three critical steps [2]: (i) strategy formulation, (ii) strategy implementation, and (iii) strategy evaluation. In strategy formulation, managers survey markets and make decisions that concentrate on their plan or generally ignore it. By choosing suitable strategies or plans, the company implements them in order to achieve the desired results. In the final step, the performance of the selected strategy is evaluated. In addition to the strategic planning process, some tools are introduced to analyze strategic possibilities. Strategic analysis is a process of seeking the operating environment of a company to formulate a strategy. This analysis consists of three main factors [3]: (i) Identification and evaluation of data relevant to strategy formulation, (ii) analyzing the internal and external environments, and (iii) use of the analytic method. There are various analytic methods in literature like SWOT analysis, PEST analysis, Porter’s five forces analysis, four corner’s analysis, value chain analysis, early warning scans, war gaming. These methods are used in various fields. Lee et al. used SWOT analysis to measure the limitations and strengths of the Korean space and satellite industry. Sometimes researchers combined them with other methods [4]. Sahani combined SWOT with the MCDM method like AHP and Fuzzy_AHP to formulate and prioritize ecotourism strategies in Western Himalaya, India [5]. Johnson et al. developed a matrix named SFA to evaluate and analyze the strategies [6]. This method consists of three main sections such as suitability, feasibility, and acceptability. The criteria that the managers take into consideration for comparison purposes should categorize in these three sections. The strategies are listed as options in this method, and each criterion gets scores basis on the experts' or managers' verdict. Then, the options are compared by these scores. The SFA covers a varied range of criteria and items. This is an excellent characteristic that helps the managers to compare the strategies from various aspects. However, this is a plausible method but has some constraints for real problems or complicated issues. It has not a distinct method for assigning a weight of criteria. This is one of the problems that make the managers less ready to use this analytic method. The SFA method structure is very similar to the MCDM methods, both of them distinguish the priorities of alternatives by evaluating the criteria. This similarity caused the researchers to decide to utilize the MCDM methods, assign weight to criteria, and evaluate the strategies' options. The SFA strategy first selects some criteria and then evaluates them, most likely the MCDM methods. This study attempts to combine the SFA framework with the MCDM methods. Showing the efficiency of this combined methodology, the researchers propose a practical case study and implement this new method on it. Section 2 reviews literature about SFA and MCDM methods and explains this study's objective. Section 3 describes the methodology of the SFA and MCDM methods. Section 4 addresses the application of this method to exporting Chilean fish and its implementation as well as the research gap and discussion. Finally, section 5 provides conclusions. Combining the Suitability-Feasibility-Acceptability (SFA) Strategy with the MCDM Approach 581 2. LITERATURE REVIEW 2.1. A summary of SFA background Nowadays, using MCDM methods in strategic planning is vital because these methods can be combined with the strategic method and can promote their outcome and accuracy. The MCDM methods with mathematical formulation could help the managers to select the optimum option or the best strategy with the highest degree of satisfaction in the board of managers [7]. In the following, several works that used the MCDM methods in the strategic fields are mentioned. Mehrjerdi used the MCDM method for selecting the strategic system with linguistic preference and gray information. They proposed a method for selecting alternatives in the presence of uncertainty and determined optimal choice among seven possible alternatives. They compared the obtained results with quantitative strategic planning matrix (QSPM), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and simple additive weighting (SAW) methods and gathered a similar ranking with TOPSIS and SAW. Their results also validate that the ranking obtained by the QSPM is inferior in the comparison methods [8]. Selecting an appropriate vendor is often a non-trivial task, in which multiple criteria need to be carefully examined. However, many decision-makers or experts select vendors based on their experience and intuition. Shyur and Shih used a hybrid MCDM model for strategic vendor selection. They proposed a five-step hybrid process, which incorporates an analytic network process (ANP) technique. More clearly, the ANP method is used to obtain the relative weights of criteria. Then, the modified TOPSIS is adopted to rank competing products in terms of their overall performance. They reported that the proposed method is practical for ranking competing vendors regarding their overall performance concerning multiple interdependence criteria. They declared that the consideration of relationships between criteria provides the organization with a way to devise and refine adequate criteria and alleviate the risk of selecting sub-optimal solutions [9]. Banihabib et al. examined the strategies to tackle water shortage for sustainable development in Shahrood, Iran. In their paper, a contentious plan has been proposed to transfer water from the Caspian Sea north of Iran to this region. They used Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis. Due to the SWOT model's inability to rank the alternatives, the developed strategies are ranked using MCDM models based on specified sustainable development criteria. The ranking model was programmed by using the compensatory models of SAW and analytical hierarchy process (AHP) and the non-compensatory model of Elimination and Choice Translating Reality III (ELECTRE III). All MCDM models' results introduced water transfer as the worst strategy for a region [10]. Hashemkhani Zolfani et al. presented a new strategic hybrid model for international market selection based on Market Attractiveness and Business Attractiveness (MABA) analysis and the EDAS method (one of the latest MCDM methods). They worked on a case in Iran's food industry and could develop the primary model of the MABA analysis in the MCDM outline. Using this model leads to selecting the most suitable and profitable market, considering several quantitative and qualitative factors [11]. As noted, one essential strategic planning, which has three assessment criteria, is the SFA strategy. It has been utilized as a powerful marketing method since the researchers received valuable strategic planning results while using that in a real problem. The following paragraphs review some research projects which used SFA in their studies. In order to get incremental improvement, companies and organizations try to attain an effective strategy. Georgise & Mindaye examined the suitability, acceptability, and feasibility of Kaizen – A Japanese concept used for continuous 582 S. H. ZOLFANI, R. BAZRAFSHAN, P. AKABERI, M. YAZDANI, F. ECER improvement- among SMEs in Ethiopia. Since organizations were eager to use this strategy, the researchers decided to evaluate Kaizen's feasibility, suitability, and acceptability for these organizations. The study results showed that although some enterprises think it is a confusing strategy, most of them are willing to implement Kaizen in their companies (acceptability). The study also found that the feasibility of Kaizen practices is possible, despite being a bit challenging. As a result, the study showed that Kaizen as an effective strategy is accepted in Southern Region, Ethiopia organizations and can improve their performance. However, still, its feasibility seems challenging [12]. Čirjevskis & Novikova investigated the commercial viability of green energy business to make an investment choice for Latvian hydropower producer and seller LLC “Green Energy Solutions”. They investigated the theoretical and practical application of such concept of the commercial viability of a strategy as an SFA, explored the latest trends of Green Energy Business in EU and Latvia, and defined strategic suitability. The research team calculated equivalent annual annuities of each alternative investment project and discussed financial feasibility to confirm disproving investments in a hydropower station or wind turbine [13]. Alimardani et al. presented a new-hybrid strategic model based on the SWARA method and Yin-Yang balance theory to design products with both international and local perspectives [14]. Dalic et al presented a new hybrid MCDM model applied in SWOT strategic tool for decision-making in a transportation company. They applied Fuzzy PIPRECIA, FUCOM, SWOT, and MARCOS methods in their study [15]. Amoozad Mehrjerdi et al. presented a hybrid MCDM model based on BWM and interval-valued intuitionistic fuzzy TODIM for evaluating strategies for implementing industry 4.0 [16]. Ullah et al. reviewed tourism resources in ecologically sensitive coastal areas of Baluchistan to assess their potential for establishing community-based ecotourism following the SFA framework. The collected information about the coastal regions was analyzed through SWOT analysis and fuzzy logic analysis. The results showed that the introduction of CBE within the selected localities without any investment in basic infrastructure and capacity building of communities would inevitably negatively impact the natural environment because the infrastructure and communities’ knowledge for developing the desired services were below the required standards [17]. Puška et al. used multi-criteria analysis methods for ranking project management programs. They perform the MARCOS method for evaluating. Since there are many software solutions for the project manager, selecting the best one is critical. So, the researchers choose four softwares: Smart sheet, Asana, Microsoft Project, and Basecamp. They evaluate them by seven scenarios and conclude that the Smart sheet is the best [18]. Pamučar & Savin choose the off-road vehicle for transportation activities in the Serbian Armed Forces because selecting the proper vehicle increases the safety, quality, and efficiency of load carried out. They used the hybrid method BWM-COPRAS for this selection. Seven criteria are introduced by them with each of them having seven sub-criteria. For verification of the results, they used BWM-MABAC and BWM-MARCA models [19]. Hashemkhani Zolfani et al. have proposed a vision-based weighting system (VIEWS) for the managers to consider time vision in their decision-making. They used a hybrid method EDAS-PMADM for this study. The three-time concept is analyzed (Current, 2025, and 2030) and shows that the ranking of alternatives is changed by time. The policymakers by this method can make good decisions for the future of their company [20]. Hasheminasab et al. implement the Circular Economic (CE) for minimizing the harmful effect of using fossil fuel. They consider three different fossil fuels (oil, gas, and coil) for selecting the most sustainable fuels. The EXTENDED-SWARA method is used for evaluating the CE criteria. Then they used the Combining the Suitability-Feasibility-Acceptability (SFA) Strategy with the MCDM Approach 583 MARCOS method for ranking, and they showed that gas is the most sustainable fuel of the two others [21]. Hashemkhani Zolfani, et al. developed a novel integrated decision-making tool for selecting the most profitable market. They consider multiple factors like: social, political, economic, and ecological. The hybrid model of Market Attractiveness and Business Attractiveness (MABA) with EDAS proposed and evaluated several international markets by this method [11]. Behzad et al studied the waste management system. They introduced seven criteria: waste generation, composting waste, recycling waste, and landfilling waste, recycling rate, waste to the energy rate, and greenhouse gas emissions from waste. They used the hybrid method BWM-EDAS for weighting and evaluating the criteria and ranking them. The five countries are considered as alternatives: Denmark, Finland, Iceland, Norway, and Sweden. The result showed that Sweden has the best waste management profile (0.9748) [22]. Hashemi et al. used the MCDM method for feature selection. They applied the TOPSIS method for evaluating multi-label data. The ridge regression algorithm is used for constructing a decision matrix; for calculating the weight of this matrix, they implement the entropy method. They ranked the features and said the user could select a desired number of features [23]. Table 1 represents some recent studies about the SFA strategy mentioned above. Table 1 Studies related to SFA strategy Goal Author/s 1 Evaluating Kaizen strategy usage among SMEs Bete Georgise & Mindaye [12] 2 Evaluating strategic options of KAIZEN (a business management concept) Bwemelo [10] 3 Assessing community-based ecotourism potentials of coastal areas of Baluchistan Ullah et al. [17] 4 Evaluating the potential success or failure of a project Abu Hassan & Moshdzir [24] 5 Making an investment choice for corporations Čirjevskis & Novikova [13] According to the above research, it has been recognized that the SFA is a valuable and productive method. The scientist and stakeholder intend to use it more than before if the degree of conformity is improved. Combining the MCDM method with strategic planning gives a significant result. Therefore, this study tries to boost the accuracy of the SFA by using an MCDM method. In general, the research question concerns the main benefits of combining the MCDM approach in the SFA concept to improve strategy development. 2.2. Research objective and novelty According to the research question, below are the main aims to reach: ▪ Improve the SFA strategy for complex problems and increase its accuracy. The SFA is used just for nominal value criteria, but this combination could use the criteria with no nominal value, and, ▪ Calculate the weight of criteria by a distinct method. The SFA method allocates criteria weights based on their importance. In other words, the more critical the criterion is, the more amount of weight it will be given during evaluations. However, this method has not introduced a specified way of calculating weights. Section 3 explains the SFA strategy and the MCDM method, which is used in this strategy. In Section 4, a case study is analyzed with these new criteria and, based on this process, concluded consequences in the last quarter. 584 S. H. ZOLFANI, R. BAZRAFSHAN, P. AKABERI, M. YAZDANI, F. ECER The proposed model has novelty due to these reasons: ▪ It reveals a new perspective for strategy formulation that improves in several aspects. This enables experts and strategists to incur. ▪ There is no study in the history of strategy planning and decision-making with multiple attributes to measure the performance of strategies. ▪ Application of the combined evaluation structure leads to an improved and reliable process that experts can comprehend. 3. METHODOLOGY This section firstly introduces the SFA strategy processes and then describes the Best- worst MCDM steps. 3.1. SFA strategy processes Child was one of the significant authors who discussed strategic choice amongst organizational theorists [24]. Čirjevskis and Novikova claimed that the concept of strategic choice initially originated from the perception that its operational strengths and opportunities define its direction [13]. Johnson et al. had a similar approach to strategic choice. They were the major contributors to the strategy choice viability by applying a clear model SFA of examining strategic opportunity through three assessment criteria: suitability, feasibility, and acceptability [25]. Strategic choices involve the options for strategy in terms of both the directions in which strategy might move and the methods by which strategy might be pursued. Once a set of strategic options has been established, it is time to evaluate their relative merits. The SFA framework suggests three criteria (see Table 2). Suitability asks whether a strategy addresses the key issues relating to the opportunities and constraints an organization faces. Acceptability asks whether a strategy meets the expectations of the stakeholders. Last, feasibility invites an explicit consideration of whether a strategy could work in practice. In other words, suitability is related to its strategic position and whether its strategic choice matches the external environment and company resources and capabilities. Feasibility is concerned with assessing the company’s internal capabilities in terms of financial resources. Finally, acceptability relates to evaluating whether the chosen strategies can meet stakeholders’ expectations in terms of outcomes. According to this model, strategic options should be evaluated before implementing them in a new context. Three ‘strategic option evaluation tests’ are suggested, which helps us evaluate this nature's strategic choice before applying it to a particular environment. These are the suitability test, acceptability test, and feasibility test. The suitability test considers whether the option is the right one in given circumstances. The acceptability test considers whether the strategic option will gain crucial support from the corresponding parties or lead to opposition or criticism. Further, the feasibility test considers whether a company can successfully carry out the strategic option [25]. Combining the Suitability-Feasibility-Acceptability (SFA) Strategy with the MCDM Approach 585 Table 2 The SAF criteria and techniques of evaluation The SAF criteria Scope Suitability (focused on external factors) ▪ Does a proposed strategy address the key opportunities and constraints an organization faces? Acceptability (focused on the internal factor) ▪ Does a proposed strategy meet the expectations of stakeholders? ▪ Is the level of risk acceptable? ▪ Is the likely return acceptable? ▪ Will stakeholders accept the strategy? Feasibility ▪ Would a proposed strategy work in practice? ▪ Can the strategy be financed? ▪ Do people and their skills exist, or can they be obtained? ▪ Can the required resources be obtained and integrated? 3.2. Best-worst method (BWM) Rezaei proposed a new MCDM method called the best-worst method (BWM). The BWM method has made substantial advancements in weight determination. According to BWM, the decision-maker identifies the best (e.g. most desirable, most important) and the worst (e.g. least desirable, least important) criteria. Pairwise comparisons are then conducted between these two criteria (best and worst) and the other ones. A max-min problem is then formulated and solved to determine the weights of different criteria. The weights of the alternatives concerning different criteria are obtained using the same process. The alternatives' final scores are derived by aggregating the weights from different criteria and alternatives, based on the best alternative which is selected [26]. BWM has been successfully applied in many areas. Torkayesh et al. applied it for the assessment of healthcare sectors in Eastern European countries [27]. Pamucar et al. addressed BWM to select the most preferred renewable energy source for a developing country [28]. Ecer performed it for the sustainability evaluation of wind plants [29]. For sustainable supplier evaluation, Ecer and Pamucar utilized the BWM technique [30]. Hashemkhani Zolfani et al. handled it for selecting the best location for a newcomer in Chile [31]. Besides, some researchers performed it successfully in various fields [32-35]. The steps of the BWM method for calculating the weights of criteria are defined below. Step 1: In this step, decision-makers determine a set of decision criteria. Step 2: After selecting decision criteria, they should separate the best and the worst criteria. Step 3: The preference of the best criterion over all the other criteria should be determined, for this we could use a number between 1 and 9. The resulting Best-to-Others vector would be: 1 2 ( , ,..., ) B B B Bn A a a a= , where aBj indicates the preference of best criterion B over criterion j and aBB =1. Step 4: The preference of all the criteria over the worst criterion is determined, and for this we could use a number between 1 and 9. The resulting Others-to-Worst vector would be: 1 2 ( , ,..., ) T w w w nw A a a a= where ajw indicates the preference of criterion j over worst criterion W and aww =1. 586 S. H. ZOLFANI, R. BAZRAFSHAN, P. AKABERI, M. YAZDANI, F. ECER Step 5: Find the optimal weights * * * 1 2 ( , ,..., ) n w w w .The optimal weight for the criteria is the one where, for each pair of wB/wj and wj/ww, wB/wj=aBj and wj/ww=ajw. To satisfy these conditions for all j should find a solution where the maximum absolute differences B Bj j w a w − and j jw w w a w − for all j is minimized. Considering the non-negativity and sum condition for the weights, the following problem emerges: min max , j jB Bj jw j w ww a a w w    − −     (1) s.t. 1 j j w = (2) 0,j for allw j (3) The above formulation could be transferred to the following formulation: Min  (4) s.t. , − B Bj j for w a w all j (5) , −  j jw w for w a w all j (6) 1= j j w (7) 0, j for allw j (8) By solving the above formulation, the optimal weights * * * 1 2 ( , ,..., ) n w w w and * are obtained [26]. 3.3. Measurement alternatives and ranking according to compromise solution (MARCOS) This method determines ideal and anti-ideal alternatives as reference values and then defines the relationship – represented as a utility function in the MARCOS method - between them and other alternatives. Though it has been introduced very recently, it attracted considerable attention from researcher communities [27], [36-41]. The following are the steps of the MARCOS method [42]. Step 1: Formation of decision-making matrix. In this step, a matrix with n criteria and m alternatives is defined. Step 2: Determination of ideal (AI) and anti-ideal solution (AAI) and extended decision matrix. min x if j beneficial and max x if j non- beneficial=   ij ij AAI (9) Combining the Suitability-Feasibility-Acceptability (SFA) Strategy with the MCDM Approach 587 max x if j non-beneficial and min x if j beneficial=   ij ij AI (10) Step 3: Normalization of the extended decision matrix. if j non-beneficial= ai ij ij x n x (11) if j beneficial= ij ij ai x n x (12) Step 4: Determination of the weighted matrix: v =n * ij ij j w (13) Step 5: Calculation of the Utility degree of alternatives Ki. − − = i i anti ideal S K S (14) + = i i ideal S K S (15) i 1 S = =  n ij i v (16) Step 6: Determination of the utility function of alternatives f(Ki). ( ) 1 ( ) 1 ( ) 1 ( ) ( ) + − + − + − + = − − + + i i i i i i i K K f K f K f K f K f K (17) Utility function in relation to the anti-ideal solution: ( ) + − + − = + i i i i K f K K K (18) Utility function in relation to the anti-ideal solution: ( ) − + + − = + i i i i K f K K K (19) Step 7: Ranking the alternatives. All alternatives are ranked as per their values of utility functions. The advantages of the MARCOS method are: it considers an anti-ideal and ideal solution at the very beginning of the formation of an initial matrix, it proposes a new way of determining utility functions and their aggregation, and the possibility to consider a large set of criteria and alternatives while maintaining the stability of the method [40]. The MARCOS method is also used in various fields like sustainable supplier selection in the healthcare industry [40], iron and steel industry [38], assessment of battery electricity [43], and integrated to other MCDM method like FUCOM [40], ITARA [39], and used as Fuzzy MARCOS [44]. 588 S. H. ZOLFANI, R. BAZRAFSHAN, P. AKABERI, M. YAZDANI, F. ECER As mentioned, the MARCOS method is proper for solving real-world business problems, helping decision-makers in multifaceted problems, and contributing to the Prospective Multiple Attribute Decision Making. 4. APPLICATION AND IMPLEMENTATION In the last decades, the farmed Atlantic salmon production was increased all over the world. Chile and Norway are recognized as the top producers by a 6% and 2% growth ratio in their production, respectively. For instance, during the first six months of 2020, Chile has produced 246,806 tons of Atlantic salmon, worth $ 1,731 million, indicating a 2.62% increase compared with the year before [45]. The greatest amount of this Chilean Salmon is exported to the US market. However, Chile could not find an acceptable market share in the European markets because of the powerful presence of its European competitor. Norway is exporting salmon not only over Europe but also over Asian countries like China and South Korea. Understandably, they would plan to increase their share of the Asian markets. Should Chile intend to capture the Asian market, it seems that the west of Asia is the best target market due to the below listed reasons: First, as a major competitor, Norway has not done any activity for exporting salmon in this region until now. Second, the region enjoys considerable potential strategic benefits like the Arabian Sea and the Indian Ocean's availability. The target countries such as Iran, Saudi Arabia, and Turkey can also play as a hub for Chile to export its salmon to other countries. Considering all the above mentioned, this study's focus is on “the export of the Atlantic Salmon of Chile to the west of Asia’s region”, using the SFA strategy. The first step of this process is to define criteria for each category of the SFA. One of the essential criteria that significantly affect foreign markets' investment is our products' "potential of the target market". Based on the FAO report in 2011, the main aquaculture producers in the west of Asia are Saudi Arabia and Iran [46]. These countries are the major producers in this region, but they cannot supply all their demands. This provides an investment opportunity for neighboring countries like Egypt to export their fishery products to the west of Asia. "Region's economic attractiveness" can be another factor to export. For example, the Emirates have the most prominent international airline in the world. Dubai International Airport had 88,242,099.000 passengers in 2017 [47]. The Emirates Group also announced that their revenue from the first six months of 2020-21 had been US$ 3.7 billion [48].Saudi Arabia is one of the places where approximately 2 million Muslims travel to this country for Hajj. Many tourists travel to Turkey and Iran annually because of their historical sites and cultural heritage. It’s figured out that West Asia is a critical and strategic location, with the potential of millions of passengers travelling to these lands. Seafood consumption is an essential issue for investors to measure and estimate people's preferences in these countries. The United Arab Emirates (UAE) and Oman are the largest seafood consumers in the region by consuming about 28.6 kg per year. The other critical criteria are the "Country Risks" like economic risk, business environment risk, political risk, commercial risk, and financing risk. One of the criteria that significantly affect the target country's selection is the "Location and cost of transportation". As the distance between the two countries (as the exporter and the importer) increases, transportation costs are seriously growing. Combining the Suitability-Feasibility-Acceptability (SFA) Strategy with the MCDM Approach 589 Hence, as shown in Table 3 and Fig. 2, this research considers three countries (Saudi Arabia, the UAE, and Oman) as the Chilean Salmon fish export destination. From the countries mentioned above, Iran and Turkey are omitted. Due to international sanctions and unstable economic situations, Iran would not be a great option. Also, since maritime transportation has been one of the consideration criteria to select the target market, Turkey does not seem to be an optimal option for this purpose. Iran and Turkey have been omitted according to the latest Trend-Economy site statistics. In 2018, Saudi Arabia, the UAE, and Oman imported fishery products $4, $5, and $15 million, respectively, and $19, $4, and $19 million 2019 [49]. Fishery importation to Saudi Arabia increased for nearly 5-times in one year. It can be concluded that Saudi Arabia has a remarkable potential for exporting fish. Economic attractiveness could be GDP growth, average inflation rate, macroeconomic stability, financial structure and development, and the target country's business environment. Table 3 The sub-criteria of the Region's economic attractiveness Sub-criteria Country Saudi Arabia UAE Oman GDP Growth volatility 78.5 86 80.4 Average inflation rate 100 100 100 Macroeconomic stability 79 71 68.9 Financial structure and development 51 46.3 36.9 Business environment 81.3 88.7 68.2 Source: Global Foreign Direct Investment Country Attractiveness [50] The annual consumption of seafood in Saudi Arabia, THE UEA, and Oman is 11.3, 24.71, and 28.54 kg/person, respectively [51]. The trend of seafood consumption per capita from 1961 to 2017 is attached in the Appendix. Transportation cost is another critical criterion that the investors should consider because they determine the direct influence on export policy. They are transporting Fishery products while noticing that the live fish should be controlled under certain conditions. A more common way of transport is via sealed containers [52]. These containers should be insulated from heat, and it is necessary to provide adequate oxygen for fish during transport. The wholesalers usually use pure bottled oxygen for oxygenating water [53]. Airplanes or ships are usually preferred for Intra-continental transportation. Although ship Freightage is less expensive than airplanes, the boat's transit time is much longer than that of the airplanes. However, as mentioned before, the fish transport system needs some other types of elements and variables. When the transition time exceeds, maintenance costs and losses of fish will increase, too. For example, the ship freighted transit time from Chile to THE UEA is about 25 to 31 days and airplane Freighted is about 1 to 3 days. In order to investigate distances, consider just the distance from the target location to Chile. The shorter length is an advantage for the target location. Table 4 shows these distances. Table 4 Distance from Chile to the target location Distance (miles) Saudi Arabia UAE Oman From Chile to Flight Ship Flight Ship Flight Ship 8551 7430 9060 7873 9166 7965 Source: [54] 590 S. H. ZOLFANI, R. BAZRAFSHAN, P. AKABERI, M. YAZDANI, F. ECER Based on Euler Hermes global study [55], the country risk consists of five parts (economic risk, business environment risk, political risk, commercial risk, and financing risk). This study uses five linguistic concepts as excellent, very good, good, bad, and worst for determining the value of these sub-criteria. Table 5 shows these values. Table 5 Linguistic assessments of country risk sub-criteria Economic Risk Business environment Risk Political Risk Commercial Risk Financing Risk Saudi Arabia Good Good Bad Worst Very good UAE Good Very good Good Worst Good Oman Bad Good Good Worst Bad Source: [56] 4.1. Research gap The first step in the SFA method is to determine the criteria. Suitability is related to opportunities and constraints that an organization faces. The five criteria, the target market’s potential, and the region’s economic attractiveness, are involved in this group. The feasibility factors examine the strategy and scan its financial capability. The consumption of seafood of the target market and the cost of transportation are relevant to this group. Finally, the acceptability usually surveys the risk of strategy, so the country risk is placed in this group. Three countries, Saudi Arabia, the UAE, and Oman are considered Option 1, Option 2, and Option 3. Table 6 shows the SFA strategy and the criteria. Table 6 The SFA strategy framework by related criteria Weight Suitability ▪ The potential of target market ▪ Region's economic attractiveness W1 W2 Feasibility ▪ Consumption of the seafood ▪ Location and cost of transportation W3 W4 Acceptability ▪ Country Risks W5 All of the criteria can be measured by nominal values, except one of them that is linguistic. The SFA strategy has not proposed a procedure for transmuting this linguistic value to nominal. One of the challenges is that the deals are not balanced, and calculating these values results in the wrong answers because data should be normalized for the measurement. SFA strategy table has a column that determines the weight of criteria. The gap is to determine the weights of each criterion, the function that MCDM methods will deliver. The MCDM method normalization steps can convert linguistic concepts to nominal ones. Some of these methods help the researchers to determine criteria weights. According to these benefits of the MCDM methods, combining these methods with the SFA strategy is considered in this study. Combining the Suitability-Feasibility-Acceptability (SFA) Strategy with the MCDM Approach 591 4.2. Calculation with the proposed MCDM model This study uses the BWM method as an MCDM method because it requires fewer comparisons and gives more trustworthy outcomes than the other weighting tools [73]. This method works by pairwise comparison of the criteria. Based on the BWM algorithm, the best and worst criteria among these five should be determined. The potential of the target market is rated as the best, and the location and transportation cost as the worst criterion. Considering Appendix from Table 8 to 22, we obtain these weights as Wpotential of target market = 0.4219, WRegion's economic attractiveness = 0.1734, WConsumption of the sea-food = 0.2601, WLocation and cost of transportation = 0.0404, and WCountry Risks= 0.104. The weights are achieved by the BWM excel file solver, which can be found in www.bestworstmethod.com. The ranking of options in the SFA method is realized by the MARCOS method. Firstly, the decision matrix is defined. The decision matrix contains the values of the alternatives according to the criteria. The criteria consist of some sub-criteria. The decision matrix is given in Table 7. The MCDM method provides the possibility to convert linguistic values to nominal. As country risk values are linguistic, it is possible to convert them to nominal values. Risk is a negative criterion that means the lower values are better preferred. The linguistic values are excellent, good, bad, and worst transmitting to numbers 1 to 5, respectively (excellent count as 1). It has to be mentioned that commercial risk is omitted from the sub- criteria of country risk because three options have the same value. The average inflation rate is also neglected from the region’s economic attractiveness for the same values. In this study, the researchers used www.mcdm.app and extracted the results. The obtained values by MARCOS are (Saudi Arabia= 0.7281, UAE= 0.5281 and Oman= 0.8287). It turns out that Oman is the best destination for the Chilean fish market, while the UAE is the worst item based on our study. Table 7 Decision matrix table Alternatives Criteria Sub-criteria Saudi Arabia UAE Oman Potential of target market 19 4 19 Region's economic attractiveness GDP Growth volatility 78.5 86 80.4 Macroeconomic stability 79 71 68.9 financial structure and development 51 46.3 36.9 business environment 81.3 88.7 68.2 Consumption of the seafood 11.3 24.71 28.54 Location and cost of transportation Flight 8551 9060 9166 Ship 7430 7873 7969 Country Risks Economic Risk 3 3 4 Business environment Risk 3 2 3 Political Risk 4 3 3 Financing Risk 2 3 4 4.3. Discussion To specify which country has a good potential for the fishery products market, this paper attempts to find the answer by utilizing the SFA strategy – as a strategic choice method- through MCDM methods. 592 S. H. ZOLFANI, R. BAZRAFSHAN, P. AKABERI, M. YAZDANI, F. ECER Since the SFA strategy does not seem very efficient for the abovementioned situation, the researchers extended it by an integrated BWM-MARCOS methodology. This combination has also increased the capability of the SFA strategy to solve complex problems. According to the SFA framework, some related criteria and options should be defined. The evaluation criteria considered are ranked from the most significant to the least important as the potential of the target market, consumption of the seafood, region's economic attractiveness, country risks, location, and transportation cost, respectively. The Selected options are the names of three countries (Saudi Arabia, Oman, and the UAE). One country should be selected among these options as the best country to export Chilean fish to. Then, the criteria and alternatives are evaluated and ranked. The results show that Oman is the most acceptable market for the Chilean fish market. Put it differently, by placing in first ranking, Oman best meets the criteria considered for the fish market. Saudi Arabia is also considered one of the top leading countries for salmon export. Among the reforms that have started in Saudi Arabia, there are projects to encourage healthy living. They comprise the goals of increasing fish consumption. Therefore, importing salmon from Chile to this country is of critical importance. In the UAE, the aquaculture imports are approaching $ 100 million and they are mostly imported from Norway, Oman, India, and Turkey. Therefore, the UAE may have a substantial potential for Chile. Fig. 1 shows the structure of this combined method for the case study. Fig. 1 The process and phases of the model Combining the Suitability-Feasibility-Acceptability (SFA) Strategy with the MCDM Approach 593 In order to verify the results, we have performed a sensitivity analysis by substituting the weights; we have noticed that the results are stable and confidential. Table 24 shows the random tests organized for analysis and Table 25 shows the ranking of the alternatives. In total, we observe that, based on 10 tests, Oman is still the best option while the UAE is judged to be the last choice. 5. CONCLUSIONS The SFA strategy is the primary research method, which introduces some criteria and options and evaluates them. By increasing the complexity of a problem, the efficiency of this strategy decreases. SFA does not consider sub-criteria, a particular way of determining the weight, and a precise structure to prioritize the options. The deficiency of SFA bears in mind the idea of developing this method by using the MCDM methods, for instance, by applying the BWM method for determining the weight of criteria and by the MARCOS method for ranking alternatives or options. In addition, the combination of the MCDM methods with the SFA increases the accuracy of the selection process. A case study has been surveyed to implement the developed SFA approach. The case study was about exporting Chilean fish to West Asia. Three countries are considered as the alternatives, including Saudi Arabia, the UAE, and Oman. The target market's potential, region's economic attractiveness, consumption of the seafood, location and cost of transportation, and country risks were five criteria selected in this study. The main challenge occurs in the process of resolving; the problem was that some criteria have nominal values and should be converted to a numeric value. This conversion in the MCDM methods is routine, but SFA does not propose a specific solution. Determining the weight of criteria in SFA has no straightforward, systematic approach. However, the BWM method calculates these weights clearly. Another problem with SFA was the absence of a normalization system. Using MCDM methods covers all of these problems. The proposed method can be used as a great tool for managers to choose the best strategy for complex and challenging problems of their company. This principle, which suggests selecting the best strategy, can be used by different sized entities from start-up teams to holding companies. This study suggests a framework by combining the advantages of BWM and MARCOS methods with the SFA strategy to identify the most appropriate target market for the Chilean fishery industry. The results showed that the best target market for Chilean fishery industry in Oman. 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Combining the Suitability-Feasibility-Acceptability (SFA) Strategy with the MCDM Approach 597 APPENDIX ADDITIONAL FIGURES AND TABLES Fig. 2 Seafood consumption of the three countries from 1961 to 2017 [51] Table 8 Enter the names of the criteria (Step 1) Criteria Criterion 1 Criterion 2 Criterion 3 Criterion 4 Criterion 5 Names of Criteria Potential of target market Region's economic attractiveness Consumption of the seafood Location and cost of transportation Country Risks Table 8 Select the Best and the Worst (Step 2) Best Potential of target market Worst Location and cost of transportation Table 9 Enter the decision-maker's preferences (Best to others: BO vector) (Step 3) Best to Others Potential of target market Region's economic attractiveness Consumption of the seafood Location and cost of transportation Country Risks Potential of target market 1 3 2 8 5 598 S. H. ZOLFANI, R. BAZRAFSHAN, P. AKABERI, M. YAZDANI, F. ECER Table 10 Enter the decision-maker's preferences (Others to Worst: OW vector) (Step 4) Others to the Worst Location and cost of transportation Potential of target market Region's economic attractiveness Consumption of the seafood Location and cost of transportation Country Risks 8 6 7 1 5 Table 11 The weights of criteria Weights Potential of target market Region's economic attractiveness Consumption of the seafood Location and cost of transportation Country Risks 0.4219 0.1734 0.2601 0.0404 0.1040 Calculating Sub-criteria weights of the Region's economic attractiveness by the BWM method: Table 12 Enter the names of the sub-criteria of Region's economic attractiveness (Step 1) Criteria Criterion 1 Criterion 2 Criterion 3 Criterion 4 Sub-Criteria GDP Growth volatility Macroeconomic stability Financial structure and development Business environment Table 13 Select the Best and the Worst (Step 2) Best Macroeconomic stability Worst Financial structure and development Table 14 Enter the decision-maker's preferences (Best to others: BO vector) (Step 3) GDP growth volatility Macroeconomic stability Financial structure and development Business environment Macroeconomic stability 4 1 8 3 Table 15 Enter the decision-maker's preferences (Others to Worst: OW vector) (Step 4) Others to the Worst Financial structure and development GDP Growth volatility Macroeconomic stability Financial structure and development Business environment 7 8 1 6 Table 16 The weights of sub-criteria of Region's economic attractiveness Weights GDP Growth volatility Macroeconomic stability Financial structure and development Business environment 0.1755 0.5425 0.0478 0.2340 Combining the Suitability-Feasibility-Acceptability (SFA) Strategy with the MCDM Approach 599 Table 17 Sub-criteria of country risks (Step 1) Criterion 1 Criterion 2 Criterion 3 Criterion 4 Economic risk Business environment risk Political risk Financing risk Table 18 Select the Best and the Worst (Step 2) Best Financing Risk Worst Political Risk Table 19 Enter the decision-maker's preferences (Best to others: BO vector) (Step 3) Best to others Economic risk Business environment risk Political risk Financing risk Financing risk 2 2 6 1 Table 20 Enter the decision-maker's preferences (Others to Worst: OW vector) (Step 4) Others to the Worst Political Risk Economic Risk Business environment Risk Political Risk Financing Risk 5 5 1 6 Table 21 The weights of sub- criteria of economy Risk attractiveness Weights Economic Risk Business environment risk Political risk Financing risk 0.25 0.25 0.0625 0.4375 Table 22 Ranking of Alternatives Weight 0.42 0.02 0.091 0.007 0.039 0.26 0.01 0.025 0.025 0.025 0.006 0.04 Beneficial (B) or Non- Beneficial (NB) Criteria Alternative 1 Alternative 2 Alternative 3 B C1 19 4 19 B C2 78.5 86 80.4 B C3 79 71 68.9 B C4 51 46.3 36.9 B C5 81.3 88.7 68.2 B C6 11.3 24.7 28.54 NB C7 8551 9060 9166 NB C8 7430 7873 7969 NB C9 3 3 4 NB C10 3 2 3 NB C11 4 3 3 NB C12 2 3 4 Table 23 Sensitivity analysis tests C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 Original weights 0.42 0.02 0.091 0.007 0.039 0.26 0.01 0.025 0.025 0.025 0.006 0.04 T1 0.42 0.02 0.091 0.006 0.039 0.26 0.01 0.025 0.025 0.025 0.007 0.04 T2 0.42 0.02 0.091 0.007 0.039 0.26 0.025 0.01 0.025 0.025 0.006 0.04 T3 0.42 0.02 0.091 0.006 0.04 0.26 0.025 0.01 0.025 0.025 0.006 0.039 T4 0.42 0.02 0.01 0.007 0.039 0.26 0.091 0.025 0.025 0.025 0.006 0.039 T5 0.42 0.02 0.091 0.007 0.039 0.26 0.01 0.025 0.025 0.025 0.04 0.006 T6 0.42 0.02 0.025 0.025 0.039 0.26 0.01 0.091 0.007 0.025 0.04 0.006 T7 0.26 0.02 0.091 0.007 0.039 0.42 0.01 0.025 0.025 0.025 0.006 0.04 T8 0.26 0.091 0.02 0.006 0.039 0.42 0.01 0.025 0.025 0.025 0.006 0.04 T9 0.26 0.02 0.091 0.006 0.039 0.42 0.01 0.04 0.006 0.025 0.025 0.025 T10 0.091 0.02 0.42 0.006 0.039 0.26 0.01 0.025 0.025 0.025 0.006 0.04 600 S. H. ZOLFANI, R. BAZRAFSHAN, P. AKABERI, M. YAZDANI, F. ECER Table 24 Ranking results of sensitivity analysis Original rank score Test 1 Test 2 Test 3 Test 4 Test 5 Test 6 Test 7 Test 8 Test 9 Test 10 Alt-1 0.7281 0.7279 0.7281 0.7279 0.7263 0.7175 0.7163 0.6308 0.6242 0.6244 0.6529 Alt-2 0.5272 0.5272 0.5272 0.5272 0.5292 0.5354 0.5357 0.6144 0.6199 0.6161 0.6585 Alt-3 0.8297 0.83 0.8297 0.8302 0.8327 0.8419 0.8437 0.8181 0.8213 0.8260 0.7098 R a n k in g Alt-1 2 2 2 2 2 2 2 2 2 2 3 Alt-2 3 3 3 3 3 3 3 3 3 3 2 Alt-3 1 1 1 1 1 1 1 1 1 1 1