Identifying and prioritizing marketing strategies for the building energy management systems using a hybrid fuzzy MCDM technique

Preventing energy waste in residential and office buildings has emerged as a critical issue in both developed and developing countries over recent decades. The growing demand for oil and energy reserves has amplified the urgency of this concern. The deployment of building energy management systems (BEMSs) can lead to timely responses to changes in environmental conditions, the prevention of energy wastage, a reduction in CO2 emissions, and an increase in the longevity of building equipment. Despite the undeniable benefits of BEMSs, their market size remains small, creating challenges for providers in reaching potential customers. This research seeks to identify and prioritize the marketing strategies for BEMSs. A case study was conducted, employing the “Strengths, Weaknesses, Opportunities, and Threats” analysis as a tool for identifying marketing strategies related to BEMSs. This method resulted in the identification of 18 distinct marketing strategies. These strategies were subsequently prioritized using a novel fuzzy multicriteria decision‐making technique, VIkor‐topSIS, considering six specific criteria. The findings of the study suggested a hierarchical influence of six criteria on the BEMS market, arranged in the following order of significance: effectiveness, cost, attainability, complexity, timing, and popularity. Furthermore, the top three marketing strategies for BEMSs were found to be internet advertising strategies, discounts to consumers, and online sales. The analysis of the results has also offered valuable insights into the strengths and weaknesses of the studied BEMS provider, as well as the opportunities and threats present within the BEMS market.

As stated in the European Commission Energy 2020 Strategy, energy is the lifeblood of society, making the optimization of energy consumption a critical global issue. 1 It is also argued in the literature that energy sustainability is one of the most urgent socioenvironmental issues in the contemporary era. 2 Over recent decades, preventing energy waste in the domestic and building sectors has become a significant concern for both industrialized and developing countries.According to data released by the European Commission in 2021, buildings account for 40% of energy consumption and 36% of CO 2 emissions in Europe, exceeding the energy consumed in the industry and transportation sectors. 3 This energy consumption relates to various building subsystems, such as Heating, Ventilating, and Air Conditioning (HVAC), safety, water, lighting, and so forth. 4Such high energy usage has promoted the advancement of building energy management systems (BEMSs).BEMS is a tool used to monitor and control a building's energy needs, and its implementation could represent a significant step toward reducing energy consumption in buildings. 5Besides energy management control, BEMS also monitors other aspects of buildings, including HVAC, lighting, intelligent control systems, fire systems, and security systems. 6According to Macarulla et al., 5 employing a BEMS solely to manage HVAC systems can lead to approximately 14% energy savings.A comprehensive review of energy-saving opportunities using energy management systems can be found in Lee and Cheng. 7 BEMS can also enhance equipment longevity by operating equipment efficiently and turning it off when unnecessary.This practice can dramatically reduce the cost of equipment maintenance and servicing.Furthermore, the ability to monitor and control all areas via a personal computer or internet access offers added convenience to residents by automating tasks and fostering a more pleasant environment. 8On a macrolevel, using BEMS reduces CO 2 emissions, promotes optimal social development benefits, and increases environmental sustainability. 9espite the benefits of using BEMSs, the market for these systems remains relatively small.Some of the challenges and barriers to the deployment of BEMSs have been discussed in a recent study by Kim and Ha. 10 Many attempts have been made to overcome the challenges by studying different aspects affecting the BEMS market, such as policy and regulations, [11][12][13] technical considerations, 14,15 economic factors, 16,17 and social issues. 18,19However, despite these efforts, the marking strategies used by the BMES providers as one of the factors affecting the deployment rate of BMESs have not yet been examined.In such circumstances, this study contributes to BEMS research by identifying and prioritizing marketing strategies through a case study.
The marketing strategies proposed in this research will assist BEMS providers in boosting their sales.Given that the number of existing marketing strategies is typically vast, implementing them simultaneously is impossible due to resource limitations. 20,21Therefore, this research aims to not only identify the marketing strategies but also prioritize the implementation of these strategies using multicriteria decision-making (MCDM) technique based on six criteria: effectiveness, cost, attainability, complication, timing, and popularity.Furthermore, the study incorporates fuzzy theory in defining parameters to accommodate uncertainties.
To identify the marketing strategies of BEMSs, this study utilizes the "Strengths, Weaknesses, Opportunities, and Threats" (SWOT) analysis.Then, the obtained strategies are prioritized and ranked using a novel hybrid fuzzy MCDM technique called VIkor-topSIS (VISIS).The VISIS technique is a combination of two popular MCDM techniques, namely, "Technique for Order of Preference by Similarity to Ideal Solution" (TOPSIS) and "VIekriterijumsko KOmpromisno Rangiranje" (VIKOR).
In light of the aim of the study, the main question of this research can be stated as follows: How can the marketing strategies of BEMS be identified and ranked using a combination of SWOT analysis and the fuzzy VISIS technique?
To respond to the main research question, the following subquestions need to be addressed.Subquestion 1: How can the strengths and weaknesses of the BEMS provider be systematically identified and analyzed?Subquestion 2: How can the threats and opportunities in the BEMS market be comprehensively assessed?Subquestion 3: How can SWOT analysis be employed to develop unique marketing strategies for BEMS?Subquestion 4: How can various criteria be effectively incorporated into the prioritization of marketing strategies, and how can their weights be determined?Subquestion 5: How are the identified marketing strategies ranked using the proposed VISIS technique?
The main contributions of this research are threefold: (1) identification of marketing strategies specifically for the BEMS sector, which has not been addressed in prior studies; (2) integration of SWOT analysis with an MCDM technique, taking into account six distinctive associated with developing underground pedestrian systems was conducted by Cui et al. 28 Solangi et al. 29 examined sustainable energy planning strategies in a case study.They ranked the strategies using the AHP and fuzzy TOPSIS techniques.Strategies for land conservation and geotourism development were studied by Datta. 30The author combined the strategies using the SWOT matrix, and the AHP technique was used to prioritize them.In another study, Aghasafari et al. 31 determined the most effective strategies for organic agriculture development by considering comprehensive factors affecting organic agriculture.The study led to the identification of 28 factors influencing organic farming and the definition of nine potential development strategies.A novel value chain model for the South Asian wind power industry, including Pakistan, India, and Bangladesh, was developed in a study conducted by Irfan et al. 32 Using SWOT analysis, 19 factors influencing the industry's competitiveness were identified.
In a study related to the hospitality sector, an alternative strategy for reviving hotel revenue in the face of the Corona pandemic was presented by Sahir and Dachyar. 33The quality of the higher education system was investigated by Fahim et al. 34 The authors integrated the AHP with entropy for ranking the variables.A SWOT analysis was performed on hydropower exploitation in Pakistan by Sibtain et al. 35 It resulted in the suggestion of 11 strategies to support the development and long-term sustainability of hydropower.In exploring the BEMS industry ecosystem, critical factors were gathered from previous research by Kim and Ha. 10 These factors were then categorized into four scopes: technology, economic, social, and institutional.In another elaboration, strategies for postmining land uses in Iran were identified by Amirshenava and Osanloo. 36Similarly, Olabi et al. 37 investigated the battery energy storage system technology in power transmission.In another study, Longsheng et al. 38 identified and prioritized the strategies for implementing sustainable waste-to-energy in Pakistan.In a different context, Yu et al. 39 identified and ranked strategies for utilizing sawdust biomass briquettes in Africa.

| Review findings and research contribution
Table 1 summarizes the reviewed studies by reporting the methods and case studies addressed.This table also displays the criteria considered for ranking the strategies in the current study and the presence of these criteria in previous research.| 4327 According to Table 1, none of the reviewed studies addressed marketing strategies in BEMS.The review also revealed that only one study related to healthcare considered effectiveness, cost, attainability, complication, timing, and popularity in ranking the alternative strategies.However, this study did not employ SWOT analysis, nor did it consider the uncertainty of qualitative parameters.The literature review also shows that while some previous studies successfully combined SWOT analysis with various MCDM techniques, they primarily used these techniques to determine the weight of internal factors (strengths and weaknesses) and external factors (opportunities and threats).They then ranked the strategies based on these weights without considering the criteria of attainability, cost, complication, effectiveness, popularity, and timing in ranking strategies obtained through SWOT analysis.
Given the literature review, the current study stands out in two ways.First, it identifies the marketing strategies of BEMS using SWOT analysis for the first time.Second, it prioritizes implementing the identified strategies by considering six aspects: effectiveness, cost, attainability, complication, timing, and popularity, employing a hybrid fuzzy MCDM technique named VISIS.
The marketing strategies identified in this research will help BEMS providers increase their sales.By boosting the sales of BEMS providers, energy consumption and CO 2 emissions will be reduced, which benefits both customers and society.Owing to resource constraints, implementing all strategies simultaneously is not feasible. 20,21Therefore, this research not only identifies marketing strategies but also prioritizes the implementation of these strategies, considering six significant criteria.The innovative aspects of the current study can be summarized as follows: • Identifying marketing strategies in the field of BEMS.

| RESEARCH METHOD
This research examines a BEMS provider to identify and prioritize BEMS marketing strategies.The process begins with analyzing the provider's strengths and weaknesses, followed by investigating market opportunities and threats within the BEMS industry.Employing SWOT analysis, the BEMS provider's marketing strategies are then identified.It is worth mentioning that other methods like "political, economic, sociocultural, and technological" analysis and Porter's Five Forces analysis also exist for identifying marketing strategies.However, these methods primarily focus on external factors and do not fully address the internal factors required for this study.The identified strategies are subsequently ranked using the VISIS technique considering six criteria: attainability, cost, complication, effectiveness, popularity, and time.The data needed for the study were collected using two questionnaires, which can be found in the Supporting Information document.Additional information regarding these questionnaires is provided in Section 3.1.

| Utilized questionnaires
This study utilized two questionnaires to gather expert opinions and collect the necessary data.Both questionnaires are standard, with their validity confirmed by previous research. 40,41A group of 10 experts, including five university professors with diverse expertise in management, strategy planning, and marketing, and five senior managers from the BEMS provider under study, completed these questionnaires.The senior managers primarily determined the internal factors (strengths and weaknesses), while the university professors mainly identified the external factors (opportunities and threats).The first questionnaire's reliability was verified with an inconsistency rate of 0.061, and the second questionnaire demonstrated reliability with Cronbach's α of 0.91.
The first questionnaire consists of pairwise comparisons between the considered criteria.In this questionnaire, each expert compared one criterion with another using the verbal expressions presented in Table 2.The numerical equivalent of each verbal expression is given in the second row of this table.The geometric mean of all expert responses was considered the final value in the pairwise comparison matrix.
The second questionnaire is utilized to determine each strategy's score for each criterion, thereby forming the decision matrix.The score for each alternative within each criterion is obtained through expert opinions using the Likert scale.Table 3 shows the choices and their T A B L E 2 Linguistic variables and their numerical equivalents used for pairwise comparison.

Linguistic variable
Same preference Slightly better Better Much better Absolutely better Quantitative value 1 3 5 7 9 associated fuzzy numbers adopted from Farokhnia and Beheshtinia. 42These fuzzy numbers reflect the ambiguities of the linguistic parameters.

| Research steps
To answer the research questions outlined in Section 1, to address the research questions presented in the introduction, the research methodology is divided into two phases, each comprising three steps.The first phase focuses on determining the marketing strategies of the BEMS provider through a SWOT analysis.As organizations face resource constraints, implementing all strategies simultaneously is not feasible.Hence, the second phase aims to prioritize the identified marketing strategies.
In the SWOT analysis, the marketing strategies are derived by considering both internal factors (strengths and weaknesses) and external factors (opportunities and threats).The first phase is then broken down into three steps.The initial step involves identifying the internal factors, including strengths and weaknesses.The second step revolves around recognizing external factors, encompassing threats and opportunities.These identified SWOTs are utilized to determine the marketing strategies using a SWOT matrix in the third step.
The second phase employs the VISIS technique to prioritize the marketing strategies.Within this study, multiple alternatives need to be ranked based on different criteria, each with varying degrees of importance (weights).To accomplish this, Steps 4-6 are designed to rank the strategies as alternatives using specific criteria.Step 4 determines the relevant criteria for ranking the marketing strategies and assigns their respective weights.Step 5 computes the score for each strategy in each criterion.Finally, Step 6 employs the VISIS method to determine the ranking of the marketing strategies.The research steps are illustrated in Figure 1.| 4329

| Phase 1: Determining the marketing strategies
This phase includes three steps and aims to determine the marketing strategies for BEMSs.
Step 1. Identify internal factors This step identifies the organization's strengths and weaknesses through in-depth interviews with experts.
Step 2. Identify external factors Market threats and opportunities are identified in this step through in-depth interviews with experts.
Step 3. Determining the strategies Through in-depth interviews with experts, marketing strategies are determined, and the SWOT matrix is created in this step.It is worth noting that internal and external factors that affect the organization should be identified before this step.Factors such as strength, weakness, opportunity, and threat are analyzed using the SWOT matrix.This matrix always provides four types of strategies, as explained below.

Conservative strategies (WO)
These strategies aim to take advantage of opportunities to compensate for weaknesses.

Defensive strategies (WT)
These strategies aim to reduce internal weaknesses and avoid threats from the external environment.Usually, when the organization is in a risky situation, it can overcome this crisis by using the method of company dissolution, outsourcing, reducing operations, and joining other companies.
Offensive strategies (SO) Following these strategies, the organization relies on its internal strengths to make the most of external opportunities.For this reason, most organizations tend to take advantage of these types of strategies.

Competitive strategies (ST)
Following these strategies, the organization's goal is to use methods that rely on internal strengths to prevent the negative impact of external threats and try to eliminate the threats.

| Phase 2: Ranking the strategies
This phase consists of three steps and deals with ranking the BEMS marketing strategies identified in Phase 1.
Step 4. Determining the effective criteria for prioritizing the strategies and obtaining their weights by AHP method In this step, the related criteria for ranking the strategies are identified using in-depth interviews with experts.
Then the weight of the obtained criteria is determined by AHP method.The first questionnaire is used by experts to create a pairwise comparison matrix.Considering that experts identified low uncertainties regarding the pairwise comparison matrix, crisp values represent the linguistic variables in this step.
After determining the criteria and finalizing the pairwise comparison matrix, the AHP technique is used to determine the weight of each criterion.The AHP technique is based on pairwise comparisons between alternatives and criteria and simultaneously examines qualitative and quantitative data. 43,44The main steps of the AHP technique are as follows.
• Sum the numbers of each column in the pairwise comparisons matrix.If n, x ij , and C j are, respectively, the number of criteria, the array of row i and column j of the pairwise comparisons matrix, and the sum of the arrays of column j, then (1) • Divide the pairwise comparison matrix numbers by the sum of the corresponding column to normalize.If n ij is the array of row i and column j of the normalized matrix, then • Calculate the average value of each row and consider it the corresponding criterion weight.If w i is the weight of criterion i, then Step 5. Determining the score of each strategy in each criterion this step concerns creating a decision matrix in which each strategy is considered an alternative.The second questionnaire is used in this step.Considering the ambiguities of the linguistic parameters and responders' uncertainties in determining the score of strategies for certain criteria, the triangular fuzzy number (TFN) is used to describe the ambiguines of the linguistic parameters in the decision matrix questionnaire.][47][48] This type of fuzzy number maintains the variable's value in an interval and displays the likelihood of different values in the considered interval. 49,50This presentation type also explains parameters' uncertainty well with relatively simple calculations. 51The selection of a triangular membership function was based on careful consideration of the specific problem and the characteristics of the variables involved.In this study, the variables exhibited a relatively symmetric distribution around a central value that a triangular shape could well represent and suitably approximate the relationships between input values and membership degrees.Moreover, the triangular membership function offers computational simplicity compared with other alternatives and provides better interpretability, which is very beneficial in decision-making problems.
In triangular membership, the first number is the lowest possible value, and the third number is the highest potential value.The second number also indicates the value with the highest probability of occurrence.If A a a a ̃= ( , , ) are two TFNs, the basic mathematic operations are defined as presented by Equations ( 4)-( 7). 52,53 , Moreover, the distance between two fuzzy numbers is calculated using Equation (5).
A TFN could be converted to a crisp number (i.e., defuzzification).In this research, a fuzzy number such as A a a a ̃= ( , , ) l m u is defuzzified using Equation (6).
Step 6. Ranking strategies using fuzzy VISIS technique After determining the marketing strategies, prioritization criteria, and the score of each strategy in each criterion, it is time to rank the strategies to help decision-makers choose the most effective strategy considering the available resources.This study uses a novel MCDM technique named VISIS to rank the strategies.The VISIS technique is a combination of two popular MCDM techniques, namely, TOPSIS and VIKOR.A detailed explanation of VISIS is provided in Section 3.3.

| The proposed fuzzy VISIS technique
The proposed VISIS technique is inspired by TOPSIS and VIKOR techniques.This section provides a brief overview of these three techniques and emphasizes the advantages that VISIS holds over the other two.TOPSIS and VIKOR are well-known MCDM techniques that share several similarities.Both require the criteria weights and decision matrix as inputs.They both normalize the decision matrix and obtain a weighted normalized decision matrix.Ultimately, they create an index to rank the alternatives.There are, however, differences between the two techniques' solution procedures.TOPSIS identifies the optimal alternative based on two parameters: (1) the total distance from the Positive Ideal Solution (PIS), and (2) the total distance from the Negative Ideal Solution (NIS). 54On the other hand, VIKOR seeks the best alternative based on two different parameters: (1) the total distance from PIS, and (2) the distance from PIS for each criterion (the regret index).
The VISIS technique attempts to offer a more comprehensive perspective for ranking alternatives by considering three parameters: (1) the total distance from PIS, (2) the total distance from NIS, and (3) the distance from PIS for each criterion (the regret index).In other words, the primary advantage of the VISIS technique lies in integrating the TOPSIS and VIKOR perspectives in ranking the alternatives.This comprehensive view considers more aspects in the ranking process, leading to more reliable results.
In this study, fuzzy numbers are used to express the uncertainty in the parameters' values.Therefore, Fuzzy TOPSIS, fuzzy VIKOR, and fuzzy VISIS are explained in the following sections.In all of the three techniques, it is assumed that there are n criteria (C 1 , C 2 , …, C n ) and m alternatives (A 1 , A 2 , …, A m ).Moreover, the weight of each criterion and the decision matrix are inputs of the techniques.Considering the fuzzy decision matrix X ̃, each array is denoted by x a b c ̃= ( , , ) ij ij ij ij representing the score alternative i in criterion j as presented in Equation (10). 55HESHTINIA ET AL.
where w j is the weight of criterion j, and it is determined by experts or calculated using an MCDM technique.
The notations and parameters used in the presented MCDM techniques are as follows.

| Fuzzy TOPSIS
TOPSIS is an MCDM technique that deals with ranking alternatives in which alternatives are ranked after evaluating their score in each defined criterion. 56This technique utilizes two concepts: PIS and NIS.PIS and NIS are generally defined by experts, with the objective of moving closer to the PIS and further away from the NIS.The distance of an alternative solution from PIS and NIS is measured to assess the similarity of the alternative to PIS and NIS.The best alternative is the one with the minimum distance from the PIS and the maximum distance from the NIS.The main steps of the fuzzy TOPSIS technique are outlined as follows. 55ep 1.
In these equations, J represents a set of positive type criteria (profit), and J′ represents a set of negative type criteria (cost).fpis ̃j and fnis ̃j are jth element of FPIS and FNIS, respectively.
Step 4. Determining the distance of each alternative from FPIS and FNIS using Equations ( 15) and (16).
where S i + and S i − are the sum of the distance of the alternatives from FPIS and FNIS, respectively.The d a b ( , ̃) means the distance between two fuzzy numbers ãand b ̃, which is calculated using Equation (5).
Step 5. Determine the closeness coefficient for each alternative i (CC i * ) using Equation (17).
The final rank is obtained by sorting the alternatives based on The alternative with the highest value of CC i * has a high distance from FNIS and a low distance from FPIS.Therefore, it is considered the best alternative.

| Fuzzy VIKOR
VIKOR technique is similar to the TOPSIS with the difference that the best alternative is the one with the minimum distance from PIS and the minimum or maximum distance in each criterion between the alternative and PIS. 57The main steps of the Fuzzy VIKOR technique are as follows. 44ep 1. Determine FPIS and FNIS.For each criterion, the best and worst values among all the alternatives are represented as fpis l m u ̃= ( , , ) and fnis l m u ̃= ( , , ) j j j j − − − , respectively.Equations (18)   and (19) represent the related formulation.
Step 2. Form the normalized distance matrix from the FPIS (D Step 3. Form a weighted normalized fuzzy matrix ) using Equation (21).
Step 4. Find the values of the utility index (S ̃i) that represent the total weighted distance of ith alternative from the FPIS, and the regret index (R i ) that represents the maximum distance of ith alternative from the FPIS in each criterion, using Equations ( 22) and (23).
where S S S S ̃= ( , , ) Step 5. Calculate the VIKOR index (Q ̃i) for each alternative i using Equation (24).In this equation, the first and second terms are the normalized values for the utility and regret indexes, respectively.
where S S R R S S S ̃= min ̃, ̃= min ̃, = max , = , and R R = min . Also ν is a weight for the maximum group utility strategy that can be between 0 and 1.
Step 6. Prioritize the alternatives after the defuzzification of the VIKOR index (Q ̃i).The alternative with the lowest Q ̃i value has a higher priority.

| Fuzzy VISIS
The VISIS aims to provide a more comprehensive viewpoint in ranking alternatives by combining VIKOR and TOPSIS.The main steps of the fuzzy VISIS technique are as follows.
Step 3. Form a weighted normalized fuzzy matrix for distance from the FPIS (V whose arrays are calculated according to Equations ( 27) and (28).
Step 4. Find the value of the utility index S ̃poz i that represents the total distance of ith alternative from FPIS, the hate index S ̃neg i that represents the total distance of ith alternative from FNIS, and the regret index R ̃i that represents the maximum distance of ith alternative from FPIS in each criterion, using Equations ( 29)- (31). where u , and w j is the weight of jth criterion.
Step 5. Calculate the VISIS index for each alternative i (Q ′ i ) using Equation (32).The first term is the normalized value for the utility index, the second term is the normalized value for the regret index, and the third term is the normalized value of the hate index. where , , and R R = max . Also ν 1 , ν 2 , and ν 3 are the weights for the factors of the total distance from FPIS, maximum distance from FPIS in each criterion, and total distance from FNIS, respectively.
Step 6. Prioritize the alternatives after defuzzification of the VISIS index ( ) The best alternative is the one with less value of defuzzified VISIS index.

| IMPLEMENTATION AND RESULTS
This section presents the results of applying the research steps to a real-life BEMS provider.Founded in 2010, the BEMS provider now operates through 10 branches with a staff of 115 personnel.They have 23 suppliers providing the necessary equipment for systems, such as ventilation, lighting, power, fire, and security.The provider's main clientele includes governmental organizations, construction companies, private enterprises, and individual citizens.The BEMS provider under study aims to expand its market through effective marketing strategies.
Adhering to the six steps presented in Section 3, this study aims to assist the company's decision-makers by identifying relevant strategies and ranking them according to their significance.

| Evaluation of internal factors (identifying the strengths and weaknesses of the organization)
The key strengths and weaknesses of the BEMS provider were identified through a thorough assessment of the provider's situation and conducting in-depth interviews.The factors identified are outlined in Table 4.

| Evaluation of external factors (identifying the market threats and opportunities)
The main opportunities and threats in the BEMS market were discerned through in-depth interviews and an assessment of market conditions.The identified factors are presented in Table 5.

| Forming the SWOT matrix and determining the marketing strategies
Various marketing strategies were derived based on the opinions of experts.The obtained SWOT matrix is presented in Table 6.Six criteria-attainability, cost, complexity, effectiveness, popularity, and timing-were identified based on experts' opinions to prioritize strategies.The weights of these criteria were obtained using the first questionnaire and implementing the AHP technique.The respective weights for each criterion are reported in Table 7.
The results indicate that among the six evaluated criteria, effectiveness has the highest weight in strategy assessment.Subsequent criteria in descending order of weight are cost, attainability, timing, complexity, and popularity.

| Determining the score of each strategy in each criterion
At this stage, the strategies derived from the SWOT analysis serve as alternatives, and a decision matrix is formed as demonstrated in Table 8.The scores within this matrix represent each alternative's score per criterion, obtained via the second questionnaire.The decision matrix reveals that, under the effectiveness criterion, the strategies of establishing sales agencies worldwide, establishing sales agencies in the Middle East, and improving quality received the highest scores, respectively.Moreover, for the timing criterion, online sales and internet advertising were the ones with the best scorers.For attainability, lottery prize distribution, radio advertising, and TV advertising ranked highest, respectively.From a cost perspective, telephone advertising and internet advertising earned the highest scores.Evaluating the complexity criterion, quality improvement, and online sales strategies obtained the best scores.With respect to the popularity criterion, establishing sales agencies worldwide and in the Middle East achieved the highest scores, respectively.

| Ranking strategies using the fuzzy VISIS technique
In this section, the steps of the fuzzy VISIS technique are executed.Some results, such as the total distance from FPIS (S ̃+i ) and FNIS (S ̃−i ), regret index (R ̃i), fuzzy VIKOR index (Q ̃i), defuzzified VIKOR index, and rank of each strategy, are presented in Table 9.In this study, the weights v 1 , v 2 , and v 3 are assumed to be equal and equivalent to 1/3.Table 9 demonstrates the values of parameters used in calculating the VISIS index.The results of the fuzzy VISIS technique indicate that the strategies of "internet advertising," "quality improvement," and "discount to consumers" hold the highest priority, respectively.
T A B L E 6 SWOT matrix.

| Comparison with other MCDM techniques
In this section, the results from the VISIS technique are compared with four other MCDM techniques, namely, fuzzy TOPSIS, 55 fuzzy VIKOR, 44 fuzzy WASPAS, 58 and weighted product model (WPM). 59ifferent MCDM techniques offer distinct perspectives, often leading to different rankings. 44Hence, the comparison presented in this section merely aims to provide an insight into the ranking generated by the VISIS technique when compared with other popular MCDM techniques mentioned above.Given that the proposed VISIS technique borrows concepts from TOP-SIS and VIKOR, detailed calculations for these two techniques are presented in Tables 10 and 11, respectively.As for WASPAS and WPM, only the final solutions are reported in Table 12.
As demonstrated in Table 10, according to the fuzzy TOPSIS technique, the "Establishment of sales agencies in different parts of the world" (S18) has obtained the first rank.The "Establishing sales agencies in the Middle East" (S17) and "TV Advertising" (S1) are given the second and third highest priorities, respectively.
As shown in Table 11, according to the fuzzy VIKOR technique, "internet advertising" (S3), "quality improvement" (S13), and "discounts to consumers" (S7) are ranked highest, respectively.It is worth noting that the weights of v 1 and v 2 in the fuzzy VIKOR are assumed to be equal and set to 0.5.
The rankings obtained for each strategy by all the considered techniques, including WASPAS and WPM, are presented in Table 12.This table demonstrates that the ranking provided by each technique varies.These variations may be attributed to the unique perspective of each technique in ranking the alternatives.Despite the discrepancies in the rankings provided by the different techniques, "Internet advertising" (S3) is given the highest priority by all techniques except fuzzy TOPSIS.This result can be considered an indication affirming the validity of the VISIS technique.
A pairwise comparison has been performed to compare VISIS with the other four MCDM techniques.Each technique was compared with the other four based on the sum of differences in ranking the alternatives (SDRA).The SDRA index of each technique is presented in Table 13.Assuming MCDM techniques A and B are compared, the SDRA is calculated using Equation (33).
The SDRA index represents the difference in the results of techniques A and B. A lower SDRA value implies greater similarity in the results provided by the two compared techniques.In other words, a lower SDRA index suggests less variation in results.
According to Table 13, the sum of SDRA values for fuzzy VISIS is lower than that for the other techniques (i.e., 158 differences), indicating fewer variations in the rankings when comparing fuzzy VISIS with the other four techniques.Therefore, it can be inferred that fuzzy VISIS is more consistent than the other four techniques.Furthermore, this comparison supports the validity and reliability of fuzzy VISIS." As can be observed in Table 13, fuzzy TOPSIS demonstrates the largest value of SDRA compared with other MCDM techniques.The highest values in this table are 100 (between fuzzy TOPSIS and fuzzy WPM), 90 (between fuzzy TOPSIS and fuzzy WASPAS), 66 (between fuzzy TOPSIS and fuzzy VISIS), and 64 | 4339 (between fuzzy TOPSIS and fuzzy VIKOR).This difference is due to the variation of alternative scores across different criteria.The TOPSIS method evaluates the total distance of each alternative from the PIS and NIS, denoted S i + and S i − , respectively.These indices are compensatory, meaning a large (or low) distance from PIS (or NIS) in one criterion can be offset by its low (or large) distance from PIS (or NIS) in other criteria.In other words, the focus is on the sum of distances from PIS or NIS for each criterion, disregarding their variation.Conversely, the variation of alternative scores across different criteria is considered in VISIS and VIKOR techniques by the regret index, and in WPM and WASPAS techniques by the weighted matrix for multiplications.

| Sensitivity analysis
This section provides a sensitivity analysis to evaluate the robustness of the proposed fuzzy VISIS technique.Two different analyses, detailed in Sections 4.8.1 and 4.8.2, are performed.
The first analysis investigates the sensitivity of VISIS concerning the assigned weight to each criterion.The second analysis assesses the sensitivity of VISIS concerning the coefficients of the VISIS index.Furthermore, given that VIKOR was identified as the second-best method in terms of the SDRA index (see Table 13), a detailed comparison of the sensitivity of VISIS and VIKOR is conducted.

| First sensitivity analysis
The first analysis aims to examine the impact of varying input parameters on the results, following the methodology proposed by Beheshtinia et al. 60 This analysis specifically explores how alterations in the weight of each criterion affect the VISIS results.Six scenarios are set up, each differing by the original weight of one criterion.As suggested by Vaziri and Beheshtinia, 61 in each scenario, the weight of one criterion is increased by 10% compared with its original value.In the first scenario, the weight of the first criterion is increased by 10%, and to ensure that the sum of all weights remains 1, this increment (10%) is uniformly subtracted from the other criteria.This procedure is repeated in the second through the sixth scenarios, where the weight of the second to sixth criteria is increased by 10%, respectively, and the weights of the other criteria are adjusted to maintain balance.The criteria weights in different scenarios are presented in Table 14.
All 18 strategies presented in Table 8 are ranked using VISIS across six designed scenarios, and these rankings are compared with the base scenario (i.e., the scenario with original weights for each criterion).The ranking of strategies derived from VISIS for each scenario is presented in Table 15.The last row of Table 15 shows the number of changes in ranking the alternatives (NCRA) compared with the base scenario.
Comparative results demonstrate that, apart from scenario 4, the rank order provided by VISIS remains remarkably consistent across all scenarios.As per the NCRA index, in five out of six scenarios, there are fewer than three differences in ranking the strategies.In scenario 4, the NCRA is 10, suggesting a heightened sensitivity in the ranking of alternatives to the weight of the fourth criterion (i.e., effectiveness).

| Second sensitivity analysis
This analysis investigates the influence of the main parameters' weights on the final result (i.e., the VISIS index) in the VISIS technique.The total relevant distance from the FPIS (v 1 ), the regret index (v 2 ), and the total relevant distance from FNIS (v 3 ) all contribute to the VISIS index, as presented in Equation (32).To evaluate the stability and robustness of the VISIS, a sensitivity analysis is conducted across three different scenarios, wherein distinct weights are assigned to v v , , 1 2 and v 3 .This analysis considers three scenarios, as presented in Table 16.In the first scenario, the weight assigned to the FPIS is greater than the FNIS.The second scenario attributes equal weights to all indexes.In the third scenario, the FPIS weight is considered lower than that of the FNIS.Since the sum of v 1 , v 2 , and v 3 should equate to 1, a value of 1/3 is assigned to v 1 , v 2 , and v 3 in the second scenario.When there are two parameters, the literature recommends the values of 0.8 and 0.2 to represent the dominance of one parameter over another. 62As VISIS includes three parameters, the first scenario assigns the | 4341 values of 0.5 and 0.3 to v 1 and v 2 , respectively (v 1 + v 2 = 0.8), with a value of 0.2 given to v 3 to underscore the preference for FPIS over FNIS.In a similar way, in the third scenario, to signify the preference of FNIS over FPIS, v 1 is given a value of 0.2, while v 2 and v 3 are assigned values of 0.3 and 0.5, respectively (v 2 + v 3 = 0.8).
A comparison between the results of the first and second scenarios reveals only a single difference in the final ranking of strategies.No differences are observed when comparing the results of the first and third scenarios, and merely one difference arises when comparing the second and third scenarios.These observations show the robustness of VISIS against alterations to the weight of main parameters.It should be noted that a lower VISIS index signifies a better rank.
In summary, both sensitivity analyses showed the robustness of the proposed VISIS in ranking the considered strategies.These sensitivity analyses also demonstrated the reliability and stability of VISIS in relation to both the weight assigned to prioritization criteria and the weight of VISIS parameters.

| Comparing the sensitivity of VISIS and VIKOR
The comparison in Section 4.7 showed that VIKOR was the second most reliable technique.Therefore, this section compares the sensitivity of the VISIS with VIKOR.For this purpose, the sensitivity of VIKOR is tested on the studied case, and the results are compared with those of VISIS.
In the VIKOR technique, the relevant weights of the total distance from FPIS (v 1 ) and the regret index (v 2 ) impact the VIKOR index.The scenarios considered for the VIKOR technique are presented in Table 17.In the first scenario, the weight of the total relative distance from FPIS is lower than that of the regret index.In the second scenario, both weights are equal, each being equivalent to 0.5.In the third scenario, the weight of the regret index is lower than the total relative distance from FPIS.
A comparison of the results from the first and second scenarios reveals that the final rank of strategies differs in 10 instances.Furthermore, when comparing the results of the first and third scenarios, as well as the second and third scenarios, there are 14 and 10 differences, respectively.The sensitivity analysis reveals 34 differences in all comparisons between the scenarios considered for the VIKOR technique, while there are only two differences in all comparisons for the VISIS technique.These results underscore the robustness and stability of the VISIS as compared with the VIKOR technique.

| Results analysis
As already stated, the VISIS technique incorporates the perspectives of TOPSIS and VIKOR in ranking alternatives.Therefore, the results obtained by all three techniques are analyzed and discussed here.
According to the results presented in Section 4.7, the results derived from VISIS, TOPSIS, and VIKOR are not identical.The primary reason for these differences stems   | 4343 from the unique approach each technique takes in ranking alternatives.Using the TOPSIS technique, an alternative gains a higher priority when its total distance from PIS is low, and the total distance from NIS is high.In contrast, the VIKOR technique assigns a higher priority to an alternative when the total distance from the PIS is low, and its maximum distance from the PIS across each criterion (referred to as the regret index) is also low.

VISIS index
The TOPSIS technique considers the total distance from the NIS when ranking the alternatives, a parameter that the VIKOR technique disregards.Conversely, the VIKOR technique takes into account the regret index when ranking alternatives, a parameter that the TOPSIS technique overlooks.
The VIKOR technique prioritizes alternatives that have balanced scores across all criteria over those with unbalanced scores.In other words, a lower score in any criterion leads to a worse value for the regret index, and this cannot be improved by a good score in other criteria.
Due to the nature of the problem, this study motivates the use of an MCDM technique that incorporates both compensatory and noncompensatory viewpoints.
From a compensatory perspective, the total scores of an alternative in all criteria are taken into consideration when ranking the alternatives.This means that a lower score in one criterion can be offset by a higher score in other criteria.Conversely, from a noncompensatory standpoint, an alternative is disqualified if it fails to meet a certain criterion, regardless of its performance in other criteria.
The VISIS technique is designed by incorporating elements from both viewpoints.It considers the total distance from the PIS and the total distance from the NIS, providing a compensatory viewpoint that takes the total score of an alternative in all criteria into account.Additionally, VISIS utilizes the concept of the regret index commonly found in noncompensatory techniques.This comprehensive approach yields more reliable results when ranking alternatives.
Table 18 displays the primary elements in the ranking process of TOPSIS, VIKOR, and VISIS, along with their attributes.Furthermore, a comparison of VISIS with other MCDM techniques in Table 13 justifies the use of this method.Additionally, the sensitivity analysis presented in Section 4.8 demonstrates that the results of VISIS are less sensitive to the weights considered in the VISIS index.
The results of the TOPSIS technique indicate that the strategies of "establishing sales agencies in different parts of the world" (S18), "establishing sales agencies in the Middle East" (S17), and "television advertising" (S1) have the highest priority, respectively.This is because, based on the results displayed in Table 10, these strategies have the most considerable distance from NIS (values of 0.502, 0.478, and 0.473, respectively) and the smallest distance from PIS (values of 0.102, 0.126, and 0.133, respectively) among the other alternatives.
The result of the VIKOR technique indicates that the strategies of "internet advertising" (S3), "quality improvement" (S13), and "discounts to consumers" (S7) have the highest rank, respectively.This ranking is due to the role of the regret index in the VIKOR index.The regret index emphasizes that an alternative should not have a substantial distance from PIS in any criteria.For example, although the value of the normalized utility index for the strategy of "online sales" (0.1875) is better than this value for the strategy of "quality improvement" (0.235), the final rank of the "quality improvement" strategy outperforms the "online sales" strategy (refer to Table 11).This is because the normalized value of the regret index for the "quality improvement" strategy (0.0963) is better than that for the "online sales" strategy (0.1405).
Finally, the results of the VISIS technique indicate that the strategies of "internet advertising" (S3), "discount to consumers" (S7), and "Online sales" (S11) have the highest priority, respectively.This is related to the role of the hate index in calculating the VISIS index.
The hate index emphasizes that an alternative should have a large distance from NIS.For instance, although the strategy of "Quality improvement" has a higher rank than the strategy of "discounts to consumers" in the VIKOR technique, in the VISIS technique, the strategy of "discounts to consumers" has a higher rank than the strategy of "quality improvement."This is due to the fact that the strategy of "discounts to consumers" has a value of 0.559 in the normalized hate index, but the strategy of "quality improvement" has a value of 0.537 in this index (see Table 9).

| CONCLUSIONS AND FUTURE RESEARCH DIRECTIONS
This section summarizes the research findings, limitations, and suggestions for future work.

| Summery
This research introduced a methodology for identifying and ranking BEMSs marketing strategies by combining SWOT analysis with a hybrid MCDM technique.Through SWOT analysis, eight critical strengths, six weaknesses, five opportunities, and seven threats were pinpointed, leading to the identification of 18 unique marketing strategies.Owing to resource constraints, it is unfeasible to implement all strategies simultaneously, necessitating a method for prioritization.In this regard, a novel hybrid MCDM method called VISIS was employed while incorporating fuzzy logic to manage the uncertainty inherent in qualitative parameters.To rank the strategies, VISIS employed six specific criteriaeffectiveness, costing, attainability, timing, complication, and popularity.Among these criteria, effectiveness was found to be the most crucial, followed by costing, attainability, timing, complication, and popularity.The application of VISIS also facilitated the identification of the top four marketing strategies for BEMS, which are internet advertising strategies, quality improvement, consumer discounts, and online sales.

| Research limitations and implications
The limitations of this study align with other case-based research employing MCDM methods.The final alternative ranking obtained in this research is based on the information and data from the specific case study.As such, the weight of criteria considered in ranking the strategies might differ in other cases, leading to potentially different rankings.Despite this, the identified SWOT, and proposed marketing strategies could provide | 4345 beneficial insights for other cases.Furthermore, the proposed VISIS method and the specific criteria used for ranking the marketing strategies (i.e., effectiveness, cost, attainability, complication, timing, and popularity) may not be applicable to all cases.Hence, decision-makers should adapt the criteria carefully based on the specific requirements of their case.

| Future research scopes
This study opens several avenues for future research.The current study can be extended by including additional criteria to prioritize strategies, allowing the consideration of different viewpoints on the strategic options available and their potential impacts.Moreover, employing other MCDM techniques could add further depth and dimension to the study, potentially leading to new insights into strategy prioritization.Regarding the solution method, future studies might consider combining other MCDM techniques to prioritize strategies.A comparative analysis between the results obtained from different techniques and the present study could yield interesting findings.It could highlight the strengths and weaknesses of each approach, providing more robust decisionmaking tools for practitioners.Furthermore, the application of the proposed VISIS technique could be extended beyond the current context.Other cases and domains could benefit from utilizing VISIS to rank alternatives.Such applications could offer invaluable real-world testing for this new technique, enhancing its validity and demonstrating its versatility.

T A B L E 3
Choices in the second questionnaire and their corresponding fuzzy numbers.The research steps.AHP, analytic hierarchy process; SWOT, Strengths, Weaknesses, Opportunities, and Threats; VISIS, VIkor-topSIS.BEHESHTINIA ET AL.

n:+:−
(j = 1,.,n):The number of criteria m (i = 1,.,m):The number of alternatives x a b c ̃= ( , , ) ij ij ij ij : Score alternative i in criterion j, where a ij , b ij , and c ij are low, medium, and high values of this fuzzy number w j : Weight of criterion j c* j : Maximum value of c ij between all alternative a°j: Minimum value of a ij between all alternative ṽi j : Weighted normalized score of alternative i in criterion j in TOPSIS technique S i − : Total distance of alternative i from FNIS in TOPSIS technique S i + : Total distance of alternative i from FPIS in TOPSIS technique CC* i : The closeness coefficient of alternative i d ̃ij : Normalized distance of score of alternative i in criterion j from FPIS in VIKOR technique v′ ij : Weighted normalized distance of alternative i in criterion j from FPIS in VIKOR technique FPIS fpis fpis = ( ̃, ., ̃} n 1 : jth element of the fuzzy positive ideal solution FNIS fnis fnis = ( ̃, ., ̃} n 1 : jth element of the fuzzy negative ideal solution S S S S ̃= ( , , )

T A B L E 4 4 . 4 |
Identified strengths and weaknesses.Strengths Weaknesses • S1: Risk-taking of senior managers of the organization • W1: Lack of sufficient experience of employees and managers in their field of work • S2: Intraunit cohesion • W2: High shipping cost • S3: Belief in modern management methods in the company • W3: Inadequacy of advertising • S4: Appropriateness of the company's location • W4: Price inflexibility • S5: The open mind of the company's senior managers • W5: Weakness in receiving big orders • S6: Use of modern technology • W6: Poor training in the organization • S7: Management stability • S8: Using young and energetic employees T A B L E 5 Identified opportunities and threats.Opportunities Threats • O1: Existence of bestselling export marketing • T1: Being away from project suppliers • O2: Lack of solid competitors in the geographical area • T2: Lack of cooperation between suppliers and contractors with the company • O3: Suitable location of the company in the geographical area • T3: The housing market downturn • O4: Entrance of the local specialized forces working in other provinces • T4: The negative impact of the country's economic problems on the construction industry • O5: Growing market and high sales potential in the future • T5: High product cost compared with traditional products • T6: Existence of a labor force with high education and dissatisfaction with low-level work • T7: Insufficient effort in advertising products in visitor marketing due to the lack of an independent distribution network Determining the effective criteria for prioritizing the developed strategies

T A B L E 16
The ranking of strategies by the VISIS for considered scenarios.
Normalized distance of alternative i in criterion j from FPIS , : Total distance of alternative i fromFNIS in the VISIS techniqueQ ′ i : VISIS index Used criteria and their weight.Ranking of strategies obtained by fuzzy VISIS.
T A B L E 10 Results and rankings provided by the fuzzy TOPSIS for the case study.
T A B L E 11 Results and ranking provided by fuzzy VIKOR for the case study.
T A B L E 14 The weight considered for each criterion in each scenario.The rank obtained by VISIS for each strategy in each scenario.
T A B L E 17The considered scenarios for the VIKOR technique and the obtained ranking.
T A B L E 18 The main elements affecting the ranking provided by TOPSIS, VIKOR, and VISIS.Abbreviations: NIS, Negative Ideal Solution; PIS, Positive Ideal Solution; TOPSIS, Technique for Order of Preference by Similarity to Ideal Solution; VIKOR, VIekriterijumsko KOmpromisno Rangiranje; VISIS, VIkor-topSIS.BEHESHTINIA ET AL.