1 INTRODUCTION

A country's economic progress depends heavily on foreign direct investment (FDI). Foreign investors' cash enables developing nations to improve their infrastructure, increase their production, and provide new employment possibilities (Crespo & Fontoura, 2007). FDI also serves as a means of acquiring sophisticated technologies and mobilizing foreign exchange resources. The presence of foreign currency reserves in the nation allows the central banking institutions to engage in the foreign exchange market and manage any unfavorable movements in order to stabilize foreign exchange rates. Therefore, it creates a more conducive economic climate for any economy to grow (Menkhoff, 2013). In the era of globalization, most of the developing economies have realized the importance of flows of FDI in different sectors and are making favorable policies concerning the FDI inflows (Paul, 2016). Indian economy, being categorized as a middle income developing market economy, started focuses on attracting FDI inflows in India. To encourage FDI, the Indian government has put in place an investor-friendly policy that allows FDI into the majority of industries on an automatic basis (Jana et al., 2019). With the liberalization in the FDI policy, India has become a preferred destination for FDI. India has been ranked as the ninth largest FDI recipient in the year 2019 as per the World Investment Report, 2020. The FDI inflow in India has been continuously increasing over the number of years. In 2012, it was 24,195.8 million dollars. It was increased to 50,533 million dollars in 2020 (UNCTAD World Investment Report, 2020).

There is a brighter future for the Indian economy due to its large human capital base, rapidly developing service sector, availability of sufficient number of skilled individuals, enormous market for every good or service, growing consumer influence, lack of regulation and licensing, and the presence of 400 million middle-class Indian citizens. Of all countries in the world, India offers the greatest returns on FDI (Sarkodie & Strezov, 2019). In India, FDI inflow has played a crucial role, and it is encouraged for the overall growth of the economy (Sahoo & Mathiyazhagan, 2003). Indian government is making policies to ease out restrictions for creating an investment-friendly environment for the investors. Reforms in industrial policy have resulted in a considerable reduction in the number of licenses required, the removal of business expansion limitations, and the facilitation of simple access to foreign technology and FDI. In India, sector-wise investment pattern is witnessed, and it has been observed that the FDI inflow is highest in the service sector (17.66%), followed by computer science (9.54%) and telecommunication (8.13%) from the year April 2000 to December 2019 (DPIIT, 2020). FDI inflow in any economy depends upon the multiple factors, through which other countries attracts for foreign investment. Piteli et al. (2021) investigated how inward FDI affects migrant remittance in 46 developing economies. In this study, authors established a novel framework that describes the fundamental processes for remittance interactions. Jushi et al. (2021) examined the importance of the impacts of FDI on economic growth for Western Balkan countries. They proposed the use of incoming transfer flows by introducing new policies to ensure a steady attraction of remittances.

FDI inflow to a nation is affected by a variety of factors. However, based on the degree of economic development, the links vary from one country to another. FDI inflows into India are influenced by a variety of factors (Kishor & Singh, 2015). Before investing, investors first, determine the riskiness of the investment atmosphere in the country before making a choice to invest overseas. Some of the factors that may be considered, include the availability of natural resources, a good geographic location, a large and prospective market, a stable cultural and political environment, inexpensive transportation and labor expenses, as well as supportive government and economic policy (Kumari & Sharma, 2017). FDI is also influenced by the number of attributes including sustainability related factors (Langley et al., 2021). Buchanan et al. (2012) investigated the influence of institutional quality on FDI volumes and volatility. They offered new evidence to show that the quality of educational institutions has a favorable and significant impact on FDI. Wang et al. (2013) endorsed that FDI and growth depend upon the changing policy and institutional environment impacts the investors' decision. Gupta and Singh (2016) concluded that there is a positive and significant relationship between FDI and economic growth of the country but all the sectors are not equally benefited with the FDI inflows. The nexus between FDI and economic growth is essential to study the impact of continuous investment flows on economic development (Alfaro & Charlton, 2013). Hitt (2016) determined that formal regulatory elements may be “even more essential” for foreign strategies than cultural and cognitive characteristics in the host nation. Li and Tanna (2019) stated that institutions factors are more essential than human capital factors in deciding productivity benefits from FDI. The detailed literature related to the factors affecting FDI decision in the host country is given in the next section.

Nowadays, experts use multiple analytical and scientific approaches for choosing the best alternative over many alternatives. The decision-making based on several criteria, popularly known as multi-criteria decision making (MCDM), is one of the most reliable methods used in assessing the different criteria. In MCDM, decision-makers choose the best alternative from the different independent alternatives (Ferreira et al., 2019; Gal et al., 2013; Mardani et al., 2015; Zavadskas et al., 2014). In recent years MCDM methods and applications have been used for many financial studies to address field problems such as portfolio analysis, general financial planning, capital budgeting, interest rate and risk analysis, prediction, and classification, working capital and commercial bank management, auditing, accounting, insurance, and pension fund management, government and nonprofit organizations, strategic planning, mergers, and acquisitions (Dymowa, 2011; Hallerbach & Spronk, 2002; Steuer & Na, 2003). In context to developing economies, limited studies have been done to suggest a framework for making decisions on FDI. Therefore our study will try to bridge this research gap by answering following research questions.

In this study, we employ the integrated approach comprising of analytic hierarchy process (AHP) and weighted aggregated sum product assessment (WASPAS) methods for assessing the preference of FDI in different sectors of. The first phase consisted of the AHP framework to delegate weights to different criteria's and sub-criteria's. The second phase included the use of WASPAS method to set ranks of sectors according to the preference value to various alternatives. WASPAS method differs from other approaches because it is easy to comprehend and use, but very efficient considering the weighted sum model (WSM) and weighted product model (WPM). Because of its mathematical simplicity and capability to provide more accurate results as compared to other methods, it is now being widely accepted as an efficient decision-making tool.

The rest of this article is structured in the following way. Section 2 includes a summary of FDI literature along with the integrated AHP-WASPAS methods. Section 3 deals with the problem description of the proposed work. AHP and WASPAS based procedure for determining the FDI for different sectors is given in Section 4. Section 5 describes the hierarchical problem layout and the findings of the proposed work. Finally, in the last part, conclusions are given.

2 LITERATURE REVIEW

To fulfill the paper's purpose, the literature review has been divided into two parts, that is, a literature review on FDI and its determinants and an integrated AHP-WASPAS approach used for determining the real-life application.

3 FRAMEWORK DEVELOPMENT

However, the literature reviews also found specific gaps in knowledge, whereby very few studies have studied and used MCDM for efficiency measurement and decision making in FDI. There is also a lack of studies using ECO, social factor (SOC), POL, and COUs all-together and their impact on FDI in developing nations. Last, no studies combine five significant sectors, that is, retailing and wholesaling, energy and natural resource, communication technology and information, finance and banking service, and manufacturing and industrial, for evaluating the FDI using MCDM.

The spectacular rise of FDI in the world economy is one of the most striking trends in the past two decades. The exponential rise in FDI globally in 1990 makes it a significant and central aspect of the growth policy in developed and developing countries, and policies are structured accordingly to enhance internal flows. FDI gives the host and home countries a win–win scenario (Goel et al., 2012). The Indian region is one of the world's most diverse economic regions and interlinked and enticing multi-national investment venues for investors. The following criteria are defined for FDI, based on the literature review. In India, the FDI's origin can be traced back to the British East India Company's existence. During Britain's colonial time in India, the British capital came to India (Teli, 2014). The literature that supports FDI criterion is reviewed and framework is shown in Figure 1.

Some countries are stable in term of the political situation; however, the general economic condition is weak because some external factors such as country and social criteria's play an essential role in choosing FDI. On the other hand, if the targeted country is vital in economic terms, but it is not politically stable, other external factors' role comes into play before finalizing FDI. Most of the studies focus on these two factors, that is, political and economic, but some countries and SOCs are also considered before FDI in any countries. This study focuses on the ECO, POL, COU, and SOCs that can be considered before investing in any counties.

4 SOLUTION PROCEDURE

This section outlines the various steps taken to accomplish the aims of this research. Figure 2 demonstrates different phases in carrying out this analysis. According to Figure 1, as given in Section 3, multiple economic, social, political, and country criteria have been considered the main decision criteria to choose the best sector for FDI in India.

In 2018–2019, India remained the world's fastest expanding largest economy (Ministry of Statistics and Program Implementation, Government of India). India's GDP is expected to raise much more in 2019 by 7.3%, according to the April 2019 World Economic Outlook of the International Monetary Fund. Based on the literature, this paper aims to analyze which India sectors is most suitable for FDI in India. To achieve this document, 100 academics and industrial experts using purposive sampling were chosen from online databases, and questionnaires sent to them. During the period from September to October, 2020 data was collected using mailing, telephonic interviews and two follow-ups fetched up 82 responses. The illustrated figure of political, economic, social, and country criteria and their sub-criteria used to evaluate FDI in different sectors of India are given in Figure 1.

The data analysis part was divided into two sections. The first section comprises the application of AHP to assign weights to various attributes (Section 3.1). The second section involved the application of WASPAS to assign ranks to different attributes as per the closeness index value (Section 3.2). In the current study, AHP calculates the weights, which will serve as a primary input to WASPAS method. The comprehensive research framework used for solving the proposed work is discussed below:

4.1 Determination of weights

AHP (Saaty, 1989; Wu et al., 2009) is an efficient and fast decision-making tool. It is widely used in the priority and selection of criteria's. AHP selects the strategic priorities as a compilation of weighted parameters which can be used to assess the criteria. It structures the decision to reduce decision bias and ensure each criterion has an equal chance of being prioritized and can also consensus between the decision-making is effectively identified. The steps by step procedure for obtaining the weights are described below:

Step (i) Construct pairwise comparisons; the next step after determining the weight of each attribute is to figure out the relative significance of one alternative over another.

Step (ii) Sum up the values of each column obtained from the pairwise comparison.

Step (iii) In order to get the matrix normalized, divide each element of the column of a pairwise matrix with its corresponding sum.

Step (iv) The next step is to sum up all the elements of each row obtained by using step (iii) and dividing it by the number of elements in each row. The outcome indicates each alternative the total priority rating.

Step (v) The last step is to calculate the priority vectors of the alternatives obtained by multiplying the pairwise comparisons of the alternatives matrix and the criteria weight.

Step (vi) Comparisons are assumed to be reasonably compatible in the decision matrix if the resulting “consistency ratio (CR)” is less than 10%. A “consistency index (CI)” is specified for the measurement of the CR coefficient, which is determined as follows:

(1)

where is the maximum eigenvalue of the matrix for a pair comparison However, the CR is determined by dividing the CI value by the “random consistency index (RCI)”, as shown in Equation (2) (Table 2)

(2)

TABLE 2. RCI value

N	1	2	3	4	5	6	7	8	9	10
RCI	0	0	0.52	0.89	1.11	1.25	1.35	1.4	1.45	1.49

• Abbreviation: RCI, random consistency index.

When the CR value is smaller than or equivalent to 10%, data is considered to be accurate; however, if it is greater than 10%, it is not reliable.

4.2 Determination of rank

The WASPAS method (Chakraborty & Zavadskas, 2014; Pathapalli et al., 2019) is commonly used to overcome real-world ranking issues, and it is used often to tackle multi-criteria decision-making complexities in comparing the criteria and attributes. It is a method of offsetting sum and product contrasted by defining each criterion's weight for a set of alternatives, normalizing values for each criterion, and evaluating the relative preference value using the WSM and WPM for every alternatives. The steps by step procedure for ranking the sectors are described below:

Step (i) Construct the decision matrix of m number of alternatives, and n number of evaluation criteria, and is the performance of ith alternative with respect to jth criterion

Step (ii) Construct the normalized decision matrix that requires linear normalization of the decision matrix elements using the following two equations:

For beneficial criteria,

(3)

For non-beneficial criteria,

(4)

where is the normalized value of .

Step (iii) In WASPAS method, a joint criterion of optimality is sought based on two criteria of optimality. The first criterion of optimality, that is, criterion of a mean weighted success is similar to WSM method (Chourabi et al., 2019; Rao & Venkatasubbaiah, 2016). It is a popular and well accepted MCDM approach applied for evaluating a number of alternatives in terms of a number of decision criteria. Based on WSM method, the total relative importance of ith alternative is calculated as follows:

(5)

where, is the relative weightage of the jth criterion.

On the other hand, according to WPM method (Chourabi et al., 2019; Rao & Venkatasubbaiah, 2016), the total relative importance of ith alternative is computed using the following expression:

(6)

A joint generalized criterion of weighted aggregation of additive and multiplicative methods is then proposed as follows:

(7)

Note that, higher the value of , higher the rating order of alternatives. All the above discussed steps involved in the process of evaluating the best sector for FDI is summarized below in the flowchart.

In this study, two-stage integrated MCDM methods were used to evaluate the FDI factors and to determine the most ideal industrial sector for FDI in India. The integrated AHP-WASPAS approach applied is shown schematically in Figure 2. The AHP method is used to calculate the weights of the main-criteria's and sub-criteria's, however, WASPAS method is used for evaluating the priority of different sectors. The main advantage of using WASPAS is that it is the joint generalized criterion of weighted aggregation of additive and multiplicative methods, for ranking of the alternatives. As part of its own process, this approach may be used to control the consistency of alternative ranks by doing sensitivity analyses.

5 RESULTS AND DISCUSSION

To use the AHP and WASPAS methods for figuring out the best sector for the FDI in India, some data are necessary. To collect the data, a questionnaire is distributed to the selected individuals (academicians and industry professionals) and asked them to compare each criteria with the others criteria, particularly by assigning a pairwise comparison in scale of 1–9 according to their preferences. The average value of all the criteria have been recorded for creating the hierarchy matrix of the model.

The pairwise comparison matrix of four criteria with respect to the ultimate goal was formed to estimate their relative weights. Table 3 shows the pairwise comparison of the criteria as well as their priority weights with a CR 0.0052. As can be seen, in the evaluation of the FDI, the most significant weight is given to the economic criterion followed by the country criterion.

After obtaining the main criteria's weight, the next target is to obtain the weightage of the sub-criteria's of the considered main criteria. AHP technique is applied on sub-criteria of main criteria, economics, political, social, and country criteria. It can be seen that among the sub-criteria of the economic criteria for FDI, the greatest weight is obtained by the size of the economy followed by the wage rate. For political sub-criteria, the most significant weight is obtained by the political stability followed by the bureaucracy. And also, for the social and country sub-criteria, the most significant weight is obtained by the population density followed by the level of the education, and competitiveness followed by the country size, respectively.

While reviewing the pairwise comparison matrix of all sub-criteria's of main criteria, it has been observed that all expert team decisions are compatible as CR value is lower than 0.1 for all the cases (economic sub-criteria CR value is 0.046; political sub-criteria CR value is 0.041; social sub-criteria CR is 0.087; and country sub-criteria CR value is 0.026). It means that the decisions of the expert panel can be satisfactorily analyzed for further stage. After obtaining the local weights of the criteria and sub-criteria used for evaluating the FDI in different sectors of India by using the AHP method, the next step is to obtain the global weights of criteria's and sub-criteria's, and the obtained global weight is given below (Table 4).

Table 4 shows the global weightage of the criteria's and sub-criteria's for FDI evaluation in India. Economic criteria (0.305498) is perceived to be the most important criteria for FDI in India, followed by the country criteria (0.251344), political criteria (0.231087), and social criteria (0.212071). Among the sub-criteria of economic criteria for FDI, size of the economy (0.08063) occupies the most important sub-criteria followed by wage rate (0.08063), tax rate (0.06918), and exchange rate (0.06829). Second important criteria for FDI is country criteria, among which competitiveness (0.06528) occupies the most important sub-criteria followed by country size (0.06451), labor skills (0.06168), and infrastructure (0.05987). Third important criteria for FDI is political criteria, among which foreign trade regulation (0.06625) occupies the most important sub-criteria followed by bureaucracy (0.05740), laws regulating environment pollution (0.05509), and political stability (0.05740). The last important criteria for FDI evaluation is social criteria, among which population density (0.05750) occupies the most important sub-criteria followed by level of education (0.05360), religious diversity (0.05180), and human capital development (0.04917).

Table 5 gives the alternatives value for each criteria used for the evaluation of FDI in India. Before applying the WASPAS method, the decision maker has to identify the beneficial and non-beneficial criteria for FDI. Among all the considered criteria, that is, economic, social, political, and country criteria's, decision maker decided to considered economic and country criteria's as the beneficial criteria while social and political criteria's as the non-beneficial criteria for FDI. After normalizing the Table 5 by using the Equations (3) and (4), and the decision maker next step is to get the weighted normalized matrix. WASPAS method is the combination of both WSM and WPM method. Table 6 shows the weighted sum normalized decision matrix derived by multiplying the criteria's weight with normalized value, while Table 7 shows the weighted product normalized decision matrix derived by using the normalized value to the power of weightage value of each criterion. Further, Equations (5) and (6) are used for determining the preference value of each alternatives. The obtained preference value of WSM and WPM is given in Table 8. Finally Equation (7) is used to determine the WASPAS preference value of each of the alternatives, which is given in Table 9 and graphically presented in Figure 3. The higher value of preferences, represents a higher ranking. Based on the obtained result of WASPAS, the considered sectors are ranked in the order of preference. It is observed that information technology (S4) has got the highest priority followed by retailing and e-commerce (S2), and healthcare (S3) sector. Appropriate decision for making FDI will give better return and will help not only to stakeholders but to society overall.

In Table 11, sensitivity analysis is also carried out for all the considered sectors. The purpose of the sensitivity analysis is to verify the findings and clarify the precision and deviations of in the determination of results. When ambiguity occurs in one or more characteristics, it is important to do so. It helps in the estimation of the minimal difference of existing characteristics weights which may influence the current attributes rank. Ten trials are administered by using various weight to assess the robustness of attributes. The final ranking is determined with the help of relative preference value of WASPAS method. Table 10 shows new sets of weights to analyze the sensitivity, and the ranks of sectors based on new sets of weights are shown in Table 11 and presented in Figure 4.

6 DISCUSSION

This work offers a systematic and structural approach to distinguishing the goals of FDI assessment in India. The work underlined 4 main-criteria's and 16 sub-criteria's, which impact decision for FDI in any country. FDI is crucial for economic growth. Probably that is why economic criteria is ranked first when analyzing FDI. Also, the critical factor of FDI inflows is primarily based on macroeconomic principles; among them, the economy's size is the important one for investment. Second, for FDIs, the countries with the most readily available natural resources are considered as the most favorable destination for investment. As a result, investors pursued investment options in emerging and developed economies that have good competitiveness in the market, rated as the most prominent sub-criteria on the analysis. Third, political considerations are essential to investment decision by foreign investors. Foreign investors invest only when they know that the country has a stable political environment which is rated as the most prominent sub-criteria on the analysis. Fourth criteria on the rated list is SOC for which population density, language, religious practices, and culture plays a significant role in investment decisions. They influence investment decisions dramatically. In some cases, language and culture differences will have unforeseen implications for the business.

The findings can be concluded with the following observations, there is a continuing need to extend and improve the road and highways network around the country on the importance to both economic growth and movement for goods and services. For this sector growth, government is trying to draw more private sector investment because the strong road network will be an essential pillar of a rapid economic recoversal in India and one would expect the government's budgetary contribution to building road networks to be supplemented by lucrative private sector investment options. Furthermore, India's government most important decision in economic policy is to liberalize FDI regulations in the real estate industry. External companies, outside of NRI's, were authorized to participate, via a wholly-owned corporation or through an Indian joint venture with a local association, in establishing the integrated townships and settlements. With the consideration of “Housing for all” in India, real-estate will witnessed a whooping FDI in the coming years.

The information technology sector is currently undergoing a change from a global business environment perspective. India is the most favorable place for investment because the availability of highly educated, lower-wage labor tends to minimize costs for source countries by ~60%–70%. FDI in the retailing and e-commerce sector is supposed to be the most benefited one for the investors because of India's high population and diversity, and also the government's “Make in India” program, too, aimed at raising this sector share to 25% by 2022, while also generating 100 million new jobs in this sector. It is expected that the Indian e-commerce industry has been on an upward growth trajectory and is expected to surpass the United States to become the second largest e-commerce market in the world by 2034. As most of the Indians have started shopping online rather than stepping outside their houses due to COVID outbreak, the Indian e-commerce sector witnessed an increase and proved to be the good place for FDI. India has a low per capita energy usage for a nation that is home to one-sixth of the world's population. As India sees fast economic growth and becomes a manufacturing hub, energy demand is increasing.

The growing urbanization, higher incomes and demographic growth would also stimulate energy use. To accommodate this considerable demand surge, India will need to double its electricity production by 2030, while still upholding its pledge to a 35% reduction of its carbon footprint from 2005 levels. The government has already taken several steps to promote foreign investment in this sector and will be an investor's eye-catcher. Healthcare sector is one of the major industries in India—both in terms of income and employment. Hospitals, medical supplies, medical trials, outsourcing, telemedicine, medical tourism, medical insurance, and facilities are covered under the healthcare sector. The Indian healthcare market is expanding rapidly because of its rising penetration, facilities, and spending from both public and private stakeholders. And, also 100% FDI is permitted for healthcare sector under the greenfield projects in India under the automated path. Therefore, international investors are increasingly interested in entering India's healthcare sector through investment in capital, technological interconnections, and collaborative initiatives across different segments.

7 CONCLUSION, LIMITATIONS, AND FUTURE SCOPE

The drivers to FDI across different sectors in developing countries are affected by the globalization, government policies, competitive consumer market, and market condition. In this study, authors have used a decision making framework for making investment in context to India. In the current study, to prioritize sectors for FDI, 4 main criteria, that is, ECO, SOC, POL, and COUs are considered along with 16 sub-criteria. It includes size of economy, exchange rate, tax rate, wage rate, political stability, foreign trade regulation, laws regulating environment pollution, bureaucracy, population density, human capital development, level of education, religious diversity, country size, competitiveness, infrastructure, and labor skills. For illustrating the proposed framework, five prominent sectors for the investment purpose are considered. These are petroleum and natural resource, retailing, and e-commerce, healthcare, information technology, and road and highways. Results are obtained by using the integrated approach of AHP and WASPAS techniques. Based on the results obtained, ECOs have been found to be the most important criteria for FDI in India, followed by COUs, POLs, and SOCs. Among the sub-criteria's of ECOs, POLs, SOCs, and COUs for FDI, size of the economy, foreign trade regulation, population density, and competitiveness are the most important sub factors that can affect FDI decision. Based on importance rating of these factors, we find most preferred sector for FDI. Information technology sector is found to be the most appropriate sectors for FDI, followed by the retailing and e-commerce, and healthcare sector. This study has contributed mainly in terms of developing decision making framework for FDI and use of MCDM for solving such complex problem.