Ranking the Determinants for International Education Destination Decision of Indian Students: Application of Fuzzy Analytical Hierarchical Process

Abstract

Purpose: One of the emerging educational trends is an increase in global mobility of students to pursue higher education. This article attempts at ranking the determinants shaping the Indian students’ decisions in selecting an appropriate global destination. Method: The study was conducted in two stages. In the first stage, researchers conducted exploratory factor analysis (EFA) to find the factors shaping Indian students’ decision-making process. During the second stage, fuzzy analytical hierarchical process (FAHP) method based on triangular fuzzy numbers was applied to rank the above factors. Findings: The findings reveal four primary factors, which have been ranked in the following order—quality of education, overall cost, environment and human interface and influencers’ role. Limitations: Most of the sample respondents were from the management discipline, drawn from six Indian states. Implications: The findings will be beneficial for both academic institutions and policymakers of host countries in shaping appropriate marketing mix including branding strategies for developing countries like India. Indian policymakers can use it for further augmenting the ecosystem for higher education. Originality: Globally, several researchers have identified the factors that influence the decisions of students in pursuing international education. However, the novelty of this research is that it is a first attempt in investigating and ranking of these factors using FAHP approach, especially in an Indian context.

Keywords

Educational Ecosystem FAHP Fuzzy Analytical Hierarchical Method Global Education International Education International Studies University Marketing

Executive Summary

A prominent educational trend consistently witnessed in the last few years is an increase in global mobility of students to pursue higher education. Consequently, both educational institutions and governments in host countries have started developing customized educational products and are marketing these globally to attract bright students for competitive advantage. A similar flux has been witnessed in developing countries like India. Under this backdrop, this article attempts to identify and rank the determinants shaping the Indian students’ decision of selecting an appropriate global destination for pursuing higher studies.

This study was conducted in two stages. In the first stage, exploratory factor analysis (EFA) was performed on a scale developed by Lee (2014), to find the factors shaping the Indian students’ decision-making process for selecting an appropriate higher educational institute. Data were collected from 167 respondents who had finalized to study abroad and had gone through the entire process of decision-making in the recent past. The sample profile included 51% male and 49% female participants. Varimax method of orthogonal rotation with KMO 0.752 resulted in four factors: influencer’s role, environmental and human interface, overall cost and quality education.

During the second stage, fuzzy analytical hierarchical process (FAHP) method using triangular fuzzy number was applied to rank the above factors. FAHP provides a robust and powerful explanation of the decision process under uncertain and complex environment. The AHP model used for this study is given as follows:

Domain experts from industry and academia were referred to assess the relevance of criteria and sub-criteria, identified through secondary research. Data were collected again from 21 students who had already finalized some international institutes for pursuing their higher education. The sample profile included 11 males and 10 females. The FAHP method is generally inappropriate for bigger sample size and leads to high level of inconsistency. The findings obtained through FAHP revealed four primary factors, which have been ranked in the following order: quality of education, overall cost, environment and human interface and influencers’ role with respective weightage of 0.47, 0.10, 0.27 and 0.16.

The findings of this article will be beneficial for both academic institutions and policymakers of host countries in shaping appropriate marketing mix including branding strategies for developing countries like India. Indian policymakers can also use these findings to further augment the ecosystem for higher education.

Introduction

The phenomenon of globalization has percolated even in the field of education. Students across the globe have been exploring the ‘best’ places to acquire quality education. According to the Ministry of External Affairs of India, 750,000¹ students had gone abroad to pursue education in the year 2018. The same report highlighted that Indian students were pursuing higher education in 90 countries, with most of them studying in five leading destinations (Deepalakshmi, 2018). These countries were the USA (211,703 as of July 2018), Canada (124,000), Australia (87,115), Saudi Arabia (70,800) and the United Arab Emirates (50,000) (ICEF Monitor, 2019).

As per the Reserve Bank of India report (2013), top five destinations for Indian students were the USA, Canada, the UK, Australia and EU countries. The same report quoted that the spending on tuition and hostel fees by Indians studying overseas had gone up by 44% from US$1.9 billion in 2013–2014 to US$2.8 billion in 2017–2018. Sutarwala (2019) explored five major reasons for students pursing studies abroad: to attain global recognition, to improve language skills, to improve employability, to engage in exciting extracurricular clubs and classes and to connect with international crowd.

In comparison, as per the Government of India’s statistics, 34,774 foreign students studied in India in the year 2012–2013 which had grown to 47,427 in the year 2018–2019 (Indiastat.com: Ministry of Human Resource Development, Govt. of India (ON1523) & Lok Sabha Un-Starred Question No. 1311, dated 25.11.2019). Under this backdrop, this article explores the determinants that Indian undergraduate students consider while selecting a global destination for pursuing higher education.

Major findings of this article are four primary factors: quality of education (with weightage of 0.47), overall cost (0.27), environment and human interface (0.16) and influencers’ role (0.10). The article is organized as follows: the second section provides the review of extant literature identifying various factors related to international education destination decision. The third section represents the research methodology and application of fuzzy analytical hierarchical process (FAHP) method. In the fourth section, results are discussed. The last section provides the conclusion of the research and scope for future research.

Literature Review

The UNESCO Institute for Statistics (2018) reported that learners from over 200 nationalities were pursuing their education abroad in 140+ countries. Globally, students seemed to prefer five countries for pursuing their education (UK, the USA, Canada, New Zealand and Australia), contributing to about 45% of international students (UNESCO et al., 2016). Further, as per a study conducted by OECD (2017), more than 5 million students registered for further education outside their country; this figure is likely to reach 8 million by the year 2025. This section is divided into three subsections as follows:

Reasons for Indian Students’ Global Mobility

In terms of global students’ movement, India is the second largest source country, after China. As an emerging power in the global economic world (King & Sondhi, 2018), international students’ mobility from India almost tripled from 62,342 in year 2000 to 181,872 in year 2013 (UNESCO, 2015). The British Council (2014) cited economic growth, rising incomes and growing tertiary enrolments as the primary determinants for Indian students’ global mobility. Leading destination countries have accorded top priority to India in their strategic plans for higher education (Hercog & Van de Laar, 2017). These authors attributed better academic and research ecosystem in advanced countries as reasons for their international education decision.

Benefits of International Study

Students select foreign education in order to make a better career, to access quality employment, to create a sound resume and to ensure effective engagement in issues related to global phenomenon (Kasravi, 2009; Lewis, 2016; Nyaupane et al., 2011). Smith-Paríolá and Gòkè-Paríolá (2006) found that students have the liberty to apply their classroom learnings within a global context. International education also enables learners to groom their personality/identity in addition to gaining global knowledge (Kasravi, 2009). Li et al. (2013) highlighted advantages like global expertise and career-based internship programmes while studying in reputed global institutes. Lewis (2016) reported social factors like meeting students from different cultures and ethnic backgrounds and personal factors like continuing family tradition of acquiring a foreign degree and desiring to accomplish self-determined goals as benefits of international study.

Factors Influencing Decision-making

Factors such as geographical location, cost of study, desired programme characteristics, quality degree programmes, higher education policies and culture were also found to be important in students’ decisions while selecting international education destination (Liu et al., 2018; Macionis et al., 2019; Yeravdekar & Tiwari, 2014). Sometimes, students make decisions about foreign education based on the availability of sponsorship, including financial support from family (Lu et al., 2009). Countries such as Russia, Vietnam and China provide sponsorship for student betterment (Lu et al., 2009; Nguyen, 2013).

Cost, course duration and culture are considered to be the most influencing factors for selecting an institute (Jackson, 2015; Lewis, 2016; Liu et al., 2018; Macionis et al., 2019; Nyaupane et al., 2011; Olson & Lalley, 2012). Multiple researchers discussed about the ‘Pull’ and ‘Push’ factors that shape the students’ options about foreign education (Lee, 2014; Muntasira et al., 2009; Nghia, 2015). Push factors relate to issues of the home country (e.g., political condition, lack of desired specializations, limited employment opportunities etc.) that influence students to opt for foreign education, while pull factors relate to issues of the host country (i.e., scholarship opportunity, personal safety, quality education etc.) which attract outside students to pursue education at their institutes. Recommendations from parents, friends, agents and professors also play an important role for students in selecting foreign education (Lee, 2014; Lewis, 2016; Mazzarol & Soutar, 2002; Singh et al., 2013). Socialization as well as geographical factors are also significant for international education decision (Anderson & Bhati, 2012). A brief review of related literature is given in Table 1.

Table 1.

A Brief Review of Related Literature on Determinants for International Education Destination Decision of Indian Students

	Parents/Relatives’ Recommendations	Friends’ Recommendations	Professors’ Recommendations	Agent’s Recommendations	Exciting Place to Live	Comfortable Climate (Atmosphere)	Make International Contacts	Fees Charged by the Chosen Institute	Cost of Living While Pursuing the Chosen Course	Better Quality of Education in Host Country	High Recognition of Educational Qualification Obtained from Chosen Institute
Macionis et al. (2019)					X					X
Shields (2019)									X
Schworm et al. (2017)							X				X
Lewis (2016)		X		X	X
Erselcan (2015)					X	X
Jackson (2015)					x		x				x
Counsell (2011)					X
Lee (2014)	X	X	X	X	X	X	X	X	X	X	X
Yeravdekar and Tiwari (2014)										X	X
Zheng (2014)					X	X				X
Le et al. (2013)											X
Nguyen (2013)					X					X
Singh et al. (2013)		X								X
Olson and Lalley (2012)							X				X
Nyaupane et al. (2011)							X		X		X
		X		X
Oosterbeek and Webbink (2011)								X	X
Daily et al. (2010)	X	X			X	X			X		X
Pineda (2009)							X				X
Mazzarol and Soutar (2002)	X				X	X			X

Source: The authors.

Table 2 presents the literature identifying various factors related to international education destination decision, along with their objectives and statistical tools used by the researchers.

Table 2 shows that several researchers have identified the factors that influence the decisions of students for pursuing international education. However, the novelty of our research lies in is ranking of these factors using fuzzy analytical hierarchy process (FAHP) approach, based on triangular fuzzy numbers. Therefore, this research enriches the extant literature by ranking the factors or determinants that guide Indian students in their international education destination decision. The findings can be used mainly by the academic institutions and policymakers of host countries in developing appropriate marketing mix and accordingly marketing their products/programmes for developing countries like India.

Table 2.

Studies Conducted for Identifying Factors with Statistical Tools Used

Authors	Objective of the Study	Statistical Tools
Fajarwati and Suyanto (2019)	Factors that drive foreign students to choose Indonesia as their education location	Exploratory factor analysis
Nghia (2019)	Motivating factors to study abroad	Intercept interview and independent sample t-tests
Liu et al. (2018)	Explore factors affecting students’ decisions of international education destinations	Focus group discussion and in-depth interviews
Singh and Srivastava (2018)	Explore factors that affect Indian students’ choice to study abroad.	ANOVA and ranking
Lewis (2016)	Identify factors that influence undergraduate decisions to study abroad.	Pearson’s correlation tests, descriptive statistics
Nghia (2015)	Identify the reasons influencing prospective international students to study in Vietnam	T-test and one-way ANOVA test
Lee (2014)	Explore why and how international students study at Taiwan	Analytical hierarchical process
Mohajeri Norris and Gillespie (2009)	Influence of studying abroad on US college students’ career paths	Survey based

Source: The authors.

Research Methodology

Sampling and Data Collection

The target population for this study included Indian students who opted for international studies after completing their undergraduate programmes. This necessitated the students to select a global institute rationally after considering various parameters mentioned above. Cross-sectional research design was adopted and data were collected from October 2019 to December 2019.

Further, data were collected using purposive sampling method from 167 students belonging to 6 states of India. These states were Gujarat, Maharashtra, Rajasthan, Madhya Pradesh, Telangana and National Capital Region (NCR) including New Delhi. Purposive sampling is a sampling method in which the selection of the respondents is carried out by the researcher (s) at his/her convenience (Anderson et al., 2015). Data collected were analysed using exploratory factor analysis (EFA) in the first stage using IBM SPSS version 20. EFA is a widely used statistical tool, deployed for data reduction. For performing EFA, the minimum sample size should be 10 times the number of variables (Mundfrom et al., 2005). In our study, there were 11 variables; therefore, sample size chosen (167) justified the minimum threshold of 110.

According to IEC Abroad, an international education consultancy firm, in year 2019, the percentage of students who moved abroad from India for pursuing studies were 25% (Telangana), 15% (Mumbai), 7%(Ahmedabad), 7% (Delhi), 8% (Chennai), 3% (Kolkata), 3% (Pune) and 23% (other states). This representation is almost similar to our sample. Students included in our sample were pursuing their bachelor’s degree in Management at leading business schools of Gujarat state of India. Data were collected in person by the authors to ensure that each respondent was well aware of the concepts used in current research. In addition, they had also taken up parallel coaching from renowned professional institutes in order to qualify for Global institutes of repute.

Figure 1 shows the complete research methodology that had been adopted.

Figure 1.

Source: The authors.

As per Figure 1, this research started with extensive literature review. For the first stage of primary research, authors used a standardized scale given by Lee (2014). Bartlett’s test, Kaiser–Mayer–Olkin (KMO) test and Cronbach’s alpha had been conducted for checking reliability and validity of EFA. Afterwards, FAHP was performed to rank and determine the weight of selected criteria and sub-criteria. Data were collected using cross-sectional design.

Exploratory Factor Analysis

EFA is a data reduction tool resulting into number of factors and their loadings, which is not known in advance before the computation (Lundqvist, 2014). This process began with a scale proposed by Lee (2014), which had 7 dimensions and 28 factors influencing students’ decision to study at some international institutes. Discussion with experts (from academia and industry) resulted in an understanding that some factors mentioned in the scale were irrelevant for the Indian environment. In order to validate the same, EFA was conducted to identify the latent variables from the original set of variables. Details of the sample profile of respondents have been given in Table 3.

Table 3.

Sample Profile

	Frequency	Percentage
Gender
Male	85	51
Female	82	49
Parent’s occupation
Business	126	75
Service	41	25

Source: The authors.

Bartlett’s test and KMO test have also been conducted for EFA. KMO checks the sample adequacy of the data (Abdallah & Hilu, 2015) and a value greater than 0.50 is considered as acceptable for factor analysis (Marshall et al., 2007). Statistical package IBM SPSS 20 was used to conduct EFA. The KMO value of the data collected was found to be 0.752, thereby confirming that the data collected were suitable for factor analysis. Bartlett’s test is used to check whether the sample qualifies for a multivariate normal distribution (Abdallah & Hilu, 2015), whereby its value should be less than 0.05. Bartlett’s Test of Sphericity was found to be highly significant (χ² = 1599.182, df = 378; p = 0.000 < 0.05).

Varimax method of orthogonal rotation resulted in four factors with eigenvalues greater than one. Table 4 presents the final items and their loadings. After examining the items, the following names were given to factors, that is, ‘Influencers’ Role’, ‘Environmental and Human Interface’, ‘Overall Cost’ and ‘Quality Education’. As shown in Table 4, Cronbach’s alpha for the four items had values of 0.873, 0.627, 0.880 and 0.603, respectively, showing good reliability. Values greater than 0.6 (Abdallah & Hilu, 2015) confirmed that the survey items appropriately measured the underlying constructs.

Thus, EFA was instrumental in identification of 11 determinants, as given in Table 4.

Table 4.

Results of EFA

Constructs	Component				Cronbach’s Alpha
Constructs	1	2	3	4	0.873
Parent’s/relatives recommendations	0.867
Friends’ recommendations	0.845
Professors’ recommendations	0.787
Agent’s recommendations	0.781
Exciting place to live		0.814			0.627
Comfortable climate (atmosphere)		0.692
Make international contacts		0.601
Fees charged by the chosen institute			0.846		0.880
Cost of living while pursuing the chosen course			0.845
Better quality of education in host country				0.717	0.603
High recognition of educational qualification obtained from chosen institute				0.664

Source: The authors.

Evolution of Decision-making Models

As the decision-making environment becomes more complex, the limitations of using single criterion decision-making become more evident. Moreover, decision-making is extremely subjective when dealing with single criterion problems. Henceforth, for enhancing the effectiveness of decisions, and to optimally use the resources, many researchers in the past have used the concept of multi-criteria decision-making (MCDM) (Kaganski et al., 2018; Vaidya & Kumar, 2019). MCDM analyses the decision, tests its robustness and recommends courses of action or selects the best action to be implemented (Yeap et al., 2014).

There are various methods of MCDM such as Fuzzy TOPSIS, VIKOR, AHP, ELECTRE and PROMETHEE. Over a period, it was found that all the criteria that influenced the decision-making process did not have equal weightage. In fact, every decision has been made with a certain hierarchy of factors influencing it, leading to development of AHP. Saaty (1980) developed the concept of AHP. Generally, in the AHP model, the goal is the first level, and criteria, sub-criteria and alternatives fall in the second, third and fourth levels, respectively. A hierarchical method is proposed to represent the factors related to the concerned decision (Sarfaraz et al., 2012), as shown in Figure 2.

Figure 2.

Source: The authors.

Based on their own preference, the decision-maker forms an opinion on the significance of each criterion in comparison with others, resulting in rank order. Moreover, researchers in the past have extensively used this for analysing complex decisions (Guru et al., 2020; Li et al., 2018), whereby they have demanded priority weights in a crisp form on a scale of 1 to 9.

As decisions in the real world are complex and vague, decision-makers find it arduous to provide priority weights in crisp values using AHP (Sarfaraz et al., 2012). Moreover, these crisp values are imprecise in deciding the priority weights (Tyagi, 2016). To include the influence of uncertainty in the process of decision-making, many researchers have combined AHP with fuzzy. Zadeh in year 1965, developed the Fuzzy set theory, while in 1983, Van Laarhoven and Pedrycz introduced the theory into AHP. They further suggested a method for implementing fuzzy AHP in triangular fuzzy numbers. These numbers in turn allow efficacious depiction and manipulation of linguistic variables (Calabrese et al., 2019).

FAHP provides a robust and powerful explanation of the decision process under uncertain and complex environment (Godoy, 2018; Sharma et al., 2020). To resolve the ambiguous alternative selection problems, FAHP was developed (Kannan et al., 2013). Some applications of fuzzy AHP are provided in Table 5.

Table 5.

Applications of FAHP

Authors	Fuzzy AHP Applications
Kaganski et al. (2018)	Performance measurement
Kumar et al. (2017)	Order allocation among suppliers
Sultana et al. (2015)	Selection of best supplier
Ayhan (2013)	Supplier selection
Tadic et al. (2013)	Evaluation of quality goals
Sarfaraz et al. (2012)	Evaluation of ERP for implementation
Cifci and Buyukozkan (2011)	Evaluation of green supplier
Sen et al. (2010)	Preselection of suppliers
Lee (2009)	Evaluation of buyer–supplier relationship

Source: The authors.

Data Analysis

Domain experts from industry and academia were referred to assess the relevance of identified criteria and sub-criteria identified through secondary research. After finalizing the AHP scale, 21 students who had already finalized some international institutes for pursuing their higher education were invited to take part in order to rank the identified criteria and sub-criteria. Table 6 displays a sample break-up of these students. The FAHP method is generally inappropriate with bigger sample size, resulting in a high level of inconsistency (Pun & Hui, 2001).

Table 6.

Sample Profile

	Frequency	Percentage
Gender
Male	11	52
Female	10	48
Parent’s occupation
Business	14	67
Service	7	33
Number of siblings
Zero	3	14
One of more	18	86

Source: The authors.

Triangular fuzzy numbers are employed for evaluating the preferences because of its ease to use and calculate (Kannan et al., 2013). These numbers are referred as u, m and l where u ≥ m ≥ l. The parameters u, m and l denote the upper, middle and lowest possible values, respectively. Moreover, this article employs geometric mean approach for analysis propounded by Buckley (1985). Table 7 presents fuzzy linguistic terms and corresponding triangular fuzzy numbers used for FAHP analysis.

Table 7.

AHP Scale and Associated Triangular Fuzzy Numbers

AHP		FAHP
Intensity of Importance	Definition	Fuzzy Ratings (l, m, u)	Fuzzy Reverse (l, m, u)
1	Equal	(1, 1, 1)	(1, 1, 1)
3	Moderate	(2, 3, 4)	(1/4, 1/3, 1/2)
5	Strong	(4, 5, 6)	(1/6, 1/5, 1/4)
7	Very Strongly	(6, 7, 8)	(1/8, 1/7, 1/6)
9	Extremely	(9, 9, 9)	(1/9, 1/9, 1/9)

Source: The authors.

Table 8.

Fuzzified Pairwise Comparison Matrix

Criteria*	IR	EHI	OC	QE
IR	(1, 1, 1)	(0.55, 0.65, 0.78)	(0.28, 0.32, 0.38)	(0.18, 0.20, 0.22)
EHI	(1.28, 1.54, 1.83)	(1, 1, 1)	(0.59, 0.68, 0.80)	(0.26, 0.29, 0.34)
OC	(2.63, 3.09, 3.56)	(1.25, 1.46, 1.48)	(1, 1, 1)	(0.55, 0.65, 0.76)
QE	(4.54, 5.01, 5.43)	(2.97, 3.43, 3.92)	(1.32, 1.55, 1.80)	(1, 1, 1)

Source: The authors.

Note: *Four factors or criteria mentioned in Table 4 and Figure 2, respectively.

IR—influencers’ role; EHI—environmental and human interface; OC—overall cost; QE—quality education.

The steps are described below:

Step 1: Development of fuzzified pairwise comparison matrix

\tilde{A} = {({\tilde{a}}_{i j})}_{n \times n} = [\begin{matrix} (1, 1, 1) & (l_{12}, m_{12}, u_{12}) & \dots & (l_{1 n}, m_{1 n}, u_{1 n}) \\ (l_{21}, m_{21}, u_{21}) & (1, 1, 1) & \dots & (l_{2 n}, m_{2 n}, u_{2 n}) \\ ⋮ & ⋮ & ⋮ & ⋮ \\ (l_{n 1}, m_{n 1}, u_{n 1}) & (l_{n 2}, m_{n 2}, u_{n 2}) & \dots & (1, 1, 1) \end{matrix}]

where

{\tilde{a}}_{i j} = (l_{i j}, m_{i j}, u_{i j}) = {({\tilde{a}}_{i j})}^{- 1} = (\frac{1}{u_{j i}}, \frac{1}{m_{j i}}, \frac{1}{l_{j i}}), i, j = 1, \dots, n; i \neq j

Step 2: Computation of fuzzy geometric mean value $({\tilde{r}}_{i})$ (Table 9)

Table 9.

Fuzzy Geometric Mean Value

Criteria	IR	EHI	OC	QE	Fuzzy GM Value $({\tilde{r}}_{i})$
IR	(1, 1, 1)	(0.55, 0.65, 0.78)	(0.28, 0.32, 0.38)	(0.18, 0.20, 0.22)	(0.41, 0.45, 0.51)
EHI	(1.28, 1.54, 1.83)	(1, 1, 1)	(0.59, 0.68, 0.80)	(0.26, 0.29, 0.34)	(0.66,0.74, 0.84)
OC	(2.63, 3.09, 3.56)	(1.25, 1.46, 1.48)	(1, 1, 1)	(0.55, 0.65, 0.76)	(1.16, 1.31, 1.41)
QE	(4.54, 5.01, 5.43)	(2.97, 3.43, 3.92 )	(1.32, 1.55, 1.80)	(1, 1, 1)	(2.05, 2.27, 2.49)

Source: The authors.

{\tilde{r}}_{i} = [\begin{matrix} {(l_{i 1} \times l_{i 2} \times \dots \times l_{i n})}^{\frac{1}{n}}, {(m_{i 1} \times m_{i 2} \times \dots \times m_{i n})}^{\frac{1}{n}}, \\ {(u_{i 1} \times u_{i 2} \times \dots \times u_{i n})}^{\frac{1}{n}} \end{matrix}]

Step 3: Ascertaining fuzzy weights $({\tilde{w}}_{i})$ (Table 10)

Table 10.

Computation of Fuzzy Weights

Criteria	Fuzzy Geometric Mean Value $({\tilde{r}}_{i})$	Fuzzy Weights $({\tilde{w}}_{i})$
IR	(0.41, 0.45, 0.51)	(0.08, 0.10, 0.12)
EHI	(0.66, 0.74, 0.84)	(0.13, 0.16, 0.19)
OC	(1.16, 1.31, 1.41)	(0.22, 0.27, 0.33)
QE	(2.05, 2.27, 2.49)	(0.39, 0.48, 0.57)

Source: The authors.

{\tilde{w}}_{i} = {\tilde{r}}_{i} \times {({\tilde{r}}_{1} + {\tilde{r}}_{2} + \dots + {\tilde{r}}_{n})}^{- 1}

Step 4: Calculating de-fuzzified and normalized weights (Table 11)

Table 11.

De-fuzzified and Normalized Weights

Criteria	De-fuzzified Weights $({\tilde{w}}_{i})$	Normalized Weights (W)
IR	0.10	0.10
EHI	0.16	0.16
OC	0.27	0.27
QE	0.48	0.47

Source: The authors.

w_{i} = (\frac{l_{i} + m_{i} + u_{i}}{3})

Step 5: Checking consistency

5.1: Developing of fuzzy matrices

From the fuzzy comparison matrix, that is, Table 8 and formula given below, the researchers derived a crisp comparison matrix (Table 12).

Table 12.

Crisp Comparison Matrix

Criteria	IR	EHI	OC	QE
IR	1.00	0.66	0.33	0.20
EHI	1.55	1.00	0.69	0.29
OC	3.09	1.40	1.00	0.65
QE	4.99	3.44	1.55	1.00

Source: The authors.

A = a_{i j} ({\tilde{a}}_{i j}) = (\frac{l_{i j} + m_{i j} + u_{i j}}{3})

5.2: Computation of λ_max, consistency index and consistency ratio (Tables 13 & 14)

Table 13.

RI and Suggested CR Values

N	3	4	5	6	7	8	9	10
RI	0.52	0.89	1.11	1.25	1.35	1.4	1.45	1.49
Suggested CR value	<0.05	<0.08	<0.10	<0.10	<0.10	<0.10	<0.10	<0.10

Source: Thanki et al. (2016).

Table 14.

Calculation of CI and CR

Criteria	W	W'	λ_max	CI & CR
IR	0.09	0.38	4.02	CI = (4.02 − 4)/3 = 0.006
EHI	0.15	0.63		CR = 0.006/0.89
OC	0.27	1.09		= 0.007 < 0.08
QE	0.47	1.91

Source: The authors.

λ_{\max} = \frac{1}{n} (\frac{{W^{'}}_{1}}{W_{1}} + \frac{{W^{'}}_{2}}{W_{2}} + \dots + \frac{{W^{'}}_{n}}{W_{n}})

where

W^{'} = A W = [\begin{matrix} {W^{'}}_{1} \\ {W^{'}}_{2} \\ ⋮ \\ {W^{'}}_{n} \end{matrix}]

\begin{matrix} C o n s i s t e n c y i n d e x (C I) = \frac{λ_{\max} - n}{n - 1} \\ C o n s i s t e n c y r a t i o (C R) = \frac{C I}{R a n d o m i n d e x (R I)} \end{matrix}

Table 15 shows the relative weights of criteria (RWC), local weights (LW), global weights (GW), rank and sub-criteria (SC). Local weights of sub-criteria (SC) were calculated similarly by following steps 1–5, discussed earlier. Global weights (GW) were computed by multiplying the RWC and LW of SC.

Results and Discussion

From Table 15, ‘Quality of Education’ as a criterion obtained the maximum weightage (0.47) while ‘Influencers Role’ received the minimum weightage of 0.10 among Indian students’ international education destination decision. Factors ‘Overall Cost’ and ‘Environment and Human Interface’ with weightage of 0.27 and 0.16 were ranked second and third, respectively. This shows that quality of education plays a key role in selection of the institute. This reflects the quality of faculty, infrastructure, students, alumni as well as the institute’s ranking. First ranking to quality of education may be largely attributed to the fact that it affects the final goal of students, that is, quality placements/ bright career, etc. Weightage of two sub-criteria, that is, high recognition of educational qualification obtained from chosen institute (0.54) and better quality of education in host country (0.46), show that country brand as well as the overall educational quality of the host country also matter almost equally in selecting an institute.

Table 15.

RWC, SC, LW, GW and Rank

Criteria	RWC	SC	LW of SC Using FAHP	GW of SC	Rank
IR (4)	0.10	PR	0.23	0.023	9
		FR	0.09	0.009	11
		PRR	0.53	0.053	6
		AR	0.15	0.015	10
EHI (3)	0.16	EP	0.23	0.036	8
		CC	0.33	0.052	7
		MI	0.44	0.070	5
OC (2)	0.27	FC	0.60	0.162	3
		CL	0.40	0.108	4
QE (1)	0.47	BQ	0.46	0.216	2
		HR	0.54	0.258	1

Source: The authors.

Note: PR—Parents’ recommendations; FR—friends’ recommendations; PRR—professors’ recommendations; AR—agent’s recommendations; EP—exciting place; CC—comfortable climate; MI—make international contacts; FC—fees charged; CL—cost of living; BQ—better quality of education; HR—high recognition of degree.

For Indian students, the overall cost is ranked second. Here, the sub-criteria fee charged by a chosen institute (0.6) becomes more important than another sub-criterion cost of living (0.4). This may be because students may not have any control over the fee charged by institute, but they can optimize the living cost by other means such as staying together, compromising on food, use of public transport, etc. Many bright Indian students also earn scholarship from various sources.

The overall environment (i.e., natural, sociocultural and political diversity of students) also influences students’ decision-making process. Here, the potential of making international contacts has the highest weightage (0.44), which results in global networking of students, affecting their long-term career aspirations. Influencers’ role plays the least important role, out of the four factors. This may be attributed to the fact that interested students may obtain significant information from various online sources. However, out of the four influencers, professors’ recommendations got the highest weightage of 0.53, which not only shows professors’ credibility and insights about global institutes but also that it is a necessity while applying to various global institutes of repute.

Nomenclature of four factors obtained are in line with the studies conducted by various researchers. Singh and Srivastava (2018) explored significant factors which affected the Indian students’ preference to study overseas. Through their study, they found quality of education, safety issues in the host country and reputation and ranking of the host institution as the top factors. Nyaupane et al. (2011) identified high recognition of educational qualification obtained from the chosen institute and cost of living as important factors. Outcomes of research conducted by Macionis et al. (2019) were better quality of education and exciting place to live. Research by Mazzarol and Soutar (2002) proposed cost of living, exciting place to live and comfortable climate as significant factors.

Implications

The findings of this research article have many implications for academic institutions and policymakers of host countries. First, these institutes can use the findings in shaping appropriate marketing mix including branding strategies for developing countries like India, while creating an enabling environment for target marketing. In addition to offering standard programmes with universal appeal, global academic institutes can design content and pedagogy, catering to specific needs of Indian students. Further, to keep convenience and affordability in mind, blended learning model can be explored. Such programmes can be offered alone or in collaboration with renowned institutes of India.

For example, Stanford University seeks to build a relationship with India for smooth flow of people and ideas. In addition to English, it offers instructions in Hindi and Punjabi, as well as includes specific courses for Indian needs. It has many programmes for Indian students that also impart significant insights for global audience. MIT Sloan School of Management has partnered with Indian School of Business (ISB), Hyderabad, for developing leadership talent for infrastructure and manufacturing sectors of India.

Second, host countries and academic institutes need to work together to create, nurture and augment enabling academic and sociocultural environment, to suit the competencies, learning styles and affordability of Indian students. Oxford University strives to entice Indian students. Its Felix Scholarship programme facilitates brilliant students from India to pursue graduation in all courses. It includes 100% of course fees, a grant for living expenses and part of air travel. It also offers scholarship in legal education. To enable some earning during the learning process, Australia legally permits foreign students to work up to 20 h a week. Singapore attracts many Indian students, allowing them to obtain professional degrees of Western universities at a lesser cost, remain closer to India and have desired cultural diversity.

In order to attract 20,000 Indian students to pursue their higher studies in France (from almost 10000 at present), France has recognized all degrees offered by India. Students’ recruitment also gets a boost from bi-annual education fair conducted by France in India. Thirteen French institutes have already established their offices in India, out of which, some have started academic programmes. The joint programme by Xavier University Bhubaneswar (XUB) with Emlyon Business School is one such example. The French government spends over €1 million in financial assistance to Indian students annually through various types of scholarships. The University of Melbourne has tied up with the Indian Institute of Management, Ahmedabad, for exchange programmes. Indian Institute of Management, Bangalore, is exploring tie-ups with institutes in Singapore to offer executive MBA course that will be delivered partly through the distance learning mode.

Third, Indian policymakers also can take a leaf out of this study. Indian government (both Union and State), and more specifically, the Ministry of Human Resource Development can use the findings to learn the underlying reasons for this brain drain. Government has already started various strategic initiatives for creating centres of excellence based on multiple criteria.

Fourth, Indian lawmakers (along with leading think tanks like Niti Aayog & industry bodies) and other eminent academicians can also leverage these findings for creating a conducive ecosystem (quality faculty, state-of-the-art infrastructure, robust industry–academia interface, provision of subsidies etc.), so that bright Indian students can consider pursuing their education in India itself. This will not only reverse the trend of global mobility, but it will also be an important step in attracting foreign students to pursue their higher education in India.

The National Education Policy (NEP), announced by Indian government on 29 July 2020, has a vision of creating an equitable and vibrant knowledge society by providing high-quality education to all. It states that the world’s top 100 universities will be facilitated to operate in India through a new law. Many countries including Bureau of South and Central Asian Affairs and US State Department have welcomed this move with the intent to provide partnership and collaboration opportunities.

Limitations and Future Research

Most of the sample respondents chosen in this research were from management discipline only. Further, the sample was drawn from six states of India. Similar studies among broad-based disciplines (other than management education) and across the country may generate results with higher level of depth. Second, a domain-wise (medical, engineering etc.) study can also be a worthwhile idea for research. Third, all the students chosen in current study were freshers. It may be significant to study the preferences of working executives who pursue some global education for taking their career to the next level.

Fourth, the majority of students opted for academic institutions from selected countries (US, Canada, Australia and UK). Further, research can be conducted among students who prefer to go to other countries like China and Singapore. Fifth, researchers can undertake similar studies by taking fourth level (country) as alternatives. Another research of importance for readers across the globe can be the impact of COVID-19 as one of the determinants in international education decision.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Note

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