Abstract

This study presents one of the first attempts to focus on critical success factors influencing the entrepreneurial intensity of Malaysian small and medium sized enterprises (SMEs) as they attempt to expand internationally. The aim of this paper is to evaluate and prioritize the entrepreneurial intensity among the SMEs using multicriteria decision (MCDM) techniques. In this research FAHP is used for finding the weights of criteria and subcriteria. Then for the final ranking of the companies, VIKOR (in Serbian: VlseKriterijumska Optimizacija I Kompromisno Resenje) method was used. Also, as an additional tool, TOPSIS technique, is used to see the differences of two methods applied over the same data. 5 main criteria and 14 subcriteria were developed and implemented in the real-world cases. As the results showed, two ranking methods provided different ranking. Furthermore, the final findings of the research based on VIKOR and TOPSIS indicated that the firms A3 and A4 received the first rank, respectively. In addition, the firm A4 was known as the most entrepreneurial company. This research has been done in the manufacturing sector, but it could be also extended to the service sector for measurement.

1. Introduction

Today, small and medium-sized enterprises (SMEs) are the basis of the global economy and play a vital role in job creation. The vitality and success of SMEs are known as important features in measuring an economy’s growth and future development [1]. In developing and emerging economies, SMEs and entrepreneurs play an important role as they represent a major source of employment and generate significant revenue and export earnings [2]. Small firms depend on entrepreneurs to get a firm off the ground. Although SMEs typically face considerable resource constraints, they are often successful innovators [3]. This is mainly because entrepreneurial SMEs are nimbler than their larger counterparts; they can move faster and are more flexible, proactive, and risk keen [4, 5]. As an emerging research field, entrepreneurship has received much attention over the last few decades. However, there is a lack of consensus on what precisely are the critical factors affecting entrepreneurial intense. For example, previous entrepreneurship research has investigated opportunity identification processes [6], startup processes [7] exploitation processes [8], team formation processes [9], financing processes [10], entrepreneurial exits [11], and international entrepreneurship [12, 13]. Yet, little attention has been paid to entrepreneurial intensity (EI). The entrepreneurial intention has been investigated extensively in the West [14–17], though it still remains as an understudied area in Malaysia. Applying the Western studies in Malaysian context would certainly raise a question of their suitability and applicability. At the local setting, some studies are really needed to expand the pertinency and accuracy of the results [18]. This study presents one of the first attempts to focus on critical success factors (CSFs) influencing the EI of Malaysian SMEs as they attempt to expand internationally. A better understanding of the entrepreneurial process would provide an important contribution to the entrepreneurship evaluation literature, and the entrepreneurship intensity literature would benefit from an investigation of how CSFs of entrepreneurship contribute to the SMEs performance. Based on the identified gaps in the literature, the key research questions addressed in this study are as follows: what are the CSFs of the EI? What are the priority of these critical factors? And how are the importances and preferences of these factors affect each of them?

The multicriteria decision making (MCDM) approach is a suitable approach to evaluate critical success factors (CSFs) of EI, as the nature of these evaluation models includes different perspectives and should address the allocation of limited resources. Tzeng and Huang [19] indicated that MCDM is a methodology that can consider multiple criteria at the same time [20] and helps the decision maker to estimate the best case according to the characteristics of limited available cases. The MCDM technique is a powerful tool widely used for evaluating and ranking problems containing multiple, usually conflicting, criteria. It includes several techniques, which allow rating a range of criteria and ranking them as a decision maker. It has great potential to reduce the cost and time and increase the accuracy of decisions and can be an appropriate framework for solving the problems. The concept of combining the fuzzy theory and MCDM is referred to as fuzzy MCDM (FMCDM).

The paper adds to the entrepreneurial evaluation literature in several ways. First, no prior study that we are aware of has examined critical factors of entrepreneurship as a driving force of entrepreneurial intensity. Second, few studies in the literature examine the performance of SMEs regarding this evaluation. The next contribution of this research is that there is no more study using MCDM in fuzzy environment to evaluate EI among the SMEs. This paper is intended to bridge these gaps. The remainder of the paper is as follows. In the next section, we review the existing literature. In Section 3, the methodologies such as FAHP, VIKOR, and TOPSIS are used to assess the criteria. Applications of the proposed methodologies in real-world situations and a comparison of the results are presented in Section 4. The results and discussion are discussed in Section 5. Finally, in Section 6, conclusion and future studies are given.

2. Literature Review

In this section, first available MCDM applications in the field of entrepreneurship and innovation are reviewed and then how to evaluate the EI is described.

2.1. MCDM Applications in the Field

Han et al. [21] developed an evaluation model based on fuzzy theory and analytic hierarchy process (AHP) for assessing entrepreneurial environment to help the government and entrepreneur in an interacted environment. This model provides a precise, effective, and organized decision support tool. The evaluation of entrepreneurial capacity of college students was done by Ni-Di and Yi [22]. The decision making method has been provided reasonably to pick the satisfactory self-employed student. The outcomes show a greater weight on the dimension of entrepreneurial capacity of college student evaluation, and four critical evaluation criteria related to it are personal characteristics, personal qualities, personal abilities, and environment. Rezaei et al. [23] proposed four different methodologies for measuring the entrepreneurship, including the statistical methodology, a fuzzy logic, a data envelopment analysis (DEA), and a naïve methodology. As an expert-based methodology, fuzzy logic compensates some of the limitations of the statistical methodology. Drawing on a sample of 59 startups, they measured innovativeness, risk taking, and proactiveness and subsequently compared the resulting EO scores. A naïve methodology produces a value that lies between the other results, while the entrepreneurial score from a fuzzy logic methodology is the most dissimilar from the others. Most lately Rezaei et al. [24] applied an improved fuzzy AHP for ranking the firms based on their EO score. Same as their previous work [23], the three dimensions were used: innovativeness, risk taking, and proactiveness. The results specify that proactiveness is the most significant dimension, followed by innovativeness. Also, there are noticeable differences when it comes to the weights of the items. Lu et al. [25] used an AHP and fuzzy set theory, to assess a firm’s technological innovation capability by several qualitative and quantitative criteria. Chen et al. [26] provided a quantitative MCDM approach to knowledge management in construction entrepreneurship education by means of an analytic knowledge network process (KANP). The study finds that there are eight clusters and 178 nodes in the KANP. The model and experimental research on the evaluation of teaching cases discloses that the KANP method is effective in conducting a knowledge management of the entrepreneurship education. Čančer and Knez-Riedl [27] presented a method for developing the internal ratings to best select among the business partners of a firm using AHP. Since qualitative factors come into play, special attention is given to determining not only quantitative but also qualitative criteria. Tsai and Kuo [1] applied a decision making trial and evaluation laboratory (DEMATEL), ANP, and zero-one goal programming (GP) methods, to measure entrepreneurship policies, using the Stevenson and Lundstrom [28] proposed criteria such as reducing entry and exit barriers, promotion, entrepreneurship knowledge, and financing and business support.

2.2. Entrepreneurial Intensity

In entrepreneurship research, entrepreneurial orientation (EO) has been found to have a positive impact on firm performance [29–35] and to be one of the most popular branches within entrepreneurship research [36]. Firms with high levels of EO tend to constantly scan and monitor their operating environment in order to find new opportunities and strengthen their competitive positions [37]. To define entrepreneurship, many authors [38–40] believe that entrepreneurship can be described as “the process of creating value by bringing together a unique combination of resources to exploit an opportunity.” This definition exposes that the entrepreneurship may vary in terms of extent and the number of times it occurs. Several dimensions of EO have been proposed in which entrepreneurial intensity has been highlighted as the most important one. Miller [41] appears to offer the earliest operationalization of the EO concept and propose three-dimension proactiveness, risk taking, and innovativeness. Sexton and Morris [30] mention the varying levels of entrepreneurship as entrepreneurial intensity (EI). They view EI as a function of the degree and frequency of entrepreneurship as shown in Figure 1 [30]. This is supported by Antoncic and Hisrich [42]. On the other hand, Lumpkin and Dess [43] claimed that not three but five dimensions should be used to measure entrepreneurship, namely, autonomy, competitive aggressive, proactiveness, innovativeness, and risk taking. Also, Yang et al. [44] reviewed the literature of corporate entrepreneurship (CE) and explored the relationship between CE and market performance in China by taking a disaggregated approach after developing a reliable and valid scale of CE suggested by Antoncic and Hisrich [42]. They used venturing, innovation, self-renewal, proactiveness, and market performance as an evaluation scale. The results showed that each dimension exerts differentiated impacts on market performance of firms.

Figure 1 

An illustration of entrepreneurial intensity.

Frequency of entrepreneurship refers to the number of times an enterprise acts entrepreneurially while the degree of entrepreneurship is measured by three subdimensions: innovativeness, risk taking, and proactiveness. Kraus [45] investigated the role of the EO in service firms in Austria. A significant positive relationship between EO and corporate performance could be identified, with a clear emphasis on innovative behavior as the most important subdimension. Rigtering et al. [46] studied a comparative analysis of the EO/growth relationship with service and manufacturing firms using Miller’s [41] dimensions. The findings showed that service firms have a significantly higher EO than manufacturing firms, both on the overall level and for each of the three dimensions. Scheepers et al. [47] compared the e-business EI of the Johannesburg Securities Exchange (JSE) and ICT companies. Company characteristics, organizational factors, and environmental factors were compared. It has been confirmed that EI varies among different industries or company groups. Erasmus and Scheepers [48] studied the relationship between EI and shareholder value creation. An adapted CE measurement instrument is applied in order to gauge EI, while shareholder value creation is measured by the market adjusted total share return (TSR) and the value based financial performance measure economic value added (EVA). The contribution of the study is the focus on the relationship between EI and shareholder value creation, rather than purely on the accounting-based financial performance of an enterprise. Keh et al. [35] studied the effects of EO and marketing information on the performance of SMEs among Singaporean. The results indicate that EO plays an influential role in the acquisition and utilization of marketing information and also has a direct effect on firm performance. The utilization of information regarding marketing mix decisions (particularly the promotion and place elements) positively affects firm performance, and it partially mediates the relationship between EO and firm performance. Related to entrepreneurial intentions, Lee et al. [49] investigated the influence of organizational and individual factors in Singapore. They examined why individuals intend to leave their jobs to start business ventures. Findings suggest that work environments with an unfavorable innovation climate and/or lack of technical excellence incentives influence entrepreneurial intentions, through low job satisfaction. Cardon et al. [50] measured entrepreneurial passion (EP) and provided conceptual foundations and scale validation. They developed and validated an instrument to capture EP and its inherent dimensions. The results indicate that proper measurement of entrepreneurial passion incorporates the interaction between entrepreneurs’ feelings and identity centrality for each domain.

The first subdimension, specifically innovativeness, refers to the creation of new products, services, and technologies. The second subdimension, risk taking, involves the willingness to commit significant resources to opportunities with a reasonable chance of costly failure. These risks are typically calculated and manageable. The third subdimension, proactiveness, reflects the top management orientation to pursuing enhanced competitiveness and includes initiative, competitive aggressiveness and boldness [51]. The forth subdimension, autonomy, refers to the independent action of an individual or a team in bringing forth an idea or a vision and carrying it through to completion. Generally, autonomy means the ability and will to be self-directed in the pursuit of opportunities.The fifth subdimension, competitive aggressiveness, refers to a firm’s propensity to directly and intensely challenge its competitors to achieve entry or improve the position; EO literature agrees that a competitive aggressive orientation is one of the basic characteristics of successful entrepreneurial firm activity (e.g., [29, 43]).

All the above-mentioned five dimensions have been used infrequently in the EO literature when compared with the use of the model with three dimensions. Rauch et al. [52] stated that only in one study [53] all these five dimensions have been used. However, in 30 studies [54–58], these same three dimensions have been used. In this research, we applied Lumpkin and Dess’s dimensions and developed subcriteria for evaluating of EI in fuzzy environment.

3. Fuzzy Set Theory

3.1. FAHP

The Analytic hierarchy process (AHP) introduced by Saaty [59] directs how to determine the priority of a set of alternatives and the relative importance of attributes in a MCDM problems. Yang and Chen [60] stated that the classic AHP has some deficiencies. It is useful only when data are crispy, dealing with a very unbalanced scale of judgment; uncertainty of human judgment does not take into account of natural language; provided ranking is rather imprecise; and the subjective judgment of perception, evaluation, improvement, and selection based on preference of decision makers has great influence on the AHP results. To overcome such vagueness, ambiguity, and subjectivity of the human judgment process, fuzzy set theory has been introduced [61]. Decision makers express their opinions in terms of linguistic scales. Linguistic data are converted into fuzzy numbers with the help of different membership functions. Then, it becomes easy to solve MCDM problems. Therefore, fuzzy set theory has become a helpful tool for systematizing human activities with uncertainty-based information. As Ragin [62] stated, most scholars have not been familiar with the potential of fuzzy logic for transforming social science methodologies. Although it has great capability for dealing with vagueness problems in the field of innovation and entrepreneurship as a social science; yet we find a few applications in the existing studies.

A tilde “” will be placed above a symbol if the symbol represents a fuzzy set. A triangular fuzzy number (TFN) is shown in Figure 2. A TFN is denoted simply as or . The parameter denotes the smallest possible value, the parameter the most promising value, and the parameter the largest possible value. Each TFN has linear representations on its left and right side such that its membership function can be defined as A fuzzy number can always be given by its corresponding left and right representation of each degree of membership: There are many fuzzy AHP methods proposed by various authors [63–69]. The FAHP methodology is presented in the Appendix. The weights that are obtained from fuzzy AHP are considered and used in VIKOR and TOPSIS calculations.

Figure 2 

Triangular fuzzy number .

3.2. VIKOR

Recently, the VIKOR method has been introduced as an applicable technique to implement within MCDM. Opricovic [70] developed VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) for multicriteria optimization of complex systems. This method determines the compromise solution and is able to establish the stability of decision performance by replacing the compromise solution obtained with initial weights. VIKOR can be divided into the following steps [70–75] starting from -metric used as an aggregating function in a compromise programming method [76, 77]:

In the VIKOR method, ( as ) and ( as ) are used to formulate ranking measure. The solution obtained by is with a maximum group utility (“majority” rule), and the solution obtained by is with a minimum individual regret of the “opponent.” The algorithm of the VIKOR method has the following steps.

Step 1. Determine the best and the worst values of all attribute functions, . If the th function represents a benefit, then we have where is the positive ideal solution for the th criteria and is the negative ideal solution for the th criteria. If one associates all , one will have the optimal combination, which gets the highest scores, the same as .

Step 2. Compute the values and ,   by where denotes the distance rate of the th alternative to the positive ideal solution and represents the distance rate of the th alternative to the negative ideal solution. Also are the weights of criteria, expressing their relative importance.

Step 3. Compute the values , by where , , , , and is the weight of the strategy of “the majority of criteria” (or “the maximum group utility”); here suppose that .

Step 4 (rank the alternatives). According to the values calculated by Step 3, we can rank the alternatives to make decision.

Step 5. If the following two conditions are satisfied concurrently, then the scheme with a minimum value of in ranking is considered the optimal compromise solution, such that  (C1) the alternative ) has an acceptable advantage, if , where is the alternative with the second position in the ranking list by and is the number of alternatives; (C2) the alternative ) is stable within the decision making process if it is also best ranked in and .

Step 6. Select the best alternative by choosing as a best compromise solution with the minimum value of regarding the above conditions [78, 79].

3.3. TOPSIS

Hwang and Yoon [80] originally proposed the order performance technique based on similarity to ideal solution (TOPSIS), in which the chosen alternative should not only have the shortest distance from the positive ideal reference point (PIRP), but also have the longest distance from the negative ideal reference point (NIRP), to solve the MCDM problems.In the following the steps of TOPSIS are given.

Step 7. Decision matrix is being normalized via

Step 8. The weighted normalized decision matrix is being formed by

Step 9. Positive ideal solution (PIS) and negative ideal solution (NIS) will be determined by

Step 10. The distance of each alternative from PIS and NIS will be calculated:

Step 11. The closeness coefficient of each alternative will be calculated:

Step 12. By comparing values, the ranking of alternatives is determined.

4. Implication in Real-World Cases

Malaysia is one of the countries that have an emerging economy. The number of companies is growing quickly and is now becoming a center of new business opportunities as international investors view Malaysia as the place to invest and establish their businesses. Therefore, the entrepreneurship development has become the key agenda which is evident by the introduction of mechanisms that cater to entrepreneurs [81]. In Malaysia, the government has created an enormous amount of funding towards the promotion of entrepreneurship especially for SMEs. The Malaysian government has been extremely encouraging entrepreneurship. Since the 1970s, the government has given due emphasis to increasing Malay ownership and participation in the corporate sector and high-income occupation as outlined in New Economic Policy of 1971 [82]. This objective is further charted and highlighted in the New Development Policy in 1991 through the establishment of Bumiputera Commercial and Industrial Community (BCIC), which is responsible for fostering and developing Malay and other Bumiputera groups as entrepreneurs and professionals [82]. The BCIC has been the main network through which the strengthening of entrepreneurship among the Malays in Malaysia has been encouraged. The establishment of the Ministry of Entrepreneur Development in 1995 clearly indicates the growing importance of the government role in the issue of entrepreneur development [83]. According to the Federation of Malaysian Manufacturer (FMM) directory reports, 34.7 percent of Malaysian SMEs have less than 50 employees, 32.1 percent have 51–150 employees and 33.2 percent of SMEs have more than 150 employees [84]. Hence, in this research, we studied the SMEs with less than 50 employees as they possess the highest percent among the others. In this section, we apply the aforesaid methodology to evaluate the critical factors of EI among the 30 SMEs and rank them based on VIKOR and TOPSIS methods. First data collection process and then the implementation of the methodology are presented.

This research has been conducted in three stages. First, FAHP was applied for finding the final weights of main criteria and subcriteria. Next, VIKOR was used to rank the EI of the firms and finally TOPSIS technique was adopted to compare the results with VIKOR. To get the required data of FAHP, a questionnaire was distributed. The respondents of this research were managers, assistant managers, and analysts of companies. To determine the reliability of the questionnaire, Spearman rank correlation coefficient analysis was conducted. Reliability test of the questionnaire was done at 95% confidence coefficient level. As the results show, the questionnaire has acceptable reliability. The hierarchy of the problem can be found in Figure 4, which includes three levels. The top level of the hierarchy represents the ultimate goal of the problem, while the second level of the hierarchy consists of five main criteria, which are, namely, innovativeness, proactiveness, autonomy, competitive aggressiveness, and risk taking. At the third level, these criteria are decomposed into various subcriteria.

Five criteria and fourteen subcriteria are considered, as shown in Table 1. For weighting tables, linguistic scales are used as illustrated in Table 2. Then, the main criteria and subcriteria were calculated using FAHP. Fuzzy pairwise comparisons of the main criteria and subcriteria are given in Tables 3 and 4, respectively. In the end, final weights of criteria and subcriteria using FAHP is presented in Table 5.

4.1. Application of VIKOR

In this stage, VIKOR was used to rank the EI of the firms. For this reason, the managers of the company have grouped. Decision makers from different backgrounds may define different weight vectors. They usually cause not only the inexact evaluation but also serious persecution during the decision process. Then, linguistic variables for the criteria and subcriteria weights and also the decision matrix are provided as shown in Tables 6 and 7, respectively. The values for , , and were calculated and summarized in ascending order as demonstrated in Tables 8 and 9, consequently. Here, the calculation is given for the firm A1. Calculation of the others was done similarly.

Step 13. Determine the best and the worst values as (4):  : 7 8 6 7 7 6 6 7 8 7 7 5 5 8 6 8 7 7 7 : 1 2 3 3 2 2 2 3 3 3 3 1 2 2 2 3 2 2 3.

Step 14. Compute the values and by (5):

Step 15. Compute the values by (6):

From Table 9, it has been shown that the A3 is best ranked by and also both (C1) and (C2) conditions are satisfied, meaning that and also A3 is best ranked by and . Therefore, A3 is the best selected company for the best compromise solution.

4.2. Application of TOPSIS

In this stage, TOPSIS was conducted for comparing the results with the VIKOR. After normalizing Table 7 via (7), will be obtained: