Abstract: * Prof. P. Sheela ** Mr. R.L.N. Murthy

Transcription

1 The Use of Fuzzy Analytical Hierarchy Process (FAHP) Model for the Primary Screening of Business Opportunity in the Process of Entrepreneurial Activity. Abstract: * Prof. P. Sheela ** Mr. R.L.N. Murthy Entrepreneurship is an activity of identifying a business opportunity, exploiting and transforming it into a successful business venture. But all business opportunities are not equally exploitable. There are several factors responsible for the success of a business opportunity to be best exploitable. These factors include Capital requirement, Availability of Other resources, Competition, Marketing opportunities, Government support, and Risk in execution are the most important factors that needs to be considered before taking an entrepreneurial activity. This forms a complex decision making process for the primary screening of a business opportunity with these factors. The objective of this paper is to provide a Fuzzy AHP technique as a simple and useful model for the primary screening of a business opportunity. Key terms: Fuzzy, AHP, Entrepreneurship. * Prof. P. Sheela, Department of Financial Management, GITAM Institute of Management- GITAM University ** Mr. R.L.N. Murthy, Research Scholar, GITAM Institute of Management- GITAM University

2 INTRODUCTION Entrepreneurship has been defined differently in different contexts. In its simple terms entrepreneurship is the process of exploiting a business opportunity. It involves identification of opportunity, analysis, mobilizing resources and exploiting the opportunity to make it the successful business venture. There may be a plethora of business opportunities in one s own interest. A detailed analysis of these opportunities is required for accepting them for entrepreneurial activity. This detailed analysis is complex, time-consuming and uphill task for entrepreneurs. The basic factors for any business opportunity are capital requirement, availability of other resources, competition, marketing opportunities, government support and risk of the executing it as a business venture. Favorability of these factors varies among various business opportunities. Expert service providers on entrepreneurial activity, such as consultancy firms, help in giving the entrepreneurs A Stitch in Time. These experts often involve in opportunity screening before considering it for a robust and comprehensive evaluation. This model (Fuzzy AHP) can be applied by those people or organizations which provide expert services in Business entrepreneurship development right from opportunity evaluation to development. Fuzzy AHP is widely accepted technique in such situations where the evaluator faces problem of uncertainty in giving judgment to an alternative (Opportunity) with several criteria (Factors affecting opportunity). LITERATURE REVIEW There are various theories of entrepreneurship such as economic entrepreneurship theory, Psychological entrepreneurship theory, Sociological entrepreneurship theory, Anthropological entrepreneurship theory, Resource based entrepreneurship theory, and Opportunity based entrepreneurship theory. According to opportunity based theory entrepreneurs do not cause change but exploits opportunities that create change (Simpeh, 2011). A successful entrepreneurial activity always follows a successful opportunity development process. A successful opportunity development includes opportunity recognition and evaluation. Opportunities are made rather than found. Therefore the focus should be on opportunity evaluation (Ardichvilia, Cardozob and Ray, 2003). An opportunity is a favorable set of circumstances for a new product or service or business (Barringer and Ireland, 2012). Before an opportunity is

3 exploited, a feasibility study is conducted. A popular evaluation procedure is stage gate procedure wherein the opportunity is evaluated at each stage of its development (Ardichvilia, Cardozob and Ray, 2003). But, the problem of stage gate process is that the opportunity is aborted if it fails at any stage of its development. Therefore there is a risk of wastage of efforts and resources in the process of opportunity development. In this context a tool is required for reducing this risk with minimal efforts. Primary screening of opportunity is capable of reducing such risk of futile opportunity development process. Fuzzy AHP Fuzzy AHP is widely used in various fields of research. It is used in Medical, Engineering, and Social Sciences. It is widely used as a MADM (Multi Attribute Decision Making) technique. Its use in business management are : supplier selection problem (Kahraman, Cebeci and Ulukan, 2003) (Koul and Verma, 2011) (Pang and Bai, 2013), evaluation of supply chain firms (Percin, 2008), investor risk evaluation (Yan-fen and Yu-lan, 2008), project manager selection (Liqin, Yuexian and Wenming, 2009), Internal financial control of an enterprise (Shao, 2009), evaluating bidding risk in International projects (Xinzheng and Liying, 2009), evaluation of effective factors of Organizational Indifferences (Esfahani et al., 2013). Process of FAHP model building for primary screening of business opportunity Fuzzy Set A fuzzy set A = x, μ A (x)) x X}, is a set of ordered pairs and X is a subset of the real numbers R, where μ A (x) is called the membership function which assigns to each object "x" a grade of membership ranging from zero to one. There are various Fuzzy numbers are used to define the membership of the object to the set of Objects. Triangular and Trapezoidal numbers are most widely used Fuzzy numbers. TFNs are mostly accepted and used for their intuitiveness and capacity in dealing with vagueness in human judgments.

4 Triangular Fuzzy Numbers (TFN) A triangular Fuzzy Number M is defined by a triplet l, m, u) where l, m, and u represent lower, middle and upper values of support of a fuzzy number M. A Triangular fuzzy number M is defined as M = x m l x m u l m l u m u if xε m, l if xε m, u 0 Otherwise Application of Fuzzy AHP FUZZY AHP is now widely used in Business management for finding solutions to various decision making situations in business. It is primarily consists of 5 steps. 1) Establishing hierarchy or Goal set 2) Establishing Criteria set 3) Determining evaluation matrix 4) Determining Weights of Factors 5) Final evaluation of goal Step 1: Establishing goal set The goal is defragmented as various factors. Here the goal is the Business Opportunity and the factors are capital requirement, availability of other resources, competition, marketing opportunities, government support and risk of the executing. These factors are then sub divided as various sub factors. The main goal is written in set form as U = [ U 1 U 2 U 3 U 4 U 5 U 6 ]

5 Illustration for weights determination of First Index factors First index factors are Capital Requirement, Availability of other resources, Competition, Marketing Opportunities, Government support and Risk of executing. Business Opportunity Capital Requirement (U 1 ) Availability of other resources (U 2 ) Competition (U 3 ) Marketing opportunities (U 4 ) Government support (U 5 ) Risk of the executing (U 6 ) Amount of Capital Required (X 1 ) Accessibility to Financial Institutions (X 2 ) Cost of Capital (X 3 ) Quality (X 4 ) Price (X 5 ) Timeliness (X 6 ) Availability of Substitutes (X 7 ) Degree of Competition (X 8 ) Number of Competitors (X 9 ) Market Size (X 10 ) Market Share (X 11 ) Market Accessibility (X 12 ) Policy (X 13 ) Subsidy (X 14 ) Licensing (X 15 ) Financial Risk (X 16 ) Family risk (X 17 ) Mental Risk (X 18 ) Figure: Factors for Primary Screening of Business Opportunity

6 Step 2: Establishing Criteria set An evaluation criteria set should be established for evaluating each sub factor with reference to the main goal. Evaluation set or Criteria set is defined according to the nature of problem. In this paper the evaluation set V is defined as V= (Highly favorable (HF), Moderately favorable (MF), Somewhat favorable(sf), Unfavorable (UF)) Step 3: Determining Evaluation Matrix Evaluation matrix or Relationship Matrix is determined by the evaluations made by the experts to each set of factors under main factors. Suppose for factor (X 1 ) there are 10 experts out of which 5 experts vote highly favorable, 2 experts voted moderately favorable and 3 experts voted somewhat favorable and no expert voted Unfavorable for the Opportunity under consideration, then the evaluation matrix for (X 1 ) is ( ). In the same way the evaluation values have to be calculated for all 18 sub factors. Then evaluation matrices for set of sub factors under each main factor are to be determined. Example, for the set of sub factors under the main factor Capital requirement (U 1 ) is as follows HF MF SF UF X R 1 = X X In the same manner we get R 2, R 3, R 4, R 5, and R 6 for (U 2 ), (U 3 ), (U 4 ), (U 1 ), and (U 5 ) Step 4: Determining Weights of Factors All factors may not have the same importance with respect to main goal. In the parlance of the present paper the various factors to be considered for opportunity evaluation may not have same degree of importance. Therefore the degree of importance in other words the weights of factors of same level have to be calculated. For this a pair wise comparison has to be made for same level of factors.

7 This pair wise comparison has to be made by the following 9 point scale developed by (Saaty, 2008). Table 1: Basis for pairwise comparison Scoring Pattern Relative Importance (More) Score Relative Importance (Less) Score Equal importance 1 Equal importance 1 Slightly more important 3 Slightly less important 1/3 Strongly more important 5 Strongly less important 1/5 Very Strongly more important 7 Very Strongly less important 1/7 Absolutely more important 9 Absolutely less important 1/9 Intermediate values 2,4,6,8 1/2,1/4,1/6,1/8 Suppose Factor (U 5 ) is Strongly More Important than factor (U 1 ) then score for (U 5 ) to (U 1 ) is 5 and (U 1 ) to (U 5 ) is 1/5. Likewise we need to compare each factor with the remaining factors at same level. After the pair wise comparison, values have to be converted into Triangular Fuzzy Numbers using the following conversion scale in Table 2. Table 2: Triangular Fuzzy Conversion Scale Linguistic scale for importance Basis Score Triangular Triangular fuzzy scale fuzzy reciprocal Equally important 1 (1, 1, 1) (1, 1, 1) Equally to Slightly important 2 (1, 2, 3) (1/3, 1/2, 1) Slightly more important 3 (2, 3, 4) (1/4, 1/3, 1/2) Slightly to strongly important 4 (3, 4, 5) (1/5, 1/4, 1/3) Strongly more important 5 (4, 5, 6) (1/6, 1/5, 1/4) Strongly to very strongly important 6 (5, 6, 7) (1/7, 1/6, 1/5) Very Strongly more important 7 (6, 7, 8) (1/8, 1/7, 1/6) Very strongly to absolutely important 8 (7, 8, 9) (1/9, 1/8, 1/7) Absolutely more important 9 (8, 9, 9) (1/9, 1/9, 1/8)

8 Let us take the pair wise comparisons as follows: Table 3: Pair wise Comparisons of Main Factors (U 1 ) (U 2 ) (U 3 ) (U 4 ) (U 5 ) (U 6 ) Capital Requirement (U 1 ) 1 1/ /5 1/7 Availability of other resources (U 2 ) /7 1/8 Competition (U 3 ) 1/4 1/ /4 1 Marketing Opportunities (U 4 ) 1/6 1/3 1/ /9 Government support (U 5 ) /6 1 9 Risk of executing (U 6 ) /9 1 The above pair wise comparisons have been converted to the TFNs as follows. Table 4: Conversion of Pair wise comparisons into TFNs (U 1 ) (U 2 ) (U 3 ) (U 4 ) (U 5 ) (U 6 ) Capital Requirement (U 1 ) (1, 1, 1) (1/3, 1/2, 1) (3, 4, 5) (5, 6, 7) (1/6, 1/5, 1/4) (1/8, 1/7, 1/6) Availability of other resources (U 2 ) ( 1, 2, 3) (1, 1, 1) (5, 6, 7) (2, 3, 4) (1/8, 1/7, 1/6) (1/9, 1/8, 1/7) Competition (U 3 ) (1/5, 1/4, 1/3) (1/7,1/6, 1/5) (1, 1, 1) ( 1, 2, 3) (1/5, 1/4, 1/3) (1, 1, 1) Marketing Opportunities (U 4 ) (1/7, 1/6, 1/5) (1/4, 1/3, 1/2) (1/3, 1/2, 1) (1, 1, 1) (5, 6, 7) (1/9, 1/9, 1/8) Government support (U 5 ) (4, 5, 6) (6, 7, 8) (3, 4, 5) (1/7, 1/6, 1/5) (1, 1, 1) (8, 9, 9) Risk of executing (U 6 ) (6, 7, 8) (7, 8, 9) (1, 1, 1) (8, 9, 9) (1/9, 1/9, 1/8) (1, 1, 1)

9 We determined the geometric mean r i of the TFN values of each factor as follows r i = (U i1 U i2 U i3 U i4 U i5 U i6 ) 1/6 for i = 1, 2,,n r 1 = (U 11 U 12 U 13 U 14 U 15 U 16 ) 1/6 r 1= ((1*1/3*3*5*1/6*1/8) 1/6, (1*1/2*4*6*1/5*1/7) 1/6, (1*1*5*7*1/4*1/6) 1/6 ) = (0.69, 0.84, 1.06) Likewise we calculate r 2, r 3, r 4, r 5, and r 6 for (U 2 ), (U 3 ), (U 4 ), (U 5 ), and (U 6 ) r 2= (0.72, 0.93, 1.12) r 3= (0.42, 0.52, 0.64) r 4= (0.43, 0.51, 0.67) r 5= (2.09, 2.44, 2.75) r 6= (1.83, 1.96, 2.08) Weight of each factor (U i ) can be calculated as follows: W U1 = r 1 (r 1 r 2 r 3 r 4 r 5 r 6 ) -1 W U1 = (0.69, 0.84, 1.06) ((0.69, 0.84, 1.06) (0.72, 0.93, 1.12) (0.42, 0.52, 0.64) (0.43, 0.51, 0.67) (2.09, 2.44, 2.75) (1.83, 1.96, 2.08)) -1 W U1 = (0.69, 0.84, 1.06) ((1/ ( ), 1/ ( ), 1/ ( )) W U1 = (0.08, 0.12, 0.17) = 0.12 likewise we determine weights of w U2, w U3, w U4, w U5, and w 6 W U2 = (0.09, 0.13, 0.18) = 0.13; W U3 = (0.05, 0.07, 1.0) = 0.37; W U4 = (0.05, 0.07, 0.11) = 0.08 W U5 = (0.25, 0.34, 0.44) = 0.34; W U6 = (0.22, 0.27, 0.34) = 0.28

10 Finally we need to normalize these weights for final weights of factors as W u1 = W u1 W ui n i=1, using this formula normalized weights are as follows W u1 = 0.09 ; W u2 = 0.10; W u3 = 0.28; W u4 = 0.06; W u5 = 0.26; W u6 = 0.21 Step 4 has to be followed to determine weights of all sub factors (X 1, X 2, X3...X18) under each main factor. Step 5: Final Evaluation Evaluations of sub factors under the main factors have to be determined as B i = A i *R i, where A i denotes weights of sub factors Under the main factor (U i ) and R i is the relationship matrix obtained for those sub factors. Let us take weights of 3 sub factors under Capital Requirement (U 1 ) as A 1 = ( ) and Relationship matrix R 1 R 1 = X 1 X 2 X 3 HF MF SF UF then B 1 = A 1 *R 1 is as follows B 1 = = (b 11, b 12, b 13, b 14 ) = In the same way we need to calculate B 2, B 3, B 4, B 5, and B 6 and these values will form relation matrix R as follows R = b 11 b 21 b 31 b 41 b 51 b 61 b 12 b 22 b 32 b 42 b 52 b 62 b 13 b 23 b 33 b 43 b 53 b 63 b 11 b 12 b 13 b 14 b 21 b 22 b 23 b 24 b 14 b 24 b 34 b 44 b 54 b 64

11 Let us take B 2, B 3, B 4, B 5, and B 6 as B 2 = ( ); B 3 = ( ); B 4 = ( ); B 5 = ( ); B 6 = ( ) (Values assumed For Illustrative purpose for the explanation of the model) R = Final evaluation of the main goal (Opportunity) is B= A*R, where A= weights of main factors and R is the evaluation matrix obtained as above from the evaluations of sub factors under each main factor. B= = From the above final evaluation of opportunity, If we observe the relationship matrix R, the maximum membership of Government support (5 th Row maximum value is 0.46) is showing Just Favorable, whereas in terms of Risk of executing the opportunity is Unfavorable (Maximum value of 6 th Row is 0.32). But the overall evaluation of opportunity is Highly Favorable (Final evaluation maximum value is 0.31) according to Fuzzy Maximum membership principle. It is mainly due to highly favorable conditions exist in terms of Capital Requirement (0.42), Other resources availability (0.34), Competition (0.45) and Marketing Opportunities (0.42).

12 Conclusion Fuzzy AHP is a simple and useful technique for Multi-Criteria Decision making. It has been in use in various fields of research today. It can be employed as a decision making tool even in Entrepreneurship Activities. This paper is an illustration of model building of Fuzzy AHP as a model for Primary screening of Business Opportunity with various factors affecting it. This model is useful and easy to use by entrepreneurial service experts with their knowledge about factors related to an opportunity. This model can be employed in various stages of Entrepreneurship where the entrepreneur faces a problem of complex decision making with several criteria affecting the decision variable. References 1. Allah, M.A. and Nakhaie, H. (2011) 'Entrepreneurship and risk taking', International Conference on E-business, Management and Economics (IPEDR), Singapore, Ardichvilia, A., Cardozob, R. and Ray, S. (2003) 'A theory of entrepreneurial opportunity identification and development', Journal of Business Venturing, vol. 18, no. 1, pp Barringer, B.R. and Ireland, R.D. (2012) Entrepreneurship: Successfully Launching New Ventures, 4 th edition, Pearson. 4. Bojadziev, G. and Bojadziev, M. (2007) Fuzzy Logic For Business, Finance, and Management, 2 nd edition, World Scientific Publishing Co. Pte. Ltd. 5. Dubois and J.Didier (1980) Fuzzy sets and systems, Academic Press Inc. 6. Esfahani, D.A.N., Ghorbani, O., Amiri, Z. and Farokhi, M. (2013) 'Identifying and Ranking the Effective Factors on the Organizational Indifference through Fuzzy Analytical Hierarchy Process (FAHP) (Damavand Municipality as a Case Study)', International Journal of Academic Research in Business and Social Sciences, vol. 3, no. 6, June, pp , Available: ISSN: Foss, K. and Foss, N.J. (2008) 'Understanding Opportunity Discovery and Sustainable Advantage: The Role of Transaction Costs and Property Rights', Strategic Entrepreneurship Journal, vol. 2, no. 3, September, pp Kabir, G., Hasin, M.A.A. and Khondokar, M.A.H. (2011) 'Fuzzy Analytical Hierarchy Process for Multicriteria Inventory Classification', International Conference on Mechanical Engineering (ICME2011) December 2011, pdf, Dhaka, Bangladesh, 1-6.

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