Technical Journal of Engineering and Applied Sciences Available online at www.tjeas.com ©2014 TJEAS Journal-2014-4-04/349-358 ISSN 2051-0853 ©2014 TJEAS

A combination of factor analysis and combined approach techniques (AHP-TOPSIS) for ranking

criteria and evaluating the factors affecting brand

Amir Nayeb1 , Sina jabari 2 , Mehdi Yousefi nejad Attari3

MA Student Of Industrial Engineering-University of Tehran, College Of Farabi 1

amir.nayeb@ymail.com

MA Student Of Industrial Management –Islamic Azazd University Of Tabriz Branch 2

sina.jabbary@gmail.com

Faculty Member Of Islamic Azad University Of Bonab Branch3

ABSTRACT: Nowadays, the brand personality is core and closest variable in customers’ decision making. Branding in the food industry of Iran is emerging phenomenon. This paper aims to investigate the effective factors on brand regarding the target population of 150 customers of Packard industrial bread factory in the city of Tabriz who are selected through available sampling method. In this paper, using factor analysis and combinatory approach (AHP-TOPSIS) in SPSS and EXCEL, a framework proposed in order to analyze the similarities and differences among consumers’ point of views about the effective factors on brand. Findings show that, management and customers’ political tendencies are the most effective and the least effective factors in the power of brand respectively. Keywords: brand, effective factors onbrand, Packardbread, factor analysis, AHP-TOPSIS

1. INTRODUCTION

Brand or "name, is a term, sign, symbol, design or combination of these which are used to identify goods and services of a seller or a group of sellers and also to distinguish them from their competitors presents commitment to stability and quality. Experiences have shown that a strong brand is an important tool for implementing marketing purposes to improve brand loyalty, excellent price based on brand and profit margins of increasing the flow of new products to market, to increase shareholders and their profits and to simplify customers' decision making, to reduce customers' risk and to determine their expectations [1]. Brand structure refers to a process which defines an organization and represents the heritage, value, culture, people and strategy of the brand of that organization and the start of this process associated with first experience and contact with a customer in any way and the desired result is the durability of the brand in customer's mind. Whenever the customer is willing to introduce the brand to others after the first experience and also the brand occupies his mind for the second time, it means that the brand is prioritized in his mind [2]. In recent marketing world, personality of the brand has attractive concept. The share of brand can be developed successfully through good management of contact with customer, honoring him and considering his needs. The lack of scientific view of companies and firms to marketing has led to cede their market share to foreign firms and damage their competitive strength in recent years. Scientific attention to brand and effort to successful management of its value can change the Iranian consumer's look to domestically manufactured goods and put them in their consumption basket [3]. This study examines influential factors on the power of brand and tries to provide solutions to strengthen brands in the Iranian market. In this study, referring to the scientific documentations to extract the influential factors on brand, a questionnaire was prepared that has the required comprehensiveness. The population examined in this study was 150 people which have a high level of data utility. After the completion of the questionnaire and extraction of data, the population has been analyzed in terms of gender, age and education. According to the results of the questionnaires, effective criteria on the brand were prioritized using descriptive statistics. Considering high volume of variables, for more detailed analysis and more scientific and operational results, we seek to reduce the volume of variables and form new structures for them and to do this, factor analysis was used. Factor analysis attempts to identify basic variables or factors in order to explain the pattern of correlations between the observed variables. At the end, using combinatory approach technique (AHP-TOPSIS), a model presented to analyze the similarities and differences among consumers' views about the affecting factors on the validity of brand.

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Regarding that most people who are working in the field of marketing in Iran inclined to traditional activities, but good scientific activities have been done in the field of branding such as related articles and papers. The main difference between the present paper and the other published ones in Iran is that, in this paper the subject has been studied with statistical approach which no personal opinion and criticism is included in it and only analysis with scientific value are relied on. While enjoying the benefits with using combinational approach (AHP-TOPSIS), their weaknesses such as limited number of paired comparisons and no provision of the weight of indexes are compensated. Presented decision making model uses effective criterion on the validity of the brand through rating the factors.

2. PROPPOSED METHOD In this study, after determining the effective factors on brand, the data collected using scientific resources and to collect the consumers' views a questionnaire was prepared. In this questionnaire some of the characteristics of respondents such as gender, age and education were questioned. In the second part of the questionnaire, each individual's perception of the impact of the effective factors on incumbent brand was questioned through 40 criterions. Scores of each factor (criteria) are classified in five alternatives of very low, low, medium, high, very high and for statistical analysis numerical values 1, 2,3,4,5 are assigned for factors. Questioned criterions with corresponding codes are given in table 1.

TABLE1. Effective criteria and factors on the validity of brand[4,5]

The most effective criteria and factors of brand identified through collecting and analyzing questionnaires. The reliability of the questionnaire was analyzed using alpha cronbach. For separation and classification of criteria into general correlations, one of the factor analysis methods called principal component analysis method was used. Also, using (AHP-TOPSIS) technique, the ranking of effective criteria on brand was determined .Figure 1 shows the implementation process of the proposed method.

code Name of criteria code Name of criteria

21 Innovation in advertising 1 sale

22 Innovation in products and

services 2 interest

22 Mutual confidence between

customer and manufacturer 2

Success and durability of business

22 Owning new share of market 2 Relations between consumer

and seller

22 Audiovisual media 2 Relations between consumer

and manufacturer

22 Identify the manufacturer from

customer 2

Relations between manufacturers and sellers

22 Market need 2 Culture of society

22 Customers' political orientations 2 Urbane visual identity and

shops

22 Competition 2 Impact of individuals in creating

brand

23 Internet 13 The impact of brand on

consumers

21 Modern marketing 11 Directing to consumers'

decisions

22 Social networks 12 Traditional marketing

22 transportation 12 Advertising policies

22 Distribution strategy 12 Advertising costs

22 Knowing the product 12 Selling strategy

22 Contact with management 12 Similar markets

22 Visual pollution reduction 12 Create differentiation in

character

22 Return of capital 12 Product quality

22 Initial capital 12 price

23 Geographical region 23 After sales services

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Fig 1. Implementation process of the proposed method

3- Analysis 1.3. Evaluation of individual information of consumers in the sample In this study the average age of consumers who answer the questionnaire (150 people) is 27-68 years old and their standard deviation is 6.4 years. 25.7% of consumers are 26-35 years old, 65.3% of consumers are 15-25 years old and 9% are above 36 years old. Also, 40% of consumers have high school diploma, 35% of them have BC and 25% of them have MA and higher than MA. 59% of consumers are women and 41% of them are men. 2.3. Descriptive statistics of the criteria To find the most effective factors on brand Parto chart can be used. Parto chart is plotted to show the frequencies of high and very high responses. In this chart the S1 criteria is the first priority. From 150 consumers, 98% considered sale as the most effective factor on brand, from 150 consumers 95% reported price to have huge impact on validity increase of brand, from150 consumers 90% reported relation with management to have huge impact on validity increase of the brand, from 150 consumers (less than 5%) considered transportation as a factor which has very low impact on brand and 125 consumers (less than 2%) considered political tendencies of the customers as the least effective factor on brand.

End

Reviewing the results of the

questionnaire

Identifying the criteria

Prioritizing effective criteria

with descriptive statistics

Providing questionnaire

Giving weight with AHP and

compatibility test

Adjusting

paired

comparisons

Identifying the correlative criteria

and their classification by factor

analysis

Ranking

withTOPSIS

Last results of ranking the

criteria

Comparing the

results

Review of the

literature

Experts'

experiences

Start

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Fig2.P-CHART

to customers’ political tendencies as the least effective factor on brand.

According to p-chart and table 2 it can be said that managers considered: To price, sale, relation with management and competition as the most effective factor on brand.

To factors related to the surrounding of the product rather than factors related to external environment of brand. To political tendencies, transportation and visional pollution reduction as effecless on brand. (Less than 6%) To market need, first capital, affectability of each person, traditional marketing, distribution strategy and separation in personality of each person to have less than 20% effect on validity of brand. On the other hand, they considered to sale, price, relation with management, competition, interest, geographical region, owning the new market share, success and durability of business to have more than 80% effect on the validity of brand. In table 2 the mean and variance of a number of criteria related to consumers presented. From consumers’ point of view, price and sale are the most effective criteria and political tendencies and transportation are the least effective criteria on brand. Based on 150 consumers’ views about 60% of effective factors on brand has the mean between 4 and 4.3 which indicates the high impact of these factors. In other words, it indicates that there are 15 effective factors on the success of brand. The variance of consumers’ views is less than 1 and between 0.36 and 0.67 which indicates the closeness of consumers’ views to each other.

TABLE 2. The mean and variance of a number of criteria related to consumers

criteria size ه

mean variance

sale 123 4.2200 382222

price 123

4.1200 382321 relation with management 123

12 281222 382221

competition 123 4.1222 382222

interest 123 281122 382213

geographical region 123 281322 382221

owning new share of market 123 281322 382222

success and durability of business 123 281333 382232

directing to consumers' decisions 123 283222 382222

product quality 123 283233 382222

knowing the product 123 283222 382212

mutual confidence between customer and manufacturer

123 283233 32222

sailing strategy 123 283222 382222

advertising costs 123 283322 382222

advertising policies 123 283333 382222

S1S19S36S29S2

S40S24S3S1

1S1

8S3

5S2

3S1

5S1

4S1

3S10S38S8S5

S22S26S21S16S31S30S7S4S32S6

S25S20

S17

S34

S12S9S3

9S2

7S37S33S28

10510095908580757065605550454035302520151050

FACTORS

Cu

mu

lati

ve

Pe

rce

nt

of

facto

rs

S10

S11

S12

S13

S14

S15

S16

S17

S18

S19

S1

S20

S21

S22

S23

S24

S25

S26

S27

S28

S29

S2

S30

S31

S32

S33

S34

S35

S36

S37

S38

S3

S4

S5

S6

S7

S8

S9

C1

Percent within all data.

P-CHART

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3.3. Reliability of criteria In reliability analysis proximate criteria are determined by adding up the responses of different criteria, and in contrast heterogeneous criteria are removed [6]. In the beginning, 40 criteria have been analyzed through alpha cronbach. In this method, alpha cronbach can be used for determining the reliability of a test or questionnaire with the emphasis on internal consistency. Also, in this method the components or parts of questionnaire are used to assess test reliability. If the alpha value is more than 0.7, the questionnaire has good reliability and we can be sure of internal consistency of questions. Otherwise we can identify the questions that have low correlation with other questions and remove them [7]. According to the results of this method, the alpha coefficient of questionnaire is 0.823 which shows that the questionnaire is reliable.

TABLE3. Reliability of criteria

Cronbach's Alpha

Cronbach's Alpha Based on

Standardized Items

N of Items

3.823 3.826 40

3.4. Classification of criteria using factor analysis Factor analysis refers to a class of multivariate statistical methods that their primary goal is to define the underlying structure of the data matrix [8]. It is a statistical technique that tries to justify the correlation pattern in the distribution of a visible random vector in terms of minimum number of invisible random variables which names agent [9]. This method simplifies the analyses reducing the variables (criteria) to fewer numbers of agents, or in other words classifies the criteria into several major groups. In general the analysis involves selection and measurement of a group of criteria, calculation of correlation matrix, determination of necessary factors, extraction a set of factors from correlation matrix and rotating of factors in order to enhance the interpretation capability of results. In this study effective criterion on the validity of brand divided to four factors. There are several methods for conducting factor analysis, and in this study principal component analysis (PCA) is used. According to this method, extracted factors with eigenvalue greater than the unit were selected and also, variables with absolute value of factor loading greater than 0.05 are affected in the factors [10]. According to the results of principal component analysis, 37 criteria at first are considered as 15 main factors with eigenvalue greater than 1 (according to table 3). A total eight factors expressed 67.393% of variations of criteria.

TABLE4.eigenvalues of explained variance

F Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings

Total Variance% Cumulative% Total Variance% Cumulative% Total Variance% Cumulative%

1 3.943 10.658 10.658 3.943 10.658 10.658 1.925 5.204 5.204

2 2.754 7.444 18.102 2.754 7.444 18.102 1.910 5.163 10.367

3 2.079 5.619 23.720 2.079 5.619 23.720 1.893 5.116 15.483

4 1.778 4.805 28.526 1.778 4.805 28.526 1.877 5.073 20.556

5 1.736 4.691 33.216 1.736 4.691 33.216 1.852 5.006 25.562

6 1.668 4.508 37.724 1.668 4.508 37.724 1.757 4.749 30.311

7 1.462 3.953 41.677 1.462 3.953 41.677 1.727 4.667 34.978

8

1.454 3.930 45.607 1.454 3.930 45.607 1.727 4.667 39.645

2 1.323 3.576 49.183 1.323 3.576 49.183 1.555 4.204 43.849

13 1.257 3.398 52.581 1.257 3.398 52.581 1.517 4.099 47.948

11 1.184 3.201 55.782 1.184 3.201 55.782 1.489 4.024 51.971

12 1.146 3.099 58.881 1.146 3.099 58.881 1.464 3.958 55.929

12 1.082 2.924 61.805 1.082 2.924 61.805 1.459 3.944 59.873

12 1.068 2.885 64.690 1.068 2.885 64.690 1.409 3.807 63.680

12 1.000 2.703 67.393 1.000 2.703 67.393 1.374 3.713 67.393

The proportion of the number of selected factors must be tested that KMO does this test. There is no statistical test for mentioned criteria, and large values imply that the factor analysis is confirmed. Values higher than 0.9 indicates high factor analysis, higher than 0.8 is very good, higher than 0.7 is good, higher than 0.6 is moderate, higher than 0.5 is acceptable and lower than 0.5 is unacceptable [11]. As table 4 shows, the value of

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KMO is 0.581 which is not appropriate. For raising KMO, I can be referred to anti-image matrix and remove unnecessary criteria from analysis and also, obtained better results. The criterion in this matrix is MSA which are diagonal elements of anti-image correlation matrix.

TABLE 5.KMO test results

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

.581

Bartlett’s Test of Sphericity

Approx. Chi-Square

1149.952

Df 222

Sig. 0.000

Because in many cases some criteria depend on one or several factors, so interpretation of them can be difficult. Therefore, we try a simpler interpretation of factors with rotating the factors without change in the rate of subscription. Using varimax rotation method with a change in the squares of column elements which maximize the coefficients of factors, the following results have been obtained. According to varimax the criteria divided to 8 factors. We don't do the grouping in this step. As it is mentioned before, the amount of KMO is not appropriate, so removing the 28

th criteria we raise its amount and then analyze. The removal of criteria is continued to place the

KMO to its highest value and then it drops. After removing the 28th criteria and observing KMO amount (0.581),

we remove other criteria. These criteria ( 38,8,22,26,21,16,31,30,7,4,32,6,25,20,17,34,12,9,39 and 10) are removed and KMO reached to its last increase amount (0.706).So the analysis continued with the remaining criteria.

TABLE 6. Eigenvalues of explained variance

F Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings

Total Variance% Cumulative% Total Variance% Cumulative% Total Variance% Cumulative%

1 2.937 19.581 19.581 2.937 19.581 19.581 1.917 12.781 12.781

2 1.495 9.966 29.547 1.495 9.966 29.547 1.720 11.466 24.247

3 1.364 9.097 38.644 1.364 9.097 38.644 1.656 11.042 35.288

4 1.113 7.422 46.066 1.113 7.422 46.066 1.493 9.952 45.240

2 1.076 7.172 53.239 1.076 7.172 53.239 1.200 7.999 53.239

As table 6 shows, the number of factors is 5 and using table 7 we can determine that each criteria are among which factor. The biggest number in each row is selected which showed the highest criteria correlation with its relevant factor.

TABLE7. rotated matrix of factors

Factors

1 2 3 4 5

sale .384 .631 -.172 .041 -.131 interest -.109 .774 -.008 .082 .050

Success and duration of business

.668 -.039 .031 -.258 .220

Directing to customers' decision

.040 .130 81.4 .177 -.445

Advertising policies .022 .629 .363 .065 .012 Advertising costs .075 .219 .503 .217 .267 Sailing strategy .440 .071 .240 .283 -.312 Product quality .106 .128 -.063 .804 .121

Price .015 .074- .778 .022 -.035 Creating confidence between customer and manufacturer

.116 -.012 .237 .667 -.102

Owning new share of market .173 .403 .420 -.099 -.082 Competition .624 .041 .037 .263 .180

Knowing product .494 -.084 .320 .245 .097 Relation with management .637 .248 -.055 .127 -.241

Geographical region .128 .012 .060 .085 .795

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3.5. Naming the factors The criteria analyzed to four factors and are grouped as the following: 1- Success and durability of business, advertising costs, sailing strategy, knowing the product, relation with management. 2- Sale, interest, advertising policies, price. 3- Directing to customers' decisions, competition, owing new share of market. 4- Product quality, creating confidence between customer and manufacturer. 5- Geographical region. The first factor mainly related to management problems, and also, the second factor related to financial problems, the third factor refers to competition, the forth factor indicates customer satisfaction and the fifth one indicates the related factors to geographical region. 3.6. Evaluation of combinatory approach (TOPSIS-AHP) for ranking and evaluating effective factors on brand: MADM is one of the mathematical models and indicates the problem solving approach in which one option selected from a limited number of options [12]. MADM methods are easily used and combined approaches can keep this strength and create multiple sources of knowledge [13]. In order to achieve to more efficient decisions, this study uses the combination of TOPSIS and AHP approaches in which a weakness in one is compensated with strength in another. TOPSIS was first offered by Hwang [16], and the only mental data required by TOPSIS are the weights of criteria which attracts decision makers [17]. The underlying logic of TOPSIS (regulating preferences with similarity to ideal solution) is defining positive and negative ideal solutions [18] and its base is that the selected option should have the shortest distance to the ideal solution .Positive and negative ideal solutions are hypothetical solutions in which all index values are similar to minimum and maximum values in database respectively [12]. In summary, positive ideal solution is a combination of the best available values of criteria and negative ideal solution includes the worst available values of criteria [18]. AHP method combines experts' opinions and coverts a complex decision making system into a simple hierarchical system. Then using a paired comparison, evaluation method in terms of scale is designed to assess the relative importance [18]. The functional nature of this method caused it to be used in many diverse functional fields and solving the big and complex decision making problems during the last two decades [14]. AHP processes are shown in figure 1. Table 8 represents the comparisons the characteristics of these two techniques.

TABLE 8. Comparisons the characteristics of AHP and TOPSIS TOPSIS AHP characteristics

Numerical dataMADM- Numerical dataMADM- level

Distance from PIS and DIS (absolute measurement)

Paired comparisons (relative measure Main process

Definite and clear Definite and clear index

Definite and clear Paired comparisons Deduction of weight

--- Is presented Compatibility checking

Very much 2±2 Number of comparative indices

Very much 2±2 Number of comparative options

Compensatory function Compensatory function others

Due to human limited capacity in information processing, the application of AHP has considerably restricted, and maximum paired comparisons is considered 7±2. TOPSIS method can meet the needs of paired comparisons, and capacity constraints are not occurred [13]. However, this approach requires an efficient procedure to determine the relative importance of indices. The AHP presents such procedure [8]. So, in order to achieve to advantages of both methods in ranking and selecting the best option, the combined method (AHP-TOPSIS) is used. In the following this method is explained step by step. The first step: determining the purpose of the problem, options and indices (criteria) and hierarchical structure formation The second step: decision making matrix formation; in this matrix indicates the value of index j for option I

The third step: normalizing of decision making matrix using software (the following equation)

The forth step: the formation of paired comparisons of criteria and achieving the relative importance of criteria using eigenvector or approximate methods such as arithmetic or geometric mean, in this matrix aij indicates the preference of index i in comparison to index j according to experts. If i=j then aij=1. According to preference

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judgment numbers 3, 7, 5, 9 are "medium", "strong", "very strong" and "absolute". Also, in this matrix = 1/

Paired comparisons matrices must be compatible that the validity of the questionnaire is verified. If incompatibility of matrices is verified, the questionnaire will be redistributed. The fifth step: normalized and weighted matrix formation Inconsistency rate of PCM must be formed. Paired comparison comprehensive matrix (combination of experts' opinions)

So far AHPhave been used and TOPSIS will be used from the next step. The sixth step: positive and negative ideal response formation

The seventh step: calculation of options distance from positive and negative ideal response The eighth step: calculation of relative proximity of each option to the ideal solution

The ninth step:ranking of options according to obtained relative proximity of the ninth step

According to preferred values that indicate the minimum and maximum preference of solutions, the options are arranged in descending order.

3.6.1. Creating a hierarchy of criteria and weighting them

This study is functional and to collect data the library method was used and in weighting the criteria the customers were surveyed by a questionnaire. In this part, the results of weighting criteria and ranking the options presented. It is worth mentioning that due to the incompatibility of some of paired comparison matrices (resulted from experts' opinion),the questionnaires were redistributed and modified by which the compatibility of matrices and validity of the questionnaire were confirmed. Comprehensive matrix of the main factors is as follows:

TABLE 9.Paired comparison matrix between factors F5 F4 F3 F2 F1

9 7 5 3 1 F1

7 5 3 1 1/3 F2

5 3 1 1/3 1/5 F3

3 1 1/3 1/5 1/7 F4

1 1/3 1/5 1/7 1/9 F5

22 12822 28222 28222 18222 total

TABLE 10. Normalizing factors and gaining their weight

mean F5 F4 F3 F2 F1

0.5028 0.3600 0.4286 0.5245 0.6415 382222 F1

0.2602 0.2800 0.3061 0.3147 0.2138 381222 F2

0.1344 0.2000 0.1837 0.1049 0.0713 381112 F3

0.0678 0.1200 0.0612 0.0350 0.0428 383222 F4

0.0348 0.0400 0.0204 0.0210 0.0305 383222 F5

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1.0000 1.0000 1.0000 1.0000 1.0000 183333 total

Compatibility rate of the main factors comprehensive matrix is 0.05358 which indicates high compatibility of this matrix. After the formation of paired comparison comprehensive matrix, weights of the main factors are calculated by AHP. 3.6.2. Ranking the criteria on the base of TOPSIS method Initial decision matrix was normaled using the equation 1. By multiplying each of the values of normal matrix in corresponding weights, the weighted normal matrix is calculated. By adding up the weighted values of criteria of each main factor the value of that main factor is achieved. The weighted normal decision matrix of main factors (last decision matrix) is formed through multiplication of the value of main factors a by corresponding weight of each criteria which is shown is table 11.Positive solutions from maximum of values and negative solutions from minimum of values of each criterion in table 11 is calculated as follows:

=)0/2426،0/1536 ،0/0812 ،0/0446، 0/ 0253(

=)-0/0396،-0/0167 ،-0/018 ،-0/0143،- 0/ 0141(

TABLE 11. Weighted normal decision matrix and ranking of criteria

factors

Rank

Ci Si- Si+ 2 2 2 2 1

4 0.5970 0.2293 0.1547 -0.0042 0.0023 -0.0180 0.1252 0.1394 sale

9 0.3675 0.1729 0.2975 0.0016 0.0046 -0.0008 0.1536 -0.0396 interest

3 0.5996 0.2839 0.1895 0.0070 -0.0143 0.0032 -0.0077 0.2426 Success and durability of

business

15 0.2711 0.0998 0.2684 -0.0141 0.0098 0.0502 0.0258 0.0145 directing to consumers'

decisions

8 0.3966 0.1611 0.2450 0.0004 0.0036 0.0379 0.1248 0.0080 Advertising policies

10 0.3265 0.1194 0.2463 0.0085 0.0120 0.0525 0.0435 0.0272 Advertising costs

6 0.5400 0.2085 0.1776 -0.0099 0.0157 0.0251 0.0141 0.1598 Sailing strategy

11 0.2967 0.1086 0.2574 0.0038 0.0446 -0.0066 0.0254 0.0385 Product quality

14 0.2730 0.1108 0.2952 -0.0011 0.0012 0.0812 -0.0147 0.0054 price

12 0.2902 0.1070 0.2619 -0.0032 0.0370 0.0247 -0.0024 0.0421 Creating Mutual confidence

between customer and manufacturer

7 0.4286 0.1545 0.2060 -0.0026 -0.0055 0.0439 0.0800 0.0628 Owning the new market share

2 0.6149 0.2705 0.1694 0.0057 0.0146 0.0039 0.0081 0.2266 competition

5 0.5425 0.2273 0.1916 0.0031 0.0136 0.0334 -0.0167 0.1794 Knowing the product

1 0.6585 0.2799 0.1452 -0.0077 0.0070 -0.0057 0.0492 0.2313 Relation with management

13 0.2791 0.1013 0.2618 0.0253 0.0047 0.0063 0.0024 0.0465 Geographical region

Using the relations, options distance (criteria) from positive and negative solutions and proximity coefficients of each option to these solutions is calculated. At the end, the criteria are ranked according to these scores which are shown in table 12. Table 12 shows that, relation with management is the first effective factor on brand using this method.

4. CONCLUSION

This study aimed to identify effective factors on brand in bread industry. Using descriptive statistics, factor analysis and combination approach (AHP-TOPSIS) in SPSS and EXCEL, a framework proposed in order to survey customers to understand the effective factors on brand .Based on this framework 40 effective factors on brand were determined and in order to collect the consumers’ opinion about the effectiveness of each factor, they were given a questionnaire. Collecting and analyzing the questionnaires, the most effective factors were identified. The reliability analysis of questionnaires was conducted by cronbach alpha. Using factor analysis, the 15 remaining factors were grouped into 5 main factors. Finally, ranking criteria and effective factors on brand was determined. The results are as follows: 1- According to the majority of consumers, management is the most effective factor on brand and political tendencies and transportation are the least effective factors on brand. 2- Regarding the answers, consumers consider to sale as effective factor on brand. The higher the sale, the higher the buyers’ incentive to buy more.

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3- Factor analysis of criteria shoes that the effective factors on brand are summarized to 5 groups of: management problems, financial problems, competition and geographical region. 4- To improve the influential criteria on the effective factors on brand, each criterion should be viewed in the context of aforementioned factors and those criteria which are in the sub sets of a factor are correlated with each other. 5- Using combined approach, the most effective criteria in factors are management, competition, success and durability of the business.

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