Optimized the Functional Food Industry Selection Address and Strategic Alliance

Frontiers in Finance Volume 1, 2015 www.seipub.org/ff doi: 10.14355/ff.2015.01.009

Optimized the Functional Food Industry Selection Address and Strategic Alliance Shu‐Chen Peng1,a Shu‐Chen Hsu2,b Chao‐Yen Wu3,c Department of Information Engineering, I‐Shou University

1,3

1, Section 1, Hsueh‐Cheng Road, Ta‐Hsu Hsiang, Kaohsiung 840, Taiwan Kaosiung municipal Nanzih Special School, Kaohsiung, Taiwan

2

Department of Marketing Distribution Management, Kao Yuan University, Taiwan

2

Email: a cecilia580807@gmail.com, b demi8468@hotmail.com, c cywu@isu.edu.tw Abstract The reasons that the market of functional foods grows most refer to the early coming of aging population society. This leads to the growth of the healthcare expense, the improvement of education level, and the prolongation of average age. These factors create the development of the functional foods industry. The research majors on the location of functional foods store and the development of strategic alliance, whose purpose is to evaluate superiorities of strategic alliance in the functional foods industry in Taiwan by the different predicting ways of quality and quantity. First, we investigate document and papers to find out what factor that those locations whose stores run busy business have. Then request experts to fill the questionnaire relating to their extent of identification by using InLinPreRa to analyze and obtain the weight of the successful location for functional foods industry. After the evaluation of strategic alliance from experts, we can carry off the success rate of maximizing the developmental superiorities to functional foods industry strategic alliance and provide the researching result for organizations and units who take part in strategic alliance to consult.

Keywords InLinPreRa, functional Food, Strategic Alliance

Introduction Taiwanʹs functional food market is highly competitive; there are some researchers who have found the following: The global Health Care Food Industry has been rapidly growing and demonstrated enormous economic value.

Taiwan should leverage its developmental advantage in the Health Care Food Industry to catch the opportunity.

Integrated Strategy should be focused on the following Developmental Guideline: Horizontal Integration of Industry, Government, and Academy; Leveraging the Advantage of Industrial Value Chain; Realize Trading, R&D and Manufacturing, Marketing and Sales, Strategic Alliance, and Merger & Acquisition[1]. Xu[2] proposed the Incomplete Linguistic Preference Relations method that makes sufficiently using of the provided preference information and maintains the decision maker’s consistency level avoids checking the consistency of linguistic preference relations. During the pairwise comparison, each expert can select anyone of the explicit items as the standard according to his/her preference or recognition, and then the pairwise comparison will be carried out between the adjoining items in order to obtain the original preference matrix; complete linguistic preference relation counters the fact that all of the attribute decision‐making experts carry out the pairwise comparison through preference matrix. Therefore, this study applies the systematic and structural InLinPreRa to identify the key factors of selecting functional food industry address and search strategic alliance in Taiwan. This model not only serves as a checking functional food industry address, but also helps in analysing the organizational ability by considering key success factors for strategic alliance.

Literature Review Xu[2] proposed the Incomplete Linguistic Preference Relations, during the process of pairwise comparison, each expert can select anyone of the explicit items as the standard according to his/her preference or recognition, and then the pairwise comparison would be carried out between the adjoining items in order obtain the original

69

www.seipub.org/ff Frontiers in Finance Volume 1, 2015

preference matrix; complete linguistic preference relation counters the fact that all of the attribute decision‐making experts carry out the pairwise comparison through preference matrix; when the decision maker uses pairwise comparison to compare the original preference values, and the remaining unknown variables add with the adjoining numbers that equals to 0 through the corresponding opposite numbers so as to obtain the complete matrix, this is called Incomplete Linguistic Preference Relations. Wang et al.[3] used horizontal, vertical and oblique pairwise comparison algorithms to construct a Multi‐Criteria Decision Making with Incomplete Linguistic Preference Relations model. This even allows every decision expert to choose an explicit criterion or alternative for index unrestrictedly. The relevant definitions are defined as follows: The Decision Making Matrix of Incomplete Linguistic Preference Relations Linguistic preference relations are usually used by decision makers to express their linguistic preference information based on pairwise comparisons [2, 4]. The relevant definitions are described as follows. 

Definition 1:Incomplete Linguistic Preference Additive Relation

Let A  ( aij ) nn be linguistic preference relation, if A is an incomplete linguistic preference relation, it counters the fact that decision makers can carry out pairwise comparison for attributes so as to satisfy Eqs.1. aij  S , aij  a ji  S0 , aii  S0



(1)

Definition 2: Incomplete Linguistic Consistent Additive Preference Relation:

Let A  ( aij ) nn be complete consistent additive preference relation, which counters all of the i , j , k decision makers for pairwise comparison, if a ik >S0 represents x i is better than x k ; while a kj >S0 represents x k is better than x j , then a ij >S0 can be derived the equation of x i better than x j is aij =aik  akj



(2)

Definition 3: The Algorithm Rules of Three Different Kinds of Pairwise Comparison Decision Making Matrices

Then based on equations, preference relation matrix is generated. For different known factors of decision‐ making expert’s choice, it could obtain few matrices. Xu[2] proposed the algorithm rules of three different decision making matrices and Wang et al. [3, 5] used the InLinPreRa the solve the Multi‐Criteria Decision Making. Chang et al. [6] utilized the Incomplete Linguistic Preference Relations to measure the success possibility of implementing ERP. Framework the Location of Functional Foods Store and the Development of Strategic Alliance Investigating the Success of Influential Factors on the Location of Functional Foods Store The selection of location of functional foods store is one of the most important decisions for international logistics managers owing to the need to consider various criteria that involve a complex decision process in which multiple requirements and uncertain conditions have to be taken into consideration simultaneously. The influential factors are derived though widespread investigation and consultation with several experts [7, 8].

Determining the Priority Weights of Functional Food Store Influential Factors Subjectivity and vagueness within the measuring process are dealt with using linguistic variables quantified in a scale of [-t , t ] . This study used linguistic to express their strength of preference among influential factors.

1) Linguistic Variables This study provides the evaluators simple linguistic terms quantified on a scale of [‐8, 8] to express their strength of preference among influential factors (Table 1). Linguistic variables [8] are simultaneously used to

70

Frontiers in Finance Volume 1, 2015 www.seipub.org/ff

measure the likelihood of success/failure regarding each influential factor (Table 2).

TABLE 1 LINGUISTIC TERMS FOR THE IMPORTANCE WEIGHTS OF INFLUENTIAL FACTORS

Definition

value

value

Absolutely more important (AB)

8

Less Weakly more important (LWK)

‐2

Very strongly more important (VS)

6

Less Strongly more important (LST)

‐4

Strongly more important (ST)

4

Less Very strongly more important (LVS)

‐6

Weakly more important (WK)

2

Less Absolutely more important (LAB)

‐8

Equally important (EQ)

0

TABLE 2 LINGUISTIC VARIABLES FOR THE PRIORITY RATINGS OF POSSIBLE OUTCOME

Definition

value

Very high (VH) 5

4

High (H)

2

Fair (F)

0

Less High (LH)

‐2

Less Very high (LVH)

‐4

2) Obtaining Priority Weights of Influential Factor Construct pairwise comparison matrices amongst the influential factors ( C r , r  1, 2,..., k ). The evaluators ( E e , e  1, 2,..., n ) used three types of pairwise comparisons algorithm which are horizontal vertical and oblique to construct pairwise comparison matrices. Using the horizontal comparison of matrices are as below: C1

C ( e )   aij( e ) 

k k

C1 0  C2  C   3 C4  ... ...  Ck 

C2

C3

C4

(e) a12

(e) a13

(e) a14

0  

 0 

  0

... 

... 

... 

... Ck ... a1(ke )   ...   ...    ...   0    ... 0  kxk

The remaining a ij( e ) can be calculated using Eqs.(1) and (2) to obtain the other known  of triangular second half. Finally, obtain the full preference matrix.

Transform the preference value a ij( e ) into bij( e ) in an interval scale [0, 1] , then the matrix Ct be obtained as.

Ct  f (C ( e ) ) The transformation function is given by

f : [ a, a ]  [0,1] , f ( x)  C1

Ct ( e)  bij( e)    kxk

C1  0  (e) C2 b21  C b(e)  3  31 C4  b ( e )  41 ... ...

Ck  b ( e )  k1

xa 2a

C2

C3

C4 ... Ck

b12( e )

b14( e )

0

b13( e ) (e) b23

(e) b32

0

(e) b34

(e) b42

(e) b43

0

...

...

...

bk( e2)

bk( e3)

bk( e4)

(e) b24

(3a)

... b1(ke)   ... b2( ke)   ... b3(ke)  (3b) ... b4( ke)   ... ...   ... 0  kxk

Utilize the method of average value to integrate the judgment values of n evaluators, namely

71

www.seipub.org/ff Frontiers in Finance Volume 1, 2015

C  [ pij ]kxk (4a) 1 (1) 1 n (b  b (2)  ...  b ( n ) )   b ( e ) (4b) n n e 1 i  1, 2,...,k , j =1, 2,...,k

pij 

ij

ij

ij

ij

Use hij to indicate the normalized preference values of each influential factor, such as C1

C2

C3

C1 0  (e) C2  h21  C h(e) C  [hij ]kxk = 3  31 C4  h ( e )  41 ... ...

Ck  h ( e )  k1

(e) h12

h13( e)

(e) h14

0

(e) h23

(e) h24

(e) h32

0

(e) h34

(e) h42

(e) h43

0

...

...

...

hk( e2)

hk( e3)

hk( e4)

hij 

pij

C4 ... Ck

i  1, 2,...k ,

k

... h1(ke )   ... h2( ke )   ... h3(ke )  ... h4( ke )   ... ...   ... 0  kxk

(5a)

j  1, 2,..., k

(5b)

 pij

i 1

Given the r w denoting the priority weight of influential factor r , the priority weight of each factor can be obtained, that is k

r

 hrj

w

k

j 1 k

i  1, 2,..., k

(6)

  hij

i 1 j 1

1

w, 2 w,..., k w ,

r

k r w  [0,1] ,  w 1 r 1

Determining the Priority Ratings for Possible Outcome Regarding Factors The evaluators are asked to express their subjective judgments regarding the preference ratings of possible outcome ( A i , i  1, 2,..., m ) regarding each influential factor in linguistic terms, as listed in Table 2.

The evaluators used three types of pairwise comparisons algorithm to choose the better of two possible outcomes for a set of m -1 preference data under each influential factor. Using the horizontal comparison kinds of matrices are below.

A1

r



(e)  D ( e )   r auv  mm

 A1  0 A2    A3     A4     ...

Am  

A

2

r (e)

a12

A3

 r (e) a

A4

...



... ... ...



0

 a 34





0

...

...

...

...

...







...

0

23

r (e)

Am

            r (e)  a   0  mm m-1 m

Using Eqs. (1) and (2) to obtain the corresponding value. Finally, obtain the full preference matrix.

(e) (e) is transformed in the range [‐4, 4] into r buv in an interval scale [0,1] ,then the Next, the preference value r auv

matrix r Dt be obtained as r Dt  f (r D(e ) ) . The transformation function is given by

72

Frontiers in Finance Volume 1, 2015 www.seipub.org/ff

f : [ a, a ]  [0,1] , f ( x) 

r



xa 2a

(e) Dt  [ rbuv ]mxm u, v  1, 2,..., m

(7)

Utilize the method of average value to integrate the judgment values of n evaluators, namely r

D  [ quv ]mxm

(8a)

1 r (1) r (2) (e) ( buv  buv  ...  r buv ) n 1 n (e)   r buv u  1, 2,..., m , v  1, 2,..., m n e 1

quv 



(8b)

Use ruv to indicate the normalized preference values of each influential factor, such as r

r

uv 

D  [ ruv ]mxm

quv

(9a)

u , v  1, 2,..., m

m

(9b)

 quv

u 1



Consequently, ru denoting the average rating of possible outcome u with respect to influential factor r is provided. The desired rating of each possible outcome can be obtained for each influential factor that is

u 

r



1



m

r  uv

(10)

v 1

Obtaining the priority weight for prediction

Multiplying the priority weights of influential factors by the ratings of possible outcomes, a predicted value Z u for chance in success/failure implementation is obtained as:

Zu = ru  r w (11) Empirical Case Study We have a meeting with all members to make sure they know what the model meant and how to measure the importance weights of influential factors before prediction. Seven major influential factors are considered in this problem of factor that those locations whose stores run busy business have. The seven major risk factors are (C1) Transportation convenience; (C2) Community Environment; (C3), Life function; (C4) Public facilities; (C5) Size of the venue; (C6) Sales targets; (C7) Competition.

The priority weight of each influential factor can to be obtained by Eqs.1‐10. The priority weight and rank of each influential factor assessed by eleven evaluators are listed in Table 3.

The results demonstrate that the weights of the seven important influential factors as below (C6) Sales targets 0.169; (C4) Public facilities 0.157; (C5) Size of the venue 0.149; (C3), Life function 0.147; (C1) Transportation convenience 0.129; (C7) Competition 0.126; (C2) Community Environment 0.123.

The following is the evaluation result with regular chains and cooperative chains.

Z Regular Chains  (0.647  0.129)  (0.569  0.123)  (0.534  0.147)  (0.685  0.157)  (0.569  0.149)  (0.710  0.169)  (0.655  0.126)

=0.627

Z Cooperative Chain  (0.353  0.129)  (0.431 0.123)  (0.466  0.147)  (0.315  0.157)  (0.431 0.149)  (0.290  0.169)  (0.345  0.126)

=0.373

73

www.seipub.org/ff Frontiers in Finance Volume 1, 2015

TABLE 3 THE RANK OF THE INFLUENTIAL FACTOR IMPORTANCE WEIGHT

C1

C2

C3

C3

C5

C6

C7

Total

Rank

Weight( k w )

C1

0.130

0.131

0.110

0.127

0.131

0.126

0.130

0.885

5

0.129

C2

0.124

0.125

0.104

0.121

0.124

0.118

0.125

0.841

7

0.123

C3

0.146

0.146

0.125

0.147

0.150

0.148

0.146

1.007

4

0.147

C4

0.155

0.155

0.134

0.158

0.160

0.160

0.155

1.078

2

0.157

C5

0.148

0.148

0.127

0.149

0.152

0.151

0.148

1.023

3

0.149

C6

0.168

0.167

0.147

0.174

0.155

0.178

0.170

1.160

1

0.169

C7

0.127

0.128

0.107

0.124

0.128

0.119

0.127

0.861

6

0.126

Total

6.855

1

C6 (0.169)  C4 (0.157)  C5 (0.149)  C3 (0.147)  C1 (0.129)  C7 (0.126)  C2 (0.123)

Conclusion and Contribution There are many definitions of functional foods, although with a common element of providing some functional advantage to consumers, and they are almost universally associated with food innovation. Perhaps an effect of the functional food movement has been taken to help create a greater awareness of all the different dimensions of food product development, and its legacy may be a greater integration of those dimensions. It is not a neglect able aspect that functional food products help to ensure an overall good health and/or to prevent/manage specific conditions in a convenient way. As to the Functional Food Industry, this research has found the following: (1).The global Functional Food Industry has been rapidly growing and demonstrated enormous economic value. (2).

Taiwan should leverage its developmental advantage in the Functional Food Industry to catch the opportunity. (3).

The market developmental strategy should be in the following: Deeply and Pervasively Focus in Taiwan, Initiate Market Development in Mainland China, Penetrate through South‐East Asia Market, and Deployment of Integration in Asia. (4). The Regulation Developmental Strategy should be like: Speeding up the Regulation up‐ grade, consider Parallel Policy, and focus on the Management of License Qualifications. (5). The Product Developmental Strategy should be supporting Fact Oriented Approaches, R&D Management, and Emphasize Customers’ Value. (6). Integrated Strategy should be focusing on the following Developmental Guideline: Horizontal Integration of Industry, Government, and Academy; Leveraging the Advantage of Industrial Value Chain; Realize Trading, R&D and Manufacturing, Marketing and Sales, Strategic Alliance, and Merger & Acquisition. According to the ʺFood Industry Almanac 2014ʺ published by the Food Industry Development Institute, in 2013, the market size of the health food market in Taiwan was NT$ 109.5 billion with a growth rate of 9.14%. The report also pointed out that the future of Taiwan’s Functional Food Industry will continue growing [1].

REFERENCES

[1]

Available: http://cdnet.stpi.org.tw/techroom/market/bio/bio049.htm

[2]

Z. Xu, ʺIncomplete linguistic preference relations and their fusion,ʺ Information Fusion, vol. 7, pp. 331‐337, 2006.

[3]

T. C. Wang, S. C. Hsu, and Y. C. Chiang, ʺMulti‐Criteria Decision Making with Expansion of Incomplete Linguistic Preference Relations,ʺ Wseas Transactions on Mathematics, vol. 6, pp. 817‐823, 2007.

[4]

Z. Xu, ʺA method for multiple attribute decision making with incomplete weight information in linguistic setting,ʺ Knowledge‐Based Systems, vol. 20, pp. 719‐725, 2007.

[5]

S.‐C. Hsu and T.‐C. Wang, ʺSolving multi‐criteria decision making with incomplete linguistic preference relations,ʺ Expert Systems with Applications, vol. 38, pp. 10882‐10888, 2011.

[6]

T.‐H. Chang, S.‐C. Hsu, T.‐C. Wang, and C.‐Y. Wu, ʺMeasuring the success possibility of implementing ERP by utilizing the Incomplete Linguistic Preference Relations,ʺ Applied Soft Computing, vol. 12, pp. 1582‐1591, 2012.

[7]

M.‐S. Kuo, ʺOptimal location selection for an international distribution center by using a new hybrid method,ʺ Expert Systems with Applications, vol. 38, pp. 7208‐7221, 2011.

[8]

M. Tabari, A. Kaboli, M. B. Aryanezhad, K. Shahanaghi, and A. Siadat, ʺA new method for location selection: A hybrid analysis,ʺ Applied Mathematics and Computation, vol. 206, pp. 598‐606, 2008.

74