The roles of Subcontractors' Entrepreneurship on the Relationship Commitment towards the Parent Companies (수급사업자의 기업가정신이 관계몰입을 유도하는 경로)
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- Asia Marketing Journal
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- v.13 no.1
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- pp.51-84
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- 2011
It seems essential to examine the factors that may affect relationship commitment of subcontractors to parent companies in the industrial market in Korea in an effort to construct a win-win-type cooperative network among them. Lots of studies have been focusing on the consumer goods market. Relatively few studies have been focused on industrial market. In the industrial goods market subcontractors used to sell their parts or services only to a small number of parent companies in a large quantity, resulting in decisive control of subcontractors over the quality of parent companies' finished goods. This is why relationship between subcontractors and parent companies is extremely important. From this viewpoint, this study aims to survey and analyze empirically the paths leading to relationship commitment of subcontractors toward the parent companies which are required to incite them to build up a collaborative network by means of subcontractors' entrepreneurship. For this aim, market orientation effects of entrepreneurship as well as factors of performance and trust are particularly set forth as the bases of developing hypotheses in this study in order to explore the paths from entrepreneurship to relationship commitment as follows. First, the path of entrepreneurship-market orientation-communication-trust- relationship commitment; second, the path of entrepreneurship-market orientation-performance-relationship commitment; third, the path of entrepreneurship-market orientation-transaction specific asset investment -trust-relationship commitment; and fourth, the path in which the entrepreneurship is expected to promote direct transaction specific asset investment by parent companies to induce their trust and, eventually, relationship commitment of subcontractors. The outcomes of the empirical analysis in this study may be summed up as follows: First, the conclusions of preceding studies are also supported here by the fact that the entrepreneurship of subcontractors promotes their market orientation (hypothesis 9), indicating that the entrepreneurship can facilitate collection, proliferation of and response to market informations. On the contrary, however, the assumption that the entrepreneurship of subcontractors might directly accelerate transaction specific asset investment by parent companies (hypothesis 8) is rejected. Second, although the influence of subcontractors' entrepreneurship on parent companies' investment of assets peculiar to their transactions is not affirmed, the assumption is found to be supported that subcontractors' market orientation would expedite the parent companies' investment of assets peculiar to their transactions. Moreover, it is also confirmed that parent companies' investment of assets peculiar to transactions would promote subcontractors' trust toward the parent companies (hypothesis 6), signifying that parent companies may level up their trust in subcontractors when they make great amount of efforts to invest in the assets peculiar to transactions, not behaving opportunistically, Third, the hypotheses 4 and 5 also turn out to be supported by the analysis as the former assumes that market orientation could promote communication and the latter relates that the communication between subcontractors and parent companies would prompt trust, both results in affirming that market orientation could introduce open communication to speed up sharing of information and that sharing of information by way of communication might give an impetus to trust. Fourth, the assumption that subcontractors' market orientation would expedite performance (hypothesis 3) is also proved favorably to the significant level equivalent to that of preceding studies. Fifth, same as preceding studies, it is also verified in this study that the benefit (outcomes) awarded by parent companies to subcontractors will be a direct cause exercising a positive impact upon relationship commitment(hypothesis 2) and that the trust of subcontractors toward parent companies may have affirmative influence on the relationship commitment(hypothesis 1). Overall, the first, second and third paths are identified as being supported by the hypotheses among constituent factors, while the fourth path is deemed meaningless since it is shown that the entrepreneurship exercises no effects on parent companies' investment in the assets peculiar to transactions.
Fuzzy logic based Control Theory has gained much interest in the industrial world, thanks to its ability to formalize and solve in a very natural way many problems that are very difficult to quantify at an analytical level. This paper shows a solution for treating membership function inside hardware circuits. The proposed hardware structure optimizes the memoried size by using particular form of the vectorial representation. The process of memorizing fuzzy sets, i.e. their membership function, has always been one of the more problematic issues for the hardware implementation, due to the quite large memory space that is needed. To simplify such an implementation, it is commonly [1,2,8,9,10,11] used to limit the membership functions either to those having triangular or trapezoidal shape, or pre-definite shape. These kinds of functions are able to cover a large spectrum of applications with a limited usage of memory, since they can be memorized by specifying very few parameters ( ight, base, critical points, etc.). This however results in a loss of computational power due to computation on the medium points. A solution to this problem is obtained by discretizing the universe of discourse U, i.e. by fixing a finite number of points and memorizing the value of the membership functions on such points [3,10,14,15]. Such a solution provides a satisfying computational speed, a very high precision of definitions and gives the users the opportunity to choose membership functions of any shape. However, a significant memory waste can as well be registered. It is indeed possible that for each of the given fuzzy sets many elements of the universe of discourse have a membership value equal to zero. It has also been noticed that almost in all cases common points among fuzzy sets, i.e. points with non null membership values are very few. More specifically, in many applications, for each element u of U, there exists at most three fuzzy sets for which the membership value is ot null [3,5,6,7,12,13]. Our proposal is based on such hypotheses. Moreover, we use a technique that even though it does not restrict the shapes of membership functions, it reduces strongly the computational time for the membership values and optimizes the function memorization. In figure 1 it is represented a term set whose characteristics are common for fuzzy controllers and to which we will refer in the following. The above term set has a universe of discourse with 128 elements (so to have a good resolution), 8 fuzzy sets that describe the term set, 32 levels of discretization for the membership values. Clearly, the number of bits necessary for the given specifications are 5 for 32 truth levels, 3 for 8 membership functions and 7 for 128 levels of resolution. The memory depth is given by the dimension of the universe of the discourse (128 in our case) and it will be represented by the memory rows. The length of a world of memory is defined by: Length = nem (dm(m)＋dm(fm) Where: fm is the maximum number of non null values in every element of the universe of the discourse, dm(m) is the dimension of the values of the membership function m, dm(fm) is the dimension of the word to represent the index of the highest membership function. In our case then Length=24. The memory dimension is therefore 128*24 bits. If we had chosen to memorize all values of the membership functions we would have needed to memorize on each memory row the membership value of each element. Fuzzy sets word dimension is 8*5 bits. Therefore, the dimension of the memory would have been 128*40 bits. Coherently with our hypothesis, in fig. 1 each element of universe of the discourse has a non null membership value on at most three fuzzy sets. Focusing on the elements 32,64,96 of the universe of discourse, they will be memorized as follows: The computation of the rule weights is done by comparing those bits that represent the index of the membership function, with the word of the program memor . The output bus of the Program Memory (μCOD), is given as input a comparator (Combinatory Net). If the index is equal to the bus value then one of the non null weight derives from the rule and it is produced as output, otherwise the output is zero (fig. 2). It is clear, that the memory dimension of the antecedent is in this way reduced since only non null values are memorized. Moreover, the time performance of the system is equivalent to the performance of a system using vectorial memorization of all weights. The dimensioning of the word is influenced by some parameters of the input variable. The most important parameter is the maximum number membership functions (nfm) having a non null value in each element of the universe of discourse. From our study in the field of fuzzy system, we see that typically nfm 3 and there are at most 16 membership function. At any rate, such a value can be increased up to the physical dimensional limit of the antecedent memory. A less important role n the optimization process of the word dimension is played by the number of membership functions defined for each linguistic term. The table below shows the request word dimension as a function of such parameters and compares our proposed method with the method of vectorial memorization[10]. Summing up, the characteristics of our method are: Users are not restricted to membership functions with specific shapes. The number of the fuzzy sets and the resolution of the vertical axis have a very small influence in increasing memory space. Weight computations are done by combinatorial network and therefore the time performance of the system is equivalent to the one of the vectorial method. The number of non null membership values on any element of the universe of discourse is limited. Such a constraint is usually non very restrictive since many controllers obtain a good precision with only three non null weights. The method here briefly described has been adopted by our group in the design of an optimized version of the coprocessor described in [10].
The general solution for classification and regression problems can be found by matching and modifying matrices with the information in real world and then these matrices are teaming in neural networks. This paper treats primary space as a real world, and dual space that Primary space matches matrices using kernel. In practical study, there are two kinds of problems, complete system which can get an answer using inverse matrix and ill-posed system or singular system which cannot get an answer directly from inverse of the given matrix. Further more the problems are often given by the latter condition; therefore, it is necessary to find regularization parameter to change ill-posed or singular problems into complete system. This paper compares each performance under both classification and regression problems among GCV, L-Curve, which are well known for getting regularization parameter, and kernel methods. Both GCV and L-Curve have excellent performance to get regularization parameters, and the performances are similar although they show little bit different results from the different condition of problems. However, these methods are two-step solution because both have to calculate the regularization parameters to solve given problems, and then those problems can be applied to other solving methods. Compared with UV and L-Curve, kernel methods are one-step solution which is simultaneously teaming a regularization parameter within the teaming process of pattern weights. This paper also suggests dynamic momentum which is leaning under the limited proportional condition between learning epoch and the performance of given problems to increase performance and precision for regularization. Finally, this paper shows the results that suggested solution can get better or equivalent results compared with GCV and L-Curve through the experiments using Iris data which are used to consider standard data in classification, Gaussian data which are typical data for singular system, and Shaw data which is an one-dimension image restoration problems.