The majority of small and medium-sized enterprises (SMEs) in Taiwan are original equipment manufacturers (OEMs) or original design manufacturers (ODMs). Given the growing intensity of global competition, transformation from OEM or ODM to a branded enterprise is a key concern for SMEs. SMEs’ branding processes are subject to numerous problems and among them, resistance by organization members is considered the most critical. Previous studies have largely focused on factors contributing to branding success or resistance by an organization’s employees, and few have addressed the implications of SMEs managers’ resistance to branding. To address the research gap, this study constructs a cognitive model for managers’ resistant behavior while drawing on the theory of planned behavior. Then, applying structural equation modeling, this study confirms that four exogenous variables, perceived belief, changes in job characteristics, organizational inertia, and social factors, directly influence two endogenous variables, managers’ attitude toward resistant behavior and the subjective norms of resistant behavior. Using demographic variables as independent variables, this study conducts a logistic regression analysis to predict companies resistant to branding on the basis of their background characteristics. The results highlight that the three factors with the highest predictive powers are organizational inertia, job position, and capitalization.

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Placement of multiple dies on an MCM substrate is a difficult combinatorial task in which multiple criteria need to be considered simultaneously to obtain a true multi-objective optimization. In this paper we described a MCM placement model for the multi-objective optimization problem and solved this model by the simulated annealing SA algorithm and the hybrid optimization strategy GASA (namely the combination of genetic algorithm and simulated annealing) respectively. Our design methodologies consider multiobjective component placement based on thermal reliability, routing length and chip area criteria for multi-chip module. The purpose of the multi-objective optimization placement is to enhance the system performance, reliability and reduce the substrate area by obtaining an optimal cost during multi-chip module placement design phase. For reliability considerations, the design methodology focuses on the placement of the power dissipating chips to achieve uniform thermal distribution. For route-ability consideration, the total wire length minimization is estimated by bounding box approximation method. For substrate area consideration, the area is estimated by minimum area contains all chips. The cost function is formulated by the weight sum calculation. For design flexibility, different weights can be assigned depending on designer's considerations. Various methods including simulated annealing and hybrid generic algorithm are applied to solve the placement solutions. 3-D Finite Element Analysis (FEA) is carried out to assess thermal distribution in MCM substrate. The optimization results of various weighting assignments obtained by different algorithms are compared. In addition, an auto generated optimal placement layout based on the analytical solution is also presented.