What motivates manufacturing SMEs to outsource offshore in China?

A hybrid fuzzy-AHP-TOPSIS model for evaluation of manufacturing relocation decisions

Manufacturing relocation decisions are complex because they involve combinations of location modes like offshoring or reshoring, and governance modes like insourcing or outsourcing. Furthermore, the uncertainty involved in the decision-making process makes it challenging to reach a right-shoring decision. This study presents a hybrid fuzzy-AHP-TOPSIS model to support generic relocation decisions. Industry experts were involved in a pairwise comparison of the competitive priorities’ decision criteria. A meta-synthesis of empirical studies is used to generate theoretical relocation scenarios. The presented hybrid model is used to rank the relocation scenarios in order to identify the most pertinent alternative. The resiliency of the solution is presented through a sensitivity analysis. The results indicate that the proposed hybrid model can simultaneously handle all the main relocation options involving governance modes. Based on the input data in this study, the competitive priorities criteria quality, time and cost are shown to have a strong impact, whereas the sustainability criterion has a weak impact on the choice of relocation option. The research presented in this paper contributes to the research field of manufacturing relocation by demonstrating the suitability of the hybrid fuzzy-AHP-TOPSIS model for relocation decisions and the resilience of the results. Furthermore, the research contributes to practice by providing managers with a generic relocation decision-support model that is capable of simultaneously handling and evaluating various relocation alternatives.

Using k-means clustering in international location decision

Purpose

This paper aims to investigate the application of unsupervised machine learning in the international location decision (ILD). This paper addresses the need for a fast, quantitative and dynamic location decision framework.

Design/methodology/approach

Unsupervised machine learning technique, i.e. k-means clustering, is used to carry out the analysis. In total, 24 different indicators of 94 countries, categorized into five groups, have been used in the analysis. After the clustering, the clusters have been compared and scored to select the feasible countries.

Findings

A new framework is developed based on k-means clustering that can be used in ILD. This method provides a quantitative output without personal subjectivity. The indicators can be easily added or extracted based on the preferences of the decision-makers. Hence, it was found out that the unsupervised machine learning, i.e. k-means clustering, is a fast and flexible decision support framework that can be used in ILD.

Research limitations/implications

Limitations include the generality of selected indicators and clustering algorithm used. The use of other methods and parameters may lead to alternate results.

Originality/value

The framework developed through the research intends to assist the decision-makers in deciding on the facility locations. The framework can be used in international and national domains. It provides a quantitative, fast and flexible way to shortlist the potential locations. Other methods can also be used to further decide on the specific location.

Entrepreneurial orientation and performance: the interaction effect of customer capital

Purpose

– The purpose of this study is to advance research on entrepreneurial orientation (EO), resource-based view (RBV), customer (relational) capital, and small and medium enterprises (SMEs) by examining how the interaction effect of customer capital shapes the relationship between EO and firm performance.

Design/methodology/approach

– This research is considered as a correlational rather than a casual study with 150 questionnaire returned from manufacturing SMEs. This cross-sectional study tested all hypotheses that are related to the research questions and use statistical software SPSS 17 to analyze data.

Findings

– The study found that a high customer capital strengthens the link between two dimensions of EO (innovativeness and risk taking) and weakens the link between another dimension of EO (proactiveness) and firm performance.

Research limitations/implications

– First, future studies would benefit from an enhanced development in the measurement of EO dimensions, which relies on richer and more refined conceptualizations. Second, a single informant who was asked to evaluate EO may potentially increase the degree of subjectivity and bias in the responses. Obtaining more than one respondent for the survey from each organization is always highly desirable.

Practical implications

– The results of the current study cover the limitation of the previous study by independently examining the moderating effect of customer capital as an intangible resource in the relationship between innovativeness and risk taking on firm performance. The paper expands this line of work by adding the idea that the intangible resources of a firm are more likely to contribute to sustaining superior firm performance when they are used with other factors simultaneously.

Social implications

– Environmental factors, such as government financial aid and protection of organizations outside the industry, may affect the relationship between SMEs and the agents. Establishing extra ties between Iranian firms and agents may be expensive for Iranian manufacturing firms, and they may not be able to create these ties without government support.

Originality/value

– A research gap exists in understanding how customer capital operates and endows benefits to firms that are beyond their start-up phase and are embarking on international activities. The current study tries to overcome a number of limitations of the previous framework by combining RBV and customer capital. Particularly, “the RBV's lack of specificity have raised questions as to its status as a legitimate theory, and make it difficult to design and test empirically.”