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|Title:||The overseas expansion of Indonesian contractors : motivations, entry modes choice and a neuro fuzzy based decision support system||Authors:||Utama, Wahyudi Putra||Degree:||Ph.D.||Issue Date:||2018||Abstract:||The participation of Indonesian construction enterprises in overseas marketplaces has indicated a high significant progression in recent decade. However, this contribution is relatively scanty if it is compared to other emerging economies. Similarly, there are only a few studies discussed internationalization of construction enterprises in developing markets, such as in Indonesia. Thus, this study aims to provide an empirical investigation of this phenomenon from the perspective of Indonesian construction enterprises and to design a decision-making model for supporting their overseas expansion attempts. The objectives of study are (1) to investigate the motivations encouraging the Indonesian contractors to expand operation in overseas markets, (2) to investigate the preferred choice of entry modes adopted by the Indonesian contractors to enter overseas construction markets, (3) to determine the significant international factors influencing the go/no go decision making of the Indonesian contractors on overseas construction projects, and (4) to develop a decision support system-based go/no go decision making of Indonesian contractors on overseas construction projects. An empirical approach integrating quantitative and qualitative techniques was adopted to address the objectives of this study. The primary data were collected through questionnaire sheets distributed to Indonesian large contractors. Descriptive statistical method and relative importance index were employed for data analysis. In the discussion, the survey results were triangulated with the case study and the interview findings. The results indicated that the overseas expansion of Indonesian contractors was mostly motivated by multiple motives instead of a single motive. Those main motivations were to increase profitability, to benefit competitive advantage, to expand business, to capitalize on globalization/free trade region, to respond project sponsor's invitation and to gain the international experience. Related to strategy to enter a foreign market, the results exposed that the preferred choice of modes constituted a combination of five main strategies. They are including Joint Venture Project, Branch Office, Representative Office, Sole Venture Project and Local Agent depending on the host market conditions. Meanwhile, a two-round Delphi survey was adopted to reach an experts' concensus on the important factors and the probability of risk occurance related to the international factors in OCPs. Based on the Delphi results, a significant index was used to determine the criticalty of the factors. From 31 selected factors harvested from a systematic literature review, the results indicated that there are 21 factors classified as critical factors. The top five ranking factors are: (1) quality and clarity of contract condition, (2) project scale/size, (3) complexity of project, (4) financial capability and support, and (5) types of contract.
In developing the decision model, this research adopted an integration of fuzzy system and neural network, namely Adaptive Neuro-Fuzzy Inference System (ANFIS). In order to generate the model, 110 simulation cases of OCPs obtained from an evaluation involving 11 experienced examiners were used as a data set. Each case was presented in an evaluation form containing five input criteria (project, contract, owner, host country and market) and one output criterion (go/not go). The examiners judged the score of each input (ranging from 1-the lowest to 9-the highest) and selected the output based on randomly given parameter (fuzzy number) on the 21 international factors. From 110 pairs of input-output data, 70% cases were set for training and the rest for testing (20%) and checking (10%). To measure the optimization of ANFIS model, Root Mean Square Error (RMSE) and Correlation Coefficient (R) were utilized. The result indicated that the optimum network of the model was developed via ANFIS parameters i.e. two inputs, Gaussian (gaussmf) and hybrid representing the number of membership function, type of membership function and optimization method respectively. The accuracy of this model was very good when predicting the decision on nine-real OCP cases with 88.89% accuracy. This model was also compared with another machine learning, namely General Feedforward Neural Network (GFNN). The results of GFNN validated the proposed model and evidenced some degree of primacy in terms of prediction performance and applicability. Finally, this study suggested several important issues to enhance the next researches.
|Subjects:||Hong Kong Polytechnic University -- Dissertations
Construction industry -- Indonesia -- Management
|Pages:||xvii, 238 pages : color illustrations|
|Appears in Collections:||Thesis|
View full-text via https://theses.lib.polyu.edu.hk/handle/200/9464
Citations as of Jun 4, 2023
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