Modeling foreign equity control in Sino-foreign joint ventures with neural networks

Hu, Michael Y.; Zhang, G.Peter; Chen, Haiyang

doi:10.1016/j.ejor.2003.06.002

Cited by 16 publications

(6 citation statements)

References 21 publications

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“…Chiang et al (1996) have discussed a back propagation NN approach to mutual fund net asset value forecasting. Hu et al (2004) have found that ANN can perform better than logistic regression in the modeling of foreign equities. Kimoto et al (1990) have applied modular NNs to develop a buying and selling timing prediction system for stocks on the Tokyo Stock Exchange using a high-speed learning method called supplementary learning.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance modeling of Indian business schools: a DEA‐neural network approach

Sreekumar

Mahapatra²

2011

Benchmarking: An International Journal

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Purpose -The main purpose of the present study is to develop an integrated approach combining data envelopment analysis (DEA) and neural network (NN) for assessment and prediction of performance of Indian B-schools for effective decision making as error and biasness due to human intervention in decision making is appreciably reduced. Design/methodology/approach -DEA, being a robust mathematical tool, has been employed to evaluate the efficiency of B-schools. DEA, basically, takes into account the input and output components of a decision-making unit (DMU) to calculate technical efficiency (TE). TE is treated as an indicator for performance of DMUs and comparison has been made among them. A sensitivity analysis has been carried out to study robustness of the ranking of schools obtained through DEA. Finally, NN is used to predict the efficiency when changes in inputs are caused due to market dynamism so that effective strategies can be evolved by the managers with limited available data. Findings -A total of 49 Indian B-schools are chosen for benchmarking purpose. The average score of efficiency is 0.625 with a standard deviation of 0.175 when Charnes, Cooper and Rhodes (CCR) model is used. Similarly, when the Banker, Charnes and Cooper (BCC) model is used the average score is 0.888 with a standard deviation of 0.063. The rank order correlation coefficient between the efficiency ranking obtained through CCR and BCC model is 0.736 ( p ¼ 0.000) which is significant. The peer group and peer weights for the inefficient B-schools have been identified. This is useful for benchmarking for the inefficient DMUs. They can identify the parameters in which they lack and take necessary steps for improvement. The peer group for the inefficient B-schools indicates the efficient B-schools to which the inefficient B-schools are closer in its combination of inputs and outputs. The TE obtained through DEA is used as output variable along with input variables considered in DEA as input and output parameters in a generalized regression NN during training phase. It can be observed that root mean square error is 0.009344 and 0.02323 for CCRand BCC-efficiency prediction, respectively, during training. Similarly, root mean square error is 0.08585 and 0.03279 for CCR-and BCC-efficiency prediction, respectively, during testing. Now, individual schools can generate scenario with the data within their control and test their own performance through NN model. Originality/value -This work proposes integration of DEA and NN to assist the managers to predict the performance of an individual DMU based on input consumed and generate various "what-if" scenarios. The study provides a simple but comprehensive methodology for improving performance of B-schools in India.

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Section: Literature Reviewmentioning

confidence: 99%

Performance modeling of Indian business schools: a DEA‐neural network approach

Sreekumar

Mahapatra²

2011

Benchmarking: An International Journal

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“…Reflecting on foreign equity control, for instance, managers can use AI to construct relationships between factors related to transaction cost and ownership that lead to successful joint-venture performance. Factors may include the proprietary nature of assets, the host country's environment, and cultural differences between host and home countries (Hu et al, 2004). Similarly, managers can utilize AI to learn related and unrelated collaborative venture formation patterns based on the potential collaborating partners' industry groups and home country relatedness (Nair et al, 2007).…”

Section: Learning About the Performance Of Entry Mode Choicementioning

confidence: 99%

Managing artificial intelligence in international business: Toward a research agenda on sustainable production and consumption

Hasan,

Ojala

2024

Thunderbird Intl Bus Rev

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The collaboration between artificial intelligence (AI) and humans is reshaping international business (IB) management dynamics, aiming to achieve global sustainable development. Recent IB literature indicates that managing AI brings benefits such as better resource reconfiguration, reduced transaction costs, and global sustainable development. However, existing IB literature provides only meager knowledge about the characteristics of AI and how these characteristics can be employed for international expansion at the intersection of sustainable development. In response, our aim is to construct these characteristics by employing directed qualitative content analysis of empirical AI research. Based on our three constructed characteristics of AI, we contribute to current IB literature by providing a framework to balance economic and social goals and utilizing AI for global sustainable development. Further, we provide future IB research themes to guide IB and AI research toward achieving a sustainable production and consumption agenda.

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“…Comparing the characteristics of domestic and foreign paper collaboration networks within the same discipline, it was found that both domestic and international paper collaboration networks are scale-free networks, but there are large differences in cooperation methods [13] . Liu C. et al ( 2017) characterized the topological structure, spatial pattern and proximity mechanism of the global scientific research cooperation network based on the data of co-authored papers in all subject areas in 2014 included in the Web of Science core collection [4] .…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of computer science paper collaboration network among cities in China

2024

AJBM

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Each city will cooperate in scientific research activities in order to improve its own level of scientific and technological innovation. This paper uses the computer science cooperation paper data between cities in my country from 1982 to 2014, and uses complex network analysis methods to find out:(1) my country's city paper cooperation network There are obvious core-peripheral characteristics in the degree centrality and betweenness centrality of , but its degree centrality always retains coreperipheral characteristics.

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Modeling foreign equity control in Sino-foreign joint ventures with neural networks

Cited by 16 publications

References 21 publications

Performance modeling of Indian business schools: a DEA‐neural network approach

Performance modeling of Indian business schools: a DEA‐neural network approach

Managing artificial intelligence in international business: Toward a research agenda on sustainable production and consumption

Analysis of computer science paper collaboration network among cities in China

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