Abstract
This study uses Adaptive Neuro-Fuzzy Inference System (ANFIS) to explore the nonlinear relationships between green innovation performance and corporate competitive advantage. The result indicates that green innovation performance has the nonlinear effect on the corporate competitive advantage. If companies hope to enhance their competitive advantages through green innovation, they must check their green innovation performance in advance. If their green innovation performance is low, they can obtain competitive advantages through the increase of the green innovation performance; however, if their green innovation performance is high, they can not necessarily obtain competitive advantages through the increase of the green innovation performance.
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References
Barney J.B.: Firm resources and sustained competitive advantage. J. Manag. 17(1), 99–120 (1991)
Berry M.A., Rondinelli D.A.: Proactive corporate environmental management: a new industrial revolution. Acad. Manag. Exec. 12(2), 38–50 (1998)
Chen Y.-S., Lai S.-B., Wen C.-T.: The influence of green innovation performance on corporate advantage in Taiwan. J. Bus. Ethics 67(4), 331–339 (2006)
Churchill G.A.: A paradigm for developing better measures of marketing constructs. J. Market. Res. 16(1), 64–73 (1979)
Collins J.M., Clark M.R.: An application of the theory of neural computation to the prediction of workplace behavior: an illustration and assessment of network analysis. Pers. Psychol. 46(3), 503–524 (1993)
Coyne K.P.: Sustainable competitive advantage-What it is, What it isn’t. Bus. Horizon 29(1), 54–61 (1986)
Fausett, L.: Fundamentals of neural networks: architecture, algorithms and applications. Prentice-Hall International, Englewood Cliffs (1994)
Greeno L.J., Robinson S.N.: Rethinking corporate environmental management. Columbia J. World Bus. 27(3/4), 222–232 (1992)
Häkkinen P.M.H.: Neural network used to analyze multiple perspectives concerning computer-based learning environments. Qual. Quant. 34(3), 237–258 (2000)
Hart S.L.: A natural-resource-based view of the firm. Acad. Manag. Rev. 20(4), 986–1014 (1995)
Hart S.L.: Beyond greening: strategies for a sustainable world. Harv. Bus. Rev. 75(1), 67–76 (1997)
Henriques I., Sadorsky P.: The relationship between environmental commitment and managerial perceptions of stakeholder importance. Acad. Manag. J. 42(1), 87–99 (1999)
Jang J.S.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)
Kainuma Y., Tawara N.: A multiple attribute utility theory approach to lean and green supply chain management. Int. J. Prod. Econ. 101(1), 99–108 (2006)
Kiang M.Y., Hu M.Y., Fisher D.M.: An extended self-organizing map network for market segmentation-a telecommunication example. Decis. Support Syst. 42(1), 36–47 (2006)
Kohonen T.: Self-organized foundation of topologically correct feature maps. Biol. Cybern. 43, 59–69 (1982)
Kohonen T.: The self-organization maps. Proc. IEEE 78(9), 1480–1481 (1990)
Kohonen T.: Self-organization map. Springer, New York (1995)
Lee R.C.T., Slagle J.R., Blum H.: A triangulation method for the sequential mapping of points from N-space to two-space. IEEE Trans. Comput. 26, 288–292 (1977)
Lippmann R.P.: An introduction to computing with neural nets. In: Vemuri, V. (eds) Artificial neural networks: theoretical concepts, The Computer Society, Washington, DC (1988)
Lo S.P.: An adaptive-network based fuzzy inference system for prediction of workpiece surface roughness in end milling. J. Mater. Process. Technol. 142, 665–675 (2003)
Malhotra R., Malhotra D.K.: Differentiating between good credits and bad credits using neuro-fuzzy systems. Eur. J. Oper. Res. 136(1), 190–211 (2002)
McMillan E.: Complexity, organizations and change. Routledge, London (2004)
Melin P., Castillo O.: Intelligent control of a stepping motor drive using an adaptive neuro–fuzzy inference system. Inf. Sci 170, 133–155 (2005)
Moshiri S., Cameron N.: Neural network versus econometric models. J. Forecast. 19(3), 201–217 (2000)
Nayak P.C., Sudheer K.P., Rangan D.M., Ramasastri K.S.: A neuro-fuzzy computing technique for modeling hydrological time series. J. Hydrol. 291, 52–66 (2004)
Peattie K.: Green Marketing. Pitman Publishing, London (1992)
Porter M.E.: Competitive Advantage. The Free Press, New York (1985)
Porter M.E., van der Linde C.: Green and competitive. Harv. Bus. Rev. 73(5), 120–134 (1995)
Pykett C.E.: Improving the efficiency of Sammon’s nonlinear mapping by using clustering archetypes. Electron. Lett. 14, 799–800 (1978)
Russo M.V., Fouts P.A.: A resource-based perspective on corporate environmental performance and profitability. Acad. Manag. J. 40(3), 534–559 (1997)
Shrivastava, P.: Environmental technologies and competitive advantage. Strateg. Manag. J. 16 (Special issue): 183–200 (1995)
Somers M.J.: Thinking differently: assessing nonlinearities in the relationship between work attitudes and job performance using a Bayesian neural network. J. Occup. Organ. Psychol. 74(1), 47–61 (2001)
Stacey R.D.: Complexity and creativity in organizations. Berrett-Koehler Publishers, San Francisco (1996)
Wanous M., Boussabaine H.A., Lewis J.: A neural network bid/no bid model: the case for contractors in Syria. Constr. Manag. Econ. 21(7), 737–744 (2003)
Wasserman P.D.: Advanced methods in neural computing. Van Nostrand Reinhold, New York (1994)
Wray B., Palmer A., Bejou D.: Using neural network analysis to evaluate buyer-seller relationships. Eur. J. Market. 28(10), 32–48 (1994)
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Chen, YS., Chang, KC. The nonlinear effect of green innovation on the corporate competitive advantage. Qual Quant 47, 271–286 (2013). https://doi.org/10.1007/s11135-011-9518-x
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DOI: https://doi.org/10.1007/s11135-011-9518-x