IMPACT OF LEGAL PRESSURE, CUSTOMER PRESSURE AND DYNAMIC CAPABILITIES ON GREEN INNOVATION PERFORMANCE, WITH EMPHASIS ON THE MEDIATING ROLE OF SENIOR MANAGEMENT SUPPORT, TRAINING, INVESTMENT IN RESEARCH AND DEVELOPMENT, AND RELATIONAL LEARNING CAPABILITIES

Authors

  • Mohamad Reza Babaei
  • Kiumars Kiani

DOI:

https://doi.org/10.32461/2226-3209.1.2018.177286

Abstract

Abstract. This research aims to investigate the impact of legal pressure, customer pressure and dynamic
capabilities on green innovation performance, with emphasis on the mediating role of senior management support, training, and investment in research and development and relational learning capabilities at the Oil Turbo Compressor Company. This descriptive research employs correlation and Structural equation research plan. The statistical population of the study includes managers, assistants, and experts of Oil Turbo Compressor Company, 281 subjects participate in the research. They responded to questionnaires on legal pressure, customer pressure, dynamic capabilities, senior management support, training, investment in research and development, relational learning
capabilities, and green innovation performance. Correlation coefficient and structural equation method have been used with SMARTPLS software to analyze the data. The results showed that the effect of legal pressure on learning abilities is not significant. The impact of legal pressure on senior management support, training, and investment in research and development is positive and significant. The impact of customer pressure on relational learning capabilities, senior management support, investment in research and development, and training is positive and significant. The impact of dynamic capabilities on relational learning capabilities, senior management support, and investment in research and development is positive and significant; but the effect of dynamic capabilities on training
is not significant. The impact of relational learning capabilities, senior management support, training, and investment in research and development on the green innovation performance is positive and significant.
Keywords: legal pressure, customer pressure, senior management support, training, investment in research
and development, green innovation performance.

References

Vermeir, I., & Verbeke, W. (2008). Sustainable food consumption among young adults in Belgium: Theory of

planned behaviour and the role of confidence and values. Ecological economics, 64(3), 542-553.

Hult, G. T. M. (2011). Market-focused sustainability: market orientation plus!. Journal of the Academy of

Marketing Science, 39(1), 1-6.

Maignan, I., & Ferrell, O. C. (2004). Corporate social responsibility and marketing: An integrative framework.

Journal of the Academy of Marketing science, 32(1), 3-19.

Yung, W. K., Chan, H. K., So, J. H., Wong, D. W., Choi, A. C., & Yue, T. M. (2011). A life-cycle assessment for

eco-redesign of a consumer electronic product. Journal of Engineering Design, 22(2), 69-85.

Chiou, T. Y., Chan, H. K., Lettice, F., & Chung, S. H. (2011). The influence of greening the suppliers and green

innovation on environmental performance and competitive advantage in Taiwan. Transportation Research Part E:

Logistics and Transportation Review, 47(6), 822-836.

Barbiroli, G., & Raggi, A. (2003). A method for evaluating the overall technical and economic performance of

environmental innovations in production cycles. Journal of Cleaner Production, 11(4), 365-374.

Charter, M. (1992). Greener marketing: A responsible approach to business. Greenleaf Publishing.

Ar, I. M. (2012). The impact of green product innovation on firm performance and competitive capability: the

moderating role of managerial environmental concern. Procedia-Social and Behavioral Sciences, 62, 854-864.

Porter, M., & Van der Linde, C. (1996). Green and competitive: ending the stalemate. Business and the Environment, 61-77.

Schiederig, T., Tietze, F., & Herstatt, C. (2012). Green innovation in technology and innovation management–

an exploratory literature review. R&D Management, 42(2), 180-192.

Chen, Y. S. (2006). The driver of green innovation and green image–green core competence. Journal of business ethics, 81(3), 531-543.

Seman, N. A. A., Zakuan, N., Jusoh, A., Arif, M. S. M., & Saman, M. Z. M. (2012). The relationship of green

supply chain management and green innovation concept. Procedia-Social and Behavioral Sciences, 57, 453-457.

Tseng, M. L., Wang, R., Chiu, A. S., Geng, Y., & Lin, Y. H. (2013). Improving performance of green innovation

practices under uncertainty. Journal of Cleaner Production, 40, 71-82.

Zhu, Q., Dou, Y., & Sarkis, J. (2010). A portfolio-based analysis for green supplier management using the

analytical network process. Supply Chain Management: An International Journal, 15(4), 306-319.

Huang, X. X., Hu, Z. P., Liu, C. S., Yu, D. J., & Yu, L. F. (2016). The relationships between regulatory and

customer pressure, green organizational responses, and green innovation performance. Journal of Cleaner Production,

, 3423-3433.

Delmas, M. A., & Toffel, M. W. (2008). Organizational responses to environmental demands: Opening the

black box. Strategic Management Journal, 29(10), 1027-1055.

Chiva, R., Alegre, J., & Lapiedra, R. (2007). Measuring organisational learning capability among the

workforce. International Journal of Manpower, 28(3/4), 224-242.

Albort-Morant, G., Leal-Millán, A., & Cepeda-Carrión, G. (2016). The antecedents of green innovation

performance: A model of learning and capabilities. Journal of Business Research, 69(11), 4912-4917.

Ashrafi, Sheida (2015). Investigating the Impact of Human Capital on Green Innovation Strategy and Financial

Performance. MA thesis, Tehran University.

Fayyazi, E., Ghobadian, B., Najafi, G., Hosseinzadeh, B., Mamat, R., & Hosseinzadeh, J. (2015). An

ultrasound-assisted system for the optimization of biodiesel production from chicken fat oil using a genetic algorithm

and response surface methodology. Ultrasonics sonochemistry, 26, 312-320.

Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (McGraw-Hill Series in Psychology) (Vol. 3).

New York: McGraw-Hill.

Ghiasvand, A. (2008). Application of statistics and SPSS in data analysis. Tehran: Lovieh publication.

Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement

error: Algebra and statistics. Journal of marketing research, 382-388.

Gefen, D., & Straub, D. (2005). A practical guide to factorial validity using PLS-Graph: Tutorial and annotated

example. Communications of the Association for Information systems, 16(1), 5.

Manuel, F. E., Manuel, F. P., & Manuel, F. E. (2009). Utopian thought in the western world. Harvard University

Press.

Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for

business research, 295(2), 295-336.

Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., & Lauro, C. (2005). PLS path modeling. Computational statistics

& data analysis, 48(1), 159-205.

Ringle, C. M. (2006). Segmentation for path models and unobserved heterogeneity: The finite mixture partial

least squares approach.

Seyyed Abbas Zadeh, Mir Muhammad, Amani Sari, Javad, Khezri Azar, Himan, & Ghasem Pashuei (2012). An

Introduction to Modeling Equation Generation by PLS Method and Its Application in Behavioral Sciences. Publisher:

Urmia University Press.

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