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How past decisions affect future behavior on eco‐innovation: An empirical study

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  • Ángel Peiró-Signes
  • Marival Segarra-Oña

Abstract

The environment is increasingly gaining importance for citizens and society and, therefore, for consumers. Eco‐innovation is a direct path for reducing the impact of production while providing companies with a source of competitive advantage. The automotive industry and its supply chain have a great impact on the environment, but no research has been developed on how the orientation toward the environment is evolving for the automotive industry and how future performance may be affected by current decisions. The aim of this paper is to bridge this gap by analyzing the eco‐innovative dynamism of the automobile industry. To do this, we use a panel analysis to see the point at which past behavior influences future decisions. The partial least square method is used to analyze the eco‐innovative dynamism of the automobile industry. We analyze a data set based on 159 responses of Spanish companies that belong to the automobile sector. Results show that environmental orientation drivers do not evolve over a short period while in the longer term there is an evolution. We prove that carryover effects have a great impact on the future behavior of the firms, showing that the evolution of organizations' environmental behavior is a long‐range consideration. Managerial implications arise from this paper's conclusions, as the decision‐making process is clarified.

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  • Ángel Peiró-Signes & Marival Segarra-Oña, 2018. "How past decisions affect future behavior on eco‐innovation: An empirical study," Business Strategy and the Environment, Wiley Blackwell, vol. 27(8), pages 1233-1244, December.
    • Handle: RePEc:bla:bstrat:v:27:y:2018:i:8:p:1233-1244
    DOI: 10.1002/bse.2071
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    1. Peiró-Signes, Ángel & Segarra-Oña, Marival & Trull-Domínguez, Óscar & Sánchez-Planelles, Joaquín, 2022. "Exposing the ideal combination of endogenous–exogenous drivers for companies’ ecoinnovative orientation: Results from machine-learning methods," Socio-Economic Planning Sciences, Elsevier, vol. 79(C).

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