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RETOS. Revista de Ciencias de la Administración y Economía

On-line version ISSN 1390-8618Print version ISSN 1390-6291

Abstract

DOMINGUEZ-LARA, Sergio; FERNANDEZ-ARATA, Manuel  and  BARRIG-JO, Patricia. Innovation Climate Scale: psychometric analysis in Peruvian workers. Retos [online]. 2024, vol.14, n.28, pp.277-292. ISSN 1390-8618.  https://doi.org/10.17163/ret.n28.2024.06.

The changes caused by the COVID-19 pandemic pushed organizations to attempt to remain current and competitive. Thus, the ability to respond to new demands and adapt to shifting conditions was an unavoidable requirement, which implies innovating in the development of their processes. In that sense, the growing importance of innovative work behaviors in the development of organizations has driven researchers to investigate mechanisms that foster such behaviors in workers. The objective of this study was to analyze the psychometric properties of the Innovation Climate Scale (ICS). The instrumental study involved 273 Peruvian workers (50.916 % male) ranging in age between 22 and 76 years old (Mean = 45.224), employed for the most part in the private sector (75.092 %). The psychometric analysis explored item clarity and relevance by means of assessment by expert judges (content validity). Internal structure was assessed via confirmatory factorial analyses, both of oblique models and bi-factor. Finally, scoring reliability (alpha coefficient) and construct reliability (omega coefficient) were estimated. This study finds the ICS items to be clear and relevant, a greater empirical support for the unidimensional model as its internal structure, and an adequate reliability. In conclusion there is favorable evidence of validity and reliability for the application of the ICS in Peruvian companies as a useful tool to measure the innovative climate perceived by its workers, providing information for decision making and planning of innovation strategies and organizational creativity.

Keywords : innovation; innovation climate; workers; content analysis; factorial analysis; multivariate analysis; structural equation models; reliability analysis.

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