Orientation to Organizational Learning and Its Effects on Innovation and Performance: The Colombian MSMEs Case

Orientation to Organizational Learning and Its Effects on Innovation and Performance: The Colombian MSMEs Case

Fred Davinson Contreras Palacios, Rafael Ignacio Perez-Uribe, Iván Rodrigo Vargas Ramírez, Carlos Salcedo-Perez
DOI: 10.4018/978-1-7998-3648-3.ch010
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This research demonstrates how the orientation to organizational learning affects the innovation and performance of the Colombian micro, small, and medium (MSMEs) enterprises, based on a study with 403 Colombian MSMEs, pretending that the results allow the government sector and the academy to design strategies maintain or improve, as appropriate, innovative and learning practices within these organizations. Two hypotheses were raised: 1) learning orientation positively influences business performance and 2) learning orientation positively influences business innovation. The two hypotheses are demonstrated after performing a multiple regression analysis and a broadly significant relationship was evidenced both between the orientation to learning and innovation and in each of the dimensions that make it up: innovation in products/services, processes, and management.
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Worldwide, Micro, Small, and Medium Enterprises - MSMEs generate a high percentage of the jobs created, contribute considerably to production and trade, and they play a key role to achieve competitiveness; this is why their importance is recognized in most countries, and public and private sectors work together to strengthen them (Varela, 2011). In Colombia, data shows that they comprise 99,9% of all enterprises (96,4% micro and 3,5% small and medium), generating 63% of employment and contributing with 37,5% of the GDP (Confecamaras, 2016).

Makarona & Kavoura (2019) carry out studies in this regard, indicating the need to link the generation of wealth of this type of companies with innovation and academic activities that really strengthen the relationship between industry and academia.

In Latin America and Colombia, previous conditions that boosted interest to research MSMEs have not changed but increased, due to issues such as the development of new technologies, internationalization, business environment dynamics and complexity, and the economic crisis (Dini and Stumpo, 2019). The last one especially has affected their performance, as result of the conditions of the environment, and their endogenous competencies such as technology undertaking, workers’ and entrepreneurs’ qualification, people’s knowledge, skills learning, management and productivity (Busłowska & Wiśniewska, 2017; CONPES, 2007).

Thoeni, Marshall & Campbell (2016) state that enterprises need to find management processes and techniques that allow them to achieve success, and different authors recognize that it is necessary to take adequate advantage of resources and capabilities available. How to get it and what to do to keep or increase a competitive position becomes a key topic for business management.

According to Vargo & Lusch (2014) and Sirvastava, Fahey & Christensen (2001), inimitability is higher in intangible than in tangible capabilities. In this sense, Slater & Narver (1995) consider innovation and organizational learning as capabilities that are difficult to imitate, besides, they are highlighted for their potential to generate other capabilities (Munuera et al., 2007).

Innovation is considered a critical factor for enterprises’ survival and success (Ramírez-Garzón and Pérez-Uribe, 2019; Formichella, 2005; Damanpour & Gopalakrishnan, 2001; Camelo, Romero and Valle, 2000; Rogers, 1983; Schumpeter, 1942), since innovative enterprises are more flexible, which allows them to learn and get better adapted to changes in the environment, to answer faster and better to changing needs of the society and therefore to get better results compared to those that are not (Miles & Snow, 1978; Drucker, 1985).

Key Terms in this Chapter

ROA: Return on assets.

D.Mediana: Enterprise’s size. Medium dimension

Durwin-Watson: In statistics, the Durbin-Watson statistic is a test statistic used to detect the presence of autocorrelation at lag 1 in the residuals (prediction errors) from a regression analysis. It is named after James Durbin and Geoffrey Watson.

OAGLOBAL: Relations between the orientation to global learning.

D.Madura: Enterprise’s age. Maturity dimension

INNOVAMAN: Degree of management innovation.

ROI: Return on investment.

D.Pequeña: Enterprise’s size. Small dimension.

INNOVAPC: Degree of process innovation.

INNOVAPS: Degree of product innovation.

Adjusted R2: It is a measure of corrected goodness of fit (precision of the model) used for linear models. It identifies the percentage of variance in the objective filed that is explained by the entry or entries. It tends to estimate optimistically the adjustment of the linear regression.

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