Purpose Although most of the literature supports the existence of a substitutive relationship between exploration and exploitation, some authors suggest that this relationship is complementary (ambidexterity), and others argue that there is no relationship. This paper aims to introduce organizational innovation into the analysis and discusses which of these three relationships prevails. Design/methodology/approach Analyses were performed using data from Spanish Technological Innovation Panel for the period 2008-2013. It should be emphasized that the use of panel data is essential in the analysis of the interaction of exploration and exploitation, as exploration only makes sense in the long run. Econometric strategy uses a two-stage selection model, estimated using the Wooldridge’s (1995) consistent estimator for panel data with sample selection. To perform the test, the hypothesis uses the approach of complementarity. Findings The results show that the relationships exploration-organizational innovation and exploitation-organizational innovation are complementary, provided that the analysis is performed on companies that simultaneously carry out exploration and exploitation activities, respectively. This indicates that the achievement of ambidexterity is strongly conditioned by the simultaneous realization of organizational innovations. Practical implications Managers and policymakers should be aware that the simultaneous implementation of exploration and exploitation yields better results when the corresponding organizational innovations are also implemented. Originality/value This paper extends the empirical investigation of the relationship between exploration and exploitation, seen in conjunction with organizational innovation, and using the complementarity approach as a research tool.

Achieving sustainable economic development is one of humanity’s greatest challenges, and, in this regard, the United Nations has promoted a line of research based on sustainable economic development. In view of this, our study focused on the sustainable economic development of nations, specifically, development through the deployment of information and communication technologies (ICTs). Academic researchers recognize the importance of ICT for economic and sustainable development, but there is controversy in the literature regarding two opposing points of view. First, there is a view that advances in ICT support Gross Domestic Product (GDP) growth, while, on the other hand, the view is that there is no relationship between these two factors. In view of this, we conducted a study where the objective was to determine whether investing in ICT contributes to sustainable economic development (measured by the GDP per capita) of European Union countries. We used Eurostat data and applied the partial least-squares (PLS) method to address the study. This approach allowed us to analyze European Union countries from 2014 to 2017, using fairly rigorous data. The most outstanding result was that ICT accounted for most of the explained variance in GDP per capita (GDPpp), and, specifically, the most representative indicator was “digital public services.” Therefore, we concluded that investing in the deployment of ICT supports the sustainable economic development of European Union countries. These countries should focus on investing in improved connectivity in areas with poor communications, as well as in training area inhabitants in the use and development of ICT to obtain greater development using these tools and technologies.

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Purpose The purpose of this study is to construct the first short-term financial distress prediction model for the Spanish banking sector. Design/methodology/approach The concept of financial distress covers a range of different types of financial problems, in addition to bankruptcy, which is not common in the sector. The methodology used to predict financial problems was artificial neural networks using traditional financial variables according to the capital, assets, management, earnings, liquidity and sensibility system, as well as a series of macroeconomic variables, the impact of which has been proven in a number of studies. Findings The results obtained show that artificial neural networks are a highly suitable method for studying financial distress in Spanish credit institutions and for predicting all cases in which an entity has short-term financial problems. Originality/value This is the first work that tries to build a model of artificial neural networks to predict the financial distress in the Spanish banking system, grouping under the concept of financial distress, apart from bankruptcy, other financial problems that affect the viability of these entities.

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