Julen Castillo Apraiz

Bio: Julen Castillo Apraiz is an academic researcher from University of the Basque Country. The author has contributed to research in topics: Competitive advantage & Partial least squares regression. The author has an hindex of 3, co-authored 3 publications receiving 117 citations.

The mediating role of personnel training between innovation and performance: Evidence from the German pharmaceutical industry

Abstract: Purpose – The need for companies to become more innovative has never been greater, because innovation helps them deal with a turbulent environment by providing them a sustainable competitive advantage. In this sense, it has been generally accepted that a successful innovative environment requires a welltrained work force. Nevertheless, the literature showing how personnel training drives the innovationperformance relationship in industries where innovation is a key factor is scarce, especially in high-tech industries such as the pharmaceutical industry. Thus, we build upon existing studies to contribute to the innovation and training-related literature by considering the latter as a mediating variable between innovation and business performance. Hence, we aim to assess the impact of innovation on performance, and bring new insights into the innovation-performance link by including training as a variable that drives the aforementioned relationship. Design/methodology – We apply the partial least squares structural equation modeling technique to a sample of German pharmaceutical firms. The data were collected in mid-2014 by means of a computerassisted telephone interviewing (CATI) procedure. As a result, 200 valid responses were obtained from CEOs. Findings – First, this study demonstrates that both innovation and personnel training have a significant, positive impact on performance. Second, the results suggest that training personnel does indeed positively mediate the innovation-performance link. Hence, our study helps explain how innovation effectively translates into greater levels of performance. Originality / value – We answer calls to clarify about the innovation-personnel training relationship to generate greater levels of performance in turbulent environments. Furthermore, we assess this fact in the pharmaceutical industry, where paradoxically there is a lack of studies within the aforementioned framework.

The role of competitive strategy in the performance impact of exploitation and exploration quality management practices

Abstract: This study aims to advance understanding about quality management (QM) practices by clarifying how competitive strategy conditions the impacts of exploitative and explorative QM practices on performance.,The authors apply partial least squares structural equation modeling to data from a sample of German pharmaceutical firms.,The results show that the impact of exploitative and explorative QM practices on firm performance is contingent on the competitive strategy pursued. Explorative QM practices are significantly more relevant for firms following a differentiation strategy, whereas exploitative QM practices are significantly more relevant for cost leaders. Furthermore, for strategically ambidextrous firms that follow simultaneously a cost and a differentiation focus, the interplay of the two QM practices matters.,This paper contributes to understanding which kind of management practices, exploitative and/or explorative, have greater performance impacts under certain competitive strategy conditions.

The Oxford Handbook of Innovation

Abstract: This handbook looks to provide academics and students with a comprehensive and holistic understanding of the phenomenon of innovation. Innovation spans a number of fields within the social sciences and humanities: Management, Economics, Geography, Sociology, Politics, Psychology, and History. Consequently, the rapidly increasing body of literature on innovation is characterized by a multitude of perspectives based on, or cutting across, existing disciplines and specializations. Scholars of innovation can come from such diverse starting points that much of this literature can be missed, and so constructive dialogues missed. The editors of The Oxford Handbook of Innovation have carefully selected and designed twenty-one contributions from leading academic experts within their particular field, each focusing on a specific aspect of innovation. These have been organized into four main sections, the first of which looks at the creation of innovations, with particular focus on firms and networks. Section Two provides an account of the wider systematic setting influencing innovation and the role of institutions and organizations in this context. Section Three explores some of the diversity in the working of innovation over time and across different sectors of the economy, and Section Four focuses on the consequences of innovation with respect to economic growth, international competitiveness, and employment. An introductory overview, concluding remarks, and guide to further reading for each chapter, make this handbook a key introduction and vital reference work for researchers, academics, and advanced students of innovation. Contributors to this volume - Jan Fagerberg, University of Oslo William Lazonick, INSEAD Walter W. Powell, Stanford University Keith Pavitt, SPRU Alice Lam, Brunel University Keith Smith, INTECH Charles Edquist, Linkoping David Mowery, University of California, Berkeley Mary O'Sullivan, INSEAD Ove Granstrand, Chalmers Bjorn Asheim, University of Lund Rajneesh Narula, Copenhagen Business School Antonello Zanfei, Urbino Kristine Bruland, University of Oslo Franco Malerba, University of Bocconi Nick Von Tunzelmann, SPRU Ian Miles, University of Manchester Bronwyn Hall, University of California, Berkeley Bart Verspagen , ECIS Francisco Louca, ISEG Manuel M. Godinho, ISEG Richard R. Nelson, Mario Pianta, Urbino Bengt-Ake Lundvall, Aalborg

Relative absorptive capacity and interorganizational learning

Abstract: Much of the prior research on interorganizational learning has focused on the role of absorptive capacity, a firm's ability to value, assimilate, and utilize new external knowledge. However, this definition of the construct suggests that a firm has an equal capacity to learn from all other organizations. We reconceptualize the firm-level construct absorptive capacity as a learning dyad-level construct, relative absorptive capacity. One firm's ability to learn from another firm is argued to depend on the similarity of both firms' (1) knowledge bases, (2) organizational structures and compensation policies, and (3) dominant logics. We then test the model using a sample of pharmaceutical–biotechnology R&D alliances. As predicted, the similarity of the partners' basic knowledge, lower management formalization, research centralization, compensation practices, and research communities were positively related to interorganizational learning. The relative absorptive capacity measures are also shown to have greater explanatory power than the established measure of absorptive capacity, R&D spending. © 1998 John Wiley & Sons, Ltd.

Guidelines for choosing between multi-item and single-item scales for construct measurement: A predictive validity perspective

Abstract: textEstablishing predictive validity of measures is a major concern in marketing research. This paper investigates the conditions favoring the use of single items versus multi-item scales in terms of predictive validity. A series of complementary studies reveals that the predictive validity of single items varies considerably across different (concrete) constructs and stimuli objects. In an attempt to explain the observed instability, a comprehensive simulation study is conducted aimed at identifying the influence of different factors on the predictive validity of single versus multi-item measures. These include the average inter-item correlations in the predictor and criterion constructs, the number of items measuring these constructs, as well as the correlation patterns of multiple and single items between the predictor and criterion constructs. The simulation results show that, under most conditions typically encountered in practical applications, multi-item scales clearly outperform single items in terms of predictive validity. Only under very specific conditions do single items perform equally well as multi-item scales. Therefore, the use of single-item measures in empirical research should be approached with caution, and the use of such measures should be limited to special circumstances.