Programa del congreso

Goals: This study explores the impact of Big Data Analytics (BDA) on organizational Innovation, focusing on how Learning Orientation and Knowledge Sharing mediate this relationship.

Theoretical Framework: The study investigates how BDA adoption affects innovation through learning orientation and knowledge sharing, with a foundation in organizational learning theories and the Technology Acceptance Model (TAM).

Design/Methodology: A survey of 400 healthcare employees assessed constructs such as BDA usage, Learning Orientation, Knowledge Sharing, and Innovation. Structural equation modelling (SEM) was used to test the hypotheses.

Results: Findings show that BDA usage positively affects Learning Orientation and Knowledge Sharing, with both mediating the relationship between BDA and Innovation.

Research limitation/Implications: The study is cross-sectional and industry-specific, limiting generalizability. Future research should explore longitudinal studies across diverse sectors.

Practical Implications: Organizations should foster a learning-oriented culture and encourage Knowledge Sharing to enhance BDA effectiveness and drive innovation.

Keywords: Big Data Analytics (BDA), Learning Orientation, Knowledge Sharing, Innovation, Technology Acceptance Model (TAM), Structural Equation Modelling (SEM), Healthcare

Proposed topic area: Operations and Technology Direction

Alternative thematic area: Innovation Management

Objectives: This research aims to develop a novel risk-based multicriteria approach for assessing technological R&D project risks.

Theoretical Framework: The research is grounded in previous literature on risk management in R&D projects and the international standard ISO 31000, which provides guidelines for project risk assessment. Moreover, while traditional MCDM approaches have been widely explored, their limitations in aligning with policy-based risk evaluation highlight the need for an improved methodology.

Methodology: It is proposed an innovative Probability-Impact method, adapting AHPSort to evaluate R&D project risks. The method was applied to five technological R&D projects within an engineering firm, classifying them according to risk exposure profiles in a structured risk matrix.

Findings/Implications: The proposed method effectively estimates risk exposure and categorizes technological R&D projects into predefined risk classes, providing managers with actionable insights. This study advances a risk assessment procedure aligned with ISO 31000 by incorporating sorting methods into R&D project evaluation. The findings significantly enhance coordination between policymakers and practitioners in managing risks associated with innovative technological implementations.

In the digital era, project managers face challenges such as bridging the gap between strategy and implementation and navigating the often ambiguous terms of digitalization and digital transformation. This study leverages Albert O. Hirschman’s theories to differentiate between digitalization as a trait-taking strategy and digital transformation as trait-making, offering a nuanced framework for digital projects. By clarifying these concepts, we provide actionable insights that help project managers align strategic objectives with execution, ultimately fostering more effective and sustainable digital initiatives. This research advances the project management field by delivering practical tools that enhance decision-making and adaptability in rapidly evolving digital landscapes.

Industrial district (ID) literature has traditionally overlooked the micro level of analysis and how it produces knowledge variety. Positioned in the ID strand, this article analyzes how lead firms incept new technology in IDs, that is, how lead firms shape the discontinuous stage of the ID learning system, preceding the subsequent continuous phase where local buzz applies. Utilizing mixed methods in the Castellon (ES) and Emilia-Romagna (IT) ceramic tile districts, we respond to this question by analyzing an ongoing and emergent technology (named mold-less pressing). Findings show how lead firms orchestrate innovation differently at each stage (discontinuous and continuous) of the ID learning system. Incepting a new technology differs from the continuous and incremental learning system stage, as it is shown to be non-collective in nature but dyad-based between lead firms and lead users in experimental-based and innovation-driven transactions under strong ties and trust-based relationships, co-existing with established technology in large time-spans neither encompassing the large local networks of SMEs, nor being supported by the usual local atmosphere: the cluster/district effect is not yet activated at this stage.