Intention to Adopt Industry 4.0 by Organizations in Colombia, Ecuador, Mexico, Panama, and Peru

This study aims to understand the factors that drive actors belonging to the sector of organizations in Latin America (LA) to adopt Industry 4.0. The proposed model results from the analysis and integration of the technology adoption model (TAM), green information technology adoption model (GITAM), and theory of planned behavior (TPB). To determine the predictive factors for internal organizational actors, the research team surveyed information on organizations belonging to Colombia, Ecuador, Mexico, Panama, and Peru. Information was collected from strategic, tactical, and operational personnel. Data were collected from 499 organizational actors in the productive sector, processed, and analyzed using a structural equation model with the partial least squares technique. The study model explains, first there is an influence of the variables Industry 4.0 perceived ease of use (PEU) and Industry 4.0 perceived utility (PUT) on Industry 4.0 attitude towards use (ATU). Second, there is a positive influence of Industry 4.0 technological context (ICO), Industry 4.0 subjective norm (SNO), Industry 4.0 attitude (ATT), Industry 4.0 attitude towards to use (ATU), and Industry 4.0 attitude behavioral control (BCO) on intention to adopt Industry 4.0 in the organization (IAI). Third, what was not supported is the influence of Industry 4.0 technological context (ICO) on the intention to adopt Industry 4.0 in the organization (IAI). The model results are consistent with those of other studies on technology adoption, and propose a model for Industry 4.0, which is a significant contribution to this study, especially for developing countries.

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Data Management in Industry 4.0: State of the Art and Open Challenges

 

Information and communication technologies are permeating all aspects of industrial and manufacturing systems, expediting the generation of large volumes of industrial data. This paper surveys the recent literature on data management as it applies to networked industrial environments and identifies several open research challenges for the future. As a first step, we extract important data properties (volume, variety, traffic, and criticality) and identify the corresponding data enabling technologies of diverse fundamental industrial use cases, based on practical applications. Second, we provide a detailed outline of recent industrial architectural designs with respect to their data management philosophy (data presence, data coordination, and data computation) and the extent of their distributiveness. Then, we conduct a holistic survey of the recent literature from which we derive a taxonomy of the latest advances in industrial data enabling technologies and data centric services, spanning all the way from the field level deep in the physical deployments, up to the cloud and applications level. Finally, motivated by the rich conclusions of this critical analysis, we identify interesting open challenges for future research. The concepts presented in this paper thematically cover the largest part of the industrial automation pyramid layers. Our approach is multidisciplinary, as the selected publications were drawn from two fields; the communications, networking and computation field, and the industrial, manufacturing, and automation field. This paper can help the readers to deeply understand how data management is currently applied in networked industrial environments, and select interesting open research opportunities to pursue.

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