DIFFERENCES IN THE CAPACITY OF ADOPTION OF THE ENABLING ICTS FOR INDUSTRY 4.0 IN CHILE
Name and surname of author:
Francisco Gatica-Neira, Mario Ramos-Maldonado
Technology adoption, ICT, Industry 4.0, technology promotion policy, digital transformation, technological synergy, ordered logit, statistical clusters, decision tree
DOI (& full text):
In the context of the Fourth Industrial Revolution this paper analyzes the factors that explain the degree of diffusion of some Information Technologies (ICTs) enabling Industries 4.0 in Chilean…more
In the context of the Fourth Industrial Revolution this paper analyzes the factors that explain the degree of diffusion of some Information Technologies (ICTs) enabling Industries 4.0 in Chilean companies. In this group we find technologies such as: Big data, RIFD (Radio frequency identification), Cloud computing, ERP (Enterprise requirements planning), CRM (Customer relationship management), SCM (Supply chain management) and Computer security. Through the analysis of clusters, orderly logistic regression and decision tree, based on 2,081 companies reported in the Survey of Access and Use of Information Communication Technology (ICT) in Companies 2018 (MINECON, 2020). It is concluded that there is an important difference in technological adoption based on size from the volume of sales and the amount of direct labor. It is also noted that companies that subcontract and at the same time have ICT professionals are more likely to invest in this type of technology. We detected a “technological staggering” where companies begin by incorporating Cloud Computing and ERP and then increase in the number and complexity of the technologies used, achieving greater synergies and benefits in digital transformation. It is necessary to implement mechanisms for monitoring technical change to generate public policies aimed at leveling technological adoption in small and medium-sized enterprises. This work provides a global and intersectoral view of the process of diffusion of enabling technologies for Industry 4.0 through multivariate analysis techniques and data science, being a contribution to what is currently worked on focused on the study of business cases, on the monitoring of a specific technology or on an analysis of a specific productive sector.