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What is Causal Map

Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry
Graph obtained from the cause-effect analysis of the experts on the different factors involved in decision-making.
Published in Chapter:
Key Factors in the Process of Acceptance and Implementation of Artificial Intelligence in the Hotel Sector
Alfonso Infante-Moro (University of Huelva, Spain), Juan C. Infante-Moro (University of Huelva, Spain), and Julia Gallardo-Pérez (University of Huelva, Spain)
DOI: 10.4018/978-1-7998-6985-6.ch014
Abstract
Artificial intelligence is one of the most innovative and trending technologies in the hotel sector. It is transforming the hotel sector into a novel environment in business-client relationships. For this reason, this study seeks to find, through a literary review and a causal study carried out by experts in technologies and hotels, using the methodology of fuzzy cognitive maps, what the determining factors in the decision of hotels to accept and implement artificial intelligence in their hotels and in their services are. The list obtained is made up of the following factors (in order of relevance in this decision): top management support, perceived reliability of the technology, security, relative advantage, support from information systems providers, technological organizational readiness, government pressure or incentives, compatibility, business partner pressure, customer pressure, complexity, pressure from competitors, perceived cost, characteristics of the leader or manager, and size of the company.
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More Results
A Support System for the Strategic Scenario Process
Causal maps include elements called nodes, which are allowed to have causal relationships of different strengths of positive or negative loading depicted with a number, usually in the range of from 1 (weak) to 3 (strong). The relationships of the nodes are depicted with arcs or links labeled with the assumed polarity and loading factor or strength of causality, Links with positive polarity refer to dependency (when A increases B increases proportionally to the loading factor) and negative to inverse dependency (when A increases, B decreases).
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