Category: Computing for business

At the Pragmatics laboratory, we have published an article in the journal IEEE Transactions on Professional Communication. DOI: https://doi.org/10.1109/TPC.2023.3260449

ABSTRACT:
Twitter offers tools that facilitate the diffusion of information by which companies can engage consumers to share their messages. Literature review: Communication professionals are using platforms such as Twitter to disseminate information; however, the strategies that they should use to achieve high information diffusion are not clear. This article proposes message repetition as a strategy. Research questions: 1. What is the wear-out point of Twitter? 2. How many times should a company repeat a tweet written on its brand page to maximize the diffusion for seeds? 3. How many times should a company repeat a tweet written on its brand page to maximize the diffusion while minimizing the number of consumers reaching their wear-out point for seeds? 4. How many times should
a company repeat a tweet written on its brand page to maximize the diffusion for non-seeds? 5. How many times should a company repeat a tweet written on its brand page to maximize the diffusion while minimizing the number of consumers reaching their wear-out point for both seeds and non-seeds? Research methodology: An agent-based simulation model for information diffusion is proposed as an approach to measure the diffusion of a tweet that has been repeated. The model considers that consumers can reach their wear-out point when they read a tweet several times. Results: The results of the model indicate the number of times a company should send the same tweet to achieve high information diffusion before this action has negative effects on consumers. Brand followers are key to achieving high information diffusion; however, consumers begin to feel bothered by the tweet by the sixth repetition. Conclusions: To the best of our knowledge, this is the first study to examine tweet repetition as a strategy to achieve higher information diffusion on Twitter. In addition, it extends the information diffusion literature by controlling the wear-out
effect. It contributes to both communication and computational science literature by analyzing a communication problem using an agent-based approach. Finally, this article contributes to the field of technical and professional communication by testing a strategy to reach great information diffusion, and by creating a tool that any company can use to anticipate the results of a communication campaign created in Twitter before launching it.

Our paper that discusses how to use an agent-based model to simulate the touristic preferences to travel to different provinces in Spain has been accepted in the Journal of Simulation, which has been accepted in a WoS journal Q3.

Abstract:
Spain ranks second in the world for the number of international tourists, most of whom need to plan routes during their stay in the country. These tourists have different preferences, which influence their choice of tourist routes depending on the activities offered by provinces. There are currently no customized routes according to the preferences of a travel party, which makes the supply of tourist packages complex. Due to the difficulty of obtaining data that is not always public and is subject to the particular circumstances of the data collection, we propose an agent-based simulation model implemented in Java, named ABM RoutePlanner, which simulates the behaviour of travel parties travelling through 23 provinces of peninsular Spain. The model is developed as an application of the protocol Overview, Design concepts and Details (ODD), which is primarily used as a base of various types of ABMs to represent some complex phenomena. This paper makes an applied contribution, which presents the actual routes that will allow tour operators to define strategic plans that motivate tourists to visit the provinces included in the routes; in addition, we deploy a Web application that shows these routes to any Internet user. The simulation of the model was calibrated and validated with extracted data from the TripAdvisor website and Spanish tourist surveys.