Matching the future capabilities of an artificial intelligence-based software for social media marketing with potential users’ expectations

• Published in: Technological forecasting & social change Vol. 151; p. 119794
• Main Authors: Capatina, Alexandru, Kachour, Maher, Lichy, Jessica, Micu, Adrian, Micu, Angela-Eliza, Codignola, Federica
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01-02-2020; Elsevier B.V; Elsevier Science Ltd
• Subjects: Artificial intelligence; Audience analysis; Causality; Data mining; Digital marketing; Digital media; Image analysis; Machine learning; Marketing; Mass media; Regression analysis; Regression models; Sentiment analysis; Social media; Social media marketing; Social networks; Software; Social media marketing; Audience analysis; Sentiment analysis; Image analysis; Artificial intelligence; Machine learning
• ISSN: 0040-1625; 1873-5509
• DOI: 10.1016/j.techfore.2019.119794

•In the pre-design phase, an AI Media software requires a feedback from its potential users regarding its forthcoming features.•Different configurations of the AI Media software capabilities lead to potential users’ intention to test its features.•Image analysis capabilities of the AI Media software are perceived as the most influential by Romanian potential users.•Sentiment analysis capabilities of the AI Media software are perceived as the most influential by French potential users.•Audience analysis capabilities of the AI Media software are perceived as the most influential by Italian potential users.
The increasing use of Artificial Intelligence (AI) in Social Media Marketing (SMM) triggered the need for this research to identify and further analyze such expectations of potential users of an AI-based software for Social Media Marketing; a software that will be developed in the next two years, based on its future capabilities.
In this research, we seek to discover how the potential users of this AI-based software (owners and employees from digital agencies based in France, Italy and Romania, as well as freelancers from these countries, with expertise in SMM) perceive the capabilities that we offer, as a way to differentiate our technological solution from other available in the market.
We propose a causal model to find out which expected capabilities of the future AI-based software can explain potential users’ intention to test and use this innovative technological solution for SMM, based on integer valued regression models. With this purpose, R software is used to analyze the data provided by the respondents. We identify different causal configurations of upcoming capabilities of the AI-based software, classified in three categories (audience, image and sentiment analysis), and will trigger potential users’ intention to test and use the software, based on an fsQCA approach.