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

Publication Type:

Journal Article

Authors:

Capatina, Alexandru; Kachour, Maher; Lichy, Jessica; Micu, Adrian; Micu, Angela Eliza; Codignola, Federica

Source:

Technological Forecasting and Social Change, Elsevier, Volume 151, Number June 2019, p.119794 (2020)

URL:

https://doi.org/10.1016/j.techfore.2019.119794

Keywords:

artificial intelligence, Audience analysis, Image analysis, machine learning, Sentiment analysis, Social media marketing

Abstract:

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.