CONSUMER ATTITUDES TOWARD ARTIFICIAL INTELLIGENCE: A COMPARATIVE ANALYSIS OF MEASUREMENT SCALES
Kata HORVATH
Marketing and Tourism Institute, Faculty of Economics, University of Miskolc, Miskolc, Hungary
Abstract: The economic significance of artificial intelligence (AI) is rapidly increasing, influencing industries, employment, and consumer behaviour all around the globe. As AI applications become increasingly apparent and tangible in our daily lives, understanding consumer attitudes toward AI has become essential for businesses and policymakers aiming to drive adoption and trust in such technologies. This paper firstly explores the economic relevance of AI by highlighting its impact on various fields and its role in driving economic growth. A critical aspect of harnessing the full economic potential of AI lies in the accurate measurement of consumer attitudes, as public perception influences the adoption of technology, hence its final market success. Accurate insights into public attitudes are also key to shaping policies that ensure ethical AI integration, fostering a balanced approach between innovation and societal concerns. Beyond adoption, understanding attitudes helps identify potential barriers which could hinder the widespread acceptance of AI systems. This paper then proceeds to providing a critical overview of the different scales developed for assessing consumer attitudes towards AI. These scales have been established in varied contexts, from evaluating general perceptions to measuring attitudes toward specific AI applications. The review underscores the importance of ensuring adaptability and context-specific relevance when selecting or designing these tools. Comparisons between scales reveal distinct advantages and disadvantages in relation to reliability, robustness, contextual limitations or scope. Finally, this paper aims to provide perspectives for selecting the right AI attitude scale, emphasizing different methodological considerations. These insights aim to guide researchers and practitioners in effectively measuring consumer attitudes, contributing to more informed decisions in AI based innovative processes.
Keywords: artificial intelligence; attitude; scale development; consumer behaviour
JEL classification: C10; D11; O33
