Comparative Analysis of AI Adoption Patterns in Music: An Opportunity-Ability-Motivation Approach

Sheena Lovia Boateng, Jefferson Seyanya Seneadza, Richard Boateng, Joseph Budu, Obed Kwame Adzaku Penu

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

The previous chapters presented detailed case studies of AI adoption in the music industry, focusing on three different stakeholders: a music artist and publisher (Diana Hopeson), a music producer (Stanley Dartey, also known as Paq), and a content creator and dance influencer (Lisa Quama). This chapter provides a comparative analysis of AI adoption patterns among these stakeholders using the MOA framework. By examining the experiences of Lisa, Paq, and Diana, this chapter identifies consistent patterns in their approach to integrating AI tools, highlighting the opportunities AI presents, the abilities developed to use these tools, and the motivations driving their adoption. The analysis of AI adoption among Lisa Quama, Stanley Dartey (Paq), and Diana Hopeson suggests a shift toward the Opportunity-Ability-Motivation (OAM) framework. The OAM framework offers a refined lens to understand AI adoption in the music industry, emphasizing the sequential recognition of opportunities, development of abilities, and sustained motivation. This approach provides a structured pathway for individuals and organizations to effectively integrate AI tools, enhancing efficiency, creativity, and fairness in the industry.

Original languageEnglish
Title of host publicationAI and the Music Industry
Subtitle of host publicationTransforming Production, Platforms, and Practice
PublisherTaylor and Francis
Pages137-149
Number of pages13
ISBN (Electronic)9781040332580
ISBN (Print)9781032978307
DOIs
Publication statusPublished - 1 Jan 2025
Externally publishedYes

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