INTEGRATION OF ARTIFICIAL INTELLIGENCE AND PERFORMANCE OF BROADCASTING COMPANIES IN NAIROBI CITY COUNTY, KENYA
Keywords:
Integration of Artificial Intelligence, Broadcasting Companies, AI-Driven Marketing, AI in Broadcast Technology IntegrationAbstract
The integration of Artificial Intelligence (AI) in broadcasting has significantly transformed industry operations, enhancing efficiency, audience engagement, and revenue generation. This study examines the impact of AI on the performance of broadcasting companies in Nairobi City County, Kenya, focusing on four key areas: AI-driven marketing, AI in broadcast technology integration. The research was grounded in Technology Acceptance Model (TAM), and the Resource-Based View (RBV) to analyse AI adoption and its influence on broadcasting performance indicators such as operational efficiency, audience retention, and revenue growth. The study adopted a descriptive research design to provide an in-depth examination of AI integration in broadcasting. The target population consisted of 127 managerial and technical staff from major media houses, including Nation Media Group, Standard Media Group, Royal Media Services, Kenya Broadcasting Corporation (KBC), and K24. A census approach was used, ensuring comprehensive data collection. A structured questionnaire served as the primary data collection instrument, segmented to assess AI applications in broadcasting. The study conducted a pilot test with 13 respondents representing 10% of the sample size to enhance validity and reliability. The pilot group was selected randomly from the target population and was excluded in the final study. The pilot test results confirmed the research instrument's validity and reliability, ensuring suitability for full-scale data collection. Content validity analysis yielded a CVI of 95.83%, exceeding the 0.90 threshold, while construct validity confirmed an AVE above 0.5 for all variables. Reliability testing showed strong internal consistency. Data analysis was conducted using SPSS version 28, employing descriptive and inferential statistics, including Pearson correlation and multiple regression analysis, to determine the relationship between AI adoption and broadcasting performance. The regression model assessed the impact of AI-driven innovations on the industry. The study found that Artificial Intelligence has a significant and positive influence on the performance of broadcasting companies in Nairobi City County. AI-driven marketing emerged as the most impactful factor, followed by AI in broadcast technology integration. The results indicated that AI enhances operational efficiency, improves audience targeting and retention, streamlines production workflows, and strengthens content delivery. Collectively, these findings confirm that AI is a strategic asset driving both technical and competitive advancement in the broadcasting sector. The findings offers practical insights for broadcasting firms, policymakers, and media regulators on leveraging AI for operational efficiency and competitive advantage.
Key Words: Integration of Artificial Intelligence, Broadcasting Companies, AI-Driven Marketing, AI in Broadcast Technology Integration
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