Impact of Streaming Algorithms on Classical Concert Attendance Trends

Thesis Abstract

Streaming algorithms have transformed user access to classical music by shaping discovery, personalization, and engagement across digital platforms, with potential knock-on effects on live concert attendance and sponsorship models. This study addresses the growing concern that algorithm-driven streaming ecosystems may redefine listeners’ attendance decisions for classical performances, potentially altering demographic composition, concert frequency, and revenue streams for orchestras and venues. The aim is to quantify the relationship between streaming algorithm exposure and classical concert attendance trends, and to identify mediating factors such as repertoire familiarity, artist prestige, and social listening behaviors. Specific objectives include (1) estimating the association between streaming-derived exposure metrics and seasonal attendance figures across major symphony orchestras; (2) examining how repertoire choices and programming diversity interact with streaming recommendations to influence attendance; (3) evaluating differential effects by demographic segments (age, education, geographic region) and by listener typologies (avid, casual, and occasional); (4) assessing the moderating role of streaming platform subscription status (free vs. premium) on attendance outcomes; and (5) deriving policy and strategic implications for marketing, programming, and experiential initiatives in the classical music sector. The methodology adopts a mixed-methods research design combining quantitative panel data analysis with qualitative insights. The population comprises audiences of five leading orchestras in North America and Europe over a five-year period (2019–2023). A stratified sample of 2,500 concertgoers from ticketing databases, supplemented by 1,200 survey respondents and 40 in-depth interviews with sector professionals (artistic directors, marketers, and streaming curators) will be used. Data collection instruments include (i) attendance records and subscription data provided by orchestras; (ii) streaming exposure metrics derived from platform APIs (play counts, discovery impressions, personalized recommendation signals, and time-of-day listening patterns); (iii) a standardized questionnaire capturing demographics, listening habits, and attitudes toward streaming and live events; (iv) interview guides targeting programming decisions, marketing communications, and perceived barriers to attendance. Validity and reliability will be ensured through triangulation, pilot testing of instruments, and test-retest reliability checks (Cronbach’s alpha) for the survey scales. Analytical techniques include time-series regression models to assess the impact of streaming exposure on monthly attendance, controlling for price, weather, and macroeconomic indicators; hierarchical linear modeling to capture nested data structures (individuals within venues and seasons); and propensity-score matching to mitigate selection bias between high and low streaming exposure groups. Mediation analyses will test whether repertoire familiarity and artist prestige mediate the streaming-attendance relationship. A thematic analysis of interview transcripts will contextualize quantitative findings, identifying organizational practices that amplify or mitigate attendance shifts. Theoretical framing draws on Uses and Gratifications Theory to understand consumer motivations, and the Attention Economy framework to interpret how algorithmic curation reallocates audience attention between on-demand streaming and live events. A conceptual model will juxtapose streaming exposure pathways with attendance outcomes, mediated by cultural capital and social influence. Expected findings anticipate a nuanced pattern streaming exposure may correlate with higher attendance for premieres, commercially high-profile soloists, and diverse programming, while general streaming dominance in niche classical repertoires could coincide with reduced attendance in smaller venues lacking differentiated experiences. Demographic analyses may reveal that younger, digitally native listeners exhibit weaker attendance responsiveness, whereas older, culturally invested patrons maintain steady attendance regardless of streaming activity. The study aims to contribute to knowledge by integrating algorithmic media dynamics with live performing arts consumption, offering a model to forecast attendance under evolving digital ecosystems and informing strategic interventions. The study concludes with recommendations for orchestras to optimize programming and marketing by leveraging streaming data insights, design tiered live experiences that complement algorithmic discovery, and implement targeted outreach to non-traditional audiences. It suggests policy implications for subsidy allocation, venue redesign, and collaboration between streaming platforms and live music institutions to sustain attendance and cultural participation in the digital age.