Impact of Streaming Algorithms on Classical Concert Attendance in Urban Centers

Thesis Abstract

The rapid expansion of streaming algorithms and personalized recommendation systems has transformed access to classical music content, raising questions about their impact on live concert attendance in urban centers where orchestras rely on ticket revenues and community engagement. This study addresses the problem of whether and how streaming platforms influence the propensity of urban residents to attend live classical concerts, and whether algorithmic curation dampens or redistributes demand across venues of varying prestige. The aim is to quantify the relationship between exposure to streaming recommendations and live attendance, and to identify moderating factors such as price, program diversity, and venue type. Specific objectives are to (1) measure changes in live attendance patterns in relation to streaming algorithm intensity; (2) examine demographic and psychographic profiles associated with attendance shifts; (3) assess the mediating role of perceived value, social influence, and accessibility; (4) compare attendance elasticity across major urban centers and smaller concert halls; and (5) provide actionable implications for orchestras' programming and marketing strategies. The study adopts a mixed-methods research design, combining quantitative analysis of longitudinal attendance and streaming exposure data with qualitative insights from musician, manager, and attendee interviews. The population comprises residents of five major metropolitan areas with prominent classical ensembles. A stratified random sample of 2,000 survey respondents will be drawn, complemented by attendance records from five orchestras (n=50,000 ticketed performances over four seasons) and anonymized streaming analytics from two leading platforms (n=1.2 million user-event interactions). Data collection instruments include standardized questionnaires assessing streaming exposure, attitudes toward live concerts, and demographic variables; concert attendance records; and in-depth interviews with 20 orchestra marketing managers and 30 concertgoers. Validity will be enhanced through pilot testing, triangulation across sources, and confirmatory factor analysis for latent constructs. Reliability will be ensured via Cronbach’s alpha for scales and test–retest checks where feasible. Analytical techniques will involve time-series regression to model live attendance as a function of streaming exposure metrics (recommendation probability, playlist similarity, and algorithmic personalization intensity), controlling for price, program type, seasonality, and venue characteristics. Multilevel modeling will account for nested data (individuals within neighborhoods and cities). Mediation analysis will test whether perceived value and accessibility mediate streaming exposure effects on attendance. Difference-in-differences analyses will compare urban centers with contrasting streaming ecosystems. Thematic analysis of interview transcripts will illuminate mechanisms, including crowding effects, social signaling, and trust in recommendations. A conceptual model drawing on Uses and Gratifications Theory and Expectancy-Value Theory will structure interpretation of how streaming experiences translate into live attendance decisions. Key expected findings include (i) a negative association between high-intensity streaming recommendations and marginal live attendance for lower-priced or less prestigious venues, counterbalanced by positive effects for niche programs and new repertoire in certain venues; (ii) stronger attendance responses among younger adults and those with higher cultural capital, mediated by perceived experiential value and social influence; (iii) differential effects across cities, with denser ecosystems exhibiting more pronounced substitution effects in the short term but potential long-term stabilization or growth through enhanced ecosystem engagement; and (iv) a moderating role of programming diversity, accessibility (pricing, scheduling), and venue branding. This study contributes to knowledge by elucidating the boundary conditions under which streaming algorithms affect live classical concert attendance, integrating streaming analytics with live-performance economics, and testing a model grounded in Uses and Gratifications Theory and Expectancy-Value Theory to explain consumer behavior in cultural markets. The findings will inform orchestras’ strategic decisions on pricing, repertoire planning, marketing segmentation, and collaborations with streaming platforms to leverage algorithms for enhanced audience development rather than unintended attenuation of live demand. The main conclusion anticipates that streaming algorithms influence live attendance in nuanced, context-dependent ways, with substitution effects more evident in dominant urban centers and for certain program types, while opportunities exist to convert online engagement into sustained live participation through targeted value propositions and improved access. Recommendations include adopting dynamic pricing strategies, crafting cross-channel programming, employing personalized but transparent recommendation practices, and fostering partnerships with streaming platforms to create channel-specific incentives that promote live performances.