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Impact of Video Availability on Movie Theater Attendance: A Study of Consumer Behavior

This research explores how the availability of feature films on video affects theater attendance. It examines consumer demographics, such as age, income, and household size, along with their interest in new products. The study finds that younger audiences, those with higher incomes, and smaller households tend to attend theaters more, while older individuals and larger households prefer watching videos. The findings support the theory of "windowing," where films are released in theaters before becoming available on video, revealing insights into consumer preferences and the evolving landscape of entertainment media.

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Impact of Video Availability on Movie Theater Attendance: A Study of Consumer Behavior

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  1. BACKGROUND What happens when a new medium challenges an existing one? • Several previous examples: • radio and print • “public” changes -- actually a cohort effect • television and film • at first - film profits were hurt • later - film developed it’s own niche

  2. VIDEOS VERSUSMOVIE THEATERS BACKGRO VIDEOS VERSUSMOVIE THEATERS UND Will availability of feature films on video affect theater attendance? • Windowing: • (Owen & Wildman, 1992; Litman, 1999) • A form of price discrimination • films are released to theaters before video • for a consumer, waiting offers a cost savings • Here: Examination of CONSUMERS

  3. HYPOTHESES HYPOTHESES Who will attend movies? H1: Younger people H2: Higher incomes H3: Smaller households H4: People with more interest in newer products Who will watch videos? H1: Older people H2: Lower incomes H3: Larger households H4: People with less interest in newer products

  4. METHODS • DDB’s 1996 Lifestyles data • initial mailings of 5,000 • 4,041 total respondents (70% response rate) • 3,130 supplemental survey (77% response rate) • This research: • age, income, household size • desire for the latest products • movie and video consumption questions

  5. # OF FILMS AND VIDEOS SEEN LAST YEAR # last year Attended film Rented video Bought video None 965 (31.1%) 1264 (41.4%) 931 (30.2%) 1-4 times 1051 (33.6%) 527 (17.1%) 932 (30.5%) 5-8 times 510 (16.3%) 455 (14.7%) 442 (14.5%) 9-11 times 265 (8.5%) 367 (11.9%) 226 (7.4%) 12-24 times 215 (6.9%) 414 (13.4%) 123 (4.0%) 25-51 times 68 (2.2%) 248 (8.0%) 45 (1.5%) 52 or more 29 (0.9%) 143 (4.6%) 23 (0.8%) MEAN 2.37 3.04 2.10

  6. RESULTS:NUMBER of FILMS RESULTS:# OF FILMS H1: Younger people Beta = -.191, p < .001 - SUPPORTED H2: Higher incomes Beta = .201, p < .001 - SUPPORTED H3: Smaller households Beta = -.137, p < .001 - SUPPORTED H4: People w/more interest in newer products Beta = .138, p < .001 - SUPPORTED

  7. RESULTS:RATIO of VIDEOS/FILMS H1: Older people Beta = .153, p < .001 - SUPPORTED H2: Lower incomes Beta = -.082, p < .001 - SUPPORTED H3: Larger households Beta = .189, p < .001- SUPPORTED H4: People w/less interest in newer products Beta = -.192, p = .001- SUPPORTED

  8. CONCLUSIONS Compatible with windowing strategy • MOST MOVIES: • People who value “latest” see more movies • Younger people • People with higher incomes • People from smaller families • The desire to be the first to own a product predicts “windowing” price discrimination

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