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Theorizing Online News Consumption: A Structural Model Linking Preference, Use, and Paying Intent

Theorizing Online News Consumption: A Structural Model Linking Preference, Use, and Paying Intent. H. Iris Chyi, Ph.D. Assistant Professor School of Journalism The University of Texas at Austin Angela M. Lee, M.A. Doctoral Student School of Journalism The University of Texas at Austin.

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Theorizing Online News Consumption: A Structural Model Linking Preference, Use, and Paying Intent

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  1. Theorizing Online News Consumption: A Structural Model Linking Preference, Use, and Paying Intent H. Iris Chyi, Ph.D. Assistant Professor School of Journalism The University of Texas at Austin Angela M. Lee, M.A. Doctoral Student School of Journalism The University of Texas at Austin Paper presented at the 13th International Symposium on Online Journalism, Austin, Texas, April 20-21, 2012

  2. Online news consumption • Three distinct factors • Use: Major concern in academic research • Preference • (Chyi & Lasorsa, 1999, 2002; Chyi & Chang, 2009; Chyi & Lee, 2012) • Intention to pay • (Chyi 2005; Chyi, 2012)

  3. What we know • Preference ≠ Use • Consumers do not always use what they prefer. • Use ≠ Intention to pay • Most are not willing to pay for what they use.

  4. What we don’t know • Exactly how these factors relate to each other • The influence of other variables • Age • Gender • News interest • Related media goods (e.g., print edition)

  5. Purpose of this study • To clarify the interrelationship among preference, use, and paying intent for online news, which • explains why most newspapers have difficulties monetizing online usage

  6. Proposing holistic models • Mapping key predictors of online news consumption – Model 1 • Age • Gender • News interest • Format Preference • Online Use • Online Paying Intent

  7. Proposing holistic models • Mapping key predictors of online news consumption – Model 2 • Age • Gender • News interest • Format Preference • Online Use • Online Paying Intent • Print Use

  8. Method • A random-sample online survey of 767 U.S. adults (18 years and older) • Data collection: August 3-6, 2010 • The weighted sample is reasonably representative of the U.S. Internet population.

  9. Analytical tool • Structural Equation Modeling (SEM) – ML estimation of simultaneous multiple regression analyses • Calculate the pure effect of key variables within the nexus of complex news consumption models • Test statistical fitness of theoretical models • Goodness of Fit tests

  10. Results: Model 1 Preference Online Age Pay Online Gender Male Use Online News Interest Note: **p <.01, *** p < .001. χ 2 (3) = 4.86, n.s. RMSEA = .03. CFI = .99. TLI = .97. SRMR = .02.

  11. Results: Model 1 Preference Online - .23*** Age - .21*** Pay Online - .17*** .18*** Gender Male Use Online News Interest Note: **p <.01, *** p < .001. χ 2 (3) = 4.86, n.s. RMSEA = .03. CFI = .99. TLI = .97. SRMR = .02.

  12. Results: Model 1 Preference Online - .23*** Age - .21*** Pay Online .18*** - .17*** .10*** Gender Male Use Online News Interest Note: **p <.01, *** p < .001. χ 2 (3) = 4.86, n.s. RMSEA = .03. CFI = .99. TLI = .97. SRMR = .02.

  13. Results: Model 1 Preference Online - .23*** Age - .21*** Pay Online .18*** - .17*** .10*** Gender Male Use Online .18*** .24*** News Interest Note: **p <.01, *** p < .001. χ 2 (3) = 4.86, n.s. RMSEA = .03. CFI = .99. TLI = .97. SRMR = .02.

  14. Results: Model 1 Preference Online .11** - .23*** .18*** Age - .21*** Pay Online .18*** - .17*** .10*** Gender Male Use Online .18*** .24*** News Interest Note: **p <.01, *** p < .001. χ 2 (3) = 4.86, n.s. RMSEA = .03. CFI = .99. TLI = .97. SRMR = .02.

  15. Results: Model 1 Preference Online .11** - .23*** .18*** Age - .21*** Pay Online .18*** - .17*** .10*** .12** Gender Male Use Online .18*** .24*** News Interest Note: **p <.01, *** p < .001. χ 2 (3) = 4.86, n.s. RMSEA = .03. CFI = .99. TLI = .97. SRMR = .02.

  16. Results: Model 2 Note: **p <.01, *** p < .001. χ 2 (3) = 4.86, n.s. RMSEA = .03. CFI = 1.0. TLI = .97. SRMR = .02.

  17. Key findings • The results distinguished preference from use • Format preference only has a minor influence on online news use (b = .16 to .18, p < .001). • Use is not strongly associated with paying intent (b = .12 in both models, p < .01).

  18. Determinants of paying intent • As many as five factors (age, gender, news interest, preference, and online news use) have direct impacts on paying intent. • Age (b = -.21 in both models, p < .001) and news interest (b = .18 in both models, p < .001) are the strongest predictors.

  19. Implications • News consumption is a multifaceted behavioral construct. • While younger people are more likely to pay for online news, they tend to have lower interest in news compared with other age groups. • Future research on potential intervention measures to promote news interest among young adults may explore the progression from interest, use, to paying intent, as proposed by this study.

  20. Thank you. H. Iris Chyi chyi@mail.utexas.edu @irischyi Angela M. Lee amlee229@gmail.com @angelamlee

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