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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
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)
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.
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)
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
Proposing holistic models
• Mapping key predictors of online news
consumption – Model 1
• Age
• Gender
• News interest
• Format Preference
• Online Use
• Online Paying Intent
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
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.
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
Results: Model 1
Age
Gender
Male
News
Interest
Preference
Online
Pay Online
Use
Online
Note: **p <.01, *** p < .001. χ 2
(3) = 4.86, n.s. RMSEA = .03. CFI = .99. TLI = .97. SRMR = .02.
Results: Model 1
- .23***
- .21***
.18***
- .17***
Age
Gender
Male
News
Interest
Preference
Online
Pay Online
Use
Online
Note: **p <.01, *** p < .001. χ 2
(3) = 4.86, n.s. RMSEA = .03. CFI = .99. TLI = .97. SRMR = .02.
Results: Model 1
- .23***
- .21***
.18***
- .17***
Age
Gender
Male
News
Interest
Preference
Online
Pay Online
Use
Online
Note: **p <.01, *** p < .001. χ 2
(3) = 4.86, n.s. RMSEA = .03. CFI = .99. TLI = .97. SRMR = .02.
.10***
Results: Model 1
- .23***
- .21***
.18***
- .17***
Age
Gender
Male
News
Interest
Preference
Online
Pay Online
Use
Online
Note: **p <.01, *** p < .001. χ 2
(3) = 4.86, n.s. RMSEA = .03. CFI = .99. TLI = .97. SRMR = .02.
.10***
.24***
.18***
Results: Model 1
- .23***
- .21***
.18***
- .17***
Age
Gender
Male
News
Interest
Preference
Online
Pay Online
Use
Online
.18***
Note: **p <.01, *** p < .001. χ 2
(3) = 4.86, n.s. RMSEA = .03. CFI = .99. TLI = .97. SRMR = .02.
.10***
.24***
.18***
.11**
Results: Model 1
- .23***
- .21***
.18***
- .17***
Age
Gender
Male
News
Interest
Preference
Online
Pay Online
Use
Online
.18***
Note: **p <.01, *** p < .001. χ 2
(3) = 4.86, n.s. RMSEA = .03. CFI = .99. TLI = .97. SRMR = .02.
.10***
.24***
.18***
.11**
.12**
Results: Model 2
Note: **p <.01, *** p < .001. χ2
(3) = 4.86, n.s. RMSEA = .03. CFI = 1.0. TLI = .97. SRMR = .02.
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).
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.
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.
Thank you.
H. Iris Chyi
chyi@mail.utexas.edu
@irischyi
Angela M. Lee
amlee229@gmail.com
@angelamlee

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Chyi lee

  • 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 Age Gender Male News Interest Preference Online Pay Online Use Online Note: **p <.01, *** p < .001. χ 2 (3) = 4.86, n.s. RMSEA = .03. CFI = .99. TLI = .97. SRMR = .02.
  • 11. Results: Model 1 - .23*** - .21*** .18*** - .17*** Age Gender Male News Interest Preference Online Pay Online Use Online Note: **p <.01, *** p < .001. χ 2 (3) = 4.86, n.s. RMSEA = .03. CFI = .99. TLI = .97. SRMR = .02.
  • 12. Results: Model 1 - .23*** - .21*** .18*** - .17*** Age Gender Male News Interest Preference Online Pay Online Use Online Note: **p <.01, *** p < .001. χ 2 (3) = 4.86, n.s. RMSEA = .03. CFI = .99. TLI = .97. SRMR = .02. .10***
  • 13. Results: Model 1 - .23*** - .21*** .18*** - .17*** Age Gender Male News Interest Preference Online Pay Online Use Online Note: **p <.01, *** p < .001. χ 2 (3) = 4.86, n.s. RMSEA = .03. CFI = .99. TLI = .97. SRMR = .02. .10*** .24*** .18***
  • 14. Results: Model 1 - .23*** - .21*** .18*** - .17*** Age Gender Male News Interest Preference Online Pay Online Use Online .18*** Note: **p <.01, *** p < .001. χ 2 (3) = 4.86, n.s. RMSEA = .03. CFI = .99. TLI = .97. SRMR = .02. .10*** .24*** .18*** .11**
  • 15. Results: Model 1 - .23*** - .21*** .18*** - .17*** Age Gender Male News Interest Preference Online Pay Online Use Online .18*** Note: **p <.01, *** p < .001. χ 2 (3) = 4.86, n.s. RMSEA = .03. CFI = .99. TLI = .97. SRMR = .02. .10*** .24*** .18*** .11** .12**
  • 16. Results: Model 2 Note: **p <.01, *** p < .001. χ2 (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

Editor's Notes

  1. Use: key concept, but overstudied
  2. Examining the interrelationship among these key variables, revealing the complexity of online news consumption
  3. Strongest predictors (direct effect) pay online: Age (-.21) and news interest (.18)
  4. I will talk about age and interest in slide 15/16. but you can still keep this sub-bullet point here if you want. Up to you.
  5. Point 1: … that entails multiple variables (e.g., age, gender, interest, preference, use, paying intent), and future studies should take these into consideration when assessing news consumption. In other words, when looking at paying intent, it is equally important that one turns to use, preference, interest, gender and age since all these factors influence each other when we look at the larger, more practical picture