Triple screen viewing practices : diversification or compartmentalization?

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Courtois, C., Schuurman, D. & De Marez, L. (2011). Triple screen viewing practices : diversification or compartmentalization?. In: 9th European Interactive TV Conference (EuroITV - 2011), Lisbon, Portugal, 2011-06-29. ACM.

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Triple screen viewing practices : diversification or compartmentalization?

  1. 1. Triple Screen Viewing Practices: Diversification or Compartmentalization? Cédric Courtois, Dimitri Schuurman & Lieven De Marez, IBBT-MICT-Ghent University
  2. 2. Today’s domestic environments (a) Multiple devices + (b) Multiple channels
  3. 3. Consequently … Shifting opportunities, shifting media practices? What about convergence? Each medium has its niche? (Television for linear, computer for downloading, etc.?)
  4. 4. Consequently … Shifting opportunities, shifting media practices? What about convergence? Each medium has its niche? (Television for linear, computer for downloading, etc.?) RQ1: Do we form hybrid media practices, mixing devices and channels? RQ2: How do practice patterns relate to the use of diferent devices for audiovisual consumption?
  5. 5. Methodology <ul><li>Very large scale web survey ( N = 13,312 !), addresses provided by a large telco provider </li></ul><ul><li>Demographics: </li></ul><ul><ul><li>51% females </li></ul></ul><ul><ul><li>Equal age distribution: 20-29y (16%), 30-39y (17%) 40-49y (20%), 50-59 (17%), 60-65 (7%) and 65y+ (23%). </li></ul></ul><ul><li>Extracted survey contents </li></ul><ul><ul><li>Frequency of which television, laptop, desktop and mobile phone are used to play back content obtained through a variety of channels: linear, carrier (e.g. DVD, Blue Ray), download/own recording and VoD. </li></ul></ul><ul><ul><li>Response categories: (a) never, (b) less than once per month, (c) once a month, (d) once a week and (e) daily. </li></ul></ul>
  6. 6. Results: descriptives (complete sample)
  7. 7. Results: further analysis <ul><li>Analysis of correlational structure: </li></ul><ul><ul><li>First analysis: exploratory factor analysis through principal components analysis (PCA, oblique rotation) </li></ul></ul><ul><ul><li>Ordinal data: quantification algorithm (data transformation) </li></ul></ul><ul><ul><li>Very large data set: randomly chopped up into smaller pieces (three subsamples N = +/- 5000) : PCA on one subsample </li></ul></ul>
  8. 8. Results: Exploratory PCA Component 1 Laptop - Download/Recorded .84 -.20 -.30 .16 Laptop - Carrier .77 -.17 -.26 .07 Laptop - Linear .75 -.26 -.24 .15 Laptop - VoD .68 -.06 -.18 .14 Component 2 Television - Download/Recorded .16 -.88 -.15 .06 Television - Linear .15 -.87 -.16 .05 Television - VoD .20 -.79 -.15 .07 Television - Carrier .21 -.78 -.23 .06 Component 3 Desktop - Download/Recorded .30 -.18 -.85 .11 Desktop - Carrier .24 -.15 -.79 .07 Desktop - Linear .23 -.25 -.78 .06 Desktop - VoD .21 -.07 -.66 .08 Component 4 Mobile Phone - Linear .16 -.06 -.12 .92 Mobile Phone - VoD .14 -.07 -.07 .92   % Variance explained 27.57 15.46 11.97 10.68 Eigenvalue 3.86 2.16 1.68 1.50   Cronbach’s α .72 .83 .73 .76
  9. 9. Results: Exploratory PCA Evidence of strong compartmenalization Sample-specific solution? Component 1 Laptop - Download/Recorded .84 -.20 -.30 .16 Laptop - Carrier .77 -.17 -.26 .07 Laptop - Linear .75 -.26 -.24 .15 Laptop - VoD .68 -.06 -.18 .14 Component 2 Television - Download/Recorded .16 -.88 -.15 .06 Television - Linear .15 -.87 -.16 .05 Television - VoD .20 -.79 -.15 .07 Television - Carrier .21 -.78 -.23 .06 Component 3 Desktop - Download/Recorded .30 -.18 -.85 .11 Desktop - Carrier .24 -.15 -.79 .07 Desktop - Linear .23 -.25 -.78 .06 Desktop - VoD .21 -.07 -.66 .08 Component 4 Mobile Phone - Linear .16 -.06 -.12 .92 Mobile Phone - VoD .14 -.07 -.07 .92   % Variance explained 27.57 15.46 11.97 10.68 Eigenvalue 3.86 2.16 1.68 1.50   Cronbach’s α .72 .83 .73 .76
  10. 10. Results: Confirmatory cross-sample analysis <ul><li>Multi-sample structural equation modeling </li></ul><ul><li>Violation of normality assumption + large data sets = ADF estimation </li></ul><ul><li>Satisfactory overall fit: χ 2 (213) = 1026, p < .001, CFI = .96, TLI = .94, RMSEA = .015 </li></ul><ul><li>No significant χ 2 changes for the nested constrained measurement weights (Δχ 2 = 11.13, p > .05) and structural covariances (Δχ 2 = 9.84, p > .05). </li></ul>
  11. 11. Results: Confirmatory cross-sample analysis <ul><li>Multi-sample structural equation modeling </li></ul><ul><li>Violation of normality assumption + large data sets = ADF estimation </li></ul><ul><li>Satisfactory overall fit: χ 2 (213) = 1026, p < .001, CFI = .96, TLI = .94, RMSEA = .015 </li></ul><ul><li>No significant χ 2 changes for the nested constrained measurement weights (Δχ 2 = 11.13, p > .05) and structural covariances (Δχ 2 = 9.84, p > .05). </li></ul>Confirmation of compartmentalization Still, some weak correlations between components
  12. 12. Results: Relations between the use of different devices <ul><li>Structural model; Independent variables: usage of a device for audiovisual consumption </li></ul><ul><li>Tested for three subsamples: χ 2 (405) = 2097, p < .001, CFI = .99, TLI = .98, RMSEA = .016) </li></ul><ul><li>Invariant measurement weights (Δχ 2 = 15.74, p > .05) and structural weigths (Δχ 2 = 9.49, p > .05); unlike structural covariances (Δχ 2 = 130.18, p < .001). </li></ul>
  13. 13. Results: Relations between the use of different devices <ul><li>Structural model; Independent variables: usage of a device for audiovisual consumption </li></ul><ul><li>Tested for three subsamples: χ 2 (405) = 2097, p < .001, CFI = .99, TLI = .98, RMSEA = .016) </li></ul><ul><li>Invariant measurement weights (Δχ 2 = 15.74, p > .05) and structural weigths (Δχ 2 = 9.49, p > .05); unlike structural covariances (Δχ 2 = 130.18, p < .001). </li></ul>Weak cross-effects, despite controlling for the same devices
  14. 14. <ul><li>Conclusions </li></ul><ul><li>Strong correlations of channel use within devices: mostly sticking to the same device to use all kinds of channels </li></ul><ul><li>Still, no evidence of a displacement: no negative associations between usage frequencies </li></ul><ul><li>On the contrary small positive correlations between television, laptop and desktop usage frequency (Not for mobile phones!). Using one device does not imply ignoring the other … modest spill over ( the rich get richer? ) </li></ul><ul><li>Using a device is differentialy associated with usage frequency (television > mobile phone) </li></ul><ul><li>Suggestions for further research: who are the rich, why do they choose to use multiple devices, and in what contexts? </li></ul>
  15. 15. <ul><li>Conclusions </li></ul><ul><li>Strong correlations of channel use within devices: mostly sticking to the same device to use all kinds of channels </li></ul><ul><li>Still, no evidence of a displacement: no negative associations between usage frequencies </li></ul><ul><li>On the contrary small positive correlations between television, laptop and desktop usage frequency (Not for mobile phones!). Using one device does not imply ignoring the other … modest spill over ( the rich get richer? ) </li></ul><ul><li>Using a device is differentialy associated with usage frequency (television > mobile phone) </li></ul><ul><li>Suggestions for further research: who are the rich, why do they choose to use multiple devices, and in what contexts? </li></ul>Thank you for listening… Any questions? Contact: cedric.courtois@ugent.be, www.mict.be

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