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Exploring interaction: Print and online news media synergies

  • Original Article
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OR Insight

Abstract

Synergies between print and online activity in the newspaper industry are not fully understood. Nor is their influence on newspaper circulation rates. Both issues are investigated using data from a small survey of regional newspapers. In particular, we show how cross-media synergies for print and online interaction can be estimated against a simple conceptual model. Also how different combinations of cross-media exposure can be identified. Interestingly, the study reveals that synergy is complementary and positively impacts on circulation rates.

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Notes

  1. Web 2.0 is the second generation of Website development and design on the Internet and is frequently referred to by scholars as social media (Zhang et al, 2010).

  2. The Huffington Post launched its UK version in July 2011.

  3. In blog platforms, interaction between users and bloggers is an exchange process involving the sharing of valuable information, individuals contribute and exchange their knowledge with others, with an expectation of benefiting from what is received in return.

  4. Please refer to Section ‘Definition of independent variables’, which explains how these criteria were derived.

  5. Our collaborating institution.

  6. Please note that population was used as a control variable (refer to Section ‘Measures – Control variable’).

  7. Heteroscedasticity refers to the situation in which the error term in a regression model does not have constant variance.

  8. Satorra (1990) explains that the EM algorithm is an iterative procedure that finds the MLE of the parameter vector by repeating the following steps: (1) The expectation E-step: Given a set of parameter estimates, such as a mean vector and covariance matrix for a multivariate normal distribution, the E-step calculates the conditional expectation of the complete-data log likelihood given the observed data and the parameter estimates. (2) The maximization M-step: Given a complete-data log likelihood, the M-step finds the parameter estimates to maximize the complete-data log likelihood from the E-step. The two steps are iterated until the iterations converge.

  9. Adjusted R2 is an attempt to take account of the phenomenon of statistical ‘shrinkage’ (Nagelkerke, 1991). He notes that, in regression analysis, a fitted relationship appears to perform less well on a new data set than on the data set used for fitting (in particular the value of the coefficient of determination ‘shrinks’). This idea is complementary to over-fitting and, separately, to the standard adjustment made in the coefficient of determination to compensate for the subjunctive effects of further sampling, such as controlling for the potential of new explanatory terms to improve the model by chance, that is, the adjustment formula itself causes ‘shrinkage’. But in our model's results the adjustment formula yields only minimal artificial shrinkage, in contrast to the first definition.

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Authors and Affiliations

  1. Leeds University Business School, Maurice Keyworth Building, University of Leeds, Leeds, LS2 9JT, UK

    Gary Graham

  2. Manchester Business School, University of Manchester, Manchester, M15 6PB, UK

    Jim Freeman

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  1. Gary Graham
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  2. Jim Freeman
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Correspondence to Jim Freeman.

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Graham, G., Freeman, J. Exploring interaction: Print and online news media synergies. OR Insight 25, 199–220 (2012). https://doi.org/10.1057/ori.2012.1

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