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The Cost Impact of Spam Filters: Measuring the Effect of Information System Technologies in Organizations

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Author Info

  • Caliendo, Marco

    ()
    (University of Potsdam)

  • Clement, Michel

    ()
    (University of Hamburg)

  • Papies, Dominik

    ()
    (University of Hamburg)

  • Scheel-Kopeinig, Sabine

    ()
    (University of Cologne)

Abstract

More than 70% of global e-mail traffic consists of unsolicited and commercial direct marketing, also known as spam. Dealing with spam incurs high costs for organizations, prompting efforts to try to reduce spam-related costs by installing spam filters. Using modern econometric methods to reduce the selection bias of installing a spam filter, we deploy a unique data setting implemented at a German university to measure the costs associated with spam and the costs savings of spam filters. The applied methodological framework can easily be transferred to estimate the effect of other IS technologies (e.g., SAP) implemented in organizations. Our findings indicate that central IT costs are of little relevance since the majority of spam costs stem from employees who spend working time identifying and deleting spam. The working time losses caused by spam are approximately 1,200 minutes per employee per year; these costs could be reduced by roughly 35% through the installation of a spam filter mechanism. The individual efficiency of a spam filter installation depends on the amount of spam that is received and on the level of knowledge about spam.

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Bibliographic Info

Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 3755.

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Length: 38 pages
Date of creation: Oct 2008
Date of revision:
Publication status: published in: Information Systems Research, 2012, 23 (3, Part II), 1068-1080
Handle: RePEc:iza:izadps:dp3755

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Keywords: treatment effects; propensity score matching; spam filter; spam;

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References

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  1. Glenn Harrison & John List, 2004. "Field experiments," Artefactual Field Experiments 00058, The Field Experiments Website.
  2. Michael Lechner, 2005. "Some practical issues in the evaluation of heterogeneous labour market programmes by matching methods," Labor and Demography 0505006, EconWPA.
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  7. Sanjeev Dewan & Charles Shi & Vijay Gurbaxani, 2007. "Investigating the Risk-Return Relationship of Information Technology Investment: Firm-Level Empirical Analysis," Management Science, INFORMS, vol. 53(12), pages 1829-1842, December.
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  12. Barbara Sianesi, 2002. "An evaluation of the Swedish system of active labour market programmes in the 1990s," IFS Working Papers W02/01, Institute for Fiscal Studies.
  13. Jeffrey Smith & Petra Todd, 2003. "Does Matching Overcome Lalonde's Critique of Nonexperimental Estimators?," University of Western Ontario, CIBC Centre for Human Capital and Productivity Working Papers 20035, University of Western Ontario, CIBC Centre for Human Capital and Productivity.
  14. Il-Horn Hann & Kai-Lung Hui & Yee-Lin Lai & S.Y.T. Lee & I.P.L. PNG, 2006. "Who gets spammed?," Natural Field Experiments 00271, The Field Experiments Website.
  15. DiPrete, Thomas A. & Gangl, Markus, 2004. "Assessing bias in the estimation of causal effects: Rosenbaum bounds on matching estimators and instrumental variables estimation with imperfect instruments," Discussion Papers, Research Unit: Labor Market Policy and Employment SP I 2004-101, Social Science Research Center Berlin (WZB).
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