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

Author

Listed:
  • 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.

Suggested Citation

  • Caliendo, Marco & Clement, Michel & Papies, Dominik & Scheel-Kopeinig, Sabine, 2008. "The Cost Impact of Spam Filters: Measuring the Effect of Information System Technologies in Organizations," IZA Discussion Papers 3755, Institute for the Study of Labor (IZA).
  • Handle: RePEc:iza:izadps:dp3755
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    References listed on IDEAS

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    1. Falkinger, Josef, 2007. "Attention economies," Journal of Economic Theory, Elsevier, vol. 133(1), pages 266-294, March.
    2. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
    3. Glenn W. Harrison & John A. List, 2004. "Field Experiments," Journal of Economic Literature, American Economic Association, vol. 42(4), pages 1009-1055, December.
    4. Seung-Hoon Yoo & Chul-Oh Shin & Seung-Jun Kwak, 2006. "Inconvenience cost of spam mail: a contingent valuation study," Applied Economics Letters, Taylor & Francis Journals, vol. 13(14), pages 933-936.
    5. Becker, Sascha O. & Caliendo, Marco, 2007. "mhbounds – Sensitivity Analysis for Average Treatment Effects," IZA Discussion Papers 2542, Institute for the Study of Labor (IZA).
    6. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 31-72, February.
    7. 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.
    8. 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, WZB Berlin Social Science Center.
    9. 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.
    10. Heckman, James J. & Lalonde, Robert J. & Smith, Jeffrey A., 1999. "The economics and econometrics of active labor market programs," Handbook of Labor Economics,in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 31, pages 1865-2097 Elsevier.
    11. Michael Lechner, 2002. "Some practical issues in the evaluation of heterogeneous labour market programmes by matching methods," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(1), pages 59-82.
    12. Becker, Sascha O. & Caliendo, Marco, 2007. "Sensitivity analysis for average treatment effects," Stata Journal, StataCorp LP, vol. 0(Number 1), pages 1-13.
    13. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
    14. 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.
    15. Oecd, 2005. "Spam Issues in Developing Countries," OECD Digital Economy Papers 99, OECD Publishing.
    16. Robert Kraut & Shyam Sunder & Rahul Telang & James Morris, 2005. "Pricing Electronic Mail to Solve the Problem of Spam," Yale School of Management Working Papers amz2638, Yale School of Management, revised 01 Oct 2005.
    17. A. D. Roy, 1951. "Some Thoughts On The Distribution Of Earnings," Oxford Economic Papers, Oxford University Press, vol. 3(2), pages 135-146.
    18. repec:eee:joinma:v:22:y:2008:i:1:p:21-35 is not listed on IDEAS
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    More about this item

    Keywords

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

    JEL classification:

    • M12 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Personnel Management; Executives; Executive Compensation
    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management

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