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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 of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp3755
    as

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    File URL: https://docs.iza.org/dp3755.pdf
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    References listed on IDEAS

    as
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    Cited by:

    1. Junying Lin & Zhonggen Zhang & Lingli Lv, 2019. "The Impact of Program Participation on Rural Household Income: Evidence from China’s Whole Village Poverty Alleviation Program," Sustainability, MDPI, vol. 11(6), pages 1-15, March.

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    More about this item

    Keywords

    treatment effects; propensity score matching; spam filter; spam;
    All these keywords.

    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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