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Customer disclosures, impression management and the construction of legitimacy

Author

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  • Stuart Ogden
  • Julia Clarke

Abstract

Purpose - This paper aims to explore how organizations use annual reporting for legitimacy purposes in the context of the ten recently privatised regional water companies in the UK. Although privatization required the water plcs to establish a distinctly different organizational legitimacy for themselves as customer‐focused companies commensurate with their new private sector status, it was clear from the nature of their privatization that they would experience difficulties in achieving this. Privatization did little to change their previous monopoly character, and this created discrepancy with the model of private sector companies operating in customer‐led competitive markets. Design/methodology/approach - The paper uses a content analysis of statements concerning customer service in annual reports. The analysis examines the variety of ways in which the ten water plcs deployed both assertive and defensive impression management techniques in their attempts to gain, maintain and repair their legitimacy as customer‐focused companies. Findings - The analysis emphasises the importance of the role of corporate reporting as a resource in legitimacy management. The paper also argues that, despite sustained efforts, the water plcs did not wholly succeed in persuading all their customers that the privatization of water was “a good thing”. Originality/value - The paper will be valuable to researchers and practitioners alike, as it attempts to take further one's understanding of how organizations use corporate reporting for legitimacy purposes by examining a much more extreme case of the legitimacy problem than has been previously considered in the literature: namely, the need for an entirely new basis for corporate legitimacy.

Suggested Citation

  • Stuart Ogden & Julia Clarke, 2005. "Customer disclosures, impression management and the construction of legitimacy," Accounting, Auditing & Accountability Journal, Emerald Group Publishing Limited, vol. 18(3), pages 313-345, June.
  • Handle: RePEc:eme:aaajpp:09513570510600729
    DOI: 10.1108/09513570510600729
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    Cited by:

    1. Berkin, Anil & Aerts, Walter & Van Caneghem, Tom, 2023. "Feasibility analysis of machine learning for performance-related attributional statements," International Journal of Accounting Information Systems, Elsevier, vol. 48(C).

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