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Background. Interorganisational Data Sharing (IDS) has always been considered extremely essential. Despite decades of intense research, IDS in practice is still quite problematic. This paper reports on a meta-analysis that aims to analyse and understand how data quality influences IDS. Method and Analysis. This study adopts a meta-analysis approach. A total of 69 interview transcripts gathered in two completed research projects in China were analysed using a Grounded Theory analysis approach. Results. Data quality can strongly influence IDS and can become either an enabler or a barrier. The analysis showed that good, well-structured, and well-maintained data are more likely to be required and shared through IDS, whereas bad (incomplete, unreliable, unmaintained) data is mostly unwanted by other organisations. Ugly data refers to those manipulated data that should not be shared. Conclusion. This is one of the early research projects investigating the relationship between data quality and IDS. The theoretical implications could be important and worth further exploring.

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Unravelling Interorganisational Data Sharing Conundrum – The Good Data, The Bad Data, and The Ugly Data

  • 会议论文

  • 发表日期：

    2025年

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  • Lihong Zhou,
  • Jiangfeng Hu,
  • Zengmin Wang

摘要

Background. Interorganisational Data Sharing (IDS) has always been considered extremely essential. Despite decades of intense research, IDS in practice is still quite problematic. This paper reports on a meta-analysis that aims to analyse and understand how data quality influences IDS. Method and Analysis. This study adopts a meta-analysis approach. A total of 69 interview transcripts gathered in two completed research projects in China were analysed using a Grounded Theory analysis approach. Results. Data quality can strongly influence IDS and can become either an enabler or a barrier. The analysis showed that good, well-structured, and well-maintained data are more likely to be required and shared through IDS, whereas bad (incomplete, unreliable, unmaintained) data is mostly unwanted by other organisations. Ugly data refers to those manipulated data that should not be shared. Conclusion. This is one of the early research projects investigating the relationship between data quality and IDS. The theoretical implications could be important and worth further exploring.