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

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Sustainability and Empowerment in the Context of Digital Libraries (ICADL 2024)

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Abstract

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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Acknowledgements

This study was funded by the National Key Research and Development Program of China, titled “Research on the Application Technology of Property Right Value Assessment and Right Marking of Cultural Products” (grant number 2021YFF0900400).

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Authors and Affiliations

  1. School of Information Management, Wuhan University, Wuhan, 430072, China

    Lihong Zhou, Jiangfeng Hu & Zengmin Wang

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  1. Lihong Zhou
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  2. Jiangfeng Hu
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  3. Zengmin Wang
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Correspondence to Lihong Zhou .

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Editors and Affiliations

  1. Monash University, Clayton, VIC, Australia

    Gillian Oliver

  2. Monash University, Clayton, VIC, Australia

    Viviane Frings-Hessami

  3. Charles Sturt University, Bathurst, NSW, Australia

    Jia Tina Du

  4. Tsukuba University, Tsukuba, Japan

    Taro Tezuka

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Zhou, L., Hu, J., Wang, Z. (2025). Unravelling Interorganisational Data Sharing Conundrum – The Good Data, The Bad Data, and The Ugly Data. In: Oliver, G., Frings-Hessami, V., Du, J.T., Tezuka, T. (eds) Sustainability and Empowerment in the Context of Digital Libraries. ICADL 2024. Lecture Notes in Computer Science, vol 15494. Springer, Singapore. https://doi.org/10.1007/978-981-96-0868-3_23

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  • DOI: https://doi.org/10.1007/978-981-96-0868-3_23

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  • Online ISBN: 978-981-96-0868-3

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