Abstract
Members of the organic supply chain need high-quality data to make correct investment decisions, but data with sufficient depth and quality are not widely available in Europe. The quality of available data is a key concern for both data collectors and data users. The aim of this study is to identify whether the commonly used quality attributes (accuracy, coherence, comparability, timeliness, punctuality, accessibility, relevance), which have been developed from the perspective of data collectors, are also appropriate from the perspective of end users of organic market data. A further aim is to assess whether the data quality needs of end users are being met by the existing data. The results of two surveys carried out in Europe, one of data collectors and one of end users, are presented. Sales data at retail level (values and volumes) are used as an illustrative example and the perceptions of end users are compared with the reported data collection approaches, quality checks and availability of data. Correlation analysis and principal component analysis were used to investigate the relationship between users’ perceptions of the data quality attributes and their overall perceptions of data quality. The findings suggest that data quality checks do help to improve the quality of data as perceived by end users but that people will use whatever data they can get, even if it has poor quality. This could have potentially negative consequences, such as a lack of confidence in the organic market, if important decisions are based on poor quality data. The analysis also suggests that the commonly used attributes represent two dimensions of data quality: ‘fitness for use’ which encompasses accuracy, relevance, comparability and punctuality; and ‘convenience’, which encompasses affordability, comparability, timeliness and accessibility. The attribute of comparability belongs to both dimensions as it contributes to both fitness for use and convenience. Data collectors wishing to improve the quality of their data should focus on enhancing fitness for use first and then on the convenience of their data for users.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Eckerson WW (2002) Data quality and the bottom line; TDWI Report, The Data Warehouse Institute
Eurostat (2009) ESS Standard for quality report, published by European Communities, Luxembourg, ISBN: 978-92-79-07854-5, available online at: http://epp.eurostat.ec.europa.eu/portal/page/portal/ver-1/quality/documents/ESQR_FINAL.pdf
Eurostat (2012) Quality Assurance Framework of the European Statistical System, Version 1.1, available online at: http://epp.eurostat.ec.europa.eu/cache/ITY_PUBLIC/QAF_2012/EN/QAF_2012-EN.PDF
Fabrigar L, Wegener D, MacCallum R, Strahan E (1999) Evaluating the use of exploratory factor analysis in psychological research. Psychol Methods 4(3):272–299
Feldmann C, Hamm U (2013) 3.1 Report on collection methods: classification of data collection methods, unpublished report, OrganicDataNetwork
Gerrard CL, Vieweger A, Padel S (2012) D2.1 Report on data collectors: inventory of data collecting and publishing institutions, unpublished report, OrganicDataNetwork
Hahn, M. and Linden, H., (2007) The European statistics Code of Practice for a high quality and European Statistical System, published by European Commission, Luxembourg, available online at: http://panda.hyperlink.cz/cestapdf/pdf07c1/hahn.pdf
Hamm, U. and Zanoli, R. (2006) The need for a long-term strategy for data collection on organic markets, paper at: Joint Organic Congress, Denmark (May 30-31, 2006), available online at: http://orgprints.org/7140/
Home R (2005) A new tune from an old instrument: the application of SERVQUAL to a tourism service business, Journal of Quality Assurance in Hospitality and Tourism. Special Issue on Innovation 6(3/4):187–204
Home R, Lošták M, Stolze M (2013) Survey of the organic market data needs of end users. Unpublished report, OrganicDataNetwork
Hotelling H (1933) Analysis of a complex of statistical variables into principal components. J Educ Psychol 24:417–441, 498–520
Kaiser HF (1970) A second generation Little Jiffy. Psychometrika 35:401–415
Karr AF, Sanil AP, Banks DL (2005) Data Quality: A Statistical Perspective. NISS Technical Report Nr. 151. Research Triangle Park, NC: National Institute of Statistical Sciences
Lampkin N, Rippin M (2005) Towards a framework for European organic market data information—the EISfOM vision and summary of recommendations. Proceedings of the 2nd EISfOM seminar, Brussels, November 2005
Martínez-Lorente AR, Dewhurst F, Dale BG (1998) Total quality management: origins and evolution of the term. The TQM Magazine (Bingley, United Kingdom: MCB University Publishers Ltd) 10(5):378–386
Michelsen J, Hamm U, Wynen E, Roth E (1999) The European market for organic products: growth and development, organic farming in Europe: economics and policy vol. 7. University of Hohenheim, Stuttgart, Germany
OrganicDataNetwork (2014) Data network for better European organic market information—synthesis report. Università Politecnica delle Marche, Ancona, Italy
Recke G (2004) Quality assurance issues in DCPS for agricultural commodities: an analysis of the theoretical and analytical tools and methods used in the assessment of data quality. In: Recke G, Willer H, Lampkin N, Vaughan A (eds) Development of a European information system for organic markets—improving the scope and quality of statistical data. Proceedings of the 1st EISfOM European Seminar, Berlin, Germany, pp 26–27
Sahm, H., SandersJ., Nieberg, H., Behrens, G., Kuhnert, H., Strohm, R. and Hamm, U. (2013) Reversion from organic to conventional agriculture: A review. In: Renewable Agriculture and Food Systems, vol. 28, no. 3, p. 263-275. Online: http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=8963050&fulltextType=RV&fileId=S1742170512000117
Stolze M, Lampkin N (2009) Policy for organic farming: rationale and concepts. Food Policy 34:237–244
Webler T, Danielson S, Tuler S (2009) Using Q method to reveal social perspectives in environmental research. Social and Environmental Research Institute, Greenfield MA, Downloaded from: http://www.seri-us.org/sites/default/files/Qprimer.pdf
Zanoli R, Vairo D, Solfanelli F, Padel S, Gerrard C, Lampkin N, Willer H, Stolze M, Hamm U, Feldmann C (2014) ORganic market data MAnual and CODE of Practice (Manual and Code of Practice for the initiation and maintenance of good organic market data collection and publication procedures, OrMaCode)). The website of the OrganicDataNetwork, Università Politecnica delle Marche, Ancona (IT), and Research Institute of Organic Agriculture (FiBL), Frick (CH)
Acknowledgements
Work for this publication was undertaken as part of the research project ‘Data network for better European organic market information’ (OrganicDataNetwork). This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 289376. The opinions expressed in this contribution are those of the author and do not necessarily represent the views of the European Commission. Particular thanks go to Mr. V. Masar, who allowed us to access the database of organic market data users. We would also like to thank all of the data collectors and data users who took the time to participate in the surveys.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Home, R., Gerrard, C., Hempel, C. et al. The quality of organic market data: providing data that is both fit for use and convenient. Org. Agr. 7, 141–152 (2017). https://doi.org/10.1007/s13165-016-0147-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13165-016-0147-5