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Evaluating perceived and estimated data quality for Web 2.0 applications: a gap analysis

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Abstract

To increase user satisfaction and enhance a positive image, the quality of software needs to be continuously improved. This study empirically investigates the importance of 15 quality characteristics and evaluates how well the Web 2.0 applications perform on those characteristics from a data quality perspective. Based on questionnaire responses from 279 participants and the results of importance–performance analysis, the performance of all data quality characteristics was found to be below the end user expectation. Confidentiality showed the greatest discrepancy between importance and performance.

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References

  1. Abalo, P. J., Varela, M. J., & Rial, B. A. (2006). Importance-performance analysis for services management. Psicothema, 18(4), 730–737.

  2. Alton, Y. K., & Goh, D. H. (2010). A study of Web 2.0 applications in library websites. Library & Information Science Research, 32(3), 203–211.

  3. Armstrong, J., & Overton, T. (1997). Estimating non-response bias in mail survey. Journal of Marketing Research, 15(2), 396–402.

  4. Bacon, D. R. (2003). A comparison of approaches to importance-performance analysis. International Journal of Market Research, 45(1), 55–71.

  5. Barney, S., Petersen, K., Svahnberg, M., Aurum, A., & Barney, H. (2012). Software quality trade-offs: a systematic map. Information and Software Technology, 54(7), 651–662.

  6. Behkamal, B., Kahani, M., & Akbari, M. K. (2009). Customizing ISO 9126 quality model for evaluation of B2B applications. Information and Software Technology, 51(3), 599–609.

  7. Boehm, B. W., Brown, J. R., Kaspar, J. R., Lipow, M. L., & MacCleod, G. (1978). Characteristics of software quality. New York: North-Holland Publishing Company.

  8. Caro, A., Calero, C., Caballero, I., & Piattini, M. (2008). A proposal for a set of attributes relevant for Web portal data quality. Software Quality Journal, 16(4), 513–542.

  9. Caro, A., Calero, C., & Moraga, M. A. (2011). Are web visibility and data quality related concepts? IEEE Internet Computing, 15(2), 43–49.

  10. Chu, R. K. S., & Choi, T. (2000). An importance-performance analysis of hotel selection factors in the Hong Kong hotel industry: a comparison of business and leisure travellers. Tourism Management, 21(4), 363–377.

  11. Crawford, S. D., Couper, M. P., & Lamias, M. J. (2001). Web surveys: perception of burden. Social Science Computer Review, 19(2), 146–162.

  12. Desharnais, J.-M., Abran, A., & Suryn, W. (2011). Identification and analysis of attributes and base measures within ISO 9126. Software Quality Journal, 19(2), 447–460.

  13. Dillman D.A., Tortora, R.D. & Bowker, D. (1998). Influence of plain vs. fancy design on response rates for Web surveys. Joint Statistical Meeting of the American Statistical Association.

  14. Dromey, R. G. (1995). A model for software product quality. IEEE Transactions on Software Engineering, 21(2), 146–162.

  15. Eckerson, W.W. (2002). Data quality and the bottom line: achieving business success through a commitment to high quality data. The Data Warehousing Institute, http://download.101com.com/pub/tdwi/Files/DQReport.pdf.

  16. Gillette, F., (2011). The rise and inglorious fall of myspace, Bloomberg businessweek, http://www.bloomberg.com/news/articles/2011-06-22/the-rise-and-inglorious-fall-of-myspace.

  17. Gousios, G., Karakoidas, V., Stroggylos, K., Louridas, P., Vlachos, V. & Spinellis, D. (2007). Software quality assessment of open source software, Proceedings of the 11th Panhellenic Conference on Informatics.

  18. Grady, R. B., & Caswell, D. L. (1987). Software metrics: establishing a company-wide program. New Jersey: Prentice-Hall, Inc. Upper Saddle River.

  19. ISO/IEC 14598 (1999). Information technology—software product evaluation -- Part 1: general overview.

  20. ISO/IEC 25000 (2014). Software engineering–software product quality requirements and evaluation (SQuaRE)—guide to SQuaRE.

  21. ISO/IEC 25012 (2008). Software engineering—software product quality requirements and evaluation (SQuaRE)—data quality model.

  22. ISO/IEC 25022 (2016). Systems and software engineering—systems and software quality requirements and evaluation (SQuaRE)—measurement of quality in use.

  23. ISO/IEC 25023 (2016). Systems and software engineering—systems and software quality requirements and evaluation (SQuaRE)—measurement of system and software product quality.

  24. ISO/IEC 9126 (2001). Software engineering—product quality—Part 1: quality model.

  25. Jones, C., Subramanyam, J., & Bonsignour, O. (2011). The economics of software quality. Boston: Addison-Wesley.

  26. Juran, J. M. (1988). Juran on planning for quality. New York: Free Press.

  27. Kannabiran, G., & Sankaran, K. (2011). Determinants of software quality in offshore development—an empirical study of an Indian vendor. Information and Software Technology, 53(11), 1199–1208.

  28. Kawamura, T., & Takano, K. (2014). Factors affecting project performance of IS development: evidence from Japanese IT vendors. Journal of Information Processing, 22(4), 689–700.

  29. Kim, D. J., Yue, K., Hall, S. P., & Gates, T. (2009). Global diffusion of the Internet XV: Web 2.0 technologies, principles, and applications: a conceptual framework from technology push and demand pull perspective. Communications of AIS, 24(1), 657–672.

  30. Kitchenham, B., & Pfleeger, S. L. (1996). Software quality: The elusive target. IEEE Software, 13(1), 12–21.

  31. Kitchenham, B., & Walker, J. (1989). A quantitative approach to monitoring software development. Software Engineering Journal, 4(1), 1–13.

  32. Liang, S. K., & Lien, C. T. (2007). Selecting the optimal ERP software by combining the ISO 9126 standard and fuzzy AHP approach. Contemporary Management Research, 3(1), 23–44.

  33. Martilla, J. A., & James, J. C. (1977). Importance-performance analysis. Journal of Marketing, 10(1), 13–22.

  34. McCall, J. A., Richards, P. K., & Walters, G. F. (1977). Factors in software quality. Griffiths Air Force Base: Rome Air Development Center Air Force Systems Command.

  35. Moraga, C., Moraga, M.A., Caro, A., Calero, C. (2009). SQuaRE-aligned data quality model for web portals. The 9th International Conference on Quality Software, 117–122.

  36. Murdy, S., & Pike, S. (2012). Perceptions of visitor relationship marketing opportunities by destination marketers: an importance-performance analysis. Tourism Management, 33(5), 1281–1285.

  37. Orehovački, T., Granić, A., & Kermek, D. (2013). Evaluating the perceived and estimated quality in use of Web 2.0 applications. Journal of Systems and Software, 86(12), 3039–3059.

  38. Pedram, H., Moghaddam, D. K., & Asheghi, Z. (2012). Applying the ISO 9126 model to the evaluation of an E-learning system in Iran. Information Sciences and Technology, 27(2), 496–518.

  39. Rivera, B., et al. (2016). Quality views and strategy patterns for evaluating and improving quality: usability and user experience case studies. Journal of Web Engineering, 15(5–6), 433–464.

  40. Sigala, M., Christou, E., & Gretzel, U. (2012). Investigating the exploitation of Web 2.0 for knowledge management in the Greek tourism industry: an utilisation–importance analysis. Computers in Human Behavior, 30, 800–812.

  41. Standish Group (2013). CHAOS Manifesto 2013, The Standish Group International.

  42. Tsai, W. H., Hsu, W., & Chou, W. C. (2011). A gap analysis model for improving airport service quality. Total Quality Management & Business Excellence, 22(10), 1025–1040.

  43. Westland, J. C. (2002). The cost of errors in software development: evidence from industry. Journal of Systems and Software, 62(1), 1–9.

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Correspondence to Wen-Ming Han.

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Han, W. Evaluating perceived and estimated data quality for Web 2.0 applications: a gap analysis. Software Qual J 26, 367–383 (2018). https://doi.org/10.1007/s11219-017-9365-7

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Keywords

  • Software quality
  • Data quality gap
  • Software quality management
  • ISO/IEC 25012
  • SQuaRE