Modeling Quality Level of University Portal Data using Quality Function Deployment (QFD), Case Study: Amir Kabir University of Technology

Document Type : Original Article

Authors

1 PhD. Candidate, Department of Management, Central Tehran branch, Islamic Azad University, Tehran, Iran.

2 Assistant Professor, Department of Management, Central Tehran branch, Islamic Azad University, Tehran, Iran

3 Assistant Professor, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran

Abstract

Purpose: Data Quality plays a vital role in the performance and success of an organization and can be compared to oxygen for human beings. Therefore, maintaining data quality helps organizations in low operating costs and increment in revenue. If data has not been offered
on time and adequate to each department’s quality expectation, such organization may not be able to make successful decisions. First step of data quality enhancement is the acquaintance of data quality dimensions. Identifying and grouping various dimensions of data quality allow experts to use data quality enhancement tools and methods on information improvement processes.
Methodology: The present study is applied in terms of purpose and analytical-survey research. In this paper, to enhance the level of data quality of university portals, users' requirements (voices) of a university portal have been prioritized using several data quality-related questionnaires by combining Kano & AHP techniques. A designed matrix of a 4-step QFD has been formed, which started with the users' requirements and ended with design methodologies.
Findings: As a result of combining Kano & AHP techniques, basic requirements have higher priority than others. It has been defined in this paper that entering data in various formats is the most important users' requirement. Based on QFD analysis, XML & HTML are more proper and useful in comparison to other platforms and programming languages to improve the level of data quality and fulfill users' requirements. In addition, flexibility could be the most important Engineering Characteristics parameter in designing a portal. HVR software can perform a reliable and secure platform to prepare rapid and optimized data integration.
Conclusion: The Quality Function Deployment method could be an appropriate solution to solve problems regarding data quality in the condition that it consists of programming to the designing process.
 
 

Keywords


Alsaadi, M.R.; Ahmad, S.Z. & Hussain, M. (2018a). A quality function deployment strategy for improving mobile-government service quality in the Gulf cooperation council countries. Benchmarking: An International Journal, 25(8): 3276-3295. DOI: 10.1108/BIJ-12-2017-0333
Cai, L. & Zhu, Y. (2015). The Challenges of Data Quality and Data Quality Assessment in the Big Data Era. Data Science Journal, 14(2):1-10. DOI: 10.5334/dsj-2015-002
Dania, W.A.P.; Xing, K. & Amer, Y. (2019). Collaboration quality assessment for sustainable supply chains: benchmarking. Benchmarking: An International Journal. DOI: 10.1108/BIJ-03-2018-0070
Djekic, I.; Skunca, D.; Nastasijevic, I.; Tomovic, V. & Tomasevic, I. (2018). Transformation of quality aspects throughout the chicken meat supply chain. British Food Journal, 120(5): 1132-1150.
DOI: 10.1108/BFJ-08-2017-0432
Ebrahimi Nejad Rafsanjani, M. & Pouraboli, F. (2018). Prioritization of components for improving the quality of health services by combining QFD and Cardinal (Case study: Government Hospital Laboratory in Kerman). Standard and Quality Management, 26(7): 33-46. [In Persian]
Erdil, N.O. & Arani, O. (2018). Quality function deployment: more than a design tool. International Journal of Quality and Service Sciences, 11. DOI: 10.1108/IJQSS-02-2018-0008
Farokhnia, M. & Beheshtinia, M.A. (2018). A three-dimensional house: extending quality function deployment in two organizations. Management Decision, 57(7): 1589-1608.    
DOI: 10.1108/MD-06-2017-0588
Gangurde, S.R. & Patil, S.S. (2018). Benchmark product features using the Kano-QFD approach: a case study. Benchmarking: An International Journal, 25(2): 450-470. DOI: 10.1108/BIJ-08-2016-0131
Ghasemaghaei, M. & Calic, G. (2019). Can big data improve firm decision quality? The role of data quality and data diagnosticity. Decision Support Systems, 120: 38-49.
DOI: 10.1016/j.dss.2019.03.008
Ghasemaghaei, M.; Ebrahimi, S. & Hassanein, Kh. (2018). Data analytics competency for improving firm decision-making performance. The Journal of Strategic Information Systems, 27(1): 101-113. DOI: 10.1016/j.jsis.2017.10.001
Gotzamani, K.; Georgiou, A.; Andronikidis, A. & Kamvysi, K. (2018). Introducing multivariate Markov modeling within QFD to anticipate future customer preferences in product design. International Journal of Quality & Reliability Management, 35(3): 762-778.
DOI: 10.1108/IJQRM-11-2016-0205
Hassani, M.; Shahin, A. & Kheradmandnia, M. (2018). Service quality function deployment by the C-shaped QFD 3D matrix: The case of post bank services. Benchmarking: An International Journal, 25(9): 3386-3405. DOI: 10.1108/BIJ-04-2017-0065
Haug, A.; Zachariassen, F. & Van Liempd, D. (2011). The costs of poor data quality. Journal of Industrial Engineering and Management, 4(2): 168-193. DOI: 10.3926/jiem.2011.v4n2.p168-193
Hazen, B.T.; Boone, Ch.A.; Ezell, J.D. & Jones-Farmer, L.A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154: 72-80. DOI: 10.1016/j.ijpe.2014.04.018
Khani Jazani, R. & Hasanvand, M. (2018). Identify and prioritize the factors affecting user satisfaction by Kano model and quality function development (QFD) integration methods: A Case Study at a technical and vocational workshop in Tehran. Iran J Ergon, 5(4): 26-37.     
DOI: 10.30699/jergon.5.4.26. [In Persian]
Khosravi Zadeh, E. & Zohrevandian, K. (2017). Assessment service quality at the sport science department in Arak University based on integrated approach include Servqual, Kano and Quality Function Deployment. Research on Educational Sport, 5(12): 37-60. DOI: 10.22089/res.2017.938. [In Persian]
Khosroanjom, D.; Anvary Rostamy, A.; Chawshini, R. & Ahmadzade, M. (2013). Extending Fuzzy AHP Models for Evaluating Dimensions of IT Capability and Data Quality. Industrial management, 8(25): 105-116. [In Persian]
Kwon, O.; Lee, N. & Shin, B. (2014). Data quality management, data usage experience and acquisition intention of big data analytics. International Journal of Information Management, 34(3): 387-394. DOI: 10.1016/j.ijinfomgt.2014.02.002
Marson, E. & Sartor, M. (2019). Quality Function Deployment (QFD). In: Quality Management: Tools, Methods, and Standards (pp.77-90). Emerald Publishing Limited.            
DOI: 10.1108/978-1-78769-801-720191005
Maulana, S.M. & Sharifian, Sh. (2017). Improving the quality of after-sales service of banking devices by using QFD model. In: the Proceedings of the International Conference on Innovation in Business Management and Economics. Tehran: Iranian Business Excellence Association. [In Persian]
Moğol Sever, M. (2018). Improving check-in (C/I) process: an application of the quality function deployment. International Journal of Quality & Reliability Management, 35(9): 1907-1919. DOI: 10.1108/IJQRM-03-2017-0043
Mo'meni, M. & Mormazi, H. (2007). Improving the quality of financial services by using QFD and AHP. Accounting and Auditing Reviews (formerly Accounting and Auditing),14(2): 105-124.
[In Persian]
Moossavizadeh, M.H.; Mohsenzadeh, M. & Arshadi, N. (2012). A new approach to measure believability dimension of data quality. Management Science Letters, 2: 2565-2570.
DOI: 10.5267/j.msl.2012.07.007
Pezoulas, V.C. & et al. (2019). Medical data quality assessment: On the development of an automated framework for medical data curation. Computers in Biology and Medicine, 107: 270-283.        
DOI: 10.1016/j.compbiomed.2019.03.001
Qandehari, M. & Salehzadeh, R. (2013). Improving service quality using a combined QFD and Kano model (with case study). In: Proceedings of the Second International Conference on Management, Entrepreneurship and Economic Development. Qom: Payam-e-Noor University. [In Persian]
Radmard, S.; Kiani Khoozestani, H. & Tajdaran, M. (2015). Application of Quality Function Deployment (QFD) to improve reference services quality in the Alzahra University Central Library. Journal of Academic librarianship and Information Research, 49(3): 377-393.  
DOI: 10.22059/jlib.2015.58106. [In Persian]
Shahin, A.; Salehzadeh, R. & Ghandehary, M. (2011). Proposing an Integrated Model of Clustring and QFD Approaches to Improve Quality of Services Based on Customer Type with a Case Study in Saman Bank of Qom. Industrial Management, (16): 103-118. [In Persian]
Sharif Nejad, A.; Parvaresh Rizi, A. & Poorzand, A. (2013). QFD, an Implement for Improving Management and Service Provision in Irrigation and Drainage Networks (Case Study: Ghazvin Irrigation District). Iranian Journal of Soil and Water Research,44(1): 45-56.            
DOI: 10.22059/ijswr.2013.36124. [In Persian]
Singh, A.K. & Rawani, A.M. (2019). Application of quality function deployment for the prioritization of National Board of Accreditation quality parameters. Quality Assurance in Education, 27(1): 127-139. DOI: 10.1108/QAE-11-2017-0078
Vanany, I.; Maarif, Gh.A. & Soon, J.M. (2018). Application of multi-based quality function deployment (QFD) model to improve halal meat industry. Journal of Islamic Marketing, 10(1): 97-124. DOI: 10.1108/JIMA-10-2017-0119
Warth, J.; Kaiser, G. & Kügler, M. (2011). The impact of data quality and analytical capabilities on planning performance: insights from the automotive industry. In: Wirtschaftsinformatik Proceedings.
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