Extending Fuzzy AHP Models for Evaluating Dimensions of IT Capability and Data Quality
Subject Areas : Industrial Management
Davod
Khosroanjom
1
(Corresponding Author, Master of Information Technology Management
Islamic Azad University (IAU), Piranshahr Branch, Piranshahr, Iran)
Ali Asghar
Anvary Rostamy
2
(Assistant Professor, Tarbiat Modares University)
Rasoul
Chawshini
3
(M.A in MBA)
Masoud
Ahmadzade
4
(M.A in Industrial Management)
Keywords:
Abstract :
Identifying appropriate decision-making methods shapes one of major concern of Information technology managers. Fuzzy analytical hierarchy process (FAHP) can be a good option in this field. Developing FAHP model, in this research 2 Information technology management capability criterion options is evaluated and reported, including information technology capabilities and Data quality. In this study Polling Expert opinion on options and criteria weights, shapes inputs of the model. According to obtained results, Fuzzy AHP is applicable in such decisions and is an appropriate apparatus. Findings indicate that human resource is the most important organizational information technology capability. Also, intrinsic criteria of data are the most critical dimension of Data quality.
1- Anany, L., & Thomas, C. (1998). Data as a resource: Properties, implications, and prescriptions. Sloan Management Review, Cambridge 40(1), 89-101.
2- Barney, J. (1991). Firm Resource and Sustained Competitive Advantage. Journal of Management 17(1), 99-120.
3- Bassellier, G., & B. Horner, & Benbasat, I. (2001). Information Technology Competence of Business Managers: A Definition and Research Model. Journal of Management Information Systems 17(4), 159-182.
4- Bharadwaj, A.S. (2000). Resource-Based Perspective on Information Technology Capability and Form Performance: An Empirical Investigation. MIS Quarterly, 24(1), 169-196.
5- Bhatt, G.D., & Grover, V. (2005). Types of Information Technology Capabilities and Their Role in Competitive Advantage. Journal of Management Information Systems 22(2), 253-277.
6- Broadbent, M., & Weill, P. (1997). Management by Maxim: How Business and IT Managers Can Create IT Infrastructures. Sloan Management Review, 38(3), 77-92.
7- Chang, D-Y. (1992). Extent Analysis and Synthetic Decision. Optimization Techniques and Applications, 1, 352-361.
8- Chang, D.-Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649-55.
9- Chao, H. P. Fu., & Chang P. T. H. (2008). The impact of market freedom on the adoption of third-party electronic marketplaces: A fuzzy AHP analysis. Industrial Marketing Management, 37, 698-712.
10- Deming, W. E. (1982). Quality, Productivity, and Competitive Position. Cambridge, MA: MIT Center for Advanced Engineering Study.
11- English, L. P. (1999). Improving data warehouse and business information quality: methods for reducing costs and increasing profits. John Wiley & Sons, Inc., New York, NY, USA.
12- Henderson, R., & Cockburn, I. (1994). Measuring Competence? Exploring Firm Effects in Pharmaceuticals Research. Strategic Management Journal, 15, 63-84.
13- Jerzy, M., & Mei-Chen, L. (2009). The assessment of the information quality with the aid of multiple criteria analysis. European Journal of Operational Research, 195, 850-856.
14- Kemer, K.M., & Nelson., & Cooprider, J.G. (1996). The Contribution of Shared Knowledge to IS Group Performance. MIS Quarterly, 20(4), 409- 429.
15- Ken, O. (1998). Data quality and systems theory, Association for Computing Machinery. Communications of the ACM, New York, 41(2), 66-71.
16- Kumar T.G,. & Donald, B. P. (1998). Examining data quality, Association for Computing Machinery. Communications of the ACM, New York, 41(2), 54-57.
17- Laarhoven, V. P. (1983). A fuzzy extension of Saaty’s priority theory. Fuzzy Sets and Systems, 11(3), 229-41.
18- Mata. F.J., & Furest, W.L., & Barney, J.B. (1995). Information Technology and Sustained Competitive Advantage: A Resource-Based Analysis. MIS Quarterly, 19(4), 487-504.
19- Mooney, J.G., & Gurbaxani, V. (1995). A Process Oriented Framework for Assessing the Business Value Information Technology. In Proceedings of the 16 Th International Conference on Information Systems.J.I. DeGross, G.Ariav, C.Beath, R.Hoyer, and C. (EDs.), Amsterdam, The Netherland, 1995, 17-27.
20- Omar, Kh. &Talha, H. D. (1999). Relationship marketing and data quality management, S.A.M. Advanced Management Journal; Cincinnati, 64(2), 26-33.
21- Perçin. S. (2008). Use of fuzzy AHP for evaluating the benefits of information-sharing decisions in supply chain. Journal of Enterprise Information Management 21(3), 263-284.
22- Redman, T. (1996). Data Quality in the Information Age. Artech House, Boston.
23- Reichheld, F., & Sasser, W.E. (1990). Zero Defections: Quality Comes to Services. Hardware Business Review, 68(5), 105-111.
24- Rockart, J.F. (1988). The Line Takes the Leadership IS Management in a Wired Society. Sloan Management Review, 29(4), 55-64.
25- Rust, R.T., & Zethaml, V.A., & Lemon, K.N. (2000). Driving Customer Equity: How Customer Life-time Value Is Reshaping Corporate Sterategy.Free Press, New York.
26- Shewhart, W. A. (2007). The Application of Statistics as an Aid in Maintaining Quality of a Manufactured Product. Journal of the American Statistical Association, 20(3), 546-548.
27- Simon, H. A. (1959). Theories of decision-making in economies and behavioral science. American economic review, 49(4), 83-253.
28- Szymanski, D.M., &Henard, D.H. (2001). Customer Satisfaction: A Meta-Analysis of the Empirical Evidence. Journal of the Academy of Marketing Science, 29(1), 16-35.
29- Thomas, C.R. (2008). The impact of poor data quality on the typical enterprise, Association for Computing Machinery. Communications of the ACM, New York 41(2), 79-82.
30- Tippins, M.J., &Sohi, R.S. (2003). IT Competency and Firm Performance: Is Organizational Learning a Missing Link?. Strategic Management Journal, 24(8), 745-761.
31- Treacy, M., &F.Wierseman. (1995). The Discipline of Market Leaders. Addison Wesley, Reading, MA.
32- Zeithaml, V.A. (2000). Service Quality, Profitability, and Economic Worth of Customer: What We Know and What We Need to Learn. Journal of the Academy of Marketing Science, 28(1), 67-86.
33- Zhu, K.J., & Jing, Y. & Chang, D.Y. (1999). A discussion on extent analysis method and applications of fuzzy AHP. European Journal of Operational Research, 116(2), 450-466.
34- Wang, R.Y., & Strong, D.M. (1996). Beyond accuracy: What data quality means to data consumers?. Journal of Management Information Systems, 12(4), 79-88.
_||_1- Anany, L., & Thomas, C. (1998). Data as a resource: Properties, implications, and prescriptions. Sloan Management Review, Cambridge 40(1), 89-101.
2- Barney, J. (1991). Firm Resource and Sustained Competitive Advantage. Journal of Management 17(1), 99-120.
3- Bassellier, G., & B. Horner, & Benbasat, I. (2001). Information Technology Competence of Business Managers: A Definition and Research Model. Journal of Management Information Systems 17(4), 159-182.
4- Bharadwaj, A.S. (2000). Resource-Based Perspective on Information Technology Capability and Form Performance: An Empirical Investigation. MIS Quarterly, 24(1), 169-196.
5- Bhatt, G.D., & Grover, V. (2005). Types of Information Technology Capabilities and Their Role in Competitive Advantage. Journal of Management Information Systems 22(2), 253-277.
6- Broadbent, M., & Weill, P. (1997). Management by Maxim: How Business and IT Managers Can Create IT Infrastructures. Sloan Management Review, 38(3), 77-92.
7- Chang, D-Y. (1992). Extent Analysis and Synthetic Decision. Optimization Techniques and Applications, 1, 352-361.
8- Chang, D.-Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649-55.
9- Chao, H. P. Fu., & Chang P. T. H. (2008). The impact of market freedom on the adoption of third-party electronic marketplaces: A fuzzy AHP analysis. Industrial Marketing Management, 37, 698-712.
10- Deming, W. E. (1982). Quality, Productivity, and Competitive Position. Cambridge, MA: MIT Center for Advanced Engineering Study.
11- English, L. P. (1999). Improving data warehouse and business information quality: methods for reducing costs and increasing profits. John Wiley & Sons, Inc., New York, NY, USA.
12- Henderson, R., & Cockburn, I. (1994). Measuring Competence? Exploring Firm Effects in Pharmaceuticals Research. Strategic Management Journal, 15, 63-84.
13- Jerzy, M., & Mei-Chen, L. (2009). The assessment of the information quality with the aid of multiple criteria analysis. European Journal of Operational Research, 195, 850-856.
14- Kemer, K.M., & Nelson., & Cooprider, J.G. (1996). The Contribution of Shared Knowledge to IS Group Performance. MIS Quarterly, 20(4), 409- 429.
15- Ken, O. (1998). Data quality and systems theory, Association for Computing Machinery. Communications of the ACM, New York, 41(2), 66-71.
16- Kumar T.G,. & Donald, B. P. (1998). Examining data quality, Association for Computing Machinery. Communications of the ACM, New York, 41(2), 54-57.
17- Laarhoven, V. P. (1983). A fuzzy extension of Saaty’s priority theory. Fuzzy Sets and Systems, 11(3), 229-41.
18- Mata. F.J., & Furest, W.L., & Barney, J.B. (1995). Information Technology and Sustained Competitive Advantage: A Resource-Based Analysis. MIS Quarterly, 19(4), 487-504.
19- Mooney, J.G., & Gurbaxani, V. (1995). A Process Oriented Framework for Assessing the Business Value Information Technology. In Proceedings of the 16 Th International Conference on Information Systems.J.I. DeGross, G.Ariav, C.Beath, R.Hoyer, and C. (EDs.), Amsterdam, The Netherland, 1995, 17-27.
20- Omar, Kh. &Talha, H. D. (1999). Relationship marketing and data quality management, S.A.M. Advanced Management Journal; Cincinnati, 64(2), 26-33.
21- Perçin. S. (2008). Use of fuzzy AHP for evaluating the benefits of information-sharing decisions in supply chain. Journal of Enterprise Information Management 21(3), 263-284.
22- Redman, T. (1996). Data Quality in the Information Age. Artech House, Boston.
23- Reichheld, F., & Sasser, W.E. (1990). Zero Defections: Quality Comes to Services. Hardware Business Review, 68(5), 105-111.
24- Rockart, J.F. (1988). The Line Takes the Leadership IS Management in a Wired Society. Sloan Management Review, 29(4), 55-64.
25- Rust, R.T., & Zethaml, V.A., & Lemon, K.N. (2000). Driving Customer Equity: How Customer Life-time Value Is Reshaping Corporate Sterategy.Free Press, New York.
26- Shewhart, W. A. (2007). The Application of Statistics as an Aid in Maintaining Quality of a Manufactured Product. Journal of the American Statistical Association, 20(3), 546-548.
27- Simon, H. A. (1959). Theories of decision-making in economies and behavioral science. American economic review, 49(4), 83-253.
28- Szymanski, D.M., &Henard, D.H. (2001). Customer Satisfaction: A Meta-Analysis of the Empirical Evidence. Journal of the Academy of Marketing Science, 29(1), 16-35.
29- Thomas, C.R. (2008). The impact of poor data quality on the typical enterprise, Association for Computing Machinery. Communications of the ACM, New York 41(2), 79-82.
30- Tippins, M.J., &Sohi, R.S. (2003). IT Competency and Firm Performance: Is Organizational Learning a Missing Link?. Strategic Management Journal, 24(8), 745-761.
31- Treacy, M., &F.Wierseman. (1995). The Discipline of Market Leaders. Addison Wesley, Reading, MA.
32- Zeithaml, V.A. (2000). Service Quality, Profitability, and Economic Worth of Customer: What We Know and What We Need to Learn. Journal of the Academy of Marketing Science, 28(1), 67-86.
33- Zhu, K.J., & Jing, Y. & Chang, D.Y. (1999). A discussion on extent analysis method and applications of fuzzy AHP. European Journal of Operational Research, 116(2), 450-466.
34- Wang, R.Y., & Strong, D.M. (1996). Beyond accuracy: What data quality means to data consumers?. Journal of Management Information Systems, 12(4), 79-88.