Journal of Revenue and Pricing Management

, Volume 18, Issue 3, pp 256–265 | Cite as

Towards improved understanding of success criteria for telecoms billing & revenue management systems: from implementation to practical value

  • Akaret TangsuwanEmail author
  • Paul Mason
Research Article


Billing and Revenue Management Systems (BRMS) represent a key enterprise application across the entire telecommunications industry. However, their inherent complexity makes them notoriously difficult to implement, meaning projects often either end in complete failure, or arrive late and overshoot budgetary costs. A clear understanding of the factors by which implantation success is measured will reduce the likelihood of these negative outcomes. This study therefore has two objectives: first to empirically validate an established conceptual model for IS success (developed by DeLone and McLean) in a BRMS context; specifically, we applied structural equation modelling techniques to validate the model using data obtained from key informants from several telecom service providers. We then prioritize our technical findings from the study, with a view to more targeted use of billing and revenue resources and equally important, consider the potential role of BRMS in driving pricing policy and dynamic product offerings. The novelty of our work lies in the adaptation of an existing model for predicting and measuring IS success at organizational level and the ensuing benefits accruing to companies beyond mere use of BRMS as a tool for managing accounts receivable.


Enterprise system success Billing and revenue management system DeLone and McLean IS Success Model System success in telecom Telecom billing systems 


  1. Avienzis, A., J.C. Laprie, B. Randall, and C. Landwehr. 2004. Basic Concepts & Taxonomy of Dependable & Secure Computing. IEEE Transactions on Dependable and Secure Computing 1 (1): 11–33.CrossRefGoogle Scholar
  2. Avison, D., and D. Wilson. 2002. IT Failure and the Collapse of One.Tel. In Information Systems: The e-Business Challenge, ed. R. Traunmuller, 31–46. New York: Springer.CrossRefGoogle Scholar
  3. Cadzow, J. 2001. That Rich Bloke. The Australian, 4 August.Google Scholar
  4. Chin, W.W. 1998. The Partial Least Squares Approach to Structural Equation Modelling. In Methodology for Business and Management. Modern Methods for Business Research, ed. G.A. Marcoulides, 295–336. New Jersey: Lawrence Erlbaum Associates Publishers.Google Scholar
  5. DeLone, W.H., and E.R. McLean. 2003. The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. Journal of Management Information Systems 19 (4): 9–30.CrossRefGoogle Scholar
  6. Fornell, C., and D.F. Larcker. 1981. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research 18 (1): 39–50.CrossRefGoogle Scholar
  7. Goldman, L. 2012. Telecoms Software: Software Strategies that Impact Operator Business Results. Accessed 16 Jan 2018.
  8. Hair, J.F., G.T. Hult, M. Sarstedt, and C. Ringle. 2013. A primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 2nd ed. California: Sage Publication.Google Scholar
  9. He, W., and L. Da Xu. 2014. Integration of Distributed Enterprise Applications: A Survey. IEEE Transactions on Industrial Informatics 10 (1): 35–42.CrossRefGoogle Scholar
  10. Hunter, J.M., and M.E. Thiebaud. 2003. Telecommunications Billing Systems: Implementing and Upgrading for Profitability. New York: McGraw-Hill.Google Scholar
  11. Jahanzeb, S., and S. Jabeen. 2007. Churn Management in the Telecom Industry of Pakistan: A Comparative Study of Ufone and Telenor. Journal of Database Marketing & Customer Strategy Management 14 (2): 120–129.CrossRefGoogle Scholar
  12. Lin, Q. 2007. Mobile Customer Clustering Analysis Based on Call Detail Records. Communications of the IIMA 7 (4): 95–100.Google Scholar
  13. Little, A.D. 2017. Reimagining Telco Operations in a Hyper-Digital World. Accessed 30 July 2018.
  14. Lowry, P.B., G.G. Karuga, and V.J. Richardson. 2007. Assessing Leading Institutions, Faculty, and Articles in Premier Information Systems Research Journals. Communications of the Association for Information Systems 20 (16): 142–203.Google Scholar
  15. Martinez-Ruiz, A., and T. Aluja-Banet. 2009. Toward the Definition of a Structural Equation Model of Patent Value: PLS Path Modelling with Formative Constructs. REVSTAT Statistical Journal 7 (3): 265–290.Google Scholar
  16. McKinney, V., Y. Kanghyun, and F.M. Zahedi. 2002. The Measurement of Web-customer Satisfaction: An Expectation and Disconfirmation Approach. Information Systems Research 13 (3): 296–315.CrossRefGoogle Scholar
  17. Niebuhr, J., Späne, A., Schröder, G., and Gröne, F. 2010. Evolution or Revolution? Strategies for Telecom Billing Transformation. Accessed 12 Oct 2017.
  18. Park, K.W., J. Jaesun Han, J.W. Chung, and K.H. Park. 2013. A Mutually Verifiable Billing System for the Cloud Computing Environment. IEEE Transactions on Services Computing 6 (3): 300–313.CrossRefGoogle Scholar
  19. ORACLE. 2015. Communications Billing and Revenue Management Concepts. Oracle Communications Billing and Revenue Management Documentation, Release 7.5. Accessed 2 Oct 2017.
  20. Rovinelli, R.J., and R.K. Hambleton. 1977. On the Use of Content Specialists in the Assessment of Criterion-Referenced Test Item Validity. Dutch Journal of Educational Research 2: 49–60.Google Scholar
  21. Solis, B. 2018. In Pursuit of Relevance. Digital Transformation, 11 April. Accessed 9 July 2018.
  22. Tam, M.C.Y., and V.M.R. Tummala. 2001. An Application of the AHP in Vendor Selection of a Telecommunications Systems. Omega International Journal of Management Science 29: 171–182.CrossRefGoogle Scholar
  23. Tangsuwan, A., and Mason, P. 2017. Key Success Measures for Billing and Revenue Management System in Thailand. Paper Presented at IEEE 3rd International Conference on Engineering, Technologies & Social Sciences (ICETSS 2017), 07–08 August, Bangkok, Thailand.Google Scholar
  24. TM Forum. 2014. Frameworx Best Practice: Business Metrics Scorecard (BMS): Business Metrics Solution Suite 21. New Jersey: TM Forum office.Google Scholar
  25. Venkatesh, V., M.G. Morris, G.B. Davis, and F.D. Davis. 2003. User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly 27 (3): 425–478.CrossRefGoogle Scholar
  26. Westland, J.C. 2010. Lower Bounds on Sample in Structural Equation Modeling. Electronic Commerce Research and Applications 9 (6): 476–487.CrossRefGoogle Scholar

Copyright information

© Springer Nature Limited 2018

Authors and Affiliations

  1. 1.Netcracker TechnologyBangkhenThailand
  2. 2.School of Science & TechnologyShinawatra UniversityPathumthaniThailand

Personalised recommendations