Information Technology and Management

, Volume 14, Issue 4, pp 295–314 | Cite as

Investigating the success of operational business process management systems



Business process management systems (BPMS) belong to a class of enterprise information systems that are characterized by the dependence on explicitly modeled process logic. Through the process logic, it is relatively easy to manage explicitly the routing and allocation of work items along a business process through the system. Inspired by the DeLone and McLean framework, we theorize that these process-aware system features are important attributes of system quality, which in turn will elevate key user evaluations such as perceived usefulness, and usage satisfaction. We examine this theoretical model using data collected from four different, mostly mature BPM system projects. Our findings validate the importance of input quality as well as allocation and routing attributes as antecedents of system quality, which, in turn, determines both usefulness and satisfaction with the system. We further demonstrate how service quality and workflow dependency are significant precursors to perceived usefulness. Our results suggest the appropriateness of a multi-dimensional conception of system quality for future research, and provide important design-oriented advice for the design and configuration of BPMSs.


BPM Workflow management Information systems success Technology adoption Field study Delone and Mclean 


  1. 1.
    Agostini A, De Michelis G (2000) A light workflow management system using simple process definitions. Comput Support Coop Work 9(3/4):335–363CrossRefGoogle Scholar
  2. 2.
    Bowers J, Button G, Sharrock W (1995) Workflow from within and without: technology and cooperative work on the print industry shopfloor. In Marmolin H, Sundblad Y, Schmidt K, (eds) Proceedings of the Fourth European Conference on Computer-Supported Cooperative Work, Kluwer Academic Publishers Norwell, MA, USA, pp 51–66Google Scholar
  3. 3.
    Burton-Jones A, Gallivan MJ (2007) Toward a deeper understanding of system usage in organizations: a multilevel perspective. MIS Q 31(4):657–679Google Scholar
  4. 4.
    Casati F, Ceri S, Pernici B, Pozzi G (1998) Workflow Evolution. Data Knowl Eng 24(3):211–238CrossRefGoogle Scholar
  5. 5.
    Centefelli RT, Schwarz A (2011) Identifying and testing the inhibitors of technology usage intentions. Inf Syst Res 22(4):808–823CrossRefGoogle Scholar
  6. 6.
    Chin W (1998) Issues and opinion on structural equation modelling. MIS Q 22(1):7–16Google Scholar
  7. 7.
    Cugola G (1998) Tolerating deviations in process support systems via flexible enactment of process models. IEEE Trans Softw Eng 24(11):982–1001CrossRefGoogle Scholar
  8. 8.
    Davenport TH (1993) Process innovation: reengineering work through information technology. Harvard Business School Press, BostonGoogle Scholar
  9. 9.
    Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319–340CrossRefGoogle Scholar
  10. 10.
    Delone W, Mclean ER (2004) Measuring e-Commerce a success: applying the DeLone & McLean information systems success model. Int J Electron Commer 9(1):31–47Google Scholar
  11. 11.
    Delone W, Mclean ER (2003) The DeLone and McLean model of information systems success: a ten-year update. J Manag Inf Syst 19(4):9–30Google Scholar
  12. 12.
    De Waal B, Batenburg R (2009) Do users go with the new workflow? From user participation to quality of work during WFM deployment. In Alexander T, Turpin M, Van Deventer JP (eds.) Proceedings of the 17th European Conference on Information Systems, Verona, Italy, 1–13Google Scholar
  13. 13.
    Diamantopoulos A, Siguaw JA (2006) Formative versus reflective indicators in organizational measure development: a comparison and empirical illustration. Br J Manag 17(4):263–282CrossRefGoogle Scholar
  14. 14.
    Diamantopoulos A, Winklhofer M (2001) Index construction with formative indicators: an alternative to scale development. J Market Res 38(2):269–277CrossRefGoogle Scholar
  15. 15.
    Dishaw MT, Strong DM (1999) Extending the technology acceptance model with task-technology fit constructs. Inf Manag 36(1):9–21CrossRefGoogle Scholar
  16. 16.
    Doherty NF, Perry I (2001) The cultural impact of workflow management systems in the financial services sector. Serv Ind J 21(4):147–166CrossRefGoogle Scholar
  17. 17.
    Doll WJ, Xia W, Torkzadeh G (1994) Confirmatory factor analysis of the end-user computing satisfaction instrument. MIS Q 18(4):453–461CrossRefGoogle Scholar
  18. 18.
    Dourish P (2001) Process descriptions as organizational accounting devices: the dual use of workflow technologies. In Ellis C, Zigurs I (Eds.) Proceedings of the ACM international conference on supporting group work, ACM, New York, pp 52–60Google Scholar
  19. 19.
    Dumas M, Vander Aalst WMP, Terhofstede AHM (2005) Process aware information systems: bridging people and software through process technology. Wiley, HobokenCrossRefGoogle Scholar
  20. 20.
    Ellis CA, Keddara K (2000) Ml-Dews: modeling language to support dynamic evolution within workow systems. Comput Support Coop Work 9(3/4):293–333CrossRefGoogle Scholar
  21. 21.
    Gable GG, Sedera D, Chan T (2008) Re-conceptualizing information system success: the IS-impact measurement model. J Assoc Inf Syst 9:377–408Google Scholar
  22. 22.
    Georgakopoulos D, Hornick M, Sheth A (1995) An overview of workflow management: from process modeling to workflow automation infrastructure. Distrib Parallel Databases 3(2):119–153CrossRefGoogle Scholar
  23. 23.
    Goodhue DL (1998) Development and measurement validity of a task-technology fit instrument for user evaluations of information systems. Decis Sci 29(1):105–139CrossRefGoogle Scholar
  24. 24.
    Grefen P, Pernici B, Sanchez G (1999) Database support for workflow management: the WIDE project. Kluwer Academic Publishers, NorwellCrossRefGoogle Scholar
  25. 25.
    Grover V, Jeong SR (1995) The implementation of business process reengineering. J Manag Inf Syst 12(1):109–144Google Scholar
  26. 26.
    Hair JF, Anderson R, Tatham RL, Black WC (2006) Multivariate data analysis. Prentice Hall, Upper Saddle RiverGoogle Scholar
  27. 27.
    Hammer M, Champy J (1993) Reengineering the corporation: a manifesto for business revolution. Nicholas Brealey, LondonGoogle Scholar
  28. 28.
    Heinl P, Horn S, Jablonski S, Neeb J, Stein K, Teschke M (1999) A comprehensive approach to flexibility in workflow management systems. Software Eng Notes 24(2):79–88CrossRefGoogle Scholar
  29. 29.
    Henseler J, Ringle CM, Sinkovics RR (2009) The use of partial least squares path modeling in international marketing. Adv Int Market (AIM) 20:277–320Google Scholar
  30. 30.
    Housel T, Bell A (2001) Managing and measuring knowledge. McGraw-Hill, BostonGoogle Scholar
  31. 31.
    Ives B, Olson MH, Baroudi JJ (1983) The measurement of user information satisfaction. Commun ACM 26(10):785–793CrossRefGoogle Scholar
  32. 32.
    Jablonski S, Bussler C (1996) Workflow management: modeling concepts, architecture and implementation. International Thomson Computer Press, LondonGoogle Scholar
  33. 33.
    Karagiannis D (1995) BPMS: business process management systems. ACM SIGOIS Bull 16(1):10–13CrossRefGoogle Scholar
  34. 34.
    Kueng P, Hagen C (2007) The fruits of business process management: an experience report from a Swiss bank. Bus Process Manag J 13(4):477–487CrossRefGoogle Scholar
  35. 35.
    Kueng P, Hagen C (2004) Increased performance through business process management: an experience report from a Swiss bank. In: Neely AD, Kennerley MP, Walters AH (eds) Performance measurement and management—public and private. Cranfield University, Cranfield, pp 1–8Google Scholar
  36. 36.
    Landrum H, Prybutok VR, Strutton D, Zhang X (2008) Examining the merits of usefulness versus use in an information service quality and information system success web-based model. Inf Resour Manag J 21(2):1–17CrossRefGoogle Scholar
  37. 37.
    Leymann F, Roller D, Schmidt MT (2002) Web services and business process management. IBM Syst J 41(2):198–211CrossRefGoogle Scholar
  38. 38.
    Lin H-F (2007) Measuring online learning systems success: applying the updated DeLone and McLean model. CyberPsychol Behav 10(6):817–820CrossRefGoogle Scholar
  39. 39.
    Mahmood MA, Burn JM, Gemoets LA, Jacquez C (2000) Variables affecting information technology end-user satisfaction: a meta-analysis of the empirical literature. Int J Hum Comput Stud 52(4):751–771CrossRefGoogle Scholar
  40. 40.
    Morris MG, Venkatesh V, Ackerman PL (2005) Gender and age differences in employee decisions about new technology: an extension to the theory of planned behavior, Engineering Management. IEEE Trans Eng Manag 52(1):69–84CrossRefGoogle Scholar
  41. 41.
    Nelson RR, Todd PA, Wixom BH (2005) Antecedents of information and system quality: an empirical examination within the context of data warehousing. J Manag Inf Syst 21(4):199–235Google Scholar
  42. 42.
    Nunnally JC, Bernstein IH (1994) Psychometric theory. McGraw-Hill, New YorkGoogle Scholar
  43. 43.
    Pavlou PA, Housel TJ, Rodgers W, Jansen E (2005) Measuring the return on information technology: a knowledge-based approach for revenue allocation at the process and firm level. J Assoc Inf Syst 6(7):199–226Google Scholar
  44. 44.
    Petter SDW, Delone W, Mclean E (2008) Measuring information systems success: models, dimensions, measures, and interrelationships. Eur J Inf Syst 17:236–263CrossRefGoogle Scholar
  45. 45.
    Petter SDW, Straub W, RAI A (2007) Specifying formative constructs in IS research. MIS Q 31(4):623–656Google Scholar
  46. 46.
    Pinsonneault A, Kraemer KL (1993) Survey research methodology in management information systems: an assessment. J Manag Inf Syst 10(2):75–105Google Scholar
  47. 47.
    Poelmans S (2002) Making workflow systems work: an investigation into the importance of task-appropriation fit, end-user support and other technological characteristics. Ph. D. Dissertation, Faculty of Economic and Business, KU LeuvenGoogle Scholar
  48. 48.
    Rai A, Lang SS, Welker RB (2002) Assessing the validity of is success models: an empirical test and theoretical analysis. Inf Syst Resarch 13(1):50–69CrossRefGoogle Scholar
  49. 49.
    Reijers HA, Heusinkveld S (2004) Business process management: attempted concepticide?. In Khosrow-Pour M (Ed.) Proceedings of the 14th information resources management conference on information systems, IDEA Group, Hershey, pp 128–131Google Scholar
  50. 50.
    Reijers HA, Poelmans S (2007) Re-configuring workflow management systems to facilitate a “smooth flow of work”. Int J Coop Inf Syst 15(2):155–175CrossRefGoogle Scholar
  51. 51.
    Ringle CM, Sarstedt M, Straub DW (2012) Editor’s comments: a critical look at the use of PLS-SEM in MIS quarterly. MIS Q 36(1):iii–xivGoogle Scholar
  52. 52.
    Ringle CM, Wende S, Will S (2005) SmartPLS 2.0 (M3) Beta, Hamburg.
  53. 53.
    Seddon PB (1997) A respecification and extension of the Delone and McLean model of IS success. Inf Syst Res 8(3):240–253CrossRefGoogle Scholar
  54. 54.
    Seen M, Rouse AC. Beaumont N (2007) Explaining and predicting information systems acceptance and success: an integrative model. In Hubert Österle H, Schelp J, Winter R (Eds.) Proceedings of the european conference on information systems conference proceedings, University of St. Gallen, St Gallen, pp. 1356–1367Google Scholar
  55. 55.
    Segars AH, Grover V (1993) Re-examining perceived ease of use and usefulness: a confirmatory factor analysis. MIS Q 17(4):517–525CrossRefGoogle Scholar
  56. 56.
    Van Der Aalst WMP, Reijers HA, Weijters AJMM, Van Dongen BF, Alves De Medeiros AK, Song M, Verbeek HMW (2007) Business process mining: an industrial application. Inf Syst 32(5):713–732CrossRefGoogle Scholar
  57. 57.
    Van Der Aalst WMP, Weske M, Grünbauer D (2005) Case handling: a new paradigm for business process support. Data Knowl Eng 53(2):129–162CrossRefGoogle Scholar
  58. 58.
    Van Der Aalst WMP, Van Hee KM (2002) Workflow Management: models, methods, and systems. MIT Press, Cambridge, MassachusettsGoogle Scholar
  59. 59.
    Wang Y, Liao Y (2008) Assessing eGovernment systems success: a validation of the DeLone and McLean model of information systems success. Gov Inf Q 25(4):717–733CrossRefGoogle Scholar
  60. 60.
    Weber B, Sadiq S, Reichert M (2009) Beyond rigidity dynamic process lifecycle support: a survey on dynamic changes in process-aware information systems. Comput Sci Res Develop 23(2):47–65CrossRefGoogle Scholar
  61. 61.
    Wixom BH, Todd PA (2005) A theoretical integration of user satisfaction and technology acceptance. Inf Syst Res 16(1):85–102CrossRefGoogle Scholar
  62. 62.
    Wu J-H, Wang Y-M (2006) Measuring KMS success: a respecification of the DeLone and McLean’s model. Inf Manag 43:728–739CrossRefGoogle Scholar
  63. 63.
    Wu I-L, Wu K-W (2005) A hybrid technology acceptance approach for exploring e-CRM adoption in organizations. Behav Inf Technol 24(4):303–316CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Stephan Poelmans
    • 1
  • Hajo A. Reijers
    • 2
    • 3
  • Jan Recker
    • 4
  1. 1.Faculty of Economics and Business, Business Information ManagementKU LeuvenLeuvenBelgium
  2. 2.Department of Mathematics and Computer Science, Architecture of Information Systems GroupEindhoven University of TechnologyEindhovenThe Netherlands
  3. 3.Research and Development, Business Process Management Research GroupPerceptive SoftwareNaardenThe Netherlands
  4. 4.Faculty of Science and Technology, Information Systems SchoolQueensland University of TechnologyBrisbaneAustralia

Personalised recommendations