Behavior of Organizational Agents on Managing Information Technology

  • Mark van der Pas
  • Rita Walczuch
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 857)


Improving the impact of information technology (IT) investments is potentially beneficial for our society. This study identifies triggers which influence behavior of organizational agents on managing IT. In scope of this study are the portfolio decisions regarding where to invest the IT euro, the management of IT projects and the management of the IT infrastructure. Following the theory of planned behavior, it is shown for controllers of Dutch organizations that ‘intention’ is positively associated with behavior and that ‘subjective norm’ and ‘perceived behavior control’ are positively associated with intention. For portfolio and IT infrastructure management, attitude is also positively associated with intention. Overall it is concluded that the most important levers for behavior for the focus areas are ‘social pressure’ and the explicit confirmation of the agent’s own intention. This is good news since both can be easily influenced without significant monetary investment.


Information technology Theory of planned behavior Project portfolio management Life cycle management Project management Benefits management 


  1. 1.
    Gartner: Gartner says worldwide IT spending to reach 3.7 Trillion in 2018. Press Release, Orlando, 3 October 2017Google Scholar
  2. 2.
    Strassmann, P.A.: Information Productivity Indicators of U.S. Corporations. The Information Economics Press (2000)Google Scholar
  3. 3.
    Chui, M., Manyika, J., Bughin, J., Dobbs, R., Roxburgh, C., Sarrazin, H., Sands, G., Westergen, M.: The Social Economy: Unlocking Value and Productivity Through Social Technologies. McKinsey Global Institute (2012)Google Scholar
  4. 4.
    European Commission: Exploiting the employment potential of ICTs. Commission staff working document (2012)Google Scholar
  5. 5.
    Davenport, E.: Information management: an educational perspective. Int. J. Inf. Manage. 8(4), 255–263 (1988)CrossRefGoogle Scholar
  6. 6.
    Brynjolfsson, E., McAfee, A.: The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W.W. Norton & Company, New York (2014)Google Scholar
  7. 7.
    Stone, B.: The Everything Store. Random House, UK (2013)Google Scholar
  8. 8.
    Evans, N.D.: SMAC and the evolution of IT. Computerworld, 9 December 2013Google Scholar
  9. 9.
    Bock, G.W., Kankanhalli, A., Sharma, S.: Are norms enough? The role of collaborative norms in promoting organizational knowledge seeking. Eur. J. Inf. Syst. 15(4), 357–367 (2006)CrossRefGoogle Scholar
  10. 10.
    Braun, S., Turner, R.A.: Attitudes and company practices as predictors of managers’ intentions to hire, develop, and promote women in science engineering, and technology professions. Consult. Psychol. J. Pract. Res. 66(2), 93–117 (2006)CrossRefGoogle Scholar
  11. 11.
    Frey, C.B., Osborne, M.A.: The Future of Employment: How Susceptible are Jobs to Computerization. Oxford University, Oxford (2013)Google Scholar
  12. 12.
    Jorgenson, D.W., Ho, M.S., Stiroh, K.J.: A retrospective look at the U.S. productivity growth resurgance. J. Econ. Perspect. 22(1), 3–24 (2008)CrossRefGoogle Scholar
  13. 13.
    Mithas, S., Tafti, A., Bardhan, I., Goh, J.M.: Information technology and firm profitability: mechanisms and empirical evidence. MIS Q. 36(1), 205–224 (2012)CrossRefGoogle Scholar
  14. 14.
    van Reenen, J., Bloon, N., Draca, M., Kretschmer, T., Sadun, R.: The Economic Impact of ICT. Enterprise LSE (2010)Google Scholar
  15. 15.
    van Ark, B., O’Mahony, M., Timmer, M.P.: The productivity gap between Europe and the United States: trends and causes. J. Econ. Perspect. 22(1), 25–44 (2008)CrossRefGoogle Scholar
  16. 16.
    Van der Pas, M., Furneaux, B.: Improving the predictability of IT investment business value. Completed Research Papers, Twenty-Third European Conference on Information Systems (ECIS), Münster, Germany (2015)Google Scholar
  17. 17.
    The Standish Group: The chaos report. The Standish Group (2011)Google Scholar
  18. 18.
    Flyvbjerg, B., Budzier, A.: Why your IT project may be riskier than you think. Harvard Bus. Rev. 89(9), 23–25 (2011)Google Scholar
  19. 19.
    Davern, M.J., Kauffman, R.J.: Discovering potential and realizing value from information technology investments. J. Manag. Inf. Syst. 16(4), 121–143 (2000)CrossRefGoogle Scholar
  20. 20.
    Verhoef, C.: Quantitative IT portfolio management. Sci. Comput. Program. 45(1), 1–96 (2002)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Ross, S.A.: The economic theory of agency: the principal’s problem. Am. Econ. Rev. 63(2), 134–139 (1973)Google Scholar
  22. 22.
    Markowitz, H.M.: Portfolio Selection: Efficient Diversification of Investment. BookCrafters, Michigan (1959)Google Scholar
  23. 23.
    Beringer, C., Jonas, D., Kock, A.: Behavior of internal stakeholders in project portfolio management and its impact on success. Int. J. Project Manage. 31(6), 794–803 (2013)CrossRefGoogle Scholar
  24. 24.
    Blichfeldt, B.S., Eskerod, P.: Project portfolio management - there’s more to it than management enacts. Int. J. Project Manage. 26(6), 357–365 (2008)CrossRefGoogle Scholar
  25. 25.
    Gutiérrez, E., Magnusson, M.: Dealing with legitimacy: a key challenge for project portfolio management decision makers. Int. J. Project Manage. 32(1), 30–39 (2014)CrossRefGoogle Scholar
  26. 26.
    Fornell, C.R., Larcker, D.F.: Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18(February), 39–50 (1981)CrossRefGoogle Scholar
  27. 27.
    Killen, C.P., Hunt, R.A.: Robust project portfolio management: capability evolution and maturity. Int. J. Manage. Proj. Bus. 6(1), 131–151 (2013)CrossRefGoogle Scholar
  28. 28.
    Martinsuo, M.: Project portfolio management in practice and in context. Int. J. Project Manage. 31(6), 794–803 (2013)CrossRefGoogle Scholar
  29. 29.
    Garmus, D., Herron, D.: Function point analysis – measurement practices for successful software projects. Addison-Wesley, Boston (2001)Google Scholar
  30. 30.
    van der Pas, M.: Speeding up time-to-market of IT-investments. In: Tenth IADIS International Conference on Information Systems, Budapest, Hungary (2017)Google Scholar
  31. 31.
    Lester, D.L., Parnell, J.A., Carraher, S.: Organizational life cycle: a five-stage empirical scale. Int. J. Organ. Anal. 11(4), 339–354 (2003)CrossRefGoogle Scholar
  32. 32.
    Shao, B.M., Lin, W.T.: Technical efficiency analysis of information technology investments: a two-stage empirical investigation. Inf. Manage. 39(5), 391–401 (2002)CrossRefGoogle Scholar
  33. 33.
    Hunton, J.E.: Discussant’s comments on presentations by John Lainhart and Gerald Trites. J. Inf. Syst. 13(2000 supplement), 33–35 (2000)Google Scholar
  34. 34.
    Jeffery, M., Leliveld, I.: Best practices in IT portfolio management. MIT Sloan Manag. Rev. 45(3), 41–49 (2004)Google Scholar
  35. 35.
    Ortiz de Guinea, A., Webster, J.: An investigation of information systems use patterns: technological events as triggers, the effect of time, and consequences for performance. MIS Q. 37(4), 1165–1188 (2013)CrossRefGoogle Scholar
  36. 36.
    Ajzen, I.: The theory of planned behavior. Organ. Behav. Hum. Decis. Process. J. Fundam. Res. Theory Appl. Psychol. 50, 179–211 (1991)CrossRefGoogle Scholar
  37. 37.
    Ajzen, I.: Attitudes, Personality and Behavior. Open University Press, Berkshire (2005)Google Scholar
  38. 38.
    Cohen, J., Ding, Y., Lesage, C., Stolowy, H.: Corporate fraud and managers’ behavior: evidence from the press. J. Bus. Ethics 95, 271–315 (2011)CrossRefGoogle Scholar
  39. 39.
    Fogarty, G.J., Shaw, A.: Safety climate and the theory of planned behavior: towards the prediction of unsafe behavior. Accid. Anal. Prev. 42, 1455–1459 (2010)CrossRefGoogle Scholar
  40. 40.
    Gagné, M.: A model of knowledge sharing motivation human resource management 48(4), 571–589 (2009)Google Scholar
  41. 41.
    Henle, C.A., Reeve, A.L., Pitts, V.E.: Stealing time at work: attitudes, social pressure, and perceived control as predictors of time theft. J. Bus. Ethics 94, 52–67 (2010)CrossRefGoogle Scholar
  42. 42.
    Hill, M., Mann, L., Wearing, A.J.: The effects of attitude, subjective norm and self-efficacy on intention to benchmark: a comparison between managers with experience and no experience in benchmarking. J. Org. Behav. 17(4), 313–327 (1996)CrossRefGoogle Scholar
  43. 43.
    Randall, D.M., Gibson, A.M.: Ethical decision making in the medical profession: an application of the theory of planned behavior. J. Bus. Ethics 10, 111–122 (1991)CrossRefGoogle Scholar
  44. 44.
    Sharma, P., Chrisman, J.J., Chua, J.H.: Succession planning as planned behavior: some empirical results. Family Bus. Rev. 16, 1–15 (2003)CrossRefGoogle Scholar
  45. 45.
    Chennamanenia, A., Teng, J.T.C., Rajab, M.K.: A unified model of knowledge sharing behaviors: theoretical development and empirical test. Behav. Inf. Technol. 31(11), 1097–1115 (2012)CrossRefGoogle Scholar
  46. 46.
    Hallikainen, P., Hu, Q., Frisk, E., Päivärinta, T., Eikebrokk, T.R.: The use of formal IT investment evaluation methods in organizations: a Survey of European Countries. In: AMCIS 2006 Proceedings, paper 67 (2006)Google Scholar
  47. 47.
    Hameed, M.A., Counsell, S., Swift, S.: A conceptual model for the process of IT innovation adoption in organizations. J. Eng. Tech. Manage. 29(3), 358–390 (2012)CrossRefGoogle Scholar
  48. 48.
    Kasim, A., Dzakiria, H., Scarlat, C.: Exploring the digital divide issues affecting hotel frontliners. Adv. Bus. Related Sci. Res. J. 4(2), 165–176 (2013)Google Scholar
  49. 49.
    Kwahk, K.Y., Lee, J.N.: The role of readiness for change in ERP implementation, theoretical bases and empirical validation. Inf. Manage. 45, 474–481 (2008)CrossRefGoogle Scholar
  50. 50.
    Pavlou, P.A., Fygenson, M.: Understanding and predicting electronic commerce adoption: an extension of the theory of planned behavior. MIS Q. 30(1), 115–143 (2006)CrossRefGoogle Scholar
  51. 51.
    Riemenschneider, C.K., McKinney, V.R.: Assessing the adoption of web-based e-commerce for business: a research proposal and preliminary findings. Electron. Mark. 9(1), 9–13 (1999)CrossRefGoogle Scholar
  52. 52.
    Zhou, L.: Application of TPB to punctuation usage in instant messaging. Behav. Inf. Technol. 26(5), 399–407 (2007)CrossRefGoogle Scholar
  53. 53.
    Zhu, S., Chen, J.: E-commerce use in urbanising China: the role of normative social influence. Behav. Inf. Technol. 35(5), 357–367 (2016)CrossRefGoogle Scholar
  54. 54.
    Gurbaxani, V., Wang, S.: The impact of information systems on organizations and markets. Commun. ACM 34(1), 59–73 (1991)CrossRefGoogle Scholar
  55. 55.
    Markus, M.L., Soh, C.: Banking on information technology: converting IT spending into firm performance. In: Banker, R., Kauffman, R., Mahmood, M.A. (eds.) Strategic Information Technology Management, pp. 375–404. Idea Group, Harrisburg (1993)Google Scholar
  56. 56.
    Melas, C.D., Zampetakis, L.A., Dimopouloi, A., Moustakis, V.S.: The significance of attitudes towards evidence-based practice in information technology use in the health sector: an empirical investigation. Behav. Inf. Technol. 33(12), 1248–1260 (2014)CrossRefGoogle Scholar
  57. 57.
    Teh, P.L., Yong, C.C.: Knowledge sharing in IS personnel: organizational behavior’s perspective. MIS Q. 27(1), 19–49 (2011)Google Scholar
  58. 58.
    Armitage, C.J., Conner, M.: Efficacy of the theory of planned behavior: a meta-analytic review. Br. J. Soc. Psychol. 40(4), 471–499 (2001)CrossRefGoogle Scholar
  59. 59.
    The Guardian: BSkyB will get 318GBPm settlement from Hewlett-Packard. The Guardian, 07 June 2010Google Scholar
  60. 60.
    The Guardian: White House relaxes Obamacare deadline as congress reviews ‘failures’. The Guardian, 24 October 2013Google Scholar
  61. 61.
    Pass, S., Ronen, B.: Reducing the software value gap. Commun. ACM 57(5), 80–87 (2014)CrossRefGoogle Scholar
  62. 62.
    de Reyck, B., Grushka-Cockayne, Y., Locket, M., Calderini, S.R., Moura, M., Sloper, A.: The impact of project portfolio management on information technology projects. Int. J. Project Manage. 23(6), 524–537 (2005)CrossRefGoogle Scholar
  63. 63.
    Zika-Viktorsson, A., Sundström, P., Engwall, M.: Project overload: an exploratory study of work and management in multi-project settings. Int. J. Project Manage. 24(6), 385–394 (2006)CrossRefGoogle Scholar
  64. 64.
    Bihina Bella, M.A., Eloff, J.H.P., Olivier, M.S.: Improving system availability with near-miss analysis. Netw. Secur. 10(October), 18–20 (2012)CrossRefGoogle Scholar
  65. 65.
    Vaassen, E., Bollen, L., Meuwissen, R., Vluggen, M.: Introduction into Information & Control Published in Dutch Basisboek Informatie & Control. Wolters-Noordhoff, Groningen (2006)Google Scholar
  66. 66.
    Teo, H.H., Wei, K.K., Benbasat, L.: Predicting intention to adopt interorganizational linkages: an institutional perspective. MIS Q. 27(1), 19–49 (2003)CrossRefGoogle Scholar
  67. 67.
    Coltman, T., Devinney, T.M., Midgley, D.F., Venaik, S.: Formative versus reflective measurement models: Two applications of formative measurement. J. Bus. Res. 61(12), 1250–1262 (2008)CrossRefGoogle Scholar
  68. 68.
    Diamantopoulos, A.: Formative indicators: introduction to the special issue. J. Bus. Res. 61(12), 1201–1202 (2008)CrossRefGoogle Scholar
  69. 69.
    Henseler, J., Ringle, C.M., Sinkovics, R.R.: The use of partial least squares path modelling in international marketing. Adv. Int. Mark. 20, 277–319 (2009)Google Scholar
  70. 70.
    Nunnally, J.C., Bernstein, I.H.: Psychometric Theory. McGraw-Hill, New York (1994)Google Scholar
  71. 71.
    Ringle, C.M., Wende, S., Will, S.: SmartPLS. Hamburg (2005)Google Scholar
  72. 72.
    Hansmann, K.W., Ringle, C.M.: SmartPLS Manual. University of Hamburg, Hamburg (2004)Google Scholar
  73. 73.
    Cohen, J.: Statistical Power Analysis for the Behavioral Sciences. Lawrence Erlbaum Associates, Hillsdale (1988)zbMATHGoogle Scholar
  74. 74.
    Ajzen, I., Fishbein, M.: Understanding Attitudes and Predicting Social Behavior. Prentice-Hall, Engelwood Cliffs (1980)Google Scholar
  75. 75.
    Standing, C., Holzweber, M., Mattson, J.: Exploring emotional expressions in e-word-of-mouth from online communities. Inf. Process. Manage. 52(5), 721–732 (2016)CrossRefGoogle Scholar
  76. 76.
    Riquelme, F., González-Cantergiani, P.: Measuring user influence on Twitter: a survey. Inf. Process. Manage. 52(5), 949–975 (2016)CrossRefGoogle Scholar
  77. 77.
    Alpar, P., Engler, T.H., Schulz, M.: Influence of social software features on the reuse of business intelligence reports. Inf. Process. Manage. 51(3), 235–251 (2015)CrossRefGoogle Scholar
  78. 78.
    Pratt, J.A., Cheng, L., Cole, C.: The influence of goal clarity, curiosity, and enjoyment on intention to code. Behav. Inf. Technol. 35(12), 1091–1101 (2016)CrossRefGoogle Scholar
  79. 79.
    Wang, T., Jung, C.H., Kang, M.H., Chung, Y.S.: Exploring determinants of adoption intentions towards Enterprise 2.0 applications: an empirical study. Behav. Inf. Technol. 33(10), 1048–1064 (2014)CrossRefGoogle Scholar
  80. 80.
    Greenwald, A.G, Carnot, C.G., Beach, R., Young, B.: Increasing voting behavior by asking people if they expect to vote. J. Appl. Psychol. 72(2), 315–318 (1987)CrossRefGoogle Scholar
  81. 81.
    Sherman, S.J.: On the self-erasing nature of errors of prediction. J. Pers. Soc. Psychol. 39(2), 211–221 (1980)CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.European Center for Digital TransformationRoermondThe Netherlands
  2. 2.School of Business and EconomicsMaastricht UniversityMaastrichtThe Netherlands

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