Skip to main content
SpringerLink
Log in

Candidate diversity and granularity in IT portfolio construction

  • Published:
Information Technology and Management Aims and scope Submit manuscript

Abstract

The construction of a superior IT portfolio remains an open research question in prior literature. For addressing this gap, we investigate two unique characteristics of IT investment projects that may make it more or less likely to construct a superior IT portfolio in this study. We are mainly grounded on the modern portfolio theory to develop propositions regarding the relationships among such characteristics and their impacts on IT portfolio construction performance. Our methodology combines optimization modeling, real-world data, numerical simulation (Monte Carlo), and computational experiment. One main finding shows that, for any set of candidate IT investment projects, their attribute diversity and investment granularity could jointly influence the resultant IT portfolio construction performance. Even when a very tight budget is provided, a set of candidate IT investment projects with higher diversity and granularity would still generate a superior IT portfolio. In other words, the diversity and granularity of IT portfolio construction candidates can positively affect portfolio performance, although budget limits can impose a negative impact on the performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Karhade P, Shaw MJ, Subramanyam R (2015) Patterns in information systems portfolio prioritization: evidence from decision tree induction. MIS Q 39(2):413–433

    Google Scholar 

  2. Davis JP (2003) Information technology portfolio management and the real options method. Master thesis, Naval Postgraduate School

  3. Maizlish B, Handler R (2005) IT portfolio management step-by-step: unlocking the business value of technology. Wiley, New York

    Google Scholar 

  4. Bardhan I, Sougstad R, Sougstad R (2004) Prioritizing a portfolio of information technology investment projects. J Manag Inf Syst 21(2):33–60

    Google Scholar 

  5. Cho W (2010) IT portfolio selection and IT synergy. Doctoral dissertation, University of Illinois at Urbana-Champaign, Champaign

  6. Cho W, Shaw MJ (2013) Portfolio selection model for enhancing information technology synergy. IEEE Trans Eng Manag 60(4):739–749

    Google Scholar 

  7. Cho W, Shaw MJ, Kwon HD (2013) The effect of synergy enhancement on information technology portfolio selection. Inf Technol Manag 14(2):125–142

    Google Scholar 

  8. Jeffery M, Leliveld I (2004) Best practices in IT portfolio management. MIT Sloan Manag Rev 45(3):41–49

    Google Scholar 

  9. Kaplan JD (2005) Strategic IT portfolio management: governing enterprise transformation. Pittiglio, Rabin, Todd & McGrath (PRTM), London

    Google Scholar 

  10. Weill P, Ross J (2005) A matrixed approach to designing IT governance. MIT Sloan Manag Rev 46(2):26–34

    Google Scholar 

  11. Ajjan H, Kumar RL, Subramaniam C (2016) Information technology portfolio management implementation: a case study. J Enterp Inf Manag 29(6):841–859

    Google Scholar 

  12. Mcfarlan FW (1981) Portfolio approach to information systems. Harvard Bus Rev 59(September–October):142–150

    Google Scholar 

  13. Aral S, Peter W (2007) IT assets, organizational capabilities, and firm performance: how resource allocations and organizational differences explain performance variation. Organ Sci 18(5):763–780

    Google Scholar 

  14. Chiang IR, Nunez MA (2013) Strategic alignment and value maximization for IT project portfolios. Inf Technol Manag 14(2):143–157

    Google Scholar 

  15. Zimmermann S, Katzmarzik A, Kundisch D (2012) IT sourcing portfolio management for IT services providers—an approach for using modern portfolio theory to allocate software development projects to available sites. DATA BASE Adv Inf Syst 43:24–45

    Google Scholar 

  16. Ramasubbu N, Bharadwaj A, Tayi GK (2015) Software process diversity: conceptualization, measurement, and analysis of impact on project performance. MIS Q 39(4):787–807

    Google Scholar 

  17. Subramanyam R, Ramasubbu N, Krishnan MS (2012) In search of efficient flexibility: effects of software component granularity on development effort, defects, and customization effort. Inf Syst Res 23(3):787–803

    Google Scholar 

  18. Dewan S, Shi C, Gurbaxani V (2007) Investigating the risk-return relationship of information technology investment: firm-level empirical analysis. Manag Sci 53(12):1829–1842

    Google Scholar 

  19. Tanriverdi H, Ruefli TW (2004) The role of information technology in risk/return relations of firms. J Assoc Inf Syst 5(11–12):421–447

    Google Scholar 

  20. Markowitz HM (1959) Portfolio selection: efficient diversification of investments. Wiley, New York

    Google Scholar 

  21. Elton EJ, Gruber MJ, Brown SJ, Goetzmann WN (2003) Modern portfolio theory and investment analysis, 6th edn. Wiley, New York

    Google Scholar 

  22. Harrison DA, Klein KJ (2007) What’s the difference? Diversity constructs as separation, variety, or disparity in organizations. Acad Manag Rev 32(4):1199–1228

    Google Scholar 

  23. Sher I (2018) Evaluating allocations of freedom. Econ J (R Econ Soc) 128:65–94

    Google Scholar 

  24. Nehring K, Puppe C (2002) A theory of diversity. Econometrica 70(3):1155–1198

    Google Scholar 

  25. Fichman RG (2004) Real options and IT platform adoption: implications for theory and practice. Inf Syst Res 15(2):132–154

    Google Scholar 

  26. Shenhar AJ (2001) One size does not fit all projects: exploring classical contingency domains. Manag Sci 47(3):394–414

    Google Scholar 

  27. Fridgen G, Klier J, Beer M, Wolf T (2015) Improving business value assurance in large-scale IT projects—a quantitative method based on founded requirements assessment. ACM Trans Manag Inf Syst 5(3):12–29

    Google Scholar 

  28. Lavanya N, Malarvizhi T (2008) Risk analysis and management: a vital key to effective project management. Paper presented at PMI (Project Management Institute) global congress 2008

  29. Davis JP, Bingham CB (2007) Developing theory through simulation methods. Acad Manag Rev 32(2):480–499

    Google Scholar 

  30. Harrison JR, Lin Z, Carroll GR, Carley KM (2007) Simulation modeling in organizational and management research. Acad Manag Rev 32(4):1229–1245

    Google Scholar 

  31. Kauffman RJ, Sougstad R (2008) Risk management of contract portfolios in IT services: the profit-at-risk approach. J Manag Inf Syst 25(1):17–48

    Google Scholar 

  32. Piramuthu S, Shaw MJ (2009) Learning-enhanced adaptive DSS: a design science perspective. Inf Technol Manag 10(1):41–54

    Google Scholar 

  33. Sikora R, Shaw MJ (1998) A multi-agent framework for the coordination and integration of information systems. Manag Sci 44(11):65–78

    Google Scholar 

  34. Tu YJ, Wei Z, Piramuthu S (2009) Identifying RFID-embedded objects in pervasive healthcare applications. Decis Support Syst 46(2):586–593

    Google Scholar 

  35. Wei Z, Tu YJ, Piramuthu S (2009) RFID-enabled item-level retail pricing. Decis Support Syst 48(1):169–179

    Google Scholar 

  36. Bacon CJ (1992) The use of decision criteria in selecting information systems/technology investments. MIS Q 16(3):335–353

    Google Scholar 

  37. Ives B, Learmonth G (1984) The information system as a competitive weapon. Commun ACM 27(12):1193–1201

    Google Scholar 

  38. Browning TR, Deyst JJ, Eppinger SD, Whitney DE (2002) Adding value in product development by creating information and reducing risk. IEEE Trans Eng Manag 49(4):443–458

    Google Scholar 

  39. Valacich JS, George JF (2017) Modern systems analysis and design, 8th edn. Pearson Education Limited, London

    Google Scholar 

  40. Larance W, Mark K (2004) Software project risks and their effect on outcomes. Commun ACM 47(4):68–73

    Google Scholar 

  41. Ross JW, Weill P (2002) Six IT decisions your IT people shouldn’t make. Harvard Bus Rev 80(11):84–95

    Google Scholar 

  42. Gallaugher J (2014) Information systems: a Manager’s guide to harnessing technology. Flat World Knowledge, Washington, DC

    Google Scholar 

  43. Mahoney JT (2004) Economic foundations of strategy. Sage Publications, Beverley Hills

    Google Scholar 

  44. Penrose ET (1959) The theory of the growth of the firm. Wiley, New York

    Google Scholar 

  45. Trigeorgis L (1996) Real options: managerial flexibility and strategy in resource allocation. MIT Press, Cambridge

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Management Information Systems, National Chengchi University (NCCU), No. 64, Sec. 2, Zhi-Nan Rd., Wenshan District, Taipei City, 11605, Taiwan, ROC

    Yu-Ju Tu

  2. College of Business and Public Administration, Drake University, 347 Aliber Hall, 2847 University Ave, Des Moines, IA, 50311, USA

    Yu-Hsiang Huang

  3. College of Business and Public Administration, Drake University, 332 Aliber Hall, 2847 University Ave, Des Moines, IA, 50311, USA

    Troy J. Strader

  4. Department of Business Administration, University of Illinois at Urbana-Champaign, 96 Wohlers Hall, 1206 South Sixth Street, Champaign, IL, 61820, USA

    Ramanath Subramanyam

  5. Department of Business Administration, University of Illinois at Urbana-Champaign, 385 Wohlers Hall, 1206 South Sixth Street, Champaign, IL, 61820, USA

    Michael J. Shaw

Authors
  1. Yu-Ju Tu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu-Hsiang Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Troy J. Strader
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ramanath Subramanyam
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Michael J. Shaw
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yu-Ju Tu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

A non-linear relationship between IT investment project granularity and benefit/risk is considered when generating these results (Figs. 6, 7, 8, 9, 10). These results are generated by 100 data sets × 8 scenarios × 10 risk tolerance levels. In general, such results and the results as presented above in the main text (Figs. 1, 2, 3, 4, 5) are comparable. Noticeably, the blue line in Fig. 5 (i.e., the linear situation) seems to present the greater IT portfolio benefit than that of Fig. 10 (i.e., the non-linear situation), if their respective dominances over the red lines are taken as their comparison basis. As stated, Figs. 5 and 10 are both focused on the effect of budget constraint. Moreover, Fig. 10 is specifically generated under the non-linear situation regarding granularity and benefit/risk. For example, if an IT investment project is done to 50%, this project will generate much less than 50% of the original (full size) benefit and risk, such as 25%, but still consume 50% of the original (full size) cost. In other words, this gap between benefit/risk and cost would not happen, when a linear relationship between granularity and benefit/risk is considered. As a result, this gap possibly can explain the dissimilarity between Figs. 5 and 10.

Fig. 6
figure 6

Comparison of higher diversity results and lower diversity results

Full size image
Fig. 7
figure 7

Comparison of higher granularity results and lower granularity results

Full size image
Fig. 8
figure 8

Comparison of lower diversity with higher granularity results and lower diversity with lower granularity results

Full size image
Fig. 9
figure 9

Comparison of looser budget constraint (higher or more budget) results and tighter budget constraint (lower or less budget) results

Full size image
Fig. 10
figure 10

Comparison of looser budget constraint (higher or more budget) with lower diversity and granularity results and tighter budget constraint (higher or less budget) with higher diversity and granularity results

Full size image

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tu, YJ., Huang, YH., Strader, T.J. et al. Candidate diversity and granularity in IT portfolio construction. Inf Technol Manag 21, 157–168 (2020). https://doi.org/10.1007/s10799-019-00312-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10799-019-00312-1

Keywords

Search

Navigation