Decision models for information systems planning using primitive cognitive network process: comparisons with analytic hierarchy process

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

The well-planned investment in a robust Information System (IS) is essential for the sustainability of a firm’s competitive advantage. The careful selection of a suitable adoption plan for the IS investment is vital, especially in the early preparedness stage of a system development life cycle (SDLC), as this has a long-lasting impact on the SDLC. The selection process involves a complex, multiple criteria decision making process. The adoption of a multiple criteria decision tool, the Primitive Cognitive Network Process (PCNP), an alternative of the Analytic Hierarchy Process (AHP), can be challenging due to the minor differences among objects which are not appropriately evaluated by multiplication or ratio. This commonly results in rating judgement that occurs during the selection of alternatives. To address the challenges with IS planning, this paper proposes the use of the PCNP in various decision models. Three established studies of IS projects using the AHP are revisited using the proposed PCNP to demonstrate the feasibility and usability of the PCNP. The paper discusses data conversion from the AHP to the PCNP, its merits, and limitations. The proposed method can be a applied as an alternative decision tool for IS planning for various projects including Artificial Intelligence adoption projects, cloud sourcing planning projects, and mobile deployment projects.

Original languageEnglish
Pages (from-to)1759-1785
Number of pages27
JournalOperational Research
Volume22
Issue number3
DOIs
Publication statusPublished - Jul 2022

Keywords

  • Analytic hierarchy process
  • Information system engineering
  • Pairwise comparison
  • Primitive cognitive network process
  • PROMETHEE

ASJC Scopus subject areas

  • Numerical Analysis
  • Modelling and Simulation
  • Strategy and Management
  • Statistics, Probability and Uncertainty
  • Management Science and Operations Research
  • Computational Theory and Mathematics
  • Management of Technology and Innovation

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