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

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.

Appendices

Appendix 1

See Tables 14, 15, 16, 17, 18, 19 and 20.

Table 14 Pairwise reciprocal matrices (Muralidhar et al. 1990) and consistence ratios (CRs) for IS project comparisons (Case 1)

Table 15 Prioritization results, aggregation results, and ranks using AHP (Case 1)

Table 16 Pairwise reciprocal matrices (Yang and Huang 2000) and consistence ratios (CRs) for Case 2

Table 17 Aggregation of weights from AHP results (Case 2)

Table 18 Absolute measurement score (Yang and Huang 2000), weighted decision table with aggregation results and ranks using AHP (Case 2)

Table 19 Pairwise reciprocal matrix (Wang and Yang 2007) and weight (CR = 0.036) (Case 3)

Table 20 PROMETHEE flows with AHP (Case 3)

Appendix 2: (PROMETHEE II).

On the basis of (Brans et al. 2005), the notations and details of PROMETHEE II used in this paper are presented as below.

Step 1: Formulate decision matrix

A typical m by n decision matrix O is shown Eq. (3).

Step 2: Calculate aggregated preference indices

$P_{j} \left( {T_{i} ,T_{k} } \right) = P_{j} \left( {d\left( {T_{i} ,T_{k} } \right)} \right) = P_{j} \left( {r_{ij} - r_{kj} } \right)$ is a preference function to measure how much $T_{i}$ prefers to $T_{k}$ with respect to $c_{j}$. Six types of preference functions $P\left( d \right)$’s were proposed in (Brans et al. 2005). Aggregated preference index $\pi \left( {T_{i} ,T_{k} } \right)$ shown as below indicates the degree of how $T_{i}$ is preferred to $T_{k}$ over all the criteria.

$$\pi \left( {T_{i} ,T_{k} } \right) = \frac{{\sum\limits_{j = 1}^{n} {P_{j} \left( {T_{i} ,T_{k} } \right) \cdot w_{j} } }}{{\sum\limits_{j = 1}^{n} {w_{j} } }}\forall T_{i} ,T_{k} \in T\;and\;i \ne k$$

(A1)

Step 3: Calculate outranking flow

In order to rank the alternatives, the outranking flows are defined as follows.

The positive outranking flow is of the form:

$$\phi^{ + } \left( {T_{i} } \right) = \frac{1}{m - 1}\sum\limits_{k = 1}^{m} {\pi \left( {T_{i} ,T_{k} } \right)}$$

(A2)

The negative outranking flow is of the form:

$$\phi^{ - } \left( {T_{i} } \right) = \frac{1}{m - 1}\sum\limits_{k = 1}^{m} {\pi \left( {T_{k} ,T_{i} } \right)}$$

(A3)

The net outranking flow is applied and is of the form:

$$\phi \left( {T_{i} } \right) = \phi ^{ + } \left( {T_{i} } \right) - \phi ^{ - } \left( {T_{i} } \right),\quad \forall i \in \left\{ {1, \ldots ,m} \right\}$$

(A4)

Appendix 3

See Table 21.

Table 21 Summary of Notations: See 21