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MARKETING STRATEGY AND CONSUMERS BEHAVIOR WITH ROUGH

NEUTROSOPHIC SET: AN ANALYSIS OF ROUGHNESS SIMILARITY MEASURE

FOR UNCERTAIN CRITERIA

NAME: DARISH AIDILZARIF BIN RIDZUAN FAUZIE

SUPERVISOR: DR SURIANA BINTI ALIAS

FACULTY OF COMPUTER AND MATHEMATICAL SCIENCES, UITM CAWANGAN KELANTAN KAMPUS MACHANG K242/01

ABSTRACT IMPLEMENTATION

Roughness approximation for Set A for the Food Industry sector
(P2) S RNS (A, B) = 1 ⇔ A = B if and only if B = A, which state
Marketing strategies evaluation helps in better targeting of consumers, optimal that when A = B, then obviously S RNS (A, B) = 1.

resource utilization, and better strategic decision-making under conditions of

uncertainty. This study develops an extended similarity measure arising from

rough neutrosophic sets to overcome problems of imprecision and opposition

from differing experts. The methodology has two main phases: first, it develops

the roughness-based similarity measure and, second, it applies this measure in

the evaluation of marketing strategies within the food industry sector and the

fashion retail sector. The findings show that the proposed approach properly

identifies uncertainty via rough approximations and gives structure while
performing rank ordering of strategic alternatives. The results show that This implies A = B.

experiential marketing (Alternative A3) is the best strategic tactic for the food

industry sector, opposite to augmented reality marketing (Alternative A6) in the (P3) S RNS (A, B) = S RNS (B, A)

fashion retail sector. The study is a step further in decision-support models for It is obvious that:

marketing by promoting a dependable tool for the evaluation of complicated Which states that: Roughness approximation for Set A for the Fashion Retail sector

alternatives under uncertainty. Future research may extend this method to

dynamic environments and integrate it with artificial intelligence for enhanced

strategic analysis.

Therefore,

PROBLEM STATEMENT

To expand marketing strategies, organizations must consider the constant

market flux and competition. Yet, current evaluation methods rarely account for
uncertainty or the diverse solutions customers show for a given problem. (P4) S RNS (A,C) ≤ S RNS (A, B) and S RNS (A,C) ≤ S RNS (B,C) if A ⊆ B ⊆ C,

Analyses of consumer behavior and resulting strategies often rely on when C ∈SVNS(X).

deterministic models, overlooking the complex and uncertain nature of human If A ⊆ B ⊆ C for A, B,C ∈ RNS, then:

decision-making. These models are ineffective, as they fail to consider factors S RNS (A,C) ≤ S RNS (A, B) and S RNS (A,C) ≤ S RNS (B,C).

like emotions, culture, and life experiences that influence consumer choices. Let A ⊆ B ⊆ C, which implies that: Then, we obtain the following relation: The result of calculation for Food Industry sector

This research tries to propose a model that gives more detail on the

imprecision and uncertainty aspects that are present within consumer behavior

studies, traditionally formulated under the RNS. Such models possess three

elements of belief- truth, indeterminacy, and falsity, which, when combined,

help depict a more accurate representation of the dynamic and often Combining (a), (b), and (c), we obtain:

antithetical nature that is associated with consumers’ preferences.

The result of calculation for Fashion Retail sector

Objective

and

To develop a roughness similarity measure for rough neutrosophic set by Implies that S (A,C) ≤ S (A, B)
satisfying the similarity properties. RNS RNS
and S RNS (A,C) ≤ S RNS (B,C).

To determine the roughness similarity measure for each criteria.

To apply the proposed roughness similarity measure for marketing strategy PHASE 2: The determination of a roughness similarity measure for

and consumer behavior. marketing strategy and consumer behavior Results

Linguistics values for Rough Neutrosophic Sets

Methodology Results for Food industry sector Results for Fashion retail sector

Relation between alternatives and Relation between alternatives and A3 ≺ A1 ≺ A6 ≺ A2 ≺ A5 ≺ A4 A6 ≺ A2 ≺ A4 ≺ A5 ≺ A1 ≺ A3

criteria for the Food Industry sector criteria for the Fashion Retail sector

DISCUSSION

The extended roughness similarity measure for RNS emphasizes the

calculation of roughness approximation, linking lower and upper

approximations for better decision-making.
The data collected from (S´anchez et al., 2023) were converted into RNS The comparison result of the existing with the same case study for the

data using linguistic terms as cited in (Rogulj et al., 2021). This purpose Food industry sector

focused on:

The comparison result of the existing with the same case study for the

Fashion retail sector

IMPLEMENTATION

Set B, by contrast, was deliberately generated from Set A through the Results showed that the best strategy is experiential marketing for food

Ideal Alternative A* formulation. industry, while augmented reality marketing for fashion retail, as it scored

PHASE 1: The determination of proposed roughness similarity measure. the highest similarity measure.

Relation between alternatives and Ideal Alternative A* formulation The results also demonstrated that this approach is superior in

criteria for Food Industry sector for Food Industry sector addressing uncertainty compared to other methods.

The extended rough Conclusion

neutrosophic sets between

any two RNSs A and B sastify Rough Neutrosophic of Rough Neutrosophic of

the following properties: Set A Set B This study introduced a rough neutrosophic similarity measure to

improve marketing strategy evaluation under uncertainty. By

capturing truth, indeterminacy, and falsity in a rough framework, the

method offers a clearer and more accurate comparison between

alternatives.

Unlike traditional models, it handles vague and incomplete data

effectively. The results showed that this approach identifies better-
fitting strategies by reflecting real consumer behavior. It provides

organizations with a reliable tool to support smarter and more

informed marketing decisions.
(P1) From the definition of RNSs A and B, Relation between alternatives and Ideal Alternative A* formulation

criteria for Fashion Retail sector for Fashion Retail sector
RECOMMENDATION

The truth (T) roughness

measure for Set A indicates for Roughness measure Roughness measure

X1 is calculated as follows for Set A for Set B This methodology can be expanded by including more alternatives
and expert opinions. It can also be refined by applying it to other

sectors like tourism, health marketing, and public services.

Incorporating dynamic or time-sensitive data may further enhance

decision accuracy. Overall, the framework offers flexibility and

reliability, making it valuable for marketing professionals and policy-

makers in complex decision-making.

Then, by using the extended roughness similarity measure Set A and Set B is

calculated as follows
REFERENCES

Roughness approximation for Set B Roughness approximation for Set B S´anchez, M. D. O., N´u˜nez, A. M., and Ruiz, C. V. (2023). Optimizing marketing

for the Food Industry sector for the Fashion Retail sector strategies under rough neutrosophic critic. Decision Science Letters, 12, 451–468.

Rogulj, A., Blaˇziˇc, I., and Marˇciˇc, A. (2021). Rough neutrosophic vikor: Application

to infrastructure planning problems. Soft Computing, 25, 2577–2594.