Enhancing Marketing Personalized Shopping Recommendations in the UAE: Leveraging Logic Mining and Advanced Technologies

SHWEDEH, Fanar; DABASH, Amro; DABBAGH, Tamadher AL; ABURAYYA, Ahmad

Enhancing Marketing Personalized Shopping Recommendations in the UAE: Leveraging Logic Mining and Advanced Technologies

Foundations of Management

Volume 16 (2024): Issue 1 (January 2024)

By:

Fanar SHWEDEH, Amro DABASH

, Tamadher AL DABBAGH and Ahmad ABURAYYA

Open Access

|Dec 2024

Figures & Tables

Research framework
(Source: Authors’ own research) — Research framework (Source: Authors’ own research)

G*Power version 3.1.9.4 used to compute the sample size of the study
(Source: Authors’ own research) — G*Power version 3.1.9.4 used to compute the sample size of the study (Source: Authors’ own research)

Measurement model
(Source: Authors’ own research) — Measurement model (Source: Authors’ own research)

Structural model assessment (hypothesis testing)
(Source: Author’s own research) — Structural model assessment (hypothesis testing) (Source: Author’s own research)

Planned costs for the production department for 2022 AD (Source: Authors’ own research)

Construct	Items	Item loadings	CR	AVE	Discriminant validity
DS.	ds1	0.872	0.859	0.776	Yes
-	ds2	0.918	-	-	-
-	ds3	0.852	-	-	-
CD	cd1	0.901	0.77	0.683	Yes
-	cd2	0.719	-	-	-
-	cd3	0.848	-	-	-
Price	Pi1	0.872	0.828	0.640	Yes
-	pi2	0.836	-	-	-
-	pi3	0.754	-	-	-
-	pi4	0.731	-	-	-
P brand	pb1	0.791	0.866	0.78	Yes
-	pb2	0.943	-	-	-
-	pb3	0.908	-	-	-
P. Qua	pq1	0.916	0.822	0.762	Yes
-	pq3	0.908	-	-	-
-	pq4	0.789	-	-	-
Con	con1	0.670	0.73	0.643	Yes
-	con2	0.861	-	-	-
-	con3	0.856	-	-	-
SR	sr2	0.907	0.783	0.603	Yes
-	sr3	0.856	-	-	-
-	sr4	0.892	-	-	-
-	-	-	-	-	-
PWM	pwm1	0.931	0.851	0.59	Yes
-	pwm2	0.938	-	-	-
SME	sme1	0.797	0.829	0.727	Yes
-	sme2	0.879	-	-	-
-	sme3	0.879	-	-	-
EC	ec1	0.905	0.901	0.769	Yes
-	ec2	0.886	-	-	-
-	ec3	0.883	-	-	-
-	ec4	0.832	-	-	-
SMP	smp1	0.902	0.81	0.669	Yes
-	smp2	0.901	-	-	-
-	smp3	0.616	-	-	-

HTMT correlation matrix (Source: Authors’ own research)

	CD	DS	EC	P brand	P. Qua	PWM	SME	SMP	SR	con
DS	0.849	-	-	-	-	-	-	-	-	-
EC	0.754	0.466	-	-	-	-	-	-	-	-
P brand	0.296	0.268	0.889	-	-	-	-	-	-	-
P. Qua	0.301	0.027	0.867	0.247	-	-	-	-	-	-
PWM	0.612	0.525	0.475	0.505	0.45	-	-	-	-	-
SME	0.573	0.766	0.532	0.772	0.837	0.526	-	-	-	-
SMP	0.456	0.034	0.885	0.319	0.391	0.259	0.906	-	-	-
SR	0.683	0.459	0.418	0.488	0.369	0.655	0.36	0.708	-	-
con	0.597	0.496	0.755	0.841	0.891	0.448	0.777	0.507	0.446	-
price	0.549	0.709	0.651	0.829	0.68	0.678	0.56	0.814	0.665	0.365

Collinearity analysis (Source: Authors’ own research)

Construct	VIF
Challenges	1.33
Pro Att	1.587
Trust and satisfaction	2.494

Hypothesis testing (Source: Authors’ own research)

Hypotheses	Relationships	B	Standard deviation	T stat	P-values	Decision
H₁	Challenges -> PR	0.481	0.09	5.332	0.000	Accepted
H₂	Pro Att -> PR	0.327	0.095	3.449	0.001	Accepted
H₃	Trust and Sat -> PR	0.102	0.048	2.115	0.034	Accepted

DOI: https://doi.org/10.2478/fman-2024-0021 | Journal eISSN: 2300-5661 | Journal ISSN: 2080-7279

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Language: English

Page range: 345 - 358

Published on: Dec 31, 2024

Published by: Warsaw University of Technology

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

Personalized Recommendation Systems,

Consumer Challenges,

Trust and Satisfaction,

Related subjects:

Business and economics,

Business management,

Management accounting, financial controlling, cost calculation, investment

© 2024 Fanar SHWEDEH, Amro DABASH, Tamadher AL DABBAGH, Ahmad ABURAYYA, published by Warsaw University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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Enhancing Marketing Personalized Shopping Recommendations in the UAE: Leveraging Logic Mining and Advanced Technologies

Figures & Tables

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Planned costs for the production department for 2022 AD (Source: Authors’ own research)

HTMT correlation matrix (Source: Authors’ own research)

Collinearity analysis (Source: Authors’ own research)

Hypothesis testing (Source: Authors’ own research)

Paradigm

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