Enter your keyword

ABSTRACT

DEVELOPMENT OF MEASUREMENT SCALE FOR PERSONALIZED RECOMMENDED PRODUCT ACCEPTANCE (PRPA-SCALE)

Journal: Malaysian E Commerce Journal (MECJ)
Ampadu Seth, Yuanchun Jiang, Samuel Adu Gyamfi, Debrah Emmanuel, Eric Amankwa

This is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Doi: 10.26480/mecj.02.2022.76.85

Personalized recommendation (PR) system has been established to enhance consumers experience by suggesting products for consumers. Researches in PR mostly focus on the PR “system” acceptance but not the “actual product” acceptance. Therefore, this research aims to develop an instrument that measures the psychometric indicators of personalized product recommended acceptance. The sample size for the study were made up of (N=521) consumers from various accessible online marketplace in Ghana (jumia, kikuu, tonaton, jiji, amazon, eBay, aliexpress, alibaba, and other platforms) who represent the end-users of the e-tailers recommendation system. The study was quantitative in nature. In an attempt to evaluate the Personalized Recommended Product Acceptance Scale (PRPA-SCALE), an instrument was developed then submitted to 1 psychologist, 2 Marketing experts and 2 consumer behaviour researchers to test and rate items developed for the study. The EFA and CFA were performed to validate the instrument. The finding indicated significant content validity and further provided evidence of a good model fit for the five factors extracted constructs ((Price consciousness (5 items), Product Review factor (6 items), Brand Influence (5 items), Perceived Quality (6 items) and E-tailers Promotional Factor (8 items)). To the best of our knowledge, the PRPA-scale is the first scale robustly developed to measure personalized product recommendation acceptance. This also set the foundation for future research in the area of consumers’ purchase decision processes towards recommended products.

Pages 76-85
Year 2022
Issue 2
Volume 6

Download