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Predicting Trustworthiness of an E-Commerce Platform from the Consumer Perspective


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1 Department of Computing and Informatics, University of Nairobi, Chiromo Campus, Nairobi, P.O. Box 30197, Kenya

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Internet shopping has become part and parcel of our day to day lives. Coupled with COVID-19 pandemic and the necessity to keep social distancing, many people have resorted to online shopping as a way of reducing potential exposure to the deadly virus. Online vendors have tried to follow the trends and put up online shops in unprecedented numbers. These myriad of alternatives have given room to unscrupulous vendors to also sneak in their products with an intention to defraud inexperienced online buyers. This massive number of online shops makes it impractical for an average user to assess with certainty which shop is trustworthy and which one is potentially fraudulent. In this study, we carry out a research to establish the indicators of trust in an e-commerce platform from the consumer perspective. We carry out a survey, focus group discussions and in-depth interview with a community within a public university to establish the factors they consider to conclude that an e-commerce platform is trustworthy or otherwise. We the use Exploratory Factor Analysis (EFA) and Principal Component Analysis (PCA) as our data analysis procedures. For EFA, we obtain uniqueness, factor loadings, scree plot, Eigen values, parallel analysis, optimal coordinates, and acceleration factor. For PCA, we obtain PCA Importance of Components, Loadings, Scree Plot, and biplot. We also obtain a Cronbach’ alpha of 0.959 which indicates reliable data. Further research will involve creating a model from these results which can be used as a trust adjustment factor for autonomous use in artificial intelligence driven recommender system in ecommerce platforms.

Keywords

Decision Support, e-Commerce, Recommender Systems, Scale Development, Trust.
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  • Predicting Trustworthiness of an E-Commerce Platform from the Consumer Perspective

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Authors

Edwin Ouma Ngwawe
Department of Computing and Informatics, University of Nairobi, Chiromo Campus, Nairobi, P.O. Box 30197, Kenya
Elisha Odira Abade
Department of Computing and Informatics, University of Nairobi, Chiromo Campus, Nairobi, P.O. Box 30197, Kenya
Stephen Nganga Mburu
Department of Computing and Informatics, University of Nairobi, Chiromo Campus, Nairobi, P.O. Box 30197, Kenya

Abstract


Internet shopping has become part and parcel of our day to day lives. Coupled with COVID-19 pandemic and the necessity to keep social distancing, many people have resorted to online shopping as a way of reducing potential exposure to the deadly virus. Online vendors have tried to follow the trends and put up online shops in unprecedented numbers. These myriad of alternatives have given room to unscrupulous vendors to also sneak in their products with an intention to defraud inexperienced online buyers. This massive number of online shops makes it impractical for an average user to assess with certainty which shop is trustworthy and which one is potentially fraudulent. In this study, we carry out a research to establish the indicators of trust in an e-commerce platform from the consumer perspective. We carry out a survey, focus group discussions and in-depth interview with a community within a public university to establish the factors they consider to conclude that an e-commerce platform is trustworthy or otherwise. We the use Exploratory Factor Analysis (EFA) and Principal Component Analysis (PCA) as our data analysis procedures. For EFA, we obtain uniqueness, factor loadings, scree plot, Eigen values, parallel analysis, optimal coordinates, and acceleration factor. For PCA, we obtain PCA Importance of Components, Loadings, Scree Plot, and biplot. We also obtain a Cronbach’ alpha of 0.959 which indicates reliable data. Further research will involve creating a model from these results which can be used as a trust adjustment factor for autonomous use in artificial intelligence driven recommender system in ecommerce platforms.

Keywords


Decision Support, e-Commerce, Recommender Systems, Scale Development, Trust.

References





DOI: https://doi.org/10.17010/ijcs%2F2022%2Fv7%2Fi4%2F172376