Mining the hidden seam of proximity m-payment adoption: A hybrid PLS-artificial neural network analytical approach : WestminsterResearch

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Title	Mining the hidden seam of proximity m-payment adoption: A hybrid PLS-artificial neural network analytical approach
Type	Journal article
Authors	Giovanis, A., Rizomyliotis, I., Konstantoulaki, K. and Magrizos, S.
Abstract	This study investigates the adoption of proximity mobile payment services (PMPS) using, for the first time, an extended version of the decomposed theory of planned behaviour (DTPB) and considering both the linear and non-linear relationships depicted in the proposed model. Based on a two-stage hybrid analytic methodology, the proposed model was validated empirically using a sample of 951 participants. First, partial least squares (PLS) regression was used to identify the significant drivers of PMPS acceptance predictors. Artificial neural networks (ANN) were then used to rank the relative influence of the significant adoption drivers obtained in the previous step. The PLS results indicate that the extended DTPB provides a solid theoretical framework for studying the adoption of PMPS. The results of the PLS-ANN sensitivity analysis confirmed the PLS results regarding the importance of the determinants’ of normative and controlling customers’ beliefs, although there were some contradictions concerning the determination of customer attitudes and behavioural intentions towards PMPS usage. The results are discussed and implications are offered.
Keywords	Proximity mobile payment
	NFC
	Decomposed TPB
	Perceived risk
	Consumer behaviour
	Marketing of high-tech m-services
Journal	European Management Journal
Journal citation	40 (4), pp. 618-631
ISSN	0263-2373
Year	2021
Publisher	Elsevier
Accepted author manuscript	License CC BY-NC-ND 4.0 File Access Level Open (open metadata and files)
Digital Object Identifier (DOI)	https://doi.org/10.1016/j.emj.2021.09.007
Published online	20 Sep 2021

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