Title: Apps shoppers' behaviour and the moderating effect of product standardisation/brand recognition: a maximum likelihood estimation approach

Authors: Sajad Rezaei; Naser Valaei

Addresses: Chair of Marketing and Innovation, University of Hamburg, Hamburg, Germany; Taylor's Business School, Taylor's University, Subang Jaya, Selangor, Malaysia ' Department of Marketing, KEDGE Business School, Talence, France; Sunway University Business School, Sunway University, Bandar Sunway, Malaysia

Abstract: The purpose of this study is to examine the structural relationship between subjective norm, attitude, intention, and behaviour via Apps and the moderating effect of product standardisation/brand recognition. Confirmatory maximum likelihood estimation (MLE) approach, a covariance based-structural equation modelling (CB-SEM) technique, was performed for assessment of the reflective measurements, structural relationship between latent constructs and moderation effect. A total of 340 online questionnaires (N = 340) was collected and the results support the structural relationship between the latent constructs and specify a valid model fit (positive and direct effects). In addition, multigroup moderation SEM analysis (critical ratio values) reveal that the degree of standardisation/brand recognition (standard vs. non-standard) moderates the structural relationships. This study provides a basic set of guidelines for the application of confirmatory MLE in the evaluation of direct effects (one tail hypotheses) and multigroup SEM analyses for moderation effects. Theoretical and managerial implications of the study are further discussed.

Keywords: shopping intention via apps; shopping behaviour via apps; theory of reasoned action; TRA; subjective norm; attitude toward shopping via apps; product classification; maximum likelihood estimation approach.

DOI: 10.1504/IJEMR.2018.090893

International Journal of Electronic Marketing and Retailing, 2018 Vol.9 No.2, pp.184 - 206

Received: 27 Dec 2016
Accepted: 06 Feb 2017

Published online: 18 Jan 2018 *

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