Prioritizing the Components of Online Environment to Assess Customer Experience: An Interpretive Structural Modeling Approach

Ruchi Jain Garg, Vandana (b8887092-79d1-4400-8995-75fb01e06fca, Vinod Kumar

Source Title: International Journal of E-Business Research (IJEBR)17(2)

ISSN: 1548-1131|EISSN: 1548-114X|EISBN13: 9781799859598|DOI: 10.4018/IJEBR.2021040105

MLA

Garg, Ruchi Jain, et al. "Prioritizing the Components of Online Environment to Assess Customer Experience: An Interpretive Structural Modeling Approach." IJEBR vol.17, no.2 2021: pp.1-21. http://doi.org/10.4018/IJEBR.2021040105

APA

Garg, R. J., Vandana (b8887092-79d1-4400-8995-75fb01e06fca, & Kumar, V. (2021). Prioritizing the Components of Online Environment to Assess Customer Experience: An Interpretive Structural Modeling Approach. International Journal of E-Business Research (IJEBR), 17(2), 1-21. http://doi.org/10.4018/IJEBR.2021040105

Chicago

Garg, Ruchi Jain, Vandana (b8887092-79d1-4400-8995-75fb01e06fca, and Vinod Kumar. "Prioritizing the Components of Online Environment to Assess Customer Experience: An Interpretive Structural Modeling Approach," International Journal of E-Business Research (IJEBR) 17, no.2: 1-21. http://doi.org/10.4018/IJEBR.2021040105

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Abstract

The present study aims to identify and prioritize the components of customer experience in online environment. The study employs Pareto analysis and interpretive structural modeling (ISM) to accomplish above-mentioned objective. Firstly, 36 components have been derived from extensively reviewed literature, and out of them, 15 were finalized as vital few variables having 80% influence in creating customer experience in online environment. To assess the impact of these 15 components, one outcome component ‘Customer Experience (Flow)' has been added. So, an ISM technique is applied on a total of 16 components of customer experience in online environment. The aim of this technique is to highlight the interrelationships among the components and to prioritize them. Further, the findings are strengthened by using MICMAC analysis. Results revealed that time distortion, skill, focused attention, interactivity, playfulness, start web, and involvement are found to have weak dependence powers but with strong driving powers. However, control, challenge, arousal, telepresence, flow, positive affect, and exploratory behavior were found to possess weak driving power and strong dependence power. The results of the present study carry implications for academicians and marketers handling online experience of their customers.