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Likelihood to Recommend (L2R) Prediction Using Quality of Experience (QoE) Measurements: A Longitudinal Study

Authors:
Amin Azad
Farzad Nejatimoharrami
Mark Chignell

Keywords: Video quality assessment; Quality of Experience (QoE); Comparison of rating scales; subjective evaluation; accessibility; retainability; longitudinal study; consumer satisfaction.

Abstract:
Models that predict satisfaction with a service over time need to consider the impact of emotions and remembered quality of experience in creating attitudes towards a service. However, prior research on subjective quality of experience has typically focused on experiments conducted in a single session or over a short period of time. Thus, there is a gap between our understanding of instantaneous quality of experience and longterm judgments, such as overall satisfaction and likelihood to recommend and likelihood to churn. The goal of the study in this paper was to carry out a longitudinal study that would provide initial insights into how experiences of service quality over time are mediated through emotions and memory and accumulated into longer term attitudes about the service. The longitudinal study was carried out over a period of roughly 4 weeks with around 3 sessions per week. A specially constructed online service was used where participants could select YouTube videos to view, and the service would randomly add impairments to the videos before playing back the videos and then asking questions relating to Quality of Experience, Technical Quality and overall frustration and satisfaction. In this paper, we report on the results obtained from the first 8 sessions of data.

Pages: 1 to 5

Copyright: Copyright (c) IARIA, 2019

Publication date: March 24, 2019

Published in: conference

ISSN: 2308-3964

ISBN: 978-1-61208-693-4

Location: Valencia, Spain

Dates: from March 24, 2019 to March 28, 2019