Brunel University London

About: Brunel University London is a education organization based out in London, United Kingdom. It is known for research contribution in the topics: Large Hadron Collider & Population. The organization has 10918 authors who have published 29515 publications receiving 893330 citations. The organization is also known as: Brunel & University of Brunel.

Antecedents of continuance intentions towards e-shopping: The case of Saudi Arabia

Abstract: The purpose of this study is to clarify the theoretical problem and identify factors that could explain the level of continuance intentions towards e-shopping. It aims to propose a revised technology acceptance model that integrates expectation confirmation theory to measure age differences with regard to continuance intentions towards e-shopping in Saudi Arabia. Design/methodology/approach: The sample (n=465) consists of 68.8 per cent women and 31.4 per cent men, 348 younger than 35 years old and 117 older than 35. A structural equation model confirms model fit. The model explains 65 per cent of the intention to continue shopping online. Findings- Perceived usefulness, enjoyment, and subjective norms are determinants of online shopping continuance. The structural weights are mostly equivalent between the young and old groups, but the regression path from subjective norms to perceived usefulness is not invariant, with that relationship being stronger for the younger respondents. Research limitations/implications- The research findings imply that usefulness and subjective norm contribute to continuance intentions to some extent, but it is enjoyment that leads to a higher level of continuance intentions. Online strategies cannot ignore either the direct or the indirect effects on continuance intentions in Saudi Arabia. Originality/value- The paper adds to the understudied area of online shopping continuance intentions in the Arab World in general, and Saudi Arabia in particular, by examining the effects of usefulness, enjoyment, and subjective norms on continuance intentions. © Emerald Group Publishing Limited.

Shrinkage observed-to-expected ratios for robust and transparent large-scale pattern discovery

Abstract: Large observational data sets are a great asset to better understand the effects of medicines in clinical practice and, ultimately, improve patient care. For an empirical pattern in observational data to be of practical relevance, it should represent a substantial deviation from the null model. For the purpose of identifying such deviations, statistical significance tests are inadequate, as they do not on their own distinguish the magnitude of an effect from its data support. The observed-to-expected (OE) ratio on the other hand directly measures strength of association and is an intuitive basis to identify a range of patterns related to event rates, including pairwise associations, higher order interactions and temporal associations between events over time. It is sensitive to random fluctuations for rare events with low expected counts but statistical shrinkage can protect against spurious associations. Shrinkage OE ratios provide a simple but powerful framework for large-scale pattern discovery. In this article, we outline a range of patterns that are naturally viewed in terms of OE ratios and propose a straightforward and effective statistical shrinkage transformation that can be applied to any such ratio. The proposed approach retains emphasis on the practical relevance and transparency of highlighted patterns, while protecting against spurious associations.