Dataset Information

The Application of Mobile fNIRS in Marketing Research-Detecting the "First-Choice-Brand" Effect.

ABSTRACT: Recent research in the field of "neuro-marketing" shows promise to substantially increase knowledge on marketing issues for example price-perception, advertising efficiency, branding and shopper behaviour. Recently, an innovative and mobile applicable neuroimaging method has been proposed, namely functional near-infrared spectroscopy (fNIRS). However, this method is, in the research field of marketing, still in its infancy and is, consequently, lacking substantial validity. Against this background, this research work applied a convergent validity approach to challenge the validity of (mobile) fNIRS in the field of "neuro-marketing" and consumer neuroscience. More precisely, we aim to replicate a robust and well-investigated neural effect previously detected with fMRI-namely the "first-choice-brand" effect-by using mobile fNIRS. The research findings show that mobile fNIRS appears to be an appropriate neuroimaging method for research in the field of "neuro-marketing" and consumer neuroscience. Additionally, this research work presents guidelines, enabling marketing scholars to utilise mobile fNIRS in their research work.

SUBMITTER: Krampe C

PROVIDER: S-EPMC6222120 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

The Application of Mobile fNIRS in Marketing Research-Detecting the "<i>First-Choice-Brand"</i> Effect.

Krampe Caspar C Gier Nadine Ruth NR Kenning Peter P

Frontiers in human neuroscience 20181101

Recent research in the field of "neuro-marketing" shows promise to substantially increase knowledge on marketing issues for example price-perception, advertising efficiency, branding and shopper behaviour. Recently, an innovative and mobile applicable neuroimaging method has been proposed, namely functional near-infrared spectroscopy (fNIRS). However, this method is, in the research field of marketing, still in its infancy and is, consequently, lacking substantial validity. Against this backgrou ...[more]

PMID: 30443210

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Project description:PurposeTo assess the accuracy of e-Paarvai, an artificial intelligence-based smartphone application (app) that detects and grades cataracts using images taken with a smartphone by comparing with slit lamp-based diagnoses by trained ophthalmologists.MethodsIn this prospective diagnostic study conducted between January and April 2022 at a large tertiary-care eye hospital in South India, two screeners were trained to use the app. Patients aged >40 years and with a best-corrected visual acuity <20/40 were recruited for the study. The app is intended to determine whether the eye has immature cataract, mature cataract, posterior chamber intra-ocular lens, or no cataract. The diagnosis of the app was compared with that of trained ophthalmologists based on slit-lamp examinations, the gold standard, and a receiver operating characteristic (ROC) curve was estimated. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were computed.ResultsThe two screeners used the app to screen 2,619 eyes of 1,407 patients. In detecting cataracts, the app showed high sensitivity (96%) but low specificity (25%), an overall accuracy of 88%, a PPV of 92.3%, and an NPV of 57.8%. In terms of cataract grading, the accuracy of the app was high in detecting immature cataracts (1,875 eyes, 94.2%), but its accuracy was poor in detecting mature cataracts (73 eyes, 22%), posterior chamber intra-ocular lenses (55 eyes, 29.3%), and clear lenses (2 eyes, 2%). We found that the area under the curve in predicting ophthalmologists' cataract diagnosis could potentially be improved beyond the app's diagnosis based on using images only by incorporating information about patient sex and age (P < 0.0001) and best-corrected visual acuity (P < 0.0001).ConclusionsAlthough there is room for improvement, e-Paarvai app is a promising approach for diagnosing cataracts in difficult-to-reach populations. Integrating this with existing outreach programs can enhance the case detection rate.

Project description:Traditional learning techniques have evolved slowly and have yet to adapt the course content delivery to today's students' approaches to acquiring new knowledge. However, micro-learning has become popular in e-Learning environments as a course design technique due to short attention spans, demand for small chunks of information, and time constraints. Hence, it has been selected for creating reading mobile applications provided to the nature of its learning approach. In order to describe the multiple iterations of design, development, and evaluation of this general framework, a methodology named Design-Based Research (DBR) is implemented. First, the article presents the abstract framework components and a cloud-based software architecture that allows a modular approach to creating such applications. The pathway developed through adapting the iPAC framework, which involves personalization, authenticity, and collaboration, is part of the methodology used to design the app under pedagogical and technological considerations. The process demanded the following phases: analysis and exploration, design and construction, evaluation and reflection, redesign and reconstruction, and final critical reflections. Four applied instruments also validate the framework implementation: The iPAC Rubric, an aphorisms checklist, a pre and post-test, a focus group, and a usability test taken by 28 students in a private university in Colombia. Findings indicated that Design-Based Research (DBR) methodology emerged as an appropriate tool to encounter the needs behind reading applications design due to its sequence of operations yields results successively closer to adequate usability standards and smooth implementation. They also reveal the positive impact of new types of texts on students' motivation and awareness toward other reading strategies and micro-learning. This impact indeed proved the proposed framework's effectiveness for designing micro-learning applications.

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Dataset Information

The Application of Mobile fNIRS in Marketing Research-Detecting the "First-Choice-Brand" Effect.

Publications

The Application of Mobile fNIRS in Marketing Research-Detecting the "<i>First-Choice-Brand"</i> Effect.

Similar Datasets

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