R
Ralf van der Lans
Researcher at Hong Kong University of Science and Technology
Publications - 28
Citations - 1226
Ralf van der Lans is an academic researcher from Hong Kong University of Science and Technology. The author has contributed to research in topics: Viral marketing & Online advertising. The author has an hindex of 12, co-authored 28 publications receiving 1075 citations. Previous affiliations of Ralf van der Lans include Erasmus University Rotterdam.
Papers
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Journal ArticleDOI
A Viral Branching Model for Predicting the Spread of Electronic Word of Mouth
TL;DR: The proposed viral branching model allows customers to participate in a viral marketing campaign by 1 opening a seeding e-mail from the organization, 2 opening a viral e-mails from a friend, and 3 responding to other marketing activities such as banners and offline advertising.
Posted Content
A Viral Branching Model for Predicting the Spread of Electronic Word-of-Mouth
TL;DR: In this article, the authors developed a model using the theory of branching processes to predict how many customers a viral marketing campaign will reach, and how marketers can influence this process through marketing activities.
Journal ArticleDOI
Research Note---Competitive Brand Salience
TL;DR: It is shown that the salience of brands has a pervasive effect on search performance, and is determined by two key components: the bottom-up component is due to in-store activity and package design, and the top-down component isdue to out-of-store marketing activities such as advertising.
Book
Handbook of Marketing Decision Models
TL;DR: The second edition of the Handbook of Marketing Decision Models (HMM) as discussed by the authors provides a survey of recent developments in marketing decision models, including a review of the state-of-the-art, models for managerial decision making, and future research directions.
Journal ArticleDOI
Cross-National Logo Evaluation Analysis: An Individual-Level Approach
Ralf van der Lans,Joseph A. Cote,Catherine A. Cole,Siew Meng Leong,Ale Smidts,Pamela W. Henderson,Christian Bluemelhuber,Paul Andrew Bottomley,John R. Doyle,Alexander Fedorikhin,Janakiraman Moorthy,Balasubramanian Ramaseshan,Bernd H. Schmitt +12 more
TL;DR: In this article, a Bayesian, finite-mixture, structural equation model is developed to identify latent logo clusters while accounting for heterogeneity in evaluations, and the concomitant variable approach allows cluster probabilities to be country specific.