Bibliographic record
Abstract
Citation (2014), "Editorial Advisory Board", Shopper Marketing and the Role of In-Store Marketing (Review of Marketing Research, Vol. 11), Emerald Group Publishing Limited, Bingley, pp. xi-xii. https://doi.org/10.1108/S1548-643520140000011011 Publisher: Emerald Group Publishing Limited Copyright © 2014 Emerald Group Publishing Limited Rick P. Bagozzi University of Michigan, USA Russell Belk York University, Canada Ruth Bolton Arizona State University, USA George Day University of Pennsylvania, USA Michael Houston University of Minnesota, USA Shelby Hunt Texas Tech University, USA Arun K. Jain State University of New York at Buffalo, USA Barbara Kahn University of Pennsylvania, USA Wagner Kamakura Rice University, USA Dawn Iacobucci Vanderbilt University, USA Donald Lehmann Columbia University, USA Robert F. Lusch University of Arizona, USA Debbie MacInnis University of Southern California, USA Kent B. Monroe University of Illinois, USA Nelson Ndubisi Griffith University, Australia A. Parasuraman University of Miami, USA William Perreault University of North Carolina, USA Robert A. Peterson University of Texas, USA Nigel Piercy University of Warwick, UK Jagmohan S. Raju University of Pennsylvania, USA Vithala Rao Cornell University, USA Brian Ratchford University of Texas, USA Jagdish N. Sheth Emory University, USA Itamar Simonson Stanford University, USA David Stewart Loyola Marymount University, USA Rajan Varadarajan Texas A&M University, USA Stephen L. Vargo University of Hawaii, USA Michel Wedel University of Maryland, USA Book Chapters Shopper Marketing and the Role of In-Store Marketing Review of Marketing Research Shopper Marketing and the Role of In-Store Marketing Copyright Page List of Contributors List of Reviewers Editorial Advisory Board Series Introduction: Building Accumulated Knowledge and Focusing on Needed Research Volume Introduction: Shopper Marketing & In-Store Marketing: An Introduction Mobile Shopper Marketing: Assessing the Impact of Mobile Technology on Consumer Path to Purchase Tracing the Evolution & Projecting the Future of In-Store Marketing Six Lessons for In-Store Marketing from Six Years of Mobile Eye-Tracking Research The Shopper-Centric Retailer: Three Case Studies on Deriving Shopper Insights from Frequent Shopper Data How Do Marketing Actions and Customer Mindset Metrics Influence the Consumer’s Path to Purchase? Insights from In-Store Marketing Experiments Identifying the Drivers of Shopper Attention, Engagement, and Purchase Shopper Marketing 2.0: Opportunities and Challenges Previous Volume Contents
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.016 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".