Notice bibliographique
Résumé
NAJP: Dr. Suomala, tell our readers about the work you have been doing and your plans for the immediate future. JS: My plan is again to go to UCSB for the academic year, 2015/2016, as part of a NeuroService-project, funded by the Finnish Funding Agency for Innovations (TEKES), Laurea, and various other companies. I am eager to understand human behavior in different contexts (learning at school, creativity and innovation at science and companies, and most recently, consumers' behavior and impact on the market from a neuromarketing point of view). NAJP: Now tell us about the journal that this research was published in, and your co-authors. JS: TIM Review (Technology Innovation Management Review) publishes works relating to evidence-based management science. Thus, it provides insightful content about emerging trends relevant to technology start-ups and growing technology businesses. The readers of TIM Review look for new ideas from a scientific point of view, and the readers come from business, academics and public sectors. This article is based on the thesis of one of my former students, Lauri Palokangas (2010). Lauri then worked at Nokia as a marketing manager and he liked to apply modern neuroscientific technology to the sales process. So, we decided to test how customers experience stimuli during the five steps of Nokia Flagship -store processes by using fMRI (functional magnetic resonance imaging). As a neuroscientist, and neuro-radiologist, Dr. Jussi Numminen from the University of Helsinki, had a very important role in the project as fMRI-expert both in data-collection situations, and data-analysis. My colleagues at Laurea, Dr. Jarmo Heinonen and Dr. Seppo Leminen contributed their expertise for the project. Jarmo Heinonen is an expert in methodology and Seppo Leminen is particularly skilled in business models in marketing. In addition, Professor Seppo Westerlund at Carleton University's Sprott School of Business in Ottawa had an important role as an expert in business models. Thus, it was my pleasure to work with a highly motivated, high level multidisciplinary scientific research group during the project and publications (Palokangas, Suomala, Heinonen, Maunula, & Numminen, 2012; Suomala, Hlushchuk, Heinonen, Palokangas, & Numminen, 2015; Suomala, Palokangas, Leminen, Westerlund, Heinonen, & Numminen, 2012).. NAJP: Jyrki, what exactly do you mean by neuromarketing? JS: Neuromarketing is a new multidisciplinary discipline in which scientists collect and apply neuroscientific knowledge to practical business problems by using different neuroscientific methods, from eye-tracking, skin-conductance and EEG to the fMRI. Thus, it is an evidence-based approach to practical business problems. NAJP: Using scans to determine what people might or might not is a tenuous inferential leap. What has your research found? JS: The most important result of our Nokia study was that we can collect reliable and valid data from and about the process as measured by fMRI. Thus, we showed that the process works and companies can use this method. We did not find a buy button or buying in the brain. Most of the fMRI studies until now have used the correlational method (At this moment subjects saw the screen of the smart phone and at the same time the reward areas in the seem to be activated) and these correlations do not imply a behavioral change, or behavior in the future. However, other scientists have shown that the human has valuation networks, which compute value from very biological issues (food, partner) to cultural issues like music and money. This network consists of the medial prefrontal cortex, striatum and precuneus. Scientists in the USA, like Emily Falk (professor at the University of Pennsylvania) and Rene Weber (Professor at UCSB) have developed brain as a predictive model which uses activation patterns as predictive tools in order to explain the change in human behaviors. …
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».