123 research outputs found
sj-pdf-2-jsr-10.1177_10946705221079941 for Supplemental Material for Toward Multisensory Customer Experiences: A Cross-Disciplinary Bibliometric Review and Future Research Directions
No full textSupplemental Material, sj-pdf-2-jsr-10.1177_10946705221079941 for Toward Multisensory Customer Experiences: A Cross-Disciplinary Bibliometric Review and Future Research Directions by Susan Stead, Ruud Wetzels, Martin Wetzels, Gaby Odekerken-Schröder and Dominik Mahr in Journal of Service Research</p
sj-pdf-1-jsr-10.1177_10946705221079941 for Supplemental Material for Toward Multisensory Customer Experiences: A Cross-Disciplinary Bibliometric Review and Future Research Directions
No full textSupplemental Material, sj-pdf-1-jsr-10.1177_10946705221079941 for Toward Multisensory Customer Experiences: A Cross-Disciplinary Bibliometric Review and Future Research Directions by Susan Stead, Ruud Wetzels, Martin Wetzels, Gaby Odekerken-Schröder and Dominik Mahr in Journal of Service Research</p
Bayesian model selection with applications in social science
No full textOnderzoekers in de sociale wetenschappen moeten hun studies van tevoren centraal aanmelden en tevens van tevoren aangeven welke analyses zij gaan uitvoeren. Dit betoogt Ruud Wetzels naar aanleiding van zijn onderzoek naar Bayesiaanse alternatieven voor veelgebruikte frequentistische nulhypothesetoetsen. Veel wetenschappelijk onderzoek is gebaseerd op nulhypothesetoetsen. In de psychologische wetenschap zijn deze toetsen, vooral de frequentistische nulhypothesetoetsen, essentieel. Het is bekend dat de toetsten echter belangrijke nadelen hebben. Zo kunnen de eindresultaten makkelijk verkeerd geïnterpreteerd en misbruikt worden. Om te komen tot een alternatief voor de frequentistische benadering focust Wetzels zich op een andere statische filosofie, de Bayesiaanse. Vervolgens bespreekt hij hoe Ba
Bayesian model selection with applications in social science
No full textOnderzoekers in de sociale wetenschappen moeten hun studies van tevoren centraal aanmelden en tevens van tevoren aangeven welke analyses zij gaan uitvoeren. Dit betoogt Ruud Wetzels naar aanleiding van zijn onderzoek naar Bayesiaanse alternatieven voor veelgebruikte frequentistische nulhypothesetoetsen. Veel wetenschappelijk onderzoek is gebaseerd op nulhypothesetoetsen. In de psychologische wetenschap zijn deze toetsen, vooral de frequentistische nulhypothesetoetsen, essentieel. Het is bekend dat de toetsten echter belangrijke nadelen hebben. Zo kunnen de eindresultaten makkelijk verkeerd geïnterpreteerd en misbruikt worden. Om te komen tot een alternatief voor de frequentistische benadering focust Wetzels zich op een andere statische filosofie, de Bayesiaanse. Vervolgens bespreekt hij hoe Ba
code+data of Bartlema et al. (2014)
No full textThis is the code and data to reproduce the results reported in Bartlema, A., Lee, M., Wetzels, R., & Vanpaemel, W. (2014). A Bayesian hierarchical mixture approach to individual differences: Case studies in selective attention and representation in category learning. Journal of Mathematical Psychology. 59, 132-150. doi:10.1016/j.jmp.2013.12.00
The Influence of Instagram Post Aesthetic on the Purchase Intention of Sustainable Fashion
No full textThe goal of this thesis was to find a classical or aesthetic aesthetic for brand generated content on social media that has the most positive effect on purchase intention of sustainable fashion for females in generation Z. To answer this question, an experimental study with eye-tracking was conducted to measure the visual attention that was paid to the Instagram posts. However, no significant effect was found on the type of aesthetic of visual attention that was paid by the participants. Fashion involvement was evaluated as a moderator for this relationship, however no significant moderation effect was found. Also, consumer engagement had no mediation effect on the relationship of attention on purchase intention. However, the expressive aesthetic was found to attract more attention compared to the classical aesthetic due to its higher level of complexity. Findings provide new insights in the practical and scientific field of marketing
Tailored, more than ever.
No full textopic intrigue.
This thesis explores the impact of personalized generative artificial intelligence (AI) on consumer cognition and behavior within the marketing domain. The advent of AI and its integration into personalized marketing strategies has revolutionized how businesses interact with consumers, enabling the delivery of customized content tailored to individual preferences. Despite the rapid growth of AI technologies since 2020, there remains a need for in-depth research to understand its effectiveness fully.
The research focuses on how personalized AI solutions influence consumer behavior and cognition, particularly through initial intrigue. Initial intrigue, an essential factor in consumer engagement, was hypothesized to moderate the relationship between personalized AI and consumer behavior.
A mixed-method approach was employed, involving a survey and an experiment conducted on 200 participants from Radboud University, with 117 valid responses due to technological limitations. The survey assessed participants' initial interest in financial literacy, while the experiment measured cognitive responses using eye-tracking technology and the decision to take a flyer as a behavior indicator.
Key findings revealed no significant relationship between personalization and consumer behavior, challenging prevailing assumptions about AI's effectiveness in marketing. While there was some evidence suggesting a positive relationship between pupil dilation and desired behavior, overall results did not support the hypothesis that personalization leads to enhanced cognitive and behavioral responses.
Despite the limitations, including the restricted level of personalization and generalizability, this research provides valuable insights into the nuanced impact of personalized generative AI in marketing. It underscores the necessity for ongoing refinement and a deeper understanding of consumer preferences to develop more effective marketing strategies
To what extent can Dutch football clubs, according to visual attention, influence the daily behavior of their supporters in terms of sustainability, through social media?
No full textPurpose – Contributing to the debate on the possible role of Dutch football clubs on the sustainable behavior of their supporters.
Design/methodology/approach – In an experimental study with a between-group design, 65 participants were asked to look at a possible advertisement of a football club, while their eye movements were measured. Group one saw an advertisement with a player of the club they like, and group two saw an advertisement with the logo of the club they like. An additional survey revealed their attitude towards the advertisement and intention to change their behavior. For the analysis, partial least squares structural equation modelling was used.
Findings - The findings of this study indicate that the difference between advertisements does not lead to a significant change in their visual attention. Moreover, it disproves the expected role of celebrities on visual attention and the moderating role of team identification on these two. In addition, the study also reflects that visual attention, in turn, not leads to a certain attitude toward the campaign and that this attitude does not influence behavioral intention.
Research implications – To extent marketing and sustainable social media strategy literature, the findings provide new insights into the role of endorser type, visual attention, and a possible behavioral change in their daily routine.
Managerial implications – By investigating the role of endorser type, visual attention and behavioral change, marketeers of football clubs could improve their sustainability social media strategy.
Keywords – Endorser – Team identification – Visual attention – Social media strateg
Data from 617 healthy participants performing the Iowa gambling task: a "many labs" collaboration
No full textThis data pool (N = 617) comes from 10 independent studies assessing performance of healthy participants (i.e., no known neurological impairments) on the Iowa gambling task (IGT) - a task measuring decision making under uncertainty in an experimental context. Participants completed a computerized version of the IGT consisting of 95 - 150 trials. The data consist of the choices of each participant on each trial, and the resulting rewards and losses. The data are stored as .rdata, .csv, and .txt files, and can be reused to (1) analyze IGT performance of healthy participants; (2) create a "super control group"; or (3) facilitate model-comparison efforts
A Comparison of Reinforcement Learning Models for the Iowa Gambling Task Using Parameter Space Partitioning
No full textThe Iowa gambling task (IGT) is one of the most popular tasks used to study decisionmaking deficits in clinical populations. In order to decompose performance on the IGT in its constituent psychological processes, several cognitive models have been proposed (e.g., the Expectancy Valence (EV) and Prospect Valence Learning (PVL) models). Here we present a comparison of three models—the EV and PVL models, and a combination of these models (EV-PU)—based on the method of parameter space partitioning. This method allows us to assess the choice patterns predicted by the models across their entire parameter space. Our results show that the EV model is unable to account for a frequency-of-losses effect, whereas the PVL and EV-PU models are unable to account for a pronounced preference for the bad decks with many switches. All three models underrepresent pronounced choice patterns that are frequently seen in experiments. Overall, our results suggest that the search of an appropriate IGT model has not yet come to an end
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