Tracking users' visual attention and responses to personalized advertising based on task cognitive demand

Bang, Hyejin; Wojdynski, Bartosz W.

doi:10.1016/j.chb.2015.10.025

Cited by 129 publications

(85 citation statements)

References 41 publications

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“…The most cited incidents (40.0% of the total) referred to inaccurate recommendations, what suggests that improving system's accuracy is still a major determinant of recommendation agents' performance. This corroborates with propositions from Bang and Wojdynski (2016), Aljukhadar et al (2012) and Chen et al (2004), for whom system accuracy continues to be the main indicator to be improved in recommendation research.…”

Section: Resultssupporting

confidence: 79%

“…Drawing on Rodger and Thorson's (2000) idea that the consumers' Internet motives influence the level of cognitive effort devoted to the task, we argue that the level of cognitive load involved in a certain task will influence the way consumers deal with recommendation failure. More specifically, we rely on Bang and Wojdynski (2016), who have recently found that task cognitive demand moderates the effects of personalization on attention, to defend that information-seekers may be more prone to recognize failure in recommendations than entertainment seekers. Additionally, we believe that the importance attributed to the item being recommended will be an important moderator of this relation.…”

Section: Chart 2 Comprehensive Framework For Analyzing Recommendatiomentioning

confidence: 99%

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Incomodando consumidores: quando agentes de recomendação não facilitam a nossa vida realmente

Verruck

Nique

2017

BASE

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Este é um artigo de acesso aberto, licenciado por Creative Commons Atribuição 4.0 Internacional (CC BY 4.0), sendo permitidas reprodução, adaptação e distribuição desde que o autor e a fonte originais sejam creditados. ABSTRACTIntelligent systems have been used in different types of websites with the intention of creating personalized messages and understanding consumers' needs more deeply. They are supposed to facilitate the decision-making process, make internet browsing easier and give users a sense of social feeling and personalization. So far, research in the field has focused attention on the positive aspects of using these systems. Little effort has been made, however, to try to recognize and correct situations in which they do not perform so well. This work is the result of an exploratory research destined to understand the broadness of the concept of failure in personalized environments as well as its antecedents and consequents. Based on the critical incident technique, we collected the opinion of 86 subjects in a multicultural environment and used their responses to elaborate a comprehensive framework of recommendation failure considering the different motivations for Internet use.Keywords: personalization, online shopping, recommendation agents, recommendation failure. RESUMOSistemas inteligentes têm sido usados em diferentes tipos de websites com a intenção de criar mensagens personalizadas e de entender as necessidades dos consumidores com mais profundidade. Eles supostamente facilitam o processo de tomada de decisão, tornam a navegação mais fácil e dão aos usuários uma sensação de trato social e personalização. Até agora, a pesquisa na área tem focado nos aspectos positivos de usar tais sistemas. Pouco esforço tem sido feito, entretanto, para tentar reconhecer e corrigir situações nas quais eles não apresentam um bom desempenho. Este trabalho é o resultado de uma pesquisa exploratória destinada a entender a amplitude do conceito de falha em ambientes personalizados, assim como seus antecedentes e consequentes. Baseados na técnica do incidente crítico, coletamos a opinião de 86 participantes em um ambiente multicultural e usamos suas respostas para elaborar um framework abrangente para a falha em recomendação, considerando as diferentes motivações para o uso da internet.Palavras-chave: personalização, compra on-line, agentes de recomendação, falha na recomendação.

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Section: Resultssupporting

confidence: 79%

Section: Chart 2 Comprehensive Framework For Analyzing Recommendatiomentioning

confidence: 99%

Incomodando consumidores: quando agentes de recomendação não facilitam a nossa vida realmente

Verruck

Nique

2017

BASE

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“…For instance, eye-tracking research into the effects of ads that are personalized based on demographic variables (e.g., name or gender) showed that personalized ads attract more attention than nonpersonalized ads (Bang and Wojdynski 2016). For now, we have no insights into whether behaviorally targeted ads also attract more visual attention.…”

Section: Novel Methodological Approachesmentioning

confidence: 94%

“…Similar to other forms of personalized advertising, such as location-based advertising (e.g., Ketelaar et al 2017) and ads that include people's names (Bang and Wojdynski 2016), OBA uses personal information to tailor ads in such a way that they are perceived as more personally relevant. A new dimension to this personalization is the fact that the tracking of online activities, collection of behavioral data, and dissemination of information often happen covertly (Ham and Nelson 2016;Nill and Aalberts 2014).…”

Section: Defining Online Behavioral Advertisingmentioning

confidence: 99%

Online Behavioral Advertising: A Literature Review and Research Agenda

Boerman

Kruikemeier

Borgesius

2017

Journal of Advertising

345

237

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“…Such a model is useful in evaluating whether a picture is interesting or not. This is particularly critical when designing marketing instruments (Bang and Wojdynski, 2015).…”

Section: Introductionmentioning

confidence: 99%

Development of Eye Fixation Points Prediction Model from Eye Tracking Data using Neural Network

Moch¹,

Komarudin

Susilo

2017

IJTech

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Fixation points, as the stopping location of eye movements, can be extracted to generate valuable information about a picture or an object. This information is valuable as it enables the identification of the area/part of the picture that attracts people's attention, which can be used as a consideration when making decisions in the future, for example in marketing. For this reason, in this study, a Neural Network (NN) model was developed to predict the fixation points of a picture. Specifically, the authors experimented with various transfer and training functions in the NN in order to determine which causes the fewest errors. The results show that the method used is applicable in practice since it produces MAPE (Mean Absolute Percent Error) of around 13-15% and MSE (Mean Squared Error) of 0.9-1.1%.

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Tracking users' visual attention and responses to personalized advertising based on task cognitive demand

Cited by 129 publications

References 41 publications

Incomodando consumidores: quando agentes de recomendação não facilitam a nossa vida realmente

Incomodando consumidores: quando agentes de recomendação não facilitam a nossa vida realmente

Online Behavioral Advertising: A Literature Review and Research Agenda

Development of Eye Fixation Points Prediction Model from Eye Tracking Data using Neural Network

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