When Being in a Positive Mood Increases Choice Deferral

Etkin, Jordan; Ghosh, Anastasiya Pocheptsova

doi:10.1093/jcr/ucx121

Cited by 18 publications

(17 citation statements)

References 65 publications

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“…However, affect has also been postulated to influence the semantic concepts that are retrieved and brought to bear on the interpretation of judgment‐relevant information (Bower, ; Bower, Gilligan, & Monteiro, ) and the subset of knowledge that individuals retrieve from memory (Isen, Shalker, Clark, & Karp, ; but see Bower & Mayer, ). (For other examples of the impact of affect on goal‐directed information processing, see Etkin & Ghosh, . )…”

Section: Applicationsmentioning

confidence: 99%

A Multistage, Multiprocess Analysis of Consumer Judgment: A Selective Review and Conceptual Framework

Wyer

Kardes

2020

J Consum Psychol

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Although numerous dual-process models have been brought to bear on the cognitive operations that underlie consumer judgment, they are typically limited in their ability to capture the processes that occur at different stages of cognitive activity and to specify how these processes interface. In many (if not most) cases, a mix of both automatic and controlled processes underlies judgments and decisions. We suggest a theoretical framework that potentially provides an integration of these processes and review representative research and theory that might be conceptualized in terms of this framework.

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Section: Applicationsmentioning

confidence: 99%

A Multistage, Multiprocess Analysis of Consumer Judgment: A Selective Review and Conceptual Framework

Wyer

Kardes

2020

J Consum Psychol

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“…Consumer researchers have also pursued the understanding of how mood affects choice and preference. For example, how being in a positive mood increase choice deferral (Etkin, Ghosh, Dahl, & Laboo, 2018) Attitudes transform into goals (Perugini & Bagozzi, 2001), and understanding goals has become a field of study in its own. Today, researchers are studying how goal specificity shapes motivation (Wallace, Etkin, Gita, & Bagchi, 2018) and how to help consumers to keep progress with their goals (Koo & Fishbach, 2012).…”

Section: Interpretive Consumer Behavior Researchmentioning

confidence: 99%

Consumer Behavior Research: Theoretical and Methodological Paradigms Trending

Angulo¹,

Agustiniana²,

Roldán³

2020

Temas Y Métodos De Investigación en Negocios, Administración, Mercadeo Y Contaduría

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The purpose of this chapter is to become a guide for future research agenda that allows researchers in marketing and other disciplines to find a way to drive research studies in consumer behavior. The text provides an interpretative analysis of the latest editorials, reviews and research articles published in top-tier consumer research journals. Journals such as the International Journal of Consumer Studies, Journal of Consumer Affairs, Journal of Consumer Behavior, Journal of Consumer Culture, Journal of Consumer Psychology, Journal of Consumer Research, and Psychology & Marketing among others were selected to offer a broad view of the challenges and research opportunities in this growing research area. Analyzed papers have been grouped by common topics into five theoretical streams (homoeconomicus, psychodynamic, behavioral, cognitive, and humanistic) and two methodological perspectives (positivist, interpretive). Results suggest strong affinity in recent papers to cognitive-humanistic-interpretive research focus, with online consumer behavior, transformative and culture consumer behavior and green consumption as main and commonly treated topics. Conclusions and future perspectives on consumer behavior research are, also presented.

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“…Henceforth many of the parallel research attempts have suggested to ignore the human intelligent properties and rather focus on the image specific properties for the content retrieval. Later this theory was made further stronger with the case study by J. Etkin et al [6].…”

Section: Introductionmentioning

confidence: 97%

An Adaptive Correlation Based Video Data Mining using Machine Learning

Lingam K*,

Reddy

2019

IJRTE

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With the immense growth in the multimedia contents for education and other purposes, the availability of the video contents has also increased Nevertheless, the retrieval of the content is always a challenge. The identification of two video contents based on internal content similarity highly depends on extraction of key frames and that makes the process highly time complex. In the recent time, many of research attempts have tried to approach this problem with the intention to reduce the time complexity using various methods such as video to text conversion and further analysing both extracted text similarity analysis. Regardless to mention, this strategy is again language dependent and criticised for various reasons like local language dependencies and language paraphrase dependencies. Henceforth, this work approaches the problem with a different dimension with reduction possibilities of the video key frames using adaptive similarity. The proposed method analyses the key frames extracted from the library content and from the search video data based on various parameters and reduces the key frames using adaptive similarity. Also, this work uses machine learning and parallel programming algorithms to reduce the time complexity to a greater extend. The final outcome of this work is a reduced time complex algorithm for video data-based search to video content retrieval. The work demonstrates a nearly 50% reduction in the key frame without losing information with nearly 70% reduction in time complexity and 100% accuracy on search results.

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When Being in a Positive Mood Increases Choice Deferral

Cited by 18 publications

References 65 publications

A Multistage, Multiprocess Analysis of Consumer Judgment: A Selective Review and Conceptual Framework

A Multistage, Multiprocess Analysis of Consumer Judgment: A Selective Review and Conceptual Framework

Consumer Behavior Research: Theoretical and Methodological Paradigms Trending

An Adaptive Correlation Based Video Data Mining using Machine Learning

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