Time to pay attention to attention: using attention-based process traces to better understand consumer decision-making

Mormann, Milica; Griffiths, Tom; Janiszewski, Chris; Russo, J. Edward; Aribarg, Anocha; Ashby, Nathaniel J. S.; Bagchi, Rajesh; Bhatia, Sudeep; Kovacheva, Aleksandra; Meißner, Martin; Mrkva, Kellen

doi:10.1007/s11002-020-09520-0

Cited by 9 publications

(10 citation statements)

References 35 publications

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“…The use of eye-tracking has become an important tool in decision science, and behavioral science more generally, as it provides a detailed representation of the decision process (Mormann et al, 2020). It has been used to understand the accumulation of evidence in sequential sampling models of choice (Krajbich, 2019), context effects in multi-attribute choice (Noguchi & Stewart 2014), strategic sophistication in games (Polonio et al, 2015), selfish vs. pro-social tendencies in altruistic choice (Teoh et al, 2020), simplification strategies in multi-attribute choice (Reeck et al 2017), etc. A challenge to the continued growth of eye-tracking research, and physiological research more generally, is the shift of behavioral research from brick-and-mortar labs to the internet (Goodman & Paolacci, 2017).…”

Section: Introductionmentioning

confidence: 99%

Webcam-based online eye-tracking for behavioral research

Yang¹,

Krajbich²

2020

Preprint

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Experiments are increasingly moving online (especially during the COVID epidemic). This poses a major challenge for researchers who rely on in-lab process-tracing techniques such as eye-tracking. Researchers in computer science have developed a web-based eye-tracking application (WebGazer) (Papoutsaki et al., 2016) but it has yet to see use in behavioral research. This is likely due to the extensive calibration and validation procedure (~50% of the study time) and low/inconsistent temporal resolution (Semmelmann & Weigelt, 2018), as well as the challenge of integrating it into standard experimental software. Here, we incorporate WebGazer with the most widely used JavaScript library among behavioral researchers (jsPsych) and adjust the procedure and code to reduce calibration/validation and dramatically improve the temporal resolution (from 100-1000 ms to 20-30 ms or better). We test our WebGazer/jsPsych combination with a decision-making study on Amazon MTurk. We find little degradation in spatial or temporal resolution over the course of the ~30-minute experiment. We replicate previous in-lab findings on the relationship between gaze dwell time and value-based choice. In summary, we provide an open-source, accessible, software template and tutorial for web-based eye-tracking in behavioral research that is sufficient to replicate in-lab studies with just a modest number of participants (N=38), and that is orders of magnitude faster than in-lab data collection. Moreover, we highlight that web-based eye-tracking is a useful tool for all behavioral researchers, as it can be used to ensure that study participants are humans and not machines.

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Section: Introductionmentioning

confidence: 99%

Webcam-based online eye-tracking for behavioral research

Yang¹,

Krajbich²

2020

Preprint

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“…Our research makes a step toward understanding the fundamental and possibly neurological links between attention trajectories and utility accumulation during complex decision making (Gabaix 2019; Wedel, Pieters, and Van der Lans 2022). It is also a step toward the more realistic, descriptive consumer choice theories that have been called for (Janiszewski and Van Osselaer 2021; Mormann et al 2020; Willemsen, Böckenholt, and Johnson 2011).…”

Section: Contributionmentioning

confidence: 99%

“…This prompts two questions: (1) is this attention lift present for all the brands in the set or only for some, and (2) when in the choice process does the attention lift surface? In a recent review, Mormann et al (2020, p. 387) call for more research on the complete sequences of fixations and saccades during decision-making tasks to develop more-realistic models of choice. Our study follows up on this.…”

Section: Literature Review and Knowledge Gapsmentioning

confidence: 99%

“…The predictive performance of competing models provides empirical support for their competing theoretical assumptions and is of managerial relevance. As Mormann et al (2020, p. 381) point out, “Ultimately, these enhanced models of choice should help us to more accurately predict choice,” a sentiment shared more widely (Busemeyer and Wang 2000; Hastie, Tibshirani, and Friedman 2009; Hofman et al 2021; Janiszewski and Van Osselaer 2021; Yarkoni and Westfall 2017).…”

Section: Literature Review and Knowledge Gapsmentioning

confidence: 99%

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Attention Trajectories Capture Utility Accumulation and Predict Brand Choice

Martinovici¹,

Pieters²,

Erdem³

2023

Journal of Marketing Research

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Trajectories of attention capture the accumulation of brand utility during complex decision-making tasks. Thus, attention trajectories, as reflected in eye movements, predict the final brand choice of 85% of consumers before they implement it. Even when observing eye movements in only the first quarter of the decision process, attention already predicts brand choice much better (45%) than chance levels (20%). This superior prediction performance is due to a “double attention lift” for the chosen brand. Thus, the chosen brand receives progressively more attention towards the moment of choice, and more of this attention is devoted to integrating information about the brand rather than to comparing it with other options. In contrast, the currently owned brand grabs attention early in the task, and its attention gain persists for brand-loyal and shifts for brand-switching consumers. A new attention-and-choice model used in tandem with the Bayesian K-fold Cross-Validation methodology on eye-tracking data from 325 representative consumers uncovered these attention trajectory effects. The findings contribute to closing important knowledge gaps in the attention and choice literature and have implications for marketing research and managerial practice.

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“…To investigate the influence of implied motion on consumers' attention and choice, a between-subjects design was adopted (implied motion condition vs. no implied motion condition). As visual attention deployment is typically unconscious (Holmqvist et al, 2011) and cannot be reliably approximated by a memory test (Atalay et al, 2012), an eye-tracking method is useful for attention-related studies (Mormann et al, 2020).…”

Section: Experiments 1 -Influence Of Implied Motion On Consumers' Att...mentioning

confidence: 99%