Cognitive Load in eCommerce Applications—Measurement and Effects on User Satisfaction

Schmutz, Peter; Heinz, Silvia; Métrailler, Yolanda; Opwis, Klaus

doi:10.1155/2009/121494

Cited by 59 publications

(44 citation statements)

References 41 publications

(63 reference statements)

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“…They found that the experimental system imposed no extra demands on user workload than the standard system. Schmutz et al [45] found significant correlations among mental demand, primary task completion time, and general user satisfaction in an experiment comparing four online bookstores.…”

Section: Workloadmentioning

confidence: 99%

The effect of cognitive abilities on information search for tasks of varying levels of complexity

Brennan

Kelly

Arguello

2014

Proceedings of the 5th Information Interaction in Context Symposium

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Although web search engines are designed as one-size-fits-all tools, people do not come in one size, but instead vary across many different attributes. One such attribute is cognitive ability. Because information search is primarily a cognitive activity, understanding the extent to which variations in cognitive abilities impact search behaviors and outcomes is especially important. We describe a study in which we explore how people's cognitive abilities affect their search behaviors and perceptions of workload while conducting search tasks with different levels of complexity. Twenty-one adults from the general public completed this study. We assessed participants' associative memory, perceptual speed, and visualization abilities and also measured workload. To evaluate the relationship between cognitive ability, task complexity and workload, we conducted three separate mixed factor ANOVAs corresponding to each of the abilities. Our results suggest three important trends: (1) associative memory ability had no significant effect on search behavior and workload, (2) visualization ability had a significant effect on search behavior, but not workload, and (3) perceptual speed had a significant effect on search behavior and workload. Specifically, participants with high perceptual speed ability engaged in more search activity in less time and experienced less workload. While the interactions were not significant, the differences were more pronounced for more complex tasks. We also found a significant relationship between task complexity and workload, and task complexity and search behaviors, which corroborates previous research.

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Section: Workloadmentioning

confidence: 99%

The effect of cognitive abilities on information search for tasks of varying levels of complexity

Brennan

Kelly

Arguello

2014

Proceedings of the 5th Information Interaction in Context Symposium

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“…More research is needed on the application of the dualtask method in interactive IR studies to identify secondary tasks particularly suited for use in interactive IR experiments. Except for Dennis et al study (2002) using an auditory secondary task, subsequent studies have employed visual secondary tasks (e.g., Gwizdka, 2008;Schmutz, Heinz, Métrailler, & Opwis, 2009). As online searching now occurs on a variety of handheld devices, such as tablets and smart phones, haptic secondary tasks may also need to be developed.…”

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confidence: 99%

Amount of invested mental effort (AIME) in online searching

Rieh¹,

Kim²,

Markey³

2012

Information Processing & Management

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a b s t r a c tThis research investigates how people's perceptions of information retrieval (IR) systems, their perceptions of search tasks, and their perceptions of self-efficacy influence the amount of invested mental effort (AIME) they put into using two different IR systems: a Web search engine and a library system. It also explores the impact of mental effort on an end user's search experience. To assess AIME in online searching, two experiments were conducted using these methods: Experiment 1 relied on self-reports and Experiment 2 employed the dual-task technique. In both experiments, data were collected through search transaction logs, a pre-search background questionnaire, a post-search questionnaire and an interview. Important findings are these: (1) subjects invested greater mental effort searching a library system than searching the Web; (2) subjects put little effort into Web searching because of their high sense of self-efficacy in their searching ability and their perception of the easiness of the Web; (3) subjects did not recognize that putting mental effort into searching was something needed to improve the search results; and (4) data collected from multiple sources proved to be effective for assessing mental effort in online searching.

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“…For small online businesses, measuring the relevance of search results can help understand whether the search facilities that they are using are efficient enough to provide the information that a customer needs [10]. For e-businesses, evaluating the capacity of the search engine is critical because users were found not willing to invest more time or more effort to improve their searching strategies [11]. They often settle on using simple keywords for searching and viewing only the first few pages of results by not going beyond 20 results.…”

Section: Relevance Of Search Resultsmentioning

confidence: 99%

Successful Data Science Projects: Lessons Learned from Kaggle Competition

Al-Taie

Salim

Obasa

2017

KJAR

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Abstract:The workflow from data understanding to deployment of an analytical model of a data science project begins at framing the problem at hand, a task that is typically business-oriented and requires humanto-human interaction. However, the next three steps: data understanding, feature extraction, and model building that come next in the pipeline are the key to successful data science projects. Failing to fully understand the requirements of each of these three steps can negatively affect the performance of the proposed system. Hence, the current study tries to answer the following question "What are the requirements of a successful data science project?" To answer this question, we will use the solution that we built to measure the relevance of local search results of small online e-businesses and submitted to Kaggle data science platform to shed light on why our solution did not achieve a top position among other competitors. Evaluation of the design that we submitted to the competition is going to be carried out in the spirit of the three winning submissions. Our results revealed that well-performed data preprocessing, well-defined features, and model ensembling are critical for building successful data science projects. Such a clarification provides insight into specific aspects of model design to help others including Kagglers avoid possible mistakes while approaching their data science projects.

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Cognitive Load in eCommerce Applications—Measurement and Effects on User Satisfaction

Cited by 59 publications

References 41 publications

The effect of cognitive abilities on information search for tasks of varying levels of complexity

The effect of cognitive abilities on information search for tasks of varying levels of complexity

Amount of invested mental effort (AIME) in online searching

Successful Data Science Projects: Lessons Learned from Kaggle Competition

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