The dimension of age and gender as user model demographic factors for automatic personalization in e-commerce sites

Fernandez-Lanvin, Daniel; Andres-Suarez, Javier de; González-Rodríguez, Martín; Pariente-Martinez, B.

doi:10.1016/j.csi.2018.02.001

Cited by 21 publications

(10 citation statements)

References 52 publications

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“…Gender and age are reported to play a pivotal role in regulating SNS addiction in the model [31]. However, age and gender are factors that are sufficient for automatic analysis in e-commerce [26]. Given this context, in this study, it is believed that it is necessary to perform subgroup analysis according to age and gender.…”

Section: Subgroup Effects Of User's Gender and Agementioning

confidence: 94%

Applying Transaction Cost Theory and Push-Pull-Mooring Model to Investigate Mobile Payment Switching Behaviors with Well-Established Traditional Financial Infrastructure

Wung

2020

JTAER

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The technology of mobile payments has received more attention than ever. However, whether it is possible that mobile payment will become a common payment method for consumers depends on the willingness of consumers. This study focuses on the intention from cash payment to mobile payment and adopts a qualitative and quantitative approach based on the theoretical structure of the Transaction Cost & the Push-Pull-Mooring Migration Model of Service Switching. Firstly, the Delphi Method was applied to extract three push variables (perceived trouble, perceived lack of transaction records, and difficulty of paying large amounts in cash) and three pull variables (perceived convenience, perceived benefit, and perceived time-saving). After surveying 586 samples, this proposed model was tested with a structural equation modeling approach. Results: 1. the pull facet tends to be more significant than the push facet. 2. Perceived trouble had a significant effect on push factors, while perceived convenience and perceived benefit had a significant effect on pull factors. 3. Push-&pull factors had significant effect on behavior of gender-&-age. And the-transaction-costs can divide into positive-transaction-costs and negative-transaction-costs after incorporating the Push-Pull-Mooring theory.

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Section: Subgroup Effects Of User's Gender and Agementioning

confidence: 94%

Applying Transaction Cost Theory and Push-Pull-Mooring Model to Investigate Mobile Payment Switching Behaviors with Well-Established Traditional Financial Infrastructure

Wung

2020

JTAER

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“…Thirdly, inquiry methods, designed to gathering subjective data from users, utilize both quantitative (questionnaires [60]- [62]) and qualitative (interviews [63]- [65] and focus groups [66]- [68]) techniques. Furthermore, some authors also distinguish analytical modelling methods such as cognitive task analysis [69]- [71], task environment analysis [72]- [74] and GOMS analysis (Goals, Operators, Methods and Selection rules) [75]- [77].…”

Section: Introductionmentioning

confidence: 99%

Usability of Mobile Applications: A Systematic Literature Study

Weichbroth

2020

IEEE Access

155

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Since the release of the first mobile devices, the usability of on-board applications has been the concern not only of software vendors but hardware manufacturers as well. The academia community later willingly joined the discussion on usability in terms of theory and empirical measurement, having experience and knowledge in desktop settings. At first sight, such a background should guarantee a solid foundation to conduct research on software usability in a new setting. However, a preliminary study on the subject matter revealed methodological disorder in contemporary literature. As a matter of fact, a need emerged to review existing usability definitions, attributes and measures to recognize all associated aspects. In order to fill this void, we conducted a systematic literature review on usability studies indexed by the Scopus database and devoted to mobile applications. The input volume covers 790 documents from 2001 to 2018. The data analysis shows that the ISO 9241-11 usability definition has been adopted in an unchanged form and popularized as the standard by the HCI community. Secondly, in total, 75 attributes were identified and analysed. The most frequent are efficiency (70%), satisfaction (66%) and effectiveness (58%), which directly originate from the above definition. Subsequently, the less frequent are learnability (45%), memorability (23%), cognitive load (19%) and errors (17%). The last two concern simplicity (13%) and ease of use (9%). Thirdly, in the evaluation of usability, controlled observation and surveys are two major research methods applied, while eye-tracking, thinking aloud and interview are hardly used and serve as complementary to collect additional data. Moreover, usability evaluations are often confused with user experience dimensions, covering not only application quality characteristics, but also user beliefs, emotions and preferences. All these results indicate the need for further research on the usability of mobile applications, aiming to establish a consensus in the theory and practice among all interested parties.

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“…For this reason, many researchers have done a lot of research from the perspective of customers' gender information collecting technologies and methods [24]- [26]. Gender information can be collected through questionnaires [27], recruiting volunteers [28], [29] and the information registered by the user [30]. Nonetheless, the gender information collected through these collected methods is far less than enough to contribute to the online shopping recommendation system [31].…”

Section: Related Workmentioning

confidence: 99%

Discover Customers’ Gender From Online Shopping Behavior

Meng

2022

IEEE Access

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Gender information is very important for the recommendation system in the online shopping website. However, gender data often face label missing and incorrect labelling problems caused by consumers' unwillingness to actively disclose personal information, which leads to gender estimation results that cannot meet the needs of the product recommendation system. To discover the customers' gender information, we explore the customers' online shopping behavior, especially the items viewed in the shopping session, from the dataset provided by Vietnam FPT Group. The dataset is very imbalanced while the number of female samples is 3× of the male samples. To address the imbalance issue, we cluster the female samples into three subsets and then train a two-layer classifier model to estimate the customers' gender. Experimental results demonstrate that our proposed method could achieve a combined accuracy 78% on average, and takes less than 6 seconds on average. As a data mining model for gender prediction, our approach has a lightweight network structure and less time consumption.

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The dimension of age and gender as user model demographic factors for automatic personalization in e-commerce sites

Cited by 21 publications

References 52 publications

Applying Transaction Cost Theory and Push-Pull-Mooring Model to Investigate Mobile Payment Switching Behaviors with Well-Established Traditional Financial Infrastructure

Applying Transaction Cost Theory and Push-Pull-Mooring Model to Investigate Mobile Payment Switching Behaviors with Well-Established Traditional Financial Infrastructure

Usability of Mobile Applications: A Systematic Literature Study

Discover Customers’ Gender From Online Shopping Behavior

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