Impact of recommender systems on unplanned purchase behaviours in e-commerce

Zhang, Ying; Chen, Caixia; Gu, Wen; Liu, Xiaogang

doi:10.1109/iea.2018.8387066

Cited by 14 publications

(9 citation statements)

References 67 publications

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“…Finally, the outcome that, the perceived value (PVrecP) of recommended product influence e-Loy via vendors’ reputation (e-tailers’ reputation) being sensitive to SHOPeJ, is informative to e-tailers because it symbolizes essential research concept in consumer behavior and marketing (Ying et al , 2018). This means that selling a huge volume of goods or product is not enough but making use of consumer purchase behavior data to formulate a marketing strategy that will enhance the consumers’ shopping experience.…”

Section: Discussionmentioning

confidence: 99%

“…For example, a hedonic consumer would attain a pleasurable shopping experience from distinctive recommended product information as compared to a utilitarian/functional consumer who perceives enjoyment as a result of efficient and effective product recommendations provided by the e-tailer. For this reason, e-shoppers/consumer predisposition to pursue enjoyment while shopping on e-tailers’ platforms can affect their normal purchase behavior or pattern (Ying et al , 2018). Moreover, the degree of enjoyment stimulates the sensitivity of the perceived value of the recommended product, and it is also driven by the reputation of the vendor (Alexander et al , 2011).…”

Section: Discussionmentioning

confidence: 99%

“…Accordingly, stakeholders have instituted a personalized recommendation system (Zhang et al , 2011; Hostler et al , 2011; Ya, 2012; Gültekin and Özer, 2012) whereby products are recommended to consumers based on their previous shopping activities. Personalized recommendation systems not only affect the financial performance of businesses (Pathak et al , 2010; Ying et al , 2018) but also influence prepurchase and postpurchase (e.g. repurchase intention and e-loyalty) behaviors of online consumers (Kraljević and Filipović, 2017; Kunamaneni et al , 2019).…”

Section: Introductionmentioning

confidence: 99%

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Perceived value of recommended product and consumer e-loyalty: an expectation confirmation perspective

Ampadu,

Jiang,

Gyamfi

et al. 2023

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Purpose The purpose of this study is to examine the effect of perceived value of recommended product on consumer’s e-loyalty, based on the proposition of expectation confirmation theory. Vendors’ reputation is tested as the mediator in the perceived value of recommended product and e-loyalty relationship, whereas shopping enjoyment is predicted as the moderator that conditions the perceived value of recommended product and e-loyalty relationship through vendors reputation. Design/methodology/approach Data were collected via an online survey platform and through a QR code. Partial least squares analysis, confirmatory factor analysis and structural equation modeling were used to verify the research proposed model. Findings The findings revealed that the perceived value of recommended product had a significant positive effect on E-loyalty; in addition, the perceived value of the recommended product and e-loyalty link was partly explained by e-shopper’s confidence in vendor reputation. Therefore, the study established that the direct and indirect relationship between the perceived value of the recommended product and e-loyalty was sensitive and profound to shopping enjoyment. Originality/value This study has established that the perceived value of a recommended product can result in consumer loyalty. This has successively provided the e-shop manager and other stakeholders with novel perspectives about why it is necessary to understand consumers’ pre- and postacquisition behavior before recommending certain products to the consumer.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Perceived value of recommended product and consumer e-loyalty: an expectation confirmation perspective

Ampadu,

Jiang,

Gyamfi

et al. 2023

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“…It is interesting to understand the effect of unplanned purchase behaviours of customers in e-commerce domain but on the contrary, less importance has been given to the user aesthetics and design of web page navigations [9]. The wide choices of recommender systems like collaborative filtering, content-based, social-based and context-based help in comparing algorithms and their performance on different data sets [10].…”

Section: Related Workmentioning

confidence: 99%

Investigating the Role of User Experience and Design in Recommender Systems: A Pragmatic Review

Dhruv¹,

Bakal

2021

Mobile Computing and Sustainable Informatics

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The world is growing smarter with technology-driven applications in assisting lifestyle of humans. Machine learning has predominantly helped businesses in continuously learning and evaluating human likes and dislikes. Today, deep neural networks have grabbed the attention of many researchers in developing highly complicated yet effective models for prediction and data analytics. Recommender systems being one of the applications of machine learning has caught customer attention especially in the retail and hospitality sector. After a recommendation engine is built, it is evaluated in different tangents like accuracy, recall, etc. However, there is a serious need to shift the research focus to the user experience and design aspect while using such recommender systems. This paper conducts a survey and based on the survey results, a novel conceptualized model labelled 'Cognitive Dynamic Design Engine' (CDDE) is proposed. This model will help businesses to understand customer mindset, perception and usability parameters while presenting recommendations to their customers. This model is proposed to run on top the deep neural network model that is constructed for generating recommendations. This survey is collected from masses of urban and rural parts of a geographical zone. The results of the study show that user experience and aesthetics of the recommendation given to a user inevitably have a direct effect on users buying decisions.

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“…This has changed in the last decades as much progress has been made in technological development and the possibilities have expanded. For example, it has been proven that in online shops an optimal, adapted RS can increase sales up to 35% (Lee & Hosanagar, 2014), even leading users to buy products that they were not willing to buy before (Ying et al, 2018). In other types of services, having a good RS can be a determining factor when it comes to the user choosing one company over another.…”

Section: Introductionmentioning

confidence: 99%

Job offers recommender system based on virtual organizations

et al. 2022

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Human interaction has changed considerably with the emergence of the Internet. Today, a large percentage of daily communication takes place on instant messaging applications and social networks. In fact, there has been a considerable increase in the use of social networks because new social networks are being created for specific purposes, such as the search for employment or tourism. When the amount of content on a social network is large, it is necessary to help users find content of their interest. In this regard, artificial intelligence techniques can greatly facilitate the task of searching for relevant content. This paper presents a recommender system for a business and employment oriented social network, on which users are recommended job offers and other user profiles to follow. The presented system is based on virtual organizations of agents, and uses artificial neural networks to determine whether job offers and users should be recommended or not. The system has been evaluated on a real social network; its recommendations regarding job offers and user profiles have had a high acceptance rate.

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Impact of recommender systems on unplanned purchase behaviours in e-commerce

Cited by 14 publications

References 67 publications

Perceived value of recommended product and consumer e-loyalty: an expectation confirmation perspective

Perceived value of recommended product and consumer e-loyalty: an expectation confirmation perspective

Investigating the Role of User Experience and Design in Recommender Systems: A Pragmatic Review

Job offers recommender system based on virtual organizations

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