Predicting the success of group buying auctions via classification

Chen, Chien Chin; Chung, Meng-Chieh

doi:10.1016/j.knosys.2015.09.009

Cited by 6 publications

(2 citation statements)

References 36 publications

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“…AI also gives firms the ability to predict product, service, or feature demand and sales (Cardoso & Gomide, 2007 ; Castillo et al, 2017 ; Ryoba et al, 2021 ), thereby giving firms the ability to anticipate and dynamically adjust their advertising and sales strategies (Chen et al, 2014 ; Greenstein-Messica & Rokach, 2020 ). Even further, it gives firms the ability to predict the success or failure of these strategies (Chen & Chung, 2015 ).…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence in E-Commerce: a bibliometric study and literature review

et al. 2022

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This paper synthesises research on artificial intelligence (AI) in e-commerce and proposes guidelines on how information systems (IS) research could contribute to this research stream. To this end, the innovative approach of combining bibliometric analysis with an extensive literature review was used. Bibliometric data from 4335 documents were analysed, and 229 articles published in leading IS journals were reviewed. The bibliometric analysis revealed that research on AI in e-commerce focuses primarily on recommender systems. Sentiment analysis, trust, personalisation, and optimisation were identified as the core research themes. It also places China-based institutions as leaders in this researcher area. Also, most research papers on AI in e-commerce were published in computer science, AI, business, and management outlets. The literature review reveals the main research topics, styles and themes that have been of interest to IS scholars. Proposals for future research are made based on these findings. This paper presents the first study that attempts to synthesise research on AI in e-commerce. For researchers, it contributes ideas to the way forward in this research area. To practitioners, it provides an organised source of information on how AI can support their e-commerce endeavours.

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence in E-Commerce: a bibliometric study and literature review

et al. 2022

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“…Meskipun barang-barang tersebut dilelang, namun tidak semua dari barang lelang laku terjual untuk umum. Salah satu cara untuk mengklasifikasi keberhasilan lelang barang tersebut adalah dengan menggunakan pohon klasifikasi (Chen & Chung, 2015).…”

Section: Pendahuluanunclassified

Comparison of C4.5 and C5.0 Algorithm Classification Tree Models for Analysis of Factors Affecting Auction

Fajri

Utami

Maruf

2022

IJSA

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Auction in Indonesia is carried out by the Office of State Assets and Auction Services (KPKNL). Goods auctioned at KPKNL are quite diverse including land, wood, inventory, vehicles, and other goods. However, not all of the items auctioned were sold. Because not a few items have been auctioned but no one has made an offer. The Purpose of this study is to compare two classification methods, C4.5 and C5.0 algorithm and to determine which items were successfully auctioned with those that did not and its factors. The methods that used were comparing the classification tree C4.5 algorithm and C5.0 algorithm with cross validation. From the results of the comparison of the two methods, it was found that the C5.0 Algorithm method was rated better than the C4.5 algorithm in classifying the auction results with an accuracy of 96.43% and 92.86% respectively. In this case, C5.0 has a higher precision than C4.5.

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Groupon and Groupon Now: Participating Firm’s Profitability Analysis

Ong

Vorobev

et al. 2017

Comput Econ

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The business model of Groupon has been categorized by many to be a case of daily deals or discount offerings to consumers. It is static (not responsive to business fluctuations and demands) and operates in batch mode (period-based availability). A new business model called "Flashon" is proposed to be distinct from Groupon in that Flashon is dynamic and real-time. This model is more responsive to business fluctuations-firms may choose to offer deals during business downtime and likewise withhold deals during business uptime.As and when deals are offered by firms, consumers receive deal notifications real-time. A time-continuum model for profitability analysis based on the two-period Groupon model developed by [Edelman et al. 2014] is introduced for systematic comparison of the profitability between Flashon, Groupon, and firm normal operations without offering discount vouchers. Simple case studies of the customer-demand fluctuations of different business cycles show consistant results that the Flashon model is more profitable than the Groupon model, which are both superior to the firm normal operations without offering any discount vouchers. This is given that the consumers are generally interested in purchasing the firm products or services and can be easily stimulated with attractive incentives such as the discount vouchers. The profitability analysis also provides quantitative proof that offering high discount rates or even free products or services with added values to small target fraction of the population may attract more consumers and hence generate high profit for participating firms with high popularity among the consumers.

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Predicting the success of group buying auctions via classification

Cited by 6 publications

References 36 publications

Artificial intelligence in E-Commerce: a bibliometric study and literature review

Artificial intelligence in E-Commerce: a bibliometric study and literature review

Comparison of C4.5 and C5.0 Algorithm Classification Tree Models for Analysis of Factors Affecting Auction

Groupon and Groupon Now: Participating Firm’s Profitability Analysis

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