Optimal pricing in e-commerce based on sparse and noisy data

Bauer, J.G.; Jannach, Dietmar

doi:10.1016/j.dss.2017.12.002

Cited by 62 publications

(34 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In regard to investigate mobile payment Apps users' behavior, the data mining approach, which means using a large database to build a model and find hidden special associations and features, is the process of discovering patterns in a relatively large data set using the intersection of artificial intelligence, machine learning, statistics, and databases (Bauer & Jannach, 2018;Khatwani & Srivastava, 2018;Kühl et al, 2019;Shahri et al, 2019). Many data mining models have been proposed such as classification, estimation, predictive modeling, clustering/segmentation, affinity grouping or association rules, description and visualization, as well as sequential modeling.…”

Section: Data Miningmentioning

confidence: 99%

Mobile Payment and Mobile Application (App) Behavior for Online Recommendations

Liao¹,

Ho²

2021

Journal of Organizational and End User Computing

View full text Add to dashboard Cite

A mobile application (App) is an application designed to run on a smartphone, tablet, or other mobile device. With the continuous change of mobile payment applications in smart phones and the support of the banking system, the global mobile payment population is increasing. This study examines the behaviors of Taiwan mobile payment and apps users, a total of 1,176 valid questionnaire data is divided into six sections with 29 items for a database design. This study develops a data mining approach, including clustering analysis and association rules, based on a relational database. Thus, this study shows that mobile payment not only can provide payment service but is also a critical mobile application platform for online business. Finally, we show that as users of mobile payment and apps gain additional demand and consumption ability, online operators can gradually put together mobile payment business models to enable future electronic commerce online recommendations.

show abstract

Section: Data Miningmentioning

confidence: 99%

Mobile Payment and Mobile Application (App) Behavior for Online Recommendations

Liao¹,

Ho²

2021

Journal of Organizational and End User Computing

View full text Add to dashboard Cite

show abstract

“…Price is a crucial factor for companies to be competitive in markets as transparent as today's online. In these markets, e-commerce providers often have to adjust their prices in short time intervals, for example, in order to take into account the prices that are frequently changed by their competitors [18], but they also have to be careful when defining the factors that affect prices [19].…”

Section: E-commerce Digital Transformation and Algorithmic Pricingmentioning

confidence: 99%

Algorithmic Pricing and Price Gouging. Consequences of High-Impact, Low Probability Events

2021

View full text Add to dashboard Cite

Algorithmic pricing may lead to more efficient and contestable markets, but high-impact, low-probability events such as terror attacks or heavy storms may lead to price gouging, which may trigger injunctions or get sellers banned from platforms such as Amazon or eBay. This work addresses how such events may impact prices when set by an algorithm and how different markets may be affected. We analyze how to mitigate these high-impact events by paying attention to external (market conditions) and internal (algorithm design) features surrounding the algorithms. We find that both forces may help in partially mitigating price gouging, but it remains unknown which forces or features may lead to complete mitigation.

show abstract

“…Gorodnichenko and Talavera (2017) or Gorodnichenko et al (2018) conduct investigations of strategies of firms in pricecomparison sites, which concentrates on pricing itself. Bauer and Jannach (2018) propose a machine learning‐based framework for estimating optimal prices in e‐commerce. See also Schlosser et al (2006) on the impact of website design investments on consumers' trusting beliefs and online purchase intentions.…”

Section: Relation To the Literaturementioning

confidence: 99%

Successful retailer strategies in price comparison platforms

et al. 2021

View full text Add to dashboard Cite

The choice of an appropriate e‐commerce strategy for the listing in price comparison platforms (eBay, Amazon, and price search engines) is crucial for the survival of online stores in B2C e‐commerce business. We use a comprehensive dataset from the Austrian price search engine geizhals.at to identify successful e‐commerce strategies with regard to these listing decisions. An e‐commerce strategy is a set of choices including the listing decision, availability decision, and decisions on a price path and shipping cost. We apply cluster analysis to identify the different strategies that have been used by online retailers. Using various success measures such as revenue, clicks, market share, and the survival of firms, as dependent variables in our regression analyses, we present causal evidence on the effectiveness of different e‐commerce strategies.

show abstract

Optimal pricing in e-commerce based on sparse and noisy data

Cited by 62 publications

References 21 publications

Mobile Payment and Mobile Application (App) Behavior for Online Recommendations

Mobile Payment and Mobile Application (App) Behavior for Online Recommendations

Algorithmic Pricing and Price Gouging. Consequences of High-Impact, Low Probability Events

Successful retailer strategies in price comparison platforms

Contact Info

Product

Resources

About