Cross-border e-commerce platform for commodity automatic pricing model based on deep learning

Guo, Lina

doi:10.1007/s10660-020-09449-6

Cited by 36 publications

(21 citation statements)

References 15 publications

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“…Previous studies have pointed out that cognition can be divided into two dimensions: positive cognition and negative cognition in nature ( Dubé and Menon, 2000 ). Guo (2020) also believes that when customers make purchase decisions, they not only care about prices, but also comprehensively compare the benefits and costs of available products. Therefore, this article will jointly understand the impact of live streaming features on consumers’ purchase intention from the two dimensions of consumers’ perceived value and perceived uncertainty in cross-border live streaming e-commerce.…”

Section: Theoretical Background and Hypothesis Developmentmentioning

confidence: 99%

How Live Streaming Features Impact Consumers’ Purchase Intention in the Context of Cross-Border E-Commerce? A Research Based on SOR Theory

Guo

et al. 2021

Front. Psychol.

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Given that “cross-border e-commerce + live streaming” has become an important driver of global trade but limited attention has been paid to this area, this study examines the impacts of live streaming features on the consumers’ cross-border purchase intention from the perspectives of consumers’ overall perceived value and overall perceived uncertainty based on the SOR theory. In addition, through investigating the moderating effects of saving money, this study reveals the impacts of amazing bargains in live streaming commerce. A total of 272 samples were collected by a questionnaire survey to test the proposed research model. The results show that live streaming features significantly increase consumers’ overall perceived value and purchase intention, and significantly reduce consumers’ overall perceived uncertainty; in addition, saving money further increases the impact of live streaming features on consumers’ overall perceived value. This study provides a theoretical basis and reference for cross-border e-commerce platforms and merchants to effectively leverage live streaming to influence consumers’ perception and purchase intention.

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Section: Theoretical Background and Hypothesis Developmentmentioning

confidence: 99%

How Live Streaming Features Impact Consumers’ Purchase Intention in the Context of Cross-Border E-Commerce? A Research Based on SOR Theory

Guo

et al. 2021

Front. Psychol.

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“…In the context of this solution, CNN-based image feature extraction took place and different attention mechanism strategies were developed to select image features related to the users’ evaluations on products. Then, out of all the influencing factors, the author focused on the three most important (i.e., the cross-elasticity coefficient, the tax difference, and the 3rd party platform usage fees) and performed a simulation study, resulting in the most suitable commodity pricing strategies for diverse scenarios [ 65 ].…”

Section: Resultsmentioning

confidence: 99%

Applications of Fusion Techniques in E-Commerce Environments: A Literature Review

Daskalakis¹,

Remoundou²,

Πεππές³

et al. 2022

Sensors

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The extreme rise of the Internet of Things and the increasing access of people to web applications have led to the expanding use of diverse e-commerce solutions, which was even more obvious during the COVID-19 pandemic. Large amounts of heterogeneous data from multiple sources reside in e-commerce environments and are often characterized by data source inaccuracy and unreliability. In this regard, various fusion techniques can play a crucial role in addressing such challenges and are extensively used in numerous e-commerce applications. This paper’s goal is to conduct an academic literature review of prominent fusion-based solutions that can assist in tackling the everyday challenges the e-commerce environments face as well as in their needs to make more accurate and better business decisions. For categorizing the solutions, a novel 4-fold categorization approach is introduced including product-related, economy-related, business-related, and consumer-related solutions, followed by relevant subcategorizations, based on the wide variety of challenges faced by e-commerce. Results from the 65 fusion-related solutions included in the paper show a great variety of different fusion applications, focusing on the fusion of already existing models and algorithms as well as the existence of a large number of different machine learning techniques focusing on the same e-commerce-related challenge.

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“…DL algorithms by having high levels of abstraction are expected to improve the accuracy of the prediction process (Mocanu et al 2016). The forecasting problem in the collected material refers to source forecasting (Charmchi et al 2021), demand forecasting (Nikolopoulos et al 2021;Chien et al 2020;Koç and Türkoğlu 2021;Bousqaoui et al 2021;Mocanu et al 2016;Kilimci et al 2019;Punia et al 2020;Tang and Ge 2021), sales forecasting (Weng et al 2019a;Liu et al 2020;Piccialli et al 2021), price forecasting (Weng et al 2019a, b;Guo 2020), performance forecasting (Shankar et al 2020), or a combination of these problems named as hybrid forecasting (Khan et al 2020;Wu et al 2021).…”

Section: Forecastingmentioning

confidence: 99%

“…Forecasting the price of horticultural products can be also beneficial for designing a cropping plan. In the cross-border e-commerce field, (Guo 2020) proposed a hybrid model to encode image features, and capture the image features of commodities. Then, this evaluation process is transformed into price perception.…”

Section: Fig 7 Share Of Each Category Of D1mentioning

confidence: 99%

Applications of deep learning into supply chain management: a systematic literature review and a framework for future research

Shavaki

Ghahnavieh

2022

Artif Intell Rev

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In today’s complex and ever-changing world, Supply Chain Management (SCM) is increasingly becoming a cornerstone to any company to reckon with in this global era for all industries. The rapidly growing interest in the application of Deep Learning (a class of machine learning algorithms) in SCM, has urged the need for an up-to-date systematic review on the research development. The main purpose of this study is to provide a comprehensive vision by reviewing a set of 43 papers about applications of Deep Learning (DL) methods to the SCM, as well as the trends, perspectives, and potential research gaps. This review uses content analysis to answer three research questions namely: 1- What SCM problems have been solved by the use of DL techniques? 2- What DL algorithms have been used to solve these problems? 3- What alternative algorithms have been used to tackle the same problems? And do DL outperform these methods and through which evaluation metrics? This review also responds to this call by developing a conceptual framework in a value-adding perspective that provides a full picture of areas on where and how DL can be applied within the SCM context. This makes it easier to identify potential applications to corporations, in addition to potential future research areas to science. It might also provide businesses a competitive advantage over their competitors by allowing them to add value to their data by analyzing it quickly and precisely.

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Cross-border e-commerce platform for commodity automatic pricing model based on deep learning

Cited by 36 publications

References 15 publications

How Live Streaming Features Impact Consumers’ Purchase Intention in the Context of Cross-Border E-Commerce? A Research Based on SOR Theory

How Live Streaming Features Impact Consumers’ Purchase Intention in the Context of Cross-Border E-Commerce? A Research Based on SOR Theory

Applications of Fusion Techniques in E-Commerce Environments: A Literature Review

Applications of deep learning into supply chain management: a systematic literature review and a framework for future research

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