Utilizing Models for e-Business Decision-Making: From Data to Wisdom

Editors


E-Mail Website
Collection Editor
Department of Informatics, Faculty of Economics and Business, University of Zagreb, 10020 Zagreb, Croatia
Interests: data science; artificial networks; simulation modeling; decision trees; cluster analysis; association rules; supervised learning; unsupervised learning; system dynamics
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Collection Editor
Department of Applied Economics I, History and Economic Institutions and Moral Philosophy, Social and Legal Sciences Faculty, Rey Juan Carlos University, 28033 Madrid, Spain
Interests: electronic commerce; big data; data analytics; machine learning; public policies; customer behavior; digitalization
Special Issues, Collections and Topics in MDPI journals

Topical Collection Information

Dear Colleagues,

Data has become omnipresent, as every aspect of personal and business activities is gradually being measured in numerous ways and turned into data, which is analyzed and used to form a value. In recent years, datafication has become a challenge, as processing all the data is nearly impossible due to its abundance, which is increasing exponentially year after year.

Nowadays, data is collected continuously both by humans and by machines, in both structured and non-structured manners. Relational databases, data warehouses, and data marts store the structured data that is often generated as the result of various interactions in e-businesses environments. Textual data is stored in the form of various business and other documents. Social media is also a rich source of textual data since our society has been transforming our communication platforms from analogic sources to digital, through online channels which are used both by persons and businesses for their communications. Moreover, human communication has been extended to the frequent online use of visual media, in the form of static pictures and video, which is also reflected in various e-business models. Soon, there will be more machines connected online than humans, and all of these machines, including mobile phones, the Internet-of-Things, and smart devices, among others, are collecting data.

What to do with all of these data? How to make the best use of it? Plenty of approaches have been adapted or invented to analyze these data, which are well known under various names such as data mining, knowledge discovery in databases, data science, data analytics, and others. All of them prescribe a certain process of data collection, data transformation, data analysis, model development and model deployment, combining machine learning, artificial intelligence and statistical analysis to understand it.

Because of the rich data sources and various methods that are nowadays easy to implement due to the availability of computed analytical tools, data analysis has become omnipresent. However, more data does not automatically lead to better decision making. On the contrary, plenty of data creates various problems that range from the issue of data selection to the misuse of data analysis that mixes correlation with causation. Besides, data analysis is often data-driven, and not decision-driven. In other words, decisions are often inspired by the available data, though data analysis should be driven by the decisions that are needed to improve performance. Finally, it is important to note that doing good analysis is not always a sign of improvement, as many organizations do not evolve to be real data-driven organizations and reject making decisions based on analysis, thus making them fail.

In the topic collection, we intend to focus on all of the stages in data analysis. First, we invite papers that discuss the availability, quality, and transformation of data used in e-business analysis. Second, the selection of existing, and invention of new data analysis approaches is a strong driver of improved e-business modes. Finally, the practical and theoretical implications of the data analysis in e-business are still not sufficiently explored. We invite you to participate in this topic collection with your case studies, original analysis, reviews, conceptual papers, and commentaries.

Prof. Dr. Mirjana Pejic-Bach
Dr. María Teresa Ballestar
Collection Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the collection website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Theoretical and Applied Electronic Commerce Research is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • decision-making
  • datafication
  • business performance
  • data analysis
  • data science
  • data mining
  • knowledge discovery in databases
  • big data
  • business intelligence
  • artificial intelligence
  • machine learning
  • security
  • privacy

Published Papers (19 papers)

2024

Jump to: 2023, 2022

22 pages, 1374 KiB  
Article
Strategic Third-Party Product Entry and Mode Choice under Self-Operating Channels and Marketplace Competition: A Game-Theoretical Analysis
by Biao Xu, Jinting Huang, Xiaodan Zhang and Thomas Brashear Alejandro
J. Theor. Appl. Electron. Commer. Res. 2024, 19(1), 73-94; https://doi.org/10.3390/jtaer19010005 - 05 Jan 2024
Viewed by 848
Abstract
To bolster their competitiveness and profitability, prominent e-commerce platforms have embraced dual retailing channels: self-operating channels and online marketplaces. However, a discernible trend is emerging wherein e-commerce platforms are expanding their marketplaces to encompass competitive third-party suppliers. Motivated by this trend, this study [...] Read more.
To bolster their competitiveness and profitability, prominent e-commerce platforms have embraced dual retailing channels: self-operating channels and online marketplaces. However, a discernible trend is emerging wherein e-commerce platforms are expanding their marketplaces to encompass competitive third-party suppliers. Motivated by this trend, this study sought to examine the strategic integration of a third-party product amidst the competition between a self-operating channel and a marketplace. This investigation involved the development of a game-theoretic model involving a platform and two representative suppliers—an incumbent supplier and a new entrant. Specifically, we delved into establishing an equilibrium partnership between the platform and the new entrant supplier while also evaluating the self-operating strategy of the established supplier. Our analysis uncovered a counterintuitive outcome: an escalation in the commission rate resulted in diminished profits for the established supplier. Furthermore, we ascertained that the economic implications of a competitive product entry pivot significantly on product quality. Lastly, we demonstrated that the revenue-sharing rate plays a pivotal role in influencing the self-operating strategy of the established supplier, and the market equilibrium hinges on the interplay among product quality, the commission rate, and the revenue-sharing rate. These insights provide invaluable guidance for marketers and e-commerce platforms in their strategic decision-making processes. Full article
Show Figures

Figure 1

2023

Jump to: 2024, 2022

20 pages, 1684 KiB  
Article
Analysis of Green Innovation of the E-Tailer and Supplier with a Drop Shipping Option in E-Commerce
by Yuepeng Cheng and Bo Li
J. Theor. Appl. Electron. Commer. Res. 2024, 19(1), 20-39; https://doi.org/10.3390/jtaer19010002 - 28 Dec 2023
Viewed by 935
Abstract
As customer demand for green products increases in the digital economic era, this study analyses the green innovation of the e-tailer and supplier in drop shipping models. Moreover, drop shipping e-tailers and suppliers with a drop shipping option need to make choices regarding [...] Read more.
As customer demand for green products increases in the digital economic era, this study analyses the green innovation of the e-tailer and supplier in drop shipping models. Moreover, drop shipping e-tailers and suppliers with a drop shipping option need to make choices regarding whether to provide green or normal products to the market. When a supplier with a drop shipping option produces green products, more fees may be invested in the production of green products than on normal products. The drop shipping e-tailers and suppliers with a drop shipping option can also choose to sell normal products at a low cost, as before. This study designs four models of drop shipping e-tailers and suppliers with a drop shipping option under different choices, analyzes their operational process in drop shipping models, and investigates five theorems. The optimal pricing decisions and green degree of drop shipping e-tailers and suppliers with a drop shipping option were evaluated in this study. The impacts of the green innovation factor, green elasticity coefficient, manufacturing and distribution costs on the drop shipping e-tailers and suppliers with a drop shipping option, and the effect of other environmental parameters on the green degree of green products are also analyzed through computer simulation. The findings of the simulation analysis provide valuable guidance for e-tailers and suppliers with green innovation in drop shipping models and offer important academic and practical implications for e-commerce and the digital economy. Full article
Show Figures

Figure 1

21 pages, 1155 KiB  
Article
Enhancing Traceability in Wine Supply Chains through Blockchain: A Stackelberg Game-Theoretical Analysis
by Yuxuan Kang, Xianliang Shi, Xiongping Yue, Weijian Zhang and Samuel Shuai Liu
J. Theor. Appl. Electron. Commer. Res. 2023, 18(4), 2142-2162; https://doi.org/10.3390/jtaer18040108 - 22 Nov 2023
Cited by 2 | Viewed by 1320
Abstract
Blockchain technology has been adopted to improve traceability and authenticity in wine supply chains (WSCs). However, whether through outsourcing or self-implementation of a blockchain-based wine traceability system (BTS), there are significant costs involved, as well as concerns regarding consumer privacy. Motivated by observations [...] Read more.
Blockchain technology has been adopted to improve traceability and authenticity in wine supply chains (WSCs). However, whether through outsourcing or self-implementation of a blockchain-based wine traceability system (BTS), there are significant costs involved, as well as concerns regarding consumer privacy. Motivated by observations of real-world practice, we explore the value of blockchain in enhancing traceability and authenticity in WSCs through a Stackelberg game-theoretical analysis. By comparing the equilibrium solutions of the scenarios with and without blockchain, we uncover the value of blockchain in tracing wine products. Our findings show that blockchain adoption can increase WSC prices under certain conditions. We derive the threshold for a third-party BTS service fee that determines blockchain adoption for tracing wine products and reveal the moderating effect of consumer traceability preferences and privacy concerns. Furthermore, the investigation of who should lead the implementation of BTS finds that the equal cost sharing between the manufacturer and the retailer results in no difference in BTS implementation leadership. Otherwise, the manufacturer always benefits from taking the lead in the implementation of BTS, and the retailer should undertake a leadership role in BTS implementation if they need to bear higher costs. Full article
Show Figures

Figure 1

17 pages, 2513 KiB  
Article
Deep Filter Context Network for Click-Through Rate Prediction
by Mingting Yu, Tingting Liu and Jian Yin
J. Theor. Appl. Electron. Commer. Res. 2023, 18(3), 1446-1462; https://doi.org/10.3390/jtaer18030073 - 22 Aug 2023
Viewed by 1061
Abstract
The growth of e-commerce has led to the widespread use of DeepCTR technology. Among the various types, the deep interest network (DIN), deep interest evolution network (DIEN), and deep session interest network (DSIN) developed by Alibaba have achieved good results in practice. However, [...] Read more.
The growth of e-commerce has led to the widespread use of DeepCTR technology. Among the various types, the deep interest network (DIN), deep interest evolution network (DIEN), and deep session interest network (DSIN) developed by Alibaba have achieved good results in practice. However, the above models’ use of filtering for the user’s own historical behavior sequences and the insufficient use of context features lead to reduced recommendation effectiveness. To address these issues, this paper proposes a novel article model: the deep filter context network (DFCN). This improves the efficiency of the attention mechanism by adding a filter to filter out data in the user’s historical behavior sequence that differs greatly from the target advertisement. The DFCN pays attention to the context features through two local activation units. This model greatly improves the expressiveness of the model, offering strong environment-related attributes and the adaptive capability of the model, with a significant improvement of up to 0.0652 in the AUC metric when compared with our previously proposed DICN under different datasets. Full article
Show Figures

Figure 1

16 pages, 622 KiB  
Article
A Multi-Criteria Group Decision-Making Method for Risk Assessment of Live-Streaming E-Commerce Platform
by Jiafu Su, Dan Wang, Fengting Zhang, Baojian Xu and Zhiguang Ouyang
J. Theor. Appl. Electron. Commer. Res. 2023, 18(2), 1126-1141; https://doi.org/10.3390/jtaer18020057 - 12 Jun 2023
Cited by 7 | Viewed by 1832
Abstract
Live-streaming e-commerce is the future development direction of the retail industry. When retailers choose a live-streaming e-commerce platform, they face the test of various risks of the platform, such as insecure control of capital flow, insufficient support of public domain traffic, etc. Therefore, [...] Read more.
Live-streaming e-commerce is the future development direction of the retail industry. When retailers choose a live-streaming e-commerce platform, they face the test of various risks of the platform, such as insecure control of capital flow, insufficient support of public domain traffic, etc. Therefore, it is necessary to evaluate the risks of the platform to help retailers identify the platform with the lowest risk. Considering the complexity of the risks of live-streaming e-commerce platforms and the ambiguity of the decision-makers thinking, the current method for multi-criteria group decision-making (MCGDM) method in a fuzzy environment rarely discusses the decision-makers weight for the criterion. This paper proposes interval-valued intuitionistic fuzzy multi-criteria group decision-making based on the decision-makers’ professionalism to evaluate the platform’s risks. This method determines the decision-maker’s weight for the criterion based on the professionalism of the decision-maker and uses the technique for order preference by similarity to an ideal solution (TOPSIS) method to rank the alternative platforms. Finally, a risk assessment of the agricultural product live-streaming e-commerce platforms is used as a case study to demonstrate the feasibility and effectiveness of the proposed method. This research will not only provide practical guidance for retailers to choose the live-streaming e-commerce platform with the lowest comprehensive risk but also provide ideas for the research of live-streaming e-commerce from the perspective of risk assessment. Full article
Show Figures

Figure 1

18 pages, 910 KiB  
Article
The Digital Platform, Enterprise Digital Transformation, and Enterprise Performance of Cross-Border E-Commerce—From the Perspective of Digital Transformation and Data Elements
by Yunpeng Yang, Nan Chen and Hongmin Chen
J. Theor. Appl. Electron. Commer. Res. 2023, 18(2), 777-794; https://doi.org/10.3390/jtaer18020040 - 23 Mar 2023
Cited by 11 | Viewed by 6727
Abstract
The digital trade ecosystem’s development relies on the growth of cross-border e-commerce platforms. To ensure the continued growth of China’s digital trade, it is crucial to consider the service capabilities of digital platforms and the digital transformation capabilities of cross-border e-commerce firms. This [...] Read more.
The digital trade ecosystem’s development relies on the growth of cross-border e-commerce platforms. To ensure the continued growth of China’s digital trade, it is crucial to consider the service capabilities of digital platforms and the digital transformation capabilities of cross-border e-commerce firms. This study explores the impact of these factors on the performance of cross-border e-commerce companies, with digital transformation capability acting as a mediator. Empirical research reveals that the service capability of digital platforms is composed of supply chain communication and cost control abilities, which partially mediate the relationship between digital platform serviceability and cross-border e-commerce enterprise performance. Moreover, both the service capabilities of digital platforms and the digital transformation capabilities of cross-border e-commerce companies have a positive and significant impact on enterprise performance. Full article
Show Figures

Figure 1

26 pages, 2231 KiB  
Article
A Hyper-Personalized Product Recommendation System Focused on Customer Segmentation: An Application in the Fashion Retail Industry
by Emre Yıldız, Ceyda Güngör Şen and Eyüp Ensar Işık
J. Theor. Appl. Electron. Commer. Res. 2023, 18(1), 571-596; https://doi.org/10.3390/jtaer18010029 - 11 Mar 2023
Cited by 7 | Viewed by 5648
Abstract
Providing the right products, at the right place and time, according to their customer’s preferences, is a problem-seeking solution, especially for companies operating in the retail industry. This study presents an integrated product RS that combines various data mining techniques with this motivation. [...] Read more.
Providing the right products, at the right place and time, according to their customer’s preferences, is a problem-seeking solution, especially for companies operating in the retail industry. This study presents an integrated product RS that combines various data mining techniques with this motivation. The proposed approach consists of the following steps: (1) customer segmentation; (2) adding the location dimension and determining the association rules; (3) the creation of product recommendations. We used the RFM technique for customer segmentation and the k-means clustering algorithm to create customer segments with customer-based RFM values. Then, the Apriori algorithm, one of the association rule mining algorithms, is used to create accurate rules. In this way, cluster-based association rules are created. Finally, product recommendations are presented with a rule-based heuristic algorithm. This is the first system that considers customers’ demographic data in the fashion retail industry in the literature. Furthermore, the customer location information is used as a parameter for the first time for the clustering phase of a fashion retail product RS. The proposed systematic approach is aimed at producing hyper-personalized product recommendations for customers. The proposed system is implemented on real-world e-commerce data and compared with the current RSs used according to well-known metrics and the average sales information. The results show that the proposed system provides better values. Full article
Show Figures

Figure 1

23 pages, 7544 KiB  
Article
The Impact of Topological Structure, Product Category, and Online Reviews on Co-Purchase: A Network Perspective
by Hongming Gao
J. Theor. Appl. Electron. Commer. Res. 2023, 18(1), 548-570; https://doi.org/10.3390/jtaer18010028 - 10 Mar 2023
Viewed by 1971
Abstract
Understanding the relationships within product co-purchasing is crucial for designing effective cross-selling and recommendation systems in e-commerce. While researchers often detect co-purchase rules based on product attributes, this study explores the influence of consumer behavior preferences and electronic word-of-mouth (eWOM) on co-purchase formation [...] Read more.
Understanding the relationships within product co-purchasing is crucial for designing effective cross-selling and recommendation systems in e-commerce. While researchers often detect co-purchase rules based on product attributes, this study explores the influence of consumer behavior preferences and electronic word-of-mouth (eWOM) on co-purchase formation by analyzing the topological network structure of products. Data were collected from a major Chinese e-retailer and analyzed using an exponential random graph model (ERGM) to identify the factors affecting the formation of follow-up purchases between products: the role of topological structure, product category, and online product reviews. The results showed that the co-purchase network was a sparse small-world network, with a product degree of centrality that positively impacted its sales volume within the network, suggesting a concentration effect. Cross-category purchases significantly contribute to the formation of co-purchase relationships, with a differential homophily effect. Positive ratings and review volumes were found to be key factors impacting this co-purchase formation. In addition, a higher inconsistency of positive ratings among products decreases the likelihood of co-purchase. These findings contribute to the literature on eWOM and electronic networks, and have valuable implications for e-commerce managers. Full article
Show Figures

Figure 1

22 pages, 762 KiB  
Article
Exploring the Gamification Affordances in Online Shopping with the Heterogeneity Examination through REBUS-PLS
by Xiao-Yu Xu, Syed Muhammad Usman Tayyab, Qing-Dan Jia and Kuang Wu
J. Theor. Appl. Electron. Commer. Res. 2023, 18(1), 289-310; https://doi.org/10.3390/jtaer18010016 - 07 Feb 2023
Cited by 4 | Viewed by 2578
Abstract
This study investigates, from the perspective of affordance theory, how the implementation of gamification features and mechanisms in online-shopping platforms enable consumers to enjoy immersive shopping experiences and make subsequent shopping decisions. Importantly, the technique of REBUS-PLS is applied to unveil the nature [...] Read more.
This study investigates, from the perspective of affordance theory, how the implementation of gamification features and mechanisms in online-shopping platforms enable consumers to enjoy immersive shopping experiences and make subsequent shopping decisions. Importantly, the technique of REBUS-PLS is applied to unveil the nature of heterogeneity in perceived affordances and ensure the robustness of structural-model results. The research model is tested using cross-sectional data. Our results not only confirm the effects of different types of gamification affordances on immersive experience and subsequent behavior but also reveal the existence of different consumer groups within the overall sample with respect to their behavior patterns. Apart from social connectiveness, rewardability, playfulness, and novelty all exert significant effects on the immersive experience. In addition, this study identified three distinct groups, namely, “no novelty” users, “no playfulness” users, and “no connective” users. Full article
Show Figures

Figure 1

16 pages, 1224 KiB  
Article
Optimal Software Versioning Strategy Considering Customization and Consumer Deliberation Behavior
by Wenjun Shu, Zhongdong Xiao, Ruirui Zhang and Quanyao Cao
J. Theor. Appl. Electron. Commer. Res. 2023, 18(1), 257-272; https://doi.org/10.3390/jtaer18010014 - 06 Feb 2023
Viewed by 1555
Abstract
This study investigates the optimal versioning problem when a monopoly software provider bears the deliberation cost to help reduce consumer uncertainty about SaaS customization. We develop stylized models based on different production strategies and deliberation support strategies. We consider customer deliberation behavior as [...] Read more.
This study investigates the optimal versioning problem when a monopoly software provider bears the deliberation cost to help reduce consumer uncertainty about SaaS customization. We develop stylized models based on different production strategies and deliberation support strategies. We consider customer deliberation behavior as a new perspective on the need for a free trial. Our results indicate that a short free trial leads to free riders while a long enough free trial eliminates free riders. This is because a long free trial means that consumers easily get accustomed to the product. We also find that the seller benefits from offering deliberation support. The optimal product strategy is dependent on the deliberation support cost. When the deliberation support cost is low, the seller should provide dual products; on the contrary, the single SaaS product strategy is better with a high deliberation cost. Full article
Show Figures

Figure 1

25 pages, 5534 KiB  
Article
Detecting E-Commerce Water Army through Graph Modeling on User Multiple Collusive Relationships: A Case Study of China’s Hotel Industry
by Jing Peng, Yue Wang and Yuan Meng
J. Theor. Appl. Electron. Commer. Res. 2023, 18(1), 105-129; https://doi.org/10.3390/jtaer18010006 - 05 Jan 2023
Cited by 1 | Viewed by 1747
Abstract
In the e-commerce environment, it is very common for consumers to select goods or services based on online reviews from social platforms. However, the behavior of some unscrupulous merchants who hire a “water army” to brush up on reviews of their products has [...] Read more.
In the e-commerce environment, it is very common for consumers to select goods or services based on online reviews from social platforms. However, the behavior of some unscrupulous merchants who hire a “water army” to brush up on reviews of their products has been continuously exposed, which seriously misleads consumers’ purchasing decisions and undermines consumer trust. Until now, it has been a challenging task to accurately detect the “water army”, who could easily alter their behaviors or writing styles. The focus of this paper is on some collusive clues between members of the same social platform to propose a new graph model to detect the “water army”. First is the extraction of six kinds of user collusive relationships from two aspects: user content and user behavior. Further, the use of three aggregation methods on such collusive relationships generates a user collusive relationship factor (CRF), which is then used as the edge weight value in our graph-based water army detection model. In the combination of the graph grouping method and evaluation rules on candidate subgraphs, the graph model effectively detects multiple collusive groups automatically. The experimental results based on the Mafengwo platform show that the CRF generated from the coefficient of variation (CV) method demonstrates the best performance in detecting collusive groups, which provides some practical reference for the detection of “water armies” in an e-commerce environment. Full article
Show Figures

Figure 1

2022

Jump to: 2024, 2023

18 pages, 852 KiB  
Article
Strategy Analysis of Multi-Agent Governance on the E-Commerce Platform
by Hongyang He and Bin Zhang
J. Theor. Appl. Electron. Commer. Res. 2023, 18(1), 1-18; https://doi.org/10.3390/jtaer18010001 - 21 Dec 2022
Cited by 2 | Viewed by 2364
Abstract
In the post-epidemic era, the e-commerce industry has become an important engine to promote the new round of growth in China’s economy. However, the frequent quality problems of products, such as shoddy goods and improper products in the market, not only violate the [...] Read more.
In the post-epidemic era, the e-commerce industry has become an important engine to promote the new round of growth in China’s economy. However, the frequent quality problems of products, such as shoddy goods and improper products in the market, not only violate the legitimate rights and interests of consumers and social and public interests, but also seriously restrict the steady and sound development of the e-commerce industry. This paper uses evolutionary game theory to build an evolutionary game model between the government, platform, and merchants, and it analyzes the stable evolution path of the game system and the key factors affecting product quality optimization under the situation of dual strategy set, and then it expands the game side strategy set into a continuous type and compares and explores the regulatory effects and quality output changes under the two situations. Then, it puts forward effective measures to improve the quality of e-commerce products. The findings are as follows: in the case of a binary strategy set, it is difficult for merchants to steadily evolve towards compliance management, while merchants’ violation management only has the willingness to improve their efforts when the scale of consumers is small. In the case of continuous policy set, government–enterprise cooperative supervision can realize the compliance operation of merchants, and the effort level and income of merchants are consistent with the optimal value in the case of dual policy set. The results show that the government and e-commerce platforms should adhere to the concept of dynamic regulation and adjust the regulatory strategies according to the different development stages of enterprises so as to not only give merchants sufficient development space, but also to maintain the healthy development environment of the market. At the same time, the government and e-commerce platforms should also avoid the binary choice of supervision or neglect, adopt flexible regulatory strategies, and maintain moderate flexible regulation so as to achieve the development trend of compliance, efforts, and profits of merchants. Full article
Show Figures

Figure 1

17 pages, 778 KiB  
Article
Analysis of OTT Users’ Watching Behavior for Identifying a Profitable Niche: Latent Class Regression Approach
by Dongnyok Shim, Changjun Lee and Inha Oh
J. Theor. Appl. Electron. Commer. Res. 2022, 17(4), 1564-1580; https://doi.org/10.3390/jtaer17040079 - 23 Nov 2022
Cited by 3 | Viewed by 4408
Abstract
Over-the-top (OTT) firms must overcome the hurdle of the competitive Korean media market to achieve sustainable growth. To do so, understating how users enjoy OTT and analyzing usage patterns is essential. This research aims to empirically identify a profitable niche in the Korean [...] Read more.
Over-the-top (OTT) firms must overcome the hurdle of the competitive Korean media market to achieve sustainable growth. To do so, understating how users enjoy OTT and analyzing usage patterns is essential. This research aims to empirically identify a profitable niche in the Korean OTT market by applying market segmentation theory. In addition, it investigates an effective content strategy to convert free users into paying customers belonging to profitable niche segments. The latent class regression model was applied to Korean Media Panel Survey data to divide Korean OTT customers into submarkets. According to an empirical analysis, Korean OTT users can be divided into three submarkets based on their OTT usage patterns, with the third segment serving as a profitable niche market. An additional analysis of the profitable niche market revealed that bundling content, such as foreign content, original content, and movies, is a crucial content strategy for increasing paying subscribers in a profitable niche segment. Full article
Show Figures

Figure 1

17 pages, 1082 KiB  
Article
AutoML Approach to Stock Keeping Units Segmentation
by Ilya Jackson
J. Theor. Appl. Electron. Commer. Res. 2022, 17(4), 1512-1528; https://doi.org/10.3390/jtaer17040076 - 15 Nov 2022
Cited by 1 | Viewed by 2577
Abstract
A typical retailer carries 10,000 stock-keeping units (SKUs). However, these numbers may exceed hundreds of millions for giants such as Walmart and Amazon. Besides the volume, SKU data can also be high-dimensional, which means that SKUs can be segmented on the basis of [...] Read more.
A typical retailer carries 10,000 stock-keeping units (SKUs). However, these numbers may exceed hundreds of millions for giants such as Walmart and Amazon. Besides the volume, SKU data can also be high-dimensional, which means that SKUs can be segmented on the basis of various attributes. Given the data volumes and the multitude of potentially important dimensions to consider, it becomes computationally impossible to individually manage each SKU. Even though the application of clustering for SKU segmentation is common, previous studies do not address the problem of parametrization and model finetuning, which may be extremely tedious and time-consuming in real-world applications. Our work closes the research gap by proposing a solution that leverages automated machine learning for the automated cluster analysis of SKUs. The proposed framework for automated SKU segmentation incorporates minibatch K-means clustering, principal component analysis, and grid search for parameter tuning. It operates on top of the Apache Parquet file format, an efficient, structured, compressed, column-oriented, and big-data-friendly format. The proposed solution was tested on the basis of a real-world dataset that contained data at the pallet level. Full article
Show Figures

Figure 1

21 pages, 2742 KiB  
Article
Research on Supervision Mechanism of Big Data Discriminatory Pricing on the Asymmetric Service Platform—Based on SD Evolutionary Game Model
by Bing Xu and Qin Li
J. Theor. Appl. Electron. Commer. Res. 2022, 17(4), 1243-1263; https://doi.org/10.3390/jtaer17040063 - 23 Sep 2022
Cited by 4 | Viewed by 2062
Abstract
Big data discriminatory pricing behavior of service platforms frequently occurs, which affects the legitimate rights and interests of consumers as well as the healthy development of the platform economy. The SD (System Dynamics) evolutionary game model characterizing the game relationship of a big [...] Read more.
Big data discriminatory pricing behavior of service platforms frequently occurs, which affects the legitimate rights and interests of consumers as well as the healthy development of the platform economy. The SD (System Dynamics) evolutionary game model characterizing the game relationship of a big platform, small platform, and government is constructed together with its equilibrium solutions in order to analyze the regulatory dilemma and governance mechanism against big data discriminatory pricing of service platforms. This paper finds that government punishment on the behavior of big data discriminatory pricing plays a decisive role. When the government punishment is large enough, both platforms tend towards fair pricing; when the government punishment is insufficient, the big platform always tends towards discriminatory pricing. The supply chain of the service platform falls into the regulatory dilemma of big data discriminatory pricing behavior. Due to the hidden characteristics of big data discriminatory pricing and technical challenges in authentication and proof, a third party is introduced for supervision, and an SD evolutionary game model with a collaborative supervision mechanism of the government and the third party is constructed. The results show that positive supervision of the third party can effectively regulate the big data discriminatory pricing behavior of the big platform, which has specific implications for the design of the supervision mechanism against big data discriminatory pricing of service platforms. Full article
Show Figures

Figure 1

24 pages, 4256 KiB  
Article
Service Decisions in a Two-Echelon Retailing System with Customer Returns
by Mohannad Radhi
J. Theor. Appl. Electron. Commer. Res. 2022, 17(3), 1219-1242; https://doi.org/10.3390/jtaer17030062 - 15 Sep 2022
Cited by 2 | Viewed by 1997
Abstract
Many manufacturers and retailers have already opened online stores to sell their products. Thus, manufacturers are competing as sellers, and retailers are transforming into dual-channel retailers (DCRs). Such an expansion in business scope and the wide spread of lenient return policies trigger tremendous [...] Read more.
Many manufacturers and retailers have already opened online stores to sell their products. Thus, manufacturers are competing as sellers, and retailers are transforming into dual-channel retailers (DCRs). Such an expansion in business scope and the wide spread of lenient return policies trigger tremendous return volume that requires great deal of logistical efforts. The service levels offered within online stores greatly affect channels’ demand. However, they also influence the channel choice of return for online customers, if applicable, when their purchases are unsatisfactory. Therefore, this paper studies the optimal service level for a centralized DCR. In addition, it examines the optimal levels for a decentralized two-echelon system through the implementation of Nash and Stackelberg theoretical frameworks. Important properties of optimal solutions and vital managerial insights have been devised through analytical and sensitivity analysis. It is found that a DCR may have the following tradeoff: decrease service level to increase the reward from the physical store or increase service level to enhance competitiveness of the online store. The optimal decision depends greatly on how sensitive the customers’ return behavior is to service level. In addition, as the exogenous price increases, the change in the retailer’s offered level depends significantly on the different rates of return. Full article
Show Figures

Figure 1

16 pages, 597 KiB  
Article
Customer Response Model in Direct Marketing: Solving the Problem of Unbalanced Dataset with a Balanced Support Vector Machine
by Sunčica Rogić, Ljiljana Kašćelan and Mirjana Pejić Bach
J. Theor. Appl. Electron. Commer. Res. 2022, 17(3), 1003-1018; https://doi.org/10.3390/jtaer17030051 - 21 Jul 2022
Cited by 9 | Viewed by 3525
Abstract
Customer response models have gained popularity due to their ability to significantly improve the likelihood of targeting the customers most likely to buy a product or a service. These models are built using databases of previous customers’ buying decisions. However, a smaller number [...] Read more.
Customer response models have gained popularity due to their ability to significantly improve the likelihood of targeting the customers most likely to buy a product or a service. These models are built using databases of previous customers’ buying decisions. However, a smaller number of customers in these databases often bought the product or service than those who did not do so, resulting in unbalanced datasets. This problem is especially significant for online marketing campaigns when the class imbalance emerges due to many website sessions. Unbalanced datasets pose a specific challenge in data-mining modelling due to the inability of most of the algorithms to capture the characteristics of the classes that are unrepresented in the dataset. This paper proposes an approach based on a combination of random undersampling and Support Vector Machine (SVM) classification applied to the unbalanced dataset to create a Balanced SVM (B-SVM) data pre-processor resulting in a dataset that is analysed with several classifiers. The experiments indicate that using the B-SVM strategy combined with classification methods increases the base models’ predictive performance, indicating that the B-SVM approach efficiently pre-processes the data, correcting noise and class imbalance. Hence, companies may use the B-SVM approach to more efficiently select customers more likely to respond to a campaign. Full article
Show Figures

Figure 1

18 pages, 1732 KiB  
Article
B2C E-Commerce Customer Churn Prediction Based on K-Means and SVM
by Xiancheng Xiahou and Yoshio Harada
J. Theor. Appl. Electron. Commer. Res. 2022, 17(2), 458-475; https://doi.org/10.3390/jtaer17020024 - 06 Apr 2022
Cited by 36 | Viewed by 10798
Abstract
Customer churn prediction is very important for e-commerce enterprises to formulate effective customer retention measures and implement successful marketing strategies. According to the characteristics of longitudinal timelines and multidimensional data variables of B2C e-commerce customers’ shopping behaviors, this paper proposes a loss prediction [...] Read more.
Customer churn prediction is very important for e-commerce enterprises to formulate effective customer retention measures and implement successful marketing strategies. According to the characteristics of longitudinal timelines and multidimensional data variables of B2C e-commerce customers’ shopping behaviors, this paper proposes a loss prediction model based on the combination of k-means customer segmentation and support vector machine (SVM) prediction. The method divides customers into three categories and determines the core customer groups. The support vector machine and logistic regression were compared to predict customer churn. The results show that each prediction index after customer segmentation was significantly improved, which proves that k-means clustering segmentation is necessary. The accuracy of the SVM prediction was higher than that of the logistic regression prediction. These research results have significance for customer relationship management of B2C e-commerce enterprises. Full article
Show Figures

Figure 1

34 pages, 5889 KiB  
Article
Customer Churn in Retail E-Commerce Business: Spatial and Machine Learning Approach
by Kamil Matuszelański and Katarzyna Kopczewska
J. Theor. Appl. Electron. Commer. Res. 2022, 17(1), 165-198; https://doi.org/10.3390/jtaer17010009 - 15 Jan 2022
Cited by 28 | Viewed by 14356
Abstract
This study is a comprehensive and modern approach to predict customer churn in the example of an e-commerce retail store operating in Brazil. Our approach consists of three stages in which we combine and use three different datasets: numerical data on orders, textual [...] Read more.
This study is a comprehensive and modern approach to predict customer churn in the example of an e-commerce retail store operating in Brazil. Our approach consists of three stages in which we combine and use three different datasets: numerical data on orders, textual after-purchase reviews and socio-geo-demographic data from the census. At the pre-processing stage, we find topics from text reviews using Latent Dirichlet Allocation, Dirichlet Multinomial Mixture and Gibbs sampling. In the spatial analysis, we apply DBSCAN to get rural/urban locations and analyse neighbourhoods of customers located with zip codes. At the modelling stage, we apply machine learning extreme gradient boosting and logistic regression. The quality of models is verified with area-under-curve and lift metrics. Explainable artificial intelligence represented with a permutation-based variable importance and a partial dependence profile help to discover the determinants of churn. We show that customers’ propensity to churn depends on: (i) payment value for the first order, number of items bought and shipping cost; (ii) categories of the products bought; (iii) demographic environment of the customer; and (iv) customer location. At the same time, customers’ propensity to churn is not influenced by: (i) population density in the customer’s area and division into rural and urban areas; (ii) quantitative review of the first purchase; and (iii) qualitative review summarised as a topic. Full article
Show Figures

Figure 1

Back to TopTop