Search Results (8)

Based on the Persuasion Knowledge Model (PKM), this research investigates how virtual electronic word-of-mouth (eWOM) senders’ message framing—numerical versus experiential—influences eWOM effectiveness across three experiments. We find that: (1) numerical descriptions from virtual eWOM senders significantly enhance eWOM effectiveness compared to experiential descriptions, while this effect does not emerge for human senders; (2) perceived diagnosticity mediates the relationship between message framing and eWOM effectiveness; and (3) product type moderates this effect pathway, with numerical descriptions showing stronger positive effects for search products than for experience products. This research enriches theoretical understanding of eWOM communication in interactive marketing and provides practical guidance for e-commerce companies to optimize their content marketing strategies. Full article

A rational exchange protocol is a type of e-commerce protocol that aims to maximize the participants’ own interests. The Buttyan model is commonly used to analyze the security of such protocols. However, this model has limitations in dealing with uncertainties and false messages in rational exchanges. To address these shortcomings, this paper proposes a formal analysis method based on Bayesian games. By incorporating participants’ types and beliefs, the Buttyan model is extended to enhance its ability to express uncertainties. Additionally, attack messages are introduced to simulate the potential fraudulent behaviors that participants may exploit through the security vulnerabilities in the protocol. Finally, the improved model is applied to conduct a formal analysis of a rational electronic contract signing protocol, and it is found that the protocol meets the usability requirements. The results show that this method can be effectively applied to the security analysis of rational exchange protocols, thereby enhancing the security of the e-commerce transaction process. Full article

The e-commerce data structure is a typical multiplex graph network structure, which allows multiple types of edges between node pairs. However, existing methods that rely on message-passing frameworks are not sufficient to fully exploit the rich information in multiplex graphs. To improve the performance of link prediction, we propose a novel laminated heterogeneous graph convolutional network (LHGCN) consisting of three core modules: a laminate generation module (LGM), an adaptive convolution module (ACM), and a laminate fusion module (LFM). More specifically, the LGM generates symmetric laminates that cover diverse semantics to create rich node representations. Then, the ACM dynamically adjusts the node receptive field and flexibly captures local information, thereby enhancing the representation ability of the node. Through symmetric information propagation across laminates, the LFM combines multiple laminated features to optimize the global representation, which enables our model to accurately predict links. Moreover, an elaborate loss function, consisting of positive sample loss, negative sample loss, and L2 regularization loss, drives the network to preserve critical information. Extensive experiments on various benchmarks demonstrate the superiority of our method over state-of-the-art alternatives in terms of link prediction. Full article

The Korean e-commerce market represents a large percentage of the global retail distribution market, a market that continues to grow each year, and online payments are rapidly becoming a mainstream payment method. As e-commerce becomes more active, many companies that support electronic payments are increasing the number of franchisees. Electronic payments have become an indispensable part of people’s lives. However, the types of statistical information on the results of electronic payment transactions are not consistent across companies, and it is difficult to automatically determine the error status of a transaction if no one directly confirms the error messages generated during payment. To address these issues, we propose an optimized LSTM model. In this study, we classify the error content in statistical information based on natural language processing to determine the error status of the current failed transaction. We collected 11,865 response messages from various vendors and financial companies and labelled them with an LSTM classifier model to create a dataset. We then trained this dataset with simple RNN, LSTM, and GRU models and compared their performance. The results show that the optimized LSTM model with the attention layer added to the dropout layer and the bidirectional recursive layer achieves an accuracy of about 92% or more. When the model is applied to e-commerce services, any error in the transaction status of the system can be automatically detected by the model. Full article

The volume of data is growing exponentially and becoming more valuable to organizations that collect it, from e-commerce data, shipping, audio and video logs, text messages, internet search queries, stock market activity, financial transactions, the Internet of Things, and various other sources. The major challenges are related with the way to extract insights from such a rich data environment and whether Deep Learning can be successful with Big Data. To get some insight on these topics, social network data are employed as a case study on how sentiments can affect decisions in stock market environments. In this paper, we propose a generalized Deep Learning-based classification framework for Stock Market Sentiment Analysis. This work comprises the study, the development, and implementation of an automatic classification system based on Deep Learning and the validation of its adequacy and efficiency in any scenario, particularly Stock Market Sentiment Analysis. Distinct datasets and several Deep Learning approaches with different layers and embedded techniques are used, and their performances are evaluated. These developments show how Deep Learning reacts to distinct contexts. The results also give context on how different techniques with different parameter combinations react to certain types of data. Convolution obtained the best results when dealing with complex data inputs, and long short-term layers kept a memory of data, allowing inputs which are not as common to still be considered for decisions. The models that resulted from Stock Market Sentiment Analysis datasets were applied with some success to real-life problems. The best models reached accuracies of 73% in training and 69% in certain test datasets. In a simulation, a model was able to provide a Return on Investment of 4.4%. The results contribute to understanding how to process Big Data efficiently using Deep Learning and specialized hardware techniques. Full article

The privacy and security of online transactions and information exchange has always been a critical issue of e-commerce. However, there is a certain level of tolerance (a share of 36%) when it comes to so-called governments’ rights to monitor electronic mail messages and other information exchange as resulting from the answers of respondents from 51 countries in the latest wave (2017–2020) of the World Values Survey. Consequently, the purpose of this study is to discover the most significant influences associated with this type of tolerance and even causal relationships. The variables have been selected and analyzed in many rounds (Adaptive Boosting, LASSO, mixed-effects modeling, and different regressions) with the aid of a private cloud. The results confirmed most hypotheses regarding the overwhelming role of trust, public surveillance acceptance, and some attitudes indicating conscientiousness, altruistic behavior, and gender discrimination acceptance in models with good-to-excellent classification accuracy. A generated prediction nomogram included 10 ten most resilient influences. Another one contained only 5 of these 10 that acted more as determinants resisting reverse causality checks. In addition, some sociodemographic controls indicated significant variables afferent to the highest education level attained, settlement size, and marital status. The paper’s novelty stands on many robust techniques supporting randomly and nonrandomly cross-validated and fully reproducible results based on a large amount and variety of evidence. The findings also represent a step forward in research related to privacy and security issues in e-commerce. Full article

Nowadays, word-of-mouth is a very important component of e-commerce activity because consumers are very sensitive to other people’s opinions. Depending on the companies’ politics, these opinions can be incentivized or non-incentivized. One of the major dilemmas consists in establishing which kind of word-of-mouth has more influence on customers’ perceptions. The purpose of this study is to assess the relationships between perceived argument quality (PAQ) and perceived source expertise (PSE), on the one hand, and electronic word-of-mouth adoption intention on the other hand, for an incentivized message compared to a non-incentivized message. We processed answers from two different random groups by using adapted PAQ and PSE inventories of questions. The constructs, latent variables and items were analyzed in IBM Amos software, and our findings confirm the hypotheses regarding the relationship between the attributes of the message (argument quality and source expertise) and message credibility. Additionally, we found a significant positive relationship between message credibility and electronic word-of-mouth adoption intention. Our research also explores the moderating role of the message type (incentivized vs. non-incentivized) in the relationships above, and we discovered that the message type significantly moderates the relationship between perceived argument quality and credibility, but the type of message does not moderate the relationship between message credibility and eWOM adoption intention. Full article

Online auctions are a popular and convenient way to engage in ecommerce. However, the amount of auction fraud has increased with the rapid surge of users participating in online auctions. Shill bidding is the most prominent type of auction fraud where a seller submits bids to inflate the price of the item without the intention of winning. Mechanisms have been proposed to detect shill bidding once an auction has finished. However, if the shill bidder is not detected during the auction, an innocent bidder can potentially be cheated by the end of the auction. Therefore, it is essential to detect and verify shill bidding in a running auction and take necessary intervention steps accordingly. This paper proposes a run-time statistical algorithm, referred to as the Live Shill Score, for detecting shill bidding in online auctions and takes appropriate actions towards the suspected shill bidders (e.g., issue a warning message, suspend the auction, etc.). The Live Shill Score algorithm also uses a Post-Filtering Process to avoid misclassification of innocent bidders. Experimental results using both simulated and commercial auction data show that our proposed algorithm can potentially detect shill bidding attempts before an auction ends. Full article