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On most e-commerce websites, there are two crucial factors that customers rely on to assess product quality and dependability: customer reviews provided online and related ratings. Reviews offer feedback to customers about the product's merits, reasons for negative reviews, and feelings of satisfaction or dissatisfaction with the provided service. As for ratings, they express customer opinions about the product's quality as numerical values from one to five (one or two for the worst opinion, three for the neutral opinion, and four or five for the best opinion). Usually, the customer reviews may be inconsistent with their relevant ratings; the customer may write the worst review despite providing a four- or five-star rating or write the best review with only a one- or two-star rating. Due to this inconsistency, customers may need help to identify relevant information. Therefore, it is required to develop a model that can classify reviews as either positive or negative, depending on the polarity of thoughts, to demonstrate if there is an inconsistency between customer reviews and their actual ratings by comparing them with the ratings resulting from the model. This paper proposes an efficient deep learning (DL) model for classifying customer reviews and assessing whether there is inconsistency. The recommended model's performance and stability are examined on a large dataset of product reviews from Amazon e-commerce. The experimental findings showed that the proposed model dominates and significantly outperforms its peers regarding prediction accuracy and other performance measures. [ABSTRACT FROM AUTHOR]

While workers of all stripes are compelled to embrace uncertainty under conditions of neoliberalism, ideologies of risk assume a particular guise in the platform economy, wherein laborers are exhorted to 'put yourself out there'. Given the attendant harms associated with public visibility – especially for women and other marginalized groups – it seems crucial to explore platform-dependent laborers' experiences of 'putting themselves out there'. This article draws upon in-depth interviews with 23 social media influencers and content creators, sampled from across platforms, content niches and subjectivities. Our analysis revealed that vulnerability is a structuring concept in the influencer economy – one that operates at multiple, often overlapping levels. First, the commercial logic of authenticity casts personal vulnerability as a strategy for building community and accruing followers. But influencers' individual disclosures were often entangled with their social identities (e.g., gender, race, sexuality, ability and body type), which rendered them socially vulnerable to targeted antagonism from audiences. Interviewees experienced a range of harms, from identity-based hate and harassment to concerted take-down campaigns. These personal and social vulnerabilities were compounded by the vulnerabilities of platform-dependent labor: not only did participants identify the failures of platforms to protect them, some shared a sense that these companies exacerbated harms through a commercial logic that incentivizes antagonism. After examining the emotional labor necessary to manage such platform vulnerabilities, we close by reiterating the unique precarity of platform labor, wherein participants lack the social and legal protections typically afforded to 'vulnerable workers'. [ABSTRACT FROM AUTHOR]

Attention on customer citizenship behavior (CCB) has increased markedly in recent years for both researchers and practitioners. However, existing research lacks deep understanding of the antecedents of CCB in social commerce, especially from the perspective of seller-consumer interaction relationships. Drawing on complexity theory, this study investigated the impacts of the causal configurations of three types of seller-consumer interaction relationships (experience of parasocial interaction (PSI), social interaction, and parasocial relationship (PSR)) on CCB in social commerce. To test this proposition, this study adopted fuzzy-set qualitative comparative analysis (fsQCA) on a sample of 380 experienced social commerce consumers. Our findings indicate that the combination of social interaction and PSR leads to a high CCB among social commerce consumers. Moreover, borrowing from social exchange theory, we further employed partial least squares structural equation modeling (PLS-SEM) to reanalyze the research data. The PLS-SEM results are consistent with the fsQCA results. [ABSTRACT FROM AUTHOR]

Summary: Food accessibility was considerably impacted by restrictions during the coronavirus disease 2019 (COVID‐19) pandemic, leading to growth in the online food retail sector, which offered contact‐free delivery. This systematic review aimed to assess the change in use of online food retail platforms during COVID‐19. The secondary aim was to identify diet‐related chronic disease risk factors including dietary intake, eating behaviors, and/or weight status associated with the use of online food retail platforms during the pandemic. The review was registered in the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42022320498) and adhered to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines. Nine electronic databases were searched between January 2020 and October 2023. Studies that reported the frequency or change in use of online groceries, meal delivery applications, and/or meal‐kit delivery services before and during the pandemic were included. A total of 53 studies were identified, including 46 cross‐sectional studies, 4 qualitative studies, 2 longitudinal cohort studies, and 1 mixed‐methods study. Overall, 96% (43/45) of outcomes showed an increase in the use of online groceries during COVID‐19, while 55% (22/40) of outcomes showed a decrease in meal delivery applications. Eight of nine outcomes associated the use of online food retail with weight gain and emotional eating. Further research is needed to investigate the links between online food retail and obesity. [ABSTRACT FROM AUTHOR]

With the emerging era of E‐commerce and online shopping, people are also in a habit to receive default product recommendations on the web pages that they access. Google is already providing such suggestions. Till now recommendations were made only based on previous sentiments or feedback or ratings, but this research has improved the product recommendation method by including one more parameter for the same. This article represents two parameters for making predictions of product allocation to a new customer. These parameters are ratings given by the existing users for that particular product and the region to which the new customer belongs. Following these parameters, a prediction model and an algorithm, Improved_Collab_Similarity, have been implemented. The dataset has been developed where India as a country along with all its States has been considered for products which are popular for their creation based on regional and ancient skills of the people belonging to that area. Results for the mentioned prediction model have been discussed in this article where generally precision increases with the increase in a number of products but at some points, it does not increase when a smaller number of that product was purchased by the customers. [ABSTRACT FROM AUTHOR]

Background: This systematic review contributes to the understanding of the characteristics of built food environments that may be associated with choices of alternative protein foods (APF). Using the built food environment typology proposed by Downs et al., we investigated various environmental structures (e.g., supermarkets, other retailers, farmers' markets, restaurants, schools, and online vendors) and the characteristics that may facilitate or hinder consumers' choices. For example, facilitators and barriers may refer to the physical characteristics of environmental structures, food presentation practices, the organizational strategies or policies operating in the setting, or the actions that retailers or consumers engage in while selling, serving, choosing, trying, or purchasing APF in these environmental structures. Methods: A systematic review (PROSPERO database preregistration; no. CRD42023388700) was conducted by searching 13 databases for peer-reviewed journals focusing on the fields of economics and business, agriculture, medical sciences, and social sciences. Data searches, coding, and quality evaluations were conducted by at least 2 researchers. A total of 31 papers (36 original studies) were included. The risk of bias was evaluated with the Joanna Briggs Institute quality evaluation tool, with 24 publications presenting low risk of bias. Results: The findings indicate that perceived and actual availability facilitate consumers' APF choices across a built food environment. Several barriers/facilitators were associated with APF choices in specific types of built food environments: the way food is presented in produce sections (supermarkets), consumer habits in terms of green and specialty shopping (grocery stores), and mismatches among retailer actions in regard to making APF available in one type of food environment structure (e-commerce) and consumers' preferences for APF being available in other food environment structures (supermarkets, grocery stores). The effect of a barrier/facilitator may depend on the APF type; for example, social norms regarding masculinity were a barrier affecting plant-based APF choices in restaurants, but these norms were not a barrier affecting the choice of insect-based APF in restaurants. Conclusions: Addressing barriers/facilitators identified in this review will help in developing environment-matching interventions that aim to make alternative proteins mainstream. Trial registration: PROSPERO database registration: #CRD42023388700. [ABSTRACT FROM AUTHOR]

This paper studies the recycling and remanufacturing mode and sales channel issues in the closed-loop supply chain. Specifically, this study establishes an e-commerce closed-loop supply chain consisting of a manufacturer and an e-commerce platform, and divides the recycling model into recycling by the manufacturer or recycling by the platform. Considering two common sales models in e-commerce platforms: the resale model and agency model, combined with the recycling model, four different research scenarios are formed. We use backward induction to solve the Stackelberg game problem and explore the remanufacturing and channel strategies of the manufacturer and the e-commerce platform. The research results show that for the manufacturer, under the same recycling model, when consumers' preference for remanufactured products and the sensitivity of recycling volume to recycling prices are low, he will prefer the resale model. Under the same sales model, the manufacturer always prefers the recycling model in which he is responsible for recycling. However, the choice of platform is contrary to that of the manufacturer. In the resale model, both the manufacturer and the platform will choose to recycle by themselves, which cannot achieve a win-win situation. Under the agency model, when consumers' preference for remanufactured products is high and the sensitivity coefficient of recycling volume to recycling price is low, supply chain members can achieve a win-win situation, and the scope of the win-win situation decreases as the unit production cost of new products increases. In addition, rising consumer preference for remanufactured products will lead to lower consumer surplus. [ABSTRACT FROM AUTHOR]

This study examines the effects of the rising live streaming e-commerce on the 3DP supply chain, employing system dynamics to develop separate models for pure polymer and polymer-metal mixed printing. The analysis focuses on optimizing the 3DP supply chain configuration. Results indicate that, based solely on printing time, cost, and quality metrics, Corporate-live-3DP services are optimal for live commerce scenarios. However, despite this, Private-live-3DP maintains a substantial consumer base in practice, as evidenced by literature data and case studies. Both models pose significant challenges to conventional supply chains, necessitating adaptation. For Corporate-live-3DP, optimization strategies may include technology advancements, digital transformation, agile manufacturing, global network optimization, innovative management, collaborative R&D, fine-tuned inventory control, quality system upgrades, talent development, and organizational restructuring. Conversely, Private-live-3DP can be optimized through consolidation of private 3D printing resources, demand prediction and order optimization, supply chain collaboration platforms, quality management extensions, inventory strategy adjustments, increased transparency, regulatory compliance, and risk mitigation measures. [ABSTRACT FROM AUTHOR]

As an emerging business modality and Internet format, live streaming e-commerce has developed rapidly since its emergence in 2016, especially since the outbreak of the COVID-19 epidemic in late 2019, when an increasing number of businesses from other industries attracted participation. However, with the development of the live streaming e-commerce industry, the industry's market environment is becoming increasingly chaotic. Therefore, during this period, government departments continuously formulate and implement relevant industry policies. In order to exploring the cooperation network structure, policy content distribution, and implementation effectiveness characteristics among publishers, this paper constructs a three-dimensional analysis framework of policy from the perspective of policy tools, policy effectiveness evaluation and policy publishers. The results show that in terms of policy tools, the overall structure of policy tools in the live streaming e-commerce industry is unreasonable, and different types of policy tools are significantly diverse. The proportion of environmental policy tools is greater than that of demand-based and supply-based policy tools, accounting for 62.97%, and among them, the tools related to industry regulation and management account for the largest proportion of the total, which greatly suppresses the enthusiasm of various entities in the industry for development. In terms of policy effectiveness evaluation, most of the policies do not formulate detailed long-, medium-, or short-term goals, nor are the policy priorities, incentive measures, or action modes perfect, indicating that the government's pushing and pulling forces for the live streaming e-commerce industry are insufficient. Finally, in the subject dimension of policy release, the synergy of relevant subjects is constantly improving, but there is also a phenomenon of over-concentration in the synergistic departments. [ABSTRACT FROM AUTHOR]

The "Three Rural Issues", encompass challenges related to agriculture, farmer, and rural area, which hold significant importance in driving comprehensive rural revitalization efforts in China. Farmer entrepreneurship, as a crucial means to enhance productivity, create job opportunities, and increase residents' income, has gradually become a key driving force in promoting rural revitalization in the new stage of development in China. With the rapid development of rural e-commerce, farmer entrepreneurship has encountered new opportunities. This study utilizes the 2020 China Family Panel Studies (CFPS) data and employs a structural equation model (SEM) to analyze the direct impact of rural e-commerce participation on farmer entrepreneurial behavior, considering factors such as human capital, social capital, and network infrastructure. This study further explores the indirect effects and mechanisms of e-commerce participation as a mediating variable and analyzes the impact and mechanisms on agricultural entrepreneurship behavior. The findings are as follows: (1) E-commerce participation significantly promotes farmer entrepreneurial behavior; (2) E-commerce participation as a mediating variable has a positive indirect effect on the relationship between social trust, network infrastructure, human capital, and farmer entrepreneurial behavior; (3) E-commerce participation has a significant positive influence on farmer entrepreneurship in the agricultural sector, and farmers with higher levels of network infrastructure and human capital have a higher probability of choosing agricultural entrepreneurship under the influence of e-commerce participation. Finally, this study provides policy recommendations in terms of infrastructure construction, entrepreneurial policy environment, and education level, aiming to optimize the situation of farmer entrepreneurship and contribute to the comprehensive promotion of rural revitalization.Overall, the research in this paper effectively combines theory and empirical evidence to outline the direct and indirect impact mechanisms of rural e-commerce participation on farmers' entrepreneurial behavior and agriculture-related entrepreneurial behavior and to test the effects of their impacts. First, most of the existing literature deals with farmers in individual sample areas, while the sample selected in this paper is farmers in the whole country, which is relatively more generalizable; second, most of the previous studies explore the level of e-commerce in the inter-provincial or county areas, while this paper expands the empirical study of rural e-commerce on the entrepreneurial behavior of farmers and the micro-period of agricultural entrepreneurial behavior, and focuses on the impacts of the e-commerce activities of farmers on their entrepreneurial behavior. [ABSTRACT FROM AUTHOR]

Recent years have witnessed the rise of digital platforms that allow economic agents to arrange ever more fine‐grained contracts. This article zooms in on labour‐based platforms that permit the hire of labour in a just‐in‐time fashion (and are part of the broader trend towards on‐demand work). Its principal contribution comes in three parts. First, exposing the frequently overlooked diversity of labour‐based platforms, the article proposes to distinguish platform companies, which directly sell services and then purchase the labour needed to provide them, and broker platforms, which act merely as intermediaries. Second, it examines how platforms of each type pose distinct threats to labour justice, thereby extending Daniel Halliday's (2021) analysis. Drawing on empirical studies of the experiences of platform workers, it identifies a power imbalance as the root cause of these threats across all platforms. Finally, the article assesses three strategies to counter these threats: introducing stricter regulation, turning platforms into worker‐run co‐ops, and improving outside job opportunities. In exploring how each strategy could be implemented for specific platforms, it makes the case for always discussing labour‐based platforms with one eye to their specific structural features and the other eye to the broader labour market. [ABSTRACT FROM AUTHOR]

The rapid expansion of social commerce (S-commerce) has necessitated the development of effective advertising strategies. Despite claims that gamification offers several benefits when integrated with S-commerce, its significance in emerging markets remains unclear. This study was designed to evaluate the influence of gamification on advertising effectiveness in emerging markets such as Vietnam. Personalization and privacy concerns (S-commerce's recent prominent factors) were also assessed. A quantitative approach was used in this study. Data were collected through a survey of 477 individuals exposed to gamification advertising on S-commerce platforms. The results demonstrate that gamification significantly influences the intention to interact with advertisements through both hedonic and utilitarian mechanisms. However, this factor only affects attitudes toward advertising through hedonic mechanisms. The influence of attitudes toward advertising on consumer behavioral intentions was also reinforced in our study. Moreover, the empirical findings show that personalization significantly influences gamification and attitudes toward advertising. Finally, privacy concerns were found to have no effect on gamification and attitudes toward advertising and, simultaneously, produced only a small negative effect on the intention to interact with advertisements. [ABSTRACT FROM AUTHOR]

Watching live streams as a constituent of the online buying process is a recently emerged trend that demands empirical investigation. For this purpose, the researcher proposed a conceptual model using S-O-R framework to investigate the relationships among perceived shopping values (utilitarian value, hedonic value, symbolic value) and buyers' engagement and purchase intention in live streaming. We assess the model's performance through an empirical measurement by conducting a survey of 300 customers who have made purchases through Facebook live streaming on social media. The path analysis results revealed that utilitarian value is significantly related to customer engagement and purchase intention while hedonic value is found to have no effect on customer engagement but significant effect on purchase intention. Symbolic value is also found to effect customer engagement significantly. The results also revealed that customer engagement mediates the relationship between utilitarian value, symbolic value, and purchase intention. Our findings also deliver some theoretical and practical implications for live commerce sellers and online platforms on how to effectively utilize live streaming features to strategically market their products. [ABSTRACT FROM AUTHOR]

Should successful e-commerce websites be boring but functional or should they provide immersive flow experiences? Past research showed that providing online consumers with immersive flow experiences is one of the goals of e-commerce websites; to do so can create a competitive advantage. This study challenges this assumption and explores, in the Turkish context, whether achieving a pseudo-flow experience should be the goal. We build on data from a questionnaire survey of 649 active users of idefix.com, the largest e-commerce website in Turkey, and adapt flow theory to identify the drivers of online shopping satisfaction and repurchase intentions. The data was analyzed using partial least squares structural equation modeling (PLS-SEM). Results revealed that affective and cognitive experiential states have a direct positive effect on online shopping satisfaction and repurchase intention. This research contributes to the debate on consumers' experiences in electronic commerce by expanding flow theory. We introduce and define 'pseudo-flow' in e-commerce which is the novelty of this research. [ABSTRACT FROM AUTHOR]

Virtual reality (VR) tourism e-commerce platforms (VRTEPs) can provide trial experience for tourists, which is easy to trigger irrational impulse buying. Based on cognitive emotion theory and S-O-R model, this study innovatively explores the impact of the multi cue-interaction stimulation (the interactivity of VR display and the empathy of text display) of VRTEPs on consumers' impulse buying intention through the mediating effect of new cognitive and emotional responses, namely, interactive pleasure (perceived usefulness and immersion). The results reveal that: (1) The interactivity of VR display stimulates interactive pleasure, then positively affects impulse buying intention. (2) The empathy of text display only stimulates perceived usefulness to positively affect impulse buying intention. This study also finds that the interactive comparison has a negative moderating effect on the relationship between immersion and impulse buying intention. These findings contribute to developing the new theory for effectively marketing using highly interactive new technology on VRTEPs. [ABSTRACT FROM AUTHOR]

Unfair competition on internet platforms (UCIP) has become a critical issue restricting the platform economy's healthy development. This paper applies evolutionary game theory to study how to utilize multiple subjects' synergy to supervise UCIP effectively. First, the "multi-agent co-governance" mode of UCIP is constructed based on the traditional "unitary supervision" mode. Second, the government and internet platform evolutionary game models are built under two supervision modes. Finally, MATLAB is used to simulate and analyze the evolutionary stage and parameter sensitivity. In addition, we match the model's evolutionary stage with China's supervisory process. The results show that (1) the Chinese government's supervision of UCIP is in the transitional stage from "campaign-style" to "normalization." (2) Moderate government supervision intensity is essential to guide the game system to evolve toward the ideal state. If the supervision intensity is too high, it will inhibit the enthusiasm for supervision. If the supervision intensity is too low, it cannot form an effective deterrent to the internet platforms. (3) When the participation of industry associations and platform users is low, it can only slow down the evolutionary speed of the game system's convergence to the unfavorable state. Nevertheless, it cannot reverse the evolutionary result. (4) Maintaining the participation level of industry associations and platform users above a specific threshold value while increasing punishment intensity will promote the transition of government supervision from the "campaign-style" to the "normalization" stage. This paper provides ideas and references for the Chinese government to design a supervision mechanism for UCIP. [ABSTRACT FROM AUTHOR]

In recent years, the global e-commerce landscape has witnessed rapid growth, with sales reaching a new peak in the past year and expected to rise further in the coming years. Amid this e-commerce boom, accurately predicting user purchase behavior has become crucial for commercial success. We introduce a novel framework integrating three innovative approaches to enhance the prediction model's effectiveness. First, we integrate an event-based timestamp encoding within a time-series attention model, effectively capturing the dynamic and temporal aspects of user behavior. This aspect is often neglected in traditional user purchase prediction methods, leading to suboptimal accuracy. Second, we incorporate Graph Neural Networks (GNNs) to analyze user behavior. By modeling users and their actions as nodes and edges within a graph structure, we capture complex relationships and patterns in user behavior more effectively than current models, offering a nuanced and comprehensive analysis. Lastly, our framework transcends traditional learning strategies by implementing advanced meta-learning techniques. This enables the model to autonomously adjust learning parameters, including the learning rate, in response to new and evolving data environments, thereby significantly enhancing its adaptability and learning efficiency. Through extensive experiments on diverse real-world e-commerce datasets, our model demonstrates superior performance, particularly in accuracy and adaptability in large-scale data scenarios. This study not only overcomes the existing challenges in analyzing e-commerce user behavior but also sets a foundation for future exploration in this dynamic field. We believe our contributions provide significant insights and tools for e-commerce platforms to better understand and cater to their users, ultimately driving sales and improving user experiences. [ABSTRACT FROM AUTHOR]

This paper develops a theoretical model of determinants influencing multimodal fake review generation using the theories of signaling, actor-network, motivation, and human–environment interaction hypothesis. Applying survey data from users of China's three leading E-commerce platforms (Taobao, Jingdong, and Pinduoduo), we adopt structural equation modeling, machine learning technique, and Bayesian complex networks analysis to perform factor identification, path analysis, feature factor importance ranking, regime division, and network centrality analysis of full sample, male sample, and female sample to reach the following conclusions: (1) platforms' multimodal recognition and governance capabilities exert significant negative moderating effects on merchants' information behavior, while it shows no apparent moderating effect on users' information behavior; users' emotional venting, perceived value, reward mechanisms, and subjective norms positively influence multimodal fake review generation through perceptual behavior control; (2) feature factors of multimodal fake review generation can be divided into four regimes, i.e., regime 1 includes reward mechanisms and perceived social costs, indicating they are key feature factors of multimodal fake review generation; merchant perception impact is positioned in regime 2, signifying its pivotal role in multimodal fake review generation; regime 3 includes multimodal recognition and governance capabilities, supporting/disparaging merchants, and emotional venting; whereas user perception impact is positioned in regime 4, indicating its weaker influence on multimodal fake review generation; (3) both in full sample, male sample, and female sample, reward mechanisms play a crucial role in multimodal fake review generation; perceived value, hiring review control agency, multimodal recognition and governance capabilities exhibit a high degree of correlation; however, results of network centrality analysis also exhibit heterogeneity between male and female samples, i.e., male sample has different trends in closeness centrality values and betweenness centrality values than female sample. This indicates that determinants influencing multimodal fake review generation are complex and interconnected. [ABSTRACT FROM AUTHOR]

In the current landscape, product prices exhibit significant variations across diverse ecommerce platforms. Numerous price comparison websites have emerged to aid users in finding their desired products at the best prices. However, these systems face challenges such as irrelevant search results, outdated product prices, and accuracy issues. This study seeks to address these challenges by developing a user-friendly system. Our system comprises two components. JSoup is employed for data collection, while TF-IDF, SVD, and Cosine Similarity are integrated for product offerings. The proposed platform gathers data from reputable retailers, analyzes it to extract product information, and presents the findings to consumers. We evaluated the system's performance using real data obtained from various marketplaces. The results demonstrate that our system achieved an acceptable accuracy rate when compared to similar industrial solutions and relevant literature. Consequently, users are empowered to make more informed purchasing decisions, leveraging the capabilities provided by the proposed system. [ABSTRACT FROM AUTHOR]

The extensive study was conducted to enhance the prediction of customer turnover in an online retail and distribution organization. The study combines data from surveys, consumer comments, and financial records to uncover themes from textual assessments using state-of-the-art methodologies. Methods such as Dirichlet Multilayer Perceptron Mixing, Latent Dirichlet Allocation and Random Sampling fall within this category. In addition to its usage for assessing geographic data for location-based consumer segmentation, DBSCAN is a crucial tool for this investigation. Model development for churn prediction and root cause analysis makes use of logistic regression and extreme gradient boosting. The statistical and practical benefits of the proposed paradigm are shown via comparison to existing options. A model's predictive efficacy may be evaluated using the area under the curve or the lift metric. The research also introduces the concept of "Consumer-driven energy-efficient WSNs architecture for Personalization and contextualization in E-commerce Systems," which suggests using wireless sensor networks (WSNs) to collect data efficiently, provide customized service and provide context for online purchases. Overall, the research demonstrates the effectiveness of machine learning in harnessing consumer input for strategic decision-making, illuminating the potential of creative sensor network integration in enhancing e-commerce personalization and contextualization. [ABSTRACT FROM AUTHOR]

With the rapid development of Artificial Intelligence (AI) and Big Data (BD), they provide people with new ways and tools in information acquisition, processing and dissemination. The purpose of this study is to deeply discuss the influence of AI and BD technologies on the development of Information Security (InfoSec) in media economy, and analyze the opportunities and challenges it brings. Firstly, the evaluation system of the influence of BD technology on the development of InfoSec, a media enterprise, is constructed. Then, this study discusses the development mode of the multilateral platform of media economy supported by BD technology. Finally, taking S company and X platform as examples, this study analyses the influence of BD technology on the development of InfoSec and the development mode of media economy supported by BD technology. The research results show that with the support of BD technology, the proportion of InfoSec products in enterprises has reached 0.2426, and the InfoSec score of S company has reached 77.74, reaching a high level. The profit margin of digital network media is three times that of traditional media. The number of users of multilateral digital media economic platform X has increased by 21%, and its turnover has increased by 54%. AI and BD technologies have brought opportunities and challenges to the InfoSec development of media economy. While applying these technologies, it is necessary to take effective measures to protect users' privacy and strengthen the information review and verification mechanism to ensure the sustainable development of media economy. The uniqueness of this study lies in the construction of a comprehensive BD technology impact assessment system and the discussion of the development model of multilateral media economic platform, which provides a powerful reference and guidance for media enterprises. In addition, this study also reveals the positive influence of BD technology on InfoSec and the development of media economy, which provides beneficial enlightenment for the future development of media industry. In response to the challenges of privacy and InfoSec, this study puts forward important policy and practical suggestions to ensure the sustainable growth of media economy and the protection of users' rights and interests. Therefore, this study has important contribution and innovative value in both theory and practice. [ABSTRACT FROM AUTHOR]

With the rapid development of social mode virtualization and electronic component technology, the application of data science and Internet of Things (IoT) technology in the field of e-commerce is gradually increasing. This study aims to explore how these emerging technologies can enhance the advantage of Chinese e-commerce companies in international competition. By comprehensively analyzing the massive data generated by online social networking and the application of IoT sensor technology in logistics and enterprise management, this paper proposes a decision support model based on data analysis. Research methods include data collection, data analysis and case studies. The results of the study show that data analytics and IoT technologies can effectively improve the efficiency of e-commerce operations and customer experience. The conclusion is that these technologies not only contribute to the domestic development of e-commerce enterprises, but also play a non-negligible role in international competition. This research has important implications for understanding the practical applications and potential of new technologies in the field of e-commerce. [ABSTRACT FROM AUTHOR]

This study investigates the role of conformity in shaping the purchasing decisions of young consumers within the food and beverage (F&B) sector. Additionally, we explore how conformity moderates the relationship between influencer credibility and purchase intention while examining the link between psychological ownership and purchase intention. Data was collected from 380 consumers between the age of 18 and 30 in Hanoi, Vietnam who regularly follow food reviews on prominent social media platforms. Structural equation modeling (SEM) were employed for data analysis. We find that: (1) influencer credibility, attitude toward advertising, attitude toward the product, and psychological ownership significantly impact the online purchase intentions of young consumers; (2) informative conformity strengthens the connection between psychological ownership and purchase intention. When consumers express a desire to purchase a product, exposure to positive influencer-driven information further solidifies their purchase intent; (3) psychological ownership and attitude toward advertising act as mediators, bridging the gap between influencer credibility and purchase intention. This study offers managerial insights to improve influencer marketing effectiveness for F&B brands targeting young consumers in Vietnam and Asia, highlighting the importance of conformity, psychological ownership, and attitude toward advertising. [ABSTRACT FROM AUTHOR]

Dark patterns and digital nudging are persuasive techniques that influence customer decision-making in the digital world. This study analyzed 80 articles published in reputed peer-reviewed journals and international conferences using the Theory-Context-Characteristics-Methods (TCCM) framework. The paper offers five significant contributions. First, it adds to the theoretical understanding of the area. Second, the study identifies various contexts in which dark patterns and digital nudging are employed. Third, it elucidates the characteristics (key variables) of dark patterns and digital nudging. Fourth, the study determines the methodologies used in prior studies in the field. Lastly, the paper proposes future research areas in the field. By presenting a comprehensive overview of the research in the field, the study could stimulate scholarly research and serve as a valuable base for future researchers. Furthermore, insights into dark patterns and digital nudging will educate online customers and allow managers to devise strategies to mitigate customer pain points. [ABSTRACT FROM AUTHOR]

The study aimed to examine the direct impact of website design and security on trust and loyalty, with trust serving as a mediator. The research focused on Generation Z consumers purchasing e-commerce products in Indonesia, with 307 respondents. Data collection utilized a Google Form questionnaire survey distributed via social media. Structural equation modeling with AMOS 21 was employed for data analysis. Results indicated positive impacts on loyalty and trust in Generation Z consumers stem from good website design and robust security. Trust was found to positively mediate the relationship between exogenous and endogenous variables. The findings suggest that e-commerce companies in Indonesia can enhance loyalty and trust by prioritizing website design, and security, and investing in strategies that build consumer trust through online marketing and security measures. [ABSTRACT FROM AUTHOR]

Integrating the E-commerce system with an enterprise resource planning tool can help the firm improve performance, maintain customers, and increase sales. In Enterprise Resource Planning, integration features can be provided either as developed features or as separate assignments and contributions. Problems with the online platform, improper addresses, rejected payments, and especially apparent transactions are frequent problems for online buyers. The enhanced Adaptive Ant Colony Optimization is utilized to optimize the rural E-commerce express of transportation. Several innovative routes can lower the downlink transportation cost and reach all collecting places with a fast delivery route. Convolutional Neural Networks were utilized to increase the collective innovation of the E-commerce platform and simplify network communication. E-commerce is a mechanism used to market information services and products. Hence, ERP-AACO-CNN has been designed to integrate Enterprise Resource Planning and E-commerce, and business operations can stream smoothly from the front to the back of the business. Statistics on sales orders, customers, stock levels, price, and essential performance measurement systems. The automated invoices, frequent communications, financial report preparation, product and service delivery, and material requirements planning. The most significant results will likely finance businesses that employ it as a stimulant for a wide-ranging process improvement. In addition, E-commerce is a valuable innovation that connects buyers and sellers in various corners of the globe. Customer satisfaction is projected to be more significant than fault detection at 95.2 % accuracy for the proposed method's E-commerce system with the superior value. According to client demand, an E-commerce system is the most accurate development at a given input level, and a future ERP is 64.9% efficient. The proposed approach has a 24.5% random error rate and a 13.2% mean square error rate. A comparison of E-commerce and enterprise ERP precision to the proposed technique yields 83.8% better results. [ABSTRACT FROM AUTHOR]

The most critical task of a social network is to identify a central node. Numerous methods for determining centrality are documented in the literature. It contributes to online commerce by disseminating news, advertisements and other content via central nodes. Existing methods capture the node's direct reachability. This study introduces a novel method for quantifying centrality in a fuzzy environment. This measurement takes into account the reachability of nodes and their direct connections. Several critical properties have been demonstrated. A small Facebook network is used to illustrate the issue. Additionally, appropriate tables and graphs present a comparative study with existing methods for centrality measurement. [ABSTRACT FROM AUTHOR]

Over the last few years, livestreaming e-commerce has shown rapid growth and has become an important form of e-commerce. However, the potential mechanisms of interpersonal interaction's influence on purchase intention in livestreaming e-commerce have yet to be fully investigated. Based on the SOR (Stimulus-Organism-Response) framework, this study reveals the association between interpersonal interaction (consumer–anchor interaction and consumer–consumer interaction), psychological distance, consumer purchase intention, and the positive role of brand identification and time pressure in this context of influential relationships. The results of analyzing 603 questionnaires show that psychological distance between consumers and products plays a mediating role in the effect of interpersonal interaction on purchase intention. Meanwhile, this study found that consumers' brand identification with the products in the live room was effective in enhancing the direct effect of interpersonal interaction in the model. Additionally, the time pressure associated with limited-time sales was also found to be effective in enhancing the effects of interpersonal interaction and psychological distance on purchase intention. The results of this study reveal the potential influence mechanisms of interpersonal interactions with various identities in livestreaming e-commerce, providing theoretical guidance and practical insights for practitioners in the field. [ABSTRACT FROM AUTHOR]

In the ever-expanding online fashion market, businesses in the clothing sales sector are presented with substantial growth opportunities. To utilize this potential, it is crucial to implement effective methods for accurately identifying clothing items. This entails a deep understanding of customer preferences, niche markets, tailored sales strategies, and an improved user experience. Artificial intelligence (AI) systems that can recognize and categorize clothing items play a crucial role in achieving these objectives, empowering businesses to boost sales and gain valuable customer insights. However, the challenge lies in accurately classifying diverse attire items in a rapidly evolving fashion landscape. Variations in styles, colors, and patterns make it difficult to consistently categorize clothing. Additionally, the quality of images provided by users varies widely, and background clutter can further complicate the task of accurate classification. Existing systems may struggle to provide the level of accuracy needed to meet customer expectations. To address these challenges, a meticulous dataset preparation process is essential. This includes careful data organization, the application of background removal techniques such as the GrabCut Algorithm, and resizing images for uniformity. The proposed solution involves a hybrid approach, combining the strengths of the ResNet152 and EfficientNetB7 architectures. This fusion of techniques aims to create a classification system capable of reliably distinguishing between various clothing items. The key innovation in this study is the development of a Two-Objective Learning model that leverages the capabilities of both ResNet152 and EfficientNetB7 architectures. This fusion approach enhances the accuracy of clothing item classification. The meticulously prepared dataset serves as the foundation for this model, ensuring that it can handle diverse clothing items effectively. The proposed methodology promises a novel approach to image identification and feature extraction, leading to impressive classification accuracy of 94%, coupled with stability and robustness. [ABSTRACT FROM AUTHOR]

In this article, I analyse the Steam platform as a configuration of several market contexts. I argue that Valve, the owner of Steam, maintains a classical retail market catering to large game publishers, while the emphasis of Valve's own game titles is in alternative market contexts based on player-driven economies. I first discuss Steam as a distinct type of platform economy due to its roots in player-driven game economies. I then introduce the notion of 'markets as fields' as my analytical framework. Based on this framework, I describe three different contexts of economic transaction: the Steam Game Store, the Steam Community Workshop and the Steam Community Market. I analyse how the approximately 46,500 game titles published on the platform are distributed across these contexts and how these games can be ranked with regard to current player numbers, and discuss Valve's position as combined platform owner, game developer and game publisher. [ABSTRACT FROM AUTHOR]

The paper examines modern re-conceptualisations and re-contextualisations of publicness, proposing theoretical and empirical advancements in its conceptualisation. It critically analyses two significant developments following the English translation of Habermas's seminal work, "Strukturwandel der Öffentlichkeit." On the one hand, the book (re)affirmed the key role of communication and media in democratic politics and instigated a broad acceptance of the public sphere as a fundamental concept in academic discourse, leading to renewed research efforts and innovative developments. However, the subsequent conceptual fragmentation of (the concept of) the public sphere raised concerns about the loss of its original critical epistemic value. On the other hand, Habermas's book obscured important sociological traditions, contributing to a divide between normative theory and sociological analysis, exemplified by a neglect of the habitual roots of public opinion and contractual bonds in the evolution of publics. These aspects gain relevance within the context of the platform economy and artificial intelligence governing internet communication. The paper concludes by introducing the concept of the contractual public, which draws on the evolving dynamics between public and private spheres, and proposing four strategies to revitalise publicness. [ABSTRACT FROM AUTHOR]

As television is embracing a new set of internet-related technologies, the medium is transitioning from broadcasting to streaming. With it, a new mode of distribution has emerged: the streaming platform. This research makes a three-pronged effort to assess their impact on the TV industry: it analyses the way platforms monetize content; it distinguishes types of streaming platforms based on a set of criteria that includes supply-chain arrangements and the way they structure commercial transactions among different sets of participants, and it considers the ownership of streaming services. This article contributes to media and communication studies by combining the platform literature with global value chain (GVC) theory in order to foster our understanding of streaming platforms. It contextualizes streaming platforms in the history of television and analyses how they are transforming the medium. [ABSTRACT FROM AUTHOR]

In e-commerce, the session-based personalized recommendation remains challenging due to the limited user information within a single session. Merely relying on a user's local data is insufficient. It is vital to consider global data, extracting insights from sessions across all users to glean collaborative information. However, using all session information will waste computing resources. Moreover, much of the global data may not be pertinent to the current user, thereby undermining the quality of recommendations. To address this, we introduce the concept of personalized next session (PNS), selectively referencing sessions most relevant to the user to enhance the limited local data. This work is the first to adopt a deep network architecture study that incorporates the concept of PNS to recommend the next item for a user in the current session. We evaluated our approach on several real-world datasets, and the results show that our model outperforms state-of-the-art recommendation methods. [ABSTRACT FROM AUTHOR]

Background: Human papillomavirus (HPV) vaccination is a promising step toward cervical cancer elimination. This study was conducted to investigate the knowledge, attitude, and HPV vaccine uptake among female adults in mainland China based on a large e-commerce platform. Methods: We conducted a cross-sectional online survey of female adults between March 4 to April 20, 2022. The survey consisted of sociodemographic information, related knowledge, vaccination uptake, and attitudes toward vaccination. We included women aged 18–45 years in the final analysis. Logistic regressions were conducted to explore influencing factors associated with related knowledge, HPV vaccination uptake, and willingness to be vaccinated. Results: In total, 3,572 female adults (34 years, IQR 30–39) were included in the analysis. The majority of the participants were highly educated (78.7%) with a high monthly family income (79.0%). The median HPV knowledge score was 8.25 out of 11. More than 75% of respondents were unvaccinated, while 95.8% of unvaccinated female adults are willing to be vaccinated. Variables such as age, insurance, vaccination history, and whether one had heard of the HPV vaccine influence HPV vaccination practice (all p-values < 0.05). The main barriers to vaccination were vaccine inaccessibility and the high cost of the vaccine. Conclusion: The findings of our study highlight a moderate knowledge level, poor vaccination rate, and strong willingness to be vaccinated among Chinese female adults who were better educated and wealthier. Targeted health education and practical support should be provided in the future, to reduce gaps between vaccine uptake and vaccine acceptance. Significance: What is already known on this subject?: Sustained high human papillomavirus (HPV) vaccination coverage is crucial for cervical cancer prevention. People's knowledge and acceptance of HPV vaccination largely influence vaccination uptake. Previous studies have indicated a lack of awareness, knowledge, and low vaccine uptake in mainland China. What this study adds?: The findings of our study highlight a moderate knowledge level, poor vaccination rate, and strong willingness to vaccination among Chinese female adults who were better educated and wealthier. This national-level survey may guide future health education and accelerate the HPV vaccination program's further roll-out targeting women at different social levels in mainland China. [ABSTRACT FROM AUTHOR]

This study examines how technology quality, experience quality, and herd behaviour in live streaming commerce affect customers' purchase intention. We proposed an integrated research model based on the elaboration likelihood model (ELM) and herd behaviour. This study used covariance-based structural equation modelling (CB-SEM) to analyse data and assess the research model and hypotheses. We surveyed 872 Chinese customers who have experience in live streaming commerce, from which the data of 845 were used to test the hypotheses. Our findings show that good technology and experience quality lead customers to discount their own information and imitate their peers. Customers' herd behaviour positively affects their purchase intention. Further, discounting own information positively mediates the indirect link between live streaming commerce quality (technology quality and experience quality), imitation, and customers' purchase intention. This study is the first to combine live streaming commerce quality and herd behaviour to investigate customers' purchase intention in live streaming commerce. It highlights the value of incorporating herd behaviour into the ELM and adds to the body of knowledge by providing a deeper insight into customers' purchase intention in live streaming shopping. It also has managerial implications for live streaming commerce practitioners to sever the sustainable growth of e-commerce. [ABSTRACT FROM AUTHOR]

Recognising the rapid growth and enormous potential of gamification and social media advertising in today's digital age, along with the increasing popularity of social commerce (s-commerce), especially in emerging markets, this study analysed attitudes towards gamification advertising in the Vietnamese s-commerce context. This study followed a quantitative approach by conducting interviews with 386 users who interacted with gamification advertising on s-commerce platforms in Vietnam. The empirical results indicate that perceived gamification in advertising (as measured by the experience of gamification ads) has a significant positive indirect effect on attitudes towards advertising via hedonic and utilitarian values. In addition, this study experimentally verified the impact of the prominent characteristics of s-commerce (interactivity, personalisation, and privacy concerns) on both the gameful experiences of ads and attitudes towards advertising in the s-commerce market. Our research has meaningful implications for marketers and social networking platform developers, in that marketers should be more proactive in gamifying advertisements to improve customer attitudes towards them. For social networking platform developers, it is essential to develop optimal algorithms to enhance the interactivity of the platform by taking advantage of a database to generate relevant advertising recommendations for users. [ABSTRACT FROM AUTHOR]

Consumers usually endorse tourism products differently when sharing positive electronic word-of-mouth (eWOM). This research examines the relative persuasiveness of two endorsement styles, that is, explicit endorsement (e.g., "I recommend it") and implicit endorsement (e.g., "I love it"). Drawing on the persuasion knowledge model, we propose that explicit endorsements are less persuasive than implicit endorsements because the former trigger stronger persuasion knowledge. We further argue that source trustworthiness mitigates the persuasion effect difference between the two endorsement styles. This article assesses these hypotheses across different sources (anonymous reviewers, friends, influencers), channels (online community, social commerce platform, social networking app), and products (hotels, restaurants) using secondary data analysis and two experiments. Three studies provide support for our hypotheses. By revealing the relationship between endorsement styles and eWOM persuasiveness, this article provides important implications for implementing effective product endorsement. [ABSTRACT FROM AUTHOR]

In today's steady development of e-commerce, improving enterprise competitiveness is a major challenge that e-commerce enterprises need to face. There are problems with incomplete and inaccurate evaluation systems in commonly used competitiveness evaluation systems. To address these concerns, the study establishes a model for evaluating business-to-business relationships by analyzing indicators of competitiveness. It also examines the algorithm process and neural network structure and incorporates them into the evaluation model. The experiment shows that through training the back propagation neural network, it is discovered that the mean square error of the samples is 9.9869e-06 when the training frequency reaches 3200, resulting in the completion of convergence of the back-propagation neural network. The utilization of back propagation neural networks to enhance the evaluation model of enterprise competitiveness has yielded positive outcomes. Suggestions have been proposed to improve the competitiveness of diverse e-commerce enterprises. This study can furnish enterprise managers with decision support, enhancing enterprise competitiveness and enabling sustainable development. [ABSTRACT FROM AUTHOR]