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Since 2012, researchers have explored various factors influencing Bitcoin prices. Up until the end of July 2023, more than 9100 research papers on cryptocurrencies were published and indexed in the Web of Science Clarivate platform. The objective of this paper is to analyze the impact of publications on Bitcoin prices. This study aims to uncover significant themes within these research articles, focusing on cryptocurrencies in general and Bitcoin specifically. The research employs latent Dirichlet allocation to identify key topics from the unstructured abstracts. To determine the optimal number of topics, perplexity and topic coherence metrics are calculated. Additionally, the abstracts are processed using BERT-transformers and Word2Vec and their potential to predict Bitcoin prices is assessed. Based on the results, while the research helps in understanding cryptocurrencies, the potential of academic publications to influence Bitcoin prices is not significant, demonstrating a weak connection. In other words, the movements of Bitcoin prices are not influenced by the scientific writing in this specific field. The primary topics emerging from the analysis are the blockchain, market dynamics, transactions, pricing trends, network security, and the mining process. These findings suggest that future research should pay closer attention to issues like the energy demands and environmental impacts of mining, anti-money laundering measures, and behavioral aspects related to cryptocurrencies. [ABSTRACT FROM AUTHOR]

The latest effort in delivering computing resources as a service to managers and consumers represents a shift away from computing as a product that is purchased, to computing as a service that is delivered to users over the internet from large-scale data centers. However, with the advent of the cloud-based IoT and artificial intelligence (AI), which are advancing customer experience automations in many application areas, such as recommender systems (RS), a need has arisen for various modifications to support the IoT devices that are at the center of the automation world, including recent language models like ChatGPT and Bard and technologies like nanotechnology. This paper introduces the marketing community to a recent computing development: IoT-driven fog computing (FC). Although numerous research studies have been published on FC "smart" applications, none hitherto have been conducted on fog-based smart marketing domains such as recommender systems. FC is considered a novel computational system, which can mitigate latency and improve bandwidth utilization for autonomous consumer behavior applications requiring real-time data-driven decision making. This paper provides a conceptual framework for studying the effects of fog computing on consumer behavior, with the goal of stimulating future research by using, as an example, the intersection of FC and RS. Indeed, our conceptualization of the "fog-based recommender systems" opens many novel and challenging avenues for academic research, some of which are highlighted in the later part of this paper. [ABSTRACT FROM AUTHOR]

Modern e-businesses heavily rely on advanced data analytics for product recommendations. However, there are still untapped opportunities to enhance user interfaces. Currently, online stores offer a single-page version to all customers, overlooking individual characteristics. This paper aims to identify the essential components and present a framework for enabling multiple e-commerce user interfaces. It also seeks to address challenges associated with personalized e-commerce user interfaces. The methodology includes detailing the framework for serving diverse e-commerce user interfaces and presenting pilot implementation results. Key components, particularly the role of algorithms in personalizing the user experience, are outlined. The results demonstrate promising outcomes for the implementation of the pilot solution, which caters to various e-commerce user interfaces. User characteristics support multivariant websites, with algorithms facilitating continuous learning. Newly proposed metrics effectively measure changes in user behavior resulting from different interface deployments. This paper underscores the central role of personalized e-commerce user interfaces in optimizing online store efficiency. The framework, supported by machine learning algorithms, showcases the feasibility and benefits of different page versions. The identified components, challenges, and proposed metrics contribute to a comprehensive solution and set the stage for further development of personalized e-commerce interfaces. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has been one of the most severe disruptions to normal life, impacting how businesses operate. The academic literature in the areas of supply chain and operations management has been trying to explain how this has affected decision-making in businesses. However, the existing literature has predominantly overlooked organisational culture and behavioural economic theories. This paper contends that considering the decisions made in supply chain disruption management involve groups and the individuals within them, the relevance of behavioural economic concepts becomes paramount. As such, the objective of this paper is to conduct an integrative literature review, utilising the purposive sampling method to explore the dearth of academic work connecting behavioural economic theories and organisational culture to supply chain disruption management. Additionally, the paper aims to offer guidelines for future research in this domain. Enhancing our comprehension of these domains concerning supply chain disruption management would empower firms to better anticipate their parties' decisions, refine their decision-making models, and cultivate stronger relationships with suppliers and customers. [ABSTRACT FROM AUTHOR]

Cryptocurrencies, like Bitcoin and Ethereum, have garnered global attention in recent years as digital alternatives to traditional fiat currencies. This paper explores the complex landscape of cryptocurrency adoption, consumer behavior, and perceptions. Beginning with the origin of cryptocurrencies and the dominance of Bitcoin with its USD 1.23 trillion market capitalization, the paper highlights popular online platforms facilitating Bitcoin trading. It also examines the varying legal statuses and regulations across different countries, with a notable divide between Eastern and Western nations, attributed to factors like wealth, risk tolerance, and government restrictions. The role of blockchain technology as the foundation of cryptocurrencies is explained, emphasizing its role in ensuring secure and transparent transactions. The paper delves into the processes involved in handling cryptocurrencies, including the blockchain, exchanges, wallets, and mining. Consumer behavior and the factors influencing cryptocurrency usage are analyzed, with a focus on speculation, algorithm trust, spending power, and demographics. Survey findings and case studies from diverse geographical areas reveal patterns of adoption and local consumer perceptions. The paper concludes by discussing the cryptocurrency market's inherent volatility and sensitivity to regulatory changes, as well as the different types of cryptocurrencies and online exchanges shaping this evolving financial landscape. Overall, it offers insights into the complex dynamics surrounding cryptocurrency adoption and its potential impact on global finance. [ABSTRACT FROM AUTHOR]

It is crucial to explore the impact of digital financial inclusion on tourism for national economic development. This paper utilizes panel data from 256 prefecture-level cities in China between 2011 and 2019 to examine the influence of digital financial inclusion on tourism. The findings demonstrate that digital financial inclusion significantly contributes to the development of the tourism industry. Notably, its coverage breadth, depth of use, and level of digitalization also have positive effects. Mechanism analysis reveals that digital financial inclusion facilitates the growth of tourism by supporting the development of tourism enterprises and enhancing consumer spending. Heterogeneity analysis further reveals regional and urban disparities in the promotion of digital financial inclusion, with the effect being more pronounced in the eastern region and larger cities. In comparison to existing studies, this paper delves into the mechanisms through which digital financial inclusion impacts tourism, as well as investigates regional and city size discrepancies. Consequently, governments should strive to foster the development of digital financial inclusion to attract market players and promote the advancement of residents' consumption, thereby bolstering tourism development. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) was born from the fusion of virtual and physical space and became the initiator of many scientific fields. Economic sustainability is the key to further development and progress. To keep up with the changes, it is necessary to adapt economic models and concepts to meet the requirements of future smart environments. Today, the need for electronic commerce (e-commerce) has become an economic priority during the transition between Industry 4.0 and Industry 5.0. Unlike mass production in Industry 4.0, customized production in Industry 5.0 should gain additional benefits in vertical management and decision-making concepts. The authors' research is focused on e-commerce in a three-layer vertical IoT environment. The vertical IoT concept is composed of edge, fog, and cloud layers. Given the ubiquity of artificial intelligence in data processing, economic analysis, and predictions, this paper presents a few state-of-the-art machine learning (ML) algorithms facilitating the transition from a flat to a vertical e-commerce concept. The authors also propose hands-on ML algorithms for a few e-commerce types: consumer–consumer and consumer–company–consumer relationships. These algorithms are mainly composed of convolutional neural networks (CNNs), natural language understanding (NLU), sequential pattern mining (SPM), reinforcement learning (RL for agent training), algorithms for clicking on the item prediction, consumer behavior learning, etc. All presented concepts, algorithms, and models are described in detail. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) agents are widely used in the retail and distribution industry. The primary objective was to investigate whether the gender of AI agents influences trust and grounding. This paper examined the influence of AI agent gender and brand concepts on trust and grounding within virtual brand spaces. For this purpose, it used two independent variables: brand concept (functional vs. experiential) and AI agent gender (male vs. female). The dependent variables included AI agent trust and grounding. The study revealed that in virtual brand spaces centered around a functional concept, male AI agents generated higher levels of trust than female AI agents, whereas, when focused on an experiential concept, female AI agents induced higher levels of grounding than male AI agents. Furthermore, the findings indicate that the association between customers' identification with AI agents and recommendations for actual brand purchases is mediated by trust and grounding. These findings support the idea that users who strongly identify with AI agents are more inclined to recommend brand products. By presenting alternatives that foster the establishment and sustenance of a meaningful, sustainable relationship between humans and AI, this study contributes to research on human–computer interactions. [ABSTRACT FROM AUTHOR]

This paper examines how businesses in Taiwan's servicescapes are adapting to the growing trend of mobile payments and innovation ecosystems. Through the analysis of four case studies, we uncover the strategies these firms employ to address the challenges posed by changing consumer payment habits. Our research reveals that these companies are establishing efficient mechanisms within their ecosystems, supported by well-structured organizational frameworks. By leveraging innovation ecosystems, they are reshaping financial services and promoting collaborative growth among participants through technology, platforms, resource sharing, and knowledge exchange. This collaborative approach is driving significant changes in the sector, helping these businesses navigate through various challenges while fostering innovation and growth. Additionally, the scarcity of comprehensive observations of the digital payment ecosystem highlights the necessity for further exploration of actor interactions, regulatory mechanisms, and ecosystem management strategies. Such research efforts are crucial for enhancing our understanding of the evolving landscape of digital payments and innovation ecosystems, facilitating informed decision-making and promoting sustainable development in this dynamic industry. [ABSTRACT FROM AUTHOR]

The emergence of the crowdsourcing platform enables seekers to obtain higher-quality services at lower costs. High-quality services are often provided by high-quality solvers, which is the key to the sustainable development of crowdsourcing platforms. Therefore, how to attract more high-quality solvers to participate needs to be focused on. Most previous studies that used stock data to measure crowdsourcing performance failed to describe the contest process of high-quality solvers' behavior. Different from the previous study, this paper explores the information signals that influence the participation of high-quality solvers in the dynamic process of crowdsourcing contests. Based on the creative projects of the Winvk platform, dynamic models affecting the participation of high-quality solvers are constructed from the perspective of reducing information asymmetry, and the effects of quality signals and intention signals are explored in depth. The results show that for logo design projects, clear information display and monetary mechanisms have a significant impact on alleviating information asymmetry and attracting the participation of high-quality solvers. Interestingly, the effect of market competition on high-quality solvers shows a U-shaped change. The research results provide a reference for enterprises to reduce information asymmetry, obtain high-quality solutions, and enrich the theoretical application in the field of crowdsourcing. [ABSTRACT FROM AUTHOR]

Meeting customer requirements in software project management, even for large digital enterprises, proves challenging due to unpredictable human factors. It involves meticulous planning and environmental factor analysis, ultimately benefiting both companies and customers. This paper came as a natural extension of our previous work where we left ourselves curious about what impact environmental complexity factors (ECFs) have in a use case point (UCP) approach. Additionally, we wanted to possibly decrease the mean magnitude relative error (MMRE) with deep learning models such as long-short-term-memory (LSTM) and gradient recurrent unit (GRU). The data augmentation technique was used to artificially increase the number of projects, since in the industry world, digital enterprises are not keen to share their data. The LSTM model outperformed the GRU and XGBoost models, while the average MMRE in all phases of the experiment for all models achieved 4.8%. Moreover, the post-agnostic models showed the overall and individual impact of eight ECFs, where the third ECF "team experience" on a new project has been shown as the most influential one. Finally, it is important to emphasize that effectively managing human factors within ECFs in UCPs can have a significant impact on the successful completion of a project. [ABSTRACT FROM AUTHOR]

Studying customer behavior and anticipating future trends is a challenging task, as customer behavior is complex and constantly evolving. To effectively anticipate future trends, businesses need to analyze large amounts of data, use sophisticated analytical techniques, and stay up-to-date with the latest research and industry trends. In this paper, we propose a comprehensive framework to identify trends in consumer behavior using multiple layers of processing, including clustering, classification, and association rule learning. The aim is to help a major retailer in Saudi Arabia better understand customer behavior by utilizing the power of big data analysis. The proposed framework is presented as being generalized to gain insight into the generated big data and enable data-driven decision-making in other relevant domains. We developed this framework in collaboration with a large supermarket chain in Saudi Arabia, which provided us with over 1,000,000 sales transaction records belonging to around 30,000 of their loyal customers. In this study, we apply our proposed framework to those data as a case study and present our initial results of consumer clustering and association rules for each cluster. Moreover, we analyze our findings to figure out how we can further utilize intelligence to predict customer behavior in clustered groups. [ABSTRACT FROM AUTHOR]

The digital economy and the sharing economy have changed the role citizens may acquire in society. Citizens can perform at least two roles from the open government perspective: on the one hand, they can be passive users/demanders of information and, on the other hand, they can provide or produce the information in an active manner. The objective of this paper is to offer a theoretical model to explain citizens' incentives to participate in open government projects. Which is the opportunity cost of participation for the citizen? Which are the drivers of the preferences for the social good? This model is based on the utility function and consumption theory. We complement the theoretical framework with an exploratory–descriptive analysis based on a case study's primary data about citizen participation. In democracy projects where citizens actively collaborate and could earn monetary gains or become entrepreneurs, the opportunity cost of participation is lower than in a passive type and the amount of the social good depends on the preferences. Preferences for social goods are related to community experiences and e-government and they also affect the decision to participate. Very few studies in the field of open government have pretended to explain citizens' participation by using microeconomic foundations. [ABSTRACT FROM AUTHOR]

Social media platforms have allowed many people to publicly express and disseminate their opinions. A topic of considerable interest among researchers is the impact of social media on predicting the stock market. Positive or negative feedback about a company or service can potentially impact its stock price. Nevertheless, the prediction of stock market movement using sentiment analysis (SA) encounters hurdles stemming from the imprecisions observed in SA techniques demonstrated in prior studies, which overlook the uncertainty inherent in the data and consequently directly undermine the credibility of stock market indicators. In this paper, we proposed a novel model to enhance the prediction of stock market movements using SA by improving the process of SA using neutrosophic logic (NL), which accurately classifies tweets by handling uncertain and indeterminate data. For the prediction model, we use the result of sentiment analysis and historical stock market data as input for a deep learning algorithm called long short-term memory (LSTM) to predict the stock movement after a specific number of days. The results of this study demonstrated a predictive accuracy that surpasses the accuracy rate of previous studies in predicting stock price fluctuations when using the same dataset. [ABSTRACT FROM AUTHOR]

The rapid growth of e-commerce has significantly increased the demand for advanced techniques to address specific tasks in the e-commerce field. In this paper, we present a brief survey of machine learning and deep learning techniques in the context of e-commerce, focusing on the years 2018–2023 in a Google Scholar search, with the aim of identifying state-of-the-art approaches, main topics, and potential challenges in the field. We first introduce the applied machine learning and deep learning techniques, spanning from support vector machines, decision trees, and random forests to conventional neural networks, recurrent neural networks, generative adversarial networks, and beyond. Next, we summarize the main topics, including sentiment analysis, recommendation systems, fake review detection, fraud detection, customer churn prediction, customer purchase behavior prediction, prediction of sales, product classification, and image recognition. Finally, we discuss the main challenges and trends, which are related to imbalanced data, over-fitting and generalization, multi-modal learning, interpretability, personalization, chatbots, and virtual assistance. This survey offers a concise overview of the current state and future directions regarding the use of machine learning and deep learning techniques in the context of e-commerce. Further research and development will be necessary to address the evolving challenges and opportunities presented by the dynamic e-commerce landscape. [ABSTRACT FROM AUTHOR]

Adopting subscription models has become a popular trend in e-commerce. Digital content platforms often use free trials to attract potential subscribers. However, rigorous research on platforms' free trial strategies is lacking. Our paper aims to fill this gap using a horizontal duopoly framework. We investigate both the scenario of market symmetry and of market asymmetry. We study the benchmark case with no free trial for each scenario and derive equilibrium results with free trial promotion. We find that the platform will not benefit from free trials unless consumers' learning rate exceeds a threshold. When a free trial is introduced in market symmetry, a higher rate of consumers' learning leads to a higher price of subscription and a shorter free trial length. In addition, when the network effect is incorporated, the platform will introduce a lower subscription price and a shorter free trial length. Under market asymmetry, our results show that the platform faces a higher threshold of consumers' learning rate. Additionally, it will offer a higher price and a longer period of free trial compared with those under the market symmetry scenario. Overall, our study provides useful managerial insights for online content platforms when considering their subscription strategies. [ABSTRACT FROM AUTHOR]

This paper builds a theoretical framework to model individualization in online markets. In a market with consumers of varying levels of demand, a seller offers multiple product bundles and prices. Relative to brick-and-mortar stores, an online seller can use pricing algorithms that can observe a buyer's online behavior and infer a buyer's type. I build a generalized model of price discrimination with Bayesian learning where a seller offers different bundles of the product that are sized and priced contingent on the posterior probability that the consumer is of a given type. Bayesian learning allows the seller to individualize product menus over time as new information becomes available. I explain how this strategy differs from first- or second-degree price discrimination models and how Bayesian learning over time affects equilibrium values and welfare. [ABSTRACT FROM AUTHOR]

With the exponential expansion of e-commerce, an immense volume of historical sales data has been generated and amassed. This influx of data has created an opportunity for more accurate sales forecasting. While various sales forecasting methods and models have been applied in practice, existing ones often struggle to fully harness sales data and manage significant fluctuations. As a result, they frequently fail to make accurate predictions, falling short of meeting enterprise needs. Therefore, it is imperative to explore new models to enhance the accuracy and efficiency of sales forecasting. In this paper, we introduce a model tailored for sales forecasting based on a Transformer with encoder–decoder architecture and multi-head attention mechanisms. We have made specific modifications to the standard Transformer model, such as removing the Softmax layer in the last layer and adapting input embedding, position encoding, and feedforward network components to align with the unique characteristics of sales forecast data and the specific requirements of sales forecasting. The multi-head attention mechanism in our proposed model can directly compute the dot product results in a single step, addressing long-term time-dependent computation challenges while maintaining lower time complexity and greater interpretability. This enhancement significantly contributes to improving the model's accuracy and efficiency. Furthermore, we provide a comprehensive formula representation of the model for the first time, facilitating better understanding and implementation. We conducted experiments using sales datasets that incorporate various factors influencing sales forecasts, such as seasons, holidays, and promotions. The results demonstrate that our proposed model significantly outperforms seven selected benchmark methods, reducing RMSLE, RMSWLE, NWRMSLE, and RMALE by approximately 48.2%, 48.5%, 45.2, and 63.0%, respectively. Additionally, ablation experiments on the multi-head attention and the number of encoder–decoders validate the rationality of our chosen model parameters. [ABSTRACT FROM AUTHOR]

Credit rationing hindered the development of MSEs. Big data credit technology creates a great opportunity to address this issue. Then, how does big data credit technology affect and to what extent can it alleviate the credit rationing of MSEs? Based on the bounded rationality economic man hypothesis, the evolutionary game model of banks and MSEs under the traditional mode and big data credit technology are constructed, respectively, in this paper, and the evolutionary trajectory of bank-enterprise credit strategies under the two modes are comparatively analyzed. The results show that it is hard to alleviate the credit rationing of MSEs under the traditional mode. However, under big data credit technology, when the overall credit level of MSEs is high, the credit rationing of MSEs will be effectively alleviated. When the overall credit level of MSEs is too low, it is difficult to determine whether big data credit technology can alleviate the credit rationing of MSEs. In order to verify the feasibility of big data credit technology in alleviating the credit rationing of MSEs, a simulation experiment is conducted to compare the differences in the credit rationing of MSEs with different credit levels under the two credit modes. We found that the credit rationing of MSEs is always lower under big data credit technology than under the traditional mode. Therefore, big data credit technology can effectively alleviate the credit rationing of MSEs under any circumstances. The research provides theoretical support for banks to apply big data credit technology to achieve a win-win situation for both parties. [ABSTRACT FROM AUTHOR]

IoT (Internet of Things)-enabled products are increasingly used by consumers and continuously propagate in daily life. Billions of networked objects not only increase the complexity of development but also raise user interaction and adaptation to individual needs. The more non-expert users are involved in decision making, interaction, and adaptation processes, the more user-centric IoT design is crucial, particularly when the number of elderly users is steadily increasing. In this paper, we study the capabilities of adopting IoT products through user-informed adaptation in a major IoT application domain, home healthcare. We review evidence from established practice in the field on how users can be supported when aiming to adapt medical IoT (M-IoT) home applications to their needs. We examine the empirically grounded use of IoT sensors and actuators, as well as the adaptation process users adopt when using an IoT application in a personalized environment. Our analysis (technological evidence) reveals various IoT devices that have already been applied in M-IoT adaptation settings to effectively support users. Our analysis reveals that only few empirically sound findings exist on how users actually perceive interactive adaptation features and redesign M-IoT applications. Based on the analysis of these empirically grounded findings, we suggest the development of a domain-specific user-centric adaptation feature. Specifically, we exemplify a tangible adaptation device for user-informed M-IoT application in home healthcare. It has been developed prototypically and tested in an environment for personalized home healthcare. [ABSTRACT FROM AUTHOR]

This research has undertaken a systematic literature review (SLR) of articles focusing on the acceptance of fintech payment services by identifying 84 peer-reviewed articles published in international scientific journals from 2015 to April 2023. This paper uses the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol to gather relevant articles and the theory, context, constructs, and methodology (TCCM) framework to analyse them. The conducted SLR has several findings. First, the Technology Acceptance Model (TAM) is the main theory used to examine consumers' acceptance of fintech payment services. Second, studies in this area have been conducted in 24 countries, with a focus on Indonesia, Malaysia, and China. The study themes identified include fintech payment apps, Buy Now Pay Later (BNPL), mobile payment, fintech services, e-wallet, and Islamic Fintech. Third, the perceived usefulness, trust, perceived ease of use, and attitude are the four main constructs found to have a significant association with behavioural intention. Finally, most studies (64) rely on quantitative methods, particularly questionnaires. Based on the findings, this study identifies research gaps and provides a future research agenda. The review also has practical implications for policymakers and corporations in developing strategies and policies promoting the acceptance of fintech payment services. Limitations include B2C focus, exclusion of B2B behavior, lack of targeting specific user demographics, and reliance on secondary data. These present opportunities for further research. [ABSTRACT FROM AUTHOR]

This research intends to identify factors in the adoption of mobile government services, recognizing the main mobile government acceptance factors through different models, namely, the theory of reasoned action (TRA), the theory of planned behavior (TPB), the technology acceptance model (TAM), the technology readiness index (TRI), and the unified theory of acceptance and use of technology (UTAUT). Reviewing prior studies on factors that influence mobile government acceptance in Egypt and addressing local challenges help in the development of effective strategies for successful implementation, considering opportunities and challenges in value-added services (VAS). Therefore, the main objectives of this paper are as follows: we critically review prior studies to identify the main mobile government acceptance factors, explore the identified dimensions of mobile government acceptance, and recognize further insights into the challenges that exist in the Egyptian context. Data are gathered through interviews with technology experts and customers with technological experience in Egypt. The findings of the study show that the factors affecting mobile government adoption in the Egyptian context are identified as follows: facilitating conditions, opportunities and challenges, technology and information systems, perceived usefulness, challenges like a lack of trust, cost, privacy, age, and several other obstacles. This research assists scholars and practitioners with insights into how to create and promote an application that is deemed attractive for the adoption of m-government services. [ABSTRACT FROM AUTHOR]

The pricing of ride-hailing platforms (e.g., Didi Rider and Uber) is heavily and simultaneously influenced by the cross-group network effect and congestion effect. To analyze the bilateral pricing of ride-hailing platforms under the influence of these two effects, in this paper we construct a game-theoretic model under four different scenarios and analyze the equilibrium outcomes. The results show that: (1) when both passengers and drivers are sensitive to hassle costs, if the cross-group network effect on the passenger side is higher than that on the driver side, then the platform's pricing on both sides increases with the increase in the congestion effect, otherwise the prices on both sides of the platform decrease with the increase in the congestion effect; (2) when passengers are sensitive to hassle costs and drivers are sensitive to price, if the ratio for passengers' and drivers' different perceptions of price and hassle cost is greater than a certain threshold, then the platform's pricing on the passenger side increases with the increase in the congestion effect and the platform's pricing on the driver side decreases with the increase in the congestion effect, otherwise the platform's pricing on the passenger side decreases with the increase in the congestion effect and the platform's pricing on the drivers' side increases with the increase in the congestion effect; (3) when passengers are sensitive to price and drivers are sensitive to hassle costs, if the ratio for passengers' and drivers' different perceptions of price and hassle costs is greater than a certain threshold, then the platform's pricing on the passenger side decreases with the increase in the congestion effect and the platform's pricing on the drivers' side increases with the increase in the congestion effect, otherwise the platform's pricing on the passenger side increases with the increase of the congestion effect and the platform's pricing on the driver side decreases with the increase in the congestion effect; (4) when both passengers and drivers are price-sensitive, if the cross-group network effect on the passengers' side is larger than that on the drivers' side, then the platform should decrease its pricing on both sides with the increase in the congestion effect, otherwise, if the cross-group network effect on the passengers' side is less than that on the drivers' side, the platform should increase its pricing on both sides with the increase in the congestion effect; (5) the platform is able to generate the highest profit in each scenario, and the results of the profit comparison between the four scenarios depends on the cross-group network effects and the congestion effects on both the passengers' and the drivers' sides. [ABSTRACT FROM AUTHOR]

Accounts receivable financing is one of the most prominent financing approaches in supply chain finance; nevertheless, in the actual financing process, financial institutions and SMEs have credit risk and information asymmetry risk, which leads to frequent nonpayment and collaboration fraudulent loans. This paper introduces central bank digital currency into traditional accounts receivable financing and solves the credit risk and information asymmetry risk using two technologies of central bank digital currency: digital technology and blockchain technology; digital technology enables the supervision of capital flow, and blockchain technology enables for access to logistics and information flow. In the context of using central bank digital currency technology, this paper builds an evolutionary game model of whether financial institutions use central bank digital currency and whether SMEs repay the loan, compares the evolutionary stabilization strategies of financial institutions and SMEs, calculates and analyzes the model's impact, investigates changes in the decision-making and evolutionary paths of both parties, and then conducts numerical simulation analysis using Matlab and Python to verify the model's reliability further. According to the results, adding central bank digital currency to the traditional accounts receivable financing model can reduce the loan risk of financial institutions, increase the credibility of accounts receivable financing, expedite the implementation of accounts receivable financing, and alleviate the financing concerns of SMEs. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to assess the impact of the COVID-19 pandemic on rural e-customers' preferences in terms of last mile delivery and products purchased via the Internet. This paper is based on the theory of last mile delivery (with a strong focus on the methods of delivery) and customers' preferences. It combines both theories to fulfil the paper's goal. In the paper, the literature review and multivariate data analysis of rural e-customers in Poland were used. The hypothesis states that there is a requirement for different last mile delivery solutions in rural areas than in urban areas in Poland. The research was carried out on a sample of 1071 adult Poles, who are residents of rural areas, using the standardized and structured computer-assisted interview (CAWI). The authors used the chi-square independence test, the Student t-test, one-way analysis of variance (ANOVA), regression models, and logistic models. The research shows that more than 60% of respondents living in rural areas order goods purchased online with a home delivery method and that online shopping preferences' changes are not so significant after the occurrence of COVID-19. Both before and after the pandemic, rural e-customers made online purchases most often 2–3 times a month. Nevertheless, some differences in the preferences of rural e-customers in terms of last mile delivery and purchasing preference can be seen considering their age, gender, or family size. The survey found that smaller families were more likely to choose free home delivery with a free return after the pandemic. The research presented in this paper provides essential information for courier companies, stores, and online platforms alike. Knowledge of preferences and their changes after the occurrence of COVID-19 can support the reorganization of last mile delivery to meet the needs of e-customers and to adapt to the increasing demands for sustainable transportation. [ABSTRACT FROM AUTHOR]

Electronic commerce appeared as a new way of managing businesses in the digital era. However, it has also been accelerated by the recent pandemic situation. Retailers had to find new strategies of reaching customers in the online environment. Thus, concepts such as multi-channel and omni-channel retailing have gained the attention of both retailers and researchers in this field. This paper aims at using a text-mining approach in order to reveal the researchers focus on this theme in a period that also precedes and covers the COVID-19 pandemic. The research methodology follows five steps that are necessary in order to obtain a relevant collection of documents that will further provide the content to be analyzed. These steps refer to: (1) Creating the database of documents for analysis purposes; (2) identifying geographic areas for separating the collection's documents; (3) framing a thematic dictionary of descriptors; (4) exploring the text using text mining approach; and (5) correspondence analysis. The discussion of the main findings is constructed starting with the geographic and the temporal distribution of documents and the design of a thematic dictionary of descriptors. Then, exploring the content of the documents provides information on the frequency of descriptors and reveals clusters of descriptors along with a link analysis. All of them are presented separately on geographic regions. Finally, the correspondence analysis of descriptors versus years provides the proximity maps and reveals the preferred topics and less approached themes. Among the main findings, one can highlight: (1) The greatest contributor in terms of documents related to the theme of interest is the United States; (2) a higher number of connections (and stronger) among descriptors for America as compared to the other two regions; (3) some categories of descriptors are specific to a particular year, which means that there are different themes under the researchers lens depending on the period; (4) the most frequently used descriptors are included in the following categories from the dictionary: Online retail environment and Consumer behavior, regardless of the region. In the end of this paper, research limitations and guidelines for future research are elaborated. [ABSTRACT FROM AUTHOR]

This paper presents an empirical analysis of e-commerce data obtained through Google Analytics (GA) from two small businesses' perspectives: an IT components company and a tourism agency website located within the same county in Romania. The objective of our study is to examine the enduring effects of the COVID-19 pandemic and seasonal variations over the last four years. The data collection spanned from January 2019, predating the onset of the COVID-19 pandemic, until mid-February 2023. To facilitate our analysis, we categorize the GA metrics into groups that encompassed website performance, site accessibility, and user behavior for the IT company. As for the tourism agency, we focus on website accessibility, user behavior, and marketing campaigns. Our goal is to empirically group or associate GA metrics according to their intrinsic meaning and check if each group reflects a certain latent concept (such as user behavior or site accessibility). Furthermore, our study aims to formulate and test five hypotheses regarding the immediate and long-lasting impact of the COVID-19 pandemic on the operations of small businesses. Our contribution consists of formulating and verifying the five hypotheses by providing descriptive data from the results of the Pearson correlation test, empirically grouping the GA metrics and verifying whether they reflect certain latent factors or topics, interpreting the results from the application of the ANOVA technique and Scarpello's adaptation of the one factor test, respectively. [ABSTRACT FROM AUTHOR]

This paper develops a duopoly model for user-generated content (UGC) platforms, which compete for consumers and content producers in two-sided markets characterized by network externalities. Each platform has the option to invest in a content quality assurance (CQA) system and determine the level of advertising. Our model reveals that network effects are pivotal in shaping the platforms' optimal strategies and user behavior, specifically in terms of single vs. multi-homing. We find that when network effects for producers are weak, consumers tend to engage in multi-homing while producers prefer single-homing. Conversely, strong network effects lead to the opposite behavior. Furthermore, our model demonstrates that user behavior and network effects dictate whether a platform is incentivized to incorporate advertisements and/or invest in CQA. Generally, weak network effects prompt a platform to invest in a CQA system, unless both consumers and producers engage in multi-homing. Our model's results highlight the importance for platform companies to evaluate the extent of network effects on their platform in order to anticipate user behavior, which subsequently informs the optimal CQA and advertising strategy. [ABSTRACT FROM AUTHOR]

For more than two decades, developed countries have been confronted with two trends that have implications for the emergence of engaging senior citizens in the digital environment. On the one hand, there is an increasing proportion of senior citizens in the total population. On the other hand, the application of ICT in all areas of life and business is accelerating. This paper investigates the relationship between self-efficacy, social support, and social influence on Internet usage among senior citizens in Croatia. Survey research was conducted on a sample of Croatian senior citizens, and a structural equation mode was developed for testing the research hypothesis. Self-efficacy influenced both the Intensity and obstacles of Internet usage in a positive and negative manner, respectively. Social influence directly decreased the obstacles to Internet usage, while the relationship with the Intensity of the Internet was indirect through self-efficacy. Social support had only an indirect association with Intensity of Internet usage. Results have relevant implications for programmes aiming to enhance Internet usage among senior citizens, which should focus on the educational programmes fostering perceived self-efficacy of Internet usage among senior citizens. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Conversational robots powered by artificial intelligence (AI) are intensively implemented in the insurance industry. This paper aims to determine the current level of acceptance among consumers regarding the use of conversational robots for interacting with insurers and seeks to identify the factors that influence individuals' behavioral intention to engage with chatbots. To explain behavioral intention, we tested a structural equation model based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. It was supposed that behavioral intention is influenced by performance expectancy, effort expectancy, social influence, and trust, and by the moderating effect of insurance literacy on performance expectancy and effort expectancy. The study reveals a significant overall rejection of robotic technology among respondents. The technology acceptance model tested demonstrates a strong ability to fit the data, explaining nearly 70% of the variance in behavioral intention. Social influence emerges as the most influential variable in explaining the intention to use conversational robots. Furthermore, effort expectancy and trust significantly impact behavioral intention in a positive manner. For chatbots to gain acceptance as a technology, it is crucial to enhance their usability, establish trust, and increase social acceptance among users. [ABSTRACT FROM AUTHOR]

Condominiums and similar properties use a stratum to manage daily operations, and owners fund it through strata fees. While existing strata fee management systems may be able to handle such funds, such systems could be more inherently transparent. It is possible to leverage the digital ledger from blockchain networks and smart contracts to build a fully transparent strata fee management system. This paper proposes designing a strata fee management system based on a smart contract in the Ethereum network. Both strata corporations and homeowners can interact with the smart contract to execute common procedures such as paying strata fees and handling expenses. Using smart contracts for strata fee management, it is believed that the chance of fraud by strata corporations is lowered compared to other systems. [ABSTRACT FROM AUTHOR]

With the vigorous development of e-commerce, efficient order picking in e-commerce warehouses has attracted the attention of many scholars. To analyze the issues about order-picking efficiency currently being studied by relevant scholars in e-commerce warehouses, this paper reviews the literature on the application of order-picking strategy and efficiency optimization direction from 2020 to 2022. That mainly falls into two categories of picking systems: "picker-to-parts" and "parts-to-picker". In the "picker-to-parts" picking system, more attention is paid to the picking strategies of storage assignment and order batching and the integration of multiple picking strategies. In contrast, in the "parts-to-picker" picking system, the main attention is on the man-machine cooperation in the Mobile Robot Fulfillment System (RMFS) and the Automated Storage and Retrieval System (AS/RS), as well as the coordination of the picking station. Further, this paper proposes future research directions for two categories of picking systems: further studying the order splitting strategy and order delivery issues; considering the dynamic uncertainties; combining the automated picking system with different picking strategies, and so on. [ABSTRACT FROM AUTHOR]

With the development of Internet technology, online shopping has become increasingly popular. Owing to the improvement of living standards, the quality of e-service has become one of the important criteria for online shopping, with online shopping consultation being one of the key services. At the same time, the emergence of new technologies such as Artificial Intelligent (AI) has allowed online retailers to increase the availability of non-human online shopping consultation services. Therefore, this paper investigates the service decision problem between human and non-human online shopping consultation services for online retailers in the online duopoly market. By constructing a Hotelling improvement model and applying it in a new way, considering consumer preferences for human services, this paper explores the impact of the optimal service level of human online shopping consultation services and consumers' sensitivity to the service level of human services on online retailers' pricing, service decisions, etc. Our research results show that consumers' sensitivity to the service level of human online shopping consultation services has an impact on the demand and profit of online retailers. In addition, human online shopping consultation services are not always beneficial to the profitability. Furthermore, when two online retailers compete, the utility of the non-human online retailer's service to consumers can influence the service decisions of the other online retailer. [ABSTRACT FROM AUTHOR]

In 2020, the COVID-19 pandemic had a major impact on China's foreign trade. Therefore, the Chinese government has proposed a "dual cycle" policy to promote economic development. In 2021, China's cross-border e-commerce B2B exports accounted for 60 percent. Therefore, this paper studies the impact of government actions on the development of cross-border e-commerce B2B export enterprises under the background of "dual cycle" policy. First, the policies related to the cross-border e-commerce industry in the "dual circulation" policy are screened, and the LDA topic model is used to classify them, i.e., sorting by topic intensity as "fiscal policy", "tax policy", "customs clearance policy", "payment policy" and "talent policy". After that, based on the analysis results of the LDA topic model, a theoretical basis for the impact of different policies on cross-border e-commerce B2B export companies is established; then an evolutionary game model between the government and cross-border e-commerce B2B export enterprises is constructed. This article also carried out experiments to verify our analysis. The simulation results show that: (1) The government's appropriate increase in subsidies, tax incentives, infrastructure investment, talent introduction and cultivation, optimized payment system, and supervision can promote enterprises to participate in cross-border e-commerce B2B export trading; (2) excessive government supervision reduces enterprises' enthusiasm to participate in cross-border e-commerce B2B export trading; (3) the government's subsidies, tax incentives, and supervision strength have the greatest impact on whether enterprises participate in cross-border e-commerce B2B export trading, followed by the government's investment in cross-border e-commerce infrastructure, the introduction and cultivation of cross-border e-commerce talents, and the improvement of the payment system. Finally, this paper puts forward relevant policy recommendations to promote the development of cross-border e-commerce B2B export enterprises. [ABSTRACT FROM AUTHOR]

The explosion of e-commerce creates new opportunities for courier companies to thrive, making the industry one of the success stories, due to its sustainability and resilience during the pandemic. As customers become more familiar with COVID-19, they adopt new online purchasing behaviors toward branding preferences. The purpose of this paper is to analyze the impact of risk familiarization on courier companies' digital branding. This paper investigates the application of the psychometric paradigm by Fischhoff ho suggested risk novelty as a key factor for the level of risk perception. Five big companies with global trading activities were selected and analyzed on a three-period time: the year before, the first year, and the second year of the COVID-19 pandemic, by using passive crowdsourcing data. The results indicate that after the first year of the pandemic, online customers' risk perception of COVID-19 hazards decreased, and consumers returned to their pre-COVID-19 behavior regarding brand preference. However, the dramatic escalation of new infections caused by new COVID-19 mutations reversed their online purchasing attitude from non-branded to branded preferences. The outcomes of the research can be used by scientists and supply chain risk managers to redefine risk mitigation strategies, COVID-related information marketing strategies and innovation investments within the industry. The research further introduces dynamic simulation modeling to be used as a risk management tool in favor of courier companies' proper resource allocation and digital optimization. [ABSTRACT FROM AUTHOR]

In recent years, online retail has developed rapidly. However, as consumer demands become increasingly sophisticated, the traditional online retail model has encountered difficulties with respect to meeting consumers' needs. As a result, numerous retailers with offline physical stores have emerged in the online retail industry. This paper constructs a game model for the invasion of the market by e-commerce retailers with offline physical stores in a context in which a traditional, online-only incumbent retailer is already in the market. Compared with the new entrant, the incumbent has the advantage of an established good reputation, and consumers prefer the products of the incumbent. This research shows that as consumers' product valuations increase, the following three situations may occur: a partially covered market, a multiple-equilibrium market or a fully covered market. In a partially covered market, the incumbency advantage does not affect the entrant. In a multiple-equilibrium market or fully covered market, the incumbency advantage impacts the profits of the entrant. However, in a fully covered market, if the incumbency advantage is too large, the profits of both retailers are damaged. Finally, this paper finds that offline physical stores can provide positive benefits to entrants. When consumers are highly sensitive to services, opening offline physical stores is an effective intrusion strategy that entrants can use to overcome the incumbency advantage. [ABSTRACT FROM AUTHOR]

A plethora of studies have examined the emerging technology of blockchain and its applications in accounting, management, and enterprise resource planning systems (ERPs). Blockchain technology (BT) can change the architecture of today's ERPs and overcome the limitations of these centralized systems. The aim of this study is twofold. First, this paper defines and analyzes the deployment of an innovative architecture of a Blockchain as an Ecosystem (BaaE) platform proposing a conceptual model of the Triple Entry Accounting (TEA) transforming the current accounting practices. Second, the paper explores the integration of cost management, supply chain, and inventory management on BT providing the significant challenges and benefits and suggesting an agenda for future research. The authors conduct an exploratory qualitative analysis of an extensive body of literature, from 81 journals. The paper's innovative contribution and primary objective is to explore, address, and employ this emerging BaaE platform technology that could potentially be integrated with TEA. Further, the study examines the theoretical, technical, and business aspects regarding TEA, since there is limited research evidence in this field. Additionally, the study tries to identify the implications of BaaE in the area of cost management, supply chain, and inventory management from an ecosystem perspective. This effort can assist organizations and practitioners in understanding and further examining this emerging technology. [ABSTRACT FROM AUTHOR]

Understanding how competitors act in a market is a critical component of strategic decision-making. In this paper, we propose a method to extract firm events from the textual content generated by firms in the market and explore the competitive relationships among firms based on the spatiotemporal homogeneity of events of different firms. To this end, we first introduce experts to define a series of business events based on the content of corporate-generated texts; then, we propose algorithms to extract and enrich the feature words (triggers) of these business events to form better event classifiers. We subsequently use these classifiers to identify the business events recorded in all online texts published by companies. Finally, based on these results, we can obtain a sequence of activities/events for each firm in the market, which can be used to identify the evolutionary patterns of firms' behavior in the market, as well as their potential competitive relationships. Considering that competition between companies in the market appears to be continuous at the strategic level, but the implementation of competitive behavior is expressed through their "events" in the market, identifying whether companies are "competing" in the market requires timely observation of the information about "events" in the market. However, obtaining accurate market information is complex and costly. Therefore, this study provides a way to bridge the gap between social media data and market competition "events". [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Decision makers' behavioral preferences have always been important in coordinating the supply chain. Decision makers need to choose a partner wisely to increase the profitability of the entire supply chain, especially in the competitive e-commerce environment. In this paper, we examine a two-echelon e-commerce supply chain with one retailer and one supplier using the most popular wholesale price contract to facilitate collaboration. Traditional research has shown that the classical expectation model cannot coordinate the supply chain. We apply the focus theory of choice to describe the retailer's behavior as a follower, and we examine the impact of the retailer's pricing decisions on the supplier under different focus preferences and the coordination for the entire supply chain. The lower the parameter φ, which represents the degree of positivity, and the higher the parameter κ, which represents the level of confidence, the closer the profit of the whole supply chain is to the coordination result—both are visualized through numerical experiments and images. In the case of φ determination, the lower the κ, the better the supply chain coordination. The finding implies that the retailer may be able to coordinate the supply chain and produce better results than the expectation model when he or she makes choices using a positive evaluation system that includes both higher levels of optimism and lower levels of confidence. The findings of the FTC model can simultaneously offer a theoretical foundation for expanding collaboration among supply chain participants and management insights for decision makers to choose cooperation partners. [ABSTRACT FROM AUTHOR]

Observing the fast development of livestreaming, this paper investigates its adoption on the retail platform and examines its impact on merchants. We develop a game-theoretic model in which a leading retailer and a third-party seller engage in price competition. Our model fully considers the initiative of live streamers in this asymmetric competition. We find that the streamer's cost and the seller's initial awareness are two key factors affecting the adoption of livestreaming. Specifically, when the streamer's cost is low, or it is intermediate and the seller's initial awareness is high, the retailer adopts and opens livestreaming and the seller also adopts it; when both factors are intermediate, the retailer adopts livestreaming but does not open it to the seller; when both factors are high, the retailer adopts and opens livestreaming but the seller does not adopt it; otherwise, the retailer does not adopt livestreaming. Our results also suggest that the presence of livestreaming benefits the retailer but may hurt the seller especially when the seller's initial awareness is high. Our findings provide relevant and useful implications for both the platform retailer and third-party seller in their livestreaming decisions. [ABSTRACT FROM AUTHOR]

The deployment of Blockchain technology in the tourism industry is already becoming a reality with the gradual emergence of innovative business models. At its core is the promise of improving the efficiency of the tourism service value chain and enhancing the quality of the service provided to the end customer. This paper analyses research trends focused on using Blockchain technology in tourism. The aim is to determine how this technology impacts the tourism sector and its sustainability. A systematic review, descriptive bibliometric analysis, and network analysis based on co-authorship, co-citation, and keyword analysis criteria, among others, have been used. The results reveal that the subject matter analysed is generating a growing trend in academic research in the fields of sustainable management and supply chain efficiency. The activities in the tourism sector that are incorporating this technology to a greater extent are those related to the areas of marketing, logistics, and smart business models, according to the data extracted from the analysis. This technology already enables the application of solutions that predict and promote tourist behaviour based on sustainable behaviour and consumption habits, generating value for the different stakeholders. [ABSTRACT FROM AUTHOR]

The global shipping industry faces many uncertainties which impact on how organisations within this sector will perform in the future. Research in the critical success factors which impact the global shipping industry in the digital era is lacking. This study plugs the gap in the literature by identifying four key critical success factors which are innovation capability, risk governance capability, leadership and strategic capability, and technological capability. In addition, this study also found three organisational performance measures that are useful for senior management teams within the industry, namely, financial performance, operational performance and marketing performance. The results were then triangulated and validated by the case study method using a global shipping organisation. The findings establish a set of critical success factors and the corresponding relationships between the identified critical success factors and the identified organisational performance measures. The paper also provides managerial insights for industry practitioners for defining, prioritising and allocating resources in order to improve organisational performance. [ABSTRACT FROM AUTHOR]

With the development of financial technology (referred to as fintech), the risks faced by fintech companies have received increasing attention. This paper uses the Sentence Latent Dirichlet Allocation (Sent-LDA) topic model to comprehensively identify risk factors in the fintech industry based on textual risk factors disclosed in Form 10-K. Furthermore, this paper analyzes the importance of risk factors and the similarities of the risk factors for the whole fintech industry and different fintech sub-sectors from the perspectives of risk factor types and risk factor contents. In the empirical analysis, 53,452 risk factor headings of 34 fintech companies included in the KBW Nasdaq Financial Technology Index (KFTX) over the period 2015–2019 are collected. The empirical results show that 20 risk factors of the fintech industry are identified. However, the important risk factors vary differently among different fintech sub-sectors. For the analysis of risk factor similarity, mean values of similarity of risk factor types and the similarity of risk factor contents both increased from 2015 to 2019, which indicates that the risks faced by fintech companies are becoming increasingly similar. The mean value of similarity of risk factor contents is 42.13%, while the mean value of similarity of risk factor types is 80.93%. Thus, although the types of risk factors faced by different fintech companies are similar, the contents of risk factors disclosed by different companies are still quite different. The comprehensive identification of fintech risk factors lays an important foundation for the further measurement and management of risks in the fintech industry. In the feature, we will further make effective risk estimations of the fintech industry based on the identified fintech risk factors. [ABSTRACT FROM AUTHOR]

Although research on risk management (RM) in small- and medium-sized enterprises (SMEs) in general and regarding supply chains (SCs) has increased recently, our understanding is still rather fragmented and underdeveloped. This refers particularly to new types of risks such as dynamic crises or emerging risks associated with digital transformation (DT). Therefore, the purpose of this exploratory paper is to investigate RM in SMEs in SCs. More precisely, the aim is to identify patterns that can be used to group SMEs according to their risk behavior (i.e., risk attitude and perception). Drawing from a data set of 181 European SMEs, this paper empirically conceptualizes a typology of SMEs. The typology consists of four distinct types of SMEs that emerged from a cluster analysis: collective risk eliminators, collective playing it safe seekers, collective risk-ignoring knights of fortune, and collective neglecting imperturbable ones. The findings indicate that different risk behavior leads to different degrees of collaboration within the SC. Furthermore, the close interconnection between RM as found in the different clusters and the respective firm's innovation performance can be shown. By acknowledging the heterogeneity found in SMEs, this paper breaks away from mainstream research that tends to consider SMEs as a homogeneous entity. [ABSTRACT FROM AUTHOR]

This paper reviews the primary scientific articles applicable to the logistics industry, and specifically those relating to cross-border e-commerce in China. The authors focused on reviewing the articles about the current status of cross-border e-commerce in China and the factors affecting its development, with the aim of highlighting literature gaps. The authors used a systematic literature review (SLR) to identify, gather, and analyze 60 primary papers selected from international peer-reviewed journals and international conference proceedings between 2001 and 2020. Chinese cross-border e-commerce has experienced a trend of steady progress, although several challenges remain. These challenges include, but are not limited to, low custom clearance efficiency, complex monitoring and supervision, tax rebate settlement challenges, payment risks, insufficient talent within the Chinese industry, and the lack of scientific management guidelines. The significant contributions of this paper include critical highlights of the current gaps and future research themes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Over recent years, social commerce has evolved into a powerful segment of e-commerce, creating new opportunities for brands of all types and sizes. However, if social commerce is to continue to grow and deliver the many benefits it promises, it must address a number of key challenges, including privacy, trust, and ethical concerns. This paper explores the extent to which privacy issues affect the attitudes and behaviours of social media platform (SMP) users towards social commerce, and investigates whether these attitudes and behaviours are a function of cultural context. The approach adopted for the research is a two-stage method, which initially uses semi-structured interviews of social-commerce users to identify their key privacy concerns. These concerns are then used to develop, using the theory of reasoned action (TRA), a structural model that facilitates the formation of hypotheses which relate users' attitudes to privacy to subsequent behaviour. This model is assessed by analysing the responses to a questionnaire from a large sample of participants. This allows us to evaluate the general accuracy of the model and to compare culturally distinct subgroups (Saudi vs. Chinese) using partial least-squares analysis. Results show good support for all of our hypotheses and indicate that there are clear cultural effects. One of these effects is the inadequacy of privacy policies implemented by SMP providers, regarding culturally specific ethical concerns. [ABSTRACT FROM AUTHOR]

To reduce resource loss and environmental pollution, green CLSC has become a hot issue that manufacturing enterprises pay attention to. In green CLSC, manufacturers would pay attention to the fairness of profit distribution when making sales efforts. Therefore, this paper studies a green closed-loop supply chain (CLSC) considering manufacturer sales efforts and fairness concerns. Then, the centralized model and decentralized model are built and analyzed. Afterward, a profit-sharing contract between members is designed to coordinate the supply chain. We made the following observations: (1) The manufacturers' fairness concerns would reduce product green degree, sales effort and recycling rate of used products, which is not conducive to the sustainable development of the green closed-loop supply chain. (2) When the manufacturers' fairness concerns are gradually strengthened, the optimal decisions would deviate even more from the optimal equilibrium results. (3) When the coefficient of fairness concerns and the ratio of profit-sharing satisfy a certain range, Pareto improvement can be effectively realized. [ABSTRACT FROM AUTHOR]