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Using Chinese A-share listed companies from 2011 to 2020 as a research sample, this paper examines the relationship between negative media coverage and corporate ESG performance using a two-way fixed-effects model. It is found that, first, negative media coverage can effectively promote corporate ESG performance. Second, the mediation mechanism study shows that negative media coverage positively promotes corporate ESG performance by increasing the degree of corporate financing constraints and information asymmetry and prompting corporations to change their ESG governance level. Third, the results of the heterogeneity test find that the positive relationship between negative media coverage and corporate ESG performance is more pronounced among firms without executives with overseas backgrounds, and the positive relationship between the two is more significant after the promulgation of China’s Code of Governance for Listed Companies in 2018. Fourth, further discussion revealed that negative media coverage has the strongest promotion effect on the performance of corporate environmental governance, followed by social governance performance, and lastly, corporate governance performance. The research in this paper contributes to an in-depth understanding of the impact of negative media coverage on corporate ESG performance and provides empirical evidence to facilitate policy formulation related to the role of media monitoring and to fully utilize the media’s role in corporate ESG governance.

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.

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.

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.

The paper examines the nexus between working capital management (WCM) and financial performance of listed non-financial firms in Ghana. An unbalanced panel data for the period 2008 to 2021 was used for the study. It is observed that the residual terms of the models were cross-sectionally independent and all the series were first-differenced stationary and cointegrated in the long term The elasticities of the predictors were explored via the Fully Modified Ordinary Least Squares (FMOLS) and the Dynamic Ordinary Least Squares (DOLS) techniques. The findings of the study indicate that WCM proxied by accounts receivable period (ACP), accounts payment period (APP), and inventory turnover period (ITP) have significant positive effect on firms’ financial performance measured by return on assets (ROA), return on equity (ROE), and return on capital employed (ROCE). This suggests that the working capital management practices of non-financial firms in Ghana improve their financial performance. Also, firm size and asset growth improve firm financial performance. On the causalities between the variables, bidirectional causalities between ACP, APP, ITP, size, and thecompanies’ ROA, ROE, and ROCE are disclosed. Finally, causality from growth to the ROA, ROE, and ROCE of the firms are unraveled. It is recommended that policy makers of non-financial firms in Ghana should not overlook WCM practices in their financial decisions, since ignoring them could seriously compromise the firms’ short- and long-term sustainability.

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.

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.

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.

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.

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.

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.

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.

Contemporary economies worldwide appreciate the underlying importance of service sectors, culminating in sectorial growth and satisfactory performance of agri-preneurship. This study is premised on two simple explanations that cascaded into two hypothetical questions: (i) Are there inspiring factors for individuals to start up their own agri-preneurship business? (ii) Are there factors hindering individuals from starting up their own agri-preneurship business? These two hypothetical constructs triggered four primary arguments established in the study. This study was conducted in Mbombela local Municipality in category ‘B’, which is made up of the Ehlanzeni District, Mpumalanga South Africa. From a population of 1021 farmers, 458 samples of respondents were obtained and considered to be realistic for the study. The study embraced the mixed research approach, and the field survey was done between the months of February 2022 and June 2022. Descriptive statistics, involving the frequency count and percentages, were used to determine the motives and obstacles of starting up one’s own business. The hypothesis was assessed by employing multinominal logistic regression to determine the relationship between demographic variables and predictor variables. The findings revealed that potential agri-preneurs are affected by an array of inspiring factors in the decision-making process. Further, the results revealed that agri-preneurial spirit is inhibited by numerous challenges. In the shared view of respondents, there was a portmanteau of obstacles impeding the commencement of one’s own business. The results also showed that, in the self-realization variant, age (p < 0.035) and marital status (p < 0.033) were found to be correlated and statistically significant as an inspiration for setting up an agri-preneurship business, while the self-satisfaction variant specified marital status (p < 0.001) to be significant and to negatively (β −1.564) influence inspiration for agri-preneurship. In the independence variant category, two crucial variables, level of formal education (p < 0.005) and farm experience (p < 0.007), were found to be correlated and statistically significant in determining agri-preneurial decisions. This study has several policy propositions: the government and allied stakeholders must consider the motives that inspire potential investors in agriculture. The government must also oversee the reasons for young and old agri-preneurs leaving agri-businesses because of failure. This study underscores the need to subjectively analyze agri-preneurial inspiring factors and obstacles to performance. The contributions of agri-preneurial businesses to economic growth and poverty alleviation justify the need for a concerted effort to motivate potential investors. Another pertinent contribution of the study is highlighting the obstacles of agri-preneurs to enable the government to support and prepare responsive, innovative, and resilient agri-preneurs in South Africa. This paper recommends that starting up one’s own agri-preneurial business is a good idea for abating hunger and unemployment.