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The high frequency of disruption and dislocation of many industries, the migration to low-cost countries of different assets and activities, the increase in systemic risk, the birth of social and ecological constraints, as well as the new worldwide competitors require businesses and the overall society to change. In a so-called Industry 4.0. era, understanding the impact of artificial intelligence (AI) in developed as well as in underdeveloped economies has become increasingly crucial. The purpose of this study is to shed the light on the peculiarities of Chinese AI assessing the state of art of AI in this unique and valuable context.

Through a research based on a qualitative data analysis, the present paper suggests a new way to analyse AI and to support a better understanding of the local Chinese aspects influencing its development and implementation.

The development and implementation of AI in China required tailor solutions which account for the following three main dimensions: the location (i.e. territorial extension, the administrative boundaries); the government approach; and the human capital.

The analysis presents a broad level activity. In addition, the paper focused on Chinese scientific literature and different types of data (i.e. institutional documents, professional reports, websites and speeches in Chinese). The paper used a multi-faceted approach, including also the tacit knowledge of the authors about the context under investigation.

In social media, crisis information susceptible of generating different emotions could be spread at exponential pace via multilevel super-spreaders. This study aims to interpret the multi-level emotion propagation in natural disaster events by analyzing information diffusion capacity and emotional guiding ability of super-spreaders in different levels of hierarchy.

We collected 47,042 original microblogs and 120,697 forwarding data on Weibo about the “7.20 Henan Rainstorm” event for empirical analysis. Emotion analysis and emotion network analysis were used to screen emotional information and identify super-spreaders. The number of followers is considered as the basis for classifying super-spreaders into five levels.

Official media and ordinary users can become the super-spreaders with different advantages, creating a new emotion propagation environment. The number of followers becomes a valid basis for classifying the hierarchy levels of super-spreaders. The higher the level of users, the easier they are to become super-spreaders. And there is a strong correlation between the hierarchy level of super-spreaders and their role in emotion propagation.

This study has important significance for understanding the mode of social emotion propagation and making decisions in maintaining social harmony.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2024-0192.

The purpose of this study is to examine the effects of banking innovations (INNs) on customer experience (EXP), satisfaction (SAT) and loyalty (LOY).

The author evaluated the data using a structural equation method-artificial neural network (SEM-ANN) method. The author’s results show the presence of relationship between INN, EXP, SAT and LOY. In this study, the node layers of ANNs add an input layer, hidden layers and an output layer. Each “node” acts as an artificial neuron that communicates with others. The ANN model takes the variables from the SEM analysis as input neurons.

The author observed the significant effects between INN, EXP, SAT and LOY using the normalised importance generated by the multilayer perceptron used in the feed-forward back propagation of the ANN methodology. In this study, the ANN model can predict LOY through service innovation, with a forecast accuracy of 77.6%.

By applying neural network modelling, this research helps us understand how service innovation affects customer behaviour. For the first time, the author examined service innovations' direct and indirect impact on loyalty through EXP and SAT. The author made a significant conceptual contribution by using a non-compensatory model of ANNs to circumvent the limitations of linear models.

Different external supply scenarios faced by the retailers will affect their choice of strategy when supply is disrupted and becomes far less than demand, urgently. This study focuses on analyzing both demand and supply side response strategies to meet customer demand and reduce the impact of the shortage during supply disruptions.

According to the quantity of products that the external market can provide, the external supply scenarios were divided into sufficient-type external supply and learning-type external supply. A two-echelon perishable goods supply chain was analyzed, and three kinds of contingency strategy models for downstream retailers were investigated. First, in the sufficient external supply scenario, the optimal price and transshipment quantity to maximize retailer's profits is discussed. Second, in the scenario of learning-type external supply, this study analyzes the optimal decision in three mechanisms of the hybrid strategy and their application: price priority mechanism, quantity priority mechanism and price–quantity balance mechanism. Furthermore, the influence of penalty cost and supply on the priority orders of different mechanisms was studied.

Results show that comparing the two pure strategies (pricing strategy and transshipment strategy)it was noted that the hybrid strategy produces the best results in sufficient-type external supply scenario. In the learning-type external supply scenario, a numerical study has shown the existence of three areas in case of penalty cost and supplier's capacity, and each areas has different priority orders of the three mechanisms. Under the situation of learning external supply, the retailer's optimal strategy is affected by parameters such as penalty cost and supply volume.

The main innovation of the work lies in the following: First; the external supply situation was divided into sufficiency type and learning type, which improves the external situation faced by retailers after the outbreak of emergencies, helps retailers understand the external situation, conforms to the actual situation and has certain practical application value. Second; in the context of learning external supply, there are three coping strategies for retailers, including: Price priority mechanism, Quantity priority mechanism and Pricing and transshipment balance mechanism. This will help retailers make strategic choices, make more scientific management decisions and improve the supply chain emergency management theory. Third; the demand side response was managed through the change of external supply during supply side recovery period and supply disruption. The proposed model enables managing and analyzing supply disruption efficiently and effectively via handling uncertainty by considering all aspects of decision-making process. The proposed model can be applied in various fields such as vegetable and fruit, fresh food, etc.

The global recession caused by the COVID-19 pandemic has led to the closure of thousands of village-owned enterprises (VOEs), which are community-managed enterprises that operate in the hostile rural areas in emerging economies. Thus, considering that a Schumpeterian view of economic downturn sees recessions as times where old products/services decline while new products/services emerge, this paper aims to explore the specific innovation-based diversification strategies that matter for the survival of emerging economy VOEs in recession periods to develop new theoretical insights.

The study is based on multiple-case studies of 13 leading VOEs operating in the rural areas of Java Island in Indonesia, an emerging economy. The data was analysed using within-case and cross-case analyses.

Overall, a number of major novel findings have emerged from the analysis, based on which the authors developed several new propositions. First, from the perspectives of both new product and new service diversification, “unrelated diversification” is the primary resilience strategy that seems to be associated with the survival of VOEs in the COVID-19 recession, over and above “related diversification”. Second, from an industrial sector diversification perspective, the most dominant resilient strategy for surviving the recession is “unrelated diversification into tertiary sectors (service sector)”, over and above diversification into the primary sector (agriculture, fisheries and mining) and secondary sector (manufacturing and construction).

The authors contribute to the literature on entrepreneurship in emerging economies by identifying the resilience diversification strategies that matter for the survival of VOEs in recession.

The study aims to explore how integrating recent fundamental values (RFVs) from conventional accounting studies enhances the accuracy of a machine learning (ML) model for predicting stock return movement in Indonesia.

The study uses multilayer perceptron (MLP) analysis, a deep learning model subset of the ML method. The model utilizes findings from conventional accounting studies from 2019 to 2021 and samples from 10 firms in the Indonesian stock market from September 2018 to August 2019.

Incorporating RFVs improves predictive accuracy in the MLP model, especially in long reporting data ranges. The accuracy of the RFVs is also higher than that of raw data and common accounting ratio inputs.

The study uses Indonesian firms as its sample. We believe our findings apply to other emerging Asian markets and add to the existing ML literature on stock prediction. Nevertheless, expanding to different samples could strengthen the results of this study.

Governments can regulate RFV-based artificial intelligence (AI) applications for stock prediction to enhance decision-making about stock investment. Also, practitioners, analysts and investors can be inspired to develop RFV-based AI tools.

Studies in the literature on ML-based stock prediction find limited use for fundamental values and mainly apply technical indicators. However, this study demonstrates that including RFV in the ML model improves investors’ decision-making and minimizes unethical data use and artificial intelligence-based fraud.

This paper aims to investigate the effects of volatility transmission among Bitcoin and other leading cryptocurrencies, namely, Binance USD, BNB, Cardano, Dogecoin, Ethereum, Polkadot, Polygon, Solana, Tether, USD Coin and XRP.

The multivariate BEKK-GARCH model is used with the daily data set from 1 January 2017 to 31 March 2023. The data set is analysed in its entirety and is also the COVID-19 epidemic period.

The study reveals that while the volatility of cryptocurrency prices is influenced by their own historical shocks and volatility, there is proof of the effects shock transmission among Bitcoin and other notable cryptocurrencies. Furthermore, the authors identify the spillover effects of volatility among all 11 pairs and provide evidence that conditional correlations with varying time constants are present, and predominantly positive for both the entire and COVID-19 outbreak periods.

The findings will be helpful to market experts who want to avoid losses in traditional assets. To develop the best risk management and hedging strategies, businesses might use the information to build asset portfolios or personalise payment methods. The use of such data by investors and portfolio managers could aid in the development of investment opportunities, risk insurance plans or hedging strategies for the management of financial portfolios.

To the best of the authors’ knowledge, the use of the BEKK-GARCH model for examining the effects of volatility spillover among Bitcoin and the other eleven top cryptocurrencies has not been previously documented.

Demonetization and pandemic-related restrictions in India propelled the usage of mobile payments (M-payments). The culture of online smartphone transactions is expected to rise over the coming years, even after things return to normal. This study aims to unveil the factors that escalate the satisfaction levels of M-payment users and eventually stimulate them to continue using M-payments for their daily activities.

This study evaluated the intention to continue using M-payments for 710 users utilizing structural equation modeling and augmenting the technology acceptance model (TAM) as well as the expectation confirmation model (ECM). Mediation and moderation analysis examined the proposed model's direct and indirect relationships.

The findings unveil that perceived value co-creation participation, service quality and cognitive processing magnify user satisfaction, significantly escalating M-payment continuance usage intention. Perceived value co-creation participation and user satisfaction with M-payment partially mediate the linkage among the constructs. Furthermore, perceived usefulness strengthens the link, while perceived severity of security threats weakens the linkage between user satisfaction with M-payment and continuance usage intention.

The study's findings could benefit M-payment service providers, users, policymakers and the telecom industry to strengthen India's digital payment framework.

The perceived value co-creation participation and cognitive processing domain have not garnered much attention in the M-payment literature. The study strives to comprehend these constructs by widening the purview of TAM and ECM models. It also measures the moderating role of perceived severity of security threats and perceived usefulness to unfurl potential linkages between the identified constructs.

The purpose of this paper is to investigate the effect of service quality and relationship quality on customer loyalty in different stages of the relationship life cycle in online banking services.

A total of 651 Iranian online banking customers participated in the research by completing questionnaires. The research hypotheses were tested using structural modeling technique.

The results showed that the relationship quality on customer loyalty in online banking services is affected by the relationship life cycle. The results also showed that online service quality, in the form of Utilitarian quality and Hedonic quality, has a positive effect both directly and indirectly on customer loyalty through online relationship quality.

In this paper, the relationship dynamics was achieved through adding the relationship life cycle variable to the model. However, the study was a cross-sectional research and different results might be obtained if data was collected longitudinally.

In an online banking service, the role of relationship quality in the prediction of customer loyalty is reduced as the relationship ages. Therefore, marketers need to consider other marketing actions to continue their relationship with the customer in the long run.

This paper examines customer loyalty to online banking services from dynamic perspective by introducing relationship life cycle as a moderating variable for the first time. Therefore, the main contribution of this paper is to develop the relationship marketing literature in the field of relationship dynamics and to challenge the effectiveness of relationship marketing in the long run.