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This study investigates the pathways for adopting IoTs and BDA technologies to improve healthcare management.

The study relied on 445 healthcare professionals' perspectives to explore different causal pathways to IoTs and BDA adoption and usage for daily healthcare management. The Fussy-set Qualitative Comparative Analysis was adopted to explore the underlying pathways for healthcare management.

The empirical analysis revealed six different configural paths influencing the acceptance and use of IoTs and BDA for healthcare improvement. Two key user topologies from the six configural paths, digital literacy and ease of use and social influence and behavioural intentions, mostly affect the paths for using digital health technologies by healthcare physicians.

Despite this study's novel contributions, limitations include the fsQCA methodology, perceptual data and the context of the study. The fsQCA methodology is still evolving with different interpretations, although it reveals new insights and as such further studies are required to explain the configural paths of social phenomena. Additionally, future research should consider other constructs beyond the UTAUT and digital literacy to illustrate configural paths to healthcare technology acceptance and usage. Again, the views of healthcare professionals are perceptual data. Hence future research on operational data will support significant contributions towards pathways to accept and use emerging technologies for healthcare improvement. Lastly, this study is from a developing country perspective where emerging digital healthcare technology is still emerging to support healthcare management. Hence, more investigation from other cross-country analyses of configural paths for digital technology deployment in healthcare will enhance the conversation with IoTs and BDA for healthcare management.

Holistically, the acceptance and use of healthcare technologies and platforms is not solely on their capabilities, but a combination of distinct factors driven by users' perspectives. This offers healthcare administrators and institutions to essentially reflect on the distinct combinations of conditions favourable to health professionals who can use IoTs and BDA for healthcare improvement.

This study is among the few scholarly works to empirically investigate the configural paths to support healthcare improvement with emerging technologies. Using fsQCA is a unique contribution to existing information system literature for configural paths for healthcare improvement with emerging digital technologies.

In this study, two postprocessing techniques, namely, conventional burnishing (CB) and ultrasonic-assisted burnishing (UAB), were applied to improve the fatigue behavior of 316 L stainless steel fabricated through selective laser melting (SLM). The effects of these processes on surface roughness, porosity, microhardness and fatigue performance were experimentally investigated. The purpose of this study is to evaluate the feasibility and effectiveness of ultrasonic-assisted burnishing as a preferred post-processing technique for enhancing the fatigue performance of additively manufactured components.

All samples were subjected to a sandblasting process. Next, the samples were divided into three distinct groups. The first group (as-Built) did not undergo any additional postprocessing, apart from sandblasting. The second group was treated with CB, while the third group was treated with ultrasonic-assisted burnishing. Finally, all samples were evaluated based on their surface roughness, porosity, microhardness and fatigue performance.

The results revealed that the initial mean surface roughness (Ra) of the as-built sample was 11.438 µm. However, after undergoing CB and UAB treatments, the surface roughness decreased to 1.629 and 0.278 µm, respectively. Notably, the UAB process proved more effective in eliminating near-surface pores and improving the microhardness of the samples compared to the CB process. Furthermore, the fatigue life of the as-built sample, initially at 66,000 cycles, experienced a slight improvement after CB treatment, reaching 347,000 cycles. However, the UAB process significantly enhanced the fatigue life of the samples, extending it to 620,000 cycles.

After reviewing the literature, it can be concluded that UAB will exceed the capabilities of CB in terms of enhancing the surface roughness and, subsequently, the fatigue performance of additive manufactured (AM) metals. However, the actual impact of the UAB process on the fatigue life of AM products has not yet been thoroughly researched. Therefore, in this study, this paper used the burnishing process to enhance the fatigue life of 316 L stainless steel produced through the SLM process.

The new customer churn prediction (CCP) utilizing deep learning is developed in this work. Initially, the data are collected from the WSDM-KKBox’s churn prediction challenge dataset. Here, the time-varying data and the static data are aggregated, and then the statistic features and deep features with the aid of statistical measures and “Visual Geometry Group 16 (VGG16)”, accordingly, and the features are considered as feature 1 and feature 2. Further, both features are forwarded to the weighted feature fusion phase, where the modified exploration of driving training-based optimization (ME-DTBO) is used for attaining the fused features. It is then given to the optimized and ensemble-based dilated deep learning (OEDDL) model, which is “Temporal Context Networks (DTCN), Recurrent Neural Networks (RNN), and Long-Short Term Memory (LSTM)”, where the optimization is performed with the aid of ME-DTBO model. Finally, the predicted outcomes are attained and assimilated over other classical models.

The features are forwarded to the weighted feature fusion phase, where the ME-DTBO is used for attaining the fused features. It is then given to the OEDDL model, which is “DTCN, RNN, and LSTM”, where the optimization is performed with the aid of the ME-DTBO model.

The accuracy of the implemented CCP system was raised by 54.5% of RNN, 56.3% of deep neural network (DNN), 58.1% of LSTM and 60% of RNN + DTCN + LSTM correspondingly when the learning percentage is 55.

The proposed CCP framework using the proposed ME-DTBO and OEDDL is accurate and enhances the prediction performance.

Autonomous underwater vehicle (AUV) is widely used in resource prospection and underwater detection due to its excellent performance. This study considers input saturation, nonlinear model uncertainties and external ocean current disturbances. The containment errors can be limited to a small neighborhood of zero in finite time by employing control strategy. The control strategy can keep errors within a certain range between the trajectory followed by AUVs and their intended targets. This can mitigate the issues of collisions and disruptions in communication which may arise from AUVs being in close proximity or excessively distant from each other.

The tracking errors are constrained. Based on the directed communication topology, a cooperative formation control algorithm for multi-AUV systems with constrained errors is designed. By using the saturation function, state observers are designed to estimate the AUV’s velocity in six degrees of freedom. A new virtual control algorithm is designed through combining backstepping technique and the tan-type barrier Lyapunov function. Neural networks are used to estimate and compensate for the nonlinear model uncertainties and external ocean current disturbances. A neural network adaptive law is designed.

The containment errors can be limited to a small neighborhood of zero in finite time so that follower AUVs can arrive at the convex hull consisting of leader AUVs within finite time. The validity of the results is indicated by simulations.

The state observers are designed to approximate the speed of the AUV and improve the accuracy of the control method. The anti-saturation function and neural network adaptive law are designed to deal with input saturation and general disturbances, respectively. It can ensure the safety and reliability of the multiple AUV systems.

The purpose of this study is to explore the effect and mechanism of the digital economy’s influence on the binary margin of agricultural exports.

Based on the theoretical analysis of the mechanism of the digital economy’s influence on the binary margin of agricultural exports, this study empirically examines the effect and mechanism of the digital economy’s influence on the binary margin of agricultural exports based on China’s customs export data from 2011 to 2016.

The relevant findings are threefold. (1) The digital economy significantly improves the binary margin of agricultural exports, and its effect on the intensive margin is stronger than that on the expansive margin. After the expansive margin is subdivided, the effects on the three sub-variables of the expansive margin are in the following order: old products exported to new markets > new products exported to old markets > new products exported to new markets. (2) The heterogeneity analysis reveals that the digital economy has a stronger role in promoting the binary margin of exports for enterprises in the eastern region, high-income countries as the destination of exports and state-owned enterprises. (3) Mechanism analysis shows that the digital economy promotes the binary margin of agricultural exports by reducing trade costs and intensifying market competition.

First, in terms of research perspective, although there are some studies on the impact of the digital economy on export trade in existing literature, the research objects mainly focus on manufacturing enterprises. In fact, agricultural trade is susceptible to natural conditions and seasonal factors, and countries may impose more SPS measures and TBT measures on agricultural trade due to risk considerations. The relationship between the digital economy and agricultural trade also has its own characteristics, but there are few research studies in this area. At present, only Liu and Gao (2022), based on the data of total imports and exports of different agricultural products from 2004 to 2018, have established a vector auto-regressive model to empirically analyse the heterogeneous dynamic impact of the digital economy on the trade volume of agricultural products. In addition, Ma and Guo (2023) conducted an empirical test on the total effect, regional heterogeneity and threshold effect of the digital economy on agricultural export trade based on China’s provincial panel data from 2011 to 2020. Therefore, under the new circumstances of continuous integration of digital technology and agriculture, this study interprets the impact effect and mechanism of the digital economy on the binary margin of agricultural exports from the perspective of the digital economy, providing new research perspectives and approaches for promoting the growth of agricultural exports. Second, in terms of theoretical analysis, the above studies have not been fully analysed in terms of the specific mechanism of the impact of the digital economy on agricultural exports. Based on the positive and negative characteristics of agricultural trade, this study introduces two kinds of roles into the theoretical analysis framework to comprehensively determine the trade impact effect of the digital economy. Third, in terms of research design, this study empirically examines the impact of the digital economy on the binary margin of agricultural products, passing a series of robustness tests and investigating the mediating roles of trade cost and market competition effects, producing an empirical basis for China to leverage the digital economy to promote the binary margin of agricultural exports.

This chapter critically evaluates whether football can attain recognition as a national sport in China. Article No. 11, released by the Chinese government in 2015, aimed to develop a new national strategy centralised on the sport of football to foster consumption and enhance national soft power. Consequently, this also means encouraging Chinese football fans to support the national football team. Comparing the significance of local football clubs and the national football team to Chinese football fans is deemed meaningless and unable to generate useful information to comprehend Chinese people's attitudes towards local and national communities. Through literature comparisons with established Chinese national sports such as Chinese martial arts, badminton and table tennis, the discussion reveals that football currently falls short of meeting the general criteria of invention and popularity to be considered a Chinese national sport. In the specific Chinese context, it also proves that football fails to meet the criterion of politics, hindering its identification as a national sport. Consequently, the chapter rebuts the assumption and advocates for the validity of comparing how fans assess their fandom for local and national football teams.

This study aims to investigate the impact of digital transformation on supply chain resilience. Additionally, the paper examines the mediating effect of supply chain process integration as well as the moderating effect of environmental uncertainty in the relationship between digital transformation and supply chain resilience.

Drawing on digital empowerment theory, this study proposes a theoretical model. Using survey data collected from 216 enterprises in China, the study employs structural equation modeling to validate the theoretical model.

The results reveal that digital transformation has a significant impact on supply chain resilience. Three dimensions of supply chain process integration, namely, information flow integration, physical flow integration, and financial flow integration mediate the relationship between digital transformation and supply chain resilience. In addition, environmental uncertainty including market uncertainty and technology uncertainty positively moderates the relationship between digital transformation and supply chain resilience.

First, this paper provides empirical evidence on both the direct and indirect effects of digital transformation on supply chain resilience. Second, this paper enriches the understanding of how supply chain integration impacts supply chain resilience in the digital transformation era by adopting a more granular perspective of process integration rather than broad external and internal integrations. Furthermore, this paper extends the knowledge of the role of external environment in digital transformation and supply chain risk management by examining the moderating effects of market uncertainty and technology uncertainty.

Literature has recognised entrepreneurship education as the main conduit through which entrepreneurial behaviours, attitudes and actions can be built, enacted and delivered. Since the founding of new ventures is largely a resourceful founder-driven enterprise, entrepreneurship education has largely centred on galvanising and shifting the mindsets and cognition of the entrepreneur. Yet, despite over 60 years of delivering entrepreneurship education programmes, hard evidence of the generation of high-growth-oriented and sustainable ventures has been scarce as student entrepreneurship intentions do not always translate into successful venture creation. This is largely because of the complexities of the practicality of entrepreneurial education particularly, the dissonance between acquired education in business schools and the knowledge and competencies needed in the entrepreneurial field. Such dissonance can be attributed to the lack of clarity on the pedagogical approach that most resonates with entrepreneurial action, the diversity in assessment methods and the scholarly illusion pertaining to how pedagogical approaches can be channelled to the generation of growth-oriented ventures. Drawing on Girox's concepts of transformative critical pedagogy (including pedagogy of repression), Socratic dialogue, Hegelian dialectic and Yrjö Engeström's transformative expansive agency, I demonstrate how a flipped transformative critical pedagogy can be harnessed in digitally enhanced learning environments to create new entrepreneurial possibilities for facilitating critical inquiry, complex problem-solving, innovation for the market and fostering tolerance for failure in ambiguous entrepreneurial contexts.

This study aims to investigate how chatbots’ warmth and competence affect customer behavioural expectation (i.e. purchase, recommendation) through perceived humanness and perceived persuasiveness. Moreover, prior knowledge of chatbot is considered the boundary condition of the effects of chatbots’ warmth and competence.

A lab-in-field experiment with 213 participants and a scenario-based experiment of 186 participants were used to test the model using partial least squares structural equation modelling via SmartPLS 4.

Chatbot warmth positively affects customer behavioural expectation through perceived humanness while chatbot competence positively affects customer behavioural expectation through perceived persuasiveness. Prior knowledge of chatbot positively moderates the effect of chatbot warmth on perceived humanness.

This study provides nuanced insights into the effects of chatbots’ warmth and competence on customer behavioural expectation. Future studies could extend the model by exploring additional boundary conditions of the effects of chatbots’ warmth and competence in different generations.

This study offers insightful suggestions for marketing managers on how to impress and convert online customers through designing verbal scripts in customer−chatbot conversations that encourage the customers to anthropomorphise the chatbots.

This study probes into the effects of chatbots’ warmth and competence on customer behavioural expectation by proposing and examining a novel research model that incorporates perceived humanness and perceived persuasiveness as the explanatory mechanisms and prior knowledge of chatbot as the boundary condition.

Social commerce (SC) is a new genre in electronic commerce (e-commerce) that has great potential. This study proposes a new research framework to address deficiencies in existing social commerce research frameworks (e.g. the information model).

In the era of Industrial Revolution 4.0 technologies and new social commerce (s-commerce) models, the authors believe that there is an immediate need for a new research framework. The authors analysed the progress of the s-commerce paradigm between 2003 and 2023 by applying longitudinal science mapping. The authors then developed a research framework based on the themes in the strategic diagrams and evolution map.

From 2003 to 2010, studies on s-commerce mainly focused on social networking sites, virtual communities, social shopping and analytic approaches. From 2011 to 2015, it shifted to s-commerce, consumer behaviour, Web 2.0, artificial intelligence, social technologies, online shopping, user studies, data gathering methods, applications, service-based social commerce constructs, e-commerce and cognitive factors. Social commerce remained the primary research paradigm from 2017 to 2023.

The SC framework may be analogous to popular research frameworks such as technology-organisation-environment (T-O-E) and stimulus-organism-response (S-O-R). Based on this SC framework, researchers may gain a better understanding by determining the factors of the social, commercial, technological and behavioural dimensions.

The authors redefined s-commerce and developed an SC framework. Practical guidelines for the SC framework and an exemplary research model are presented. Overall, this study offers a new research agenda for the extant understanding of s-commerce, with the SC framework as the next frontier of the theoretical advancements and applications of s-commerce.