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In this digital age and risk society, this study aims to explore innovative strategies for E-retailers during supply chain disruptions to construct a more resilient supply chain system.

Various game theoretical models are constructed to analyze four supply chain scenarios. Meanwhile, sufficient numerical analysis was conducted to observe the impact of key parameters on supply chain strategies.

Multiple crucial factors exert a comprehensive influence on E-retailers’ decisions on sourcing and pricing, leading to the diversity and complexity of decision-making conditions. First, with the increased probability of disruption, the purchase quantities of the E-retailer from different suppliers are not in a linear changing pattern, and the total purchase quantity is allocated variably between different suppliers. Second, the variation in disruption severity (partial or complete) results in the shift of decisions between single-sourcing and dual-sourcing. Responsive pricing is conducive to increasing the purchase quantity and profits under partial disruption; its advantages are diminished when completely disrupted. Third, higher commission rates usually have a detrimental impact on profit, whereas responsive pricing may mitigate this impact.

Unlike the previous single perspective, this study innovatively explores strategies from the hybrid perspective of sourcing and pricing. By extracting two key factors (disruption probability and severity), it realizes the scientific characterization of supply chain disruptions. These achievements boost theoretical innovation. Concentrating on E-retailers, it avoids the generalization of conclusions and enhances the application value.

This study aims to investigate the “digitalization paradox” in manufacturing digital transformation, where significant investments in digital technology may not necessarily lead to increased returns. Specifically, it explores the intricate relationship between digital technology convergence, financial performance, productivity and technological innovation in listed Chinese manufacturing firms, drawing upon theories of digital innovation and knowledge networks.

Using a large panel data from 747 listed firms in China’s manufacturing sector and their 428,927 patents spanning from 2013 to 2022, this research first quantifies manufacturing firm-level digital technology convergence through patent network analysis. Furthermore, this study employs hierarchical regression analysis and the instrumental variable method to investigate the curvilinear relationship between digital technology convergence and financial performance. Furthermore, the moderating role of firms’ productivity and technological innovation is tested.

Three types of firm-level digital technology convergence (DTC) are delineated and quantified: local authority in digital convergence (DegreeDTC), convergence with heterogeneous digital knowledge (BetweenessDTC) and shortest-path convergence with digital technologies (ClosenessDTC, where a higher value signifies a more conservative and shorter path in adopting digital technologies). Network visualization shows that manufacturing firms' DTC has consistently increased over time. Contrary to traditional assumptions, our research reveals a U-shaped relationship between DTC (specifically, DegreeDTC and BetweenessDTC) and financial performance. This relationship is characterized by a negative correlation at lower levels and a positive one at higher levels. The joint effect of firms’ productivity and technological innovation significantly strengthens this relationship. These findings are robust across a series of robustness checks.

Our findings offer practical insights for both managers and policymakers. We recommend a balanced approach to digital innovation management within the technology convergence paradigm. Manufacturing firms can generate economic value by strategically choosing to either shrink or expand their digital technology application areas, thereby reducing uncertainties related to emerging convergent businesses. Additionally, the study underscores the synergistic strategy of combining innovation with productivity. Within the DTC business context, integrating productivity with technological innovation not only enhances cost flexibility but also improves problem-solution matching, ultimately amplifying synergistic benefits.

To the best of our knowledge, this is the first study to apply a digital technology co-occurrence network to unveil nuanced relationships in “DTC – finance performance” within the manufacturing sector. It challenges conventional thinking regarding the common positive effect of digital innovation and technological convergence. This study provides a comprehensive analysis of DTC, financial performance, productivity and technological innovation dynamics, as well as offers managerial implications for managers and policymakers.

• (1)

  We quantify manufacturing firm-level DTC through patent network analysis and find consistent increases over time.

• (2)

  A significant U-shaped relationship between DTC and financial performance, being negative at lower levels and positive at higher levels.

• (3)

  The joint effect of firms’ productivity and technological innovation reinforces this relationship by distributing costs and enhancing synergistic benefits.

• (4)

  We challenge existing literature by uncovering a complex relationship in “DTC – finance performance”, contrary to popular belief of a monotonic effect of digital innovation or technological convergence.

We quantify manufacturing firm-level DTC through patent network analysis and find consistent increases over time.

A significant U-shaped relationship between DTC and financial performance, being negative at lower levels and positive at higher levels.

The joint effect of firms’ productivity and technological innovation reinforces this relationship by distributing costs and enhancing synergistic benefits.

We challenge existing literature by uncovering a complex relationship in “DTC – finance performance”, contrary to popular belief of a monotonic effect of digital innovation or technological convergence.

This research examines how responsible deployment of ChatGPT in the UAE’s government sector, guided by New Public Management principles, can enhance customer journeys by integrating services across government bodies. Through semi-structured interviews with UAE government officers, the study investigates this approach’s benefits, challenges, and applications for achieving efficient and integrated public service delivery.

This research adopts a qualitative approach, purposive sampling strategy, and semi-structured interviews to explore the subjective viewpoints of 20 high-level UAE government authorities. The thematic analysis uncovers ChatGPT’s benefits, challenges, and applications, aligning with New Public Management principles.

Thematic analysis reveals four themes: Benefits and Applications of ChatGPT, Challenges, Strategies to Overcome Challenges, and Steps for Customer Journey Enhancement through ChatGPT.

The analysis is based on participant responses provided during the interviews, which may be subject to biases or incomplete information. Secondly, the study focuses solely on the provided applications and participant responses, limiting the generalizability of the conclusions to other contexts.

The implementation of ChatGPT in the government sector has practical implications for transforming its operations and enhancing communication, efficiency, decision-making, and service offerings: citizen engagement, streamlined processes, and informed governance.

This study uniquely examines ChatGPT’s role in government, offering insights into communication, efficiency, decision-making, and service offerings. Identifying hurdles enriches understanding of ChatGPT’s practical integration in government.

While the application of blockchain technology in the organic food supply chain has been increasingly recognized, the extant knowledge of how blockchain-driven traceability influences consumer perceptions and purchase intentions remains underexplored. Grounded in the stimulus-organism-response theory, this study aims to construct a moderated mediation model to examine blockchain-enabled traceability’s direct and indirect impacts on organic food purchase intention through perceived blockchain-related information transparency, considering the moderating role of blockchain-based trust.

A purposive sample of 5,326 Vietnamese consumers was surveyed using the PROCESS macro to test the proposed hypotheses.

The findings indicate that blockchain-enabled traceability significantly enhances perceived blockchain-related information transparency, which positively influences organic food purchase intention. Furthermore, blockchain-based trust was found to positively moderate both the direct effect of transparency on purchase intention and the indirect impact of traceability on purchase intention through transparency.

Practical and managerial insights for stakeholders in the organic food sector are also discussed.

These results contribute to the literature by extending the stimulus-organism-response model to the context of blockchain technology in supply chains and highlighting the critical role of trust in moderating the effectiveness of technological innovations.

This study aims to investigate the effects of three dimensions of ChatGPT strategic value – organization support (OS), managerial productivity (IM) and decision aids (DA) – on the business sustainability (BS) of Malaysian public universities.

A quantitative methodology was adopted for this study to examine the relationships between ChatGPT strategic value and the BS of Malaysian public universities.

The study found that two dimensions of ChatGPT strategic value, namely, OS and IM, influence BS, whereas DA do not.

To the best of the author’s knowledge, this study is the first to address the relationship between ChatGPT strategic value and BS in a specific context – Malaysian public universities – providing new contributions to theory by extending the literature on the topic.

The findings are expected to guide universities in Malaysia in leveraging ChatGPT strategic value for enhancing BS.

To the best of the author’s knowledge, this empirical study is the first in the literature to examine the relationships between ChatGPT strategic value and BS in the education sector. Supported by an original conceptual model, the insights provided should extend the literature dedicated to ChatGPT strategic value and BS in the context of a South Asian economy.

Many studies have shown that from a theoretical and empirical point of view, downside risk-based measures of risk are better than the traditional ones. Despite academic appeal and practical implications, downside risk has not been thoroughly examined in markets outside developed country markets. Using downside beta as a measure of downside risk, this study examines the relationship between downside beta and stock returns in Indian equity market, an emerging market with unique investor, asset and market characteristics.

This is an empirical study done by using ranked portfolio return analysis and regression analysis methodologies.

The study results show that downside risk, as measured by downside beta, is distinctly priced in the Indian equity market. There is a direct positive relationship between downside beta and contemporaneous realized returns, indicating a premium for downside risk. Downside risk carries a higher weightage than upside potential in the aggregate return of the stock portfolios. Downside beta is a better measure of systematic risk than conventional market beta and downside coskewness.

The empirical results support the adoption of downside beta in practice and provide a case for replacing traditional beta with downside beta in asset pricing applications, trading and investment strategies, and capital allocation decision-making.

This is one of the first in-depth studies examining downside beta in Indian equity markets using a broad sample of individual stock returns covering a wide time range of 22 years. To the best of our knowledge, this study is the first one to compare downside beta and downside coskewness using individual stock data from the Indian equity market.

Research on decision rights partitioning and its impact on platform performance has predominantly focused on single rights, leading to inconclusive results. This study is driven by a more nuanced objective of exploring diverse governance models that can enhance the performance of sharing platforms across different contexts. Rather than delegating single decision right to users, this approach partitions several essential decision rights concurrently throughout the transaction process. By examining the complex relationships between multiple decision rights partitioning and platform performance, this study identifies and explains suitable governance models that are tailored to specific contextual factors for improving the performance of sharing platforms.

Collecting data from 60 sharing platforms in China, this study employs a combination of cluster and configuration analyses to address research questions.

The study explores three strategic decision rights partitioning modes widely adopted by sharing platforms. It further identifies four governance models for sharing platforms, which are termed as conservative seller model, conservative buyer model, aggressive seller model and aggressive buyer model, related to certain contextual factors.

In addressing platform governance as key to sharing platform success, the study contributes to the literature by investigating how multiple-rights partitioning portfolios and strategic differentiation in decision rights partitioning can enhance platform performance.

This study aims to investigate the effect of powder recycling on the microstructure of plasma-sprayed Ni625-WC composite coating and to verify the feasibility of Ni625-WC powder recycling by comparing the corrosion resistance of the coatings in high-temperature and pressure CO₂ environment.

Recycling powder is an efficient way to improve the utilization rate of metal powder during plasma spraying. The plasma-sprayed Ni625-WC composite coatings with original powder (OC) and recovered powder (RC) were analytically compared by using scanning electron microscope (SEM) equipped with an energy-dispersive spectroscopy, X-ray diffractometer, and X-ray photoelectron spectroscopy. The corrosion resistance of the Ni625-WC composite coatings was characterized in a self-designed high-temperature and pressure autoclave by an electrochemical workstation.

The results showed that there is massive M₂₃C₆ in OC and acicular M₂₃C₆ in RC. The WC particles in RC are more uniformly distributed, and the area ratios of WC particles to Inconel 625 matrix are 2.37% higher than OC. RC showed high corrosion resistance, and the recycling of Ni625-WC powder is feasible.

The feasibility of Ni625-WC powder recycling was verified from the microstructure evolution and electrochemical behavior of the coatings.

The purpose of this study is to address the critical need for patent value evaluation within patent management, particularly in the context of the digital economy. Recognizing the importance of utilizing historical data, this research aims to uncover effective methodologies that enhance the appraisal of patent value, which is vital for informed decision-making in the management of scientific and technological advancements.

This study introduces a comprehensive evaluation model by analyzing various factors that influence patent value. An index system is constructed that integrates technical, economic and legal aspects to facilitate a nuanced assessment of patents. The methodological core of this research is the development of an XGBoost patent value appraisal model, which incorporates Bayesian optimization to refine the evaluation process. The model’s validity is tested through empirical analysis of patents in the rapidly evolving sector of cloud computing.

The empirical results demonstrate that the XGBoost model, strengthened by Bayesian optimization, outperforms traditional categorization techniques. The proposed model shows superior performance in terms of accuracy, precision, recall rate and operational feasibility. These findings indicate a significant improvement in the precision of patent potential and value assessments, leading to more reliable and actionable insights for patent management.

This study introduces a novel patent evaluation model that combines XGBoost with Bayesian optimization. XGBoost enhances performance by integrating weak learners, ideal for complex, nonlinear problems like patent valuation. Bayesian optimization refines hyperparameters efficiently using prior distributions and known results. Its practical implications for patent management and technology exploration are substantial, offering a new tool for strategic decision-making.

Linear addition is commonly used to generate ensemble forecasts for decomposition ensemble models but traditionally treats individual modes with equal weights for simplicity. Using Taiwan air passenger flow as an empirical case, this study examines whether incorporating weighting for individual single-mode forecasts assessed by grey relational analysis into linear addition can improve the accuracy of the decomposition ensemble models used to forecast air passenger demand.

Data series are decomposed into several single modes by empirical mode decomposition, and then different artificial intelligence methods are applied to individually forecast these decomposed modes. By incorporating the correlation between each forecasted mode series and the original time series into linear addition for ensemble learning, a genetic algorithm is applied to optimally synthesize individual single-mode forecasts to obtain the ensemble forecasts.

The empirical results in terms of level and directional forecasting accuracy showed that the proposed decomposition ensemble models with linear addition using grey relational analysis improved the forecasting accuracy of air passenger demand for different forecasting horizons.

Accurately forecasting air passenger demand is beneficial for both policymakers and practitioners in the aviation industry when making operational plans.

In light of the significance of improving the accuracy of decomposition ensemble models for forecasting air passenger demand, this research contributes to the development of a weighting scheme using grey relational analysis to generate ensemble forecasts.