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The purpose of this paper is to provide an overview of the computational progress in the development of hydrogen-fired gas turbines. This review aims to identify suitable combustion models, appropriate NO_x chemistry mechanisms and NO_x emission levels for effective utilization of hydrogen as an alternative fuel in gas turbines.

Hydrogen is recognized as a potential alternative fuel for achieving exceptionally low emissions in gas turbines. The developments in conventional, trapped vortex combustor and micromix combustors are discussed, along with various computational models aimed at accurately predicting combustion and emission characteristics. The results of numerical simulations were then discussed with emphasis on their role in optimizing the combustor geometry.

Computational studies that were used to optimize the combustor geometry to reduce NO_x emissions and the flashback phenomenon are discussed. To retrofit existing gas turbines for hydrogen fuel, minor modifications that are required were discussed by analyzing extensive literature. The influence of key design and geometrical parameters on NO_x emissions and the appropriate selection of combustion models for numerical simulations in optimizing various combustion systems are elaborated.

The review emphasizes the computational studies in the progress of hydrogen-fired gas turbine developments. The previous reviews were primarily focused on the combustion technologies for hydrogen-fired gas turbines. This comprehensive review focuses on the key design parameters, flame structure, selection of combustion models, combustion efficiency improvement and impact of parametric studies on NO_x formation of various combustion systems, in particular hydrogen combustion for gas turbine applications.

The study’s purpose is to investigate the constraints and facilitators influencing skiing participation in Beijing. This research includes three segments based on the frequency of skiing participation (i.e. non-, low-frequency-, and high-frequency skiers). By doing so, the study offers an enhanced understanding of the Chinese skiing market and unveils insights assisting industry professionals to effectively address their customers' diverse needs and expectations.

An online survey was developed based on prior research and consisted of four sections: (1) skiing participation; (2) constraints; (3) facilitators; (4) demographics. Items in the constraint and facilitator scale were measured using a 7-point Likert scale. A total of 409 participants completed the survey. The participants included 137 non-skiers, 134 low-frequency skiers, and 138 high-frequency skiers.

Through an exploratory factor analysis, three constructs emerged: general constraints, facilitators and learning constraints. As expected, facilitators were a positive predictor of skiing participation. Importantly, the emergent construct of learning constraints was a negative predictor of skiing and yet, the construct of general constraints was insignificant. Furthermore, the three segments differ significantly in household status, income, and education level.

These results support previous research noting the relevance in skiing participation of the dimensions: facilitators and learning constraints. The findings point to the need for ski resorts in Beijing to offer instructional sessions for beginners so they may become familiar with skiing fundamentals and enhance their confidence, particularly among nonskiers and low-frequency skiers.

The purpose of this paper is to give compiled information on previously applied cotton fabric surface modifications. The paper covered most of the modifications done on cotton fabric to improve its properties or to add some functional properties. The paper presented mostly studied research works that brought a significant surface improvement on cotton fabric.

Different previous works on surface modifications of cotton fabrics such as pilling, wrinkle and microbial resistance, hydrophobicity, cationization, flame retardancy and UV-protection characteristics were studied and their methods of modification including the main findings are well reported in this paper.

Several modification treatments on surface modification of cotton fabrics indicated an improvement in the desired properties in which the modification is needed. For instance, the pilling tendency, wrinkling, microbial degradation and UV degradation drawbacks of cotton fabric can be overcome through different modification techniques.

To the best of the author’s knowledge, there are no compressive documents that covered all the portions presented in this review. The author tried to cover the surface modifications done to improve the main properties of cotton fabric.

Smart contracts are written in high-level programming languages, compiled into Ethereum Virtual Machine (EVM) bytecode, deployed onto blockchain systems and called with the corresponding address by transactions. The deployed smart contracts are immutable, even if there are bugs or vulnerabilities. Therefore, it is critical to verify smart contracts before deployment. This paper aims to help developers effectively and efficiently locate potential defects in smart contracts.

GethReplayer, a smart contract testing method based on transaction replay, is proposed. It constructs a parallel transaction execution environment with two virtual machines to compare the execution results. It uses the real existing transaction data on Ethereum and the source code of the tested smart contacts as inputs, conditionally substitutes the bytecode of the tested smart contract input into the testing EVM, and then monitors the environmental information to check the correctness of the contract.

Experiments verified that the proposed method is effective in smart contract testing. Virtual environmental information has a significant effect on the success of transaction replay, which is the basis for the performance of the method. The efficiency of error locating was approximately 14 times faster with the proposed method than without. In addition, the proposed method supports gas consumption analysis.

This paper addresses the difficulty that developers encounter in testing smart contracts before deployment and focuses on helping develop smart contracts with as few defects as possible. GethReplayer is expected to be an alternative solution for smart contract testing and provide inspiration for further research.

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 study aims to develop a scale using item response theory (IRT) to assess the entrepreneurial potential for digital transformation in Brazilian companies.

IRT was used to develop a scale for entrepreneurial potential in digital transformation. This scale was constructed from a questionnaire, covering the domains: (1) data-driven culture, (2) openness to knowledge and adaptation to change, (3) connectivity and (4) creativity and innovation. The questionnaire was administered to a sample of 216 entrepreneurs from small business enterprise (SBE) and startups in Brazil.

A questionnaire was developed and validated to assess the latent trait of entrepreneurial potential within the context of digital transformation. Additionally, a three-level scale of entrepreneurial potential was established: low (level I), intermediate (level II) and high (level III). The interpretation of this scale provides valuable information on which domains, such as data-driven culture, innovation, among others, can be enhanced to improve the potential of entrepreneur for digital transformation.

The sample was limited to small Brazilian companies and startups, which may restrict the applicability of the results to other business or geographic contexts. Additionally, the items evaluated in the scale may not fully capture all nuances of entrepreneurial potential for digital transformation. Future research should consider including new items that cover a broader range of entrepreneurial characteristics.

The findings of this study have significant practical implications for the Brazilian entrepreneurial ecosystem, the entrepreneurs themselves, public policy makers and entrepreneurship support institutions. These results can guide digital transformation strategies, adjustments in public policies and investments, thereby promoting economic development and innovation in the country.

This study stands out for using IRT as a robust methodology to develop an interpretative scale to assess entrepreneurial potential in the digital transformation era. By focusing on Brazilian SBEs and startups, the study offers an original contribution on how these companies are handling the challenges of digitalization and identifying areas for improvement to further promote digital transformation among entrepreneurs.

This study aims to address the aspects of product and process innovation strategies and their determining factors to understand their characteristics in clothing manufacturing and contribution for a successful and competitive clothing industry.

This general review is based on literature data of previous studies on innovation that transcend and cover the aspects of innovation applicable in the clothing industry. Although the scope of discussion is theoretically broad, it focusses on the context of innovation strategies in clothing manufacturing and the determinant factors indicating the acquisition and implementation of product and process-related innovation activities, simultaneously exploring and linking their implications for adopting, managing and integrating enterprise activities to the values of desired innovation novel models.

Based on theoretical background and pragmatic generalizations, product and process innovation strategies in clothing manufacturing firms tend to incline more towards computer-integrated technologies and concepts meant to promote product development, process optimization and organizational integration. Industry, technological and R&D factors tend to significantly determine innovation capability of a clothing firm.

This review generates integrated conceptual frameworks for product and process innovation strategies applicable in clothing firms and their determinant factors as prelude to empirical validation.

Organisations using advanced technology, like ChatGPT, for executing their marketing practices are proliferating, but such fast growth also comes with different adverse impacts of ChatGPT. This interaction of ChatGPT with the humanly implemented marketing 5.0 approach complements the marketing effectiveness. However, while considering the brighter aspects of this techno-marketing integration, marketers should also keep its dark side in mind. Therefore, this chapter investigates the integration of AI-enabled ChatGPT into marketing 5.0 practices. However, both the concepts under study are growing in terms of literature, and the research gap is even more extended when considering their associated views. Furthermore, significantly less literature is available emphasising the negative aspects of this advanced technology. This chapter bridges these gaps by reviewing the literature and presenting the gold-plating effect of ChatGPT usage while implementing marketing 5.0 practices. It also proposes a framework for showing the relationship between ChatGPT utilisation and practicing marketing 5.0, depicting the dark side of this techno-marketing integration. It also emphasised the need for conscious and learned associations between the concepts under study.

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.