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The purpose of this study is to solve the problem of longer dead-time in the rear bridge leg switches and lower efficiency in the Four-Switch Buck-Boost LLC Resonant Converter.

The paper adopts time-domain analysis to derive the time-domain expression for optimal dead time, analyzing the conditions for achieving soft switching of the transistors. It further explores the relationship between the dead time of the bridge arm switching transistors and the input/output of the converter under different operating conditions. Specifically, the dead time of the upper bridge arm transistors increases with the converter input voltage and decreases with the output current. In contrast, the dead time of the lower bridge arm transistors is independent of the converter output current and decreases with increasing converter input voltage.

By simulating and constructing a 500 W experimental prototype, experimental results indicate that designing the dead time of the switch according to the optimal dead time proposed in this paper significantly improves efficiency when the converter operates from heavy load to full load. When the transformer takes minimum input, maximum input and intermediate bus voltage inputs respectively, its peak efficiency is increased by 0.6%, 1.7% and 1.1%, respectively, compared to the traditional four-switch Buck–Boost LLC resonant converter.

Experimental validation confirms the correctness of the optimal dead time design and analyzes the impact of different operating conditions of the converter on the dead time. This is of significant importance for the rational design of switch dead times and the enhancement of converter efficiency.

This paper aims to provide a reference for the development of digital transformation from the perspective of manufacturing process links.

This paper applies canonical correlation analysis based on digital technology patents in the key links of manufacturing industries (product design, procurement, product manufacturing, warehousing and transportation, and wholesale and retail) and the related indicators of economic benefits of regions in China.

(1) The degree of digitalization of manufacturing process links is significantly correlated with economic benefits. (2) The improvement of the degree of digitalization in the “product design” link, the “warehousing and transportation” link, the “product manufacturing” link and the “wholesale and retail” link has significant impacts on the economic benefits of manufacturing industry. (3) The digital degree of the “procurement” link has no obvious influence on the economic benefits of manufacturing industry.

The research results can provide reference for the formulation and implementation of micro policies. The strategy of improving the level of digital transformation of key links of manufacturing industry is put forward to better promote both the digital transformation of manufacturing industry and economic development.

This paper innovatively studies the relationship between digitalization of manufacturing process links and economic benefits. The findings can provide theoretical and empirical support for the digital transformation of China's manufacturing industry and high-quality development of economy.

This paper examines the willingness to pay (WTP) for insurance premiums and identifies the factors that influence it in fruit, greenhouse vegetables and dairy farming. It evaluates Albanian farmers' WTP for insurance products, emphasizing the key farm characteristics and factors that shape this willingness, offering valuable insights for establishing subsidized insurance.

The study is based on a structured rapid survey conducted in 2019 with 808 farmers and interviews with key experts and operators in the insurance market. Authors use a double-bounded dichotomous choice contingent valuation technique to gauge WTP for insurance products.

Results indicate that farmers' WTP ranges between 1.18 and 2.15% of their income from the main crop, depending on the farming activity, with dairy and greenhouse farmers showing a higher WTP than fruit farmers. WTP is positively related to exposure to climate hazards, engagement in modern market channels and contracting.

The study used three different types of farms; thus, the findings are not strictly relevant to one activity. Subsequent research could employ choice experiments to evaluate alternative insurance schemes focusing on one sector.

The paper contributes to the literature by empirically testing various determinants for WTP for which there are no valid findings by using contingent choice analysis to explore WTP for crop insurance.

This study aims to examine the impact of board gender diversity on sustainable growth by considering the mediating role of investment efficiency (INVEFF) in this relationship and the threshold effect between board gender diversity and INVEFF. This investigation focuses on the Gulf Cooperation Council (GCC) region, which is characterized by rapid socio-economic transformations and a recent emphasis on gender diversity.

Panel data regressions are applied to estimate the impact of board gender diversity on INVEFF using companies listed in the GCC in 2013–2022 as a sample. The estimations consider subsamples of underinvestment and overinvestment, as well as the pre- and post-COVID-19 pandemic periods.

The empirical results show a nonlinear impact of board gender diversity on INVEFF, a relationship that is more pronounced in the underinvestment subsample. The results indicate that INVEFF mediates the relationship between board gender diversity and corporate sustainable growth, which helps companies optimize their board composition to enhance their sustainable growth strategies.

These findings could inform GCC regulators in mandating further increases in women’s presence on boards of directors to improve INVEFF. This study examined only GCC-listed companies. Future research should investigate other factors influencing INVEFF and conduct comparative studies across Middle Eastern and North African countries to consider different regulatory and economic contexts and to examine compliance with international standards.

This study reveals the significant nonlinear impact of board gender diversity on INVEFF and the mediation of INVEFF in the relationship between board gender diversity and sustainable growth. These findings will help companies optimize their board of directors’ composition by increasing the presence of women on boards to improve their INVEFF and sustainable growth. This study aims to develop knowledge that will not only benefit companies regarding the potential impact of board gender diversity but also help international communities create better gender equality within companies.

To the best of the author’s knowledge, this study is the first to explore the relationship between board gender diversity and INVEFF in the emerging economies of the GCC region. It is also the first to examine the nonlinear relationship between board gender diversity and INVEFF and the mediating role of INVEFF in the relationship between board diversity and sustainable growth. This study contributes to the understanding of the financial impact of board gender diversity in improving corporate INVEFF and sustainable growth.

This study quantitatively investigates the impacts of digital and learning orientations on supply chain resilience (SCR) and firm performance (FP), aiming to fill the gaps in understanding their specific impacts in the context of Industry 4.0 developments and supply chain disruptions.

This study utilized survey techniques and structural equation modelling (SEM) to gather and analyse data through a questionnaire based on a seven-point Likert scale. Hypotheses were formulated based on an extensive literature review and tested using Amos software.

The study confirms SCR’s significant impact on FP, aligning with existing research on resilience’s role in organizational competitiveness. This study uncovers the nuanced impacts of digital and learning orientations on SCR and FP. Internal digital orientation (DOI) positively impacts SCR, while external digital orientation (DOE) does not. Specific dimensions of learning orientation – shared vision (LOS), open-mindedness (LOO) and intraorganizational knowledge sharing (LOI) – enhance SCR, while commitment to learning (LOC) does not. SCR mediates the relationship between DOI and FP but not between DOE and FP.

This research focuses on digital and learning orientations, recommending that future studies investigate other strategic orientations and examine the specific contributions of various digital technologies to SCR across diverse contexts.

The empirical findings emphasize the significance of developing internal digital capabilities and specific learning orientations to enhance SCR and FP, aligning these initiatives with resilience strategies.

This study advances knowledge by distinguishing the impacts of internal and external digital orientations and specific learning dimensions on SCR and FP, offering nuanced insights and empirical validation.

Despite the important role that suppliers have in enhancing the environmental performance of a buyer firm, previous research has not investigated the individual-level motivations of supplier employees (representatives) in supplier-to-supplier environmental knowledge sharing. Thus, we use insights from the coopetition literature to examine how buyer firms can encourage supplier-to-supplier environmental knowledge sharing with the aim of improving the buyer’s environmental performance.

We empirically test our model using an online vignette-based experiment administered to supply chain managers. We contextualized our results using insights from interviews with senior managers representing firms operating in a broad array of industries.

We find that a supplier representative’s personal environmental values influence their commitment to an environmental consortium with a rival firm, and they are subsequently willing to share proprietary environmental knowledge. In turn, these relationships are moderated by situational factors including competitive intensity and buyer power.

The study of coopetition is an emerging stream of research in operations management. Our findings improve the understanding on how a focal actor within a buyer–supplier coopetitive network can promote environmental knowledge sharing behavior.

The study aims to investigate consumer preferences across 25 attributes of plant-based milk (PBM) products and examine the key predictors and underlying mechanisms of consumer purchase decisions of PBM alternatives.

This study employed a multidimensional approach to investigate consumer preferences and the determinants of PBM purchasing decisions. Drawing on data from 819 online surveys conducted in the Jing-Jin-Ji region of China in 2021, we measured consumer preferences across 25 specific attributes and other individual characteristics. Purchasing decisions were framed as a two-stage process – the decision to purchase (frequency) and the decision on how much to pay (WTP). The Least Absolute Shrinkage and Selection Operator (LASSO) model was utilized to examine these dimensions separately, and the selected predictors were incorporated into OLS linear and Heckman’s two-stage regression analyses to establish the underlying mechanisms.

The findings indicate that consumers exhibit a strong preference for freshness and the absence of spoilage, followed by taste experiences such as taste and aroma. Preferences for milk preservation significantly increase the purchase frequency of PBM, while preference for calorie content has a negative and significant impact. Preferences for milk preservation, aroma and processing methods can also significantly increase WTP. Preferences vary across PBM categories. Social influence, knowledge and advertising exposure positively impact purchase frequency and WTP. Consumers with low food neophobia tend to be more responsive to product-related factors, such as freshness, calorie content and processing methods, in their purchase decisions.

This study contributes to the extant literature by comprehensively examining the determinants of consumer purchase decisions for PBM alternatives. The findings provide practical implications for marketers and policymakers, highlighting the strategic product attributes, consumer segments and marketing levers that can effectively target and cater to consumer preferences for PBM alternatives.

Social media texts as a data source in depression research have emerged as a significant convergence between Information Management and Public Health in recent years. This paper aims to sort out the depression-related study conducted on the text on social media, with particular attention to the research theme and methods.

The authors finally selected research articles published in Web of Science, Wiley, ACM Digital Library, EBSCO, IEEE Xplore and JMIR databases, covering 57 articles.

(1) According to the coding results, Depression Prediction and Linguistic Characteristics and Information Behavior are the two most popular themes. The theme of Patient Needs has progressed over the past few years. Still, there is a lesser focus on Stigma and Antidepressants. (2) Researchers prefer quantitative methods such as machine learning and statistical analysis to qualitative ones. (4) According to the analysis of the data collection platforms, more researchers used comprehensive social media sites like Reddit and Facebook than depression-specific communities like Sunforum and Alonelylife.

The authors recommend employing machine learning and statistical analysis to explore factors related to Stigmatization and Antidepressants thoroughly. Additionally, conducting mixed-methods studies incorporating data from diverse sources would be valuable. Such approaches would provide insights beneficial to policymakers and pharmaceutical companies seeking a comprehensive understanding of depression.

This article signifies a pioneering effort in systematically gathering and examining the themes and methodologies within the intersection of health-related texts on social media and depression.

With recent advances in artificial intelligence, the hospitality industry has introduced the concept of unmanned smart hotels staffed by service robots instead of human employees. Research is needed to understand consumers’ receptivity to such an innovation. This paper examines factors associated with consumers’ potential resistance to using automated service hotels via two sequential studies. Given that younger generations of consumers are typically early adopters of advanced technology and innovative services, our sampling approach focused on this consumer group.

Two studies were conducted. Study 1 proposed and empirically tested a theoretical model. Results revealed that attitude, subjective norms and perceived behavioral control each positively influenced individuals’ intentions to use unmanned smart hotels. In Study 2, we further investigated aspects informing perceived security, a key variable in the use of unmanned smart hotels.

Findings showed how people’s beliefs about unmanned smart hotels and security control assurances led to perceived security. These perceptions were shaped by perceived physical risks, privacy concerns, website design and hotel reputation. Overall, this research provides theoretical and practical implications for various stakeholders associated with unmanned smart hotels.

Findings of this study suggested that managers of unmanned smart hotels should design user-friendly, secure processes and offer comprehensive support resources to enhance customer experience and usage.

The findings provide a holistic understanding of consumers’ receptivity to unmanned smart hotels.

This study aims to explore the factors influencing the evolution of emerging technology innovation network (ETIN) in combination with the key attributes and life cycle of emerging technologies, particularly the impact of multiple knowledge attributes and technology life cycle on the ETIN evolution.

This study collects 5G patent data and their citation information from the Derwent Innovations Index to construct a 5G technology innovation network (5GIN) as a sample network and conducts an empirical analysis of the 5GIN using the temporal exponential random graph model (TERGM).

The results indicate that during the 5GIN evolution, the network scale continues to expand and exhibits increasingly significant core-periphery structure, scale-free characteristic, small-world characteristic and community structure. Furthermore, the findings suggest that the multiple knowledge attributes based on the key attributes of emerging technologies, including knowledge novelty, coherence, growth and impact, have a significant positive influence on the ETIN evolution. Meanwhile, the temporal evolution of ETIN is also found to be correlated with the life cycle of emerging technologies.

This study extends the exploration of emerging technology research from a complex network perspective, providing a more realistic explanatory framework for the factors influencing ETIN evolution. It further highlights the important role that multiple knowledge attributes and the technology life cycle play within this framework.