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Digital inclusive finance has a positive promotion effect on the development of the national economy, but little research exists on how digital inclusive finance affects high-quality consumption in economically developed regions. Therefore, to fill the gap, this paper aims to study the impact of digital inclusive finance on high-quality consumption development using the economically developed regions of Jiangsu, Zhejiang and Shanghai as examples.

Firstly, the entropy method is used to construct the index of high-quality consumption among residents. Then, the municipal-level data of Jiangsu, Zhejiang and Shanghai from 2011 to 2020 are used to test the impact. Subsequently, the mechanism of action test and heterogeneity analysis are conducted.

The results show that digital inclusive finance has a positive role in promoting the high-quality consumption of residents in Jiangsu, Zhejiang and Shanghai. At the same time, digital inclusive finance can promote high-quality consumption through its own digital payment and internet insurance channels. There is regional heterogeneity in the impact.

To the best of the authors’ knowledge, this study is the first to examine whether and how digital inclusive finance affects high-quality consumption. The authors consider multiple dimensions, such as consumption level, consumption structure, consumption ability, consumption environment and consumption mode, to measure high-quality consumption. The findings provide valuable insights for policymakers, investors and regulators in planning regulations.

The purpose of this paper is to provide rich, qualitative insights around internal communication in strategic communication agencies, addressing the evolutions in expectations and best practices for agency leadership through COVID-19.

Qualitative interview study with 18 US-based leaders of public relations and advertising agencies to examine their experiences of leading and managing strategic communication teams during COVID-19.

Synthesized findings around changes in leadership values and important facets of ongoing internal crisis communication led to the development of the following five categories—Improvisation and Flexibility, Transparency and Trust, Ownership and Embodiment, Care and Empathy, Relationships and Resilience.

Using a high-value sample, the study is the first (to the best of the authors' knowledge) to focus on the crucial context of agencies and internal communication around COVID-19; diversity, equity, and inclusion (DEI); and other pandemic-era challenges. It provides theoretical implications around ongoing, internal crisis communication and practical implications for agency leaders in crisis.

In the context of investment decisions, the intricate interplay between exogenous shocks and their influence on investor confidence significantly shapes their behaviors and, consequently, their outcomes. Investment decisions are influenced by uncertainties, exogenous shocks as well as the sentiments and confidence of investors, factors typically overlooked by decision-makers. This study will meticulously examine these multifaceted influences and discern their intricate hierarchical nuances in the sentiments of industrial entrepreneurs during the COVID-19 pandemic.

Employing the robust framework of the generalized linear latent and mixed models (GLLAMM), this research will thoroughly investigate individual and group idiosyncrasies present in diverse data compilations. Additionally, it will delve deeply into the exogeneity of disturbances across different sectors and regions.

Relevant insights gleaned from this research elucidate the adverse influence of exogenous forces, including pandemics and financial crises, on the confidence of industrial entrepreneurs. Furthermore, a significant discovery emerges in the regional analysis, revealing a notable homogeneity in the propagation patterns of industrial entrepreneurs' perceptions within the sectoral and regional context. This finding suggests a mitigation of regional effects in situations of global exogenous shocks.

Within the realm of academic inquiry, this study offers an innovative perspective in unveiling the intricate interaction between external shocks and their significant impacts on the sentiment of industrial entrepreneurs. Furthermore, the utilization of the robust GLLAMM captures the hierarchical dimension of this relationship, enhancing the precision of analyses. This approach provides a significant impetus for data-informed strategic directions.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

Research on leaders’ knowledge management systems focuses exclusively on how leaders gather and disseminate knowledge in collaboration with external actors. Not much is known about how leaders address the psychological aspects of employees and strategize internal communication. In addition, while previous work has treated high uncertainty as a default feature of crisis, this study aims to propose that perceived uncertainty varies in experience/meaning and has a crucial bearing on the relative balance of cognitive/emotional load on the leader and behavioral/psychological responses.

The authors contribute by qualitatively examining the role of leader knowledge systems in designing communication strategies in the context of the COVID-19 crisis by investigating communication characteristics, style, modes and the relatively unaddressed role of compassion/persuasion. In this pursuit, the authors interviewed 21 C-suite leaders, including chief executive officers, chief marketing officers, chief financial officers, chief human resource officers and founders, and analyzed their data using open, axial and selective coding, which were later extracted for representative themes and overarching dimensions.

Drawing from grounded theory research, the authors present a framework of knowledge systems and their resultant communication with employees in high uncertain and low uncertain crises. The authors highlight interactions of a set of concepts – leaders’ preparedness, leaders’ support to employees tailored communication adapted to perceived uncertainty, leading to enhanced trust – in the achievement of outcomes related to balancing operational and relational systems with employees. The findings suggest that a structured process of communication helps employees mitigate any concern related to uncertainty and feel confident in their leadership.

The research has implications for leaders in managing their knowledge systems, for human esources practitioners in designing effective internal communication programs, as well as for scholars in knowledge management, communication and leadership.

The purpose of this study is to investigate the thermoforming process of 3D-printed parts made from polylactic acid (PLA) and explore its application in producing wrist-hand orthoses. These orthoses were 3D printed flat, heated and molded to fit the patient’s hand. The advantages of such an approach include reduced production time and cost.

The study used both experimental and numerical methods to analyze the thermoforming process of PLA parts. Thermal and mechanical characteristics were determined at different temperatures and infill densities. An equivalent material model that considers infill within a print is proposed. Its practical use was proven using a coupled finite-element analysis model. The simulation strategy enabled a comparative analysis of the thermoforming behavior of orthoses with two designs by considering the combined impact of natural convection cooling and imposed structural loads.

The experimental results indicated that at 27°C and 35°C, the tensile specimens exhibited brittle failure irrespective of the infill density, whereas ductile behavior was observed at 45°C, 50°C and 55°C. The thermal conductivity of the material was found to be linearly related to the temperature of the specimen. Orthoses with circular open pockets required more time to complete the thermoforming process than those with hexagonal pockets. Hexagonal cutouts have a lower peak stress owing to the reduced reaction forces, resulting in a smoother thermoforming process.

This study contributes to the existing literature by specifically focusing on the thermoforming process of 3D-printed parts made from PLA. Experimental tests were conducted to gather thermal and mechanical data on specimens with two infill densities, and a finite-element model was developed to address the thermoforming process. These findings were applied to a comparative analysis of 3D-printed thermoformed wrist-hand orthoses that included open pockets with different designs, demonstrating the practical implications of this study’s outcomes.

Motivated by the organizational challenge coined the great discontent, employees are dissatisfied with their jobs, see minimal opportunities for growth and are actively searching for new roles. This research aims to take a novel approach to internal communication strategy by introducing employability culture and leadership empowerment as mechanisms for supporting employees' career growth and additional positive workplace outcomes.

An online survey was designed and administered in the United States. The final sample size includes 425 full-time employees working in a variety of roles, industries and work arrangements.

Findings point to the inherent need for revised internal communication strategy that goes beyond managing and disseminating information. Organizations must develop cultures and their leaders in ways that empower employees and help them understand the meaning of their work. Employability culture, or an organization's support for developing employees' adaptive skills as work roles change, positively predicted employees' perceptions of their career growth opportunities at their current place of employment, employee loyalty and engagement, and job satisfaction. Leadership empowerment behaviors also positively predicted all previously listed workplace variables. These perceptions as influenced by work arrangement (onsite, hybrid, fully remote) and younger versus older generations were also analyzed.

Research findings offer new strategies for internal communications. Internal communication teams can partner alongside executive leadership to develop a culture that helps employees envision how their skills and expertise translates to different areas of the organization, empowering them to find meaning in their work, and be driven to support organizational growth.

The main purpose of this paper is to examine the adoption of big data analytics (BDA) in the Tanzania banking industry by investigating the influence of technological, environmental and organizational (TOE) factors while exploring the moderating role of perceived risk (PR).

The study employed a qualitative research design, and the research instrument was developed using per-defined measurement items adopted from prior studies; the items were slightly adjusted to fit the current context. The questionnaires were distributed to top and middle managers in selected banks in Tanzania using the snowball sampling technique. Out of 360 received responses, 302 were considered complete and valid for data analysis. The study employed partial least squares structural equation modeling (PLS-SEM) to examine the developed conceptual framework.

Top management support and financial resources emerged as influential organizational factors, as did competition intensity for the environmental factors. Notably, bank size and perceived trends showed no significant impacts on BDA adoption. The study's novelty lies in revealing PR as a moderating factor, weakening the link between technological readiness, perceived usefulness and the intent to adopt BDA.

This study extends literature by extending the TOE model, through examining the moderating roles of PR on technological factors. Furthermore, the study provides useful managerial support for the adoption of BDA in banking in emerging economies.

The expansion of bike-sharing enterprises has led to a series of social problems, and due to the high breakage rate and high recycling cost of bike sharing, enterprises are reluctant to maintain them. Therefore, government regulation does play a leading role in maintaining bike sharing. This study’s purpose is to investigate how the government should regulate the bike-sharing enterprises that maintain bicycles.

Firstly, the authors assume that there is only one bike-sharing enterprise and establish a game model that the government regulates the enterprise. Furthermore, the authors extend the model to the case that there are two competing enterprises in the market. Finally, through numerical analysis, the influence of various factors on the government strategy and revenue is analyzed.

The authors find that the regulatory probability of two enterprises are regulated by the government is larger than that of one enterprise. When two bike-sharing enterprises compete without government regulation, both will choose the non-self-discipline strategy, thus falling into the Prisoner's Dilemma. If the government regulates them, then both enterprises will choose the self-discipline strategy. Finally, through numerical analysis, it is found that the self-discipline behavior of bike-sharing enterprises is related to the government regulatory probability, the cost of self-discipline, the probability of being reported and the penalties. Interestingly, the cost of government regulation will not affect the regulation probability of government.

This research provides a theoretical reference for the government to make the regulation strategies of bike sharing and achieve the sustainable development of bike sharing.

This research provides a theoretical reference for the government to make the regulation strategies of bike sharing and achieve the sustainable development of bike sharing.

This study examines how the different attributes of authentic leadership influence trust and employee organization fit and how such influences differ by gender and the level of positions during the COVID-19 pandemic.

The study employed a survey to examine US employees' perceptions toward different attributes of authentic leadership during the COVID-19 pandemic.

The study showed that self-awareness, balanced processing and internalized moral perspective positively relate to trust in the employer, mediated through employee–organization fit. However, relational transparency has a backfiring effect, negatively related to trust through the mediation of employee–organization fit. Additionally, this study highlights the differences in gender and level of positions in reactions to authentic leadership.

This study contributes to the understanding of internal public relations in a turbulent crisis time by proposing a mediated model that explains the effects of authentic leadership on employees' trust through their fit with the organization. Additionally, it identified that gender and position level are important factors moderating such effects.