Based on the population change data of 2005–2009, 2010–2014, 2015–2019 and 2005–2019, the shrinking cities in Northeast China are determined to analyze their spatial distribution pattern. And the influencing factors and effects of shrinking cities in Northeast China are explored by using multiple linear regression method and random forest regression method. The results show that: 1) In space, the shrinking cities in Northeast China are mainly distributed in the “land edge” areas represented by Changbai Mountain, Sanjiang Plain, Xiaoxing’an Mountain and Daxing’an Mountain. In terms of time, the contraction center shows an obvious trend of moving northward, while the opposite expansion center shows a trend of moving southward, and the shrinking cities gather further; 2) in the study of influencing factors, the results of multiple linear regression and random forest regression show that socio-economic factors play a major role in the formation of shrinking cities; 3) the precision of random forest regression is higher than that of multiple linear regression. The results show that per capita GDP has the greatest impact on the contraction intensity, followed by the unemployment rate, science and education expenses and the average wage of on-the-job workers. Among the four influencing factors, only the unemployment rate promotes the contraction, and the other three influencing factors inhibit the formation of shrinking cities to various degrees.

This study delves into the evolving landscape of smart city development in Kazakhstan, a domain gaining increasing relevance in the context of urban modernization and digital transformation. The research is anchored in the quest to understand how specific technological factors influence the formation of smart cities within the region. To this end, the study adopts a Spatial Autoregressive Model (SAR) as its core analytical tool, leveraging data on server density, cloud service usage, and electronic invoicing practices across various Kazakhstani cities. The crux of the research revolves around assessing the impact of these selected technological variables on the smart city development process. The SAR model’s application facilitates a nuanced understanding of the spatial dynamics at play, offering insights into how these factors vary in influence across different urban areas. A key finding of this investigation is the significant positive correlation between the adoption of electronic invoicing and smart city development, a result that stands in contrast to the relatively insignificant impact of server density and cloud service usage. The conclusion drawn from these findings underscores the pivotal role of digital administrative processes, particularly electronic invoicing, in driving the smart city agenda in Kazakhstan. This insight not only contributes to the academic discourse on smart cities but also holds practical implications for policymakers and urban planners. It suggests a strategic shift towards prioritizing digital administrative innovations over mere infrastructural or technological upgrades. The study’s outcomes are poised to guide future smart city initiatives in Kazakhstan and offer a reference point for similar emerging economies embarking on their smart city journeys.

This study aimed to examine the impact of working conditions and sociopsychological factors on job satisfaction among office workers. Using data from the 2017–2018 Working Conditions Survey, exploring how workplace conditions and sociopsychological elements could impact job satisfaction. This study examined data from 9801 workers to explore the effects of working conditions and psychosocial environments on job enthusiasm, which subsequently impacts job satisfaction. Analyzing 1416 office workers, it found that fewer working hours, better work-life balance, improved work conditions, and lower depression levels enhance job enthusiasm, significantly affecting job satisfaction. The work environment had the most substantial impact, encompassing relationships with colleagues, task completion time, and confidence. Work-life imbalance and depression were also significant, with work-life balance being crucial for modern society, especially the younger generation. Poor working conditions and unstable psychosocial environments negatively affect job enthusiasm and satisfaction, with findings supporting previous research on job stress and turnover intentions in various industries. This study highlights the need for organizational policies that support these aspects to improve overall employee well-being and productivity.

Business model innovation (BMI) has garnered substantial academic and corporate attention in recent decades. Researchers have not yet agreed on the most complicated BMI practices in the high-tech startups (HTS). Despite being the second-biggest economy in the world today, China has done little research on the practice of business model innovation in China’s high-tech startups. This study addresses the factors that impact the business model innovation of high-tech startups in China. Our study aims to fill the research gap by visualising and analysing, using systematic literature review (SLR) analyses and reviewing 36 in-depth articles, from 688 academic literature sources. Relevant publications from Scopus, Springer, ScienceDirect, Web of Science, IEEE Xplore, and the JDM e-library expose the current research status from 2013 to December 2023 without bias. We conducted a literature-based investigation to identify essential insights on the BMI factors in the literature and derived a high-tech startup’s BMI critical factor. Our study shows that three main factors affect the innovation of business models in high-tech startups in China. The findings raise managers’, entrepreneurs’, and executives’ knowledge of corporate resource bricolage and cognitive style constraints in business model innovation and their pros and cons. The findings will help Chinese academics understand enterprises’ institutional environment and resource bricolage as final suggestions and proposals for corporates, regulators, and policymakers are presented.

The article considers an actual problem of organizing a safe and sustainable urban transport system. We have examined the existing positive global experience in both infrastructural and managerial decisions. Then to assess possible solutions at the stage of infrastructure design, we have developed the simulation micromodels of transport network sections of the medium-sized city (Naberezhnye Chelny) with a rectangular building type. The models make it possible to determine the optimal parameters of the traffic flow, under which pollutant emissions from cars would not lead to high concentrations of pollutants. Also, the model allows to obtain the calculated values of the volume of emissions of pollutants and the parameters of the traffic flow (speed, time of passage of the section, etc.). On specific examples, the proposed method’s effectiveness is shown. Case studies of cities of different sizes and layouts are implementation examples and possible uses proposed by the models. This study has shown the rationality of the suggested solution at the stage of assessing infrastructure projects and choosing the best option for sustainable transport development. The proposed research method is universal and can be applied in any city.

The ongoing dissemination of globalization and digitalization may suggest that personal relationships are becoming less crucial in the context of retail banking and financial services. In Hungary, in addition to private banking, which is associated with high income levels, personal banking also plays an important role. The objective of this study is to develop a model that can identify the factors that determine customer satisfaction and their relative importance. Furthermore, the aim is to incorporate gender and age as moderator variables to identify demographic differences in satisfaction. The analysis was conducted via a questionnaire survey in October to November 2023 employing a purposive sampling approach in a university environment, as the respondents are likely to possess the highest level of existing financial knowledge within this population. The 214 valid responses were analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach, with the objective of contributing to the development of theory in this field of study. The results demonstrate that perception (β = 0.519) and reliability (β = 0.253) collectively explained 51.8% of the variance in satisfaction. Moreover, the results indicate that perception accounts for 49.2% of the variance in reliability, suggesting the existence of an indirect effect on satisfaction. Therefore, the findings suggest that, despite the advent of digital banking, face to face service remains a pertinent concern in Hungary, and financial institutions should prioritize the factors that shape customer satisfaction. The study contributes to the literature and to the development of customer loyalty strategies for banks based on these findings.