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This article aims to explore the geographic distribution of open access practices in the world from a diffusionist perspective.

The article applies a tempo‐spatial analysis to examine the diffusion movement of open access practices from the West to the entire world during the past several decades. Both maps and tables are used to support the analysis. The diffusionist theory is reviewed and applied to the understanding of open access.

The paper discovers that technology is not the only factor determining the diffusion pattern of information systems as discussed in the literature. Cultural dissimilarities across countries have played a significant role in open access development. Open access can only be effectively established after it meets local standards.

The findings help understanding of why open access has a disproportionate growth among developing countries, and even among developed countries, where the ICT infrastructure has been in place.

Few studies have taken a transnational view to analyze open access geography at the global level, and few have been able to synthesize models to interpret diverse discoveries. Furthermore, a chronological evaluation tracing the history of open access spatial expansion is absent in the literature.

Development of urban-rural integration is essential to fulfill sustainable development goals worldwide, and comprehension about urban-rural integration types has been highlighted as increasingly relevant for an efficient policy design. This paper aims to utilize an unsupervised machine learning approach to identify urban-rural integration typologies based on multidimensional metrics regarding economic, population and social integration in China.

The study introduces partitioning around medoids (PAM) for the identification of urban-rural integration typologies. PAM is a powerful tool for clustering multidimensional data. It identifies clusters by the representative objects called medoids and can be used with arbitrary distance, which help make clustering results more stable and less susceptible to outliers.

The study identifies four clusters: high-level urban-rural integration, urban-rural integration in transition, low-level urban-rural integration and early urban-rural integration in backward stage, showing different characteristics. Based on the clustering results, the study finds continuous improvement in urban-rural integration development in China which is reflected by the changes in the predominate type. However, the development still presents significant regional disparities which is characterized by leading in the east regions and lagging in the western and central regions. Besides, achievement in urban-rural integration varies significantly across provinces.

The machine learning techniques could identify urban-rural integration typologies in a multidimensional and objective way, and help formulate and implement targeted strategies and regionally adapted policies to boost urban-rural integration.

This is the first paper to use an unsupervised machine learning approach with PAM for the identification of urban-rural integration typologies from a multidimensional perspective. The authors confirm the advantages of this machine learning techniques in identifying urban-rural integration types, compared to a single indicator.

This study aims to examine the various factors and conditions pertaining to the rise of the sharing economy.

After framing the sharing economy concept, the study adopts a multidisciplinary approach and relies on the extant literature to analyze and classify eight major groups of factors behind the rise of the sharing economy.

The analysis indicates that the sharing economy (1) represents a significant paradigm shift emphasizing utilization rather than possession and relying on mutual trust, collaboration and reciprocity; (2) benefits from fundamental transformations such as the world population growth, global urbanization, surge in the world middle class and the convergence of tastes and preferences; (3) relies on technological innovation but is affected by socio-cultural and psychological conditions; (4) is driven by emerging trends in consumption, marketing and working conditions; (5) benefits from lax or nonexistent regulation and taxation; and (6) is recognized as clean and environmentally friendly.

This paper's chief contribution resides in adopting a multidisciplinary perspective to offer an in-depth analysis of the various types of factors behind the rise of the sharing economy.

This paper aims at analyzing salient cultural transformations and their implications for business and management.

First, the interpretative approach is explained, and its adoption is justified. Then, seven major cultural transformations associated with globalization are identified and analyzed. Finally, business and management implications are discussed.

The cultural trends/transformations caused by or associated with globalization include convergence, divergence, hybridization, the clash of cultures/civilizations, diversity, multiculturalism, time-space compression, temporal acceleration, short-termism, risk, insecurity and uncertainty.

This study, like any other interpretative study, is limited in its internal validity. Furthermore, some scholars may have different perspectives on cultural transformations, such as clash of cultures, diversity, multiculturalism and risk society.

At the methodological level, this paper adopts an interpretative research design and takes into consideration historical, contextual and social components of culture. While the culture in management is often conceptualized as bipolar and mutually exclusive dimensions, this study offers a more versatile conceptualization of culture.

The purpose of this paper is to use Weibo as a window to examine the Chinese netizens’ online attitudes and responses to two sets of population policy: the Selective Two-Child Policy (Phase 2) and the Universal Two-Child Policy. The population policy change from the rigid One-Child Policy to the Selective Two-Child Policy then to the Universal Two-Child Policy aroused great attention of the Chinese people.

This research uses the crawler technique to extract data on the Sina Weibo platform. Through opinion mining of Weibo posts on two sets of population policy, the Weibo users’ online opinions on the Two-Child Policy are analyzed from two perspectives: their attention intensity and sentiment tendency. The research also use the State Bureau of Statistics of China’s national population data between 2011 and 2016 to examine the Chinese people’s actual birth behaviors after implementing the two different sets of the Two-Child Policy.

The research findings indicate that the Selective Two-Child Policy (Phase 2) and the Universal Two-Child Policy are good examples of thematic public sphere of Weibo. Weibo posts on the two sets of the Two-Child Policy have undergone different opinion cycles. People from economically developed regions and populous regions have paid more attention to both sets of Two-Child Policy than their counterparts in the less developed and less populated regions. Men pay more attention to the Two-Child Policy than women do. Despite people’s huge attention to the new population policy, the population growth after the policy is not sustainable.

The new population policy alone is difficult to boost China’s population within a short period of time. The Chinese Government must provide its people with enough incentives and supporting welfare to make the population growth happen.

These findings have important implications for understanding the dynamics of online opinion formation and changing population policy in China.

This paper aims to identify, classify and study emerging social and business trends associated with the outbreak of the Covid-19 pandemic.

By adopting the meta-synthesis method, the study scrutinizes, synthesizes and interprets the findings from a pool of publications. This approach results in identifying 53 key ideas that are classified under nine dominant trends.

The study identifies and examines nine major trends caused and intensified by the Covid-19 pandemic, i.e. the rise of authoritarianism, the new era of corporate welfare, deep imbalances in public finances, exacerbated inequalities, higher risks of poverty and famine, the dominance of giant corporations, the increasing influence of big tech, the accelerated innovation and the fluidity of work and organizations. The paper suggests that these trends are the continuation of the past three decades’ transformations, are contributing to the rising concentration of power and wealth and are leading to a new type of globalization marked by high connectivity and low tangibility.

The originality of this paper resides in adopting a multidisciplinary approach to analyzing various social and business dimensions of a complex phenomenon. While the study should not be viewed as a comprehensive investigation, it offers a groundwork for further research on Covid-19.

This paper aims to develope an integrated image processing method to investigate the spatiotemporal dynamics of Phragmites invasion in the Detroit River International Wildlife Refuge on the basis of publically available sources.

This new approach integrates the standard time-series analysis of Landsat images with USDA National Agriculture Imagery Program (NAIP) imagery and USGS Digital Orthophoto Quarter Quads (DOQQ) datasets, which are either classified or manually interpreted with the aid of ground control points. Three different types of spatiotemporal dimensions are designed to test this integrated time-series image analysis method: the selected sites and selected time-points with high spatial resolution and sufficient validation data points, the intermediate time-series with continued yearly images and periodical validation data, and the long time-series with periodical images without enough validation data. The support vector machine (SVM) method was used to classify the Landast TM sequence images to detect the Phragmites invasion.

The habitat map produced by NAIP images and field collection data shows that the total Phragmites area of DRIWR in 2010 is 4221.87 acres without treatment areas and similar with the removed non-vegetation method. It is confirmed that the pre-classification method can obtain more accurate results.

The test results show that the Landsat-5 data can be used for long-term environmental management and monitoring of Phragmites invasion and can achieve rehabilitation of invasion areas.

The growth of the Chinese economy has resulted in a significant increase in construction and demolition waste (CDW), and regional differences in CDW generation are gradually increasing. The purpose of this study is to investigate the regional differences in CDW generation and the driving factors that influence CDW generation in different areas of China. To provide a systematic advisement for local governments to select the appropriate policy, reduce CDW generation.

The generation of CDW was calculated by region, based on the area estimation method, from 2005 to 2018. The relationship between CDW generation and economic development, and the driving factors of CDW generation in different regions of China, was investigated using the environmental Kuznets curve (EKC) model and the STIRPAT theoretical model.

CDW generation of China increased at the average annual growth rate of 10.86% from 2005 to 2018. The main areas of CDW generation were concentrated in the eastern and central regions, while the proportion of CDW generation in the northeast region decreased gradually, and the changes varied significantly across different regions. The EKC between CDW generation and economic development was established for the whole country, North China, Northeast China, East China, Central South China, Southwest China and Northwest China. Three main factors based on the STIRPAT theoretical model were identified and explained into a framework to reduce CDW generation. The results provided a useful theoretical basis and data support guide for devising effective policies and regulations for the Chinese context.

The findings from this study can ultimately support policymakers and waste managers in formulating effective policies for waste management strategies and CDW-specific legislation. Additionally, it can help the coordinated reduction of CDW generation across regions in China and can support construction enterprises (in their development strategies), similar developing economies and foreign firms planning to operate in China.

This study contributes to the field through the STIRPAT model on driving factors of CDW generation in the Chinese context, in different regions.