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This study aims to find a dynamic model in an effort to optimize tourism performance in ecotourism destinations. The model structure is built based on competitive performance in geographic areas and the application of ecotourism elements that are integrated with big data innovation through artificial intelligence technology.

Data analysis is performed through dynamic system modeling. Simulations are carried out in three models: First, existing simulation models. Second, Scenario 1 is carried out by utilizing a causal loop through innovation of big data-based artificial intelligence technology to ecotourism elements. Third, Scenario 2 is carried out by utilizing a causal loop through big data-based artificial intelligence technology on aspects of ecotourism elements and destination competitiveness.

This study provides empirical insight into the competitiveness performance of destinations and the performance of implementing ecotourism elements if integrated with big data innovations that will be able to massively demonstrate the growth of sustainable tourism performance.

This study does not use a primary database, but uses secondary data from official sources that can be accessed by the public.

The paper includes implications for the development of intelligent technology based on big data and also requires policy innovation.

Sustainable tourism development.

This study finds the expansion of new theory competitiveness of ecotourism destinations.

The purpose of this paper is to study the operation mechanism of the ecosystem of crowd innovation space. Though the crowd innovation space is a new product of China's innovation-driven strategy, there are some barriers in operation. So, this problem is worthy of study.

In this study, data were obtained through four-month field investigation and semistructured interview, then classified and analyzed through grounded theory, because grounded theory is conducive to the exploration and discovery of new theories.

This study finds that the relationship between makerspace and entrepreneurs is strong social relational embeddedness. The relationship between crowd innovation space and governments and investment institutions is economic relational embeddedness. Under these social network ties, entrepreneurs, crowd innovation space, social investment institutions and so on can interact directly with each other to different degrees, carry out value cocreation activities and improve the benefits of all elements in the ecosystem and the ecosystem itself.

This study researches the operation mechanism of crowd innovation space ecosystem and identifies the ties between various elements in the ecosystem on the perspective of social network, which is conducive to improve the self-generating capacity of crowd innovation space and enhance the success rate of entrepreneurship.

This study aims to examine the trend of industrial competition between the US and China, which is the most crucial determinant in the future development of the global economy. For decades, the global economy has strengthened the global production network based on the division of labor between countries. Thus, the ripple effect of competition between the two countries should be analyzed in terms of the global production network. Therefore, this study uses the product space model, which explains the development process of industries with comparative advantage by country. We constructed the model based on the products of HS 4-digit code for the 2010–2019 period. The analysis results on the trend of the industrial competitiveness of major countries are as follows. First, the current industrial competitiveness of China is concentrated on low-tech industries. In the case of high-tech items, China shows a tendency of lower export sophistication compared to major manufacturing powerhouses such as Germany, the US, Japan, and Korea. Second, with respect to the possibility of a future industrial structure upgrade evaluated by density, the trend of China overtaking other manufacturing powerhouses is observed. As implied by the product space model, the advancement of the industrial structure through active participation in international trade enhances the industrial competitiveness. Therefore, the outcome of US-China industrial competition depends on who ensures more openness and industrial complexity.

The purpose of this paper is to examine the influence of export logistics components: shipment arrangements, timely delivery, customs quality, trade infrastructure, and tracking and tracing on export competitiveness of firms in the East African Community (EAC).

The study adopted the Structural Gravity Model and the Poisson pseudo-maximum likelihood (PPML). PPML a nonlinear estimation method was applied in STATA on a balanced panel data for the period of 2007–2018. Data were obtained from World Bank International Trade Centre (ITC), World Bank Logistics Performance Index (LPI) and World Bank development indicators.

Results show that timely delivery and tracking and tracing of exports are positive and significant predictors of export competitiveness in EAC countries. Conversely, shipment arrangements, customs quality and trade infrastructure have no influence on export competitiveness.

The results of this study show that export logistics components of shipment arrangements, customs quality and trade infrastructure do not matter at the present in improving export competitiveness in the EAC. There is a need to examine the intricate nature of the EAC economy to further this study's findings.

The EAC partner states should embrace deep integration by removing the behind the border trade barriers in addition to other trade restrictions, to create a common economic space among member states. This will further shrink the delivery time and the tracking and tracing of exports hence improving the competitiveness of EAC exports within the region and outside. Also, common and harmonized trade policies and regulations should be implemented through mutual recognition agreements where countries agree to recognize one another's conformity assessments.

This study explains the complex dynamic interactions of export logistics factors in the EAC using quantitative data and that this interaction has an effect on the export competitiveness in import-dominated countries with less harmonization in their trade policies.

As the contradiction between economic development, resource and environment has become increasingly prominent, low-carbon competitiveness has received worldwide focus. This study aims to examine low-carbon competitiveness in 31 provinces (cities and regions) of China.

An evaluation index system for low-carbon competitiveness in China has been constructed, which is composed of 25 economic, social, environmental and policy indicators. To study the state of low-carbon competitiveness and resistance to China’ development of low-carbon competitiveness, this study uses a combination of the catastrophe progression model, the spatial autocorrelation model and the barrier method.

China’ low-carbon competitiveness gradually decreases from coastal to inland areas: the Tibet and Ningxia Hui autonomous regions are the least competitive regions, while the Shandong and Jiangsu provinces are the most competitive areas. The spatial correlation of the 31 provinces’ low-carbon competitiveness is very low and lacks regional cooperation. This study finds that the proportion of a region’ wetland area, the proportion of tertiary industries represented in its GDP and afforestation areas are the main factors in the development of low-carbon competitiveness. China should become the leader of carbon competitiveness by playing the leading role in the Eastern Region, optimizing the industrial structure, improving government supervision and strengthening environmental protection.

The paper provides a quantitative reference for evaluating China’ low-carbon competitiveness, which is beneficial for environmental policymaking. In addition, the evaluation and analysis methods offer relevant implications for developing countries.

This paper outlines how Design for Lean Six Sigma methods aided a medical device manufacturing company in developing a new strategic space management and approval process for its manufacturing site.

The project demonstrates the application of the Design for Lean Six Sigma and structured Define, Measure, Analyse, Design, and Verify methodology in designing and implementing a process that enables the case study manufacturing site to improve its space utilisation and free up space.

The project was validated in one manufacturing department, and the Design for Lean Six Sigma methodology resulted in creating 15% new space for that area, with opportunities identified to free up 44.7% of the total manufacturing floor space and realise over €2.2 million cost savings as well as start to manufacture new products launched.

The manuscript highlights for the first time how the Design for Lean Six Sigma methodology can be utilised for space utilisation and can be leveraged by other manufacturers. The current study's limitations are that it is a single-site case study application. Future longitudinal case studies on Design for Lean Six Sigma application in more manufacturing space utilisation projects would be useful. This study has implications for identifying best practices for Design for Lean Six Sigma methodology application in the device industry, thus improving the state of the art for introducing new manufacturing lines.

This is the first published work to utilise Design for Lean Six Sigma methodology for space utilisation in a medical device company. This review will provide medical devices and other manufacturing organisations with recommendations on utilising Design for Lean Six Sigma and design for improved space utilisation to reduce costs.

The global economy and air transport business have been negatively affected owing to the COVID-19 pandemic outbreak. As countries tighten restrictions on international movements, the growing emphasis on air cargo places pressure on airports to maintain and upgrade their cargo policies, facilities and operations. Hence, ensuring the competitiveness of cargo airports is pivotal for their survival under volatile global demand. This study aims to evaluate the importance of competitiveness factors for cargo airports and identify areas for further improvement.

This study applies the Best-Worst Method (BWM) to assess the cargo airports' competitiveness factors.

The results identified “Transport Capacity” as the most significant competitiveness factor, implying that airport connectivity is crucial in promoting cargo transportation at hub airports. This result was followed by “Airport Operations' and Facilities' Capacity” and “Economic Growth.”. Additionally, the results identified Hong Kong International Airport as the best-performing cargo airport, followed by Aéroport de Paris-Charles de Gaulle and Incheon International Airport, respectively. Furthermore, both selected European airports are the most competitive airports in terms of “Financial Performance” and appear to be aware of the significance of their brand value.

This study forms a reference framework for evaluating cargo airports’ competitive positions, which may help identify airports’ relative strengths and weaknesses. Moreover, this framework can also serve as a tool to facilitate the strategic design of airports that can accommodate air cargo demand flexibly under demand uncertainty.

This study aims to assess the technological capability of Chinese internet platforms (BAT: Baidu, Alibaba, Tencent) compared to US ones (GAFA: Google, Amazon, Facebook, Apple). More specifically, this study explores Baidu’s technological catching-up process with Google by analyzing their patent textual information.

The authors retrieved 26,383 Google patents and 6,695 Baidu patents from PATSTAT 2019 Spring version. The collected patent documents were vectorized using the Word2Vec model first, and then K-means clustering was applied to visualize the technological space of two firms. Finally, novel indicators were proposed to capture the technological catching-up process between Baidu and Google.

The results show that Baidu follows a trend of US rather than Chinese technology which suggests Baidu is aggressively seeking to catch up with US players in the process of its technological development. At the same time, the impact index of Baidu patents increases over time, reflecting its upgrading of technological competitiveness.

This study proposed a new method to analyze technology mapping and evolution based on patent text information. As both US and China are crucial players in the internet industry, it is vital for policymakers in third countries to understand the technological capacity and competitiveness of both countries to develop strategic partnerships effectively.

This editorial proposes three fundamental issues in the international economic field evoked by the US-China trade war. The first is the intensifying conflict between protectionism measures and the coherence with relevant global trade rules. The second is decoupling between the US and China and subsequent evolution in the global production network. The third is the digression in the trade flow between the US and China and the resulting change in innovative capabilities and manufacturing competitiveness among major industrial countries. This editorial also offers the long-term perspective of the current global trade war and how the world encounters repercussion of openness and protectionism as well as times of prosperity and uncertainty.

Globally, urban planners and decision makers are pursuing place-based initiatives to develop and enhance urban infrastructure to optimise city performance, competitiveness and sustainability credentials. New discourses associated with big data, Building Information Modelling, SMART cities, green and biophilic thinking inform research, policy and practice agendas to varying extents. However, these discourses remain relatively isolated as much city planning is still pursued within traditional sectoral silos hindering integration. This research explores new conceptual ground at the Smart – Natural City interface within a safe interdisciplinary opportunity space. Using the city of Birmingham UK as a case study, a methodology was developed championing co-design, integration and social learning to develop a conceptual framework to navigate the challenges and opportunities at the Smart-Natural city interface. An innovation workshop and supplementary interviews drew upon the insights and experiences of 25 experts leading to the identification of five key spaces for the conceptualisation and delivery at the Smart-Natural city interface. At the core is the space for connectivity; surrounded by spaces for visioning, place-making, citizen-led participatorylearning and monitoring.The framework provides a starting point for improved discussions, understandings and negotiations to cover all components of this particular interface. Our results show the importance of using all spaces within shared narratives; moving towards ‘silver-green’ and living infrastructure and developing data in response to identified priorities. Whilst the need for vision has dominated traditional urban planning discourses we have identified the need for improved connectivity as a prerequisite. The use of all 5 characteristics collectively takes forward the literature on socio-ecological-technological relationships and heralds significant potential to inform and improve city governance frameworks, including the benefits of a transferable deliberative and co-design method that generates ownership with a real stake in the outcomes.