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The purpose of this study is to explore the relationship between big data and supply chain platform in China’s retail industry. With the emergence of big data resources and technologies, the business pattern of new retail advocates the combination of online and offline channels. Supply chain platform plays a key role in the implementation of retail activities, which has gradually become a research hotspot in the cross field of operations management and information system.

Through the method of literature review and case study, this study empirically explores how big data shapes supply chain platform to support new forms of online retail by grounded theory.

The model framework is validated by reliability test and coding method to process survey materials. The results identify the overall antecedents of supply chain platform and reveal positive effects between big data and new retail. The findings help firm managers build a big data-driven supply chain to support new retail.

There are insufficient studies on theoretical frameworks and interaction relationships among big data, supply chain platform and new retail.

As the complexity and uncertainty of megaprojects make it difficult for traditional management models to address the difficulties, this paper aims to design a performance incentive contract through IT applications, thereby promoting the formation of an information-based governance mechanism for megaprojects and facilitating the transformation and upgrading of the construction management model of megaprojects to informatisation.

This paper introduced IT applications into the performance assessment and used the proportion of IT applications replacing traditional manual management as a variable. It analysed different replacement ratios to obtain the optimal solution for the change of contractors behaviours and promote the optimal performance incentive for the informatisation in megaprojects.

The results show that under the condition of the optimal replacement ratio, achieving the optimal state of a mutual win-win situation is possible for the benefit of both sides. The counter-intuitive finding is that the greater the replacement ratio is not, the better, but those other constraints are also taken into account.

This study enriched the research of the performance configuration incentive from a practical perspective. It extended the research framework of IT incentive mechanisms in the governance of megaprojects from a management theory perspective. It clarified the role of IT applications in incentive mechanisms and the design process of optimal incentive contracts under different performance incentive states. The incentives made the contractors work harder to meet the owner's requirements, and it could improve the efficiency of megaprojects, thus better achieving megaproject objectives.

Bacterial cellulose (BC) is an ideal alternative filtering material. However, current functionalization approaches for BC have not been fully discovered industrially as well as academically applying textile processing. This study aims to create a sustainable fabric-like membrane made of BC/activated carbon (AC) for applications in filtration using textile padding method, to protect people from respiratory pandemics.

Fabric-like BC is first mechanically dehydrated then AC is loaded via a textile padding step. The finishing efficacy, properties of fabric-like BC/AC and NaOH pretreatment are analyzed and characterized by scanning electron microscope (SEM), field emission scanning electron microscope (FE SEM), X-ray diffraction (XRD), CIELab color space, color strength (K/S), nitrogen adsorption-desorption isotherm including Brunauer–Emmett–Teller (BET) specific surface area and Barrett–Joyner–Halenda (BJH) pore size and volume.

This research results in a fabric-like BC/AC with pore diameters of 3.407 ± 0.310 nm, specific surface area of 115.28 m²/g and an efficient scalable padding process, which uses 8 times less amount of chemical and nearly 30 times shorter treating duration than conventional methods.

Our globe is now consuming an alarming amount of non-degradable disposable masks resulting in massive trash buildup as a future environmental problem. Besides, current disposable masks requiring a significant upfront technological investment have posed challenges in human protection from respiratory diseases, especially for countries with limited conditions. By combining a sustainable material (BC) with popular padding method of textile industry, the fabric-like BC/AC will offer sustainable and practical values for both humankind and nature.

This research has offered an effective padding process to functionalize BC, and a unique fabric-like BC/AC membrane for filtration applications.

This research presents machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic using multisource Internet data.

To develop the prediction models, this research utilizes multisource Internet data from TripAdvisor travel forum and Google Trends. Temporal factors, posts and comments, search queries index and previous tourist arrivals records are set as predictors. Four sets of predictors and three distinct data compositions were utilized for training the machine learning models, namely artificial neural networks (ANNs), support vector regression (SVR) and random forest (RF). To evaluate the models, this research uses three accuracy metrics, namely root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE).

Prediction models trained using multisource Internet data predictors have better accuracy than those trained using single-source Internet data or other predictors. In addition, using more training sets that cover the phenomenon of interest, such as COVID-19, will enhance the prediction model's learning process and accuracy. The experiments show that the RF models have better prediction accuracy than the ANN and SVR models.

First, this study pioneers the practice of a multisource Internet data approach in predicting tourist arrivals amid the unprecedented COVID-19 pandemic. Second, the use of multisource Internet data to improve prediction performance is validated with real empirical data. Finally, this is one of the few papers to provide perspectives on the current dynamics of Indonesia's tourism demand.

Energy conservation and environmental protection industry (ECEPI) is a strategic choice to promote energy conservation and emission reduction, develop green economy and circular economy. However, China’s ECEPI is still in the stage of rapid development and the overall scale is relatively small, what development periods have the ECEPI experienced? This study aims to contribute to a better understanding of collaborative innovation evolution based on social network analysis from the perspective of multi-dimensional proximity.

Methodologically, this study uses social network analysis method to explore the co-evolution of multidimensional collaboration networks. It divides China’s ECEPI into four periods based on national policies from 2001 to 2020. This contribution constructs collaborative innovation networks from geographical, technological and organizational proximity.

The results show that the collaborative innovation network was initially formed in the central region of China, gradually expanded to neighboring cities and the core positions of Beijing, Jiangsu and Guangdong have been continuously consolidated. C02F has been the core of the collaboration networks, and the research focus has gradually shifted from the treatment of wastewater, sewage or sludge to the separation field. Enterprises always occupy a dominant position in the collaboration networks.

This research investigates the dynamic evolution process of collaborative innovation network in China’s ECEPI from the perspective of multidimensional proximity, explores the community structure, important nodes and multidimensional proximity features in the network, expands the research perspective on evolution characteristics of innovative network and the research field of social network analysis. Theoretically, this study enriches collaborative innovation theory, social network theory and multi-dimensional proximity theory.