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Traditional Chinese Medicine (TCM) is a complete medical healthcare system that encompasses acupuncture, acupressure, moxibustion, herbal medicine, diet, tui na massage, and exercises (tai chi and qigong) among other traditional therapies. It uses herbs and natural resources to produce the traditional medicines and focuses on maintaining the balance between body and mind. As such, many aspects of TCM can be considered as green and sustainable. While there is market demand for TCM in some countries and among some communities, some others are still not aware of TCM. Moreover, there are fewer discussions in the academic platforms on TCM. This case highlights the scenario of TCM based on Malaysia’s perspective and discusses its challenges and prospects.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

It is possible to see effective use of Artificial Intelligence-based systems in many fields because it easily outperforms traditional solutions or provides solutions for the problems not previously solved. Prediction applications are a widely used mechanism in research because they allow for forecasting of future states. Logical inference mechanisms in the field of Artificial Intelligence allow for faster and more accurate and powerful computation. Machine Learning, which is a sub-field of Artificial Intelligence, has been used as a tool for creating effective solutions for prediction problems.

In this chapter the authors will focus on employing Machine Learning techniques for predicting data for future states of economic using techniques which include Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, Dynamic Boltzmann Machine, Support Vector Machine, Hidden Markov Model, Bayesian Learning on Gaussian process model, Autoregressive Integrated Moving Average, Autoregressive Model (Poggi, Muselli, Notton, Cristofari, & Louche, 2003), and K-Nearest Neighbor Algorithm. Findings revealed positive results in terms of predicting economic data.

In an omnichannel environment, customers switch channels from product discovery to eventual purchase decision strategically. Hence, the biggest challenge for retailers nowadays is how to operate an effective omnichannel strategy. To improve inventory operational efficiency, this chapter investigates the influences of price setting and customers’ return probability on inventory forecasting. Subsequently, we explore how retailers participate in providing appropriate information delivery and product fulfillment. Specifically, a stylized newsvendor model, which incorporates customers’ showrooming behavior, is developed to address retailers’ inventory problem. Furthermore, we compare the benefits of buy-online-and-pick-up-in-store (BOPS) and showroom strategy which originates offline but is completed online. Three main findings are obtained as follows: (1)online and offline inventory order quantities augment with the ascending of pricing offline and online, respectively. Meanwhile, the inventory decisions increase when customers’ return probability declines; (2) the implementation of showroom helps retailers expand their pure online market coverage than BOPS, while it reduces the total inventory quantity if the proposition of unit online inventory cost accounting for product price exceeds physical store; and (3) showroom strategy is more profitable than BOPS option as long as unit online inventory cost is small enough. In addition, we find this boundary where showroom increases total profit expands with the attenuating of return probability.

Business models are constantly innovating to adopt international trends. Business models are shifting toward sustainable practices to meet expectations and standards and keep on with the rest of the competitors in their industries. Recently, sustainability took a greater emphasis, and this chapter seeks to describe the main sustainability initiatives generated in international markets. Through a qualitative study made of secondary sources, strategies aligned to different sustainability initiatives are shown. Interestingly, adopting technology, digital transformation, and blockchain can enhance sustainable development.

The idea of implementing supply chain management (SCM) principles for the construction industry was embraced by construction stakeholders to enhance the sector's performance. The analysis from the literature revealed that the implementation of SCM in the construction industry enhances the industry's value in terms of cost-saving, time savings, material management, risk management and others. The construction supply chain (CSC) can be managed using the pull or push system. This chapter also discusses the origin and proliferation of SCM into the construction industry. The chapter revealed that the concept of SCM has passed through five different eras: the creation era, the use of ERP, globalisation stage, specialisation stage and electronic stage. The findings from the literature revealed that we are presently in the fourth industrial revolution (4IR) era. At this stage, the SCM witnesses the adoption of technologies and principles driven by the 4IR. This chapter also revealed that the practice of SCM in the construction industry is centred around integration, collaboration, communication and the structure of the supply chain (SC). The forms and challenges hindering the adoption of these practices were also discussed extensively in this chapter.

Digitalization can drive the sustainable development of society and industry. However, a lot of the opportunities are tightly related to the risks. This chapter investigates how academic performance in terms of scientific publications at the intersection of digitization, sustainable development, and industry brings added value to the research field. The analysis focuses on the academic papers that research the relationship between digitalization, sustainable development, and industry, analyzing comparatively two primary academic databases, Scopus and Web of Science (WoS). To do that, two samples of scientific publications were investigated. The first sample comprised 703 documents indexed in WoS, while the second was formed by 566 documents indexed in Scopus. The analyzed period for WoS is 1996–2022, while for Scopus is 2000–2022. The empirical results indicated that the research field's primary issues include digitalization, digital transformation, the construction industry, sustainability, COVID-19, and innovation for both WoS and Scopus. The most relevant authors on this cross-cutting subject in the WoS database are Beier G., Aigbavboa C., and Kumar M. In contrast, in the Scopus database, we found Beier G., Ghobakhloo M., and Iranmanesh M. At the same time, the most typical journals are the International Journal of Production Research, Sustainability, and Computers in Industry for WoS and Sustainability, Journal of Cleaner Production, and Procedia CIRP for Scopus.

This chapter reviews three main issues in the interactions between air transport and high-speed rail (HSR) in China, namely the interaction between low-cost carriers (LCCs) and HSR, HSR speed effect on airlines, and airline–HSR integration. Studies on these three aspects of airline–HSR interactions have yet been well reviewed, and our chapter aims to fill in this gap. In this chapter, we comprehensively survey literature on the topics, especially studies on Chinese markets that have recently witnessed major HSR developments (and have planned further large-scale HSR expansion in the coming years). Our review shows that, first, compared to full-service carriers, LCCs face fiercer competition from HSR. However, the expansion of HSR network in China can be better coordinated with LCC development. Second, HSR speed exerts two countervailing effects on airline demand and price (the “travel-time” effect and “safety” effect, respectively). Specifically, an HSR speed reduction can have a positive effect on airlines due to longer HSR travel time, but a negative effect on airlines due to improved perception on HSR safety. Third, airline–HSR integration can be implemented through cooperation between airlines and HSR operators and through co-location of airports and HSR stations and can have important implications for intermodal transport and social welfare.