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The study aims to facilitate the medical tourists visiting emerging countries for various kinds of ailments by ranking the possible destinations to avail medical treatments.

A Fuzzy Analytical Hierarchical Process (FAHP) with a mixed-method approach is applied to analyze data collected from patients and substantiate it with medical tour operators in India to gain managerial insights on the choice-making patterns of the patients.

India is a preferred emerging market location due to the low cost and high medical staff quality. India offers value for money, whereas Singapore and Thailand are preferred destinations for quality and technology.

The study will facilitate the emerging markets' governments, hospitals and medical tourists to understand the importance of various determinants responsible for availing medical treatment outside their country.

The study recommends that cost and quality care are the patients' prime focus; government policies must provide clear guidelines on what the hospitals and country environment can offer and accordingly align the marketing strategies.

This study is the first attempt to rank various factors affecting medical tourism using the FAHP approach.

This paper aims to study the agent learning from expert demonstration data while incorporating reinforcement learning (RL), which enables the agent to break through the limitations of expert demonstration data and reduces the dimensionality of the agent’s exploration space to speed up the training convergence rate.

Firstly, the decay weight function is set in the objective function of the agent’s training to combine both types of methods, and both RL and imitation learning (IL) are considered to guide the agent's behavior when updating the policy. Second, this study designs a coupling utilization method between the demonstration trajectory and the training experience, so that samples from both aspects can be combined during the agent’s learning process, and the utilization rate of the data and the agent’s learning speed can be improved.

The method is superior to other algorithms in terms of convergence speed and decision stability, avoiding training from scratch for reward values, and breaking through the restrictions brought by demonstration data.

The agent can adapt to dynamic scenes through exploration and trial-and-error mechanisms based on the experience of demonstrating trajectories. The demonstration data set used in IL and the experience samples obtained in the process of RL are coupled and used to improve the data utilization efficiency and the generalization ability of the agent.

Although the challenges associated with big data are increasing, the question of the most suitable big data analytics (BDA) platform in libraries is always significant. The purpose of this study is to propose a solution to this problem.

The current study identifies relevant literature and provides a review of big data adoption in libraries. It also presents a step-by-step guide for the development of a BDA platform using the Apache Hadoop Ecosystem. To test the system, an analysis of library big data using Apache Pig, which is a tool from the Apache Hadoop Ecosystem, was performed. It establishes the effectiveness of Apache Hadoop Ecosystem as a powerful BDA solution in libraries.

It can be inferred from the literature that libraries and librarians have not taken the possibility of big data services in libraries very seriously. Also, the literature suggests that there is no significant effort made to establish any BDA architecture in libraries. This study establishes the Apache Hadoop Ecosystem as a possible solution for delivering BDA services in libraries.

The present work suggests adapting the idea of providing various big data services in a library by developing a BDA platform, for instance, providing assistance to the researchers in understanding the big data, cleaning and curation of big data by skilled and experienced data managers and providing the infrastructural support to store, process, manage, analyze and visualize the big data.

The study concludes that Apache Hadoops’ Hadoop Distributed File System and MapReduce components significantly reduce the complexities of big data storage and processing, respectively, and Apache Pig, using Pig Latin scripting language, is very efficient in processing big data and responding to queries with a quick response time.

According to the study, there are significantly fewer efforts made to analyze big data from libraries. Furthermore, it has been discovered that acceptance of the Apache Hadoop Ecosystem as a solution to big data problems in libraries are not widely discussed in the literature, although Apache Hadoop is regarded as one of the best frameworks for big data handling.

Drawing on social network and information diffusion theories, the authors study the impact of the structural characteristics of a seller’s local social network on her promotion effectiveness in social commerce.

The authors define a local social network as one formed by a focal seller, her directly connected users and all links among these users. Using data from a large social commerce website in China, the authors build econometric models to investigate how the density, grouping and centralization of local social networks affect the number of likes received by products posted by sellers.

Local social networks with low density, grouping and centralization are associated with more likes on sellers’ posted products. The negative effects of grouping and centralization are reduced when density is high.

The paper deepens the understanding of the determinants of social commerce success from a network structure perspective. In particular, it draws attention to the role of sellers’ local social networks, forming a foundation for future research on social commerce.

This paper aims to solve the major assessment problem in matching the satisfaction of psychological gratification and mission accomplishment pertaining to volunteers with the disaster rescue and recovery tasks.

An extended belief rule-based (EBRB) method is applied with the method's input and output parameters classified based on expert knowledge and data from literature. These parameters include volunteer self-satisfaction, experience, peer-recognition, and cooperation. First, the model parameters are set; then, the parameters are optimized through data envelopment analysis (DEA) and differential evolution (DE) algorithm. Finally, a numerical mountain rescue example and comparative analysis between with-DEA and without-DEA are presented to demonstrate the efficiency of the proposed method. The proposed model is suitable for a two-way matching evaluation between rescue tasks and volunteers.

Disasters are unexpected events in which emergency rescue is crucial to human survival. When a disaster occurs, volunteers provide crucial assistance to official rescue teams. This paper finds that decision-makers have a better understanding of two-sided match objects through bilateral feedback over time. With the changing of the matching preference information between rescue tasks and volunteers, the satisfaction of volunteer's psychological gratification and mission accomplishment are also constantly changing. Therefore, considering matching preference information and satisfaction at two-sided match objects simultaneously is necessary to get reasonable target values of matching results for rescue tasks and volunteers.

Based on the authors' novel EBRB method, a matching assessment model is constructed, with two-sided matching of volunteers to rescue tasks. This method will provide matching suggestions in the field of emergency dispatch and contribute to the assessment of emergency plans around the world.

Given the increasing volatility, uncertainty, complexity, and ambiguity, egalitarian ecosystems may play an important role to establish equality among various stakeholders. With this idea, the study aimed to understand conflicts and challenges in creating an egalitarian ecosystem in the application-based cab aggregator (ABCA) market.

Narratives of various stakeholders involved in the ABCA business were collected. The study involved narrations from direct and indirect stakeholders up to saturation till common themes were found. Grounded theory methodology using constant comparison was explored to interpret the results. After the results were obtained, root cause analysis was undertaken using the why–why methodology to understand ground-level reality.

In total, 13 major issues were identified using grounded theory for narrative analysis that cab aggregator companies, driver-partners, and riders faced. The stakeholders' inability in the ecosystem to see each other's problems could be accorded to their self-interest, rational boundedness and asymmetric information. These findings collude with Banaji et al. (2004) and Chugh et al. (2005).

This study explained each stakeholder's perspectives about their counterparts that influence non-egalitarianism. The study further suggested possible areas for solving the issues and promoting cooperation.

Using textual analysis the authors study the relationship between social media sentiments and stock markets during the COVID-19 pandemic.

The study analysis is based on a sample of 1,616,007 tweets over the period January to June 2021 for seven countries. The authors process the tweets via the VADER analyzer thereby producing both positive and negative sentiment measures.

Particularly, the authors prove that higher positivism is associated with a short-term increase in stock prices. On the other side, negativism relates inversely to stock prices with long-term impact, in the case of English-spoken countries. Notably, the study results remain robust to the inclusion of various control variables, including virtual fear and Google vaccine indexes. Finally, the authors prove that positivism is associated with higher returns and lower volatility in the short-run, while negativism is linked with lower returns in the short run.

The study analysis also has significant policy implications for researchers, investors and policymakers. First, researchers can employ our measures to quantify market sentiments and expand their research arsenal to incorporate social media trends, thus providing better explanatory power. Second, during times of severe uncertainty such as in a pandemic period, investors could beneficially take into account our textual measures and empirical results when using asset pricing models or constructing their portfolios. Third, the finding that the stock market is heavily governed by sentimental behaviors, especially during crisis periods, implies that policymakers including central banks, governments and capital market commissions must consider these sentiments before exerting their policies. In this regard, governments can effectively develop policy tools and approaches to manage recovery from the pandemic, which translates to greater long-term economic resilience. Moreover, central banks should accordingly adjust their monetary policy measures in order to stabilize financial markets, and by extension, to stop the pandemic from turning into a renewed financial crisis. For example, asset purchase program is considered the main instrument of this kind of intervention.

The authors confirm that this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere. The paper should be of interest to readers in the areas of finance.

This study focuses on an emerging market, China, and investigates the effects of corporate research and development (R&D) spending and subsidies on stock market reactions to seasoned equity offering (SEO) announcements.

The study uses a sample of SEOs announced over the period of 2003–2018 in the Chinese A-share market. The cumulative abnormal stock returns (CARs) are adopted to measure the stock market response to SEOs. The R&D spending-to-sales ratio (R&D subsidies) in 2 years before SEO announcements is used to measure the pre-SEO R&D spending (R&D subsidies). The instrumental variable (IV) regression method is applied to address the endogeneity problem in the robustness test.

This study demonstrates that firms with high R&D spending suffer stock overpricing and experience a negative market reaction when they announce SEOs, but R&D subsidies alleviate stock overpricing and mitigate the negative relationship between R&D spending and SEO market reactions.

Although the prior studies have demonstrated that information asymmetry, which causes stock overpricing, explains negative stock market reactions to SEOs, it is unclear if a certain factor that causes information asymmetry affects SEO market reactions. This study fills this gap and focuses on R&D spending, demonstrating that R&D spending is negatively related to SEO performance.