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The purpose of this paper is to examine the mechanism of how peer review helpfulness evaluation in online review communities is established, drawing upon the internalization and identification routes of persuasion effect.

Based on book reviews selected from Douban.com (a prestigious review community in China), this study used econometric models to investigate the effects of both reviews and reviewers’ characteristics on peer review helpfulness evaluation in review communities.

Review internalization is more persuasive than reviewers’ identification in peer evaluations, in terms of both short and long reviews. Reviews with extreme negative ratings tend to obtain higher level of helpfulness evaluation than those with positive or moderate ratings. The influence of reviewers’ characteristics is a significant cue in helping consumers to establish the trust perception in the context of short reviews, while its function diminishes in the context of long reviews, thus suggesting the importance of reviewers’ identification for short reviews in review communities.

The findings will enhance current understanding of peer review review helpfulness evaluation in online review communities and help practitioners administrate community reviews intelligently, help members write better reviews and customers in their product browsing experience.

First, this study enriches review evaluation research in review communities by demonstrating the importance of internalization and identification lens of persuasion effect when explaining review helpfulness; second, this study helps to confirm the existing findings that reviews with extreme negative ratings are more helpful than those with moderate or positive ratings in review communities; third, this study proposes a new perspective pertaining to the relationship between reviewers’ identification and helpfulness evaluation.

The purpose of this paper is to establish an accurate model to quantify the effect of conductor roughness on insertion loss (IL) and provide improved measurements and suggestions for manufacturing good conductive copper lines of printed circuit board.

To practically investigates the modified model of conductor roughness, three different kinds of alternate oxidation treatments were used to provide transmission lines with different roughness. The IL results were measured by a vector net analyzer for comparisons with the modified model results.

An accurate model, with only a 1.8% deviation on average from the measured values, is established. Compared with other models, the modified model is more reliable in industrial manufacturing.

This paper introduces the influence of tiny roughness structures on IL. Besides, this paper discusses the effect of current distribution on IL.

This chapter investigates the impact of the COVID-19 pandemic on economic stimulus policies. Based on data from 156 economies, empirical results show that in the medium term, cumulative effect of COVID-19 pandemic is positively correlated with the economic stimulus policies but not in the short term. Heterogeneity tests show that while economic policies are used in developed economies more often, restrictive measures in developing countries are likely used as a substitution; deaths have a positive impact on economic stimulus policies but confirmed cases not. The results suggest that the pandemic may reinforce economic inequality due to potential stimulus policy capabilities, requiring international coordination and assistance to low-and-middle income countries in various aspects.

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.

This study aims to integrate perspectives of transaction costs and capabilities to investigate how the configurational fit between outsourcing strategies, asset specificity of projects and the capabilities of general contractors could achieve high outsourcing performance.

Employing fuzzy-set qualitative comparative analysis, this study analyzes 31 outsourcing cases in construction projects to examine outsourcing performance under different combinations of transaction costs and capabilities.

The findings highlight six different but functionally equivalent configurations to high outsourcing performance, which vary according to attributes of projects and capabilities of contractors. Further, this study develops four context-dependent paths that link outsourcing strategies to high outsourcing performance: leading practitioner, collaborative manager, partnership dependent, and struggler.

How do contractors in construction projects navigate outsourcing practices to high performance? This study provides a configurational perspective to make outsourcing strategy decisions. Based on the firm’s capabilities and project asset specificity, contractors are encouraged to select from six different but functionally equivalent combinations and thereby four outsourcing strategies, namely leading practitioner, collaborative manager, partnership dependent, and struggler, to achieve high outsourcing performance.

This study contributes to the body of knowledge by offering a holistic perspective that integrates production and transaction costs to analyze contractors’ outsourcing strategies, contributing to the integration of the transaction cost perspective and capability perspective.

Successful digital transformation involves all areas which bring new impacts and challenges to the leadership of the enterprise. From the perspective of organizational identification, the authors construct a theoretical model of digital leadership–digital strategic consensus–digital transformation and explore the different moderated mediation effects of diversity types.

This paper obtains data from 351 Chinese science and technology enterprises and uses regression analysis and bootstrap analysis to test the research hypotheses.

The results demonstrate that digital leadership has a positive impact on digital transformation. Digital strategic consensus partially mediates the linkage between digital leadership and digital transformation. Disparity diversity and variety diversity positively moderate the mediating role of digital strategic consensus between digital leadership and digital transformation, respectively; and separation diversity negatively moderates the mediating role of digital strategic consensus between digital leadership and digital transformation.

The research innovatively measures digital leadership and digital transformation. It expands the application of leadership, strategic consensus, diversity and other related theories in a digital context and provides a decision-making basis for enterprises' digital transformation.

The purpose of this paper is to provide the historical background of genealogical records and analyze the value of Chinese genealogical research through the study of names and genealogical resources.

The paper examines the historical evolution and value of Chinese genealogical records, with the focus on researching the Islamic Chinese names used by the people living in Guilin. The highlight of this paper includes the analysis and evolution of the Islamic Chinese names commonly adopted by the local people in Guilin. It concludes with the recommendations on emphasizing and making the best use of genealogical records to enhance the research value of Chinese overseas studies.

The paper covers the history of Islam and describes how the religion was introduced into China, as well as Muslims' ethnicity and identity. It also places focus on the importance of building a research collection in Asian history and Chinese genealogy.

This research study has a strong subject focus on Chinese genealogy, Asian history, and Islamic Chinese surnames. It is a narrow field that few researchers have delved into.

The results of this study will assist students, researchers, and the general public in tracing the origin of their surnames and developing their interest in the social and historical value of Chinese local history and genealogies.

The study of Chinese surnames is, by itself, a particular field for researching the social and political implications of contemporary Chinese society during the time the family members lived.

Very little research has been done in the area of Chinese local history and genealogy. The paper would be of value to researchers such as historians, sociologists, ethnologists and archaeologists, as well as students and anyone interested in researching a surname origin, its history and evolution.

This study aims to explore the evolutionary pattern of discussion topics over time in an online depression self-help community.

Using the Latent Dirichlet Allocation (LDA) method, the authors analyzed 17,534 posts and 138,567 comments posted over 8 years on an online depression self-help group in China and identified the major discussion topics. Based on significant changes in the frequency of posts over time, the authors identified five stages of development. Through a comparative analysis of discussion topics in the five stages, the authors identified the changes in the extent and range of topics over time. The authors discuss the influence of socio-cultural factors on depressed individuals' health information behavior.

The results illustrate an evolutionary pattern of topics in users' discussion in the online depression self-help group, including five distinct stages with a sequence of topic changes. The discussion topics of the group included self-reflection, daily record, peer diagnosis, companionship support and instrumental support. While some prominent topics were discussed frequently in each stage, some topics were short-lived.

While most prior research has ignored topic changes over time, the study takes an evolutionary perspective of online discussion topics among depressed individuals. The authors provide a nuanced account of the progression of topics through five distinct stages, showing that the community experienced a sequence of changes as it developed. Identifying this evolutionary pattern extends the scope of research on depression therapy in China and offers a deeper understanding of the support that individuals with depression seek, receive and provide online.

Social media texts as a data source in depression research have emerged as a significant convergence between Information Management and Public Health in recent years. This paper aims to sort out the depression-related study conducted on the text on social media, with particular attention to the research theme and methods.

The authors finally selected research articles published in Web of Science, Wiley, ACM Digital Library, EBSCO, IEEE Xplore and JMIR databases, covering 57 articles.

(1) According to the coding results, Depression Prediction and Linguistic Characteristics and Information Behavior are the two most popular themes. The theme of Patient Needs has progressed over the past few years. Still, there is a lesser focus on Stigma and Antidepressants. (2) Researchers prefer quantitative methods such as machine learning and statistical analysis to qualitative ones. (4) According to the analysis of the data collection platforms, more researchers used comprehensive social media sites like Reddit and Facebook than depression-specific communities like Sunforum and Alonelylife.

The authors recommend employing machine learning and statistical analysis to explore factors related to Stigmatization and Antidepressants thoroughly. Additionally, conducting mixed-methods studies incorporating data from diverse sources would be valuable. Such approaches would provide insights beneficial to policymakers and pharmaceutical companies seeking a comprehensive understanding of depression.

This article signifies a pioneering effort in systematically gathering and examining the themes and methodologies within the intersection of health-related texts on social media and depression.

This study provides a bibliometric analysis of business literature on the interlinks of fintech, climate risks, and sustainable finance. Fintech growth promotes national environmental efficiency and green finance by decreasing carbon intensity toward the net-zero target. National fintech growth moderates the impact of environmental, social, and governance investment on bank efficiency. Fintech mitigates the loan bankruptcy risk imposed by climate risks with strict mortgage lending decisions due to climate concerns. Fintech applications in banking systems optimize financing costs and increase the accessibility of money for firms, decreasing corporate greenwashing behaviors and promoting green innovation. The existing literature leaves room for future studies on fintech to promote climate finance with important policies for climate action toward Sustainable Development Goals.