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The purpose of this paper is to propose a type of hybrid bearing lubricated with supercritical carbon dioxide (S-CO₂) and to investigate the stiffness and damping characteristics of the bearing under hydrostatic status.

Established a test rig for radial bearings lubricated with S-CO₂ and used it to measure the dynamic coefficients by recording the relative and absolute displacements of bearing. Test bearing is mounted on a nonrotating, stiff shaft. Using static loading experiments to obtain structural stiffness. The dynamic coefficient regularities of the test bearing under hydrostatic status were revealed through dynamic loading experiments.

Experiment results indicate that test bearing displayed increased stiffness when subjected to high excitation frequencies and low excitation forces, as well as elevated damping when exposed to low excitation frequencies and low excitation forces. Additionally, an increase in either environmental pressure or hydrostatic recess pressure can elevate the dynamic coefficient. The effect of temperature on the dynamic coefficient is more pronounced around the critical temperature of S-CO₂.

Designed a type of hybrid bearing for use in the Brayton cycle that is lubricated with S-CO₂ and uses hydrostatic lubrication during start-stop and hydrodynamic lubrication during high-speed operation. The hybrid bearing reduces the wear and friction power consumption of gas bearing. However, few experimental analyses have been conducted by researchers in this field.

A new business model online to offline (O2O) has emerged in recent years. Similar to many new models at an early stage, O2O has inconsistent definitions which not only inhibit its adoption but also poorly differentiate O2O from other existing business models. To resolve the two issues, the authors propose an approach of definition development.

To show the usefulness of the approach, the authors demonstrate the differences among O2O and other business models with the use of the distinctive definition and thereby evaluate adoption of O2O from a practical perspective and identify research directions from a theoretical perspective based on the differences.

The authors' proposed approach of definition development integrates the work of Tatarkiewicz (1980) and Nickerson et al. (2013). The approach generates a distinctive definition of O2O with important analytical dimensions which help decision-making of adoption of O2O.

The paper aims to make several contributions. First, on theoretical contribution, the authors confine the scope of O2O studies and facilitate accumulation of more coherent knowledge of O2O. The authors help O2O evolve from a “buzz word” of successful stories in real businesses to a more serious concept from an academic perspective. Second, from a practical perspective, the authors' definition provides business executives with critical evaluative dimensions for gauging the adoption of O2O. Lastly, from a methodological perspective, the proposed approach can be used in future to define an emerging concept in real life businesses.

This paper intends to study how geographic heterogeneity in urban vibrancy, especially in human capital creation, helps explain persist firm valuation dispersion across cities in China.

This paper studies geographic differences in firm valuations of 1,023 listed companies headquartered in 35 major cities in China from 2001 to 2018. The authors estimate panel regressions of local firm Tobin's q on city fixed effects or city endowed attributes in human capital creation after controlling industry-year fixed effects as well as a set of firm and city time variant attributes.

The results show persistent, significant city-to-city differences in Tobin's q, especially among large, mature or high labor-intensive firms. To explain such geographic differences in firm valuations, the authors identify several factors of the endowed city competitive advantages in creating human capital that play important roles in explaining the persistent geographic firm valuation premia.

This paper provides the first systematic analysis of urban vibrancy in human capital supply in explaining persistent geographic firm valuation dispersion in China. The evidence suggests that city endowed comparative advantages in supplying human capital have created long-lasting, and growing, shareholder wealth by attracting and retaining talents and human resources in local firms.

The purpose of this paper is twofold: first, to explore how China uses a social credit system as part of its “data-driven authoritarianism” policy; and second, to investigate how datafication, which is a method to legitimize data collection, and dataveillance, which is continuous surveillance through the use of data, offer the Chinese state a legitimate method of monitoring, surveilling and controlling citizens, businesses and society. Taken together, China’s social credit system is analyzed as an integrated tool for datafication, dataveillance and data-driven authoritarianism.

This study combines the personal narratives of 22 Chinese citizens with policy analyses, online discussions and media reports. The stories were collected using a scenario-based story completion method to understand the participants’ perceptions of the recently introduced social credit system in China.

China’s new social credit system, which turns both online and offline behaviors into a credit score through smartphone apps, creates a “new normal” way of life for Chinese citizens. This data-driven authoritarianism uses data and technology to enhance citizen surveillance. Interactions between individuals, technologies and information emerge from understanding the system as one that provides social goods, using technologies, and raising concerns of privacy, security and collectivity. An integrated critical perspective that incorporates the concepts of datafication and dataveillance enhances a general understanding of how data-driven authoritarianism develops through the social credit system.

This study builds upon an ongoing debate and an emerging body of literature on datafication, dataveillance and digital sociology while filling empirical gaps in the study of the global South. The Chinese social credit system has growing recognition and importance as both a governing tool and a part of everyday datafication and dataveillance processes. Thus, these phenomena necessitate discussion of its consequences for, and applications by, the Chinese state and businesses, as well as affected individuals’ efforts to adapt to the system.

Tracking trends in new technology funding patterns is essential for venture scaling. The emerging advanced digital technologies (ADT) such as virtual reality (VR), artificial intelligence (AI), blockchain and Internet-of-things (IoT) promote business innovation adaptations, and in turn, reshape the industrial landscape. To attract nascent funding for such prospective projects among the public, well-articulated project pitches that are equipped with effective marketing communication convey the projects' importance and marketability. Specifically, when the entrepreneurs and the crowdfunding platform users interact via different types of crowdfunding platforms, pitch framing, including the signaling of ADT terms, project location and fundraising goal, becomes imperative to help facilitate crowdfunding success.

Drawing on data collected from six leading US-based equity and reward-based crowdfunding platforms in 2020, an empirical study was performed. Using the text analysis approach, the authors examined the positive effects of incorporating technology orientation on crowdfunding success. While the effect between the project description's signaling of geographic location, fundraising goal and articulation style on fundraising success, while controlling for project and platform characteristics.

The results suggested that the technology-orientated projects are more likely to achieve better fundraising outcomes. Taking crowdfunding platform types, project locations, minimum fundraising goals and articulation with analytical and authentic into consideration, the results still hold.

Building on the theoretical framework of signaling theory, the authors consider the crowdfunding-specific contextual factors to enhance the understanding of the positivity impact of technology orientation. By such addition, it facilitates more effective strategic composition of entrepreneurs' fundraising conversations.

This paper aims to explore netizen’s opinions on cryptocurrency under the lens of emotion theory and lexicon sentiments analysis via machine learning.

An automated Web-scrapping via RStudio is performed to collect the data of 15,000 tweets on cryptocurrency. Sentiment lexicon analysis is done via machine learning to evaluate the emotion score of the sample. The types of emotion tested are anger, anticipation, disgust, fear, joy, sadness, surprise, trust and the two primary sentiments, i.e. negative and positive.

The supervised machine learning discovers a total score of 53,077 sentiments from the sampled 15,000 tweets. This score is from the artificial intelligence evaluation of eight emotions, i.e. anger (2%), anticipation (18%), disgust (1%), fear (3%), joy (15%), sadness (3%), surprise (7%), trust (15%) and the two sentiments, i.e. negative (4%) and positive (33%). The result indicates that the sample primarily contains positive sentiments. This finding is theoretically significant to measure the emotion theory on the sampled tweets that can best explain the social implications of the cryptocurrency phenomenon.

This work is limited to evaluate the sampled tweets’ sentiment scores to explain the social implication of cryptocurrency.

The finding is necessary to explain the recent phenomenon of cryptocurrency. The positive sentiment may describe the increase in investment in the decentralised finance market. Meanwhile, the anticipation emotion may illustrate the public’s reaction to the bubble prices of cryptocurrencies.

Previous studies find that the social signals, e.g. word-of-mouth, netizens’ opinions, among others, affect the cryptocurrencies’ movement prices. This paper helps explain the social implications of such dynamic of pricing via sentiment analysis.

This study contributes to theoretically explain the implications of the cryptocurrency phenomenon under the emotion theory. Specifically, this study shows how supervised machine learning can measure the emotion theory from data tweets to explain the implications of cryptocurrencies.

This paper aims to present a transformation mechanism designed for a miniature throw-able robot, including the mechanical model, related analysis and experiments.

The robot can be thrown into suspicious areas. It keeps in a ball-shaped configuration during throwing and uses the driving motors to implement transformation of the mobile form. A foldable tail is also released out as a third point to guarantee the stability of the robot.

By transformation, the robot possesses the overall shock protection like a regular spherical robot and also has detection ability and agile mobility as a two-wheeled robot.

An innovative transformation mechanism was designed, analyzed and tested. The mechanism is suitable for a throw-able robot which is simple in structure, small in volume and light in weight. Effectiveness of the transformation design has been validated through experiments.

There is a strong prerequisite for organizations to analyze customer review behavior to evaluate the competitive business environment. The purpose of this study is to analyze and predict customer reviews of halal restaurants using machine learning (ML) approaches.

The authors collected customer review data from the Yelp website. The authors filtered the reviews of only halal restaurants from the original data set. Following cleaning, the filtered review texts were classified as positive, neutral or negative sentiments, and those sentiments were scored using the AFINN and VADER sentiment algorithms. Also, the current study applies four machine learning methods to classify each review toward halal restaurants into its sentiment class.

The experiment showed that most of the customer reviews toward halal restaurants were positive. The authors also discovered that all of the methods (decision tree, linear support vector machine, logistic regression and random forest classifier) can correctly classify the review text into sentiment class, but logistic regression outperforms the others in terms of accuracy.

The results facilitate halal restaurateurs in identifying customer review behavior.

Sentiment and emotions, according to appraisal theory, form the basis for all interactions, facilitating cognitive functions and supporting prospective customers in making sense of experiences. Emotion theory also describes human affective states that determine motives and actions. The study looks at how potential customers might react to a halal restaurant’s consensus on social media based on reviewers’ opinions of halal restaurants because emotions can be conveyed through reviews.

This study applies machine learning approaches to analyze and predict customer sentiment based on the review texts toward halal restaurants.