Search results

The purpose of this study is to prolong the service life of the Al–Si alloy cylinder and achieve the objective of energy saving and emission reduction by the composite treatments.

Chemical etching + laser texturing + filled MoS₂ composite treatment was applied to the friction surface of aluminum–silicon (Al–Si) alloy cylinder. The friction coefficient and wear loss were measured to characterize the tribology property of cylinders.

The composite-treated Al–Si alloy cylinder had the lowest friction coefficient and weight loss. The friction coefficient and weight loss of the composite treatment were approximately 27.08% and 54.17% lower than those of the untreated sample, respectively. The laser micro-textures control the release of solid lubricant to the interface of friction pairs slowly, which prolongs the service life of cylinders.

The synergistic effect of the chemical etching + laser texturing + filled MoS2 modified the tribology properties of Al–Si alloy cylinder. The chemical etching raised the silicon particles to bear the load, and laser micro-textures control the release of solid lubricant to improve the lubrication property.

The paper aims to investigate the friction and wear properties of three surface-modified piston rings matched with a chromium-plated cylinder liner.

Samples were taken from the chromium-plated cylinder liner, Cr-Al2O3 ring, CrN ring and Mo ring. Tribo-tests were conducted on a reciprocating sliding tribometer under fully formulated engine oils. Friction coefficients and wear depths of three friction pairs were tested. Surface morphologies of cylinder liners and piston rings before and after test were analyzed.

Experimental results show that in the Cr-Al2O3 piston ring, scuffing occurred easily when matched with the chromium-plated cylinder liner; compared with the Mo ring, the CrN ring could decrease the wear depth of the piston ring from 2.7 to 0.2 μm, and the wear depth of cylinder liner remained; however, the friction coefficient increased from 0.113 to 0.123. The tribological performances of three surface-modified piston rings were significantly different when they matched with chromium-plated cylinder liner.

Chromium-plated cylinder liner and the three kinds of surface-modified piston rings have excellent friction and wear properties, respectively. However, according to the systematic characteristics of internal combustion (IC) engine tribology, only the appropriate cylinder liner–piston ring can improve the tribological performance of the IC engine. This paper reports the tribological performance of three surface-modified piston rings matched with a chromium-plated cylinder liner. The results can be used as reference for the design of high-power-density diesel engine.

Studies on mining text and generating intelligence on human resource documents are rare. This research aims to use artificial intelligence and machine learning techniques to facilitate the employee selection process through latent semantic analysis (LSA), bidirectional encoder representations from transformers (BERT) and support vector machines (SVM). The research also compares the performance of different machine learning, text vectorization and sampling approaches on the human resource (HR) resume data.

LSA and BERT are used to discover and understand the hidden patterns from a textual resume dataset, and SVM is applied to build the screening model and improve performance.

Based on the results of this study, LSA and BERT are proved useful in retrieving critical topics, and SVM can optimize the prediction model performance with the help of cross-validation and variable selection strategies.

The technique and its empirical conclusions provide a practical, theoretical basis and reference for HR research.

The novel methods proposed in the study can assist HR practitioners in designing and improving their existing recruitment process. The topic detection techniques used in the study provide HR practitioners insights to identify the skill set of a particular recruiting position.

To the best of the authors’ knowledge, this research is the first study that uses LSA, BERT, SVM and other machine learning models in human resource management and resume classification. Compared with the existing machine learning-based resume screening system, the proposed system can provide more interpretable insights for HR professionals to understand the recommendation results through the topics extracted from the resumes. The findings of this study can also help organizations to find a better and effective approach for resume screening and evaluation.

As an unhealthy dependence on social media platforms, social media addiction (SMA) has become increasingly commonplace in the digital era. The purpose of this paper is to provide a general overview of SMA research and develop a theoretical model that explains how different types of factors contribute to SMA.

Considering the nascent nature of this research area, this study conducted a systematic review to synthesize the burgeoning literature examining influencing factors of SMA. Based on a comprehensive literature search and screening process, 84 articles were included in the final sample.

Analyses showed that antecedents of SMA can be classified into three conceptual levels: individual, environmental and platform. The authors further proposed a theoretical framework to explain the underlying mechanisms behind the relationships amongst different types of variables.

The contributions of this review are two-fold. First, it used a systematic and rigorous approach to summarize the empirical landscape of SMA research, providing theoretical insights and future research directions in this area. Second, the findings could help social media service providers and health professionals propose relevant intervention strategies to mitigate SMA.

The purpose of this paper is to deconstruct the multi-faceted dimensions of Chinese travellers’ image of boutique hotels with a large amount of online textual data from social media (53,427 reviews written from 2014 to 2018), reinforcing the value creation of user-generated content via social media.

With the aid of Python, a computer language, online textual reviews (53,427 reviews) of 86 high-end boutique hotels in seven cities (Beijing, Shanghai, Hangzhou, Nanjing, Chengdu, Qingdao and Sanya) were collected from the top-ranked online travel agency in China, Ctrip.com. Then, the overall perceived image of boutique hotels was revealed with the aid of Python.

The results showed multiple dimensions of the image of boutique hotels. The overall image can be grouped into eight dimensions (room, service, food, environment, entertainment, location, price and value, and uniqueness). An affective image based on eight dimensions was further developed in the Chinese boutique hotel context. It appears that online data from social media are beneficial for hotel managers to learn travellers’ overall perceptions of boutique hotels and help put more effective management strategies in place in the hospitality industry.

The relationship between cognitive image and affective image should be further investigated in future research. Theoretical implications are discussed from both cognitive image and affective image perspectives in the boutique hotel context. Managerial implications are highlighted to help industry managers understand the travellers’ perceptions of the hotels, via online data from social media, and put more effective hotel strategies in hospitality industry.

By using textual online data from social media, this paper deconstructs both the cognitive image and the affective image of boutique hotels. The dimensions of the most frequently mentioned concepts related to the Chinese boutique hotel industry are profoundly deconstructed, as is the uniqueness of the image of boutique hotels. The work is valuable for promoting effective marketing strategies in the hotel industry.

The paper aims to identify the emergent themes of hotel guests’ satisfaction, to compare the distribution of the attributes of the themes between Chinese and North American guests and to compare the importance of the themes for different satisfaction levels between Chinese and North American guests from a cross-cultural perspective.

By adopting Python (a computer language), the word-frequency method was used to identify emergent themes of hotel guests’ satisfaction. Topic modeling was adopted to compare the attributes distribution of each theme and the features of satisfaction between Chinese and North American guests.

First, three themes were identified including functionality, staff and price. Functionality can be further categorized into five subthemes, namely, room, travel, food, environment and hotel facility. Second, the distribution of the attributes of the themes between Chinese and North American guests was compared from a cross-cultural perspective. Chinese guests tend to mention both lifestyles- and social norms–related attributes and expect personalized service, while North American guests mainly prefer to describe lifestyle-related attributes and prefer standardized service. Third, the study compared the changing importance of the themes (functionality, staff and price) for different satisfaction levels between Chinese and North American guests. As the satisfaction level decreases, the importance of functionality decreases, that of staff increases and that of price remain stable for Chinese guests. In contrast, the importance of each theme has fluctuated mildly from the high to the low satisfaction level for North American guests.

Proposed managerial implications are to highlight lifestyle- and social norms-related attributes, as well as personalized service for Chinese guests. However, lifestyle-related attributes and standardized service should be facilitated for North American guests. Specific suggestions were made to help improve hotel performance such as the good performance of functional-related attributes, which could enhance satisfaction and better staff performance, which would reduce dissatisfaction.

By mining big data, this study investigated hotel guests’ satisfaction from a dynamic instead of a static perspective. This study provides some rare insights into differences in key attributes influencing satisfaction levels of Chinese versus North American guests staying in luxury hotels in China. This study also takes a novel approach to examine the dynamics of the importance of the various themes at different satisfaction levels, and contrast these dynamics between Chinese and North American guests. The findings offer valuable insight into market segmentation and management in the hospitality industry.

China's stock market, which serves as an example of emerging markets, is steadily maturing in the context of globalization. In order to analyze the pricing mechanism of China's stock market, this paper builds a six-factor model to address the market features that are structurally efficient but not entirely efficient.

This study updates the Fama–French factor model's construction process to account for the unique features of China's stock market before creating a model that incorporates size, volume, value, profitability, and profit-income factors based on institutional investors' trading behavior and research preferences. The SWS three-tier sector stock index's monthly and quarterly data for the years 2016–2021 are used as samples for this study.

The results imply that China's stock market is structurally efficient and exhibits high levels of rationality in the region dominated by institutional investors. Specifically, big-size and high-volume portfolios that perform well in terms of liquidity can receive trading premiums. Growth-style sectors prevail at present, and investing in sectors with strong profitability and reliable financial reporting data can produce better returns.

The research on China's stock market can be extended to improve the understanding of the development process of similar emerging markets, thereby promoting their improvement. To enhance the development of emerging markets, the regulators should attach great importance to the role of local institutional investors in driving the market to maturity. It is crucial to adopt a structured approach to examine the market pricing mechanism throughout the middle stage of the transition from developing to mature markets.

This study offers a structured viewpoint on asset pricing in growing emerging markets by combining the multi-factor pricing model approach with behavioral finance theories.

The authors hope to filter valuable information from online reviews, obtain objective and accurate information about the demands of auto consumers and help auto companies develop more reasonable production and marketing strategies for healthy and sustainable development. This paper aims to discuss the aforementioned objectives.

The authors collected review data from online automotive forums and generated a corpus after pre-processing. Then, the authors extracted consumer demands and topics using the LDA model. Finally, the authors used a trained Word2vec tool to extend the consumer demand topics.

Different types of vehicle consumers have the same demands, such as “Space,” “Power Performance,” and “Brand Comparison,” and distinct demands, such as “Appearance,” “Safety,” “Service,” and “New Energy Features”; consumers who buy new energy vehicles are still accustomed to comparing with the brands or models of fuel vehicles; new energy vehicles consumers pay more attention to services and service quality during the purchasing and using process.

The development time of new energy vehicles is relatively short, with some models being available for only one year or even six months. The smaller amount of available data may impact the applicability of topic models. The sample size, especially for new energy vehicles, needs to be increased to improve the general applicability of topic models further.

First, this measure helps online review websites improve their existing review publication mechanisms, enhance the overall quality of online review content, increase user traffic and promote the healthy development of online review websites. Second, this allows for timely adjustments in future product production and sales plans and further enhances automotive companies' ability to leverage online reviews for Internet marketing.

The authors have improved the accuracy and stability of the fused topic model, providing a scientific and efficient research tool for multi-dimensional topic mining of online reviews. With the help of research results, consumers can more easily understand the discussion topics and thus filter out valuable reference information. As a result, automotive companies may gain information about consumer demands and product quality feedback and thus quickly adjust production and marketing strategies to increase sales and market share.

The purpose of this paper is to propose a graph-based representation approach for evolutionary knowledge under the big data circumstance, aiming to gradually build conceptual models from data.

A semantic data model named meaning graph (MGraph) is introduced to represent knowledge concepts to organize the knowledge instances in a graph-based knowledge base. MGraph uses directed acyclic graph–like types as concept schemas to specify the structural features of knowledge with intention variety. It also proposes several specialization mechanisms to enable knowledge evolution. Based on MGraph, a paradigm is introduced to model the evolutionary concept schemas, and a scenario on video semantics modeling is introduced in detail.

MGraph is fit for the evolution features of representing knowledge from big data and lays the foundation for building a knowledge base under the big data circumstance.

The representation approach based on MGraph can effectively and coherently address the major issues of evolutionary knowledge from big data. The new approach is promising in building a big knowledge base.

Food SMEs is the backbone of local and world economy. Even while food SMEs are aware of the potential advantages of implementing supply chain analytics (SCA), only a small number of companies use data-based decision-making. This is because of technophobia. In light of this, the purpose of this study is to investigate the factors that have an impact on SCA adoption which in turn influence the sustainable performance of firms.

The data were collected from 221 managers working in food-related SMEs in China by using a questionnaire-based survey. The framework of this study was validated using a rigorous statistical procedure using the technique, namely, partial least squares structural equation modelling.

The findings of this study suggest that all modified UTAUT components (i.e. performance expectancy, effort expectancy, social influence, facilitating conditions and technophobia) significantly influence SCA adoption. Moreover, the existing study highlights and confirms the significance of adopting SCA to improve sustainable performance.

This research is novel, as it extends and investigates the theoretical framework based on UTAUT theory in SCA context and its impact on sustainable organizational performance. In addition, the factor of technophobia is tested in SCA context. This study has several contributory managerial implications for food SMEs.