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Previous experience is a critical factor affecting entrepreneurial activities; however, it has not been fully studied in the existing literature. This study attempts to comprehensively reveal the routes and mechanisms of occupational experience that affect entrepreneurial activities and assess the entrepreneurial potential of different occupational practitioners.

By matching occupational characteristics with entrepreneurs' competence, this study proposes ten hypotheses about how occupational experience affects entrepreneurial entry and performance. This empirical study is based on the Occupational Information Network database and Chinese survey data. Factor and regression analyses were used in the empirical research.

This study verifies that different occupational practitioners have varied entrepreneurial potential. Occupational experience, including occupational uncertainty, market contact and social capital, gained from previous experience significantly affects entrepreneurial entry. Meanwhile, occupational characteristics, including management experience, marketing experience, social capital, financial capital, risk-taking ability and creativity, accumulated from previous experience, have a significant impact on entrepreneurial performance.

This study is a pioneering attempt to reveal the relationship between occupational experience and entrepreneurial activities. The transmission mechanism of previous experiences affecting entrepreneurial activities is comprehensively revealed by relaxing the assumption of a representative occupation. These findings provide a theoretical foundation for empirical evidence and have important practical value.

Preprint has become an important vehicle for academic communications and discussions. However, in preprint, there is a lack of a sufficient quality control mechanism such as peer review, which is a proven quality assurance practice that is used in traditional academic publishing services. To address the problem leveraging on the power of this practice, the authors introduce into preprint a self-organizing peer review method by applying the concept of token economy and the blockchain technology.

Specifically, this paper proposes an idea that applies the token economy concept to the design of the incentive and penalty mechanisms for peer reviewers in preprint to assure the qualities of its publications. Steemit has been studied to demonstrate the characteristics of the mechanisms.

A token economy-enhanced framework for self-organizing peer review in preprint is also proposed. The resulting preprint system is an academic community-oriented, self-organizing and blockchain-based content publishing system that is designed to run on both permissioned and permissionless blockchains.

First, since peer review is on a voluntary basis and not profits oriented, the “monetary” incentive and penalty mechanisms borrowed from Steemit may conflict with academic ethics. Second, the authors proposed to deploy the authors’ token economy on blockchain, but the current mainstream decentralized blockchain services are too few to warrant a foreseeable successful future for the authors’ application. In fact, as the flagship of blockchain 2.0, the Ethereum blockchain suffers from the problem of scalability, which leads to its applications' lower performances, longer response times and eventually more negative user experiences as time goes by. Finally, the authors’ proposed version of preprint has not been implemented, and hence, its practical effectiveness and acceptance by academia are yet to be evaluated.

In this paper, the authors proposed a token economy-based framework for self-organizing peer review in preprint leveraging on blockchain technology. This framework encourages positive interactions between authors and reviewers, which helps to establish a healthy academic ecology that produces more contents with better qualities. Application of a solution based on the authors’ framework should impact the current academic communities by offering a new academic peer reviewing tool that has a built-in mechanism for self-behavior correction and quality assurance.

Through adaption, the framework can be applied to other domains as well. In such domains, a large amount of feedbacks from partakers are needed and there exists a tremendous amount of work to filter noises in feedbacks so as to ensure that as many the quality ones as possible are delivered for a variety of purposes. The authors’ framework essentially impacts almost all domains where there exists a need to collect and filter large amount of feedbacks, and using the authors’ framework-based solution is cost-saving, which can be seen as a major potential contribution of the research.

The incentive and penalty mechanisms encourage positive interactions between authors and reviewers, and it helps to establish a healthy academic ecology that produces high-volume contents with good qualities.

This paper aims to investigate the effects of different ultrasonic-assisted loading degrees on the microstructure, mechanical properties and the fracture morphology of Cu/Zn+15%SAC0307+15%Cu/Al solder joints.

A new method in which 45 μm Zn particles were mixed with 15% 500 nm Cu particles and 15% 500 nm SAC0307 particles as solders (SACZ) and five different ultrasonic loading degrees were applied for realizing the soldering between Cu and Al at 240 °C and 8 MPa. Then, SEM was used to observe and analyze the soldering seam, interface microstructure and fracture morphology; the structural composition was determined by EDS; the phase of the soldering seam was characterized by XRD; and a PTR-1102 bonding tester was adopted to test the average shear strength.

The results manifest that Al–Zn solid solution is formed on the Al side of the Cu/SACZ/Al joints, while the interface IMC (Cu₅Zn₈) is formed on the Cu side of the Cu/SACZ/Al joints. When single ultrasonic was used in soldering, the interface IMC (Cu₅Zn₈) gradually thickens with the increase of ultrasonic degree. It is observed that the proportion of Zn or ZnO areas in solders decreases, and the proportion of Cu–Zn compound areas increases with the variation of ultrasonic degree. The maximum shear strength of joint reaches 46.01 MPa when the dual ultrasonic degree is 60°. The fracture position of the joint gradually shifts from the Al side interface to the solders and then to the Cu side interface.

The mechanism of ultrasonic action on micro-nanoparticles is further studied. By using different ultrasonic loading degrees to realize Cu/Al soldering, it is believed that the understandings gained in this study may offer some new insights for the development of low-temperature soldering methodology for heterogeneous materials.

This paper aims to investigate consumers’ demand patterns for products with nutritional benefits and products with no nutritional benefits across processed healthy and unhealthy foods. This paper integrates price changes (i.e. increases and decreases) into a demand model and quantifies their relative impact on the quantity of food purchased. First, how demand patterns vary across processed healthy and unhealthy products is investigated; second, how demand patterns vary across nutrition-benefited (NB) products and non-nutrition-benefited (NNB) products is examined; and third, how consumers respond to price increases and decreases for NB across processed healthy and unhealthy foods is investigated.

Here, a demand model quantifying scenarios for price changes in consumer food choice behaviour is proposed, and controlled for heterogeneity at household, store and brand levels.

Consumers exhibit greater sensitivity to price decreases and less sensitivity to price increases across both processed healthy and unhealthy foods. Moreover, the research shows that consumers’ demand sensitivity is greater for NNB products than for NB products, supporting our prediction that NB products have higher brand equity than NNB products. Furthermore, the research shows that consumers are more responsive to price decreases than price increases for processed healthy NB foods, but more responsive to price increases than price decreases for unhealthy NB foods. The findings suggest that consumers exhibit a desirable demand pattern for products with nutritional benefits.

Although studies on the effects of nutritional benefits on demand have proliferated in recent years, researchers have only estimated their impact without considering the effect of price changes. This paper contributes by examining consumers’ price sensitivity for NB products across processed healthy and unhealthy foods based on consumer scanner data, considering both directionalities of price changes.

The purpose of this paper is to explore how leaders' emotional intelligence (EI) influences subordinates' trust and to examine the roles played in the process by abusive supervision (a negative leadership) and leader‐member exchange (LMX) (a positive leadership).

According to revelations in the case of Foxconn's jumping events and LMX theory, this paper argues that low levels of leaders' EI affect their subordinates' perception of abusive supervision and tends to cause their mistrust of employers in return, further damaging the employer‐employee relationship. Tension will develop or be intensified among such relationships as time evolves and relationship length extends. A superior‐subordinate matching questionnaire survey was conducted among enterprises in Shenzhen, China. About 202 valid samples were eventually collected. The data were analyzed through correlation analysis, regression analysis, CFA, EFA and SEM using SPSS and LISREL.

The EI of superiors has a significant positive impact on the personal trust between subordinates and superiors, in which both abusive supervision and LMX play a partial mediating role; and the relationship length of superiors and subordinates plays a moderating role between LMX and affective trust.

The paper advises that when selecting leaders, more emphasis should be placed on EI, and managers should be trained to improve their emotional skills.

The paper extends the research on the antecedent and consequence variables of abusive supervision in Chinese enterprises, discussing both positive and negative leadership.

This paper presents a new approach of productivity estimation of cutter suction dredger operation through data mining and learning from real-time big data.

The paper used big data, data mining and machine learning techniques to extract features of cutter suction dredgers (CSD) for predicting its productivity. ElasticNet-SVR (Elastic Net-Support Vector Machine) method is used to filter the original monitoring data. Along with the actual working conditions of CSD, 15 features were selected. Then, a box plot was used to clean the corresponding data by filtering out outliers. Finally, four algorithms, namely SVR (Support Vector Regression), XGBoost (Extreme Gradient Boosting), LSTM (Long-Short Term Memory Network) and BP (Back Propagation) Neural Network, were used for modeling and testing.

The paper provided a comprehensive forecasting framework for productivity estimation including feature selection, data processing and model evaluation. The optimal coefficient of determination (R²) of four algorithms were all above 80.0%, indicating that the features selected were representative. Finally, the BP neural network model coupled with the SVR model was selected as the final model.

Machine-learning algorithm incorporating domain expert judgments was used to select predictive features. The final optimal coefficient of determination (R²) of the coupled model of BP neural network and SVR is 87.6%, indicating that the method proposed in this paper is effective for CSD productivity estimation.

The main purpose of this study is to explore the effect of three dimensions of “home feeling” – home-as-practical, home-as-social and home-as-attachment – on the online rating of homestays and additionally considers the accommodation’s attribute performance and level of sharing.

To achieve the research aims, more than 9,738,335 items of user-generated content concerning 743,953 Airbnb listings covering 35 cities were collected as the study data. These data are analyzed through hierarchical regression.

The results show that all three dimensions of home feeling positively affect the online rating; all three dimensions negatively moderate the relationship between attribute performance and online rating; the size of the moderating effect of each dimension on the relationship between attribute performance and online rating gradually increases in the order home-as-practical, home-as-social and home-as-attachment; and as the level of sharing increases, the moderating effect of home feeling on the relationship between attribute performance and online rating diminishes.

This study contributes to the literatures on the role of home feeling in homestays, the online rating of homestays and the motivations of guests who choose different room types. The findings of this study can help hosts better understand the formation of online rating of homestays, make targeted improvements in rooms and services and create a home feeling for specific degrees of sharing. This in turn will help them to improve the online rating of their homestays, establish an excellent online reputation and, ultimately, increase sales.

This study advances knowledge by confirming three dimensions of home feeling not only have direct positive impacts on online rating but also mitigate the impact of attribute performance on online rating. This effect differs significantly in magnitude with the degree of sharing.

Black-Scholes Imolied volatility (8SIV) has a few drawbacks. One is that the model Is not much successful in fitting the option prices. and It Is n야 guaranteed the model is correct one. Second. the usual tradition in using the BSIV is that only at-the-money Options are used. It is well-known that IV's of In-the-money or Qut-of-the-money ootions are much different from those estimated from near-the-money options.

In this regard, a new model is confronted with Korean market data. Brittenxmes and Neuberger (2000) derive a formula for volatility which is a function of option prices‘ Since the formula is derived without using any option pricing model. volatility estimated from the formula is called model-tree implied volatillty (MFIV). MFIV overcomes the two drawbacks of BSIV. Jiang and Tian (2005) show that. with the S&P index Options (SPX), MFIV is suoerlor to historical volatility (HV) or BSIV in forecasting the future volatllity.

In KOSPI 200 index options, when the forecasting performances are compared, MFIV is better than any other estimated volatilities. The hypothesis that MFIV contains all informations for realized volatility and the other volatilities are redundant is oot rejected in any cases.

Heterogeneous graphs, composed of diverse nodes and edges, are prevalent in real-world applications and effectively model complex web-based relational networks, such as social media, e-commerce and knowledge graphs. As a crucial data source in heterogeneous networks, Node attribute information plays a vital role in Web data mining. Analyzing and leveraging node attributes is essential in heterogeneous network representation learning. In this context, this paper aims to propose a novel attribute-aware heterogeneous information network representation learning algorithm, AAHIN, which incorporates two key strategies: an attribute information coverage-aware random walk strategy and a node-influence-based attribute aggregation strategy.

First, the transition probability of the next node is determined by comparing the attribute similarity between historical nodes and prewalk nodes in a random walk, and nodes with dissimilar attributes are selected to increase the information coverage of different attributes. Then, the representation is enhanced by aggregating the attribute information of different types of high-order neighbors. Additionally, the neighbor attribute information is aggregated by emphasizing the varying influence of each neighbor node.

This paper conducted comprehensive experiments on three real heterogeneous attribute networks, highlighting the superior performance of the AAHIN model over other baseline methods.

This paper proposes an attribute-aware random walk strategy to enhance attribute coverage and walk randomness, improving the quality of walk sequences. A node-influence-based attribute aggregation method is introduced, aggregating neighboring node attributes while preserving the information from different types of high-order neighbors.