Search results

Ethical consumption is an integral component for the sustainable development in the world and is especially challenging in the Western consumer society. This research demonstrates that mindfulness, a Buddhism-based notion, is associated with two related and distinctive approaches of ethical consumption: refinement and reduction. It examines the psychological mechanisms underlying the effects of mindfulness on these two approaches of ethical consumption.

Self-report data were collected through an online survey with consumers from western societies (N = 523).

The findings show (1) that the significance of mindfulness on both approaches of ethical consumption and (2) that the contrast between the different mechanisms underlying them. Specifically, the mindfulness–consumption refinement link is fully mediated by connectedness-to-nature whereas the mindfulness–consumption reduction link is fully mediated by connectedness-to-nature and self-control. A series of supplementary studies further confirmed the proposed model.

It demonstrates the multifaceted and complex nature of ethical consumption, which is positively associated with mindfulness but through distinctive psychological mechanisms.

The multifaceted and complex nature of ethical consumption and its underlying drivers need special attention. Mindfulness can be an effective means to boost ethical consumption behavior. Meanwhile, nurturing the sense of connectedness to nature and self-control capability facilitates the path-through of the positive impacts of mindfulness

The findings can be adopted to enhance the effectiveness of mindfulness practice in promoting ethical consumption towards achieving the Sustainable Consumption goal, especially in the West.

The paper makes original contribution by conceptualizing two interrelated and distinctive approaches of ethical consumption and shows how mindfulness promotes both through different mediating pathways. Overall, this study paints a clearer picture how mindfulness relates to ethical consumption.

To improve the accuracy of stock price trend prediction in the field of quantitative financial trading, this paper takes the prediction accuracy as the goal and avoid the enormous number of network structures and hyperparameter adjustments of long-short-term memory (LSTM).

In this paper, an adaptive genetic algorithm based on individual ordering is used to optimize the network structure and hyperparameters of the LSTM neural network automatically.

The simulation results show that the accuracy of the rise and fall of the stock outperform than the model with LSTM only as well as other machine learning models. Furthermore, the efficiency of parameter adjustment is greatly higher than other hyperparameter optimization methods.

(1) The AGA-LSTM algorithm is used to input various hyperparameter combinations into genetic algorithm to find the best hyperparameter combination. Compared with other models, it has higher accuracy in predicting the up and down trend of stock prices in the next day. (2) Adopting real coding, elitist preservation and self-adaptive adjustment of crossover and mutation probability based on individual ordering in the part of genetic algorithm, the algorithm is computationally efficient and the results are more likely to converge to the global optimum.

Multi-objective is a complex problem that appears in real life while these objectives are conflicting. The swarm intelligence algorithm is often used to solve such multi-objective problems. Due to its strong search ability and convergence ability, particle swarm optimization algorithm is proposed, and the multi-objective particle swarm optimization algorithm is used to solve multi-objective optimization problems. However, the particles of particle swarm optimization algorithm are easy to fall into local optimization because of their fast convergence. Uneven distribution and poor diversity are the two key drawbacks of the Pareto front of multi-objective particle swarm optimization algorithm. Therefore, this paper aims to propose an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.

In this paper, the proposed algorithm uses adaptive Cauchy mutation and improved crowding distance to perturb the particles in the population in a dynamic way in order to help the particles trapped in the local optimization jump out of it which improves the convergence performance consequently.

In order to solve the problems of uneven distribution and poor diversity in the Pareto front of multi-objective particle swarm optimization algorithm, this paper uses adaptive Cauchy mutation and improved crowding distance to help the particles trapped in the local optimization jump out of the local optimization. Experimental results show that the proposed algorithm has obvious advantages in convergence performance for nine benchmark functions compared with other multi-objective optimization algorithms.

In order to help the particles trapped in the local optimization jump out of the local optimization which improves the convergence performance consequently, this paper proposes an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.

Modelling methods can be helpful for understanding vibrations of beam structures including cracks, as well as for early detection of crack. This study aims to provide an analytical modelling approach for a cantilever beam considering a slant vertical crack along its height. However, previous uniform crack methods cannot be used for describing this case. The results from the analytical, finite element (FE) and experimental methods are compared to verify the vibration problem.

A massless rotational spring model is adopted to describe the crack. An extended method based on the calculation method for a uniform vertical edge crack is proposed to obtain the stiffness of the slant case. The beam is divided into a series of independent thin slices along the beam height. An Euler–Bernoulli beam model is applied to formulate each slice. The crack in each slice is considered as a uniform one. The transfer matrix method in the literature is used to obtain the beam vibration frequencies and mode shapes. Influences of crack location and sizes on the natural frequencies for the cantilever beam, as well as the mode shapes, are analysed. An established FE model and test results in the listed references are used to validate the developed method.

The numerical results show that the rotational stiffness at the cracked section and the natural frequencies of the beam decrease by increasing the crack sizes; the natural frequencies for the beam are greatly influenced by the crack sizes and location; the first natural frequency decreases with the distance from the beam fixed end to the crack location; the value of the first natural frequency reaches a minimum value when the crack is at the beam fixed end; the value of the second natural frequency is a minimum value when the crack is at the beam middle; and the value of the third natural frequency is a minimum value when the crack is at the beam free end. Saltation is observed in some mode shapes at the crack location, especially for larger crack depths; but, the mode shapes of the beam are slightly influenced by the vertical crack.

This study gives a useful analytical modelling method for free vibration analysis for the cantilever beam with a vertical crack, which can overcome the disadvantages of the previous uniform crack methods.

Understanding the customers’ habits of getting cosmetic products information is of great importance to enhance the effectiveness of advertising and marketing strategy of cosmetics. In China, as females are the major target customers of cosmetics, this paper aims to analyze the information behaviors of obtaining cosmetics information of different women groups, including the preferences of information channels and the corresponding means of getting information.

This research is based on a questionnaire survey. A total of 419 effective questionnaires were collected from females who had bought cosmetics in China. Descriptive statistics and chi-square analysis were used to analyze the data.

According to the result of the questionnaire survey, women preferred to get information through social platforms and physical stores. And the primary reason of the choice was that they believed the information from the channel they chose was reliable. The survey also revealed that age, educational level and occupation would significantly affect their preferences of information channels.

There was no previous research which analyzed Chinese women consumers’ information-seeking behaviors about cosmetic products information as well as the influence factors of the behaviors.

Traditional library book recommendation methods are mainly based on association rules and user profiles. They may help to learn about students' interest in different types of books, e.g., students majoring in science and engineering tend to pay more attention to computer books. Nevertheless, most of them still need to identify users' interests accurately. To solve the problem, the authors propose a novel embedding-driven model called InFo, which refers to users' intrinsic interests and academic preferences to provide personalized library book recommendations.

The authors analyze the characteristics and challenges in real library book recommendations and then propose a method considering feature interactions. Specifically, the authors leverage the attention unit to extract students' preferences for different categories of books from their borrowing history, after which we feed the unit into the Factorization Machine with other context-aware features to learn students' hybrid interests. The authors employ a convolution neural network to extract high-order correlations among feature maps which are obtained by the outer product between feature embeddings.

The authors evaluate the model by conducting experiments on a real-world dataset in one university. The results show that the model outperforms other state-of-the-art methods in terms of two metrics called Recall and NDCG.

It requires a specific data size to prevent overfitting during model training, and the proposed method may face the user/item cold-start challenge.

The embedding-driven book recommendation model could be applied in real libraries to provide valuable recommendations based on readers' preferences.

The proposed method is a practical embedding-driven model that accurately captures diverse user preferences.

This paper aims to study largely recent aspects of entrepreneurship and innovation in China. It synthesizes the research in the current special issue (SI) of Multinational Business Review (MBR) on this topic. In addition, this paper differs from other work on this topic in examining entrepreneurship and innovation from a more global standpoint with relevant international effects.

The paper provides an overview of the literature on entrepreneurship, innovation and key related topics such as firm and economic growth, as well as linking this research to related international works. It also summarizes the papers of the SI.

The authors’ analysis suggests that the study of entrepreneurship and innovation should be placed in the context of a country’s economic development and institutional environment as well as the firm internationalization trajectories and business models. In addition, the authors believe that a good understanding of economic growth in a transition economy like China (which is a key goal of China’s recent emphasis on innovation) is facilitated by understanding the comparative advantages and disadvantages of an economy with respect to the global innovation system.

The authors’ study explores the local-global and parent-subsidiary connectivity and co-evolution of firm strategies and the institutional environment in entrepreneurship and innovation in emerging and transition economies. The authors summarize and synthesize the papers in this SI to provide the results as well as some directions for future research in the domain of entrepreneurship, innovation and new venture creation, which is believed to be a key engine of economic growth in the coming years.

This paper aims to re-examine the role of institutions in the rise of made-in-China multinationals. Specifically, the paper seeks to understand how changes in the global environment, especially global financial crisis, have solidified the Chinese government's role in pushing and encouraging Chinese firms to engage in outbound foreign direct investment (OFDI) activities.

This is a conceptual paper. The analysis is based on a large number of publicly available sources, including research papers, government documents, and reports. The paper strives to triangulate the validity of the data with multiple sources.

The study finds that while the role of the state in China has been evolving since the start of the economic reforms in 1978, by no means has it been lessened. Instead, the state has asserted its role specifically to grow Chinese multinationals in size and in number, by leveraging the financial resources accumulated over the last 30 years, by taking advantage of the cheap assets made available globally by the recent financial crisis and by institutionalizing its “Go Global” strategy.

The study implies that the role of the state will be further solidified through China's national goal of enhancing competitiveness via knowledge acquisition through OFDI and simultaneously, multinationals’ OFDI initiatives and strategies will be reinforced by the state's economic policies and goals while their commercial interests will take on an increasing importance in the global marketplace and their behavior will co-evolve with and be reshaped by local, national, and international environments. The paper suggests that future studies employ co-evolutionary theory to investigate the role of state-owned enterprises (especially the functions of their CEOs) as well as non-state actors in shaping the institutional framework in China. Future studies should verify some of the ideas with empirical data and strive to triangulate different data sources to increase data quality.

The study also provides implications to Chinese policy makers on how to balance the government's role as conductor, enabler, protector, and constrainer while allowing Chinese multinationals to integrate into the global market for the benefit of both China and the world economy.

This study represents an original contribution to this topic. The research contributes to the study of globalization of Chinese enterprises by exploring the renewed dynamic relationship between the state and the firm after the 2008 global financial crisis.

The purpose of this paper is to review the theoretical background, methodological extensions, and empirical applications of the Engel curve, which is applied to the research of the change in farmers’ welfare and food demand in China after the economic reform in 1978, compared with the statistics of income and food consumption.

This paper mainly uses the traditional method of Engel curve, which is compared with income growth and food consumption, to study farmers’ welfare improvement in rural China.

The Engel coefficients identify three different stages for farmers’ welfare change after 1978. The first stage is the period between 1978 and 1988, in which farmers’ welfare has been continuously enhanced due to the institutional bonus of the 1978 economic reform and increased government purchase price of agricultural products. The second stage is the period between 1989 and 1995, in which farmers’ welfare has been slightly deteriorated mainly due to the end of institutional reform bonus, suppressed food prices, relative high inflation, and instable political situation. The third stage is the period after 1995, in which farmers’ welfare returns to a growing path, as the dual price system was abolished, the transition from a planned economy to a market economy had been completed, and the government carried out protective policies for agriculture and started to heavily subsidize agriculture. The Engel coefficient still remained at a very high level at 0.59 in 1995, but it continuously decreased to 0.33 in 2015. The welfare enhancement for farmers mainly results from deepened market-oriented reform, protective policies for agriculture, and prevalent off-farm employment. The Engel coefficient is also linked to food demand elasticities. Along with the decreasing Engel coefficient in the past 40 years, income elasticities also continuously decrease from 0.55 in 1978 to 0.08 in 2015. Food demand is very inelastic now, and any further increase in income will not substantially increase food demand any more.

Inequality has not been analyzed.

This paper reviews the methodological advantages of the Engel curves, and uses it to identify different stages of welfare change and estimate income elasticities of food demand for farmers in China after the 1978 economic reform.