Search results

In the search for alternative income-generating assets, the paper addresses the following question, one that the literature has yet to answer: what is a reasonable allocation, if any, to asset-backed securities within a 60–40% stock-bond balanced portfolio of mutual funds?

The authors apply the Black–Litterman model of Modern Portfolio Theory to test the efficacy of adding asset-backed securities to the classic 60–40% stock-bond portfolio of mutual funds. The authors use out-of-sample tests of one, three, five, and ten years to determine a reasonable asset allocation. The data are monthly and range from January 2000 through September 2021.

The statistical evidence indicates a modest reward-risk added value from the addition of asset-backed securities, as measured by the Sharpe “reward-to-variability” ratio, in holding periods of three, five, and ten years. Based on the findings, the authors conclude that a reasonable asset allocation for income-seeking, risk-averse investors who follow the classic 60%–40% stock-bond allocation is 8%–10%.

The findings apply to a stock-bond balanced portfolio of mutual funds. Other fund combinations could produce different results.

Investors and money managers can use the findings to improve portfolio performance.

For investors seeking higher income-generating securities in the current record-low interest rate environment, the authors determine a reasonable asset allocation range on asset-backed securities. This study is the first to provide such direction to these investors.

With the signing of the Civil Rights Act of 1964, discrimination in employment with respect to the hiring, promotion and pay of minorities and women became illegal in the United States.¹ Yet, 40 years later, earnings differentials still persist between certain minorities and white non-Hispanics and between women and men. For example, although the ratio of black men's earnings to those of white men and of black women's to white women's have increased considerably over the past 50 years, the black–white ratio was still only 78% in 2003 among men and 87% among women (Fig. 1). Hispanic–white wage differentials are larger than the black–white differential among both men and women (Figs. 2 and 3). And despite a significant narrowing in the gender gap, the ratio of women's earnings to men's was about 76% in 2003 (Fig. 4).²

The paper aims to clarify the influence of different picture contents on consumer's willingness to click pictures when shopping for agricultural products online and examine the intermediary mechanism and boundary conditions of the impact.

The paper opted for an empirical study based on the cue utilization theory and information processing theory, including three experiments to test the existence, intermediary mechanism and boundary conditions of the impact of online picture contents of agricultural products on consumers' clicking intention.

The paper provides empirical insights about the influence of picture contents on consumer's willingness to click when shopping for agricultural products online. The picture of product's production environment or grower on the search result page can effectively improve consumer's willingness to click the product under dual systemic information processing modes. Compared with product pictures, pictures displaying products and production environment can stimulate more cognitive system processing, and pictures displaying products and its growers can stimulate more emotional system processing, both resulting in higher click intention. However, the above effects only exist in the context of non-branded agricultural products.

The research results not only provide practical guidance for merchants, but also fill the gap in the research on the impact of picture contents on consumers in the field of agricultural products in online marketing.

The paper aims to clarify the influence of different picture contents on consumer's willingness to click pictures when shopping for agricultural products online and examine the intermediary mechanism and boundary conditions of the impact.

The paper opted for an empirical study based on the cue utilization theory and information processing theory, including 3 experiments to test the existence, intermediary mechanism and boundary conditions of the impact of online picture contents of agricultural products on consumers' clicking intention.

The paper provides empirical insights about the influence of picture contents on consumer's willingness to click when shopping for agricultural products online. The picture of product's production environment or grower on the search result page can effectively improve consumer's willingness to click the product under dual-systemic information processing modes. Compared with product pictures, pictures displaying products and production environment can stimulate more cognitive system processing, and pictures displaying products and its growers can stimulate more emotional system processing, both resulting in higher click intention. However, the above effects only exist in the context of non-branded agricultural products.

The research results can not only provide practical guidance for merchants, but also fill the gap in the research on the impact of picture contents on consumers in the field of agricultural products in online marketing.

This paper aims to explore the effect of teacher–student collaboration on academic innovation in universities in different stages of collaboration.

Based on collaboration life cycle, this paper divided teacher–student collaboration into initial, growth and mature stages to explore how teacher–student collaboration affects academic innovation.

Collecting data from National Science Foundation of China, the empirical analysis found that collaboration increases the publication of local (Chinese) papers at all stages. However, teacher–student collaboration did not significantly improve the publication of international (English) papers in the initial stage. In the growth stage, teacher–student collaboration has a U-shaped effect on publishing English papers, while its relationship is positive in the mature stage.

The results offer suggestions for teachers and students to choose suitable partners and also provide some implications for improving academic innovation.

This paper constructed a model in which the effect of teacher–student collaboration on academic innovation in universities was established.

Mega construction projects (MCPs), which play an important role in the economy, society and environment of a country, have developed rapidly in recent years. However, due to frequent social conflicts caused by the negative social impact of MCPs, social risk control has become a major challenge. Exploring the relationship between social risk factors and social risk from the perspective of risk evolution and identifying key factors contribute to social risk control; but few studies have paid enough attention to this. Therefore, this study aims to systematically analyze the impact of social risk factors on social risk based on a social risk evolution path.

This study proposed a social risk evolution path for MCPs explaining how social risk occurs and develops with the impact of social risk factors. To further analyze the impact quantitatively, a social risk analysis model combining structural equation model (SEM) with Bayesian network (BN) was developed. SEM was used to verify the relationship in the social risk evolution path. BN was applied to identify key social risk factors and predict the probabilities of social risk, quantitatively. The feasibility of the proposed model was verified by the case of water conservancy projects.

The results show that negative impact on residents’ living standards, public opinion advantage and emergency management ability were key social risk factors through sensitivity analysis. Then, scenario analysis simulated the risk probability results with the impact of different states of these key factors to obtain management strategies.

This study creatively proposes a social risk evolution path describing the dynamic interaction of the social risk and first applies the hybrid SEM–BN method in the social risk analysis for MCPs to explore effective risk control strategies. This study can facilitate the understanding of social risk from the perspective of risk evolution and provide decision-making support for the government coping with social risk in the implementation of MCPs.

Early fault detection of bearing plays an increasingly important role in the operation of rotating machinery. Based on the properties of early fault signal of bearing, this paper aims to describe a novel hybrid early fault detection method of bearings.

In adaptive variational mode decomposition (AVMD), an adaptive strategy is proposed to select the optimal decomposition level K of variational mode decomposition. Then, a criterion based on envelope entropy is applied to select the optimal intrinsic mode functions (OIMF), which contains most useful fault information. Afterwards, local tangent space alignment (LTSA) is used to denoising of OIMF. The envelope spectrum of the OIMF is used to analyze the fault frequency, thereby detecting the fault. Experiments are conducted in a simulated signal and two experimental vibration signals of bearings to verify the effect of the new method.

The results show that the proposed method yields a good capability of detecting bearing fault at an early stage. The new method can extract more useful information and can reduce noise, which can provide better detection accuracy compared with the other two methods.

An adaptive strategy based on center frequency is proposed to select the optimal decomposition level of variational mode decomposition. Envelope entropy is used to fault feature selection. Combining the advantage of the AVMD-envelope entropy and LTSA, which suits the nature of the early fault signal. So, the proposed method has better detection accuracy, which provides a good alternative for early fault detection of bearings.

The purpose of this study is to analyse and discuss the influencing factors of user experience in university mobile libraries and the improvement path of user experience in the context of mobile learning.

The study adopted the grounded theory research method, and the sample included 28 students from five universities, with mobile libraries as the research objects and semi-structured interview as data acquisition method. A step-by-step coding analysis of the original interview materials was conducted, which comprehensively identified the main concerns and problems encountered by users of the university mobile library apps especially in the mobile learning behaviour mode, and then a theoretical model of the influencing factors of the app user experience of the university mobile library was constructed.

A theoretical model of influencing factors was constructed, which determined that system quality, interaction quality, content quality, interface quality and function quality were the key elements of mobile library user experiences. Furthermore, based on the research results and user feedback obtained in the research process, the content and key points relating to the user experience can be elaborated in detail. In addition, this study was able to determine users' perspectives and their behavioural characteristics when engaging in mobile learning.

This study establishes a theoretical model of the factors influencing of the user experience of university mobile libraries based on mobile learning, which could provide a valuable reference for the design of other programs and strategies to promote user learning experiences of mobile library app in colleges and universities.

The purpose of this paper is to systematically review the platform literature and synthesize the various topics of research into a common framework to reveal the relations between platform-based service innovation, system design and other platform-related factors.

A quantitative descriptive analysis led to an overview of the distribution of research focuses of the 187 sample articles identified by a well-established search strategy. A qualitative in-depth review was then used to clarify the detailed research topics and generate an overall conceptual model to link them, with a focus on platform-based service innovation and system design.

In total, 11 research topics of three research perspectives were identified and linked by a framework that accounts for the relationships between platform-based service innovation and system design and their influences on platform evolution. A small panel of industry experts validated the accuracy and utility of the proposed framework.

This paper provides an integrated framework for separately developed research perspectives and the topics investigated in the platform literature. Through the proposed framework, this paper helps to improve the knowledge on platform study and management, and lays a foundation for exploring the research opportunities in platform-based service innovation and system design.

The purpose of this paper is to formulate and simulate the model for vehicle routing problem (VRP) on a practical application in logistics distribution.

Based on the real data of a distribution center in Utica, Michigan, USA, the design of VRP is modeled as a multi‐objective optimization problem which considers three objectives. The non‐dominated sorting genetic algorithm II (NSGA‐II) is adopted to solve this multi‐objective problem. On the other hand, the VRP model is simulated and an object‐oriented idea is employed to analyze the classes, functions, and attributes of all involved objects on VRP. A modularized objectification model is established on AnyLogic software, which can simulate the practical distribution process by changing parameters dynamically and randomly. The simulation model automatically controls vehicles motion by programs, and has strong expansibility. Meanwhile, the model credibility is strengthened by introducing random traffic flow to simulate practical traffic conditions.

The computational results show that the NSGA‐II algorithm is effective in solving this practical problem. Moreover, the simulation results suggest that by analyzing and controlling specific key factors of VRP, the distribution center can get useful information for vehicle scheduling and routing.

Multi‐objective problems are seldom considered on VRPs, yet they are of great practical value in logistics distribution. This paper is mainly focused on multi‐objective VRP which is derived from a practical distribution center. The NSGA‐II algorithm is applied in this problem and the AnyLogic software is employed as the simulation tool. In addition, this paper deals with several key factors of VRP in order to control and simulate the distribution process. The computational and simulation results regarding VRPs constitute the main contribution of our paper.