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This paper aims to present a new method, named as augmented polynomial dimensional decomposition (PDD) method, for robust design optimization (RDO) and reliability-based design optimization (RBDO) subject to mixed design variables comprising both distributional and structural design variables.

The method involves a new augmented PDD of a high-dimensional stochastic response for statistical moments and reliability analyses; an integration of the augmented PDD, score functions, and finite-difference approximation for calculating the sensitivities of the first two moments and the failure probability with respect to distributional and structural design variables; and standard gradient-based optimization algorithms.

New closed-form formulae are presented for the design sensitivities of moments that are simultaneously determined along with the moments. A finite-difference approximation integrated with the embedded Monte Carlo simulation of the augmented PDD is put forward for design sensitivities of the failure probability.

In conjunction with the multi-point, single-step design process, the new method provides an efficient means to solve a general stochastic design problem entailing mixed design variables with a large design space. Numerical results, including a three-hole bracket design, indicate that the proposed methods provide accurate and computationally efficient sensitivity estimates and optimal solutions for RDO and RBDO problems.

This paper aims to explore the influence of organisational justice (including distributional justice, procedural justice and interactional justice) and ethical leadership on employees’ innovation behaviour and to analyse the role of ethical leadership in regulating the relationship between organisational justice and employees’ innovation behaviour.

This paper explores the key factors affecting employees’ innovation behaviour from three forms of organisational justice (distributional justice, procedural justice and interactional justice) and ethical leadership. The questionnaire survey method is used to survey Chinese industrial workers. A total of 323 valid questionnaires are collected, and the model is verified by SmartPLS.

The study found that distributional justice and interactional justice in organisational justice had a significant positive influence on employees’ innovation behaviour. Ethical leadership had a significant positive influence on employees’ innovation behaviour and interactional justice. At the same time, ethical leadership has a significant regulatory effect on the relationship between interactional justice and employees’ innovation behaviour. The study also found that procedural justice had a significant positive influence on distributional justice and interactional justice.

This paper is limited to industrial workers in Hubei, China, and the research objects need further expansion. In terms of research methods, future research will use other laboratory research methods, combining experimental environments with real-world situations, enhancing the scientific nature of research methods and increasing the credibility of research results.

In management practice, organisations can take various measures to improve the interaction level of employees, improve employees’ perception of organisational distribution justice by establishing a scientific distribution system, give full play to the role of ethical leadership, implement more ethical management and stimulate employees' innovation behaviour.

This research theorises the relationship between distributional justice, interactional justice and employees' innovation behaviour and enriches the research system of employees' innovation behaviour. At the same time, it examines the regulatory effect of ethical leadership on the relationship between interactional justice and employees' innovation behaviour, which helps enrich the mechanism of ethical leadership affecting employees' innovation behaviour path. It clarifies the mechanism of the interaction among distributional justice, procedural justice and interactional justice and expands the research theory of organisational justice.

Investigates the feedback to product designers of engineering and production costs in five industrial equipment firms. Reports that, despite the ubiquity of cost as an important design criterion, and the role that feedback should play in both individual and organizational learning, there were several significant problems: (1) Cost feedback was given as the difference between outcome and estimate in order to remove the effect of external factors, but this feedback then confounded the performance of estimation and design activities. (2) Distributional information in historical cost feedback was usually overlooked. The result was an excessive attention to detailed planning, consistent under‐estimation, and persistently negative feedback. (3) Designers and supervisors disagreed about the predictability of costs. Supervisors drew stronger inferences from feedback because they believed particular outcomes were more representative. (4) Engineering cost outcomes had poor reliability owing to the incentives to smooth cost discrepancies over different elements of the design; as a result it was unclear which were the problematic elements and opportunities were lost for calibrating the estimating process. This calibration also suffered from cost measurements being made at a higher level of aggregation than cost estimates. (5) The considerable delays between making design decisions and observing cost outcomes made it hard to learn cost‐effective design strategies through experience. There were instances where designers simply never found out how much it cost to engineer and produce their designs.

The existing literature has a lack of modeling of procedural fairness concerns in the supply chain level. This paper aims to investigate how procedural fairness concerns affect channel decisions, performance and coordination.

This paper considers a supply chain consisting of one supplier and one retailer who have procedural fairness concerns in a classic Stackelberg game setting. The model is set in sales promotional environment. According to the existing literature, engagement is used to depict fair process. Some findings are made through analyzing respective decisions of the supplier and the retailer under the influence of procedural fairness concerns.

The results show that the channel efficiency can be improved when the retailer exhibits procedural fairness concerns, but if the aversion to unfair process exceeds a certain threshold, the retailer cannot benefit from it. Besides, the retailer profits more when he cares about distributional fairness, although the whole channel surplus can be improved by procedural fairness concerns.

This is the first paper to study the influences of procedural fairness concerns on supply chain decisions and channel performance. Finally, a mechanism combining a wholesale price contract with slotting allowances is proposed to coordinate the supply chain.

Alongside structural equation modeling (SEM), the complementary technique of partial least squares (PLS) path modeling helps researchers understand relations among sets of observed variables. Like SEM, PLS began with an assumption of homogeneity – one population and one model – but has developed techniques for modeling data from heterogeneous populations, consistent with a marketing emphasis on segmentation. Heterogeneity can be expressed through interactions and nonlinear terms. Additionally, researchers can use multiple group analysis and latent class methods. This chapter reviews these techniques for modeling heterogeneous data in PLS, and illustrates key developments in finite mixture modeling in PLS using the SmartPLS 2.0 package.

The COVID-19 pandemic has wrought havoc on economies around the world. The purpose of this study is to learn about the distributional impacts of the pandemic.

The authors contribute new theoretical and empirical evidence on the distributional impacts of the pandemic on different income groups in a multicountry setting. The authors analyze rich individual-level survey data covering 6,082 respondents from China, Italy, Japan, South Korea, the United Kingdom and the United States. The results are robust to various econometric models, including ordinary least squares (OLS), Tobit and ordered probit models with country-fixed effects.

The authors find that while the outbreak has no impact on household income losses, it results in a 63% reduction in the expected own labor income for the second-poorest income quintile. The pandemic impacts are most noticeable for savings, with all the four poorer income quintiles suffering reduced savings ranging between 5 and 7% compared to the richest income quintile. The poor are also less likely to change their behaviors regarding immediate prevention measures against COVID-19 and healthy activities. The authors also found countries to exhibit heterogeneous impacts.

Designing tailor-made social protection and health policies to support the poorer income groups in richer and poorer countries can generate multiple positive impacts that help minimize the negative and inequality-enhancing pandemic consequences. These findings are relevant not only for COVID-19 but also for future pandemics.

The authors theoretically and empirically investigate the impacts of the pandemic on poorer income groups, while previous studies mostly offer empirical analyses and focus on other sociodemographic factors. The authors offer a new multicountry analysis of several prevention measures against COVID-19 and specific health activities.

In order to determine the status quo of PLS path modeling in international marketing research, we conducted an exhaustive literature review. An evaluation of double-blind reviewed journals through important academic publishing databases (e.g., ABI/Inform, Elsevier ScienceDirect, Emerald Insight, Google Scholar, PsycINFO, Swetswise) revealed that more than 30 academic articles in the domain of international marketing (in a broad sense) used PLS path modeling as means of statistical analysis. We assessed what the main motivation for the use of PLS was in respect of each article. Moreover, we checked for applications of PLS in combination with one or more additional methods, and whether the main reason for conducting any additional method(s) was mentioned.

This paper suggests to combine different kind of “other-regarding” preferences as an approach to fair behavior which is observed in controlled experiments. We assert that participants in two-person experiments have a good will capital which may be described by altruistic preferences. These preferences guide a large fraction of participants when they have to make distributional choices in one-stage games. We further show that in games with more than one stage the previous action of the other person may cause reciprocal feelings in addition to the altruistic preferences. A friendly (unfriedly) act of the other person may increase (decrease) the good will capital of the participants. Upon these findings, we conclude that a combination of altruism and reciprocity is able to describe the variety of behavior in several experiments despite their differing strategic context.