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The potential contradictory impact of empowering leadership may obfuscate its effectiveness. Empowering leadership is characterized not only by increased employees' autonomy but also by additional responsibilities and work demands, which may trigger different motivational processes for employees and lead to different perceptions of job stressors. This research aims to explore such contradictory impacts of empowering leadership on employees' perceived stressors by clarifying the complex motivational processes (intrinsic/extrinsic) experienced by employees when facing empowering leadership, as well as the boundary condition of employees' regulatory focus based on regulatory focus theory and self-determination theory (SDT).

The authors examine the proposed theoretical model using a two-wave survey, with the data being collected from 294 employees working at a hotel in China.

The results show that both intrinsic and extrinsic motivation mediate the relationship between empowering leadership and employees' perceived stressors and demonstrate the moderating role of an employee's regulatory focus. Specifically, a high promotion focus strengthens the relationship between empowering leadership and intrinsic motivation, while a high prevention focus strengthens the relationship between empowering leadership and extrinsic motivation.

Managers should consider the attributes of front-line service employees (e.g. are they promotion-focused or prevention-focused?) when demonstrating empowering leadership to prevent employees from misinterpreting that leadership as a source of stress.

This research helps to reconcile previously conflicting findings on empowering leadership by clarifying the complex motivational processes behind it. Furthermore, this research adopts a regulatory focus perspective to suggest that the reason why employees respond to their leaders differently is inherently associated with each employee's motivational tendencies.

The purpose of this study is to identify the dynamic parameters of journal bearings in asymmetric rotor systems without additional test runs or excitations.

An asymmetric rotor-bearing test rig was set up for the identification experiment. Comparations were made between the measured response of the asymmetric rotor and the symmetric rotor. The mathematical model of the asymmetric rotor is established by the finite element method. The identification algorithm is based on the model of the rotor and the measured vibration response to identify bearing parameters. The influence of modeling error and measurement noise on the identification results are numerically analyzed. The dynamic parameters of the journal bearings under different rotational speeds are identified and compared with the theoretical values calculated by the perturbation method.

The experiment results show that the vibration characteristics of the asymmetric rotor and the symmetric rotor are different. The numerical evaluation of the identification algorithm shows that the algorithm is accurate and has good robustness to modeling error and measurement noise. The identified dynamic parameters agree reasonably well with the parameters derived from the theoretical bearing model.

The proposed identification method uses the unique vibration characteristics of asymmetric rotors to identify the bearing dynamic parameters. As the method does not require excitations or additional test runs, it is suitable for the field test.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0096/

Asymmetric rotating machinery supported by oil film bearings is relatively common in practical applications. The purpose of this study is to propose a method for estimating the oil film parameters of the bearings in an asymmetric rotor-bearing system.

The proposed method requires the finite element model and translational displacement responses at the center of mass and bearings locations to form a regression equation to estimate the unknown parameters. Due to the transverse stiffness of the asymmetric rotor is not symmetrical, the analysis and parameter estimation procedures are performed in a rotating coordinate. Numerical simulations were carried out to illustrate the vibration characteristics of the asymmetric rotor system. The proposed method is applied to the simulated responses to estimate the assumed oil film parameters. The influence of the estimated parameter deviations on the rotor dynamic characteristics is discussed.

The vibration characteristics of asymmetric rotors are different from those of symmetrical rotors. The bearing parameters estimated by the proposed method are close to the assumed values, within a maximum error of 9%. The deviations of the estimated parameters have little effect on the vibration characteristic of the rotor system.

The proposed method does not require changing the rotational speed or applying additional excitation force to the rotor, which is suitable for the field test.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2023-0111/

The purpose of this study is to explore the seismic damage mechanism of the Dayemaling Bridge during the Maduo earthquake and discuss the seismic damage characteristics of the high-pier curved girder bridge.

In this study, the numerical simulation method is used to analyze the seismic response using synthetic near-field ground motion records.

The near-field ground motion of the Maduo earthquake has an obvious directional effect, it is more likely to cause bridge seismic damage. Considering the longitudinal slope of the bridge and adopting the continuous girder bridge form, the beam end displacement of the curved bridge can be effectively reduced, and the collision force of the block and the bending moment of the pier bottom are reduced, so the curved bridge with longitudinal slope is adopted.

Combined with the seismic damage phenomenon of bridges in real earthquakes, the seismic damage mechanism and vulnerability characteristics of high-pier curved girder bridges are discussed by the numerical simulation method, which provides technical support for the application of such bridges in high seismic intensity areas.

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.

Multi-tier supply chain risk management (MSCRM) is an evolving and dynamic field, as any defaults or glitches in supply chains can potentially harm the efficiency and competitiveness of the entire supply chain. This study aims to investigate barriers to MSCRM in the automotive and civil aircraft industries.

This study adopts an inductive case study research design. The case analysis includes two parts. First, the within-case analysis develops case profiles and identifies critical categories. Second, the cross-case analysis compares MSCRM patterns across the cases.

This study argues that narrow information sharing and communication covering only the immediate supply chain partners obstruct the efficiency of MSCRM. Similarly, high dependency on strategic alliances with suppliers hinders efficient MSCRM. Additionally, relying on information and communication technologies (ICT) increases companies' exposure to risks and poses another barrier to efficient MSCRM.

Further research should be pursued to expand generalizability and test the validity of the findings using other forms of data collection and methodologies, such as large-scale surveys, experiments or secondary data across different sectors and typical supply networks.

This study provides empirical evidence on the obstacles faced by companies during the process of MSCRM. These findings can guide practitioners in developing initiatives to overcome these challenges.

This study is among the first to investigate the barriers to MSCRM in the automotive and civil aircraft industries using in-depth case studies across three tiers of the supply chain.

This study aims to evaluate the effectiveness of different prefabricated construction (PC) policies using a case study in Wuhan, considering the local context.

The effectiveness of PC policies is falling behind expectations. The main reason lies in an insufficient understanding of the policy impacts. An agent-based model was built by choosing the residential sector in a typical large city of Wuhan, China, as the study case. Different cost reduction scenarios were introduced for investigating the PC policy effectiveness. The proposed model and simulation approach can be used for other cities and generalized to the whole Chinese PC industry with the potential to include more local policies and corresponding data.

Simulation results show that carbon emission reduction will be between 60,000 and 80,000 tons with policy incentives, nearly double that of the no policy intervention scenario. The target of 30% PC in all new buildings by 2026 in China is achievable with the subsidy policies of linear cost reduction, or cost reduction conforms to the learning curve.

Simulation results of three kinds of policy show that subsidy policy optimization is necessary regarding reducing the level of subsidy needed. The carbon credit policy is not essential since it has little influence on PC development. Implementing the project procurement restriction policy is not recommended if the scale of development of PC is more important than achieving the development target.

This study can help the government and developers make better policy and strategic decisions on PC development and boost the sustainability transition of the construction industry.

The purpose of this paper is to establish a combined model for selecting key indexes of complex equipment, and then improve the cost forecasting precision of the model. The problem how to choose the key elements of complex products has always been concerned on many fields, such as cost assessment, investment decision making, etc.

Using Grey System Theory to establish a cost estimation model of complicated equipment is more reasonable under the few data and poor information. Therefore, this paper constructs cost index’s system of complex equipment, and then quantitative and qualitative analysis methods are utilized to calculate the grey entropy between the characteristic parameter and the behavior parameters. Further, establish the grey relational clustering matrix of the behavior sequences by using the grey relative incidence analysis. Finally, the authors select key indicators according to the grey degree.

The experiment demonstrates that the cost key parameters of complex equipment can be successfully screened out by the proposed approach, and the cost estimation accuracy of complicated products is improved.

The method proposed in this paper could be utilized to solve some practical problems, particularly the selection of cost critical parameters for complex products with few samples and poor information. Taking the cost key indexes of civil aircraft as an example, the results verified the validity of the GICM model.

In this paper, the authors develop the method of GICM model. Taking the data of civil aircraft as an example, the authors screen the key indicators of complex products successfully, and improve the prediction accuracy of the GM (1, N) model by using the selected parameters, which provides a reference for some firms.

Sensor arrays and pattern recognition-based electronic nose (E-nose) is a typical detection and recognition instrument for indoor air quality (IAQ). The E-nose is able to monitor several pollutants in the air by mimicking the human olfactory system. Formaldehyde concentration prediction is one of the major functionalities of the E-nose, and three typical machine learning (ML) algorithms are most frequently used, including back propagation (BP) neural network, radial basis function (RBF) neural network and support vector regression (SVR).

This paper comparatively evaluates and analyzes those three ML algorithms under controllable environment, which is built on a marketable sensor arrays E-nose platform. Variable temperature (T), relative humidity (RH) and pollutant concentrations (C) conditions were measured during experiments to support the investigation.

Regression models have been built using the above-mentioned three typical algorithms, and in-depth analysis demonstrates that the model of the BP neural network results in a better prediction performance than others.

Finally, the empirical results prove that ML algorithms, combined with low-cost sensors, can make high-precision contaminant concentration detection indoor.

In response to food scares related to high levels of agro-chemical residues sometimes found on foods, consumers in China increasingly demand for organic foods. As little is known on consumer perceptions and attitudes toward organic food products, the purpose of this paper is to gain insight into the purchase of organic food products by consumers and to explore the main factors driving this process.

Mixed (quantitative and qualitative) market survey approaches were used to provide a potentially deeper insight into consumer’s perspective and could help get a better picture of the complex factors involved.

The main trigger for purchasing organic food products is that consumers expect them to be healthier and safer. The respondents who have bought organic food products tend to have a higher education level and disposable incomes, be families with children and be older than those who have not bought them. The main barriers to increasing the market share of organic food products are the consumers’ lack of knowledge, the relatively high price premium and the lack of availability of organic food products. Informing consumers about unique characteristics of organic production methods, the decline in the prices of and increasing the availability of organic food products to its customers might be a promising strategy to develop the market for organic food products.

This paper provides a unique insight into buying behavior and attitudes of organic consumers in China. The results of the research could provide information for the organic food industry to expand its market and improve its profitability and reliability.