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On the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the perspective of electricity stability. On the other hand, this paper is to address the problem of lack of causal relationship in the existing research on the association analysis of residential electricity consumption behavior and basic information data.

First, the density-based spatial clustering of applications with noise method is used to extract the typical daily load curve of residents. Second, the degree of electricity consumption stability is described from three perspectives: daily minimum load rate, daily load rate and daily load fluctuation rate, and is evaluated comprehensively using the entropy weight method. Finally, residential customer labels are constructed from sociological characteristics, residential characteristics and energy use attitudes, and the enhanced FP-growth algorithm is employed to investigate any potential links between each factor and the stability of electricity consumption.

Compared with the original FP-growth algorithm, the improved algorithm can realize the excavation of rules containing specific attribute labels, which improves the excavation efficiency. In terms of factors influencing electricity stability, characteristics such as a large number of family members, being well employed, having children in the household and newer dwelling labels may all lead to poorer electricity stability, but residents' attitudes toward energy use and dwelling type are not significantly associated with electricity stability.

This paper aims to uncover household socioeconomic traits that influence the stability of home electricity use and to shed light on the intricate connections between them. Firstly, in this article, from the perspective of electricity stability, the characteristics of the power consumption of residents' users are refined. And the authors use the entropy weight method to comprehensively evaluate the stability of electricity usage. Secondly, the labels of residential users' household characteristics are screened and organized. Finally, the improved FP-growth algorithm is used to mine the residential household characteristic labels that are strongly associated with electricity consumption stability.

1. The stability of electricity consumption is important to the stable operation of the grid.

2. An improved FP-growth algorithm is employed to explore the influencing factors.

3. The improved algorithm enables the mining of rules containing specific attribute labels.

4. Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

The stability of electricity consumption is important to the stable operation of the grid.

An improved FP-growth algorithm is employed to explore the influencing factors.

The improved algorithm enables the mining of rules containing specific attribute labels.

Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

Tribological research is complex and multidisciplinary, with many parameters to consider. As traditional experimentation is time-consuming and expensive due to the complexity of tribological systems, researchers tend to use quantitative and qualitative analysis to monitor critical parameters and material characterization to explain observed dependencies. In this regard, numerical modeling and simulation offers a cost-effective alternative to physical experimentation but must be validated with limited testing. This paper aims to highlight advances in numerical modeling as they relate to the field of tribology.

This study performed an in-depth literature review for the field of modeling and simulation as it relates to tribology. The authors initially looked at the application of foundational studies (e.g. Stribeck) to understand the gaps in the current knowledge set. The authors then evaluated a number of modern developments related to contact mechanics, surface roughness, tribofilm formation and fluid-film layers. In particular, it looked at key fields driving tribology models including nanoparticle research and prosthetics. The study then sought out to understand the future trends in this research field.

The field of tribology, numerical modeling has shown to be a powerful tool, which is both time- and cost-effective when compared to standard bench testing. The characterization of tribological systems of interest fundamentally stems from the lubrication regimes designated in the Stribeck curve. The prediction of tribofilm formation, film thickness variation, fluid properties, asperity contact and surface deformation as well as the continuously changing interactions between such parameters is an essential challenge for proper modeling.

This paper highlights the major numerical modeling achievements in various disciplines and discusses their efficacy, assumptions and limitations in tribology research.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2023-0076/

The study aims to examine the impact of intellectual and social capital in funding businesses.

The study made use of fixed-effects panel data models with a sample of 142 countries from the five continents during the period 1998–2018.

It was found that human capital (HC), relational capital, structural capital and social capital play a role in investors’ decisions to fund a business. The study revealed that investors’ funding decisions in low human development index countries are based mainly on education, while those in high human development index countries are based mainly on the creativity component of HC and on relational, structural and social capital.

The study needs to be replicated using firm-level data within each country. Moreover, the search for new proxies for intellectual and social capital (although the list of variables is exhaustive) both at the country and firm level, constitutes an interesting avenue for future research.

Countries should pay attention to intellectual and social capital to encourage business activity. In particular, low human development countries should strengthen HC, such as the school enrollment rate, with early entrepreneurial training and increase research and development investments, while high human development countries should continue to foster strategic alliances, protect intellectual property and maintain or increase the level of trust in the country.

The study contributes to literature by being the first to explore such a variety of intellectual and social capital variables from a country-level perspective.

El estudio tiene como objetivo examinar el impacto del capital intelectual y social en la financiación de las empresas.

Utilizamos modelos de datos de panel de efectos fijos con una muestra de 142 países de los cinco continentes durante el periodo 1998-2018.

Encontramos que el capital humano (CH), el capital relacional, el capital estructural y el capital social juegan un papel en las decisiones de los inversionistas para financiar un negocio. Encontramos que las decisiones de financiamiento de los inversionistas en los países con bajo índice de desarrollo humano se basan principalmente en la educación, mientras que las de los países con alto índice de desarrollo humano se basan principalmente en el componente de creatividad del CH y en el capital relacional, estructural y social.

Sugerimos replicar el estudio utilizando datos a nivel de empresa dentro de cada país. Por otra parte, la búsqueda de nuevos indicadores de capital intelectual y social (aunque nuestra lista de variables es exhaustiva) tanto a nivel de país como de empresa, constituye una vía interesante para futuras investigaciones.

Los países deben prestar atención al capital intelectual y social para fomentar la actividad empresarial. En particular, los países con bajo desarrollo humano deberían fortalecer el CH, como la tasa de matriculación escolar, con una formación empresarial temprana y aumentar las inversiones en investigación y desarrollo, mientras que los países con un alto nivel de desarrollo humano deberían seguir fomentando las alianzas estratégicas, proteger la propiedad intelectual y mantener o aumentar el nivel de confianza en el país.

El estudio contribuye a la literatura al ser el primero en explorar tal variedad de variables de capital intelectual y social desde una perspectiva a nivel de país.

Large optical mirror processing systems (LOMPSs) consist of multiple subrobots, and correlated disturbance terms between these robots often lead to reduced processing accuracy. This abstract introduces a novel approach, the nonlinear subsystem adaptive dispersed fuzzy compensation control (ADFCC) method, aimed at enhancing the precision of LOMPSs.

The ADFCC model for LOMPS is developed through a nonlinear fuzzy adaptive algorithm. This model incorporates control parameters and disturbance terms (such as those arising from the external environment, friction and correlation) between subsystems to facilitate ADFCC. Error analysis is performed using the subsystem output parameters, and the resulting errors are used as feedback for compensation control.

Experimental analysis is conducted, specifically under the commonly used concentric circle processing trajectory in LOMPS. This analysis validates the effectiveness of the control model in enhancing processing accuracy.

The ADFCC strategy is demonstrated to significantly improve the accuracy of LOMPS output, offering a promising solution to the problem of correlated disturbances. This work holds the potential to benefit a wide range of practical applications.

This review aims to give a critical view of the wheel/rail high frequency vibration-induced vibration fatigue in railway bogie.

Vibration fatigue of railway bogie arising from the wheel/rail high frequency vibration has become the main concern of railway operators. Previous reviews usually focused on the formation mechanism of wheel/rail high frequency vibration. This paper thus gives a critical review of the vibration fatigue of railway bogie owing to the short-pitch irregularities-induced high frequency vibration, including a brief introduction of short-pitch irregularities, associated high frequency vibration in railway bogie, typical vibration fatigue failure cases of railway bogie and methodologies used for the assessment of vibration fatigue and research gaps.

The results showed that the resulting excitation frequencies of short-pitch irregularity vary substantially due to different track types and formation mechanisms. The axle box-mounted components are much more vulnerable to vibration fatigue compared with other components. The wheel polygonal wear and rail corrugation-induced high frequency vibration is the main driving force of fatigue failure, and the fatigue crack usually initiates from the defect of the weld seam. Vibration spectrum for attachments of railway bogie defined in the standard underestimates the vibration level arising from the short-pitch irregularities. The current investigations on vibration fatigue mainly focus on the methods to improve the accuracy of fatigue damage assessment, and a systematical design method for vibration fatigue remains a huge gap to improve the survival probability when the rail vehicle is subjected to vibration fatigue.

The research can facilitate the development of a new methodology to improve the fatigue life of railway vehicles when subjected to wheel/rail high frequency vibration.

This research investigates the mechanism by which big data capability enables superior supply chain resilience (SCRe) by empirically examining the links among big data analytics (BDA), supply chain flexibility (SCF) and SCRe, with innovation-focused complementary assets (CA-I) as the moderator.

Extensive surveys were conducted to gather 308 responses from Malaysian manufacturing firms in order to explore this framework. The structural and measurement models were examined and evaluated by using partial least squares structural equation modelling.

The findings revealed that BDA is linked to flexibilities in a manufacturing firm’s value chain, which in turn is related to the firm’s SCRe. However, the association between BDA and SCRe is surprisingly non-significant. Additionally, CA-I was discovered to moderate the connections between all of the constructs, except for the relationship between BDA and SCRe. Such findings imply that with the aim of enhancing resilience, a company should concentrate on SCF; and that BDA capability is a prerequisite for increasing these flexibilities.

This research extrapolates the findings of previous studies regarding BDA’s influence on SCRe by investigating the indirect effect of SCF, as well as the moderating influence of CA-I. This research is one of the first few studies to empirically examine the relationships between BDA, SCF and SCRe across manufacturing firms, with CA-I acting as a moderator.

Despite its crucial role in ensuring food safety, traceability remains underutilized by small and medium-sized enterprises (SMEs), a vital component of China’s agricultural supply chain, thereby compromising the integrity of the supply chain traceability system. Therefore, this study sets out to explore the factors influencing SMEs’ adoption of traceability systems and the impact of these factors on SMEs’ intent to adopt such systems. Furthermore, the study presents a model to deepen understanding of system adoption in SMEs and provides a simulation demonstrating the evolutionary trajectory of adoption behavior.

This study considers the pivotal aspects of system adoption in SMEs, aiming to identify the influential factors through a grounded theory-based case study. Concurrently, it seeks to develop a mathematical model for SMEs’ adoption patterns and simulate the evolution of SMEs’ adoption behaviors using the Q-learning algorithm.

The adoption of traceability among SMEs is significantly influenced by factors such as system attributes, SMEs’ capability endowment, environmental factors and policy support and control. However, aspects of the SMEs’ capability endowment, specifically their learning rate and decay rate, have minimal impact on the adoption process. Furthermore, group pressure can expedite the attainment of an equilibrium state, wherein all SMEs adopt the system.

This study fills the existing knowledge gap about the adoption of traceability by SMEs in China’s agricultural supply chain. This study represents the pioneer study that identifies the factors influencing SMEs’ adoption and examines the effects of these factors on their traceability adoption, employing a multi-methodological approach that incorporates grounded theory, mathematical modeling and the Q-learning algorithm.

Considering that corporate social responsibility (CSR) addresses a wide range of claims from multiple stakeholders, companies must determine their CSR scope. This paper aims to examine what factors influence a firm’s decision in its scope of CSR. In exploring what factors influence CSR scope, the authors examine the relationship between a firm’s prosocial orientation and CSR and further examine its boundary conditions by the existence of CSR department.

This study uses a data set – the Social Value Survey – administered by the Center for Social Value Enhancement Studies based in the context of Korean firms. Based on 86 firm responses, statistical models were performed to test hypotheses.

The findings show that a firm’s prosocial orientation is positively associated with CSR scope. Further, this study shows that there is a negative moderating effect of the CSR department for the relationship between the prosocial orientation and CSR scope.

This study attempts to contribute to the extensive line of work on the antecedents of CSR by exploring the simultaneous existence of various drivers of CSR and the interplay between the drivers. And this study enhances the understanding on what factors influence the decision of CSR scope within a complex system of diverse stakeholder relationships. Additionally, this study has potentially shed light on the role of CSR departments to determine CSR scope.

Drawing from cognitive dissonance theory (CDT) and attribution ambiguity theory (AAT), this paper aims to examine how employees interact with queer customers within the hospitality service and the ways that queer representations regulate emotions when discriminated against by normative gender roles.

This study used a mixed method. Study 1 used firm-level secondary data to analyze hospitality firms’ efforts in diversity, equity and inclusion (DEI) and the effects on firms’ profit margins and customer satisfaction. In Study 2, an experimental design was used to understand how employees’ assailing behavior toward queer customers interacts with employee feelings of guilt and impacts their sabotage and organizational citizenship behavior via self-serving bias. Study 3 further explored how queer customer victimization interacts with stress to influence their perceptions of organization DEI authenticity and corporate social responsibility (CSR) through resilience.

Hospitality firms’ DEI efforts were associated with varying outcomes, including higher profit margins but lower customer satisfaction, while guilt weakened the impact of employees’ assailing behavior on their outcomes and customer stress amplified the effect of assailing behavior on queer customers’ perceptions of DEI authenticity and CSR through resilience.

Hospitality organizations should take proactive measures to address self-serving bias among employees. Moreover, fostering an inclusive culture is crucial, with managers playing a pivotal role in facilitating discussions and creating an environment that values diversity, inclusivity and respect for all employees.

The study makes a remarkable contribution to hospitality literature by focusing on CDT and AAT in providing valuable implications for DEI advocators to be aware of the tensions between heteronormativity and queer representations in service encounters.

In the channel-mix era, the customer journey involves combining channels during all the stages of the decision-making process, such that creating and maintaining relationships with consumers poses a challenge to retailers. This work aims to explore what role brands play in this issue by analyzing what impact the perceived benefits of brand channel-mix have on consumer self–brand connection (SBC) and what their effect is in enduring consumer–brand relationships (i.e. future channel-mix use and word of mouth [WOM]). This paper also explores the moderating role of product involvement in these relations.

The authors carried out a personal questionnaire with a sample of 288 consumers who were recruited after leaving one of the stores of a clothing brand that is a successful example of distribution channel management.

Insofar as consumers perceive channel-mix benefits, SBC will be higher and (or as a result) their future intentions with the brand will be more intense. In addition, the results show that product involvement moderates the relationship between SBC and channel-mix use intention and WOM.

This work contributes to channel-mix, relationship marketing, brand and product involvement literature by analyzing how customers may be retained in the channel-mix era through brand management and by considering product category involvement. This study merges brand and product variables to explore their impact on relationship marketing within channel-mix behaviors.