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Mass participation sport events, such as running events, have the potential to foster social capital among event participants. The purpose of this study is to investigate the interrelationships among sport event participation, social capital and various (behavioral, psychological, informational and negative) outcomes.

Following Putnam's social capital approach, a research model was developed and tested using confirmatory factor analysis and structural equation modeling, based on survey data from 301 runners with varied running histories and event experiences.

Results showed a limited impact of sport event participation on participants' social capital, indicating that the temporary interactions within the event timeframe were insufficient to generate sustainable social capital among event participants. Nevertheless, significant relationships were found between social capital and behavioral, psychological and informational outcomes of social capital, suggesting that social capital can be converted to a range of benefits for participants and the event community.

Event marketers and sponsors should take strategic actions to enhance participants' social experience and cultivate social capital, which may help them gain support from the event community irrespective of past experiences with participation.

This study extends Putnam's social capital framework into mass participation sport event context. As an initial effort to quantitatively test the linkage among event participation, social capital and various outcomes, this study offers empirical insights into the role of sport event participation in generating long-term social benefits for event participants.

The purpose of this paper is to provide a comprehensive review of a non-contact full-field optical measurement technique known as digital image correlation (DIC).

The approach of this review paper is to introduce the research pertaining to DIC. It comprehensively covers crucial facets including its principles, historical development, core challenges, current research status and practical applications. Additionally, it delves into unresolved issues and outlines future research objectives.

The findings of this review encompass essential aspects of DIC, including core issues like the subpixel registration algorithm, camera calibration, measurement of surface deformation in 3D complex structures and applications in ultra-high-temperature settings. Additionally, the review presents the prevailing strategies for addressing these challenges, the most recent advancements in DIC applications across quasi-static, dynamic, ultra-high-temperature, large-scale and micro-scale engineering domains, along with key directions for future research endeavors.

This review holds a substantial value as it furnishes a comprehensive and in-depth introduction to DIC, while also spotlighting its prospective applications.

To address the problem that the current train operation mode that train selects one of several offline pre-generated control schemes before the departure and operates following the scheme after the departure, energy-saving performance of the whole metro system cannot be guaranteed.

A cooperative train control framework is formulated to regulate a novel train operation mode. The classic train four-phase control strategy is improved for generating specific energy-efficient control schemes for each train. An improved brute force (BF) algorithm with a two-layer searching idea is designed to solve the optimisation model of energy-efficient train control schemes.

Case studies on the actual metro line in Guangzhou, China verify the effectiveness of the proposed train control methods compared with four-phase control strategy under different kinds of train operation scenarios and calculation parameters. The verification on the computation efficiency as well as accuracy of the proposed algorithm indicates that it meets the requirement of online optimisation.

Most existing studies optimised energy-efficient train timetable or train control strategies through an offline process, which has a defect in coping with the disturbance or delays effectively and promptly during real-time train operation. This paper studies an online optimisation of cooperative train control based on the rolling optimisation idea, where energy-efficient train operation can be realised once train running time is determined, thus mitigating the impact of unpredictable operation situations on the energy-saving performance of trains.

The artificial intelligence (AI) based total productive maintenance (TPM) condition based maintenance (CBM) approach through Industry 4.0 transformation can well predict the breakdown in advance to eliminate breakdown.

Meeting the customer requirement as per the delivery schedule with the existing resources are always a big challenge in industries. Any catastrophic breakdown in the equipment leads to increase in production loss, damage to machines, repair cost, time and affects delivery. If these breakdowns are predicted in advance, the breakdown can be addressed before its occurrence and the demand supply chain can be met. TPM is one of the essential operational excellence tool used in industries to utilize the existing resources of a plant in a optimal way. The conventional time based maintenance (TBM) and CBM approach of TPM in Industry 3.0 is time consuming and not accurate enough to achieve zero down time.

The proposed AI and IIoT based TPM is achieved in a digitalized data oriented platform to monitor and control the health status of the machine which may reduce the catastrophic breakdown by 95% and also improves the quality rate and machine performance rate. Based on the identified key signature parameters related to major breakdown are measured using the sensors, digitalised by programmable logic controller (PLC) and monitored by supervisory control and data acquisition (SCADA) and predicted in server or cloud.

Long short term memory based deep learning network was developed as a regression forecasting model to predict the remaining useful life RUL of the part or assembly and based on the predictions, corrective action has been implemented before the occurrence of breakdown. The reliability and consistency of the proposed approach are validated and horizontally deployed in similar machines to achieve zero downtime.

The paper aims to reveal the impact of relationship marketing (RM) practices adopted by companies in emerging markets on their market and financial performance (FP) over a long-term, 13-year perspective.

The research design combines primary empirical data from 229 Russian companies, based on the Contemporary Marketing Practices (CMP) survey, and objective FP data from official statistical databases for 2008–2020 to verify the impact of RM practices on market and FP in the long term.

The research underlines the significant impact of RM practices. It is important to notice that the effect of product development (PD) on marketing performance is mediated by competitor orientation. PD affects market and FP, whose roles vary with the return on assets (ROA).

Research design supplements the subjective survey data with the objective FP data on the ROA to avoid common method bias.

Implementation of RM practices by Russian companies can increase their effectiveness of performance in the long term.

This research shows the positive impact of RM practices on the FP of Russian firms over the past 13 years.

Digitalization is increasingly important for promoting authentic CSR practices. Firms with higher CSR levels motivate their employees to pursue their goals and demonstrate their social responsibility. However, the literature has not adequately examined how firm-level digitalization influences corporate sustainability from a governance perspective. This study aims to fill this gap by exploring how digitalization affects CSR disclosure, a key aspect of sustainability, at the firm level. Furthermore, this study also aims to investigate how governance factors, such as management power, internal control and minority shareholder pressure, moderate this effect.

This study employs a fixed effect model with robust standard errors to analyze how digitalization and CSR disclosure are related and how this relationship is moderated by governance heterogeneity among Chinese A-share companies from 2010 to 2020. The sample consists of 2,339 firms, of which 360 are SOEs and 1,979 are non-SOEs. To ensure robustness, this study has excluded the observations in 2020 to avoid the effects of COVID-19 and used an alternative measure of CSR disclosure based on the HEXUN CSR disclosure index. Furthermore, this study also explores the link in various corporate-level CSR settings.

The regression findings reveal that: First, Chinese A-share firms with higher digitalization levels disclose less CSR information. This finding holds for both SOEs and non-SOEs. Second, stronger management power has a negative moderating effect that weakens the link between digitalization and CSR disclosure, and this effect is mainly driven by SOEs. Third, internal control attenuates the negative association between firm digitalization and CSR disclosure, which is more pronounced in SOEs. Finally, minority shareholders exacerbate the negative relationship between digitalization and CSR disclosure, and this effect is more evident in non-SOEs. These results are robust to excluding the potential COVID effect and using an alternative HEXUN CSR disclosure index measure.

Digitalization and sustainability have been widely discussed at a macro level, but their relationship at a micro level has been largely overlooked. Moreover, there is hardly any evidence on how governance heterogeneity affects this relationship in emerging economies, especially China. This paper addresses these issues by providing empirical evidence on how digital transformation influences CSR disclosure in China, a context where digitalization and CSR are both rapidly evolving. The paper also offers implications for both practitioners and policymakers to design appropriate digital strategies for firm development from diverse business perspectives.

This study aims to ascertain the significance of retail service quality dimensions on shopping frequency at supermarkets in Durban. This study also adopts the Retail Service Quality Scale (RSQS) to South African supermarket consumers.

Primary data were collected from 399 consumers through mall intercepts using an adapted RSQS. Non-probability convenience sampling was utilised in selecting participants from different malls in Durban. To analyse data the SPSS software was utilised with multiple regression analysis to confirm relationships between variables of the study.

Results indicate a significant linear relationship between retail service quality and shopping frequency. Two of the three dimensions of retail service quality (atmospherics and reliability) have a positive and significant influence on the shopping frequency of consumers whereas one dimension (policy) showed no significant influence on the dependent variable.

Further studies are recommended in ascertaining the reasons behind an insignificant relationship between policy items of service quality and the shopping frequency of consumers.

This study highlights the managerial implications of retail service quality on improved shopping frequency of consumers.

This study suggests a lesser emphasis on policy items specifically personal interaction amongst Durban consumers on their shopping frequency. This might be due to cultural differences as well as the importance of self-service and privacy in supermarkets. Furthermore, this study demonstrates the role of context in providing deviations in retail service quality measurement and conceptualisation.

This paper aims to reveal the influence of the presentation of online product information (POPI) on consumer attitudes in the context of online buying digital products.

Two main experimental designs are used to collect data. The ANOVA, t-test and Bootstrap methods are applied to check hypotheses.

Findings of Study 1 indicate that if the POPI is combined with different types of celebrity endorsement (CE) (real vs virtual), the self-brand connection will be changed and further influence consumer attitudes toward digital products. Study 2 verifies the diverse moderating effects of the type of virtual CE. The CRP (central-route presentation) online product information with SVCE (super-realistic-digital virtual CE) can decrease consumer attitudes, while the PRP (peripheral-route presentation) online product information with AVCE (anthropomorphic virtual CE) can enhance consumer attitudes.

E-commerce enterprises should optimize the current layout of POPI by considering diverse matchings between POPI and CE to increase consumer attitudes. Moreover, marketers could make various schemes of POPI considering (virtual) CE and self-brand connection.

Findings contribute to understanding the relationship between POPI and consumer attitudes considering the mediation of self-brand connection and the mediations of virtual/real CE. Additionally, this study bridges the gap between research on virtual CE and business practices.

The recent innovations of Industry 4.0 have made it possible to easily collect data related to a production environment. In this context, information about industrial equipment – gathered by proper sensors – can be profitably used for supporting predictive maintenance (PdM) through the application of data-driven analytics based on artificial intelligence (AI) techniques. Although deep learning (DL) approaches have proven to be a quite effective solutions to the problem, one of the open research challenges remains – the design of PdM methods that are computationally efficient, and most importantly, applicable in real-world internet of things (IoT) scenarios, where they are required to be executable directly on the limited devices’ hardware.

In this paper, the authors propose a DL approach for PdM task, which is based on a particular and very efficient architecture. The major novelty behind the proposed framework is to leverage a multi-head attention (MHA) mechanism to obtain both high results in terms of remaining useful life (RUL) estimation and low memory model storage requirements, providing the basis for a possible implementation directly on the equipment hardware.

The achieved experimental results on the NASA dataset show how the authors’ approach outperforms in terms of effectiveness and efficiency the majority of the most diffused state-of-the-art techniques.

A comparison of the spatial and temporal complexity with a typical long-short term memory (LSTM) model and the state-of-the-art approaches was also done on the NASA dataset. Despite the authors’ approach achieving similar effectiveness results with respect to other approaches, it has a significantly smaller number of parameters, a smaller storage volume and lower training time.

The proposed approach aims to find a compromise between effectiveness and efficiency, which is crucial in the industrial domain in which it is important to maximize the link between performance attained and resources allocated. The overall accuracy performances are also on par with the finest methods described in the literature.

The proposed approach allows satisfying the requirements of modern embedded AI applications (reliability, low power consumption, etc.), finding a compromise between efficiency and effectiveness.

The purpose of this study is to prepare ZnFe₂O₄ nanospheres, sheet MoS₂ and three ZnFe₂O₄@MoS₂ core-shell composites with various shell thicknesses, and add them to the base oil for friction and wear tests to simulate the wear conditions of hybrid bearings.

Through the characterization and analysis of the morphology of wear scars and the elemental composition of friction films, the tribological behavior and wear mechanism of sample materials as lubricant additives were investigated and the effects of shell thickness and sample concentration on the tribological properties of core–shell composite lubricant additives were discussed.

The findings demonstrate that each of the five sample materials can, to varying degrees, enhance the lubricating qualities of the base oil and that the core–shell nanocomposite sample lubricant additive has superior lubricating properties to those of ZnFe₂O₄ and MoS₂ alone, among them ZnFe₂O₄@MoS₂-2 core–shell composites with moderate shell thickness performed most ideally. In addition, the optimal concentration of the ZnFe₂O₄@MoS₂ lubricant additive was 0.5 Wt.%, and a concentration that was too high led to particle deposition and affected the friction effect.

In this work, ZnFe₂O₄@MoS₂ core–shell composites were synthesized for the first time using ZnFe₂O₄ as the carrier and the lubrication mechanism of core–shell composites and single materials were compared and studied, which illustrated the advantages of core–shell composite lubricant additives. At the same time, the influence of different shell thicknesses on the lubricant additives of core–shell composites was studied.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2022-0367/