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Leveraging the power of corporate social responsibility (CSR) is strategically important to corporations. Although various studies have explored the positive effects of CSR, few have been devoted to the investigation of CSR related to service employees from the internal marketing perspective. Therefore, this study fills this research gap by developing a conceptual model based on social influence theory to explain how internal CSR initiatives affect service employees' attitudes and behaviors.

This study develops and empirically tests a theoretical model examining the impact of internal CSR initiatives (i.e. internal dissemination of and management support for CSR) on service employees' attitudes toward an organization (i.e. employee–company identification and value congruence), which ultimately enhance their organizational citizenship behaviors (OCBs) toward customers, other employees and the organization. Survey data from 271 frontline employees of service firms actively involved in CSR-related activities were examined with structural equation modeling.

The results suggest that perceived internal dissemination of and management support for CSR affect service employees' citizenship behaviors toward customers, other employees and the organization through the mediation of employee–company identification and value congruence.

Internal CSR initiatives are highly related to service employees' various OCBs that are beneficial to improving the service performance of firms. Therefore, it is imperative for companies to devote attention to internal marketing dissemination while promoting CSR. In addition, as managers' attitudes and behaviors trickle down to employees, service supervisors' support of CSR activities plays a significant role in forming employee perceptions of a firm's CSR dedication.

This study represents one of the first to view internal CSR initiatives as an effective internal marketing lever. Moreover, the relationship between internal CSR initiatives and service employees' OCBs – OCB toward customers, other employees and the organization – is proposed and tested with an empirical model, providing significant contributions.

Keeping both employees and customers highly engaged has become a critical issue for service firms, especially for high-contact and highly customized services. Therefore, it is essential to engage employees and customers during service interactions for better service outcomes. However, past research on employee and customer engagement has primarily focused on brands and organizations. Little research has concentrated on service interactions as the objects of engagement. To fill this research gap, this study aims to clarify and define service engagement behaviors (SEBs), identify various employee and customer SEBs and develop a model to investigate the relationships between these behaviors.

A theoretical framework was developed based on social contagion theory and service-dominant (S-D) logic to explore the effects of employee SEBs on customer SEBs through customer perceptions of relational energy and interaction cohesion. Dyadic survey data collected from 293 customer-employee pairs in various high-contact and highly customized service industries were examined through structural equation modeling.

Results show that employee SEBs (service role involvement, customer orientation behavior and customer empowerment behavior) positively influence relational energy and interaction cohesion, which in turn affect customer SEBs (service exploration behavior and service coordination behavior).

This study represents pioneering research to conceptualize SEBs. Different from the extant literature on engagement, SEBs capture the proactive and collaborative engagement behaviors of employees and customers in service interactions. Various employee and customer SEBs were identified and an empirical model was proposed and tested to investigate the effect of employee SEBs on customer SEBs through relational energy and interaction cohesion.

This study develops a universal game-based blended museum learning service (UGBMLS) to engage museum visitors of different ages and promote their return visits. This study aims to provide a single service with flexible learning content matching the diverse needs and preferences for different ages. The contextual influences are considered to promote participatory experiences and meaningful social interactions. An experiment is conducted herein to understand how visitors of different ages experience the museum's services as well as to address the design issues in aiming to satisfy the different age groups' needs.

A UGBMLS, which includes a real-world adventure game system with flexible learning content and various types of learning support, was provided for different age groups. To investigate how the visitors of different ages evaluate their experiences with the UGBMLS, a mixed-method study was conducted. In total, two questionnaires were employed to examine the visitors' museum experiences and subjective measurements of cognitive load. In addition, observation and group interviews were used to explain the quantitative results.

The results show that the UGBMLS can engage diverse visitors of different ages in museum visits and promote their revisit motivations. However, there are significant differences among different age groups. The design issues in seeking to satisfy the diverse needs of different age groups are noted.

This study provides a feasible and cost-effective approach to support diverse visitors of different ages learning in museums.

This study provides an effective solution for engaging diverse visitors of different ages with a unified service. The results can be used to better understand the needs of different age groups and to effectively improve their museum experiences, thereby boosting their motivation regarding return visits. This study provides a feasible and cost-effective approach to support diverse visitors' learning in museums.

The aim of this study is to experimentally analyze the friction and wear responses of different steels to different surface films generated in oil-lubricated tribological contacts.

Tribological experiments were conducted using a 100Cr6 bearing ball sliding against a V155 carbon steel disk and 316L stainless steel disk, respectively. Lubricants with additives known to form zinc dialkyl-dithiophosphate (ZDDP) or Ca tribofilms were used.

Both of the ZDDP and Ca tribofilms helped stabilize the friction coefficient of the carbon steel and stainless steel. The ZDDP tribofilm could effectively protect the carbon steel from wear, in contrast to the stainless steel, whereas the wear of both carbon steel and stainless steel could be significantly reduced by the Ca tribofilm. In the case of neither ZDDP nor Ca tribofilms formation, the 100Cr6 ball was worn by the V155 disk and generated a special surface topography. A polishing wear mechanism was proposed to explain the wear of the 100Cr6 ball.

This study clearly shows the different friction and wear responses of steels to the different surface films and the response is dependent on the tested steel.

Quantitative fretting wear prediction is of practical significance for industrial components. This study aims to establish a fretting wear model considering the formation of tribolayers and provide better fretting wear prediction.

Based on the characteristics for the formation of tribolayers, the ratio of fretting amplitude to nominal contact area length in the fretting direction is used to characterize their formation and contribution to the wear volume. The wear volume is then associated with the product of the friction energy and the ratio of fretting amplitude to nominal contact area length.

Better prediction in the wear volume can be achieved with the proposed fretting wear model by taking the formation of tribolayers into consideration.

The contribution of the formation of tribolayers to the wear volume is considered in the model and better prediction can be achieved.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2023-0004/

This paper aims to develop a curriculum-based virtual and physical mobile (CVPM) learning model, which integrates related physical and e-learning resources associated with elementary school curricula and supports elementary education.

This model provides a highly participative and well-monitored environment with fruitful interactions among students, teachers and museum staff. A practical system was implemented in the National Museum of Natural Science in Taiwan.

The results show that the proposed model can effectively extend related learning resources to satisfy the requirements of elementary education. In addition, this model can engage students’ self-directed explorations through problem-solving learning content during the learning process.

This learning model was implemented in a one-day theme-based learning activity. Generic learning outcomes and qualitative analysis were used to examine the students’ perceptions and the teachers’ feedback of this learning model, respectively.

The results show that both students and teachers highly appreciated this learning model. The model not only strengthens the connection between museum and elementary curricula but also enhances students’ perceptions and positive attitudes toward natural science courses. Finally, this study offers suggestions for future research.

This study develops a CVPM learning model which integrates elementary school courses to satisfy the requirements of elementary education and meet museums’ educational strategies for schools. The practical learning system and services have been successfully implemented and are widely available to elementary schools in Taiwan.

This paper aims to propose an online and on-site cyclical learning model (OOCLM). It considers how combining digital applications can promote a museum’s virtuous learning cycle between online and on-site spaces for children.

A practical cyclical learning service has been successfully implemented in a science museum in Taiwan. This provides a thematic game-based learning environment, allowing all the children to create their unique museum experiences before, during and after their visit. A questionnaire was developed to examine the children’s perceptions of the OOCLM to ascertain whether they were satisfied with the pre-visit, on-site visit and post-visit services offered.

The learning model considered the contextual factors that influence digital applications in museums. The digital and physical resources are well integrated, and the museum’s online and on-site services are linked to effectively promote children’s cyclical learning.

The results show that most of the children highly appreciated the learning model. The model presents an interactive learning environment for children’s cyclical learning and repeat visits.

The OOCLM considers the related contextual influences of digital applications in museum learning; it effectively bridges the museum’s online and on-site services to promote the museum’s virtuous learning cycle and long-term museum learning resource management. This study provides a benchmark example to develop sustainable cyclical learning services for target visitor groups and to motivate their long-term interaction with the museum.

The purpose of this study is to propose the time series decomposition approach to analyze and predict the failure data of the repairable systems.

This study employs NHPP to model the failure data. Initially, Nelson's graph method is employed to estimate the mean number of repairs and the MCRF value for the repairable system. Second, the time series decomposition approach is employed to predict the mean number of repairs and MCRF values.

The proposed method can analyze and predict the reliability for repairable systems. It can analyze the combined effect of trend‐cycle components and the seasonal component of the failure data.

This study only adopts simulated data to verify the proposed method. Future research may use other real products' failure data to verify the proposed method. The proposed method is superior to ARIMA and neural network model prediction techniques in the reliability of repairable systems.

Results in this study can provide a valuable reference for engineers when constructing quality feedback systems for assessing current quality conditions, providing logistical support, correcting product design, facilitating optimal component‐replacement and maintenance strategies, and ensuring that products meet quality requirements.

The time series decomposition approach was used to model and analyze software aging and software failure in 2007. However, the time series decomposition approach was rarely used for modeling and analyzing the failure data for repairable systems. This study proposes the time series decomposition approach to analyze and predict the failure data of the repairable systems and the proposed method is better than the ARIMA model and neural networks in predictive accuracy.

Analyzing and forecasting reliability is increasingly important for enterprises. An accurate product reliability forecasting model cannot only learn and track a product's reliability and operational performance, but can also offer useful information that allows managers to take follow‐up actions to improve the product's quality and cost. The Generalized Autoregressive Conditional Heteroskedastic (GARCH) model is already extensively used to analyze and forecast time series data. However, the GARCH model has not been used to analyze and forecast the failure data of repairable systems. Based on these concerns, this study proposes the GARCH model to analyze and forecast the field failure data of repairable systems.

This paper proposes the GARCH model to analyze and forecast the field failure data of repairable systems. Empirical results from electronic systems designed and manufactured by suppliers of the Chrysler Corporation are presented and discussed.

The proposed method can analyze and forecast failure data for repairable systems. Not only can this method analyze failure data volatility, it can also forecast the future failure data of repairable systems.

Advanced progress in the field of reliability prediction estimation can benefit engineers or management authorities by providing important decision support tools in which the prediction accuracy suggests financial and business outcomes as well as other outcome application results.

The purpose of this paper is to propose an accurate product reliability prediction model in order to enhance product quality and reduce product costs.

This study proposes a new method for predicting the reliability of repairable systems. The novel method employed constructs a predictive model by integrating neural networks and genetic algorithms. Findings – The novel method employed constructs a predictive model by integrating neural networks and genetic algorithms. Genetic algorithms are used to globally optimize the number of neurons in the hidden layer, the learning rate and momentum of neural network architecture. Research limitations/implications – This study only adopts real failure data from an electronic system to verify the feasibility and effectiveness of the proposed method. Future research may use other product's failure data to verify the proposed method. The proposed method is superior to ARIMA and neural network model prediction techniques in the reliability of repairable systems. Practical implications – Based on the more accurate analytical results achieved by the proposed method, engineers or management authorities can take follow‐up actions to ensure that products meet quality requirements, provide logistical support and correct product design. Originality/value – The proposed method is superior to other prediction techniques in predicting the reliability of repairable systems.