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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.

To propose an accurate product reliability prediction model in order to enhance product quality and reduce product costs.

This study proposes a method for analysing and forecasting field failure data for repairable systems. The novel method constructs a predictive model by combining the seasonal autoregressive integrated‐moving average (SARIMA) method and neural network model.

Current methods for analysing and forecasting field failure data for repairable systems do not consider the seasonal effect in the data. The proposed method can not only analyse the trends and seasonal vibration of the data, but can also forecast the short‐ and long‐term reliability of the system based on only a small amount of historical data.

This study adopts only 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.

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 proposed method is superior to other prediction techniques in predicting future real failure data.

This study aims to investigate the association between cognitive moral development (CMD) and unethical pro-organizational behaviour (UPB) by taking purchasing employees as research subjects. The moderating effect of perceived leader’s UPB is also explored.

Data were collected through a two-stage questionnaire survey on purchasing employees in companies across a spectrum of industries in Taiwan, and 492 purchasing employees were analysed in the study.

Research findings reveal that employees at the conventional level of CMD are more likely to conduct UPB than those at the pre-conventional and post-conventional levels. Perceived leader’s UPB will moderate the association between CMD and UPB. Employees’ UPB is strongly associated with their CMD when they perceive their leaders as being likely to perform UPB.

Although a variety of factors influencing UPB have been proposed in the literature, none of them have analysed the association between CMD and UPB. However, CMD is one important factor affecting ethical decision-making. The present study can promote further understanding of the role of CMD in UPB and contribute to a growing body of research on CMD and UPB.

While many companies begin to promote ethically produced products, much remains to be known about consumers' buying intention toward these products. This paper attempts to integrate the theory of planned behavior and the Hunt–Vitell theory of marketing ethics to explore the buying intention toward ethically produced food products in a developing economy.

Data were collected through a questionnaire survey in Bangladesh. Structural equation modeling technique was used to test the research model.

Research findings showed that deontological evaluation and teleological evaluation have significantly positive effects on perceived behavioral control and subjective norm. Perceived behavioral control, subjective norm, attitude, hedonic and utilitarian value have significantly positive effects on buying intention toward ethically produced foods.

The results are practically and theoretically meaningful because the integrated model holds well explanatory power to predict consumers' intention toward buying ethical foods and thereby understand consumers' ethical decision-makings.

Using Kunming, the capital of China's southwest Yunnan Province, as an example, this mixed-methods research examines three interacting dimensions of social change in contemporary China: migration, ethnicity, and education. In particular, it sheds light on the issue of educational achievement of migrant children, especially children of ethnic minority background. The quantitative portion of the study is based on data gathered from over 700 sample students, teachers, and principals who participated in the “2008 Kunming Migrant Children's Survey.” A two-level hierarchical linear modeling (HLM) is employed to identify student- and school-level factors and to estimate the impacts of these factors on migrant children's academic achievement. The qualitative portion of the study is based primarily on the data collected through in-depth individual interviews and focus-group discussions with 97 migrant students, teachers, and school principals from 10 government and nongovernment migrant children's schools in Kunming between 2008 and 2009. The qualitative and quantitative results highlight four interrelated groups of educational barriers experienced by migrant students in pursuing compulsory education: institutional, socioeconomic, cultural, and psychological barriers. In particular, cultural and psychological barriers, including difficulty in school adaptation, low self-esteem, lack of family support, and discrimination against ethnic minorities due to their different religious beliefs and ethnic traditions, are found to have exerted particularly significant negative influences on academic achievements of ethnic minority students.

The aim of this paper is to provide a comprehensive and detailed review of the state-of-the-art mechanisms of knowledge sharing in the education field as well as directions for future research.

In the current study, a systematic literature review until June 2017 is presented, which has been on the education’s mechanisms of knowledge sharing. The authors identified 237 papers, which are reduced to 71 primary studies through the paper selection process.

By providing the state-of-the-art information, the challenges and issues, this survey will directly support academics, researchers and practicing professionals in their understanding of knowledge sharing developments in education.

There are several limitations in this study. First, this study limited the search for articles to Google scholar and four online databases. There might be other academic journals, which may be able to provide a more comprehensive picture of the articles related to the knowledge sharing in education. Second, non-English publications were excluded from this study. The authors believe research regarding the application of knowledge sharing techniques have also been discussed and published in other languages. In addition, more studies need to be carried out using other methodologies such as interviews.

The paper presents a comprehensive structured literature review of the articles’ mechanisms of knowledge sharing in the education field. The paper’s findings can offer insights into future research needs.

The Chinese population is rapidly ageing before they are rich. The purpose of this paper is to describe healthcare seeking behaviour and the critical factors associated with healthcare seeking behaviour.

Using a purposive sampling method, the authors recruited 44 adults aged 60 years or older from three provinces, representing the developed (Shanghai), undeveloped (Ningxia) regions and the regions in between (Hubei). From July to September 2008, using a semi-structured guide, the authors interviewed participants in focus group discussions.

The healthcare needs for chronic and catastrophic diseases were high; however, the healthcare demands were low and healthcare utilizations were even lower owing to the limited accessibility to healthcare services, particularly, in underdeveloped rural areas. “Too expensive to see a doctor” was a prime complaint, explaining substantial discrepancies between healthcare needs, demands and use. Care seeking behaviour varied depending on insurance availability, perceived performance, particularly hospital services, and prescription medications. Participants consistently rated increasing healthcare accessibility as a high priority, including offering financial aid, and improving service convenience. Improving social security fairness was the first on the elderly’s wish list.

Healthcare demand and use were lower than needs, and were influenced by multiple factors, primarily, service affordability and efficiency, perceived performance and hospital service quality.