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This study aims to develop a comprehensive LOHAS (lifestyles of health and sustainability) restaurant service quality scale by using a rigorous qualitative and quantitative research process to effectively measure the service quality of LOHAS restaurants. Moreover, this study aims to further identify the Kano quality characteristics and strategic meanings of service attributes in LOHAS restaurants.

This study designed the preliminary items of the service quality scale for LOHAS restaurants (LORSERV scale) based on relevant literatures and expert interview procedures. This study identified the goodness of fit of the questionnaire content, construct validity and validity of the LORSERV scale using exploratory factor analysis and confirmatory factor analysis. The moderated regression was conducted to identify the Kano quality characteristics and strategic meanings of each service attribute in LOHAS restaurants.

The results indicated that the LORSERV scale included seven dimensions (internal sense of happiness, transitiveness, environment, healthy catering, service commitment, green practicability and thoughtfulness), for a total of 33 items. According to the results of the Kano model, the seven service attributes were categorized into the attractive quality. A total of 25 service attributes were categorized into the one-dimensional quality, and one service attribute was categorized into the must-be quality.

The contribution of this study is that the scale could facilitate operators of LOHAS restaurants to effectively understand customer perceptions of service quality and serve as a reference to upgrade and improve service quality. The identification of Kano quality characteristics for each service attribute is conducive for LOHAS restaurants to understand the strategic meanings of each service attribute and can serve as a reference to make distinctive service strategies to reach sustainable operations.

This study aims to adapt a neural network based fuzzy time series model to improve Taiwan's tourism demand forecasting.

Fuzzy sets are for modeling imprecise data and neural networks are for establishing non‐linear relationships among fuzzy sets. A neural network based fuzzy time series model is adapted as the forecasting model. Both in‐sample estimation and out‐of‐sample forecasting are performed.

This study outperforms previous studies undertaken during the SARS events of 2002‐2003.

The forecasting model only takes the observation of one previous time period into consideration. Subsequent studies can extend the model to consider previous time periods by establishing fuzzy relationships.

Non‐linear data is complicated to forecast, and it is even more difficult to forecast nonlinear data with shocks. The forecasting model in this study outperforms other studies in forecasting the nonlinear tourism demands during the SARS event of November 2002 to June 2003.

The purpose of this paper is to establish mechanisms for process improvement so that production efficiency and product quality can be expected, and create a sustainable development in terms of circular economy.

The authors obtain a critical value from statistical hypothesis testing, and thereby construct a process capability indices chart, which both lowers the chance of quality level misjudgment caused by sampling error and provides reference for the processes improvement in poor quality levels. The authors used the bottom bracket of bicycles as an example to demonstrate the model and methods proposed in this study.

This approach enables us to plot multiple quality characteristics, despite varying attributes and specifications, onto the same process capability analysis chart. And it therefore increases accuracy and precision to reduce rework and scrap rates (reduce), increase product availability, reduce maintenance frequency and increase reuse (reuse), increase the recycle rates of components (recycle) and lengthen service life, which will delay recovery time (recovery).

Parts manufacturers in the industry chain can upload their production data to the cloud platform. The quality control center of the bicycle manufacturer can utilized the production data analysis model to identify critical-to-quality characteristics. The platform also offers reference for improvement and adds the improvement achievements and experience to its knowledge management to provide the entire industry chain. Feedback is also given to the R&D department of the bicycle manufacturer as reference for more robust product designs, more reasonable tolerance designs, and selection criteria for better parts suppliers, thereby forming an intelligent manufacturing loop system.

This paper aims to link academic classifications of service innovation with practical activities by firms to detail the essence of service innovation.

This research employs both qualitative and quantitative analyses. The qualitative study features interviews with senior managers from 590 companies, covering nine industries in Taiwan, to gather practitioners ' perspectives on service innovation. A content analysis details specific forms of service innovation. The quantitative study provides a homogeneity test and two-sample proportions test to examine differences in service innovation perspectives/activities across organizational characteristics.

The interview data link three types of service innovations to 11 associated elements and 25 labels, derived from 659 potential service innovation incidents (550 new service concepts, 82 new service processes, and 27 new service business models). This study also shows that elements of service innovations vary by company size, service innovation experience, and industry life cycle.

The three types of service innovations enable businesses to benchmark and modify their current service innovation activities. Service managers can use the results of this study to develop their own service innovation strategies and concrete action plans.

This pioneering study links the viewpoints of academics with practical service innovation activities and empirically shows that service innovation is dissimilar, depending on various organization characteristics.