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This paper develops a smart system based on the concept of Industry 4.0 to prevent customer dissatisfaction. The value of this prevention system is that it enables hoteliers to interact with customers by understanding what they like/dislike from their behaviors via data analysis. Therefore, this system helps hoteliers to enhance service quality by predicting service issues.

The system, named the dissatisfaction identification system (DIS), is developed. A total of 127 service items were examined by a hotel manager who preset the threshold values for the measurement of service quality. A big data set for the questionnaire survey is statistically generated by a pseudorandom number generator and 10,000 mock data sets are taken as input for comparison.

The results indicated that 36 out of 127 service items are identified as service issues for the participating hotel. Examples include customer code number 01d, “Space of parking lot is adequate” in the safety management category, and number 05a, “A hotel's service time meets my needs” in the front office service category. The items identified require improvement action plans for preventing customer dissatisfaction.

This paper offers a new perspective paper emphasizing customer dissatisfaction using a big data-driven technology system. The DIS, prevention system, is developed to aid hotels by enhancing their relationships with customers using a data-driven approach.

The purpose of this study is to identify the factors that influence the development of service robots, and to apply a service innovation strategic mindset to the hotel industry in Taiwan.

A mixed-methods approach, combining an expert panel and semi-structured interviews using a SMART SWOT ranking survey, was applied to understand the perspectives of hotel owners and robotics experts.

Fifty-three items were collected regarding the demand side of the hospitality market, showing that the Taiwan hospitality industry has good potential to implement service robots. Sixty-one items on the supply side of business were collected, showing that this robotics service can help hotels handle seasonal employment and labor utilization. The SWOT analysis identifies “The fun and curiosity aroused in consumers can enhance the promotion of service robots”, “Lack of talent in system integration”, “Taiwan’s aging society can increase the demand for service robots” and “China and South-east countries have aggressively poached talent in the global robotics market” as key issues.

Few studies have investigated robotics service for hotels using the six-dimensional service innovation model. This model helps identify six factors and implies that this new service concept can position hotels to better compete by using IT and relationship marketing strategies.

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.

The purpose of this paper is to investigate the relationship between potential affecting factors and the local communities’ willingness to pay (WTP) for housing earthquake insurance (HEI) in the context of ethnic minority communities.

A literature review was done to identify possible factors affecting WTP for HEI. Fieldwork was conducted in 2017 in Dali Minority Autonomous Prefecture, where the first Chinese HEI was launched in 2015. Interviews were done in two earthquake-prone counties, as the main ethnic minority communities in the area. A total of 536 questionnaires were collected and used as empirical data for testing the impacts mechanism.

Respondents’ risk perception, risk exposure, self-prevention behaviors, government aid, insurance experience and sociodemographic characteristics were hypothesized as theoretical indicators correlated to WTP for HEI. Empirical analysis results predict that WTP for HEI is significantly influenced by risk perception, insurance experience, government aid, and age and out-migrating labors. It is evident that higher risk perception and more insurance experience lead to stronger desire for HEI coverage. However, dependency on government aid negatively affects WTP for HEI. Moreover, WTP for HEI is negative in relation to age and out-migrating labors. Surprisingly, ethnic-culture factors were not statistically significant to WTP for HEI.

This paper is an attempt to identify and verify factors affecting WTP for HEI, bridging the gap of inadequate research on WTP for HEI in ethnic minority communities.

Following the advancement of financial technology (FinTech) in modern society to assist investment decisions, robo-advisers had become widely used in mean-variance analyses to allow investors to formulate efficiently diversified investment portfolios. However, the concept of robo-advisers is new, and few studies have addressed issues related to them. To help readers to better understand robo-advisor adoption by a wide range of potential customers, this study explores integrated models of the attitudes (ATT) and behavioural intentions (BI) of users towards robo-advisors.

A survey was used to gather data to determine how the unified theory of acceptance and use of technology (UTAUT) applies to robo-advisors. A total of 242 valid questionnaires were collected.

The results of our study show that performance expectations (PE), effort expectancy (EE), and social influences (SI) may have indirect influences on robo-advisor behavioural intention (BI) through attitudes. Furthermore, behavioural intention was also positively influenced by facilitating conditions (FC). Moreover, the investment to income ratio (IIR) moderated the relationship between the PE, EE, and SI influence on ATT. Additionally, experience is found to positively strengthen the relationship between EE and SI on ATT, and it moderated the relationship between FC and BI.

This research is expected to expand the use of UTAUT to tools in the field of financial investments and help financial sectors formulate suitable marketing strategies.