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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 build a comprehensive model and examine the relationship among green brand positioning (GBP), green brand knowledge (GBK), attitude toward green brand (AGB), and green purchase intention (GPI).

A questionnaire survey was deployed to collect data from the members of Taiwan's Lifestyles of Health and Sustainability (LOHAS) Club, obtaining 425 valid samples which were analyzed with structural equation modeling.

GBP and GBK influence green brand attitudes separately. GBK affects green brand attitudes. Meanwhile, green brand attitudes influence GPIs. Another finding indicates that the mediating effects exist.

By applying the environmental knowledge-attitude-intention paradigm to green brand research, it was empirically supported the existence of a GBK-attitude-intention hierarchy in the context of GPIs.

GBP can be used as brand marketing strategy to improve consumers’ GBK and form positive green brand attitudes as well as enhance GPIs.

Proposing two novel concepts, i.e. GBK and green brand attitude to develop and test the framework of this study.

The aim of this study was to draw on the theory of planned behavior (TPB) and attempt to identify the factors influencing the customer decision to purchase via online auctions, focusing on how managers selling via online auctions can modify product positioning and promotion decisions in order to make their offerings more congruent with these factors.

The study employed a survey instrument to collect data from Yahoo!Kimo website consumers in Taiwan, obtaining 450 samples which were analyzed with structural equation modeling.

Attitude toward online auctions, perceived behavioral control and past related experiences significantly and positively influence the intention to purchase on online auctions, whereas subjective norm does not have such influence. Additionally, past related experiences have a positive effect on perceived behavioral control.

There has been a relative dearth of work on online auction customer behavior. By applying the TPB to online auction research, it was empirically supported that behavioral intention to purchase via online auctions is determined by attitude and perceived behavior control. Past related experiences were further integrated, discovering that they can strengthen perceived behavioral control.

Recommendations are put forward in order to help better align product positioning and promotion decisions in online auctions with customer attitudes, perceived behavioral control, and past related experiences. These constructs may also be capable of explaining employee actions in general managerial contexts, thus extending the contribution of the paper beyond the limited world of online auctions.

This study integrated online auctions, the theory of planned behavior and consumer decision‐making philosophies in order to develop and empirically test a theoretical framework of consumer decision making in online auctions.

Based on literature development, the purpose of this paper is to propose a conceptual framework and the theoretical model in order to examine the influence of leadership, organizational learning (OL), and organizational innovation (OI) in Taiwan’s financial and information technology industries.

Structural equation modeling is employed to evaluate the degree of influence each variable has on the others and whether their relationships differ in different industry. This study investigates a selection of firms from the top 100 financial and the top 1,000 high-technology enterprises in Taiwan in 2012 as the population. A total of 377 valid responses were collected from 2012 to 2013.

The research results indicate that OL acts as a full mediator between leadership and OI. Furthermore, industry type has moderating effect in the proposed research model.

Thus, considering appropriate leadership styles through learning to innovate might be a well leadership model to be further considered by different industries in the global countries.

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.

To support digital humanities research more effectively and efficiently, this study develops a novel Knowledge Graph Analysis Tool of People and Organizations (KGAT-PO) for the Digital Humanities Research Platform for Biographies of Chinese Malaysian Personalities (DHRP-BCMP) based on artificial intelligence (AI) technology that would not only allow humanities scholars to look at the relationships between people but also has the potential for aiding digital humanities research by identifying latent relationships between people via relationships between people and organizations.

To verify the effectiveness of KGAT-PO, a counterbalanced design was applied to compare research participants in two groups using DHRP-BCMP with and without KGAT-PO, respectively, to perform people relationship inquiry and to see if there were significant differences in the effectiveness and efficiency of exploring relationships between people, and the use of technology acceptance between the two groups. Interviews and Lag Sequential Analysis were also used to observe research participants’ perceptions and behaviors.

The results show that the DHRP-BCMP with KGAT-PO could help research participants improve the effectiveness of exploring relationships between people, and the research participants showed high technology acceptance towards using DHRP-BCMP with KGAT-PO. Moreover, the research participants who used DHRP-BCMP with KGAT-PO could identify helpful textual patterns to explore people’s relationships more quickly than DHRP-BCMP without KGAT-PO. The interviews revealed that most research participants agreed that the KGAT-PO is a good starting point for exploring relationships between people and improves the effectiveness and efficiency of exploring people’s relationship networks.

The research’s limitations encompass challenges related to data quality, complex people relationships, and privacy and ethics concerns. Currently, the KGAT-PO is limited to recognizing eight types of person-to-person relationships, including couple, sibling, parent-child, friend, teacher-student, relative, work, and others. These factors should be carefully considered to ensure the tool’s accuracy, usability, and ethical application in enhancing digital humanities research.

The study’s practical implications encompass enhanced research efficiency, aiding humanities scholars in uncovering latent interpersonal relationships within historical texts with high technology acceptance. Additionally, the tool’s applications can extend to social sciences, business and marketing, educational settings, and innovative research directions, ultimately contributing to data-driven insights in the field of digital humanities.

The research’s originality lies in creating a Knowledge Graph Analysis Tool of People and Organizations (KGAT-PO) using AI, bridging the gap between digital humanities research and AI technology. Its value is evident in its potential to efficiently uncover hidden people relationships, aiding digital humanities scholars in gaining new insights and perspectives, ultimately enhancing the depth and effectiveness of their research.

The basic assumption is that there is a symmetric relationship between review valence and rating, but what if review valence and rating were linked asymmetrically? There are few studies which have investigated the situations in which positive and negative online reviews exert different influences on ratings. This study considers brand strength as having an important moderating role because the average rating of existing reviews for a particular product is a heuristic cue for decision makers. Thus, the purpose of this paper is to argue that an asymmetric relationship between review content valence and numerical rating will depend on brand strength.

The authors have conducted a sentiment analysis via text mining, using self-developed computer programs to retrieve a data set from the TripAdvisor website.

This study finds there is an asymmetric relationship between review valence (verbal) and numerical rating. The authors further find brand strength to have an important moderating role. For a stronger brand, negative review content will have a greater impact on numerical ratings than positive review content, while for a weaker brand, positive review content will have a greater impact on numerical ratings than negative review content.

Marketers could adopt sentiment analysis via text mining of online reviews as a valid measure or predictor of consumer satisfaction or numerical ratings. Strong brands should direct more attention to negative reviews, because in such reviews the negative impact transcends the positive. In contrast, weak brands should aim to exploit as many positive reviews as possible to minimize the impact of any negative reviews.

This study finds there is an asymmetric relationship between review valence (verbal) and numerical rating and considers brand strength to play an important moderating role. The authors have used real data from the TripAdvisor website, which allow people to express themselves in an unsolicited manner, and linked these with the results from the sentiment analysis.

This study aims to examine the consequences when audit committees have different economic incentives (i.e. incentive-based compensation) to switch auditors.

The author focuses on companies experiencing an auditor switching event (client-initiated dismissals) and uses Heckman’s (1997) two-stage estimation procedure to control endogenous bias. Audit committee quality is measured by the level of incentive-based compensation. Accrual quality and abnormal audit fees are examined over the periods of auditor switches.

Using 1,087 US companies between 2006 and 2014, the author found that audit committees’ incentive-based compensation is negatively (positively) associated with accruals quality (abnormal audit fees) only when companies switch from Big 4 to non-Big 4 auditors or switch within non-Big 4 auditors. For companies that switch from non-Big 4 to Big 4 auditors, she found no evidence.

This study provides a detailed discussion of the consequences of audit committee quality. The findings also contribute to the literature by concluding that economic incentives are associated with ineffective oversight, particularly after auditor switches.

Sarbanes–Oxley Act and its associated regulations significantly expanded the oversight role of audit committees. However, regulators bypassed restrictions on audit committee compensation. Accordingly, the author suggests that regulators focus on the issue of economic incentives to improve audit committee quality.

Minimal research has been conducted on the role of audit committees when companies switch to a new external auditor. The author shows that when companies switch auditors, incentive-based compensation significantly affects the monitoring quality of audit committees.

In the face of a changing and turbulent environment, an organizational learning culture (OLC) is crucial for the long-term operation of an organization. A learning culture provides the capacity to effectively integrate employees, and it also provides structure so that an organization can move forward via continuous learning and change. Few empirical results are available from Chinese companies enduring an organizational change. To bridge this research gap, this study investigated the relationships among an OLC, job satisfaction, turnover intentions and job performance during organizational change.

A quantitative approach with structural equation modeling (SEM) and bootstrapping estimation was used to test hypotheses developed from a sample of 434 employees in a restructured telecommunications company in Taiwan.

Employees who experienced a higher learning culture had lower levels of turnover intentions and exhibited better job performance. Job satisfaction had a negative impact on employee turnover intentions but a positive impact on job performance. Moreover, job satisfaction fully mediated the relationships between an OLC and employee turnover intentions and job performance. When encountering organizational planned changes, a vibrant learning culture gave employees a higher level of satisfaction in their jobs and workplace. Although unexpected challenges often appeared during the organizational changes, employees with a higher level of job satisfaction tended to fulfill their own job duties and showed fewer turnover intentions.

In investigating issues related to organizational change, this study provides managerial insights and addresses strategies for facilitating the adoption of an OLC into the design and implementation of a better workplace environment.

This study aims to examine how the perceived importance of work values differs among the three generations (Cultural Revolution, Social Reform and Millennial) in the Chinese workforce.

Grounded in work values and generation theories, hypotheses were tested by empirical data collected from 464 Chinese employees from companies located in the Yangtze River Delta of China. A one-way multivariate analysis of covariance and a series of one-way analysis of covariance and t-tests were conducted to compare the three generations with respect to work values.

The results revealed significant generational differences existing in China with respect to extrinsic–intrinsic work values measured by the work-need typology (Huseman and Hatfield, 1990). After controlling for demographic variables, Millennial employees were found to show the highest preference for both extrinsic and intrinsic work values, followed by the Social Reform generation, whereas the Cultural Revolution generation scored lowest. Additionally, important similarities across the three generations were also found.

These findings highlight the complex nature of generational phenomena and suggest the need to further develop a deep appreciation and understanding of the underlying reasons for those generational differences and similarities.

Drawing from generation and work values theory, the authors developed a theoretical framework that allows us to directly compare the three generations in the Chinese workforce with respect to the magnitude of importance each generation attaches to various work priorities. The present study represents an important initial step in throwing more light on the mechanisms underlying the observed generational differences and similarities in work values.