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Facing the competition pressure of internationalization and diversification, the semiconductor industry of Taiwan has to increase the activation/utilization rate of machines, enhance flow speed and values, cut down delivery and reduce costs in an efficient way in reaction to a shortening product life cycle and the global market requirements. As a result, introduction of ERP has become a critical factor of enhancing competitiveness. The purpose of this study is establish a systematic evaluation and improvement mechanism to locate the risk priority number (RPN) of implementation items via failure mode and effects analysis (FMEA) for semiconductor related industries in Taiwan while introducing ERP.

A standardized system introduced performance matrix based on the performance evaluation matrix (PEM) will be established in accordance with the locations of severity (S), occurrence (O) and detection (D) and the three RPN indices, in the PEM. Performance levels will be assessed and the performance improvement strategy introduced by the system will be formulated. Finally, items falling within the non‐appropriate performance zone will be specified through the quality function development (QFD) method.

From the results of the case study, the proposed systematic evaluation and improvement on the performance of introducing ERP for the semiconductor industry in Taiwan can be conducted in an efficient way.

All that the management needs to do is to correspond to the positions of these RPN indices of implementation items on the performance matrix. Performance levels will be assessed and the performance improvement strategy introduced by the system will be formulated.

The PEM is demonstrated to be suitable to define the best countermeasure can be sought to serve as a reference for the semiconductor related industries in Taiwan to introduce ERP.

The purpose of this paper is to introduce a novel personal scientific document retrieval system. The most common approach taken for the storage of personal documents is to construct a hierarchical folder structure. Most users prefer searching for documents by manually traversing their organizational hierarchy until reaching the location where the target item is stored, then locating the specific documents within its directory or folder. However, this is very time-consuming, especially when the number of personal scientific documents is very large. Unfortunately, related personal information management (PIM) systems, which provide solutions for managing various types of personal information, have thus far made little progress at managing personal scientific documents.

In this paper, we introduce the design of a personal scientific document retrieval system, namely, LocalContent. It is composed of database indexing and retrieval stages. During indexing, term feature extraction from scientific documents is performed by the natural language processing technique. The extracted terms are stored in the inverted index for later retrieval. For retrieval, a graphical user interface is provided by LocalContent, which allows users to search their personal scientific documents.

The evaluation results based on 20 different personal archives taken from 20 graduate students show that LocalContent is simple to use and can facilitate the search for relevant scientific documents. Moreover, these users were willing to have a system which provides specialized search functions like LocalContent to explore their personal scientific documents in the future.

LocalContent is a novel scientific document retrieval system and provides several particular functions of LocalContent including displaying the content summary of the query term frequency in each specific section of the retrieved documents, querying by local section specification and providing a number of recommended keywords related to the query terms.

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.

Online reviews are increasingly available for a wide range of products and services in e-commerce. Most consumers rely heavily on online reviews when making purchase decisions, so an important topic is that of understanding what makes some online reviews helpful in the eyes of consumers. Researchers have demonstrated the benefits of the presence of customer reviews to an online retailer, however, few studies have investigated how images in review content and the facial expressions of reviewers’ avatars influence the judgment of online review helpfulness. This study draws on self-construal theory, attribution theory and affect-as-information theory to empirically test a model of the interaction effects of images in review content and the facial expressions of reviewers’ avatars on online review helpfulness. Furthermore, the purpose of this paper is to identify an underlying mechanism of causal attribution toward store performance on the above effects.

This study conducted two online experiments. Study 1 is a 2 (images in review content: one person with a product vs a group of people with a product) ×2 (facial expression of the reviewer’s avatar: happy vs angry) between-subjects design. Study 2 is a 3 (image: product alone vs one person with a product vs a group of people with a product) ×2 (facial expression of the reviewer’s avatar: happy vs angry) ×3 (valence of the review: positive vs negative vs neutral) between-subjects design.

The results indicate that when consumers were exposed to a happy-looking avatar, they were likely to express higher perceptions of online review helpfulness in response to an image showing a group of people in a restaurant than they would for an image of one person in the same situation. In contrast, when consumers were exposed to an angry-looking avatar, their perceptions of online review helpfulness did not differ in response to images of either a group of people or of one person. Furthermore, cause attribution toward store performance mediated the interaction between images in content of reviews and the facial expression of a reviewer’s avatar on the perceptions of online review helpfulness.

The authors provide insights into how to develop guidelines on how online reviews should be written so that readers perceive them to be helpful, and how to design effective reward mechanisms for customer feedback.

Compared with previous studies, this study provides further contributions in three ways. First, it contributes to the literature on review content by showing which images in reviews are deemed to be helpful. Second, it extends previous findings from the literature relating to online peer reviews by demonstrating the importance of facial expressions in reviewers’ avatars (i.e. happy vs angry) when explaining helpfulness, rather than the strength of purchase intent. Third, this study contributes by further highlighting a novel mechanism which shows that a causal attribution toward store performance motivates the perceptions of online review helpfulness.

Electricity demand forecasting has always been a key issue, and inaccurate forecasts may mislead policymakers. To accurately predict China’s electricity demand up to 2030, this paper aims to establish a cross-validation-based linear model selection system, which can consider many factors to avoid missing useful information and select the best model according to estimated out-of-sample forecast performances.

With the nine identified influencing factors of electricity demand, this system first determines the parameters in four alternative fitting procedures, where for each procedure a lot of cross-validation is performed and the most frequently selected value is determined. Then, through comparing the out-of-sample performances of the traditional multiple linear regression and the four selected alternative fitting procedures, the best model is selected in view of forecast accuracy and stability and used for forecasting under four scenarios. Besides the baseline scenario, this paper investigates lower and higher economic growth and higher consumption share.

The results show the following: China will consume 7,120.49 TWh, 9,080.38 TWh and 11,649.73 TWh of electricity in 2020, 2025 and 2030, respectively; there is hardly any possibility of decoupling between economic development level and electricity demand; and shifting China from an investment-driven economy to a consumption-driven economy is greatly beneficial to save electricity.

Following insights are obtained: reasonable infrastructure construction plans should be made for increasing electricity demand; increasing electricity demand further challenges China’s greenhouse gas reduction target; and the fact of increasing electricity demand should be taken into account for China’s prompting electrification policies.

Most current condition‐based maintenance (CBM) systems using proportional hazards model (PHM) assume that enough historical data are available. However, in many practical cases, it is usually costly to collect much historical data prior to real practice (model implementation). This paper aims to focus on the necessity and benefits of updating a PHM with new samples generated in the process of model implementation.

First, an updating scheme is presented and embedded into the entire cycle of PHM establishment, its application/implementation, and its updating. Next, a simulation evaluation is conducted based on a typical degradation model.

By updating a PHM using newly generated samples, the precision and reliability of residual life (RL) prediction can be improved, especially close to system failure.

The current version of PHM is typically for non‐repairable systems or those systems only receiving renewal maintenance. Further research should focus on the inclusion of the effect of imperfect maintenance.

The updating scheme enables maintenance practitioners to more precisely and reliably predict the RL of an in‐operation system, and enhances further CBM decision making.

This paper highlights the necessity and benefits of updating a PHM using emerging new training resources, which has not received enough attention in existing research/practice of a PHM.

The purpose of this paper is to provide a variety of viewpoints to illustrate the mechanism of the deck-stay interaction with the appropriate initial shapes of cable-stayed bridges, which is validated by a symmetrical structure.

Based on the smooth and convergent bridge shapes obtained by the initial shape analysis, the one-element cable system (OECS) and multi-element cable system (MECS) models of the symmetric harp cable-stayed bridge are developed to verify the applicability of the analytical model and numerical formulation from the field observations in the authors’ previous work. For this purpose, the modal analyses of the two finite element models are conducted to calculate the natural frequency and normalized mode shape of the individual modes of the bridge. The modal coupling assessment is also performed to obtain the generalized mass ratios among the structural components for each mode of the bridge.

The findings indicate that the coupled modes are attributed to the frequency loci veering and mode localization when the “pure” deck-tower frequency and the “pure” stay cable frequency approach one another, implying that the mode shapes of such coupled modes are simply different from those of the deck-tower system or stay cables alone. The distribution of the generalized mass ratios between the deck-tower system and stay cables are useful indices for quantitatively assessing the degree of coupling for each mode. For each identical group of stay cables in the MECS model, the local modes with similar natural frequencies and normalized mode shapes consist of the participation of one or more stay cables. These results are demonstrated to fully understand the mechanism of the deck-stay interaction with the appropriate initial shapes of cable-stayed bridges.

It is important to investigate the deck-stay interaction with the appropriate initial shape of a cable-stayed bridge. This is because such initial shape not only reasonably provides the geometric configuration as well as the prestress distribution of the bridge under the weight of the deck-tower system and the pretension forces in the stay cables, but also definitely ensures the satisfaction of the relations for the equilibrium conditions, boundary conditions and architectural design requirements. However, few researchers have studied the deck-stay interaction considering the initial shape effect. The objective of this paper is to fully understand the mechanism of the deck-stay interaction with the appropriate initial shapes of cable-stayed bridges, which is validated by a symmetrical structure. The modal coupling assessment is also performed for quantitatively assessing the degree of coupling for each mode of the bridge.

The aims of this study are twofold: to explore the influence of the typicality of brand story and regulatory focus on the effectiveness of argument strength and product evaluations and to examine the mediating role of being hooked.

The study performed two experiments which showed how the measured or induced regulatory focus of a consumer in a given situation moderates the relationship between typicality of the brand story and product evaluations.

The results show that prevention-focused individuals rely on the substance of the message after reading an atypical brand story, whereas promotion-focused individuals are more likely to be hooked by an atypical brand story. Moreover, the findings have also revealed that being hooked mediated the results of the interaction effects of the typicality of brand story and regulatory focus on product evaluation.

A better understanding of the interplay effects between the perceived typicality of brand story and the regulatory focus of target audiences has the potential to help marketers increase the persuasiveness of their communication messages.

To the authors’ knowledge, this is the first piece of research to examine how the typicality of brand story and regulatory focus can influence the likelihood of a consumer being hooked. Moreover, the present study is among the first to show that regulatory focus is an important moderator of the effects of typicality, and this contributes to the literature of categorization theory.

The purpose of this paper is to propose a predictive maintenance (PdM) system for hybrid degradation processes with continuous degradation and sudden damage to improve maintenance effectiveness.

The PdM system updates the degradation model using partial condition monitoring information based on degradation type judgment. In addition, an extended multi-step-ahead updating stopping condition is adopted for performance enhancement of the PdM system.

An extensive numerical investigation compares the performance of the PdM system with the corresponding preventive maintenance (PM) policy. By carefully choosing the updating stopping condition, the PdM policy performs better than the corresponding PM policy.

The proposed PdM system is applicable to single-unit systems. And the continuous degradation process should be well modeled by the stochastic linear degradation model (Gebraeel et al., 2009).

In literature, there are abundant studies on PdM policies for continuous degradation processes. However, research on hybrid degradation processes still focuses on condition-based maintenance policy and a PdM policy for a hybrid degradation process is still unreported. In this paper, a PdM system for hybrid degradation processes with continuous degradation and sudden damage is proposed. The PdM system decides PM schedules by fully utilizing the condition monitoring data of each specific product, and can hopefully improve maintenance effectiveness.

Aiming to find out how to incorporate green building into the architectural curriculum, this study aims to explore the psychological path for cultivating architectural students’ awareness and motivation to learn the green design concepts and related technologies.

Based on a global review of relevant architectural courses in universities, a set of green building learning behaviors was proposed and a survey was conducted in architectural schools in South China to verify the “value-belief-norm” theory through the lens of green building learning behaviors. The psychological path that affects students’ green building learning behaviors was analyzed using structural equation modeling.

The results showed that biospheric and altruistic values could directly affect students’ motivation to learn green building, while personal norms served as the mediating condition for personal values and beliefs, and ultimately improved motivation.

The study suggests that the cultivation of environmental awareness and a sense of the ecological crisis should be developed through foundation courses, by establishing an ecological architecture curriculum, to more effectively guide students to learn and practice green building.

This study, for the first time, applied the “value-belief-norm” theory, which was developed to explain the psychological path for pro-environmental behaviors, to green building learning behaviors of architectural students.