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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.

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.

This paper develops a generalizable, machine-learning-based method for measuring established marketing constructs using passive analysis of consumer-generated textual data from service reviews. The method is demonstrated using topic and sentiment analysis along dimensions of an existing scale: lodging quality index (LQI).

The method induces numerical scale ratings from text-based data such as consumer reviews. This is accomplished by automatically developing a dictionary from words within a set of existing scale items, rather a more manual process. This dictionary is used to analyze textual consumer review data, inducing topic and sentiment along various dimensions. Data produced is equivalent with Likert scores.

Paired t-tests reveal that the text analysis technique the authors develop produces data that is equivalent to Likert data from the same individual. Results from the authors’ second study apply the method to real-world consumer hotel reviews.

Results demonstrate a novel means of using natural language processing in a way to complement or replace traditional survey methods. The approach the authors outline unlocks the ability to rapidly and efficiently analyze text in terms of any existing scale without the need to first manually develop a dictionary.

The technique makes a methodological contribution by outlining a new means of generating scale-equivalent data from text alone. The method has the potential to both unlock entirely new sources of data and potentially change how service satisfaction is assessed and opens the door for analysis of text in terms of a wider range of constructs.

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.

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. Several studies have demonstrated the benefits of the presence of customer reviews to an online retailer, but the issue of what makes online reviews helpful to a consumer in the process of making a purchase decision remains uninvestigated. The paper aims to discuss this issue.

Given the strategic potential of online reviews, this study drew on past research to develop a conceptual understanding of the components of helpfulness and to further empirically test the model using actual online review data from iPeen.com in Taiwan. A content analysis of 989 reviews across four products identified the interplay effects of review sidedness, reviewer’s expertise, and product type on the helpfulness of an online review.

For search goods, consumers consider two-sided reviews to be more helpful than one-sided reviews when the reviewers are experts in writing such articles, whereas they consider two-sided reviews to be equally helpful as one-sided reviews when the reviewers are novices. Conversely, for experience goods, consumers consider one-sided reviews to be more helpful than two-sided reviews when the reviewers are experts in writing review articles, but they consider one-sided reviews to be equally helpful as two-sided reviews when the reviewers are novices.

With an understanding of how review sidedness affects online review helpfulness, online retailers could establish the policy for promoting the helpfulness of reviews more effectively.

This research yields at least three important contributions: first, it contributes to the message sidedness literature by showing which arguments (one- or two-sided) are deemed to be helpful; second, it contributes to the online peer review literature by demonstrating the importance of considering product type and heuristic cues (i.e. the reviewer’s expertise) when explaining helpfulness; and third, the results in this research demonstrate that people are drawn to dual-processing; that is, the judgment of online review helpfulness is determined by heuristic cues (e.g. the status of the reviewer) and systematic processing (e.g. review content).

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.

The goal of the study is to develop an integrated, non-sequential framework for excellent public relations management for the financial services sector in Ghana. The study empirically analyzes how PR is conceptualized and practiced in relation to the purpose (models) and roles (activities).

The study adopted the qualitative in-depth interview approach to gauge the views of 22 PR practitioners.

Practitioners of most communication departments have no PR background, and this influenced their conceptualization of PR. Essentially, PR in Ghana is shaped, to a large extent, by a western ideology. PR is however influenced greatly by strong interpersonal relationships, which is premised largely on the culture of the country. What is obvious is that PR in the financial services sector is not fully strategic. Although practitioners occupy managerial roles, they are limited in their ability to make impact due a lack of understanding by management about the purpose of PR and budgetary constraints.

The use of a qualitative approach means the result cannot be generalized. As the study focussed on one sector, it will be essential to look at other sectors using a generalized sample.

The study developed an integrative non-sequential PR framework based on literature and empirical findings. The framework differs in certain aspects from some of the recommendations made by literature for an excellent PR practice.

Building upon the framework of glocalization, the purpose of this paper is to summarize the development of National Basketball Association (NBA) in Chinese market, explore its successful and unsuccessful places, and propose strategies of glocalization for the NBA as well as other overseas sport leagues.

The current case study was organized by summarizing the developmental history of NBA in China, analyzing its current promotional practices, investigating into its marketing strategies, and extrapolating practical references for other sport leagues aiming to penetrating into the Chinese marketplace.

The current case study concluded that when facing the current challenges, the NBA needs to bring authentic American cultural commodities while adding Chinese characteristics to accommodate local fans. Meanwhile, the NBA management needs to continue seeking ways to work out and through the differences in government models and cultural contexts between China and USA. In addition, this study suggested that the research framework of glocalization would be an ever intriguing inquiry needed for other sport organizations or leagues seeking expansion to overseas markets.

A thorough case study with the NBA that has achieved huge successes in Chinese markets will provide valuable implications for sport leagues to broaden their overseas markets.

This research examines the role of a therapist’s attributes, namely, expertise, sociability, likability and mind-set similarity, in building trust, satisfaction and commitment amongst visitors in Indian wellness resorts and hotels.

The text mining approach was adopted to collect a large corpus of 3,94,373 online reviews from TripAdvisor, Google Reviews and hotels.com. Reviews were taken from 1,677 resorts and hotels that deal in spa and wellness care across India. This study uses unsupervised Naïve Bayes classification and n-gram lexical TF-IDF vectorizer method to classify and find the sentiment of the reviews shared by the visitors of the wellness resorts. Additionally, multiple linear regression is performed to understand the impact of the therapist’s identified attributes on the visitor’s relationship quality.

The research found positive sentiment towards the therapist’s likability, and visitors seemed satisfied with the overall wellness service. The sentiment towards trust and commitment is low. The study also found significant links between likability and expertise in building the relationship quality between the therapist and the visitors. The expertise of the therapist enhances visitors’ trust and willingness to return. The therapist’s likability nature helps in increasing visitor satisfaction.

This study helps to understand the service personnel's level of relationship with the customer in hospitality services. Further, the study empirically verifies the important factors that build relationship quality in Indian wellness services.

The present study argues the need for greater clarity in understanding the customer perception of the services provided by wellness therapists in Indian wellness resorts and hotels. The study guides hotel managers to perform training of wellness therapists to improve customer satisfaction. Using the findings of the current study, managers can prioritize therapists’ attributes and realign their core strategies and provide satisfying wellness services to customers.

This study demonstrates the essential qualities a therapist should develop to enhance the relationship with the resort visitors and foster trust, commitment and satisfaction. The study goes a step further by using a vast database of online data for deep insights into the visitor’s view and the use of machine learning for amplifying results.