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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.

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.

The purpose of this study is to increase the thermal and mechanical properties of the photopolymer by filling with the copper powder for the application of rapid tooling.

In this study, the photopolymer is filled with the different loading of copper powder for investigating the thermal and mechanical properties of the copper/photopolymer composite. The thermal properties of the copper/photopolymer composite are characterized with the degradation temperature and with the thermal conductivity. The mechanical properties of copper/photopolymer composite are performed with the tensile strength and hardness testing. Moreover, the copper/photopolymer composite is imaged by using a scanning electron microscopic with energy dispersive spectroscopy.

The tensile strength of the copper/photopolymer composite is increased over 45 per cent at 20 phr copper loading. The hardness of the photopolymer has a negative correlation with the increasing copper loading and is decreased about 28.5 per cent at 100 phr copper loading. The degradation temperature of the copper/photopolymer composite is increased about 7.2 per cent at 70 phr copper loading. The thermal conductivity of the copper/photopolymer composite is increased over 65 per cent at 100 phr copper loading.

The photopolymer used in rapid prototyping system is generally fragile and has poor thermal properties. This study improves the thermal and mechanical properties of the photopolymer with the copper filling which has been never investigated in the field of rapid prototyping applications.

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).

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 examine whether matching pay policy with innovation strategy really improves firm performance.

Three technology‐based service sectors (software, information system integration, and IC design) comprise the analytical samples. A hierarchical multiple regression method is adopted to examine the research hypotheses.

Examinations reveal that the positive effect which pay policy combined with innovation strategy has on firm performance is only found in IC design service firms.

Industry serves as a moderator in the relationship between the match and firm performance. However, this examination concentrates on the technology‐based service sectors only.

Matching pay policy and innovation strategy cannot be regarded as a panacea for improving firm performance.

This study makes an interesting contribution to understanding the strategic perspective of compensation.

Climate engineering management (CEM) as an emerging and cross-disciplinary subject gradually draws the attention to researchers. This paper aims to focus on economic and social impacts on the technologies of climate engineering themselves. However, very few research concentrates on the management of climate engineering. Furthermore, scientific knowledge and a unified system of CEM are limited.

In this paper, the concept of CEM and its characteristics are proposed and elaborated. In addition, the framework of CEM is established based on management objectives, management processes and supporting theory and technology of management. Moreover, a multi-agent synergistic theory of CEM is put forward to guide efficient management of climate engineering, which is composed of time synergy, space synergy, and factor synergy. This theory is suitable for solving all problems encountered in the management of various climate engineering rather than a specific climate engineering. Specifically, the proposed CEM system aims to mitigate the impact of climate change via refining and summarizing the interrelationship of each component.

Overall, the six research frontiers and hotspots in the field of CEM are explored based on the current status of research.

In terms of the objectives listed above, this paper seeks to provide a reference for formulating the standards and norms in the management of various climate engineering, as well as contribute to policy implementation and efficient management.

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 helpfulness vote is a type of aggregate user representation that, by measuring the quality of an online review based on certain criteria, can allow readers to find helpful reviews more quickly. Although widely applied in practice, the effectiveness of the voting mechanism is unsatisfactory. This paper uses the heuristic–systematic model and the theory of dynamics of reviews to shed light on the effect of various information cues (product ratings, word count and product attributes in the textual content of reviews) on online reviews’ aggregative voting process. It proposes a conceptual model of seven empirically tested hypotheses.

A dataset of user-generated online hotel reviews (n = 6,099) was automatically extracted from Ctrip.com. In order to measure the variable of product attributes as a systematic cue, the paper uses Chinese word segmentation, a part-of-speech tag and word frequency statistics to analyze online textual content. To verify the seven hypotheses, SPSS 17.0 was used to perform multiple linear regression.

The results show that the aggregative process of helpfulness voting can be divided into two stages, initial and cumulative voting, depending on whether voting is affected by the previous votes. Heuristic (product ratings, word count) and systematic cues (product attributes in the textual content) respectively exert a greater impact on the two stages. Furthermore, the interaction of heuristic and systematic cues plays an important role in both stages, with a stronger impact on the cumulative voting stage and a weaker one on the initial stage.

This paper’s findings can be used to explore improvements to helpfulness voting by aligning it with an individual’s information process strategy, such as by providing more explicating heuristic cues, developing different methods of presenting relevant cues to promote the voting decision at different stages, and specifying the cognitive mechanisms when designing the functions and features of helpfulness voting.

This study explores the aggregative process of helpfulness votes, drawing on the study of the dynamics of online reviews for the first time. It also contributes to the understanding of the influence of various information cues on the process from an information process perspective.