Jiho Kim, Hanjun Lee and Hongchul Lee
This paper aims to find determinants that can predict the helpfulness of online customer reviews (OCRs) with a novel approach.
Abstract
Purpose
This paper aims to find determinants that can predict the helpfulness of online customer reviews (OCRs) with a novel approach.
Design/methodology/approach
The approach consists of feature engineering using various text mining techniques including BERT and machine learning models that can classify OCRs according to their potential helpfulness. Moreover, explainable artificial intelligence methodologies are used to identify the determinants for helpfulness.
Findings
The important result is that the boosting-based ensemble model showed the highest prediction performance. In addition, it was confirmed that the sentiment features of OCRs and the reputation of reviewers are important determinants that augment the review helpfulness.
Research limitations/implications
Each online community has different purposes, fields and characteristics. Thus, the results of this study cannot be generalized. However, it is expected that this novel approach can be integrated with any platform where online reviews are used.
Originality/value
This paper incorporates feature engineering methodologies for online reviews, including the latest methodology. It also includes novel techniques to contribute to ongoing research on mining the determinants of review helpfulness.
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Keywords
Hanjun Lee, Keunho Choi, Donghee Yoo, Yongmoo Suh, Soowon Lee and Guijia He
Open innovation communities are a growing trend across diverse industries because they provide opportunities of collaborating with customers and exploiting their knowledge…
Abstract
Purpose
Open innovation communities are a growing trend across diverse industries because they provide opportunities of collaborating with customers and exploiting their knowledge effectively. Although open innovation communities can be strategic assets that can help firms innovate, firms nonetheless face the challenge of information overload incurred due to the characteristic of the community. The purpose of this paper is to mitigate the problem of information overload in an open innovation environment.
Design/methodology/approach
This study chose MyStarbucksIdea.com (MSI) as a target open innovation community in which customers share their ideas. The authors analyzed a large data set collected from MSI utilizing text mining techniques including TF-IDF and sentiment analysis, while considering both term and non-term features of the data set. Those features were used to develop classification models to calculate the adoption probability of each idea.
Findings
The results showed that term and non-term features play important roles in predicting the adoptability of ideas and the best classification accuracy was achieved by the hybrid classification models. In most cases, the precisions of classification models decreased as the number of recommendations increased, while the models’ recalls and F1s increased.
Originality/value
This research dealt with the problem of information overload in an open innovation context. A large amount of customer opinions from an innovation community were examined and a recommendation system to mitigate the problem was proposed. Using the proposed system, the firm can get recommendations for ideas that could be valuable for its business innovation in the idea generation phase, thereby resolving the information overload and enhancing the effectiveness of open innovation.
Details
Keywords
Successful open innovation requires that many ideas be posted by a number of users and that the posted ideas be evaluated to find ideas of high quality. As such, successful open…
Abstract
Purpose
Successful open innovation requires that many ideas be posted by a number of users and that the posted ideas be evaluated to find ideas of high quality. As such, successful open innovation community would have inherently information overload problem. The purpose of this paper is to mitigate the information problem by identifying potential idea launchers, so that they can pay attention to their ideas.
Design/methodology/approach
This research chose MyStarbucksIdea.com as a target innovation community where users freely share their ideas and comments. We extracted basic features from idea, comment and user information and added further features obtained from sentiment analysis on ideas and comments. Those features are used to develop classification models to identify potential idea launchers, using data mining techniques such as artificial neural network, decision tree and Bayesian network.
Findings
The results show that the number of ideas posted and the number of comments posted are the most significant among the features. And most of comment-related sentiment features found to be meaningful, while most of idea-related sentiment features are not in the prediction of idea launchers. In addition, this study show classification rules for the identification of potential idea launchers.
Originality/value
This study dealt with information overload problem in an open innovation context. A large volume of textual customer contents from an innovation community were examined and classification models to mitigate the problem were proposed using sentiment analysis and data mining techniques. Experimental results show that the proposed classification models can help the firm identify potential idea launchers for its efficient business innovation.
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Huijun Yang, Hanqun Song, Qing Shan Ding and Hanjun Wang
Drawing on signalling theory and focusing on independent restaurants, this study aims to investigate how business signals (transparency information and exposure) affect business…
Abstract
Purpose
Drawing on signalling theory and focusing on independent restaurants, this study aims to investigate how business signals (transparency information and exposure) affect business transparency, food authenticity and, ultimately, purchase intentions.
Design/methodology/approach
Using a 2 × 2 between-subject experimental design, Study 1 examines the recipe and an internet-famous restaurant, and Study 2 assesses the food supply chain and a celebrity-owned restaurant. Analysis of covariance and PROCESS are used to analyse the data.
Findings
The results suggest that while revealing information on recipes and food supply chains positively affects business transparency, exposure has no significant impact. Additionally, secret recipes and revealed food supply chains contribute to higher food authenticity, whilst being a celebrity owner or internet-famous restaurant negatively affects food authenticity.
Research limitations/implications
Restaurant managers must be strategic and selective about the kinds of business signals they wish to reveal to customers. Secret recipes lead to higher food authenticity, whereas the revealed recipes and revealed food supply chains elicit higher business transparency. Independent restaurants should not rely on celebrity owners or seek internet fame, as neither type of exposure contributes to transparency or authenticity.
Originality/value
This study advances the theoretical understanding of signalling theory relating to the determinants of transparency and food authenticity in a hospitality context. Contrary to previous studies, it reveals that exposure, as a transparency signal, has no impact on either business transparency or food authenticity. It extends knowledge and understanding of different types of independent restaurants, especially internet-famous restaurants.
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Spark ignition (SI) engines are used in a wide area in the transportation industry, from road vehicles to piston-prop aircraft. On the other hand, the decrease in reserves of…
Abstract
Purpose
Spark ignition (SI) engines are used in a wide area in the transportation industry, from road vehicles to piston-prop aircraft. On the other hand, the decrease in reserves of fossil fuels used in SI engines and the increase in greenhouse gas emissions makes the use of alternative fuels inevitable. In this paper, optimization of in-cylinder combustion and engine performance parameters by intake-charge conditions [i.e. intake-air temperature, injection timing and exhaust gas recirculation (EGR)] in a hydrogen (H2)-fueled small SI engine is performed.
Design/methodology/approach
Experimental studies were performed at a 1,600 rpm engine speed of a single-cylinder, air-cooled engine having a stroke volume of 476.5 cm3, maximum output power of 13 HP and torque of 25 Nm. The hydrogen-fueled SI engine was operated by a lean air-fuel mixture (ϕ = 0.6) under wide-open throttle (WOT) conditions.
Findings
The findings of the paper show that improvements can be achieved in in-cylinder combustion, indicated engine performance, exhaust NOx emissions with optimum intake-air temperature, the start of H2 injection and the ERG rate.
Practical Implications
It has been determined that a 32°C intake-air temperature, 395°C (bTDC) start of H2 injection, and 5%–10% EGR rates are the most suitable values for the examined hydrogen fueled SI engine.
Originality Value
Hydrogen is a usable alternative fuel for SI engines used in a wide area from road vehicles to piston-prop aircraft engines. However, a number of problems remain that limit hydrogen fueled SI engines to some extent, such as backfire, a decrease of engine power, and high NOx emissions. Therefore, it is appropriate to examine the effects of intake-charge conditions on in-cylinder combustion, engine performance, and NOx emissions parameters in a hydrogen fuelled SI engine.
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Shaizatulaqma Kamalul Ariffin, Thenmoli Mohan and Yen-Nee Goh
This paper aims to examine the relationship between six factors of consumers’ perceived risk and consumers’ online purchase intentions. In particular, this study will examine the…
Abstract
Purpose
This paper aims to examine the relationship between six factors of consumers’ perceived risk and consumers’ online purchase intentions. In particular, this study will examine the relationship between financial risk, product risk, security risk, time risk, social risk and psychological risk and online purchase intention.
Design/methodology/approach
Survey method was used for the purpose of data collection, and quantitative analysis was used to test the hypotheses. A total of 350 respondents participated on an online survey, and data were quantitatively analyzed via IBM SPSS Statistics 24.
Findings
The findings from this study suggest consumers’ perceived risks when they intend to purchase online. Five factors of perceived risk have a significant negative influence on consumer online purchase intention, while social risk was found to be insignificant. Among these factors, security risk is the main contributor for consumers to deter from purchasing online.
Practical implications
This study provides useful information to online retailers in electronic commerce (e-commerce) activities. Previous studies show that many online retailers are still facing some risks in online business, and this will affect the transaction and performance of the retailers. It is hoped that the findings can help online retailers to formulate strategies to reduce risks in the online shopping environment, especially security risks for better e-commerce.
Originality/value
The development of online shopping has led to some challenges to consumers, which comprise security of payment, data protection, the validity and enforceability of e-contract, insufficient information disclosure, product quality and enforcement of rights. This issue emerged because many online retailers do not understand the main factors that will contribute to consumers’ perceived risk. Consumers’ perceived risks will influence consumer attitudes toward online shopping and purchase behaviors. Studies on consumers’ perceived risks toward online purchase intentions are still inconclusive. Thus, this paper fills the gap in the research area.
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The purpose of this study is to examine employee imagination and implications for entrepreneurs of China. In 2015, the European Group of Organization Studies released a call for…
Abstract
Purpose
The purpose of this study is to examine employee imagination and implications for entrepreneurs of China. In 2015, the European Group of Organization Studies released a call for papers highlighting poor knowledge of employee imagination in organizations. To address this need, the current study hypothesizes employee imagination consisting of seven conditions common to the organizational experience of Chinese Entrepreneurs.
Design/methodology/approach
The current paper reviews the Chinese enterprising context. Cases from China are used to illustrate the effects of proposed conditions and their value for entrepreneurs and innovators in businesses undergoing change.
Findings
Employee imagination underpins and conditions how Chinese employees make sense of their organizations and better understand the process of organizational change. From the viewpoint of human resource management, emphasis on coaching and developing imagination enables businesses to stay competitive and adapt to environmental demands such as lack of information, too much information or the need for new information.
Research limitations/implications
The proposed conditions apply to the Chinese context; however, their application to wider contexts is suggested and requires attention.
Practical implications
Employee imagination was found to be a powerful tool, which facilitates the process of organizational change management.
Originality/value
Theoretically, the research adds new insights to knowledge of a poorly understood organizational behavior topic – employee imagination. Practically, the research findings provide mangers with knowledge of conditions, which could be adopted as powerful tools in facilitating organizational change management.