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Although co-creation draws attention from researchers and practitioners, the concept is theoretically discussed, and it is not known enough how to measure co-created value (CCV) substantially at service encounters. This study aims to conceptualize CCV from the service-dominant (S-D) logic perspective and develop a CCV scale for hospitality services.

In addition to the conventional psychometric procedure for scale development, this study combined text-mining techniques and interviews to generate items to capture the concept of CCV comprehensively. Exploratory and confirmatory factor analyses were conducted using two different surveys. Moreover, structural equation modeling was performed to test concurrent validity.

The study developed a CCV scale, including four sub-dimensions: CCV-in-use, CCV-in-interaction, CCV-in-involvement and CCV-in-experience. The validity test results demonstrated that the new scale effectively measured CCV in a hospitality setting.

The multidimensional constructs and the scale that this study developed will contribute to empirical research and improve understanding of CCV at the service encounter. Moreover, managers can enhance their competitive advantages by identifying and evaluating factors to facilitate CCV.

The study reconceptualized CCV, drawing on a resource-based view from S-D logic, and developed a scale to measure the degree to which customers perceive CCV. Furthermore, it achieved methodological advancement in adopting text mining of online reviews for the scale development process.

This study aims to provide an understanding of the concept of service innovation resulting from emerging technologies and suggest areas for future hospitality and tourism research. By thoroughly reviewing previous literature, this study provides the basis for improving customer service with service innovation.

This study examines the existing body of knowledge from leading hospitality, tourism and business journals by performing content analysis.

This study reveals the multifaceted aspects of service innovation practices using emerging technologies. Findings provide an evidence base to future studies by highlighting the role of technology in hospitality and tourism service innovation.

The major contribution of this study is the demonstration of an approach for both academic researchers and service providers how they can use the technology to improve customers’ perceived value, experience and engagement.

本论文旨在讨论新兴科技对服务创新的应用以及酒店和旅游管理领域中的未来发展方向。本论文通过全面回顾文献，对服务创新中的客户服务提供基础理解。

本论文通过对酒店、旅游、以及商业领域顶尖期刊文献做文本分析，以达到研究目的。

本论文提供了新兴科技对服务创新措施的多方面讨论。研究结果强调了科技对酒店和旅游管理创新中的重要地位，对未来研究做出了指导性意见。

本论文的主要贡献在于向学术研究人员和服务提供商展示，如何运用科技来加强客户感知价值、体验、以及客户参与。

服务创新、顾客价值、顾客体验、顾客参与、价值共创、科技、批判性文献综述

文献综述

Big data analytics allows researchers and industry practitioners to extract hidden patterns or discover new information and knowledge from big data. Although artificial intelligence (AI) is one of the emerging big data analytics techniques, hospitality and tourism literature has shown minimal efforts to process and analyze big hospitality data through AI. Thus, this study aims to develop and compare prediction models for review helpfulness using machine learning (ML) algorithms to analyze big restaurant data.

The study analyzed 1,483,858 restaurant reviews collected from Yelp.com. After a thorough literature review, the study identified and added to the prediction model 4 attributes containing 11 key determinants of review helpfulness. Four ML algorithms, namely, multivariate linear regression, random forest, support vector machine regression and extreme gradient boosting (XGBoost), were used to find a better prediction model for customer decision-making.

By comparing the performance metrics, the current study found that XGBoost was the best model to predict review helpfulness among selected popular ML algorithms. Results revealed that attributes regarding a reviewer’s credibility were fundamental factors determining a review’s helpfulness. Review helpfulness even valued credibility over ratings or linguistic contents such as sentiment and subjectivity.

The current study helps restaurant operators to attract customers by predicting review helpfulness through ML-based predictive modeling and presenting potential helpful reviews based on critical attributes including review, reviewer, restaurant and linguistic content. Using AI, online review platforms and restaurant websites can enhance customers’ attitude and purchase decision-making by reducing information overload and search cost and highlighting the most crucial review helpfulness features and user-friendly automated search results.

To the best of the authors’ knowledge, the current study is the first to develop a prediction model of review helpfulness and reveal essential factors for helpful reviews. Furthermore, the study presents a state-of-the-art ML model that surpasses the conventional models’ prediction accuracy. The findings will improve practitioners’ marketing strategies by focusing on factors that influence customers’ decision-making.

This study aims to uncover how heuristic information cues (HIC) and systematic information cues (SIC) of online reviews influence review helpfulness and examine a moderating role of social influence in the process of assessing review helpfulness. In particular, this study conceptualizes a theoretical framework based on dual-process and social influence theory (SIT) and empirically tests the proposed hypotheses by analyzing a broad set of actual customer review data.

For 4,177,377 online reviews posted on Yelp.com from 2004 to 2018, this study used data mining and text analysis to extract independent variables. Zero-inflated negative binomial regression analysis was conducted to test the hypothesized model.

The present study demonstrates that both HIC and SIC have a significant relationship with review helpfulness. Normative social influence cue (NSIC) strengthened the relationship between HIC and review helpfulness. However, the moderating effect of NSIC was not valid in the relationship between SIC and review helpfulness.

This study contributes to the extant research on review helpfulness by providing a conceptual framework underpinned by dual-process theory and SIT. The study not only identifies determinants of review helpfulness but also reveals how social influences can impact individuals’ judgment on review helpfulness. By offering a state-of-the-art analysis with a vast amount of online reviews, this study contributes to the methodological improvement of further empirical research.

本论文旨在揭示网络评论的启发性信息源和系统性信息源对于评论有用性的影响, 以及检验社会影响在评论有用性的调节作用。其中, 本论文基于双重历程理论和社会影响理论来构建理论模型, 并且利用实际数据来验证假设, 通过分析一系列实际客户评论数据。

本论文样本数据为2004年至2018年Yelp.com上面的4,177,377网络评论。本论文采用数据挖掘和文本分析的方法来提取自变量。本论文采用零膨胀负二项回归模型来验证假设。

研究结果表明, 启发性和系统性信息源都对网络评论有用性有着显著作用。规范性社会影响加强了启发性信息源对评论有用性的作用。然而, 规范性社会影响对系统性信息源与评论有用性的关系并未起到有效的调节作用。

本论文对现有评论有用性的文献有着补充贡献, 其采用双重历程理论和社会影响理论来构建理论模型。本论文不仅指出评论有用性的影响因素, 而且展示了社会影响如何影响个人对评论有用性的判断。本论文的样本数据庞大, 数据分析夯实, 这对于进一步的实际测量研究有着方法改进方面的贡献。