scholarly journals Does cultural distance affect online review ratings? Measuring international customers’ satisfaction with services leveraging digital platforms and big data

2020 ◽  
Author(s):  
Marcello M. Mariani ◽  
Michela Matarazzo
Keyword(s):  
Big Data ◽  
Service Providers ◽  
Cultural Distance ◽  
Online Reviews ◽  
Big Data Analysis ◽  
Online Review ◽  
Related Factors ◽  
Digital Platforms ◽  
International Visibility ◽  
Satisfaction With Services

AbstractThe advent and development of digital platforms has helped enhance the international visibility of brands, products and services, and has also introduced a proliferation of online reviews. This study develops a big data analysis of customer online reviews of hospitality services to gauge the extent to which the cultural distance among service providers and their customers influences online review ratings. By examining almost 715,000 online reviews written by hotel customers from more than 100 different nationalities, the effect of national cultural differences among service customers and providers (namely cultural distance) on online review ratings is innovatively scrutinized. The paper, by considering reviewers’ behavioral features, demographics, and trip-related factors, reveals that the effect of national cultural distance on online review ratings is negative. Several implications for practitioners are also discussed.

scholarly journals The study of the effect of online review on purchase behavior

2020 ◽  
Vol 4 (1) ◽  
pp. 73-86 ◽  
Author(s):  
Jinghuan Zhang ◽  
Wenfeng Zheng ◽  
Shan Wang
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Online Reviews ◽  
Big Data Analysis ◽  
Influential Factors ◽  
Purchase Behavior ◽  
Behavioral Experiment ◽  
Content Type ◽  
Online Purchase ◽  
Network Big Data

Purpose The purpose of this paper is to explain the difference and connection between the network big data analysis technology and the psychological empirical research method. Design/methodology/approach This study analyzed the data from laboratory setting first, then the online sales data from Taobao.com to explore how the influential factors, such as online reviews (positive vs negative mainly), risk perception (higher vs lower) and product types (experiencing vs searching), interacted on the online purchase intention or online purchase behavior. Findings Compared with traditional research methods, such as questionnaire and behavioral experiment, network big data analysis has significant advantages in terms of sample size, data objectivity, timeliness and ecological validity. Originality/value Future study may consider the strategy of using complementary methods and combining both data-driven and theory-driven approaches in research design to provide suggestions for the development of e-commence in the era of big data.

Mining and modelling the dynamic patterns of service providers in cellular data network based on big data analysis

2013 ◽  
Vol 10 (12) ◽  
pp. 25-36 ◽  
Author(s):  
Liu Jun ◽  
Li Tingting ◽  
Cheng Gang ◽  
Yu Hua ◽  
Lei Zhenming
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Service Providers ◽  
Big Data Analysis ◽  
Data Network ◽  
Dynamic Patterns

A new method for evaluating tour online review based on grey 2-tuple linguistic

Kybernetes ◽  
2014 ◽  
Vol 43 (3/4) ◽  
pp. 601-613 ◽  
Author(s):  
Chuanmin Mi ◽  
Xiaofei Shan ◽  
Yuan Qiang ◽  
Yosa Stephanie ◽  
Ye Chen
Keyword(s):  
Big Data ◽  
Social Network ◽  
Social Responsibility ◽  
Evaluation Method ◽  
Evaluation Model ◽  
Online Reviews ◽  
New Method ◽  
Online Review ◽  
Future Research ◽  
Content Type

Purpose – Tour social network data that are heterogeneous contain not only the quantitative structured evaluation data, but also the qualitative non-structured data. This is a big data scenario. How to evaluate tour online review and then recommend to potential tourists quickly and accurately are important parts of social responsibility of tour companies. The purpose of this paper is to propose a new method for evaluating tour online review based on grey 2-tuple linguistic. Design/methodology/approach – The phenomenon of “poor information” exists in some big data scenario. According to social responsibility, grey 2-tuple linguistic evaluation model for tour online review is proposed. Findings – Tour social networks contain data that are valuable to each individual on tourism industry's value chain. Grey 2-tuple linguistic evaluation model can be used for evaluating tour online reviews. This is a systems thinking method that takes social responsibility into account. Research limitations/implications – Due to the complex links among reviewers in social network, network mining approaches and models are expected to be added to this research in the near future. Practical implications – Grey 2-tuple linguistic evaluation method can contribute to the future research on evaluating a variety of tour social network comment data in the real world. Originality/value – A new evaluation method for making evaluation and recommendations based on tour social network comment information is proposed.

scholarly journals Are environmental-related online reviews more helpful? A big data analytics approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marcello Mariani ◽  
Matteo Borghi
Keyword(s):  
Big Data ◽  
North America ◽  
Data Analytics ◽  
Negative Binomial ◽  
Big Data Analytics ◽  
Online Reviews ◽  
Environmental Discourse ◽  
Content Type ◽  
Digital Platforms ◽  
The Impact

Purpose Based on more than 2.7 million online reviews (ORs) collected with big data analytical techniques from Booking.com and TripAdvisor.com, this paper aims to explore if and to what extent environmental discourse embedded in ORs has an impact on electronic word-of-mouth (e-WOM) helpfulness across eight major destination cities in North America and Europe. Design/methodology/approach This study gathered, by means of Big Data techniques, 2.7 million ORs hosted on Booking.com and TripAdvisor, and covering hospitality services in eight different destinations cities in North America (New York City, Miami, Orlando and Las Vegas) and Europe (Barcelona, London, Paris and Rome) over the period 2017–2018. The ORs were analysed by means of ad hoc content analytic dictionaries to identify the presence and depth of the environmental discourse included in each OR. A negative binomial regression analysis was used to measure the impact of the presence/depth of online environmental discourse in ORs on e-WOM helpfulness. Findings The findings indicate that the environmental discourse presence and depth influence positively e-WOM helpfulness. More specifically those travelers who write explicitly about environmental topics in their ORs are more likely to produce ORs that are voted as helpful by other consumers. Research limitations/implications Implications highlight that both hotel managers and platform developers/managers should become increasingly aware of the importance that customer attach to environmental practices and initiatives and therefore engage more assiduously in environmental initiatives, if their objective is to improve online review helpfulness for other customers reading the focal reviews. Future studies might include more destinations and other operationalizations of environmental discourse. Originality/value This study constitutes the first attempt to capture how the presence and depth of hospitality services consumers’ environmental discourse influence e-WOM helpfulness on multiple digital platforms, by means of a big data analysis on a large sample of online reviews across multiple countries and destinations. As such it makes a relevant contribution to the area at the intersection between big data analytics, e-WOM and sustainable tourism research.

Beyond self-selection: the multilayered online review biases at the intersection of users, platforms and culture

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiangyou Shen ◽  
Bing Pan ◽  
Tao Hu ◽  
Kaijun Chen ◽  
Lin Qiao ◽  
...  
Keyword(s):  
Service Providers ◽  
Online Reviews ◽  
Online Review ◽  
Digital Environment ◽  
Multiple Sources ◽  
Content Type ◽  
Additional Layer ◽  
Self Selection ◽  
User Data ◽  
Restaurant Reviews

PurposeOnline review bias research has predominantly focused on self-selection biases on the user’s side. By collecting online reviews from multiple platforms and examining their biases in the unique digital environment of “Chinanet,” this paper aims to shed new light on the multiple sources of biases embedded in online reviews and potential interactions among users, technical platforms and the broader social–cultural norms.Design/methodology/approachIn the first study, online restaurant reviews were collected from Dianping.com, one of China's largest review platforms. Their distribution and underlying biases were examined via comparisons with offline reviews collected from on-site surveys. In the second study, user and platform ratings were collected from three additional major online review platforms – Koubei, Meituan and Ele.me – and compared for possible indications of biases in platform's review aggregation.FindingsThe results revealed a distinct exponential-curved distribution of Chinese users’ online reviews, suggesting a deviation from previous findings based on Western user data. The lack of online “moaning” on Chinese review platforms points to the social–cultural complexity of Chinese consumer behavior and online environment that goes beyond self-selection at the individual user level. The results also documented a prevalent usage of customized aggregation methods by review service providers in China, implicating an additional layer of biases introduced by technical platforms.Originality/valueUsing an online–offline design and multi-platform data sets, this paper elucidates online review biases among Chinese users, the world's largest and understudied (in terms of review biases) online user group. The results provide insights into the unique social–cultural cyber norm in China's digital environment and bring to light the multilayered nature of online review biases at the intersection of users, platforms and culture.

Product optimization design based on online review and orthogonal experiment under the background of big data

2020 ◽  
pp. 095440892094369
Author(s):  
Hang Liu ◽  
Tao Cui ◽  
Mingxin He
Keyword(s):  
Sustainable Development ◽  
Big Data ◽  
Optimization Design ◽  
Orthogonal Experiment ◽  
Original Data ◽  
Online Reviews ◽  
Online Review ◽  
Customer Needs ◽  
Product Optimization ◽  
Product Appearance

The individuation and diversity of customer demands motivates enterprises to compete in various aspects of services, such as product categories, cost, performance, marketing and after-sales service. Among them, product optimization design is the most important link, which determines whether an enterprise can achieve sustainable development in the market of fierce competition. For this reason, enterprises must implement various competitive strategies in terms of product diversity and characteristics. To solve this challenge, optimizing innovative product design is the key to help enterprises gain competitive advantage in the market. This paper presents a product optimization design based on online review and orthogonal experiment under the background of big data to meet customer needs. First, the big data of online reviews are collected from the web platform through the data collector, and the original data are processed by the apriori algorithm. Secondly, the fuzzy clustering was used to analyze the processed online reviews data to obtain customer needs. Finally, the product is optimized by orthogonal test based on customer needs. This paper analyzes the case of Huawei Honor 9 mobile phone, and obtains that the product appearance is the most concerned and dissatisfied product attribute of customers, and then optimizes the design of product appearance to improve customer satisfaction. Through this method, enterprises can improve the design of products according to the needs of customers in time, effectively overcome the defects of products, and help enterprises to establish a good relationship with customers, so as to promote the sustainable development of enterprises in the competitive market.

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