Digital Marketing Platforms and Customer Satisfaction: Identifying eWOM Using Big Data and Text Mining

Big data analytics provides many opportunities to develop new avenues for understanding hospitality management and to support decision making in this field. User-generated content (UGC) provides benefits for hotel managers to gain feedback from customers and enhance specific product attributes or service characteristics in order to increase business value and support marketing activities. Many scholars have provided significant findings about the determinants of customers’ satisfaction in hospitality. However, most researchers primarily used research methodologies such as customer surveys, interviews, or focus groups to examine the determinants of customers’ satisfaction. Thus, more studies must explore how to use UGC to bridge the gap between guest satisfaction and online reviews. This paper examines and compares the aspects of satisfaction and dissatisfaction of Greek hotels’ guests. Text analytics was implemented to deconstruct hotel guest reviews and then examine their relationship with hotel satisfaction. This paper helps hotel managers determine specific product attributes or service characteristics that impact guest satisfaction and dissatisfaction and how hotel guests’ attitudes to those characteristics are affected by hotels’ market positioning and strategies.

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Business Data Analytics Applications to Online Product Reviews and Nationalism

International Journal of Data Analytics ◽

10.4018/ijda.2021070102 ◽

2021 ◽

Vol 2 (2) ◽

pp. 27-39

Author(s):

Charles C. Willow

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Smart Phone ◽

Consumer Preference ◽

Online Reviews ◽

Global Market ◽

Product Reviews ◽

Purchase Decisions ◽

Control Factors

This paper investigates the data analytics between consumer purchase decisions relative to the on-line reviews. The multi-attributes associated with purchase decisions are comprised of nationalism and consumer preference to be correlated with online reviews using big data analytics. By far, a small fraction of meaningful studies have sought to correlate nationalism and ethnocentrism with big data analytics to date. Globally accepted generic products are selected to expedite the process of data engineering. Two sets were arranged: passenger automobiles for transportation with an estimated $9 trillion global market and the smart phone, boosting its market size of approximately $5 billion. Both products provide minimized cultural, linguistic, gender, age, and/or custom barriers of entry for prospective digital consumers, thereby allowing relatively unrestricted engagement with online reviews and purchases. A series of hypothesis tests indicate that there is a positive correlation between nationalism and automobiles. As to smart cell phones, however, nationalism had nominal control factors. Multi-variate analytics were performed by using R and Tableau Public.

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

International Journal of Contemporary Hospitality Management ◽

10.1108/ijchm-06-2020-0548 ◽

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.

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Exploring influential factors affecting guest satisfaction

Journal of Hospitality and Tourism Technology ◽

10.1108/jhtt-07-2018-0054 ◽

2020 ◽

Vol 11 (1) ◽

pp. 137-153 ◽

Cited By ~ 3

Author(s):

Minwoo Lee ◽

Yanjun (Maggie) Cai ◽

Agnes DeFranco ◽

Jongseo Lee

Keyword(s):

Social Media ◽

Big Data ◽

Online Reviews ◽

Influential Factors ◽

Business Analytics ◽

Content Type ◽

Factors Affecting ◽

Starting Point ◽

Guest Satisfaction ◽

Significant Factors

Purpose Electronic word of mouth in the form of user-generated content (UGC) in social media plays an important role in influencing customer decision-making and enhancing service providers’ brand images, sales and service innovations. While few research studies have explored real content generated by hotel guests in social media, business analytics techniques are still not widely seen in the literature and how such techniques can be deployed to benefit hoteliers has not been fully explored. Thus, this study aims to explore the significant factors that affect hotel guest satisfaction via UGC and business analytics and also to showcase the use of business analytics tools for both the hospitality industry and the academic world. Design/methodology/approach This study uses big data and business analytics techniques. Big data and business analytics enable hoteliers to develop effective and efficient strategies improving products and services for guest satisfaction. Therefore, this study analyzes 200,431 hotel reviews on Tripadvisor.com through business analytics to explore and assess the significant factors affecting guest satisfaction. Findings The findings show that service, room and value evaluations are the top-three factors affecting overall guests’ satisfaction. While brand type and negative emotions are negatively associated with guests’ satisfaction, all other factors considered were positively associated with guests’ satisfaction. Originality/value The current study serves as a great starting point to further explore the relationship between specific evaluation factors and guests’ overall satisfaction by analyzing user-generated online reviews through business analytics so as to assist hoteliers to resolve performance-related problems by analyzing service gaps that exist in these influential factors.

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Hotel’s online reviews and ratings: a cross-cultural approach

International Journal of Contemporary Hospitality Management ◽

10.1108/ijchm-05-2018-0413 ◽

2019 ◽

Vol 31 (5) ◽

pp. 2054-2073 ◽

Cited By ~ 6

Author(s):

Ramona Diana Leon

Keyword(s):

Power Distance ◽

Online Reviews ◽

Uncertainty Avoidance ◽

Hospitality Management ◽

Web Based ◽

Content Type ◽

Cultural Specificity ◽

Hotel Managers ◽

The Impact

Purpose This paper aims to examine the impact of cultural specificity on hotel’s online reviews and ratings. Design/methodology/approach Using Hofstede’s scale of cultural differences, it analyzes 1,821 comments about the Catalonia Sagrada Familia Hotel across 77 countries. Logistic regression is used for data analysis. Findings It is found that detailed reviews tend to be provided by the guests who belong to a low-power distance culture, are collectivistic, are masculine, have a low uncertainty avoidance, are long-term orientated or are indulgent. On the other hand, the customers who tend to deviate from the prior average ratings come from high-power distance societies, are individualists, are feminists, belong to a high uncertainty avoidance culture, are long-term oriented or are indulgent. Originality/value These findings extend the hospitality management literature and potentially help the hotel managers to better understand their customers’ behavior in a web-based environment.

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Factors contributing to the helpfulness of online hotel reviews

International Journal of Contemporary Hospitality Management ◽

10.1108/ijchm-03-2015-0107 ◽

2016 ◽

Vol 28 (10) ◽

pp. 2156-2177 ◽

Cited By ~ 51

Author(s):

Linchi Kwok ◽

Karen L. Xie

Keyword(s):

Big Data ◽

Large Data ◽

Online Reviews ◽

Opinion Leaders ◽

Data Set ◽

Consumer Reviews ◽

Content Type ◽

Hotel Managers ◽

Hospitality Managers ◽

The Impact

Purpose This paper aims to examine the factors contributing to the helpfulness of online hotel reviews and to measure the impact of manager response on the helpfulness of online hotel reviews. Design/methodology/approach This investigation used a linear regression model that drew upon 56,284 consumer reviews and 10,797 manager responses from 1,405 hotels on TripAdvisor.com for analysis. Findings The helpfulness of online hotel reviews is negatively affected by rating and number of sentences in a review, but positively affected by manager response and reviewer experience in terms of reviewer status, years of membership, and number of cities visited. Manager response moderates the influence of reviewer experience on the helpfulness of online hotel reviews. Research limitations/implications Using the data from hotels in five major cities in Texas, the results may not be necessarily generalized to other markets, but the important role that manager response plays in online reviews is assessed with big data analysis. Practical implications The results suggest hospitality managers should strategically identify opinion leaders among reviewers and proactively influence the helpfulness of the reviews by providing manager response. Additionally, this study makes recommendations to webmasters of social media platforms in terms of advancing the algorithm of featuring the most helpful online reviews. Originality/value This study is at the frontier of research to explain how hotel managers can proactively identify opinion leaders among consumers and use manager response to influence the helpfulness of consumer reviews. Additionally, the results also provide new insights to the influence of reviewer demographic background on the helpfulness of online reviews. Finally, this study analyzed a large data set on a scale that was not available in traditional guest survey studies, responding to the call for big data applications in the hospitality industry.

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A Review on Big Data Analytics in Business

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset21841130 ◽

2018 ◽

pp. 210-214

Author(s):

Manbir Sandhu ◽

Purnima, Anuradha Saini

Keyword(s):

Big Data ◽

Big Data Analytics ◽

Smart Devices ◽

Data Streaming ◽

Huge Amount ◽

Business Units ◽

Enormous Amount ◽

Tools And Techniques ◽

Heath Care ◽

Data Analytic

Big data is a fast-growing technology that has the scope to mine huge amount of data to be used in various analytic applications. With large amount of data streaming in from a myriad of sources: social media, online transactions and ubiquity of smart devices, Big Data is practically garnering attention across all stakeholders from academics, banking, government, heath care, manufacturing and retail. Big Data refers to an enormous amount of data generated from disparate sources along with data analytic techniques to examine this voluminous data for predictive trends and patterns, to exploit new growth opportunities, to gain insight, to make informed decisions and optimize processes. Data-driven decision making is the essence of business establishments. The explosive growth of data is steering the business units to tap the potential of Big Data to achieve fueling growth and to achieve a cutting edge over their competitors. The overwhelming generation of data brings with it, its share of concerns. This paper discusses the concept of Big Data, its characteristics, the tools and techniques deployed by organizations to harness the power of Big Data and the daunting issues that hinder the adoption of Business Intelligence in Big Data strategies in organizations.

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Big Data Analytics: The Next Big Thing

The Management Accountant Journal ◽

10.33516/maj.v54i5.20-24p ◽

2019 ◽

Vol 54 (5) ◽

pp. 20

Author(s):

Dheeraj Kumar Pradhan

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics

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Strategy in Discovery Mode - Wie Big Data & Analytics strategisches Denken verdrängen kann

WiSt - Wirtschaftswissenschaftliches Studium ◽

10.15358/0340-1650-2020-5-11 ◽

2020 ◽

Vol 49 (5) ◽

pp. 11-17

Author(s):

Thomas Wrona ◽

Pauline Reinecke

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics

Big Data & Analytics (BDA) ist zu einer kaum hinterfragten Institution für Effizienz und Wettbewerbsvorteil von Unternehmen geworden. Zu viele prominente Beispiele, wie der Erfolg von Google oder Amazon, scheinen die Bedeutung zu bestätigen, die Daten und Algorithmen zur Erlangung von langfristigen Wettbewerbsvorteilen zukommt. Sowohl die Praxis als auch die Wissenschaft scheinen geradezu euphorisch auf den „Datenzug“ aufzuspringen. Wenn Risiken thematisiert werden, dann handelt es sich meist um ethische Fragen. Dabei wird häufig übersehen, dass die diskutierten Vorteile sich primär aus einer operativen Effizienzperspektive ergeben. Strategische Wirkungen werden allenfalls in Bezug auf Geschäftsmodellinnovationen diskutiert, deren tatsächlicher Innovationsgrad noch zu beurteilen ist. Im Folgenden soll gezeigt werden, dass durch BDA zwar Wettbewerbsvorteile erzeugt werden können, dass aber hiermit auch große strategische Risiken verbunden sind, die derzeit kaum beachtet werden.

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