Exploring influential factors affecting guest satisfaction

2020 ◽  
Vol 11 (1) ◽  
pp. 137-153 ◽  
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.

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.

Detecting customers knowledge from social media big data: toward an integrated methodological framework based on netnography and business analytics

2020 ◽  
Vol 24 (4) ◽  
pp. 799-821 ◽  
Author(s):  
Pasquale Del Vecchio ◽  
Gioconda Mele ◽  
Giuseppina Passiante ◽  
Demetris Vrontis ◽  
Cosimo Fanuli
Keyword(s):  
Social Media ◽  
Big Data ◽  
Customer Relationship Management ◽  
Value Creation ◽  
Relationship Management ◽  
Customer Relationship ◽  
Business Analytics ◽  
Content Type ◽  
Social Big Data ◽  
Practical Implications

Purpose This paper aims to demonstrate how the integration of netnography and business analytics can support companies in the process of value creation from social big data by leveraging on customer relationship management and customer knowledge management (CKM). Design/methodology/approach This paper adopts the methodology of a single case study by using desk analysis, netnography and business analytics. The context of analysis has been identified into the case of Aurora Company, a well-known producer of fountain pens. Findings The case demonstrates how the integration of big data analytics and netnography is relevant for the development of a customer relationship management strategy. The results obtained have been categorized according to the three main categories of customer knowledge, such as knowledge for, from and about customer. Research limitations/implications This paper presents implications for the advancement of the theory on CKM by demonstrating, as the acquisition, storage and management of data generated by customers on social media require the adoption of a cross-disciplinary approach resulting from the integration of qualitative and quantitative approaches. The framework is structured as methodological tool to detect knowledge in virtual community. Practical implications Practical implications arise for managers and entrepreneurs in terms of value creation from knowledge assets generated on social big data through the management of the customers’ relationship and data-driven innovation patterns. Originality/value This paper offers an original contribution of integration of well-established research streams. The focus on the knowledge under the perspectives of information assets for, from and about customers in the debate on value creation and management of big data is an element of value offered by this study in addition to the comprehension of strategies of social customer relationship management as actual initiative embraced by a company in the leveraging of innovation and tradition.

The role of key online reviews in affecting online hotel booking: an empirical investigation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dong Zhang ◽  
Pengkun Wu ◽  
Chong Wu
Keyword(s):  
Binary Logistic Regression ◽  
Online Reviews ◽  
Limited Attention ◽  
Content Type ◽  
Factors Affecting ◽  
Mediating Role ◽  
Antecedents And Consequences ◽  
Hotel Managers ◽  
Hotel Booking

Purpose The importance of online reviews on online hotel booking has been widely acknowledged. However, not all online reviews affect consumers equally. Compared with common online reviews, key online reviews (KORs) have a greater influence on consumers' decisions and online hotel booking. This study takes the first step to investigate the factors affecting the identification of KORs and the role of KORs in online hotel booking.Design/methodology/approach To test the research hypotheses, this study develops a crawler to obtain 551,600 online reviews of 650 hotels in ten representative large cities in China. This study first uses a binary logistic regression to identify KORs by combining review content quality and reviewer characteristics and then uses a log-regression model to investigate the role of KORs in online hotel booking.Findings This study mined the factors affecting the identification of KORs by analyzing review contents and reviewer characteristics. Our results revealed that KORs play a mediating role in the effects of review content and reviewer characteristics on online hotel booking.Originality/value This study focuses on KORs, which have received limited attention in research but are important to practitioners. Specifically, this study investigates the antecedents and consequences of KORs. Our results enable hotel managers to manage online reviews effectively, particularly KORs.

Assessment of the significance of factors affecting the growth of women entrepreneurs: study based on experience categorization

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sucheta Agarwal ◽  
Veland Ramadani ◽  
Leo-Paul Dana ◽  
Vivek Agrawal ◽  
Jitendra Kumar Dixit
Keyword(s):  
Emerging Economies ◽  
Women Entrepreneurs ◽  
Uttar Pradesh ◽  
Sustainable Growth ◽  
Gender Orientation ◽  
Content Type ◽  
Factors Affecting ◽  
Emerging Nations ◽  
Practical Implications ◽  
Significant Factors

Purpose The ascent of women enterprising community (WEC) in a couple of decades draws the attention of various government and non-government bodies. Literature has mentioned various studies that focus on the factors affecting the success or failure of women entrepreneurs (WEs), but understanding of the ranking of the factors depending on the experiences of different WEs is needed. This study aims to identify the significant factors essential for the growth of WEC. Design/methodology/approach This study examines the factors through interview of 33 WEs having different entrepreneurial experiences (less than 1 year, more than 1 year but less than 10 years and more than 10 years of experiences) from different regions of Uttar Pradesh, India, and with the help of analytical hierarchical process, ranks the factors affecting the sustainable growth of WEs. Findings Through analysis, significant factors have been identified such as determination, education, entrepreneurial resilience, personal satisfaction and provide employment, and these factors have been analysed according to the different experiences of WEs. An investigation of ranking these factors of WEC, especially in the emerging nations, can assist policymakers in designing projects that improve the mindfulness associated with women enterprise and define the compelling methodologies. Practical implications The growth of the WEC is significantly affected by gender orientation ways of thinking as driven by entrepreneurship models. Originality/value This study gives a direction to policymakers by emphasizing on significant factors of various stages of enterprise development for the encouragement of WEs in the emerging economies.

Sharing tourism experiences in social media

2017 ◽  
Vol 29 (1) ◽  
pp. 179-225 ◽  
Author(s):  
Marios D. Sotiriadis
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Academic Research ◽  
Online Reviews ◽  
Content Type ◽  
Main Research ◽  
Research Themes ◽  
Consumer Perspective ◽  
Use Of Social Media ◽  
The Impact

Purpose The purpose of this paper is twofold: to perform a synthesis of academic research published between 2009 and 2016 regarding the changes in tourism consumer behavior brought about by the use of social media (SM); and to suggest a set of strategies for tourism businesses to seize opportunities and deal with resulting challenges. Design/methodology/approach A volume of 146 peer-reviewed journal articles were retrieved from two major databases. Content analysis of this academic research has been performed, exploring the effects of online reviews on tourism consumers and providers. Findings The content analysis identified three main research themes that were investigated by scholars and classified into two major categories, namely, consumer perspective and provider perspective: the antecedents (factors motivating and influencing tourists); the influence of online reviews on consumer behaviour; and the impact of these reviews on tourism businesses (providers’ perspective). Research limitations/implications This study is based on a literature review and outcomes reported by previous studies; hence, the suggestions are indicative rather than conclusive. Some publication sources were not included. Practical implications This paper suggests a range of adequate strategies, along with operational actions, formulated for industry practitioners in the fields of management and marketing. Originality/value It provides an update of the state of published academic research into SM and an integrated set of management and marketing strategies for tourism providers in seizing the opportunities and dealing with the challenges raised in a digital context.

Tit for tat: understanding the responding behavior of property hosts on peer-to-peer rental platforms

2021 ◽  
Vol 33 (3) ◽  
pp. 1105-1126
Author(s):  
Sai Liang ◽  
Xiaoxia Zhang ◽  
Chunxiao Li ◽  
Hui Li ◽  
Xiaoyu Yu
Keyword(s):  
Service Providers ◽  
Online Reviews ◽  
Peer To Peer ◽  
Theoretical Development ◽  
Effective Response ◽  
Content Type ◽  
Starting Point ◽  
Tit For Tat ◽  
Hotel Managers ◽  
The Impact

Purpose Due to their very different contexts, the responses made by property hosts to online reviews can differ from those posted by hotel managers. Thus, the purpose of this study is to investigate the determinants of the responding behavior of hosts on peer-to-peer property rental platforms. Design/methodology/approach This study applied a comprehensive framework based on the theory of planned behavior. Empirical models are constructed based on 89,967 guest reviews with their associated responses to reveal the responding pattern of property hosts. Findings Unlike hotel managers, property hosts are more likely to reply to positive than to negative reviews; moreover, when they do choose to respond to negative reviews, they are likely to do so negatively, in a “tit-for-tat” way. This study also finds that one reason for the difference of responding patterns between property hosts and hotel managers is the hosts’ lack of experience of consumer relationship management and service recovery. Research limitations/implications This study provides a good start point for future theoretical development regarding effective responding strategy on peer-to-peer property rental platforms, as well as some useful implications for practitioners. Originality/value This study is an early attempt to analyze the impact of the particularity of emerging platforms on the responding behavior of service providers based on a comprehensive conceptual framework and empirical model thus provides a good starting point for the further investigation of effective response strategies on these emerging platforms.

Forecasting daily attraction demand using big data from search engines and social media

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fengjun Tian ◽  
Yang Yang ◽  
Zhenxing Mao ◽  
Wenyue Tang
Keyword(s):  
Social Media ◽  
Big Data ◽  
Search Engine ◽  
Elastic Net ◽  
Percentage Error ◽  
Mean Square ◽  
Social Media Data ◽  
Content Type ◽  
Forecasting Error ◽  
Media Data

Purpose This paper aims to compare the forecasting performance of different models with and without big data predictors from search engines and social media. Design/methodology/approach Using daily tourist arrival data to Mount Longhu, China in 2018 and 2019, the authors estimated ARMA, ARMAX, Markov-switching auto-regression (MSAR), lasso model, elastic net model and post-lasso and post-elastic net models to conduct one- to seven-days-ahead forecasting. Search engine data and social media data from WeChat, Douyin and Weibo were incorporated to improve forecasting accuracy. Findings Results show that search engine data can substantially reduce forecasting error, whereas social media data has very limited value. Compared to the ARMAX/MSAR model without big data predictors, the corresponding post-lasso model reduced forecasting error by 39.29% based on mean square percentage error, 33.95% based on root mean square percentage error, 46.96% based on root mean squared error and 45.67% based on mean absolute scaled error. Practical implications Results highlight the importance of incorporating big data predictors into daily demand forecasting for tourism attractions. Originality/value This study represents a pioneering attempt to apply the regularized regression (e.g. lasso model and elastic net) in tourism forecasting and to explore various daily big data indicators across platforms as predictors.

Machine Learning for Business Analytics

2021 ◽  
pp. 232-256
Author(s):  
Kağan Okatan
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Media ◽  
Big Data ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Business Analytics ◽  
Business Intelligence Systems ◽  
Long Time ◽  
Rules Of The Game

All these types of analytics have been answering business questions for a long time about the principal methods of investigating data warehouses. Especially data mining and business intelligence systems support decision makers to reach the information they want. Many existing systems are trying to keep up with a phenomenon that has changed the rules of the game in recent years. This is undoubtedly the undeniable attraction of 'big data'. In particular, the issue of evaluating the big data generated especially by social media is among the most up-to-date issues of business analytics, and this issue demonstrates the importance of integrating machine learning into business analytics. This section introduces the prominent machine learning algorithms that are increasingly used for business analytics and emphasizes their application areas.

scholarly journals Airline passengers’ perceptions of service quality: themes in online reviews

2019 ◽  
Vol 31 (2) ◽  
pp. 855-873 ◽  
Author(s):  
Ana Brochado ◽  
Paulo Rita ◽  
Cristina Oliveira ◽  
Fernando Oliveira
Keyword(s):  
Social Media ◽  
Design Methodology ◽  
Online Reviews ◽  
Value For Money ◽  
Content Type ◽  
The Core ◽  
Content Analyses ◽  
Airline Passengers ◽  
Media Platform ◽  
Post Purchase

PurposeThis paper aims to identify the main themes shared in online reviews by airline travellers, as well as which of these themes were linked with higher and lower value for money ratings.Design/methodology/approachThe research used mixed content analyses (i.e. quantitative and qualitative) to examine 1,200 reviews of six airline companies shared by airline travellers in a social media platform.FindingsThe analyses revealed nine themes in descriptions of airline travel experiences. These are the core services during “flights”, “airport” operations, crew and ground “staff”, ticket “classes”, “seats”, inflight “services”, “entertainment”, overall experiences of “airlines” and post-purchase recommendations of with which companies to “fly”. Low value for money ratings are linked with the “airport” and “flights” themes.Originality/valueThe results offer useful insights into airline travellers’ overall experiences based on social media information and facilitate the identification of the main themes linked with different value for money ratings.

Effect of crosslinking agents on the strength characteristics of cellulosic fabrics using 23 32 mixed level factorial

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Farhana Naeem ◽  
Fareha Asim ◽  
Muhammad Tufail
Keyword(s):  
High Strength ◽  
Strength Characteristics ◽  
Blend Ratio ◽  
Tear Strength ◽  
Content Type ◽  
Factors Affecting ◽  
Acrylate Copolymer ◽  
Curing Method ◽  
Crosslinking Agents ◽  
Significant Factors

Purpose Low pilling and wrinkle-free appearance of cellulosic fabrics are always demanded. Resin finishes are applied to improve these properties, but there is an adverse effect of the resin finish as it tends to reduce the strength of the fabrics. Therefore, the effect of the two most important finishes; anti-pilling and resin finish, on the strength characteristics of 100% viscose and 50:50 Viscose/cotton plain and satin fabrics were investigated in this paper. The purpose of this study is to identify significant factors affecting the strength of fabrics finished with crosslinking agents [non-ionic acrylate copolymer and (dimethyloldihydroxyethyleneurea)]. Design/methodology/approach A statistical model of 23 32 mixed level factorial design was used for the study. Appratan N9211 (A) and Arkofix NF (B) were tested at three concentrations, whereas three factors fabric; weave (C), blend ratio (D) and curing method (E) were tested at two levels. The performance of the finish was evaluated by two response variables, which were tensile and tear strength. Findings The various conditions of high strength values of the fabrics were presented in this paper. It was found that the tear strength of the fabrics increased after finishing except for 50:50 viscose/cotton plain fabric, whereas the tensile strength of plain fabrics is better at shock cure and for a satin normal cure is better. The model adequacy plots exhibit that the assumptions of normality and independence are not desecrated. Moreover, the values of “predicted R2” are in reasonable agreement with the “adjusted R2,” which confirms that models have been accounted for most of the inconsistency. Originality/value This paper is a part of my PhD dissertation. Unlike the previous studies, this paper investigated the effect of two crosslinking agents, Appretan N9211 as anti-pilling and Arkofix NF as wrinkle resistant agents on 100% viscose and 50:50 viscose/cotton plain and satin. Three different concentrations of both the crosslinking agents were used. Also, fixation of the finishes was carried out at a normal cure and shock cure.

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