Journal of Hospitality and Tourism Technology

Park, Eunhye (Olivia); Chae, Bongsug (Kevin); Kwon, Junehee

2020 Journal of Hospitality and Tourism Technology

doi: 10.1108/jhtt-08-2017-0075

The purpose of this study was to explore influences of review-related information on topical proportions and the pattern of word appearances in each topic (topical content) using structural topic model (STM).Design/methodology/approachFor 173,607 Yelp.com reviews written in 2005-2016, STM-based topic modeling was applied with inclusion of covariates in addition to traditional statistical analyses.FindingsDifferences in topic prevalence and topical contents were found between certified green and non-certified restaurants. Customers’ recognition in sustainable food topics were changed over time.Research limitations/implicationsThis study demonstrates the application of STM for the systematic analysis of a large amount of text data.Originality/valueLimited study in the hospitality literature examined the influence of review-level metadata on topic and term estimation. Through topic modeling, customers’ natural responses toward green practices were identified.

Becken, Susanne; Alaei, Ali Reza; Wang, Ying

2020 Journal of Hospitality and Tourism Technology

doi: 10.1108/jhtt-09-2017-0090

Destination monitoring is crucial to understand performance and identify key points of differentiation. Visitor satisfaction is an essential driver of destination performance. With the fast-growing volume of user-generated content through social media, it is now possible to tap into very large amounts of data provided by travellers as they share their experiences. Analysing these data for consumer sentiment has become attractive for destinations and companies. The idea of drawing on social media sentiment for satisfaction monitoring aligns well with the broader move towards smart destinations and real-time information processing. Thus, this paper aims to examine whether the electronic word of mouth originating from Twitter posts offers a useful source for assessing destination sentiment. Importantly, this research examines what caveats need to be considered when interpreting the findings.Design/methodology/approachThis research focusses on a prominent tourist destination situated on Australia’s East Coast, the Gold Coast. Using a geographically informed filtering process, a collection of tweets posted from within the Gold Coast destination was created and analysed. Metadata were analysed to assess the population of Twitter users, and sentiment analysis, using the Valence Aware Dictionary for Sentiment Reasoning algorithm, was performed.FindingsTwitter posts provide considerable information, including about who is visiting and what sentiment visitors and residents express when sending tweets from a destination. They also uncover some challenges, including the “noise” of Twitter data and the fact that users are not representative of the broader population, in particular for international visitors.Research limitations/implicationsThis paper highlights limitations such as lack of representativeness of the Twitter data, positive bias and the generic nature of many tweets. Suggestions for how to improve the analysis and value of tweets as a data source are made.Practical implicationsThis paper contributes to understanding the value of non-traditional data sources for destination monitoring, in particular by highlighting some of the pitfalls of using information sources, such as Twitter. Further research steps have been identified, especially with a view to improving target-specific sentiment scores and the future employment of big-data approaches that involve integrating multiple data sources for destination performance monitoring.Social implicationsThe identification of cost-effective ways of measuring and monitoring guest satisfaction can lead to improvements in destination management. This in turn will enhance customer experience and possibly even resident satisfaction. The social benefits, especially at times of considerable visitation pressure, can be important.Originality/valueThe use of Twitter data for the monitoring of visitor sentiment at tourist destinations is novel, and the analysis presented here provides unique insights into the potential, but also the caveats, of developing new, smart systems for tourism.

Lado-Sestayo, Rubén; Vivel-Búa, Milagros

2020 Journal of Hospitality and Tourism Technology

doi: 10.1108/jhtt-08-2017-0072

The purpose of this paper is to design an algorithm to predict hotel profitability by means of deep learning techniques.Design/methodology/approachThe methodology consists of a multi-layered neural network that includes a lag of profitability as the input. Furthermore, other input variables are related to hotel and tourist destinations; the raw data for hotel and tourist destinations were collected from multiple public access data sources.FindingsThe results show that the proposed model has a high predictive capacity of hotel profitability in all the years studied (2005-2011), according to the performance metrics evaluated within the sample. Thus, the authors can conclude that deep learning algorithms can be a useful tool to evaluate hotel performance.Practical implicationsThe algorithm designed in this research could be of interest to improve decision-making processes related to profitability, for example, in evaluating the creation of new hotels. Moreover, the model provides a quick and efficient analyses that could be of interest to investors and lenders. In particular, they could compare investment alternatives in the hotel sector. Also, according to the results, the location variables are important determinants of hotel profitability, and consequently, hotel managers should collaborate with the tourist destination managers to improve profitability. From an internal perspective, hotel managers should focus on the management of human resources.Originality/valueThis paper is the first empirical study that predicts hotel profitability using deep learning techniques. In addition, this methodology is applied to analyse hotel profitability, for the first time, in the Spanish market. This market is an ideal analytical framework because of its heterogeneity with respect to hotel supply in terms of seasonality and coastal characteristics, among others.

Xie, Karen L.; Lee, Young Jin

2020 Journal of Hospitality and Tourism Technology

doi: 10.1108/jhtt-03-2017-0026

When shopping for hotels online, consumers usually follow a sequential process of search, click-through and book. How to maximize consumer conversion on the path to purchase and prevent potential customers from giving up the online search remains an important topic to hotel marketers and online travel agents (OTAs). The purpose of this study is to understand how informational cues displayed in an online hotel search process, including quality indicators, brand affiliation, incentives (discounted price and promotion) and position in the search results, influence consumer conversion from one stage to another.Design/methodology/approachThe authors collected clickstream data of hotel search from Expedia. The data include information on individual consumers’ click-through and booking, as well as events leading up to the conversions (or failure to convert) from search, click-through to book. It contains 940,164 hotels searched and displayed in 39,574 online search queries made by users in a regional US market between November 1, 2012 and June 20, 2013. The modeling strategy comprised the Heckman model and random effects model, which integrated sequential consumer behavior in different problem-solving stages while accounting for heterogeneity across different hotels online.FindingsThe authors find that consumers rely on informational cues displayed online to make decisions about hotel booking. Specifically, consumers tend to click through hotels with higher consumer-generated ratings and industry-endorsed ratings. However, they tend to rely on consumer-generated ratings rather than industry-endorsed ratings when committing to a booking. Moreover, consumers are strongly responsive to incentives (discounted price and promotion) when clicking-through and booking a hotel. Finally, the likelihood of consumer conversions from search to click-through and booking is higher for hotels with brand affiliation and higher positions in the search results.Originality/valueThis research provides critical managerial implications of online search for hotel marketers and OTAs. The results inform hotel marketers and OTAs on how consumers respond to informational cues displayed in their search process and how these informational cues influence consumer conversion from one stage to another. The sequential problem-solving process of search, click-through and booking disclosed in this study also helps hotel marketers to identify customer conversion opportunities using effective informational cues.

Höpken, Wolfram; Müller, Marcel; Fuchs, Matthias; Lexhagen, Maria

2020 Journal of Hospitality and Tourism Technology

doi: 10.1108/jhtt-08-2017-0059

The purpose of this study is to analyse the suitability of photo-sharing platforms, such as Flickr, to extract relevant knowledge on tourists’ spatial movement and point of interest (POI) visitation behaviour and compare the most prominent clustering approaches to identify POIs in various application scenarios.Design/methodology/approachThe study, first, extracts photo metadata from Flickr, such as upload time, location and user. Then, photo uploads are assigned to latent POIs by density-based spatial clustering of applications with noise (DBSCAN) and k-means clustering algorithms. Finally, association rule analysis (FP-growth algorithm) and sequential pattern mining (generalised sequential pattern algorithm) are used to identify tourists’ behavioural patterns.FindingsThe approach has been demonstrated for the city of Munich, extracting 13,545 photos for the year 2015. POIs, identified by DBSCAN and k-means clustering, could be meaningfully assigned to well-known POIs. By doing so, both techniques show specific advantages for different usage scenarios. Association rule analysis revealed strong rules (support: 1.0-4.6 per cent; lift: 1.4-32.1 per cent), and sequential pattern mining identified relevant frequent visitation sequences (support: 0.6-1.7 per cent).Research limitations/implicationsAs a theoretic contribution, this study comparatively analyses the suitability of different clustering techniques to appropriately identify POIs based on photo upload data as an input to association rule analysis and sequential pattern mining as an alternative but also complementary techniques to analyse tourists’ spatial behaviour.Practical implicationsFrom a practical perspective, the study highlights that big data sources, such as Flickr, show the potential to effectively substitute traditional data sources for analysing tourists’ spatial behaviour and movement patterns within a destination. Especially, the approach offers the advantage of being fully automatic and executable in a real-time environment.Originality/valueThe study presents an approach to identify POIs by clustering photo uploads on social media platforms and to analyse tourists’ spatial behaviour by association rule analysis and sequential pattern mining. The study gains novel insights into the suitability of different clustering techniques to identify POIs in different application scenarios.

Farrish, John; Edwards, Chase

2020 Journal of Hospitality and Tourism Technology

doi: 10.1108/jhtt-07-2017-0046

This paper aims to examine technostress and asks whether it is an illness requiring accommodation under the terms of the Americans with Disabilities Act. It further explores the notion that hospitality employers may contribute to employee technostress and examines employers' potential legal liability. Finally, it recommends steps employers can take to avoid legal liability.Design/methodology/approachTechnostress is defined in terms of job demand and resource theory. It explores how technology overload can contribute to employee technostress.FindingsAs there is currently no legal definition for technostress, courts will be guided by the standard of what a reasonably prudent individual would do to guard against a particular threat.Research limitations/implicationsThe courts have yet to rule on whether technostress constitutes an illness requiring accommodation. It is therefore possible that technostress will not be classified as such. Still, operators should not make themselves a target for litigation.Practical implicationsEmployers would be wise to craft policies that reduce the risk of technostress in the workplace to mitigate both its causes and effects.Social implicationsVery little research has been conducted examining the impact of technostress in the workplace. The obligation of employers to accommodate employees suffering from the effects of technostress will be litigated soon. This will have a significant impact on the culture surrounding catering and room sales.Originality/valueNo studies have been undertaken as yet to anticipate its effects on employees and what steps employers must take to accommodate employees who suffer from it. This paper fills that gap and, more importantly, does so before the issue is litigated.

Nadkarni, Sanjay; Kriechbaumer, Florian; Rothenberger, Marcus; Christodoulidou, Natasa

2020 Journal of Hospitality and Tourism Technology

doi: 10.1108/jhtt-12-2018-0120

The purpose of this study is to explore the use of Internet of Things (IoT) in hospitality and examine its relationship with Big Data. Drawing upon theoretical and practical considerations, it lays a foundation for its adoption in practice and future research.Design/methodology/approachThis paper uses a conceptual approach. It demonstrates the use of IoT and its impact on Big Data in hospitality through exemplars. The paper further explores the convergence of IoT, Big Data and hospitality in the context of the literature, value attributes and vendor offerings. Theoretical models from information systems and business are used to support the concepts proposed.FindingsThe study compiles and contextualizes the applications of IoT in hospitality by applying an input–process–output model, demonstrating the link to Big Data. The resulting value dimensions are represented by the IoT–Big Data triple impact intensity model.Research limitations/implicationsAn outlook toward the future trajectory of IoT adoption is provided by proposing to extend the prevalent social, mobile, analytics and cloud framework with an IoT component.Practical implicationsPractical implications of the use of IoT and Big Data in hospitality on information technology infrastructure, business models, security and standardization highlight the scope for further empirical research.Originality/valueBy synthesizing IoT applications in hospitality and by bringing to light their relationship with Big Data, the study demonstrates how IoT, Big Data and hospitality converge – a synthesis that has thus far been largely unexplored. This study lays the groundwork for increased deployment of IoT and Big Data in hospitality and future academic research in this area.

Barreda, Albert A.; Nusair, Khaldoon; Wang, Youcheng; Okumus, Fevzi; Bilgihan, Anil

2020 Journal of Hospitality and Tourism Technology

doi: 10.1108/jhtt-02-2018-0016

The study aims to develop a theoretical model that portrays the antecedents of emotional attachment in the travel context by combining branding, marketing and information systems theories.Design/methodology/approachThe authors gather empirical data through a Web-based questionnaire from 236 respondents. The proposed theory-driven model is examined empirically by using confirmatory factor analysis and structural equation modeling.FindingsThe findings suggest that social media rewards and benefits impact users’ brand commitment. Social media interactivity and rewards help building a stronger brand image. Brand commitment and brand image, in turn, affect emotional attachment positively.Research limitations/implicationsOther unexamined constructs may add to the explanation of building brands using social media platforms. As this is an exploratory study in relation to enhancing emotional attachment in an online travel setting, other constructs such as brand page commitment, annoyance, social benefits and telepresence may be considered in future studies.Practical implicationsPractitioners might encounter ways to influence favorable perceptions and brand commitment when consumers use social media sites. The model addresses questions regarding the significant role of social media activities on influencing brand image and brand commitment that in turn influence the development of a strong emotional attachment.Social implicationsThis study examined the effects of social media activities including interactivity, psychological benefits and rewards on brand image and brand commitment, and the effects of brand image and brand commitment on emotional attachment in the travel context. The results offer further verification for the theory-based model presented in the study. Evidently, statistically significant and meaningful associations exist among the factors.Originality/valueThe key contribution of this study is that it presents and validates a theory-driven model that reveals the antecedents of sustainable emotional attachment. The proposed framework stresses the positive relationships among constructs and offers research basis for expansion in other settings.

Lee, Minwoo; Cai, Yanjun (Maggie); DeFranco, Agnes; Lee, Jongseo

2020 Journal of Hospitality and Tourism Technology

doi: 10.1108/jhtt-07-2018-0054

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/approachThis 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.FindingsThe 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/valueThe 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.

Prima Lita, Ratni; Fitriana Faisal, Ranny; Meuthia, Meuthia

2020 Journal of Hospitality and Tourism Technology

doi: 10.1108/jhtt-11-2017-0124

This study aims to identify the effect of entrepreneurial orientation and organizational culture on organizational innovation and organizational performance among small- and medium-sized enterprises (SMEs) on the creative industry which is supporting tourism in Indonesia.Design/methodology/approachA quantitative approach was used to distribute questionnaires using a purposive sampling technique to 183 SME’s owners of the creative industry that produce and trade the products directly to the customers. A partial least square (PLS) was conducted to analyze the data.FindingsEntrepreneurial orientation and organizational culture have a significant effect on innovation, which in turn, influences the performance. Interestingly, innovation does not have a significant influence on performance as well as does not mediate the influence between entrepreneurial orientation and organizational performance.Research limitations/implicationsThe issue of innovation in this study was measured by many indicators that reflected the organizational innovation. Further studies may investigate other specific types of innovation.Practical implicationsBoth entrepreneurs and government should establish more technological support, business incubation centers and counseling organizations to encourage performance in the future.Originality/valueSocio-cultural diversity such as entrepreneurial orientation and natural resources especially the culture of Indonesia can inspire creative industries to continue to innovate and after that can lead them to improve their performance, especially in the tourism area.