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Industry 4.0 applications can accelerate data driven decision making culture in organizations. Such data driven culture can have a profound impact on the organizational capabilities underlying product and process innovation. While there is a relatively developed body of literature on the effect of data driven culture on organizational performance, there is virtually no study that has examined how Industry 4.0 influences the data driven culture of organizations and how in its turn such culture influences both product and process innovation. Furthermore, the role of organizational data driven culture has seldom been examined in relation to organizational sustainability performance. Against this backdrop, the aim of this study is to examine the role of emerging Industry 4.0 technologies on the data driven culture of organizations and analyze if and how such data driven culture influences organizational performance ultimately translating into competitive advantage. By leveraging the Resource Based View (RBV) and Dynamic Capabilities theory, we developed a theoretical model and tested it using a PLS-SEM approach on a sample of 416 organizations. We found that adoption of industry 4.0 technologies influences organizational performance by improving social, competitive, and financial performance of the organizations relying on data driven culture and improved innovative capabilities. • Data driven culture positively influences organizational innovation capabilities. • Data driven culture positively influences sustainability performance. • Sustainability performance ultimately translates into competitive advantage. [ABSTRACT FROM AUTHOR]

• We develop and test a DEA model embedding eWOM among the outputs. • Including eWOM leads to differences in the mean and ranking of hotel efficiency. • Hotels improving in the efficiency ranking are less than those decreasing. • eWOM as an output of DEA models generates differentiated impacts across hotel classes. • e-WOM could inform Strategic Performance Measurement Systems. This paper develops and tests an innovative DEA model in the hospitality sector, by originally embedding online customer ratings among the outputs of the model. Based on a sample of 268 independent hotels located in Rome (Italy), we test a eWOM-informed DEA model and find that the introduction of online ratings among the outputs of the model significantly affects the assessment of hotels' efficiency regardless of hotel category. The efficiency rankings generated by the DEA models embedding eWOM are radically different compared to those resulting from DEA models exclusively based on financial variables. The number of hotels improving their position in the efficiency rankings is lower than the number of hotels decreasing in the ranking. However, the average efficiency variation is positive and higher for 2- and 3- stars hotels than for 4- and 5- stars hotels. Implications for researchers in hospitality and tourism, managers and practitioners are discussed. [ABSTRACT FROM AUTHOR]

Highlights • Almost half a million Booking.com online reviews written by hotel guests are used. • An empirical study on Italy and Russia is conducted. • Use of domestic language exerts a positive impact on online ratings. • Findings do not vary across type of destination or country. • Hotels and online review platform managers should look at language communities. Abstract In an increasingly global travel market, hospitality services encounters involve growing interactions between providers and customers often belonging to different nationalities and cultures and speaking different languages. Extant hospitality management literature has explored the influence of language on service evaluations mostly in offline settings. This study innovatively captures the effect of the language used in online hotel reviews on online consumer ratings in two distinctively different destinations located in culturally different countries: Italy and Russia. Based on almost half a million Booking.com online reviews written by hotel guests in Moscow and Rome, we illuminate if and to what extent domestic vs. foreign language use affects online customer satisfaction. We find that the use of domestic language exerts a positive impact on online ratings in both countries. Implications for hospitality practitioners and managers, developers and managers of online review platforms, and customers of hotel services are discussed. [ABSTRACT FROM AUTHOR]

Abstract This study examines the role played by submission devices (mobile vs. desktop) in online travel reviewing behaviour. We analyse over 1.2 million online reviews from Booking.com and detect the presence and distinctive features of online reviews submitted by mobile devices. Our findings indicate that 1) the share of online reviews submitted by mobile increased at a very high rate over time (higher than the growth rate of those submitted by desktop); 2) there is a systematic and statistically significant difference between the features and distributions of online reviews submitted through mobile devices vs. online reviews submitted through desktops. We raise awareness of the role played by submission devices in online travel behaviour research and present implications for future research. Highlights • Examines differences in mobile vs. desktop eWOM. • Finds growth rate to be over 12 times higher for mobile online reviews. • Identifies significant differences in features and distributions of mobile reviews. • Draws attention to need for considering submission device in online review research. [ABSTRACT FROM AUTHOR]

The purpose of this study is to continue the discussion initiated by Mellinas et al. (2015, 2016) on the effects of the Booking.com rating system and more widely on the use of the OTA as a data source in academic tourism and hospitality research. We enrich and complement the original work by Mellinas et al. (2015) by empirically investigating the effects of the Booking.com rating system on the distribution of hotel ratings for the overall population of hotels located in London over two years. Based on more than 1.2 million online reviews, we show that the overall distribution of hotel scores is significantly left-skewed. Moreover, we find that the degree of skewness is positively associated with hotel class: lower-class hotels exhibit distributions of ratings that are statistically less skewed than higher-class hotels. [ABSTRACT FROM AUTHOR]

This paper explores how the national tourism organizations (NTOs) of the top 10 most-visited countries by international tourists strategically employ Facebook to promote and market their destinations. Based on big data retrieved from the NTOs’ Facebook pages, and leveraging advanced metrics for capturing user engagement, the study sheds light on the factors contributing to superior level of social activity. The findings indicate that the way Facebook is tactically employed varies significantly across the NTOs. The panel data regression analyses suggest that engagement is positively affected by posting visual content (namely photos), and posting during the weekends, and negatively affected by evening posting. Post frequency displays no statistically significant effect on social engagement. The study also shows that most of the NTOs (except for Italy, Spain, Turkey and the UK) deploy Facebook with a top-down approach, and spontaneous user-generated content (UGC) is allowed to a very little extent. [ABSTRACT FROM AUTHOR]

Deploying big data analytical techniques to retrieve and analyze a large volume of more than 2.7 million online consumer reviews (OCRs), this work sheds light on how mobile devices used by consumers to post online reviews influence their satisfaction with services. More specifically, we conduct a multi-platform study of TripAdvisor.com and Booking.com OCRs pertaining to hotel services across eight leading tourism destination cities in the American and European continents over the period 2017–2018. By adopting multivariate regression analyses, we show that OCR ratings are positively influenced by the use of mobile devices on Booking.com. The opposite effect is observed on TripAdvisor. These asymmetric effects can be explained in light of different online review policies across the platforms analyzed. Theoretical and managerial contributions and implications for digital platforms, big data analytics, electronic word of mouth, and marketing research are examined. • Deploys >2.7 million online consumer reviews (OCRs) and big data analytics • Multi-platform study of TripAdvisor.com and Booking.com OCRs is conducted. • OCR ratings are influenced by online review policies. • Mobile devices influence positively OCR valence when lenient review policies. • Mobile devices influence negatively OCR valence when strict review policies. [ABSTRACT FROM AUTHOR]

This paper adopts a managerial perspective to revisit an original case study of the “Pink Night” festival presented by Giovanardi et al. (2014) in an earlier issue of this journal. Our in-depth qualitative study contributes to the event tourism planning and management literature in three ways. First, we shed light on how and why competing Destination Management Organisations (DMOs) cooperate to plan, develop and manage event tourism. Second, we introduce and describe the brand new concept of the meta-event, which is the main theoretical contribution of this work. Third, we elucidate the role of meta-events as brand architecture tools to rebrand and reposition wide tourism areas. We illustrate the theoretical and managerial implications of the meta-event concept for event tourism studies and destination managers. [ABSTRACT FROM AUTHOR]

This work explores how Italian regional Destination Management Organisations (DMOs) strategically employ Facebook to promote and market their destinations, and improves on the current metrics for capturing user engagement. Based on big data analysis from the regional DMOs' Facebook pages, supplemented with semi-structured interviews conducted with DMO managers, the study sheds light on the factors contributing to superior level of social activity. The findings indicate that the way Facebook is tactically and strategically employed varies significantly across Italian regional DMOs. Visual content (namely photos) and moderately long posts have a statistically-significant positive impact on DMOs' Facebook engagement, whereas high post frequency, and early daily timing (in the morning) of posts have a negative impact on engagement. Last but not least, the study shows that most of the regional DMOs (except for Trentino, Tuscany, and Sicily) deploy Facebook with a top-down approach, allowing for little spontaneous user generated content (UGC). [ABSTRACT FROM AUTHOR]

This work introduces, develops, and empirically applies an innovative approach aimed at assessing selling prices based on the value perceived by the customers, as measured by electronic word-of-mouth (eWOM) in the guise of online reviews. To achieve this aim, it applies a constant return to scale Data Envelopment Analysis (DEA) approach where the price is the input, and the value attributes are the outputs measured through eWOM in the form of online reviews. We empirically apply the model to the hotel sector by considering both the prices and the service attributes (i.e., staff, location, cleanliness, comfort, facilities and free wi-fi) of 364 hotels based in two leading Italian tourism destinations: Milan and Rome. Our findings suggest that online review analytics can be suitably embedded into analytical models to assess prices. The index developed innovatively supports value-based pricing by means of online review analytics and it is easy-to-perform, and parsimonious as it is based on widely available information on the Internet. • E-Wom analytics can support the assessment of price/value relationships. • A DEA based approach to support value based price assessment is presented. • The approach compares the price (input) to the service attributes (outputs). • The approach is applied to 364 Italian hotels of two leading tourism destinations. [ABSTRACT FROM AUTHOR]

The interaction with social robots is supposed to be a unique and emotionally charged activity. Based on the diffusion of innovations literature, subjective feelings represent a driver of the innovation diffusion process. Yet, to date, no study has comprehensively assessed consumers' emotional responses over time to interactions with social robots. Thus, the study aims to address this research gap by combining innovation diffusion and psychology literature. The emotional content of customers' self-reported communication on social robots deployed across international hotels is categorized through Plutchik's wheel of emotions by using advanced text analytics techniques to track and analyze its evolution over time. Findings show that consumers generally express positive emotions towards social robots. Trust , anticipation and joy are the most frequently expressed emotions. Empirical results from multivariate regression analysis indicate that joy has the greatest magnitude and that anticipation and surprise do not significantly influence consumers' opinions and comments. Negative emotions are less frequent but have a significantly negative impact, which might be considered by hotel managers willing to introduce social robots. • 3627 self-reported comments on social robots for world‑leading hotels are analysed. • Trust , anticipation and joy are the most frequently expressed emotions by reviewers. • Either positive or negative emotions influence robot sentiment polarity. • Joy has the greatest impact on robot sentiment polarity. • Anticipation and surprise do not significantly affect robot sentiment polarity. [ABSTRACT FROM AUTHOR]

• Informativeness and entertainment are the most important predictors of ad value. • Ad irritation reduces ad effectiveness significantly. • Ad value is more important than the attitude toward the ad for purchase intention. • S-O-R framework was integrated with an advertising value model. The exponential growth of mobile phone usage has shifted the attention of marketers toward digital advertising as a tool for targeted communication. However, the increased usage of digital advertising exposes consumers to excessive amounts of advertisements, which may trigger their annoyance and raise concerns about overall advertising effectiveness. In a moderated mediation model, this study examined how digital advertising value influences consumers' attitudes toward advertising and purchase intention at different levels of advertising irritation using the S-O-R framework and the advertising value model. Data were collected from 272 university students to test the conceptual model, and a multi-analytic approach combining partial least squares structural equation modeling and necessary condition analysis (NCA) was adopted. Results show that advertising value has a stronger effect on purchase intention than attitude toward advertising and that advertising irritation is a strong negative moderator that significantly reduces overall advertising effectiveness. NCA results also show varying degrees of predictor necessity. These findings can help advertisers improve their strategies to maximize the effectiveness of digital advertising. [ABSTRACT FROM AUTHOR]

Abstract: This conference communication illustrates the major outcomes emerging from the EIASM Conference on Tourism Management and Tourism Related Issues held in Nice (France), September 20–21, 2012. While a number of managerial issues pertaining to the tourism field were dealt with, this communication covers three specific areas of interest for destination management and marketing: (1) the increasing competition among tourism destinations and the rise and consolidation of BRIC countries as outbound travel markets; (2) the modification of the target markets for established tourism destinations; and (3) the increasing importance of collaboration and especially coopetition not only within a tourism destination but also among destinations. [Copyright &y& Elsevier]

• Develops a typology of online review platforms (ORPs). • Describes Research-driven online review platform (RORP). • RORPs combine digital experimentation with AI-based big data analytics capabilities. • RORPs generate novel innovation insights through digital innovation experimentation. • RORPs operate and deliver value through innovation analytics. Big data analytics constitute one of the driving forces of the fourth industrial revolution and represent one of the founding pillars of Industry 4.0. They are increasingly leveraged to create business insights from online reviews of products and services by a wide range of organizations and firms. In this work, we develop a typology of online review platforms (ORPs) and describe a novel platform, research-driven online review platform (RORP), that combines the science and rigor of very large-scale, low-cost, fast-paced, and complex digital experimentation using real-world customers on digital platforms with the power of modern AI-based big data analytics capabilities (BDAC) to generate novel innovation insights for the digital age. Using multiple real-world case studies, we illustrate how RORPs operate and deliver value through innovation analytics, and serve as a powerful tool for digital innovation experimentation, enabling firms to innovate more effectively and transform their business models to adapt to rapidly changing market conditions. We shed light on the BDAC requirements, as well as the benefits and challenges of using RORPs and innovation analytics, particularly in the post-COVID-19 world, and offer strategic and operational implications for entrepreneurs and innovation managers. [ABSTRACT FROM AUTHOR]

• Online reviews used to track customers' discourse about hospitality service robots. • 50,000 TripAdvisor online reviews from 19 international hotels are analysed. • Trend analyses show different levels of robotic discourse across the hotels. • On average online reviews covering robots increase over time. • Reviews covering robots show different and distinctive features. [ABSTRACT FROM AUTHOR]

• The study unravels cultural differences between service providers and customers. • Online reviews related to hospitality services in three countries analyzed. • Examines conjoint effect of nationality and language differences on online ratings. • Influence of national cultural distance on online ratings is destination-dependent. • Usage of a common language is positively associated with online review valence. International travelling has increased the likelihood of service interactions between customers and providers of different nationalities, frequently speaking various language, and repeatedly experiencing issues with intercultural communication. By examining more than 700,000 online reviews written by hotel guests of 101 different nationalities, using 84 different languages in their reviews, this study unravels cultural differences and examines through a direct measurement approach the simultaneous influence of national and linguistic differences between service customers and providers on online review valence. After controlling for reviewers' demographics, behavioral features, trip related factors and location of service provider, the study findings reveal that while the influence of national cultural distance on online ratings is country- and destination- dependent, the usage of a common language is positively associated with online review valence irrespective of the destination/country where the service provider is located. Theoretical and practical implications are discussed. [ABSTRACT FROM AUTHOR]