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More hotels are beginning to embrace green practices given increasing awareness of sustainable development. The purpose of this study is to examine factors contributing to hotels’ participation in TripAdvisor’s GreenLeaders program.

Based on a sample of 48,064 hotels from 328 destinations in 29 countries, the authors leverage a multi-level logit model to examine antecedents of GreenLeaders participation. A multi-level ordered logit model is then estimated to uncover factors influencing the ranking of this participation.

Empirical results indicate that hotels with a larger size, a higher class, a better online reputation, greater reliance on business travelers, fewer neighboring hotels and a more long-term-oriented culture are more apt to join the program. Online reputation factors, hotel size and the number of neighboring hotels explain GreenLeaders hotels’ rankings. A series of robustness checks reinforces the results.

The results shed light on green program design and promotion. These findings can help hotel practitioners identify ideal target markets and better use their organizational resources to establish green programs. Several strategies can be implemented to promote hotels’ commitment to sustainability and to encourage guests’ awareness of and involvement in green practices.

This study enriches knowledge of sustainable hospitality and tourism. The findings of this study also address corporate social responsibility by analyzing factors that can promote and inhibit GreenLeaders program participation. Further, as a complement to hotel- and location-specific factors, the authors scrutinize the effects of cultural features in shaping green strategies.

The purpose of this paper is to examine the extent to which workplace policies and practices are related to participation in, and frequency and duration of, workplace training, controlling for worker and workplace characteristics.

The authors regress variables depicting participation, frequency and duration of workplace training on workplace policies and control variables. In the case of participation in training, the dependent variable is binary; hence, the authors use a logit model. To examine the number of times which employees participate in training and the number of days they spend training the authors use a Tobit model. The Lewbel (2012) method is used to examine whether there is a causal relationship between workplace policies and the frequency, and duration, of training.

The findings suggest that about half of the workplace policies considered are positively correlated with the incidence and breadth of workplace training. There is also some support for the view that bundling of policies is positively correlated with the provision of workplace training. The Lewbel (2012) results suggest a causal relationship between a bundle of workplace policies and the frequency, and duration, of workplace training. There is, however, no evidence that workplace policies designed to devolve responsibilities to workers and incentivize staff polarizes skills through resulting in more training for professional staff over others.

The authors use matched employer and employee cross-sectional data for Shanghai in China. To this point most studies that have examined the determinants of training use data for Europe or the USA. There are few studies of this sort for countries in other regions and, in particular, developing or transition countries. There are no studies at all on the relationship between workplace policies and practices designed to promote organizational performance and training in developing or transitional countries. This study addresses this gap in the understanding of the factors related to on-the-job training in transitional countries, such as China.

Growing health concerns amid the COVID-19 pandemic have led guests to focus on various aspects of hotel cleanliness. This study aims to investigate whether customers’ perceived importance of hotel cleanliness during their stay depends on local pandemic severity and moderators of the pandemic–cleanliness relationship.

Based on TripAdvisor data from 26,519 reviews in 2020 for 2,024 hotels across the USA, this study evaluated the importance of hotel cleanliness using the estimated coefficient of the cleanliness score in a regression of overall hotel rating scores.

Results of a multilevel ordered logit model confirmed that a more difficult local pandemic situation rendered cleanliness more important during hotel stays. Additionally, the effect of the pandemic was more pronounced among specific groups: men and travelers with more expertise, and guests staying in hotels without COVID-19 protocols for linen cleaning, with a lower average rating, with a larger size and in a more urbanized location.

This study represents a pioneering effort to assess how pandemics shape people’s (perceived) importance of cleanliness during hotel stays based on revealed data. Despite potential managerial relevance, a number of the moderating variables included in this study, such as traveler expertise and hotel location, have never been studied within the context of cleanliness perceptions during a pandemic.

Recently, a small but burgeoning literature has argued that tax non-compliance cannot be fully explained using the conventional rational economic actor approach which views non-compliance as occurring when the pay-off is greater than the expected cost of being caught and punished. Instead, a social actor approach has emerged which views tax non-compliance as higher when “tax morale”, defined as the intrinsic motivation to pay taxes, is low. To advance this social actor model, the purpose of this paper is to evaluate the individual and national heterogeneity in tax morale, which is crucial if tax compliance is to be improved.

To do this, the authors report data from the 2010 Life in Transition Survey on tax morale in 35 Eurasian countries.

Logit econometric analysis reveals, on the one hand, that there is higher tax morale among middle-aged, married, homeowners with children, with a university degree and employed, and on the other hand, that there is higher tax morale in more developed countries with stronger legal systems and less corruption, and higher levels of state intervention in the form of both taxation and expenditure.

Rather than continue with the rational actor approach, this paper reveals that how an emergent social actor approach can help to more fully explain tax non-compliance and results in a different policy approach focused upon changing country-level economic and social conditions associated with low tax morale and thus non-compliance.

These results display the specific populations with low tax morale which need targeting when seeking to tackle tax non-compliance.

This paper provides a new way of explaining and tackling tax non-compliance in Eurasian countries.

This paper aims to investigate the effect of the flat tax system on self-employment by necessity and by opportunity. Specifically, the paper examines whether individuals decide to switch from wage-employment to self-employment by necessity or by opportunity when government imposes a flat tax system.

To analyze the association of a flat tax system with occupational choice this paper uses both multinomial and ordinary logit models. In the multi-nominal logit model, this study separates dependent variables into three categories: wage employee, self-employed by necessity and self-employed by opportunity. In the second step of analyzes using the ordinary logit model, this paper studies only self-employed individuals by distinguishing them according to their preferences.

The results suggest that, in countries with the imposed flat tax system, the probability of being self-employed by necessity is low, while the probability of being self-employed by opportunity is high. Moreover, better economic growth in the country also elevates the chances of individuals to be self-employed by opportunity.

Out novel contribution is documenting that flat tax system in transition countries increases the number of individuals self-employed by opportunity compared to self-employed by necessity.

This study aims to investigate the factors influencing the adoption of data analytics in performance management. By examining the role of organizational and environmental contexts, this study contributes to the existing literature by proposing a novel and detailed technology-organization-environment (TOE) model for the complex interplay between firm characteristics and the adoption of data analytics. The results offer valuable insights and practical implications for organizations seeking to leverage data analytics for effective performance management.

The research draws upon a data set encompassing over 21,869 companies operating across all European Union member states. A multilevel logistic regression model was developed to evaluate the influence of organizational and environmental factors on the likelihood of adopting performance analytics in organizations.

The findings indicate that the lack of awareness of the benefits of data analytics and its practical application to address specific business challenges is a significant barrier to its adoption. Organizational contexts, such as variable-pay systems, employee training, hierarchical structures and frequency of monetary rewards, also influence the adoption of data analytics.

The study informs managers about the strategic role of data analytics capabilities in performance management for improved business intelligence and driving data culture.

The study helps managers understand the strategic role of data analytics capabilities in performance management, leading to improved business intelligence and fostering a data-driven culture in five key areas: structural alignment, strategic decision-making, resource allocation, performance improvement and change management.

The study advances the TOE theory, making it a more detailed and complete framework, particularly applicable to the adoption of performance analytics. It identifies the main factors of adoption that play a crucial role in this process.

Improving the diversity of recommendation information has become one of the latest research hotspots to solve information cocoons. Aiming to achieve both high accuracy and diversity of recommender system, a hybrid method has been proposed in this paper. This study aims to discuss the aforementioned method.

This paper integrates latent Dirichlet allocation (LDA) model and locality-sensitive hashing (LSH) algorithm to design topic recommendation system. To measure the effectiveness of the method, this paper builds three-level categories of journal paper abstracts on the Web of Science platform as experimental data.

(1) The results illustrate that the diversity of recommended items has been significantly enhanced by leveraging hashing function to overcome information cocoons. (2) Integrating topic model and hashing algorithm, the diversity of recommender systems could be achieved without losing the accuracy of recommender systems in a certain degree of refined topic levels.

The hybrid recommendation algorithm developed in this paper can overcome the dilemma of high accuracy and low diversity. The method could ameliorate the recommendation in business and service industries to address the problems of information overload and information cocoons.

Although the use of data from different levels is very common in international marketing research, the practice of employing multi‐level analysis techniques is relatively new. The paper aims to provide an application of a specific case of multi‐level modelling – where the dependent variable is dichotomous, which is often the case in marketing research (e.g. whether a consumer buys the brand or not, whether he/she is aware of the brand or not, etc.)

A hierarchical generalized linear model is employed.

Since this is a technical paper, the authors would like to emphasize the process rather than the empirical findings. In summary, the paper: provides a brief theoretical overview of Hierarchical Linear Modeling and Hierarchical Generalized Linear Modeling; illustrates the application of the method using the domains of consumers within countries and a dichotomous dependent variable; focuses on interpretation of log‐odds results; and concludes with practical issues and research implications.

The main value of this research is to demonstrate how to employ multi‐level models when the dependent variable is dichotomous. Multi‐level techniques are quite new in international marketing research, although nested data structures are relatively common in our field. This is a technical paper that guides the researchers as to how to apply and interpret the results when modeling such data with a dichotomous dependent variable.

The global financial and economic crisis resulting from the US housing crisis has shown that house prices can have far-reaching consequences for the real economy. For macroprudential supervision, it is, therefore, necessary to identify house price bubbles at an early stage to counteract speculative price developments and to ensure financial market stability. This paper aims to develop an early warning system to signal speculative price bubbles.

The results of explosivity tests are used to identify periods of excessive price increases in 18 industrialized countries. The early warning system is then based on a logit and an ordered logit regression, in which monetary, macroeconomic, regulatory, demographic and private factors are used as explanatory variables.

The empirical results show that monetary developments have the highest explanatory power for the existence of house price bubbles. Further, the study reveals currently emerging house price bubbles in Norway, Sweden and Switzerland.

The results implicate a new global housing boom, particularly in those countries that did not experience a major price correction during the global financial crisis.

The ordered logit model is an advanced approach that offers the advantage of being able to differentiate between different phases of a house price bubble, thereby allowing a multi-level assessment of the risk of speculative excesses in the housing market.