Bettina Grün

Wirtschaftsuniversität Wien | WU · Department of Finance, Accounting and Statistics

The aim of the present paper is to highlights a novel collective problem-solving mechanism that has the potential to turbo-charge efforts to make people behave in more environmentally sustainable ways, and to illustrate the effectiveness of such an approach in a field study.

Behavioural change research for environmental sustainability is currently guided almost exclusively by a cognitive paradigm, which assumes that cognitive constructs drive behaviour and must be influenced to change it. This study challenges this dominant paradigm and tests two non-cognitive theoretical constructs – respect for authority and empathy...

The tourism industry must reduce the negative impacts of its operations on the environment to secure its own prosperity into the future and to contribute to humanity’s collective aim of more sustainable production and consumption. An increasing number of studies in sustainable tourism have attempted to develop and test in the field the effectivenes...

Different approaches to determining two-sided interval estimators for risk measures such as Value-at-Risk (VaR) and conditional tail expectation (CTE) when modeling loss data exist in the actuarial literature. Two contrasting methods can be distinguished: a nonparametric one not relying on distributional assumptions or a fully parametric one relyin...

In this work, we propose an efficient implementation of mixtures of experts distributional regression models which exploits robust estimation by using stochastic first-order optimization techniques with adaptive learning rate schedulers. We take advantage of the flexibility and scalability of neural network software and implement the proposed frame...

Survey measurement scales are expected to be stable – to generate the same values across two timepoints and under unchanged conditions. In scale development, stability is assessed by calculating a scale's test-retest reliability – a prerequisite to validity. Yet, a systematic review shows that test-retest reliability values are reported for only 23...

The tourism industry must reduce the negative impacts of its operations on the environment to secure its own prosperity into the future and to contribute to humanity’s collective aim of more sustainable production and consumption. An increasing number of studies in sustainable tourism have attempted to develop and test in the field the effectivenes...

Multivariate panel data of mixed type are routinely collected in many different areas of application, often jointly with additional covariates which complicate the statistical analysis. Moreover, it is often of interest to identify unknown groups of units in a study population using such data structure, i.e., to perform clustering. In the Bayesian...

We present a novel approach for modelling heterogeneous gas flow patterns in gas transmission networks by means of mixtures of generalised nonlinear models (GNMs). We focus on the modelling of the maximum daily gas flow to predict gas flow at low temperatures, so called design temperatures. We present finite mixtures of GNMs as a suitable framework...

Multi-site field experimentation is critical to creating practically relevant context-independent and scientifically robust knowledge (Viglia & Dolnicar 2020). Yet, field experimentation is not common in tourism research. When used, it is typically implemented at one specific field site (e.g., Kallbekken & Sælen 2013; Kneževič Cvelbar et al. 2019),...

Estimating political positions of lawmakers has a long tradition in political science. We present the time varying text based ideal point model to study the political positions of lawmakers based on text data. In addition to identifying political positions, our model also provides insights into topical contents and their change over time. We use ou...

Hydropeaking is known for its adverse impacts on river ecosystems. However, the implementation of mitigation measures is still largely pending due to conflicting priorities of ecology and economics, which require scenario building to assess trade-offs. Therefore, widely applicable and standardized tools are needed to analyze hydropeaking hydrology...

Survey measurement scales are expected to be stable – to generate the same values across two measurements and under unchanged conditions. In scale development, stability is assessed by calculating a scale’s test-retest reliability – a prerequisite to validity. Yet, a systematic review indicated that test-retest reliability values are reported for o...

Cluster analysis aims at partitioning data into groups or clusters. In applications, it is common to deal with problems where the number of clusters is unknown. Bayesian mixture models employed in such applications usually specify a flexible prior that takes into account the uncertainty with respect to the number of clusters. However, a major empir...

Finite mixture models are a useful statistical model class for clustering and density approximation. In the Bayesian framework finite mixture models require the specification of suitable priors in addition to the data model. These priors allow to avoid spurious results and provide a principled way to define cluster shapes and a preference for speci...

Tourists bite off more than they can chew at hotel breakfast buffets. Food waste from hotel buffets means unnecessary food cost for hotels as well as an unnecessary burden on the environment. The present study measured food waste at a hotel breakfast buffet and identified the following guest and breakfast characteristics as being significantly asso...

Most practical interventions the tourism industry deploys to make tourists behave in more environmentally sustainable ways when they are at their premises or destination – such as the request to reuse towels to protect the environment – rely on attention and cognitive processing. We propose that focusing instead on habit, as the key construct, will...

Background Beta amyloid (Aβ) causes synaptic dysfunction leading to neuronal death. It is still controversial if the magnitude of Aβ deposition correlates with the degree of cognitive impairment. Diagnostic imaging may lead to a better understanding the role of Aβ in development of cognitive deficits. The aim of the present study was to investigate...

Appeals to people’s pro-environmental values have been shown to trigger pro-environmental behavior across a range of contexts. The present study tests the potential of such interventions in a hedonic context where behavioral change does not generate utilitarian benefits (tourism). Results from a field experiment in a four-star hotel in Slovenia ind...

Vaccine hesitancy is one of the main obstacles facing the tourism industry in its recovery from the COVID-19 pandemic. Many people are sceptical about the COVID-19 vaccine and decide not to get vaccinated. Our research aims to test the effectiveness of using travel-related incentives to overcome vaccine hesitancy. We investigate (1) whether travel-...

This paper investigates heterogeneity of preferences for disability services within the theoretical framework of consumption values. We conducted interviews with people with a disability and disability service providers to develop survey items, then conducted a survey with 2000 adult Australian residents who either had a disability or were carers o...

Relative risks are estimated to assess associations and effects due to their ease of interpretability, e.g., in epidemiological studies. Fitting log-binomial regression models allows to use the estimated regression coefficients to directly infer the relative risks. The estimation of these models, however, is complicated because of the constraints w...

This paper investigates heterogeneity of preferences for disability services within the theoretical framework of consumption values. We conducted interviews with people with a disability and disability service providers to develop survey items, then conducted a survey with 2000 adult Australian residents who either had a disability or were carers o...

In model-based clustering, the Galaxy data set is often used as a benchmark data set to study the performance of different modeling approaches. Aitkin (Stat Model 1:287–304) compares maximum likelihood and Bayesian analyses of the Galaxy data set and expresses reservations about the Bayesian approach due to the fact that the prior assumptions impos...

Recent research in loss modeling resulted in a growing number of classes of statistical models as well as additional models being proposed within each class. Empirical results indicate that a range of models within or between model classes perform similarly well, as measured by goodness-of-fit or information criteria, when fitted to the same data s...

Ample empirical evidence in tourism research points to the fact that the environmentally sustainable behaviour of tourists is not driven by the same factors as the same behaviour at home. Most critically, values and beliefs do not manifest in behaviour to the same degree when people are on vacation. In this study we introduce habit as a potential d...

This paper investigates heterogeneity of preferences for disability services within the theoretical framework of consumption values. We conducted interviews with people with a disability and disability service providers to develop survey items, then conducted a survey with 2000 adult Australian residents who either had a disability or were carers o...

The harmful tourist behaviour of taking a lot of food from a buffet, but not eating it all, remains under-researched. This study gains key insights into drivers of plate waste. Observational data show that: dinner buffets are worse than breakfast buffets; the latest breakfast serving time is worse than the earliest; high-end breakfast buffets are w...

Model uncertainty is a pervasive problem in regression applications. Bayesian model averaging takes model uncertainty into account and identifies robust determinants. However, it requires the specification of suitable model priors. Mixture model priors are appealing because they explicitly account for different groups of covariates as robust determ...

Data-driven market segmentation is heavily used by academic tourism and hospitality researchers to create knowledge, and by data analysts in tourism industry to generate market insights. The stability of market segmentation solutions across repeated calculations is a key quality indicator of a segmentation solution. Yet, stability is typically igno...

Twenty percent of all global greenhouse emissions are food-related. Tourism and hospitality contribute significantly, with food accounting for nearly half of the waste these sectors produce. One type of food waste – plate waste – could easily be avoided. Plate waste is the food people leave behind uneaten on their plates. It does not increase the e...

The harmful tourist behaviour of taking a lot of food from a buffet, but not eating it all, remains under-researched. This study gains key insights into drivers of plate waste. Observational data show that: dinner buffets are worse than breakfast buffets; the latest breakfast serving time is worse than the earliest; high-end breakfast buffets are w...

Survey data quality suffers when respondents have difficulty completing complex tasks in questionnaires. Cognitive load theory informed the development of strategies for educators to reduce the cognitive load of learning tasks. We investigate if these cognitive load reduction strategies can be used in questionnaire design to reduce task difficulty...

The new model class of mixtures of generalised nonlinear models (GNMs) is introduced. The model is specified, identifiability issues discussed, the fitting in a maximum likelihood framework using the expectation-maximisation (EM) algorithm outlined and an appropriate computational implementation introduced. The new model class is applied to capture...

In model-based clustering, the Galaxy data set is often used as a benchmark data set to study the performance of different modeling approaches. Aitkin (2001) compares maximum likelihood and Bayesian analyses of the Galaxy data set and expresses reservations about the Bayesian approach due to the fact that the prior assumptions imposed remain rather...

Survey data quality suffers when respondents have difficulty completing complex tasks in questionnaires. Cognitive load theory informed the development of strategies for educators to reduce the cognitive load of learning tasks. We investigate whether these cognitive load reduction strategies can be used in questionnaire design to reduce task diffic...

Mixture models represent the key modelling approach for Bayesian cluster analysis. Different likelihood and prior specifications are required to capture the prototypical shape of the clusters. In addition, the mixture modelling approaches also crucially differ in the specification of the prior on the number of components and the prior on the compon...

Twenty percent of all global greenhouse emissions are food-related. Tourism and hospitality contribute significantly, with food accounting for nearly half of the waste these sectors produce. One type of food waste – plate waste – could easily be avoided. Plate waste is the food people leave behind uneaten on their plates. It does not increase the e...

This article reports on a quasi-experimental study in which the use of emissions-intensive, water hungry thick cotton serviettes at hotel breakfast buffets was reduced by 95% by changing the default to recycled paper serviettes. The outcome is better for the environment, reduced costs for the hotel and does not influence guest satisfaction.

Changing default settings has proven to be a powerful approach to influencing consumer decisions without denying consumers the possibility of choosing freely. This is only the second study investigating the effectiveness of defaults in tourism, and the first testing also the combined effect of default changes and pro-environmental appeals in the co...

Within a Bayesian framework, a comprehensive investigation of the model class of mixtures of finite mixtures (MFMs) where a prior on the number of components is specified is performed. This model class has applications in model-based clustering as well as for semi-parametric density approximation, but requires suitable prior specifications and infe...

Bayesian model averaging (BMA) is a statistical method to rigorously take model uncertainty into account. This chapter gives a coherent overview on the statistical foundations and methods of BMA and its usefulness for forecasting, but also for the identification of robust determinants. The focus is given on economic applications. We describe the BM...

Purpose Data-driven market segmentation is heavily used by academic tourism and hospitality researchers to create knowledge and by data analysts in tourism industry to generate market insights. The stability of market segmentation solutions across repeated calculations is a key quality indicator of a segmentation solution. Yet, stability is typical...

Changing default settings has proven to be a powerful approach to influencing consumer decisions without denying consumers the possibility of choosing freely. This is only the second study investigating the effectiveness of defaults in tourism, and the first testing also the combined effect of default changes and pro-environmental appeals in the co...

Composite models have a long history in actuarial science because they provide a flexible method of curve-fitting for heavy-tailed insurance losses. The ongoing research in this area continuously suggests methodological improvements for existing composite models and considers new composite models. A number of different composite models have been pr...

Economic theory does not always specify the functional relationship between dependent and explanatory variables, or even isolate a particular set of covariates. This means that model uncertainty is pervasive in empirical economics. In this paper, we indicate how Bayesian semi‐parametric regression methods in combination with stochastic search varia...

This chapter discusses strategic marketing areas that need to be integrated with the target segment decision (positioning and competition), and the tactical marketing decisions that follow from all of those strategic decisions in relation to product development and modification, pricing, distribution channel choice, and advertising and promotion. A...

Market segmentation is a strategic process and implies a long-term commitment to catering best possibly to the needs of a subset of the market. Because of its long-term nature, it is critical to evaluate the effectiveness of the segmentation strategy, and to ensure that target segments do not change over time. If they do, adjustments to the market...

Market segmentation analysis, irrespective of the algorithm used to extract segment, is exploratory in nature. Before performing the actual extraction, it is useful to gain preliminary insight into the data. This chapter discusses different ways of gaining an understanding of the data structure, and introduces pre-processing methods that may be req...

This chapter defines market segmentation analysis, offers a few alternative segmentation approaches, and introduces the ten step process of market segmentation analysis. This chapter also introduces natural, reproducible and constructive segmentation, reflecting that market segments may naturally exist, but typically do not and, therefore, have to...

In Step the big decisions are made. Informed by all the insights gained during the entire market segmentation analysis, the time has come to commit. Of the many available market segments, one or a small number have to be chosen and declared target segments. This critical step builds on the segments extracted in Step 5, profiled in Step 6, and descr...

Market segmentation analysis is driven primarily by the desire of an organisation to better cater to a part of the market and, in so doing, secure a competitive advantage. At the end of the segmentation analysis, the organisation needs to select one or more target segments. To make this selection process as easy as possible, it is useful to think a...

Market segmentation is a long-term strategic commitment. It is critical, therefore, for any organisation that intends to adopt a segmentation strategy, to be aware of the consequences, and to make an informed decision. This chapter discusses key barriers to the successful adoption of market segmentation. The chapter also offers a checklist of quest...

Once a small number of market segments has been selected on the basis of their segment profiles, these segments need to be described in detail using additional information. The detailed description of market segments is critical; it informs target segment selection. This chapter introduces a number of methods, including techniques from graphical st...

This chapter explains the purpose of marketing and marketing planning, clarifies the difference between strategic marketing and tactical marketing, highlights the asymmetry between the two areas, and outlines the role of market segmentation within strategic marketing. Market segmentation is defined, and the benefits and costs of committing to a mar...

The outcome of a market segmentation analysis is only as good as the data upon which it is based. This chapter discusses a range of alternative sources of data that can serve as input for extracting market segments. Key potential dangers associated with each of those sources are discussed. A checklist summarises a number of questions that may assis...

Segment extraction leads to one or more segmentation solutions. They may have been pre-selected on the basis of statistical criteria. But statistical criteria are no substitute for user assessment. The profiling stage is the opportunity for all members of the segmentation team to inspect what characterises each of the resulting market segments, and...

This chapter focuses on the task of grouping consumers and, in so doing, revealing naturally existing or creating artificial market segments. The chapter covers algorithms falling into three categories: distance-based methods, model-based methods, and algorithms integrating variable selection with the task of extracting market segments. In addition...

Mixture models extend the toolbox of clustering methods available to the data analyst. They allow for an explicit definition of the cluster shapes and structure within a probabilistic framework and exploit estimation and inference techniques available for statistical models in general. In this chapter an introduction to cluster analysis is provided...

Surveys provide critical insights into consumer satisfaction and experience. Excessive survey length, however, can reduce data quality. We propose using constrained principle components analysis to shorten the survey length in a data-driven way by identifying optimal items with maximum information. The method allows assessing item elimination poten...

We introduce a new measure of bivariate jointness to assess the degree of inclusion dependency between pairs of explanatory variables in Bayesian Model Averaging analysis. Building on the discussion concerning appropriate statistics to assess covariate inclusion dependency in this context, a set of desirable properties for bivariate jointness measu...

Tourist behavior has a critical impact on the environmental sustainability of tourism. The hedonic nature of tourism and lack of an economic incentive make tourist behavior particularly hard to change. Making tourists behave more environmentally friendly would have substantial environmental benefits. This is the aim of the present study. Three alte...

Irrespective of the method used, market segmentation analysis is exploratory in nature. This means that any analysis, with any kind of data, will lead to a result, and different competing solutions might emerge where no clear best solution is discernible. It is critical, therefore, to be aware of all potential methodological pitfalls. This chapter...

Customer service is a critical element of a hotel's strategy and an important lever for differentiation of the hotel's offer. Over the last two decades, information systems have contributed to the transformation of customer interactions, enabling an unprecedented scale and scope of service personalization in the tourism industry. This paper reports...

Tourists bite off more than they can chew at hotel breakfast buffets. Food waste from hotel buffets means unnecessary food cost for hotels as well as an unnecessary burden on the environment. The present study measured food waste at a hotel breakfast buffet and identified the following guest and breakfast characteristics as being significantly asso...

Appeals to people’s pro-environmental values have been shown to trigger pro-environmental behavior across a range of contexts. The present study tests the potential of such interventions in a hedonic context where behavioral change does not generate utilitarian benefits (tourism). Results from a field experiment in a four star hotel in Slovenia ind...

This paper reviews and evaluates the wide range of supply and demand side measures employed and tested to reduce the environmental impacts of tourist accommodation. It focuses on the importance of understanding market segments and their pro-environmental behaviour by exploring the personal and travel characteristics significantly associated with pr...

In this paper, we propose an alternative approach for flexible modeling of heavy tailed, skewed insurance loss data exhibiting multimodality, such as the well-known data set on Danish Fire losses. Our approach is based on finite mixture models of univariate distributions where all K components of the mixture are assumed to be from the same parametr...

Market segmentation is extensively used in tourism studies. The quality of segmentation studies is critical as they form a basis for strategic decisions. Tourism researchers frequently use a large set of variables as a segmentation base. This leads to the problem of high-dimensionality in the analysis. In addition, it is unclear if all variables ar...

Hydro peaking causes an important environmental impact on running water ecosystems. Many affected rivers have a poor ecological status. In rivers affected by hydro peaking, the flow conditions are highly complex and difficult to grasp. To develop a general framework for detecting and characterizing sub-daily flow fluctuations, we analysed more than...

Survey data frequently serve as the basis for market segmentation studies. Survey data, however, are prone to a range of biases. Little is known about the effects of such biases on the quality of data-driven market segmentation solutions. This study uses artificial data sets of known structure to study the effects of data problems on segment recove...

Survey data collected for market segmentation studies is typically ordinal in nature. As such, it is susceptible to response styles. Ignoring response styles can lead to market segments which do not differ in beliefs, but merely in how segment members use survey answer options and which possibly occur in addition to the belief segments. We propose...

Women make an increasingly important contribution to the skilled workforce. Nations like Australia that suffer skill shortages need not only to attract but also retain more skilled female migrants. The latter has become an important policy issue in Australia because of modest retention rates. While much of the relevant literature focuses upon migra...

We propose the use of Latent Class Analysis methods to analyze the covariate inclusion patterns across specifications resulting from Bayesian Model Averaging exercises. Using Dirichlet Process clustering, we are able to identify and describe dependency structures among variables in terms of inclusion in the specifications that compose the model spa...

The level-free version of the Forced-Choice Binary measure of brand benefit beliefs was introduced in a recent article in IJMR (Dolnicar et al. 2012) and was shown to yield more stable hence more reliable and trustworthy results than the shorter 'Pick-Any' measure and the longer '7-Point Scale' measure. The aims of the present article are (1) to ex...

Cut points in immunogenicity assays are used to classify future specimens into anti-drug antibody (ADA) positive or negative. To determine a cut point during pre-study validation, drug-naive specimens are often analyzed on multiple microtiter plates taking sources of future variability into account, such as runs, days, analysts, gender, drug-spiked...

The use of a finite mixture of normal mixtures model in model-based clustering allows to capture non-Gaussian data clusters. However, identifying the clusters from the normal components is challenging and in general either achieved by imposing constraints on the model or by using post-processing procedures. Within the Bayesian framework we propose...

Mining frequent itemsets and association rules is a popular and well researched approach for discovering interesting relationships between variables in large databases. The R package arules presented in this paper provides a basic infrastructure for creating and manipulating input data sets and for analyzing the resulting itemsets and rules. The pa...

We compare the predictive ability of Bayesian methods which deal simultaneously with model uncertainty and correlated regressors in the framework of cross-country growth regressions. In particular, we assess methods with spike and slab priors combined with different prior specifications for the slope parameters in the slab. Our results indicate tha...

The publication of statistical results based on the use of computational tools requires that the data as well as the code are provided in order to allow to reproduce and verify the results with reasonable effort. However, this only allows to rerun the exact same analysis. While this is helpful to understand and retrace the steps of the analysis whi...

Maximum likelihood estimation of the concentration parameter of von Mises-Fisher distributions involves inverting the ratio [Formula: see text] of modified Bessel functions and computational methods are required to invert these functions using approximative or iterative algorithms. In this paper we use Amos-type bounds for [Formula: see text] to de...

In the framework of Bayesian model-based clustering based on a finite mixture of Gaussian distributions, we present a joint approach to estimate the number of mixture components and identify cluster-relevant variables simultaneously as well as to obtain an identified model. Our approach consists in specifying sparse hierarchical priors on the mixtu...