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The purpose of this paper is to investigate consumers’ wine preferences, recycling attitudes and behaviors and socio-demographic data in an effort to build market segment profiles of those willing to transport wine bottles back to winery tasting rooms to be recycled, interest in standard-weight glass-alternative packaging and, various cork-alternative bottle closures.

A two-stage online survey was administered to 714 wine consumers residing in the Mid-Atlantic region of the USA during two periods in March 2016. Exhaustive chi-square automatic interaction detector (ECHAID) decision tree predictive analysis was used to identify the market segments.

A majority of survey participants were willing to bring empty wine bottles to a winery for recycling (85 percent). Collectively, 77 percent of participants were members of just three of the eight segments developed using a decision tree predictive algorithm, with 90 percent of participants in these segments willing to bring empty wine bottles to a winery tasting room to be recycled. Two segments were comprised of Millennials and Generation X, and the third of Baby Boomers, two having a moderate to extreme interest in natural cork used to seal wine bottles, and similar.

An ECHAID classification tree method was used to develop eight consumer segments. Identifying characteristics that describe consumers likely to return empty wine bottles to a winery benefits recycling efforts and could possibly encourage additional sales.

This study aims to characterize several wine consumer segments who were “likely” to sample (i.e. taste before purchasing) wine from vineyards using cover crops, a sustainable production practice that reduces herbicide applications, and identify those with a greater probability of being a viable target market based on survey responses.

A total of 956 wine consumers from the Mid-Atlantic and boarding US states were separated into segments based on an ECHAID (exhaustive Chi-square automatic interaction detector) classification tree from internet survey responses.

Out of the 12 created segments, 6 (n = 530, 72% of training data) contained participants who were at least 1.02 times (index score =102%) more “likely” to try the wine compared to the overall sample and were willing to pay $18.99 for a 750-mL bottle of the wine, which included a $1 surcharge to cover associated production costs. Of these, three (n = 195, 26%) had the greatest potential for which a marketing plan could be developed (index scores of 109%–121%), with over half in each segment willing to pay $20.99 for the bottle of wine, which could motivate growers to consider implementing this sustainable strategy.

Although several segments of participants were “likely” to sample the sustainably produced wine, an ECHAID classification tree allowed us to identify participants who would not pay $18.99 for a 750-mL bottle of wine, even after learning about the use of cover crops and the trade-off ($1 bottle surcharge). By narrowing the number of potential “likely” segments to those with a greater potential of sampling the wine, more purposeful marketing strategies can be developed based on demographics, attitudes, and behaviors defined in the model.

The purpose of this paper is to explore teachers' behaviours in completing an internet survey using decision trees. Furthermore, to reduce the complexity of the decision trees, a statistical technique was used to decrease the number of input variables in the decision trees.

A dataset of 47,647 samples was used to build the decision trees. These samples were collected from an internet survey of teachers in Taiwan. The output of the decision trees was the answering time (the time taken to complete the internet questionnaire). Eight variables were selected as the inputs for the decision trees. Two techniques were employed to build the decision trees – the exhaustive chi‐squared automatic interaction detector (ECHAID) and classification and regression tree (CRT) analysis. To reduce the complexity of the decision models, factor analysis technique was used to decrease the data dimensions (number of input variables) and to obtain a simplified decision model. One‐way ANOVA was used to validate the effects of the dimension reduction.

From the results of the factor analysis, a simplified decision tree is recommended using four input variables – teaching years, school level, sex and area. The classification accuracy of the simplified model is statistically equivalent to that of the original one, which used eight input variables.

The complexity of decision trees theoretically depends on the number of input variables. This study used a statistical technique to decrease the number of input variables and thereby reduce the complexity of the decision trees. A statistical technique was employed to validate that the classification accuracy is not statistically different between the original decision model and the simplified one. The decision models proposed in this paper can be applied in estimating the answering time for completing a questionnaire during an internet survey.

Tourism and hospitality scholars have examined tourists' food experiences in the tourism context. However, little research has been focused on the experience of tasting slow food/wine and the market that consumes slow food/wine. This study aims to (1) understand the factors contributing to tourists' slow food experiences and (2) identify the market segmentation of tourists who experiences slow food and wine at a winery. Four hundred fifty-three online survey questionnaires were collected from Amazon Mechanical Turk (MTurk) platform. The critical research variable is slow food experience, which contains 16 items explained by four dimensions: (1) food, (2) place, (3) behavior, and (4) knowledge. Subsequently, a cluster analysis based on slow food experience measurement was adopted to discover the tourist market. The study identifies three markets: (1) hardcore slow food gastronomes, (2) common slow foodies, and (3) casual visitors. In the conclusion section, this study offers theoretical contributions to the slow food literature and managerial implications for tourism marketers to establish new marketing strategies.

This study aims to determine white wine drinkers’ interest in Grüner Veltliner wine, a potential signature wine for the Commonwealth of Pennsylvania in the USA and identify the consumer segments likely to look for and purchase this wine varietal.

A total of 676 wine consumers from the Mid-Atlantic region in the USA were compared based on familiarity with Grüner Veltliner wine, variety-seeking (VARSEEK) scores and the likelihood of looking for and purchasing Pennsylvania Grüner Veltliner wine.

Although only a third of participants had some experience with Grüner Veltliner wine, 77% were “somewhat interested” to “very interested” in being able to sample and taste the wine, and 66%–67% were “somewhat likely” to “very likely” to look for and purchase the wine from both growing regions. Generation, wine consumption behavior and familiarity with Pennsylvania wine and Grüner Veltliner wine differed between participants based on purchase intent and VARSEEK scores (i.e. low VARSEEK/likely, high VARSEEK/unlikely). Differences in factors motivating Grüner Veltliner purchasing were also identified between low VARSEEK/likely and high VARSEEK/likely participants.

The paper presents evidence of potential demand for a signature wine from an emerging wine region. This provides direction for target marketing and related promotional strategy, along with identifying wine consumers interested in tasting and purchasing the wine.

This paper aims to analyze the relationship between sustainability practices and the degree of innovation in the service provided by restaurants. The study identifies relevant restaurant segments in relation to sustainable practice-based service innovation so that effective actions to raise awareness and train managers and staff may be developed. Segmentation has been identified as a key tool when designing strategies and proposing actions. Yet, the use of segmentation techniques is still scarce regarding service innovation and sustainability in restaurants.

A segmentation analysis was carried out applying the CHAID algorithm from 300 valid questionnaires completed by restaurant owners or managers from coastal Ecuador, where tourism and gastronomy may be drivers of service innovation.

A typology of restaurants based on the sustainability-service innovation interrelation suggests three final segments: sustainable innovators focused on the value chain, moderate innovators focused on saving resources and restaurants with a low innovative profile.

The three segments derived from the analysis present differences in terms of the degree of implementation of sustainability practices, as well as in terms of the demographic profile of the restaurant manager. These segments are measurable, substantial, accessible and actionable, so that tailored initiatives to raise awareness and boost sustainability-oriented innovativeness among restaurant owners/managers may be targeted to each group of establishments.

The present research provides evidence of the positive relationship between sustainability practices and service innovation in foodservices. The segments of restaurants identified enable the design and implementation of actions that facilitate the transition of less sustainability-oriented restaurants towards more innovative and sustainable business models.

Among the growing interest towards market segmentation and targeted marketing, the current study adopted a scientometric approach to examine the literature on wine selection and preferences. The current review specifically attempts to shed light on the research that explores the determinants of wine preferences at multiple levels of analysis.

CiteSpace was used to compute a Document Co-Citation Analysis (DCA) on a sample of 114,048 eligible references obtained from 2,846 publications downloaded from Scopus on 24 May 2021.

An optimized network of 1,505 nodes and 4,616 links was generated. Within the network, impactful publications on the topic and thematic domains of research were identified. Specifically, two thematic macro-areas were identified through a qualitative analysis of papers included in the 7 major clusters. The first one - “Methods of Wine Making” - included clusters #0, #3, #5, #6 and #18. The second one - “Consumers' Attitudes and Preferences Towards Wine” - included clusters #1 and #2. The first thematic macro-area included more technical aspects referring to the process of wine making, while the second thematic macro-area focused more on the factors influencing individuals' preferences and attitudes towards wine. To reflect the aims of the current paper, publications giving light to the “Consumers' Attitudes and Preferences Towards Wine” macro-area were analyzed in detail.

The resulting insights may help wine makers and wine sellers optimize their work in relation to market segments and to the factors influencing individuals' purchasing behaviors.

The current study aims to predict consumer complaint status (complainers or non-complainers) based on socio-demographic and psychographic factors and further to discern the differences in behavior disposition of consumer groups concerning determinants of consumer's tendency to exit (TE).

The research used survey-based data of 600 Indian consumers of three service sectors (hotel and hospitality, automobile service centers and organized retail stores). Chi-square automatic interaction detector (CHAID) decision tree analysis was used to profile consumers.

The results indicated that occupation; income; education; industry and attitude toward complaining were significant factors in profiling consumers as complainers or non-complainers. Further, determinants of TE (discouraging subjective norms, perceived likelihood of successful complaint, lower perceived switching cost, poor employee response, negative past experience and ease of complaint process) vary significantly across the groups of complainers and non-complainers.

The research questions in this study were tested with three service sectors consumers in India, so due care should be exercised in generalizing these findings to other sectors and countries. Study replication across other service sectors and countries is recommended to improve the generalizability of these findings with wider socio-demographic samples.

Firms striving for consumer retention and aim to extend their consumer life cycle can greatly benefit from the results of this study to understand the customer complaint behavior (CCB) specific to non-complaining (exit) behavior. The future researcher may benefit from replicating and extending the model in different industries for further contribution to the CCB literature.

To the best of the author's knowledge, there is no evidence of consumer segmentation based on their complaining behavior or socio-demographic and psychographic factors by employing CHAID decision tree analysis. In addition to illustrating the use of data mining techniques such as CHAID in the field of CCB, it also contributes to the extant literature by researching in a non-Western setting like India.

This study analyses how the socio-demographic profile of the tourist, trip-related characteristics, distance, and economic conditions in the source country affect pleasure tourists' length of stay behaviours in Barbados.

The study uses “biggish” data (over 3.6 million observations), parametric models (OLS) and statistical learning models (regression trees) to develop a length of stay decision rule to segment pleasure tourists' length of stay. Our sample period is January 2004 to March 2013.

The analysis revealed a great deal of heterogeneity in the impact of the predictors across segments, which would be typically hidden from simple parametric approaches often used to model length of stay (such as OLS).

The main implication is that conventional models of length of stay should be complemented with segmentation analyses to shed some light on the heterogeneous length of stay behaviours of specific market segments.

Many studies on small tourism-specialising states focus on modelling aggregate arrivals. By modelling micro-data for Barbados, we provide insights on this aspect of tourism demand for small states. Second, very few studies use classification tools to analyse length of stay. The study contributes to the literature through its methodological approach.

This study aims to identify and validate the different clusters of wine festival visitors based on their hedonic motivation. Further, this study also sought how identified clusters were different in terms of perceived value, satisfaction and loyalty to the wine festival.

A survey was conducted during the International Beach Wine Festival held in Karnataka, India, to collect primary data from 400 visitors. Data were subjected to a two-step cluster analysis. Further, cluster segmentation based on visitors’ demographics, perceived value, satisfaction and loyalty was conducted. Decision tree analysis based on recursive partitioning algorithm was used to validate the clusters.

A two-step cluster analysis identified two distinct segments and named those as elite and informal visitors based on hedonic motivation. The cluster scores show that the elite group had the best ratings on social status, socialization and family harmony. On the other hand, the informal group had top scores for wine tasting, enjoyment, change from routine and the festival atmosphere. Decision tree analysis results indicate that social status enjoyment and taste motives differentiate an informal group from the elite group.

This study was conducted in a wine festival held in a single location. To assess the strength of the results, case studies in other regions will be of importance.

This study extended the knowledge of the wine festival by adapting hedonic motivation as a basis for wine festival segmentation. Besides, this study’s empirical findings would greatly benefit wine festival organizers to formulate an appropriate marketing strategy to target each wine festival visitors’ cluster based on the differentiating factors obtained from the decision tree modelling.