1,721,033 research outputs found
Cub model-based clustering of Likert-type data with a tourist satisfaction application
Get PDFIn investigating customer satisfaction with products or services, the most popular approach still relies on interviews or questionnaires to obtain consumers' opinions, and responses are usually measured by means of Likert-type scales. However, Likert-type data are inherently imprecise and uncertain. Thus, to obtain reliable analysis using such data, an a-posteriori correction must be adopted. The fuzzification procedure is the most common a-posteriori way to deal with uncertainty of Likert-type data. In this study, an alternative method to address the uncertainty of such data when used as input of a cluster analysis is proposed. The suggested method is based on the CUB model and the Fuzzy C-Medoids Clustering of Mixed Data algorithm and it is theoretically and empirically presented using real case study data. Advantages of the FCMd-CUB method are discussed in the conclusion section
Do satisfied cellar door visitors want to revisit? Linking past knowledge and consumption behaviors to satisfaction and intention to return
Get PDFThis study evaluates the main determinants of wine tourists' intention to revisit the winery cellar door. The proposed tourist behavior model suggests that past wine-related knowledge and behaviors as well as motivation affect satisfaction with the cellar door visit. The model suggests that actual behavior at the cellar door (number of bottles bought and amount of money spent) is dependent on the previously mentioned factors. A survey of wine tourists in the Barossa Valley, Australia, led to 676 useable questionnaires. The results of a binary logistic model show that only monthly household expenditure on wine consumption and the motive of tasting wine predict satisfaction with the cellar door visit. A negative binomial model shows that the probability to buy more bottles at the winery increases if the visitor is from Australia, satisfied with the visit, has tasted wine at the cellar door, is younger, spends more on monthly household consumption of wine, and was primarily visiting to buy wine. However, intention to revisit is predicted only by satisfaction, awareness of the winery before the visit, motives of buying and tasting wine, and some sociodemographic characteristics. Implications for the management of visitor behavior and the cellar door experience are also discussed
Combining textual and rating data from online reviews to cluster consumers
No full textIn today’s competitive global market, businesses seek a profound understanding of consumer behavior to gain a competitive edge. Leveraging Big Data and Web 2.0, companies can efficiently gather vast amounts of timely online information at minimal cost. Online review platforms like TripAdvisor ask users to provide textual reviews and ratings regarding the overall product/service and its key aspects. The challenge is identifying the best tools to extract as much reliable information as possible combining all data available. This paper proposes the Clustering Online Evaluations using Rating, Topic, and Sentiment (COERTS) method, allowing businesses to gain comprehensive insights into customer satisfaction by analyzing textual and rating data within a cluster framework. By identifying groups of similar clients, businesses can extract valuable information about customer experiences, enabling targeted improvements for unsatisfied consumers. Techniques such as sentiment analysis, emotional analysis, and the Latent Dirichlet Allocation model preprocess textual data. Additionally, Likert-type variables representing overall ratings and specific aspects are fuzzified to address response uncertainty and heterogeneity. After preprocessing, a suitable clustering algorithm organizes the data effectively. The presented procedure is theoretically explained and illustrated through a case study, showcasing its advantages in enhancing customer satisfaction, increasing retention, and re-attracting former customers
Analysing Imprecise and Dependent Information
Get PDFThis paper presents a discussion on how to analyze imprecise and dependent information using traditional econometric models, supervised and unsupervised Machine Learning techniques. The discussion includes the presentation and analysis of real example data from the tourism field to
familiarize the readers with imprecise and dependent information. Further developments in the treatment of such specific data are discussed in the conclusion
An ensemble Machine Learning algorithm for Lead Time Prediction
Get PDFIn this research project a real case-study based on an ensemble Machine Learning algorithm aims to predict the lead time of a product is presented. Specifically, the prediction has been achieved by employing a clustering algorithm as a preprocessing method and comparing several supervised Machine Learning algorithms to determine which one is most suitable for the industry under analysis. The primary aim of this article is to
assess the effectiveness of the fuzzy clustering algorithm in enhancing the performance of the prediction algorithm. Our analysis reveals that the Random Forest yields more accurate prediction. Furthermore, the application of a fuzzy clustering algorithm as pre-processing method proves to be
advantageous in terms of predictive accuracy
Bagged fuzzy clustering for fuzzy data: An application to a tourism market.
Get PDFSegmentation has several strategic and tactical implications in marketing products and services. Despite hard clustering methods having several weaknesses, they remain widely applied in marketing studies. Alternative segmentation methods such as fuzzy methods are rarely used to understand consumer behaviour. In this study, we propose a strategy of analysis, by combining the Bagged Clustering (BC) method and the fuzzy C-means clustering method for fuzzy data (FCM-FD), i.e., the Bagged fuzzy C-means clustering method for fuzzy data (BFCM-FD). The method inherits the advantages of stability and reproducibility from BC and the flexibility from FCM-FD. The method is applied on a sample of 328 Chinese consumers revealing the existence of four segments (Admirers, Enthusiasts, Moderates, and Apathetics) of the perceived images of Western Europe as a tourist destination. The results highlight the heterogeneity in Chinese consumers' place preferences and implications for place marketing are offered
Fuzzy segmentation of postmodern tourists.
Get PDFIn postmodern tourism, the experiences of each tourist could not be summarized only through a unique perspective but multiple and disjointed perspectives are necessary. The aim of this paper is to create a nexus between postmodern tourist and fuzzy clustering, and to propose a suitable clustering procedure to segment postmodern tourists. From a methodological perspective, the main contribution of this paper is related to the use of the fuzzy theory from the beginning to the end of the clustering process. Furthermore, the suggested procedure is capable of analysing the uncertainty and vagueness that characterise the experiences and perceptions of postmodern consumers. From a managerial perspective, fuzzy clustering methods offer to practitioners a more realistic multidimensional description of the market not forcing consumers to belong to one cluster. Moreover, the results are easy and comprehensible to read since they are similar to those obtained with more traditional clustering techniques
The challenge of publishing research about a never-ending subject for marketing scholars: The country of origin
Get PDFThe Country of Origin (COO) represents one of the main topics in the marketing literature and a large body
of knowledge about it has already been published. This commentary essay tries to explain why it seems to be
a never-ending subject for marketing scholars and the reason why the paper we published in this Journal few
years ago contributed to the literature and has achieved the Google i-10 high citation-impact ranking.
Analysing the effect of COO on a specific factor such as brand associations, the use of a methodology that
cope with the critics of some scholars about the overstressed of COO in the past research, and the selection
of an emerging market - the Chinese one - as country in which testing the COO have helped our paper to be
cited. Based on these elements, some future research topics are also suggested
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