Behavioural market segmentation using the bagged clustering approach based on binary guest survey data: exploring and visualizing unobserved heterogeneity.

2009 ◽  
pp. 243-252
Author(s):  
S. Dolnicar ◽  
F. Leisch
Keyword(s):  
Survey Data ◽  
Market Segmentation ◽  
Unobserved Heterogeneity ◽  
Clustering Approach

scholarly journals Unsupervised Hierarchical Clustering Approach for Tourism Market Segmentation Based on Crowdsourced Mobile Phone Data

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2972 ◽  
Author(s):  
Jorge Rodríguez ◽  
Ivana Semanjski ◽  
Sidharta Gautama ◽  
Nico Van de Weghe ◽  
Daniel Ochoa
Keyword(s):  
Hierarchical Clustering ◽  
Market Segmentation ◽  
Wearable Sensors ◽  
Essential Element ◽  
Cost Effective ◽  
Mobile Phone Data ◽  
Mobility Data ◽  
Tourism Management ◽  
Clustering Approach ◽  
Spatio Temporal

Understanding tourism related behavior and traveling patterns is an essential element of transportation system planning and tourism management at tourism destinations. Traditionally, tourism market segmentation is conducted to recognize tourist’s profiles for which personalized services can be provided. Today, the availability of wearable sensors, such as smartphones, holds the potential to tackle data collection problems of paper-based surveys and deliver relevant mobility data in a timely and cost-effective way. In this paper, we develop and implement a hierarchical clustering approach for smartphone geo-localized data to detect meaningful tourism related market segments. For these segments, we provide detailed insights into their characteristics and related mobility behavior. The applicability of the proposed approach is demonstrated on a use case in the Province of Zeeland in the Netherlands. We collected data from 1505 users during five months using the Zeeland app. The proposed approach resulted in two major clusters and four sub-clusters which we were able to interpret based on their spatio-temporal patterns and the recurrence of their visiting patterns to the region.

scholarly journals A Clustering Approach for Characterizing Voter Types: An Application to High-Dimensional Ballot and Survey Data

2020 ◽  
Author(s):  
Shiro Kuriwaki
Keyword(s):  
Survey Data ◽  
Large Scale ◽  
Clustering Algorithm ◽  
High Dimensional ◽  
Palm Beach County ◽  
Spatial Voting ◽  
Clustering Approach ◽  
Swing Voters ◽  
State And Local ◽  
Spatial Voting Model

Large-scale ballot and survey data hold the potential to uncover the prevalence of swing voters and strong partisans in the electorate. However, existing approaches either employ exploratory analyses that fail to fully leverage the information available in high-dimensional data, or impose a one-dimensional spatial voting model. I derive a clustering algorithm which better captures the probabilistic way in which theories of political behavior conceptualize the swing voter. Building from the canonical finite mixture model, I tailor the model to vote data, for example by allowing uncontested races. I apply this algorithm to actual ballots in the Florida 2000 election and a multi-state survey in 2018. In Palm Beach County, I find that up to 60 percent of voters were straight ticket voters; in the 2018 survey, even higher. The remaining groups of the electorate were likely to cross the party line and split their ticket, but not monolithically: swing voters were more likely to swing for state and local candidates and popular incumbents.

Market Segmentation: Group versus Individual Behavior

1968 ◽  
Vol 5 (3) ◽  
pp. 264-270 ◽  
Author(s):  
Frank M. Bass ◽  
Douglas J. Tigert ◽  
Ronald T. Lonsdale
Keyword(s):  
Survey Data ◽  
Market Segmentation ◽  
Group Versus ◽  
Group Behavior ◽  
Theoretical Framework ◽  
Individual Behavior ◽  
Socioeconomic Variables ◽  
Grocery Products

The argument that socioeconomic variables do not provide an adequate basis for market segmentation of grocery products is disputed. A theoretical framework for segmentation measurement in terms of group behavior is developed and applied to survey data.

The Link between Relative Pay and Job Satisfaction Revisited

2020 ◽  
Author(s):  
Andreas Eberl ◽  
Matthias Collischon
Keyword(s):  
Job Satisfaction ◽  
Social Networks ◽  
Survey Data ◽  
Time Trends ◽  
Fixed Effects ◽  
Work Values ◽  
Unobserved Heterogeneity ◽  
Reference Group ◽  
Omitted Variable Bias ◽  
Variable Bias

Abstract This paper investigates the connection between job satisfaction and comparison pay (defined as a person’s rank within a reference group) with SOEP Data. Based on work values and social networks, we argue that the existing literature neglects heterogeneities in individual job satisfaction as well as wage trajectories along the career path. Thus, previous studies based on survey data likely overestimate the connection between job satisfaction and comparison pay. We use fixed-effects individual slopes models to account for heterogeneous time trends between individuals. We find no statistically significant correlation between comparison pay and job satisfaction. We conclude that previous estimates were biased by not accounting for idiosyncratic trends in job satisfaction due to unobserved heterogeneity, which led to an omitted variable bias.

Economic Value for University Services: Modelling and Heterogeneity Analysis

2017 ◽  
Vol 59 (5) ◽  
pp. 671-690
Author(s):  
Raquel Sánchez-Fernández ◽  
David Jiménez-Castillo ◽  
Angeles Iniesta-Bonillo
Keyword(s):  
Least Squares ◽  
Survey Data ◽  
Unobserved Heterogeneity ◽  
Economic Value ◽  
Perceived Value ◽  
Structural Models ◽  
Finite Mixture ◽  
Heterogeneity Analysis ◽  
University Services ◽  
Value Model

The study described here develops a perceived value model, from the alumni's perspective, to determine the sources of economic value universities must focus on to enhance satisfaction, organisational image and identification. The assessment of university audiences' perceived value of service is increasingly critical for universities to become more innovative and competitive, yet research rarely examines the nature, effects or perceptions of value in this context. The study also aims to identify alumni-specific differences in the model, considering the existence of unobserved heterogeneity. Survey data from a sample of 500 alumni were examined using partial least squares (PLS) and Finite Mixture PLS. Overall results support the model, but the heterogeneity analysis differentiates between two latent classes in the number of sources of economic value and the intensity and significance of the proposed relationships. The findings provide useful theoretical and practical insights, and highlight the importance of uncovering heterogeneity in structural models.

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