scholarly journals How to Avoid Random Market Segmentation Solutions

2017 ◽  
Vol 57 (1) ◽  
pp. 69-82 ◽  
Author(s):  
Dominik Ernst ◽  
Sara Dolnicar
Keyword(s):  
Market Segmentation ◽  
Tourism Industry ◽  
Data Driven ◽  
Knowledge Development ◽  
Data Sets ◽  
Random Solution ◽  
Segmentation Strategy ◽  
Segmentation Analysis ◽  
Consumer Data

Tourism researchers and the tourism industry rely heavily on data-driven market segmentation analysis for both knowledge development and market insight. Most algorithms used in data-driven market segmentation are exploratory; they do not generate one single stable result. Only when data are well-structured (when very clear, distinct market segments exist in the data) are repeated calculations likely to generate the same segmentation solution. When data lack structure, which is frequently the case in empirical consumer data sets, repeated calculations lead to different solutions. Running a market segmentation analysis once only can therefore lead to an entirely random solution that does not represent a strong foundation for developing a long-term market segmentation strategy. The present study (1) explains the problem, (2) assesses how high the risk is of random solutions occurring in tourism market segmentation studies, and (3) recommends an approach that can be used to avoid random solutions.

scholarly journals Improving the stability of market segmentation analysis

2021 ◽  
Author(s):  
Homa Hajibaba ◽  
Bettina Grün ◽  
Sara Dolnicar
Keyword(s):  
Market Segmentation ◽  
Quality Indicator ◽  
Binary Data ◽  
Tourism Industry ◽  
Data Driven ◽  
Segmentation Analysis ◽  
Tourism And Hospitality ◽  
The Stability

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 ignored, risking that the segmentation solution arrived at is random. The present study offers an overview of market segmentation analysis and proposes a new procedure to increase the stability of market segmentation solutions derived from binary data.

scholarly journals Market Segmentation Analysis in Tourism: A Perspective Paper

2021 ◽  
Author(s):  
Sara Dolnicar
Keyword(s):  
Market Segmentation ◽  
Target Market ◽  
Market Segments ◽  
Tourist Destination ◽  
The Core ◽  
Segmentation Strategy ◽  
Segmentation Analysis ◽  
Similar Product ◽  
Product Preferences

No two tourists are the same. This insight stands at the core of market segmentation. Pursuing a segmentation strategy as a tourist destination or a tourism business means catering to the specific needs of certain types of tourists (market segments), rather than attempting to satisfy the needs of the entire tourist market by effectively targeting the average tourist. But which market segments should a tourist destination or business target? Market segmentation analysis helps answer this question. Market segmentation analysis is “the process of grouping consumers into naturally existing or artificially created segments of consumers who share similar product preferences or characteristics” (Dolnicar, Grün & Leisch, 2018, p. 11).

Asian Americans and Pacific Islanders: Employment Issues in the United States

2011 ◽  
Vol 9 (1-2) ◽  
pp. 58-69
Author(s):  
Marlene Kim
Keyword(s):  
United States ◽  
Asian Americans ◽  
The United States ◽  
Current Data ◽  
Pacific Islanders ◽  
Native Hawaiians ◽  
Data Sets ◽  
High Poverty ◽  
Unemployment Rates

Asian Americans and Pacific Islanders (AAPIs) in the United States face problems of discrimination, the glass ceiling, and very high long-term unemployment rates. As a diverse population, although some Asian Americans are more successful than average, others, like those from Southeast Asia and Native Hawaiians and Pacific Islanders (NHPIs), work in low-paying jobs and suffer from high poverty rates, high unemployment rates, and low earnings. Collecting more detailed and additional data from employers, oversampling AAPIs in current data sets, making administrative data available to researchers, providing more resources for research on AAPIs, and enforcing nondiscrimination laws and affirmative action mandates would assist this population.

Epidemiological Evidence that Maternal Influenza Contributes to the Aetiology of Schizophrenia

1993 ◽  
Vol 163 (4) ◽  
pp. 522-534 ◽  
Author(s):  
W. Adams ◽  
R. E. Kendell ◽  
E. H. Hare ◽  
P. Munk-Jørgensen
Keyword(s):  
Data Sets ◽  
Time Lags ◽  
Epidemiological Evidence ◽  
Intrauterine Development ◽  
Increased Risk ◽  
Schizophrenic Patients ◽  
The Relationship ◽  
Monthly Incidence ◽  
Maternal Influenza

The epidemiological evidence that the offspring of women exposed to influenza in pregnancy are at increased risk of schizophrenia is conflicting. In an attempt to clarify the issue we explored the relationship between the monthly incidence of influenza (and measles) in the general population and the distribution of birth dates of three large series of schizophrenic patients - 16 960 Scottish patients born in 1932–60; 22 021 English patients born in 1921–60; and 18 723 Danish patients born in 1911–65. Exposure to the 1957 epidemic of A2 influenza in midpregnancy was associated with an increased incidence of schizophrenia, at least in females, in all three data sets. We also confirmed the previous report of a statistically significant long-term relationship between patients' birth dates and outbreaks of influenza in the English series, with time lags of - 2 and - 3 months (the sixth and seventh months of pregnancy). Despite several other negative studies by ourselves and others we conclude that these relationships are probably both genuine and causal; and that maternal influenza during the middle third of intrauterine development, or something closely associated with it, is implicated in the aetiology of some cases of schizophrenia.

scholarly journals Accelerating In-Transit Co-Processing for Scientific Simulations Using Region-Based Data-Driven Analysis

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 154
Author(s):  
Marcus Walldén ◽  
Masao Okita ◽  
Fumihiko Ino ◽  
Dimitris Drikakis ◽  
Ioannis Kokkinakis
Keyword(s):  
Large Scale ◽  
Data Driven ◽  
Data Sets ◽  
Output Constraints ◽  
Data Driven Approach ◽  
Scientific Simulations ◽  
Multiple Metrics ◽  
In Transit ◽  
Multiple Compression ◽  
Large Scale Simulations

Increasing processing capabilities and input/output constraints of supercomputers have increased the use of co-processing approaches, i.e., visualizing and analyzing data sets of simulations on the fly. We present a method that evaluates the importance of different regions of simulation data and a data-driven approach that uses the proposed method to accelerate in-transit co-processing of large-scale simulations. We use the importance metrics to simultaneously employ multiple compression methods on different data regions to accelerate the in-transit co-processing. Our approach strives to adaptively compress data on the fly and uses load balancing to counteract memory imbalances. We demonstrate the method’s efficiency through a fluid mechanics application, a Richtmyer–Meshkov instability simulation, showing how to accelerate the in-transit co-processing of simulations. The results show that the proposed method expeditiously can identify regions of interest, even when using multiple metrics. Our approach achieved a speedup of 1.29× in a lossless scenario. The data decompression time was sped up by 2× compared to using a single compression method uniformly.

scholarly journals A Novel LSTM Model with Interaction Dual Attention for Radar Echo Extrapolation

2021 ◽  
Vol 13 (2) ◽  
pp. 164
Author(s):  
Chuyao Luo ◽  
Xutao Li ◽  
Yongliang Wen ◽  
Yunming Ye ◽  
Xiaofeng Zhang
Keyword(s):  
Short Term Memory ◽  
Weather Forecast ◽  
Vital Role ◽  
Data Sets ◽  
Short Term ◽  
Learning Techniques ◽  
Radar Echo ◽  
Hidden States ◽  
Better Than

The task of precipitation nowcasting is significant in the operational weather forecast. The radar echo map extrapolation plays a vital role in this task. Recently, deep learning techniques such as Convolutional Recurrent Neural Network (ConvRNN) models have been designed to solve the task. These models, albeit performing much better than conventional optical flow based approaches, suffer from a common problem of underestimating the high echo value parts. The drawback is fatal to precipitation nowcasting, as the parts often lead to heavy rains that may cause natural disasters. In this paper, we propose a novel interaction dual attention long short-term memory (IDA-LSTM) model to address the drawback. In the method, an interaction framework is developed for the ConvRNN unit to fully exploit the short-term context information by constructing a serial of coupled convolutions on the input and hidden states. Moreover, a dual attention mechanism on channels and positions is developed to recall the forgotten information in the long term. Comprehensive experiments have been conducted on CIKM AnalytiCup 2017 data sets, and the results show the effectiveness of the IDA-LSTM in addressing the underestimation drawback. The extrapolation performance of IDA-LSTM is superior to that of the state-of-the-art methods.

Tourism Productivity Theory and Measurement for Policy Implications: The Case of Australia

2019 ◽  
Vol 59 (2) ◽  
pp. 247-266
Author(s):  
Tien Duc Pham
Keyword(s):  
Labor Productivity ◽  
Holistic Approach ◽  
Tourism Industry ◽  
Policy Implications ◽  
Policy Makers ◽  
Average Growth ◽  
Multifactor Productivity ◽  
Capital Productivity ◽  
Productivity Theory

Tourism productivity measures are quite diverse, not always compatible and usually based partly on labor productivity for hotels and restaurants. This article develops a holistic approach that integrates the principles of the growth accounting framework and tourism satellite account to measure multifactor productivity, labor productivity and capital productivity for the Australian tourism industry. This study shows that tourism has been identified as a reservoir for other industries through the ebbs and flows of labor demands. Compared with the rest of the economy, the average growth of labor productivity—that is, income per unit of labor—for tourism is stagnant, and has reached an unprecedented low, six times below the market sector average, mainly because of low multifactor productivity. The results are valuable for policy makers and the lobbying groups wanting to identify areas of need for policy changes to ensure the healthy long-term growth of tourism.

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