Two Step Cluster Analysis for Tourist Segmentation Coastal Object for Green Marketing Strategy

Nurjannah, .; Darmanto, Darmanto; Solimun, .; Astuti, Ani Budi; Fernandes, Adji Achmad Rinaldo; Amaliana, Luthfatul; Yanti, Indah; Isaskar, Riyanti

doi:10.1088/1755-1315/239/1/012019

Cited by 7 publications

(6 citation statements)

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“…where BIC R -Bayesian information criterion; m r -ratio in r-cluster developed during the hierarchical clustering stage; L k -number of groups in k categorical variables [41].…”

Section: Methodsmentioning

confidence: 99%

The Classification of Work and Offenses of Professional Drivers from Slovakia and the Czech Republic

Poliak,

Benus,

Mazanec

et al. 2024

Applied Sciences

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To achieve the elimination of the negative impacts of transport on road safety, the European Union is taking various measures resulting from its commitment to improve road safety. The main objective of this paper is to assess the impact of social legislation on road transport safety using real research and to identify the factors influencing the violation of the legislation depending on the type of transport carried out in domestic or international road freight transport. Answers from the respondents are used in the segmentation of truck drivers based on input data describing the factors influencing the violation of traffic rules. Two-step cluster analysis is a suitable method for the segmentation of truck drivers based on input data. These data are obtained through a questionnaire from a total of 472 Czech and Slovak truck drivers. The results show that the optimal number of types of truck drivers with different characteristics is four. All of the clusters are described and compared. The majority of truck drivers do not violate social legislation for several reasons, such as traffic accidents (almost 67%), complications in meeting the loading or unloading deadline (less than 88%), poorly planned transport routes (more than 90%) and driving home (almost 80%).

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“…where BIC R -Bayesian information criterion; m r -ratio in r-cluster developed during the hierarchical clustering stage; L k -number of groups in k categorical variables [41].…”

Section: Methodsmentioning

confidence: 99%

The Classification of Work and Offenses of Professional Drivers from Slovakia and the Czech Republic

Poliak,

Benus,

Mazanec

et al. 2024

Applied Sciences

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show abstract

“…where: 𝐵𝐼𝐶 𝑅 Bayesian information criterion, 𝑚 𝑟 ratio in r-cluster developed during the hierarchical clustering stage, 𝐿 𝑘 the number of groups in k categorical variables [28] BIC with a lower value indicates the optimal number of clusters, and the "optimal" number of clusters has the lowest BIC value. In addition, we also monitor the large Ratio of BIC Changes and large Ratio of Distance Measures.…”

Section: Methodsmentioning

confidence: 99%

The Classification of Work and Offenses of Professional Drivers from Slovakia and the Czech Republic

Poliak,

Vichova,

Mazanec

et al. 2024

Preprint

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In order to achieve the elimination of the negative impacts of transport on road safety, the European Union is taking various measures resulting from its commitment to improve road safety. One of the instruments for achieving the objectives set is social legislation for the work of drivers in road freight and passenger transport. In line with the objective of improving the quality of working conditions for road transport drivers and road safety, the European Union applies Regulation (EC) No 561/2006 on the harmonisation of certain social legislation relating to road transport, which lays down rules on driving times, breaks and rest periods for drivers operating in road freight and passenger transport. The main objective of the paper is to assess the impact of social legislation on road transport safety by means of real research and to identify the factors influencing the violation of the legislation depending on the type of transports carried out in domestic or international road freight transport.

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“…As the data set contained continuous and categorical variables, the Two-Step Clustering method was used. Many previous studies adopted this methodology in market segmentation (Borges Tiago et al, 2016;Hadjikakou et al, 2014;Nurjannah et al, 2019). The same method was used to pre-process the data and identify the unusual cases that were not identified separately for single variables but for the whole dataset to secure the high quality of used data and acceptable dataset variability.…”

Section: Methodsmentioning

confidence: 99%

“… Length of Stay (in nights) Average Room Rate (in CZK) Type of Online Distribution Channel (as the rate parity strategy is adopted by the accommodation facility, the distribution channels were treated on the aggregated level) Day of Reservation Creation (day of the week category) Day of Arrival (day of the week category) Lead Time (or Booking Horizon) (in days before arrival) Type of Offer (payment conditions – BAR or Non-refundable) As the data set contained continuous and categorical variables, the Two-Step Clustering method was used. Many previous studies adopted this methodology in market segmentation (Borges Tiago et al, 2016; Hadjikakou et al, 2014; Nurjannah et al, 2019). The same method was used to pre-process the data and identify the unusual cases that were not identified separately for single variables but for the whole dataset to secure the high quality of used data and acceptable dataset variability.…”

Section: Methodsmentioning

confidence: 99%

Understanding customer's online booking intentions using hotel big data analysis

Chalupa

Petříček

2022

Journal of Vacation Marketing

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The presented article focuses on the issue of customer segmentation in the hospitality industry and its use for price optimisation. To identify the market segments article uses the Two-Step cluster analysis. The analysis was based on the seven variables (length of stay, average room rate, distribution channel, reservation day, day of arrival, lead time and payment conditions). Six clusters were identified as following segments: Corporates, Early Bird Bookers, Last Minute Bookers, Product Seekers, Values Seekers and Last Minute Bookers. To optimise the price for these segments, article works with the coefficient of price elasticity of demand for the presented market segments. The price elasticity of demand is measured by the log-log regression analysis. Data were colected from the four-star hotel in Prague, Czech Republic and analysis is based on more than 9000 transactions. Last minute bookers segment was the only one with the positive coefficient of price elasticity and has the lowest value of lead time (9,27 in average). Product seekers have the highest coefficient of price elasticity (−3,413) and highest average room rate (3573 CZK in average). Early bird bookers, Long time stayers, Corporates and Value seekers was identified as pricely inelastic.

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Two Step Cluster Analysis for Tourist Segmentation Coastal Object for Green Marketing Strategy

Cited by 7 publications

References 0 publications

The Classification of Work and Offenses of Professional Drivers from Slovakia and the Czech Republic

The Classification of Work and Offenses of Professional Drivers from Slovakia and the Czech Republic

The Classification of Work and Offenses of Professional Drivers from Slovakia and the Czech Republic

Understanding customer's online booking intentions using hotel big data analysis

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