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Potentials of digital approaches in a tourism industry with changing customer needs – a quantitative study

Häfner Felix, Härting Ralf-Christian, Kaim Raphael

Annals of Computer Science and Information Systems

2020

Vol. 21

553--557

In recent years, the tourism industry has been undergoing a period of transformation, not least due to innovative and disruptive business models entering the market. Companies are increasingly focusing on customer needs and try to meet them with digital approaches. The early recognition of rapidly changing needs and their reactions is challenging. This paper examines four factors influencing the potentials of the changing customer needs and ongoing digitization in the tourism industry. A hypothesis model developed in previous qualitative research is examined in this paper with a quantitative approach using structural equation modeling. 157 responses from the target group were analyzed to test the factors digital marketing, data mining, digital services and online travel communities. The results of this paper show that digital marketing and data mining have a positive as well as highly significant influence on the potentials of digital approaches in a tourism industry with changing customer needs.

Multi-criteria approach to viral marketing campaign planning in social networks, based on real networks, network samples and synthetic networks

Karczmarczyk Artur, Jankowski Jarosław, Wątróbski Jarosław

Annals of Computer Science and Information Systems

2019

Vol. 18

663--673

Spreading of information within social media and techniques related to viral marketing take more and more attention from companies focused on targeting audiences within electronic systems. Recent years resulted in extensive research centered around spreading models, selection of initial nodes within networks and identification of campaign characteristics affecting the assumed goals. While social networks are usually based on complex structures and high number of users, the ability to perform detailed analysis of mechanics behind the spreading processes is very limited. The presented study shows an approach for selection of campaign parameters with the use of network samples and theoretical models. Instead of processing simulations on large network, smaller samples and theoretical networks are used. Results showed that knowledge derived from relatively smaller structures is helpful for initialization of spreading processes within the target network of larger size. Apart from agent based modeling, multi-criteria methods were used for evaluation of results from the perspective of costs and performance.

An approach to customer community discovery

Korczak Jerzy, Pondel Maciej, Sroka Wiktor

Annals of Computer Science and Information Systems

2019

Vol. 18

675--683

In the paper, a new multi-level hybrid method of community detection combining a density-based clustering with a label propagation method is proposed. Many algorithms have been applied to preprocess, visualize, cluster, and interpret the data describing customer behavior, among others DBSCAN, RFM, k-NN, UMAP, LPA. In the paper, two key algorithms have been detailed: DBSCAN and LPA. DBSCAN is a density-based clustering algorithm. However, managers usually find the clustering results too difficult to interpret and apply. To enhance the business value of clustering and create customer communities, the label propagation algorithm (LPA) has been proposed due to its quality and low computational complexity. The approach is validated on real life marketing database using advanced analytics platform Upsaily.