Decentralized Payment Networks Digital Marketing Campaigns’ Evaluation Through Estimating Web Page Factors

Damianos P. Sakas and Nikolaos T. Giannakopoulos ()
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Damianos P. Sakas: BICTEVAC LABORATORY: Business Information and Communication Technologies in Value Chains Laboratory, Department of Agribusiness and Supply Chain Management, School of Applied Economics and Social Sciences, Agricultural University of Athens
Nikolaos T. Giannakopoulos: BICTEVAC LABORATORY: Business Information and Communication Technologies in Value Chains Laboratory, Department of Agribusiness and Supply Chain Management, School of Applied Economics and Social Sciences, Agricultural University of Athens

A chapter in Computational and Strategic Business Modelling, 2024, pp 3-11 from Springer

Abstract: Abstract Technology has been the driver of change and nowadays blockchain is one of its most recent innovations. Various application of blockchain technology have emerged, with cryptocurrencies being one of them. Cryptocurrency organizations provide the ability for decentralized payments, since enabling the development of decentralized payment networks (DPNs). The process of enhancing the digital marketing of DPNs has simultaneously arisen. A key issue for this enhancement could be the DPNs’ Web page factors, which determine a website’s efficiency. The authors gathered for 60 consecutive days analytic data from five DPNs’ websites, regarding technical and behavioral Web page factors, and analyzed them. Data elaboration was held via correlation and regression analysis, followed by the development of fuzzy cognitive mapping (FCM) and dynamic modeling (DM) processes. Throughout this paper, DPNs’ digital marketing campaigns can be evaluated by specific Web page factors, like crawl depth, raw internal link, and incoming internal links, since these factors appear to have a strong connection with DPNs’ organic campaign metrics.

Keywords: Decentralized payment networks (DPNs); Digital marketing; Website technical factors; Dynamic modeling; Decision support systems (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-41371-1_1

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