Purpose – The purpose of this research work is to find a methodology for the strategic development of competitive advantage for information technology (IT) companies (Mezger and Violani, 2011). The ultimate aim of this project is to develop a methodological approach on this issue, based on dynamic simulation models (DSM) (Wirahadikusumah and Abraham, 2003). With the aid of DSM, senior managements of organizations will have the opportunity to make decisions of assured success. This success shall be guaranteed by the realization of entrepreneurial activity in a safe and inexpensive computing environment before actual investment. Design/methodology/approach – This paper highlights the advantages of the dynamic modelling of systems aiming at developing competitive advantage for IT companies (Ordóñez de Pablos, 2006). In this research, we have used the science of design and the research methodology for testing the concept of modelling as well as the process of modelling. The models have been completed through a series of alternations and iterations in the design, development, simulation, testing and evaluation. Findings – This paper examines the interface among several dimensions for the development of dynamic models. The validity and usefulness of those models in the process of decision-making has been confirmed by the usage of dynamic models in various sectors. Originality/value – This paper applies the system and the concepts of dynamic modelling, which are pioneering elements as to their nature and evolution. Although the sector, where the modelling was applied, is an IT company, the concepts and principles investigated, developed and validated can be applied to most enterprises.
In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.
The tourism sector increasingly relies on technology to acquire new clients in a world overflowing with information. So, the main question that needs to be answered is:What digital marketing strategy should be adopted to attract customers and built digital brand name by incorporating websites and social media big data? The authors of this research utilize web analytics and big data to build an innovative methodology in an effort to address this issue. After the data collection, statistical analysis was implemented, followed by a fuzzy cognitive map and an agent-based simulation model in order to illustrate the usage of social media and user experience in multichannel marketing. The findings suggest that, in contrast to the websites of other industries, such as logistics, where customers want to finish their inquiries as quickly as possible and leave the webpage, it is advantageous for tourism websites to keep customers’ attention moreon their website in order to increasevisibility. Additionally, the research further highlights the importance of personalization and user-engagement content to e-WOM, suggesting to tourism businesses to encourage posts made by customers and employees.
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