Destination image analytics through traveller-generated content

The explosion of content generated by users, in parallel with the spectacular growth of social media and the proliferation of mobile devices, is causing a paradigm shift in research. Surveys or interviews are no longer necessary to obtain users' opinions, because researchers can get this information freely on social media. In the field of tourism, online travel reviews (OTRs) hosted on travel-related websites stand out. The objective of this article is to demonstrate the usefulness of OTRs to analyse the image of a tourist destination. For this, a theoretical and methodological framework is defined, as well as metrics that allow for measuring different aspects (designative, appraisive and prescriptive) of the tourist image. The model is applied to the region of Attica (Greece) through a random sample of 300,000 TripAdvisor OTRs about attractions, activities, restaurants and hotels written in English between 2013 and 2018. The results show trends, preferences, assessments, and opinions from the demand side, which can be useful for destination managers in optimising the distribution of available resources and promoting sustainability

Web 2.0 and destination marketing: current trends and future directions

Over the last decade, destination marketers and Destination Marketing Organizations (DMOs) have increasingly invested in Web 2.0 technologies as a cost-effective means of promoting destinations online, in the face of drastic marketing budgets cuts. Recent scholarly and industry research has emphasized that Web 2.0 plays an increasing role in destination marketing. However, no comprehensive appraisal of this research area has been conducted so far. To address this gap, this study conducts a quantitative literature review to examine the extent to which Web 2.0 features in destination marketing research that was published until December 2019, by identifying research topics, gaps and future directions, and designing a theory-driven agenda for future research. The study’s findings indicate an increase in scholarly literature revolving around the adoption and use of Web 2.0 for destination marketing purposes. However, the emerging research field is fragmented in scope and displays several gaps. Most of the studies are descriptive in nature and a strong overarching conceptual framework that might help identify critical destination marketing problems linked to Web 2.0 technologies is missing

Content marketing model for leading web content management

This paper is envisaged to provide the Ukrainian businesses with suggestions for a content marketing model for the effective management of website content in order to ensure its leading position on the European and world markets. Our study employed qualitative data collection with semi-structured interviews, survey, observation methods, quantitative and qualitative methods of content analysis of regional B2B companies, as well as the comparative analysis. The following essential stages of the content marketing process as preliminary search and analysis, website content creation, promotion and distribution, and content marketing progress assessment were identified and classified in detail. The strategic decisions and activities at each stage of the process showed how a company’s on-site and off-site content can be used as a tool to establish the relationship between the brand and its target audience and increase brand visibility online. This study offered several useful insights into how website content, social media and various optimization techniques work together in engaging with the target audience and driving website traffic and sales leads. We constructed and described the content marketing model elaborated for effective web content management that can be useful for those companies that start to consider employing content marketing strategy for achieving business goals and increasing a leadership position

Technology-driven online marketing performance measurement: lessons from affiliate marketing

Although the measurement of offline and online marketing is extensively researched, the literature on online performance measurement still has a number of limitations such as slow theory advancement and predominance of technology- and practitioner-driven measurement approaches. By focusing on the widely employed but under-researched affiliate marketing channel, this study addresses these limitations and evaluates the effectiveness of practitioner-led online performance assessment. The paper offers a comprehensive review of extant performance measurement research across traditional, online and affiliate marketing and, employing grounded theory, presents a qualitative in-depth analysis of 72 online forum discussions and 37 semi-structured interviews with the major affiliate marketing stakeholders. As a result, the research identifies a growing need for change in the technology-pushed measurement approaches in affiliate marketing, and proposes actionable improvement recommendations for affiliate and online marketing managers

The moderating influence of device characteristics and usage on user acceptance of smart mobile devices

This study seeks to develop a comprehensive model of consumer acceptance in the context of Smart Mobile Device (SMDs). This paper proposes an adaptation of the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT2) model that can be employed to explain and predict the acceptance of SMDs. Also included in the model are a number of external and new moderating variables that can be used to explain user intentions and subsequent usage behaviour. The model holds that Activity-based Usage and Device Characteristics are posited to moderate the impact of the constructs empirically validated in the UTAUT2 model. Through an important cluster of antecedents the proposed model aims to enhance our understanding of consumer motivations for using SMDs and aid efforts to promote the adoption and diffusion of these devices

Digital marketing actions that achieve a better attraction and loyalty of users: an analytical study

Currently, the digital economy contributes decisively to an increase in competitiveness, especially as a digital transformation involves migrating to new technological models where digital marketing is a key part of growth and user loyalty strategies. Internet and Digital Marketing have become important factors in campaigns, which attract and retain Internet users. This study aims to identify the main ways in which users can be gained and retained by using Digital Marketing. The Delphi method with in-depth interviews was the methodology used in this study. The results of the research show the most important actions for achieving user recruitment and loyalty with Digital Marketing from the opinions of consulted experts. The limitations of this study are those related to the number of experts included in the study, and the number of research papers consulted in the literature review. The literature review and the results of this research are used to propose new solid research with a consolidated critical methodology. This research deals with a new approach that will optimize web technologies for the evolution of user trends, and therefore, will be of academic and professional use for marketing managers and web solution developers. The conclusions of the investigation show the key factors, discarding others that do not affect the optimization of conversions in B2C businesses such as the duration of the session and the rebound percentage. Likewise, the results of the research identify the specific actions that must be carried out to attract and retain users in B2C companies that use the Digital Marketing ecosystem on the Internet. The requirements for companies that wish to implement a model to optimize conversions using the current digital economy are also shown.info:eu-repo/semantics/publishedVersio

Cultural heritage appraisal by visitors to global cities: the use of social media and urban analytics in urban buzz research

An attractive cultural heritage is an important magnet for visitors to many cities nowadays. The present paper aims to trace the constituents of the destination attractiveness of 40 global cities from the perspective of historical-cultural amenities, based on a merger of extensive systematic databases on these cities. The concept of cultural heritage buzz is introduced to highlight: (i) the importance of a varied collection of urban cultural amenities; (ii) the influence of urban cultural magnetism on foreign visitors, residents and artists; and (iii) the appreciation for a large set of local historical-cultural amenities by travelers collected from a systematic big data set (emerging from the global TripAdvisor platform). A multivariate and econometric analysis is undertaken to validate and test the quantitative picture of the above conceptual framework, with a view to assess the significance of historical-cultural assets and socio-cultural diversity in large urban agglomerations in the world as attraction factors for visitors. The results confirm our proposition on the significance of urban cultural heritage as a gravity factor for destination choices in international tourism in relation to a high appreciation for historical-cultural amenities.info:eu-repo/semantics/publishedVersio

A Cluster-based Recommender System

Introduction: E-commerce is growing rapidly offering a vast number of products and services to the users. Facing with a wide range of options, users cannot decide which one would be the most suitable option. Recommender systems help users to find the most suitable item easier and faster. To do this, recommender systems apply machine learning algorithms to user’s data to build sophisticated models to predict the user’s behavior in the future. There are many recommender systems employed by companies to increase their profitability. Some examples include Amazon, Movielens, Youtube, Facebook, and Linkedin. Objectives: The aim of this project is to provide a cluster-based recommender system which cluster users based on their history (previous interactions with the system) to increase the accuracy of recommendations. Method: The proposed approach consists of two phases: offline and online. In the offline phase, users are clustered using genetic algorithm. In the online phase, the appropriate cluster or clusters and neighborhood are selected for the target user. Then, his/her interesting items (not chosen yet) are determined using interesting items of his/her neighbors. Results: After implementing the proposed approach for the recommender system, it was evaluated in terms of accuracy (the portion of recommended items which have been interesting for the users) and compared it with several existing recommender systems. The results show that our approach outperforms other approaches. Conclusions: Having a good recommender system encourages users to buy new products, find new friends, or watch new videos. On the contrary, an inaccurate recommender system may discourage the users and motivates them to sign out of the system or ignore all recommendations. The approach we proposed for recommendation achieved promising results. We hope by completing the project we can use this approach in developing commercial recommender systems