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The performance of behavioral targeting (BT) mainly relies on the effectiveness of user classification since advertisers always want to target their advertisements to the most relevant users. In this paper, the authors frame the BT as a user classification problem and describe a machine learning–based approach for solving it.

To perform such a study, two major research questions are investigated: the first question is how to represent a user’s online behavior. A good representation strategy should be able to effectively classify users based on their online activities. The second question is how different representation strategies affect the targeting performance. The authors propose three user behavior representation methods and compare them empirically using the area under the receiver operating characteristic curve (AUC) as a performance measure.

The experimental results indicate that ad campaign effectiveness can be significantly improved by combining user search queries, clicked URLs and clicked ads as a user profile. In addition, the authors also explore the temporal aspect of user behavior history by investigating the effect of history length on targeting performance. The authors note that an improvement of approximately 6.5% in AUC is achieved when user history is extended from 1 day to 14 days, which is substantial in targeting performance.

This paper confirms the effectiveness of BT on user classification and provides a validation of BT for Internet advertising.

This study aims to conduct performance and clustering analyses with the help of Digital Government Reference Library (DGRL) v16.6 database examining the role of emerging technologies (ETs) in public services delivery.

VOSviewer and SciMAT techniques were used for clustering and mapping the use of ETs in the public services delivery. Collecting documents from the DGRL v16.6 database, the paper uses text mining analysis for identifying key terms and trends in e-Government research regarding ETs and public services.

The analysis indicates that all ETs are strongly linked to each other, except for blockchain technologies (due to its disruptive nature), which indicate that ETs can be, therefore, seen as accumulative knowledge. In addition, on the whole, findings identify four stages in the evolution of ETs and their application to public services: the “electronic administration” stage, the “technological baseline” stage, the “managerial” stage and the “disruptive technological” stage.

The output of the present research will help to orient policymakers in the implementation and use of ETs, evaluating the influence of these technologies on public services.

The research helps researchers to track research trends and uncover new paths on ETs and its implementation in public services.

Recent research has focused on the need of implementing ETs for improving public services, which could help cities to improve the citizens’ quality of life in urban areas. This paper contributes to expanding the knowledge about ETs and its implementation in public services, identifying trends and networks in the research about these issues.

Turkey is a maritime country with its current merchant fleet and shipyards, geographical location, young population and growth potential. Clustering, being one of the important improvement methods of global competition power, is widely used in the maritime sector. Analysing the clustering level and potential of Istanbul, which is the major city of Turkey, in regard to economic and social aspects is a basic step for increasing global competitiveness in this sector. This study aims to measure the clustering level of Istanbul’s maritime sector and also define the effect of clustering level on firm performance.

The clustering levels of Istanbul’s maritime transportation and supporting firms, shipyards and maritime equipment manufacturers are measured by means of a survey based on Porter’s diamond theory in this paper. The relationship between clustering level and firm performance is defined by using simple linear regression and fuzzy linear regression methods. The weights of the criteria are calculated by means of entropy method.

It is concluded that despite its deficits, Istanbul’s maritime sector has significant potential to become a major maritime cluster not only in its region but also worldwide. The effect of clustering level on firm performance was observed to be statistically significant, but not high. The results of the simple linear regression and fuzzy linear regression methods are compared.

According to the author’s knowledge, this paper is the first study using fuzzy linear regression and entropy methods to analyse maritime clusters. It evaluates the effect of clustering level on firm performance in the case of Istanbul maritime sector.

The purpose of this paper is to review studies on the use of virtual reality (VR) and gamification to engage students in higher education for marketing issues to identify the research topics, the research gaps and to prepare a future research agenda.

A literature review is performed based on two search terms applied to Web of Science, resulting in a final pool of 115 articles. A text-mining approach is used to conduct a full-text analysis of papers related to VR and gamification in higher education. The authors also compare the salient characteristics presented in the articles.

From this analysis, five major research topics are found and analysed, namely, teaching methodologies and education, experience and motivation, student engagement, applied theories in VR and gamification. Based on this and following the theory concept characteristics methodology framework, the paper provides directions for future research.

There is no comprehensive review exploring the topics, theories, constructs and methods used in prior studies concerning VR and gamification applied to higher education services based on all the articles published in well-regarded academic journals. This review seeks to provide deeper insights, to help scholars contribute to the development of this research field.

En este documento se revisan los estudios sobre el uso de la realidad virtual (RV) y la Gamificación para involucrar a los estudiantes en la educación superior en marketing, se identifican los principales temas de investigación tratados, las lagunas de investigación y se sugiere una agenda futura de investigación.

Una revisión de la literatura basada en dos búsquedas en Web of Science (WOS) permitió identificar 115 artículos. La minería de textos se utilizó para realizar un análisis de texto completo de los artículos relacionados con RV y gamificación en la educación superior. Los autores también compararon las características más destacadas de los artículos.

A partir de este análisis, se encuentran y analizan cinco grandes temas de investigación: metodologías de enseñanza y educación, experiencia y motivación, compromiso de los estudiantes, aplicadas a la RV y la gamificación. Basándose en esto y siguiendo el marco del TCCM, el artículo proporciona una agenda futura de investigación.

No hay una revisión exhaustiva que explore los temas, las teorías, construcciones y métodos utilizados en estudios anteriores relativos a RV y gamificación aplicados a servicios de educación superior basados en todos los artículos publicados en revistas académicas. Esta revisión proporciona una panorámica más detallada y sugiere a los académicos nuevas líneas de trabajo para seguir desarrollando este campo de investigación.

本文的目的是回顾为了提高学生参与高等教育而使用虚拟现实(VR)和游戏化的营销方面的研究，以确定它们的研究主题，研究差距，并以此准备未来的研究议程。

本文的文献综述是基于Web of Science的两个搜索词进行的，最终搜索出115篇文章。本文采用文本挖掘方法，对与高等教育中的虚拟现实和游戏化相关的论文进行全文分析。作者还比较了这些文章中呈现的显著特征。

从这一调查中，我们发现并分析了五大研究主题，即教学方法与教育、体验与动机、学生参与、虚拟现实应用理论和游戏化。在此基础上，遵循理论概念、特征、方法论框架，为今后的研究提供了方向。

目前，在权威学术期刊上发表的所有文章，都没有对以往关于虚拟现实和游戏化应用于高等教育服务的研究的主题、理论、结构和方法进行全面的综述。本文旨在提供更深刻的见解，以帮助学者们为这一研究领域的发展做出贡献。

In the most recent years, social, innovative, economic and political changes in the European context have characterized consumers' behaviours. The paper aims to understand if the use of electronic commerce differs in a situation characterized by uncertainty.

An innovative approach to categorising online consumer behaviour considers the self-determination theory and basic psychological needs in an uncertain scenario. The research is based on a quantitative analysis obtained by clustering algorithms on a sample of 1,000 digital users in European countries. A structured questionnaire was administered online and distributed through the leading online social platforms and direct mailing.

The results show online activities during changes in consumer behaviour patterns and retailers' strategies. This research will allow online retail managers and practitioners to obtain important information to help them define appropriate customer-oriented strategic actions to enhance value in the electronic context for both customers and firms.

The innovation of this research approaches the categorization of online consumer behaviour by exploiting the self-determination theory in an uncertain scenario. Precisely, the novelty of this research is to highlight three detailed categories of electronic commerce consumers, namely, unwilling, halfback and digital, to collect, store and disseminate information about these categories of Online Consumers Behaviours.