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Humanoid robots that are artificially intelligent have infiltrated almost every aspect of life, including libraries, and can do things that humans are incapable of doing with greater efficiency in the library. It is on that note that this study has examined artificial intelligence (AI) humanoid robots for public libraries reference services. This study aims to discuss AI and draw the link between it and robotic technology.

This study adopted conceptual analysis and review of literature on AI humanoid robots for public libraries reference services.

This study focused not only on humanoid robots but also discovered that there are other types of robots relevant to libraries including chatbot, telepresence and shelve-reading robots as well as general robots such as aerospace robots, drones and army robots, commercial entertainment robots, industrial robots and medical-surgical robots. This paper also reported the usefulness of robots in libraries, and zero in on robots and reference services in the library. Lastly, this study reported the impact of humanoid robots on reference services in public libraries.

This paper is the original idea from the authors and does not reflect on any copyrighted materials. This study recommended among others that public libraries (in collaboration with their parent organisations) devise a strategic plan for new and emerging technologies such as humanoid robots.

As a relatively new computing paradigm, crowdsourcing has gained enormous attention in the recent decade. Its compliance with the Web 2.0 principles, also, puts forward unprecedented opportunities to empower the related services and mechanisms by leveraging humans’ intelligence and problem solving abilities. With respect to the pivotal role of search engines in the Web and information community, this paper aims to investigate the advantages and challenges of incorporating people – as intelligent agents – into search engines’ workflow.

To emphasize the role of the human in computational processes, some specific and related areas are studied. Then, through studying the current trends in the field of crowd-powered search engines and analyzing the actual needs and requirements, the perspectives and challenges are discussed.

As the research on this topic is still in its infancy, it is believed that this study can be considered as a roadmap for future works in the field. In this regard, current status and development trends are delineated through providing a general overview of the literature. Moreover, several recommendations for extending the applicability and efficiency of next generation of crowd-powered search engines are presented. In fact, becoming aware of different aspects and challenges of constructing search engines of this kind can shed light on the way of developing working systems with respect to essential considerations.

The present study was aimed to portrait the big picture of crowd-powered search engines and possible challenges and issues. As one of the early works that provided a comprehensive report on different aspects of the topic, it can be regarded as a reference point.

This study aims to analyse the effectiveness of a static promotional banner located on a hotel reservation website in terms of capturing the visitor’s visual attention by exploring how this impact depends on the user’s degree of internet experience.

An experiment was conducted using the eye-tracking methodology, in addition to a self-administered questionnaire. Through eye-tracking technology, eye movements were recorded whilst participants explored a generic hotel website. The factors used in the analyses were the position of the banner on the website and participants’ experience as internet users.

The findings showed that positioning a banner at certain locations on the webpage may lead to a better recall, which, in part, seems to result from the visual attention that such locations attract. The mediation analysis showed that the bottom-right and bottom-left positions have a negative effect on banner recall due, in part, to the shorter attention times and the smaller number of fixations those positions induce. Although the visitor’s level of internet experience affected his/her visual attention towards the banner, its impact on banner recall was non-significant. Results are discussed considering which variables produce greater effectiveness in capturing the user’s attention.

The paper draws several implications for the marketing literature, hospitality management and society in general.

The study is the first to analyse the impact of the position of a static ad on users’ visual attention and memory, considering the user’s degree of internet experience.

Este estudio analiza la eficacia de un banner promocional estático ubicado en un sitio web de reservas de un hotel en términos de captación de la atención visual del visitante examinando cómo este efecto depende del grado de experiencia del usuario en Internet.

Se llevó a cabo un experimento utilizando la metodología de seguimiento ocular (eye tracking), junto con un cuestionario autoadministrado. A través de la tecnología de eye tracking, se registraron los movimientos oculares mientras los participantes exploraban el sitio web genérico de un hotel. Los factores considerados en los análisis fueron la posición del banner en el sitio web y la experiencia de los participantes como usuarios de Internet.

Los hallazgos mostraron que colocar un banner en ciertas ubicaciones de la página web puede conducir a un mayor recuerdo, lo que, en parte, parece ser el resultado de la atención visual que atraen tales posiciones. El análisis de mediación mostró que las posiciones inferior-derecha e inferior-izquierda tienen un efecto negativo en el recuerdo del banner debido, en parte, a los tiempos de atención más cortos y al menor número de fijaciones que provocan esas posiciones. Aunque el nivel de experiencia de Internet del visitante afectó su atención visual hacia el banner, su impacto en el recuerdo del banner no fue significativo. Los resultados son discutidos considerando qué variables producen mayor efectividad a la hora de captar la atención del usuario.

El artículo presenta una serie de implicaciones para la literatura de marketing, la gestión hotelera y la sociedad en general.

Nuestro estudio es el primero en analizar el impacto de la posición de un anuncio estático en la atención visual y la memoria de los usuarios, considerando el nivel de experiencia del usuario en Internet.

本研究分析了位于酒店预订网站上的静态促销横幅在吸引访问者视觉注意力方面的效果, 并探索了这种效果如何受到用户的互联网体验程度的影响。

本文除了采用自填问卷的方式, 还使用眼动追踪方法进行了实验。通过眼动追踪技术, 记录了参与者在浏览一般酒店网站时的眼动情况。分析中使用的因素是网站上横幅的位置和参与者作为互联网用户的体验。

调查结果显示, 将横幅定位在网页上的某些位置可能会导致更佳的记忆, 这种效果在一定程度上是由于横幅的位置吸引了视觉注意力。调解分析表明, 横幅在右下角和左下角的位置对的回忆有负面影响, 部分原因是这些位置引起的注意时间较短、固定次数较少。虽然访问者的互联网体验水平影响了他/她对横幅的视觉注意力, 但其对横幅记忆的影响并不显著。讨论结果时考虑到了某些变量在吸引用户的注意力方面具有更明显的效果。

本文对市场营销文献、酒店管理和社会都具有一定的借鉴意义。

本次研究是第一个分析静态广告的位置对用户视觉注意力和记忆的影响的研究, 考虑到用户的互联网体验程度。

This paper examines the impact of political instability on the investors' behavior, measured by Google search queries, and on the dynamics of stock market returns.

First, by using the DCC-GARCH model, the authors examine the effect of investor sentiment on the Tunisian stock market return. Second, the authors employ the fully modified dynamic ordinary least square method (FMOL) to estimate the long-term relationship between investor sentiment and Tunisian stock market return. Finally, the authors use the wavelet coherence model to test the co-movement between investor sentiment measured by Google Trends and Tunisian stock market return.

Using the dynamic conditional correlation (DCC), the authors find that Google search queries index has the ability to reflect political events especially the Tunisian revolution. In addition, empirical results of fully modified ordinary least square (FMOLS) method reveal that Google search queries index has a slightly higher effect on Tunindex return after the Tunisian revolution than before this revolution. Furthermore, by employing wavelet coherence model, the authors find strong comovement between Google search queries index and return index during the period of the Tunisian revolution political instability. Moreover, in the frequency domain, strong coherence can be found in less than four months and in 16–32 months during the Tunisian revolution which show that the Google search queries measure was leading over Tunindex return. In fact, wavelet coherence analysis confirms the result of DCC that Google search queries index has the ability to detect the behavior of Tunisian investors especially during the period of political instability.

This study provides empirical evidence to portfolio managers that may use Google search queries index as a robust measure of investor's sentiment to select a suitable investment and to make an optimal investments decisions.

The important research question of how political instability affects stock market dynamics has been neglected by scholars. This paper attempts principally to fill this void by investigating the time-varying interactions between market returns, volatility and Google search based index, especially during Tunisian revolution.

While research has identified a consistent link between startup intent and entrepreneurship education (EE) intentions, studies also indicate that many entrepreneurs lack the EE they need. However, research examining factors that explain why certain individuals with high startup intent pursue EE while others do not is rare. Given this, the purpose of this paper is to examine how individual characteristics moderate the startup intent EE intentions relationship.

Survey data were gathered on 199 US adults. Moderators examined include attitudes toward education, perceived entrepreneurial efficacy, propensity for risk taking and the Big Five personality traits. Linear regression models were used to test each of the moderation relationships predicted.

Notable findings suggest that extroversion, openness to experience, agreeableness, perceived entrepreneurial efficacy and risk propensity reduce the chances that individuals with high startup intent will pursue EE, while viewing education as instrumental enhances the relationship.

Study findings imply that EE programs might not be reaching critical target markets, suggest that EE programs might need to be modified to attract individuals with high startup intent and indicate that individual characteristics are key factors that determine why certain individuals with high startup intent pursue EE while others with the same desires do not pursue EE.

This study builds on previous work that looks at the relationship between startup intent and EE intentions by investigating how individual characteristics either amplify or diminish the relationship, increasing scholarly knowledge about why certain individuals with high startup intent pursue EE while others do not.

The purpose of this study is to show that closure-based classification and regression models provide both high accuracy and interpretability.

Pattern structures allow one to approach the knowledge extraction problem in case of partially ordered descriptions. They provide a way to apply techniques based on closed descriptions to non-binary data. To provide scalability of the approach, the author introduced a lazy (query-based) classification algorithm.

The experiments support the hypothesis that closure-based classification and regression allow one to both achieve higher accuracy in scoring models as compared to results obtained with classical banking models and retain interpretability of model results, whereas black-box methods grant better accuracy for the cost of losing interpretability.

This is an original research showing the advantage of closure-based classification and regression models in the banking sphere.

For companies that intend to respond to the modern conscious consumers' needs, a great competitive advantage is played on the ability to incorporate sustainability messages in marketing communications. The aim of this paper is to address this important priority in the web context, building a semantic algorithm that allows content managers to evaluate the quality of sustainability web contents for search engines, considering the current semantic web development.

Following the Design Science (DS) methodological approach, the study develops the algorithm as an artefact capable of solving a practical problem and improving the operation of content managerial process.

The algorithm considers multiple factors of evaluation, grouped in three parameters: completeness, clarity and consistency. An applicability test of the algorithm was conducted on a sample of web pages of the Google blog on sustainability to highlight the correspondence between the established evaluation factors and those actually used by Google.

Studying content marketing for sustainability communication constitutes a new field of research that offers exciting opportunities. Writing sustainability contents in an effective way is a fundamental step to trigger stakeholder engagement mechanisms online. It could be a positive social engineering technique in the hands of marketers to make web users able to pursue sustainable development in their choices.

This is the first study that creates a theoretical connection between digital content marketing and sustainability communication focussing, especially, on the aspects of search engine optimization (SEO). The algorithm of “Sustainability-contents SEO” is the first operational software tool, with a regulatory nature, that is able to analyse the web contents, detecting the terms of the sustainability language and measuring the compliance to SEO requirements.

Question-answering (QA) systems are being increasingly applied in learning contexts. However, the authors’ understanding of the relationship between such tools and traditional QA channels remains limited. Focusing on question-answering learning activities, the current research investigates the effect of QA systems on students' learning processes and outcomes, as well as the interplay between two QA channels, that is, QA systems and communication with instructors.

The authors designed and implemented a QA system for two university courses, and collected data from questionnaires and system logs that recorded the interaction between students and the system throughout a semester.

The results show that using a QA system alone does not improve students' learning processes or outcomes. However, the use of a QA system significantly improves the positive effect of instructor communication.

This study contributes to the literature on learning and education technology, and provides practical guidance on how to incorporate QA tools in learning.

This research work aims to arise from developing new communication channels for customer service in micro, small and medium enterprises (MSMEs), such as chatbots. In particular, the results of the usability testing of three chatbots implemented in MSMEs are presented.

The methodology employed includes participants, chatbot development platform, research methodology, software development methodology and usability test to contextualize the study's results.

Based on the results obtained from the System Usability Scale (SUS) and considering the accuracy of the chatbot's responses, it is concluded that the level of satisfaction in using chatbots is high; therefore, if the chatbot is well integrated with the communication systems/channels of the MSMEs, the client receives an excellent, fast and efficient service.

The paper analyzes chatbots for customer service and presents the usability testing results of three chatbots implemented in MSMEs.

Semantic retrieval and discovery of datasets published as open data remains a challenging task. The datasets inherently originate in the globally distributed web jungle, lacking the luxury of centralized database administration, database schemes, shared attributes, vocabulary, structure and semantics. The existing dataset catalogs provide basic search functionality relying on keyword search in brief, incomplete or misleading textual metadata attached to the datasets. The search results are thus often insufficient. However, there exist many ways of improving the dataset discovery by employing content-based retrieval, machine learning tools, third-party (external) knowledge bases, countless feature extraction methods and description models and so forth.

In this paper, the authors propose a modular framework for rapid experimentation with methods for similarity-based dataset discovery. The framework consists of an extensible catalog of components prepared to form custom pipelines for dataset representation and discovery.

The study proposes several proof-of-concept pipelines including experimental evaluation, which showcase the usage of the framework.

To the best of authors’ knowledge, there is no similar formal framework for experimentation with various similarity methods in the context of dataset discovery. The framework has the ambition to establish a platform for reproducible and comparable research in the area of dataset discovery. The prototype implementation of the framework is available on GitHub.