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In this paper the authors aim to review the state-of-the-art literature on online review systems and their impacts on consumer behavior and retailers' performance with the aim of identifying research gaps related to different design features of review systems and developing future research agenda.

The authors conducted a systematic review based on PRISMA 2020 protocol, focusing on studies published in the domains of retailing and marketing. This procedure resulted in 48 selected papers investigating the design features of retailer online review systems.

The authors identify eight design features that are controllable by retailers in an online review system. The design features have been researched independently in previous literature, with some features receiving more attention. Most selected studies focus on the design features adapted metrics and review presentations, while other features are generally neglected (e.g. rating dimensions). Previous literature argues that design features affect consumer behaviors and retailers' performance. However, the interactions among the features are still neglected in the literature, creating a relevant gap for future research.

This paper distinguishes between different types of retailer online review systems based on how they are implemented. The authors summarize the state-of-the-art of relevant literature on design features of online review systems and their effects on consumer- and retailer-related outcome variables. This systematic literature review distinguishes between online reviews provided on websites controlled by retailers (internal systems) and third-party websites (external systems).

The purpose of this study is to investigate how much visual attention is given to banner ads embedded in Web page content dependent on whether the user’s task is goal- or not goal-oriented, as well as the interplay between attention, banner location, banner click and banner recognition.

The authors used a within-subjects design where 100 participants performed two tasks – reading a news and finding where to click next – on a Web page containing three banner ads embedded into the website content. The authors gathered behavioral and eye-tracking data.

Consumers disregard banner ads when they are performing a focused task (reading news). Visual attention paid to the banners while reading – but not while free browsing – and banner location do not impact ad clicking. In addition, it is not necessary to pay full attention to a banner ad to be able to recognize it afterward.

The strategy of embedding banners in the main content of a Web page leads to higher visual attention when consumers are browsing a Web page compared to a focused task (e.g. reading). It also increases ad recognition over time compared to benchmark levels for ads placed in traditional positions.

Previous studies mainly assessed effectiveness of banners located at the top or lateral of a Web page. The authors used eye tracking as an objective measure of visual attention to banner ads embedded in Web page content and behavioral metrics to assess ad interest and measured ad recognition over time.

Investigar cuánta atención visual se presta a los banners publicitarios incrustados en el contenido de una página Web en función de si la tarea del usuario está orientada a un objetivo o no, así como la interacción entre la atención, la ubicación del banner, el clic en el banner y el reconocimiento del banner.

Se utilizó un diseño entre sujetos en el que 100 participantes realizaban dos tareas – leer una noticia y encontrar dónde hacer clic a continuación – en una página Web que contenía tres banners publicitarios incrustados en el contenido del sitio Web. Se recogieron datos conductuales y de seguimiento ocular.

Los consumidores no prestan atención a los banners publicitarios cuando están realizando una tarea concentrada (leer noticias). La atención visual prestada a los banners durante la lectura – pero no durante la navegación libre – y la ubicación de los banners no influyen en el hecho de hacer clic en los anuncios. Además, no es necesario prestar toda la atención a un banner publicitario para poder reconocerlo después.

Los estudios anteriores evaluaban principalmente la eficacia de los banners situados en la parte superior o lateral de una página Web. Nosotros utilizamos el seguimiento ocular como medida objetiva de la atención visual a los banners incrustados en el contenido de la página Web y métricas de comportamiento para evaluar el interés por el anuncio, y medimos el reconocimiento del anuncio a lo largo del tiempo.

La estrategia de incrustar banners en el contenido principal de una página Web aumenta la atención visual de los consumidores cuando navegan por una página Web en comparación con una tarea específica (por ejemplo, leer). También aumenta el reconocimiento del anuncio a lo largo del tiempo en comparación con los niveles de referencia de los anuncios colocados en posiciones tradicionales.

研究用户对嵌入在网页内容中的横幅广告的视觉注意程度, 取决于用户的任务是否以目标为导向, 以及注意、横幅位置、横幅点击和横幅识别之间的相互作用。

我们采用了主体内设计, 100名参与者在一个含有三个嵌入网站内容的横幅广告的网页上执行两项任务–阅读新闻和寻找下一步的点击位置。我们收集了行为和眼球追踪数据。

消费者在执行重点任务（阅读新闻）时忽略了横幅广告。阅读时对横幅广告的视觉关注–而不是自由浏览时–以及横幅广告的位置并不影响广告点击。此外, 不一定要完全注意横幅广告才能在事后认出它。

以前的研究主要评估位于网页顶部或侧面的横幅广告的效果。我们用眼动仪作为对嵌入网页内容的横幅广告的视觉注意力的客观测量, 用行为指标来评估广告的兴趣, 并测量了广告在一段时间内的识别度。

在网页的主要内容中嵌入横幅广告的策略导致消费者在浏览网页时, 与重点任务（如阅读）相比, 视觉注意力更高。与放置在传统位置的广告的基准水平相比, 它也会随着时间的推移增加广告识别度。

This paper aims to propose a new method to derive custom dynamic cyber risk metrics based on the well-known Goal, Question, Metric (GQM) approach. A framework that complements it and makes it much easier to use has been proposed too. Both, the method and the framework, have been validated within two challenging application domains: continuous risk assessment within a smart farm and risk-based adaptive security to reconfigure a Web application firewall.

The authors have identified a problem and provided motivation. They have developed their theory and engineered a new method and a framework to complement it. They have demonstrated the proposed method and framework work, validating them in two real use cases.

The GQM method, often applied within the software quality field, is a good basis for proposing a method to define new tailored cyber risk metrics that meet the requirements of current application domains. A comprehensive framework that formalises possible goals and questions translated to potential measurements can greatly facilitate the use of this method.

The proposed method enables the application of the GQM approach to cyber risk measurement. The proposed framework allows new cyber risk metrics to be inferred by choosing between suggested goals and questions and measuring the relevant elements of probability and impact. The authors’ approach demonstrates to be generic and flexible enough to allow very different organisations with heterogeneous requirements to derive tailored metrics useful for their particular risk management processes.

The study aims to analyze the synergy of artificial intelligence (AI), with scientometrics, webometrics and bibliometrics to unlock and to emphasize the potential of the applications and benefits of AI algorithms in these fields.

By conducting a systematic literature review, our aim is to explore the potential of AI in revolutionizing the methods used to measure and analyze scholarly communication, identify emerging research trends and evaluate the impact of scientific publications. To achieve this, we implemented a comprehensive search strategy across reputable databases such as ProQuest, IEEE Explore, EBSCO, Web of Science and Scopus. Our search encompassed articles published from January 1, 2000, to September 2022, resulting in a thorough review of 61 relevant articles.

(1) Regarding scientometrics, the application of AI yields various distinct advantages, such as conducting analyses of publications, citations, research impact prediction, collaboration, research trend analysis and knowledge mapping, in a more objective and reliable framework. (2) In terms of webometrics, AI algorithms are able to enhance web crawling and data collection, web link analysis, web content analysis, social media analysis, web impact analysis and recommender systems. (3) Moreover, automation of data collection, analysis of citations, disambiguation of authors, analysis of co-authorship networks, assessment of research impact, text mining and recommender systems are considered as the potential of AI integration in the field of bibliometrics.

This study covers the particularly new benefits and potential of AI-enhanced scientometrics, webometrics and bibliometrics to highlight the significant prospects of the synergy of this integration through AI.

In the last few years, the size of Linked Open Data (LOD) describing artworks, in general or domain-specific Knowledge Graphs (KGs), is gradually increasing. This provides (art-)historians and Cultural Heritage professionals with a wealth of information to explore. Specifically, structured data about iconographical and iconological (icon) aspects, i.e. information about the subjects, concepts and meanings of artworks, are extremely valuable for the state-of-the-art of computational tools, e.g. content recognition through computer vision. Nevertheless, a data quality evaluation for art domains, fundamental for data reuse, is still missing. The purpose of this study is filling this gap with an overview of art-historical data quality in current KGs with a focus on the icon aspects.

This study’s analyses are based on established KG evaluation methodologies, adapted to the domain by addressing requirements from art historians’ theories. The authors first select several KGs according to Semantic Web principles. Then, the authors evaluate (1) their structures’ suitability to describe icon information through quantitative and qualitative assessment and (2) their content, qualitatively assessed in terms of correctness and completeness.

This study’s results reveal several issues on the current expression of icon information in KGs. The content evaluation shows that these domain-specific statements are generally correct but often not complete. The incompleteness is confirmed by the structure evaluation, which highlights the unsuitability of the KG schemas to describe icon information with the required granularity.

The main contribution of this work is an overview of the actual landscape of the icon information expressed in LOD. Therefore, it is valuable to cultural institutions by providing them a first domain-specific data quality evaluation. Since this study’s results suggest that the selected domain information is underrepresented in Semantic Web datasets, the authors highlight the need for the creation and fostering of such information to provide a more thorough art-historical dimension to LOD.

The purpose of this study is to assess the value of altmetrics or other indicators, showcasing the impact of academic output, which is seen too often correlated with the citation count.

This study considered three reputed journals of Library and Information Science (LIS) published by Elsevier. A total of 1,164 articles were found in these journals from 2016 to 2020 and the relationships between altmetric attention scores (AAS) and citations were examined. The analysis was extended to compare the grouped data set based on percentile ranks of AAS like top 50%, top 25%, top 10% and top 1%.

Using Spearman correlation analysis, the findings reveal a positive correlation between AAS and citations with different significant levels for all articles, and articles with AAS, as well as for normalized AAS in the top 50%, top 25%, top 10% and top 1% data set. For the three journals International Journal of Information Management (IJIM), Journal of Informetrics (JIF) and Library and Information Science Research (LISR), a significant positive correlation is observed across all data sets. But an unexpected result was observed: in the case of the top 50% of articles for the IJIM and JIF showed no significant correlation but the LISR journal showed a positive correlation for the whole data set. This journal though has fewer articles in comparison to the other two.

A source item that is highly cited may not be having high social media attention as reflected in the findings. This demarcates AAS with citations implying various factors on which these measurements are dependent. The study distinguishes these metrics lucidly. There is not a single guideline or uniformity in assessing the correlation found. But the problem is that the interpretation of the correlation strength affects the conclusion of the study. Moreover, this study will be a role model as a draft for librarians to select relevant journals for their libraries and will facilitate authors in the choice of the publication outlets for their papers, particularly concerning the journals that have both visibility and research impact.

The study reported devising a comprehensive tool to validate AAS as a measure of scholarly impact to include appropriate social media sources and verify its relationship with other metrics. To the best of the authors’ knowledge, this paper is the first attempt to discover the correlation between AAS and citations for the highly impactful LIS journal published by Elsevier. The empirical evidence lies in the citation and altmetric data extracted from the dimension database.

In academics and industry, significant efforts have been made to lead planners and control teams in evaluating project performance and control. In this context, numerous control metrics have been devised and put into practice, often with little emphasis on analyzing their underlying concepts. To cover this gap, this research aims to identify and analyze a holistic list of control metrics and their functionalities in the construction industry.

A multi-step analytical approach was conducted to achieve the study’s objectives. First, a holistic list of control metrics and their functionalities in the construction industry was identified. Second, a quantitative analysis based on social network analysis (SNA) was implemented to discover the most important functionalities.

The results revealed that the most important control metrics' functionalities (CMF) could differ depending on the type of metrics (lagging and leading) and levels of control. However, in general, the most significant functionalities include managing project progress and performance, evaluating the look-ahead level’s performance, measuring the reliability and stability of workflow, measuring the make-ready process, constraint management and measuring the quality of construction flow.

This research will assist the project team in getting a comprehensive sensemaking of planning and control systems and their functionalities to plan and control different dynamic aspects of the project.

The purpose of this paper is to study the implications of managing human resource (HR) flexibility and absorptive capacity (AC) of knowledge for the implementation of agile innovation methods in the new product development (NPD) process of manufacturing firms.

To achieve this, the authors have carried out a meta-analysis by defining first a research question that determines the design of bibliometric data and compilation. The study question is: How firms can support agile initiatives in innovation considering their HR flexibility and AC? The Web of Science Collection has been consulted in three steps with several search strings. The analysis of 161 references without duplicities in the period 1997–2021 provides the foundations for exploring the research question.

The authors propose research hypotheses to analyze the interrelationship between HR flexibility, AC and agile innovation according to the literature-based discussion. The authors have identified and develop metrics to pilot the transition to agile that have been also adapted to specific innovation departures points before the transition. The authors also propose and discuss some organizational changes and practical guidelines to pilot transitions to agile that should be useful to the firm’s strategic decision of implementing the most adequate type of agile innovation.

Even though flexibility and innovation are very important needs for many firms, agile schemes are still under-studied topics in the NPD of manufacturing firms. There are no specific studies in the literature that analyze the interrelationship between HR flexibility, AC and agile innovation, identifying common aspects and different approaches. Thus, this study is of original nature and the developed managerial tools, composed of metrics and organizational changes, will be useful to managers and permitted the authors to achieve the proposed goal.

As the number of web applications is increasing day by day web mining acts as an important tool to extract useful information from weblogs and analyse them according to the attributes and predict the usage of a website. The main aim of this paper is to inspect how process mining can be used to predict the web usability of hotel booking sites based on the number of users on each page, and the time of stay of each user. Through this paper, the authors analyse the web usability of a website through process mining by finding the web usability metrics. This work proposes an approach to finding the usage of a website using the attributes available in the weblog which predicts the actual footfall on a website.

PROM (Process Mining tool) is used for the analysis of the event log of a hotel booking site. In this work, authors have used a case study to apply the PROM (process mining tool) to pre-process the event log dataset for analysis to discover better-structured process maps than without pre-processing.

This article first provided an overview of process mining, then focused on web mining and later discussed process mining techniques. It also described different target languages: system nets (i.e. Petri nets with an initial and a final state), inductive miner and heuristic miner, graphs showing the change in behaviour of the dataset and predicting the outcome, that is the webpage having the maximum number of hits.

In this work, a case study has been used to apply the PROM (process mining tool) to pre-process the event log dataset for analysis to discover better-structured process maps than without pre-processing.

This paper evaluates the precision of four metasearch engines (MSEs) – DuckDuckGo, Dogpile, Metacrawler and Startpage, to determine which metasearch engine exhibits the highest level of precision and to identify the metasearch engine that is most likely to return the most relevant search results.

The research is divided into two parts: the first phase involves four queries categorized into two segments (4-Q-2-S), while the second phase includes six queries divided into three segments (6-Q-3-S). These queries vary in complexity, falling into three types: simple, phrase and complex. The precision, average precision and the presence of duplicates across all the evaluated metasearch engines are determined.

The study clearly demonstrated that Startpage returned the most relevant results and achieved the highest precision (0.98) among the four MSEs. Conversely, DuckDuckGo exhibited consistent performance across both phases of the study.

The study only evaluated four metasearch engines, which may not be representative of all available metasearch engines. Additionally, a limited number of queries were used, which may not be sufficient to generalize the findings to all types of queries.

The findings of this study can be valuable for accreditation agencies in managing duplicates, improving their search capabilities and obtaining more relevant and precise results. These findings can also assist users in selecting the best metasearch engine based on precision rather than interface.

The study is the first of its kind which evaluates the four metasearch engines. No similar study has been conducted in the past to measure the performance of metasearch engines.