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Completing construction projects within the planned schedule has widely been considered as one of the major project success factors. This study investigates the use of technologies to address delays in construction projects and aims to address three research questions (1) to identify the adopted technologies and proposed solutions in the literature, (2) to explore the reasons why the delays cannot be prevented despite disruptive technologies and (3) to determine the major strategies to prevent delays in construction projects.

In total, 208 research articles that used innovative technologies, methods, or tools to avoid delays in construction projects were investigated by conducting a comprehensive literature review. An elaborative content analysis was performed to cover the implemented technologies and their transformation, highlighted research fields in relation to selected technologies, focused delay causes and corresponding delay mitigation strategies and emphasized project types with specific delay causes. According to the analysis results, a typological framework with appropriate technological means was proposed.

The findings revealed that several tools such as planning, imaging, geo-spatial data collection, machine learning and optimization have widely been adopted to address specific delay causes. It was also observed that strategies to address various delay causes throughout the life cycle of construction projects have been overlooked in the literature. The findings of the present research underpin the trends and technological advances to address significant delay causes.

Despite the technological advancements in the digitalization era of Industry 4.0, many construction projects still suffer from poor schedule performance. However, the reason of this is questionable and has not been investigated thoroughly.

This paper constitutes the first academic study to be made of Google Discover as applied to online journalism.

This paper constitutes the first academic study to be made of Google Discover as applied to online journalism. The study involved conducting 61 semi-structured interviews with experts that are representative of a range of different professional profiles within the fields of journalism and search engine positioning (SEO) in Brazil, Spain and Greece. Based on the data collected, the authors created five semantic categories and compared the experts' perceptions in order to detect common response patterns.

This study results confirm the existence of different degrees of convergence and divergence in the opinions expressed in these three countries regarding the main dimensions of Google Discover, including specific strategies using the feed, its impact on web traffic, its impact on both quality and sensationalist content and on the degree of responsibility shown by the digital media in its use. The authors are also able to propose a set of best practices that journalists and digital media in-house web visibility teams should take into account to increase their probability of appearing in Google Discover. To this end, the authors consider strategies in the following areas of application: topics, different aspects of publication, elements of user experience, strategic analysis and diffusion and marketing.

Although research exists on the application of SEO to different areas, there have not, to date, been any studies examining Google Discover.

The peer-review history for this article is available at: https://publons.com/publon/10.1108/OIR-10-2022-0574

The COVID-19 pandemic triggered an increase in online traffic, with many assuming that this technology would facilitate coping through active social connections. This study aims to interrogate the nature of this traffic-engagement relationship by distinguishing between passive (e.g. browsing) and active (e.g. reacting, commenting and sharing) engagement, and examining behavioral shifts across platforms.

Three field studies assessed changes in social media engagement during the COVID-19 pandemic. These studies included social media engagement with the most followed accounts (Twitter), discussion board commenting (Reddit) and news content sharing (Facebook).

Even though people spent more time online during the pandemic, the current research finds people were actively engaging less. Users were reacting less to popular social media accounts, commenting less on discussion boards and even sharing less news content.

While the current work provides a systematic observation of engagement during a global crisis, it does not claim causality based on its correlational nature. Future research should test potential mechanisms (e.g. anxiety, threat and privacy) to draw causal inference and identify possible interventions.

The pandemic shed light on a complex systemic issue: the misunderstanding and oversimplification of how online platforms facilitate social cohesion. It encourages thoughtful consideration of online social dynamics, emphasizing that not all engagement is equal and that the benefits of connection may not always be realized as expected.

This research provides a postmortem on the traffic-engagement relationship, highlighting that increased online presence does not necessarily translate to active social connection, which might help explain the rise in mental health issues that emerged from the pandemic.

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.

Social media are increasingly being used in teaching and learning in higher education. This paper aims to explore multiple case studies to better understand how instructors decide to incorporate social media into learning.

This qualitative case study used the technology acceptance model (TAM) to explore five instructors' use of social media for teaching and learning, particularly the pedagogical reasons and goals driving their use of social media. Participant interviews, course documentation and social media observation data were collected to answer the research questions.

Findings suggest that an instructor's social media knowledge and awareness of instructional goals are important for the use of social media in learning. Three pedagogical objectives of the use of social media were found across five participants: collaborative learning, dialog and discussion, and authentic learning.

Previous studies have explored potential pedagogical uses of social media tools, however studies that attempt to understand how and why instructors decide to use particular social media tools are underreported.

Disinformation on social media is a serious issue. This study examines the effects of disinformation on COVID-19 vaccination decision-making to understand how social media users make healthcare decisions when disinformation is presented in their social media feeds. It examines trust in post owners as a moderator on the relationship between information types (i.e. disinformation and factual information) and vaccination decision-making.

This study conducts a scenario-based web survey experiment to collect extensive survey data from social media users.

This study reveals that information types differently affect social media users' COVID-19 vaccination decision-making and finds a moderating effect of trust in post owners on the relationship between information types and vaccination decision-making. For those who have a high degree of trust in post owners, the effect of information types on vaccination decision-making becomes large. In contrast, information types do not affect the decision-making of those who have a very low degree of trust in post owners. Besides, identification and compliance are found to affect trust in post owners.

This study contributes to the literature on online disinformation and individual healthcare decision-making by demonstrating the effect of disinformation on vaccination decision-making and providing empirical evidence on how trust in post owners impacts the effects of information types on vaccination decision-making. This study focuses on trust in post owners, unlike prior studies that focus on trust in information or social media platforms.

This study aims to conceptualise a customer-centric model based on an online customer experience (OCE) construct, mediated by e-loyalty (EL) and e-trust (ET), to improve the continuous usage intention (CUI) of Indian digital banks from Generation Y and Z perspectives.

This study used an online survey method to gather data from a sample of 466 digital banking users, from which usable questionnaires were obtained. The obtained data were subjected to thorough analysis using PLS-SEM to further study the research hypotheses.

The main factors that determine digital banks’ OCE are perceived enjoyment, e-service quality, information quality and e-convenience. Additionally, relevant constructs were evaluated using an importance-performance map analysis.

This study used convenience sampling for the urban population using digital banking; therefore, the outcome may be generalised to a limited extent. It would be valuable to imitate studies in other countries to strengthen digital banking further.

There is a lack of research on digital banking and OCE in India; thus, this study helps rectify this issue while providing valuable insights. This study differs from others in that it examines the connections between OCE, EL, ET and the bottom line of financial institutions, using these factors as dependent variables instead of traditional measures.

Wikipedia's inclusive editorial policy permits unrestricted participation, enabling individuals to contribute and disseminate their expertise while drawing upon a multitude of external sources. News media outlets constitute nearly one-third of all citations within Wikipedia. However, embracing such a radically open approach also poses the challenge of the potential introduction of biased content or viewpoints into Wikipedia. The authors conduct an investigation into the integrity of knowledge within Wikipedia, focusing on the dimensions of source political polarization and trustworthiness. Specifically, the authors delve into the conceivable presence of political polarization within the news media citations on Wikipedia, identify the factors that may influence such polarization within the Wikipedia ecosystem and scrutinize the correlation between political polarization in news media sources and the factual reliability of Wikipedia's content.

The authors conduct a descriptive and regression analysis, relying on Wikipedia Citations, a large-scale open dataset of nearly 30 million citations from English Wikipedia. Additionally, this dataset has been augmented with information obtained from the Media Bias Monitor (MBM) and the Media Bias Fact Check (MBFC).

The authors find a moderate yet significant liberal bias in the choice of news media sources across Wikipedia. Furthermore, the authors show that this effect persists when accounting for the factual reliability of the news media.

The results contribute to Wikipedia’s knowledge integrity agenda in suggesting that a systematic effort would help to better map potential biases in Wikipedia and find means to strengthen its neutral point of view policy.

This study employs bibliometric analysis to map the research landscape of social media trending topics during the COVID-19 pandemic. The authors aim to offer a comprehensive review of the predominant research organisations and countries, key themes and favoured research methodologies pertinent to this subject.

The authors extracted data on social media trending topics from the Web of Science Core Collection database, spanning from 2009 to 2022. A total of 1,504 publications were subjected to bibliometric analysis, utilising the VOSviewer tool. The study analytical process encompassed co-occurrence, co-authorship, citation analysis, field mapping, bibliographic coupling and co-citation analysis.

Interest in social media research, particularly on trending topics during the COVID-19 pandemic, remains high despite signs of the pandemic stabilising globally. The study predominantly addresses misinformation and public health communication, with notable focus on interactions between governments and the public. Recent studies have concentrated on analysing Twitter user data through text mining, sentiment analysis and topic modelling. The authors also identify key leading organisations, countries and journals that are central to this research area.

Diverging from the narrow focus of previous literature reviews on social media, which are often confined to particular fields or sectors, this study offers a broad view of social media's role, emphasising trending topics. The authors demonstrate a significant link between social media trends and public events, such as the COVID-19 pandemic. The paper discusses research priorities that emerged during the pandemic and outlines potential methodologies for future studies, advocating for a greater emphasis on qualitative approaches.

The peer-review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2023-0194.