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The purpose of this paper is to investigate the potential of altmetric and webometric indicators to aid with funding agencies’ evaluations of their funding schemes.

This paper analyses a range of altmetric and webometric indicators in terms of suitability for funding scheme evaluations, compares them to traditional indicators and reports some statistics derived from a pilot study with Wellcome Trust-associated publications.

Some alternative indicators have advantages to usefully complement scientometric data by reflecting a different type of impact or through being available before citation data.

The empirical part of the results is based on a single case study and does not give statistical evidence for the added value of any of the indicators.

A few selected alternative indicators can be used by funding agencies as part of their funding scheme evaluations if they are processed in ways that enable comparisons between data sets. Their evidence value is only weak, however.

This is the first analysis of altmetrics or webometrics from a funding scheme evaluation perspective.

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 demonstrates that aggregated data from the Repository Analytics and Metrics Portal (RAMP) have significant potential to analyze visibility and use of institutional repositories (IR) as well as potential factors affecting their use, including repository size, platform, content, device and global location. The RAMP dataset is unique and public.

The webometrics methodology was followed to aggregate and analyze use and performance data from 35 institutional repositories in seven countries that were registered with the RAMP for a five-month period in 2019. The RAMP aggregates Google Search Console (GSC) data to show IR items that surfaced in search results from all Google properties.

The analyses demonstrate large performance variances across IR as well as low overall use. The findings also show that device use affects search behavior, that different content types such as electronic thesis and dissertation (ETD) may affect use and that searches originating in the Global South show much higher use of mobile devices than in the Global North.

The RAMP relies on GSC as its sole data source, resulting in somewhat conservative overall numbers. However, the data are also expected to be as robot free as can be hoped.

This may be the first analysis of aggregate use and performance data derived from a global set of IR, using an openly published dataset. RAMP data offer significant research potential with regard to quantifying and characterizing variances in the discoverability and use of IR content.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-08-2020-0328

The purpose of this paper is to examine the extent to which HTTPS encryption and Google Analytics services have been implemented on academic library websites, and discuss the privacy implications of free services that introduce web tracking of users.

The home pages of 279 academic libraries were analyzed for the presence of HTTPS, Google Analytics services and privacy-protection features.

Results indicate that HTTPS implementation on library websites is not widespread, and many libraries continue to offer non-secured connections without an automatically enforced redirect to a secure connection. Furthermore, a large majority of library websites included in the study have implemented Google Analytics and/or Google Tag Manager, yet only very few connect securely to Google via HTTPS or have implemented Google Analytics IP anonymization.

Librarians are encouraged to increase awareness of this issue and take concerted and coherent action across five interrelated areas: implementing secure web protocols (HTTPS), user education, privacy policies, informed consent and risk/benefit analyses.

Third-party tracking of users is prevalent across the web, and yet few studies demonstrate its extent and consequences for academic library websites.

Institutional and commercial web profiles that provide biobibliographic information about researchers are used for promotional purposes but also as information sources. In the latter case, the profiles' (re)presentations of researchers may be used to assess whether a researcher can be trusted. The article introduces a conceptual framework of how trust in researchers may be formed based on how the researchers' experiences and achievements are mobilized on the profiles to tell a multifaceted story of the “self.”

The framework is an analytical product which draws on theories of trust as well as on previous research focused on academic web profiles and on researchers' perceptions of trust and credibility. Two dimensions of trust are identified as central to the theoretical construction of trust, namely competence and trustworthiness.

The framework outlines features of profile content and narrative that may influence the assessment of the profile and of the researcher's competence and trustworthiness. The assessment is understood as shaped by the frames of interpretation available to a particular audience.

The framework addresses the lack of a trust perspective in previous research about academic web profiles. It provides an analysis of how potential trust in the researcher may be formed on the profiles. An innovative contribution is the acknowledgement of both qualitative and quantitative indicators of trustworthiness and competence, including the richness of the story told about the “self.”

The purpose of the research was to find out if there are any differences in the readability score between abstracts published in scientific journals from library and information science with and without an impact factor. Therefore, the author made a comparison between the readability of abstracts from one journal with (Journal of Documentation) and one journal without (Knjižnica or Library) an impact factor.

As a measure of readability, the Flesch Reading Ease Readability Formula was used. Then, with the help of statistical experts, a comparison of the readability scores between the abstracts of two selected journals was performed.

The results showed that some statistically important differences exist between the abstracts published in the Journal of Documentation and Knjižnica. The statistically important differences were found in the number of words and sentences in abstracts and in the readability of abstracts included in the research. Therefore, it can be said that there exists a statistically important difference between abstracts with and without an impact factor.

The primary purpose was to find out whether there is a statistically important difference in the readability score of abstracts with and without an impact factor in the field of library and information science. Some similar research studies have been conducted in other scientific fields.

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.

Explore the use of Gender-Fair Language (GFL) by influencers on Instagram.

The clustering methodology. A digital Bag-of-Words (BoW) Method called GFL Clustering BoW Methodology to identify whether an inclusive marketing (IM) strategy can be used. Thus, this research has a methodological and practical contribution to increasing the number of marketing technology tools.

This study is original as it proposes an inclusive digital marketing strategy and contributes with methods associated with digital transfers in order to improve marketing strategies, tactics and operations for inclusive content with a data integrity approach.

Due to the limitations of the application programming interface (API) of the social network Instagram, a limited number of text data were used, which allowed for retrieving the last 12 publications of each studied profile. In addition, it should be considered that this study only includes the Spanish language and is applied to a sample of influencers from Chile.

The practical contribution of this study will lead to a key finding for the definition of communication strategies in both public and private organizations.

The originality of this work lies in its attractive implications for nonprofit and for-profit organizations, government bodies and private enterprises in the measurement of the success of campaigns with an IM communicational strategy and to incorporate inclusive and non-sexist content for their consumers so as to contribute to society.

本研究擬探究有影響力的人士在使用即時電報 (Instagram) 時、如何使用性別中立語言。

研究使用了聚類分析法;具體來說, 研究人員採用一個叫 GFL聚類詞袋法的數位詞袋分析法, 去確定研究可否使用信息管理策略。因此, 本研究在行銷科技方面、添加了一個工具, 就此而言, 本研究在學術的研究法和實務方面、均作出貢獻。

本研究建議了一個包括一切的數位行銷策略;研究亦構建了若干與數位傳輸有關的方法, 以能利用數據完整性的理念, 為行銷策略、行銷戰術和市場營銷, 在內容的全面包含度方面取得改善。

因為社交網站即時電報的應用程式介面有其局限, 故使用了少量的文本數據, 這可使每個被探討的傳略的最後12個發佈能被撿回。另外需注意的是、本研究只涵蓋西班牙語, 而且, 研究使用的樣本只是來自智利有影響力的人士。

本研究在實務方面的貢獻是、它為探討在公共機構和私營機構內使用的溝通策略的定義上、帶來重要的啟發和發現。

本研究的原創性在於它給營利和非營利組織、政府機關和私人企業帶來頗具吸引力的啟示。而這些啟示是與測量以包括一切的行銷溝通策略進行的專門活動是否成功有關的。另外, 涵蓋一切和無性別歧視的內容被納入供消費者使用, 以此為社會帶來裨益。

Sustainable customer relationship management (SCRM) is a combination of business strategy, customer-oriented business processes and computer systems that seeks to integrate sustainability into customer relationship management. The purpose of this paper is to contribute to the body of knowledge of marketing, business management and computer systems research domains by classifying in research categories the current state of knowledge on SCRM, by analysing the major research streams and by identifying a future research agenda in each research category.

To identify, select, collect, synthesise, analyse and evaluate all research published on SCRM, providing a complete insight in this research area, the PRISMA methodology, content analysis and bibliometric tools are used.

In total, 139 papers were analysed to assess the trend of the number of papers published and the number of citations of these papers; to identify the top contributing countries, authors, institutions and sources; to reveal the findings of the major research streams; to develop a classification framework composed by seven research categories (CRM as a key factor for enterprise sustainability, SCRM frameworks, SCRM computer tools and methods, case studies, SCRM and sustainable supply chain management, sustainable marketing and knowledge management) in which academics could expand SCRM research; and to establish future research challenges.

This paper have an important positive social and environmental impact for society because it will lead to an increase in the number of green and socially conscious customers with an ethical behavior, while also transforming business processes, products and services, making them more sustainable.

Customer relationship management in the age of sustainable development is an increasing research area. Nevertheless, to the authors' knowledge, there are no systematic literature reviews that identify the major research streams, develop a classification framework, analyse the evolution in this research field and propose a future research agenda.

The study aims to investigate the utilisation of open research data repositories (RDRs) for storing and sharing research data in higher learning institutions (HLIs) in Tanzania.

A survey research design was employed to collect data from postgraduate students at the Nelson Mandela African Institution of Science and Technology (NM-AIST) in Arusha, Tanzania. The data were collected and analysed quantitatively and qualitatively. A census sampling technique was employed to select the sample size for this study. The quantitative data were analysed using the Statistical Package for the Social Sciences (SPSS), whilst the qualitative data were analysed thematically.

Less than half of the respondents were aware of and were using open RDRs, including Zenodo, DataVerse, Dryad, OMERO, GitHub and Mendeley data repositories. More than half of the respondents were not willing to share research data and cited a lack of ownership after storing their research data in most of the open RDRs and data security. HILs need to conduct training on using trusted repositories and motivate postgraduate students to utilise open repositories (ORs). The challenges for underutilisation of open RDRs were a lack of policies governing the storage and sharing of research data and grant constraints.

Research data storage and sharing are of great interest to researchers in HILs to inform them to implement open RDRs to support these researchers. Open RDRs increase visibility within HILs and reduce research data loss, and research works will be cited and used publicly. This paper identifies the potential for additional studies focussed on this area.