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The purpose of this study are to identify the factors influencing the adoption of big data in libraries, determine the challenges causing the hindrance of big data implementation and reveal the best practices for the efficient adoption of big data in libraries.

A systematic literature review was applied to address the objectives of the study. Twenty-two studies published in peer-reviewed journals were selected to conduct the study.

The findings of the study revealed that decision-making, service enhancement, professional development and preservation factors influenced the adoption of big data technologies in libraries. The study also displayed that challenges of infrastructure, technical skills, data management and legal considerations caused barriers to the adoption of big data in libraries. Results also revealed that training and professional development, guidelines and policies establishment, leadership and strategic planning and resource allocation proved fruitful in the efficient adoption of big data applications in libraries.

The study offers theoretical implications for future investigators through the provision of innovative literature on the factors, challenges and best practices associated with big data in the context of librarianship. The study has also provided practical implications for management bodies by offering guidelines for the successful adoption of big data in libraries.

This study was designed to discover the readiness of the higher education commission (HEC)-recognized journals of Pakistan in terms of human, financial and technological resources, technical expertise, institutional support, availability of open access (OA) policy, availability of guidance and training, willingness, motivation and so on for OA journal publishing and to expose the challenges in OA journal publishing.

A quantitative research approach was used and a structured questionnaire was developed to meet the objectives of this study. A survey method was used to collect data from the editors of all 329 HEC-recognized journals in Pakistan.

The respondents of all the HEC-recognized journals of Pakistan are neutral and are not of the view that they have sufficient financial, human, technological/infrastructural resources and technical expertise to continue/initiate an OA journal publishing. ‘No academic reward’; and ‘no monetary reward for the editorial staff’ are both enormous challenges for OA journal publishing. The perceived challenges of OA have a negative impact on readiness for OA publishing. The readiness level of the respondents of the OA journals is higher as compared to the readiness level of the respondents of non-OA journals.

This study covered the lists of HEC-recognized journals of 2019. More studies may be conducted based on updated lists of HEC-recognized journals. Qualitative studies may also be conducted to discover the readiness of the HEC-recognized journals of Pakistan for OA journal publishing.

This study is the first comprehensive study on this phenomenon and is an effort to fill this gap to invigorate scholarly literature. It may attract the attention of policymakers, funding bodies, parent institutions of the journals and the HEC regarding the readiness of journals in terms of financial, human, technological/infrastructural resources, technical expertise of the journals and challenges of journals to prompt the OA journal publishing paradigm.

This paper focuses on image-to-text manuscript processing through Handwritten Text Recognition (HTR), a Machine Learning (ML) approach enabled by Artificial Intelligence (AI). With HTR now achieving high levels of accuracy, we consider its potential impact on our near-future information environment and knowledge of the past.

In undertaking a more constructivist analysis, we identified gaps in the current literature through a Grounded Theory Method (GTM). This guided an iterative process of concept mapping through writing sprints in workshop settings. We identified, explored and confirmed themes through group discussion and a further interrogation of relevant literature, until reaching saturation.

Catalogued as part of our GTM, 120 published texts underpin this paper. We found that HTR facilitates accurate transcription and dataset cleaning, while facilitating access to a variety of historical material. HTR contributes to a virtuous cycle of dataset production and can inform the development of online cataloguing. However, current limitations include dependency on digitisation pipelines, potential archival history omission and entrenchment of bias. We also cite near-future HTR considerations. These include encouraging open access, integrating advanced AI processes and metadata extraction; legal and moral issues surrounding copyright and data ethics; crediting individuals’ transcription contributions and HTR’s environmental costs.

Our research produces a set of best practice recommendations for researchers, data providers and memory institutions, surrounding HTR use. This forms an initial, though not comprehensive, blueprint for directing future HTR research. In pursuing this, the narrative that HTR’s speed and efficiency will simply transform scholarship in archives is deconstructed.

This study aims to assess the digital literacy competencies among students in higher learning institutions in Kazakhstan.

A survey design was used. Simple random sampling was used to draw sample. Primary data were collected using Web/online questionnaires (Google Form). A total of 370 online questionnaires were disseminated to the respondents to their email addresses. Quantitative data collected were analyzed using MS Excel 2010. Thus, descriptive statistics were computed and the results were further presented in tables, charts and figures.

Results reveal that students are very competent in using digital technologies to communicate and share their educational digital contents, whereas they indicate moderate competence and incompetence in other essential digital literacy skills crucial for their academic pursuits. Furthermore, this study revealed that students frequently use digital technologies for educational purposes, with statistical analysis [t(381) = 4.562, p < 0.00001, two-tailed] indicating a significant difference between the extent and purpose of their digital usage. Moreover, findings identified health issues, technical issues and the constantly changing of hardware and software as primary challenges faced students when engaging with digital technologies.

This study is new in the context of Kazakhstan analyzing the digital literacy competencies among students, with a particular focus on elucidating the five fundamental facets of such competencies. This study therefore, recommends the implementation of comprehensive and consistent training programs aimed at imparting necessary digital literacy skills to students.

This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

Factors like bid price, submission time, and number of bidders influence the procurement process in public projects. These factors and the award criteria may impact the project’s financial compliance. Predicting budget compliance in construction projects has been traditionally challenging, but Machine Learning (ML) techniques have revolutionised estimations.

In this study, Portuguese Public Procurement Data (PPPData) was utilised as the model’s input. Notably, this dataset exhibited a substantial imbalance in the target feature. To address this issue, the study evaluated three distinct data balancing techniques: oversampling, undersampling, and the SMOTE method. Next, a comprehensive feature selection process was conducted, leading to the testing of five different algorithms for forecasting budget compliance. Finally, a secondary test was conducted, refining the features to include only those elements that procurement technicians can modify while also considering the two most accurate predictors identified in the previous test.

The findings indicate that employing the SMOTE method on the scraped data can achieve a balanced dataset. Furthermore, the results demonstrate that the Adam ANN algorithm outperformed others, boasting a precision rate of 68.1%.

The model can aid procurement technicians during the tendering phase by using historical data and analogous projects to predict performance.

Although the study reveals that ML algorithms cannot accurately predict budget compliance using procurement data, they can still provide project owners with insights into the most suitable criteria, aiding decision-making. Further research should assess the model’s impact and capacity within the procurement workflow.

Previous research predominantly focused on forecasting budgets by leveraging data from the private construction execution phase. While some investigations incorporated procurement data, this study distinguishes itself by using an imbalanced dataset and anticipating compliance rather than predicting budgetary figures. The model predicts budget compliance by analysing qualitative and quantitative characteristics of public project contracts. The research paper explores various model architectures and data treatment techniques to develop a model to assist the Client in tender definition.

The digital marketing landscape is rapidly evolving, but the integration of visual content still heavily depends on human expertise. Driven by the quest for innovative marketing strategies that resonate with family-oriented consumers, this study seeks to bridge this gap by applying machine learning to analyze visual content in the maternity and baby care product sector.

This study incorporates a range of machine learning techniques – including open science framework feature detection, panoptic segmentation, customized instance segmentation, and face detection calculation methods – to analyze and predict the appeal of images, thereby enhancing user engagement and parent-child intimacy.

The exploration of various ML models, such as DT, LightGBM, RIPPER algorithm, and CNNs, has offered a comparative analysis that addresses a methodological gap in the existing literature, which frequently depends on isolated model evaluations. According to our quadrant analysis with respect to engagement rate and parent-child intimacy, the selection of a model for real-world applications depends on balancing performance and interpretability.

The proposed system offers a series of actionable recommendations designed to enhance customer engagement and foster brand loyalty. This study contributes to image design in maternity and baby care marketing and provides analytical insights for recommendation systems.

In this chapter, the authors critically examine the application of unmeasured latent method factors (ULMFs) in human resource and organizational behavior (HROB) research, focusing on addressing common method variance (CMV). The authors explore the development and usage of ULMF to mitigate CMV and highlight key debates concerning measurement error in the HROB literature. The authors also discuss the implications of biased effect sizes and how such bias can lead HR professionals to oversell interventions. The authors provide evidence supporting the effectiveness of ULMF when a specific assumption is held: a single latent method factor contributes to the data. However, the authors dispute this assumption, noting that CMV is likely multidimensional; that is, it is complex and difficult to fix with statistical methods alone. Importantly, the authors highlight the significance of maintaining a multidimensional view of CMV, challenging the simplification of a CMV as a single source. The authors close by offering recommendations for using ULMFs in practice as well as more research into more complex forms of CMV.

This study aims to analyze the adoption of open access (OA) publishing in social sciences within central universities in India, focusing on various aspects such as the growth of OA literature, the use of different OA routes and collaboration patterns in OA publications.

Ten central universities were selected based on their rankings in the National Institute Ranking Framework 2022. Data on OA publishing in social sciences were collected from the Social Science Citation Index of the Web of Science (WoS) database using the advanced search query “(CU=India OR AD=India) AND PY=(2003–2022) NOT PY=(2023).” Data analysis was conducted using MS Excel (v16.0), BibExcel (version 2017), Biblioshiny (version 4.1.2) and Google Open Refine (version 3.7).

The study found that 30.40% of total publications were OA, with BHU as leading institute in OA publishing. OA publishing in social sciences saw a consistent increase, peaking in 2022 with 209 publications. “Sustainability” and “Plos One” were among the top ten journals, with 103 and 34 OA papers, respectively. OA publications had a higher mean citation rate than closed access publications. Collaboration with seven and nine authors had higher mean citation rates, while six-author collaborations were lower. Indian researchers received the most citations collaborating with the USA, UK and Australia. The Netherlands and Saudi Arabia received the fewer citations, when collaborating with Indian authors.

The study’s main limitation is its reliance on WoS data, excluding many OA publications from smaller or specialized journals. Additionally, the focus on high-ranked central universities may not represent the entire academic landscape, as OA publishing patterns vary across other institutions and disciplines.

The study’s findings suggest that advancing OA publishing in social sciences at Indian universities requires raising awareness of OA concepts, enhancing institutional support and policies and informing researchers about funding opportunities. Emphasizing Gold OA and funding publication fees can broaden access to research. Universities with low OA ratios should adopt similar policies, mandate public research deposits and develop technical infrastructure. Encouraging multi-author collaborations can boost research impact and citation rates. Insights from the study can help institutions and policymakers shape effective OA strategies, enhancing the visibility and impact of social science research.

This is the first study analyzing the adoption of OA in the field of social sciences in high-ranked central universities in India. It has implications for promoting OA and increasing accessibility to research outputs. Universities with higher OA ratios can lead in this regard and encourage others to adopt similar practices for overall OA growth.

This study examined brand hate within the context of the (German) Bundesliga and (Australian) National Rugby League (NRL). The study pursued two research questions: (1) What types of brand hate were expressed towards the Bundesliga and the NRL? (2) To what extent did hateful comments attract more likes than non-hateful comments?

Brand hate was studied in the context of competition restrictions in 2020 due to the Covid-19 pandemic. We analysed reader comments posted below online articles published in three German (119 articles and 8,975 comments) and three Australian online newspaper articles (116 articles and 4,858 reader comments). The data were analysed deductively.

Non-parametric tests found that all types of brand hate were expressed. Approximately 85% of the hateful comments were mild, or more specifically, cold (n = 445 or approximately 53%), or cool (n = 250 or approximately 30%), or hot (n = 20 or approximately 2%). Hateful comments attracted more likes than non-hateful comments.

This study advances our understanding of how negative brand perceptions underpin an extreme negative emotional reaction in the form of brand hate. The empirical evidence enables brand managers to better address disgusted, angry, or contemptuous consumers (or stakeholders) and consider whether the feeling is enduring, strong or weak, and linked to either aggressive or passive behaviours.