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The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned from single modalities, such as text, images, audio and video, to integrated multimodal forms. This paper identifies key trends, gaps and areas of focus in the field. Furthermore, it proposes a theoretical organizational framework based on deep learning to address the challenges of managing archives in the era of big data.

Via a comprehensive systematic literature review, the authors investigate the field of multimodal archive resource organization and the application of deep learning techniques in archive organization. A systematic search and filtering process is conducted to identify relevant articles, which are then summarized, discussed and analyzed to provide a comprehensive understanding of existing literature.

The authors' findings reveal that most research on multimodal archive resources predominantly focuses on aspects related to storage, management and retrieval. Furthermore, the utilization of deep learning techniques in image archive retrieval is increasing, highlighting their potential for enhancing image archive organization practices; however, practical research and implementation remain scarce. The review also underscores gaps in the literature, emphasizing the need for more practical case studies and the application of theoretical concepts in real-world scenarios. In response to these insights, the authors' study proposes an innovative deep learning-based organizational framework. This proposed framework is designed to navigate the complexities inherent in managing multimodal archive resources, representing a significant stride toward more efficient and effective archival practices.

This study comprehensively reviews the existing literature on multimodal archive resources organization. Additionally, a theoretical organizational framework based on deep learning is proposed, offering a novel perspective and solution for further advancements in the field. These insights contribute theoretically and practically, providing valuable knowledge for researchers, practitioners and archivists involved in organizing multimodal archive resources.

This study aims to adjust Thesis-type properties on Schema.org using metadata models and standards (MS) (Bibframe, electronic thesis and dissertations [ETD]-MS, Common European Research Information Format [CERIF] and Dublin Core [DC]) to enrich the Thesis-type properties for better description and processing on the Web.

This study is applied, descriptive analysis in nature and is based on content analysis in terms of method. The research population consisted of elements and attributes of the metadata model and standards (Bibframe, ETD-MS, CERIF and DC) and Thesis-type properties in the Schema.org. The data collection tool was a researcher-made checklist, and the data collection method was structured observation.

The results show that the 65 Thesis-type properties and the two levels of Thing and CreativeWork as its parents on Schema.org that corresponds to the elements and attributes of related models and standards. In addition, 12 properties are special to the Thesis type for better comprehensive description and processing, and 27 properties are added to the CreativeWork type.

Enrichment and expansion of Thesis-type properties on Schema.org is one of the practical applications of the present study, which have enabled more comprehensive description and processing and increased access points and visibility for ETDs in the environment Web and digital libraries.

This study has offered some new Thesis type properties and CreativeWork levels on Schema.org. To the best of the authors’ knowledge, this is the first time this issue is investigated.

This study aims to provide insight into Library and Information Science (LIS) research in India using scientometric approaches. Web of Science (WoS) and SCOPUS databases were used for data retrieval. The study examines productivity in terms of source types, gender distribution, document formats, authorship and other factors. In addition, this study sought to identify trends or patterns in the research preferences of LIS scientists through text analysis.

Data were downloaded from the WoS and Scopus databases over 22 years and analysed using VOSviewer, Orange, Biblioshiny and CRExplorer softwares.

The findings reveal that 5,692 out of the 9,384 documents in both databases underwent the final examination. In total, 466 different sources produced all of those papers. Author analysis revealed that 6,603 different authors authored 5,692 documents. There were 4,209 male and 1,063 female authors. Furthermore, India shares maximum collaborations with the USA and England. The spectrogram features nine significant peaks corresponding to Lotka’s, Bradford’s and similar laws. Text analysis revealed that Indian LIS researchers have consistently investigated open access and digital or open libraries.

The findings of this study will provide readers with a better understanding of India’s contribution to LIS. In addition, the study will help academics identify research gaps and undiscovered areas in the Indian context that require further investigation.

Not many studies highlight Indian research trends and international collaboration in LIS. This study highlights research trends, collaboration and gender productivity in LIS. The most cited references and trending topics were also identified using reference publication year spectroscopy and text analysis techniques.

The purpose of this study is to comprehend how AI aids marketers in engaging customers and generating value for the company by way of customer engagement (CE). CE is a popular area of research for scholars and practitioners. One area of research that could have far-reaching ramifications with regard to strengthening CE is artificial intelligence (AI). Consequently, it becomes extremely important to understand how AI is helping the marketer reach customers and create value for the firm via CE.

A detailed approach using both systematic review and bibliometric analysis was used. It involved identifying key research areas, the most influential authors, studies, journals, countries and organisations. Then, a comprehensive analysis of 50 papers was carried out in the four identified clusters through co-citation analysis. Furthermore, a content analysis of 42 articles for the past six years was also conducted.

Emerging themes explored through cluster analysis are CE concepts and value creation, social media strategies, big data innovation and significance of AI in tertiary industry. Identified themes for content analysis are CE conceptualisation, CE behaviour in social media, CE role in value co-creation and CE via AI.

CE has emerged as a topic of great interest for marketers in recent years. With the rapid growth of digital media and the spread of social media, firms are now embarking on new online strategies to promote CE (Javornik and Mandelli, 2012). In this review, the authors have thoroughly assessed multiple facets of prior research papers focused on the utilisation of AI in the context of CE. The existing research papers highlighted that AI-powered chatbots and virtual assistants offer real-time interaction capabilities, swiftly addressing inquiries, delivering assistance and navigating customers through their experiences (Cheng and Jiang, 2022; Naqvi et al., 2023). This rapid and responsive engagement serves to enrich the customer’s overall interaction with the business. Consequently, this research can contribute to a comprehensive knowledge of how AI is assisting marketers to reach customers and create value for the firm via CE. This study also sheds light on both the attitudinal and behavioural aspects of CE on social media. While existing CE literature highlights the motivating factors driving engagement, the study underscores the significance of behavioural engagement in enhancing firm performance. It emphasises the need for researchers to understand the intricate dynamics of engagement in the context of hedonic products compared to utilitarian ones (Wongkitrungrueng and Assarut, 2020). CEs on social media assist firms in using their customers as advocates and value co-creators (Prahalad and Ramaswamy, 2004; Sawhney et al., 2005). A few of the CE themes are conceptual in nature; hence, there is an opportunity for scholarly research in CE to examine the ways in which AI-driven platforms can effectively gather customer insights. As per the prior relationship marketing studies, it is evident that building relationships reduces customer uncertainty (Barari et al., 2020). Therefore, by using data analysis, businesses can extract valuable insights into customer preferences and behaviour, equipping them to engage with customers more effectively.

The rapid growth of social media has enabled individuals to articulate their thoughts, opinions and emotions related to a brand, which creates a large amount of data for VCC. Meanwhile, AI has emerged as a radical way of providing value content to users. It expands on a broader concept of how software and algorithms work like human beings. Data collected from customer interactions are a major prerequisite for efficiently using AI for enhancing CE. AI not only reduces error rates but, at the same time, helps human beings in decision-making during complex situations. Owing to built-in algorithms that analyse large amounts of data, companies can inspect areas that require improvement in real time. Time and resources can also be saved by automating tasks contingent on customer responses and insights. AI enables the analysis of customer data to create highly personalised experiences. It can also forecast customer behaviour and trends, helping businesses anticipate needs and preferences. This enables proactive CE strategies, such as targeted offers or timely outreach. Furthermore, AI tools can analyse customer feedback and sentiment across various channels. This feedback can be used to make necessary improvements and address concerns promptly, ultimately fostering stronger customer relationships. AI can facilitate seamless engagement across multiple digital channels, ensuring that customers can interact with a brand through their preferred means, be it social media, email, or chat. Consequently, this research proposes that practitioners and companies can use analysis performed by AI-enabled systems on CEB, which can assist companies in exploring the extent to which each product influences CE. Understanding the importance of these attributes would assist companies in developing more memorable CE features.

This study examines how prominent CE and AI are in academic research on social media by identifying research gaps and future developments. This research provides an overview of CE research and will assist academicians, regulators and policymakers in identifying the important topics that require investigation.

The purpose of this study is to conduct a bibliometric analysis to discover the published information on the subject of “Library Automation Software.”

This study is based on bibliometric research to analyze the growth of literature on the subject area “Library Automation Software.” The data were retrieved from the leading database Web of Science to analyze the scholarly publications on the topic during 2001–2022. To analyze the bibliometric data on the subject, the VOSviewer, Biblioshiny and MS Excel software were used.

Results manifest that the topic of “Library Management Systems” has been greatly used by several authors as a title to publish their research work. The USA emerged as the most productive country. The most used keywords to retrieve literature on the topic were “Library Automation,” “Library Management Systems,” “KOHA,” “Integrated Library Systems” and “Open Source Software.”

To the best of the authors’ knowledge, this is the first bibliometric study in Pakistan that has been conducted to measure the productivity of literature at the international level on library automation.

This paper aims to check the fragmentation of knowledge across multiple sources of evidence, identifying, scrutinizing and outlining suggestions concerning the challenges researchers face when using multiple sources of data to identify studies.

This study produced a comprehensive database of 15,848 items from Scopus, Web of Science and EBSCO on the organizational growth and decline topics. The analyses carried out to check the fragmentation of scientific knowledge and the challenges in identifying studies have made use of the basic data frame functions in R’s language and the Bibliometrix and Corpus R’s packages.

This study confirms the fragmentation of scientific knowledge as well as it identifies the following challenges: missing information in key fields, nonexistence of standards in terminology, limitations on data extraction, duplicates and multiple formats of cited reference. Additionally, it suggests practical coping procedures and advances implications for stakeholders and an agenda for future research.

This study provides valuable and practical examples with empirical confirmation of scientific knowledge fragmentation and offers an integrated view of many challenges in the process of identifying studies. Moreover, by offering suggestions to address these challenges, this study not only offers a practical guide to scientific researchers but also initiates a wider discussion regarding knowledge organizing in social sciences.

The purpose of this study is to analyse and compile the literature on various option pricing models (OPM) or methodologies. The report highlights the gaps in the existing literature review and builds recommendations for potential scholars interested in the subject area.

In this study, the researchers used a systematic literature review procedure to collect data from Scopus. Bibliometric and structured network analyses were used to examine the bibliometric properties of 864 research documents.

As per the findings of the study, publication in the field has been increasing at a rate of 6% on average. This study also includes a list of the most influential and productive researchers, frequently used keywords and primary publications in this subject area. In particular, Thematic map and Sankey’s diagram for conceptual structure and for intellectual structure co-citation analysis and bibliographic coupling were used.

Based on the conclusion presented in this paper, there are several potential implications for research, practice and society.

This study provides useful insights for future research in the area of OPM in financial derivatives. Researchers can focus on impactful authors, significant work and productive countries and identify potential collaborators. The study also highlights the commonly used OPMs and emerging themes like machine learning and deep neural network models, which can inform practitioners about new developments in the field and guide the development of new models to address existing limitations.

The accurate pricing of financial derivatives has significant implications for society, as it can impact the stability of financial markets and the wider economy. The findings of this study, which identify the most commonly used OPMs and emerging themes, can help improve the accuracy of pricing and risk management in the financial derivatives sector, which can ultimately benefit society as a whole.

It is possibly the initial effort to consolidate the literature on calibration on option price by evaluating and analysing alternative OPM applied by researchers to guide future research in the right direction.

This paper aims to introduce the experience-dominant (Ex-D) logic model, which synthesizes the creation, perceptions and outcomes of Ex-D logic. It is designed to offer valuable insights for strategic managerial applications and future research directions.

Employing a qualitative approach by using eight selected product launch events from reviewed 100 event videos and 55 in-depth interviews with industrial managers to develop an Ex-D logic model, and data were coded and analysed via NVivo.

Results show that the firm’s Ex-D logic is operationalized as the mentalizing of the three types of customer needs (service competence, hedonic excitations and meaning making), the materializing of three types of customer experiences and customer journeys (service experience, hedonic experience and brand experience) and the moderating of three types of customer values (service values, hedonic values and brand values).

This study has implications for adding new insights into existing theory on dominant logic and customer experience management and also offers actionable recommendations for managerial applications.

This study sheds light on the importance of Ex-D logic from a strategic point of view and provides an organic view of the firm. It distinguishes firm perspective from customer perspective, firm experience from customer experience and firm journey from consumer journey.

This study aims to explore the findings related to data literacy skills for students to succeed in the digital age labor market and the role of university-industry collaborations (UICs) in the co-design and co-delivery of curriculum for the development of students’ data literacy.

The study uses an interview-based research methodology to gather insights from industry partners and stakeholders. The interviews focus on identifying key data literacy skills, understanding the significance of these skills and exploring the role of UICs in enhancing students’ data literacy.

The findings reveal several important data literacy skills for students. The most commonly mentioned skills include data evaluation/analysis, identifying the relevance of data and data protection in a sensitive manner. Participants also emphasized the importance of recognizing the interrelationships among data, adapting data across different contexts and strategically combining diverse data. The study emphasizes the role of universities in providing a well-rounded educational setting that fosters the development of data literacy skills. Additionally, it highlights the value of practical collaborations between universities and industries, enabling students to apply theoretical knowledge in real-world contexts.

The study highlights the interconnected nature of various data skills and emphasizes the significance of data literacy in navigating the complexities of the digital age labor market. It also sheds light on the role of UICs in codesigning and codelivering curricula to enhance students’ data literacy. The findings provide valuable insights into the practical implications for UICs in preparing students for the data-driven job market.

Despite being an authentic carrier of various cultural practices, the human body is often underutilised to access the knowledge of human body. Digital inventions today have created new avenues to open up cultural data resources, yet mainly as apparatuses for well-annotated and object-based collections. Hence, there is a pressing need for empowering the representation of intangible expressions, particularly embodied knowledge within its cultural context. To address this issue, the authors propose to inspect the potential of machine learning methods to enhance archival knowledge interaction with intangible cultural heritage (ICH) materials.

This research adopts a novel approach by combining movement computing with knowledge-specific modelling to support retrieving through embodied cues, which is applied to a multimodal archive documenting the cultural heritage (CH) of Southern Chinese martial arts.

Through experimenting with a retrieval engine implemented using the Hong Kong Martial Arts Living Archive (HKMALA) datasets, this work validated the effectiveness of the developed approach in multimodal content retrieval and highlighted the potential for the multimodal's application in facilitating archival exploration and knowledge discoverability.

This work takes a knowledge-specific approach to invent an intelligent encoding approach through a deep-learning workflow. This article underlines that the convergence of algorithmic reckoning and content-centred design holds promise for transforming the paradigm of archival interaction, thereby augmenting knowledge transmission via more accessible CH materials.