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The purpose of this study is to provide a systematic literature review on taxonomy alignment methods in information science to explore the common research pipeline and characteristics.

The authors implement a five-step systematic literature review process relating to taxonomy alignment. They take on a knowledge organization system (KOS) perspective, and specifically examining the level of KOS on “taxonomies.”

They synthesize the matching dimensions of 28 taxonomy alignment studies in terms of the taxonomy input, approach and output. In the input dimension, they develop three characteristics: tree shapes, variable names and symmetry; for approach: methodology, unit of matching, comparison type and relation type; for output: the number of merged solutions and whether original taxonomies are preserved in the solutions.

The main research implications of this study are threefold: (1) to enhance the understanding of the characteristics of a taxonomy alignment work; (2) to provide a novel categorization of taxonomy alignment approaches into natural language processing approach, logic-based approach and heuristic-based approach; (3) to provide a methodological guideline on the must-include characteristics for future taxonomy alignment research.

There is no existing comprehensive review on the alignment of “taxonomies”. Further, no other mapping survey research has discussed the comparison from a KOS perspective. Using a KOS lens is critical in understanding the broader picture of what other similar systems of organizations are, and enables us to define taxonomies more precisely.

Knowledge graphs (KGs) are structured knowledge bases that represent real-world entities and are used in a variety of applications. Many of them are created and curated from a combination of automated and manual processes. Microdata embedded in Web pages for purposes of facilitating indexing and search engine optimization are a potential source to augment KGs under some assumptions of complementarity and quality that have not been thoroughly explored to date. In that direction, this paper aims to report results on a study that evaluates the potential of using microdata extracted from the Web to augment the large, open and manually curated Wikidata KG for the domain of touristic information. As large corpora of Web text is currently being leveraged via large language models (LLMs), these are used to compare the effectiveness of the microdata enhancement method.

The Schema.org taxonomy was used as the source to determine the annotation types to be collected. Here, the authors focused on tourism-related pages as a case study, selecting the relevant Schema.org concepts as point of departure. The large CommonCrawl resource was used to select those annotations from a large recent sample of the World Wide Web. The extracted annotations were processed and matched with Wikidata to estimate the degree to which microdata produced for SEO might become a valuable resource to complement KGs or vice versa. The Web pages themselves can also serve as a context to produce additional metadata elements using them as context in pipelines of an existing LLMs. That way, both the annotations and the contents itself can be used as sources.

The samples extracted revealed a concentration of metadata annotations in only a few of the relevant Schema.org attributes and also revealed the possible influence of authoring tools in a significant fraction of microdata produced. The analysis of the overlapping of attributes in the sample with those of Wikidata showed the potential of the technique, limited by the disbalance of the presence of attributes. The combination of those with the use of LLMs to produce additional annotations demonstrates the feasibility of the approach in the population of existing Wikidata locations. However, in both cases, the effectiveness appears to be lower in the cases of less content in the KG, which are arguably the most relevant when considering the scenario of an automated population approach.

The research reports novel empirical findings on the way touristic annotations with a SEO orientation are being produced in the wild and provides an assessment of their potential to complement KGs, or reuse information from those graphs. It also provides insights on the potential of using LLMs for the task.

Localization, glocalization, and standardization advertising strategies have scarcely been examined in the context of internationally acknowledged heritage products aimed at young domestic consumers in emerging markets. This study investigated two essential advertising cues: endorser nationality (local vs Western) and language (local vs English). National pride and gender effects were also analyzed.

Eight brochure types were constructed to represent localized, glocalized, and standardized print advertisements and examine their effects on brand image and purchase intention. MANOVA, MANCOVA, and moderated mediation analysis were employed to test the model.

The localization presenting same-sex endorsement is the best fit for promoting an internationally acknowledged heritage product to young, educated domestic consumers who have a low-to-moderate level of national pride (NP).

This study provides theoretical implications in localization, NP, and gender effect in ad strategy.

This study fills a literature gap regarding the effects of localization, glocalization, and standardization advertising strategies on culturally bound heritage products aimed at young consumers in emerging markets. The moderating effect of NP adds to the novelty of this study.

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.

Although there is a growing body of literature on how celebrity involvement impacts the effectiveness of destination marketing, the underlying mechanisms of that relationship are still underexplored. Based on the affect transfer and meaning transfer theories, this study aims to examine the impact of celebrity attachment on customer delight toward K-culture and K-culture attachment, affective and cognitive images of Korea, and the intention to visit Korea.

Online survey data were collected from 2,614 US residents, representing various demographic characteristics. For the data analysis, the partial least squares-structural equation modeling was conducted to evaluate the structural model and test the hypotheses.

The results showed that celebrity attachment is positively related to customer delight toward K-culture and K-culture attachment, which, in turn, positively influences affective and cognitive images of Korea. Additionally, K-culture attachment positively influences cognitive and affective images of Korea, which are positively related to the intention to visit Korea.

By using the affect transfer theory and meaning transfer theory, this study provides valuable insights into how consumer’s attachment to celebrities has spillover effects on the decision-making process. This study also adds a new concept, customer delight connected to cultural experience, in the context of destination marketing.

By understanding the importance and influence of people’s intimacy with media characters, practitioners can apply parasocial relationship theory, affect transfer theory and meaning transfer theory to their marketing strategies.

As one of the few empirical studies that examines the impact of celebrity attachment on consumers’ perceptions and behaviors, this study can make significant contributions to the destination marketing literature.

Celebrities communicating about environmental sustainability on social media have the power to inspire young adults to engage in pro-environmental behavior, such as reducing their consumption behavior or only buying local and organic food. However, at the same time, celebrities’ carbon-rich and luxurious lifestyles might generate skepticism when they preach about environmental action. Thus, this study aims to shed light on the effects of celebrity pro-environmental messages on young adults’ perceived authenticity and greenwashing and, subsequently, on young adults’ pro-environmental behavior. Moreover, this study examined the moderation effect of congruent (vs incongruent) messages in the celebrity’s social media profile depicting an environmentally friendly (vs unfriendly) lifestyle.

The authors conducted a 3 (celebrity pro-environmental messages: with concrete action claim vs without vs control group) x 2 (celebrity message-lifestyle congruence: congruent vs incongruent) between-subjects experimental study (N = 400) with young adults (16–26 years old).

Results showed a significant positive effect of celebrity pro-environmental messages with concrete green action claims on authenticity perceptions only when the social media profile depicted a congruent environmentally friendly lifestyle. Moreover, higher perceived authenticity of the celebrity by social media audiences led to a higher likelihood of young adults’ engagement in pro-environmental behavior.

To the best of the authors’ knowledge, this study is the first to consider celebrity message characteristics and young adults’ perceptions of authenticity and greenwashing when investigating the effects of celebrity pro-environmental messages on young adults’ pro-environmental behavior.

The purpose of this work is to explore the new possibilities enabled by the recent introduction of RDF-star, an extension that allows for statements about statements within the Resource Description Framework (RDF). Alongside Named Graphs, this approach offers opportunities to leverage a meta-level for data modeling and data applications.

In this extended paper, the authors build onto three modeling use cases published in a previous paper: (1) provide provenance information, (2) maintain backwards compatibility for existing models, and (3) reduce the complexity of a data model. The authors present two scenarios where they implement the use of the meta-level to extend a data model with meta-information.

The authors present three abstract patterns for actively using the meta-level in data modeling. The authors showcase the implementation of the meta-level through two scenarios from our research project: (1) the authors introduce a workflow for triple annotation that uses the meta-level to enable users to comment on individual statements, such as for reporting errors or adding supplementary information. (2) The authors demonstrate how adding meta-information to a data model can accommodate highly specialized data while maintaining the simplicity of the underlying model.

Through the formulation of data modeling patterns with RDF-star and the demonstration of their application in two scenarios, the authors advocate for data modelers to embrace the meta-level.

With RDF-star being a very new extension to RDF, to the best of the authors’ knowledge, they are among the first to relate it to other meta-level approaches and demonstrate its application in real-world scenarios.

This study aims to follow a rigorous approach to identify, critically analyze and synthesize 75 papers published from 2000 to 2022.

The study presents a systematic literature review on identity expressiveness (IE), clarifying and expanding what is currently known about the concept.

To synthesize current knowledge on IE, the study uses the overarching framework of antecedents-phenomenon-consequences, using this same framework to identify gaps and future research directions. The findings show individual and brand-related factors such as the need for uniqueness and anthropomorphism as antecedents of IE, and eWOM/WOM, impulse purchases and upgrading to more exclusive lines as consequences of IE.

The study contributes to theory by synthesizing and mapping current understanding of the state of knowledge on the concept of IE while highlighting gaps in the extant literature and paving future research directions for scholars in the field.

The study offers useful insights for practitioners, broadening marketers’ actionable options in identity-based marketing. Marketers can use insights from this study to inform marketing strategy and communication campaigns for different types of brands.

To the best of the authors’ knowledge, this study is the first of its kind and offers an integrative review of the current literature on IE, thus enhancing understanding of the concept, its antecedents and consequences. The study also contributes to knowledge by highlighting future research priorities for researchers in this field of enquiry.

In recent years, there has been a growing interest in the potential of data analytics to enhance project delivery. Yet many argue that its application in projects is still lagging behind other disciplines. This paper aims to provide a review of the current use of data analytics in project delivery encompassing both academic research and practice to accelerate current understanding and use this to formulate questions and goals for future research.

We propose to achieve the research aim through the creation of a systematic review of the status of data analytics in project delivery. Fusing the methodology of integrative literature review with a recently established practice to include both white and grey literature amounts to an approach tailored to the state of the domain. It serves to delineate a research agenda informed by current developments in both academic research and industrial practice.

The literature review reveals a dearth of work in both academic research and practice relating to data analytics in project delivery and characterises this situation as having “more gap than knowledge.” Some work does exist in the application of machine learning to predicting project delivery though this is restricted to disparate, single context studies that do not reach extendible findings on algorithm selection or key predictive characteristics. Grey literature addresses the potential benefits of data analytics in project delivery but in a manner reliant on “thought-experiments” and devoid of empirical examples.

Based on the review we articulate a research agenda to create knowledge fundamental to the effective use of data analytics in project delivery. This is structured around the functional framework devised by this investigation and highlights both organisational and data analytic challenges. Specifically, we express this structure in the form of an “onion-skin” model for conceptual structuring of data analytics in projects. We conclude with a discussion about if and how today’s project studies research community can respond to the totality of these challenges. This paper provides a blueprint for a bridge connecting data analytics and project management.

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.