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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.

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.

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.

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.

Although situational crisis communication theory (SCCT) is centered on rationality and cognitive information processing, it ignores that people are also driven by irrationality and non-cognitive information processing. The purpose of this study aims to fill this gap by examining how gender stereotypes, based on perceived spokesperson sex influence the public’s perceptions of crisis response messages.

A 2 (industry type: automotive vs daycare industry) × 2 (spokesperson’s sex: male vs female) × 2 (crisis response appeal: rational vs emotional) between-subject online experiment was conducted to examine the effect of gender stereotype in crisis communication.

Results showed that either matching spokesperson sex with sex differed industry or matching sex differed industry with appropriate crisis response appeal can generate a more positive evaluation of the spokesperson and the organization. The results also revealed under which circumstances, the attractiveness of different sex of the spokesperson can either promote or mitigate people’s perceptions of the organization. Furthermore, when people are aware of a spokesperson’s sex, in a female-associated industry, a mismatching effect of a positive violation of a male-related stereotype overrides a matching effect of a female-related stereotype in crisis communication.

This study is among the first to identify how the gender of a spokesperson and industry type affect publics’ crisis response.

Mobility as a service (MaaS) is a new model of bundling personal transport options from multiple providers to provide frictionless service. The model integrates information and service to enable users to search, book and pay through a single digital platform. The aim of MaaS is to minimize individual car ownership and revolutionize personal mobility habits in favour of sustainable modes. Thus, it is important to understand consumers' readiness for MaaS. This study explains the factors that influence MaaS adoption.

A comprehensive review of the related literature has been conducted, leading to the development of a conceptual model for investigating the key factors that influence MaaS adoption. The model draws upon the digital platform, schema congruity and planned behaviour theories. The model is then tested using structural equation modelling and survey data collected from 331 respondents.

The findings indicate that MaaS information schema congruity, the digital platform's personalizability, customizability, functional integration, network integration and governance of data together with attitude towards MaaS and subjective norm concerning MaaS influence MaaS adoption. Furthermore, MaaS digital platform expectation and MaaS information schema congruity contribute to users' attitude towards MaaS use.

The paper highlights the design and informational attributes of MaaS platforms that influence their adoption and contributes an integrated research model that links digital platform characteristics, behavioural factors and information schema congruity. Future research can use the constructs and measures we developed in this study to build deeper understanding of what contributes to the success of digital platforms.

Despite ongoing research into archival metadata standards, digital archives are unable to effectively represent records in their appropriate contexts. This study aims to propose a knowledge graph that depicts the diverse relationships between heterogeneous digital archive entities.

This study introduces and describes a method for applying knowledge graphs to digital archives in a step-by-step manner. It examines archival metadata standards, such as Records in Context Ontology (RiC-O), for characterising digital records; explains the process of data refinement, enrichment and reconciliation with examples; and demonstrates the use of knowledge graphs constructed using semantic queries.

This study introduced the 97imf.kr archive as a knowledge graph, enabling meaningful exploration of relationships within the archive’s records. This approach facilitated comprehensive record descriptions about different record entities. Applying archival ontologies with general-purpose vocabularies to digital records was advised to enhance metadata coherence and semantic search.

Most digital archives serviced in Korea are limited in the proper use of archival metadata standards. The contribution of this study is to propose a practical application of knowledge graph technology for linking and exploring digital records. This study details the process of collecting raw data on archives, data preprocessing and data enrichment, and demonstrates how to build a knowledge graph connected to external data. In particular, the knowledge graph of RiC-O vocabulary, Wikidata and Schema.org vocabulary and the semantic query using it can be applied to supplement keyword search in conventional digital archives.

Luxury consumption has evolved, and two important reasons behind the change include globalization and the COVID-19 crisis. These factors have led to the rise of new luxury consumption, which is different from traditional luxury consumption. This study examines how consumers’ identities shape their intentions to consume traditional luxury and new luxury brands.

The theoretical underpinnings of the schema congruity theory and heuristic systematic framework were applied to understand the role of identities in determining consumers’ regulatory focus, price luxuriousness inference and preference for traditional and new luxury brands.

Findings suggest that the global identity of consumers shapes their promotion focus and price luxuriousness inferences. However, their local identities induce a prevention goal. Consumers with such a goal are unlikely to make price luxuriousness inferences. Further, these inferences lead to the choice of traditional luxury over new luxury brands. The results also establish the moderating effects of consumer flexibility.

The extant literature is inconclusive on the role of globalization in luxury consumption and ignores new luxury brands. The current study shows the impact of identities and regulatory focus on traditional and new luxury consumption. The findings also indicate consumers’ regulatory focus and price luxuriousness inference as the reasons behind the influence. The paper also implies that consumers open to renting, sharing or buying second-hand goods will prefer new luxury over traditional luxury brands.

Drawing on embodied cognition theory, this study examined the impact of midair, gesture-based somatosensory augmented reality (AR) experience on consumer delight and stickiness intention. The mediating effects of three psychological states for body schema (i.e. natural symbol sets, vivid memory and human touch) on the relationships between somatosensory AR and consumer delight/stickiness intention are determined. By filling gaps in the research, we hope to provide guidance on how to drive delightful somatosensory AR marketing.

Two experiments were conducted (Study 1 and Study 2) to test the research model and hypotheses. These experiments compared the effects of the “presence” (midair, gesture-based) and “absence” (mouse-based traditional website) conditions in somatosensory AR on consumer body schema and the creation of a delightful virtual shopping experience (i.e. consumer delight and stickiness intention).

The consumer delight and stickiness intention created in the presence condition was much higher than those in the absence condition. Consumers appeared to prefer engaging in a midair gesture-based somatosensory AR experience and exploring an augmented metaverse reality to interacting with a mouse-based traditional website. We also found that giving online consumers more somatosensory activities and kinesthetic experiences effectively inspired three psychological states of body schema in online consumers.

The results contribute to the AR experience and somatosensory marketing literature by revealing the role of natural symbol sets, vivid memory and the sense of human touch. This research breaks through the long-developed research paradigm on consumer delight, which has been limited to traditional entities and web contexts. We also extend embodied cognition theory to the study of somatosensory AR marketing.

Data protection requirements heavily increased due to the rising awareness of data security, legal requirements and technological developments. Today, NoSQL databases are increasingly used in security-critical domains. Current survey works on databases and data security only consider authorization and access control in a very general way and do not regard most of today’s sophisticated requirements. Accordingly, the purpose of this paper is to discuss authorization and access control for relational and NoSQL database models in detail with respect to requirements and current state of the art.

This paper follows a systematic literature review approach to study authorization and access control for different database models. Starting with a research on survey works on authorization and access control in databases, the study continues with the identification and definition of advanced authorization and access control requirements, which are generally applicable to any database model. This paper then discusses and compares current database models based on these requirements.

As no survey works consider requirements for authorization and access control in different database models so far, the authors define their requirements. Furthermore, the authors discuss the current state of the art for the relational, key-value, column-oriented, document-based and graph database models in comparison to the defined requirements.

This paper focuses on authorization and access control for various database models, not concrete products. This paper identifies today’s sophisticated – yet general – requirements from the literature and compares them with research results and access control features of current products for the relational and NoSQL database models.