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The purpose of this study is twofold: first, to provide a nuanced understanding of the information concept that encapsulates both its universal foundations and context-dependent intricacies, and second, to propose a theoretical framework that integrates these diverse viewpoints, thereby contributing to the theoretical discourse and practical applications in information science/studies (IS).

The aim of this paper is to explore the relationship between universalism and pluralism in connection with semiotics. The paper adopts a conceptual approach based on semiotics to address the long-standing debate over the definition of information in IS. By engaging with two foundational semiotic theories—C.S. Peirce's pragmatic semiotics and Ferdinand de Saussure's structuralist semiotics – the research seeks to bridge the conflicting perspectives of universalism and pluralism within the field.

The findings reveal that Peirce's semiotics, with its universalist core, views information as dynamic and shaped by context, while Saussure's approach, which is rooted in linguistics, emphasizes the relational aspects of sign systems and connects them to pluralism. This comparative analysis facilitates a broad understanding of information that transcends the limitations of singular perspectives. In conclusion, the paper seeks to demonstrate that the integration of universalist and pluralist perspectives through semiotics offers a more holistic approach to the study of information in IS. It contributes to the field by suggesting that the tension between these perspectives is not only reconcilable but also essential for a richer and more complete understanding of information. This theoretical foundation paves the way for future research and practical advancements in IS and advocates for approaches that recognize the complexity and multifaceted nature of information.

The study engages with theoretical frameworks, predominantly semiotic theories by C.S. Peirce and Ferdinand de Saussure. While this provides a deep conceptual understanding, the lack of empirical data may limit the practical applicability of the findings. The research focuses on two semiotic perspectives, which, although foundational, do not represent the entirety of semiotics or information theory. Other significant semiotic theories and perspectives could provide alternative or complementary insights. The concepts of universalism and pluralism in information science are highly intricate and multifaceted. This study's approach to synthesizing these concepts, while comprehensive, might oversimplify some aspects of these complex paradigms.

The paper's originality lies in its unique integration of semiotic theories into the discourse of IS. This integration offers a novel perspective on understanding the concept of information, bridging the gap between universalist and pluralist approaches. The study presents a new conceptual framework for understanding information that combines elements of universalism (consistent, objective understanding of information) and pluralism (context-dependent, subjective interpretations). This dual approach is relatively unexplored in IS literature, providing fresh insights into the complex nature of information.

The purpose of this study is to develop a domain independent, cost-effective, time-saving and semi-automated ontology generation framework that could extract taxonomic concepts from unstructured text corpus. In the human disease domain, ontologies are found to be extremely useful for managing the diversity of technical expressions in favour of information retrieval objectives. The boundaries of these domains are expanding so fast that it is essential to continuously develop new ontologies or upgrade available ones.

This paper proposes a semi-automated approach that extracts entities/relations via text mining of scientific publications. Text mining-based ontology (TmbOnt)-named code is generated to assist a user in capturing, processing and establishing ontology elements. This code takes a pile of unstructured text files as input and projects them into high-valued entities or relations as output. As a semi-automated approach, a user supervises the process, filters meaningful predecessor/successor phrases and finalizes the demanded ontology-taxonomy. To verify the practical capabilities of the scheme, a case study was performed to drive glaucoma ontology-taxonomy. For this purpose, text files containing 10,000 records were collected from PubMed.

The proposed approach processed over 3.8 million tokenized terms of those records and yielded the resultant glaucoma ontology-taxonomy. Compared with two famous disease ontologies, TmbOnt-driven taxonomy demonstrated a 60%–100% coverage ratio against famous medical thesauruses and ontology taxonomies, such as Human Disease Ontology, Medical Subject Headings and National Cancer Institute Thesaurus, with an average of 70% additional terms recommended for ontology development.

According to the literature, the proposed scheme demonstrated novel capability in expanding the ontology-taxonomy structure with a semi-automated text mining approach, aiming for future fully-automated approaches.

This study aims to introduce the model of implementation to run the smart production factories. The study also aims to investigate the Industry 4.0 technologies as enablers to deal with challenges in the way of implementation.

This contribution benefits from two teams of experts to evaluate the challenges and technologies of Industry 4.0. The Hanlon method is applied to evaluate, rank and prioritise the challenges which are initially scored by experts’ Team 1. Then, the adjacency matrix among enablers and challenges is extracted through the opinions of experts’ Team 2. The study also uses fuzzy cognitive map (FCM) to evaluate the real weights of technologies and challenges, rank and prioritise subsequently.

A total of 8 challenging obstacles and 24 key technologies have been evaluated. The findings reveals that recruit and retention of experienced managers, undefined return on investment and recruit and retention of multi-skilled workers are the most serious challenges in the way of implementing smart production factories. Furthermore, big data, IT-based management and Internet of Things are the top-ranked key enablers to face the challenges.

To the best of the authors’ knowledge, this study is one of the pioneering studies that uses Hanlon method to evaluate industrial challenges. Integrating Hanlon method and FCM leads to a comprehensive model of evaluation and ranking which is another novelty of this contribution. Although many research studies have been released to implement the smart factories, practical model of implementation for production factories is identified as a literature gap.

The purpose of this study is to review literature on the relationship between leadership and workplace learning, to critically analyze and discuss findings and to suggest future research paths based on the synthesis.

This study applied a refined literature review process leading to a selection of 40 articles, which originated from 14 internationally acclaimed journals.

When explaining leadership influence regarding individual and team learning, the concepts of role modeling behavior, relational support and negotiation of meaning is significant. If leaders provide support, show exemplary behavior and negotiate individual arrangements with employees, workplace learning development is positively affected.

Future studies should focus on empirical cases further illustrating how the leader–employee relationship is formed in practice, to further understand differences in leadership influence on employee workplace learning.

The gathered knowledge implicates that carefully designed leadership training programs and personalized work arrangements between leader and employees are beneficial for leader’s ability to influence employee workplace learning.

The reviewed studies were solely published in top management journals, which resulted in an original literature selection. This study also discusses implicit or articulated assumptions about the view of learning in the selected studies, offering additional understanding about the underlying learning views in leadership–workplace learning research.

The main aim of this article is to broaden the notion of strategic intent in public relations. It also develops an understanding of the social value of what can be defined as the first modern health communication campaign in Europe based on strategic intents and the development of modernity.

The study is based on both historical research and empirical material from the Norwegian tuberculosis campaign from 1889 up to 1913, when Norwegian women achieved suffrage. The campaign is analysed in the framework of modernity and social theory. The literature on lobbying and social movements is also used to develop a theoretical framework for the notion of strategic intent.

The study shows that strategic intent can be divided into two layers: (1) the implicit strategic intent is the real purpose behind the communication efforts, whereas (2) the explicit intent is found directly in the communication efforts. The explicit intent may be presented as a solution for the good of society at the right political moment, giving an organisation the possibility to mobilise for long-term social changes, in which could be the implicit intent.

The distinction between explicit and implicit strategic intent broadens our understanding on how to make long-term social changes as well as how social and political changes occur in modern societies. The article also gives a historical account of what is here defined as the first modern health communication campaign in Europe and its social value.

The identification of network user relationship in Fancircle contributes to quantifying the violence index of user text, mining the internal correlation of network behaviors among users, which provides necessary data support for the construction of knowledge graph.

A correlation identification method based on sentiment analysis (CRDM-SA) is put forward by extracting user semantic information, as well as introducing violent sentiment membership. To be specific, the topic of the implementation of topology mapping in the community can be obtained based on self-built field of violent sentiment dictionary (VSD) by extracting user text information. Afterward, the violence index of the user text is calculated to quantify the fuzzy sentiment representation between the user and the topic. Finally, the multi-granularity violence association rules mining of user text is realized by constructing violence fuzzy concept lattice.

It is helpful to reveal the internal relationship of online violence under complex network environment. In that case, the sentiment dependence of users can be characterized from a granular perspective.

The membership degree of violent sentiment into user relationship recognition in Fancircle community is introduced, and a text sentiment association recognition method based on VSD is proposed. By calculating the value of violent sentiment in the user text, the annotation of violent sentiment in the topic dimension of the text is achieved, and the partial order relation between fuzzy concepts of violence under the effective confidence threshold is utilized to obtain the association relation.

This paper aims to study the role of self-concept in consumer relationships with anthropomorphised conversational artificially intelligent (AI) agents. First, the authors investigate how the self-congruence between consumer self-concept and AI and the integration of the conversational AI agent into consumer self-concept might influence such relationships. Second, the authors examine whether these links with self-concept have implications for mental well-being.

This study conducted in-depth interviews with 20 consumers who regularly use popular conversational AI agents for functional or emotional tasks. Based on a thematic analysis and an ideal-type analysis, this study derived a taxonomy of consumer–AI relationships, with self-congruence and self–AI integration as the two axes.

The findings unveil four different relationships that consumers forge with their conversational AI agents, which differ in self-congruence and self–AI integration. Both dimensions are prominent in replacement and committed relationships, where consumers rely on conversational AI agents for companionship and emotional tasks such as personal growth or as a means for overcoming past traumas. These two relationships carry well-being risks in terms of changing expectations that consumers seek to fulfil in human-to-human relationships. Conversely, in the functional relationship, the conversational AI agents are viewed as an important part of one’s professional performance; however, consumers maintain a low sense of self-congruence and distinguish themselves from the agent, also because of the fear of losing their sense of uniqueness and autonomy. Consumers in aspiring relationships rely on their agents for companionship to remedy social exclusion and loneliness, but feel this is prevented because of the agents’ technical limitations.

Although this study provides insights into the dynamics of consumer relationships with conversational AI agents, it comes with limitations. The sample of this study included users of conversational AI agents such as Siri, Google Assistant and Replika. However, future studies should also investigate other agents, such as ChatGPT. Moreover, the self-related processes studied here could be compared across public and private contexts. There is also a need to examine such complex relationships with longitudinal studies. Moreover, future research should explore how consumers’ self-concept could be negatively affected if the support provided by AI is withdrawn. Finally, this study reveals that in some cases, consumers are changing their expectations related to human-to-human relationships based on their interactions with conversational AI agents.

This study enables practitioners to identify specific anthropomorphic cues that can support the development of different types of consumer–AI relationships and to consider their consequences across a range of well-being aspects.

This research equips marketing scholars with a novel understanding of the role of self-concept in the relationships that consumers forge with popular conversational AI agents and the associated well-being implications.