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This study addresses the gap in understanding text analytics within the service domain, focusing on new service development to provide insights into key research themes and trends in text analytics approaches to service development. It explores the benefits and challenges of implementing these approaches and identifies potential research opportunities for future service development. Importantly, this study offers insights to assist service providers to make data-driven decisions for developing new services and optimising existing ones.

This research introduces the hybrid thematic analysis with a systematic literature review (SLR-TA). It delves into the various aspects of text analytics in service development by analysing 124 research papers published from 2012 to 2023. This approach not only identifies key practical applications but also evaluates the benefits and difficulties of applying text analytics in this domain, thereby ensuring the reliability and validity of the findings.

The study highlights an increasing focus on text analytics within the service industry over the examined period. Using the SLR-TA approach, it identifies eight themes in previous studies and finds that “Service Quality” had the most research interest, comprising 42% of studies, while there was less emphasis on designing new services. The study categorises research into four types: Case, Concept, Tools and Implementation, with case studies comprising 68% of the total.

This study is groundbreaking in conducting a thorough and systematic analysis of a broad collection of articles. It provides a comprehensive view of text analytics approaches in the service sector, particularly in developing new services and service innovation. This study lays out distinct guidelines for future research and offers valuable insights to foster research recommendations.

User-generated content was explored to understand the public discourse surrounding the Vessel, a star architecture in New York. Through text analytics, the study aims to uncover topics, sentiments and themes in public opinion regarding this controversial building from social media data.

This study utilized a big data and text analytics approach, employing topic modeling with the BERTopic technique, sentiment analysis with roBERTa and thematic analysis on 10,259 Reddit comments pertaining to the Vessel.

The comments were grouped into 20 topics and seven themes, shedding light on discussions regarding the Vessel’s philosophy of existence, critiques of the architect’s approach, evaluations of project success or failure and considerations of the project’s future. Negative sentiments dominate the discourse, reflecting widespread criticism and skepticism towards the project.

The manual data collection method, due to API restrictions, precluded tracking evolving trends over time. Nevertheless, the study provides insights for architects, urban planners, policymakers and stakeholders involved in public space design and management, highlighting the importance of considering user feedback from social media platforms.

This study enriches our comprehension of how users perceive star architecture in the age of social media, focusing on hidden layers of discourse surrounding a controversial iconic building. By combining topic modeling and sentiment analysis, the study offers a novel approach to analyzing architectural public debates on social media platforms like Reddit.

The aim of this study is to review the literature on how intellectual capital (IC) contributes to the decarbonization efforts of firms. It explores how carbon accounting can measure the components of IC in decarbonization efforts to balance profitability with environmental and social goals, particularly in promoting decent work and economic growth (Sustainable Development Goal [SDG] 8 and its targets [2, 5, 6, 8]). Moreover, it emphasises the importance of multi-stakeholder partnerships for sharing knowledge, expertise, technology, and financial resources (SDG17-Target 17.G) to meet SDG8.

As a consolidated methodological approach, a systematic literature review (SLR) was used in this study to fill the existing research gaps in sustainability accounting. To consolidate and clarify scholarly research on IC towards decarbonization, 149 English articles published in the Scopus database and Google Scholar between 1990 and 2024 were reviewed.

The results highlight that the current research does not sufficiently cover the intersection of carbon accounting and IC in the analysis of decarbonization practices. Stakeholders and regulatory bodies are increasingly pressuring firms to implement development-focused policies in line with SDG8 and its targets, requiring the integration of IC and its measures in decarbonization processes, supported by SDG17-Target 17.G. This integration is useful for creating business models that balance profitability and social and environmental responsibilities.

The integration of social dimension to design sustainable business models for emission reduction and provide a decent work environment by focusing on SDG17-Target 17.G has rarely been investigated in terms of theory and practice. Through carbon accounting, IC can be a key source of SDG8-Targets 8.[2, 5, 6, 8] and SDG17-Target 17.G. Historically, these major issues are not easily aligned with accounting research or decarbonization processes.

Hair loss is often overlooked but psychologically challenging. However, the emergence of online health communities provides opportunities for hair loss patients to seek social support through self-disclosure. Nevertheless, not all disclosures receive the desired support. This research explores what patients disclose within the community and how their health narrative (content, form and linguistic style) regarding self-disclosure influences the social support they receive.

This study investigated a 13-year-old online support group for Chinese hair loss patients with nearly 240,000 members. Using structural topic modeling, Linguistic Inquiry and Word Count, and a negative binomial model, the research analyzed the content of self-disclosure and the interrelationships between social support and three narrative dimensions of self-disclosure.

Self-disclosures are classified into 14 topics, grouped under analytical, informative and emotional categories. Emotion-related self-disclosures, whether in content or effective word use, receive deeper social support. Longer and image-rich posts attract more support in quantity, but not necessarily in quality, while cognitive words have a limited impact.

This study addresses the previously overlooked population of hair loss patients within online health communities. It employs a more comprehensive health narrative framework to explore the relationship between self-disclosure and social support, utilizing unsupervised structural topic modeling methods to mine text. The research offers practical implications for how patients seek support and for healthcare professionals in developing doctor-patient communication strategies.

Researchers, with the widespread acceptance of Web-based technologies by companies, have recently discovered a new type of social capital through these mass communication tools, but there is still limited knowledge about its formation. Therefore, this study specifically aims to conceptualize and validate brand social capital (BSC) by analyzing the role of the online brand community’s social media capital (OBCSC).

Research data was collected using a questionnaire with 39 closed-ended questions. Participants, among the 220 questionnaires distributed, only returned 140 acceptable questionnaires, indicating a response rate of 64%. The statistical population of the study included managers and employees of e-commerce companies active in social media in the field of B2C who introduce and sell their products and services on various types of social networking websites. This study performed data analysis using structural equation modeling with partial least squares.

The results showed that OBCSC has a positive and significant effect on the integration of brand knowledge, branding co-creation and sense of belonging to the brand community, and in addition, using the mediating role of these three variables, it also has a positive effect on BSC. This study rejects only hypothesis 8 among all the hypotheses formulated, which shows that the sense of belonging to the brand community has no significant effect on branding co-creation.

By conceptualizing a new phenomenon called BSC and how its conversion mechanism is, this research defines a specific and formulated path to better identify the results of the organizational use of social media. In addition, it significantly contributes to increasing managers’ understanding of the importance of online brand community activities in internalizing customer brand knowledge within the company and turning it into wealth.

Unlocking the potential of Big Data Analytics (BDA) has proven to be a transformative factor for the Architecture, Engineering and Construction (AEC) industry. This has prompted researchers to focus attention on BDA in the AEC industry (BDA-in-AECI) in recent years, leading to a proliferation of relevant research. However, an in-depth exploration of the literature on BDA-in-AECI remains scarce. As a result, this study seeks to systematically explore the state-of-the-art review on BDA-in-AECI and identify research trends and gaps in knowledge to guide future research.

This state-of-the-art review was conducted using a mixed-method systematic review. Relevant publications were retrieved from Scopus and then subjected to inclusion and exclusion criteria. A quantitative bibliometric analysis was conducted using VOSviewer software and Gephi to reveal the status quo of research in the domain. A further qualitative analysis was performed on carefully screened articles. Based on this mixed-method systematic review, knowledge gaps were identified and future research agendas of BDA-in-AECI were proposed.

The results show that BDA has been adopted to support AEC decision-making, safety and risk assessment, structural health monitoring, damage detection, waste management, project management and facilities management. BDA also plays a major role in achieving construction 4.0 and Industry 4.0. The study further revealed that data mining, cloud computing, predictive analytics, machine learning and artificial intelligence methods, such as deep learning, natural language processing and computer vision, are the key methods used for BDA-in-AECI. Moreover, several data acquisition platforms and technologies were identified, including building information modeling, Internet of Things (IoT), social networking and blockchain. Further studies are needed to examine the synergies between BDA and AI, BDA and Digital twin and BDA and blockchain in the AEC industry.

The study contributes to the BDA-in-AECI body of knowledge by providing a comprehensive scope of understanding and revealing areas for future research directions beneficial to the stakeholders in the AEC industry.

The study aims to provide concrete service remediation and enhancement for LLM developers such as getting user forgiveness and breaking through perceived bottlenecks. It also aims to improve the efficiency of app users' usage decisions.

This paper takes the user reviews of the app stores in 21 countries and 10 languages as the research data, extracts the potential factors by LDA model, exploratively takes the misalignment between user ratings and textual emotions as user forgiveness and perceived bottleneck and uses the Word2vec-SVM model to analyze the sentiment. Finally, attributions are made based on empathy.

The results show that AI-based LLMs are more likely to cause bias in user ratings and textual content than regular APPs. Functional and economic remedies are effective in awakening empathy and forgiveness, while empathic remedies are effective in reducing perceived bottlenecks. Interestingly, empathetic users are “pickier”. Further social network analysis reveals that problem solving timeliness, software flexibility, model updating and special data (voice and image) analysis capabilities are beneficial in breaking perceived bottlenecks. Besides, heterogeneity analysis show that eastern users are more sensitive to the price factor and are more likely to generate forgiveness through economic remedy, and there is a dual interaction between basic attributes and extra boosts in the East and West.

The “gap” between negative (positive) user reviews and ratings, that is consumer forgiveness and perceived bottlenecks, is identified in unstructured text; the study finds that empathy helps to awaken user forgiveness and understanding, while it is limited to bottleneck breakthroughs; the dataset includes a wide range of countries and regions, findings are tested in a cross-language and cross-cultural perspective, which makes the study more robust, and the heterogeneity of users' cultural backgrounds is also analyzed.

The purpose of this study is to develop a conceptual model, ProvKOS, for tracking the provenance of change activities in a knowledge organization system (KOS). By extending current provenance practices, this model represents dynamic changes in a KOS more effectively.

We take a five-step approach to develop the conceptual model, including content analysis of KOS editorial data, environmental scan of existing provenance models, development of persona-specific provenance questions and a participatory design with stakeholders to ensure the model’s utility.

We introduce (1) a taxonomy of editorial activities for a KOS; (2) a conceptual model ProvKOS, which extends existing models PROV and Simple Knowledge Organization Systems (SKOS). We also provide detailed data dictionaries for the entities, activities and warrants classes proposed in the model. A use case on “gender dysphoria” in Dewey Decimal Classifications (DDCs) is provided to illustrate the implementation of ProvKOS. This shows ProvKOS’s ability to capture KOS changes effectively and to link external resources relating to the changes.

Further validation may be needed to implement the ProvKOS model across various types of KOSs.

ProvKOS can help improve machine readability, querying and analysis of a KOS. Especially within the linked data environment, the enhanced provenance documentation through ProvKOS can enable a network of KOSs, which will then inform better linked data or knowledge graph designs.

By facilitating better tracking of changes within a KOS and across KOSs, ProvKOS can enhance the accessibility and usability of knowledge bases across different cultural and social contexts, thus better supporting inclusive information practices.

The proposed model is novel in two ways: one, its ability to represent dynamic change activities in a KOS, which has not been discussed anywhere else; two, it supports the interconnectivity across KOSs by providing a “warrant” class to substantiate the context of changes.

This study aims to analyze the distribution of novelty among scholarly papers in the field of library and information science (LIS) in China. Specifically, this study explores the distribution of novelty of papers in various journals, research topics and different periods. It is possible to understand the characteristics of LIS research in China and what factors have influenced it.

This paper collects articles published in Chinese library science journals indexed by the Chinese Social Sciences Citation Index from 2000 to 2022. The BERTopic model is used based on abstracts of the papers and to obtain the topic of each paper. Based on the combination innovation theory of reference pairs cited by focal papers, novelty scores of all papers are calculated. Next, this paper analyzes the novelty of papers under different topics. Finally, this paper analyzes the differences in author collaboration patterns across various topics, aiming to explain how these differences relate to the novelty of papers from a collaborative perspective.

This study shows that archival research topics have lower novelty than papers on journal evaluation and patent technology in Chinese LIS. Research papers in this field are gradually becoming more novel over time. Papers on different topics and with varying degrees of novelty exhibit distinct author collaboration patterns, with low-novelty topics more frequently featuring solo authorship, while high-novelty topics tend to involve a higher percentage of inter-institutional collaboration.

This study investigates the novelty characteristics of research papers on different topics in the field of LIS in China. The authors’ contribution includes visualizing research hotspots and trends in the field and analyzing authors’ collaboration patterns at the level of research topics, thereby providing new perspectives on the factors affecting the novelty of these papers.

There is little to no empirical research on the phenomenon of ways in which the racism of whiteness transpires within the faculties and classrooms of US-based ALA-accredited library and information science (LIS) education programs. We do have scholars publishing meaningful work exploring diversity-equity-inclusion topics and initiatives to evolve the LIS discourse on these issues (Honma, 2005; Chancellor, 2019; De LaRosa et al., 2021; Gibson, 2019; Mehra et al., 2023; Colón-Aguirre et al., 2022; Hands, 2022). This research substantiates the conceptual research that exists by empirically exposing the ways in which the racism of whiteness functions at the interpersonal level of work culture in LIS programs (i.e. the academy) in the US.

Adapting Baima and Sude’s (2020) modified Delphi Method, a focus group of 13 BIPOC (Black, Indigenous, and People of Color) library and information science faculty members in the United States were recruited to participate in a one-time 60-min virtual Zoom session. Participants were engaged in three iterative rounds of reflective inquiry to reach a consensus of experience. The study design was embedded with critical race theory-based (CRT) ethnographic methods such as testimony (counterstorytelling), collective affirmation (shared narratives), and silence.

BIPOC LIS faculty (tenure-track and tenured) have similar ideas about whiteness and how it is operationalized as micro- and macro-aggressions in the LIS academic workplace, most significantly inside the classroom. The experience of whiteness was prevalent among all study participants in two areas: workplace meetings with faculty colleagues and classroom sessions (face-to-face and online) with students.

The findings offer empirical evidence to support the prolific conceptual literature in LIS discourse concerning ways in which critical race theory (CRT) interrogates LIS’s socio-professional injustices and inequities (e.g. Gibson et al., 2018; Stauffer, 2020; Leung and Lopez-McKnight, 2021; Jennings and Kinzer, 2022; Snow and Dunbar, 2022). There remains a dearth of empirical research that reports how whiteness is reproduced in the practices, knowledge, and resources that make up the ethos of the LIS faculty meeting and classroom. Documenting the testimonies of BIPOC LIS faculty solidifies the existence of whiteness as a toxic reality in the LIS academy.