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This paper aims to conduct an in-depth analysis of the shortcomings of apps’ privacy policies and to propose improvement and optimization strategies, which are of great significance for establishing a transparent and responsible privacy protection framework that ensures compliant collection and use of users’ information and effective protection of their privacy.

This paper obtained privacy policy texts for 100 shopping apps through Web crawlers and manual downloads. Based on the perspective of perceived usefulness, thematic analysis is conducted through the latent Dirichlet allocation topic model and comparison with existing policies. Based on the perspective of perceived ease of use, readability analysis is conducted through content analysis and formula calculation.

The apps privacy policies can be divided into seven themes. The authors benchmark these seven topics with the Personal Information Protection Law of the People’s Republic of China, the E-Commerce Law of the People’s Republic of China and the General Data Protection Regulation. It is found that there are omissions in the information collection and use and juvenile protection of the existing apps. Through the indicators’ readability analysis and calculation, it is found that the existing apps privacy policies have good performance in the readability indicators such as naming method, frame directory and so on. However, text personalization and text readability need to be improved and optimized.

At the theoretical level, this paper constructs a model from the dual perception perspectives of perceived usefulness and perceived ease of use and analyses the apps’ privacy policy texts at a fine-grained level. At the practical level, based on large-scale apps’ privacy policy text data, this paper conducts multi-dimensional research from theme analysis, authoritative law benchmarking analysis, content analysis and text readability calculation and analysis. At the same time, this paper identifies the current problems of apps’ privacy policies and puts forward countermeasure suggestions for their content improvement and optimization.

Our research investigated the potential effects of incorporating artificial intelligence (AI) techniques into scholarly publications, specifically big language models. The study employs a qualitative methodology and web content analysis to understand various publishers' guidelines. It examines the potential applications and outcomes of AI in publishers. The analysis has revealed insightful findings regarding the use and implications of AI tools in academic publishing. Agglomeration analysis has uncovered distinct clusters of terms, indicating semantic relationships and thematic cohesion within the dataset. Notably, ‘Large’ and ‘Models’ have formed a coherent cluster, highlighting the significance of large-scale language models in scholarly discourse. Similarly, factor analysis has identified thematic clusters related to AI usage, emphasising aspects such as accuracy, responsibility and the role of authors in AI-assisted work. Semantic mapping has further elucidated thematic dimensions, highlighting linguistic frameworks, work-related constructs, methodological frameworks, AI technologies and publication dynamics. Evaluation metrics have consistently demonstrated cohesion, coherence and lexical diversity across varying numbers of topics, underscoring the robustness of the semantic mapping approach. Additionally, the Silhouette coefficient has provided insights into cluster quality, indicating strong cohesion within specific thematic clusters while hinting at potential overlaps in others. Co-occurrence matrix and cross-tabulation analysis have revealed association and frequency distribution patterns among terms, shedding light on prevalent themes and topics within the dataset. Finally, the proximity plot has illustrated the strength of associations between keywords and accuracy, emphasising central themes and moderate thematic relevance.

The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.

Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.

To address the shortcomings of existing academic user information needs identification methods, such as low efficiency and high subjectivity, this study aims to propose an automated method of identifying online academic user information needs.

This study’s method consists of two main parts: the first is the automatic classification of academic user information needs based on the bidirectional encoder representations from transformers (BERT) model. The second is the key content extraction of academic user information needs based on the improved MDERank key phrase extraction (KPE) algorithm. Finally, the applicability and effectiveness of the method are verified by an example of identifying the information needs of academic users in the field of materials science.

Experimental results show that the BERT-based information needs classification model achieved the highest weighted average F1 score of 91.61%. The improved MDERank KPE algorithm achieves the highest F1 score of 61%. The empirical analysis results reveal that the information needs of the categories “methods,” “experimental phenomena” and “experimental materials” are relatively high in the materials science field.

This study provides a solution for automated identification of academic user information needs. It helps online academic resource platforms to better understand their users’ information needs, which in turn facilitates the platform’s academic resource organization and services.

This paper aims to illustrate, by means of a content analysis of 278 weekly School Meeting minutes, the ways in which student voice is actualized in one democratic free school in Germany.

This paper uses a qualitative content analysis methodology of 278 weekly School Meetings minutes.

This paper uses Fielding’s (2012) patterns of partnership typology to illustrate what counts as student voice and participation in a democratic free school.

Limitations included being reliant on translations of German texts, some missing minutes from the entire set, the lack of a single author for the minutes (and thus degree of detail differs) and the fact that the School Meeting minutes make reference to other meetings for various sub-committees for which no minutes exist, and thus, findings on the degree of student voice may be limited. And because this is a study of one school, generalizability may be difficult. Future research into these sub-committee meetings would prove helpful as well as content analyses of other democratic free schools’ meeting minutes.

This study can help people more deeply understand what goes on in democratic free schools and what student voice and participation can mean within this context.

This paper provides a comprehensive review of the influential and intellectual aspects of the literature on the Gulf Cooperation Council (GCC) region's banking activities.

This study undertakes a bibliometric meta-analysis review of the GCC region banking literature, covering 199 articles published between 2004 and 2022, extracted from the Web of Science (WoS) database, followed by a content analysis of highly cited papers.

This paper identifies the influential aspects of the GCC region banking literature in terms of journals, articles, authors, universities and countries. The paper also identifies and discusses five major research clusters: (1) bank efficiency; (2) corporate governance (CG) and disclosure; (3) performance and risk-taking; (4) systemic risk, bank stability and risk spillovers and (5) intellectual capital (IC). Finally, it identifies gaps in the literature and highlights some important research issues that provide directions for future research.

This paper is limited to the articles indexed in the WoS database and written in English. Though the WoS database encompasses a wide range of multidisciplinary journals, there is a chance that some relevant articles are not included in the WoS database or written in another language.

This study provides regulators, practitioners and academics with valuable insight and an in-depth understanding of the banking system of the GCC region.

To the best of the authors' knowledge, this is the first review paper on GCC region banking literature. This study provides regulators, practitioners and academics with valuable insight and an in-depth understanding of the banking system of the GCC region.

This study addresses how algorithmic recommendations and their affordances shape everyday information practices among young people.

Thematic interviews were conducted with 20 Finnish young people aged 15–16 years. The material was analysed using qualitative content analysis, with a focus on everyday information practices involving online platforms.

The key finding of the study is that the current affordances of algorithmic recommendations enable users to engage in more passive practices instead of active search and evaluation practices. Two major themes emerged from the analysis: enabling not searching, inviting high trust, which highlights the how the affordances of algorithmic recommendations enable the delegation of search to a recommender system and, at the same time, invite trust in the system, and constraining finding, discouraging diversity, which focuses on the constraining degree of affordances and breakdowns associated with algorithmic recommendations.

This study contributes new knowledge regarding the ways in which algorithmic recommendations shape the information practices in young people's everyday lives specifically addressing the constraining nature of affordances.

Researching on lesson studies in initial training of physical education (PE) teachers in Portugal, our group is especially interested in the potential of this methodology for developing the pedagogical content knowledge (PCK) of future PE teachers. The purpose of the present text is to synthesize the knowledge relating to this subject.

Exploratory searches in four multiple search databases were carried out, resulting in 51 records. Information about authors, year of publication, purposes, methodology and results was collected and analyzed.

Results highlight the diversity of purposes. The interview is recurrent in the qualitative studies, while the questionnaire is used in all the large quantitative studies. Regarding the findings, it appears the centrality of PCK, its development potential and its measurability, the consistency of the concept and its evolutionary dynamics, as well as the constructive criticism of initial teacher training models and practices.

To emphasize the relationship between lesson studies and the development of PCK in initial teacher training of PE teachers.

With the disruptive evolution of artificial intelligence (AI), the roles of robotics in hospitality and tourism are shifting rapidly from automatic to emotional works. Although academics have focused on examining robotic applications in hospitality and tourism, concrete images of the different roles of robots are unclear. Thus, this study aims to systematically aggregate and evaluate existing robotic technology-related studies published in Social Science Citation Index-listed hospitality and tourism journals to link the fragmented knowledge and provide an up-to-date overview of robotic technology in hospitality and tourism.

This study retrieved 134 robotic technology-related articles and used descriptive and content analyses to analyze the retrieved papers thoroughly.

The top keyword identified was service robot. Robotic technologies are categorized into AI-supplemented, AI-generated, robotic technology anthropomorphism (RTA)-facilitated and RTA-mediated.

Future studies can consider exploring service robots further from the perspectives of suppliers in tourism. A more comprehensive categorization of robotic technologies is also recommended.

This study contributes to the robotic research realm by providing a holistic view of robotic applications in hospitality and tourism research. This study also attempts to pin down the potential research directions to guide researchers in expanding future studies.

随着人工智能（AI）的颠覆性发展, 机器人在酒店和旅游业中的角色正迅速从自动化工作转变为情感性工作。尽管学术界已开始研究机器人在酒店和旅游业中的应用, 但对于机器人不同角色的具体图像仍不清晰。本研究系统性地汇总并评估了发表在社会科学引文索引（SSCI）列出的酒店与旅游期刊中的现有机器人技术相关研究, 以连接零散的知识, 并提供关于酒店与旅游业机器人技术的最新概览。

本研究检索了134篇与机器人技术相关的文章, 并采用描述性分析和内容分析方法对检索到的文章进行了深入分析。

最常见的关键词是服务机器人。机器人技术分为AI辅助、AI生成、机器人技术拟人化(RTA)促进和RTA中介。

未来的研究可以考虑从旅游供应商的角度进一步探索服务机器人。还建议对机器人技术进行更全面的分类。

本研究通过提供酒店与旅游研究中机器人应用的整体视角, 丰富了机器人研究领域。本研究还尝试明确潜在的研究方向, 为未来的研究扩展提供指导。

The field of international business (IB) formally welcomes and frequently calls for case study research, but the proportion of case study papers appearing in IB journals remains very small. This paper aims to support efforts to redress this imbalance by addressing an overlooked yet critical issue: the (mis)use of tenses when theorizing from case study findings. The authors reveal a pervasive use of the present tense and argue that this leads to decontextualization and, in turn, over-generalization. The paper also suggests ways in which this problem may be avoided in the future, thereby improving the credibility and status of case-based research and helping to de-marginalise it within IB.

A qualitative content analysis was applied to all (2,627) papers published between 2011 and 2021 in four leading IB journals. In total, 171 case study papers were identified over these 11 years, and a deeper content analysis was then performed to measure the extent of decontextualization/over-generalization implied by the inappropriate use of the present tense in the discussion and theorisation of research findings.

This study found that, out of 171 case study papers identified, 141 (82.5%) provided at least two instances of over-generalization as implied by the misuse of the present tense. However, some of these papers were found to feature statements that could be claimed to mitigate such inappropriate generalization. These mitigating factors included the repeated use of adverbial phrases denoting context and the use of a “propositional style” that clearly distinguished contextual findings from speculative, decontextualized generalizations. Nevertheless, 71 of the 171 (41.5%) papers still demonstrated inappropriate generalization, even after allowing for mitigating factors.

This study reveals a problematic writing practice and one which has arguably significantly contributed to the “decontextualization” problem critiqued in IB and management studies more broadly. The study also offers further insights into how decontextualization might be avoided, arguing that this problem would be significantly reduced if tenses were used appropriately in discussing and theorizing case study findings. Additionally, the study highlights the continued marginalization of qualitative research methods in IB and reinforces calls to address it.