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Information opacity in donation crowdfunding activities has constrained the healthy development of China’s public welfare activities. Addressing the trust crisis and enhancing public engagement warrants further investigation. This study aims to uncover the moderating effect of activity transparency by utilizing data from 1,029 donation crowdfunding projects on the Sina Weibo Public Welfare Social Platform. In this way, we seek to elucidate the impact of donation crowdfunding events on fundraising ability.

This study selects text complexity, number of supporters, creator experience, and social capital as explanatory variables; innovatively selects the number of updates of online crowdfunding activities and total reading volume as moderating variables; selects the number of shares of crowdfunding activities as a mediating variable; and constructs a moderated mediation multiple regression model for fundraising ability.

Our findings indicate that independent variables, such as text complexity, number of supporters, and social capital, can significantly affect the dependent variable, fundraising ability. However, creator experience does not influence fundraising ability. Furthermore, social interaction has a mediating effect, whereas activity transparency has a reverse moderating effect. These results indicate that social interaction can enhance the fundraising ability of donation crowdfunding events. However, with an increase in information transparency, the fundraising ability of social media decreases.

The originality of this research is in clarifying the internal factors affecting fundraising ability through induction, making bold assumptions, and focusing on how social media’s effective interaction and activity transparency will affect public welfare crowdfunding fundraising ability.

Color psychology theory reveals that complex images with very varied palettes and many different colors are likely to be considered unattractive by individuals. Notwithstanding, web content containing social signals may be more attractive via the initiation of a social connection. This research investigates a predictive model blending variables from these theoretical perspectives to determine crowdfunding success.

The research is based on data from 176,614 Kickstarter projects. A number of machine learning and artificial intelligence techniques were employed to analyze the listing images for color complexity and the presence of people, while specific language features, including socialness, were measured in the listing text. Logistic regression was applied, controlling for several additional variables and predictive model was developed.

The findings supported the color complexity and socialness effects on crowdfunding success. The model achieves notable predictive value explaining 56.4% of variance. Listing images containing fewer colors and that have more similar colors are more likely to be crowdfunded successfully. Listings that convey greater socialness have a greater likelihood of being funded.

This investigation contributes a unique understanding of the effect of features of both socialness and color complexity on the success of crowdfunding ventures. A second contribution comes from the process and methods employed in the study, which provides a clear blueprint for the processing of large-scale analysis of soft information (images and text) in order to use them as variables in the scientific testing of theory.

This study aims to explore the intelligibility of communication with stakeholders as a result of accrual accounting adoption. It focuses on changes in the use of visual forms and the readability of text that occurred immediately after the adoption of accrual accounting in performance reports of Italian public universities.

The authors collect the stakeholder section of performance reports published before and after accrual accounting adoption. Then, the authors use manual and computer-assisted textual analysis. Finally, the authors explore the data using principal component analysis and qualitative comparative analysis.

This study demonstrates that switching from cash to accrual accounting provokes immediate changes in communication patterns. It confirms the significant reduction of readability and increase in visual forms after accruals accounting adoption. The results indicate that smaller universities especially put effort into increasing intelligibility while implementing a more complex accounting system. This study also finds a relation between the change in readability and the change in visual forms that are complementary, with the exception of several very large universities.

The findings underline the possibility of neutralising the adverse effects of accounting reform associated with its complexity and difficulties in understanding by the use of visual forms and attention to the document’s readability.

This paper adds a new dimension to the study of public sector accounting from the external stakeholder perspective. It provides further insight into the link between accrual accounting adoption and readability, together with the use of visual forms by universities.

The purpose of this paper is twofold: (1) to map and analyze existing complexity typologies and (2) to develop a framework for characterizing complexity-based approaches.

This study was conducted in three stages: (1) initial identification of typologies related to complexity following a structured procedure based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol; (2) backward and forward review to identify additional relevant typologies and (3) content analysis of the selected typologies, categorization and framework development.

Based on 17 selected typologies, a comprehensive overview of complexity studies is provided. Each typology is described considering key concepts, contributions and convergences and differences between them. The epistemological, theoretical and methodological diversity of complexity studies was explored, allowing the identification of the main schools of thought and authors. A framework for characterizing complexity-based approaches was proposed including the following perspectives: ontology of complexity, epistemology of complexity, purpose and object of interest, methodology and methods and theoretical pillars.

This study examines the main typologies of complexity from an integrated and multidisciplinary perspective and, based on that, proposes a novel framework to understanding and characterizing complexity-based approaches.

The general data protection regulation (GDPR) was designed to address privacy challenges posed by globalisation and rapid technological advancements; however, its implementation has also introduced new hurdles for companies. This study aims to analyse and synthesise the existing literature that focuses on challenges of GDPR implementation in business enterprises, while also outlining the directions for future research.

The methodology of this review follows the preferred reporting items for systematic reviews and meta-analysis guidelines. It uses an extensive search strategy across Scopus and Web of Science databases, rigorously applying inclusion and exclusion criteria, yielding a detailed analysis of 16 selected studies that concentrate on GDPR implementation challenges in business organisations.

The findings indicate a predominant use of conceptual study methodologies in prior research, often limited to specific countries and technology-driven sectors. There is also an inclination towards exploring GDPR challenges within small and medium enterprises, while larger enterprises remain comparatively unexplored. Additionally, further investigation is needed to understand the implications of emerging technologies on GDPR compliance.

This study’s limitations include reliance of the search strategy on two databases, potential exclusion of relevant research, limited existing literature on GDPR implementation challenges in business context and possible influence of diverse methodologies and contexts of previous studies on generalisability of the findings.

The originality of this review lies in its exclusive focus on analysing GDPR implementation challenges within the business context, coupled with a fresh categorisation of these challenges into technical, legal, organisational, and regulatory dimensions.

Given the persistent receptive skills challenges faced by language learners and the numerous merits of using technologically-enhanced learning in the form of mobile-assisted language learning in language teaching, this study decides to incorporate a context-analysis-practice-exploration lesson-planning framework with mobile learning to improve the receptive skills of learners. This step is necessary on the recommendation by scholarly research that while implementing technology, teacher training, digital literacy and expertise, incorporating good teaching practices and lesson planning frameworks may assist in yielding positive results.

This study decides to utilize a mixed-method research design through an experimental approach to elicit information from 90 EFL students concerning the effectiveness of incorporating the context-analysis-practice-exploration framework in a mobile-leaning mode instructional delivery to improve their reading and listening skills.

After collecting and analyzing data, the study found that while the participants perceived enhanced-mobile learning to make lessons enjoyable, integrating this framework and m-learning improved their reading skills. This study used notable findings to make pedagogical conclusions on the objective of this study.

This study contributes through empirical evidence on the effectiveness of incorporating mobile learning and a responsive lesson planning framework towards improving English as a foreign language learners' learning outcome. Besides, this study is the first to probe and unpack the effectiveness of a responsive lesson-planning framework in language education.

Computer-assisted instruction (CAI) has been used to combat reading challenges, namely reading accuracy and rate for learners with intellectual, developmental and learning disabilities (IDLD). Whilst most reading CAI effectiveness has been studied in English, other transparent languages have less evidence. This study provides a systematic review and meta-analysis of CAI effectiveness for transparent language reading for K-3 learners with IDLD.

This study systematically reviews academic peer-reviewed studies from 2010 to 2023 with either randomised controlled treatment (RCT) or single-case treatments. Articles were searched from the ACM Digital Library, Google Scholar, IEEE Xplore, ERIC, PsychINFO and Science Direct databases, references and systematic review articles. Reading component skills effect sizes were computed using the random effect sizes model.

11 RCT studies of reading CAI for transparent languages with 510 learners with IDLD were found. A random effect sizes (Cohen’s d) of CAI on individual reading component skills were d = 0.24, p-value = 0.063 and confidence interval (CI) 95% (−0.068–0.551) for phonics and phonemic awareness d = 0.41, p-value = 0.000 and CI 95% (0.175–0.644). Given an average intervention dosage of 1.8 h weekly for a maximum of 16 weeks, CAI had better retention with d = 1.13, p-value = 0.066 and CI 95%(−0.339–2.588). However, these results must be interpreted with a concern of only using published studies.

The study contributes to quantitative CAI effectiveness for transparent language reading components for learners with IDLD.

This research explores the differential effects of pixel-level and object-level visual complexity in firm-generated content (FGC) on consumer engagement in terms of the number of likes and shares, and further investigates the moderating role of image brightness.

Drawing on a deep learning analysis of 85,975 images on a social media platform in China, this study investigates visual complexity in FGC.

The results indicate that pixel-level complexity increases both the number of likes and shares. Object-level complexity has a U-shaped relationship with the number of likes, while it has an inverted U-shaped relationship with the number of shares. Moreover, image brightness mitigates the effect of pixel-level complexity on likes but amplifies the effect on shares; contrarily, it amplifies the effect of object-level complexity on likes, while mitigating its effect on shares.

Although images play a critical role in FGC, visual data analytics has rarely been used in social media research. This study identified two types of visual complexity and investigated their differential effects. We discuss how the processing of information embedded in visual content influences consumer engagement. The findings enrich the literature on social media and visual marketing.

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.