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Since the implementation of the regulatory inquiry system, research on its impact on information disclosure in the capital market has been increasing. This article focuses on a specific area of study using Chinese annual report inquiry letters as the basis. From a text mining perspective, we explore whether the textual information contained in these inquiry letters can help predict financial restatement behavior of the inquired companies.

Python was used to process the data, nonparametric tests were conducted for hypothesis testing and indicator selection, and six machine learning models were employed to predict financial restatements.

Some text feature indicators in the models that exhibit significant differences are useful for predicting financial restatements, particularly the proportion of formal positive words and stopwords, readability, total word count and certain textual topics. Securities regulatory authorities are increasingly focusing on the accounting and financial aspects of companies' annual reports.

This study explores the textual information in annual report inquiry letters, which can provide insights for other scholars into research methods and content. Besides, it can assist with decision making for participants in the capital market.

We use information technology to study the textual information in annual report inquiry letters and apply it to forecast financial restatements, which enriches the research in the field of regulatory inquiries.

This paper aims to simulate vital corrective actions (CAs) affecting system availability through a system dynamics approach based on the results obtained by analyzing the causal relationships among failure modes and effects analysis elements.

A stock and flow diagram has been developed to simulate system behaviors during a timeframe. Some improvement scenarios regarding the most necessary CAs according to their strategic priority and the possibility of eliminating root causes of critical failure modes in a roller-transmission system have been simulated and analyzed to choose the most effective one(s) for the system availability. The proposed approach has been examined in a steel-manufacturing company.

Results indicated the most effective CAs to remove or diminish critical failure causes that led to the less reliability of the system. It illustrated the impacts of the selected CAs on eliminating or decreasing root causes of the critical failure modes, lessening the system’s failure rate and increasing the system availability more effectively.

Results allow managers and decision-makers to consider different maintenance scenarios without wasting time and more cost, choosing the most appropriate option according to system conditions.

This study innovation would be the dynamic analysis of interactions among failure modes, effects and causes over time to predict the system behavior and improve availability by choosing the most effective CAs through improvement scenario simulation via VENSIM software.

This paper aims to establish an efficient maintenance management system tailored for healthcare facilities, recognizing the crucial role of medical equipment in providing timely and precise patient care.

The system is designed to function both as an information portal and a decision-support system. A knowledge-based approach is adopted centered on Semantic Web Technologies (SWTs), leveraging a customized ontology model for healthcare facilities’ knowledge capitalization. Semantic Web Rule Language (SWRL) is integrated to address decision-support aspects, including equipment criticality assessment, maintenance strategies selection and contracting policies assignment. Additionally, Semantic Query-enhanced Web Rule Language (SQWRL) is incorporated to streamline the retrieval of decision-support outcomes and other useful information from the system’s knowledge base. A real-life case study conducted at the University Hospital Center of Oran (Algeria) illustrates the applicability and effectiveness of the proposed approach.

Case study results reveal that 40% of processed equipment is highly critical, 40% is of medium criticality, and 20% is of negligible criticality. The system demonstrates significant efficacy in determining optimal maintenance strategies and contracting policies for the equipment, leveraging combined knowledge and data-driven inference. Overall, SWTs showcases substantial potential in addressing maintenance management challenges within healthcare facilities.

An innovative model for healthcare equipment maintenance management is introduced, incorporating ontology, SWRL and SQWRL, and providing efficient data integration, coordinated workflows and data-driven context-aware decisions, while maintaining optimal flexibility and cross-departmental interoperability, which gives it substantial potential for further development.

This study aims to introduce and define the concept of phygital brand community (PBC). It discusses the potential conflicts that can arise from engaging in multiple PBCs and propose an enriched netnographic methodological approach to explore the role of PBC engagement overlap and its influence on the phygital experience.

Following a critical analysis of the inherent limitations of netnographic methodological approaches in the context of PBCs, this study develops an enriched netnographic research protocol that accounts for the challenges of engagement overlap among PBCs.

This study proposes two methods of analysis, namely, “participatory netnography” and “witness netnography,” which are derived from a mixed-methodology approach that integrates elements of netnography.

The findings of this study underscore the requisite methodological refinements imperative for enhancing netnographic analysis, particularly in its application for a better comprehension of individual behaviors within the realm of PBCs. In pursuit of this objective, the identified adjustments encompass ethical considerations, evaluation methods and their application in a digital milieu, where intricate mechanics and technologies frequently elude conventional methodologies.

In this study, the authors present a novel conceptualization of PBCs, highlighting their role and development, as well as the challenges they pose. To adequately capture the impact of PBC engagement overlap, the authors propose the need for an enriched mixed-methodological approach.

This study aims to address the existing knowledge gap by investigating teachers’ adoption of ChatGPT for educational purposes. The study specifically focuses on identifying the factors that motivate and inhibit teachers in adoption of ChatGPT in higher education institutions (HEIs).

This research has used interpretative phenomenological analysis – a qualitative approach. Through in-depth interviews among the teachers, data was collected to identify the motivating and inhibiting factors that impacted teachers’ willingness to adopt ChatGPT. The data was collected from 48 teachers working across HEIs of Uttarakhand region in India.

The analysis revealed seven themes under motivating factors that encourage teachers to adopt ChatGPT for their educational purposes. These include time factor, tool for competitive edge, learning enhancement tool for students, research facilitator, benefits in educational settings, troubleshooter and easy to use. On the other hand, inhibiting factors comprise five themes, which include technical difficulties, limited features for educational and research purposes, tool for handicapping innovation and creativity, lack of personal touch and ethical considerations.

The findings will be valuable for HEIs in establishing policies that promote the appropriate and effective use of ChatGPT. Moreover, the study provides recommendations to ChatGPT solution providers for improving ChatGPT services for effective adoption of ChatGPT among teachers and implementation at HEIs. Further, it contributes to the body of literature by filling a knowledge gap about teacher adoption of ChatGPT in the HEIs. Through qualitative research, the study has pinpointed specific motivating and inhibiting factors that affect teacher adoption of ChatGPT.

Unlike previous studies that primarily explored the potential advantages and drawbacks of ChatGPT in education, this research study delves deeper into the topic. It makes a substantial contribution to our understanding of ChatGPT adoption among teachers by identifying distinct factors that either motivate or inhibit teachers from adopting ChatGPT for job related purposes. The study provides novel insights that were previously mislaid, thereby introducing a fresh perspective to the existing literature

This article conceptualizes and constructs a comprehensive framework that can better help to answer that question – Who is accountable for social and public problems? – theoretically and practically.

Tracing the drivers behind two phenomena “accountability hole” and “accountability black hole”, stemming from “pushing power game” and “pulling power game”, respectively, this study considers (1) the three actors of society: citizens (civil society), corporations (market) and civil servants (government), and (2) the principal-agent relationship between the three actors in the face of social and public problems. As a result, the 4CAs framework that contains the three actors’ collaborative accountabilities to one another is presented.

The 4CAs model emphasizes (1) all three actors function as agents that are accountable to one another, (2) collaborative accountability beyond collaborative governance and (3) repowering citizens and corporations beyond just empowering them, i.e. returning their inherent rights and obligations to serve one another.

The 4CAs model may function as a descriptive and prescriptive lens through which the trilemma between market failure, government failure and citizen failure can be re-assessed and balanced. The model can also be used as a set of indicators for assessing and helping a society to better resolve the social and public problems collectively.

There is a sparsity of research that considers how to overcome implementation challenges for interventions in special school settings where specialist teaching methods are involved. Successful implementation has often relied on considerable researcher involvement, making them inaccessible and not sustainable for the majority of special schools. The purpose of this study was to implementa train-the-trainer approach to train teaching staff to use the Teaching Early Numeracy to Children with Developmental Disabilities (TEN-DD) programme in a large special school in the UK, thereby significantly reducing researcher involvement in its implementation.

One staff member was trained to become the school lead for TEN-DD and trained other teaching staff in the school on implementation. This study recruited 13 students aged between 12 and 16 years of age with developmental disabilities to receive TEN-DD. Pre- and post-intervention tests on a standardised numeracy measure were conducted.

A train-the-trainer model was developed and successfully delivered to train teaching staff in TEN-DD. A standardised outcome measure indicated that ten students made improvements to their numeracy skills after teachers trained using this approach delivered TEN-DD for between 3 and 10 months.

Very little research has been carried out to better understand methods for overcoming implementation challenges for delivering evidence-based teaching programmes at scale to students with developmental disabilities who attend special schools. To the best of the authors’ knowledge, this study reports the results of the first evaluation of using a train-the-trainer model for the delivery of a numeracy intervention (TEN-DD), whereby there was no involvement of researchers in implementation beyond the initial training of the school lead. This model of training for interventions may be more sustainable for special schools and help improve the uptake of evidence-based interventions.

This study aims to identify significant factors, analyse them using the consistent fuzzy preference relations (CFPR) method and forecast the probability of successful deployment of the internet of things (IoT) and cloud computing (CC) in Gujarat, India’s building sector.

From the previous studies, 25 significant factors were identified, and a questionnaire survey with personal interviews obtained 120 responses from building experts in Gujarat, India. The questionnaire survey data’s validity, reliability and descriptive statistics were also assessed. Building experts’ opinions are inputted into the CFPR method, and priority weights and ratings for probable outcomes are obtained to forecast success and failure.

The findings demonstrate that the most important factors are affordable system and ease of use and battery life and size of sensors, whereas less important ones include poor collaboration between IoT and cloud developer community and building sector and suitable location. The forecasting values demonstrate that the factor suitable location has a high probability of success; however, factors such as loss of jobs and data governance have a high probability of failure. Based on the forecasted values, the probability of success (0.6420) is almost twice that of failure (0.3580). It shows that deploying IoT and CC in the building sector of Gujarat, India, is very much feasible.

Previous studies analysed IoT and CC factors using different multi-criteria decision-making (MCDM) methods to merely prioritise ranking in the building sector, but forecasting success/failure makes this study unique. This research is generally applicable, and its findings may be utilised for decision-making and deployment of IoT and CC in the building sector anywhere globally.

Pandemics are frequent events, and the impact of each pandemic makes a strong and long-term effect on companies and markets. Given the potential impact of the COVID-19 pandemic, it is important to investigate the crisis from a different perspective to know how companies have sustained growth in markets. The purpose of this paper is to understand how profit-oriented customer-centric companies (small, medium and large) have responded and adapted to COVID-19 crisis, using the complexity theory.

Drawing upon the complexity theory, a humble attempt is made to develop theoretical propositions by conceptualizing companies as complex adaptive systems. The paper examines companies from three dimensions (i.e. internal mechanism, environment and coevolution).

Companies self-organize, emerge into new states and become adaptive to the changing environment. Companies create knowledge to understand the dynamic anatomy and design survival and growth strategies during and post COVID-19 era. Complex adaptive systems perspective provides companies with insights to deal with complex issues raised due to COVID-19 pandemic. They can handle the impact of pandemic efficiently with complex adaptive systems by developing and implementing appropriate strategies post-COVID-19.

The study reveals how companies evolve and emerge into as complex adaptive systems to adapt themselves to the highly dynamic environment, which are uncertain, unpredictable, nonlinear and multifaceted, in the context of COVID-19. Implications for theory and practice of viewing companies as complex adaptive systems and coevolving structures in the COVID-19 context are discussed.

Research on the effects of feedback frequency has reported mixed findings. To tackle this problem, the current study focuses on specific feedback signs (i.e. negative feedback). By integrating the face management theory and attribution theory, this study examined the mediating effect of trust in supervisors and the moderating effect of employee-attributed performance promotion motives for negative feedback.

A field study with 176 participants and two supplemental experiments with 143 and 100 participants, respectively, were conducted to test the theoretical model.

Results revealed that the frequency of supervisory negative feedback negatively influenced employees’ trust in supervisors, which in turn influenced employees’ perceptions of feedback utility and learning performance. These indirect effects can be alleviated when employees have high degrees of performance promotion attribution for supervisor motives.

This research extends feedback research by integrating feedback frequency with a specific sign of feedback and revealing a moderated mediation effect of the negative feedback frequency.