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A majority of higher education institutions (HEIs) in Oman, and internationally, have adopted English as the language of education, driven by its power and its globally accepted status as the language of knowledge and communication. Such an internationalisation policy has been inadequately evaluated to examine its actual effects. This paper aims at analysing the existing literature with a view to hypothesise the effects of adopting English as a medium of instruction (EMI) on establishing intellectual capital in the Omani context.

The paper employs a case study design that draws on data generated through a systematic review of 94 peer-reviewed papers that are synthesised using thematic analysis.

The findings indicate that EMI negatively affects the optimal creation of intellectual capital through limiting access to HE, hindering knowledge transfer, impeding Omanis' employability and hindering faculty's professional growth. EMI leads HEIs to mirror the supplying countries' cultures in terms of materials, ideologies and standards. It affects teaching and research quality, training and communication, the sense of equity, belonging and self-worth amongst students and the relationships amongst faculty members. It also increases reliance on external stakeholders.

The paper highlights the interconnection between the forms of intellectual capital and how some components are antecedents to the creation of the intellectual capital forms. It establishes the moderating role the language of instruction plays in relation to the three sub forms of intellectual capital in higher education.

The paper calls for maximising higher education intellectual capital through adopting bilingual rather than monolingual higher education. It calls upon policymakers to revisit the assumptions underlying higher education systems in order to optimise their outcomes.

The paper is the first one that sheds light on the role of language in intellectual capital construction. Such a moderating role has received almost no attention in the higher education literature that is largely busy quantifying its outcomes rather than ensuring they are actually sustainably generated.

A lot has been discussed about Peter Drucker, and there exists significant written content admiring or criticizing his work as a management writer. This paper aims to offer a holistic analysis of Peter Drucker’s written contributions to better understand his views of society, government and organizations of all kinds.

Many have written about Peter Drucker and his considerable impact on the practical and philosophical foundations of modern management. Yet, there has been no systematic scholarly evaluation of Drucker as a writer, although many have praised and criticized his written work on management. In this study, the authors offer an analysis of Peter Drucker’s written contributions to evaluate his central contributions, as well as how he communicated his ideas on society and management.

A comprehensive analysis of Drucker’s word usage and writing style throughout his writing career forms an evidence-based approach to better understand his viewpoints and objectively evaluate the criticisms surrounding his work.

This research contributes to a better understanding of Peter Drucker’s central contributions, concerns and sentiments, as it relates to not only business management but also to his views of society, government and organizations of all kinds. A reconsideration of Drucker as a writer presents possible implications for the practice of management.

This paper presents participants' experiences of the collaborative process. The paper introduces the World Heritage Site and presents the central learning outcomes of a process through which researchers and practitioners sought to develop an empirically-grounded, site-specific and practice-led research agenda relevant to World Heritage management. The purpose of this paper is to discuss an approach to improving collaboration between researchers and practitioners in the World Heritage field.

From September 2021 to April 2022, a research-practice team comprised of social scientists and managers of the Rjukan-Notodden Industrial Heritage Site in Norway participated in a capacity-building pilot initiative under the ICCROM-IUCN World Heritage Leadership programme. Bringing researchers and practitioners together in a joint collaborative process, the Heritage Place Lab (HPL) pilot programme aimed to function as an incubator for developing research agendas for World Heritage Sites.

The paper demonstrates that close collaboration between heritage researchers and practitioners can benefit World Heritage Sites, offering managers valuable inputs for informed and inclusive decision-making. In the case of the Rjukan-Notodden Industrial Heritage Site, an improved understanding of the diversities in local values would benefit management, as existing management issues underpin the site's complexity. Furthermore, fruitful collaborations between heritage researchers and World Heritage managers depend on an overlapping and reflexive understanding of central concepts. This can be achieved through collaborative research-practice processes but is likely to require careful time management.

Focussing on the collaborative process between World Heritage researchers and practitioners, and using additional information for comparable World Heritage examples obtained online, this paper shows how research interests and management challenges can be developed and aligned more successfully through cooperation and improved communication over time. Beyond the specific results for the Rjukan-Notodden Industrial Heritage Site, the broader discussion presented on the challenges and importance of addressing the complexities of managing heritage sites will be valuable for other World Heritage Sites and managers.

This study aims to predict the financial performance of Islamic banks with sentiments of management from the textual information in annual reports.

The study uses data from 33 Islamic banks in six Islamic countries from 2006 to 2020. The authors estimate the model using the system GMM because it helps dealing with endogeneity problem, which are inherent in panel data.

The findings of the study reveal that there is a strong relationship between the sentiment expressed by management in annual reports and the current (future) financial performance of Islamic banks. The higher the positive sentiments of management, the better financial performance. In addition, the study also suggests that negative sentiments using term frequency-inverse document frequency is linked to a decrease in banks’ financial performance.

The study does not present the Islamic view on sentiment analysis in the context of Islamic scriptures due to the unavailability of a relevant dictionary.

The findings of the study suggest that developing accurate models with the help of textual information for performance prediction of Islamic banks help shareholders, regulators and policymakers avoid devastating events. Using textual information may also help reduce the information asymmetry between the management and shareholders, which may lead to more efficient bank supervision. The study can also help investors evaluate their prospective investments in the Islamic bank.

To the best of the authors’ knowledge, this study is the first of its kind that uses management sentiments for performance prediction of the Islamic banking sector. It may add a valuable contribution to the existing literature.

This study aims to investigate a unique approach to learning languages through self-directed online learning. Specifically, it explores the self-management abilities and skills learners need while learning a language outside traditional classroom settings when using mobile-assisted learning technology.

A mixed-methods approach was used in this study, including an online survey of 84 people and 10 semi-structured interviews.

Findings reveal the significant role of specific and well-defined learning goals in enhancing learners’ performance. These goals can be either self-initiated by the learners themselves or defined by the technological features of the learning platform. However, the presence of distractions in learners’ daily lives presents challenges to effective time management, affecting learners both physically and psychologically. A key aspect of self-directed language learning lies in the learners’ ability to seek out relevant human and material resources beyond the confines of a single mobile-assisted language learning (MALL) tool. The authenticity of these resources is crucial in ensuring meaningful and effective learning experiences.

Understanding how learners navigate and discover valuable resources is a central focus of this study. This research offers valuable insights into the field of self-directed language learning, revealing the pivotal role of self-management skills with mobile-assisted learning technology. The findings contribute to the broader field of language education and offer practical implications for educators and developers seeking to optimize self-directed language learning experiences through innovative and technologically driven approaches.

MALL is often ideal for individualized informal learning, but the existing literature focuses heavily on formal learning situations, underestimating the importance of MALL practices in various informal settings. Most research reports on MALL-based self-directed learning primarily sample traditional English-learning university students. Therefore, there is a need for research on how nontraditional older adult learners self-direct their language learning with mobile technology outside the classroom.

AI-based chatbots are revamping employee communication in organizations. This paper examines the adoption of AI-based employee experience chatbots by employees.

The proposed model is developed using behavioral reasoning theory and empirically validated by surveying 1,130 employees and data was analyzed with PLS-SEM.

This research presents the “reasons for” and “reasons against” for the acceptance of AI-based employee experience chatbots. The “reasons for” are – personalization, interactivity, perceived intelligence and perceived anthropomorphism and the “reasons against” are perceived risk, language barrier and technological anxiety. It is found that “reasons for” have a positive association with attitude and adoption intention and “reasons against” have a negative association. Employees' values for openness to change are positively associated with “reasons for” and do not affect attitude and “reasons against”.

This is the first study exploring employees' attitude and adoption intention toward AI-based EEX chatbots using behavioral reasoning theory.

This study analyses the advantages and weaknesses of using Facebook to aid the learning of Japanese as a foreign language.

A questionnaire survey was conducted to collect data from 100 Hong Kong Japanese language learners (who are generally fluent in Chinese and English), ranging from total amateur to advanced learners (Japanese Language Proficiency Test (JLPT) qualified at different levels).

The authors' results suggest that the advantages of using Facebook to help learn Japanese include: (1) serving as a free-of-charge, casual, and convenient learning platform; (2) enriching learners' knowledge beyond the language learning and (3) encouraging interactive and collaborative learning with other users for practicing the language. However, the low credibility and unstructured educational materials posted on Facebook and being easily distracted by other Facebook feeds are the major weaknesses of learning a language through Facebook. Furthermore, the authors' result shows that Facebook is especially effective for Japanese learning when learners fall into either one of the following groups: young, female, or intermediate (N2/3) learners.

Scant studies focus on the aid of learning Japanese via Facebook, especially Hong Kong learners' perceptions, or generally in the East. Therefore, this study aims to fill this research gap. The authors' findings will facilitate the students, teachers, and language institutions from Hong Kong and other countries to improve the students' effectiveness in learning and teaching Japanese.

The number of construction dispute cases has maintained a high growth trend in recent years. The effective exploration and management of construction contract risk can directly promote the overall performance of the project life cycle. The miss of clauses may result in a failure to match with standard contracts. If the contract, modified by the owner, omits key clauses, potential disputes may lead to contractors paying substantial compensation. Therefore, the identification of construction project contract missing clauses has heavily relied on the manual review technique, which is inefficient and highly restricted by personnel experience. The existing intelligent means only work for the contract query and storage. It is urgent to raise the level of intelligence for contract clause management. Therefore, this paper aims to propose an intelligent method to detect construction project contract missing clauses based on Natural Language Processing (NLP) and deep learning technology.

A complete classification scheme of contract clauses is designed based on NLP. First, construction contract texts are pre-processed and converted from unstructured natural language into structured digital vector form. Following the initial categorization, a multi-label classification of long text construction contract clauses is designed to preliminary identify whether the clause labels are missing. After the multi-label clause missing detection, the authors implement a clause similarity algorithm by creatively integrating the image detection thought, MatchPyramid model, with BERT to identify missing substantial content in the contract clauses.

1,322 construction project contracts were tested. Results showed that the accuracy of multi-label classification could reach 93%, the accuracy of similarity matching can reach 83%, and the recall rate and F1 mean of both can reach more than 0.7. The experimental results verify the feasibility of intelligently detecting contract risk through the NLP-based method to some extent.

NLP is adept at recognizing textual content and has shown promising results in some contract processing applications. However, the mostly used approaches of its utilization for risk detection in construction contract clauses predominantly are rule-based, which encounter challenges when handling intricate and lengthy engineering contracts. This paper introduces an NLP technique based on deep learning which reduces manual intervention and can autonomously identify and tag types of contractual deficiencies, aligning with the evolving complexities anticipated in future construction contracts. Moreover, this method achieves the recognition of extended contract clause texts. Ultimately, this approach boasts versatility; users simply need to adjust parameters such as segmentation based on language categories to detect omissions in contract clauses of diverse languages.

Purpose: This paper aims to study the impact of adoption eXtensible Business Reporting Language (XBRL) on earnings management.

Design/methodology/approach: This study uses a sample of all firms listed on the Indonesian stock exchange, except for finance and real-estate sectors from 2012 to 2019, with a total of 2,560 firms–years with panel data analysis.

Findings: Four findings in this study are listed as follow. First, the surprising result is that accrual earnings management increase after the adoption of XBRL. Second, after the adoption of XBRL, there was an increase in real earnings management. Third, the results of the study prove that the use of Big 4 auditors will weaken the increase in real earnings management after the adoption of XBRL. Finally, this study shows that after the adoption of XBRL, it turns out that both accrual and real earnings management experienced an increase.

Originality/value: This study contributes to providing an evaluation note to IDX regulators that the goals they want to achieve have not been achieved. This study provides empirical evidence for the debate over whether the adoption of XBRL is beneficial.

The purpose of this study is to examine the relationship between explanatory language, audit fees and audit hours to demonstrate that auditors use explanatory language in audit reports to explain perceived audit risk.

The authors construct the sentiment value, a novel audit risk proxy derived from audit reports, using big data analysis. The relationship between sentiment value and explanatory language is then investigated. The authors present the validity of their new metric by examining the relationship between sentiment value and accounting quality, taking audit fees and hours into account.

The authors first find that reporting explanatory language is positively related to audit fees. More importantly, the authors provide an evidence that explanatory language in audit reports is indicative of increased audit risk as it is negatively correlated with sentiment value. As a positive (negative) sentimental value means that the audit risk is low (high), the results indicate that auditors describe explanatory language in a negative manner to convey the inherent audit risk and receive higher audit fees from the risky clients. Furthermore, the relationship is strengthened when the explanatory language is more severe, such as reporting the multiple numbers of explanatory language or going-concern opinion. Finally, the sentiment value is correlated with accounting quality, as measured by the absolute value of discretionary accruals.

Contrary to previous research, the authors’ findings suggest that auditors disclose audit risks of client firms by including explanatory language in audit reports. In addition, the authors demonstrate that their new metric effectively identifies the audit risk outlined qualitatively in audit report. To the best of the authors’ knowledge, this is the first study that establishes a connection between sentiment analysis and audit-related textual data.