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This study aims to examine the effect of audit client’s use of blockchain (BC) on auditing accounting estimates (AEs), especially the inherent risk (IR), control risk (CR) and collection of audit evidence.

The study used a questionnaire to collect data for a sample of 249 auditors. A partial least squares method is used to test the hypotheses.

The results showed positive relationship between audit client’s use of BC and both IR and CR when auditing AEs. The results also showed the BC improves the collection of sufficient and appropriate audit evidence when auditing AEs.

This study did not address all the risks associated with auditing AEs, including fraud, detection, sampling and nonsampling risks, and the procedures and tests for auditing AEs.

There are several implications of this research, including that it informs the revision of auditing standards and guidelines to correspond with successive technological changes, which subsequently clarify the roles and responsibilities of auditors, and the study findings will also cause changes to the design and form of audit procedures so as to obtain sufficient and appropriate audit evidence.

To the best of the authors’ knowledge, this study is considered the first of its kind that deals with the effects of audit client’s use of BC on audit AEs in the Middle East and North Africa region. This study also presented different sets of measures as proxies for measuring IR, CR and AE.

The commercialization of research produced by universities constitutes a core facet of academic entrepreneurship (AE). Academic literature reveals the need to shed light on entrepreneurial processes in higher education institutions (HEIs). This study intends to fill this gap by researching the mechanisms for facilitating AE and the variables that can moderate the relationship between such mechanisms and AE in Portuguese HEIs.

Our research model aims to assess the mechanisms of academic entrepreneurship (AE) within a sample of 125 Portuguese public higher education institutions (HEIs). To test our research hypotheses, we employed a structural equation model (SEM) using the partial least squares (PLS) method. Additionally, our evaluation examines the potential moderating effects of incubator programs, support initiatives, and proof-of-concept programs (PoCs). Our research model seeks to evaluate the mechanisms for facilitating AE and explore the effects of including incubator programs, support initiatives, and PoCs as moderators. The seven variables (Research mobilization, Unconventionality, Industry collaboration, University policies, Incubator programs and support initiatives, Proof-of-concept programs, and academic entrepreneurship) were measured using a 7-point Likert scale.

The results revealed that different drivers of AE influence the creation and development of entrepreneurial activities. Our findings also show the moderating effects of incubator programs, support initiatives, and proof-of-concept programs on AE. We find that incubator programs, other support initiatives, and PoCs maintain a moderating effect on AE and benefit their respective HEIs.

The study examines only the Portuguese HEI context. Therefore, generalizing these results necessitates reservations. However, the responses came from various actors in HEIs, from different academic backgrounds and research interests. This makes the results more generalizable. Limitations are evident in external validity, given that we gathered the data over a relatively short period.

Observed factors are explored to gain a deeper understanding of their influence on the mechanisms of AE. The implications arise from the new perspective presented and the methodology used to identify mechanisms capable of fostering AE. We hope this research will encourage other researchers to study this topic further.

the engagement of universities at the global level should be emphasised in future policy. While universities in innovation systems often have a local focus, their engagement in innovation ecosystems transcends the boundaries of geographic locations.

PoCs had a significant positive moderating effect on the impact of research mobilization and university policies on AE. Thus, we find interactions between universities and industry boost AE. This study demonstrates how AE benefits HEIs by extending orientation towards mobilizing research, unconventional approaches, cooperation with industry, and university policy implementation. We thus advocate a new approach, demonstrating the influence that the mobility of research, unconventionality, industry collaboration, and university policies hold over AE.

This paper aims to seek accounting graduates' perspectives on the demand for accounting in their workplaces, on the gaps in accounting education (AE), and on the future of the accounting profession, inspired by the new definition of accounting proposed by Carnegie et al. (2021, 2022, 2023a), to adopt a strong focus on sustainable development goals (SDGs) in AE to inculcate tertiary students with the skills that lead them to approach and apply accounting as a multidimensional technical, social and moral (TSM) practice.

The online qualitative survey was distributed to 100 randomly selected New Zealand accounting graduates in order to gather insights from their workplaces. All responses from the 30 graduates who completed the questionnaire underwent qualitative analysis using Leximancer software, which automatically identifies high-level concepts and insights and offers interactive visualizations without bias.

The graduates’ experiences underscore the ongoing significance of technical skills in the New Zealand workplace. They emphasized the lack of non-technical skills training, stressed the necessity of diverse business knowledge and highlighted the importance of automation and digital skills.

The implications for transforming AE involve adopting an activist approach to integrate a TSM perspective into teaching and learning and being open to an interdisciplinary approach to expose tertiary students to the impact of accounting on sustainable development, including collaboration with professional bodies for real-world experiences.

The importance of engaging with SDG-related narratives is stressed to stimulate further discussion, debate and research aimed at identifying practical solutions for AE as a facilitator for SDGs in realizing accounting as a TSM practice.

This study aims to investigate the conditional indirect effect of academic self-efficacy in the interplay between academic motivation and academic satisfaction through academic engagement among university students.

A cross-sectional study was performed on 1,638 Vietnamese university students (31.9% males and 68.1% females) aged 16 to 36 (Mean = 20.06, SD = 1.428). The participants filled out a questionnaire with the Vietnam versions of the General Self-Efficacy Scale, Academic Motivation Scale, Academic Life Satisfaction Scale and Academic Engagement Scale. Model 4 and Model 7 in the PROCESS macro were used for the mediation analysis and the moderated mediation analysis.

Results showed that the indirect effect of academic engagement on the academic motivation-academic satisfaction link was significant. Furthermore, academic self-efficacy moderated this indirect effect. The indirect effect was stronger among students with high academic self-efficacy and weaker among students with low academic self-efficacy.

This study’s findings contribute to educational research on academic satisfaction and can be used by institutions of higher education and educators to enhance academic satisfaction among university students.

Automatic dependent surveillance-broadcast (ADS-B) is the foundational technology of the next generation air transportation system defined by Federal Aviation Authority and is one of the most precise ways for tracking aircraft position. ADS-B is intended to provide greater situational awareness to the pilots by displaying the traffic information like aircraft ID, altitude, speed and other critical parameters on the Cockpit Display of Traffic Information displays in the cockpit. Unfortunately, due to the initial proposed nature of ADS-B protocol, it is neither encrypted nor has any other innate security mechanisms, which makes it an easy target for malicious attacks. The system is vulnerable to various active and passive attacks like message ingestion, message deletion, eavesdropping, jamming, etc., which has become an area of concern for the aviation industry. The purpose of this study is to propose a method based on modified advanced encryption standard (AES) algorithm to secure the ADS=B messages and increase the integrity of ADS-B data transmissions.

Though there are various cryptographic and non-cryptographic methods proposed to secure ADS-B data transmissions, it is evident that most of these systems have limitations in terms of cost, implementation or feasibility. The new proposed method implements AES encryption techniques on the ADS-B data on the sender side and correlated decryption mechanism at the receiver end. The system is designed based on the flight schedule data available from any flight planning systems and implementing the AES algorithm on the ADS-B data from each aircraft in the flight schedule.

The suitable hardware was developed using Raspberry pi, ESP32 and Ra-02. Several runs were done to verify the original message, transmitted data and received data. During transmission, encryption algorithm was being developed, which has got very high secured transmission, and during the reception, the data was secured. Field test was conducted to validate the transmission and quality. Several trials were done to validate the transmission process. The authors have successfully shown that the ADS-B data can be encrypted using AES algorithm. The authors are successful in transmitting and receiving the ADS-B data packet using the discussed hardware and software methodology. One major advantage of using the proposed solution is that the information received is encrypted, and the receiver ADS-B system can decrypt the messages on the receiving end. This clearly proves that when the data is received by an unknown receiver, the messages cannot be decrypted, as the receiver is not capable of decrypting the AES-authenticated messages transmitted by the authenticated source. Also, AES encryption is highly unlikely to be decrypted if the encryption key and the associated decryption key are not known.

Implementation of the developed solution in actual onboard avionics systems is not within the scope of this research. Hence, assessing in the real-time distances is not covered.

The authors propose to extend this as a software solution to the onboard avionics systems by considering the required architectural changes. This solution can also bring in positive results for unmanned air vehicles in addition to the commercial aircrafts. Enhancement of security to the key operational and navigation data elements is going to be invaluable for future air traffic management and saving lives of people.

The proposed solution has been practically implemented by developing the hardware and software as part of this research. This has been clearly brought out in the paper. The implementation has been tested using the actual ADS-B data/messages received from using the ADS-B receiver. The solution works perfectly, and this brings immense value to the aircraft-to-aircraft and aircraft-to-ground communications, specifically while using ADS-B data for communicating the position information. With the proposed architecture and minor software updates to the onboard avionics, this solution can enhance safety of flights.

This study aims to address this unexplored influence of international assignment types on the development, transfer and utilization of career capital by assigned repatriates from host to home country. In response to existing literature gap, it aligns with the need for qualitative case studies that delve into threats to the self-reinforcement of repatriates' career capital.

By mean of a qualitative case study, this paper deepens understanding of linkages and processes in career capital development and clarify the interplay between individual interpretations of career actions and the organizational context in which they unfold. Nineteen qualitative interviews with assigned repatriates explored the impact of exposure to new career contexts.

This study provides valuable insights into the complex dynamics of career capital development and transfer during international assignments. It elucidates the impact of career context on assigned repatriates' career capital, emphasizing challenges in career capital generation, dispersion and absorption within multinational enterprises. It contributes to understanding the complexities of (new) managerial capacity development by revealing varied effects that international assignments can exert on individuals' immediate competencies and career capital.

If the assigned expatriate/repatriate’s understanding of the firm’s assignment motive, and their own motive (understanding/reason) for the assignment corresponds then expectations of outcomes can be better managed. Organizations otherwise run the risk of perpetuating inequities in the career development opportunities of employees.

Studies on career capital emphasize its qualities or examine different globally mobile employee types. Yet there's a gap in understanding how the type of assignment impacts career capital development, transfer and utilization. This research fills this void by investigating the international transfer of career capital from host to home country specifically for assigned repatriates.

Nutrition labels provide a cost-effective method of conveying nutrition information to consumers. This study aimed to assess the use of nutrition facts panels, knowledge of traffic light labelling (TLL) and perceived healthiness of food items using TLL among consumers.

A web-based cross-sectional study was conducted among adults in the United Arab Emirates (UAE) (n = 1,322). TLL knowledge score was derived for each participant. Conjoint analysis was used to calculate the utilities and relative importance of the perceived healthiness scores for four attributes (fat, saturated fat, total sugar, salt) at the aggregate level.

Participants had a positive attitude towards TLL but were less familiar with TLL than the nutrition facts panel (47.4 vs 85.8%). The mean TLL knowledge score was 3.6 out of 7 (51.6%). Younger age, higher education, higher income, and health-related qualifications were associated with higher scores. Conjoint analysis showed that participants tend to choose products with greener labels, especially for sugars (80.1%) and avoid red labels for fats. Sugars had the highest percentage value of relative importance compared to the other attributes (27.1%).

The study outcomes offer valuable insights into the extent of consumer awareness, comprehension and utilization of nutrition facts panels in the UAE. These findings contribute essential knowledge for a deeper understanding of the impact of nutrition labels on consumer behaviour and decision-making in the region.

Extending the springboard perspective with the resource dependence theory, the authors posit that cross-border mergers and acquisitions (M&As) are a new channel for emerging economy firms (EEFs) to enhance their technology capabilities. This study aims to examine the impact of cross-border M&As initiated by EEFs on their technology augmentation vis-à-vis matched domestic M&A cases and investigate the factors influencing the difference in post-merger innovation capability.

This paper estimates the post-acquisition innovation capability of acquirers from emerging economies (EEs) that engage in cross-border M&As. To remove possible selection bias, the authors leverage a difference-in-difference-style approach in combination with a matched sample constructed by pairing each cross-border M&A case with a similar domestic deal. The data set contains 266 cross-border M&As and 266 matched domestic M&A deals between 2003 and 2011, whereby acquirers are based in 6 EEs and targets are in 36 countries consisting of both EEs and advanced economies (AEs).

The present empirical results show that cross-border M&As engaged by EEFs are an important engine for improving EEFs’ innovation capability through technology augmentation. The main empirical results are as follows. First, compared with matched domestic acquirers with similar characteristics, EE cross-border M&As have a positive effect on innovation capability. Second, the positive effect of the EEFs’ cross-border M&As relative to the matched domestic M&As on innovation capability is driven largely by cross-border M&As with targets in AEs. Third, the increase in post-M&A innovation capability of the EE cross-border acquirers comes mainly from deals where targets are based in countries with relatively superior human capital and innovation capability than those of the acquirers.

To the best of the authors’ knowledge, this study is the first systematic study of whether cross-border M&As serve as an effective channel of technology augmentation for EE acquirers compared to matched domestic acquirers with similar characteristics.

Depression is a mental health problem characterized by a persistent sense of sadness and loss of interest. EEG signals are regarded as the most appropriate instruments for diagnosing depression because they reflect the operating status of the human brain. The purpose of this study is the early detection of depression among people using EEG signals.

(i) Artifacts are removed by filtering and linear and non-linear features are extracted; (ii) feature scaling is done using a standard scalar while principal component analysis (PCA) is used for feature reduction; (iii) the linear, non-linear and combination of both (only for those whose accuracy is highest) are taken for further analysis where some ML and DL classifiers are applied for the classification of depression; and (iv) in this study, total 15 distinct ML and DL methods, including KNN, SVM, bagging SVM, RF, GB, Extreme Gradient Boosting, MNB, Adaboost, Bagging RF, BootAgg, Gaussian NB, RNN, 1DCNN, RBFNN and LSTM, that have been effectively utilized as classifiers to handle a variety of real-world issues.

1. Among all, alpha, alpha asymmetry, gamma and gamma asymmetry give the best results in linear features, while RWE, DFA, CD and AE give the best results in non-linear feature. 2. In the linear features, gamma and alpha asymmetry have given 99.98% accuracy for Bagging RF, while gamma asymmetry has given 99.98% accuracy for BootAgg. 3. For non-linear features, it has been shown 99.84% of accuracy for RWE and DFA in RF, 99.97% accuracy for DFA in XGBoost and 99.94% accuracy for RWE in BootAgg. 4. By using DL, in linear features, gamma asymmetry has given more than 96% accuracy in RNN and 91% accuracy in LSTM and for non-linear features, 89% accuracy has been achieved for CD and AE in LSTM. 5. By combining linear and non-linear features, the highest accuracy was achieved in Bagging RF (98.50%) gamma asymmetry + RWE. In DL, Alpha + RWE, Gamma asymmetry + CD and gamma asymmetry + RWE have achieved 98% accuracy in LSTM.

A novel dataset was collected from the Central Institute of Psychiatry (CIP), Ranchi which was recorded using a 128-channels whereas major previous studies used fewer channels; the details of the study participants are summarized and a model is developed for statistical analysis using N-way ANOVA; artifacts are removed by high and low pass filtering of epoch data followed by re-referencing and independent component analysis for noise removal; linear features, namely, band power and interhemispheric asymmetry and non-linear features, namely, relative wavelet energy, wavelet entropy, Approximate entropy, sample entropy, detrended fluctuation analysis and correlation dimension are extracted; this model utilizes Epoch (213,072) for 5 s EEG data, which allows the model to train for longer, thereby increasing the efficiency of classifiers. Features scaling is done using a standard scalar rather than normalization because it helps increase the accuracy of the models (especially for deep learning algorithms) while PCA is used for feature reduction; the linear, non-linear and combination of both features are taken for extensive analysis in conjunction with ML and DL classifiers for the classification of depression. The combination of linear and non-linear features (only for those whose accuracy is highest) is used for the best detection results.

This study aims to examine the research output on cyber insurance from 2002 to 2021 through an extensive bibliometric analysis. It examines the cyber insurance resources and how the process of cyber insurance works.

This paper uses Scopus and VOSviewer to analyze cyber insurance papers. Using 503 papers from Scopus, this paper enhances the understanding of cyber insurance through collaborative network maps of experts and researchers.

The study comprehensively evaluates the development of cyber research. The results show that the number of research articles on cyber insurance has significantly increased since 2009.

The study's results offer practical implications for researchers to gain knowledge on the latest trends and developments in the domain. In addition, the study highlights the significance of cyber insurance in mitigating financial risks linked to cyberattacks, potentially boosting the investment of more organizations in such policies. Furthermore, practitioners can enhance their understanding of the various types of cyber insurance policies and their coverage.

Our results are likely to encourage practitioners, computer scientists, auditors, accountants and lawyers to contribute further to corporate strategies, data analytics and business operations to mitigate cyber risk consequences. In addition, understanding regarding the cyber insurance concept formed between experts and researchers is limited. This paper fills this gap by evaluating and identifying the development of cyber insurance literature.