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The purpose of this study is to develop and test the causal relationships between alcohol consumption, social support, illness perception, fatigue and functional status among persons with liver cirrhosis. The hypothesized model was developed based on the theory of unpleasant symptoms.

A cross-sectional study was conducted among 400 persons with liver cirrhosis by stratified random sampling. Data were collected at outpatient departments from four public hospitals across three regions in Thailand. Six self-report questionnaires were utilized to collect data from March to August 2020. The developed model was verified via a structural equation modeling analysis.

The proposed model fit with the empirical data considering from χ²/df = 2.397 (p = 0.061), root mean square error of approximation (RMSEA) = 0.056, comparative fit index (CFI) = 0.985, Tucker–Lewis index (TLI) = 0.981, and standardized root mean square residual (SRMR) = 0.048. The model explained 71.30% of the total variance in functional status. Social support was the most influential factor affecting functional status both directly and indirectly through illness perception. Illness perception had a negative direct effect on functional status and a positive indirect effect on functional status through fatigue. Fatigue had a negative direct effect on functional status. Alcohol consumption was found as the lowest influential factor affecting functional status.

This is the first study that explores the characteristics of functional status among persons with liver cirrhosis comprehensively. A causal relationship among various variables found in this study would shed light as important fundamental data for developing interventions to enhance functional status among this population.

The study aim is to evaluate the contribution of Blockchain technology (Cryptobanking) using expected operating model (EOM) to address the pain points in reconciliation at middle and back-office operational levels in assessing the significance of this technology on return on investment.

A structured questionnaire was designed to collect primary data using a stratified sampling method from 120 respondents working in leading Investment banks operating in the geographical locality of urban Bangalore. Demographic variables, accounting variables, data reporting variables, approach variables, variables of EOM were considered to validate the hypothesis with the help of statistical tools, namely ANOVA, and Multiple Stepwise Regression Analysis.

The results obtained confirm that there is significant difference in reconciliation with implementation of an innovative business process. Financial analysis is the highest predictor of ROI when integrated with technology as the adapted Blockchain innovation in reconciliation is the most influencing factor in enhancing, improving ROI playing a pivotal role in the Investment banks.

Blockchain technology (Cryptobanking) facilitates in transforming the reconciliation process of these banks with improved operational efficiency. Blockchain and settlement platforms offer inter-organization solutions facilitating in the reconciliation of various transactions in real-time through a trust-based network in the form of digital settlements with better consortiums.

This paper provides remarkable insight into the structural components of a firm's core competence and its development via research and development (R&D) activities for innovation and exporting activities.

The authors have used a positivist design and a deductive methodology. The authors have examined the extant literature developing a theoretical framework to empirically investigate the relationships between a firm's core competence, organisational learning (OL), tacitness, dynamic capability and R&D activities. To carry out this investigation, the authors have collected stratified sample data from 330 firms operating in North East England, a peripheral region of England.

The authors have found that there are indeed significant statistical relationships between these structural components, R&D activities and a firm's core competence, and this nexus is pertinent to innovation and exporting. Furthermore, it is found that North East England is significantly constrained by the lack of finance, technological capability, experts and brain drain. Based on these findings, the authors propose a cooperative R&D framework to narrow down these constraints to assist firms in developing core competencies for innovation and exporting in peripheral regions.

There is an urgent need to investigate the incidence of knowledge-driven activities, R&D, the extent of innovation and exporting activities of firms operating in North East England, a peripheral region of the United Kingdom (UK).

This study provides an original and systematic investigation of the firm's core competence and its formation via key structural components for innovation and exporting within an empirical framework.

This research aimed to study the impact of compensation on employee retention and turnover intentions among healthcare employees. The study also tested the mediation role of job satisfaction in the relationship.

In the present study, self-administrated questionnaires were distributed among 600 doctors working in public hospitals of Pakistan, following stratified sampling. The data analysis was conducted through SPSS and smart-PLS.

Results of the present study supported all the hypotheses (H1–H7), such as the significant relationship of compensation with employee retention and turnover intentions. Results further confirmed the mediation effect of job satisfaction between compensation and employee retention as well as compensation and turnover intentions.

This study is useful for policymakers and organizational managers since the study provides guidelines on employee retention and high turnover intentions and how these factors are influenced by improved compensation.

This study sheds light on the relationship of compensation together with employee retention and turnover intentions through the mediating role of job satisfaction in healthcare context, which was overlooked in the existing literature.

The purpose of this study is to evaluate the effect of learner–learner and learner–facilitator interactions on learner satisfaction and their substitutability.

A quantitative survey research focusing on 130 students was used to collect data. Stratified sampling was preferred for the study, with a Likert type research instrument being administered online.

Learner–learner and learner–facilitator interactions mediate the effect of e-learning on learner satisfaction. These sets of interactions act as independent mediators, each playing a pivotal role in enhancing learner satisfaction. The interactions are however not substitutable.

The findings of the study will provide insights to academic administrators, to ensure that the two sets of interactions contribute to an effective and conducive e-learning environment and ultimately learner satisfaction. Deliberate efforts should be made to design increased e-learning interactivity into the cyber learning environment for effective learning.

The findings result in a ranking of learner–learner and learner–facilitator interactions for increased learner satisfaction. The interactions cannot be substituted without affecting the level of learner satisfaction. The interactions play a complementarity role, and collectively, enrich the quality of e-learning. A collective deployment is recommended.

Creating employment for all seems to be impossible in emerging nations as the unemployment rate is rising faster than the number of job openings available. Studies on the other hand show that creating jobs is simple if the right resources and opportunities are made available. The present study aims to examine the effects of various demographic social and environmental factors on the entrepreneurial intention of engineering undergraduates in Chhattisgarh state.

Correlational research design was incorporated in the present study. The author applied stratified random sampling to collect samples of 1,000 engineering undergraduates enrolled in their third and fourth years in different technical colleges/universities in Chhattisgarh state. Structural equation modelling and confirmatory factor analysis were applied to analyse the data.

The results revealed that demographic, social and environmental factors greatly influenced engineering undergraduates’ entrepreneurial intention. However, age and occupation do not predict their self-employment intention.

The present research itself is a novel study, especially in Chhattisgarh state, where the area of examining the effects of demographic, social and environmental factors on entrepreneurial intention among technical undergraduates has been limitedly explored.

Equity is an issue that pervades all aspects of primary care provision for children and as such is a recurring theme in the Models of Child Health Appraised project. All European Union member states agree to address inequalities in health outcomes and include policies to address the gradient of health across society and target particularly vulnerable population groups. The project sought to understand the contribution of primary care services to reducing inequity in health outcomes for children. We focused on some key features of inequity as they affect children, such as the importance of good health services in early childhood, and the effects of inequity on children, such as the higher health needs of underprivileged groups, but their generally lower access to health services. This indicates that health services have an important role in buffering the effects of social determinants of health by providing effective treatment that can improve the health and quality of life for children with chronic disorders. We identified common risk factors for inequity, such as gender, family situation, socio-economic status (SES), migrant or minority status and regional differences in healthcare provision, and attempted to measure inequity of service provision. We did this by analysing routine data of universal primary care procedures, such as vaccination, age at diagnosis of autism or emergency hospital admission for conditions that can be generally treated in primary care, against variables of inequity, such as indicators of SES, migrant/ethnicity or urban/rural residency. In addition, we focused on the experiences of child population groups particularly at risk of inequity of primary care provision: migrant children and children in the state care system.

Intrapreneurial ability (IA) of employees strengthens an organization's internal as well as external growth. Employees' IA makes innovation a continuous practice and augments organization's intellectual capital (IC). This intellectual capital-based intrapreneurial ability (ICIA) helps professionals to effectively handle changes in the business ecosystem by creating innovative solutions. The onus of assessing and inculcating ICIA is a joint responsibility of both academia and industry. In academia, teacher as a servant leader (TASL) contributes towards building ICIA of working professionals (WP) by enhancing their self-efficacy (SE). The paper aims to strengthen the industry–academia interface by analyzing the role of TASL and SE in influencing the ICIA of WP.

Using a stratified sampling technique, data from 387 WP is analyzed on SmartPLS-4 to study the interrelationship between the stated constructs and the role of SE as a mediator between TASL and ICIA. PLSpredict is used to study the predictive relevance of the proposed model.

High R² = 0.654 shows that 65% of ICIA is determined by SE and TASL; reflecting model's robustness. SE partially mediates the relationship between TASL and ICIA. Results reported a higher ICIA of male WP than their female counterpart. The results indicate the low predictive accuracy of the model.

The proposed model of industry–academia partnership allows assessment of ICIA for enhancing corporate value in the present gig economy. The study also highlights the relevance of ICIA, particularly, for developing economies. In knowledge-driven economy, exploring the new ICIA will help organizations to draft a more robust performance measurement system.

This unique industry–academia partnership studies the role of TASL towards enhancing SE and ICIA of WP. The novelty of ICIA would enrich and provide a new perspective in IA literature. Additionally, the study also examines the role of gender in the ICIA of WP.

The purpose of this paper is to investigate the differences of audit quality of financial statements among auditors, including Big 4 and non-Big 4 auditors.

By employing cross-sectional analysis of compliance (a proxy of audit quality) of goodwill impairment testing of listed firms in the context of Hong Kong, the variation of audit quality of financial statements of auditees has been shown.

Audit quality of Big 4 auditors is viewed to be higher than that of non-Big 4 audit firms and the homogeneity of audit quality among Big 4 auditors is not long accepted, but variation.

Even though unqualified opinions have been given on the auditors’ reports, the quality of financial statements audit is a skeptical issue because of the high level of non-compliance of goodwill impairment testing under International Financial Reporting Standards.

This study does emphasize the higher audit quality of financial statements of Big 4 auditors than that of non-Big 4 auditors and stresses the variation of audit quality among Big 4 auditors.

User-generated social media comments can be a useful source of information for understanding online corporate reputation. However, the manual classification of these comments is challenging due to their high volume and unstructured nature. The purpose of this paper is to develop a classification framework and machine learning model to overcome these limitations.

The authors create a multi-dimensional classification framework for the online corporate reputation that includes six main dimensions synthesized from prior literature: quality, reliability, responsibility, successfulness, pleasantness and innovativeness. To evaluate the classification framework’s performance on real data, the authors retrieve 19,991 social media comments about two Finnish banks and use a convolutional neural network (CNN) to classify automatically the comments based on manually annotated training data.

After parameter optimization, the neural network achieves an accuracy between 52.7 and 65.2 percent on real-world data, which is reasonable given the high number of classes. The findings also indicate that prior work has not captured all the facets of online corporate reputation.

For practical purposes, the authors provide a comprehensive classification framework for online corporate reputation, which companies and organizations operating in various domains can use. Moreover, the authors demonstrate that using a limited amount of training data can yield a satisfactory multiclass classifier when using CNN.

This is the first attempt at automatically classifying online corporate reputation using an online-specific classification framework.