Search results

This study aimed to evaluate the effectiveness of using a virtual patient (VP) software program in increasing clinical reasoning skills confidence among medical students.

A quasi-experimental design was used to compare the outcomes of students’ confidence levels on clinical reasoning between problem-based learning (PBL) and VP program with 122 pre-clinical Year 2 medical students. History taking, physical examination, clinical investigations and diagnosis was investigated using four PBL cases in the oncology block. Pre- and post-differences in mean confidence scores between the arms were compared, as well as mean difference by type of case and skill category.

A total of 122/156 (78.2%) students participated; n = 55 were from 7 VP groups and n = 67 were from 13 control arm groups. For the primary outcome, the VP arm showed a statistically significant increase in confidence measures among 11/16 (4 cases × 4 skills) categories, compared to 4/16 for the control arm. The proportion of the students who indicated an improved confidence was statistically significant for the cervical cancer case for physical examination (60.0% improved in VP vs 12.5% in control), investigation (60.0% VP vs 18.8% control) and diagnosis (60.0% in VP vs 25.0% in control). Finally, analysis by case showed an increment in overall mean scores from the start to end of the case within the VP arm while the pattern was erratic in the control arm cases.

The study results showed that incorporating the VP into PBL was more effective in increasing students’ clinical reasoning confidence levels compared to the usual PBL. As the study utilized existing PBL cases, it demonstrates how medical schools can incorporate digital VP tools into pre-clinical years before students' transition to learning from actual patients in the clinical years.

Knowledge loss (KL) is still an unfortunate fact, causing many challenges, including poor organisational performance, despite prior efforts to investigate knowledge retention methods. Therefore, this study identifies early approaches to combat KL and poor organisational performance, shifting focus on employee personality traits.

Grounded on the social exchange theory (SET) cross-section data from 400 Chinese construction knowledge employees were used to investigate the role of internal work locus of control (IWLC) on job rotation (JR), KL and organisational performance. The data were analysed through IBM SPSS Statistics 25 and SmartPLS 4 software.

The results demonstrated that IWLC minimises KL and positively influences JR. Then, JR negatively influences KL and decreases in organisational performance. The negative mediating effect of JR in the relationship between IWLC, KL and decreased organisational performance were also found. Finally, KL proved to positively influence a decrease in organisational performance.

This study contributes to the new understanding of individual behaviour and its influence on organisational outcome variables. Specifically, for ultimate KL prevention and organisational performance improvement, an organisation should understand its employees' behaviours to establish progressive collective learning and knowledge sharing.

This study contributes to the new understanding of individual behaviour and its influence on organisational outcome variables. Specifically, for ultimate KL prevention and organisational performance improvement, an organisation should understand its employees’ behaviours to establish progressive collective learning and knowledge sharing.

This study is the first attempt to explore the influence of personality traits in the early minimisation of KL, particularly the role of IWLC and JR in combating KL and improving organisational performance.

Electronic wallets (e-wallet) systems have revolutionized transactional activities, prompting individuals to adopt digital payment methods. This study investigates the determinants of the continuance intention of the e-wallet system Touch 'n Go in Malaysia. We propose a framework integrating the diffusion of innovation theory (DOI) and mental accounting theory, thereby assessing the impacts of product-related factors (convenience, compatibility, perceived risk and perceived compatibility) and the moderating role of personal-related factors [personal innovativeness (PI) and personal anxiety].

Data were collected involving 381 Malaysian Touch 'n Go users. The partial least squares structural equation modeling (PLS-SEM) was applied as a data analysis technique.

As demonstrated by the results, convenience serves as a facilitator, while perceived risk acts as an inhibitor, influencing the continuance intention of the application. Compatibility and perceived complexity are found to be insignificant antecedents. However, neither PI nor personal anxiety was found as moderators.

This research brings contributions to the electronic commerce literature by extending the understanding of the predictors and moderators of the continuance intention of Touch 'n Go users in Malaysia. We discuss how these findings implicate the two theories and offer insights for service providers and policymakers to promote the post-adoption intentions of Touch 'n Go.

The purpose of the research is to investigate customer shopping behaviour for Italian market, extra virgin olive oil (EVOO) using the quick response (QR) code. The study uses means-end chain (MEC) theory to explore the connections between product attributes, personal values and consumer preferences.

The theory of means-end chain helps to uncover the underlying reasons why consumers choose specific products by examining the links between tangibles. There is a conceptual model developed with the help of MEC. The conceptual framework comprises product information and marketing communication, which constitute the sensory and non-sensory aspects of consumption behaviour, and it is empirically tested by CB-SEM technique. QR code usefulness and loyalty are treated as mediators to evaluate shopping behaviour to purchase EVOO.

The study has used MEC theory and has extended this theory. This study found that there is a profound impact of technology (QR code) on the buying behaviour of EVOO for the Italian customers. This study has also found that product information and product communication play a critical role towards shopping behaviour of Italian consumers regarding EVOO related products.

Innovation and technology adoption, especially the use of QR codes in understanding product features, has been an attractive area for research exploration. This could include details about the product’s origin, production methods and nutritional information. Such technology and innovation can change the business process management landscape of food-product distribution process. Not many studies are available which examine customers’ interest and shopping behaviour using QR codes, especially in the context of EVOO. Thus, this research study has a profound impact on the Italian and other European market.

In the Italian market, EVOO is more than just a product, it is a symbol of tradition, quality and cultural heritage. EVOO is not just a condiment, it is an essential ingredient that contributes to the overall flavour of dishes. This study explores the usage of QR code for EVOO related products. This is a unique study as there are a very few research studies available in this area of business process management and technology usage related to EVOO products. Moreover, this research study adds values to the body of literature of business process management, technology usage, innovation and food technology. Thus, this study is unique and adds value to the existing literature.

This study proposes a novel method utilising a speech-word pair bipartite network to examine the correlation structure between members of parliament (MPs) in the context of the UK- China relationship.

We construct MP-word pair bipartite networks based on the co-occurrence relationship between MPs and words in their speech content. These networks are then mapped into monopartite MPs correlation networks. Additionally, the study calculates correlation network indicators and identifies MP communities and factions to determine the characteristics of MPs and their interrelation in the UK-China relationship. This includes insights into the distribution of key MPs, their correlation structure and the evolution and development trends of MP factions.

Analysis of the parliamentary speeches on China-related affairs in the British Parliament from 2011 to 2020 reveals that the distribution and interrelationship of MPs engaged in UK-China affairs are centralised and discrete, with a few core MPs playing an integral role in the UK-China relationship. Among them, MPs such as Lord Ahmad of Wimbledon, David Cameron, Lord Hunt of Chesterton and Lord Howell of Guildford formed factions with significant differences; however, the continuity of their evolution exhibits unstableness. The core MP factions, such as those led by Lord Ahmad of Wimbledon and David Cameron, have achieved a level of maturity and exert significant influence.

The research has several limitations that warrant acknowledgement. First, we mapped the MP-word pair bipartite network into the MP correlation network for analysis without directly analysing the structure of MPs based on the bipartite network. In future studies, we aim to explore various types of analysis based on the proposed bipartite networks to provide more comprehensive and accurate references for studying UK-China relations. In addition, we seek to incorporate semantic-level analyses, such as sentiment analysis of MPs, into the MP-word -pair bipartite networks for in-depth analysis. Second, the interpretations of MP structures in the UK-China relationship in this study are limited. Consequently, expertise in UK-China relations should be incorporated to enhance the study and provide more practical recommendations.

Firstly, the findings can contribute to an objective understanding of the characteristics and connotations of UK-China relations, thereby informing adjustments of focus accordingly. The identification of the main factions in the UK-China relationship emphasises the imperative for governments to pay greater attention to these MPs’ speeches and social relationships. Secondly, examining the evolution and development of MP factions aids in identifying a country’s diplomatic focus during different periods. This can assist governments in responding promptly to relevant issues and contribute to the formulation of effective foreign policies.

First, this study expands the research methodology of parliamentary debates analysis in previous studies. To the best of our knowledge, we are the first to study the UK-China relationship through the MP-word-pair bipartite network. This outcome inspires future researchers to apply various knowledge networks in the LIS field to elucidate deeper characteristics and connotations of UK-China relations. Second, this study provides a novel perspective for UK-China relationship analysis, which deepens the research object from keywords to MPs. This finding may offer important implications for researchers to further study the role of MPs in the UK-China relationship.

This study proposes a novel scheme for analysing the correlation structure between MPs based on bipartite networks. This approach offers insights into the development and evolving dynamics of MPs.

This study examines whether the risk management committee (RMC) mitigates earnings management (EM) in Nigeria.

The study used a sample of 365 firm-year observations of Nigerian-listed nonfinancial companies from 2018 to 2022. Driscoll and Kraay’s fixed-effect standard error regression model is used to test the hypotheses.

The study finds that RMC size, expertise, meeting frequency and membership overlapping with the audit committee have a negative effect on both accrual earnings management (AEM) and real earnings management (REM). While RMC independence is found to have a negative effect on REM. Moreover, additional tests reveal that RMC effectiveness is significantly associated with lower EM practices. Further analysis using the industry level finds that RMC attributes mitigate EM practices in some industries. The results remain after rigorous, robust analysis for endogeneity and alternative regressions.

This study is limited to a sample of Nigerian-listed nonfinancial service companies for a period of five years, resulting in the non-generalizability of the findings to different contexts as the countries’ internal policies and regulations varied.

The findings have important implications for regulators, policymakers and investors that a stand-alone RMC can effectively help to evaluate potential risk activities and implement a proper risk management system, thereby mitigating EM practices. The result can help investors, analysts and other stakeholders across the international community in considering RMC information to evaluate potential risk and earnings management practices.

Following the NCCG 2018 reform in Nigeria that requires listed firms to create a standalone RMC, this study is among the earliest that examines the effect of RMC attributes on EM practices and emerging markets. As such, the findings may draw the attention of regulators and policymakers across the African market and the international community to the monitoring role of RMC attributes in mitigating EM practices.

The study presented healthcare service quality, lean thinking and Six Sigma to enhance patient satisfaction. Moreover, the notion of machine learning is combined with lean service quality to bring about the fundamental benefits of predicting patient waiting time and non-value-added activities to enhance patient satisfaction.

The study applied the define, measure, analyze, improve and control (DMAIC) method. In the define phase, patient expectation and perception were collected to measure service quality gaps, whereas in the measure phase, quality function deployment (QFD) was employed to measure the high-weighted score from the patient's voice. The root causes of the high weighted score were identified using a cause-and-effect diagram in the analysis phase.

The study employed a random forest, neural network and support vector machine to predict the healthcare patient waiting time to enhance patient satisfaction. Performance comparison metrics such as root-mean-square error (RMSE), mean absolute error (MAE) and R2 were accessed to identify the predictive model accuracy. From the three models, the prediction performance accuracy of the support vector machine model is better than that of the neural network and random forest models to predict the actual data.

Lean service quality improvement using DMAIC, QFD and machine learning techniques can be generalized to predict patient waiting times. This study provides better realistic insights into patient expectations by announcing waiting times to enable data-driven service quality deliveries.

Prior studies lack lean service quality, Six Sigma and waiting time prediction to reduce healthcare waste. This study proposes lean service quality improvement through lean Six Sigma (LSS), i.e. DMAIC and machine learning techniques, along with QFD and cause-and-effect diagram.