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Supply chain collaboration is increasingly important in today’s competitive business environment. While prior research has acknowledged the role of relationships in facilitating collaboration, few studies have considered inter-personal and inter-organisational attributes simultaneously. This study addresses this gap by investigating these two levels of the manufacturer-supplier relationship in Vietnam’s fishery industry and their impacts on supply chain collaboration.

The study employed a quantitative approach. A questionnaire was distributed to 635 fishery manufacturers in Vietnam by the drop-and-collect method. EFA and CFA were used to assess the reliability and validity of the measurement model, while CB-SEM was employed for structural model assessment and hypothesis testing.

Supply chain collaboration encompasses incentive alignment, collaborative communication, decision synchronisation, and information sharing. All antecedents, including commitment, inter-organisational trust, reciprocity, and inter-personal trust, positively affect collaboration. Inter-personal trust positively influences the other antecedents, and inter-organisational trust enhances commitment. Furthermore, there are partial mediating effects among these relationship attributes. However, no significant moderating effect regarding firm size is observed.

The study extends the social exchange theory to encompass both inter-personal and inter-organisational relationship attributes. Additionally, it pioneers in investigating the interrelatedness of these antecedents. By addressing the research gap in the Vietnamese fishery supply chain, it offers valuable insights for both academics and practitioners, contributing to theoretical understanding and practical implications in supply chain collaboration.

To map nurses’ actions performed during the care transitions from hospital to home of Covid-19 patients.

A scoping review based on the Joanna Briggs Institute guidelines was carried out. We searched in seven databases: PubMed/MEDLINE, BDENF, LILACS, SciELO, Embase, Scopus, Web of Science and Google Scholar. A two-step screening process and data extraction was performed independently by two reviewers. The findings were summarized and analyzed using a content analysis technique.

Of the total 5,618 studies screened, 21 were included. The analysis revealed nurses’ actions before and after patient’ discharge, sometimes planned and developed with the interprofessional team. The nurses’ actions included to plan and support patients’ discharge, to adapt the care plan, to use screening tools and monitor patients’ clinical status and needs, to provide health orientation to patients and caregivers, home care and face-to-face visiting, to communicate with patients, caregivers and other health professionals with phone calls and virtual tools, to provide rehabilitation procedures, to make referrals and to orient patients and families to navigate in the health system.

The results provide a broader understanding of the actions taken and challenges faced by nurses to ensure a safe care transition for Covid-19 patients from hospital to home. The interprofessional integration to discharge planning and the clinical nursing leadership in post-discharge monitoring were highlighted.

The nurses’ actions for Covid-19 patients performed during care transitions focused on coordination and discharge planning tailored to the needs of patients and caregivers at the home setting. Nurses monitored patients, with an emphasis on providing guidance and checking clinical status using telehealth tools.

Drawing on the personalization–privacy paradox and guided by means–end analysis, this study explores how consumers balance their concerns for privacy and the benefits of smart home device personalization and the role that trust plays in the process. More specifically, this study aims to investigate how perceptions of smart device personalization and privacy concerns are shaped by consumers’ experiences and the role of trust in the deliberation process.

In-depth interviews were conducted across diverse demographic groups of smart device users to shed light on the balancing act between personalization and privacy.

The study found that product experience, ownership type, perceived value of convenience and control and quality of life via “smart things” are key motivators for product usage. The benefits of tailored recommendations and high relevance are balanced against the risks of echo chamber effects and loss of control. The results also show the role of active involvement in the privacy calculus and trust level. The study points to the significance of an ecosystem-based service/business model in gaining consumer confidence when they balance between personalization and privacy.

Although many studies have explored trust, privacy concerns and personalization in an artificial intelligence (AI)-related context, few have addressed trust in the context of both smart devices and the personalization–privacy paradox. As such, this study adds to the existing literature by incorporating the concept of trust and addressing both privacy concerns and personalization in the AI context.

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.

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.

The purpose of this paper is to provide an organizing lens for viewing the distinct contributions to knowledge production from those research communities addressing the impact of competitive strategy on company performance in general, and the influence of cost leadership and differentiation strategy on organizational performance in detail.

The research methodology was based on the PRISMA review, and thematic analysis based on an iterative process of open coding was analyzed and then the sample was analyzed by illustrating the research title, objectives, method, data analysis, sample size, variables and country.

The main factor that influenced the competitive strategy is strategic growth; strategic growth has a significant influence on competitive strategy. Furthermore, competitive strategy will boost firm network, performance measurement and organization behavior. In the same way, the internal goal factor will enhance organizational effectiveness. Also, a differentiation strategy will support management practice factors, strategic positions, product price, product characteristics and company performance.

This study contributes to the literature by identifying a framework of competitive strategy factors, company performance factors, cost leadership strategy factors, differentiation strategy factors and competitive strategy with global market factors. This study provides a complete picture and description of the resulting body knowledge in competitive strategy and organizational performance.

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.