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Value-based healthcare suggested using patient-reported information to complement the information available in the medical records and administrative healthcare data to provide insights into patients' perceptions of satisfaction, experience and self-reported outcomes. However, little attention has been devoted to questions about factors fostering the use of patient-reported information to create value at the system level.

Action research design is carried out to elicit possible triggers using the case of patient-reported experience and outcome data for breast cancer women along their clinical pathway in the clinical breast network of Tuscany (Italy).

The case shows that communication and engagement of multi-stakeholder representation are needed for making information actionable in a multi-level, multispecialty care pathway organized in a clinical network; moreover, political and managerial support from higher level governance is a stimulus for legitimizing the use for quality improvement. At the organizational level, an external facilitator disclosing and discussing real-world uses of collected data is a trigger to link measures to action. Also, clinical champion(s) and clear goals are key success factors. Nonetheless, resource munificent and dedicated information support tools together with education and learning routines are enabling factors.

Current literature focuses on key factors that impact performance information use often considering unidimensional performance and internal sources of information. The use of patient/user-reported information is not yet well-studied especially in supporting quality improvement in multi-stakeholder governance. The work appears relevant for the implications it carries, especially for policymakers and public sector managers when confronting the gap in patient-reported measures for quality improvement.

This study aims to enhance understanding of malnutrition's effect on academic achievement of primary school students.

This is a descriptive, cross-sectional design built on Roy's adaptation model (RAM). This study uses a random cluster sample, consisting of 453 primary school students. Contextual stimuli (mother's educational level, income and child’s breakfast eating) and focal stimuli (wasting, thinness, body mass index and stunting) were examined regarding adaptive responses to student’s academic achievement.

The investigation revealed that Model 1, which took into account factors of age, gender, the frequency of breakfast, income, the number of family members and the education of mothers, explained 12% (R² = 0.12) of the variance in academic achievement. Stuntedness (β = −3.2 and p < 0.01), BMI (β = 0.94 and p < 0.001), family income per month (β = 5.60 and p < 0.001) and mother's education (β = 2.79 and p < 0.001) were the significant predictors in Model 2.

This study provides evidence that malnutrition is associated with ineffective academic achievement. Moreover, variables such as the mother's level of education, family income and the child’s breakfast consumption have a significant impact on academic achievements.

RAM is a useful framework for determining factors affecting people's reactions to difficult circumstances.

The purpose of this study is to propose a framework for the convergence of maturity model and education and evaluation in accounting.

The present research was conducted in two phases. In the first phase, to determine the indicators of convergence of the maturity model and education and evaluation in accounting, a Meta-Synthesis method was used. The conceptual model includes two dimensions of “Teaching and learning processes” and “Evaluation methods"; five levels of initial, repeatable, defined, managed and optimized; and a total number of 35 indicators. In the second phase, a questionnaire was developed, and academics as accounting faculty members in Iranian public universities were employed to fill out the questionnaire electronically and present a final framework. Having received the questionnaires, 66 questionnaires were analyzed statistically.

The results showed that the two dimensions of “Teaching and learning processes” and “Evaluation methods” considering initial, repeatable, defined, managed and optimized levels include 35 indicators, which form a framework for the convergence of maturity model and education and evaluation in accounting. The results show that both dimensions have positive and significant regression path coefficients in the convergence model. Moreover, the dimension of teaching and learning processes has the highest regression path coefficient indicating a greater impact on the convergence model. Besides, all five levels have positive and significant regression path coefficients with dimensions. Finally, in this study, all indicators were prioritized according to five levels.

Due to the success of maturity models and the urgent developments that require transformative improvements in accounting education, maturity models can respond to the challenges associated with education and learning in accounting. Thus, conceiving an image of the convergence of maturity model, education and evaluation in accounting seems imperative which has been scarcely investigated previously.

Drawing upon the stimulus-organism-response model, this study aims to explore the impact of soft aspects of service quality on revisit intention through the mechanism of perceived empathy.

For the examination of the hypothesized relationships, the study adopts structural equation modelling to analyse the data of 562 respondents (i.e. 281 family members and 281 inpatients).

The empirical results suggest that service quality increased family member empathy perception, which, in turn, improved inpatients’ revisit intentions.

Past studies have focused on the roles of overall service quality. The authors have extended the literature by examining the specific but important aspect of service quality and its effects on emotional response. Importantly, the study explains that the affective reactions of a patient’s family, fastened with perceived empathy, have a central role in influencing the patients’ subsequent reactions. Moreover, the prior studies collected the data either from hospital employees or patients. However, in the present study, the authors used a unique sample (family members as well as patients) to have a deeper understanding. Thus, the study enhances the literature on the stimuli-response (i.e. service quality – revisit intentions) relationship in the context of service marketing in general and health care in specific. Important academic and managerial contributions and recommendations for future research are discussed.

Research on artificial intelligence (AI) and its potential effects on the workplace is increasing. How AI and the futures of work are framed in traditional media has been examined in prior studies, but current research has not gone far enough in examining how AI is framed on social media. This paper aims to fill this gap by examining how people frame the futures of work and intelligent machines when they post on social media.

We investigate public interpretations, assumptions and expectations, referring to framing expressed in social media conversations. We also coded the emotions and attitudes expressed in the text data. A corpus consisting of 998 unique Reddit post titles and their corresponding 16,611 comments was analyzed using computer-aided textual analysis comprising a BERTopic model and two BERT text classification models, one for emotion and the other for sentiment analysis, supported by human judgment.

Different interpretations, assumptions and expectations were found in the conversations. Three subframes were analyzed in detail under the overarching frame of the New World of Work: (1) general impacts of intelligent machines on society, (2) undertaking of tasks (augmentation and substitution) and (3) loss of jobs. The general attitude observed in conversations was slightly positive, and the most common emotion category was curiosity.

Findings from this research can uncover public needs and expectations regarding the future of work with intelligent machines. The findings may also help shape research directions about futures of work. Furthermore, firms, organizations or industries may employ framing methods to analyze customers’ or workers’ responses or even influence the responses. Another contribution of this work is the application of framing theory to interpreting how people conceptualize the future of work with intelligent machines.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

Although there is a growing body of literature on how celebrity involvement impacts the effectiveness of destination marketing, the underlying mechanisms of that relationship are still underexplored. Based on the affect transfer and meaning transfer theories, this study aims to examine the impact of celebrity attachment on customer delight toward K-culture and K-culture attachment, affective and cognitive images of Korea, and the intention to visit Korea.

Online survey data were collected from 2,614 US residents, representing various demographic characteristics. For the data analysis, the partial least squares-structural equation modeling was conducted to evaluate the structural model and test the hypotheses.

The results showed that celebrity attachment is positively related to customer delight toward K-culture and K-culture attachment, which, in turn, positively influences affective and cognitive images of Korea. Additionally, K-culture attachment positively influences cognitive and affective images of Korea, which are positively related to the intention to visit Korea.

By using the affect transfer theory and meaning transfer theory, this study provides valuable insights into how consumer’s attachment to celebrities has spillover effects on the decision-making process. This study also adds a new concept, customer delight connected to cultural experience, in the context of destination marketing.

By understanding the importance and influence of people’s intimacy with media characters, practitioners can apply parasocial relationship theory, affect transfer theory and meaning transfer theory to their marketing strategies.

As one of the few empirical studies that examines the impact of celebrity attachment on consumers’ perceptions and behaviors, this study can make significant contributions to the destination marketing literature.