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Programming education is being rapidly transformed by generative AI tools and educators must determine how best to support students in this context. This study aims to explore the experiences of programming educators and students to inform future education provision.

Twelve students and six members of faculty in a small technology-focused university were interviewed. Thematic analysis of the interview data was combined with data collected from a survey of 44 students at the same university. Self-determination theory was applied as an analytical framework.

Three themes were identified – bespoke learning, affect and support – that significantly impact motivation and learning outcomes in programming education. It was also found that students are already making extensive use of large language models (LLMs). LLMs can significantly improve learner autonomy and sense of competence by improving the options for bespoke learning; fostering emotions that are conducive to engendering and maintaining motivation; and inhibiting the negative affective states that discourage learning. However, current LLMs cannot adequately provide or replace social support, which is still a key factor in learner motivation.

Integrating the use of LLMs into curricula can improve learning motivation and outcomes. It can also free educators from certain tasks, leaving them with more time and capacity to focus their attention on developing social learning opportunities to further enhance learner motivation.

To the best of the authors’ knowledge, this is the first attempt to explore the relationship between motivation and LLM use in programming education.

Small retail stores in the luxury market face significant challenges due to fluctuations in market demand. This task turns challenging as it requires effectively coordinating and translating customer needs into specific requirements that align with retail goals and available resources. However, limited empirical research exists investigating how managers can address service value and quality attributes in small retail stores. This article aims to bridge this gap by investigating the role of quality function deployment (QFD) in improving market and quality requirements management in small retail stores.

Based on the case study, a customer survey was initially conducted to gather information on critical characteristics valued in the luxury retail segment. QFD was used to assist the company in identifying and prioritizing key quality attributes to meet customer requirements effectively.

The findings demonstrate that implementing QFD in small luxury retail stores empowers managers to identify previously neglected product and service quality aspects. The article shows that QFD informs organizational adaptations that align with the demands of the retail market, leading to an improved ability to meet customer expectations and enhance customer value through the development of enhanced products and services. The study showcases the efficacy of the tested methodology in effectively capturing and prioritizing both tangible and intangible customer needs in retail.

Findings offer valuable insights to retail managers of small luxury stores, providing actionable market-oriented strategies. By implementing the recommended practices, managers can improve the store’s competitiveness and better cater to the customer base.

This study contributes to bridging persistent knowledge gaps by addressing the unique context of small luxury retail stores and introducing the application of QFD in this setting. The insights gained from this research are relevant to both retailing and quality management literature. Considering the growing prevalence of transformations in the retail industry, the study provides practical implications for retail managers in effectively navigating these changes.

The crude oil market plays a key role in addressing the issue of energy economics. This paper aims to detect the causality relationship between the crude oil market and economy based on the financial system.

This paper used the static and dynamic Hatemi-J Bootstrap Toda–Yamamoto and Diebold–Yilmaz connectedness index. The Hatemi-J Bootstrap Toda-Yamamoto approach allows researchers to use nonstationary data and that method is robust to nonnormal distribution and heteroscedasticity. The Diebold–Yilmaz connectedness index model provides researchers to detect the power of connectedness besides linkage direction. The analyzed period is the span from January 3, 2005 to October 3, 2022.

The results show bidirectional causality in the full sample but unidirectional causality before and after the 2008 financial crisis. During the 2008 financial crisis period and the COVID-19 period, there was a bidirectional and unidirectional causality, respectively. The connectedness approach indicates that the crude oil market affects financial stress through investors’ risk preferences.

The Diebold–Yilmaz spillover index model is based on vector autoregression methods with a stationarity precondition. However, some of the five dimensions that constitute the financial stress index (FSI) are nonstationary in level. Therefore, the authors takes the first difference of the nonstationary data.

The linkage between the crude oil market and the FSI provides useful information for investors and policymakers. For instance, this paper indicates that an investor wanted to forecast future value of the crude oil (financial stress) should consider the current and past values of financial stress (crude oil). Moreover, policymaker should consider the crude oil market (FSI) to make a policy proposal for financial system (crude oil market).

Recently, indicators of economic activity levels (economic policy uncertainty, implied volatility index) have begun to be considered to analyze the relationship between energy and the economy but very little is known in the literature about the leading and lagging roles of data in subsample periods and the linkage channel. The other originality of this research is using the new econometric approaches.

The aim of this study is twofold: (1) to gain insight into the different perspectives on the relationship between patient and person centeredness and (2) to learn more about the differences between non-academic and academic stakeholders in the healthcare system.

This mixed-methods study includes a scoping review on person and patient centeredness and in-depth interviews with patients, caregivers, staff and management of healthcare organizations. The data were analyzed by following the six phases of Braun and Clarke.

The analysis of the data showed four different perspectives on patient versus person centeredness: (1) they are synonyms; (2) one term is favorite; (3) they should be in balance; and (4) person centeredness is the surplus on top of patient centeredness.

There are different perspectives on patient versus person centeredness. Perspectives differ between people and can change over time. Some people feel like a patient all the time, other people feel like a person all the time, and some feel like a patient at one point in time and as a person at another point in time.

These different perspectives can have important implications for the so-called moments of truth. In their role as patients, people value functional encounters and in their identity as people they value meaningful encounters with caregivers.

By unraveling these different perspectives, novel insights were found in the different perspectives people can take.