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This study aims to uncover apple preference and consumption in Indonesia, to disclose the risk of non-halal contamination of apples and the importance of maintaining the halal integrity of apples along the supply chain and to uncover the impacts of food miles of apples along supply chain segmentation.

This study adopted mixed research methods under a fully mixed sequential dominant status design (QUAN → qual). Data were collected through a survey in some Indonesian provinces (N = 396 respondents). Samples were collected randomly from individual consumers. The qualitative data were collected through interviews with 15 apple traders in Indonesia. Data were analysed using crosstab, chi-square and descriptive analysis.

First, Muslim consumers believe in the risk of chemical treatment of apples because it can affect the halal status of apples. Second, Indonesian consumers consider the importance of halal certification of chemical-treated apples and the additives for apple treatments. Third, the insignificance of domestic apple preference contributes to longer food miles at the first- and middle-mile stages (preference for imported apples). Fourth, apple consumption and shopping distance contribute to the longer food miles problem at the last-mile stage. Fifth, longer food miles have negative impacts, such as emissions and pollution, food loss and waste, food insecurity, financial loss, slow development of the local economy and food unsafety.

This research has implications for the governments, farmers, consumers (society) and business sectors.

This study proposes a framework of food miles under a halal supply chain (halal food miles) to reduce the risk of food miles and improve halal integrity. The findings from this research have theoretical implications for the development of the food mile theory, halal food supply chain and green supply chain.

The study assesses pre-service teachers' attitudes, confidence in research and intentions to use research by comparing the effectiveness of practical versus theoretical training in research methods courses.

This natural experiment examines the impact of the adaptations made to a research methods course for 848 pre-service teachers, due to the COVID-19 pandemic. The participants were naturally divided into two cohorts, each attending either the applied version, which required the completion of a whole research study, or the theoretical version, which did not require data collection and analyses. The data were collected through three questionnaires measuring attitudes, confidence and intentions toward research.

Inferential statistics revealed that pre-service teachers in the theoretical course (1) had more positive attitudes toward research and less anxiety, (2) had more intentions to integrate research in their daily practice and (3) had more confidence in engaging in educational research, compared to the pre-service teachers who completed the applied version of the course.

This study uniquely capitalizes on course adaptations, imposed due to the pandemic, to compare attitudes toward research. This is an ideal comparison because comparing attitudes of participants from different contexts involves many confounding variables. The study’s significance is amplified as, it not only elucidates the variances in attitudes, but also underscores the intricate relationship these attitudes share with the design of research courses. The insights yielded by this study offer substantial potential for reshaping pedagogical strategies in research methods instruction, thereby serving as a cornerstone for future educational innovations.

This study aims to predict the dark side of knowledge management risk to innovation in Portuguese small and medium enterprises (SMEs). It examines the spinner innovation model factors of knowledge creation, knowledge transfer, private knowledge, public knowledge and innovation in uncertain environments.

The authors developed a conceptual model to support the analysis. The survey data stemmed from a sample of 208 Portuguese SMEs in Portugal. The authors analyzed the primary data from the ad hoc survey using the data mining (deep learning) technique.

The research sets out and tests factors relevant to understanding how to predict innovation in uncertain business environments. This study identifies four factors fostering innovation in SMEs: knowledge creation, knowledge transfer, public knowledge management and private knowledge management. Knowledge creation showed the best return and presented the closest relationship with innovation.

Innovation models generally measure the relationships between variables and their impacts on the economy (economic and regional development). Predictive models are considered in the literature as a gap to be filled, especially in an uncertain environment in the SME context.