The scarcity of water resources, climate change, and water-wasting behavior have contributed to a worsening water crisis in many countries. This has raised concerns among farmers and increased pressure on governments. Digital technologies provide effective solutions to reduce resource waste; therefore, exploring farmers’ willingness to implement water-smart farming technologies to reduce waste, especially in developing countries, requires further analysis. To address this gap, this paper aims to investigate the factors that influence farmers’ intention to minimize water waste in Algeria. The theory of planned behavior was extended with the constructs of perceived usefulness of water-smart farming and knowledge of water waste reduction. Primary data were collected from 202 farmers to test the model. The empirical evidence suggests that attitudes, knowledge about water waste reduction, perceived usefulness, and perceived behavioral control significantly predict farmers’ intention to reduce waste. These factors explained 54.6% of the variation in intention. However, social influence was not found to be a significant antecedent of intentions. This paper’s findings can provide useful insights for various stakeholders on how to encourage farmers to reduce water waste and offer guidance on strategies for achieving sustainability in agriculture.

Drug waste poses a real threat to human health and the environment. Therefore, recycling and sustainability scholars have recently sought practical solutions to the drug waste problem. Furthermore, several governments have made significant efforts to reduce the negative effects of waste, such as establishing programs to take back unwanted drugs (expired and unused) for recycling (i.e., drug reuse or drug redispersing). However, many of these programs have failed to achieve their goals. In this context, it is expected that creating green start-ups to collect unwanted drugs will contribute to solving this problem. Accordingly, this study aims to investigate the antecedents of intentions to create green start-ups for collecting unwanted drugs. To this end, the authors integrate start-up self-efficacy and drug waste collection-related knowledge constructs into the theory of planned behavior (TPB). Using a self-administered paper questionnaire and relying on a convenience sample, data were collected from 328 students in six Algerian universities. A hierarchical multiple regression was conducted to test the proposed study model. Theresults revealed that perceived behavioral control (PBC) (β = 0.187, p < 0.001), attitudes (β = 0.182, p = 0.002), start-up self-efficacy (β = 0.169, p = 0.001), drug waste collection-related knowledge (β = 0.161, p < 0.001), and subjective norms (β = 0.088, p < 0.05) have a positive significant effect on the intention to create a start-up to collect unwanted drugs. In conclusion, this paper contributes to reducing drug waste by investigating the drivers of intention to create green start-ups for collecting unwanted drugs. Therefore, our study is expected to provide valuable insights for hospitals, pharmacies, pharmaceutical manufacturers, environmental protection associations, and stakeholders interested in reverse logistics.

The rapid expansion of digital platforms has significantly influenced consumer purchasing behaviors, particularly in the agri-food sector. Therefore, this paper investigates the key factors driving customers’ intention to use green agri-food delivery apps (GAFDAs) by integrating trust and electronic word of mouth (eWOM) into the Theory of Planned Behavior (TPB) framework. Additionally, this study examines gender as a moderating variable, assessing whether its influence alters the relationships between key determinants and behavioral intention. Data were collected from 252 Algerian consumers, and the proposed model was tested using SmartPLS 4 and SPSS 26.0. The results confirm that attitude, subjective norms, perceived behavioral control (PBC), trust, and eWOM positively and significantly influence the intention to use GAFDAs, with PBC emerging as the strongest predictor. Moreover, gender moderates the effect of trust on behavioral intention, with trust significantly influencing men’s adoption decisions but not those of females. In contrast, subjective norms and PBC are stronger predictors for female consumers. These findings highlight the importance of gender-specific marketing strategies to enhance GAFDA adoption. This study contributes to the literature by extending TPB with trust, eWOM, and gender moderation, offering valuable insights for marketers, policymakers, and app developers promoting sustainable food consumption.

Product waste in grocery supply chains remains a major concern for multiple stakeholders, particularly retailers, due to the direct financial losses it generates and the potential risks it poses to customer health and safety. In this context, digital technologies—especially artificial intelligence (AI)—offer promising opportunities to improve retail performance and reduce waste. Accordingly, this study aims to investigate the factors influencing retailers’ intentions to adopt AI-based solutions for product waste reduction. To achieve this objective, the Technology Acceptance Model (TAM) was extended by incorporating three additional constructs (i.e., perceived ethical responsibility, product waste reduction-related knowledge, and perceived economic utility of AI for product waste reduction). Data were collected from a purposive sample of 214 grocery retailers operating in major cities in northern Algeria. Structural Equation Modeling (SEM) was employed to test the proposed research model and hypotheses. The results indicate that retailers’ behavioral intentions to use AI for product waste reduction are significantly influenced by perceived economic utility of AI, AI for product waste reduction-related knowledge, perceived usefulness, and perceived ease of use. In contrast, perceived ethical responsibility for product waste reduction did not exhibit a statistically significant effect, although its relationship with behavioral intention was positive. This study contributes to the growing literature on AI adoption for waste reduction in the retail sector, particularly within developing country contexts, and offers practical insights for policymakers and industry stakeholders seeking to promote the adoption of digital technologies for sustainable supply chain management.

Underserved communities face persistent challenges due to limited access to healthcare services. Digital volunteering offers opportunities for healthcare professionals to support these populations remotely. This study examined factors associated with healthcare workers’ intentions to participate in digital healthcare volunteering in Algeria. An extended technology acceptance model was used, incorporating perceived organizational support (OS), altruism, and social responsibility. A convenience sample of 142 healthcare workers completed a survey, and hierarchical regression analysis was conducted. Results indicated that perceived ease of use, social responsibility, altruism, perceived OS, and perceived usefulness were each significantly associated with intentions to engage in digital volunteering. The extended model explained 75.6% of the variance in intention, highlighting the relevance of psychological, organizational, and ethical factors. These findings provide insights for policymakers, healthcare organizations, and developers seeking to support digital volunteering initiatives. Limitations include the cross-sectional design and the use of convenience sampling, which may affect generalizability. Future research should consider longitudinal designs, larger and more diverse samples, and cross-cultural comparisons to validate and extend these findings.