Digitalization of the Logistics Process in Short Food Supply Chains.:An online Viable System Model application during the COVID-19 pandemic

This paper reports an ongoing exercise concerning the design of a logistics App to support operations within Farmers’ Markets in Mexico. This exercise is part of a wider research agenda focused on ‘Supporting Alternative Food Networks’ (SAFeNET). This is a research agenda to conceive, build, implement, and develop better-informing decision-making processes that support effective and efficient AFNs (also known as Short Food Supply Chains) logistics operations in a digital environment, through smooth flows of goods and information among producers, AFNs coordinators, and consumers. This view calls for taking a systemic approach to help collectives of people to improve their autonomy and viability. Initial plans were to conduct this collaborative design exercise, using the Viable System Model (VSM) as a conversational tool. Accordingly, a series of face-to-face interviews and a focus group were planned. However, the lockdown due to COVID-19 forced researchers to abandon the face-to-face option and conduct the primary data collection online. The VSM intervention had to be adapted for its use on an online platform, in such a way that the platform would support knowledge building interactively, with a series of participants. This paper describes the format and visual appearance of the online VSM framework, its application, and the lessons learned through this exercise. Two points deserve to be highlighted: First, although the exercise outcome was very valuable for the next stage of the design, the participants’ capacity for collective and individual reflection during the workshop was limited. Second, participants continued adding comments via the adopted online visual collaboration platform after the workshop ended, showing an understanding of the process and commitment beyond the researchers’ expectations. The outcomes from this experiment are promissory, suggesting that online Systems Thinking interventions deserve further development

Challenges and Opportunities of Generative AI for Higher Education as Explained by ChatGPT

ChatGPT is revolutionizing the field of higher education by leveraging deep learning models to generate human-like content. However, its integration into academic settings raises concerns regarding academic integrity, plagiarism detection, and the potential impact on critical thinking skills. This article presents a study that adopts a thing ethnography approach to understand ChatGPT’s perspective on the challenges and opportunities it represents for higher education. The research explores the potential benefits and limitations of ChatGPT, as well as mitigation strategies for addressing the identified challenges. Findings emphasize the urgent need for clear policies, guidelines, and frameworks to responsibly integrate ChatGPT in higher education. It also highlights the need for empirical research to understand user experiences and perceptions. The findings provide insights that can guide future research efforts in understanding the implications of ChatGPT and similar Artificial Intelligence (AI) systems in higher education. The study concludes by highlighting the importance of thing ethnography as an innovative approach for engaging with intelligent AI systems and calls for further research to explore best practices and strategies in utilizing Generative AI for educational purposes

Beyond links and chains in food supply: a community OR perspective

This theoretical paper complements traditional OR approaches to improve micro-businesses’ performance. When looking at local micro-businesses, we find that current supply chain and operations theory that focuses on efficiency and economic-based criteria for chain and network integration, is inapplicable and external organisation inappropriate. An illustration shows how traditional modelling exercises may fall short in better-informing independent-minded micro-entrepreneurs on how to collaborate, even though they recognise benefits from such endeavour. The illustration concerns consideration of food micro-producers, not as links constituting a chain, but as members of a community. This paper explores two different approaches to apply Community OR research principles. On one hand, the application of OR methods to phenomena in the ‘community’; on the other, the development of research on ‘community operations’; which are symbolised as C+OR and CO+R respectively. These approaches are associated to two different research languages: of needs and for interactions. Main contributions of this paper are: first, we show that collaboration does not always need shared aims. Second, we offer a circular process where the identification of collective actions may help organisations to improve individually; and vice versa. Third, we suggest how to develop the role of a stronger collective actor by means of collaboration

Integrating Industry 4.0 in higher education using challenge-based learning: An intervention in Operations Management

This is a set of files concerning students' comments on a CBL intervention.The data analysis process was based on a six-phase thematic analysis approach (Braun and Clarke, 2006) using NVivo software. The analysis involved an iterative process of coding, categorization of codes into overarching themes, and naming of final codes and themes. The results identified a range of themes and codes regarding how students experienced CBL. Overall, some similarity exists among the experiences reported by different teams. Using a six-phase thematic analysis (Braun and Clarke, 2006) and NVivo software, we identified four main themes that relate to 1) ‘The challenge’, 2) ‘Teamwork’, 3) ‘Feelings’ and 4) ‘Challenge-based learning’.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Using Generative Artificial Intelligence Tools to Explain and Enhance Experiential Learning for Authentic Assessment

The emergence of generative artificial intelligence (GenAI) requires innovative educational environments to leverage this technology effectively to address concerns like academic integrity, plagiarism, and others. Additionally, higher education needs effective pedagogies to achieve intended learning outcomes. This emphasizes the need to redesign active learning experiences in the GenAI era. Authentic assessment and experiential learning are two possible meaningful alternatives in this context. Accordingly, this article investigates how GenAI can enhance teaching and learning by constructively addressing study situations beyond conventional learning approaches and cultivating high-order skills and knowledge acquisition. This study employs thing ethnography to examine GenAI tools’ integration with authentic assessment and experiential learning and explore implementation alternatives. The results reveal insights into creating human-centered and GenAI-enhanced learning experiences within a constructive alignment. Specific examples are also provided to guide their implementation. Our contributions extend beyond the traditional use of GenAI tools as mere agents-to-write or agents-to-answer questions to become agents-to-support experiential learning for authentic assessment. These findings underscore the transformative role of GenAI tools in enhancing teaching and learning efficacy and effectiveness. The limitations in treating GenAI tools as subjects in thing ethnography are acknowledged, with potential for future implementation evaluation

Designing experiential learning activities with generative artificial intelligence tools for authentic assessment

Purpose This article investigates the application of generative artificial intelligence (GenAI) in experiential learning for authentic assessment in higher education. Recognized for its human-like content generation, GenAI has garnered widespread interest, raising concerns regarding its reliability, ethical considerations and overall impact. The purpose of this study is to explore the transformative capabilities and limitations of GenAI for experiential learning. Design/methodology/approach The study uses “thing ethnography” and “incremental prompting” to delve into the perspectives of ChatGPT 3.5, a prominent GenAI model. Through semi-structured interviews, the research prompts ChatGPT 3.5 on critical aspects such as conceptual clarity, integration of GenAI in educational settings and practical applications within the context of authentic assessment. The design examines GenAI’s potential contributions to reflective thinking, hands-on learning and genuine assessments, emphasizing the importance of responsible use. Findings The findings underscore GenAI’s potential to enhance experiential learning in higher education. Specifically, the research highlights GenAI’s capacity to contribute to reflective thinking, hands-on learning experiences and the facilitation of genuine assessments. Notably, the study emphasizes the significance of responsible use in harnessing the capabilities of GenAI for educational purposes. Originality/value This research showcases the application of GenAI in operations management education, specifically within lean health care. The study offers insights into its capabilities by exploring the practical implications of GenAI in a specific educational domain through thing ethnography and incremental prompting. Additionally, the article proposes future research directions, contributing to the originality of the work and opening avenues for further exploration in the integration of GenAI in education