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This pathway paper highlights how geopolitics, risk and ethics affect critical minerals (CMs) supply chains (SCs). It identifies pathways to enable operations and SC management scholars to support this under-researched industry.

Qualitative research was undertaken in partnership with the Critical Minerals International Alliance (CMIA). Interviews were conducted with senior industry leaders from across CMs supply networks.

The CMs industry is distinctly different from conventional SCs and would greatly benefit from the development and application of operations and SC management theories.

The four pathways that require scholars’ attention comprise risk and resilience, SC opacity, supply constraints and ethics.

CM s are essential for products such as smart phones and the technologies required for decarbonisation and achieving net zero. The pathways address multifaceted challenges of benefit to industry stakeholders.

Improving the understanding of CMs SCs will support the decarbonisation agenda. Reducing the opacity within SCs would help address governance issues and curb unethical behaviours.

This paper draws on the expertise and insights gained from industry leaders. It establishes pathways and proposes theories and research questions for addressing the impact of geopolitics on CM operations and SCs.

The impact of banking deregulation on firms and economic growth is heavily researched, but not the effects on banks’ risk-taking. This study aims to investigate the impact of China’s 2009 banking deregulation on bank risk-taking, particularly from a balance sheet capacity perspective.

Using a difference-in-differences approach, this study examines how deregulation affects bank risk-taking. A three-stage regression strategy is employed to conduct mechanism analysis.

The results reveal that deregulated banks exhibit higher levels of risk-taking. Mechanism analysis confirms the bank balance sheet capacity channel: deregulation helps strengthen the net interest margin of deregulated banks, which enhances their balance sheet capacity and subsequently increases their risk appetite. In addition, deregulation improves firms’ access to long-term credit in regions with limited credit availability, especially for smaller firms, thereby expanding the financial sector’s service outreach.

While banking deregulation enhances credit availability for firms and supports the real economy, it also raises banks’ risk-taking, posing challenges to financial stability. Our study highlights the trade-off between supporting the real economy and maintaining financial stability under banking deregulation.

This study fills a gap in research on the effects of banking deregulation on bank risk-taking, highlighting the critical role of balance sheet capacity in this process.

This study investigates how consumers perceive the packaging of Philippine coffee social enterprise products and how this impression affects their willingness to purchase based on sensory expectations, brand attitude, product quality perception, and price perception.

Following a positivist research philosophy, this study used empirical methods, surveying 263 coffee consumers. The coffee packaging prototypes varied across material and graphic designs. The data was analyzed statistically using the Friedman test and Spearman correlation.

It was found that packaging elements elicit an equal share of emotional responses. Graphics have a greater impact than materials. Females are more affected by visuals, while materials influence males more. Further, packaging design correlates positively and significantly with consumer impressions and willingness to buy.

The sampling methodology limits generalizability. Future studies can use integrated models to analyze the effects of additional packaging variables like color and shape.

Coffee enterprises should focus on graphic rather than material elements. Paper packs with patterned graphics are most attractive to consumers. Targeted gender-sensitive packaging designs are needed. Standardized packaging can help build the Philippine coffee social enterprise industry and support small-scale farmer livelihoods. Environmentally sustainable materials should be prioritized.

The findings contribute to the success and growth of small-scale farmers and social enterprises in the Philippines. These businesses can attract more consumers, increase their market share, and ultimately generate more significant social impact by implementing packaging design strategies that effectively communicate product quality, sustainability, and social value.

The study uniquely integrates diverse methods to provide holistic insights into jointly analyzing the effects of packaging materials and graphics. It proposes an expanded conceptual role of packaging in shaping product perceptions using the affective response framework and Kansei approach.

This study aims to examine manufacturers’ strategic responses to consumer migration from offline to online channels, focusing on how these shifts affect their channel selection and business strategies.

This research uses a theoretical framework using a Stackelberg game model to analyze manufacturers’ decision-making processes amid evolving consumer behaviors. It intricately explores the strategic implications across three distinct channel structures: manufacturer direct sales (MD), retailer resale (RR) and retailer agency (RA), focusing on their economic outcomes and market dynamics. This approach is instrumental in decoding the multifaceted nature of channel migration and its impact on manufacturer–retailer relationships in the digital marketplace.

The research reveals that in MD and RA scenarios, as channel migration intensifies, manufacturers tend to lower both wholesale and online retail prices. Conversely, in the RR scenario, the set wholesale price is intricately linked to the market share, with higher prices set for smaller offline market shares. From a strategic standpoint, MD emerges as the optimal choice for maximizing manufacturer profits, while RA takes precedence when considering the entire supply chain’s profitability, particularly under high commission costs.

This research illuminates the impact of channel migration on manufacturers’ pricing strategies and channel selection. It not only advances the understanding of consumer behavior in multichannel retail environments but also offers practical insights for businesses in effectively managing online and offline channels.

This paper aims to analyse how local agri-food systems (LAFS), compared to other production models, can offer innovative responses to the important environmental challenges facing food production under the twin transition. These responses are more conducive to community inclusion and local development.

The paper combines territorial development, clusters and industrial districts literature with studies on agri-food industry environmental problems and twin transition technologies to develop an agri-food systems typology. This typology is based on a territorial approach to environmental challenges of food production and serves to illustrate the ways in which LAFS can provide innovative responses to these challenges.

The study allows to visualise the differences between LAFS and other agri-food production models, showing how the operationalisation and implementation of digitisation occur at territorial level and how rural communities are involved in the process. The theoretical proposal emphasises not assuming that technology is inherently beneficial but ensuring that its implementation is inclusive and generates social value for the communities.

The paper aims to enrich future research by adopting a territorial perspective to study the twin transition challenges associated with food production systems.

This study aims to work toward understanding the entrepreneurship ecosystem of agricultural social enterprises in the Philippines by exploring the interactions between policy, culture, supports and human capital domains.

The authors considered using an exploratory single-embedded case study approach, involving methodological triangulation of document analysis, semistructured interviews and participant observation. The authors analyzed the data using a narrative approach to map the ecosystem.

Through the research, the authors discovered that while each domain functions effectively individually, disconnects exist when interacting collectively as an ecosystem. The authors come to know that there is no policy consensus on social enterprise definitions, which limits specialized policy support. Although support services like incubators are available, the authors observed that awareness and accessibility vary based on location and business maturity. The authors also noted that human capital helps translate concepts into frameworks, but research tailored to agriculture and social entrepreneurship is limited. The authors come to the conclusion that collaboration and openness across domains are needed to strengthen connections and synergies.

The study was geographically limited to Luzon Island, and the authors did not include the finance and markets domains of the ecosystem model in the analysis.

Based on the findings, the authors identify strategies to reinforce connections, such as increasing awareness of support services, developing tailored policies for social enterprises, conducting specialized research and promoting collaboration across domains. The authors are convinced that implementing these strategies can further develop the agricultural social entrepreneurship ecosystem.

The study provides unique empirical insights into the agricultural social entrepreneurship ecosystem in the Philippines. The authors captured the narratives and experiences of key ecosystem stakeholders along the process. The authors have confidence that what the authors found can strategically guide policymakers and support organizations, educational institutions and social entrepreneurs to accelerate ecosystem development for greater social impact.

The informal sector contributes significantly to the growth of the Nigerian economy, accounting for over 40 percent of the annual growth of the gross domestic product and employing a majority of the nation's workforce. Indigenous manufacturing in the informal sector, which affects all spheres of the economy, can be deployed to promote sustainable development in Nigeria. Nigerian informal manufacturing is largely based on the use of indigenous technology, and as a result, this provides an opportunity for such players in the sector to consider the role their operations could play in promoting sustainable development in the country. This, they can realize by keying into the principles of sustainability and moderating the impact of their operations on the environment, society, and the economy at large. Nigeria, among other African countries, has persistently suffered the adverse effects of pollution and abuse of the environment in terms of flooding, deforestation, and desertification, among others. Those employing indigenous technology in manufacturing in the informal sector should therefore be encouraged to take the lead in Africa in order to promote and guarantee environmental sustainability, biodiversity and sustainable development in general by imbibing the culture of waste recycling, waste reduction, waste conversion, using green energy and the application of environmental and related resources with moderation, taking into consideration the interests of the future generation. In general, efforts should be made towards modernizing the application of indigenous technology in informal manufacturing in order to enhance sustainability in the country.

This paper is the genesis for robots and robotic technology and their introduction to the Caribbean Academic library community. This paper aims to explore the specific areas that this technology can improve as well as their adaptability and dynamic yet multifaceted nature it possesses.

A thorough assessment of literature was done of all developed libraries that are employing the services of robots and robotic technology in their daily operations. Additionally, a meticulous analysis was done of all Caribbean Libraries that have explored, are currently exploring or actively explored the implementation of robots and robotic technology for effective use in their libraries.

Seamless functionality as well as the reduction of mundane repetitive tasks by library staff is at the fore. Efficacy and heightened levels of accuracy are also found to be a great factor for implementation as well as speed of retrieval and offsite storage are further benefits to the implementation of robots and robotic technology.

This research primarily assessed material on robotics and robotic technology that offers unprecedented efficacy and accuracy in the processing of information and tasks assigned as well as smooth location and retrieval of library material resulting in reduction in wait time for all library users.

To the best of the author’s knowledge, this paper is the first of its kind and is intended to trigger a “light bulb” in the minds of decision-makers and managers of Library spaces as to the potential robots and robotic technology has on fostering greater levels of efficacy in certain key areas of libraries and help improve user services while adding to the theoretical body of knowledge available in the field on this fast rising area.

The purpose of this paper is to study the relationship between the attitudes of principals, municipality-specific aspects of reform implementation, and principals’ resistance to change.

The collected data are based on a multi-level structure. The levels of analysis are at the school level (school principal) and at the municipality level. Therefore, the research question posed in this study is examined using a quantitative multi-level analysis.

The results show that both the personal attitudes of school principals and adjustments made by the school presidency of the municipality affect the school principals’ willingness to change.

The study’s focus on schools limits the ability to generalize the results to apply to other organizations. Nevertheless, schools are an important object of study for change management research because they share crucial organizational characteristics with other organizations in the public sector.

Studies that have looked at the change reactions of leaders in the public school sector have rarely examined individual and collective factors together. We focus on both, since the municipalities in many countries have a certain amount of leeway in implementing reforms, meaning that their involvement is of central importance for a successful change process.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.