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This study aims to explore the potential use of drones in special library services, aiming to enhance accessibility, services and reliability. It examines how drones can provide library materials to individuals unable to access traditional services and addresses challenges associated with drone implementation.

This study involves a literature review and case studies to analyze the feasibility and benefits of incorporating drones into special libraries. This study also discusses the synergy between drone technology and artificial intelligence (AI) in enhancing library operations.

Drones have the potential to transform special libraries by automating tasks, improving efficiency and expanding outreach. Their application ranges from inventory management and book retrieval to security, surveillance and outreach initiatives. AI-powered drones can provide real-time data on library usage and enhance cost-effectiveness. However, challenges including costs, privacy concerns and regulatory frameworks need to be addressed.

The integration of drones and AI in special library services presents a novel approach to revolutionizing library operations. This study uniquely combines these technologies, emphasizing the importance of proactive consideration of challenges and prospects for successful implementation.

This paper aims to illustrate the growing role of drones in applications involving physical tasks.

Following a short introduction, this first provides a brief introduction to drone technology. It then describes and discusses products and applications involving physical tasks in agricultural and forestry, maritime rescue, firefighting and product delivery. Finally, brief conclusions are drawn.

Excluding military applications, drones were initially used primarily for image acquisition. Numerous different designs have since been developed with greatly varying wing configurations, payloads, flight duration, power sources and other features which are increasingly being used to conduct physical tasks. In the applications considered here drones are applying agrochemicals and dispersing crop and tree seeds; saving lives by deploying lifeboats and buoyancy aids; extinguishing fires in high-rise buildings and forests; and delivering groceries, food, mail, medicines and humanitarian aid, often in and to remote locations.

This study provides a detailed insight into selection of applications in which drones conduct physical tasks.

This paper aims to propose a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas. The objective of the model is to perform analytical studies, evaluate the performance of drone delivery systems for humanitarian logistics and can support the decision-making on the operational design of the system – on where to locate drone take-off points and on assignment and scheduling of delivery tasks to drones.

This simulation model captures the dynamics and variabilities of the drone-based delivery system, including demand rates, location of demand points, time-dependent parameters and possible failures of drones’ operations. An optimization model integrated with the simulation system can update the optimality of drones’ schedules and delivery assignments.

An extensive set of experiments was performed to evaluate alternative strategies to demonstrate the effectiveness for the proposed optimization/simulation system. In the first set of experiments, the authors use the simulation-based evaluation tool for a case study for Central Florida. The goal of this set of experiments is to show how the proposed system can be used for decision-making and decision-support. The second set of experiments presents a series of numerical studies for a set of randomly generated instances.

The goal is to develop a simulation system that can allow one to evaluate performance of drone-based delivery systems, accounting for the uncertainties through simulations of real-life drone delivery flights. The proposed simulation model captures the variations in different system parameters, including interval of updating the system after receiving new information, demand parameters: the demand rate and their spatial distribution (i.e. their locations), service time parameters: travel times, setup and loading times, payload drop-off times and repair times and drone energy level: battery’s energy is impacted and requires battery change/recharging while flying.

The study aims to optimize truck routes by minimizing social and economic costs. It introduces a strategy involving diverse drones and their potential for reusing at DNs based on flight range. In HTDRP-DC, trucks can select and transport various drones to LDs to reduce deprivation time. This study estimates the nonlinear deprivation cost function using a linear two-piece-wise function, leading to MILP formulations. A heuristic-based Benders Decomposition approach is implemented to address medium and large instances. Valid inequalities and a heuristic method enhance convergence boundaries, ensuring an efficient solution methodology.

Research has yet to address critical factors in disaster logistics: minimizing the social and economic costs simultaneously and using drones in relief distribution; deprivation as a social cost measures the human suffering from a shortage of relief supplies. The proposed hybrid truck-drone routing problem minimizing deprivation cost (HTDRP-DC) involves distributing relief supplies to dispersed demand nodes with undamaged (LDs) or damaged (DNs) access roads, utilizing multiple trucks and diverse drones. A Benders Decomposition approach is enhanced by accelerating techniques.

Incorporating deprivation and economic costs results in selecting optimal routes, effectively reducing the time required to assist affected areas. Additionally, employing various drone types and their reuse in damaged nodes reduces deprivation time and associated deprivation costs. The study employs valid inequalities and the heuristic method to solve the master problem, substantially reducing computational time and iterations compared to GAMS and classical Benders Decomposition Algorithm. The proposed heuristic-based Benders Decomposition approach is applied to a disaster in Tehran, demonstrating efficient solutions for the HTDRP-DC regarding computational time and convergence rate.

Current research introduces an HTDRP-DC problem that addresses minimizing deprivation costs considering the vehicle’s arrival time as the deprivation time, offering a unique solution to optimize route selection in relief distribution. Furthermore, integrating heuristic methods and valid inequalities into the Benders Decomposition approach enhances its effectiveness in solving complex routing challenges in disaster scenarios.

This study presents a systematic literature review (SLR) of the interdisciplinary literature on drones in last-mile delivery (LMD) to extrapolate pertinent insights from and into the logistics management field.

Rooting their analytical categories in the LMD literature, the authors performed a deductive, theory refinement SLR on 307 interdisciplinary journal articles published during 2015–2022 to integrate this emergent phenomenon into the field.

The authors derived the potentials, challenges and solutions of drone deliveries in relation to 12 LMD criteria dispersed across four stakeholder groups: senders, receivers, regulators and societies. Relationships between these criteria were also identified.

This review contributes to logistics management by offering a current, nuanced and multifaceted discussion of drones' potential to improve the LMD process together with the challenges and solutions involved.

The authors provide logistics managers with a holistic roadmap to help them make informed decisions about adopting drones in their delivery systems. Regulators and society members also gain insights into the prospects, requirements and repercussions of drone deliveries.

This is one of the first SLRs on drone applications in LMD from a logistics management perspective.

After completion of the case study, students will be able to evaluate the journey of launching a business in an emerging market context and judge how opportunities and challenges can be navigated to build sustainable enterprises; assess the relevance of individual attributes and process skills that are necessary for entrepreneurial agency to transform social structures through entrepreneurial action; formulate an argument highlighting the role of the entrepreneurial ecosystem in growing a competitive business in an emerging market context; make an informed decision and critique how accelerators and incubators affect the development of ideas and access to finance in South Africa; and propose various strategic options available for technology entrepreneurs, considering the challenges they face in emerging economies.

In April 2023, Queen Ndlovu, CEO and founder of QP Drone Tech, a provider of drone business solutions, was considering options to fulfil her original dream of manufacturing drones in South Africa. She had encountered obstacles to achieving the same in 2019, and had decided to focus on providing commercial drone consulting services. However, her dream had not extinguished, and in 2022, she decided to restart her efforts. She found practical support from The Innovation Hub, an incubator that was supporting her business, which enabled her to enhance the prototype of her drone. She then had to think about how she would manufacture drones locally by ensuring she had access to production infrastructure, funding, partners and customers. Would she be able to gain a competitive advantage that would differentiate her from competitors? Or should she reconsider whether she should be manufacturing in the first place, as there are risks and benefits for smaller businesses in this regard.

This case is intended for discussion in postgraduate diploma in business and Master of Business Administration courses.

Teaching notes are available for educators only.

CSS 3: Entrepreneurship.

The purpose of the paper is to analyse the sequence of forces acting as barriers in the usage of drones in the construction industry using interpretive structural modelling (ISM). The usage of drones in the construction industry is brought out phase-wise with the help of literature and live cases. Barriers to the usage of drones in construction and steps to derive the interaction between them are described in detail.

The extraction of barriers to the usage of drones in construction is done through cases and supported by the literature. The identification of the interaction between the barriers is done through multi-criteria decision models, namely, ISM and Matriced Impacts Croises Multiplication Appliquee a un Classement (MICMAC) and the results are presented in the form of a hierarchical structure. The paper highlights the potential for the usage of drones in every phase of construction across three stages of construction and eight different applications.

The findings on the interaction between barriers show that technical and research and development-related barriers have a higher driving power, ultimately influencing negativity among stakeholders in drone usage for construction. By analysing interrelationships between barriers, management can frame suitable strategies to adopt drone usage in projects. Awareness about the strength of certain barriers can help management take steps to mitigate the same.

By analysing interrelationships between barriers, management can frame suitable strategies to adopt drone usage in projects. A major limitation is a restriction of the study area to the Indian subcontinent. However, the authors believe that the results can be applied across countries where drone technology is at the nascent stage.

Awareness about the strength of certain barriers can help stakeholders take steps to mitigate the same.

The results of this research also give some inputs to the government’s drone policy for wider usage of drones in the construction industry.

To the best of the authors’ knowledge, most studies on drones in construction industry bring out a list various challenges to their adoption. In this study, the authors have gone further to perform a hierarchical sequencing of barriers to drone adoption based on challenges faced in an emerging economy like India.

The present study intends to discover and analyze the determinants of users' intention to use (ITU) drone-based online food delivery (OFD) services. The study mainly focuses on the drone-based food delivery system in India and its implications.

This study has used the purposive sampling method. With the support of the technology acceptance model (TAM) and the theory of planned behavior (TPB), a theoretical model was developed conceptually. Later, the model was validated using the partial least square-structure equation modeling (PLS-SEM) technique with consideration of 324 responses mainly from university students in Delhi- National Capital Region (NCR).

The findings reveal that all the determinants are positively and significantly related to ITU, except for perceived behavioral control that does not influence the consumer’s ITU drone-based OFD services. The study also shows that how food delivery system through drone can revolutionize the entire food delivery system in India.

The present study has developed a unique model that can be used by practitioners, future researchers in this field and policymakers in government departments. The present study is limited to Delhi-NCR in India, and thus, there is an issue of generalizability in the present study.

This study has examined the future of food delivery system through drone-based system. Thus, the leaders in the food industry will be better positioned to understand consumers' intentions to use OFD services using drones and be able to make more informed decisions about investment in drone technology in their respective organizations.

The present study has combined both the technology adoption model and the TPB and developed a theoretical model. The study enriches the literature on drone-based OFD services. Since users' acceptance of OFD services using drones is an under-researched area, the present study will make a meaningful contribution to bring the body of literature in this domain.

The purpose of this study is to evaluate food supply chain stakeholders’ intention to use Industry 5.0 (I5.0) drones for cleaner production in food supply chains.

The authors used a quantitative research design and collected data using an online survey administered to a sample of 264 food supply chain stakeholders in Nigeria. The partial least square structural equation model was conducted to assess the research’s hypothesised relationships.

The authors provide empirical evidence to support the contributions of I5.0 drones for cleaner production. The findings showed that food supply chain stakeholders are more concerned with the use of I5.0 drones in specific operations, such as reducing plant diseases, which invariably enhances cleaner production. However, there is less inclination to drone adoption if the aim was pollution reduction, predicting seasonal output and addressing workers’ health and safety challenges. The findings outline the need for awareness to promote the use of drones for addressing workers’ hazard challenges and knowledge transfer on the potentials of I5.0 in emerging economies.

To the best of the authors’ knowledge, this study is the first to address I5.0 drones’ adoption using a sustainability model. The authors contribute to existing literature by extending the sustainability model to identify the contributions of drone use in promoting cleaner production through addressing specific system operations. This study addresses the gap by augmenting a sustainability model, suggesting that technology adoption for sustainability is motivated by curbing challenges categorised as drivers and mediators.

The purpose of this study is to examine the factors influencing consumers' intentions to use drone food delivery services from the perspective of the theory of consumption values (TCV).

Data collection involves the utilisation of self-administered questionnaires. Subsequently, 305 data were gathered from Malaysian consumers and subjected to analysis through partial least squares structural equation modelling (PLS-SEM).

This study demonstrated that functional, social, emotional and epistemic values, as well as personal innovativeness, can strongly predict intentions. However, neither the conditional value nor environmental concerns were significant predictors of intentions.

The study is the first of its kind to use the TCV from the perspective of a developing country to understand consumers' intentions to use drone food delivery services.