We are on an unfavorable trajectory, as the world population is expected to increase by five billion people by the end of the century (UN Population Division, 2018), leading to a greater demand for food. We currently experiencing food security concerns that threaten humanity in the twenty-first century, and the public is looking for big food system opportunities. These high-quality scientific findings must be turned into policy and action as quickly as possible (Fanzo et al., 2020a). Combating global food insecurity and malnutrition requires a more holistic approach to food system thinking and planning (Fanzo et al., 2020a). With limited resources, food systems will have to feed a growing and changing population while also dealing with environmental degradation, climate change, and loss of biodiversity (Willett et al., 2019). Climate-related natural disasters, market distortions, and politics are all wreaking havoc on food systems (Barrett, 2020). As every part of the food system, contributes to climate change, the core truths about how food systems operate should adapt as a result of climate change (Hoegh-Guldberg et al., 2018). When land-use changes are taken into account, food systems account for 21–34 percent of world emissions (Fanzo et al., 2020a).

While COVID-19 has exposed vulnerabilities, it hastens technological transformation toward a sustainable global economy through bold policies (Franco et al., 2020). Favorably changing the food systems ensures that the food we produce is available to everyone and that the food system continues to be a vehicle for poverty reduction and improved food security for all (Fanzo et al., 2020a). The various components of the food system include the food supply chain, food environments, individual factors, consumer behavior, and external drivers. The food system encompasses all the people and activities involved in growing, transporting, supplying, and consuming food (Fanzo et al., 2020b). This study specifically focuses on the food supply chain of the food system. Kasza et al. (2019) referred supply chain as a network of organizations connected by backward and forward integration, typically in the several stages of the production process and delivery operations that ensure a product or service reaches the consumer.

Industries are digitizing the supply chain because of the creation of new digital systems and technology within Industry 4.0 and 5.0. Using IT-enabled procedures, connection, integration of systems, and web-enabled features, the definition of a digital supply chain is the development of information systems and the use of innovative technology to improve the supply chain’s agility and integration while also improving customer service and organization’s long-term survival (Ageron et al., 2020). According to Havenga (2018), logistics is a crucial step in the supply chain process that prepares, executes, and successfully regulates the movement and storage of commodities. Additionally, logistics is made up of a variety of tasks and procedures, such as fleet management, inventory management, and transportation (Havenga, 2018). Organizations from all around the world are beginning to realize the importance of logistics for the agri-food sector.

Drones are compared to automated planes and are often referred to as Unmanned Aerial Vehicles (UAVs), Remote Piloted Aircraft Systems (RPAS), Unpiloted Aircraft Systems (UAS), Quadcopters, etc. (Adepoju, 2022). Drone technology has received much interest, and like smartphones, it is anticipated that drones will become a regular part of our lives (Jasim et al., 2022). Drones have been used historically for a while. Drones, however, were mostly developed and explored in the past for military uses (Adepoju, 2022). Drone technology consists of a flying robot that may be operated remotely using flight software and Global Positioning Systems (Adepoju, 2022). The word “drone” is often used to describe autonomously controlled aircraft, those as submarines or ground-based autonomous vehicles (Yaacoub et al., 2020). A drone is a flying device that may either be piloted remotely or automatically through software to collect data. According to the type of drone, there are several categories of drones. The many drone varieties include multirotor, single-rotor, and fixed-wing aircraft (Wang et al., 2016). These technologies are designed with sophisticated stabilization mechanisms, sensors, and flying cameras that can carry out certain tasks and capture high-definition video (Adepoju, 2022).

The Internet of Drones (IoD) is a new era, where a fleet of drones is deployed and harvests the needed data under the supervision of a ground station server (GSS) over a wireless channel (Martos et al., 2021). Drones have drawn a lot of interest in improving the value of operations (Yurek and Ozmutlu, 2018). Drone technology is used in various sectors, including the medical, environmental, and service industries. Drones are easy to use and update often to reflect the newest technological advancements. Drones are utilized for various purposes, including agriculture, photography, disaster relief efforts, military surveillance activities, and industrial monitoring. The drone’s technological capabilities have advanced so swiftly that they are now a viable choice not only for delivery operations but also for passenger usage and transportation (Rejeb et al., 2021).

Companies must use innovative technology for effective and smooth business operations to compete in the globalized business world (Ramadan et al., 2017). Drone technology has digitally transformed the food industry and gained much usage in the food supply chain. Drone technologies have become crucial in the food system process because of the digitization of the agricultural industry, often known as agriculture 5.0. Businesses can use drones as cutting-edge tools to boost the responsiveness and effectiveness of their logistics (Sah et al., 2021). Due to their ability to fly, speed, and autonomy, drones have previously been investigated for their possibilities as delivery vehicles for logistics and humanitarian aid distribution (Shavarani, 2019). Drones can improve environmental sustainability and reducing delivery times since they are fueled by electricity (Hwang et al., 2019).

The industry needs to think more widely about food logistics and take the idea of the food supply chain into consideration. The supply chain is unquestionably responsive to the supply chain’s complexity and comprises a sizable number of producers, suppliers, and customers. Stakeholders can now obtain and analyze data previously inaccessible at any stage of the food supply chain through drone technology usage. The outcome serves as the basis for new process development and improvement in the food supply chain. In the supply chain, logistics is the planning, execution, and management of the safe, effective movement and storage of goods, services, and information to satisfy customer demands (Croom et al., 2018). Drones’ high mobility may considerably optimize various logistical activities while lowering supply chain costs. Flexible logistics systems are required because modern logistics and supply chains have become more dynamic, complicated, and technology-driven. These systems must be able to adapt to customer requirements more quickly and effectively. In a world that is getting more complex, the added complexity of introducing drones is less frightening than it would have been 10, or even five, years ago.

There is a significant, unexplored global market for on-demand food delivery services as technology improves people’s quality of life (Liu, 2019). Congested or isolated places will benefit from drone logistics in the supply chain since they offer a greater level of service and accessibility in less time than conventional delivery methods (Kim et al., 2021). The period of food delivery is the key benefit of using drones in the food supply chain and logistics as a food delivery system; due to the perishable nature of the items and the need for on-time delivery, drones are ideal for this application (Doole et al., 2018). The regulations and permissions for drones are one of the barriers to drone logistics (Kuschke and Cassim, 2019). Drones are a brand-new, cutting-edge technology that is ever-evolving and responding to users’ demands. Using drones has been shown to lower carbon emissions, increase efficiency, decrease labor costs, save lives, and expedite delivery (Ayamga et al., 2021).

Several studies have explored drone usage in the supply chain using various approaches. For instance, an exploratory case study by Sermuksnyte-Alesiuniene et al. (2021) analyzed how digital technology can enhance food supply chain operations. Singh et al. (2021) used a simulation model to highlight the importance of a robust supply chain during pandemics and the potential disruptions to the food supply chain. Waris et al. (2022) applied an extended technology acceptance model (TAM) to investigate customer use of drone technology for food delivery services. Rejeb et al. (2021) conducted a systematic literature review to examine the potential benefits and challenges of drones in supply chain management and logistics but did not specifically address drone adoption in the food supply chain. A review of existing studies reveals that none have utilized the TOE framework to explore drone adoption in the food supply chain and logistics. Therefore, this study addresses this gap by applying the TOE framework to explore factors affecting drone adoption in the supply chain.

The study adopted the Technological-organizational-environmental (TOE) framework developed by Tornatzky and Fleisher (1990). The TOE framework is the most suitable theoretical framework to explore different factors affecting the adoption in various contexts (Baker, 2012). The TOE framework offers an effective analytical tool for examining both possibilities and challenges linked to the adoption, adaptation, and incorporation of technical breakthroughs into a business strategy of an organization (Oliveira and Martins, 2011). From an organization perspective, the TOE framework outlines three areas of consideration in organizations that impact the adoption of the decision-making process at the application level (Tornatzky and Fleischer, 1990). The three elements that offer a complete view of drone adoption in the food supply chain are technological, organizational, and environmental.

The what or the who being researched is the unit of analysis (Babbie, 2016b). For this research study, organization was the unit of analysis. The organizations’ features include their size, composition, place, and collective descriptions of their members (Babbie and Mouton, 2001). The UOA comprises organizations that utilize drones in the food supply chain. The study explores drone adoption in the food supply chain. Its main objective was to explore factors that affect drone adoption in the food supply chain.

The study used a literature matrix as our research instrument. Content analysis is a technique for mapping symbolic data into a data matrix suitable for statistical analysis (Roberts, 2015). The literature matrix will be used to find and analyze useful literature and group it under its subcategories. Using search terms like “Drones,” “food supply chain,” “logistics,” “food delivery services,” “food systems,” and “TOE framework,” the literature was searched to find all publications pertinent to the research issue. To prepare the content for categorization, which was adopted from the technology, environmental, and organizational (TOE) framework factors as shown in Table 1, all articles published from 2019 to 2022 were selected resulting in 50 articles. These articles were then manually coded in an Excel spreadsheet using word frequency analysis to find patterns in the qualitative data. This establishes the basis for converting qualitative data into quantitative data, which may then be subjected to additional data and statistical analysis using an SPSS statistics software tool to generate frequencies, analysis of variance (ANOVA), and correlations of TOE factors as provided in Table 1.

The study used secondary data which is data that has previously been collected for another purpose (Sharma, 2018). The data was collected electronically from electronic databases such as Ebscohost, Google Scholar, Mendeley, Scopus, and ScienceDirect Journals. These databases which contain published articles relevant to the study were accessible through the university account. A portion of the target population is known as the research population, from which the sample is drawn (Hu, 2014). The research population included published articles on drone adoption in the food supply chain. The research sample was 50 peer-reviewed articles published from 2019 to 2022. The journal articles were selected based on the keywords. A convenience (opportunity) sampling approach was used as a non-probability strategy to collect the relevant information. Convenience sampling is used in research to identify target research objects that satisfy certain practical requirements, such as ease of access, geographic closeness, availability at a specific time, or a willingness to contribute (Dörnyei, 2007). The convenience sampling approach was selected for the study because it involved sourcing and choosing articles published from 2019 to 2022, focusing on the factors affecting the adoption of drones in the food supply chain. The study targeted organizations that used drones in the food supply chain.

The systematic literature review (SLR) approach of content analysis was used to answer the research question and achieve the study’s objective. The systematic literature review (SLR) approach is versatile to adopt quantitative and qualitative research methods. The data collection method was qualitative, using convenience sampling to acquire and analyze data by inputting keywords related to the research topic into the search engines of specific scientific databases. Content analysis was employed to identify the presence of keywords within a set of qualitative data. This method allows researchers to quantify and assess the presence, meanings, and relationships within the data. According to Siddaway et al. (2019), a systematic review involves using structured and explicit procedures to identify, select, and evaluate relevant research on a specific issue, as well as to collect and analyze data from the included studies. As per Siddaway et al. (2019), the SLR process consists of five steps: scoping, planning, identification, screening, and eligibility. The SLR content analysis approach was suited for the study since the study was a non-empirical study that utilizes secondary data to answer the research question using published articles. Statistical methods were used to analyze and summarize the results of the included studies.

Data analysis is the process of obtaining solutions to problems by analysis of data. The main analytic processes are to find problems, assess the availability of acceptable data, decide on appropriate methodologies for addressing the topics of interest, implement the techniques, and finally analyze, summarize, and present the results (Sharma, 2018). The data from selected 50 published articles were manually coded and then analyzed using quantitative content analysis. According to Mouton (2001, pp. 165–166), content analysis is a study that analyses the content of a text or document. The study needs to be qualitative and make use of secondary data, and the key research question needs to be exploratory or descriptive (Mouton, 2001).

The study used the literature matrix method to categorize data collected from published articles. The qualitative data was organized based on the technological, organizational, and environmental framework constructs variables that affect drone adoption in the food supply chain. Reliability refers to the extent to which a measure of a concept is stable and consistent (Bell et al., 2022). Inter-coder reliability a numerical measure of agreement among multiple coders on coding the same data was used for the manual coded data (O’Connor and Joffe, 2020). Cronbach’s alpha was employed to assess the reliability of the TOE factors. Therefore, the qualitative data used in the study was transformed into codes for the quantitative data analysis. The quantitative data was analyzed using statistical analysis software, SPSS, to obtain statistical results.

This section demonstrates how technological, organizational, and environmental aspects influence the use of drones in the food supply chain.

This section presents the analysis of variance (ANOVA) and correlation analysis results of technology, organization, and environmental factors variables across several demographic variables such as year published, research method, research type, research framework, and study region. Only variables with significant differences are presented on the factors affecting the adoption of drones in the food supply chain.