## 3 Materials and Methods  
The main objective of this research was to extract the roles of agricultural social networks in AVC digitalization to define an AVC digitalization framework toward an inclusive digital economy. We achieved this objective through an interpretive research paradigm and case study research strategy. A case study refers to \"an empirical inquiry that investigates a contemporary phenomenon in depth and within its real-life context\" (p. 240, [80]). This research applied two data collection methods. First, a systematic literature review (SLR), following the three-step process suggested by [PERSON], [PERSON] [81], was undertaken to identify the corpus for automated content analysis. A Google Scholar \begin{table}
\begin{tabular}{c c} \hline \hline
**Enabling Digital Technologies** & **Technology Applications in AVC Digitalization** \\ \hline Internet of Things (IoTs) & Coordination, and logistics [64; 65], quality management [66], smart farming [30] \\ \hline Blockchain & Traceability of supply sources and transparency of food sources [10], food safety [67] \\ \hline Artificial intelligence & Intelligent farm machines, greenhouse monitoring, drone-based crop imaging, social media \\  & and modernization of supply chains [11], precision agriculture [12] \\ \hline Big data & Decision-making based on data and sustainable agriculture [68; 69; 70] \\ \hline Augmented reality & Digital agriculture [71], precision farming [72] \\ \hline System integration & Integrated agricultural farm management [15] \\ \hline Machine learning & Digital agriculture and precision farming [73], crop disease detection, yield prediction, \\  & weed detection, water management, and crop recognition [74] \\ \hline Edge computing & Big data processing [75; 76] and smart AI applications in agriculture [77] \\ \hline Cloud computing & Increased efficiency in the AVC [78] \\ \hline Ubiquitous connectivity & Increasing connectivity along the AVC with the use of different digital devices and \\  & platforms to access and share agricultural information [79] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A summary of technologies enabling the digitalization of agricultural value chains. search was executed with the keywords \"social network\" and \"agriculture value chain\", creating a result set of 130 papers. We screened the corpus and excluded duplicate papers, papers written in languages other than English, and papers not relevant to our research. This resulted in a final set of 55 papers for detailed analysis to identify agriculture social network themes and concepts. The overall objective of this paper was to develop an AVC digitalization framework for implementing resilience principles in enabling an inclusive digital ecosystem. A strategy to attain this goal was to understand the various ASNs within the AVC. The 11 themes identified via content analysis of the literature with the aid of _Lexinancer_ software fundamentally informed the design of the research questionnaires for the case study. Once the themes and concepts had been identified, we used a case study to collect data via a questionnaire, interviews, and focus groups [80]. The case study focused on indigenous vegetables in Kisii County, Kenya. Kisii County is an agricultural region in which food security systems are under threat due to the effects of climate change [82]. The data collection process we applied was similar to the approach taken by [PERSON], [PERSON] [34] and [PERSON], [PERSON] [83]. The data collection methods utilized in the case study survey included questionnaires, focus group discussions, and interviews. Interviews were conducted with the agricultural extension officers to elicit information on (1) demographic information, (2) digital literacy levels, and (3) the application of digital devices in the provision of the extension of services. Two sets of questionnaires were designed for two different categories of respondents, namely farmer and trader representative groups.