## 2 Theoretical Background
This model of the integration of various data sources is supported by (1) efficient technology adoption processes that enable the development and optimization of decision-making processes [31] in the different links from a bidirectional perspective and (2) models of articulation and trust between the links to promote data exchange. Regarding the first, it cannot simply be assumed that just because a technology exists, it is known and used effectively [32], since the more complex the technology is, the higher the level of cooperation required between the parties [33; 34]. Under this logic, specialized literature has been analyzing the factors that inhibit or enhance the adoption and use process under different technology acceptance models, such as the Technology Acceptance Model (TAM), which has been proposed to understand the drivers of human behavior [35]. The analysis of the decision-making factors allows understanding adoption profiles and the special requirements in each chain link, facilitating the development of strategies and policies so that decision-makers can accelerate the process. Regarding the second element, the high complexity of the Food Informatics model requires the participation of all interested parties, who have different roles in the data value chain [24], as well as data exchange, collaboration, acquisition, and management frameworks, forcing a change in the data ecosystem [36]. As stated before, technologies related to Food Informatics are changing the way value is created for small and medium-sized companies [37], where data becomes another asset that must be managed through business models that encourage those involved to collect and exchange data in an accessible and interoperable way that allows for the reuse of these, creating and capturing value to develop advanced analytics processes, and that does not only involve one link, but, on the contrary, has a complete view of the multiple data sources. Transparency and traceability in the food industry have become central elements for consumers and companies. In the case of consumers, there is a need for more information, and regarding the processors and manufacturing companies, they must mitigate food challenges and reduce costs and fraud to improve their impact on the market [38]. Under this logic, in Table 1, the technologies associated with the Food Informatics model and its potential applications in the different links of the chain are shown. Regarding the adoption processes of Food Informatics technologies, different theoretical approaches have been developed based on models that have their foundation in the discipline of sociology. Among these, the case of the study carried out by [PERSON] (1962) is highlighted [39]. Each model postulates variables that attempt to explain the principles on which behaviors are established around the decisions of adopting (or not) a technology from the exploiter perspective. These include the theory of reasoned action (TRA), the theory of planned behavior (TPB), the theory of diffusion of innovation (IDT or DOI), the unified theory of acceptance and use of technology (UTAUT), and the technology acceptance model (TAM) ([PERSON] and [PERSON], 2017) [40].