Scholars from different perspectives agree that agritourism can be the right tool to balance the needs of tourists with those of rural communities, offering real opportunities for economic and social development, while mitigating undesirable impacts on the environment. This paper aims to provide a holistic outlook of the different perspectives under which scientific literature deals with the topic of agritourism as a means to support the sustainable development of rural areas. To reach this aim, we performed a systematic review of the scientific literature in order to point out the linkages between agritourism and sustainability. We analyzed papers through a text mining solution based on the Latent Dirichlet Allocation (LDA) technique to point out the main topics around which the scientific literature on agritourism and sustainability has grown. Topics are further categorized in themes by means of an agglomerative hierarchical clustering procedure. Results are further analyzed to highlight the strengths and weaknesses of the current streams of the literature.

It is widely recognized that the adoption of collaborative organization models in the agri-food sector can help farmers in rural areas to reach sustainable development goals. In any case, a holistic and coherent view of sustainability, organizational models and supporting technologies in the agri-food sector is still not present in the scientific literature. With this paper, we aim to fill this gap and to propose a framework that is useful to help scholars and practitioners in analyzing and designing sustainable Collaborative Networks in the agri-food sector

Multi-criteria decision methods (MCDMs) are used as an effective tool to support decision makers (DMs) in critical decision processes. These methods are used in several fields of application by analyzing static decision-making problems in which it is assumed that the decision is made at a precise moment. By increasing the complexity of decision-making problems and operating in increasingly competitive production sectors, very often analyzing a decision-making problem in a static way is not enough. This paper deals with considering the temporal variable in the construction of a dynamic MCDM, which takes into account historical and current data in order to learn from the past; and prospective also allowing to have a forecasting perspective of future data through the use of techniques that work in this sense. Our approach was tested in a multinational company in the manufacturing sector. The results show that the use of dynamic approaches allows DMs to obtain more precise alternative rankings given the information they exploit from the past; furthermore, the use of the prospective model, integrated with the dynamic one, makes it possible to provide greater detail on the possible future rankings of the alternatives that update their positions based on the feedback received. The approach allows for drawing advantages from a management point of view as it defines a complete decision support tool for the choices related to the planning and control of production processes. Our approach can be implemented in corporate information systems. Furthermore, the involvement of the DM in the construction of the model helps to define a learning process that feeds the decision-making process by generating greater awareness of the DM on the choices to be made.