M.Sc. Jéssica Alves Justo Mendes
Jessica is Research Associate (PhD) at the University of São Paulo (USP), the best university in Latin America. It’s also Researcher at the University of São Paulo in the group of Change and Innovation Management, since March 2020. Guest researcher at Leuphana University of Lüneburg, from March 2023 to August 2023. Researcher at the Federal University of Pernambuco at the Center for Decision Systems and Information Development, from March 2018 to January 2020. Jessica has a Master’s degree in Production Engineering.
Jessica is dedicated full time to research activities
Responsabilities: Development of the research project: “Proposal of a Maturity Model for AgTechs” funding by CAPES; Collaboration in the research group of Change and Innovation Management; Development of administrative activities related to the research group of Change and Innovation Management; Supervision of junior researchers; Collaboration in the development of partnerships between international universities in Germany.
Consulting Hours: by appointment
Areas of interest: AgTechs, Greentechs and Start-ups; Sustainability Indicators; Maturity Models; Digital Transformation of Agriculture; Innovation Managementn
e-mail: jessy.a.j.mendes@usp.br OR jessy.mendes10@gmail.com
Current work (PhD):
Title: Proposol of a Maturity Model for AgTechs
Theme: Evaluation of maturity in agricultural startups (AgTechs)
Goal: to propose a maturity model for AgTechs to improve their businesses towards sustainable growth of the 4th agricultural revolution.
Abstract: The agribusiness sector is currently facing several challenges regarding the digital transformation (DT) of modern agriculture (MA). One of the main features of DT is the digital and disruptive technologies that are currently molding the fourth agrarian revolution or Agriculture 4.0. In this sector, AgTechs, which can be defined as start-ups, micro and small agribusiness that develop and commercialize sustainable agriculture technologies, are the ones that are basing their business on the new concepts and technologies brought on by Agriculture 4.0. However, despite the growing importance of AgTechs for MA, their level of mortality is high. To address this issue, we propose to understand their maturity level, which can help them to identify improvement points and reach a desired future state. Therefore, this research proposes the development of a maturity model (MM) geared towards the specific needs of AgTechs. A MM specific to AgTechs is innovative, for, despite the growing number of MMs developed to 4.0 environments, none were explicitly applied to the agribusiness sector. The main methodology to develop this MM is the design science research, which was supported by a systematic literature review (SLR) to identify the theoretical constructs of DT in MA, an exploratory literature review to identify the most relevant MMs for the AgTechs specificities and the application of surveys (in Germany and Brazil) with AgTechs specialist, to validate the model’s applicability. We hope that the results of this research, i.e., our MM, will spread new knowledge in the field of MA, helping AgTechs to identify points of improvement, which can, in turn, aid them to reduce their high mortality rate.
Keywords: agtechs; digital transformation; modern agriculture; agriculture 4.0; maturity models, sustainability
Previous work (Master):
Title: Experiment with numerical simulation to evaluate the performance of the FITradeoff method
Theme: Use of numerical simulations to evaluate the performance of FITradeoff, an additive multicriteria method
Goal: Propose a guide for the identification of opportunities of value in the context of circular economy.
Abstract: The use of partial information in the decision-making process increasingly shows a useful and effective approach to multicriteria decision making, as it can save the time and cognitive effort of decision makers (DM). The multicriteria method Flexible and Interactive Tradeoff (FITradeoff) elicits the DM’s preferences through an interactive and flexible process, using partial information through a question-and-answer process in which the decision maker declares his preference for hypothetical consequences, evaluating tradeoffs between criteria. The advantages of FITradeoff over other additive methods were evidenced in case studies presents in the literature; however, no studies have so far evaluated the factors that influence FITradeoff’s performance in relation to the increase in the number of questions asked to the DM in order to find a recommendation. In this context, the present work evaluated, through a large number of simulated scenarios, the way in which FITradeoff behaves against the following factors: variation in the distribution of scale constants, increase in the number of criteria and increase in the number of alternatives. The simulations showed that, during the ranking phase, FITradeoff can reduce the number of potentially optimal alternatives (POA) to up to 8 POA in 97% of cases. It was also proved that the form of the distribution of the scale constants values is the factor that most affects the performance of FITradeoff, followed by the increase in the number of criteria. It was also possible to conclude that the increase in the number of alternatives has little influence on the number of questions asked to the DM. Through the results obtained in this research, the efficiency of the method was proven from the point of view of reducing the number of questions when compared to other elicitation procedures, such as the traditional Tradeoff.
Keywords: Multicriteria decision making. Multicriteria methods with partial information. FITradeoff method. Simulations in multicriteria problems
ORCID: https://orcid.org/0000-0003-0251-0023
RESEARCHID: https://www.webofscience.com/wos/author/record/AAK-6779-2021
ACADEMIC CURRICULUM (LATTES): http://lattes.cnpq.br/2788891281400093
PROFESSIONAL ADDRESS: University of Sao Paulo - Campus of Sao Carlos Av. Trab. São Carlense, 400 - Parque Arnold Schimidt, São Carlos - SP, 13566-590 Department of Production Engineering Operations management laboratory (second floor)