Innovation management model for functional food ingredients and additives. Alignment with hype cycle, Python S-curves, and open innovation variables

Abstract

Objective: The objective of this article is to generate an innovation management model for organizations in the functional food ingredients and additives sector, based on prioritization of variables, stakeholder consultation, technology surveillance, HypeCycle for emerging technologies and S-curves. Method and analysis: A literature review was conducted on innovation management models and associated articles were selected from which the relevant variables. Three surveys were used; one for the statistical analysis of 97 initial variables, prioritizing a total of 32 variables that were the input for survey II; survey III validated a graphical conceptual model of innovation management; the HypeCycle and S-curves Were used to analyze the emergence component as input for open innovation strategies. three possible innovation management models for the sector were validated by stakeholders of an organization in the sector, from a statistical point of view, mode, modal frequency and consensus percentages were used in the 3 surveys and non-linear regression models, T value, P value and Durwin Watson in the calculations of S curves. Findings: From the literature review, 97 variables were identified, prioritizing 32 of them by stakeholders in survey one. Excellent, good, average and poor states were generated on these variables in survey two, including analysis of relevance and congruence for each question, the Survey 3 presented three graphic concept models for prioritization, obtaining 1 MGI prioritized for the sector, in the Hypecycle methodology 13 emergency components related to nanoencapsulation were identified, in S curves 13 nonlinear regression models were applied in nanoencapsulation, mixing and drying obtaining inflection points in the years 2016 – 2020 for nanoencapsulation technology, 2020–2030 for mixing and 2033 – 2042 for drying technology. Finally, 7 open innovation platforms with previous innovation challenges in food and agribusiness were identified. Conclusions: The MGI proposed for the sector under analysis corresponds to a structured one that facilitates the identification, development and commercialization of new ingredients and functional food additives, improving competitiveness and response to market and user demands.Furthermore, the alignment with the Hypecycle or over-expectation cycles with the proposed MGI allowed us to capture how this methodology helps manage the adoption of emerging technologies in nanoencapsulation, mixing and drying. Regarding the analysis variables, the research showed the importance of integrating these variables with external collaborations, such as collective collaboration, and with open innovation platforms, to promote the co-creation and expansion of knowledge in the food ingredients and additives industry. functional in Colombia, the complementary S-curve methodology allowed us to discover that the life cycle of the mixing and drying technology has not reached its inflection point, which constitutes an opportunity for organizations in the sector. This could be validated through of Python libraries and code to validate turning point calculations.This article is relevant to explore new alternatives for the co-development of innovation management models - MGI -, applied to specific sectors taking into account open innovation variables. The novelty of the research lies in the fact that the results obtained, aligned with the prioritization of variables, consultation with stakeholders, technological surveillance, Hypecycle and S-curves, become inputs for specific strategies and challenges of Open Innovation for ingredient and additive industries. functional foods and opportunities for R&D&i activities and projects in this sector.

Keywords

• Management model
• Open innovation
• Emerging technologies
• Technological surveillance
• Hype Cycle
• S curves