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Catic 2021 d1-1 - Cadenas agroproductivas - Santiago Quintero

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Catic 2021 d1-1 - Cadenas agroproductivas - Santiago Quintero

  1. 1. Caracterización de cadenas agroproductivas - Sistema de Innovación Local PhD Santiago Quintero PRIMER ENCUENTRO DE INNOVACIÓN LOCAL Y CADENAS AGROPRODUCTIVAS - CACAO Y AGROTIC
  2. 2. The Supply Chain (SC) are characterized by their high economic and social potential for generating new knowledge and markets for developing countries. (Pietrobelli & Rabellotti, 2011) Introduction 01 Understood as the process through which firms create specific knowledge and capabilities through learning (Quintero, 2016). The specialization approach 03 Complex Adaptive System (CAS) The specialization is a complex phenomenon that emerges along with learning (Holland, 1992). 02 Is an important factor to understand the voluntary effort they make to acquire the necessary capabilities to compete in a SC (Lund, 2004; Lundvall, 2007) . Accumulation of capabilities Modelling and simulation have proven to be an appropriate methodology for the analysis of such phenomena. Nevertheless, existing models have limitations related to the characterization of their agents and market forces and a few methods that provide a longitudinal perspective of the dynamics of the actors’ behavior such systems and a rigorous analysis of so called emerging phenomena such as specialization.
  3. 3. Introduction ¿What is the set of learning patterns followed by the actors in a SC that lead emerging phenomena of specialization? RQ Objetive Design and develop an instrument that would allow to estimate the level of the TCI of the most representative actors of the SC of Coffee and Avocado, in order to analyze the specialization which are essential to the construction and accumulation of Technological Capabilities for Innovation (TCI) (Quintero, Ruiz & Robledo, 2017) and Specialization in these SC It is important to conduct studies and analyses to understand how specialization emerges from the patterns of accumulation of TCI (Albino, Carbonara, & Giannoccaro, 2016; Quintero, 2016) The Coffee SC has represented the main product of the Colombian economy (2018) 2,3 Billion US (TCI, 2019a) (2017) 2,6 Billion US Despite the dynamism and their organizational and systemic problems, there is evidence of fragmentation, interconnection and low level of associativity, which hinders the interaction and development of technological learning processes (Quintero, 2016) Hypothesis A tool that allows to measure the variation of the CTI in the Coffee and Avocado SC will allow to discover the learning patterns that lead to understanding the emergent phenomenon of specialization. List of importing markets for a product exported by Colombia product: 0901 Coffee Exported Value, USD Thousand Years List of importing markets for a product exported by Colombia product: 080440 Avocado Exported Value, USD Thousand Years ADFDVDZVZDVZ The Avocado SC has become a very profitable sector as a result of its great potential in international markets (2018) 62.732 US (2017) 52.948 US (TCI, 2019b)
  4. 4. Theoretical framework Capabilities Are understood as an actor's ability to make use of resources in order to perform a task or activity (Hafeez, Zhang, & Malak, 2002). Diffusion and Linkage Capabilities R&D Capabilities Appropriation to production and Marketing Capabilities It´s the dynamic process of acquisition of TCI (Quintero, 2016). It depend on the learning factor adopted by the system resulting from the interaction among actors. Technological learning Patterns of Specialization /1 (Carlsson et al., 2002) TCI Competitive advantage The congruence model (Nadler & Tushman, 1980) The hypothesis of the model is that the higher the level of congruence of its components, the more effective the organization it will be Generation Diffusion Use
  5. 5. Methodology Figure 1. Proposed Methodology. Source: elaborated by the authors from Quintero, Ruiz and Robledo, (2017) Specialization Stage 1. Specialized literature search Stage 2. Supply Chain Characterization Stage 3. Tool Development Stage 3. Statistic analysis Stage 4. Technological Capabilities Analysis Interview Structured Sources Non unstructured sources Specialized Database Implementation Verification “Information Data” Technological Capabilities Measurement Data collection Stratified finite populations Percentage increase Descriptive analysis Learning factor Math finding Input
  6. 6. Findings Measure Research Development Diffusion Linkage Appropriation Market Year 2008 2013 2018 2008 2013 2018 2008 2013 2018 2008 2013 2018 2008 2013 2018 2008 2013 2018 sample 43 59 93 43 59 93 43 59 93 43 59 93 43 59 93 43 59 93 Average 1.23 0.92 0.61 1.1 0.81 0.57 2.9 3.09 2.78 3.58 4.17 4.29 2.13 2.53 2.39 0.97 1.5 1.14 Standard deviation 2.67 2.37 2.02 2.44 2.15 1.83 2.43 2.26 2.17 2.3 1.91 1.68 2.2 2.09 1.99 1.99 1.69 1.53 Variation Coefficient (%) 217 259 330 221 266 319 84 73 78 64 46 39 103 82 83 204 112 134 Learning factor - -0.07 -0.09 - -0.07 -0.08 - 0.02 -0.03 - 0.05 0.01 - 0.05 -0.02 - 0.1 -0.06 Porcentual increment (%) - -3.44 -3.44 - -3.22 -2.67 - 2.11 -3.44 - 6.56 1.33 - 4.44 -1.56 - 5.89 -4 Table 1. Descriptive statistical results of the avocado SC Characterization. Supply Chain Characterization
  7. 7. Findings The accumulation of TCI reflected in the percentage variations are not significant enough to identify a specialization 2013 2018 5.89% -4.0% 2013 4.44% 2018 -1.56% 2013 6.56% 2018 1.33% 2013 2.11% 2018 -3.44% 2013 -3.22% 2018 -2.67% Development Capability 2013 -3.22% 2018 -2.67% Research Capability Diffusion Capability Linkage Capability Appropriation Capability Marketing Capability 93 Actors 10% Population
  8. 8. Findings Measure Research Development Diffusion Linkage Appropriation Market Year 2008 2013 2018 2008 2013 2018 2008 2013 2018 2008 2013 2018 2008 2013 2018 2008 2013 2018 sample 209 235 256 209 235 256 209 235 256 209 235 256 209 235 256 209 235 256 Average 0.15 0.16 0.19 0.16 0.15 0.16 1.01 1.06 1.19 1.32 1.42 1.59 0.99 1.19 1.52 0.18 0.26 0.42 Standard deviation 0.94 1.01 1.1 0.99 0.98 1.01 1.1 1.16 1.36 1.16 1.23 1.45 1.03 1.1 1.15 0.83 0.98 1.21 Variation Coefficient (%) 609 624 594 606 636 618 109 109 114 88 86 91 104 93 75 457 382 285 Learning factor - 0.01 0.04 - -0.01 0.01 - 0.01 0.03 - 0.02 0.03 - 0.04 0.06 - 0.08 0.1 Porcentual increment (%) - 0.11 0.33 - -0.11 0.11 - 0.56 1.44 - 1.11 1.89 - 2.22 3.67 - 0.89 1.78 Supply Chain Characterization Table 2. Descriptive statistical results of the coffee SC Characterization.
  9. 9. Findings The accumulation of TCI reflected in the percentage variations are not significant enough to identify a specialization 2013 2018 0.89% 1.78% 2013 2.22% 2018 3.67% 2013 1.11% 2018 1.89% 2013 0.56% 2018 1.44% 2013 -0.11% 2018 0.11% 2013 0.11% 2018 0.33% Development Capability Research Capability Difussion Capability Linkage Capability Appropriation Capability Marketing Capability 256 Actors 10% Population
  10. 10. Conclusions • The methodology used in this work allows the calculation of the percentage variation and the identification of patterns of specialization of TCI. • Through the analysis of the patterns of specialization in SC, it is shown that they have not accumulated their TCI and, as a result, there is a lack of competitiveness. • With respect to learning factors, in the coffee chain, it was noted that the marketing capability had a greater learning factor in 2018, whereas for the avocado chain, learning factors were only representative in the marketing capability of the year 2013. • The avocado and coffee SC show shortcomings in the development of new knowledge to help build and accumulate TCI to obtain competitive advantages in Global Value Chains (GVCs). • For a future paper, we intend to use the data collected for an agent based simulation model. The objective will be to simulate different policy scenarios and strategies to help understand the learning dynamics and patterns of specialization of the TCI in the SC.
  11. 11. References Albino, V., Carbonara, N., & Giannoccaro, I. (2007). Supply chain cooperation in industrial districts: A simulation analysis. European Journal of Operational Research, 177(1), 261-280. Carlsson, B., Jacobsson, S., Holmén, M., & Rickne, A. (2002). Innovation systems: analytical and methodological issues. Research policy, 31(2), 233-245. Edquist, C. (2013). Systems of innovation: technologies, institutions and organizations. Routledge. Hafeez, K., Zhang, Y., & Malak, N. (2002). Determining key capabilities of a firm using analytic hierarchy process. International journal of production economics, 76(1), 39-51. Holland, J. H. (1992). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press. ITC. (2019a). List of importing markets for a product exported by Colombia product: 0901 Café . Obtenido de Trade Map: https://www.trademap.org/Country_SelProductCountry_TS_Graph.aspx?nvpm ITC. (2019b). List of importing markets for a product exported by Colombia product: 0804 Avocado. Obtenido de Trade Map: https://www.trademap.org/Country_SelProductCountry_TS_Graph.aspx?nvpm Lund, R. (2004). The organization of actors’learning in connection with new product development. In Product Inovation, Interactive Learning and Economic Performance (pp. 129-153). Emerald Group Publishing Limited Lundvall, B. (2007). “National Innovation Systems - Analytical Concept and Development Tools”. Industry and Innovation,14(1), 95-119. Nadler, D., & Tushman, M., (1980). A congruence model for organization problem solving. In Managing strategic innovation and change: A collection of readings, Tushman M., and Anderson P. (eds.), New York: Oxford University Press, 159-171 Penrose, E. (1959). The Theory of the Growth of the Firm. New York: John Wiley. Pietrobelli, C., & Rabellotti, R. (2011). Global Value Chains Meet Innovation Systems: Are There Learning Opportunities for Developing Countries? . World Development, 39(7), 261–1269. Quintero Ramírez, S. Aprendizaje en los sistemas regionales de innovación: Un modelo basado en agentes (Doctoral dissertation, Universidad Nacional de Colombia-Sede Medellín). Quintero Ramírez, S., Castañeda, R., Lugo, W., & Robledo Velásquez, J. (2017). Learning in the regional innovation systems: An agent based model. Cuadernos de Administración (Universidad del Valle), 33(57), 7-20. Robledo, J. (2016). Introducción a la gestión de la tecnología y la innovación. Medellín: Universidad Nacional de Colombia.

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