000130760 001__ 130760
000130760 005__ 20240131210810.0
000130760 0247_ $$2doi$$a10.1080/10438599.2018.1433582
000130760 0248_ $$2sideral$$a109710
000130760 037__ $$aART-2019-109710
000130760 041__ $$aeng
000130760 100__ $$0(orcid)0000-0002-1246-6751$$aFatas-Villafranca, F.$$uUniversidad de Zaragoza
000130760 245__ $$aConsumer social learning and industrial dynamics
000130760 260__ $$c2019
000130760 5060_ $$aAccess copy available to the general public$$fUnrestricted
000130760 5203_ $$aIn this paper, we propose an agent-based model in which industrial dynamics depend on consumer social learning and firm innovation efforts. We draw on behavioral economics and consumer psychology to model consumer learning as a process of social adaptation-cum-individual novelties which operates within a stochastic dynamic network. In our model, consumers create original patterns of behavior, but they also imitate similar others through a (degree-dependent) influence-biased process of change. Likewise, consumer behavior is shaped by firms which attempt to capture larger market shares. Thus, we propose a model in which consumers update their position (tastes) in a product characteristics space through innovation and adaptation, and co-evolve with profit-seeking firms which observe and shape evolving consumer behavior. We simulate the resulting market process obtaining trajectories and stationary states for the degree of industrial concentration, the number of producers, and certain features of the industry lifecycle. The analysis of the model reveals how three demand parameters–consumer ‘insistence’ (capturing inertia in decision-making), the ‘locality’ of consumer learning, and consumer ‘loyalty’ to firms–affect industry evolution. Likewise, the model generates a continuum of limit industrial structures–from perfect competition, to oligopolies or monopolies–with said demand parameters influencing the stationary states.
000130760 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc$$uhttp://creativecommons.org/licenses/by-nc/3.0/es/
000130760 590__ $$a1.563$$b2019
000130760 591__ $$aECONOMICS$$b167 / 371 = 0.45$$c2019$$dQ2$$eT2
000130760 592__ $$a0.923$$b2019
000130760 593__ $$aManagement of Technology and Innovation$$c2019$$dQ1
000130760 593__ $$aEconomics, Econometrics and Finance (miscellaneous)$$c2019$$dQ1
000130760 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000130760 700__ $$aFernández-Márquez, C.M.
000130760 700__ $$aVázquez, F.J.
000130760 7102_ $$14000$$2415$$aUniversidad de Zaragoza$$bDpto. Análisis Económico$$cÁrea Fund. Análisis Económico
000130760 773__ $$g28, 2 (2019), 119-141$$pEcon. innov. new technol.$$tEconomics of innovation and new technology$$x1043-8599
000130760 8564_ $$s2206842$$uhttps://zaguan.unizar.es/record/130760/files/texto_completo.pdf$$yPostprint
000130760 8564_ $$s1834874$$uhttps://zaguan.unizar.es/record/130760/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000130760 909CO $$ooai:zaguan.unizar.es:130760$$particulos$$pdriver
000130760 951__ $$a2024-01-31-19:18:41
000130760 980__ $$aARTICLE