Multiple peer effects in the diffusion of innovations on social networks: a simulation study

Peer effects in innovation adoption decisions have been extensively studied. However, the underlying mechanisms of peer effects are generally not explicitly accounted for. Gaps in this knowledge could lead to misestimation of peer effects and inefficient interventions. This study examined the role of two mechanisms—sharing experiences (namely, experience effect) and externalities—in the adoption of an agricultural innovation. By referring to the diffusion process of a new crop in Chinese villages, we developed a simulation model that incorporated experience effect and externality effect on a multiplex network. The model allowed us to estimate the influence of each specific effect and to investigate the interplay of the positive and negative directions of the effects. The main results of simulation experiments were the following: (1) a negative externality effect in the system caused the diffusion of innovation to vary around a middle-level rate, which resulted in a fluctuating diffusion curve rather than a commonly found S-shaped one; (2) in the case of full diffusion, experience effect significantly shaped the diffusion process at the early stage, while externality effect mattered more at the late stage; and (3) network properties (i.e. connectivity, transitivity, and network distance) imposed indirect influence on diffusion through specific peer effects. Overall, our study illustrated the need to understand specific causal mechanisms when studying peer effects. Simulation methods such as agent-based modelling provide an effective approach to facilitate such understanding.

[1]  Marco Janssen,et al.  Simulating Market Dynamics: Interactions between Consumer Psychology and Social Networks , 2003, Artificial Life.

[2]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[3]  James E. West,et al.  From Natural Variation to Optimal Policy? the Importance of Endogenous Peer Group Formation * 1 Data 1.1 the Dataset 2 Experimental Design and Sorting Methodology , 2022 .

[4]  Mason A. Porter,et al.  Multilayer networks , 2013, J. Complex Networks.

[5]  Bernard Fortin,et al.  Identification of Peer Effects through Social Networks , 2007, SSRN Electronic Journal.

[6]  G. Deffuant,et al.  An Individual‐Based Model of Innovation Diffusion Mixing Social Value and Individual Benefit1 , 2005, American Journal of Sociology.

[7]  Robert J. Morris,et al.  Reducing the Complexity of an Agent-Based Local Heroin Market Model , 2014, PloS one.

[8]  Stephen Kinsella,et al.  Peer Effects in the Diffusion of Innovations: Theory and Simulation , 2015 .

[9]  G. Imbens,et al.  Social Networks and the Identification of Peer Effects , 2013 .

[10]  Scott A. Neslin,et al.  The Development and Impact of Consumer Word of Mouth in New Product Diffusion , 2009 .

[11]  Alan M. Frieze,et al.  Random graphs , 2006, SODA '06.

[12]  M. Kremer,et al.  Peer Effects and Alcohol Use Among College Students , 2003 .

[13]  S. Wellek.  Testing Statistical Hypotheses of Equivalence and Noninferiority , 2010 .

[14]  Imran Rasul,et al.  Social Networks and Technology Adoption in Northern Mozambique , 2002 .

[15]  Bruce Sacerdote,et al.  credit, including © notice, is given to the source. From Natural Variation to Optimal Policy? The Lucas Critique Meets Peer Effects , 2011 .

[16]  E. Rogers.  Diffusion of Innovations , 1962 .

[17]  Elmar Kiesling,et al.  Agent-based simulation of innovation diffusion: a review , 2011, Central European Journal of Operations Research.

[18]  Hang Xiong,et al.  Characteristics of Chinese rural networks: Evidence from villages in central China , 2017 .

[19]  Florian Ederer,et al.  Understanding Mechanisms Underlying Peer Effects: Evidence from a Field Experiment on Financial Decisions , 2014 .

[20]  Pilar Alcalde.  A behavioral model of non-linear peer effects in cognitive achievement , 2013 .

[21]  Hang Xiong,et al.  Characteristics of Chinese Rural Network: Evidence from Villages in Central China , 2015 .

[22]  Alan T. Sorensen,et al.  Social learning and health plan choice. , 2006, The Rand journal of economics.

[23]  Arun G. Chandrasekhar,et al.  The Diffusion of Microfinance , 2012, Science.

[24]  Boleslaw K. Szymanski,et al.  Threshold-limited spreading in social networks with multiple initiators , 2013, Scientific Reports.

[25]  Kaivan Munshi.  Social learning in a heterogeneous population: technology diffusion in the Indian Green Revolution , 2004 .

[26]  Santiago L. Rovere,et al.  Exploring associations between micro-level models of innovation diffusion and emerging macro-level adoption patterns , 2012, ArXiv.

[27]  Mark S. Granovetter.  Threshold Models of Collective Behavior , 1978, American Journal of Sociology.

[28]  Jonathan Guryan,et al.  Using technology to explore social networks and mechanisms underlying peer effects in classrooms. , 2008, Developmental psychology.

[29]  Renana Peres,et al.  The impact of network characteristics on the diffusion of innovations , 2014 .

[30]  Magne Mogstad,et al.  Peer Effects in Program Participation , 2012, SSRN Electronic Journal.

[31]  Timothy G. Conley,et al.  Learning About a New Technology: Pineapple in Ghana , 2010 .

[32]  Jacob Goldenberg,et al.  Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth , 2001 .