“Leveraging Inter-Firm Influence in the Diffusion of Energy Efficiency Technologies: An Agent-Based Model,” Yingying Shi, Beijing Institute of Technology
Abstract: Energy-efficiency technologies (EETs) are crucial for saving energy and reducing carbon dioxide (CO2) emissions. However, the diffusion of EETs in small and medium-sized enterprises (SMEs) is rather slow. Literature shows the interactions between innovation adopters and potential adopters have significant impacts on innovation diffusion. Enterprises lack the motivation to share information, and EETs usually lack observability, which suppress the inter-firm influence. Therefore, an information platform together with proper policies encouraging or forcing enterprises to disclose EET-related information should be helpful for harnessing inter-firm influence to accelerate EET diffusion in SMEs.
To explore whether and how such an information platform affects EET diffusion in SMEs, this study builds an agent-based model to mimic the EET diffusion process. A series of controlled numerical experiments are conducted. The results show that the information platform is a double-edged sword that can notably accelerate EET diffusion by approximately 47% but may also boost negative information to diffuse even faster, which delays massive adoption of EETs in SMEs. Increasing network density and the intensity of the influence from each enterprise are effective to speed EET diffusion but their impacts diminish drastically after reaching 0.05 and 0.15, respectively, and eventually harm the stability of the system, which hampers EET diffusion. Hence, the findings implicate that EET suppliers should carefully launch their promising but immature products; policies that can reduce the perceived risk by enterprises and the effort to maintain an informative rather than judgmental information platform can prominently mitigate the negative side effect.
Bio: Yingying Shi is working toward a Ph.D. from the School of Management and Economics, Beijing Institute of Technology. Her research interests include applying the index decomposition analysis in energy consumption and carbon dioxide emissions, using agent-based modeling
“On the Disruptive Innovation Strategy of Renewable Energy Technology: An Agent-Based Model,” Yongchao Zeng, Beijing Institute of Technology
Abstract: Renewable energy technologies (RETs) are crucial for solving the world’s energy dilemma. However, the diffusion rate of RETs is still far from satisfactory. One critical reason is that conventional energy technologies (CETs) as incumbent competitors dominate energy markets. Emergent technologies that have inferior initial performance at first but eventually become new market dominators are frequently observed in various industries, which can be explained with the disruptive innovation theory (DIT). DIT suggests that instead of competing with incumbent technologies in the dominated dimension, redefining the competition on a two-dimensional basis is wise. Aiming at applying DIT to RET diffusion, this research builds an agent-based model considering the order of entering a market, price, preference changing, and RET improvement rate to simulate the competition dynamics between RETs and CETs.
The findings include that (1) the order of entering a market is crucial for the success of a technology; (2) disruptive innovation is an effective approach to coping with the disadvantage of RETs as late-comers; (3) generally, lower price, higher consistency with consumer preference, and higher improvement rate in the conventional dimension are beneficial to RET diffusion; and (4) paradoxically, increasing the RET improvement rate in the conventional dimension is helpful to diffusion when network density is low, while it is harmful when network density is high. The findings implicate that actively launching disruptive innovation strategy is “worth a shot”; RET suppliers should closely follow changing consumer preference, particularly in a dense network where both preferences and technologies evolve quickly.
Bio: Yongchao Zeng is a Ph.D. student at the Beijing Institute of Technology, majoring in management science and engineering. His interests include agent-based modeling, behavior decision making, network science, data analysis, evolutionary optimization algorithms, and their integration.
Yingying Shi and Yongchao Zeng