From Acting What’s next to Speeding Trap: Co-Evolutionary Dynamics of an Emerging Technology-Leader

JEL Classifications: O33, O53, L63[[abstract]]How does technological innovation emerge and evolve? We approach such an inquiry by synthesizing the perspectives of dynamic capabilities and co-evolutionary dynamics to portray organizational routines and multi-phase strategic renewals of an emerging technology-leader. To untangle the emergence of technological innovation, we conducted a longitudinal case study on the first and the largest dedicated semiconductor foundry, TSMC, located in the emerging economy of Taiwan. The firm-case of TSMC illustrates two unique co-evolutionary paths, that is, transforming from industry-latecomer to technology-leader and from process innovation to product innovation. We found multi-motor co-evolutionary dynamics between TSMC and the semiconductor industry, where its co-evolutionary mechanism of managed selection in its creating phase of mature process-innovation (1987-1998) has migrated to hierarchical renewal in its extending phase of advanced process-innovation (1999-2001), and then to holistic renewal in its modifying phase of product-innovation (2002-2007). During such paths, our research discovered a unique type of organizational routines, acting what’s next because TSMC has proactively searched for potential problems sooner than its competitors. However, such routines, although driving technological innovation, also lead to a unique type of success-trap, that is, speeding trap. When an emerging technology-leader fundamentally changes the industrial structures to over-specs, the growth driven by technology speeding may trap such a leader in a loop of over-exploration.[[sponsorship]]The authors are grateful to the research grant from the National Science Council (NSC) in Taiwan. The earlier manuscript of this paper was presented at the 2009 Annual Meeting of Academy of International Business (AIB) in San Diego, USA.[[notice]]補正完畢[[journaltype]]國外[[ispeerreviewed]]Y[[booktype]]紙本[[booktype]]電子版[[countrycodes]]CA

Optimal Sizing of Electrical Energy Storage Systems using Inventory Models

AbstractThis paper adopts a single-period newsvendor model with supply uncertainties to be used for optimally sizing an electrical energy storage system (EESS) for an apartment house with a photovoltaic (PV) system. Hence, typical inventory cost components and supply chain characteristics are translated to the EESS application. The results show that inventory management and energy storage can be aligned. The optimal size of the EESS takes into account the total cost of the storage system including energetic losses as well as the costs for energy supply from the own energy systems and from the energy supplier