Core or Periphery: Where Should Firms Locate Exploring Innovators? Exploring With an NK Model B. Heydari, S. Chattopadhyay, S. Padhee, S. Karim Strategic Management Society (SMS) 42nd Annual Conference in London September 2022
Innovation Flow in Engineering System Design Teams: Core and Periphery and the Role of Complexity The Council of Engineering Systems Universities (CESUN) at Eighth International Engineering Systems Symposium, Charlottesville. October 2021
Evolution of Innovation Networks at Different Stages of Technology Life cycle Institute for Operations Research and the Management Sciences (INFORMS) Annual Meeting, Virtual November 2020
Journal Papers & Publications
Core or Periphery: Examining where to allocate exploring inventors and the impact on breakthrough innovation. B. Heydari, S. Chattopadhyay, S. Padhee, S. Karim
Recent work on search has highlighted that inventors search behavior consists of two dimensions–whether they search (adoption) and where they search (proximity)–but has not studied how these two dimensions of individual’s search behavior together impact firms’ performance. We use a computational model to examine how firms’ innovation performance is affected by inventor heterogeneity along these search dimensions, their network positions within a core-periphery network, and the complexity of their innovation landscape. We seek to answer the question: in firms with innovation landscapes reflecting differing levels of complexity, where should firms allocate their heterogeneous inventors in order to achieve faster breakthrough innovations? The intuition from our model highlights critical tradeoffs that firms face between reaping benefits from knowledge diffusion through the network and incurring costs from this same knowledge diffusion, depending on the complexity of the innovation landscape. We show that (a) firms’ innovation is shaped both individually and jointly by the two dimensions of their search behavior and (b) when allocating on the basis of search distance, the adoptive behavior of neighbors will shape the effectiveness of individuals’ search. The results bring to light unexamined connections within the literature on innovation which has not treated the two dimensions of search (i.e., proximity and adoption) as interrelated constructs, and highlight that it is imperative for scholars on innovation to consider both dimensions when addressing questions on search and innovation.
Evolution of Design Teams throughout Industry Life Cycle: Interplay of Innovation and Complexity. S. Padhee, N. Lore, B. Heydari
This paper studies how innovation teams can be optimally configured to yield the best possible performance at different stages of a certain technology's life cycle, which correspond to different levels of environmental complexity. To conduct our analysis, we have employed computational simulations of communities searching NK landscapes at varying levels of complexity. We studied how the relative proportion of exploring agents to exploiting agents in a community impacts the evolution of scores over time, and conducted additional investigations into the role of specialization (i.e., the agents' propensity to take their preferred action) and density (i.e., the expected width of social groups within the community). We discover that majority-explorer teams are to be preferred when complexity is high and over the long run, whereas majority-exploiter teams are more effective in the short run and at low complexities. Furthermore, we show that higher levels of specialization yield better results at higher complexities, and that majority-explorer teams benefit the most from higher levels of density. We conclude that different team compositions are to be preferred at different stages of maturity, and that selecting a time horizon for operations is of crucial importance when designing an innovation team.
Identifying Evolution of Innovation Networks at Different Stages of Technology Life Cycle: Evidence from Patent-Citation Networks. S. Padhee, B. Heydari
Technological life cycles are driven by the changes in the shape and level of innovation, yet innovation rate is not directly observable and is difficult to trace. Innovation measured by patent citations cannot be estimated solely by quantity, whereas quality-adjusted quantity measurements are still prone to bias. Our paper uses three steps to uncover the latent innovation that automatically accounts for the quality and quantity components instead of decoupling them. First, we analyze a simplified decision making problem to create a conceptual understanding of how firms' allocation strategy shape the evolution of innovation. Next, in order to observe the structure of innovation in radio frequency ‘CMOS’ technology, we build dynamic patent-citation networks (PCN) and develop an agent-based simulation to replicate underlying innovation network formation mechanisms. Comparing the real and synthetic data, we examine `age-dependent bias' and isolate latent innovation rates in PCN. Finally, mapping pivotal patent assignees — innovators, we calculate the diversity of innovators in the technology market. Identifying innovation patterns, we show that, early on, innovation structures are less diverse and exploratory as most innovative firms prioritize high value breakthrough innovation, but this gradually changes, and a lot of new innovators try to capitalize on extensive research collaborations. This grows and matures eventually until value creation and commercialization become an expensive endeavor and only a few prominent players stay in the market publishing valuable patents. Compared with the usual abundance of patent publications at this stage, the result is unusual. In reality though, our results show how the abundance that appears are less significant publications as innovating firms at this stage are mostly driven by exploitation in the technology market.
Short-term Rentals Improve Locals´ Experience of Neighborhood Eateries Evidence from the impact of Airbnb on Restaurants Quality in Boston. B. Heydari, Y. Bart, D.T. O‘ Brain, S. Padhee
Parametric Study on laser drilling of Al/SiCp metal matrix composite S. Padhee, S. Pani, S.S. Mahapatra
Multi-objective Parametric Optimization of Powder Mixed Electro-discharge Machining using Response Surface Methodology and Non-Sorted Genetic Algorithm S. Padhee, N. Nayak, S. Panda, P. Dhal, S.S. Mahapatra
Assessment of safety performance in Indian industries using fuzzy approach G.S. Beriha, B. Patnaik, S.S. Mahapatra, S. Padhee
Prediction of spontaneous heating susceptibility of Indian coals using fuzzy logic and artificial neural network model H.B. Sahu, S. Padhee, S. Pani, S.S. Mahapatra
Optimization of Fused Deposition Modeling (FDM) Process Parameters Using Bacterial Foraging Technique S. Panda, S. Padhee, A. K. Sood, S.S. Mahapatra
padhee.s@northeastern.edu
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