A model of the learning process with local knowledge externalities illustrated with an integrated graphical framework

In this paper we present a theoretical model of the learning process with knowledge externalities to R&D and other learning inputs within a region, a technological district, an industry or a technological cluster with fast rates of accumulation of new technological knowledge. As there are several definitions of localized technological and learning opportunities (according to the technical space, or to the regional space) and of localized technological knowledge, we can therefore find several possible applications to the generic model. The analysis of the learning firm interacting with a specific region in the production of new technological knowledge is just one of them. The analytical model we develop is amenable to a graphical representation. Thus we provide in the first place a unifying graphical framework, consisting of a four-quadrant picture to analyze the process of knowledge accumulation by learning firms located and operating in a specific region or industry, which simultaneously stresses the nature of the basic learning process and the importance of true knowledge spillovers in the generation of new knowledge. We adopt the following approach to the construction of spillover stocks or pools. First, the magnitude of the state of aggregate knowledge in a region or industry is reconstructed through the historic accumulation of flows of knowledge. Thus, the aggregate level of knowledge can always be updated after every learning loop, or at every moment of discrete time, whose unit of measurement we might assume at the outset of our analysis. Secondly, every firm within a region or industry is treated symmetrically regarding spillover effects and magnitudes. Such statement meaning that the amount of aggregate knowledge borrowed from any available source, either the region or industry under analysis or some other distant region or industry, is regarded as the same by every firm. And finally, we model both the loss of appropriation of benefits from innovation and the distance between different technological bases or regional sources in terms of single parameters, or instantaneous rates of growth, weighting respectively the leakage and the absorption intensities of flows and stocks of knowledge. Several theoretical predictions about the direction and magnitude of the knowledge spillovers can therefore be deducted from parametric changes in the leakage and absorption functions of our model arising from, among other things: - Improvements in information technology and falling communication costs observed in the economic system at general. - Improvements in technological communication systems within specific technological districts. - The establishment of explicit cooperative relations and effective access to the pool of collective knowledge, or instead any improvements of the mutuality and trust conditions, within the group of firms located and operating within a specific region. - The increasing of competitive pressures, or the working of any other mechanism for lowering the appropriation of a firm’s gains from innovation, in an array of industrial sectors. One interesting theoretical result is then derived from our full model. With such purpose in mind, we consider first the existence of a relevant competitive situation where appropriation and communication are both dependent upon the number of receiving and sending firms within the region. Whereas the amount of technological leakage per firm increases with the number of firms effectively operating within the region, ceteris paribus; the extent of absorption per firm also increases with the number of firms effectively communicating within the region, ceteris paribus. Apparently, there is a trade-off between such appropriation conditions and communication conditions. In the long-run, the addition of firms eventually exhausts the net positive effects of taking part in an effective network, and so we can establish an equilibrium number of firms operating in the region.

A Model of the Learning Process with Local Knowledge Externalities Illustrated with an Integrated Graphical Framework

We present a unified graphical framework accounting for the nature and impact of spillover effects. The dynamics of the learning process with a specific spillover transfer mechanism can be illustrated by recurring to this four-quadrant picture. In particular, a whole cycle of technological learning is explained with help of such a graphical representation of the basic learning process in the presence of knowledge spillovers. We hypothesize two different functional specifications of spillover exchanges among firms within a local innovation system. Each conceivable shape for the knowledge transfer relationship among firms expresses a possible mode and intensity of information processing arising from technology spillovers. A general proposition regarding the relative efficiency of the two alternative formal models with spillovers effects is derived. The basic models with spillover effects are then extended in several relevant directions.Learning; knowledge; technology spillovers; knowledge externalities; local innovation systems

Acquiring and sharing tacit knowledge in software development teams: An empirical study

Context: Sharing expert knowledge is a key process in developing software products. Since expert knowledge is mostly tacit, the acquisition and sharing of tacit knowledge along with the development of a transactive memory system (TMS) are significant factors in effective software teams. Objective: We seek to enhance our understanding human factors in the software development process and provide support for the agile approach, particularly in its advocacy of social interaction, by answering two questions: How do software development teams acquire and share tacit knowledge? What roles do tacit knowledge and transactive memory play in successful team performance? Method: A theoretical model describing the process for acquiring and sharing tacit knowledge and development of a TMS through social interaction is presented and a second predictive model addresses the two research questions above. The elements of the predictive model and other demographic variables were incorporated into a larger online survey for software development teams, completed by 46 software SMEs, consisting of 181 individual team members. Results: Our results show that team tacit knowledge is acquired and shared directly through good quality social interactions and through the development of a TMS with quality of social interaction playing a greater role than transactive memory. Both TMS and team tacit knowledge predict effectiveness but not efficiency in software teams. Conclusion: It is concluded that TMS and team tacit knowledge can differentiate between low- and high-performing teams in terms of effectiveness, where more effective teams have a competitive advantage in developing new products and bringing them to market. As face-to-face social interaction is key, collocated, functionally rich, domain expert teams are advocated rather than distributed teams, though arguably the team manager may be in a separate geographic location provided that there is frequent communication and effective use of issue tracking tools as in agile teams

Patents and Knowledge Diffusion:The Effect of Early Disclosure

We study how the timing of information disclosure affects the diffusion of codified technical information. On November 29, 2000, the American Inventors Protection Act (AIPA) reduced the default publication time of patents at the United States Patent and Trademark Office (USPTO) to 18 months. We analyze the effects of this change by means of a regression discontinuity design with time as an assignment variable and a complementary difference-in-differences analysis. Our study shows that information flows from patents measured by forward citations, increased. Interestingly, the degree of localization within geographic boundaries remained unchanged and technological localization even increased moderately. Moreover, the effect of early disclosure on citations from patents filed by patent attorney service firms is particularly strong. These results imply that knowledge diffusion stemming from speedier disclosure of technical information is confined to the existing attention scope and absorptive capacity of inventors and organizations

A Finite State Automaton Representation And Simulation Of A Data/Frame Model Of Sensemaking

This thesis presents the application of a finite state automaton (FSA) to analytic modeling of Data/Frame Model (DFM) of sensemaking. A FSA is chosen for the DFM simulation because of its inherent characteristics to mimic changes in system behaviors and transitional states akin to the dynamic information changes in dynamic and unstructured emergencies. It also has the ability to capture feedback and loops, transitions, and spatio-temporal events based on iterative processes of an individual or a group of sensemakers. The thesis has exploited the human-driven DFM constructs for analytical modeling using Laboratory Virtual Instrumentation Engineering Workbench (LabVIEW) software system. Sensemaking times, problem stage time (PST), and nodeto-node (NTN) transition times serve as the major performance factors. The results obtained show differences in sensemaking times based on problem complexity and information uncertainty. An analysis of variance (ANOVA) statistical analysis, for three developed fictitious scenarios with different complexities and Hurricane Katrina, was conducted to investigate sensemaking performance. The results show that sensemaking performance was significant with an F (3,177) of 16.78 and probability value less than 0.05, indicating an overall effect of sensemaking information flow on sensemaking. Tukey’s Studentized Range Test shows the significant statistical differences between the complexities of Hurricane Katrina (HK) and medium complexity scenario (MC), HK and low complexity scenario (LC), high complexity scenario (HC) and LC, and MC and LC

Modelling innovation support systems for development

The present article offers a concise theoretical conceptualization on the contribution of innovation to regional development. These concepts are closely related to geographical proximity, knowledge diffusion and filters, and clustering. Institutional innovation profiles and regional patterns of innovation are two mutually linked, novel conceptual elements in this article. Next to a theoretical framing, the paper offers also a new methodology to analyse institutional innovation profiles. Our case study addresses three Portuguese regions and their institutions, included in a web-based inventory of innovation agencies which offered the foundation for an extensive data base. This data set was analyzed by means of a recently developed Principal Coordinates Analysis followed by a Logistic Biplot approach (leading to a Voronoi mapping) to design a systemic typology of innovation structures where each institution is individually represented. There appears to be a significant difference in the regional innovation patterns resulting from the diverse institutional innovation profiles concerned. These profiles appear to be region-specific. Our conclusion highlights the main advantages in the use of the method used for policy-makers and business companies

Is distance dying at last? Falling home bias in fixed effects models of patent citations

We examine the "home bias" of knowledge spillovers (the idea that knowledge spreads more slowly over international boundaries than within them) as measured by the speed of patent citations. We present econometric evidence that the geographical localization of knowledge spillovers has fallen over time, as we would expect from the dramatic fall in communication and travel costs. Our proposed estimator controls for correlated fixed effects and censoring in duration models and we apply it to data on over two million patent citations between 1975 and 1999. Home bias is exaggerated in models that do not control for fixed effects. The fall in home bias over time is weaker for the pharmaceuticals and information/communication technology sectors where agglomeration externalities may remain strong.

Developing computer-based assessment as a tool to support enquiry led learning

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Master of Science (MSc) by ResearchThis research explores the possibility of developing Computer-based Assessment (CBA) as a tool to support enquiry-led learning. In this approach learners explore and unpack thoughts and ideas that help them to learn and solve problems. A critical feature of this is feedback and this research focussed on how to design and supply feedback in CBA. Two lines of research were sourced: Computer-assisted Assessment (CM) and Improving Formative Assessment (IFA). Specifically, performance data was collected, analysed and evaluated from the statistical results of 3 CSA tests (approximately 100 undergraduates per test) and from qualitative feedback, the dialogic question and answer responses of (approximately 30 learners x 100 responses) engaged on level 3 activity of the National Qualifications Framework (NQF). The outcome of the research is the development of Kilauea exemplar, a theoretical model of an enquiry led item type applied in a subject specific domain