Systemic absorptive capacity: creating early-to-market returns through R&D alliances

For most complex emergent technologies, product-market success depends on efficient linkages between changing lead innovators within the R&D process. In this paper, our unit of analysis is a complex high technology product and the system of alliance linkages formed to progress a product through R&D milestones. We present a model and evidence for advancing our understanding of how achieving early-to-market returns depends on systemic absorptive capacity. This systemic absorptive capacity is the cumulative efficiency in the use of absorptive capacity to link changing lead innovators across successive milestones in R&D product development. We advance propositions of how systemic absorptive capacity can explain performance differences between rival product development systems competing for early-to-market returns with similar products through accelerating speed to market, cost and quality advantages. These explanations are contrasted with the conclusions of previous studies that have focused on absorptive capacity of single firms or single alliances in RD.

The market for preferences

Learning processes are widely held to be the mechanism by which boundedly rational agents adapt to environmental changes. We argue that this same outcome might also be achieved by a different mechanism, namely specialisation and the division of knowledge, which we here extend to the consumer side of the economy. We distinguish between high-level preferences and low-level preferences as nested systems of rules used to solve particular choice problems. We argue that agents, while sovereign in high-level preferences, may often find it expedient to acquire, in a pseudo-market, the low-level preferences in order to make good choices when purchasing complex commodities about which they have little or no experience. A market for preferences arises when environmental complexity overwhelms learning possibilities and leads agents to make use of other people's specialised knowledge and decision rules.

Genetic algorithm-based spinning reserve dispatching in a competitive electricity market

Power systems rely greatly on ancillary services in maintaining operation security. As one of the most important ancillary services, spinning reserve must be provided effectively in the deregulated market environment. This paper focuses on the design of an integrated market for both electricity and spinning reserve service with particular emphasis on coordinated dispatch of bulk power and spinning reserve services. A new market dispatching mechanism has been developed to minimize the ISO's total payment while ensuring system security. Genetic algorithms are used in the finding of the global optimal solutions for this dispatching problem. Case studies and corresponding analyses haw been carried out to demonstrate and discuss the efficiency and usefulness of the proposed market.

Equity market linkage and multinational trade accords: The case of NAFTA

This paper examines the impact of multinational trade accords on the degree of stock market linkage using NAFTA as a case study. Besides liberalizing trade among the U.S., Canada and Mexico, NAFTA has also sought to strengthen linkage among stock markets of these countries. If successful, this could lessen the appeal of asset diversification across the North American region and promote a higher degree of market efficiency. We assess the possible impact of NAFTA on market linkage using cross-correlations, multivariate price cointegrating systems, speed of convergence, and generalized variance decompositions of unexpected stock returns. The evidence proves robust and consistently indicates intensified equity market linkage since the NAFTA accord. The results also suggest that interdependent goods markets in the region are a primary reason behind the stronger equity market linkage observed in the post-NAFTA period. (c) 2005 Elsevier Ltd. All rights reserved.

Institutional governance systems and variations in national competitive advantage: An integrative framework

Creating competitive industries has become one of the key tasks of governments. Different adaptation outcomes in industries across nations cannot be accounted for fully simply by an emphasis on firm-level capabilities, market-driven policies, or state-level policies. We propose an integrative framework that draws on both the strategic management and political economy literature to explain variations in national industrial competitiveness.. We discuss differences with respect to institutional characteristics and capabilities, competitive outcomes, conditions of best fit, and who bears the cost of industry adaptation.

Electricity market price spike forecast with data mining techniques

Electricity market price forecast is a changeling yet very important task for electricity market managers and participants. Due to the complexity and uncertainties in the power grid, electricity prices are highly volatile and normally carry with spikes. which may be (ens or even hundreds of times higher than the normal price. Such electricity spikes are very difficult to be predicted. So far. most of the research on electricity price forecast is based on the normal range electricity prices. This paper proposes a data mining based electricity price forecast framework, which can predict the normal price as well as the price spikes. The normal price can be, predicted by a previously proposed wavelet and neural network based forecast model, while the spikes are forecasted based on a data mining approach. This paper focuses on the spike prediction and explores the reasons for price spikes based on the measurement of a proposed composite supply-demand balance index (SDI) and relative demand index (RDI). These indices are able to reflect the relationship among electricity demand, electricity supply and electricity reserve capacity. The proposed model is based on a mining database including market clearing price, trading hour. electricity), demand, electricity supply and reserve. Bayesian classification and similarity searching techniques are used to mine the database to find out the internal relationships between electricity price spikes and these proposed. The mining results are used to form the price spike forecast model. This proposed model is able to generate forecasted price spike, level of spike and associated forecast confidence level. The model is tested with the Queensland electricity market data with promising results. Crown Copyright (C) 2004 Published by Elsevier B.V. All rights reserved.

Fixing the system? The experience of service users of the quasi-market in disability services in Australia

A qualitative study involving semi-structured interviews with 31 people with disabilities and 32 carers in the state of Queensland, Australia, found that their experience of supportive service delivery had not improved despite reforms of the service delivery system driven by a version of the quasi-market model. Instead of delivering increased consumer choice and improved efficiency in service delivery, service users experienced inadequate service supply, service cutbacks, and an increased emphasis on cost subsidisation and assessment processes. Additionally, few consumers felt that individualised funding arrangements had personally delivered the benefits which the quasi-market model and associated policy paradigm had indicated that they should receive. For many consumers, the notion of consumer 'choice' around service provision was fictitious and they felt that any efficiency gains were at the agency level, largely at the consumers' cost. It is concluded that there appears to be no particular benefit to service users of quasi-market reforms, particularly in policy contexts where service delivery systems are historically under-funded.

A complex systems approach to the value of ecological resources

A theory of value sits at the core of every school of economic thought and directs the allocation of resources to competing uses. Ecological resources complicate the modem neoclassical approach to determining value due to their complex nature, considerable non-market values and the difficulty in assigning property rights. Application of the market model through economic valuation only provides analytical solutions based on virtual markets, and neither the demand nor supply-side techniques of valuation can adequately consider the complex set of biophysical and ecological relations that lead to the provision of ecosystem goods and services. This paper sets out a conceptual framework for a complex systems approach to the value of ecological resources. This approach is based on there being both an intrinsic quality of ecological resources and a subjective evaluation by the consumer. Both elements are necessary for economic value. This conceptual framework points the way towards a theory of value that incorporates both elements, so has implications for principles by which ecological resources can be allocated. (c) 2005 Elsevier B.V. All rights reserved.

Impact of weather dependency on planning decisions in the australian national electricity market

A long-term planning method for the electricity market is to simulate market operation into the future. Outputs from market simulation include indicators for transmission augmentation and new generation investment. A key input to market simulations is demand forecasts. For market simulation purposes, regional demand forecasts for each half-hour interval of the forecasting horizon are required, and they must accurately represent realistic demand profiles and interregional demand relationships. In this paper, a demand model is developed to accurately model these relationships. The effects of uncertainty in weather patterns and inherent correlations between regional demands on market simulation results are presented. This work signifies the advantages of probabilistic modeling of demand levels when making market-based planning decisions.

A new methodology for generation investment in the national electricity market of Australia

Market administrators hold the vital role of maintaining sufficient generation capacity in their respective electricity market. However without the jurisdiction to dictate the generator types, locations and timing of new generation, the reliability of the system may be compromised by delayed entry of new generation. This paper illustrates a new generation investment methodology that can effectively present expected returns from the pool market; while concurrently searching for the type and placement of a new generator to fulfil system reliability requirements.

A genetic algorithm based method for deregulated electricity market dispatch

In a deregulated electricity market, optimizing dispatch capacity and transmission capacity are among the core concerns of market operators. Many market operators have capitalized on linear programming (LP) based methods to perform market dispatch operation in order to explore the computational efficiency of LP. In this paper, the search capability of genetic algorithms (GAs) is utilized to solve the market dispatch problem. The GA model is able to solve pool based capacity dispatch, while optimizing the interconnector transmission capacity. Case studies and corresponding analyses are performed to demonstrate the efficiency of the GA model.