Exploring the role of market learning capability in competitive strategy

It has been argued that a firm's capacity to learn from its market is a source of both innovation and competitive advantage. However, past research has failed to conceptualize market-focused learning activity as a capability having the potential to contribute to competitive advantage. Prior innovation research has been biased toward technological innovation. However, there is evidence to suggest that both technological and non-technological innovations contribute to competitive advantage reflecting the need for a broader conceptualization of the innovation construct. Past research has also overlooked the critical role of entrepreneurship in the capability building process. Competitive advantage has been predominantly measured in terms of financial indicators of performance. In general, the literature reflects the need for comprehensive measures of organizational innovation and competitive advantage. This paper examines the role of market-focused learning capability in organizational innovation-based competitive strategy. The paper contributes to the strategic marketing theory by developing and refining measures of entrepreneurship, market-focused learning capability, organizational innovation and sustained competitive advantage, testing relationships among these constructs.

Learning business strategies for competitive electronic marketplaces

This paper presents a Multi-Agent Market simulator designed for developing new agent market strategies based on a complete understanding of buyer and seller behaviors, preference models and pricing algorithms, considering user risk preferences and game theory for scenario analysis. This tool studies negotiations based on different market mechanisms and, time and behavior dependent strategies. The results of the negotiations between agents are analyzed by data mining algorithms in order to extract rules that give agents feedback to improve their strategies. The system also includes agents that are capable of improving their performance with their own experience, by adapting to the market conditions, and capable of considering other agent reactions.

Didactics–promoting collaborative work and personal learning environment. Critical, creative and autonomous roles in a competitive society

School has evolved from a place where knowledge is provided to a place where learners are helped to develop their professional and social skills. Consequently, education must evolve through big challenges in order to face the changes of society in the XXIst century

Temperament and self-based correlates of cooperative, competitive and individualistic learning preferences

People vary in the extent to which they prefer cooperative, competitive or individualistic achievement tasks. In the present research, we conducted two studies designed to investigate correlates and possible roots of these social interdependence orientations, namely approach and avoidance temperament, general self-efficacy, implicit theories of intelligence, and contingencies of self-worth based in others’ approval, competition, and academic competence. The results indicated that approach temperament, general self-efficacy, and incremental theory were positively, and entity theory was negatively related to cooperative preferences (|r| range from .11 to .41); approach temperament, general self-efficacy, competition contingencies, and academic competence contingencies were positively related to competitive preferences (|r| range from .16 to .46); and avoidance temperament, entity theory, competitive contingencies, and academic competence contingencies were positively related, and incremental theory was negatively related to individualistic preferences (|r| range from .09 to .15). The findings are discussed with regard to the meaning of each of the three social interdependence orientations, cultural differences among the observed relations, and implications for practicioners.

Transformative Learning Theory and Its Relevance to Managing Group Dynamics in a Competitive Work Environment

In any environment, group dynamics would exist. How we deal with it in a competitive work environment defines who we are using transformative learning. This paper provides useful information from a number of theorists who share perspectives on the complex nature of groups.

Learning organizations: Empirically investigating metaphors

Learning organizations are a special form of organization where enhancing learning is a strategy to increase intellectual capital. Developing learning organizations has become an imperative for many managers, since an organization's learning methods and rate may be the only source of sustainable competitive advantage. However, learning organization theory tends to be prescriptive and rhetorical, with empirical research still relatively new. This paper contributes to the literature by reporting case-study research in progress based on four Australian organizations. In the organizations studied, use of the learning organization metaphor was coupled with an emergent metaphor: organization as `family". By employing structure mapping of metaphor within analytical induction, both established methods but not combined before, this paper shows how theory might be developed from metaphor.

E-Learning as a shared service in shared services centers

Book Subtitle International Conference, CENTERIS 2010, Viana do Castelo, Portugal, October 20-22, 2010, Proceedings, Part II

Collaborative learning in a qeb-accessible workbench

Web-based course management and delivery is regarded by many institutions as a key factor in an increasingly competitive education and training world, but the systems currently available are largely unsatisfactory in terms of supporting collaborative work and access to practical science facilities. These limitations are less important in areas where “pen-and-paper” courseware is the mainstream, but become unacceptably restrictive when student assignments require real-time teamwork and access to laboratory equipment. This paper presents a web-accessible workbench for electronics design and test, which was developed in the scope of an European IST project entitled PEARL, with the aim of supporting two main features: full web access and collaborative learning facilities.

Adaptive learning in agents behaviour: A framework for electricity markets simulation

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.

Demonstration of the Multi-Agent Simulator of Competitive Electricity Markets

Electricity markets are complex environments with very particular characteristics. A critical issue concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, performed so that the competitiveness could be increased, but with exponential implications in the increase of the complexity and unpredictability in those markets’ scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behavior. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This paper presents the Multi-Agent System for Competitive Electricity Markets (MASCEM) – a simulator based on multi-agent technology that provides a realistic platform to simulate electricity markets, the numerous negotiation opportunities and the participating entities.

Reinforcement Learning Based on the Bayesian Theorem for Electricity Markets Decision Support

This paper presents the applicability of a reinforcement learning algorithm based on the application of the Bayesian theorem of probability. The proposed reinforcement learning algorithm is an advantageous and indispensable tool for ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to electricity market negotiating players. ALBidS uses a set of different strategies for providing decision support to market players. These strategies are used accordingly to their probability of success for each different context. The approach proposed in this paper uses a Bayesian network for deciding the most probably successful action at each time, depending on past events. The performance of the proposed methodology is tested using electricity market simulations in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). MASCEM provides the means for simulating a real electricity market environment, based on real data from real electricity market operators.

Learning sustainability by developing a solar dryer for microalgae retrieval

The development of nations depends on energy consumption, which is generally based on fossil fuels. This dependency produces irreversible and dramatic effects on the environment, e.g. large greenhouse gas emissions, which in turn cause global warming and climate changes, responsible for the rise of the sea level, floods, and other extreme weather events. Transportation is one of the main uses of energy, and its excessive fossil fuel dependency is driving the search for alternative and sustainable sources of energy such as microalgae, from which biodiesel, among other useful compounds, can be obtained. The process includes harvesting and drying, two energy consuming steps, which are, therefore, expensive and unsustainable. The goal of this EPS@ISEP Spring 2013 project was to develop a solar microalgae dryer for the microalgae laboratory of ISEP. A multinational team of five students from distinct fields of study was responsible for designing and building the solar microalgae dryer prototype. The prototype includes a control system to ensure that the microalgae are not destroyed during the drying process. The solar microalgae dryer works as a distiller, extracting the excess water from the microalgae suspension. This paper details the design steps, the building technologies, the ethical and sustainable concerns and compares the prototype with existing solutions. The proposed sustainable microalgae drying process is competitive as far as energy usage is concerned. Finally, the project contributed to increase the deontological ethics, social compromise skills and sustainable development awareness of the students.