Showing papers on "Knowledge acquisition published in 2010"
TL;DR: A blended learning approach was not superior to a QCs approach for improving knowledge about dementia management, however, a subgroup of GPs who were motivated to actually use the online modules had a gain in knowledge.
Abstract: The implementation of new medical knowledge into general practice is a complex process. Blended learning may offer an effective and efficient educational intervention to reduce the knowledge-to-practice gap. The aim of this study was to compare knowledge acquisition about dementia management between a blended learning approach using online modules in addition to quality circles (QCs) and QCs alone. In this cluster-randomised trial with QCs as clusters and general practitioners (GPs) as participants, 389 GPs from 26 QCs in the western part of Germany were invited to participate. Data on the GPs' knowledge were obtained at three points in time by means of a questionnaire survey. Primary outcome was the knowledge gain before and after the interventions. A subgroup analysis of the users of the online modules was performed. 166 GPs were available for analysis and filled out a knowledge test at least two times. A significant increase of knowledge was found in both groups that indicated positive learning effects of both approaches. However, there was no significant difference between the groups. A subgroup analysis of the GPs who self-reported that they had actually used the online modules showed that they had a significant increase in their knowledge scores. A blended learning approach was not superior to a QCs approach for improving knowledge about dementia management. However, a subgroup of GPs who were motivated to actually use the online modules had a gain in knowledge. Current Controlled Trials ISRCTN36550981.
678 citations
TL;DR: In this article, the authors argue that these gains from R&D outsourcing need to be balanced against the "pains" that stem from a dilution of firm-specific resources, the deterioration of integrative capabilities and the high demands on management attention.
Abstract: The outsourcing of research and development (R&D) activities has frequently been characterized as an important instrument to acquire external technological knowledge that is subsequently integrated into a firm's own knowledge base However, in this paper we argue that these ‘gains’ from R&D outsourcing need to be balanced against the ‘pains’ that stem from a dilution of firm-specific resources, the deterioration of integrative capabilities and the high demands on management attention Based on a panel dataset of innovating firms in Germany, we find evidence for an inverse U-shaped relationship between R&D outsourcing and innovation performance This relationship is positively moderated by the extent to which firms engage in internal R&D and by the breadth of formal R&D collaborations: both serve as an instrument to increase the effectiveness of R&D outsourcing
490 citations
TL;DR: 3-D vision on humanoid robots with complex oculomotor systems is often difficult due to the modeling uncertainties, but it is shown that these uncertainties can be accounted for by the proposed approach.
Abstract: Acquisition of new sensorimotor knowledge by imitation is a promising paradigm for robot learning. To be effective, action learning should not be limited to direct replication of movements obtained during training but must also enable the generation of actions in situations a robot has never encountered before. This paper describes a methodology that enables the generalization of the available sensorimotor knowledge. New actions are synthesized by the application of statistical methods, where the goal and other characteristics of an action are utilized as queries to create a suitable control policy, taking into account the current state of the world. Nonlinear dynamic systems are employed as a motor representation. The proposed approach enables the generation of a wide range of policies without requiring an expert to modify the underlying representations to account for different task-specific features and perceptual feedback. The paper also demonstrates that the proposed methodology can be integrated with an active vision system of a humanoid robot. 3-D vision data are used to provide query points for statistical generalization. While 3-D vision on humanoid robots with complex oculomotor systems is often difficult due to the modeling uncertainties, we show that these uncertainties can be accounted for by the proposed approach.
334 citations
TL;DR: A new approach for automatic learning of terminological ontologies from text corpus based on probabilistic topic models, which shows that the method outperforms other methods in terms of recall and precision measures.
Abstract: Probabilistic topic models were originally developed and utilized for document modeling and topic extraction in Information Retrieval. In this paper, we describe a new approach for automatic learning of terminological ontologies from text corpus based on such models. In our approach, topic models are used as efficient dimension reduction techniques, which are able to capture semantic relationships between word-topic and topic-document interpreted in terms of probability distributions. We propose two algorithms for learning terminological ontologies using the principle of topic relationship and exploiting information theory with the probabilistic topic models learned. Experiments with different model parameters were conducted and learned ontology statements were evaluated by the domain experts. We have also compared the results of our method with two existing concept hierarchy learning methods on the same data set. The study shows that our method outperforms other methods in terms of recall and precision measures. The precision level of the learned ontology is sufficient for it to be deployed for the purpose of browsing, navigation, and information search and retrieval in digital libraries.
218 citations
TL;DR: In this paper, a conceptual framework is developed by integrating the relational view, organizational learning theory, and the resource-based view, and empirical evidence using large-sample data to test the model and find that trust and interaction creates a basis for knowledge acquisition across alliance partners.
188 citations
TL;DR: In this paper, the authors investigated how the vendor firms in China respond to cross-border outsourcing trends differently by examining the different effects of entrepreneurial orientation (EO) and market orientation (MO), as well as their interaction, on local vendors' acquisition of knowledge from foreign outsourcers.
Abstract: From the perspective of the vendors in emerging countries (VECs), this article investigates how the vendor firms in China respond to cross-border outsourcing trends differently by examining the different effects of entrepreneurial orientation (EO) and market orientation (MO), as well as their interaction, on local vendors' acquisition of knowledge from foreign outsourcers in cross-border outsourcing. We find that the knowledge acquisition of the vendors positively affects firm performance. Thus, the vendors need to correctly choose their strategic orientation to improve the knowledge acquisitions. Our results show the EO of the vendors has a positive effect on the knowledge acquisition, but the relationship between MO and the knowledge acquisition is an inverted U-shape. Further, the interactive effect between EO and MO on the knowledge acquisition is positive. All these findings extend the literature in organizational learning and cross-border outsourcing, and suggest that VECs should not only correctly choose their strategic orientation, but also pay more attention to the orientation interaction in acquiring knowledge from their partners through cross-border outsourcing so that they can more efficiently improve their performance.
159 citations
TL;DR: In this paper, the authors investigate how knowledge acquired from alliance partners affects organizational knowledge creation, which in turn leads to innovative performance and propose that the knowledge-innovation relationship is stronger in international alliances than domestic alliances.
Abstract: Strategic alliances play a critical role in global innovation. Firms can overcome resource constraints and achieve superior innovative performance not only by using internal resources but also by acquiring knowledge-based capabilities from alliance partners. In this study, the authors investigate how knowledge acquired from alliance partners affects organizational knowledge creation, which in turn leads to innovative performance. The authors propose that the knowledge–innovation relationship is stronger in international alliances than domestic alliances. The results from a survey of 127 German firms engaged in strategic alliances confirm that knowledge creation mediates the effect of knowledge acquisition on innovative performance and that international alliances strengthen the effect of knowledge creation on innovative performance. In addition, the authors find that interfirm cooperation and competition coexist in strategic alliances and that both factors increase knowledge acquisition, though f...
157 citations
TL;DR: An ontology of PGx relationships built starting from a lexicon of key pharmacogenomic entities and a syntactic parse of more than 87 million sentences from 17 million MEDLINE abstracts is described, creating a network of 40,000 relationships between more than 200 entity types with clear semantics.
117 citations
TL;DR: This study investigates the relationships between knowledge acquisition, absorptive capability, and innovation capability on Taiwan’s knowledge-intensive industries using a structural equation model, which is constructed based on the data sampled from financial and manufacturing industries and the 362 returned valid research samples.
Abstract: This study investigates the relationships between knowledge acquisition, absorptive capability, and innovation capability on Taiwanâ??s knowledge-intensive industries using a structural equation model, which is constructed based on the data sampled from financial and manufacturing industries, and the 362 returned valid research samples. By testing five hypotheses, the research results find that absorptive capacity is the mediator between knowledge acquisition and innovation capability, and that knowledge acquisition has a positive effect on absorptive capacity. In addition, we used a multi-group approach and found that industry is a moderator between knowledge acquisition and innovation capability. Finally, a conclusion including research findings, discussion, implication, and future works is presented.
115 citations
TL;DR: In this article, the authors focus on the way in which owner-managers in smaller firms improve their businesses through the creation of "strategic space" which refers to the process by which ownerman managers are able to access resources, motivation and capability to review existing practices.
Abstract: The authors focus on the way in which owner-managers in smaller firms improve their businesses through the creation of ‘strategic space’. The term ‘strategic space’ refers to the process by which owner-managers are able to access resources, motivation and capability to review existing practices. The starting point is the owner-manager's human capital and their capacity to engage in critical reflection about their business. We highlight three concepts central to the creation of strategic space, first, social capital, which refers to the network relationships that provide access to a wide range of resources and information. Second, absorptive capacity, which describes the way in which organizational members identify, acquire and utilize knowledge from external sources. Third, mediating artefacts, which represent existing knowledge but also facilitate the translation and transformation of understanding within and between communities of practice. This process is essential to the renewal of knowledge and knowi...
112 citations
TL;DR: This paper presents a system, known as Concept-Relation-Concept Tuple-based Ontology Learning (CRCTOL), for mining ontologies automatically from domain-specific documents and presents two case studies where CRCTOL is used to build a terrorism domain ontology and a sport event domain ontologies.
Abstract: Domain ontologies play an important role in supporting knowledge-based applications in the Semantic Web. To facilitate the building of ontologies, text mining techniques have been used to perform ontology learning from texts. However, traditional systems employ shallow natural language processing techniques and focus only on concept and taxonomic relation extraction. In this paper we present a system, known as Concept-Relation-Concept Tuple-based Ontology Learning (CRCTOL), for mining ontologies automatically from domain-specific documents. Specifically, CRCTOL adopts a full text parsing technique and employs a combination of statistical and lexico-syntactic methods, including a statistical algorithm that extracts key concepts from a document collection, a word sense disambiguation algorithm that disambiguates words in the key concepts, a rule-based algorithm that extracts relations between the key concepts, and a modified generalized association rule mining algorithm that prunes unimportant relations for ontology learning. As a result, the ontologies learned by CRCTOL are more concise and contain a richer semantics in terms of the range and number of semantic relations compared with alternative systems. We present two case studies where CRCTOL is used to build a terrorism domain ontology and a sport event domain ontology. At the component level, quantitative evaluation by comparing with Text-To-Onto and its successor Text2Onto has shown that CRCTOL is able to extract concepts and semantic relations with a significantly higher level of accuracy. At the ontology level, the quality of the learned ontologies is evaluated by either employing a set of quantitative and qualitative methods including analyzing the graph structural property, comparison to WordNet, and expert rating, or directly comparing with a human-edited benchmark ontology, demonstrating the high quality of the ontologies learned. © 2010 Wiley Periodicals, Inc.
TL;DR: A conceptual framework, based on taxonomy of the most important argumentation models, approaches and systems found in the literature, is proposed, which highlights the similarities and differences between these argueation models.
Abstract: Understanding argumentation and its role in human reasoning has been a continuous subject of investigation for scholars from the ancient Greek philosophers to current researchers in philosophy, logic and artificial intelligence. In recent years, argumentation models have been used in different areas such as knowledge representation, explanation, proof elaboration, commonsense reasoning, logic programming, legal reasoning, decision making, and negotiation. However, these models address quite specific needs and there is need for a conceptual framework that would organize and compare existing argumentation-based models and methods. Such a framework would be very useful especially for researchers and practitioners who want to select appropriate argumentation models or techniques to be incorporated in new software systems with argumentation capabilities. In this paper, we propose such a conceptual framework, based on taxonomy of the most important argumentation models, approaches and systems found in the literature. This framework highlights the similarities and differences between these argumentation models. As an illustration of the practical use of this framework, we present a case study which shows how we used this framework to select and enrich an argumentation model in a knowledge acquisition project which aimed at representing argumentative knowledge contained in texts critiquing military courses of action.
TL;DR: This paper brings together research from two different fields – user modelling and web ontologies – in attempt to demonstrate how recent semantic trends in web development can be combined with the modern technologies of user modelling.
Abstract: This paper brings together research from two different fields – user modelling and web ontologies – in attempt to demonstrate how recent semantic trends in web development can be combined with the modern technologies of user modelling. Over the last several years, a number of user-adaptive systems have been exploiting ontologies for the purposes of semantics representation, automatic knowledge acquisition, domain and user model visualisation and creation of interoperable and reusable architectural solutions. Before discussing these projects, we first overview the underlying user modelling and ontological technologies. As an example of the project employing ontology-based user modelling, we present an experiment design for translation of overlay student models for relative domains by means of ontology mapping.
TL;DR: In this article, the role played by the cognitive dimension of social capital on knowledge acquisition in clustered firms was analyzed and it was found that knowledge access depends on the capacity of the firms to share visions, goals, values, and culture with other actors in the local neighborhood.
Abstract: This paper analyzes the role played by the cognitive dimension of social capital on knowledge acquisition in clustered firms. We have applied a structural model to an empirical survey of the Spanish footwear industry. Findings detect a significant indirect effect of the district membership on knowledge acquisition through cognitive social capital development. In contrast with the assumption of direct and free access to common knowledge in territorial agglomerations, findings suggested that knowledge access depends on the capacity of the firms to share visions, goals, values, and culture with other actors in the local neighborhood. The social capital perspective provides a solid base to explain heterogeneity among firm members in the industrial district in order to access common knowledge and capacities. Findings suggested that institutions involved in a district must favour efforts to boost the collective representation as well as common goals and vision.
TL;DR: The results show that the depth and the breadth of its owner's technical and industrial experiences best explained absorptive capacity of an SME.
Abstract: Purpose – The purpose of this paper is to examine the relationship between knowledge acquisition, knowledge absorptive capacity, and innovation performance in small and medium enterprises (SMEs).Design/methodology/approach – Questionnaire data were collected from research and development (R&D) managers or owners of 49 SMEs of the bicycle industry in Taiwan. The questionnaire was designed to measure variables including: knowledge absorptive capacity, knowledge acquisition of company, technical and industrial experiences of owner's and the R&D staff, innovation performance measures, and control variables.Findings – The results show that the depth and the breadth of its owner's technical and industrial experiences best explained absorptive capacity of an SME. In turn, the absorptive capacity and the knowledge acquisition activities of an SME affect its innovation performance.Research limitations/implications – The findings show that, first, SME owners' technical and industrial experiences are contributing fa...
TL;DR: The analysis indicates that expert power has a positive influence on the extent of knowledge acquisition and dissemination practices and the impact of coercive, legitimate, and reward power to be contingent on the organizational size.
Abstract: Purpose – The purpose of this paper is to identify the key leadership characteristics (in the form of social power) needed in a knowledge‐based firm that can influence knowledge workers (KWs) to participate actively in creating, sharing, and using knowledge.Design/methodology/approach – Data measuring top leaders social power and knowledge management (KM) practices is gathered from 402 KWs representing 180 Multimedia Super Corridor status firms in Malaysia.Findings – The analysis indicates that expert power has a positive influence on the extent of knowledge acquisition and dissemination practices. Legitimate power is found to impede knowledge acquisition practices. Furthermore, reliance on referent power no longer works in a knowledge‐based context. Finally, the paper found the impact of coercive, legitimate, and reward power to be contingent on the organizational size.Research limitations/implications – Besides leaders potential to influence, there may be other factors that could influence the extent of...
01 Dec 2010
TL;DR: Modifications of classical similarity measures are proposed based on a contextualized and scalable version of IC computation in the Web by exploiting taxonomical knowledge to avoid the measures’ dependency on the corpus pre-processing to achieve reliable results and minimize language ambiguity.
Abstract: Estimation of the degree of semantic similarity/distance between concepts is a very common problem in research areas such as natural language processing, knowledge acquisition, information retrieval or data mining. In the past, many similarity measures have been proposed, exploiting explicit knowledge--such as the structure of a taxonomy--or implicit knowledge--such as information distribution. In the former case, taxonomies and/or ontologies are used to introduce additional semantics; in the latter case, frequencies of term appearances in a corpus are considered. Classical measures based on those premises suffer from some problems: in the first case, their excessive dependency of the taxonomical/ontological structure; in the second case, the lack of semantics of a pure statistical analysis of occurrences and/or the ambiguity of estimating concept statistical distribution from term appearances. Measures based on Information Content (IC) of taxonomical concepts combine both approaches. However, they heavily depend on a properly pre-tagged and disambiguated corpus according to the ontological entities in order to compute accurate concept appearance probabilities. This limits the applicability of those measures to other ontologies ---like specific domain ontologies- and massive corpus ---like the Web-. In this paper, several of the presented issues are analyzed. Modifications of classical similarity measures are also proposed. They are based on a contextualized and scalable version of IC computation in the Web by exploiting taxonomical knowledge. The goal is to avoid the measures' dependency on the corpus pre-processing to achieve reliable results and minimize language ambiguity. Our proposals are able to outperform classical approaches when using the Web for estimating concept probabilities.
TL;DR: Simulation results prove the fact that the proposed knowledge acquisition with a swarm-intelligence approach (KASIA) outperforms classical learning approaches in terms of final results and computational effort.
Abstract: Knowledge acquisition is a long-standing problem in fuzzy-rule-based systems. In spite of the existence of several approaches, much effort is still required to increase the efficiency of the learning process. This study introduces a new method for the fuzzy-rule evolution that forms an expert system knowledge: the knowledge acquisition with a swarm-intelligence approach (KASIA). Specifically, this strategy is based on the use of particle-swarm optimization (PSO) to obtain the antecedents, consequences, and connectives of the rules. To test the feasibility of the suggested method, the inverted-pendulum problem is studied, and results are compared for two of the most extensively used methodologies in machine learning: the genetic-based Pittsburgh approach and the Q-learning-based strategy, i.e., state-action-reward-state-action (SARSA). Moreover, KASIA is analyzed as a learning strategy in fuzzy-rule-based metascheduler design for grid computing, and performance is compared with other scheduling strategies based on genetic learning and existing scheduling approaches, i.e., EASY-backfilling and ESG+local periodical search. To be more precise, simulation results prove the fact that the proposed strategy outperforms classical learning approaches in terms of final results and computational effort. Furthermore, the main advantage is the capability to control convergence and its simplicity.
09 Feb 2010
TL;DR: A new approach to generate association rules on numeric data and a modified equal width binning interval approach to discretizing continuous valued attributes are introduced to help the health doctors to explore their data and to understand the discovered rules better.
Abstract: The discovery of knowledge from medical databases is important in order to make effective medical diagnosis. The aim of data mining is extract the information from database and generate clear and understandable description of patterns. In this study we have introduced a new approach to generate association rules on numeric data. We propose a modified equal width binning interval approach to discretizing continuous valued attributes. The approximate width of the desired intervals is chosen based on the opinion of medical expert and is provided as an input parameter to the model. First we have converted numeric attributes into categorical form based on above techniques. Apriori algorithm is usually used for the market basket analysis was used to generate rules on Pima Indian diabetes data. The data set was taken from UCI machine learning repository containing total instances 768 and 8 numeric attributes.We discover that the often neglected pre-processing steps in knowledge discovery are the most critical elements in determining the success of a data mining application. Lastly we have generated the association rules which are useful to identify general associations in the data, to understand the relationship between the measured fields whether the patient goes on to develop diabetes or not. We are presented step-by-step approach to help the health doctors to explore their data and to understand the discovered rules better.
TL;DR: Simulated clinical experience was found to be as effective as traditional clinical experience in promoting students' knowledge acquisition and significant knowledge gain was identified associated with both simulated and traditional clinical experiences.
Abstract: Although simulated clinical experience is being used increasingly in nursing education, vital evidence related to knowledge acquisition associated with simulated clinical experience does not exist. This intervention study used a 2×2 crossover design and equivalence testing to explore the effects of simulated clinical experiences on undergraduate students' (n = 74) knowledge acquisition in a fundamentals of nursing course. Following random assignment, students participated in laboratory-based simulated clinical experiences with high-fidelity human patient simulators and traditional clinical experiences and completed knowledge pretests and posttests. Analysis identified significant knowledge gain associated with both simulated and traditional clinical experiences, with the groups' knowledge scores being statistically significantly equivalent. A priori equivalence bounds around the difference between the groups were set at ± 5 points. Simulated clinical experience was found to be as effective as traditional clinical experience in promoting students' knowledge acquisition.
TL;DR: Michael Schneider and Elsbeth Stern place knowledge acquisition at the very heart of the learning process, albeit that the quality of the knowledge is as necessary as the quantity and that "knowledge" should be understood much more broadly than (but including) knowing facts.
Abstract: Michael Schneider and Elsbeth Stern place knowledge acquisition at the very heart of the learning process, albeit that the quality of the knowledge is as necessary as the quantity and that "knowledge" should be understood much more broadly than (but including) knowing facts. They summarise the cognitive perspective through ten "cornerstones". Learning: i) is essentially carried out by the learner; ii) should take prior knowledge importantly into account; iii) requires the integration of knowledge structures; iv) balances the acquisition of concepts, skills and meta-cognitive competence; v) builds complex knowledge structures by hierarchically organising more basic pieces of knowledge; vi) can valuably use structures in the external world for organising knowledge structures in the mind; vii) is constrained by the capacity limitations of human information-processing; viii) results from a dynamic interplay of emotion, motivation and cognition; ix) should develop transferable knowledge structures; x) requires time and effort.
TL;DR: A correct and timely diagnosis mechanism of pump failures by knowledge acquisition through a fuzzy rule-based inference system which could approximate human reasoning is provided and the proposed approach is tested and applied to a petrochemical industry.
Abstract: Pump operating problems may be either hydraulic or mechanical and there is interdependence between the failure diagnoses of these two categories Consequently, a correct diagnosis of a pump failure needs to consider many symptoms and hydraulic or mechanical causes But, due to nonlinear, time-varying behavior and imprecise measurement information of the systems it is difficult to deal with pumps failures with precise mathematical equations, while human operators with the aid of their practical experience can handle these complex situations, with only a set of imprecise linguistic if-then rules and imprecise system state, but this procedure is time consuming and needs the knowledge of human experts and experienced maintenance personnel The purpose of this study is to provide a correct and timely diagnosis mechanism of pump failures by knowledge acquisition through a fuzzy rule-based inference system which could approximate human reasoning The proposed fuzzy inference system by: (1) reduction of human error, (2) reduction of repair time (3) creation of expert knowledge which could be used for training (4) reduction of unnecessary expenditures for upgrades and finally, (5) reduction of maintenance costs, will improve the maintenance process The novelty of this work is the knowledge acquisition (the extraction of linguistic rules) through the interactive impact of the critical failure modes on the both hydraulic and mechanical operating parameters including flow rate, discharge pressure, NPSHR (Net Positive Suction Head Required), BHP (Brake Horse Power), efficiency, vibration and temperature The proposed approach is tested and applied to a petrochemical industry
TL;DR: In this article, the authors found that managerial knowledge that has more sticky characteristics than technology is acquirable know-how and also showed that trust between parents, international experience of IJV employees, and foreign parent's support in various managerial functions will considerably increase the extent of knowledge acquisition for IJVs.
Abstract: Although there is a general agreement amongst scholars that international joint venture (IJV) is a vehicle to acquire technology and knowledge from foreign parents, key factors differentiating high knowledge acquirers from low knowledge acquirers are not yet conclusive. For the purpose of this study, the samples are divided into two groups based on the extent of knowledge acquisition using cluster analysis. Then the two groups are examined to identify the main factors classifying the groups by using logistic regression. This study finds that managerial knowledge that has more sticky characteristics than technology is acquirable know-how. It also shows that trust between parents, international experience of IJV employees, and foreign parent’s support in various managerial functions will considerably increase the extent of knowledge acquisition for IJVs. In conclusion, based on the findings from this study, this paper offers suggestions to IJVs and multinational enterprises investing in Korea.
01 Jun 2010
TL;DR: This paper uses the Web as a massive learning corpus to retrieve data and to infer information distribution using highly contextualized queries aimed at improving the quality of the result.
Abstract: Class descriptors such as attributes, features or meronyms are rarely considered when developing ontologies. Even WordNet only includes a reduced amount of part-of relationships. However, these data are crucial for defining concepts such as those considered in classical knowledge representation models. Some attempts have been made to extract those relations from text using general meronymy detection patterns; however, there has been very little work on learning expressive class attributes (including associated domain, range or data values) at an ontological level. In this paper we take this background into consideration when proposing and implementing an automatic, non-supervised and domain-independent methodology to extend ontological classes in terms of learning concept attributes, data-types, value ranges and measurement units. In order to present a general solution and minimize the data sparseness of pattern-based approaches, we use the Web as a massive learning corpus to retrieve data and to infer information distribution using highly contextualized queries aimed at improving the quality of the result. This corpus is also automatically updated in an adaptive manner according to the knowledge already acquired and the learning throughput. Results have been manually checked by means of an expert-based concept-per-concept evaluation for several well distinguished domains showing reliable results and a reasonable learning performance.
TL;DR: This study investigated the process of spatial knowledge acquisition in younger adults, middle-aged adults, and older adults in a desktop virtual environment, where participants learned a way through a virtual maze, had to recall landmarks that were present in the maze, and had to draw an overview of the maze.
Abstract: This study investigated the process of spatial knowledge acquisition in younger adults (20-30 years), middle-aged adults (40-50 years), and older adults (60-70 years) in a desktop virtual environment, where participants learned a way through a virtual maze, had to recall landmarks that were present in the maze, and had to draw an overview of the maze. The results revealed a general decline in spatial memory of the elderly, that is, in the time needed to learn a new route, in the retrieval of landmarks from memory (landmark knowledge), and in the ability to draw a map (configurational knowledge). When the route with landmarks was perfectly learned, however, there was no age dependent difference in finding the correct route without landmarks in the virtual maze (retrieval of route knowledge). Therefore, we conclude that not all aspects of spatial knowledge acquisition and spatial memory degrade with increasing age during adulthood.
TL;DR: Findings show that external knowledge acquisition takes place through three different processes that raise important differences and similarities with the SECI model, and some implications for management practice can be derived.
Abstract: Purpose – The purpose of this paper is to try to assess the applicability of the SECI model (Nonaka and Takeuchi) to the processes of external knowledge acquisition for firms located on knowledge‐intensive clusters. The paper's intended contribution lies in improving our understanding about the different mechanisms that organizations can use to learn from this kind of environment.Design/methodology/approach – The paper uses survey data obtained from a sample of knowledge‐intensive firms from Boston's Route 128, with custom tailored measurement scales. It applies a quantitative method based on questionnaire answers.Findings – Findings show that external knowledge acquisition takes place through three different processes that raise important differences and similarities with the SECI model.Research limitations/implications – Conclusions can only be generalized to firms located in knowledge‐intensive clusters. Nevertheless, some implications for management practice can be derived. Tacit knowledge from the en...
TL;DR: In this paper, a conceptual framework is established for the exploration of the way to acquire knowledge and the effectiveness of the acquired knowledge in maximising the maritime logistics value, and its relevant theoretical and practical implications are discussed.
Abstract: This paper introduces the concept of maritime logistics value as one of the most significant strategic goals that maritime operators want to achieve. The value is reflected in operational efficiency (e.g. reducing lead time and business costs) and service effectiveness (e.g. flexibility, responsiveness and reliability in the service). Drawing from key theories and practices in strategic management such as knowledge-based perspective and social network embeddedness perspective, this paper adopts a knowledge management strategy as a way to create and sustain the maritime logistics value. A conceptual framework is established for the exploration of the way to acquire knowledge and the effectiveness of the acquired knowledge in maximising the maritime logistics value. Following the parameters of this framework, the positive relationship between social networks, knowledge acquisition and maritime logistics value is identified, and its relevant theoretical and practical implications are discussed.
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TL;DR: Results reveal that participants taking a holistic systems-based view of a client environment develop more coherently organized mental models that increase their likelihood of identifying management representations that are inconsistent with industry evidence.
Abstract: Auditors are required to understand dynamic business environments as part of the performance of analytical procedures. Prior evidence, though, suggests that auditors have difficulty understanding such environments. This study reports an experimental investigation of techniques that help auditors to identify incorrect management representations by developing expectations that are both accurate and adaptive to changing business conditions. My predictions suggest that analyzing a dynamic client environment through a systems perspective enhances information processing ability, which then improves both evidence discrimination and the assimilation of new audit evidence. Results reveal that participants taking a holistic systems-based view of a client environment develop more coherently organized mental models that increase their likelihood of identifying management representations that are inconsistent with industry evidence. Furthermore, these participants more efficiently use their information processing ability, thereby improving assimilation of newly learned evidence to understand how changing business conditions affect their initial expectations.
TL;DR: The proposed eFSM model elicits highly interpretable semantic knowledge in the form of Mamdani-type if-then fuzzy rules from low-level numeric training data that collectively generalizes and describes the salient associative mappings between the inputs and outputs of the underlying process being modeled.
Abstract: Fuzzy rule-based systems (FRBSs) have been successfully applied to many areas However, traditional fuzzy systems are often manually crafted, and their rule bases that represent the acquired knowledge are static and cannot be trained to improve the modeling performance This subsequently leads to intensive research on the autonomous construction and tuning of a fuzzy system directly from the observed training data to address the knowledge acquisition bottleneck, resulting in well-established hybrids such as neural-fuzzy systems (NFSs) and genetic fuzzy systems (GFSs) However, the complex and dynamic nature of real-world problems demands that fuzzy rule-based systems and models be able to adapt their parameters and ultimately evolve their rule bases to address the nonstationary (time-varying) characteristics of their operating environments Recently, considerable research efforts have been directed to the study of evolving Tagaki-Sugeno (T-S)-type NFSs based on the concept of incremental learning In contrast, there are very few incremental learning Mamdani-type NFSs reported in the literature Hence, this paper presents the evolving neural-fuzzy semantic memory (eFSM) model, a neural-fuzzy Mamdani architecture with a data-driven progressively adaptive structure (ie, rule base) based on incremental learning Issues related to the incremental learning of the eFSM rule base are carefully investigated, and a novel parameter learning approach is proposed for the tuning of the fuzzy set parameters in eFSM The proposed eFSM model elicits highly interpretable semantic knowledge in the form of Mamdani-type if-then fuzzy rules from low-level numeric training data These Mamdani fuzzy rules define the computing structure of eFSM and are incrementally learned with the arrival of each training data sample New rules are constructed from the emergence of novel training data and obsolete fuzzy rules that no longer describe the recently observed data trends are pruned This enables eFSM to maintain a current and compact set of Mamdani-type if-then fuzzy rules that collectively generalizes and describes the salient associative mappings between the inputs and outputs of the underlying process being modeled The learning and modeling performances of the proposed eFSM are evaluated using several benchmark applications and the results are encouraging
TL;DR: Findings confirm the predicted superiority of the before-plus-during format in scientific reasoning and knowledge acquisition in low prior knowledge students.