Knowledge spaces and places: From the perspective of a “born-global” start-up in the field of urban technology

The success of cities increasingly relies on its capacity to capitalize on its knowledge base, but also on its potential to anchor external knowledge and the strategies of knowledge-based firms. In this paper we analyze how a “born global” start-up firm is linked to different types of places, and how it explores and exploits different territorial innovation potentials. Our case company—i.e., Living PlanIT—develops, tests and sells smart city software to processes real-time information collected through sensors embedded in a city’s buildings and infrastructure towards energy savings and manifold efficiency gains. The paper illustrates how the interaction with different places and knowledge-based cities provides unique resources for the technology development, search, experimentation, market formation and societal legitimation. Beyond focusing on a place’s fixed knowledge assets, the paper empirically assesses the innovation functions of different types of knowledge-cities and temporary “non-places” such as international high-level events.     

ERP systems as an enabler of sustained business process innovation: A knowledge-based view

This research examines the relationship between ERP systems and innovation from a knowledge-based perspective. Building upon the multi-dimensional conceptualization of absorptive capacity by Zahra and George [Zahra, S.A., George, G., 2002. Absorptive capacity: a review, reconceptualization, and extension. Academy of Management Journal 27 (2), 185–203], a theoretical framework is developed to specify the relationships between ERP-related knowledge impacts and potential/realized absorptive capacity for business process innovation. The implication of the knowledge-based analysis in this paper is that ERP systems present dialectical contradictions, both enabling and constraining business process innovation. The model highlights areas where active management has potential to enhance the capabilities of a firm for sustained innovation of its business processes. Future research directions are also outlined.     

Position paper: Benchmarking the performance of global and emerging knowledge cities

Knowledge-based development has become a new urban policy approach for the competitive cities of the global knowledge economy era. For those cities seeking a knowledge-based development, benchmarking is an essential prerequisite for informed and strategic vision and policy making to achieve a prosperous development. Nevertheless, benchmarked knowledge-based development performance analysis of global and emerging knowledge cities is an understudied area. This paper aims to contribute to the field by introducing the methodology of a novel performance assessment model—that is the Knowledge-Based Urban Development Assessment Model—and providing lessons from the application of the model in an international knowledge city performance analysis study. The assessment model puts renowned global and emerging knowledge cities—that are Birmingham, Boston, Brisbane, Helsinki, Istanbul, Manchester, Melbourne, San Francisco, Sydney, Toronto, and Vancouver—under the knowledge-based development microscope. The results of the analysis provide internationally benchmarked snapshot of the degree of achievements in various knowledge-based urban development performance areas of the investigated knowledge cities, and reveals insightful lessons on scrutinizing the global perspectives on knowledge-based development of cities.     

Research on radical innovation implementation through knowledge reuse based on knowledge flow: A case study on academic teams

To better understand and apply knowledge reuse through knowledge flow when radical innovation is expected, we select an extremely important reuse knowledge industry, i.e., academic research field, and adopt the “spatiotemporal” perspective to analyze the principles and processes of knowledge reuse. We have divided knowledge reuse into four stages, namely, knowledge search, knowledge evaluation, knowledge recombination, and knowledge creation, thereby developing a knowledge reuse mechanism model centered on these stages. This study differs from the previous ones primarily in our adoption of the new perspective in knowledge management research, and dynamically analyze knowledge reuse in the context of knowledge flow.     

Factors influencing beliefs for adoption of a learning analytics tool: An empirical study

Present research and development offer various learning analytics tools providing insights into different aspects of learning processes. Adoption of a specific tool for practice is based on how its learning analytics are perceived by educators to support their pedagogical and organizational goals. In this paper, we propose and empirically validate a Learning Analytics Acceptance Model (LAAM) of factors influencing the beliefs of educators concerning the adoption a learning analytics tool. In particular, our model explains how the usage beliefs (i.e., ease-of-use and usefulness perceptions) about the learning analytics of a tool are associated with the intention to adopt the tool. In our study, we considered several factors that could potentially affect the adoption beliefs: i) pedagogical knowledge and information design skills of educators; ii) educators' perceived utility of a learning analytics tool; and iii) educators' perceived ease-of-use of a learning analytics tool. By following the principles of Technology Acceptance Model, the study is done with a sample of educators who experimented with a LOCO-Analyst tool. Our study also determined specific analytics types that are primary antecedence of perceived usefulness (concept comprehension and social interaction) and ease-of-use (interactive visualization).     

Does the source of external knowledge matter? Examining the role of customer co-creation and partner sourcing in knowledge creation and innovation

External knowledge is widely acknowledged to be critical for organizational innovation. However, we lack understanding of how different sources of knowledge—namely knowledge from customers and partners—influence innovation. Grounded in the knowledge-based view of the firm, we theoretically develop and empirically test a model of mechanisms through which two forms of external knowledge acquisition i.e., customer co-creation and partner sourcing, foster knowledge creation and innovation. We posit that the different forms of external knowledge acquisition contribute differentially toward innovation outcomes. Finally, we examine the moderating role of knowledge dissemination capability on the relationships between external knowledge of both forms. We test our proposed model using data gathered from 655 organizations in two knowledge intensive sectors i.e., financial and information technology. Our model explains 65 % of the variance in knowledge creation and highlights that organizations with higher degree of knowledge creation are more likely to innovate through new patents. We extend prior research on customer co-creation and organizational innovation by identifying intervening organizational variables and mechanisms that explicate the effects of external knowledge on innovation. The results provide important implications for organizations regarding the capabilities needed to utilize external knowledge for innovation.     

DynamiCITY: Revealing city dynamics from citizens social media broadcasts

Social media and mobile devices have revolutionized the way people communicate and share information in various contexts, such as in cities. In today’s “smart” cities, massive amounts of multiple forms of geolocated content is generated daily in social media, out of which knowledge for social interactions and urban dynamics can be derived. This work addresses the problem of detecting urban social activity patterns and interactions, by modeling cities into “dynamic areas”, i.e., coherent geographic areas shaped through social activities. Social media users provide the information on such social activities and interactions in cases when they are on the move around the city neighborhoods. The proposed approach models city places as feature vectors which represent users visiting patterns (social activity), the time of observed visits (temporal activity), and the context of functionality of visited places category. To uncover the dynamics of city areas, a clustering approach is proposed which considers the derived feature vectors to group people’s activities with respect to location, time, and context. The proposed methodology has been implemented on the DynamiCITY platform which demonstrates neighborhood analytics via a Web interface that allows end-users to explore neighborhoods dynamics and gain insights for city cross-neighborhood patterns and inter-relationships.     

Knowledge-based programs

Summary.  Reasoning about activities in a distributed computer system at the level of the knowledge of individuals and groups allows us to abstract away from many concrete details of the system we are considering. In this paper, we make use of two notions introduced in our recent book to facilitate designing and reasoning about systems in terms of knowledge. The first notion is that of a knowledge-based program. A knowledge-based program is a syntactic object: a program with tests for knowledge. The second notion is that of a context, which captures the setting in which a program is to be executed. In a given context, a standard program (one without tests for knowledge) is represented by (i.e., corresponds in a precise sense to) a unique system. A knowledge-based program, on the other hand, may be represented by no system, one system, or many systems. In this paper, we provide a sufficient condition for a knowledge-based program to be represented in a unique way in a given context. This condition applies to many cases of interest, and covers many of the knowledge-based programs considered in the literature. We also completely characterize the complexity of determining whether a given knowledge-based program has a unique representation, or any representation at all, in a given finite-state context. Received: October 1995 / Accepted: February 1997     

School Innovative Management Model and Strategies: The Perspective of Organizational Learning

Abstract

Along with knowledge-based economy coming, knowledge and innovation have been the core elements of national competitive power. Education plays an important role to improve individual or organizational creativity and innovational power. School innovational management is conducive to promoting creativity education. Additionally, it helps schools to acquire competitive power so as to develop constantly. This study discusses the related concepts and content of learning school and school innovative management; analyzes the impact factors of school organizational learning; and establishes the model of school innovative management based organizational learning. From the perspective of organizational learning, the study presents the strategies of school innovative management using for reference for China's schools.     

A methodology for applying students' interactive task performance scores from a multimedia-based performance assessment in a Bayesian Network

Computer-based simulations are increasingly being used in educational assessment. In most cases, the simulation-based assessment (SBA) is used for formative assessment, which can be defined as assessment for learning, but as research on the topic continues to grow, possibilities for summative assessment, which can be defined as assessment of learning, are also emerging. The current study contributes to research on the latter category of assessment. In this article, we present a methodology for scoring the interactive and complex behavior of students in a specific type of SBA, namely, a Multimedia-based Performance Assessment (MBPA), which is used for a summative assessment purpose. The MBPA is used to assess the knowledge, skills, and abilities of confined space guard (CSG) students. A CSG supervises operations that are carried out in a confined space (e.g., a tank or silo). We address two specific challenges in this article: the evidence identification challenge (i.e., scoring interactive task performance), and the evidence accumulation challenge (i.e., accumulating scores in a psychometric model). Using expert ratings on the essence and difficulty of actions in the MBPA, we answer the first challenge by demonstrating that interactive task performance in MBPA can be scored. Furthermore, we answer the second challenge by recoding the expert ratings in conditional probability tables that can be used in a Bayesian Network (a psychometric model for reasoning under uncertainty and complexity). Finally, we validate and illustrate the presented methodology through the analysis of the response data of 57 confined space guard students who performed in the MBPA.     

Doctor-patient interaction in telemedicine: Logic of choice and logic of care perspectives

This paper focuses on telemedicine implementation, which can be used to extend modern medical knowledge to remote areas in developing countries. By examining doctor- patient interactions in the context of a telemedicine program in India, we posit how the behaviour of the actors interacting over virtual media is determined by interplay between two dominant institutional logics, namely logic of care and logic of choice. The paper draws on the tenets of institutional logics to extend the theoretical understanding about processes of engagement of actors with a new technology and explicates how the engagement evolves with the use of technology. The research emphasizes the essential role of considering the dynamics of logic of care and logic of choice in the design and implementation process.     

Qualitative findings of students’ perception on practice of self-regulated strategies in online community discussion

With rapid emergence to the birth of the knowledge era, an active and interactive learning environment is fundamental to any teaching and learning process. Over time, negative implications of traditional learning environment are mounting, and measures must be taken to challenge it. With extensive research on available learning tools, it has become more feasible to provide our students with better learning opportunities. Such was the anticipation in the Malaysian educational context. Hence, this paper aims to address qualitative findings of a study on self-regulated learning strategies conveyed through the proposed interactive e-learning community (iELC). In duration of eight weeks, students (n = 50) from regular national secondary schools were subjected to online learning tasks aimed to advance strategies on self-regulated learning. To date, strategies of self-regulated learning have been perceived as key to learner to succeed academically and after the schooling years. By means of semi-structured interview, data were collected from small group and one-to-one perception. Results were directed at the overall development of self-regulated learning strategies and its diffusion in the iELC discussion platform. Conclusively, these findings add significantly to the understanding of self-regulated learning strategies in an online learning environment.     

Sustainability SI: Logistics Cost and Environmental Impact Analyses of Urban Delivery Consolidation Strategies

Among new, innovative city logistics strategies, urban delivery consolidation has received increasing academic and practical attention mostly in Europe and Japan. It is believed to bring cost savings and environmental benefits with the right setting. This paper demonstrates an alternative modeling framework to examine, from the strategic planning perspective, the effectiveness of urban delivery consolidation in terms of monetary logistics cost, energy consumption and PM_2.5 emissions with respect to a number of operational (e.g., rent cost, customer demand) and policy factors (e.g., commercial vehicle size restriction in city centers). The framework consists of two key modeling components: the Continuous Approximation (CA) method to model urban delivery (the so-called last-mile delivery) and the Motor Vehicle Emission Simulator (MOVES by the U.S. Environmental Protection Agency) to estimate the energy consumption and PM_2.5 emissions associated with the logistics activities. It is found that the potential logistics and environmental benefits of UCC could come from either improving the utilization of the vehicle capacity through consolidation, or shifting the more expensive storage cost from customers in the city center to the less expensive UCC rent cost—due to a less centralized location and/or government subsidy or other cost sharing mechanisms—outside of the city center. However, UCC could achieve those benefits compared to non- consolidation strategies only under certain conditions, for example when there is an economy of scale or high customer density (i.e., high shipping volume) in the service area. The paper discusses in detailed under what assumptions and conditions UCC could work. Study limitations and future work are also presented.     

Computing inter-document similarity with Context Semantic Analysis

We propose a novel knowledge-based technique for inter-document similarity computation, called Context Semantic Analysis (CSA). Several specialized approaches built on top of specific knowledge base (e.g. Wikipedia) exist in literature, but CSA differs from them because it is designed to be portable to any RDF knowledge base. In fact, our technique relies on a generic RDF knowledge base (e.g. DBpedia and Wikidata) to extract from it a Semantic Context Vector, a novel model for representing the context of a document, which is exploited by CSA to compute inter-document similarity effectively. Moreover, we show how CSA can be effectively applied in the Information Retrieval domain. Experimental results show that: (i) for the general task of inter-document similarity, CSA outperforms baselines built on top of traditional methods, and achieves a performance similar to the ones built on top of specific knowledge bases; (ii) for Information Retrieval tasks, enriching documents with context (i.e., employing the Semantic Context Vector model) improves the results quality of the state-of-the-art technique that employs such similar semantic enrichment.     

Inductive learning: Algorithms and frontiers

Machine learning is a major subfield of artificial intelligence. It has been seen as a feasible way of avoiding the knowledge bottleneck problem in knowledge-based systems development. Research on machine learning has concentrated in the main on inductive learning. This paper surveys the current inductive learning research. The three typical inductive algorithms, AQ11, ID3 and HCV, are summarized with their main features being analyzed and three research frontiers, i.e., constructive learning, incremental learning and learning from data bases, in inductive learning are introduced.     

Knowledge patterns and spatial dynamics of industrial districts in knowledge cities: Hsinchu,Taiwan

In the era of a global knowledge economy, urban regions that seek to increase their competitive edge and become destinations for talent and investment have little chance of achieving these goals without forming effective knowledge-based urban development strategies. Hence, the development of clusters of knowledge-based corporations has become an important strategic factor in increasing the competitiveness of knowledge cities. Whereas previous studies have tended to focus on the characteristics of local clusters and the causes of their success, empirical studies of the long-term development of local knowledge-based industries are few. Accordingly, this investigation takes the knowledge city region-Hsinchu as its subject, and quantitatively analyzes the correlation between the spatial dynamics of knowledge in major industries and innovation based on empirical data. This finding shows that steadily developing industries in the Hsinchu region have continued to strengthen their new knowledge of product development and innovation. An overview of innovative activities of firms also revealed that their knowledge patterns have been changing from patterns of internal dependency to a locality-based, broader networking and agglomeration pattern.     

Learning to acquire: how serial acquirers build organisational knowledge for information systems integration

This paper develops a knowledge-based model of information systems (IS) integration in acquisition-based growth programs. Previous research has found important differences in the acquirers’ abilities for acquisition IS integration, and that these differences play key roles in explaining the economic benefits of acquisition-based growth. A knowledge-based view of acquisition IS integration contributes explanation to how differences in abilities arise. Our knowledge-based model is developed by an analytic induction approach, comprising both deductive and inductive analysis of the industry group Trelleborg. The analyses yield five propositions that explain the building of knowledge for acquisition IS integration by the mechanisms of routine refinement, superstitious learning, expertise building, sub-activity refinement and related expertise building. Together, the five propositions form a knowledge-based model of acquisition IS integration. The model suggests that ‘serial acquirers’ form the knowledge necessary for attending to an IS integration challenge over a series of heterogeneous acquisitions. Differences in acquirers’ abilities for acquisition IS integration that depend on knowledge variations are therefore persistent and hard to overcome for the inexperienced acquirer.     

Interactive transfer of expertise: Acquisition of new inference rules

teiresias is a program designed to provide assistance on the task of building knowledge-based systems. It facilitates the interactive transfer of knowledge from a human expert to the system, in a high level dialog conducted in a restricted subset of natural language. This paper explores an example of teiresias in operation and demonstrates how it guides the acquisition of new inference rules. The concept of meta-level knowledge is described and illustrations given of its utility in knowledge acquisition and its contribution to the more general issues of creating an intelligent program.     

Three perspectives of data mining

This paper reviews three recent books on data mining written from three different perspectives, i.e., databases, machine learning, and statistics. Although the exploration in this paper is suggestive instead of conclusive, it reveals that besides some common properties, different perspectives lay strong emphases on different aspects of data mining. The emphasis of the database perspective is on efficiency because this perspective strongly concerns the whole discovery process and huge data volume. The emphasis of the machine learning perspective is on effectiveness because this perspective is heavily attracted by substantive heuristics working well in data analysis although they may not always be useful. As for the statistics perspective, its emphasis is on validity because this perspective cares much for mathematical soundness behind mining methods.