Search results

The purpose of this paper is to propose an appropriate symbolic representation, as well as its metaphorical interpretation, to illustrate the special role of information in the knowledge acquisition process.

Besides the literature review, this is a speculative study based on a symbolic and metaphorical point of view.

The proposed symbolic representation was derived from the conceptual designation of information “as a flow” and, accordingly, by the corresponding redrawing of the data-information-knowledge-wisdom (DIKW) pyramid. The knowledge acquisition process is symbolically represented by the growth of a “tree of knowledge” which is planted on a “data earth,” filled with “information sap” and lit by the rays of the “sun of the mind,” a new symbol of the concept of wisdom in the DIKW model. As indicated, a key concept of this metaphorical interpretation is the role of “information sap” which rises from the roots of the “tree of knowledge” to the top of the tree and it is recognized as an invisible link between “world of data” and “world of knowledge.” This concept is also proposed as a new symbolic representation of the DIKW model.

On the basis of specific symbolic-metaphorical representation, this paper provides a relatively new concept of information which may help bridge observed gaps in the understanding of information in various scientific fields, as well as in its understanding as an objective or subjective phenomenon.

Traditional classification algorithms always have an incorrect prediction. As the misclassification rate increases, the usefulness of the learning model decreases. This paper presents the development of a wisdom framework that reduces the error rate to less than 3% without human intervention.

The proposed WisdomModel consists of four stages: build a classifier, isolate the misclassified instances, construct an automated knowledge base for the misclassified instances and rectify incorrect prediction. This approach will identify misclassified instances by comparing them against the knowledge base. If an instance is close to a rule in the knowledge base by a certain threshold, then this instance is considered misclassified.

The authors have evaluated the WisdomModel using different measures such as accuracy, recall, precision, f-measure, receiver operating characteristics (ROC) curve, area under the curve (AUC) and error rate with various data sets to prove its ability to generalize without human involvement. The results of the proposed model minimize the number of misclassified instances by at least 70% and increase the accuracy of the model minimally by 7%.

This research focuses on defining wisdom in practical applications. Despite of the development in information system, there is still no framework or algorithm that can be used to extract wisdom from data. This research will build a general wisdom framework that can be used in any domain to reach wisdom.

In line with the cognitive viewpoint on the phenomenon of information, the constructivist tradition based on Maturana and Varela's theory of knowing, and some aspects of Shannon's theory of communication, the purpose of this paper is to shed more light on the role of information, data, and knowledge in the cognitive system (domain) of the observer.

In addition to the literature review, a proposed description of the communication and knowledge acquisition processes within the observer's cognitive system/domain is elaborated.

The paper recognizes communication and knowledge acquisition as separate processes based on two roles of information within the observer's cognitive system, which are emphasized. The first role is connected with the appropriate communication aspects of Shannon's theory related to encoding cognitive entities in the cognitive domain as data representations for calculating their informativeness. The second role involves establishing relations between cognitive entities encoded as data representations through the knowledge acquisition process in the observer's cognitive domain.

In this way, according to the cognitive viewpoint, communication and knowledge acquisition processes are recognized as important aspects of the cognitive process as a whole. In line with such a theoretical approach, the paper seeks to provide an extension of Shannon's original idea, intending to involve the observer's knowledge structure as an important framework for the deepening of information theory.

Knowledge- and communication-intensive domains still long for a better support of creativity that considers legal requirements, compliance rules and administrative tasks as well, because current systems focus either on knowledge representation or business process management. The purpose of this paper is to discuss our model of integrated knowledge and business process representation and its presentation to users.

The authors follow a design science approach in the environment of patent prosecution, which is characterized by a highly standardized, legally prescribed process and individual knowledge study. Thus, the research is based on knowledge study, BPM, graph-based knowledge representation and user interface design. The authors iteratively designed and built a model and a prototype. To evaluate the approach, the authors used analytical proof of concept, real-world test scenarios and case studies in real-world settings, where the authors conducted observations and open interviews.

The authors designed a model and implemented a prototype for evolving and storing static and dynamic aspects of knowledge. The proposed solution leverages the flexibility of a graph-based model to enable open and not only continuously developing user-centered processes but also pre-defined ones. The authors further propose a user interface concept which supports users to benefit from the richness of the model but provides sufficient guidance.

The balanced integration of the data and task perspectives distinguishes the model significantly from other approaches such as BPM or knowledge graphs. The authors further provide a sophisticated user interface design, which allows the users to effectively and efficiently use the graph-based knowledge representation in their daily study.

This paper aims to examine the relationship between organizational learning (OL) and technology through the lens of strategic factors and to ascertain future research directions.

The systematic literature review method was applied in three stages to the 76 articles obtained from Scopus, Web of Science, Google Scholar and EBSCO databases.

This research revealed the evolution of the role of OL in innovation, performance, knowledge management and technological adoption and showcases a detailed conceptual model relating technology outcomes (technological innovation and capabilities) to OL outcomes (technology absorptive capacity, technological proactivity, as well as information technology [IT] and organization process alignment).

This review includes articles mainly in English and excludes conference proceedings.

This research attempts to guide managers and policymakers to foster an organizational culture conducive to technological adoption and OL. It helps organizations develop strategies for new product development, including strategic alliances and strategic leadership.

This review formalizes the linkages between technological absorptive capacity, technological proactivity and IT with technological innovation and capabilities. It identifies research gaps and elucidates future research directions.

The purpose of this paper is to present a process-theory-based model of big data value creation in a business context. The authors approach the topic from the viewpoint of a single firm.

The authors reflect current big data literature in two widely used value creation frameworks and arrange the results according to a process theory perspective.

The model, consisting of four probabilistic processes, provides a “recipe” for converting big data investments into firm performance. The provided recipe helps practitioners to understand the ingredients and complexities that may promote or demote the performance impact of big data in a business context.

The model acts as a framework which helps to understand the necessary conditions and their relationships in the conversion process. This helps to focus on success factors which promote positive performance.

Using well-established frameworks and process components, the authors synthetize big data value creation-related papers into a holistic model which explains how big data investments translate into economic performance, and why the conversion sometimes fails. While the authors rely on existing theories and frameworks, the authors claim that the arrangement and application of the elements to the big data context is novel.

Aid knowledge management (KM) and business intelligence (BI) practitioners explore and exploit the Intelligence Age complex venture model focusing on intelligence as an emergent behavior. The paper aims to extend the discrete model used by classical system engineering (SE) for a wisdom, knowledge, information, data, and measurement (WKIDM) pyramid to add a wrapper of emergent intelligence to support successful decision making and implementation.

Building on previous theoretical complex venture work, this research explores the value of extending the WKIDM or “Knowledge Pyramid” model proposed by classical SE and KM approaches. The resultant IWKIDM model builds on the insights derived from chaos and complexity theories; KM research; observations of several acquisition successes and failures; and doctoral research on agile enterprise decision support.

The paper finds that successful classical SE complicated systems models built with the closed system assumptions of linearity, predictability, and context independence do not scale to the needed open system Intelligence Age solutions. It is necessary to build on a Complex Venture model that guides the engineering solutions that: leverage emergent behavior insights to develop an improved intelligence model for the interaction of complex venture intellectual capital (i.e., self‐organizing agents) with the WKIDM pyramid entities and the intelligence products consumer context; and examine WKIDM pyramid levels of abstraction for detachable and complex representations (e.g., explicit versus tacit knowledge).

A complex venture conceptual model informs the architecture and systems engineering acquisition practices for new solution category to empower the venture's intellectual capital to produce needed emergent intelligence.

In this chapter, the author explores the principles of responsible management education through the lens of Taoism. This chapter begins by introducing the concepts of knowledge-inquiry and wisdom-inquiry and highlights the differences between the two in the context of management education. The author emphasizes the importance of wisdom-inquiry in management education, arguing that it allows individuals to not only understand and analyze information but also to apply ethical considerations when making decisions. This chapter delves into how to synthesize knowledge and wisdom in education, highlighting the need for a balance between technical skills and ethical awareness in management education. This chapter concludes with an examination of the principles of managing talent by balancing competence and character. The author discusses how to hire for character and train for competence in human resources management and development. This approach involves focusing on developing individuals' character traits, such as integrity, compassion and empathy, in addition to their technical skills. This chapter demonstrates the value of incorporating Taoist principles into management education. When the importance of wisdom-inquiry, balancing competence and character, and developing ethical leaders is emphasized, management education can prepare individuals to navigate the complexities of the modern business landscape while promoting responsible business practices.

This conceptual paper aims to contribute to the knowledge management (KM) literature by seeking to determine whether wisdom management (WM) will replace KM in future.

This exploratory paper follows the interpretivist research philosophy and the deductive approach. The data collection is based on selected literatures from three disciplines (KM, philosophy and psychology). The findings were qualitatively analysed.

The findings are threefold: (1) the discussion of wisdom has been either neglected or superficially discussed in the KM literature; (2) despite the fact that wisdom is widely discussed and researched in philosophy and psychology disciplines, there is no commonly agreed upon definition of wisdom, and a dichotomy exists between the implicit and explicit theories of wisdom; (3) wisdom research in philosophy and psychology disciplines provides valuable input to KM by identifying the dimensions, components and characteristics of wisdom and wise individuals.

Important sources may have been unintentionally overlooked in this paper. This paper identifies the need for empirical research and discussion about WM as the next potential phase of KM. It offers several implications for researchers, managers and management educators as this paper shows that WM is emerging as a new discipline.

This paper makes a theoretical contribution to the fifth phase of KM by drawing attention to wisdom and WM as the next potential phase of KM.