Knowledge Visualization for Research Design

This chapter presents a case of information and communication technology use in doctoral research processes. In particular, it presents the use of the Idea Puzzle software as a knowledge visualization tool for research design at the University of Auckland. The chapter begins with a review of previous contributions on knowledge visualization and research design. It then presents the Idea Puzzle software and its application at the University of Auckland. In addition, the chapter discusses the results of a large-scale survey conducted on the Idea Puzzle software in 71 higher education institutions as well as its first usability testing at the University of Auckland. The chapter concludes that the Idea Puzzle software stimulates visual integrative thinking for coherent research design in the light of Philosophy of Science.

Supporting strategic management decisions

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings Knowledge visualization with a strategic digital twin system can help optimize a firms decision making. Originality The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

The Use of Visual Templates for Sharing Knowledge in Team Meetings at Work: A Qualitative Case Study.

The present study explores the use of visual templates to facilitate knowledge sharing in real life team meetings. Ten real life teams belonging to ten different departments of the same organisation have been invited to take part in a case study replicating a team meeting. The purpose of the case study was to compare how and why meetings supported or not by visual templates lead to diverging meeting output, participants’ behaviours, and perceptions. The visual template used in the case study is a matrix mapping interest and power of stakeholders. The present qualitative research built on two disciplines, namely knowledge visualization in communication research and group interaction analysis in small group research. The phenomenon of knowledge sharing in meeting discussions was explored through a social constructionist lens with some embodied cognitivist elements borrowing from the 4E cognition framework. The verbal statements of the meeting participants were coded with the Act4Teams coding scheme. Participants’ perceptions have been collected through an individual questionnaire and focus group discussions. Field notes, artefacts and photographs completed the data set. The field research confirmed that also real team are helped by visual templates when it comes to delivering on their meeting objectives. A visual template can help make tacit knowledge tangible. It helps recall the knowledge visualized on the template. It nudges the discussions towards a concrete output. A visual template stimulates disagreement and avoid groupthink. It enables silence which in turn helps teams take distance to review critically the knowledge sharing process. It also helps the team organise the knowledge shared. The research contributed to bringing together knowledge visualization and group interaction analysis. It allowed the Dunning-Kruger effect to be observed as the satisfaction affirmed by teams without tangible meeting output was comparable to those with a tangible output. It also contributed some insights on how making thinking visible can help researchers unveil patterns relating to the phenomenon studied using visual ethnography approaches.

Integrated knowledge visualization and the enterprise digital twin system for supporting strategic management decision

PurposeThis paper proposes an integrated knowledge visualization and digital twin system for supporting strategic management decisions. The concepts and applications of strategic architecture have been illustrated with a concrete real-world case study and decision rules of using the strategic digital twin management decision system (SDMDS) as a more visualized, adaptive and effective model for decision-making.Design/methodology/approachThis paper integrates the concepts of mental and computer models and examines a real case's business operations by applying system dynamics modelling and digital technologies. The enterprise digital twin system with displaying real-world data and simulations for future scenarios demonstrates an improved process of strategic decision-making in the digital age.FindingsThe findings reveal that data analytics and the visualized enterprise digital twin system offer better practices for strategic management decisions in the dynamic and constantly changing business world by providing a constant and frequent adjustment on every decision that affects how the business performs over both operational and strategic timescales.Originality/valueIn the digital age and dynamic business environment, the proposed strategic architecture and managerial digital twin system converts the existing conceptual models into an advanced operational model. It can facilitate the development of knowledge visualization and become a more adaptive and effective model for supporting real-time management decision-making by dealing with the complicated dependence of constant flow of data input, output and the feedback loop across business units and boundaries.

IP Analytics and Machine Learning Applied to Create Process Visualization Graphs for Chemical Utility Patents

Researchers must read and understand a large volume of technical papers, including patent documents, to fully grasp the state-of-the-art technological progress in a given domain. Chemical research is particularly challenging with the fast growth of newly registered utility patents (also known as intellectual property or IP) that provide detailed descriptions of the processes used to create a new chemical or a new process to manufacture a known chemical. The researcher must be able to understand the latest patents and literature in order to develop new chemicals and processes that do not infringe on existing claims and processes. This research uses text mining, integrated machine learning, and knowledge visualization techniques to effectively and accurately support the extraction and graphical presentation of chemical processes disclosed in patent documents. The computer framework trains a machine learning model called ALBERT for automatic paragraph text classification. ALBERT separates chemical and non-chemical descriptive paragraphs from a patent for effective chemical term extraction. The ChemDataExtractor is used to classify chemical terms, such as inputs, units, and reactions from the chemical paragraphs. A computer-supported graph-based knowledge representation interface is developed to plot the extracted chemical terms and their chemical process links as a network of nodes with connecting arcs. The computer-supported chemical knowledge visualization approach helps researchers to quickly understand the innovative and unique chemical or processes of any chemical patent of interest.

Seeing knowledge

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings Knowledge visualization is a crucial tool for managers of organizations to adopt in order to boost competitive advantage through stakeholder engagement and knowledge transfer. Originality/value The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Visualizing knowledge for decision-making in Lean Production Development settings. Insights from the automotive industry

PurposeThe paper focuses on how knowledge visualization supports the development of a particular multiobjective decision-making problem as a portfolio optimization problem in the context of interorganizational collaboration between universities and a large automotive company. This paper fits with the emergent knowledge visualization literature because it helps to explain decision-making related to the development of a multiobjective optimization model in Lean Product Development settings. We investigate how using ad hoc visual tools supports knowledge translation and knowledge sharing, enhancing managerial judgment and decision-making.Design/methodology/approachThe empirical case in this study concerns the setting up of a multiobjective decision-making model as a portfolio optimization problem to analyze and select alternatives for upgrading the lean production process quality at an FCA plant.FindingsThe study shows how knowledge visualization and the associated tools work to enable knowledge translation and knowledge sharing, supporting decision-making. The empirical findings show why and how knowledge visualization can be used to foster knowledge translation and sharing among individuals and from individuals to groups. Knowledge visualization is understood as both a collective and interactional process and a systematic approach where different players translate their expertise, share a framework and develop common ground to support decision-making.Originality/valueFrom a theoretical perspective, the paper expands the understanding of knowledge visualization as a system of practices that support the development of a multiobjective decision-making method. From an empirical point of view, our results may be useful to other firms in the automotive industry and for academics wishing to develop applied research on portfolio optimization.

A knowledge visualization approach to identify and discovery inner areas: a pilot application in the province of Lecce

PurposeThe paper aims to propose a knowledge visualization approach and algorithm to support public decision makers to define the inner areas, which represents a strategic topic in the European debate about territorial inequality and development.Design/methodology/approachThe study has been developed by following the design science research, which includes six steps: problem identification and motivation; identification of the objectives for a solution; design and development; demonstration; evaluation; and communication. As for the design and development step, the proposed approach and algorithm ground on association mining to discover hidden relationships existing among municipalities. They have been applied to analyse the 97 municipalities of the Lecce province, and each municipality has been described through 30 multi-domain indicators organized into seven categories, whose data have been collected from institutional datasets, local sources or web-scraping process.FindingsA set of complementary analyses has been generated through the construction of dynamic and interactive knowledge maps that show “similar” municipalities according to the indicators selected.Originality/valueThe approach and algorithm proposed allow discovering similarities existing among distinct municipalities, based on the analysis of a set of multi-domain indicators. The approach may complement or completely substitute the existing ones used to define inner areas, thus overcoming both the methodological limits of the “top-down” line imposed by the central legislator, and the “bottom-up” paradox consisting in the illusion that single (and often small) towns have the economic and cognitive resources necessary to implement effective territorial mapping and development strategies. In such a way, policy makers can be aware on similarities existing among distinct towns and can thus share cognitive and financial resources to define a common plan and a set of practices for territorial development.

Exploring knowledge visualization in the digital age: an analysis of benefits and risks

PurposeThe purpose of this paper is to explore the main benefits and risks of knowledge visualization in the current digital age.Design/methodology/approachThe paper is based on a qualitative and explorative research to frame the benefits and risks of knowledge visualization. The emerging views of 57 small and medium-sized entrepreneurs (SMEs) managers are examined.FindingsThe findings reveal both benefits and risks related to knowledge visualization. The two aggregate dimensions (i.e. benefits and risks) are supported by six second-order and five second-order categories, respectively. On one side, the main benefits highlighted in the study are related to: stakeholder engagement, flexibility, knowledge transfer, signaling role, agility and interactivity; on the other side, the risks identified are related to: complexity, absorptive capacity, divergences, capabilities and ineffectiveness.Originality/valueThe research highlights novel insights in the emerging field of knowledge visualization and extends current literature. It provides useful implication from both a theoretical and practical point of view.