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The concept of smart city has recently become more topical in academic and policy discussions. This idea is considered a complex, non-consensual subject since its definition has not yet been agreed upon by most authors in the relevant fields. The need to identify and measure smart city indicators has also given rise to many different evaluation procedures. However, the available frameworks have yet to overcome challenges in structuring and measuring all the evaluation parameters of the cities in question. Thus, methods still need to be developed and applied that can structure criteria used to assess smart city success.

This study sought to show cognitive mapping's tangible usefulness as an expedient tool for strategic analysis, using smart cities as a complex object of study. To this end, various cognitive maps were constructed and compared using the Strategic Options Development and Analysis (SODA) approach.

Cognitive mapping's advantages and limitations in the strategic visualization research context are analyzed and discussed.

The authors know of no prior work reporting comparative analysis of this methodological approach in the same research context.

This article aims to explore the so-called illusion of explanatory depth (IOED) of managers regarding their understanding of digital technologies and examines the effect of knowledge visualization one’s current understanding and decision making. Its purpose is to show that managers think they know more than they do and that this affects decision making but can be reduced through knowledge visualization.

In two experiments with experienced managers, the authors investigate the size and impact of the IOED bias in decision making and examine if sketched self-explanations are as effective as written self-explanations to reduce the bias.

The findings show that experienced managers suffer from a significant illusion concerning their explanatory understanding of digital technologies and that sketching one’s current level of explanatory understanding of these technologies supports the accurate calibration of one’s knowledge. The findings indicate that sketching knowledge is a helpful modality for the detection and subsequent recalibration of biased knowledge in domain-dependent decision making.

This article is the first to explore the effect of sketched knowledge externalization on the calibration of explanatory knowledge of managers. It extends the literature on both, the IOED and on knowledge visualization as an instrument of knowledge calibration.

The purpose of this paper is to analyse current challenges and to articulate the preconditions for data-driven, fact-based product portfolio management (PPM) based on commercial and technical product structures, critical business processes, corporate business IT and company data assets. Here, data assets were classified from a PPM perspective in terms of (product/customer/supplier) master data, transaction data and Internet of Things data. The study also addresses the supporting role of corporate-level data governance.

The study combines a literature review and qualitative analysis of empirical data collected from eight international companies of varying size.

Companies’ current inability to analyse products effectively based on existing data is surprising. The present findings identify a number of preconditions for data-driven, fact-based PPM, including mutual understanding of company products (to establish a consistent commercial and technical product structure), product classification as strategic, supportive or non-strategic (to link commercial and technical product structures with product strategy) and a holistic, corporate-level data model for adjusting the company’s business IT (to support product portfolio visualisation).

The findings provide a logical and empirical basis for fact-based, product-level analysis of product profitability and analysis of the product portfolio over the product life cycle, supporting a data-driven approach to the optimisation of commercial and technical product structure, business IT systems and company product strategy. As a virtual representation of reality, the company data model facilitates product visualisation. The findings are of great practical value, as they demonstrate the significance of corporate-level data assets, data governance and business-critical data for managing a company’s products and portfolio.

The study contributes to the existing literature by specifying the preconditions for data-driven, fact-based PPM as a basis for product-level analysis and decision making, emphasising the role of company data assets and clarifying the links between business processes, information systems and data assets for PPM.

This paper aims to offers suggested practices for dealing with the challenge of providing relevant and reliable information to boards.

Notes and reports from formal governance reviews have been considered from five organizations where board information was flagged as a key area for improvement. The cases were chosen from dozens of organizations the author worked with over a 10-year period.

The paper explains that boards struggle to process information because of challenges such as group dynamics and cognitive biases. Key themes identified reveal that both the type of information and how it is presented matters to boards. Most significantly, giving more information to boards is not always better.

This is not an empirical study but instead seeks to use themes identified in practice as the base for suggestions for boards to consider when seeking relevant and reliable information to make decisions.

This paper makes practical suggestions on how boards and managers can ensure boards receive appropriate information from managers. These include having a clear philosophy for presenting information to the board, being clear on the story that is told, using knowledge visualization when appropriate, explaining how information is relevant to decision items and information items and appointing a steward to oversee the process if needed.

The struggle around board information has been noted in both research and practice. This paper empowers boards and managers with proactive strategies to steward processes and procedures related to board information.

This 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.

This 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.

The 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.

In 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.

Managing a project involves taking a number of critical decisions that can have a crucial impact on the success or failure of the initiative. The analytical definition and visualization of the main components of a project can support project managers engaged to address the right issues at the right time. This article aims to identify crucial crossroads in the management of a project and to provide a visual representation of knowledge involved into a system of project components and decisions.

A design science process is adopted to define the initial goals and requirements and to develop the knowledge visualization framework. Expert feedback is also gathered to obtain a preliminary validation of the framework.

Moving from a system view of project dimensions, we identify eight types of strategic decisions, i.e. growth, problem shifting, goals balancing, escalation, rewarding, resource allocation, problem fixing and cooperation. We then present a visualization map of project decision making addressing six categories of knowledge (i.e. “what-knowledge”, “how-knowledge”, “who-knowledge”, “why-knowledge”, “what for-knowledge”, “when-knowledge”).

The framework needs further theoretical refinement in terms of more fine-grained decision types, other determinants and the reciprocal influence in the management of project activities.

The article can support project managers attempting to build a comprehensive view of project decisions, and it can be a basis to develop novel types of knowledge management systems for project-related applications.

The article proposes a new approach to sustain strategic decision making in project management by adopting a knowledge visualization view. Moreover, it provides an operational tool for managers and analysts at different levels engaged into the management of a project.

Data visualizations in some form or another have served as decision-support tools for many centuries. In conjunction with advancements in information technology, data visualizations have become more accessible and more efficient to generate. In fact, virtually all enterprise resource planning and human resource (HR) information system vendors offer off-the-shelf data visualizations as part of decision-support dashboards as well as stand-alone images and displays for reporting. Plus, advances in programing languages and software such as Tableau, Microsoft Power BI, R, and Python have expanded the possibilities of fully customized graphics. Despite the proliferation of data visualization, relatively little is known about how to design data visualizations for displaying different types of HR data to different user groups, for different purposes, and with the overarching goal of improving the ways in which users comprehend and interpret data visualizations for decision-making purposes. To understand the state of science and practice as they relate to HR data visualizations and data visualizations in general, we review the literature on data visualizations across disciplines and offer an organizing framework that emphasizes the roles data visualization characteristics (e.g., display type, features), user characteristics (e.g., experience, individual differences), tasks, and objectives (e.g., compare values) play in user comprehension, interpretation, and decision-making. Finally, we close by proposing future directions for science and practice.

This paper reports on an intervention for improving the strategic decision making and strategic planning in a specific quasi-public organization: the Illinois Soybean Program Operating Board and on how decision support systems can alter perceptions of the decision making environment, which in turn affect strategic planning. The study hypothesis is that if the use of a sophisticated 3-D modeling tool, the Protein Consumption Dynamics (PCD) model, broadens perspectives to include a more global and long-term outlook, then the quality of planning should be enhanced. Before and after questionnaires are used to capture the changes in 121 soy industry decision- makers' perceptions of the decision making environment. The perceptions of soybean industry decision makers change to reflect more long-term thinking about the industry, indicating that the PCD model's visualized presentation of complex information did influence strategic behavior.

This study was developed in response to the need to develop age-friendly smart living environments due to the complex demands placed on society by the ageing of the population. This study aims to analyse the potential that knowledge visualisation offers collaborative decision-making applied to the development of a multiple criteria framework supporting knowledge management (KM) through knowledge collaboration (KC) and knowledge sharing (KS) in the context of regional innovation systems (RIS).

Using a socio-technical approach, knowledgeable and experienced representatives of RIS innovation actors were brought together to develop a constructivist multiple criteria framework that integrates knowledge visualisation and collaborative decision-making techniques (i.e. cognitive mapping and system dynamics).

The study introduces a multiple criteria model supporting KM encompassing conditions and practices of RIS innovation actors facilitating and encouraging KC and KS. The potential for knowledge visualisation in collaborative decision-making is explored in great depth and illustrated in a case study setting.

The panel members who participated in this study consider our methodological proposal to be extremely versatile and see great potential for further applications in RIS contexts.

The combined use of cognitive mapping and system dynamics according to the strategic options development and analysis (SODA) approach offers a holistic and well-informed perspective on the issue in question. The literature reports no prior work of this methodological combination in the same research context.