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This study aims to examine the use of data visualization as a tool to support practitioner-led organizational learning within the emergency services. The authors investigate how data visualization can support visual communication and the analysis of emergency response data to promote system improvement.

The authors investigate if communication data, presented as node-link diagrams, can be understood and evaluated by firefighters. Objective understanding of the communication network is measured quantitatively, while subject judgement of the emergency response system is measured qualitatively and compared to prior system evaluation outcomes. The authors compare different data visualization layouts and assess their value in supporting practitioner evaluation of emergency response systems.

The authors find that while firefighters are largely unfamiliar with their use, data visualizations function as a tool for visual communication and analysis. The authors identify the importance of visualization design and the difficulty in representing characteristics of a dynamic network within static diagrams. The authors also find some correlation between layout design and how respondents interpret visual data.

Results demonstrate the value of data visualization to support practitioner-led organizational learning and suggest future work to support the development of emergency response management.

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.

Quality management systems are commonly applied to meet the increasingly stringent requirements for product quality in discrete manufacturing industries. However, traditional experience-driven quality management methods are incapable of handling heterogeneous data from multiple sources, leading to information islands. This study aims to present a quality management key performance indicator visualization (QM-KPIVIS) system to enable integrated quality control and ultimately ensure product quality.

Based on multiple heterogeneous data, an integrated approach is proposed to quantify explicitly the relationship between Internet of Things data and product quality. Specifically, this study identifies the tracing path of quality problems based on multiple heterogeneous quality information tree. In addition, a hierarchical analysis approach is adopted to calculate the key performance indicators of quality influencing factors in the quality control process.

Proposed QM-KPIVIS system consists of data visualization, quality problem processing, quality optimization and user rights management modules, which perform in a well-coordinated manner. An empirical study was also conducted to validate the effectiveness of proposed system.

To the best of the authors’ knowledge, this study is the first attempt to use industrial Internet of Things and multisource heterogeneous data for integrated product quality management. Proposed approach is more user-friendly and intuitive compared to traditional empirically driven quality management methods and has been initially applied in the manufacturing industry.

Cultural heritage archives rely on environmental monitoring devices, such as dataloggers or more complex networked systems, to ensure collection preservation through collecting temperature, humidity, light and/or air quality measures. Existing systems are often costly, inflexible and do not use a modern, internet of things (IoT) approach. This paper aims to determine the suitability of currently popular general-purpose IoT devices, standards and technologies to the environmental monitoring needs of archivists, as well as identify any challenges.

This paper describes an exploratory study detailing the design, construction and usability testing of a do-it-yourself datalogger and data dashboard system, which seeks to manage previously identified trade-offs in cost, required technical skill and maintainability.

The environmental monitoring system presented met archivists’ needs well and was generally noted to be easy-to-use, efficient and an improvement on existing systems. This suggests that an IoT approach can support archivists’ needs in this area.

Potential limitations of this study include lack of archival staff with sufficient technical training to maintain such a system and the rapid pace of IoT evolution yielding unstable and constantly changing technologies.

The system design presented in this work provides a blueprint for cultural heritage organizations desiring a fuller-featured, lower cost environmental monitoring system for archival collections.

This research takes a novel user-centered, open-source, IoT approach to construct an environmental monitoring system that is designed directly from archivists’ requirements and is extensible for future needs.

The study aims to identify the gaps and the potentialities of citizen-generated data in four axes of warning system: (1) risk knowledge, (2) flood forecasting and monitoring, (3) risk communication and (4) flood risk governance.

Research inputs for this work were gathered during an international virtual dialogue that engaged 40 public servants, practitioners, academics and policymakers from Brazilian and British hazard and risk monitoring agencies during the Covid-19 pandemic.

The common challenges identified were lack of local data, data integration systems, data visualisation tools and lack of communication between flood agencies.

This work instigates an interdisciplinary cross-country collaboration and knowledge exchange, focused on tools, methods and policies used in the Brazil and the UK in an attempt to develop trans-disciplinary innovative ideas and initiatives for informing and enhancing flood risk governance.

This study aims to focus on providing a computerized classification testing (CCT) system that can easily be embedded as a self-assessment feature into the existing legacy environment of a higher education institution, empowering students with self-assessments to monitor their learning progress and following strict data protection regulations. The purpose of this study is to investigate the use of two different versions (without dashboard vs with dashboard) of the CCT system during the course of a semester; to examine changes in the intended use and perceived usefulness of two different versions (without dashboard vs with dashboard) of the CCT system; and to compare the self-reported confidence levels of two different versions (without dashboard vs with dashboard) of the CCT system.

A total of N = 194 students from a higher education institution in the area of economic and business education participated in the study. The participants were provided access to the CCT system as an opportunity to self-assess their domain knowledge in five areas throughout the semester. An algorithm was implemented to classify learners into master and nonmaster. A total of nine metrics were implemented for classifying the performance of learners. Instruments for collecting co-variates included the study interest questionnaire (Cronbach’s a = 0. 90), the achievement motivation inventory (Cronbach’s a = 0. 94), measures focusing on perceived usefulness and demographic data.

The findings indicate that the students used the CCT system intensively throughout the semester. Students in a cohort with a dashboard available interacted more with the CCT system than students in a cohort without a dashboard. Further, findings showed that students with a dashboard available reported significantly higher confidence levels in the CCT system than participants without a dashboard.

The design of digitally supported learning environments requires valid formative (self-)assessment data to better support the current needs of the learner. While the findings of the current study are limited concerning one study cohort and a limited number of self-assessment areas, the CCT system is being further developed for seamless integration of self-assessment and related feedback to further reveal unforeseen opportunities for future student cohorts.

The purpose of this paper is to describe the process of designing, developing and evaluating a prototype of an open government data (OGD) platform that provided user-centred experiences.

Based on the OGD lifecycle, an OGD prototype was created, which involved the system functionality, user interface, standard specification and security mechanism. The main functionalities of the system included data acquisition, data processing and data management. A usability test was conducted following the prototype implementation.

The usability test indicated that an OGD platform was desired to help the public to find, access, reuse and share government data effectively and efficiently. Functions, such as visualization, local download and digital watermark should be provided and integrated into the platform.

This paper provided a complete case study on the design of an OGD platform and a reference for information system developers to design such system in the future.

Advanced analytics‐driven data analyses allow enterprises to have a complete or “360 degrees” view of their operations and customers. The insight that they gain from such analyses is then used to direct, optimize, and automate their decision making to successfully achieve their organizational goals. Data, text, and web mining technologies are some of the key contributors to making advanced analytics possible. This paper aims to investigate these three mining technologies in terms of how they are used and the issues that are related to their effective implementation and management within the broader context of predictive or advanced analytics.

A range of recently published research literature on business intelligence (BI); predictive analytics; and data, text and web mining is reviewed to explore their current state, issues and challenges learned from their practice.

The findings are reported in two parts. The first part discusses a framework for BI using the data, text, and web mining technologies for advanced analytics; and the second part identifies and discusses the opportunities and challenges the business managers dealing with these technologies face for gaining competitive advantages for their businesses.

The study findings are intended to assist business managers to effectively understand the issues and emerging technologies behind advanced analytics implementation.

The study investigated the effect of big data analytics capabilities (BDACs) on reverse logistics (strategic and tactical) decisions and finally on remanufacturing performance.

The primary data were collected using a structured questionnaire and an online survey sent to South African manufacturing companies. The data were analysed using partial least squares based structural equation modelling (PLS–SEM) based WarpPLS 6.0 software.

The results indicate that data generation capabilities (DGCs) have a strong association with strategic reverse logistics decisions (SRLDs). Data integration and management capabilities (DIMCs) show a positive relationship with tactical reverse logistics decisions (TRLDs). Advanced analytics capabilities (AACs), data visualisation capabilities (DVCs) and data-driven culture (DDC) show a positive association with both SRLDs and TRLDs. SRLDs and TRLDs were found to have a positive link with remanufacturing performance.

The theoretical guided results can help managers to understand the value of big data analytics (BDA) in making better quality judgement of reverse logistics and enhance remanufacturing processes for achieving sustainability.

This research explored the relationship between BDA, reverse logistics decisions and remanufacturing performance. The study was practice oriented, and according to the authors’ knowledge, it is the first study to be conducted in the South African context.

This chapter presents findings from a recently conducted process for obtaining Accounting Advisory Board (AAB) input related to Master of Accountancy curriculum of one university. Board members represent both large and small public accounting firms as well as corporate offices of Fortune 500 companies and non-profit organizations. AAB input includes perceptions of the relative importance of over 160 candidate topics for the courses making up the program’s infrastructure, as well as written comments noting other potential topics and pedagogical approaches to consider. Comparisons of topic rankings reveal a strong level of consistency among Board member types for the traditional accounting courses with structured content, as opposed to those courses involving more systems-related topics or having a wider range of specialized topics. Furthermore, the authors compare Board perceptions regarding topic necessity to those of faculty and note faculty reactions. Specifically, the authors find that faculty ranking consistency with the Board is weak, illustrating the importance of seeking curricular Board input on an ongoing basis. To “close the loop,” faculty incorporated many curriculum changes, involving both the topics to be covered and the overall approach to the course.