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Designing effective business analytics (BA) platforms that visualise data, provide deep insights and support data-driven decision-making is a challenging task. Understanding the elements shaping BA platform design is crucial for success. The purpose of this study is to explore the impact of visualisation on usability (UI) and user experience (UX) while emphasising the importance of insights understanding in BA platform design.

This paper presents a case study following a startup’s journey as it undergoes two redesign phases for its BA platform. A combination of quantitative and qualitative methods is used to assess UX/UI and insights understanding of the platform. Indicatively this included semi-structured interviews, observations, think-aloud techniques and surveys to monitor runtime per task, number of errors, users’ emotions and users’ understanding.

Our findings suggest that modifications in aesthetics and information visualisation positively influence overall usability, UX, and understanding of platform insights – a critical aspect for the success of the startup.

Our goal is not to make a methodological contribution, but to illustrate how companies, constrained by time and pressure, navigate platform changes without meticulous design and provide learnings on important elements while designing BA platforms.

This paper concludes with suggested methods for assessing BA platforms and recommends practical practices to follow. These practices include recommendations on important elements for BA platform users, such as navigation and interactivity, user control and personalisation, visual consistency and effective visualisation.

This study contributes to practice as it presents a real-life case and offers valuable insights for practitioners.

Financial firms are looking for better ways to harness the power of data analytics to improve their decision quality in the financial modeling era. This study aims to explore key factors influencing big data analytics-driven financial decision quality which has been given scant attention in the relevant literature.

The authors empirically examined the interrelations between five factors including technology capability, data capability, information quality, data-driven insights and financial decision quality drawing on quantitative data collected from Jordanian financial firms using a cross-sectional questionnaire survey.

The SmartPLS analysis outcomes revealed that both technology capability and data capability have a positive and direct influence on information quality and data-driven insights without any direct influence on financial decision quality. The findings also point to the importance and influence of information quality and data-driven insights on high-quality financial decisions.

The study for the first time enriches the knowledge and relevant literature by exploring the critical factors affecting big data-driven financial decision quality in the financial modeling context.

This paper aims to curate open research knowledge graph (ORKG) with papers related to ontology learning and define an approach using ORKG as a computer-assisted tool to organize key-insights extracted from research papers.

Action research was used to explore, test and evaluate the use of the Open Research Knowledge Graph as a computer assistant tool for knowledge acquisition from scientific papers.

To extract, structure and describe research contributions, the granularity of information should be decided; to facilitate the comparison of scientific papers, one should design a common template that will be used to describe the state of the art of a domain.

This approach is currently used to document “food information engineering,” “tabular data to knowledge graph matching” and “question answering” research problems and the “neurosymbolic AI” domain. More than 200 papers are ingested in ORKG. From these papers, more than 800 contributions are documented and these contributions are used to build over 100 comparison tables. At the end of this work, we found that ORKG is a valuable tool that can reduce the working curve of state-of-the-art research.

As a sector, higher education is at the low end of innovation rankings. The challenges we face – demographic, technological, political, and pedagogical – will require sustained innovation at a strategic level. Recent research with mature companies has identified exemplars in strategic innovation (e.g., O’Connor, Corbett, & Peters, 2018). This work explores whether – and how – higher education institutions might adapt insights from the corporate sector for strategic innovation in teaching and learning.

The introductory section provides an overview of the nature of strategic innovation (and why it is hard to sustain), strategic issues facing higher education, and the status and challenges of sustaining strategic innovation for teaching. The next two sections describe insights from research with corporate exemplars of sustaining strategic innovation. Each section uses a scenario from higher education as a proof-of-concept test to explore the application of the corporate sector insights for strategic innovation in higher education teaching and learning.

The final section of the chapter discusses the planned next steps to prototype and test adaptation of these corporate sector insights with institutional innovation leaders in higher education, as well as additional potential sources of insights (from other research in the corporate sector and from strategic innovation in the public sector).

As the field of action-oriented research becomes increasingly diffuse and diverse, this paper seeks to identify common ground across the multiple modalities of action research and collaborative management research through articulating and exploring a general empirical method that is grounded in the recognizable structure of human knowing. This method is grounded in: attention to observable data (experience), envisaging possible explanations of that data (understanding), and preferring as probable or certain the explanations, which provide the best account for the data (judgment). Engaging this method requires the dispositions to perform the operations of attentiveness, intelligence, and reasonableness, to which responsibility is added when we seek to take action. This paper seeks to provide insight into the multiple modalities of action research and collaborative management research and to illustrate how each modality engages the recognizable operations of human knowing.