A Conceptual Framework for Survey-Based Student Experience Optimization Using Bi Tools in Higher Education

Background: Business intelligence tools play an important role for businesses across all industries for their data and information management solutions. By harnessing the capabilities of business intelligence, companies are able to predict and better meet customer needs. This study investigates factors that inhibit managers’ use of business intelligence tools in a public organisation in South Africa, where there has been evidence of a very low uptake of business intelligence tools. Objective: The objective of this study was to establish factors that inhibit the use of business intelligence and data analytics tools by managers in public organisations. Organisations are likely to lose their competitive advantage and experience a negative impact on efficiency and service delivery if managers do not make use of analytics in strategic decision-making. Methods: The study utilised both qualitative and quantitative data. Qualitative data were collected from managers through interviews, whilst questionnaires and daily login recordings were used to collect quantitative data. Quantitative data were analysed using statistical methods, whilst qualitative data were analysed by thematic analysis. Results: The research revealed a wide range of factors that inhibit the use of business intelligence tools by managers. These factors range from personal, system quality, organisational, macro-environmental, behavioural beliefs and attitudes, effort perceptions and social influence to facilitating conditions. Conclusion: The article provides a comprehensive contextual situation regarding poor use of business intelligence and analytics tools in a large metropolitan public entity. The findings from the organisation under study would be deemed to apply to similar organisations throughout the country.

Oil and gas companies are increasingly using data analytics to improve drilling performance. This paper provides an example of using a business intelligence (BI) tool to analyze drilling data in the Permian Basin. The BI tool helped to improve operation decisions through the use of a visual report. A database, consisting of massive amounts of historical drilling data, is analyzed using the BI tool to better understand drilling performance and predict average operations performance in the area. A historical drilling database is created based on the bottomhole assembly (BHA) run data and analyzed by the BI tool to review the well performance, in addition to identifying any hazards and summarizing the optimum drilling system during the planning phase. With the help of the BI tool, the drilling database can be displayed in an interactive way to further understand the drilling performance in the area; e.g., the top performing drill bit, drilling system, downhole mud motor configuration, and estimated drilling time for the section of interest. As a result, engineers will find it easier to identify the potentially top performing wells along with drilling hazards in offset wells. The engineers can evaluate the well details and identify the best drilling practices to optimize drilling performance and eliminate downhole incidents. Using the BI tool helps reduce the data mining time and offers a fast, improved method for gaining technical insights into the drilling operation. These descriptive analytics help to simplify the complex data sets, which are valuable for uncovering patterns that offer data set understanding. With the visualization results, experts can focus on data diagnostics analytics to make suggestions for drilling operation improvements and corrections. Furthermore, these analytical data can be used as inputs for more advanced predictive (forecast drilling performance) or prescriptive analytics (drilling optimization) that deliver real-time insights for making improved business decisions.

This paper provides a comprehensive overview of the current trends in Business Intelligence (BI) tools within the finance sector, with a specific focus on the divergent landscapes of the USA and Africa. As the financial industry evolves, the integration of advanced BI tools has become imperative for informed decision-making and sustainable growth. This review examines key developments, challenges, and opportunities shaping the implementation of BI tools in both regions. In the USA, financial institutions are witnessing a surge in BI tool adoption, driven by the increasing need for real-time data analytics, regulatory compliance, and risk management. The integration of artificial intelligence and machine learning algorithms in BI tools is enabling predictive analytics, empowering financial professionals to make strategic decisions in a rapidly changing market environment. Additionally, the emergence of cloud-based BI solutions is enhancing scalability and accessibility, facilitating seamless collaboration and data-driven insights. Contrastingly, the African financial landscape is experiencing a gradual but promising shift towards BI tool utilization. The continent, characterized by diverse economies and regulatory frameworks, faces unique challenges such as infrastructure limitations and varying levels of technological readiness. However, the potential for BI tools to revolutionize financial operations, improve transparency, and facilitate financial inclusion is evident. Governments and financial institutions are investing in technology infrastructure and skill development to harness the transformative power of BI tools. This review underscores the importance of understanding regional nuances in the adoption of BI tools, recognizing that effective implementation requires tailored strategies. As both the USA and Africa navigate the evolving financial landscape, the convergence of technological advancements and strategic planning will undoubtedly play a pivotal role in shaping the future of BI tools in the finance sector

- In today’s data-intensive business environment, organizations increasingly depend on Business Intelligence (BI) tools to support evidence-based decision-making and sustain competitive advantage. This study examines how managers at Mind Box LLC perceive and utilize BI tools as Decision Support Systems (DSS) in their routine and strategic decision processes. Using a descriptive research design, primary data were collected from 100 managers across departments through a structured questionnaire, complemented by secondary data from journals and company documents. The analysis employed percentage statistics, chi-square tests, correlation, and one-way ANOVA to evaluate BI usage patterns, perceived benefits, challenges, and satisfaction levels. Results show high adoption of BI tools, with 75% of managers using them at least weekly and a majority reporting moderate to advanced BI knowledge. Managers report that BI tools improve decision quality, reduce uncertainty and risk, enable faster decisions, and contribute to competitive advantage. Key barriers include insufficient training, data quality issues, and system complexity, while no significant differences in satisfaction were found across managerial levels. The findings underscore that BI tools are effective DSS enablers when supported by robust data governance, continuous training, user-friendly design, and a data-driven culture. The study offers practical recommendations for strengthening BI adoption and maximizing its decision support value at Mind Box and similar technology-driven organizations. This study contributes to the growing literature on BI-enabled decision-making in technology-driven organizations. Keywords: Business Intelligence, Decision Support Systems, managerial perception, data-driven decision-making, Mind Box.

Objective: The objective of this paper is to explore the antecedents and consequents of student experience in higher education settings. Several variables such as co-creation value, social environment, physical environment and relational benefits are predicted as antecedents and consequents of student experience. Methodology: The authors proposed the conceptual framework to identify antecedents and consequents of student experience in higher education. Findings: Theoretically there is a possibility to create and the use of co creation in the higher education context to enhance student experience. The other factors should also be considered, such as physical environment (ambient, design and IT), social environment (Employee displayed emotion, and customer climate), and relational benefits. The findings suggest the necessary changes in how higher education institutions should be marketed with more focus on creating, communicating, and delivering value to enhance student experience. Value Added: The author’s perspective on antecedents and consequents of student experience is a new interesting theme in higher education marketing. The paper proposed a testable propositions regarding the antecedents and consequences of student experience.

Business Intelligence (BI) is one of the fastest growing software sector and software vendors are rapidly developing multiple BI tools to support the growing data analysis needs of organisations. In order to be sustainable in a briskly changing turbulent environment, organisations need to have access to information about their operational performance. BI tools play a vital role in supporting the decision makers at different organisational levels. As these tools are becoming critical in decision making, it has become not only an information technology concern but also a management concern. Without proper governance it would be impractical to achieve the value that BI tools offer. Adopting a BI governance framework in organisations will lead to common principles and clear ownership over information. Additionally, appropriate alignment between corporate governance and BI governance can yield more benefits. This paper provides an insight into the importance and value of BI tools. Key functionalities of BI tools have also been highlighted. Different challenges in gaining true value from BI tools have been examined. Four phases of developing a BI governance framework have been illustrated. The alignment between BI governance and corporate governance has also been explored with a recommended model. Exploratory analysis of two organisations (Premier Healthcare Alliance & BellSouth Telecommunications) to identify how they have utilised BI tools and adopted BI governance has been briefly carried out. The paper posits that if the steps for developing a BI framework are adopted by organisations and the BI framework is aligned with the corporate framework, BI deployment and usage will be successful with reduced risk levels.

PurposeHigher education institutions (HEIs) face a number of challenges in effectively managing and reporting on sustainability information, such as siloes of data and a limited distribution of information. Business intelligence (BI) can assist in addressing the challenges faced by organisations. The purpose of this study was to propose a BI framework for strategic sustainability information management (the Sustainable BI Framework) that can be used in HEIs.Design/methodology/approachThe research applied the design science research methodology whilst using a South African HEI as a case study. The problems with sustainability information management were identified, and a theoretical framework was proposed. In addition, a practical BI software tool was developed as proof of concept to address these problems and to assist with the management of strategic sustainability information in an HEI.FindingsThe proposed sustainability BI tool was evaluated through heuristic and usability evaluations with senior management. The results indicated that the usability of the BI tool was positively rated and that the framework can assist in overcoming the constraints that HEIs face in effectively managing sustainability information.Research limitations/implicationsThe research was limited to a single case. However, the theoretical framework was derived from and expanded on existing stakeholder theory, sustainability reporting theory and literature on BI dashboard development. The framework was implemented successfully in the Sustainable BI Tool prototype at the case study, and the results reveal in-depth information regarding information management for sustainability reporting in higher education.Practical implicationsThe Sustainable BI Tool is a solution that integrates data from multiple areas of sustainability and provides a single integrated view of the information to stakeholders. The information is provided through performance dashboards, which provide predictive capabilities to enable management to report on sustainability and determine if the institution is meeting its strategic goals. The lessons learnt can also assist other HEIs considering implementing BI for sustainability reporting.Social implicationsImproved sustainability reporting for HEIs provided by the BI framework can improve the environmental and social impact of the educational community.Originality/valueThis study provides the most comprehensive framework for guiding the design of a BI tool to assist in effectively managing sustainability information in HEIs.

With the domains of big data, knowledge management (KM), and knowledge capital (KC) gaining tremendous interest from practitioners and academics, this paper examines creating intelligent knowledge and the assimilation of both tacit and explicit knowledge to build intelligent organizations. The paper focuses on the transformation of traditional organizations into intelligent organizations by converting their participants' real-world experiences and skills that reside in their tacit knowledge into articulated, transformed data and codified knowledge. This process is aimed at building KC brought to higher level of intelligence with the integration of business intelligence (BI) and the use of BI tools. The paper proposes the use of a concern-issue-problem opportunity (CIPO) model integrated with BI to outline a knowledge management process designed for the capture of heuristics and expert knowledge that can be applied to optimize the factors of time and money.

India's higher education industry is rapidly evolving and progressing in its use of technology. Educational institutions can strategically improve their overall performance and decision-making processes by integrating data warehousing and business intelligence tools. The purpose of this research paper is to examine higher education operations in greater detail about the advantages provided by data warehousing and business intelligence applications. We aim to clarify how these technology tools and pedagogical practices work in concert to improve institutional effectiveness, academic quality, and administrative efficiency. This study aims to shed light on the transformative potential of data-driven decision-making in the context of higher education in India through empirical analysis and case studies. The study's conclusions ultimately seek to provide useful recommendations for instructors, administrators, legislators, and other interested parties committed to enhancing the higher education environment

The act of applying game mechanics and dynamics (which have been traditionally used in video games) into business applications is being widely trialled in an effort to make conventional business software a bit more participative, fun and engaging. This new trend, named `gamification' has its believers and of course, its critics who still need convincing that the concept is an effective and beneficial business tool worthy of investment. The literature reveals that user engagement of business intelligence (BI) tools is much lower than expected and investors are failing to get a good return on their investment (ROI). So, a software prototype will be designed and developed to add gamification to a BI tool to determine its effect upon the user engagement levels of test participants. The experimental study will be evaluated using the comprehensive User Engagement Scale (UES) to see if there are improvements in areas such as; aesthetics, perceived usability, endurability, novelty, felt involvement and focused attention. The results of this unique study should demonstrate whether or not `gamifying' a BI tool has the potential to increase an individual's motivation to use BI software more often.

This study extends the existing literature of users' acceptance of business intelligence technologies by proposing a comprehensive model integrating personality five factors model (FFM) and technology acceptance model (TAM) to investigate the intention to use business intelligence (BI) tools to support business decision making. We expect that this study will present an example of the integration between FFM and TAM to provide a better understanding about the relationship between personality and intention to use within the context of BI.

Tracking and monitoring construction projects are essential tasks to make decisions on the projects. There are various techniques to collect and store data from the construction site. In the current construction sector, informatics play an important role in collecting and interpreting data from construction processes. In recent studies, various technologies such as RFID, UAV are used for surveying the construction site. However, there is a study about collecting and interpreting the collected data is still needed. In this study, the collected data is categorized by labeling with several dimensions (e.g. location, contractor) using business intelligence and data warehouse tools. Moreover, this technique allows us to construct relationships between activities and generate practical information. The major implication of this study for engineering managers is monitoring the public demands and construction processes within one framework.

Business intelligence techniques and tools are used more and more in all the domains of human activity, being able to bring to light new information from the volumes of data that are gathered from the activities carried on. In the educational domain, as in other fields of activity, business intelligence tools can be employed with success. The current paper aims to analyze the impact of learning activities, based on data available related to a number of courses that were carried on over a period of three years (October 2010 - September 2013). The large majority of the courses was carried on through a European funded project and was aimed to develop the entrepreneurship competences of the participants through relatively short term trainings (24 hours spread over several days - 20 hours theoretical activities and 4 hours practical activities). Related to these courses, the available database consists of information related to the participant's background and the courses followed by them, with the outcomes of the courses (for example businesses started after graduation). Among the analysis that can be performed based on the available database are: participants starting a business by age; participants starting a business by field of study / level of education; participants starting a business by region / county of residence/ place of residence; participants starting a business by status on the labor market. The conclusions of the study can be useful to different learning organizations that carry on entrepreneurship and other training courses in order to optimize their activities and better select the participants to maximize the impact of the learning programs they are offering.

This study proposes the use of Business Intelligence (BI) tools to support obtaining technical and economic indicators for the management of electrical energy in consumer units with photovoltaic generation added to storage systems, focusing on consumption, photovoltaic generation and storage of electrical energy in its operation. To this end, data was collected using telemetry devices connected to bidirectional electrical energy meters, allowing the acquisition of electrical quantities. The detailed analysis of the UCs energy profile over time provides valuable insights to optimize the use of electrical energy, reduce costs and promote sustainability, through automation and business intelligence resources. The availability of the expenses and revenues included in the UC's monthly billing makes it possible to analyze the values recorded in the invoices over a given period and allows the actual costs to be compared with the calculated projections, identifying possible deviations and verifying the accuracy of the estimates. Through BI tools, it is possible to identify trends and measure impacts for future projections, opening a range of innovative applications with regard to seasonality and optimization opportunities, contributing to end-user diagnosis through graphs, dashboards and customizable alarms . Furthermore, the platform creates a robust database for recording and analyzing the history of paid invoices, allowing the generation of important indicators for UC energy management.

This paper explores the design and implementation of compliance-focused financial reporting systems using a robust integration of SQL, Tableau, and Business Intelligence (BI) tools, specifically Excel. In the face of evolving regulatory mandates such as SOX (Sarbanes-Oxley Act), IFRS (International Financial Reporting Standards), and Basel III, organizations are under increasing pressure to maintain transparency, accuracy, and timeliness in their financial reporting. Leveraging a technical toolkit comprising structured query language (SQL), Tableau dashboards, and advanced Excel models, this study demonstrates how a streamlined data architecture can enable real-time, auditable, and regulator-ready financial reports. The system design begins with SQL-driven data modeling to ensure the integrity, traceability, and normalization of financial data from multiple source systems. SQL stored procedures and views are developed to automate data extraction, transformation, and loading (ETL), eliminating manual errors and enhancing audit readiness. Tableau is then used to create dynamic visualizations and compliance dashboards that allow stakeholders to monitor financial health, reconcile transactions, and detect anomalies. These dashboards integrate governance controls, flagging deviations from compliance thresholds in real time. Advanced Excel formulas and pivot tables complement this workflow by supporting in-depth variance analysis, control testing documentation, and regulatory submissions. A key contribution of this work is the development of a repeatable compliance-reporting framework that emphasizes version control, data lineage, and role-based access. By embedding risk metrics, policy thresholds, and audit trails within the reporting interface, the system minimizes regulatory risks while improving operational efficiency. Case studies from investment funds and corporate finance illustrate how this integrated architecture enhances both internal control effectiveness and external audit alignment. This technical synthesis provides a blueprint for finance professionals, auditors, and IT teams to collaborate in developing scalable, compliance-ready financial reporting systems that fulfill both operational and regulatory mandates in a digitized finance ecosystem. It highlights how bridging data engineering with financial analytics through SQL, Tableau, and Excel ensures not just compliance, but strategic foresight.