Enhancing Competitive Advantage through Business Intelligence System Adoption: A Strategic Framework for Improved Decision-Making in Organisations | Chapter 2 | Information Management and Technology: The Proceedings of the 10 th International Conference on Business and Management Dynamics (ICBMD), Edition 1

This paper aims to address gaps in managerial competencies by using Business intelligence systems to enhance competitive advantage. It focuses on adopting a business intelligence system as a digital strategy to improve business intelligence and address a long-time problem of information failures in organisations. The flow of quality information to make quality decisions is often ignored when measuring competitive advantage, yet, the quality of the information received by decision-makers can have a huge impact on the performance of an organisation. The question remains: How are managers getting the information to make decisions in this technological era? In some instances, information sent to decision-makers is still being prepared manually contrary to the demands of the fourth and the upcoming fifth industrial revolution where managers are expected to interact with gadgets. This paper explored ways of ensuring that managers can develop BI reports and configure the BI system to report directly to them to improve business intelligence. It answers the following questions: Do managers have the capacity to configure gadgets to report to them directly? What should be done to achieve this? We conducted qualitative research, to get a deep understanding. The results revealed that senior managers still over-rely on information supplied by humans, often prone to manipulation and errors. Supervisors try to block the free flow of information through a behaviour called gatekeeping. Senior managers lack the technical skills to develop BI reports in the BI system. This paper proposes the stages an organisation goes through to overcome this challenge. The practical implications of the findings suggest that, when BI systems are implemented effectively and utilized correctly, they contribute to informed decision-making and enhance competitive positioning in organisations.

 

Author (s) Details

 

Garikayi Emmanuel Gondo
University of South Africa (UNISA), Midrand, Republic of South Africa.

Lorna B Holtman
International Education and Partnerships, Durban University of Technology, ML Sultan Campus, PO Box: 1334, Durban, 4000, Republic of South Africa.

 

Please see the book here:- https://doi.org/10.9734/bpi/mono/978-93-49238-37-4/CH2

SpaceConnect: A Framework for Modelling and Managing Behavioral States of Evolutive Agents | Chapter 5 | Research and Applications Towards Mathematics and Computer Science Vol. 8

In the natural world, all objects and entities (agents) evolves and changes their state continually e.g. a village may become "Town". To capture and depict the evolution of agents, a commonly used approach is the utilization of a multi-dimensional model. This model is used in most of traditional and highly powerful analytical systems which data are being represented in multi-dimensional data structure, enabling efficient data tracking and monitoring. Such systems are popular in organizations and institutions that store and interpret historical cumulative data for forecasting and decision-making purposes, such as data warehousing. However, when dealing with unconventional evolutionary spaces, handling and representing the agent’s evolution using a multi-dimensional model becomes challenging. One of the key challenges lies in managing reactive and dynamic data, which are being more specified and solicited, and may include and stimulate a massive amount of knowledge. In this chapter, a meticulous/methodical framework is proposed for modeling and managing of evolving agents or structural entities (metaphors, e.g. Restaurants, Hospitals, Factories, etc.). These agents can exist within the same or different spaces (as organizational structures); they evolve, interact and transit. They are inter-dependent, and have analytical state-full characteristics (so far, seen as expressive dimensions). The framework provides solutions for analysis and prediction, along with an analytical methodology support that shows; how agents evolve, how evolutions propagate, how evolutions stimulate the evolution of other agents etc. The chapter extensively discusses the fundamental features, principles, and foundations that illustrated in preceding work “hyper-space navigational framework renovated with SpaceConnect”, and also experimented with an indicative case study.

Author(s) Details:

Mohamed Dbouk,
Faculty of Sciences (I), Lebanese University, Beirut, Lebanon.

Hamid Mcheick,
Université du Québec à Chicoutimi, Québec, Canada.

Ebrahim Al-Almani,
Doctoral School of Science and Technology, Lebanese University, Beirut, Lebanon and Faculty of Science & Engineering, The National University, Sana’a, Yemen.

Please see the link here: https://stm.bookpi.org/RATMCS-V8/article/view/13074

Big Data Analytics for Dealing with COVID-19: An Innovative Trend in Business Intelligence | Chapter 05 | New Innovations in Economics, Business and Management Vol. 8

 Most commercial and scientific activity include data and data analysis. It assists businesses and people in properly understanding the issue and making the best option possible. The goal of this chapter is to explore big data analysis, which is a growing field that involves analysing enormous and unstructured data that is created and gathered. The chapter also emphasises the significance and necessity of Big Data Analytics, as well as its important application and utilisation during Covid-19. Bid data analysis has been demonstrated to have a vital role in numerous fields in various research.

Author(S) Details

Murtaza Farooque
Dhofar University, Sultanate of Oman.

Mohammed Abdul Imran Khan
Dhofar University, Sultanate of Oman.

Mohammed Aref Abdul Rashid
Dhofar University, Sultanate of Oman.

View Book:- https://stm.bookpi.org/NIEBM-V8/article/view/6736

Analyzing the Procedure of Using Business Intelligence Resources in the Publication of Entrepreneurship Culture: An Applied Study | Chapter 8 | Modern Perspectives in Economics, Business and Management Vol. 2

 Business skills development is crucial for both researchers and experts, and it is incorporated into the early years of schooling. According to Howard Gardner's view, intelligence is made up of nine skills that pupils possess, known as Multiple Intelligence (theoretical intelligence, language, spiritual, musical, natural, movable, personal, social and emotional intelligence). These intelligences have demonstrated their ability to teach students such skills and talents during their school years in order to help them start a future business. This research is regarded as a great learning opportunity since it enables us to discover opportunities and invest in increasing the skills and knowledge required to serve society today and in the future. These skills educate students how to negotiate, communicate, and work as part of a team, all of which are crucial components of the company entrepreneurial architecture.

Author (s) Details

Rasha H. Abbood
Ministry of Higher Education and Scientific Research, Iraq.

Nagham H. Al- Nama'a
Al - Nahrain University, Iraq.

View Book :- https://stm.bookpi.org/MPEBM-V2/article/view/2326

A Descriptive Classification of Association Item Sets from Large Data Sets Based on User Awareness Using Hybrid Approach | Chapter 4 | Emerging Trends in Engineering Research and Technology Vol. 11

 There is a large amount of data to be generated in business intelligence due to rising data in business applications. Data interpretation and prediction is a very vigorous idea for determining the outcomes of data based on decision-making analysis. Some of the machine learning-related techniques such as clustering, grouping, neural network-based approaches and association rule-based approaches have historically been used to explore and analyse business data in order to provide efficient data processing. Because of increasing data analysis in business intelligence-related applications, static machine learning approaches were not satisfied with establishing an association in real-time data sets between different attributes. So we suggest Advanced & Hybrid Computer in this article, Learning Methodology (AHMLA) for efficient data analysis of various high-dimensional data related attributes. Our proposed plan improves customer experience, reporting generations of business intelligence applications based on user understanding. An experimental outcome of the proposed method offers better output with regard to various parameters with regard to current approaches. Experimental findings illustrate successful data creation with different relationships between attributes. A further enhancement of the proposed approach is to facilitate the optimization of attribute relationships from large high-dimensional datasets between different item sets.

Author (s) Details

Srihari Varma Mantena
Department of CSE, Acharya Nagarjuna University, Guntur, AP, India.

C. V. P. R. Prasad
Department of CSE, Acharya Nagarjuna University, Guntur, AP, India.

View Book :- https://bp.bookpi.org/index.php/bpi/catalog/book/335