scholarly journals Modeling of Business Intelligence Systems Using the Potential Determinants and Theories with the Lens of Individual, Technological, Organizational, and Environmental Contexts-A Systematic Literature Review

2020 ◽  
Vol 10 (9) ◽  
pp. 3208 ◽  
Author(s):  
Sumera Ahmad ◽  
Suraya Miskon ◽  
Tawfeeq Abdullah Alkanhal ◽  
Iskander Tlili
Keyword(s):  
Decision Making ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Business Intelligence ◽  
Large Scale ◽  
Intelligence System ◽  
Frequency Of Use ◽  
Study Results ◽  
Decision Making Processes ◽  
The Individual

Race towards industry 4.0 increases the hyper competition and puts pressure on organizations to integrate the advanced technologies. Business intelligence system (BIS) is one of the top prioritized technologies that attracted the significant attention of policy-makers and industry experts due to its ability to provide more informed and intelligent knowledge for decision-making processes. It is evident by literature that organizations and industries are prone to integrate the BIS at large scale, but more than 70% BIS projects fail to give the expected results. Hence, it is pertinent to explore the significant determinants that influence the BIS adoption and acceptance in organizations. Although previous literature did not have any comprehensive review relevant to the individual, technological, organizational, and environmental determinants. Therefore, the current study tries to narrow this gap by a systematic literature review (SLR) of 84 studies that were published during the period of 2011–2020. A total of 93 determinants are identified based on content analysis by using text mining techniques of Yoshikoder and human coding skills. The identified determinants are ranked according to their frequency of use. A theoretical framework has been developed with potential determinants and theories. The study results will enrich the recent BIS literature and improve the understanding of practitioners’ decision-making processes to leverage maximum value from the adoption of BIS.

Business Intelligence System Design and its Consequences for Knowledge Sharing, Collaboration, and Decision-Making

2020 ◽  
pp. 382-402
Author(s):  
Lapo Mola ◽  
Cecilia Rossignoli ◽  
Andrea Carugati ◽  
Antonio Giangreco
Keyword(s):  
Decision Making ◽  
Knowledge Sharing ◽  
Business Intelligence ◽  
Two Phase ◽  
Intelligence System ◽  
Business Intelligence Systems ◽  
Managerial Implications ◽  
Decision Making Processes

This exploratory study analyses the effects of the technical and organisational characteristics of business intelligence systems (BIS) on knowledge sharing, collaboration, and decision-making processes. The authors conducted a two-phase multi-method investigation. First, we surveyed 30 enterprises using BIS on a regular basis; then, we engaged in an in-depth case study with one of the respondent companies. Our results show that, on average, the technical and organisational characteristics of the BIS are positively associated with an increase in knowledge sharing, leading to an improvement in internal collaboration that subsequently brings improvement in the quality of decision-making. This case study adds that the way the BIS is designed and appropriated in organisations is important in obtaining such results is. A BIS being designed so that it can be appropriated by the general employee base is key in obtaining the desired organizational impacts. This suggests some requirements for BIS design that we will discuss in terms of theoretical and managerial implications.

Business Intelligence System Design and its Consequences for Knowledge Sharing, Collaboration, and Decision-Making

2015 ◽  
Vol 11 (4) ◽  
pp. 1-25 ◽  
Author(s):  
Lapo Mola ◽  
Cecilia Rossignoli ◽  
Andrea Carugati ◽  
Antonio Giangreco
Keyword(s):  
Decision Making ◽  
Knowledge Sharing ◽  
Business Intelligence ◽  
Two Phase ◽  
Intelligence System ◽  
Business Intelligence Systems ◽  
Managerial Implications ◽  
Decision Making Processes

This exploratory study analyses the effects of the technical and organisational characteristics of business intelligence systems (BIS) on knowledge sharing, collaboration, and decision-making processes. The authors conducted a two-phase multi-method investigation. First, we surveyed 30 enterprises using BIS on a regular basis; then, we engaged in an in-depth case study with one of the respondent companies. Our results show that, on average, the technical and organisational characteristics of the BIS are positively associated with an increase in knowledge sharing, leading to an improvement in internal collaboration that subsequently brings improvement in the quality of decision-making. This case study adds that the way the BIS is designed and appropriated in organisations is important in obtaining such results is. A BIS being designed so that it can be appropriated by the general employee base is key in obtaining the desired organizational impacts. This suggests some requirements for BIS design that we will discuss in terms of theoretical and managerial implications.

Implementation System of Business Intelligence System In The Company

2017 ◽  
Vol 1 (2) ◽  
Author(s):  
Abdul Hamid Arribathi ◽  
Maimunah Maimunah ◽  
Devi Nurfitriani
Keyword(s):  
Decision Making ◽  
Business Intelligence ◽  
Research Library ◽  
Decision Making Process ◽  
Intelligence System ◽  
Business Intelligence Systems

This study aims to determine the stages that must be implemented in building a Business Intelligence System structured and appropriate in building Business Intelligence Systems in an organization, and understand the important aspects that must be considered for investment development Business Intelligence System is increasing. Business must be based on the conditions and needs of the organization in achieving the desired goals. If these conditions occur, then the decision-making process will be better and more accurate. The purpose of this study is to determine the important aspects that must be understood and prepared in using the Business Intelligence System in an organization. The method used is the explanation as well as the research library of several books, articles and other literature.

A Study on the Analytics Tool Used in Decision Making at Small and Medium Enterprise

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Vivek N. Bhatt
Keyword(s):  
Decision Making ◽  
Business Intelligence ◽  
Small And Medium Enterprises ◽  
Small Scale ◽  
Know How ◽  
Decision Making Styles ◽  
Routine Basis ◽  
Medium Enterprise ◽  
Decision Making Processes ◽  
Medium Enterprises

The article focuses on the study of prevailing decision making styles of Small Scale Industrial (SSI) Units. It presents data collected from 200 SSI units from Bhavnagar – a coastal city of Gujarat, India. The objective of writing the article is to depict heuristic decision patterns of small and medium enterprises, and the rare use of analytical or statistical business intelligence tools in decision making processes. It would be interesting to study the design of decision taken on routine basis in small units, poorly equipped with technology and technical know-how. The paper is descriptive in terms, and lays a lucid picture of present decision making processes.

scholarly journals Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Alan Brnabic ◽  
Lisa M. Hess
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Real World ◽  
Learning Algorithms ◽  
External Validation ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Machine Learning Methods

Abstract Background Machine learning is a broad term encompassing a number of methods that allow the investigator to learn from the data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision making. Methods This systematic literature review was conducted to identify published observational research of employed machine learning to inform decision making at the patient-provider level. The search strategy was implemented and studies meeting eligibility criteria were evaluated by two independent reviewers. Relevant data related to study design, statistical methods and strengths and limitations were identified; study quality was assessed using a modified version of the Luo checklist. Results A total of 34 publications from January 2014 to September 2020 were identified and evaluated for this review. There were diverse methods, statistical packages and approaches used across identified studies. The most common methods included decision tree and random forest approaches. Most studies applied internal validation but only two conducted external validation. Most studies utilized one algorithm, and only eight studies applied multiple machine learning algorithms to the data. Seven items on the Luo checklist failed to be met by more than 50% of published studies. Conclusions A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of machine learning methods to inform patient-provider decision making. There is a need to ensure that multiple machine learning approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that decisions for patient care are being made with the highest quality evidence. Future work should routinely employ ensemble methods incorporating multiple machine learning algorithms.

scholarly journals A Systematic Literature Review of Multi-Criteria Decision-Making Methods for Sustainable Selection of Insulation Materials in Buildings

2021 ◽  
Vol 13 (2) ◽  
pp. 737
Author(s):  
Indre Siksnelyte-Butkiene ◽  
Dalia Streimikiene ◽  
Tomas Balezentis ◽  
Virgilijus Skulskis
Keyword(s):  
Decision Making ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Energy Demand ◽  
Energy Performance ◽  
Future Research ◽  
Multi Criteria Decision Making ◽  
Insulation Materials ◽  
Advantages And Disadvantages ◽  
Selection Of

The European Commission has recently adopted the Renovation Wave Strategy, aiming at the improvement of the energy performance of buildings. The strategy aims to at least double renovation rates in the next ten years and make sure that renovations lead to higher energy and resource efficiency. The choice of appropriate thermal insulation materials is one of the simplest and, at the same time, the most popular strategies that effectively reduce the energy demand of buildings. Today, the spectrum of insulation materials is quite wide, and each material has its own specific characteristics. It is recognized that the selection of materials is one of the most challenging and difficult steps of a building project. This paper aims to give an in-depth view of existing multi-criteria decision-making (MCDM) applications for the selection of insulation materials and to provide major insights in order to simplify the process of methods and criteria selection for future research. A systematic literature review is performed based on the Search, Appraisal, Synthesis and Analysis (SALSA) framework and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. In order to determine which MCDM method is the most appropriate for different questions, the main advantages and disadvantages of different methods are provided.

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