Improving Decision Quality: The Role of Business Intelligence

2016 ◽  
Vol 57 (1) ◽  
pp. 58-66 ◽  
Author(s):  
Lucian L. Visinescu ◽  
Mary C. Jones ◽  
Anna Sidorova
Keyword(s):  
Business Intelligence ◽  
Decision Quality

scholarly journals Heart and Mind: Essential Components in Sound Decision-Making

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 364-364
Author(s):  
Michaela Clark ◽  
Julie Hicks Patrick ◽  
Michaela Reardon
Keyword(s):  
Decision Making ◽  
Decision Quality ◽  
Cluster Solution ◽  
Cognitive Resources ◽  
Cluster Membership ◽  
Essential Components ◽  
Decision Making Processes ◽  
Affective Components ◽  
Heart And Mind

Abstract Consumer tasks permit an ecologically-valid context in which to examine the contributions of affective and cognitive resources to decision-making processes and outcomes. Although previous work shows that cognitive factors are important when individuals make decisions (Patrick et al., 2013; Queen et al.), the role of affective components is less clear. We examine these issues in two studies. Study 1 used data from 1000+ adults to inform a cluster analysis examining affective aspects (importance, meaningfulness) of making different types of decisions. A 4-cluster solution resulted. In Study 2, we used affective cluster membership and cognitive performance as predictors of experimental decision-making outcomes among a subset of participants (N = 60). Results of the regression (F(2, 40) = 6.51, p < .01, R2 = .25.) revealed that both the affective clusters (b = .37, p = .01) and cognitive ability (b = -.30, p = .04) uniquely contributed to the variance explained in decision quality. Age did not uniquely contribute. Results are discussed in the context of developing measures that enable us to move the field forward.

scholarly journals Role of Business Intelligence in Banking Sector for Achieving Competitive Advantage- An Empirical Analysis

2021 ◽  
Vol 19 (1) ◽  
Keyword(s):  
Competitive Advantage ◽  
Business Intelligence ◽  
Quantitative Research ◽  
Banking Sector ◽  
Statistical Tests ◽  
Firm Strategy ◽  
Middle Level ◽  
Mediating Role ◽  
The Impact

With recent advancements in information technology, organizations’ capability to acquire and analyze data for efficient decision making has increased. Good strategies promote alignment among processes and technology in use, which may result in better firm performance. However, there has been little focus on how firm strategies and business intelligence (BI) systems might play their part in forming organizational information and getting a competitive edge. Therefore, the purpose of conducting this study is to investigate the impact of firm strategy on firm competitive advantage with mediating role of BI adoption and moderating role of BI capabilities. For this, a quantitative research methodology was used, and data was collected from 300 middle-level managers in Pakistan's telecom sector. Statistical tests such as descriptive statistics, correlation, reliability analysis, one-way ANOVA, confirmatory factor analysis, and mediation analysis through Hayes process were performed using SPSS and AMOS. The findings revealed a positive link between firm strategy and competitive advantage, with business intelligence adoption serving as a mediating factor. Business intelligence capabilities positively moderate the relationship between BI adoption and competitive advantage. Hence, all proposed hypotheses (H1, H2, and H3) were approved. The contribution and Limitation of the study are also discussed.

The Role of Data Mining for Business Intelligence in Knowledge Management

2015 ◽  
pp. 12-33 ◽  
Author(s):  
Kijpokin Kasemsap
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Human Resources ◽  
Organizational Performance ◽  
Business Intelligence ◽  
Statistical Significance ◽  
Practical Applications ◽  
Business Goals ◽  
Emerging Trends

This chapter introduces the role of Data Mining (DM) for Business Intelligence (BI) in Knowledge Management (KM), thus explaining the concept of KM, BI, and DM; the relationships among KM, BI, and DM; the practical applications of KM, BI, and DM; and the emerging trends toward practical results in KM, BI, and DM. In order to solve existing BI problems, this chapter also describes practical applications of KM, BI, and DM (in the fields of marketing, business, manufacturing, and human resources) and the emerging trends in KM, BI, and DM (in terms of larger databases, high dimensionality, over-fitting, evaluation of statistical significance, change of data and knowledge, missing data, relationships among DM fields, understandability of patterns, integration of other DM systems, and users' knowledge and interaction). Applying DM for BI in the KM environments will enhance organizational performance and achieve business goals in the digital age.

Knowledge Management and Business Analytics

2019 ◽  
pp. 1-42
Author(s):  
Nirali Nikhilkumar Honest ◽  
Atul Patel
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Business Intelligence ◽  
Business Performance ◽  
Virtual Organization ◽  
Data Driven ◽  
Business Analytics ◽  
Broad Category ◽  
Business Operations

Knowledge management (KM) is a systematic way of managing the organization's assets for creating valuable knowledge that can be used across the organization to achieve the organization's success. A broad category of technologies that allows for gathering, storing, accessing, and analyzing data to help business users make better decisions, business intelligence (BI) allows analyzing business performance through data-driven insight. Business analytics applies different methods to gain insight about the business operations and make better fact-based decisions. Big data is data with a huge size. In the chapter, the authors have tried to emphasize the significance of knowledge management, business intelligence, business analytics, and big data to justify the role of them in the existence and development of an organization and handling big data for a virtual organization.

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