Search Results - subject:"Business Intelligence"

Entwicklung eines ICP-LLM zur Unterstützung der Erstellung eines Intelligence Collection Plans (ICP)

Baur, Ralf; Donner, Ole; Koch, Niklas; Schulz, André; … - 2025

Die Erstellung eines Intelligence Collection Plans (ICP) stellt einen zentralen Arbeitsschritt im Schnittpunkt aus der Direction-Phase (siehe Abbildung 1) und dem Intelligence Requirement Management (IRM) innerhalb des Intelligence Cycle dar. Dieser soll ermöglichen, Informationsbedarfe...

Persistent link: https://www.econbiz.de/10015481450

Assessing sustainability of green supply chain performance : the roles of agile innovative products, business intelligence readiness, innovative supply chain process integration, a...

Al-Shboul, Moh'd Anwer - In: Journal of open innovation : technology, market, and … 11 (2025) 1, pp. 1-14

This study aims to investigate the impact of business intelligence readiness (BIR), innovative supply chain process …

Persistent link: https://www.econbiz.de/10015358641

Machine learning in predicting firm performance : a systematic review

Hezam, Yaseen; Luong, Hoa; Anthonysamy, Lilian - In: China Accounting and Finance Review 27 (2025) 3, pp. 309-339

Purpose - This study investigates the application of machine learning (ML) techniques in predicting firm performance, responding to the challenges posed by the large volumes of data required for accurate predictions. It aims to assess the effectiveness of various ML methods and algorithms used...

Persistent link: https://www.econbiz.de/10015438516

A systematic literature review on the impact of business intelligence on organization agility

Malawani, Luay; Sanguinoa, Ramón; Tato Jiménez, Juan Luis - In: Administrative Sciences : open access journal 15 (2025) 7, pp. 1-18

critical for companies to maintain competitiveness and sustainability. Business intelligence (BI) is pivotal in enabling … contribution by examining the relationship between business intelligence (BI) and organizational agility (OA) through a systematic …

Persistent link: https://www.econbiz.de/10015436173

Artificial intelligence-driven business intelligence for strategic energy and ESG management : a systematic review of economic and policy implications

Khaddam, Amineh A.; Alzghoul, Amro - In: International Journal of Energy Economics and Policy : IJEEP 15 (2025) 4, pp. 635-650

Persistent link: https://www.econbiz.de/10015472032

Applications and competitive advantages of data mining and business intelligence in SMEs performance : a systematic review

Tsiu, Shao V.; Ngobeni, Mfanelo; Mathabela, Lesley; … - In: Businesses 5 (2025) 2, pp. 1-49

of data mining and business intelligence (BI) tools. This systematic literature review scrutinizes the deployment and …, impeding effective utilization. Our review, grounded in the analysis of business intelligence tools used indicates that …

Persistent link: https://www.econbiz.de/10015434557

Tech-driven strategies : leveraging information technology in business management

Borah, Samarjeet (ed.); Nemade, Bhushankumar (ed.); … - 2025

business intelligence and analytics provide support for business strategy in a data-driven decision-making regime, and indeed …

Persistent link: https://www.econbiz.de/10015386937

Innovative approaches to accounting education : incorporating business intelligence tools

Yassin, Mohammed M.; Toumeh, Ahmad A. - In: Cogent business & management 11 (2024) 1, pp. 1-15

This study explores innovative approaches to accounting education by incorporating business intelligence (BI) tools …

Persistent link: https://www.econbiz.de/10015449014

Strategic key elements in big data analytics as driving forces of IoT manufacturing value creation : a challenge for research framework

Rajnoha, Rastislav; Hadač, Jakub - In: IEEE transactions on engineering management : EM ; a … 71 (2024), pp. 90-105

Persistent link: https://www.econbiz.de/10015404195

A data quality multidimensional model for social media analysis

Aramburu, María José; Berlanga, Rafael; Lanza-Cruz, Indira - In: Business & information systems engineering 66 (2024) 6, pp. 667-689

Persistent link: https://www.econbiz.de/10015195613