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Journal of Business Analytics - Taylor & Francis | 2024 Impact Factor:1.6 | Cite Score:3.7 | Q2

Journal of Business Analytics With Cite Score

Cite Score and Journal Rank of Journal of Business Analytics

  • About: The Journal of Business Analytics (JBA) is a peer-reviewed journal that focuses on the application of analytics in business contexts. It covers a range of topics including data analysis, business intelligence, predictive modeling, decision support systems, and the use of data-driven insights for strategic decision-making. The journal aims to publish high-quality research that advances the understanding and practice of business analytics and its impact on organizational performance.
  • Objective:
    The primary objective of JBA is to advance the field of business analytics by disseminating significant research findings, innovative methodologies, and practical applications. The journal seeks to provide a platform for researchers, practitioners, and business professionals to share their work on analytics tools, techniques, and case studies. JBA aims to enhance the use of data and analytics in business decision-making and to promote the development of new approaches and technologies in the field.
  • Interdisciplinary Approach:
    JBA embraces an interdisciplinary approach, inviting contributions from fields such as statistics, computer science, management science, economics, and information systems. This approach ensures a comprehensive exploration of business analytics topics, integrating diverse perspectives and methodologies. By promoting interdisciplinary research, the journal aims to address complex business challenges and develop integrated solutions that leverage data and analytics.
  • Impact:
    The journal has a significant impact on both academic research and business practice in the field of business analytics. It is widely cited by researchers, practitioners, and industry professionals interested in the latest developments and applications of business analytics. The research published in JBA contributes to the advancement of analytics techniques, tools, and strategies that improve business performance and decision-making. The journal serves as a valuable resource for professionals involved in data analysis, business intelligence, and strategic planning.
  • Significance:
    JBA plays a crucial role in advancing the study and practice of business analytics by providing a platform for high-quality research and practical insights. Its contributions support the development of innovative analytics solutions and methodologies that address current and future business challenges. The journals commitment to excellence and interdisciplinary focus make it an essential resource for anyone involved in business analytics research, development, and application. Through its rigorous scholarship and broad coverage, JBA helps shape the future of business analytics and its role in enhancing organizational decision-making.

  • Editor-in-Chief:  Dursun Delen

  • Scope: The Journal of Business Analytics focuses on research and practice related to the use of analytics in business decision-making. Its scope includes, but is not limited to:
  • Business Analytics Techniques: Research on various analytical techniques used in business, including descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.
  • Data Analysis and Visualization: Studies on methods and tools for analyzing and visualizing business data, including statistical analysis, data mining, and data visualization techniques.
  • Big Data Analytics: Exploration of techniques and technologies for handling and analyzing large and complex datasets, including big data platforms, frameworks, and tools.
  • Business Intelligence: Research on business intelligence tools and systems used to support strategic decision-making, including dashboards, reporting, and data warehousing.
  • Predictive Modeling: Studies on models and algorithms for predicting future business outcomes, including regression analysis, machine learning models, and time series forecasting.
  • Optimization: Research on optimization techniques applied to business problems, including linear programming, integer programming, and heuristic methods.
  • Decision Support Systems: Exploration of systems that assist in decision-making processes, including decision-making frameworks, simulation models, and decision support technologies.
  • Data Mining: Research on methods for discovering patterns and relationships in business data, including clustering, association rule mining, and anomaly detection.
  • Customer Analytics: Studies on techniques for analyzing customer data to gain insights into customer behavior, preferences, and segmentation.
  • Operations Analytics: Exploration of analytical methods applied to business operations, including supply chain management, inventory control, and process optimization.
  • Financial Analytics: Research on the application of analytics in financial decision-making, including risk management, portfolio optimization, and financial forecasting.
  • Marketing Analytics: Studies on the use of analytics in marketing, including campaign analysis, market segmentation, and customer relationship management.
  • Human Resources Analytics: Research on the application of analytics to human resources management, including talent acquisition, employee performance, and workforce planning.
  • Healthcare Analytics: Exploration of analytics applied to healthcare management, including patient data analysis, clinical decision support, and healthcare operations optimization.
  • Business Strategy and Analytics: Studies on the integration of analytics into business strategy, including strategic planning, competitive analysis, and performance measurement.
  • Ethics and Privacy: Research on ethical considerations and privacy issues related to the use of analytics in business, including data security, consent, and responsible data use.
  • Case Studies and Applications: Detailed case studies showcasing the application of analytics in various business contexts, including real-world examples and best practices.
  • Latest Research Topics for PhD in Computer Science
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  • Print ISSN:  2573-234X

    Electronic ISSN:  2573-2358

  • Abstracting and Indexing:  Scopus

  • Imapct Factor 2024:  1.6

  • Subject Area and Category:  Business, Management and Accounting, Management Information Systems, Computer Science, Information Systems, Engineering, Industrial and Manufacturing Engineering

  • Publication Frequency:  

  • H Index:  12

  • Best Quartile:

    Q1:  

    Q2:  Industrial and Manufacturing Engineering

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  • Cite Score:  3.7

  • SNIP:  0.889

  • Journal Rank(SJR):  0.382