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مقاله انگلیسی رایگان در مورد هوش تجاری در کارت امتیازی متوازن – الزویر ۲۰۲۳

 

مشخصات مقاله
ترجمه عنوان مقاله هوش تجاری در کارت امتیازی متوازن: تحلیل کتاب سنجی
عنوان انگلیسی مقاله Business Intelligence in Balanced Scorecard:Bibliometric analysis
نشریه الزویر
انتشار مقاله سال ۲۰۲۳
تعداد صفحات مقاله انگلیسی ۱۲ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
نوع نگارش مقاله
مقاله پژوهشی (Research Article)
مقاله بیس این مقاله بیس نمیباشد
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۲٫۲۶۷ در سال ۲۰۲۰
شاخص H_index ۹۲ در سال ۲۰۲۲
شاخص SJR ۰٫۵۶۹ در سال ۲۰۲۰
شناسه ISSN ۱۸۷۷-۰۵۰۹
فرضیه ندارد
مدل مفهومی ندارد
پرسشنامه ندارد
متغیر ندارد
رفرنس دارد
رشته های مرتبط مدیریت
گرایش های مرتبط مدیریت کسب و کار – مدیریت استراتژیک
نوع ارائه مقاله
ژورنال
مجله  علوم کامپیوتر پروسدیا – Procedia Computer Science
دانشگاه Institute of Western China Economic Research, Southwestern University of Finance and Economics, China
کلمات کلیدی کارت امتیازی متوازن – هوش تجاری – تحلیل کتابسنجی
کلمات کلیدی انگلیسی Balanced Scorecard – business intelligence – bibliometric analysis
شناسه دیجیتال – doi
https://doi.org/10.1016/j.procs.2022.09.470
لینک سایت مرجع https://www.sciencedirect.com/science/article/pii/S1877050922013655
کد محصول e17326
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
Abstract
۱ Introduction
۲ Research characteristics
۳ Analysis of scientific articles concerning business intelligence
۴ Analysis of scientific articles concerning Balanced Scorecard
۵ Summary
References

بخشی از متن مقاله:

Abstract

     The aim of the research described in the article was to determine how and to what extent business intelligence solutions were presented in scientific articles concerning Balanced Scorecards published until 2022. The bibliometric analysis method was used in the course of the research. As a result of data analyses, the following have been determined: key phrases describing business intelligence in scientific articles concerning the Balanced Scorecard, the number of their occurrences, the structure of research, and countries in which researchers carry out scientific activity in the field of the Balanced Scorecard, which constitute the article’s contribution to science. Areas in which there are research gaps, where the practical significance of this research manifests, and what is particularly important for researchers of the Balanced Scorecard, have also been identified.

Introduction

     The reason for my interest in studying the relations between Business Intelligence (BI) and Balanced Scorecard (BSC) solutions consists in an assumption that such a relation is natural and results from the need to ensure rapid processing and presenting many different data monitored in the Balanced Scorecard, which I observed in the implementation practice of the Balanced Scorecard. This assumption seems correct since it is based on data defined and processed in a Balanced Scorecard – in the data’s large amount, varied (both qualitative and quantitative) nature, in many sources of the data’s origin in an enterprise, the required high frequency of their refreshing, the need to ensure high quality of the data itself, as well as the way the data is monitored, processed and analysed, the need to adjust the range of available results to the positions, etc.

     When launching a Balanced Scorecard, it is usually assumed to implement certain solutions in the field of business intelligence in order to analyse and illustrate the results of measuring the activities of enterprises. In balance scorecard implementations specifically, I noticed that selecting and launching business intelligence solutions supporting Balanced Scorecard in its operation remains to be launched in the second place, is launched gradually, or stops at the planning stage. In this research, I have decided to verify my doubts concerning the scope of the actual presence of business intelligence solutions in the practical service of a Balanced Scorecard. An additional argument for undertaking this task consists in my interest in learning about the practical functioning of Balanced Scorecard structures, their compliance with theoretical assumptions, their impact on accounting, management accounting, measuring results, enterprise strategies, as well as organizational and IT support solutions. I found only four articles [1] [2] [3] [4] related to bibliometric analysis of Balanced Scorecard and some of its aspects but none of it is related to business intelligence (in response for a query to Web of Science: ALL=(“balance* scorecard*”) AND ALL=(“business* intelligence*”) AND ALL=(“bibliometric*”)) but I found no article which could be comparable to this article. I am additionally motivated by that lack of research with a similar thematic scope and methodology. By this article I begin exploring this research area by recognizing the relation between a Balanced Scorecard and business intelligence.

Summary

     As a result of the research presented in the article, I obtained answers to the following research questions: 1a) In 9779 articles recognized as related to business intelligence it was identified 20,303 phrases co-occurred with the phrase “business intelligence”. After deeper analysis and establishing a bottom level of amount at least on 30 co-occurrences in the analysed articles it was found 169 phrases co-occurred with the phrase “business intelligence”

     ۱b) 33 phrases from among those co-occurring with the phrase “business intelligence” (approx. 20%) concern or describe organizational, technical, conceptual and software tools, phenomena and techniques related to business intelligence and these are: business intelligence, artificial intelligence, big data, business analytics, business intelligence system, cloud computing, dashboard, data analytics, data integration, data mining, data science, data visualization, data warehouse, database, decision support, deep learning, enterprise resource planning, framework, fuzzy logic, information management, information system, information technologies, internet of things, machine learning, neural network, online analytical process, olap, prediction, predictive analytics, risk management, semantic web, text mining and web mining.

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