دانلود مقاله هوش تجاری و تجزیه و تحلیل در بخش صنعت بانکداری
خلاصه
1. معرفی
2. بررسی ادبیات و پیشینه
3. چارچوب نظری و توسعه فرضیه ها
4. روش تحقیق
5. نتایج تحقیق
6. بحث
7. مشارکت
8. نتیجه گیری
منابع
Abstract
1. Introduction
2. Literature Review and Background
3. Theoretical Framework and Hypotheses Development
4. Research Methodology
5. Research Results
6. Discussion
7. Contributions
8. Conclusions
References
چکیده
هدف این مطالعه بررسی عواملی است که بر استفاده از هوش تجاری و تجزیه و تحلیل (BIA) در بخش بانکی تاثیر می گذارد. بر اساس بررسی ادبیات جامع، یک مدل نظری برای بررسی تأثیر سه عامل کلیدی بر پذیرش و استفاده از هوش تجاری و تجزیه و تحلیل در بخش بانکداری، یعنی عوامل فناوری سازمانی و محیطی ایجاد شد. این مطالعه از بسته آماری علوم اجتماعی (SPSS) برای تجزیه و تحلیل داده های جمع آوری شده از 120 کارمند بانک عرب اردن استفاده کرد. نتایج نشان دهنده تأثیر حیاتی نه تنها وجود زیرساخت داده و فناوری، بلکه اهمیت و در دسترس بودن پشتیبانی و قابلیت های مدیریت و منابع انسانی است. این مطالعه نشان می‌دهد که مهم‌تر از آن، برنامه‌ریزی موفق برای هوش تجاری و تجزیه و تحلیل باید فراتر از جنبه‌های فناوری باشد تا از مزایای کامل چنین فناوری‌هایی به‌ویژه در بخش بانکداری بهره‌مند شود. با این حال، ما استدلال می کنیم که تحقیقات بیشتری به خصوص در زمینه کشورهای در حال توسعه نیاز است تا به طور کامل درک کنیم که چگونه بخش های بانکی می توانند با موفقیت هوش تجاری و تجزیه و تحلیل را پیاده سازی و استفاده کنند.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
This study aims to examine the factors that influence business intelligence and analytics (BIA) usage in the banking sector. Based on a comprehensive literature review, a theoretical model was developed to explore the impact of three key factors on business intelligence and analytics adoption and usage in the banking sector, namely technological, organizational, and environmental factors. The study used the Statistical Package for the Social Sciences (SPSS) to analyze data collected from 120 employees of Jordan Arab bank. The results revealed the critical impact of not only the existence of data and technology infrastructure but also the importance and availability of management and human resources support and capabilities. This study suggests that, more importantly, successful planning for business intelligence and analytics should go beyond the technology aspects to gain the full benefits of such technology, especially in the banking sector. Yet, we argue that more research needs to be conducted, especially in the context of developing countries, to fully understand how banking sectors can successfully implement and utilize business intelligence and analytics.
Introduction
Business intelligence and analytics (BIA) is considered one of the most critical technologies, systems, practices, and applications that help organizations develop a deeper understanding of business data and gain a competitive advantage while improving operations and product development and strengthening relationships with customers [1,2]. BIA has an even more important role in the banking sector by enabling experts and managers to make better, accurate, timely, and relevant decisions so as to increase the productivity and profitability of the bank and be able to comply with the different regulatory and environmental dimensions of this sector [3].
BIA, nowadays, is a trendy issue and a compulsory prerequisite for creating an outstanding corporate image, which goes in line with implementing a successful plan regarding using technology extensively. Thus, this supports business decisions and gains a competitive advantage in today’s dynamic environment, which requires outstanding efforts for dedicating massive budgets to research and development (R&D). Data are a focal point and are considered the fuel of the future since they can be processed efficiently and used effectively in supporting risky occurrences and decisions that can be heavily reflected in the performance of corporates [4,5].
Business intelligence (BI) is an umbrella term that includes structures, tools, databases, applications, and methodologies to analyze data by converting raw data into meaningful and helpful information to support business managers’ decisions [6]. Banking areas, such as branch performance, sales, risk assessment, electronic banking, customer segmentation, and retention, are generally excellent for applying various business concepts and analytics, technologies, and tools, including data mining (DM), data warehousing, and decision support systems (DSS). Therefore, top management must constantly focus on solving challenging problems and seizing opportunities for the banking sector to succeed and excel in today’s business environment. This requires computerized support for managerial decision making, thereby implying a need for decision support, business intelligence, and analytics systems [7]. Business intelligence systems (BIS) emerged from technical solutions that provide data integration, analytical capabilities, and data mining to provide stakeholders at various levels with valuable information to make their decisions effectively and successfully.
Conclusions
Business intelligence and analytics is a key tool that deals with large amounts of diverse data, whether structured or unstructured [2], thus providing banks with the huge value of achieving better operations and great competitive advantages. With the dynamic environment we are facing nowadays, every bank should emphasize the importance of implementing a massive business-intelligence system to keep up with the fast pace of technology. In this study, we studied the factors that influence the use of business intelligence and analytics in the banking sector. Different factors have been examined, including technical, organizational, and environmental dimensions. The results of the analysis confirmed not only the importance of these factors on business intelligence and analytics adoption and use but also on the huge value for the banking sector that can be achieved by such technology. In fact, it is extremely important to understand the dynamics of these factors, not only to achieve better usage of business intelligence, but also to better plan and implement such technologies in the banking sector.
The results presented in this study reveal that to achieve better fact-based decision making, business-intelligence technologies require the presence of quality-level data-related infrastructure capabilities, great senior-management support and better “market environment” awareness. Yet, the researchers believe that more factors with more comprehensive and larger populations need to be studied in order to fully understand the critical success factor of BIA usage and utilization. That is, further research can test the proposed model in different contexts, such as the Jordanian public-shareholding firms as they invest heavily in BIA systems, and, in turn, more generalizable results can be obtained. In addition, future research could apply the model to developed countries to compare the results with those of developing counties. Finally, the researchers examined the impact of BIA on firm performance and innovation capabilities, but more research is needed to examine the BIA adoption on organizational effectiveness in a holistic view. To conclude, the results of this study are of supreme implication to the literature in this research area and can motivate additional crucial studies to shed more light on their standings.