استفاده از هوش تجاری و تجزیه و تحلیل (BIA) در بانکداری (مطالعه موردی: بانک صادرات استان یزد)
  •   آرشیو : نسخه تابستان 1402 - جلد اول
  •   کد پذیرش : 12177
  •   موضوع : مدیریت مالی
  • نویسنده/گان : | غلامرضا عسکرزاده، سید جلال ناظم منبری
  •   زبان : فارسی
  •   نوع مقاله : پژوهشی
  •   چکیده مقاله به فارسی : هدف این مطالعه بررسی عوامل موثر بر استفاده از هوش تجاری و تجزیه و تحلیل (BIA) در بخش بانکی است. بر اساس یک بررسی جامع ادبیات، یک مدل نظری برای بررسی تأثیر سه عامل کلیدی بر هوش تجاری و پذیرش و استفاده از تجزیه و تحلیل در بخش بانکداری، یعنی عوامل تکنولوژی، سازمانی و محیطی ایجاد شد. جامعه این پژوهش شامل 117 نفر از کارمندان بانک صادرات یزد می باشد. حجم نمونه با استفاده از جدول مورگان محاسبه شده و تعداد 90 نفر نمونه مورداستفاده قرارگرفته است. داده هاي لازم ازطريق پرسشنامه هاي استاندارد جمع آوري شده است.يافته هاي تحقيق نشان می¬دهدكه عوامل تکنولوژی، سازمانی و محیطی ارتباط معنی داری با میزان استفاده از هوش تجاری و تجزیه و تحلیل (BIA) دارد.
  •   لیست منابع : Al-Okaily, A.; Al-Okaily, M.; Teoh, A.P.; Al-Debei, M. An Empirical Study on Data Warehouse Systems Effectiveness: The Case of Jordanian Banks in the Business Intelligence Era. EuroMed J. Bus. 2022, ahead-of-print. [CrossRef]
    Ajah, I.A.; Nweke, H.F. Big data and business analytics: Trends, platforms, success factors and applications. Big Data Cogn.Comput. 2019, 3, 32. [CrossRef]
    Nithya, N.; Kiruthika, R. Impact of Business Intelligence Adoption on performance of banks: A conceptual framework. J. Ambient.Intell. Humaniz. Comput. 2021, 12, 3139–3150. [CrossRef]
    Al-Okaily, A.; Al-Okaily, M.; Teoh, A.P. Evaluating ERP systems success: Evidence from Jordanian firms in the age of the digitalbusiness. VINE J. Inf. Knowl. Manag. Syst. 2021, ahead-of-print. [CrossRef]
    Al-Okaily, M.; Al-Okaily, A. An Empirical Assessment of Enterprise Information Systems Success in a Developing Country: TheJordanian Experience. TQM J. 2022, ahead-of-print. [CrossRef]
    Turban, E.; Sharda, R.; Delen, D. Decision Support and Business Intelligence System; Prentice Hall: Englewood Cliffs, NJ, USA, 2011.
    Moro, S.; Cortez, P.; Rita, P. Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latentDirichlet allocation. Expert Syst. Appl. 2015, 42, 1314–1324. [CrossRef]
    Al-Madadha, A.; Al Khasawneh, M.H.; Al Haddid, O.; Al-Adwan, A.S. Adoption of Telecommuting in the Banking Industry:A Technology Acceptance Model Approach. Interdiscip. J. Inf. Knowl. Manag. 2022, 17, 443–470. [CrossRef]
    Aws, A.L.; Ping, T.A.; Al-Okaily, M. Towards business intelligence success measurement in an organization: A conceptual study.J. Syst. Manag. Sci. 2021, 11, 155–170.
    Wells, D. Business Analytics—Getting the Point. BeyeNetwork. 2008. Available online: http://www.b-eyenetwork.com/view/71 33 (accessed on 5 January 2022).
    Shollo, A.; Galliers, R.D. Towards an understanding of the role of business intelligence systems in organisational knowing. Inf. Syst. J. 2016, 26, 339–367. [CrossRef]
    Davenport, T.H. Competing on analytics. Harv. Bus. Rev. 2006, 84, 98.
    Wixom, B.H.; Watson, H.J.;Werner, T. Developing an enterprise business intelligence capability: The Norfolk Southern journey. MIS Q. Exec. 2011, 10, 61–71.
    Turban, E.; Sharda, R.; Aronson, J.E.; King, D. Business Intelligence: A Managerial Approach; Pearson Prentice Hall: Upper Saddle River, NJ, USA, 2008; pp. 58–59.
    Al-Khatib, A.W.; Al-ghanem, E.M. Radical innovation, incremental innovation, and competitive advantage, the moderating role of technological intensity: Evidence from the manufacturing sector in Jordan. Eur. Bus. Rev. 2022, 34, 344–369. [CrossRef]
    Rao, G.K.; Kumar, R. Framework to integrate business intelligence and knowledge management in banking industry. Rev. Bus.Technol. Res. 2011, 4. [CrossRef]
    Vassiliadis, P. A survey of extract–transform–load technology. Int. J. Data Warehous. Min. (IJDWM) 2009, 5, 1–27. [CrossRef]
    Kimball, R.; Ross, M. The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling; JohnWiley & Sons: Hoboken, NJ,USA, 2011.
    Jackson, J. Data mining; a conceptual overview. Commun. Assoc. Inf. Syst. 2002, 8, 19. [CrossRef]
    Han, J.; Kamber, M.; Pei, J. Data Mining: Concepts and Techniques, 3rd ed.; The Morgan Kaufmann Series in Data Management Systems; Morgan Kaufmann: San Francisco, CA, USA, 2011.
    Bhasin, M.L. Data mining: A competitive tool in the banking and retail industries. Chart. Acc. New Delhi 2006, 55, 588.
    Al-Qudah, A.A.; Hamdan, A.; Al-Okaily, M.; Alhaddad, L. The impact of green lending on credit risk: Evidence from UAE’s banks. Environ. Sci. Pollut. Res. 2022. [CrossRef] [PubMed]
    Curko, K.; Bach, M.P.; Radonic, G. Business intelligence and business process management in banking operations. In Proceedings of the 2007 29th International Conference on Information Technology Interfaces, Dubrovnik, Croatia, 25–28 June 2007; pp. 57–62.
    Mortezaei, A.; Sangari, M.S.; Nazari-Shirkouhi, S.; Razmi, J. The Impact of Business Intelligence (BI) Competence on Customer Relationship Management (CRM) Process: An Empirical Investigation of the Banking Industry. J. Inf. Technol. Manag. 2018, 10, 209–234.
    Awa, H.O.; Ukoha, O.; Emecheta, B.C. Using TOE theoretical framework to study the adoption of ERP solution. Cogent Bus. Manag. 2016, 3, 1196571. [CrossRef]
    Rouhani, S.; Ashrafi, A.; Ravasan, A.Z.; Afshari, S. Business intelligence systems adoption model: An empirical investigation. J. Organ. End User Comput. (JOEUC) 2018, 30, 43–70. [CrossRef]
    Malladi, S.; Krishnan, M. Determinants of usage variations of business intelligence & analytics in organizations—An empirical analysis. In Proceedings of the Thirty Fourth International Conference on Information Systems, Milan, Italy, 15–18 December 2013; pp. 1–22.
    Tornatzky, L.; Fleischer, M. The Process of Technology Innovation; Lexington Books: Lexington, MA, USA, 1990.
    Tassey, G. The Roles and Economic Impacts of Technology Infrastructure, Version 3. National Institute of Standards and Technology; 2008. Available online: https://www.nist. gov/system/files/documents/2017/05/09/Measurement_Infrastr_Roles_Impacts_v3.pdf (accessed on 18 June 2022).
    Al-Bashayreh, M.; Almajali, D.; Altamimi, A.; Masa’deh, R.E.; Al-Okaily, M. An Empirical Investigation of Reasons Influencing Student Acceptance and Rejection of Mobile Learning Apps Usage. Sustainability 2022, 14, 4325. [CrossRef]
    Mungree, D.; Rudra, A.; Morien, D. A framework for understanding the critical success factors of enterprise business intelligence implementation. In Proceedings of the 9th Americas Conference on Information Systems, Chicago, IL, USA, 15–17 August 2013.
    Yeoh,W.; Koronios, A. Critical success factors for business intelligence systems. J. Comput. Inf. Syst. 2010, 50, 23–32.
    Ramamurthy, K.R.; Sen, A.; Sinha, A.P. An empirical investigation of the key determinants of data warehouse adoption. Decis.Support Syst. 2008, 44, 817–841. [CrossRef]
    Al-Okaily, M.; Alghazzawi, R.; Alkhwaldi, A.F.; Al-Okaily, A. The effect of digital accounting systems on the decision-making quality in the banking industry sector: A mediated-moderated model. Glob. Knowl. Mem. Commun. 2022, ahead-of-print. [CrossRef]
    Giovinazzo, W. BI: Only as Good as Its Data Quality. Information Management Special Reports. Available online:http://www.information management.com /specialreports /2009_157/business_intelligence_bi_data_quality_governance_decision_making-10015888-1.html (accessed on 3 December 2021).
    Hujran, O.; Alarabiat, A.; Al-Adwan, A.S.; Al-Debei, M. Digitally transforming electronic governments into smart governments: SMARTGOV, an extended maturity model. Inf. Dev. 2021. [CrossRef]
    Eckerson, W. Smart companies in the 21st century: The secrets of creating successful business intelligence solutions. TDWI Rep.Ser. 2003, 7, 1–38.
    Bijker, M.; Hart, M. Factors influencing pervasiveness of organisational business intelligence. In BUSTECH 2013, the Third International Conference on Business Intelligence and Technology; European Research Institute in Service Science (ERISS): Valencia, Spain, 2013; pp. 21–26.
    Watson, H.J.;Wixom, B.H. The current state of business intelligence. Computer 2007, 40, 96–99. [CrossRef]
    LaValle, S.; Lesser, E.; Shockley, R.; Hopkins, M.S.; Kruschwitz, N. Big data, analytics and the path from insights to value. MIT Sloan Manag. Rev. 2011, 52, 21–32.
    Luftman, J.; Derksen, B.; Dwivedi, R.; Santana, M.; Zadeh, H.S.; Rigoni, E. Influential IT management trends: An international study. J. Inf. Technol. 2015, 30, 293–305. [CrossRef]
    AL-Khatib, A.W. Can Big Data Analytics Capabilities Promote a Competitive Advantage? Green Radical Innovation, Green Incremental Innovation and Data-Driven Culture in a Moderated Mediation Model. Bus. Process Manag. J. 2022, ahead-of-print.[CrossRef]
    Al-Omoush, K.S.; Al Attar, M.K.; Saleh, I.H.; Alsmadi, A.A. The drivers of E-banking entrepreneurship: An empirical study. Int. J. Bank Mark. 2019, 38, 485–500. [CrossRef]
    Alsmadi, A.; Alfityani, A.; Alhwamdeh, L.; Al_hazimeh, A.; Al-Gasawneh, J. Intentions to use FinTech in the Jordanian banking industry. Int. J. Data Netw. Sci. 2022, 6, 1351–1358. [CrossRef]
    Themistocleous, M.; Irani, Z.; Kuljis, J.; Love, P.E. Extending the information system lifecycle through enterprise application integration: A case study experience. In Proceedings of the 37th Annual Hawaii International Conference on System Sciences, Big Island, HI, USA, 5–8 January 2004.
    Ramakrishnan, T.; Jones, M.C.; Sidorova, A. Factors influencing business intelligence (BI) data collection strategies: An empirical investigation. Decis. Support Syst. 2012, 52, 486–496. [CrossRef]
  •   کلمات کلیدی به فارسی : هوش تجاری و تجزیه و تحلیل؛ سیستم های اطلاعاتی؛ فناوری اطلاعات؛ بانک صادرات.
  •   چکیده مقاله به انگلیسی : The purpose of this study is to investigate the factors affecting the use of business intelligence and analysis (BIA) in the banking sector. Based on a comprehensive literature review, a theoretical model was developed to examine the impact of three key factors on business intelligence and the adoption and use of analytics in the banking sector, namely technological, organizational and environmental factors. The population of this research includes 117 employees of Saderat Bank of Yazd. The sample size was calculated using Morgan's table and 90 samples were used. The necessary data has been collected through standard questionnaires. Research findings show that technological, organizational and environmental factors have a significant relationship with the use of business intelligence and analysis (BIA).
  •   کلمات کلیدی به انگلیسی : business intelligence and analysis; information systems; Information technology; Export Bank
  •  صفحات : 92-108
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