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نتیجه جستجو - هوش تجاری

تعداد مقالات یافته شده: 81
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1 چارچوب حاکمیتی هوش تجاری در دانشگاه: مطالعه موردی دانشگاه دو لا کاستا
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 25
دانشگاه ها و شرکت ها دارای فرآیندهای تصمیم گیری هستند که به آنها اجازه می دهد تا به اهداف سازمانی دست پیدا کنند. در حال حاضر، تحلیل داده ها نقش مهمی در ایجاد دانش، بدست آوردن الگوهای مهم و پیش بینی استراتژی ها ایفا می کنند.این مقاله طراحی چارچوب نظارت هوش تجاری را برای دانشگاه دو لا کاستا ارائه کرده است که به آسانی برای سازمان های دیگر هم قابل استفاده است. برای این منظور، تشخیص انجام شده به منظور شناسایی میزان بلوغ تحلیلی انجام شده است. با استفاده از این چشم انداز، مدلی برای تقویت فرهنگ سازمانی ، زیر ساختارها، مدیریت داده، تحلیل داده و نظارت ارائه شده است.این مدل در بر گیرنده تعریف چارچوب نظارتی، اصول هدایت کننده، استراتژی ها، نهادهای تصمیم گیرنده و نقش ها می باشد. بنابراین، این چارچوب برای استفاده از کنترل های موثر جهت اطمینان از موفقیت پروژه های هوش تجاری و دست یابی به اهداف برنامه توسعه همراه با چسم انداز تحلیلی سازمان ارائه شده است.
کلمات کلیدی: هوش تجاری | نظارت | دانشگاه | تحلیل | تصمیم گیری
مقاله ترجمه شده
2 Managing complex engineering projects: What can we learn from the evolving digital footprint?
مدیریت پروژه های پیچیده مهندسی: از ردپای دیجیتال در حال تحول چه می توانیم یاد بگیریم؟-2020
The challenges of managing large complex engineering projects, such as those involving the design of infrastructure, aerospace and industrial systems; are widely acknowledged. While there exists a mature set of project management tools and methods, many of todays projects overrun in terms of both time and cost. Existing literature attributes these overruns to factors such as: unforeseen dependencies, a lack of understanding, late changes, poor communication, limited resource availability (inc. personnel), incomplete data and aspects of culture and planning. Fundamental to overcoming these factors belies the challenge of how management information relating to them can be provided, and done so in a cost eff ;ective manner. Motivated by this challenge, recent research has demonstrated how management information can be automatically generated from the evolving digital footprint of an engineering project, which encompasses a broad range of data types and sources. In contrast to existing work that reports the generation, verification and application of methods for generating management information, this paper reviews all the reported methods to appraise the scope of management information that can be automatically generated from the digital footprint. In so doing, the paper presents a reference model for the generation of managerial information from the digital footprint, an appraisal of 27 methods, and a critical reflection of the scope and generalisability of data-driven project management methods. Key findings from the appraisal include the role of email in providing insights into potential issues, the role of computer models in automatically eliciting process and product dependencies, and the role of project documentation in assessing project norms. The critical reflection also raises issues such as privacy, highlights the enabling technologies, and presents opportunities for new Business Intelligence tools that are based on real-time monitoring and analysis of digital footprints.
Keywords: Big Data | Project Management | Business Intelligence | Knowledge Workers
مقاله انگلیسی
3 Managing complex engineering projects: What can we learn from the evolving digital footprint?
مدیریت پروژه های مهندسی پیچیده: از رد پای دیجیتال در حال تکامل چه می توان یاد گرفت؟-2020
The challenges of managing large complex engineering projects, such as those involving the design of infra- structure, aerospace and industrial systems; are widely acknowledged. While there exists a mature set of project management tools and methods, many of todays projects overrun in terms of both time and cost. Existing literature attributes these overruns to factors such as: unforeseen dependencies, a lack of understanding, late changes, poor communication, limited resource availability (inc. personnel), incomplete data and aspects of culture and planning. Fundamental to overcoming these factors belies the challenge of how management in- formation relating to them can be provided, and done so in a cost effective manner. Motivated by this challenge, recent research has demonstrated how management information can be automatically generated from the evolving digital footprint of an engineering project, which encompasses a broad range of data types and sources. In contrast to existing work that reports the generation, verification and application of methods for generating management information, this paper reviews all the reported methods to appraise the scope of management information that can be automatically generated from the digital footprint. In so doing, the paper presents a reference model for the generation of managerial information from the digital footprint, an appraisal of 27 methods, and a critical reflection of the scope and generalisability of data-driven project management methods. Key findings from the appraisal include the role of email in providing insights into potential issues, the role of computer models in automatically eliciting process and product dependencies, and the role of project documentation in assessing project norms. The critical reflection also raises issues such as privacy, highlights the enabling technologies, and presents opportunities for new Business Intelligence tools that are based on real-time monitoring and analysis of digital footprints.
Keywords: Big Data | Project Management | Business Intelligence | Knowledge Workers
مقاله انگلیسی
4 Big Data Compliance for Innovative Clinical Models
مطابقت داده های بزرگ برای مدل های بالینی نوآورانه-2018
In the healthcare sector, information is the most important aspect, and the human body in particular is the major source of data production: as a result, the new challenge for world healthcare is to take advantage of these huge amounts of data de-structured among themselves. In order to benefit from this advantage, technology offers a solution called Big Data Analysis that allows the management of large amounts of data of a different nature and coming from different sources of a “computerized” healthcare, as there are considerable changes made by the input of digital technology in all major health areas. Clinical intelligence consists of all the analytical methods made possible through the use of computer tools, in all the processes and disciplines of extraction and transformation of crude clinical data into significant insights, new purposes and knowledge that provide greater clinical efficacy and best health pronouncements about past performance, current operations and future events. It can therefore be stated that clinical intelligence, through patient data analysis, will become a standard operating procedure that will address all aspects of care delivery. The purpose of this paper is to present clinical intelligence approaches through Data Mining and Process Mining, showing the differences between these two methodologies applied to perform “real process” extraction to be compared with the procedures in the corporate compliance template (the so called “Model 231”) by “conformance checking”.
Keywords: Big Data healthcare , Clinical intelligence , Data Mining , Process Mining , Business intelligence
مقاله انگلیسی
5 The power of a thumbs-up: Will e-commerce switch to social commerce?
توان یک توافق: آیا تجارت الکترونیک قابل تبدیل به تجارت اجتماعی است؟-2018
By taking advantage of social networking capabilities, social commerce provides features that encourage customers to share their personal experiences. The popularity of online social networks has driven the purchase decisions of buyers on social commerce sites, but few studies have explored why consumers switch between e-commerce (product-centered) and social (social-centered) commerce sites. In applying the push–pull–mooring model, the objective of this study was to gain an understanding of specifically how push, pull, and mooring factors shape their switching intentions. The findings revealed that push effect, in terms of low transaction efficiency, drives customers away from e-commerce sites, whereas the pull effects, including social presence, social support, social benefit, and self-presentation, attract customers to social commerce sites. Moreover, mooring effects, including conformity and personal experience, strengthened consumers’ behavior in switching between e-commerce and social commerce sites. Besides, conformity was also found to moderate the influences of social presence, social support, social benefit, and efficiency on switching intention, whereas personal experience moderated the effects of social benefit, self-presentation, and efficiency on switching intention. Such an understanding assists online retailers in understanding online shoppers’ switching behaviors, and thus turning social interactions into profits and sales.
keywords: Switching intention| Push–pull–mooring framework| Social commerce|E-commerce
مقاله انگلیسی
6 The impact of past performance on information valuation in virtual communities: Empirical study in online stock message boards
تاثیر عملکرد گذشته بر روی ارزش گذاری اطلاعات در جوامع مجازی: مطالعه تجربی در صفحه های پیام رسانی آنلاین بورس-2018
Unlike other types of virtual communities, individuals participate in online stock message boards for their material needs rather than non-material needs (e.g., sense of belonging). They may seek and read others’ opinions to make better investment performances. The value of information in online stock message boards may vary from person to person according to their past investment experiences. However, little is known regarding how their past investment performance influences the value of others’ opinions. Therefore, our study investigates how individuals process others’ opinions on stock message boards for their investment decisions when they have different levels of past investment performance. We proposed the unique research model with two paths consisting of both online stock message board factors and individual factors to determine continuous use of online stock message boards. We conducted SEM analysis with 452 questionnaire data. The results, first, showed that message boards factors (e.g., satisfaction using others’ opinions) have a positive impact on continuous intention to use while their own satisfaction with past investment activities has a negative impact on continuous intention to use. In addition, we believe that this is one of the few papers to examine the moderating role of self-attribution bias on the effects of stock investment performance. Our results indicated that investors with strong self-attribution bias lower the usefulness of opinions when losing money while they increased confidence in their investment-related abilities when achieving a profit from investment. This study will help to support and extend the theory of IS continuance model while providing practical insights for online stock message board managers by suggesting ways to vitalize online stock message boards.
keywords: Information processing behavior| Stock message boards| Self-attribution| IS continues model
مقاله انگلیسی
7 Business intelligence for patient-centeredness: A systematic review
هوش تجاری برای بیمار محوری : مرور سیستماتیک -2018
This study utilized a systematic review to provide an overall understanding of how aca demic research can be incorporated into business intelligence (BI) to ensure patient centeredness (PC). Using the BI maturity model, this study analyzed findings of previous studies from four time periods within the period 2000–2016 to determine how BI can facil itate PC through organization, human-orientation, and technology, as well as other PC specific conditions. Our results indicate that the number of BI applications that include PC have continued to grow since 2010, and that they primarily focus on the dimensions of organization, humanism, and PC-specific conditions; additionally, we noted that a time-based correlation exists between the related results. This study then explored the extent to which BI supports the subdimensions of PC (e.g., principles, enablers, and activ ities). Finally, future research focuses and directions were proposed.
Keywords: Business intelligence ، Patient-centeredness ، Systematic review ، Clinician–patient relationship ، Patient involvement ، Health data
مقاله انگلیسی
8 Research Landscape of Business Intelligence and Big Data analytics: A bibliometrics study
چشم انداز تحقیقات هوش تجاری و تحلیل داده های بزرگ: مطالعه کتابشناختی-2018
Business Intelligence that applies data analytics to generate key information to support business deci sion making, has been an important area for more than two decades. In the last five years, the trend of “Big Data” has emerged and become a core element of Business Intelligence research. In this article, we review academic literature associated with “Big Data” and “Business Intelligence” to explore the devel opment and research trends. We use bibliometric methods to analyze publications from 1990 to 2017 in journals indexed in Science Citation Index Expanded (SCIE), Social Science Citation Index (SSCI) and Arts & Humanities Citation Index (AHCI). We map the time trend, disciplinary distribution, high-frequency keywords to show emerging topics. The findings indicate that Computer Science and management in formation systems are two core disciplines that drive research associated with Big Data and Business Intelligence. “Data mining”, “social media” and “information system” are high frequency keywords, but “cloud computing”, “data warehouse” and “knowledge management” are more emphasized after 2016.
Keywords: Bibliometrics ، Big Data analytics ، Business Intelligence ، Research trend
مقاله انگلیسی
9 An event-extraction approach for business analysis from online Chinese news
یک دیدگاه استخراج رویداد برای تحلیل کسب و کار از اخبار آنلاین چینی-2018
Extracting events from business news aids users to perceive market trends, be aware of competitors’ strategies, and to make valuable investment decisions. Prior research lacks event extraction in the area of business and event based business analysis, especially in Chinese language. We propose a novel business event-extraction approach integrating patterns, machine learning models and word embedding technology in deep learning, which is applied to extract events from online Chinese news. Word embedding and a semantic lexicon are utilized to extend an event trigger dictionary with high accuracy. Then the trigger features in the dictionary are introduced into a machine learning classification algorithm to implement more refined event-type recognition. Based on a scalable pattern tree, the event type that is discovered is used to find the best-suited pattern for extracting event elements from online news. Experimental results show the effectiveness of the proposed approach. In addition, empirical studies demonstrate the practical value of extracted events, especially in finding the relationships between news events and excess returns for stock, and analyzing industry trends based on events in China.
keywords: Business events |Business intelligence |Chinese text analytics |Event extraction |Explanatory econometrics |Machine learning models |Natural language processing |Online news |Patterns |Word embedding
مقاله انگلیسی
10 Human resources for Big Data professions: A systematic classification of job roles and required skill sets
مدیریت منابع انسانی برای تخصص های داده های بزرگ: یک دسته بندی سیستماتیک نقش های شغلی و سری مهارتهای مورد نیاز-2018
The rapid expansion of Big Data Analytics is forcing companies to rethink their Human Resource (HR) needs. However, at the same time, it is unclear which types of job roles and skills constitute this area. To this end, this study pursues to drive clarity across the heterogeneous nature of skills required in Big Data professions, by analyzing a large amount of real-world job posts published online. More precisely we: 1) identify four Big Data ‘job families’; 2) recognize nine homogeneous groups of Big Data skills (skill sets) that are being demanded by companies; 3) characterize each job family with the appropriate level of competence required within each Big Data skill set. We propose a novel, semi-automated, fully replicable, analytical methodology based on a combination of machine learning algorithms and expert judgement. Our analysis leverages a significant amount of online job posts, obtained through web scraping, to generate an intelligible classification of job roles and skill sets. The results can support business leaders and HR managers in establishing clear strategies for the acquisition and the development of the right skills needed to leverage Big Data at best. Moreover, the structured classification of job families and skill sets will help establish a common dictionary to be used by HR recruiters and education providers, so that supply and demand can more effectively meet in the job marketplace.
keywords: Big Data |Business intelligence |Human resources management |Machine learning |Topic modeling
مقاله انگلیسی
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