Int. J. Bus. Intell. Res.最新文献

{"title":"Best Practices for Culturally Sensitive Data Visualizations","authors":"Michael S. Gendron, Christopher Hutwelker, Krzysztof Kisz","doi":"10.4018/IJBIR.2016070101","DOIUrl":"https://doi.org/10.4018/IJBIR.2016070101","url":null,"abstract":"Organizations are undergoing significant changes in their business environment. Competing on a global scale means that organizations must better understand cultural issues of their customers, employees, and key stakeholders. As business analytics grows in importance, managers are sharing data visualizations to a variety of different cultures and beliefs. When creating culturally sensitive data visualizations, a set of best practices is required to assist managers to make timely and accurate decisions with the least possible cultural bias. This article aims to develop these best practices by analyzing cultural traits, communication habits, and other differences between the western and eastern global regions.","PeriodicalId":404696,"journal":{"name":"Int. J. Bus. Intell. Res.","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123184117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From General to Specific: Understanding Individual Characteristics and their Relationship with Neural Recordings during Media Consumption","authors":"Adriane B. Randolph, J. Burkhalter","doi":"10.4018/IJBIR.2016070103","DOIUrl":"https://doi.org/10.4018/IJBIR.2016070103","url":null,"abstract":"Neurophysiological methods and recording techniques are increasingly being embraced to enhance business intelligence about consumers' behavior. Researchers have found evidence linking individual characteristics with variations in mental processing and user literacy for neurally-controlling computer interfaces. The work presented here seeks to better understand the relationship between individual characteristics and neural activations as recorded by electroencephalography EEG while participants viewed certain types of media online. A study conducted with 21 right-handed individuals demonstrates that the individual characteristics of smoking, hand dexterity, and experience playing certain types of video games correlate with neural activations in the frontal lobe, reflecting arousal and engagement. These correlations indicate the need to control for particular participant characteristics when conducting studies using neurophysiological recording techniques and expand considerations for incorporating such novel, yet insightful tools into the business intelligence practice.","PeriodicalId":404696,"journal":{"name":"Int. J. Bus. Intell. Res.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128747527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An End-User Metadata Model on Object and Element Levels for Business Intelligence Users","authors":"Y. Verbitskiy, W. Yeoh","doi":"10.4018/IJBIR.2016070104","DOIUrl":"https://doi.org/10.4018/IJBIR.2016070104","url":null,"abstract":"The effective use of metadata can offer end users an improved understanding and greater level of assurance during the Business Intelligence BI report analysis process. This paper reports key findings from a case study that investigates critical end-user metadata issues in a large Australian organization. The findings led to the development of an end-user metadata model on object report and cube and element term and column levels, which can support effective BI use and potentially increase user satisfaction at the case organization. The adoption and use of BI applications by business stakeholders may be improved by incorporating the end-user metadata model.","PeriodicalId":404696,"journal":{"name":"Int. J. Bus. Intell. Res.","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132953633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Depicting Data Quality Issues in Business Intelligence Environment through a Metadata Framework","authors":"Te-Wei Wang, Y. Verbitskiy, W. Yeoh","doi":"10.4018/IJBIR.2016070102","DOIUrl":"https://doi.org/10.4018/IJBIR.2016070102","url":null,"abstract":"Modern business intelligence systems depend highly on high quality data. The core of data quality management is to identify all possible sources of data quality problems. To achieve this goal, an extensive metadata infrastructure is the most promising solution. Through theoretical metadata model investigation, the authors identified a set of data quality dimensions by carefully examining the data quality management principles and applied those principles to current BI environment. They summarize the analysis by proposing a BI data quality framework.","PeriodicalId":404696,"journal":{"name":"Int. J. Bus. Intell. Res.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134207213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Virtuous Business Intelligence","authors":"N. McBride","doi":"10.4018/IJBIR.2015070101","DOIUrl":"https://doi.org/10.4018/IJBIR.2015070101","url":null,"abstract":"This paper examines three approaches to ethics and focuses on the development of character and the practice of virtue in business intelligence BI. The paper describes BI as a tool for mediating the relationships between pairs of stakeholders such as management and customer. Three aspects of the relationship which benefit ethically from the practice of virtues are discussed: the purpose of the BI, the prejudices behind the BI and the power of the stakeholders. The connection between the ethics of BI and the corporate ethics is discussed. Without the practice of virtues, BI may be recruited to support corporate vices of exploitation, exposure, exclusion, coercion, control and concealment. The paper seeks to highlight the importance of ethical issues in BI practice and suggests the development of an ethical balanced scorecard as a vehicle for developing ethical senstitivity.","PeriodicalId":404696,"journal":{"name":"Int. J. Bus. Intell. Res.","volume":"157 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133673787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Market Sensing Capability, Entrepreneurial Orientation, Product Innovativeness Success, Speed to Market and SMEs Performance","authors":"Elia Ardyan","doi":"10.4018/IJBIR.2015070102","DOIUrl":"https://doi.org/10.4018/IJBIR.2015070102","url":null,"abstract":"The purpose of this research is to determine market sensing capability, product innovation success, speed to market, and entrepreneurial orientation on SME's performance. The sample of this research are 168 SME's owner or manager in Java, Indonesia. This research uses Structural Equation Model to hypothesis test and uses AMOS 21 in analysis data. Results of the research find that Entrepreneurial orientation and product innovativeness success have positive and significant effect on SME's performance but market sensing capability and speed to market have no significant effect. Another result of this study finds that market sensing capability has significant effect on speed to market and product innovativeness success but entrepreneurial orientation has no significant effect on product innovativeness effect.","PeriodicalId":404696,"journal":{"name":"Int. J. Bus. Intell. Res.","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127337134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Big Data Business Intelligence in Bank Risk Analysis","authors":"Nayem Rahman, Shane Iverson","doi":"10.4018/IJBIR.2015070104","DOIUrl":"https://doi.org/10.4018/IJBIR.2015070104","url":null,"abstract":"This paper provides an overview of big data technologies and best practices from the standpoint of business intelligence BI applications in the banking industry. The authors discussed current challenges in banking industry that could be addressed by using big data technologies. Based on their research, they provided a list of big data tools and technologies in terms of an ecosystem that are suitable for real-time data processing and capable in bank fraud detection and prevention, and other bank risk analysis. They highlighted how business intelligence could be leveraged with the help of emerging big data technologies.","PeriodicalId":404696,"journal":{"name":"Int. J. Bus. Intell. Res.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127753023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Effect of Behavioral Factors on Stock Price Prediction using Generalized Regression and Backpropagation Neural Networks Models","authors":"P. Hanafizadeh, A. Hashemi","doi":"10.4018/IJBIR.2014100104","DOIUrl":"https://doi.org/10.4018/IJBIR.2014100104","url":null,"abstract":"With regard to the importance of behavioral factors on stock price, which has been mentioned by researchers, this study includes four behavioral factors overconfidence, representativeness, over reaction and under reaction in addition to fundamental and technical factors as inputs for neural network models to evaluate the effectiveness of these behavioral factors on stock price prediction accuracy of 10 companies of DJIA index. Multi-layer perceptron MlP and generalized regression neural networks are used in this research as models to find the best model for each company based on unique characteristics of its own financial data. This study shows the mentioned behavioral factors are effective on accuracy of predictions of 8 out of 10 companies.","PeriodicalId":404696,"journal":{"name":"Int. J. Bus. Intell. Res.","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115655711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Business Intelligence as a Service: A Vendor's Approach","authors":"M. Spruit, T. D. Boer","doi":"10.4018/IJBIR.2014100103","DOIUrl":"https://doi.org/10.4018/IJBIR.2014100103","url":null,"abstract":"Demand for business intelligence BI applications continues to grow even at a time when demand for most information technology IT products is low, showing the importance of BI products for a modern organization. However, globalization changes the way organizations use BI, where geographic location and time independency is becoming more and more important. Gartner's hype-cycle on BI depicts the technology of BI as a Service as being almost on top of the hype cycle, indicating there are high expectations of this new technology. This research advances on existing literature on business intelligence and cloud computing from a development perspective by introducing the concept of business intelligence as a service BIaaS. The most important deliverable is the BIaaS capability maturity model CMM that is introduced here. The BIaaS CMM explains the conceptual model of BIaaS by the creation of the first BIaaS capability model containing key capabilities of BIaaS. The capability model is further enhanced with maturity levels depicting the importance of each BIaaS capability, a maturity matrix suggesting a roadmap for BIaaS solution development, and a BIaaS assessment model introducing a tool for finding problem areas in existing BIaaS solutions. The developed BIaaS CMM ought to support starting BIaaS vendors to develop BIaaS solutions by providing an assessment tool for BIaaS solutions. The assessment outcome provides the current maturity of the BIaaS solution and also includes problem areas for solution improvement. The introduction of the CApability Maturity Positioning CAMP method for the development of a maturity matrix, which results in the BIaaS maturity model, is significantly different from conventional maturity modeling. To calculate the weight of each capability from the BIaaS capability model, a thorough product review of existing business intelligence and cloud computing products is performed. Analysis of the results and normalizing the outcome of that analysis together with the introduction of a calculation mapping, is input for the creation of the maturity matrix. The maturity matrix is the essential foundation for the developed business intelligence as a Service capability maturity model, which is the main deliverable of this research.","PeriodicalId":404696,"journal":{"name":"Int. J. Bus. Intell. Res.","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127276196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personal Financial Aggregation and Social Media Mining: A New Framework for Actionable Financial Business Intelligence (AFBI)","authors":"Vipul Gupta, Sameer Khanna, Iljoo Kim","doi":"10.4018/IJBIR.2014100102","DOIUrl":"https://doi.org/10.4018/IJBIR.2014100102","url":null,"abstract":"Consumers have been banking and trading online for several years now. More ambitious and tech savvy consumers have also been constructing an overview of their financial life by using Personal Finance software like Quicken and online tools such as Yodlee and Mint.com. Since late 1999, Personal Financial Aggregators PFAs have started offering internet based services to automate this process of account aggregation. This web account aggregation allows individuals to log onto one Web site and view all of their online accounts in one place. Online accounts that can be aggregated include financial sites bank, credit card, brokerage, insurance, etc. as well as lifestyle-based sites travel awards, email, chat rooms, etc.. The idea behind Personal Financial Aggregation is to offer consumers their own personal portal from which they can see all their finances at a glance, balance and rebalance accounts, make investments, pay bills, etc. In addition to this Web data aggregation, consumers are relying on social media sites such as facebook, tweeter and other internet forums to get financial advice from each other and also to critique various financial products and services. As a result, many Financial Institutions FIs are using social media analysis and mining to shape their businesses. FIs include consumer banks, brokerages, insurance, wealth management firms, etc. This paper presents a framework for financial institutions that combines social media mining, web mining, online advice engines, and web aggregation. This framework can be utilized by FIs to analyze online buzz about their products/services and combine those insights with web aggregation and online advice to create different revenue streams and to offer personalized bundled products and services. The authors conducted interviews with various executives at the Global Financial institutions and insurance companies to test and validate this framework. A comprehensive review of top service providers and vendors that can enable and drive this framework is also discussed in this paper, followed by managerial implications, benefits and challenges.","PeriodicalId":404696,"journal":{"name":"Int. J. Bus. Intell. Res.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126581151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}