4,356 research outputs found

    Valid knowledge: The economy and the academy

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    The future of Western universities as public institutions is the subject of extensive continuing debate, underpinned by the issue of what constitutes valid knowledge. Where in the past only prepositional knowledge codified by academics was considered valid, in the new economy enabled by information and communications technology, the procedural knowledge of expertise has become a key commodity, and the acquisition of this expertise is increasingly seen as a priority by intending university students. Universities have traditionally proved adaptable to changing circumstances, but there is little evidence to date of their success in accommodating to the scale and unprecedented pace of change of the Knowledge Economy or to the new vocationally-oriented demands of their course clients. And in addition to these external factors, internal ones are now at work. Recent developments in eLearning have enabled the infiltration of commercial providers who are cherry-picking the most lucrative subject areas. The prospect is of a fracturing higher education system, with the less adaptable universities consigned to a shrinking public-funded sector supporting less vocationally saleable courses, and the more enterprising universities developing commercial partnerships in eLearning and knowledge transfer. This paper analyses pressures upon universities, their attempts to adapt to changing circumstances, and the institutional transformations which may result. It is concluded that a diversity of partnerships will emerge for the capture and transfer of knowledge, combining expertise from the economy with the conceptual frameworks of the academy

    Monitoring E-commerce Adoption from Online Data

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    [EN] The purpose of this paper is to propose an intelligent system to automatically monitor the firms¿ engagement in e-commerce by analyzing online data retrieved from their corporate websites. The design of the proposed system combines web content mining and scraping techniques with learning methods for Big Data. Corporate websites are scraped to extract more than 150 features related to the e-commerce adoption, such as the presence of some keywords or a private area. Then, these features are taken as input by a classification model that includes dimensionality reduction techniques. The system is evaluated with a data set consisting of 426 corporate websites of firms based in France and Spain. The system successfully classified most of the firms into those that adopted e-commerce and those that did not, reaching a classification accuracy of 90.6%. This demonstrates the feasibility of monitoring e-commerce adoption from online data. Moreover, the proposed system represents a cost-effective alternative to surveys as method for collecting e-commerce information from companies, and is capable of providing more frequent information than surveys and avoids the non-response errors. This is the first research work to design and evaluate an intelligent system to automatically detect e-commerce engagement from online data. This proposal opens up the opportunity to monitor e-commerce adoption at a large scale, with highly granular information that otherwise would require every firm to complete a survey. In addition, it makes it possible to track the evolution of this activity in real time, so that governments and institutions could make informed decisions earlier.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness with Grant TIN2013-43913-R, and by the Spanish Ministry of Education with Grant FPU14/02386.Blazquez, D.; Domenech, J.; Gil, JA.; Pont Sanjuan, A. (2018). Monitoring E-commerce Adoption from Online Data. Knowledge and Information Systems. 1-19. https://doi.org/10.1007/s10115-018-1233-7S119Arias M, Arratia A, Xuriguera R (2013) Forecasting with Twitter data. ACM Trans Intell Syst Technol 5:1–24. https://doi.org/10.1145/2542182.2542190Arora SK, Youtie J, Shapira P, Gao L, Ma T (2013) Entry strategies in an emerging technology: a pilot web-based study of graphene firms. Scientometrics 95:1189–1207. https://doi.org/10.1007/s11192-013-0950-7Barcaroli G, Nurra A, Scarnò M, Summa D (2014) Use of web scraping and text mining techniques in the istat survey on information and communication technology in enterprises. In: Proceedings of quality conference, pp 33–38Barcaroli G, Nurra A, Salamone S, Scannapieco M, Scarnò M, Summa D (2015) Internet as data source in the istat survey on ict in enterprises. Austrian J Stat 44:31. https://doi.org/10.17713/ajs.v44i2.53Blazquez D, Domenech J (2014) Inferring export orientation from corporate websites. Appl Econ Lett 21:509–512. https://doi.org/10.1080/13504851.2013.872752Blazquez D, Domenech J (2017) Big data sources and methods for social and economic analyses. Technol Forecast Soc Change. https://doi.org/10.1016/j.techfore.2017.07.027Blazquez D, Domenech J (2017) Web data mining for monitoring business export orientation. Technol Econ Dev Econ. https://doi.org/10.3846/20294913.2016.1213193Bollen J, Mao H, Zeng X (2011) Twitter mood predicts the stock market. J Comput Sci 2:1–8. https://doi.org/10.1016/j.jocs.2010.12.007Bughin J (2015) Google searches and twitter mood: nowcasting telecom sales performance. NETNOMICS: Econ Res Electron Netw 16:87–105. https://doi.org/10.1007/s11066-015-9096-5Bulligan G, Marcellino M, Venditti F (2015) Forecasting economic activity with targeted predictors. Int J Forecast 31:188–206. https://doi.org/10.1016/j.ijforecast.2014.03.004Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) Smote: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357Choi H, Varian H (2009) Predicting the present with Google Trends. http://static.googleusercontent.com/external_content/untrusted_dlcp/www.google.com/en//googleblogs/pdfs/google_predicting_the_present.pdf . Accessed 9 Dec 2016Choi H, Varian H (2012) Predicting the present with Google Trends. Econ Record 88:2–9. https://doi.org/10.1111/j.1475-4932.2012.00809.xCooley R, Mobasher B, Srivastava J (1997) Web mining: information and pattern discovery on the world wide web. In: Proceedings of the ninth ieee international conference on tools with artificial intelligence. IEEE Computer Society, Newport Beach, CA, USA, pp 558–567. https://doi.org/10.1109/TAI.1997.632303Domenech J, de la Ossa B, Pont A, Gil JA, Martinez M, Rubio A (2012) An intelligent system for retrieving economic information from corporate websites. In: IEEE/WIC/ACM international joint conferences on web intelligence (WI) and intelligent agent technologies (IAT), Macau, China, pp 573–578. https://doi.org/10.1109/WI-IAT.2012.92Ecommerce Foundation (2016) Global B2C E-commerce Report 2016Edelman B (2012) Using internet data for economic research. J Econ Perspect 26:189–206. https://doi.org/10.1257/jep.26.2.189Einav L, Levin J (2014) The data revolution and economic analysis. Innov Policy Econ 14:1–24. https://doi.org/10.1086/674019Eurostat (2008) NACE Rev. 2 Statistical classification of economic activities in the European Communities. EUROSTAT Methodologies and Working papers, Office for Official Publications of the European Communities, LuxembourgEurostat (2016) ICT usage and e-commerce in enterprises. http://ec.europa.eu/eurostat/statistics-explained/index.php/E-commerce_statistics . Accessed 12 Dec 2016Fan J, Han F, Liu H (2014) Challenges of Big Data analysis. Natl Sci Rev 1:293–314. https://doi.org/10.1093/nsr/nwt032Fondeur Y, Karamé F (2013) Can Google data help predict French youth unemployment? Econ Model 30:117–125. https://doi.org/10.1016/j.econmod.2012.07.017Griffis SE, Goldsby TJ, Cooper M (2003) Web-based and mail surveys: A comparison of response, data, and cost. J Bus Logist 24:237–258. https://doi.org/10.1002/j.2158-1592.2003.tb00053.xHand C, Judge G (2012) Searching for the picture: forecasting UK cinema admissions using google trends data. Appl Econ Lett 19:1051–1055. https://doi.org/10.1080/13504851.2011.613744Hao W, Walden J, Trenkamp C (2013) Accelerating e-commerce sites in the cloud. 10th Anual Consumer Communications and Networking Conference (CCNC). IEEE, IEEE, pp 605–608Hasan B (2016) Perceived irritation in online shopping: the impact of website design characteristics. Comput Hum Behav 54:224–230. https://doi.org/10.1016/j.chb.2015.07.056Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference and prediction, 2nd edn. Springer, BerlinHastie T, Tibshirani R, Friedman J (2013) The elements of statistical learning: data mining, inference and prediction, 3rd edn. Springer, BerlinHe LJ (2012) The application of web mining ontology system in e-commerce based on FCA, vol 149. Springer, Berlin, pp 429–432. https://doi.org/10.1007/978-3-642-28658-2_65Hernández B, Jiménez J, Martín MJ (2009) Key website factors in e-business strategy. Int J Inf Manag 29:362–371. https://doi.org/10.1016/j.ijinfomgt.2008.12.006INE (2016) Encuesta de uso de TIC y Comercio Electrónico en las empresas 2015-2016. http://ine.es/dynt3/inebase/?path=/t09/e02/a2015-2016 , http://ine.es/dynt3/inebase/?path=/t09/e02/a2015-2016 . Accessed 9 Oct 2016James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning, vol 112. Springer Texts in Statistics. Springer, New YorkJungherr A, Jürgens P (2013) Forecasting the pulse. Internet Res 23:589–607. https://doi.org/10.1108/IntR-06-2012-0115Kim T, Hong J, Kang P (2015) Box office forecasting using machine learning algorithms based on SNS data. Int J Forecast 31:364–390. https://doi.org/10.1016/j.ijforecast.2014.05.006Kosala R, Blockeel H (2000) Web mining research. ACM SIGKDD Explor Newsl 2:1–15. https://doi.org/10.1145/360402.360406Kuhn M, Johnson K (2013) Applied predictive modeling, vol 810. Springer, BerlinKulkarni G, Kannan P, Moe W (2012) Using online search data to forecast new product sales. Decision Support Syst 52:604–611. https://doi.org/10.1016/j.dss.2011.10.017Lee Y, Kozar KA (2006) Investigating the effect of website quality on e-business success: an analytic hierarchy process (ahp) approach. Decision Support Syst 42:1383–1401. https://doi.org/10.1016/j.dss.2005.11.005Li Y, Arora S, Youtie J, Shapira P (2016) Using web mining to explore Triple Helix influences on growth in small and mid-size firms. Technovation. https://doi.org/10.1016/j.technovation.2016.01.002Menardi G, Torelli N (2014) Training and assessing classification rules with imbalanced data. Data Min Knowl Discov 28:92–122. https://doi.org/10.1007/s10618-012-0295-5Munzert S, Rubba C, Meißner P, Nyhuis D (2015) Automated data collection with R: a practical guide to web scraping and text mining. Wiley, ChichesterOliveira T, Martins MF (2010) Understanding e-business adoption across industries in European countries. Ind Manag Data Syst 110:1337–1354. https://doi.org/10.1108/02635571011087428ONS (2016) E-commerce and ICT Activity: 2015. https://www.ons.gov.uk/businessindustryandtrade/itandinternetindustry/bulletins/ecommerceandictactivity/2015 . Accessed 5 Dec 2016Ordanini A, Rubera G (2010) How does the application of an it service innovation affect firm performance? A theoretical framework and empirical analysis on e-commerce. Inf Manag 47:60–67. https://doi.org/10.1016/j.im.2009.10.003Peytchev A (2013) Consequences of survey nonresponse. Ann Am Acad Political Soc Sci 645:88–111. https://doi.org/10.1177/0002716212461748Poggi N, Carrera D, Gavaldà R, Ayguadé E, Torres J (2014) A methodology for the evaluation of high response time on e-commerce users and sales. Inf Syst Front 16:867–885. https://doi.org/10.1007/s10796-012-9387-4Pokorný J, Škoda P, Zelinka I, Bednárek D, Zavoral F, Kruliš M, Šaloun P (2015) Big Data movement: a challenge in data processing, Studies in Big Data, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-319-11056-1_2R Core Team (2015) R: a language and environment for statistical computing, Vienna, Austria. https://www.R-project.org/ . Accessed 25 Mar 2015Roche X (2014) HTTrack. http://www.httrack.com . Accessed 10 Nov 2014Rodríguez-Ardura I, Meseguer-Artola A (2010) Toward a longitudinal model of e-commerce: environmental, technological, and organizational drivers of B2C adoption. Inf Soc 26:209–227. https://doi.org/10.1080/01972241003712264Rosaci D, Sarnè G (2014) Multi-agent technology and ontologies to support personalization in B2C e-commerce. Electron Commer Res Appl 13:13–23. https://doi.org/10.1016/j.elerap.2013.07.003Shih HY (2012) The dynamics of local and interactive effects on innovation adoption: the case of electronic commerce. J Eng Technol Manag 29:434–452. https://doi.org/10.1016/j.jengtecman.2012.06.001Sohrabi B, Mahmoudian P, Raeesi I (2012) A framework for improving e-commerce websites usability using a hybrid genetic algorithm and neural network system. Neural Comput Appl 21:1017–1029. https://doi.org/10.1007/s00521-011-0674-7Stoll KU, Hepp M (2013) Detection of e-commerce systems with sparse features and supervised classification. In: 10th international conference on e-business engineering (ICEBE), IEEE, Coventry, United Kingdom, pp 199–206. https://doi.org/10.1109/ICEBE.2013.30Suchacka G, Borzemski L (2013) Simulation-based performance study of e-commerce Web server system-results for FIFO scheduling. Springer, Berlin, pp 249–259Swets J (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293. https://doi.org/10.1126/science.3287615Thorleuchter D, Van den Poel D (2012) Predicting e-commerce company success by mining the text of its publicly-accessible website. Expert Syst Appl 39:13,026–13,034. https://doi.org/10.1016/j.eswa.2012.05.096Tibshirani R (1996) Regression shrinkage and selection via the Lasso. J R Stat Soc Ser B (Methodol) 58:267–288Varian HR (2014) Big Data: new tricks for econometrics. J Econ Perspect 28:3–28. https://doi.org/10.1257/jep.28.2.3Vicente MR, López-Menéndez AJ, Pérez R (2015) Forecasting unemployment with internet search data: does it help to improve predictions when job destruction is skyrocketing? Technol Forecast Soc Change 92:132–139. https://doi.org/10.1016/j.techfore.2014.12.005Youtie J, Hicks D, Shapira P, Horsley T (2012) Pathways from discovery to commercialisation: using web sources to track small and medium-sized enterprise strategies in emerging nanotechnologies. Technol Anal Strateg Manag 24:981–995. https://doi.org/10.1080/09537325.2012.724163Zhang Y, Fang Y, Wei KK, Ramsey E, McCole P, Chen H (2011) Repurchase intention in B2C e-commerce—a relationship quality perspective. Inf Manag 48:192–200. https://doi.org/10.1016/j.im.2011.05.003Zhao WX, Li S, He Y, Wang L, Wen JR, Li X (2016) Exploring demographic information in social media for product recommendation. Knowl Inf Syst 49:61–8

    Inferring export orientation from corporate websites

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    This is an author's accepted manuscript of an article published in: “Applied Economics Letters"; Volume 21, Issue 7, 2014; copyright Taylor & Francis; available online at: http://dx.doi.org/10.1080/13504851.2013.872752The purpose of this article is to infer indicators about the export orientation of firms from the analysis of their corporate websites. Using a dataset of manufacturing firms, two logistic regressions were performed and compared: one considering some firm structural variables, and another considering some web-based variables. Results showed that the website features are good predictors of the export orientation of firms, performing as well as the classic economic variables.Blázquez Soriano, MD.; Doménech I De Soria, J. (2014). Inferring export orientation from corporate websites. Applied Economics Letters. 21(7):509-512. doi:10.1080/13504851.2013.872752S509512217Bonaccorsi, A. (1992). On the Relationship Between Firm Size and Export Intensity. Journal of International Business Studies, 23(4), 605-635. doi:10.1057/palgrave.jibs.8490280DA, Z., ENGELBERG, J., & GAO, P. (2011). In Search of Attention. The Journal of Finance, 66(5), 1461-1499. doi:10.1111/j.1540-6261.2011.01679.xDzielinski, M. (2012). Measuring economic uncertainty and its impact on the stock market. Finance Research Letters, 9(3), 167-175. doi:10.1016/j.frl.2011.10.003Freund, C. L., & Weinhold, D. (2004). The effect of the Internet on international trade. Journal of International Economics, 62(1), 171-189. doi:10.1016/s0022-1996(03)00059-xGirma, S., Greenaway, avid, & Kneller, R. (2004). Does Exporting Increase Productivity? A Microeconometric Analysis of Matched Firms. Review of International Economics, 12(5), 855-866. doi:10.1111/j.1467-9396.2004.00486.xLee, J., & Morrison, A. M. (2010). A comparative study of web site performance. Journal of Hospitality and Tourism Technology, 1(1), 50-67. doi:10.1108/17579881011023016Murphy, J., & Scharl, A. (2007). An investigation of global versus local online branding. International Marketing Review, 24(3), 297-312. doi:10.1108/02651330710755302Nassimbeni, G. (2001). Technology, innovation capacity, and the export attitude of small manufacturing firms: a logit/tobit model. Research Policy, 30(2), 245-262. doi:10.1016/s0048-7333(99)00114-6Preis, T., Reith, D., & Stanley, H. E. (2010). Complex dynamics of our economic life on different scales: insights from search engine query data. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 368(1933), 5707-5719. doi:10.1098/rsta.2010.0284Spence, M. M. (2003). Small Business Economics, 20(1), 83-103. doi:10.1023/a:1020200621988Varian, H. R. (2010). Computer Mediated Transactions. American Economic Review, 100(2), 1-10. doi:10.1257/aer.100.2.1Wholey, J. S., & Hatry, H. P. (1992). The Case for Performance Monitoring. Public Administration Review, 52(6), 604. doi:10.2307/97717

    A Conceptual Framework for Definition of the Correlation Between Company Size Categories and the Proliferation of Business Information Systems in Hungary Download article

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    Based on a conceptual model, this paper aims to explore the background of the decision-making process leading to the introduction of business information systems among enterprises in Hungary. Together with presenting the problems arising in the course of the implementation of such systems, their usage patterns are also investigated. A strong correlation is established between the size of an enterprise, the scope of its business activities and the range of the business information systems it applies

    Design and Evaluation of Web-Based Economic Indicators: A Big Data Analysis Approach

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    Tesis por compendio[ES] En la Era Digital, el creciente uso de Internet y de dispositivos digitales está transformando completamente la forma de interactuar en el contexto económico y social. Miles de personas, empresas y organismos públicos utilizan Internet en sus actividades diarias, generando de este modo una enorme cantidad de datos actualizados ("Big Data") accesibles principalmente a través de la World Wide Web (WWW), que se ha convertido en el mayor repositorio de información del mundo. Estas huellas digitales se pueden rastrear y, si se procesan y analizan de manera apropiada, podrían ayudar a monitorizar en tiempo real una infinidad de variables económicas. En este contexto, el objetivo principal de esta tesis doctoral es generar indicadores económicos, basados en datos web, que sean capaces de proveer regularmente de predicciones a corto plazo ("nowcasting") sobre varias actividades empresariales que son fundamentales para el crecimiento y desarrollo de las economías. Concretamente, tres indicadores económicos basados en la web han sido diseñados y evaluados: en primer lugar, un indicador de orientación exportadora, basado en un modelo que predice si una empresa es exportadora; en segundo lugar, un indicador de adopción de comercio electrónico, basado en un modelo que predice si una empresa ofrece la posibilidad de venta online; y en tercer lugar, un indicador de supervivencia empresarial, basado en dos modelos que indican la probabilidad de supervivencia de una empresa y su tasa de riesgo. Para crear estos indicadores, se han descargado una diversidad de datos de sitios web corporativos de forma manual y automática, que posteriormente se han procesado y analizado con técnicas de análisis Big Data. Los resultados muestran que los datos web seleccionados están altamente relacionados con las variables económicas objeto de estudio, y que los indicadores basados en la web que se han diseñado en esta tesis capturan en un alto grado los valores reales de dichas variables económicas, siendo por tanto válidos para su uso por parte del mundo académico, de las empresas y de los decisores políticos. Además, la naturaleza online y digital de los indicadores basados en la web hace posible proveer regularmente y de forma barata de predicciones a corto plazo. Así, estos indicadores son ventajosos con respecto a los indicadores tradicionales. Esta tesis doctoral ha contribuido a generar conocimiento sobre la viabilidad de producir indicadores económicos con datos online procedentes de sitios web corporativos. Los indicadores que se han diseñado pretenden contribuir a la modernización en la producción de estadísticas oficiales, así como ayudar a los decisores políticos y los gerentes de empresas a tomar decisiones informadas más rápidamente.[CA] A l'Era Digital, el creixent ús d'Internet i dels dispositius digitals està transformant completament la forma d'interactuar al context econòmic i social. Milers de persones, empreses i organismes públics utilitzen Internet a les seues activitats diàries, generant d'aquesta forma una enorme quantitat de dades actualitzades ("Big Data") accessibles principalment mitjançant la World Wide Web (WWW), que s'ha convertit en el major repositori d'informació del món. Aquestes empremtes digitals poden rastrejar-se i, si se processen i analitzen de forma apropiada, podrien ajudar a monitoritzar en temps real una infinitat de variables econòmiques. En aquest context, l'objectiu principal d'aquesta tesi doctoral és generar indicadors econòmics, basats en dades web, que siguen capaços de proveïr regularment de prediccions a curt termini ("nowcasting") sobre diverses activitats empresarials que són fonamentals per al creixement i desenvolupament de les economies. Concretament, tres indicadors econòmics basats en la web han sigut dissenyats i avaluats: en primer lloc, un indicador d'orientació exportadora, basat en un model que prediu si una empresa és exportadora; en segon lloc, un indicador d'adopció de comerç electrònic, basat en un model que prediu si una empresa ofereix la possibilitat de venda online; i en tercer lloc, un indicador de supervivència empresarial, basat en dos models que indiquen la probabilitat de supervivència d'una empresa i la seua tasa de risc. Per a crear aquestos indicadors, s'han descarregat una diversitat de dades de llocs web corporatius de forma manual i automàtica, que posteriorment s'han analitzat i processat amb tècniques d'anàlisi Big Data. Els resultats mostren que les dades web seleccionades estan altament relacionades amb les variables econòmiques objecte d'estudi, i que els indicadors basats en la web que s'han dissenyat en aquesta tesi capturen en un alt grau els valors reals d'aquestes variables econòmiques, sent per tant vàlids per al seu ús per part del món acadèmic, de les empreses i dels decisors polítics. A més, la naturalesa online i digital dels indicadors basats en la web fa possible proveïr regularment i de forma barata de prediccions a curt termini. D'aquesta forma, són avantatjosos en comparació als indicadors tradicionals. Aquesta tesi doctoral ha contribuït a generar coneixement sobre la viabilitat de produïr indicadors econòmics amb dades online procedents de llocs web corporatius. Els indicadors que s'han dissenyat pretenen contribuïr a la modernització en la producció d'estadístiques oficials, així com ajudar als decisors polítics i als gerents d'empreses a prendre decisions informades més ràpidament.[EN] In the Digital Era, the increasing use of the Internet and digital devices is completely transforming the way of interacting in the economic and social framework. Myriad individuals, companies and public organizations use the Internet for their daily activities, generating a stream of fresh data ("Big Data") principally accessible through the World Wide Web (WWW), which has become the largest repository of information in the world. These digital footprints can be tracked and, if properly processed and analyzed, could help to monitor in real time a wide range of economic variables. In this context, the main goal of this PhD thesis is to generate economic indicators, based on web data, which are able to provide regular, short-term predictions ("nowcasting") about some business activities that are basic for the growth and development of an economy. Concretely, three web-based economic indicators have been designed and evaluated: first, an indicator of firms' export orientation, which is based on a model that predicts if a firm is an exporter; second, an indicator of firms' engagement in e-commerce, which is based on a model that predicts if a firm offers e-commerce facilities in its website; and third, an indicator of firms' survival, which is based on two models that indicate the probability of survival of a firm and its hazard rate. To build these indicators, a variety of data from corporate websites have been retrieved manually and automatically, and subsequently have been processed and analyzed with Big Data analysis techniques. Results show that the selected web data are highly related to the economic variables under study, and the web-based indicators designed in this thesis are capturing to a great extent their real values, thus being valid for their use by the academia, firms and policy-makers. Additionally, the digital and online nature of web-based indicators makes it possible to provide timely, inexpensive predictions about the economy. This way, they are advantageous with respect to traditional indicators. This PhD thesis has contributed to generating knowledge about the viability of producing economic indicators with data coming from corporate websites. The indicators that have been designed are expected to contribute to the modernization of official statistics and to help in making earlier, more informed decisions to policy-makers and business managers.Blázquez Soriano, MD. (2019). Design and Evaluation of Web-Based Economic Indicators: A Big Data Analysis Approach [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/116836TESISCompendi

    Collaborative system: higher education search engine

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    Malaysian universities or colleges has been one of the main attractive educational destinations to foreigners for the last decade. Malaysian educational institutions are recognized in opening doors of knowledge that fulfill student’s lives with skills, awareness and enthusiasm. There are over 20 public universities and 60 private colleges that offer a complete range of programs leading to highly regarded and internationally recognized qualifications. Despite the vast information made available by these institutions on their websites, majority of potential students face challenges in choosing the right program, courses and university that suits their needs. This research study the innovative method which could be developed and leveraged by the educational industries, specifically the Ministry of Higher Education Malaysia (MOHE) in close collaboration with these universities and colleges, in using search engine comparator technology whereby assisting potential students in making selection. The study raises awareness of relevant collaboration initiatives that could be adopted and replicated to facilitate students and parents in making well-informed decision

    Improving Collaborative Learning Using Pervasive Embedded System-Based Multi-Agent Information and Retrieval Framework in Educational Systems

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    E-learning is a form of Technology SupportedEducation where the medium of instruction is throughDigital Technologies, particularly Computer Technology.An instance is the use of search engines like Google andYahoo, which aid Collaborative Learning. However, thewidespread provision of distributed, semi-structuredinformation resources such as the Web has obviouslybrought a lot of benefits; but it also has a number ofdifficulties. These difficulties include people gettingoverwhelmed by the sheer amount of information available,making it hard for them to filter out the junk andirrelevancies and focus on what is important, and also toactively search for the right information. Also, people easilyget bored or confused while browsing the Web because ofthe hypertext nature of the web, while making it easy to linkrelated documents together, it can also be disorienting. Toalleviate these problems, the Web Information Food ChainModel was introduced. How effective has this been with thedynamic nature of computing technologies? Pervasivecomputing devices enable people to gain immediate accessto information and services anywhere, anytime, withouthaving to carry around heavy and impractical computingdevices. Thus, the bulky PCs become less attractive andbeing slowly eroded with the development of a newgeneration of smart devices like wireless PDAs, smartphones, etc. These embedded devices are characterized bybeing unobtrusively embedded; completely connected;intuitively intelligent; effortlessly portable and mobile; andconstantly on and available. This paper presents the use ofembedded systems and Intelligent Agent-Based WebInformation Food Chain Model in Multi-Agent Informationand Retrieval Framework (IIFCEMAF), to realizing fullpotentials of the internet, for users’ improved system ofcollaborative e-learning in education

    E-CRM and CMS systems: potential for more dynamic businesses

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    Any change in customer’s behaviour affects the customer’s value. In addition, profitability and economic viability also change. Most companies still do not know entirely their customer base characteristics. They find difficult to define criteria that segment their customer base to find high-value customers. They need to focus on target selections to carry on with marketing campaigns which involve high investments. Given the potential of e-CRM and CMS as powerful tools to guide customer-oriented understanding and analysis, greater attention is required. Several companies, operating within the same business and having access to the same information and technology, differ in e-CRM performance. Without sufficient evidence, managers are prone to making investment decisions that are neither efficient nor effective. So it is imperative to base the decision of e-CRM and CMS adoption, on not only their analytical power, but also on economic viability criteria for sustainable business dynamic

    Islamic Economy Through Online Community (IEOC) Issues on Information Gathering & Storing

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    Knowledge Communities are communities of interest that come together to share knowledge that affects performance. Knowledge Management envisions getting the right information within the right context to the right person at the right time for the right business purpose. Communities are more aware and concern of sharing and transfer the knowledge. The rapid development of web technology had made the World Wide Web an important and popular application platform for disseminating and searching for information as well as conducting business. As a huge source, World Wide Web has allowed unprecedented sharing of ideas and information on a scale never seen before. The use of Web and its exponential growth are now well known, and they are causing a revolution in the way people use computers and perform daily tasks. Therefore Islamic Economy thru Online Community [IEOC] intention was to proposed for an avenue of knowledge sharing and experience for the community. Issue on Information Gathering and Storing is discussed in this project paper where it concentrates on how data are being managed and used. The target users of this website are among consumers and business personnel. In developing the project, the methodology comprises of four ( 4) phase: System Planning and Strategy , System Analysis and Design , System Implementation and System Testing. The tools used comprises of Macromedia Dreamweaver MX 2004, Joomla Open Source, Apache Web Server and PHP scripting language. In the end of this paper, conclusion and recommendation part will discuss for future enhancement. ii
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