The Role of the Mangement Sciences in Research on Personalization

We present a review of research studies that deal with personalization. We synthesize current knowledge about these areas, and identify issues that we envision will be of interest to researchers working in the management sciences. We take an interdisciplinary approach that spans the areas of economics, marketing, information technology, and operations. We present an overarching framework for personalization that allows us to identify key players in the personalization process, as well as, the key stages of personalization. The framework enables us to examine the strategic role of personalization in the interactions between a firm and other key players in the firm's value system. We review extant literature in the strategic behavior of firms, and discuss opportunities for analytical and empirical research in this regard. Next, we examine how a firm can learn a customer's preferences, which is one of the key components of the personalization process. We use a utility-based approach to formalize such preference functions, and to understand how these preference functions could be learnt based on a customer's interactions with a firm. We identify well-established techniques in management sciences that can be gainfully employed in future research on personalization.CRM, Persoanlization, Marketing, e-commerce,

Short-Video Marketing in E-commerce: Analyzing and Predicting Consumer Response

This study analyzes and predicts consumer viewing response to e-commerce short-videos (ESVs). We first construct a large-scale ESV dataset that contains 23,001 ESVs across 40 product categories. The dataset consists of the consumer response label in terms of average viewing durations and human-annotated ESV content attributes. Using the constructed dataset and mixed-effects model, we find that product description, product demonstration, pleasure, and aesthetics are four key determinants of ESV viewing duration. Furthermore, we design a content-based multimodal-multitask framework to predict consumer viewing response to ESVs. We propose the information distillation module to extract the shared, special, and conflicted information from ESV multimodal features. Additionally, we employ a hierarchical multitask classification module to capture feature-level and label-level dependencies. We conduct extensive experiments to evaluate the prediction performance of our proposed framework. Taken together, our paper provides theoretical and methodological contributions to the IS and relevant literature

An Exploratory Study of Patient Falls

Debate continues between the contribution of education level and clinical expertise in the nursing practice environment. Research suggests a link between Baccalaureate of Science in Nursing (BSN) nurses and positive patient outcomes such as lower mortality, decreased falls, and fewer medication errors. Purpose: To examine if there a negative correlation between patient falls and the level of nurse education at an urban hospital located in Midwest Illinois during the years 2010-2014? Methods: A retrospective crosssectional cohort analysis was conducted using data from the National Database of Nursing Quality Indicators (NDNQI) from the years 2010-2014. Sample: Inpatients aged ≥ 18 years who experienced a unintentional sudden descent, with or without injury that resulted in the patient striking the floor or object and occurred on inpatient nursing units. Results: The regression model was constructed with annual patient falls as the dependent variable and formal education and a log transformed variable for percentage of certified nurses as the independent variables. The model overall is a good fit, F (2,22) = 9.014, p = .001, adj. R2 = .40. Conclusion: Annual patient falls will decrease by increasing the number of nurses with baccalaureate degrees and/or certifications from a professional nursing board-governing body

Exploring demographic information in social media for product recommendation

In many e-commerce Web sites, product recommendation is essential to improve user experience and boost sales. Most existing product recommender systems rely on historical transaction records or Web-site-browsing history of consumers in order to accurately predict online users’ preferences for product recommendation. As such, they are constrained by limited information available on specific e-commerce Web sites. With the prolific use of social media platforms, it now becomes possible to extract product demographics from online product reviews and social networks built from microblogs. Moreover, users’ public profiles available on social media often reveal their demographic attributes such as age, gender, and education. In this paper, we propose to leverage the demographic information of both products and users extracted from social media for product recommendation. In specific, we frame recommendation as a learning to rank problem which takes as input the features derived from both product and user demographics. An ensemble method based on the gradient-boosting regression trees is extended to make it suitable for our recommendation task. We have conducted extensive experiments to obtain both quantitative and qualitative evaluation results. Moreover, we have also conducted a user study to gauge the performance of our proposed recommender system in a real-world deployment. All the results show that our system is more effective in generating recommendation results better matching users’ preferences than the competitive baselines

Generating Effective Recommendations Using Viewing-Time Weighted Preferences for Attributes

Recommender systems are an increasingly important technology and researchers have recently argued for incorporating different kinds of data to improve recommendation quality. This paper presents a novel approach to generating recommendations and evaluates its effectiveness. First, we review evidence that item viewing time can reveal user preferences for items. Second, we model item preference as a weighted function of preferences for item attributes. We then propose a method for generating recommendations based on these two propositions. The results of a laboratory evaluation show that the proposed approach generated estimated item ratings consistent with explicit item ratings and assigned high ratings to products that reflect revealed preferences of users. We conclude by discussing implications and identifying areas for future research

A Survey and Taxonomy of Sequential Recommender Systems for E-commerce Product Recommendation

E-commerce recommendation systems facilitate customers’ purchase decision by recommending products or services of interest (e.g., Amazon). Designing a recommender system tailored toward an individual customer’s need is crucial for retailers to increase revenue and retain customers’ loyalty. As users’ interests and preferences change with time, the time stamp of a user interaction (click, view or purchase event) is an important characteristic to learn sequential patterns from these user interactions and, hence, understand users’ long- and short-term preferences to predict the next item(s) for recommendation. This paper presents a taxonomy of sequential recommendation systems (SRecSys) with a focus on e-commerce product recommendation as an application and classifies SRecSys under three main categories as: (i) traditional approaches (sequence similarity, frequent pattern mining and sequential pattern mining), (ii) factorization and latent representation (matrix factorization and Markov models) and (iii) neural network-based approaches (deep neural networks, advanced models). This classification contributes towards enhancing the understanding of existing SRecSys in the literature with the application domain of e-commerce product recommendation and provides current status of the solutions available alongwith future research directions. Furthermore, a classification of surveyed systems according to eight important key features supported by the techniques along with their limitations is also presented. A comparative performance analysis of the presented SRecSys based on experiments performed on e-commerce data sets (Amazon and Online Retail) showed that integrating sequential purchase patterns into the recommendation process and modeling users’ sequential behavior improves the quality of recommendations

Sentiment analysis of ASOS product reviews using machine learning algorithms by comparing several models.

Digital ratings are crucial in improving international customer communications and impacting consumer purchasing trends. To obtain important data from a massive number of customer reviews, they must be sorted into positive and negative opinions. Sentiment analysis is a computational method for extracting emotive information from a text. In this particular research, over 3000 reviews have been obtained from the ASOS website and classified into three different sentiments: excellent, average, and bad. The obtained reviews have been pre-processed, then feature extraction is applied to the pre-processed data to remove the redundant data. Finally, distinct machine learning algorithms will be utilized to build disparate models. This research is vital as it allows the ASOS organization to gain insight into how consumers perceive about specific issues and detect urgent issues such as delivery delays and misplaced packages in the current time period before the issue goes outof control. The key results of this research show that the Nu-Support Vector Classification model obtained the highest accuracy score of 85.99% and the lowest accuracy score of 51.47% was obtained for the AdaBoost classifier model

Monitoring E-commerce Adoption from Online Data

[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