@article{
	11589_75324,
	author = { Ardito  Lorenzo  and  Messeni Petruzzelli  Antonio  and  Panniello  Umberto },
	title = {Unveiling the breakthrough potential of established technologies: an empirical investigation in the aerospace industry},
	year = {2016},
	journal = {TECHNOLOGY ANALYSIS & STRATEGIC MANAGEMENT},
	volume = {28},
	keywords = {aerospace; breakthrough technologies; Established technologies; knowledge breadth; technological evolution; Strategy and Management1409 Tourism, Leisure and Hospitality Management; Management Science and Operations Research},
	url = {http://www.tandf.co.uk/journals/titles/09537325.asp},
	doi = {10.1080/09537325.2016.1180356},	
	pages = {916--934}
}
@article{
	11589_75323,
	author = { Panniello  U  and  Hill  S  and  Gorgoglione  M },
	title = {Using context for online customer re-identification},
	year = {2016},
	journal = {EXPERT SYSTEMS WITH APPLICATIONS},
	volume = {64},
	keywords = {Context; Customer identification; Customer modeling; Engineering (all); Computer Science Applications1707 Computer Vision and Pattern Recognition; Artificial Intelligence},
	doi = {10.1016/j.eswa.2016.08.004},	
	pages = {500--511}
}
@article{
	11589_75325,
	author = { Panniello  Umberto  and  Gorgoglione  Michele  and  Tuzhilin  Alexander },
	title = {In CARSs we trust: How context-aware recommendations affect customers' trust and other business performance measures of recommender systems},
	year = {2016},
	journal = {INFORMATION SYSTEMS RESEARCH},
	volume = {27},
	keywords = {Business value of IT; Case studies; Context aware; Economics of IS; Electronic commerce; Field experiments; Recommender systems; Information Systems; Computer Networks and Communications; Information Systems and Management; Library and Information Sciences},
	url = {http://pubsonline.informs.org/doi/pdf/10.1287/isre.2015.0610},
	doi = {10.1287/isre.2015.0610},	
	pages = {182--196}
}
@conference{
	11589_59865,
	author = { Fortunato  A  and  Gorgoglione  M  and  Panniello  U },
	title = {What Drives Tv Online Engagement? The Influence Of Social Strategies And Contents},
	year = {2015},
	booktitle = {12th International Conference on Web Based Communities and Social Media}
}
@conference{
	11589_59863,
	author = { Fortunato  A  and  Gorgoglione  M  and  Panniello  U },
	title = {Modeling The Price-Relevance Relationship To Drive Users’ Decision Making},
	year = {2015},
	booktitle = {12th International Conference on e-Commerce and Digital Marketing}
}
@conference{
	11589_59866,
	author = { Fortunato  A  and  Gorgoglione  M  and  Panniello  U },
	title = {The Influence of Social TV Strategies and Contents on TV Online Engagement},
	year = {2015},
	booktitle = {The Fifth International Conference on Social Media Technologies, Communication, and Informatics}
}
@conference{
	11589_59864,
	author = { Buonamassa  D  and  Gorgoglione  M  and  Panniello  U },
	title = {Bringing Wine-Making Business Online: What Matters Most To Customers},
	year = {2015},
	booktitle = {12th International Conference on e-Commerce and Digital Marketing}
}
@article{
	11589_59861,
	author = { Panniello Umberto},
	title = {Developing a price-sensitive recommender system to improve accuracy and business performance of ecommerce applications},
	year = {2015},
	journal = {INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE STUDIES},
	volume = {6},
	abstract = {Much work has been done on recommender systems (RS) and much evidence was collected from applications about their effectiveness on business. As a consequence, the use of RS has quickly shifted from information retrieval to automatic marketing tools. The main aim of marketing tools is to positively affect customers' purchasing decisions and we know through marketing literature that purchasing decisions are strongly influenced by price. However, few works have explored the issue of including price in a recommendation engine. In this paper, we want to describe the main issues of designing this type of price-sensitive recommendation engine. We want also to demonstrate what the effect is of this design on recommendations' accuracy and on business performance. We demonstrate that including price in an RS improves the accuracy of recommendations, but it has to be properly modeled in order to also improve business performance. We have experimented with a Price-Sensitive RS in a laboratory setting and compared it to a traditional one by varying several settings},
	doi = {10.7903/ijecs.1348},	
	pages = {1--18}
}
@article{
	11589_60507,
	author = { Panniello  Umberto  and  Tuzhilin  Alexander  and  Gorgoglione  Michele },
	title = {Comparing context-aware recommender systems in terms of accuracy and diversity},
	year = {2014},
	journal = {USER MODELING AND USER-ADAPTED INTERACTION},
	volume = {24},
	abstract = {Although the area of context-aware recommender systems (CARS) has made a significant progress over the last several years, the problem of comparing various contextual pre-filtering, post-filtering and contextual modeling methods remained fairly unexplored. In this paper, we address this problem and compare several contextual pre-filtering, post-filtering and contextual modeling methods in terms of the accuracy and diversity of their recommendations to determine which methods outperform the others and under which circumstances. To this end, we consider three major factors affecting performance of CARS methods, such as the type of the recommendation task, context granularity and the type of the recommendation data. We show that none of the considered CARS methods uniformly dominates the others across all of these factors and other experimental settings; but that a certain group of contextual modeling methods constitutes a reliable "best bet" when choosing a sound CARS approach since they provide a good balance of accuracy and diversity of contextual recommendations.},
	keywords = {Accuracy; CARS; Context-aware recommender systems; Contextual modeling; Diversity; Performance measures; Post-filtering; Pre-filtering},
	url = {http://link.springer.com/article/10.1007/s11257-012-9135-y},
	doi = {10.1145/1055709.1055714},	
	pages = {35--65}
}
@article{
	11589_59884,
	author = { Panniello U.},
	title = {How to use recommender systems in e-business domains},
	year = {2014},
	journal = {WEBOLOGY},
	volume = {11},
	abstract = {Recommender systems (RS) were developed by research as a means to manage the information retrieval problem for users searching large databases. Recently they have become very popular among businesses as online marketing tools. Several online companies base their success on these systems, among other conditions. By looking at the last decades, the research on RS can be summarized into two main streams. The first research stream is focused on technical aspects of the algorithms and on identifying new ways to make them more accurate, while the second stream is focused on the effects of RS on customers. Therefore, we can draw several indications from the research on RS about the mistakes that companies should avoid when using RS. In this work we conduct an extensive literature and industrial review and we identify some crucial points managers should mind when developing a RS in order to make it as effective as possible in real world applications, or at least to avoid making it a failure},
	keywords = {E-business; E-commerce; Personalization; Recommender systems},
	url = {http://www.webology.org/2014/v11n2/a127.pdf},
	pages = {1--23}
}
@article{
	11589_8160,
	author = { Klaus Ph  and  Gorgoglione M  and  Panniello U  and  Buonamassa D  and  Nguyen B },
	title = {Are you providing the ‘right’ experiences? The case of Banca Popolare di Bari},
	year = {2013},
	journal = {INTERNATIONAL JOURNAL OF BANK MARKETING},
	volume = {31},
	abstract = {Purpose - This study proposes to model customer experience as a ‘continuum’. We adopt a customer experience quality construct and scale (EXQ) to determine the effect of customer experience on a bank’s marketing outcomes. We discuss our study’s theoretical and managerial implications, focusing on customer experience strategy design. 
Design/methodology/approach – We empirically test a scale to measure customer experience quality (EXQ) for a retail bank. We interview customers using a means-end-chain approach and soft-laddering to explore their customer experience perceptions with the bank. We classify their perceptions into the categories of ‘brand experience’ (pre-purchase), ‘service experience’ (during purchase), and ‘post-purchase experience’. After a confirmatory factor analysis, we conduct a survey on a representative customer sample. We analyze the survey results with a statistical model based on the partial least squares method. We test three hypotheses: 1) Customers’ perceptions of brand, service provider, and post-purchase experiences have a significant and positive effect on their experience quality (EXQ), 2) EXQ has a significant and positive effect on the marketing outcomes, namely share of wallet, satisfaction, and word-of-mouth, and 3) The overall effect of EXQ on marketing outcomes is greater than that of EXQ’s individual dimensions. 
Practical implications - Banks should focus their customer experience (CE) strategies on the customer experience continuum (CEC) and not on single encounters, tailoring marketing actions to specific stages in a customer’s CE process. Different organisational units interacting with customers should be integrated into CE strategies, and marketing and communication budgets should be allocated according to CEC analysis. The model proposed in this paper enables the measurement of the quality of CE and its impact on marketing outcomes, thus enabling continuous improvement in customer experience.
Findings - The results of the statistical analysis support the three hypotheses. 
Originality/value - The research proposes a different view of customer experience by modelling the interaction between company and customer as a continuum (CEC). It provides further empirical validation of the EXQ scale as a means of measuring customer experience. It also measures the impact of customer experience on a bank’s marketing outcomes. It discusses the guidelines for designing an effective customer experience strategy in the banking industry.},
	keywords = {Customer experience, customer experience strategy, customer experience quality, EXQ, service experience, scale development, loyalty, word-of-mouth}
}
@article{
	11589_687,
	author = { Lombardi S  and  Gorgoglione M  and  Panniello U },
	title = {The effect of context on misclassification costs in e-commerce applications},
	year = {2013},
	journal = {EXPERT SYSTEMS WITH APPLICATIONS},
	volume = {40},
	abstract = {The performance of customer behavior models depends on both the predictive accuracy and the cost of incorrect predictions. Previous research showed that including context in the customer behavior models can improve the accuracy. Improving the accuracy does not necessarily mean that the misclassification cost decreases. The aim of this paper is to understand whether including context in a predictive model reduces the misclassification costs and in which conditions this happens. Experimental analyses were done by varying the market granularity, the dependent variable and the context granularity. The results show that context leads to a decrease in the misclassification cost when the unit of analysis is the single customer or the micro-segment. The exceptions may occur when the unit of analysis is a segment. These findings have significant implications for companies that have to decide whether to gather context and how to exploit it best when they build predictive models.},
	keywords = {Misclassification cost, Context, Predictive model, Personalization},
	pages = {5219--5227}
}
@article{
	11589_8161,
	author = { Panniello U  and  Tuzhilin A  and  Gorgoglione M },
	title = {Comparing Context-Aware Recommender Systems in Terms of Accuracy and Diversity: Which Contextual Modeling, Pre-filtering and Post-Filtering Methods Perform the Best},
	year = {2014},
	journal = {USER MODELING AND USER-ADAPTED INTERACTION},
	volume = {23},
	abstract = {Although the area of context-aware recommender systems has made a significant progress over the last several years, the problem of comparing various contextual pre-filtering, post-filtering and contextual modeling methods remained fairly unexplored. In this paper, we address this problem and compare several contextual pre-filtering, post-filtering and contextual modeling methods in terms of the accuracy and diversity of their recommendations to determine which methods outperform the others and under which circumstances. To this end, we consider three major factors affecting performance of CARS methods, such as the type of the recommendation task, context granularity and the type of the recommendation data. We show that none of the considered CARS methods uniformly dominates the others across all of these factors and other experimental settings; but that a certain group of contextual modeling methods constitutes a reliable “best bet” when choosing a sound CARS approach since they provide a good balance of accuracy and diversity of contextual recommendations.},
	keywords = {Context-aware recommender systems, CARS, pre-filtering, post-filtering, contextual modeling, accuracy, diversity, performance measures}
}
@article{
	11589_10120,
	author = { FARAONE MF  and  GORGOGLIONE M  and  PALMISANO C  and  PANNIELLO U },
	title = {Using context to improve the effectiveness of segmentation and targeting in e-commerce},
	year = {2012},
	journal = {EXPERT SYSTEMS WITH APPLICATIONS},
	volume = {39},
	pages = {8439--8451}
}
@article{
	11589_7824,
	author = { PANNIELLO U  and  GORGOGLIONE M },
	title = {Incorporating Context Into Recommender Systems: An Empirical Comparison Of Context-Based Approaches},
	year = {2012},
	journal = {ELECTRONIC COMMERCE RESEARCH},
	volume = {12}
}
@conference{
	11589_21186,
	author = { GORGOGLIONE M  and  PANNIELLO U  and  TUZHILIN A },
	title = {The Effect of Context-Aware Recommendations on Purchases and Trust},
	year = {2011},
	publisher = {ACM},
	address = {New York},
	booktitle = {Proceedings of the 5th ACM International Conference on Recommender Systems}
}
@conference{
	11589_23489,
	author = { GORGOGLIONE M  and  PANNIELLO U },
	title = {A Contextual Modeling Approach to Context-Aware Recommender Systems},
	year = {2011},
	booktitle = {Workshop on Context-Aware Recommender Systems (CARS)}
}
@conference{
	11589_16567,
	author = { PANNIELLO U  and  GORGOGLIONE M },
	title = {Context-Aware Recommender Systems: A Comparison Of Three Approaches},
	year = {2011},
	booktitle = {Proceedings of the 5th International Workshop on New Challenges in Distributed Information Filtering and Retrieval}
}
@article{
	11589_10376,
	author = { GORGOGLIONE M  and  PANNIELLO U },
	title = {Beyond Customer Churn: Generating Personalized Actions to Retain Customers in a Retail Bank by a Recommender System Approach},
	year = {2011},
	journal = {JOURNAL OF INTELLIGENT LEARNING SYSTEMS AND APPLICATIONS (PRINT)},
	volume = {3},
	pages = {90--102}
}
@conference{
	11589_19317,
	author = { GORGOGLIONE M  and  PANNIELLO U },
	title = {Does the recommendation task affect a CARS performance?},
	year = {2010},
	publisher = {New York, NY, USA},
	address = {ACM},
	booktitle = {Proceedings}
}