Models of User Engagement

Lehmann, Janette; Lalmas, Mounia; Yom-Tov, Elad; Dupret, Georges

doi:10.1007/978-3-642-31454-4_14

Cited by 217 publications

(152 citation statements)

References 5 publications

Supporting

Mentioning

147

Contrasting

Unclassified

Order By: Relevance

“…Another example for a partial transaction support is the Australian travel agency Flight Centre that processes holiday packages only through direct customer-company communication, despite customers' ability to search through available deals and request quotes on the company's website (flightcentre.com.au/holidays). Further examples of forced The forced channel switching represents an e-commerce business model by which the company seeks to maximize customer service and user engagement through personal interactions with customers (Lehmann et al 2012;van Doorn et al 2010). This strategic decision is made consciously and does not take full use of technological opportunities to provide full support via multiple channels for customers.…”

Section: Australasian Journal Of Information Systemsmentioning

confidence: 99%

Theorizing E-Commerce Business Models: On the Impact of Partially and Fully Supported Transaction Phases on Customer Satisfaction and Loyalty

Madlberger

Matook

2017

AJIS

View full text Add to dashboard Cite

Although considerable research has been conducted on the definition and classification of ecommerce business models, little research has integrated the support and impact of distinct transaction phases within e-commerce business models. Indeed, any channel design decisions depend on the arrangement of the three transaction phases, viz. information phase, agreement phase and fulfilment phase. Whereas in literature, a complete support of transaction phases is implicitly assumed, in practice, e-commerce firms exist that consciously do not support all three transaction phases through the online channel. In cases for which only certain phases are supported in one channel, the customer is required to switch to another channel to complete the transaction. To gain an initial understanding of the relevance of transaction phases, we examine the influence of transaction phase support in one channel on satisfaction and reuse intention of an e-commerce website under consideration of users' prior experiences. We conduct a laboratory experiment involving two e-commerce business models that differ only in the number of supported transaction phases via the online channel. Empirical data was analysed using a Bayesian network approach. Our analysis indicates the types of users who value partial support compared to those users who prefer full support of transaction phases. The results will assist firms in meeting future challenges regarding user engagement and attraction to their online stores.

show abstract

Section: Australasian Journal Of Information Systemsmentioning

confidence: 99%

Theorizing E-Commerce Business Models: On the Impact of Partially and Fully Supported Transaction Phases on Customer Satisfaction and Loyalty

Madlberger

Matook

2017

AJIS

View full text Add to dashboard Cite

show abstract

“…Many implicit measures have been proposed [22] to track online behavior. These measures are classically categorized in activity (how a website is used), loyalty (return of users to a website) and popularity (how much a website is used).…”

Section: Related Workmentioning

confidence: 99%

Benchmarking Exploratory OLAP

Djedaini

Furtado

Labroche

et al. 2017

Performance Evaluation and Benchmarking. Traditional - Big Data - Interest of Things

View full text Add to dashboard Cite

Abstract. Supporting interactive database exploration (IDE) is a problem that attracts lots of attention these days. Exploratory OLAP (OnLine Analytical Processing) is an important use case where tools support navigation and analysis of the most interesting data, using the best possible perspectives. While many approaches were proposed (like query recommendation, reuse, steering, personalization or unexpected data recommendation), a recurrent problem is how to assess the effectiveness of an exploratory OLAP approach. In this paper we propose a benchmark framework to do so, that relies on an extensible set of user-centric metrics that relate to the main dimensions of exploratory analysis. Namely, we describe how to model and simulate user activity, how to formalize our metrics and how to build exploratory tasks to properly evaluate an IDE system under test (SUT). To the best of our knowledge, this is the first proposal of such a benchmark. Experiments are two-fold: first we evaluate the benchmark protocol and metrics based on synthetic SUTs whose behavior is well known. Second, we concentrate on two different recent SUTs from IDE literature that are evaluated and compared with our benchmark. Finally, potential extensions to produce an industrystrength benchmark are listed in the conclusion.

show abstract

“…In our contribution, we propose to combine both product and user point of view by (1) including product characteristics as previously done by [4,18] and (2) a metric highlighting the user engagement [14]. In contrast to [9,10,16] which focused on the interest of a particular user, our proposed user engagement metric leverages from the crowd.…”

Section: Related Workmentioning

confidence: 99%

“…The integration of the user engagement component is driven by the main aim of e-commerce application which consists in increasing the user conversion rate. Although the user engagement should be derived in accordance with the application goal, such metric emphasizes the positive aspect of the interaction [14]. In the setting of a Web application, the user's engagement is often associated with his/her interactions including visits, clicks, comments, recommendations, etc.…”

Section: The Modelmentioning

confidence: 99%

“…The latter may be characterized by several variations, accessories, and/or complementary products. Combining both approaches (product features [4,18] and user-centered [9]), our model ranks products with respect to their descriptive fields and category as well as their popularity highlighting the user engagement toward the product [14]. We evaluate our model while participating to the product search track of the Living Labs for IR (LL4IR) [17] of CLEF 2015 [11] and present also some analysis aiming at understanding the user engagement factor.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Product Feature-Based User-Centric Ranking Model for E-Commerce Search

Jabeur

Soulier

Tamine

et al. 2016

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. During the online shopping process, users search for interesting products in order to quickly access those that fit with their needs among a long tail of similar or closely related products. Our contribution addresses head queries that are frequently submitted on e-commerce Web sites. Head queries usually target featured products with several variations, accessories, and complementary products. We present in this paper a product feature-based user-centric model for product search involving, in addition to product characteristics, the user engagement toward the product. This model has been evaluated through the product search track of the LL4IR lab at CLEF 2015 in order to highlight the effectiveness of our model as well as the impact of the user engagement factor.

show abstract

Models of User Engagement

Cited by 217 publications

References 5 publications

Theorizing E-Commerce Business Models: On the Impact of Partially and Fully Supported Transaction Phases on Customer Satisfaction and Loyalty

Theorizing E-Commerce Business Models: On the Impact of Partially and Fully Supported Transaction Phases on Customer Satisfaction and Loyalty

Benchmarking Exploratory OLAP

A Product Feature-Based User-Centric Ranking Model for E-Commerce Search

Contact Info

Product

Resources

About