Fostering the User Interface Acceptance in Customer Relationship Management: A Multimedia-aided Approach

Alotaibi, Mutlaq B.; Rigas, Dimitrios

doi:10.1109/itng.2012.158

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…Several studies evaluated the effect of a face and virtual embodiment on social perceptions and behaviours with mixed results. One study found that an ECA received significantly higher ratings of trust than speech-only and textonly agents on a shopping website [65]. Another study found that for older adults, an ECA with speech and text was perceived as more trustworthy and provided better social support over a speech and text-only agent [59].…”

Section: Embodimentmentioning

confidence: 99%

“…Social perceptions refer to judgements of the intentions and psychological dispositions of others [64]. Some social perceptions that are related to relationship quality and have shown to be affected by design features include: perceived trustworthiness [33,65], warmth [54,66], and caring [67,68]. Design features may also influence social behaviours related to relationship quality including engagement (the degree of user involvement and interaction [69,70]) [71,72], degree of self-disclosure [73,74], and desire to interact again [72,75].…”

Section: Introductionmentioning

confidence: 99%

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The Effect of Design Features on Relationship Quality with Embodied Conversational Agents: A Systematic Review

Loveys

Sebaratnam

Sagar

et al. 2020

Int J of Soc Robotics

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Embodied conversational agents (ECAs) are increasingly used in healthcare and other settings to improve self-management and provide companionship. Their ability to form close relationships with people is important for enhancing effectiveness and engagement. Several studies have looked at enhancing relationships with ECAs through design features focused on behaviours, appearance, or language. However, this evidence is yet to be systematically synthesized. This systematic review evaluates the effect of different design features on relationship quality with ECAs. A systematic search was conducted on electronic databases EMBASE, PsychInfo, PubMed, MEDLINE, Cochrane Library, SCOPUS, and Web of Science in January-February 2019. 43 studies were included for review that evaluated the effect of a design feature on relationship quality and social perceptions or behaviours towards an ECA. Results synthesize effective design features and lay a scientific framework for improving relationships with ECAs in healthcare and other applications. Risk of bias for included studies was generally low, however there were some limitations in the research quality pertaining to outcome measurement and the reporting of statistics. Further research is needed to understand how to make ECAs effective and engaging for all consumers.

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Section: Embodimentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Effect of Design Features on Relationship Quality with Embodied Conversational Agents: A Systematic Review

Loveys

Sebaratnam

Sagar

et al. 2020

Int J of Soc Robotics

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“…The development of alerts based on business measures moving beyond some acceptable bounds continue to be considered an important aspect of intelligence in BI software, but such alerts are typically hard coded event triggers [7]. More recently active development in BI interfaces has began to move towards the incorporation of more intelligent techniques such as the incorporation of improved visualization methods, speech based communication, and multi-touch and multi-modal interfaces [1]. While these developments represent progress, BI interfaces generally remain bloated and often require a user to navigate through many pages of content to find important information.…”

Section: Related Workmentioning

confidence: 99%

“…From the perspective of intelligent user interfaces the primary technical problem to be addressed is the selection of KPI measures for presentation to the call center agents at the start of every call 1 . For this work we have implemented and evaluated two types of selection strategy for this purpose, i.e., a closed form solution and a classifier based ranking solution.…”

Section: Selection Strategiesmentioning

confidence: 99%

Exploring customer specific KPI selection strategies for an adaptive time critical user interface

Keck¹,

Ross²

2014

Proceedings of the 19th International Conference on Intelligent User Interfaces

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Rapid growth in the number of measures available to describe customer-organization relationships has presented a serious challenge for Business Intelligence (BI) interface developers as they attempt to provide business users with key customer information without requiring users to painstakingly sift through many interface windows and layers. In this paper we introduce a prototype Intelligent User Interface that we have deployed to partially address this issue. The interface builds on machine learning techniques to construct a ranking model of Key Performance Indicators (KPIs) that are used to select and present the most important customer metrics that can be made available to business users in time critical environments. We provide an overview of the prototype application, the underlying models used for KPI selection, and a comparative evaluation of machine learning and closed form solutions to the ranking and selection problems. Results show that the machine learning based method outperformed the closed form solution with a 66.5% accuracy rate on multilabel attribution in comparison to 54.1% for the closed form solution.

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Empirically Derived Guidelines for Multimodal Interaction in Knowledge-Based Environments

Alotaibi

Rigas

2014

2014 11th International Conference on Information Technology: New Generations

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Fostering the User Interface Acceptance in Customer Relationship Management: A Multimedia-aided Approach

Cited by 4 publications

References 16 publications

The Effect of Design Features on Relationship Quality with Embodied Conversational Agents: A Systematic Review

The Effect of Design Features on Relationship Quality with Embodied Conversational Agents: A Systematic Review

Exploring customer specific KPI selection strategies for an adaptive time critical user interface

Empirically Derived Guidelines for Multimodal Interaction in Knowledge-Based Environments

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