Importance Performance Matrix Analysis for Assessing User Experience with Intelligent Voice Assistants: A Strategic Evaluation

IF 2.8 2区社会学 Q1 SOCIAL SCIENCES, INTERDISCIPLINARY

Social Indicators Research Pub Date : 2024-06-04 DOI:10.1007/s11205-024-03362-3

Rosanna Cataldo, Martha Friel, Maria Gabriella Grassia, Marina Marino, Emma Zavarrone

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引用次数: 0

Abstract

The digital transformation, in which we have actively participated over the last decades, involves integrating new technology into every aspect of the business and necessitates a significant overhaul of traditional business structures. Recently there has been an exponential increase in the presence of Artificial Intelligence (AI) in people’s daily lives, and many new AI-infused products have been developed. This technology is relatively young and has the potential to significantly affect both industry and society. The paper focuses on the Intelligent Voice Assistants (IVAs) and the User eXperience (UX) evaluation. IVAs are a relatively new phenomenon that has generated much academic and industrial research interest. Starting from the contribution to systematization provided by the Artificial Intelligence User Experience (AIXE^®) scale, the idea is to develop an easy UX evaluation tool for IVAs that decision-makers can adopt. The work proposes the Partial Least Squares-Path Modeling (PLS-PM) to investigate different dimensions that affect the UX, and to verify if it becomes possible to quantify the impact and performance of each dimension on the general latent dimension of UX. The Importance Performance Matrix Analysis (IPMA) is utilised to evaluate and identify the primary factors that significantly influence the adoption of IVAs. IVA developers should examine the main aspects as a guide to enhancing the UX for individuals utilising IVAs.

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评估智能语音助手用户体验的重要性绩效矩阵分析：战略评估

过去几十年来，我们一直积极参与数字化转型，将新技术融入业务的方方面面，并对传统业务结构进行重大改革。最近，人工智能（AI）在人们日常生活中的应用呈指数级增长，许多新的人工智能产品应运而生。这项技术相对年轻，有可能对工业和社会产生重大影响。本文的重点是智能语音助手（IVA）和用户体验（UX）评估。IVA 是一种相对较新的现象，已引起学术界和工业界的广泛研究兴趣。从人工智能用户体验（AIXE®）量表的系统化贡献出发，本文的想法是为 IVA 开发一种决策者可以采用的简便用户体验评估工具。这项工作提出了偏最小二乘法路径建模（PLS-PM）来研究影响用户体验的不同维度，并验证是否有可能量化每个维度对用户体验一般潜在维度的影响和表现。利用重要性绩效矩阵分析法（IPMA）来评估和确定对采用 IVA 有重大影响的主要因素。IVA 开发人员应研究这些主要方面，以此为指导，提高个人使用 IVA 的用户体验。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Social Indicators Research Multiple-

CiteScore

6.30

自引率

6.50%

发文量

174

期刊介绍： Since its foundation in 1974, Social Indicators Research has become the leading journal on problems related to the measurement of all aspects of the quality of life. The journal continues to publish results of research on all aspects of the quality of life and includes studies that reflect developments in the field. It devotes special attention to studies on such topics as sustainability of quality of life, sustainable development, and the relationship between quality of life and sustainability. The topics represented in the journal cover and involve a variety of segmentations, such as social groups, spatial and temporal coordinates, population composition, and life domains. The journal presents empirical, philosophical and methodological studies that cover the entire spectrum of society and are devoted to giving evidences through indicators. It considers indicators in their different typologies, and gives special attention to indicators that are able to meet the need of understanding social realities and phenomena that are increasingly more complex, interrelated, interacted and dynamical. In addition, it presents studies aimed at defining new approaches in constructing indicators.