{"title":"Evaluating the intelligence capability of smart homes: A conceptual modeling approach","authors":"Di Wu , Weite Feng , Tong Li , Zhen Yang","doi":"10.1016/j.datak.2023.102218","DOIUrl":null,"url":null,"abstract":"<div><p>With the rapid development of Internet of Things technology, smart homes have gradually become an integral part of people’s lives, and the market share of smart homes has experienced a significant surge in recent years. As a result, there is a growing need for both producers and end-users to evaluate the intelligence of smart homes. While existing studies focus on simulating smart home environments, they do not provide an approach for automatically evaluating the intelligence of smart homes. In this study, we systematically establish a conceptual model of smart homes based on a wide range of smart home definitions, focusing on examining the factors that contribute to users feeling satisfied with their smart homes. Additionally, we proposed a framework for evaluating the intelligence capability of smart homes. To validate the effectiveness of our framework, we conducted an empirical study using an online user survey and collected 300 questionnaires about user ratings of three smart home suites. Our empirical results demonstrate that our framework is consistent with users’ perceptions of the intelligence level of smart homes. In order to further explore why users feel satisfied with their smart homes, we held a workshop with five participants. The results of our discussion showed a correlation between why users feel satisfied with their smart homes and the user needs that smart homes can fulfill.</p></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data & Knowledge Engineering","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0169023X23000782","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
With the rapid development of Internet of Things technology, smart homes have gradually become an integral part of people’s lives, and the market share of smart homes has experienced a significant surge in recent years. As a result, there is a growing need for both producers and end-users to evaluate the intelligence of smart homes. While existing studies focus on simulating smart home environments, they do not provide an approach for automatically evaluating the intelligence of smart homes. In this study, we systematically establish a conceptual model of smart homes based on a wide range of smart home definitions, focusing on examining the factors that contribute to users feeling satisfied with their smart homes. Additionally, we proposed a framework for evaluating the intelligence capability of smart homes. To validate the effectiveness of our framework, we conducted an empirical study using an online user survey and collected 300 questionnaires about user ratings of three smart home suites. Our empirical results demonstrate that our framework is consistent with users’ perceptions of the intelligence level of smart homes. In order to further explore why users feel satisfied with their smart homes, we held a workshop with five participants. The results of our discussion showed a correlation between why users feel satisfied with their smart homes and the user needs that smart homes can fulfill.
期刊介绍:
Data & Knowledge Engineering (DKE) stimulates the exchange of ideas and interaction between these two related fields of interest. DKE reaches a world-wide audience of researchers, designers, managers and users. The major aim of the journal is to identify, investigate and analyze the underlying principles in the design and effective use of these systems.