Pub Date : 2022-08-05DOI: 10.1080/07370024.2022.2077733
Claudette Pretorius, Darragh McCashin, D. Coyle
{"title":"Supporting personal preferences and different levels of need in online help-seeking: a comparative study of help-seeking technologies for mental health","authors":"Claudette Pretorius, Darragh McCashin, D. Coyle","doi":"10.1080/07370024.2022.2077733","DOIUrl":"https://doi.org/10.1080/07370024.2022.2077733","url":null,"abstract":"","PeriodicalId":56306,"journal":{"name":"Human-Computer Interaction","volume":"14 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2022-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75503808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-18DOI: 10.1080/07370024.2022.2084620
Timo Strohmann, Dominik Siemon, Bijan Khosrawi-Rad, S. Robra-Bissantz
Due to significant technological advances in the field of artificial intelligence (AI), which are driven by increased computing power, the ubiquitous availability of data, as well as new algorithms, new forms of intelligent systems and services have been developed and brought to the market (Choudhury et al., 2020; Clark et al., 2019a; Diederich et al., 2022; Kaplan & Haenlein, 2019; Ransbotham et al., 2018; Robert et al., 2020; Rzepka & Berger, 2018). In addition to specific applications in the form of virtual assistants, such as Apple’s Siri or Amazon’s Alexa, companies increasingly develop chatbots and enterprise bots to interact with customers (Diederich et al., 2022; Maedche et al., 2016; McTear et al., 2016). What all these systems have in common, is that they allow their users to interact with them using natural language, which is why the systems are summarized by the term conversational agent (CA) (Diederich et al., 2022; McTear et al., 2016). There are already various use cases for CAs today, ranging from executing smartphone functions, such as creating calendar entries or sending messages to smart home control, to interaction in the healthcare context (Ahmad et al., 2022; Elshan et al., 2022; Gnewuch et al., 2017; McTear et al., 2016; Sin & Munteanu, 2020). Thus, CAs currently offer a new way of interacting with information technology (Morana et al., 2017). Recent literature reviews show a growing interest in CAs and AI-enabled systems (Diederich et al., 2022; Elshan et al., 2022; Nißen et al., 2021; Rzepka & Berger, 2018), but mainly a limited variety of application contexts, which mostly focus on short-term interactions in marketing, sales, and support. Application scenarios that require long-term interaction are available but under-researched (Diederich et al., 2022; Elshan et al., 2022). Additionally, the current applications show that CA’s main goal is to provide personal assistant functionality, while less attention goes to the actual interaction with the system which should be improved by social behaviors being incorporated (Elshan et al., 2022; Gnewuch et al., 2017; Krämer et al., 2011; Nißen et al., 2021; Rzepka & Berger, 2018). Most of these interactions are initiated by the user and not by the CA, which means that the CA acts reactively rather than proactively. Moreover, these interactions are isolated, transactional, and based on predefined paths, as if they are starting over every time (Seymour et al., 2018). Although presently, from a technological perspective, CAs can predominantly conduct restricted conversations related to a specific topic (Diederich et al., 2022), modern language prediction models such as the Generative Pre-trained Transformer 3 (GPT-3) are able to fundamentally expand the capabilities of CAs. They achieve this by enabling open-topic and richer conversations with strong interpersonal character (Brown et al., 2020). The GPT-3 and many other recent language models are built on Transformer (Vaswani et al.,
由于人工智能(AI)领域的重大技术进步，这是由不断增强的计算能力、无处不在的数据可用性以及新算法驱动的，新形式的智能系统和服务已经开发出来并推向市场(Choudhury等人，2020;Clark et al.， 2019a;Diederich et al.， 2022;Kaplan & Haenlein, 2019;Ransbotham等人，2018;Robert et al.， 2020;Rzepka & Berger, 2018)。除了虚拟助手形式的特定应用程序，如苹果的Siri或亚马逊的Alexa，公司越来越多地开发聊天机器人和企业机器人来与客户互动(Diederich等人，2022;Maedche et al.， 2016;McTear et al.， 2016)。所有这些系统的共同点是，它们允许用户使用自然语言与它们进行交互，这就是为什么这些系统被术语会话代理(CA)所概括(Diederich et al.， 2022;McTear et al.， 2016)。如今，ca已经有了各种各样的用例，从执行智能手机功能(如创建日历条目或向智能家居控制发送消息)到医疗保健环境中的交互(Ahmad等人，2022;Elshan et al.， 2022;Gnewuch等人，2017;McTear et al.， 2016;Sin & Munteanu, 2020)。因此，ca目前提供了一种与信息技术交互的新方式(Morana et al.， 2017)。最近的文献综述显示，人们对ca和人工智能系统的兴趣越来越大(Diederich et al.， 2022;Elshan et al.， 2022;Nißen et al.， 2021;Rzepka & Berger, 2018)，但主要是有限种类的应用程序上下文，主要集中在市场营销，销售和支持中的短期交互。需要长期交互的应用场景是可用的，但研究不足(Diederich等人，2022;Elshan et al.， 2022)。此外，目前的应用表明，CA的主要目标是提供个人助理功能，而较少关注与系统的实际交互，这应该通过纳入社会行为来改善(Elshan等人，2022;Gnewuch等人，2017;Krämer等人，2011;Nißen et al.， 2021;Rzepka & Berger, 2018)。大多数这些交互都是由用户发起的，而不是由CA发起的，这意味着CA是被动的，而不是主动的。此外，这些交互是孤立的、事务性的，并且基于预定义的路径，就好像它们每次都重新开始一样(Seymour等人，2018)。虽然目前，从技术角度来看，ca主要可以进行与特定主题相关的限制性对话(Diederich等人，2022)，但现代语言预测模型，如生成预训练转换器3 (GPT-3)能够从根本上扩展ca的能力。他们通过启用具有强烈人际特征的开放式话题和更丰富的对话来实现这一目标(Brown et al.， 2020)。GPT-3和许多其他最新的语言模型都是基于Transformer (Vaswani et al.， 2017)构建的，Transformer是Google Research在2017年发明的一种神经网络架构。谷歌最近的语言模型LaMDA展示了如何通过基于现代语言的开放话题对话来实现类似人类的交互方式
{"title":"Toward a design theory for virtual companionship","authors":"Timo Strohmann, Dominik Siemon, Bijan Khosrawi-Rad, S. Robra-Bissantz","doi":"10.1080/07370024.2022.2084620","DOIUrl":"https://doi.org/10.1080/07370024.2022.2084620","url":null,"abstract":"Due to significant technological advances in the field of artificial intelligence (AI), which are driven by increased computing power, the ubiquitous availability of data, as well as new algorithms, new forms of intelligent systems and services have been developed and brought to the market (Choudhury et al., 2020; Clark et al., 2019a; Diederich et al., 2022; Kaplan & Haenlein, 2019; Ransbotham et al., 2018; Robert et al., 2020; Rzepka & Berger, 2018). In addition to specific applications in the form of virtual assistants, such as Apple’s Siri or Amazon’s Alexa, companies increasingly develop chatbots and enterprise bots to interact with customers (Diederich et al., 2022; Maedche et al., 2016; McTear et al., 2016). What all these systems have in common, is that they allow their users to interact with them using natural language, which is why the systems are summarized by the term conversational agent (CA) (Diederich et al., 2022; McTear et al., 2016). There are already various use cases for CAs today, ranging from executing smartphone functions, such as creating calendar entries or sending messages to smart home control, to interaction in the healthcare context (Ahmad et al., 2022; Elshan et al., 2022; Gnewuch et al., 2017; McTear et al., 2016; Sin & Munteanu, 2020). Thus, CAs currently offer a new way of interacting with information technology (Morana et al., 2017). Recent literature reviews show a growing interest in CAs and AI-enabled systems (Diederich et al., 2022; Elshan et al., 2022; Nißen et al., 2021; Rzepka & Berger, 2018), but mainly a limited variety of application contexts, which mostly focus on short-term interactions in marketing, sales, and support. Application scenarios that require long-term interaction are available but under-researched (Diederich et al., 2022; Elshan et al., 2022). Additionally, the current applications show that CA’s main goal is to provide personal assistant functionality, while less attention goes to the actual interaction with the system which should be improved by social behaviors being incorporated (Elshan et al., 2022; Gnewuch et al., 2017; Krämer et al., 2011; Nißen et al., 2021; Rzepka & Berger, 2018). Most of these interactions are initiated by the user and not by the CA, which means that the CA acts reactively rather than proactively. Moreover, these interactions are isolated, transactional, and based on predefined paths, as if they are starting over every time (Seymour et al., 2018). Although presently, from a technological perspective, CAs can predominantly conduct restricted conversations related to a specific topic (Diederich et al., 2022), modern language prediction models such as the Generative Pre-trained Transformer 3 (GPT-3) are able to fundamentally expand the capabilities of CAs. They achieve this by enabling open-topic and richer conversations with strong interpersonal character (Brown et al., 2020). The GPT-3 and many other recent language models are built on Transformer (Vaswani et al.,","PeriodicalId":56306,"journal":{"name":"Human-Computer Interaction","volume":"263 1","pages":"194 - 234"},"PeriodicalIF":5.3,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76715490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-14DOI: 10.1080/07370024.2022.2098129
Jaisie Sin, Cosmin Munteanu, Dongqing Chen, Jalena G. Threatt
It is often suggested that older adults (those 60 years or older) constitute a viable target market for voice user interfaces (VUIs) and that VUIs can provide many benefits for older adults. The mass media has been found to support this view, based on recent investigation on mass media’s portrayals of VUIs for older adults (Sin, Munteanu et al., 2021). The mass media was also found to suggest that older adults’ perceptions, acceptance, and adoption of VUIs rest on issues of data privacy, trust in the organizations behind VUIs, life fit and benefits conferred by VUIs, and market and government actions. This messaging can directly and indirectly influence older adults’ perceptions, and subsequent adoption, of voice user interfaces (Boothroyd, 2014; Rogers, 2010), much in the way that mass media has influenced adoption of other technologies when they emerged, such as smartphones (Yoo et al., 2010) and television (Weber & Evans, 2002). However, it is not yet known to what degree the claims made by the mass media about VUIs are supported by current academic research. It is possible that the mass media is propagating claims about VUIs that are not supported by academic literature. This is important because discrepancies between media messaging and academic findings may highlight aspects related to VUI adoption that are either not yet investigated by academic research or are portrayed in the mass media in a manner not supported by (or even contradictory to) scientific knowledge. Shedding light upon these knowledge gaps and addressing them are vital steps for the design of VUI systems in a manner that is conscious of factors that can influence VUI adoption (herein referred to as ‘adoption factors’) and sociotechnical influences of adoption (such as mass media messaging). Failure to design in a manner that sufficiently accounts for adoption factors can result in older adults’ rejection of VUI systems and, perhaps, other forms of digital technology as well (Norman, 2013; Whitenton, 2018). Furthermore, unsubstantiated claims in mass media messaging may interplay with the commercial industry’s development of VUIs, which is not only progressing much faster than academic-based research but may also be moving in a different direction than academia (Murad et al., 2019). The vigorous mass media messaging and industry push for “voice-first” devices may cause VUIs, which are positioned as greatly benefiting older adults, to instead further marginalize them with design features that exacerbate feelings of frailty, social isolation, and loneliness (Sin, Franz et al., 2021; Sin & Munteanu, 2020). For our investigation, we adopt a sociotechnical perspective (as opposed to issues of engineering performance or accuracy) of VUIs for older adults. The study of technology as sociotechnical systems (i.e., as systems with technological, social, cultural, historical, economic, and political dimensions) accounts for forces external to the technology alone as drivers of technolo
{"title":"Avoiding mixed messages: research-based fact-checking the media portrayals of voice user interfaces for older adults","authors":"Jaisie Sin, Cosmin Munteanu, Dongqing Chen, Jalena G. Threatt","doi":"10.1080/07370024.2022.2098129","DOIUrl":"https://doi.org/10.1080/07370024.2022.2098129","url":null,"abstract":"It is often suggested that older adults (those 60 years or older) constitute a viable target market for voice user interfaces (VUIs) and that VUIs can provide many benefits for older adults. The mass media has been found to support this view, based on recent investigation on mass media’s portrayals of VUIs for older adults (Sin, Munteanu et al., 2021). The mass media was also found to suggest that older adults’ perceptions, acceptance, and adoption of VUIs rest on issues of data privacy, trust in the organizations behind VUIs, life fit and benefits conferred by VUIs, and market and government actions. This messaging can directly and indirectly influence older adults’ perceptions, and subsequent adoption, of voice user interfaces (Boothroyd, 2014; Rogers, 2010), much in the way that mass media has influenced adoption of other technologies when they emerged, such as smartphones (Yoo et al., 2010) and television (Weber & Evans, 2002). However, it is not yet known to what degree the claims made by the mass media about VUIs are supported by current academic research. It is possible that the mass media is propagating claims about VUIs that are not supported by academic literature. This is important because discrepancies between media messaging and academic findings may highlight aspects related to VUI adoption that are either not yet investigated by academic research or are portrayed in the mass media in a manner not supported by (or even contradictory to) scientific knowledge. Shedding light upon these knowledge gaps and addressing them are vital steps for the design of VUI systems in a manner that is conscious of factors that can influence VUI adoption (herein referred to as ‘adoption factors’) and sociotechnical influences of adoption (such as mass media messaging). Failure to design in a manner that sufficiently accounts for adoption factors can result in older adults’ rejection of VUI systems and, perhaps, other forms of digital technology as well (Norman, 2013; Whitenton, 2018). Furthermore, unsubstantiated claims in mass media messaging may interplay with the commercial industry’s development of VUIs, which is not only progressing much faster than academic-based research but may also be moving in a different direction than academia (Murad et al., 2019). The vigorous mass media messaging and industry push for “voice-first” devices may cause VUIs, which are positioned as greatly benefiting older adults, to instead further marginalize them with design features that exacerbate feelings of frailty, social isolation, and loneliness (Sin, Franz et al., 2021; Sin & Munteanu, 2020). For our investigation, we adopt a sociotechnical perspective (as opposed to issues of engineering performance or accuracy) of VUIs for older adults. The study of technology as sociotechnical systems (i.e., as systems with technological, social, cultural, historical, economic, and political dimensions) accounts for forces external to the technology alone as drivers of technolo","PeriodicalId":56306,"journal":{"name":"Human-Computer Interaction","volume":"13 1","pages":"235 - 258"},"PeriodicalIF":5.3,"publicationDate":"2022-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83756597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-13DOI: 10.1080/07370024.2022.2081571
Robert J. Moore, Sungeun An, G. Ren
User interfaces that take human conversation as their interaction metaphor work fundamentally differently than those that employ spatial metaphors, such as a desktop or a page. While the fundamental concept in visual interface design is the layout, the fundamental concept in conversational interface design is the sequence. Each provides for the overall structure of the user experience. In the past, user-interface designers have borrowed elements from the various areas of physical design. From industrial design, they have borrowed concepts such as, buttons, levers, wheels, and more from the print industry, they have borrowed the page, typography, iconography, illustration, and photography and more. These concepts from the physical world are then adapted to persistent, visual representations on a computer screen. Of course, virtual buttons are different from physical buttons and displayed words are different from printed words, but they evoke familiar ways of interacting with the real world that are then repurposed for a computer–user interface. And graphical user interface design, web design and mobile design are mature disciplines with shared standards and communities of practitioners. However, the spatial interaction metaphors of these areas of visual design largely do not apply to the design of conversational user interfaces (Moore & Arar, 2019; Moore et al., 2020; Murad et al., 2021; Yankelovich et al., 1995). Human conversational interaction consists primarily of sequences of words and embodied actions, not of layouts of visual elements. Buttons or pages cannot be represented as a stream of words produced by different parties. Conversational interfaces are more akin to command-line interaction, which involves sequences of specialized commands. The interaction conventions of visual design, graphical, web and mobile, were invented as an alternative to language-based interfaces and are not applicable to the design of conversational user experience. Where then can UX designers find inspiration when creating conversational interfaces with their sequences of natural-language utterances? In part, they can turn to Natural Language Processing (NLP), which provides mature methods for recognizing, classifying, and generating natural-language input and output (Chowdhury, 2003; Goldberg, 2017; Graves et al., 2013; López-Cózar et al., 2011; McTear et al., 2016; Reiter & Dale, 1997). These methods help designers understand what the user said and render realistic voice responses. However, NLP provides resources primarily for managing natural language, not for managing natural conversation. NLP addresses language use in any form: novels, poems, tweets, e-mails, conversations, etc. (Berg, 2015; Mitri, 2022; Peng et al., 2018; Zhang & Gao, 2017). Any bit of natural language, be it Spanish, English, Mandarin, etc., is analyzable with NLP.
将人类对话作为交互隐喻的用户界面与使用空间隐喻(如桌面或页面)的用户界面的工作方式根本不同。视觉界面设计的基本概念是布局，而对话界面设计的基本概念是顺序。每个都提供了用户体验的整体结构。在过去，用户界面设计师从物理设计的各个领域借鉴元素。从工业设计中，他们借鉴了诸如按钮、杠杆、轮子等概念，从印刷行业中，他们借鉴了页面、排版、图像、插图和摄影等等。然后，这些来自物理世界的概念被适应为计算机屏幕上持久的视觉表现。当然，虚拟按钮不同于物理按钮，显示的文字也不同于打印的文字，但它们唤起了与现实世界交互的熟悉方式，然后将其重新用于计算机用户界面。图形用户界面设计、网页设计和移动设计是成熟的学科，有共同的标准和从业者社区。然而，这些视觉设计领域的空间交互隐喻在很大程度上不适用于会话用户界面的设计(Moore & Arar, 2019;Moore et al.， 2020;Murad et al.， 2021;Yankelovich et al.， 1995)。人类的对话互动主要由文字序列和具体的动作组成，而不是视觉元素的布局。按钮或页面不能表示为由不同方产生的单词流。会话接口更类似于命令行交互，它涉及一系列专门的命令。视觉设计、图形、网络和移动的交互约定是作为基于语言的界面的替代而发明的，并不适用于会话用户体验的设计。那么，用户体验设计师在用自然语言话语序列创建会话界面时，从哪里找到灵感呢?在某种程度上，他们可以转向自然语言处理(NLP)，它为识别、分类和生成自然语言输入和输出提供了成熟的方法(Chowdhury, 2003;戈德堡,2017;Graves et al.， 2013;López-Cózar等人，2011;McTear et al.， 2016;Reiter & Dale出版社，1997)。这些方法可以帮助设计师理解用户所说的内容并呈现真实的声音响应。然而，NLP提供的资源主要用于管理自然语言，而不是用于管理自然对话。NLP处理任何形式的语言使用:小说、诗歌、推特、电子邮件、对话等(Berg, 2015;米特里,2022;Peng et al.， 2018;Zhang & Gao, 2017)。任何自然语言，无论是西班牙语、英语、普通话等，都可以用NLP进行分析。
{"title":"The IBM natural conversation framework: a new paradigm for conversational UX design","authors":"Robert J. Moore, Sungeun An, G. Ren","doi":"10.1080/07370024.2022.2081571","DOIUrl":"https://doi.org/10.1080/07370024.2022.2081571","url":null,"abstract":"User interfaces that take human conversation as their interaction metaphor work fundamentally differently than those that employ spatial metaphors, such as a desktop or a page. While the fundamental concept in visual interface design is the layout, the fundamental concept in conversational interface design is the sequence. Each provides for the overall structure of the user experience. In the past, user-interface designers have borrowed elements from the various areas of physical design. From industrial design, they have borrowed concepts such as, buttons, levers, wheels, and more from the print industry, they have borrowed the page, typography, iconography, illustration, and photography and more. These concepts from the physical world are then adapted to persistent, visual representations on a computer screen. Of course, virtual buttons are different from physical buttons and displayed words are different from printed words, but they evoke familiar ways of interacting with the real world that are then repurposed for a computer–user interface. And graphical user interface design, web design and mobile design are mature disciplines with shared standards and communities of practitioners. However, the spatial interaction metaphors of these areas of visual design largely do not apply to the design of conversational user interfaces (Moore & Arar, 2019; Moore et al., 2020; Murad et al., 2021; Yankelovich et al., 1995). Human conversational interaction consists primarily of sequences of words and embodied actions, not of layouts of visual elements. Buttons or pages cannot be represented as a stream of words produced by different parties. Conversational interfaces are more akin to command-line interaction, which involves sequences of specialized commands. The interaction conventions of visual design, graphical, web and mobile, were invented as an alternative to language-based interfaces and are not applicable to the design of conversational user experience. Where then can UX designers find inspiration when creating conversational interfaces with their sequences of natural-language utterances? In part, they can turn to Natural Language Processing (NLP), which provides mature methods for recognizing, classifying, and generating natural-language input and output (Chowdhury, 2003; Goldberg, 2017; Graves et al., 2013; López-Cózar et al., 2011; McTear et al., 2016; Reiter & Dale, 1997). These methods help designers understand what the user said and render realistic voice responses. However, NLP provides resources primarily for managing natural language, not for managing natural conversation. NLP addresses language use in any form: novels, poems, tweets, e-mails, conversations, etc. (Berg, 2015; Mitri, 2022; Peng et al., 2018; Zhang & Gao, 2017). Any bit of natural language, be it Spanish, English, Mandarin, etc., is analyzable with NLP.","PeriodicalId":56306,"journal":{"name":"Human-Computer Interaction","volume":"16 1","pages":"168 - 193"},"PeriodicalIF":5.3,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85046836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-13DOI: 10.1080/07370024.2022.2080552
Sari R. R. Nijssen, Barbara C. N. Müller, T. Bosse, M. Paulus
ABSTRACT Robots are becoming an integral part of society, and might soon take on roles involving making morally relevant decisions. In a pre-registered experiment (n = 184), we investigated which factors modulate the extent to which we trust a robot to make a moral choice. Specifically, the effects of anthropomorphic appearance and anthropomorphic agency and affect attributions were assessed. Participants were presented with moral dilemmas in which the individual having to make a decision was a humanoid or mechanical robot. Each robot was described in vignettes in which they were attributed with agency and/or affective states. Subsequently, participants’ implicit moral trust in the robot was measured, as well as explicit trust, perceived capability of the robot, and the extent to which they felt the robot was responsible for its choice. Both agency and affective state attributions were found to impact participants’ implicit and explicit trust as well as the perceived capability of the robot. Moreover, across conditions, mechanical robots were trusted significantly more than humanoid robots to take moral choices.
{"title":"Can you count on a calculator? The role of agency and affect in judgments of robots as moral agents","authors":"Sari R. R. Nijssen, Barbara C. N. Müller, T. Bosse, M. Paulus","doi":"10.1080/07370024.2022.2080552","DOIUrl":"https://doi.org/10.1080/07370024.2022.2080552","url":null,"abstract":"ABSTRACT Robots are becoming an integral part of society, and might soon take on roles involving making morally relevant decisions. In a pre-registered experiment (n = 184), we investigated which factors modulate the extent to which we trust a robot to make a moral choice. Specifically, the effects of anthropomorphic appearance and anthropomorphic agency and affect attributions were assessed. Participants were presented with moral dilemmas in which the individual having to make a decision was a humanoid or mechanical robot. Each robot was described in vignettes in which they were attributed with agency and/or affective states. Subsequently, participants’ implicit moral trust in the robot was measured, as well as explicit trust, perceived capability of the robot, and the extent to which they felt the robot was responsible for its choice. Both agency and affective state attributions were found to impact participants’ implicit and explicit trust as well as the perceived capability of the robot. Moreover, across conditions, mechanical robots were trusted significantly more than humanoid robots to take moral choices.","PeriodicalId":56306,"journal":{"name":"Human-Computer Interaction","volume":"26 1","pages":"400 - 416"},"PeriodicalIF":5.3,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77250716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-19DOI: 10.1080/07370024.2022.2047971
Joe Cutting, Sebastian Deterding
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Pub Date : 2022-04-08DOI: 10.1080/07370024.2022.2057858
D. Mulchandani, Ala'a N. Alslaity, Rita Orji
ABSTRACT Persuasive games are widely implemented in the healthcare domain to promote behaviour change among individuals. Previous research shows that using persuasive games increases motivation and awareness, leading to a positive change in behaviour. However, there is little knowledge on which persuasive strategies will motivate people at different Stages of Behaviour Change and whether tailoring persuasive games to match users’ stages of change will increase their effectiveness with respect to their motivational appeal towards promoting disease awareness and prevention using the ARCS motivation scales and their intention to adopt the precautionary measures. To address this gap, using COVID-19 as a case study, we designed two different versions of a persuasive game, called COVID Pacman, using different persuasive strategies. The two versions of the game target the same goal of motivating the adoption of precautionary measures. We conducted a quantitative study (N=127) followed by semi-structured interviews of 18 participants. The results of conducting an ANOVA on the quantitative data and thematic analysis on the qualitative study show that tailoring the persuasive games to individual’s stages of change by using appropriate persuasive strategies increased their effectiveness with respect to their ability to motivate people to adopt the precautionary measures towards disease prevention compared to the non-tailored version.
{"title":"Exploring the effectiveness of persuasive games for disease prevention and awareness and the impact of tailoring to the stages of change","authors":"D. Mulchandani, Ala'a N. Alslaity, Rita Orji","doi":"10.1080/07370024.2022.2057858","DOIUrl":"https://doi.org/10.1080/07370024.2022.2057858","url":null,"abstract":"ABSTRACT Persuasive games are widely implemented in the healthcare domain to promote behaviour change among individuals. Previous research shows that using persuasive games increases motivation and awareness, leading to a positive change in behaviour. However, there is little knowledge on which persuasive strategies will motivate people at different Stages of Behaviour Change and whether tailoring persuasive games to match users’ stages of change will increase their effectiveness with respect to their motivational appeal towards promoting disease awareness and prevention using the ARCS motivation scales and their intention to adopt the precautionary measures. To address this gap, using COVID-19 as a case study, we designed two different versions of a persuasive game, called COVID Pacman, using different persuasive strategies. The two versions of the game target the same goal of motivating the adoption of precautionary measures. We conducted a quantitative study (N=127) followed by semi-structured interviews of 18 participants. The results of conducting an ANOVA on the quantitative data and thematic analysis on the qualitative study show that tailoring the persuasive games to individual’s stages of change by using appropriate persuasive strategies increased their effectiveness with respect to their ability to motivate people to adopt the precautionary measures towards disease prevention compared to the non-tailored version.","PeriodicalId":56306,"journal":{"name":"Human-Computer Interaction","volume":"27 1","pages":"459 - 494"},"PeriodicalIF":5.3,"publicationDate":"2022-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77375692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-30DOI: 10.1080/07370024.2022.2050725
A. Boldi, A. Rapp, Maurizio Tirassa
The COVID-19 pandemic led to dramatic changes in people's lives. The Human-Computer Interaction (HCI) community widely investigated technology use during crises. However, commercial video games received minor attention. In this article, we describe how video game play impacted the life transformations engendered by the pandemic. We administered a qualitative online survey to 330 video game players who were living in Italy during the lockdown measures. We found that the COVID-19 pandemic altered the participants' sense of time and space, reshaped both their intimate and wider social interactions, and elicited a wide spectrum of disturbing emotions. Players escaped from this unsatisfying reality into video game worlds, searching for a new normality that could compensate for the unpredictability and dangerousness of the pandemic life, as well as seeking uncertainty in the game environments to balance the flatness of the lockdown everydayness. In doing so, they "appropriated" the gaming technologies, which also led to several unexpected outcomes. Starting from these findings, we propose a model of escapism that points out four ways to escape from reality into video game worlds. Moreover, we outline some design implications that might inspire future strands of research in the field of crisis technologies. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
{"title":"Playing during a crisis: The impact of commercial video games on the reconfiguration of people’s life during the COVID-19 pandemic","authors":"A. Boldi, A. Rapp, Maurizio Tirassa","doi":"10.1080/07370024.2022.2050725","DOIUrl":"https://doi.org/10.1080/07370024.2022.2050725","url":null,"abstract":"The COVID-19 pandemic led to dramatic changes in people's lives. The Human-Computer Interaction (HCI) community widely investigated technology use during crises. However, commercial video games received minor attention. In this article, we describe how video game play impacted the life transformations engendered by the pandemic. We administered a qualitative online survey to 330 video game players who were living in Italy during the lockdown measures. We found that the COVID-19 pandemic altered the participants' sense of time and space, reshaped both their intimate and wider social interactions, and elicited a wide spectrum of disturbing emotions. Players escaped from this unsatisfying reality into video game worlds, searching for a new normality that could compensate for the unpredictability and dangerousness of the pandemic life, as well as seeking uncertainty in the game environments to balance the flatness of the lockdown everydayness. In doing so, they \"appropriated\" the gaming technologies, which also led to several unexpected outcomes. Starting from these findings, we propose a model of escapism that points out four ways to escape from reality into video game worlds. Moreover, we outline some design implications that might inspire future strands of research in the field of crisis technologies. (PsycInfo Database Record (c) 2022 APA, all rights reserved)","PeriodicalId":56306,"journal":{"name":"Human-Computer Interaction","volume":"8 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73534777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-18DOI: 10.1080/07370024.2022.2039147
Sara Falcone, G. Englebienne, J. V. Erp, D. Heylen
ABSTRACT We present a literature review and a toolbox to help the reader find the best method to design for and assess Sense of Embodiment (SoE) in several application scenarios. The main examples are based on teleoperation applications, due the challenges that these applications present. The three embodiment components that we consider to describe SoE are sense of ownership, sense of agency, and sense of self-location. We relate each embodiment component to the most often used assessment measures, test tasks, and application scenarios. The toolbox is built to efficiently design, test, and assess an embodiment experience, following seven concrete steps. We provide four main contributions: 1) a literature review of the assessment measures and strategies used to measure SoE; 2) a systematic categorization of SoE measures; 3) A categorization of the main test tasks used in SoE assessment; and 4) a toolbox consisting of seven steps as guidance to design SoE. We included several examples and tables to guide the user step by step through the design of an embodiment experience.
{"title":"Toward Standard Guidelines to Design the Sense of Embodiment in Teleoperation Applications: A Review and Toolbox","authors":"Sara Falcone, G. Englebienne, J. V. Erp, D. Heylen","doi":"10.1080/07370024.2022.2039147","DOIUrl":"https://doi.org/10.1080/07370024.2022.2039147","url":null,"abstract":"ABSTRACT We present a literature review and a toolbox to help the reader find the best method to design for and assess Sense of Embodiment (SoE) in several application scenarios. The main examples are based on teleoperation applications, due the challenges that these applications present. The three embodiment components that we consider to describe SoE are sense of ownership, sense of agency, and sense of self-location. We relate each embodiment component to the most often used assessment measures, test tasks, and application scenarios. The toolbox is built to efficiently design, test, and assess an embodiment experience, following seven concrete steps. We provide four main contributions: 1) a literature review of the assessment measures and strategies used to measure SoE; 2) a systematic categorization of SoE measures; 3) A categorization of the main test tasks used in SoE assessment; and 4) a toolbox consisting of seven steps as guidance to design SoE. We included several examples and tables to guide the user step by step through the design of an embodiment experience.","PeriodicalId":56306,"journal":{"name":"Human-Computer Interaction","volume":"47 1","pages":"322 - 351"},"PeriodicalIF":5.3,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80507609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-25DOI: 10.1080/07370024.2022.2039658
P. Hancock
To begin, I would first like to thank those who were kind enough to comment upon my paper, (Hancock, 2021) and even those whose comments were not so kind. For, after all, it is only by postulation, discussion, and resolution that we make those advances that propel science along. I am under an obligation to thank all of them who have given of their time and knowledge to make such observations. Initially, this present response was conceived so as to feature points of consensus and points of dispute and reference those commentators who participated in each. Yet I found such a framework to be stultifying, leaving me unable to address each commentary in the individual and bespoken form in which it was offered. Therefore, I have elected to respond to the commentaries seriatim, proceeding from those of greatest concord to those of greatest discord. This will be clear in what follows. Traducing the above assertion, I would however, like to identify one critical common cause; that of importance. In all of science we have to apply the “so what” criterion. In essence, what is the inherent importance and value of the effort. In the present case, there was consensus, including even those most vehement critics that the issue is vital (and see, Cheatham et al., 2019; Littman et al., 2021). The persuasion then of the members of the community sampled, is that autonomous systems and their potential for adverse impacts must concern us and concern us now. In that sense, I am highly gratified that my paper struck this common chord and hope that it also resonates with the greater community of readers.
首先，我要感谢那些对我的论文(Hancock, 2021)发表评论的人，甚至那些评论不那么友好的人。因为，毕竟，只有通过假设、讨论和决议，我们才能取得推动科学发展的进步。我有义务感谢所有贡献时间和知识进行这些观察的人。最初，本答复的构思是为了突出协商一致的观点和争议的观点，并提及每一种观点的评论者。然而，我发现这样的框架是愚蠢的，使我无法以提供的个人和口头形式来处理每一条评论。因此，我选择对这些评论逐一作出回应，从最一致的评论到最不一致的评论。这一点在下文中将会很清楚。然而，我想指出一个关键的共同原因，以驳斥上述断言;重要的事。在所有的科学中，我们都必须应用“那又怎样”的标准。从本质上讲，努力的内在重要性和价值是什么。在目前的情况下，人们达成了共识，甚至包括那些最激烈的批评者，认为这个问题至关重要(见Cheatham et al.， 2019;Littman et al.， 2021)。然后，抽样社区成员的说服是，自治系统及其潜在的不利影响必须引起我们的关注，现在就必须引起我们的关注。从这个意义上说，我非常高兴我的论文引起了大家的共鸣，并希望它也能引起广大读者的共鸣。
{"title":"Advisory adumbrations about autonomy’s acceptability","authors":"P. Hancock","doi":"10.1080/07370024.2022.2039658","DOIUrl":"https://doi.org/10.1080/07370024.2022.2039658","url":null,"abstract":"To begin, I would first like to thank those who were kind enough to comment upon my paper, (Hancock, 2021) and even those whose comments were not so kind. For, after all, it is only by postulation, discussion, and resolution that we make those advances that propel science along. I am under an obligation to thank all of them who have given of their time and knowledge to make such observations. Initially, this present response was conceived so as to feature points of consensus and points of dispute and reference those commentators who participated in each. Yet I found such a framework to be stultifying, leaving me unable to address each commentary in the individual and bespoken form in which it was offered. Therefore, I have elected to respond to the commentaries seriatim, proceeding from those of greatest concord to those of greatest discord. This will be clear in what follows. Traducing the above assertion, I would however, like to identify one critical common cause; that of importance. In all of science we have to apply the “so what” criterion. In essence, what is the inherent importance and value of the effort. In the present case, there was consensus, including even those most vehement critics that the issue is vital (and see, Cheatham et al., 2019; Littman et al., 2021). The persuasion then of the members of the community sampled, is that autonomous systems and their potential for adverse impacts must concern us and concern us now. In that sense, I am highly gratified that my paper struck this common chord and hope that it also resonates with the greater community of readers.","PeriodicalId":56306,"journal":{"name":"Human-Computer Interaction","volume":"29 1","pages":"263 - 280"},"PeriodicalIF":5.3,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90557112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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