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THE RELATIONSHIP BETWEEN HUMAN AND VIRTUAL AGENTS: A LIFE CYCLE VIEW

2023 | European Conference On Information Systems | Citations: 0
Authors: Huang, Rong; Bai, Yang; Li, Hongxiu; Suomi, Reima

Abstract: Virtual agents powered by artificial intelligence (AI) have been implemented i ... Expand

Abstract: Virtual agents powered by artificial intelligence (AI) have been implemented in different service contexts, leading to changes in our daily lives. Previous studies have examined individual users' motivations to use virtual agents and the impact of virtual agents as social objects on users. However, there is a lack of knowledge on the relationship development between human and virtual agents, which could help understand the role of virtual agents in societies. In this work, we chose the mobile app Replika as our research context and used a topic-modeling approach to explore the major topics covered in online reviews about Replika on Twitter. Applying social penetration theory, we identified four stages of the relationship between users and Replika, including relationship formation, exploration, maintenance, and destruction or termination. Our findings contribute to the literature by unraveling a life circle of the relationship development between human and virtual agents. Collapse

Semantic filters: social penetration theory

Topics: Twitter robotic social media application programming interface Python

Methods: topic model term frequency–inverse document frequency computational algorithm latent dirichlet allocation

Theories: social penetration theory
EDUCATING, EVALUATING, ADVISING, AND ENTERTAINING - DIGITAL INFLUENCERS AND THEIR NARRATIVES

2023 | European Conference On Information Systems | Citations: 0
Authors: Richter, Shahper; Richter, Alexander

Abstract: Organizations have been increasingly exploring storytelling as a valuable strat ... Expand

Abstract: Organizations have been increasingly exploring storytelling as a valuable strategy to engage customers to increase their attention and retention. Social media platforms like Instagram have become essential tools for many brands to use storytelling through influencers. In this study, we explore the narratives constructed by digital influencers, as they play a crucial role in shaping their brand image and engagement with their audience. By analysing the narrative strategies used by digital influencers, we contribute to a deeper understanding of the role and impact of digital storytelling. We identify, illustrate, and discuss four narrative strategies: educating, evaluating, advising, and entertaining. Our study motivates further research to develop a detailed conceptual understanding of digital influencers and digital storytelling. Collapse

Semantic filters: social penetration theory

Topics: Instagram social media website Snapchat marketing management

Methods: qualitative interview theory development narrative analysis qualitativ observational study participant observation

Theories: flow theory social penetration theory communication theory
Companion Bot Development: A Human Interaction-based Theoretical Lens

2023 | Americas Conference on Information Systems | Citations: 0
Authors: THIMMANAYAKANAPALYA, Sagarika Suresh; Sharman, Raj; Samant, Harshada

Abstract: Companion bots have the potential to alleviate loneliness and provide companion ... Expand

Abstract: Companion bots have the potential to alleviate loneliness and provide companionship. To accomplish this, the bot must generate appropriate conversations that are contextually relevant and interesting to the human companion. This research synthesizes 48 papers pertinent to companion bots. To understand and develop critical companion bot elements of the conversations, this paper explores prior literature to understand the human-interaction theories that have been leveraged. Collapse

Semantic filters: social penetration theory

Topics: social presence robotic artificial intelligence data breach usage intention

Methods: chatbot literature sample literature study synthesis robustness check

Theories: social presence theory social response theory social penetration theory communication theory attachment theory
Identifying Intimacy of Self-Disclosure: A Design Based on Social Penetration Theory and Deep Learning

2023 | Americas Conference on Information Systems | Citations: 0
Authors: Zhang, Xi; Qi, Chengzhu; Wei, Xin; He, Wei

Abstract: Research on the peer-to-peer (P2P) platforms, privacy, and digitalized business ... Expand

Abstract: Research on the peer-to-peer (P2P) platforms, privacy, and digitalized business environment has overwhelmingly treated the intimacy of self-disclosure as a survey-based, subjective, and cognitive construct. A few studies have conducted topic analysis using objective data, but are still limited by the difficulty of capturing the degree of intimacy, which hinders the development of the transaction antecedents of P2P platforms. Building upon social penetration theory, we propose an innovative approach to identifying the intimacy of self-disclosure using a deep learning algorithm and an expertcompiled intimacy corpus in the context of P2P platforms. Adopting a sample dataset of 10,000 hosts' self-descriptions in Airbnb, we introduce the computational and verification process of operationalizing the intimacy of self-disclosure. Through an empirical study, we demonstrate the theoretical feasibility of our quantification method of intimacy and show the potential of using deep learning to measure selfdisclosure, expanding the theoretical development of social penetration theory and self-disclosure. Collapse

Semantic filters: social penetration theory

Topics: Airbnb peer-to-peer model data breach sharing platform electronic business

Methods: computational algorithm deep learning experiment machine learning delphi study

Theories: social penetration theory
Post-Story: Influence of Introducing Story Feature on Social Media Posts
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2022 | Journal of Management Information Systems | Citations: 0
Authors: Alibakhshi, Reza; Srivastava, Shirish C.

Abstract: Driven by the need to enhance user traffic on social media (SM) platforms for in ... Expand

Abstract: Driven by the need to enhance user traffic on social media (SM) platforms for increasing their advertising revenues, SM platforms are experimenting with new content creation features. However, it is unclear if such initiatives are also beneficial for SM profile owners such as influencers, who are the prime content creators on the SM platforms who use SM posts to build their influence within their network of followers. Our study investigates the effect of introducing one such new SM feature: the "story" on the creation and consumption of SM posts. Leveraging social penetration theory, we hypothesize the influence of introducing story feature on (1) the frequency of SM post creation by profile owners and (2) the extent of follower engagement with SM posts. Employing a quasi-experimental design, we find that the introduction of the story feature reduces the frequency of SM post creation, but the enhanced self-disclosure through the story feature increases follower engagement with the SM posts. However, these effects are moderated by the situating culture of the SM communities: while low-power-distance cultures value profile owners' self-disclosure, high-power-distance cultures exhibit a mixed influence. Advancing literature on social penetration theory and SM user engagement, our study demonstrates that new self-disclosive SM content creation features do not necessarily benefit all the concerned stakeholders and that the effectiveness of such features might vary from one community to another. Hence, the intended impact of introducing new SM features needs to be carefully evaluated by SM platforms in a holistic manner. Collapse

Semantic filters: social penetration theory

Topics: Instagram technology adoption information system use social network unintended consequence

Methods: experimental group econometric modeling quasi experiment theory development regression analysis method

Theories: social penetration theory
Attachment Theory as a Framework to Understand Relationships with Social Chatbots: A Case Study of Replika
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2022 | HICSS | Citations: 10
Authors: Xie, Tianling; Pentina, Iryna

Abstract: With increasing adoption of AI social chatbots, especially during the pandemic-r ... Expand

Abstract: With increasing adoption of AI social chatbots, especially during the pandemic-related lockdowns, when people lack social companionship, there emerges a need for in-depth understanding and theorizing of relationship formation with digital conversational agents. Following the grounded theory approach, we analyzed in-depth interview transcripts obtained from 14 existing users of AI companion chatbot Replika. The emerging themes were interpreted through the lens of the attachment theory. Our results show that under conditions of distress and lack of human companionship, individuals can develop an attachment to social chatbots if they perceive the chatbots’ responses to offer emotional support, encouragement, and psychological security. These findings suggest that social chatbots can be used for mental health and therapeutic purposes but have the potential to cause addiction and harm real-life intimate relationships. Collapse

Semantic filters: social penetration theory

Topics: artificial intelligence mobile application Reddit online community robotic

Methods: chatbot qualitative interview grounded theory qualitative coding generative pre-trained transformer 3

Theories: attachment theory uncanny valley theory social response theory social penetration theory
A Relational Theory of Reputational Stability and Change
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2022 | Organization Science | Citations: 0
Authors: Zavyalova, Anastasiya; Bundy, Jonathan; Humphrey, Stephen E.

Abstract: An ongoing discussion in organizational studies has focused on the path-dependen ... Expand

Abstract: An ongoing discussion in organizational studies has focused on the path-dependent nature of organizational reputation. To date, however, there has been little explanation about when and why some constituents’ reputation judgments remain stable, whereas others are more prone to change. We contribute to this research by developing a relational theory of reputational stability and change. Our fundamental argument is that differences in constituent-organization relationships, as well as in the reputational communities that surround these relationships, affect the stability and change of reputation judgments. First, we highlight three relationship characteristics—favorability, history, and directness—and theorize that the reputation judgments of constituents with more unfavorable, longer, and more direct relationships with an organization are more stable, whereas the reputation judgments of constituents with more favorable, shorter, and more indirect relationships with the organization are less stable. We then develop the concept of reputational communities as a key source of indirect information about organizations. We highlight that the immediacy, size, and level of agreement within reputational communities affect how influential they are in changing individual constituents’ reputation judgments. Specifically, we propose that more immediate and larger reputational communities with a higher level of agreement are most likely to change individual constituents’ reputation judgments, whereas more distant and smaller reputational communities with a lower level of agreement are least likely to do so. Overall, we position constituents’ relationships with an organization and the communities that surround these relationships as central elements for understanding reputational stability and change. Collapse

Semantic filters: social penetration theory

Topics: resistance to change anonymity

Methods: theory development

Theories: social impact theory social penetration theory
Examining the determinants of the count of customer reviews in peer-to-peer home-sharing platforms using clustering and count regression techniques
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2020 | Decision Support Systems | Citations: 0
Authors: Biswas, Baidyanath; Sengupta, Pooja; Chatterjee, Dwaipayan

Abstract: The sharing economy has experienced massive growth in the short-term shared-home ... Expand

Abstract: The sharing economy has experienced massive growth in the short-term shared-home rental industry. However, few studies have investigated the determinants of the number of customer reviews received by these shared-homes. To fill this gap, we were motivated to propose an analytical framework that identified these determinants, both explicit and implicit. We applied Poisson, Quasi-Poisson, and Negative Binomial regressions with a dataset consisting of Airbnb properties from ten different cities worldwide, while successful bookings were proxied by the count of customer reviews posted by guests. We performed a cluster analysis based on the properties to generate homogeneous “cluster cities” and performed the regressions separately for each cluster. Among host-generated features, superhost, host duration, bedrooms, and amenities became significant. Among user-generated features, overall review scores and negative sentiments were significant. We also found that the “superhost” badge moderated the effects of host-generated content on the count of customer reviews. Consequently, guests paid a higher “price per night” for “superhost” properties, while they overlooked crucial attributes such as “website features.” Through these novel “cluster-specific” recommendations, our study extends the existing theories and contributes to the literature of decision analytics and tourism management. Finally, we performed a sensitivity analysis to check for the timeliness and robustness of these determinants. Collapse

Semantic filters: social penetration theory

Topics: Airbnb peer-to-peer model website participatory design collaborative consumption

Methods: cluster analysis regression analysis method design methodology sensitivity analysis historical review

Theories: social exchange theory social penetration theory transaction cost economics
A Machine Learning Method for Measuring Information Disclosure in Sharing Economy Platforms

2020 | International Conference on Information Systems | Citations: 11
Authors: Wei, Xin; He, Wei; Zhang, Xi; Zhao, Chuang; Zhao, Hongke

Abstract: Research on e-commerce, social technologies and privacy has overwhelmingly treat ... Expand

Abstract: Research on e-commerce, social technologies and privacy has overwhelmingly treated information disclosure as a survey-based, subjective, and unidimensional construct. A few studies employing semantic analysis on objective textual data, on the other hand, are constrained by the manual coding method with limited number of categories and thus prone to bias. Building upon the social penetration theory, we introduce an innovative method of measuring information disclosure using machine learning algorithms in the context of sharing economy platforms. We propose that information disclosure should be examined from two dimensions, i.e., breadth and depth, and machine learning techniques could effectively compute the high-volume factual data of information disclosure. Using 1,200 hosts’ self-description data in Airbnb as an example, we report the computational and evaluation processes of operationalizing information disclosure. The research thus provides new theoretical lens and empirical support through which information disclosure in digital age could be better understood and efficiently assessed. Collapse

Semantic filters: social penetration theory

Topics: data breach Airbnb sharing platform privacy internet technology

Methods: computational algorithm experiment machine learning topic model latent dirichlet allocation

Theories: social penetration theory
How do consumers in the sharing economy value sharing? Evidence from online reviews
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2020 | Decision Support Systems | Citations: 33
Authors: Xu, Xun

Abstract: With the rapid development of information technology, platform-facilitated colla ... Expand

Abstract: With the rapid development of information technology, platform-facilitated collaborative consumption has recently become attractive to consumers. A comparative study of consumers' online review behavior and its impact on overall satisfaction and demand in the accommodation-sharing economy and the hotel industry indicates that consumers' perceptions and behavior change gradually with changes in the level of sharing—from no sharing when staying in hotel rooms to intensive sharing when sharing rooms through collaborative consumption. Online consumer reviews focus on product and service attributes, and the influential factors of customer satisfaction and demand differ when consumers are at different accommodation-sharing levels. Not all attributes described in online reviews influence overall customer satisfaction. With a higher level of sharing, consumers' valuation changes from more to less tangible attributes. Consumers at a higher sharing level care more about social interaction and economic value than consumers at a lower sharing level. Transaction costs, particularly the information search and acquisition costs, play an important role in influencing customer purchase decisions in the sharing economy. Consumers refer to direct information for tangible attributes and to previous consumers' online reviews for intangible attributes to familiarize themselves with details before making purchase decisions. Our study provides implications that help platforms and hosts better target consumer segments with different sharing levels and more effectively utilize online reviews to generate positive electronic word of mouth to enhance consumer demand and the performance of platform economics. Collapse

Semantic filters: social penetration theory

Topics: online review sharing platform Airbnb online word of mouth word of mouth

Methods: natural language processing Student's t-test term frequency–inverse document frequency data transformation exploratory factor analysis

Theories: transaction cost economics social penetration theory social exchange theory

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Found 21 articles

THE RELATIONSHIP BETWEEN HUMAN AND VIRTUAL AGENTS: A LIFE CYCLE VIEW

EDUCATING, EVALUATING, ADVISING, AND ENTERTAINING - DIGITAL INFLUENCERS AND THEIR NARRATIVES

Companion Bot Development: A Human Interaction-based Theoretical Lens

Identifying Intimacy of Self-Disclosure: A Design Based on Social Penetration Theory and Deep Learning

Post-Story: Influence of Introducing Story Feature on Social Media Posts

Attachment Theory as a Framework to Understand Relationships with Social Chatbots: A Case Study of Replika

A Relational Theory of Reputational Stability and Change

Examining the determinants of the count of customer reviews in peer-to-peer home-sharing platforms using clustering and count regression techniques

A Machine Learning Method for Measuring Information Disclosure in Sharing Economy Platforms

How do consumers in the sharing economy value sharing? Evidence from online reviews