Accepted for publication in the Journal of the Association for Information Systems (JAIS)
On the Design of and Interaction with Conversational Agents:
An Organizing and Assessing Review of
Human-Computer Interaction Research
University of Göttingen
Alfred Benedikt Brendel
University of Göttingen
Conversational agents (CAs), described as software with which humans interact through
natural language, have increasingly attracted interest in both academia and practice, due to
improved capabilities driven by advances in artificial intelligence and, specifically, natural
language processing. CAs are used in contexts like people’s private life, education, and
healthcare, as well as in organizations, to innovate and automate tasks, for example in
marketing and sales or customer service. In addition to these application contexts, such agents
take on different forms concerning their embodiment, the communication mode, and their
(often human-like) design. Despite their popularity, many CAs are not able to fulfill expectations
and to foster a positive user experience is a challenging endeavor. To better understand how
CAs can be designed to fulfill their intended purpose, and how humans interact with them, a
multitude of studies focusing on human-computer interaction have been carried out. These
have contributed to our understanding of this technology. However, currently a structured
overview of this research is missing, which impedes the systematic identification of research
gaps and knowledge on which to build on in future studies. To address this issue, we have
conducted an organizing and assessing review of 262 studies, applying a socio-technical lens
to analyze CA research regarding the user interaction, context, agent design, as well as
perception and outcome. We contribute an overview of the status quo of CA research, identify
four research streams through a cluster analysis, and propose a research agenda comprising
six avenues and sixteen directions to move the field forward.
Keywords: Conversational Agent; Chatbot; Digital Assistant; Virtual Human; Robot;
Organizing Review; Assessing Review; Human-Computer Interaction.
Technological advances continue to drive the digital transformation and change the way we
live, work, and interact with one another (Davenport & Kirby, 2016; McAfee & Brynjolfsson,
2017). Advances in artificial intelligence (AI), such as machine learning and natural language
processing, are essential drivers in this development, making machines seemingly intelligent
and capable of conversing in natural language, creating meaning in written or spoken words
(Brynjolfsson & McAfee, 2016). Conversational agents (CAs), benefiting from these advances,
increasingly attract interest in research as well as in practice (McTear, 2017). Interacting with
a system through natural language promises to increase the ease of use and to ensure faster
completion of user requests, while creating the feeling of a human-like interaction (Følstad &
Brandtzæg, 2017). For users, CAs can function in various contexts, ranging from digital
personal assistants in mobile devices, such as Apple’s Siri or Google Assistant, to specific
purposes like in-car assistance (Laumer et al. 2019). For organizations, CAs offer the
possibility of automating and innovating processes in areas such as human resources (Liao et
al., 2018), customer service (Ashktorab et al., 2019), and sales (Vaccaro et al., 2018).
Recent examples developed by Facebook and Google underline CAs’ popularity and potential.
After launching its new Messenger platform, more than 100,000 bots appeared on Facebook
within the first year (Johnson, 2018). Further, Google demonstrated the future potential of CAs
at its 2018 developer conference by having their assistant autonomously make a hairdresser’s
appointment via a telephone conversation with a real person on the other end (Welch, 2018).
Gartner predicts that 70 percent of white-collar workers will interact with systems using
conversational interfaces in their daily work by 2022 (Goasduff, 2019). Despite their potential,
many CAs do not meet expectations and are discontinued because of flaws related to their
design, such as unappealing appearance, lacking conversational abilities, or unrealizable user
expectations (Ashktorab et al., 2019; Ben Mimoun et al., 2012; Lahoual & Fréjus, 2019; Luger
& Sellen, 2016). The complexity of designing such agents is particularly driven by the human’s
social responses to the cues incorporated in these artifacts, such as interaction via natural
language, human names, or these agents’ social roles (Feine et al., 2019; Seeger et al., 2018).
Such social responses affect the individual’s perception of these agents, and they foster high
user expectations, which are often not in line with the agents’ actual capabilities (Ben Mimoun
et al., 2012; Luger & Sellen, 2016).
In summary, designing CAs and understanding how they interact with humans remain
substantial challenges in research as well as practice (Schuetz & Venkatesh, 2020). In
research, a multitude of studies, particularly in the field of human-computer interaction (HCI),
have contributed to addressing this challenge. In particular, a renewed interest in research on
CAs emerged in the information systems (IS) and computer science (CS) communities
(McTear, 2017; Rzepka & Berger, 2018). Researchers from both disciplines investigate CAs
with different technological properties, such as the different communication modes as in voice
or text (Cho, 2019; Schroeder & Schroeder, 2018), or embodiment which can be virtual or
physical (Araujo, 2018; Seymour et al., 2018), explore different contexts, such as interactive
tutoring (Fryer et al., 2017) or customer service (Xu et al., 2017). Moreover, they focus on
different aspects related to the perception of such agents and interaction outcomes, for
example, anthropomorphism (Araujo, 2018; Seeger et al., 2018), trust (Benlian et al., 2019;
Elson et al., 2018; Schuetzler et al., 2014), or number of digital products sold (Kim et al., 2018).
In short, the design of and interaction with present-day CAs offer very many research
opportunities, and a variety of studies are available in this research area.
However, this variety of studies also comes with the challenge of gaining an overview of the
topic. Existing reviews on CAs focus on selected aspects of the interaction with and design of
CAs, such as trust (Zierau et al., 2020) or social cues (Feine et al., 2019), they investigate CAs
in specific contexts like the digital workplace (Wolff et al., 2019), or, more abstractly, review
AI-based applications (Rzepka & Berger, 2018). An overview of CA research covering different
contexts, types of CAs, and users’ perceptions is not available. Without such an overview,
researchers face difficulties in systematically identifying and addressing research gaps and
knowledge upon which to build. To address this issue, in his project we organize existing
studies on CAs, assess the status quo, and contribute avenues as well as directions for future
work in this area.
The remainder of this article is organized as follows: first, we provide a brief overview of CAs
and introduce the framework for our analysis. Next, we describe our research approach,
present overarching observations gained in our literature review, and identify four research
streams based on a cluster analysis. Building on these results, we derive six avenues
containing sixteen specific directions for future research in this area.
2 Research Background
CAs are based on the idea of interacting with users through natural language as in human-to-
human conversations (Dale, 2016; McTear, Callejas, & Griol, 2016). CAs are variously and
interchangeably termed as digital assistant, chatbot, interactive agent, etc. (Maedche et al.,
2019; Stieglitz et al., 2018). Different definitions are given for a CA, such as an agent that
“interacts with users, turn by turn by using natural language” (Comendador et al., 2015, p.
137), or “computer programs designed to respond to users in natural language, thereby
mimicking conversations between people” (Miner et al., 2016, p. 619), or a concept to “achieve
some result by conversing with a machine in a dialogic fashion, using natural language” (Dale,
2016, page 811). While these definitions each highlight different characteristics of CAs, such
as turn-taking (Comendador et al., 2015) or mimicking conversations (Miner et al., 2016), they
all share the idea of natural language interaction.
Thus, for this research, we consider CAs to be technological artifacts with which users interact
through natural language, both in written and spoken form.
2.1 An Overview of Conversational Agents
The basic idea to interact with technological artifacts through natural language already
emerged in the 1960s when Joseph Weizenbaum (1966) developed ELIZA. While the
fundamental idea of natural language interaction is the same for all CAs, these agents take on
different forms that are distinguished by communication mode, embodiment, and the context
in which they are used (Cassell et al., 1999; Cowell & Stanney, 2005; Gnewuch et al., 2017):
• Communication mode: CAs can communicate via voice (Cowan et al., 2015), text
(Schroeder & Schroeder, 2018), or both (Cho, 2019)
• Embodiment: CAs can be disembodied (Araujo, 2018), virtually embodied
(Diederich et al., 2019), or physically embodied (Nunamaker et al., 2011)
• Context: CAs can be used for general-purpose conversations or be domain-
specific, for example, for a specific task or business function (Gnewuch et al., 2017).
Multiple studies have addressed the interaction with CAs regarding user responses, such as
user trust (Elson et al., 2018; Seeger et al., 2017), authenticity (Wünderlich & Paluch, 2017),
or empathy (Leite et al., 2013; McQuiggan & Lester, 2007). CAs have been studied in different
contexts, such as in legal research (Sugumaran & Davis, 2001), lie detection (Nunamaker et
al., 2011), financial advisory (Morana et al., 2020), or data analytics (Matsushita et al., 2004).
Concerning their application in organizations, recent research has attended to different
business functions, such as human resources (Diederich, Brendel, & Kolbe, 2020; Liao et al.,
2018) or marketing and sales (Qiu & Benbasat, 2009; Vaccaro et al., 2018). In practice,
different CAs have emerged over time, such as IKEA’s Anna (Wakefield, 2016).
In the past, due to CAs reliance on simple pattern-matching, they were limited regarding both
understanding a user’s intent (i.e., the meaning behind a message) and providing purposeful
feedback (Berg, 2015; Knijnenburg & Willemsen, 2016). This often leads to CAs being
discontinued (Ben Mimoun et al., 2012). However, with advances in natural language
processing, as well as machine learning, CA capabilities have greatly improved in recent years
and led to a renewed interest in both research and practice (McTear, 2017; Oracle, 2016).
Today, most smartphones are equipped out-of-the-box with voice-based CAs, such as Google
Assistant or Siri (Burton & Gaskin, 2019), and devices such as Amazon’s Alexa are used in
private households (Purington et al., 2017) or team collaboration (Winkler et al., 2019). Further,
companies are exploring the potential of physically embodied CAs, such as SoftBank’s
humanoid robot “Pepper,” to provide services (Stock & Merkle, 2018a).
Similarly, text-based CAs, often referred to as chatbots, are increasingly available in different
contexts. For example, the Dutch airline KLM introduced a text-based CA that helps users to
find and book flights (Vogel-Meijer, 2018), the coffee shop chain Starbucks designed a CA
called “Barista” to support ordering products (Perez, 2016), and the American railroad
company Amtrak offers customer services that answer five million requests per year through
“Julie” (NextIT, 2018). Regarding CAs with a virtual interactive embodiment IPSoft developed
the agent known as “Amelia,” that, for example, automates information technology (IT) service
desk tasks for a medical supplies manufacturer and offers customer services for a telecom
provider (Ipsoft, 2020) (see Figure 1).
“Barista” by Starbucks
Virtual interactive agent
“Amelia” by IPsoft
Service robot “Pepper” by
Figure 1. Examples of Conversational Agents
2.2 Human-Computer Interaction
Human-Computer Interaction (HCI) research as a research domain dates back to the early
1980s (Card et al., 1980; Carroll, 2020). More recent HCI research investigated “the ways
humans interact with information, technologies, and tasks, especially in business, managerial,
organizational, and cultural contexts” (Zhang et al., 2002, p. 333). The first ACM Computer-
Human Interaction (CHI) conference, the premier conference on HCI, was established in 1982
(SIG CHI, 2020). Since then, this research field expanded, also into other disciplines such as
CS, (cognitive) psychology, human factors, and IS. In the IS discipline, Gerlach and Kuo (1991)
recognized the interdisciplinary nature of HCI, after which numerous conceptual and empirical
publications on HCI appeared in various disciplines (Carroll, 2020; Olson & Olson, 2003;
Zhang & Li, 2004).
The interaction between humans and computers can be related to the concept of socio-
technical systems. A socio-technical system relies on the interplay of three key elements
(Goodhue & Thompson, 1995; Heinrich et al., 2011): the human that wants to achieve a
specific goal; the task that the user has to accomplish in order to achieve the goal; the
technology (i.e., software, hardware, and data) the user utilizes to complete the task. Taking a
socio-technical perspective, Zhang and Li (2005) assessed the intellectual development of HCI
research in the IS discipline, proposing an extended framework that consists of humans
interacting with technology in a specific context, ultimately leading to a set of outcomes and
perceptions. This framework adds the dimension of perception change through technology to
our understanding of HCI.
In CA research, where the anthropomorphic design, related human-like perception, and
application context are key research objects, the socio-technical framework provides a
valuable lens to investigate the continuously growing body of knowledge on the interaction
between humans and CAs. Therefore, drawing on the framework by Zhang and Li (2005), we
will derive a framework to organize existing research, conduct a literature review, and analyze
the identified studies in order to determine current trends as well as outline an agenda for
3 Method: A Review of HCI Research on CAs
In this section, we outline our study’s research framework as well as the method we used to
identify and analyze research on CAs in IS and CS research.
3.1 A Framework for Human-Computer Interaction via Natural Language
As a lens for our review of CA studies, we draw on and adapt the research framework Zhang
and Li (2005) proposed. Drawing on this framework, we review four dimensions of CA studies:
context, human, agent, and perception and outcome (see Figure 2).
Figure 2. Research Framework
- Cultural Background
- CA Experience
- Task Experience
- Cognitive Style
- Individual Task Support
- Team Collaboration
- Customer Interface
- Individual Task Support
- Communication Mode
- (Human) Identity
- Verbal Communication
- Non-verbal Communication
Perception and Outcome
First, we consider the context in which the CA is applied. On an abstract level, we can
distinguish professional and private contexts. Professional contexts comprise, for example, the
internal use of CAs for individual task support (e.g., scheduling appointments) (Bittner &
Shoury, 2019; Fast et al., 2017), CAs in team collaboration (e.g., managing tasks within a
team) (Bittner et al., 2019; Seeber et al., 2019), and the customer interface (e.g., providing
services) (Diederich et al., 2021; Vaccaro et al., 2018; Wünderlich & Paluch, 2017). In private
life, CAs are primarily used for individual task support (e.g., searching for information online)
(Porcheron et al., 2018), education (Graesse et al., 2017), or personal health (Yokotani et al.,
2018). Further, studies that do not fit any of these two contexts are classified as “others” (e.g.,
CAs for legal research (Sugumaran & Davis, 2001)), and articles that do not fit a specific
context are considered “generic” (e.g., Candello et al., 2019).
Second, the dimension human refers to the user interacting with the CA. In general, users’
characteristics include demographic aspects such as age, gender, cultural background,
experience both with CAs and with the task at hand, as well as aspects related to individual
dispositions, including a user’s personality or cognitive style (Zhang & Li, 2005)
The third dimension, agent, includes characteristics of the CA itself. These characteristics
comprise the primary communication mode, i.e., the agent adjusted for interaction via speech,
text, or both (Gnewuch et al., 2017), as well as the agent’s embodiment, which can be physical,
as with service robots (Stock & Merkle, 2018b), virtual with an interactive avatar (Seymour et
al., 2018), virtual with a static avatar (Wünderlich & Paluch, 2017), or non-existent, for
example, a CA without any visual embodiment (Abul et al., 2018). Further, CAs exhibit different
design components comprising an identity (e.g., name), verbal communication (e.g.,
expression of emotions), and non-verbal communication (e.g., response delays to indicate
thinking and typing) (Seeger et al., 2018).
Finally, perception and outcome refer to the investigated topic relating to the use and impact
of the technology. This dimension shows how users perceive the CA during their interaction,
as well as the interaction’s impact. Following Zhang & Li's (2005) suggestions, these topics
can be divided into eight distinct categories, as shown in Table 1.
Humanness, similarity, reciprocity, social distance, social presence
Usefulness, ease of use
Attitude, satisfaction, preference
Productivity, effectiveness, efficiency
Affect, hedonic quality, enjoyment, humor, intrinsic motivation
Trust, risk, loyalty, security, privacy
Learning models, learning processes, general training
Ethical belief, ethical behavior, ethics
Influence, interdependence, interference, agreement/disagreement
Table 1. Categories and Exemplary Constructs in the Dimension Perception and Outcome
To complement the dimensions of the research framework, we further included the research
method, unit of analysis, and the theoretical grounding of the study in our review. Thereby, we
sought to better understand the methodological focus of current CA research and theories
used to inform CA design, and to improve insight on the user’s interaction with the CA.
Following Bariff & Ginzberg's (1982) explanations, we differentiate between studies on the
individual level (e.g., user reactions toward CAs), on the group level (e.g., CAs as team
members), on the organizational level (e.g., use cases for CAs in enterprises), and on the inter-
organizational level. To analyze the methods used in the studies, we drew on the research
methods Banker & Kauffman (2004) described and used in their assessment of IS research.
3.2 Identification, Coding, and Analysis of CA Literature
To collect and analyze existing studies on CAs in IS and CS research, we followed a process
based on the combination of the systematic literature review guidelines by Webster and
Watson (2002), vom Brocke et al. (2009), and Bandara et al. (2015). As our goal is to provide
an organizing and assessing review of research on CAs (Leidner, 2018), we focused on
research outcomes of the studies in the scope of this review, choosing to organize them
conceptually (Cooper, 1988) using the HCI research framework adapted from Zhang & Li's
(2005) seminal work. We took a three step approach (Table 2) which meant collecting literature
for the review (step 1), coding the studies qualitatively using the research framework (step 2),
and conducting analyses (step 3) by creating a concept matrix, investigating CA research over
time and clustering the studies to identify research streams.
Conduct database search
and identify relevant CA
Code CA studies using
dimensions from the
Create concept matrix
and descriptive statistics,
Concept matrix and four
research streams (cluster)
Table 2. Research Approach
To initiate our literature review, we identified the relevant outlets for our search process in step
1. The IS and CS communities conducted extensive work on CAs as part of HCI research,
thus we purposefully selected journals from these fields. For the IS discipline, we focused on
the Basket of Eight, and for CS, we selected four well-regarded journals that focus on HCI (i.e.,
Advances in Human-Computer Interaction, ACM Transactions on Computer-Human
Interaction, Computers in Human Behavior, and the International Journal of Human-Computer
Studies) for our search. We extended the data search by adding high-quality conference
presentations to take more recent work into account, as renewed interest in CA research
emerged only a few years ago (Pfeuffer, 2019; Rzepka & Berger, 2018). Thus, we
complemented our review with proceedings from major IS conferences (ICIS, ECIS, HICSS,
AMCIS, and PACIS) and CS conferences (ACM CHI Conference on Human Factors in
Computing Systems). To collect appropriate studies, we used the Web of Science, AISeL,
ACM Digital Library, and the websites of the respective outlets. We conducted the search in
January 2019 and updated it in November 2019, using the following search query:
((Conversational OR Interactive OR Virtual) AND Agent) OR Chatbot OR Digital Assistant)
The query returned 8,768 results in all, for which we scanned titles, abstracts, and content to
identify studies that focus on CAs. Further, we conducted a forward and backward search to
identify additional studies. After this search and filtering process, 262 studies remained in our
database (Table 3, outlets in alphabetical order).
ACM CHI Conference on Human Factors in Computing Systems
ACM Transactions on Computer-Human Interaction
Advances in Human-Computer Interaction
Americas Conference on Information Systems
Computers in Human Behavior
European Conference on Information Systems
Hawaii International Conference on System Sciences
International Conference on Information Systems
International Journal of Human-Computer Studies
Journal of Management Information Systems
Journal of the Association for Information Systems
Pacific Asia Conference on Information Systems
Note: Some studies were identified through backward and forward search
Table 3. Literature Search Results
In the second step of our research, we coded the 262 studies using the dimensions of our
research framework (see Figure 2). Complementing the framework dimensions, we coded the
research approach (empirical or conceptual), method (e.g., laboratory experiment or survey),
unit of analysis (e.g., technology or individual), and noted the study’s theoretical grounding
(e.g., Similarity-Attraction theory (Byrne, 1971; Byrne & Griffitt, 1969) or Computers Are Social
Actors Paradigm (Nass & Moon, 2000; Reeves & Nass, 1996)).
To ensure the reliability of the coding, three of the authors coded a random set of twenty
studies independently in a pre-test using a preliminary coding guideline. The authors then
discussed the coded studies to identify discrepancies and shortcomings in the codes. Based
on the results of this pre-test, we adjusted the codes and the coding guidelines (e.g., adding
“technology” as a unit of analysis for studies exclusively containing artifact descriptions or
selection of multiple codes in the dimensions “perception and outcome”) (see Appendix A).
After this pre-test, one author coded the remaining studies and, when required, discussed
uncertainties with the other authors. Based on the coded 262 studies, we carried out three
analyses in step 3 to assess the state of research on CAs. First, we created a concept matrix
to foster a conceptual understanding of the studies, going beyond descriptive content
summaries, and we viewed the distribution of characteristics in the coding dimensions
(Webster & Watson, 2002). Second, we conducted a cluster analysis to identify research
streams in the extant CA literature empirically. Following Punj & Stewart’s (1983)
recommendations, we first identified a suitable number of clusters using a hierarchical
clustering approach, Ward’s method with squared Euclidean distance, and afterwards used k-
means as an iterative partitioning technique. We selected a solution with four clusters after
reviewing the scree plot dendrogram and coefficient delta (see Appendix B). The k-means
procedure then computed seven iterations until we achieved no further significant
enhancements. Finally, we created graphical representations to show how CA research had
developed over time, both by discipline (IS, CS) and by research stream.
Our organizing and assessing review offers insight regarding the state of CA research and
allows us to derive recommendations to advance our understanding of the design of and
interaction with CAs. In this section, based on our review, we first describe our general
observations on CA research. Next, we outline four research streams identified through a
cluster analysis of the studies in our sample.
4.1 Overarching Observations
In accordance with our review framework (see Figure 2), we relate our findings to the technical
characteristics of the studied CAs (“agent”), the application context, the user (“human”), and
the perception and outcomes emerging from the interaction.
4.1.1 Agent Dimension
Regarding the agents we investigated (see Table 4), we found that around half of the studies
focused on communication with the agent via written text (e.g., chatbots (Adam & Klumpe,
2019; Vaccaro et al., 2018)), around 40 percent explored speech-based CAs (e.g., digital
assistants like Siri or Alexa (e.g., Burton & Gaskin, 2019; Winkler & Roos, 2019)), and nearly
10 percent studied both communication modes, mostly to identify commonalities or differences
in human interaction with such agents via text or speech (e.g., Schroeder & Schroeder, 2018).
Concerning the representation or embodiment of the CA, nearly half of the studies investigated
agents without any form of embodiment (i.e., without a static/interactive digital avatar or
physical appearance (e.g., Gnewuch et al., 2018; Schuetzler et al., 2018)). One-fourth of these
studies focused on virtual interactive representations (i.e., with a virtual human (e.g., Cafaro
et al., 2016; Krämer et al., 2013)). Further, 17 percent of the studies address CAs with virtual
static avatars (e.g., images (Seeger et al., 2018)), 8 percent explore agents with physical
embodiment (e.g., service robots (Stock & Merkle, 2018a, 2018b)), and a handful investigate
and compare multiple embodiments (e.g., Gong, 2008; Seymour et al., 2017).
Around half of the studies focus on verbal communication (e.g., dialogue repair strategies
(Corti & Gillespie, 2016) or expressing emotions (e.g., Beale & Creed, 2009; Niewiadomski &
Pelachaud, 2010), around one-third study the agent’s (human) identity (e.g., the impact of
agent representations on user perception (e.g., Vugt et al., 2010)), and one-fourth explores
non-verbal communication (e.g., response times (Gnewuch et al., 2018) or facial expressions
(De Rosis et al., 2003). Table 4 summarizes the types of CAs investigated in the studies.
Representation / Embodiment
1) Multiple selections possible
Table 4. Types of Agents investigated in the Studies
4.1.2 Context Dimension
Concerning the context in which CAs are applied (Table 5), 45 percent of the studies do not
explicitly specify a context, investigating only the interaction of humans and CAs generally
(e.g., Banks, 2018; Chaves & Gerosa, 2018). Around 21 percent of the studies addressed
professional contexts (e.g., customer service (Baier et al., 2018; Xu et al., 2017) or marketing
and sales (Kim et al., 2018; Vaccaro et al., 2018), 12 percent addressed education (e.g.,
interactive tutoring systems (Hobert & Wolff, 2019; Winkler & Roos, 2019), 9 percent dealt with
health applications (e.g., digital health advisors (Gambino et al., 2019; Powers & Kiesler,
2006), or behavior change agents (Sebastian & Richards, 2017). The remaining studies
focused on private individual task support (6.9%) (Porcheron et al., 2018; Purington et al.,
2017), multiple contexts (e.g., Meyer von Wolff et al., 2019), specific contexts that do not fit
the aforementioned categories (e.g., legal research (Sugumaran & Davis, 2001) or CAs as role
models (Rosenberg-Kima et al., 2008)).
Professional task support
Private task support
Table 5. CAs’ Application Contexts in the Studies
4.1.3 Human Dimension
Concerning the user, the vast majority of the studies did not distinguish between users
according to their characteristics (Table 6). For example, most experimental studies show that
there are no significant differences between control and treatment groups concerning
demographics, nor do they investigate the potential effects such characteristics have as control
variables. The influence of demographic characteristics such as users’ age (Chattaraman et
al., 2018; Kowalski et al., 2019) or gender (Braun & Alt, 2019; Meier et al., 2019) on the
perception of and interaction with CAs is discussed or controlled for in only a few of the
identified studies (10-12%). Similarly, only a fraction of the studies explores the impact of
individual experience with CAs (6%) (Fadhil & Villafiorita, 2017; Schroeder & Schroeder, 2018)
or the task at hand (2%) (Ashktorab et al., 2019; Laumer et al., 2019). Further user
characteristics, such as personality (5%) (Mou & Xu, 2017; Straßmann et al., 2018), cultural
features (2%) (Duan et al., 2018; Schlesinger et al., 2018), cognitive style (1%) (Crockett et
al., 2017), or the user’s education (0%) are very rarely studied.
Table 6. User Characteristics investigated in the Studies
4.1.4 Perception and Outcome Dimension
Regarding the agents’ perception and the interaction’s outcomes, we find that many studies
focus on constructs related to users’ attitudes toward the CA (24%), the agent’s perception
(23%), system or task performance (20%), and acceptance (17%). Concerning attitude,
Burgoon et al. (2016), for example, measure the user’s perceived sense of connectedness
with the agent after the interaction, and Vugt et al. (2010) assess perceived facial similarity
with an interactive CA.
Regarding perception, many of the reviewed studies focus on comparing different design
options (regarding identity, verbal communication, and non-verbal communication) have on
perceived anthropomorphism. For example, Seeger et al. (2018) varied an agent’s verbal and
non-verbal communication (use of self-references and emoticons) and its human-like identity
(a human person’s name and image), to study the impact on perceived anthropomorphism.
Similarly, Gnewuch et al. (2018) and Diederich et al. (2019) studied the impact of response
times and preset answer options on users’ perception of humanness in a customer service
encounter with a chatbot. Araujo (2018) explored different language styles, framings to
introduce the CA, and names in relation to anthropomorphism.
Concerning performance, CA researchers proposed different system-related measures, such
as response success rate (e.g., Liao et al., 2018) or perceived conversational ability (e.g.,
Shah et al., 2016). Additionally, some reviewed studies used performance measures related
to the interaction’s outcomes, considering, for example, purchase amounts for digital content
Alexa provided (Son & Wonseok, 2018), or completion time in microtask crowdsourcing
(Mavridis et al., 2019).
Other authors draw on constructs in acceptance models, such as the Technology Acceptance
Model (TAM) (Davis, 1989) or the Unified Theory of Acceptance and Use of Technology
(UTAUT) (Venkatesh et al., 2003). Examples of studies measuring acceptance-related
constructs are Qiu & Benbasat (2010), who assessed the perceived usefulness of a product
recommendation agent depending on different demographic embodiments, and Hobert (2019)
who drew on ease-of-use and intention to use in order to evaluate a CA intended to teach
programming to IS and CS students.
Further studies focus on constructs related to emotion (12%), such as empathy (Leite et al.,
2013; McQuiggan & Lester, 2007), trust (Saffarizadeh et al. (2017) or Sohn (2019)). Others
focus on the relationship between the user and the agent (11%), such as social roles (Seering
et al., 2019) and social distance (Kim & Mutlu, 2014). Constructs less frequently studied, are
the perceptions of CAs, and outcomes of the interaction include rather specific aspects, coded
as other (5%), which include audience effects (Candello et al., 2019), and constructs related
to learning (3%), for example, retention of learnt content (Van Der Meij, 2013). Finally, a single
study focuses on ethics, developing a moral agency scale (Banks, 2018). Table 7 summarizes
the constructs and shows their distribution in the studies.
Connectedness with the CA (Burgoon et al., 2016)
Rapport (Krämer et al., 2018)
Similarity (Vugt et al., 2010)
Anthropomorphism (Seeger et al., 2018)
Humanness (Gnewuch et al., 2018)
Uncanniness (Tinwell & Sloan, 2014)
Response success rate (Liao et al., 2018)
Conversational ability (Shah et al., 2016)
Purchase amount (Son & Wonseok, 2018)
Usefulness (Qiu & Benbasat, 2010)
Performance expectancy (Laumer et al.,2019)
Ease of use and intention to use (Hobert, 2019)
Empathy (Leite et al., 2013; McQuiggan & Lester, 2007)
Enjoyment (Bell, Sarkar, & Wood, 2019)
Compassion (Looije et al., 2010)
Cognitive and emotional trust (Saffarizadeh et al., 2017)
Perception of trust (Elson et al., 2018)
Privacy Concern (Sohn, 2019)
Social distance (Kim & Mutlu, 2014)
Social roles (Seering et al., 2019)
Relationship building (T. W. Bickmore & Picard, 2005)
Audience effects (Candello et al., 2019)
Shorthand language (Hill et al., 2015)
Speech portions in dialogue (Bittner & Shoury, 2019)
Learning outcomes (Wang et al., 2008)
Retention of learned content (Van Der Meij, 2013)
Facilitation of learning (Zhang et al., 2019)
Perceived moral agency (Banks, 2018)
1) Multiple selections possible
Table 7. Constructs related to Perception and Outcome investigated in the Studies
4.1.5 Complementary Findings
There are different observations regarding the discipline (IS, CS), research approach
(empirical or conceptual), research methods, and unit of analysis. Overall, we identified 262
studies on CAs, of which two-thirds had been published in CS journals and conference
proceedings, and one-third from the IS discipline. We noted a substantially increased interest
in CAs since 2016, in both the IS and CS disciplines (see Figure 4 in Appendix C). While about
half of the studies in our CS sample were published before 2016 (48%), the vast majority of IS
studies (85%) were published after 2016, indicating that the IS discipline recently increased
the interest in CAs as a research phenomenon.
The studies in our sample comprise empirical (84%) as well as conceptual work (16%).
Regarding the research methods used in the reviewed studies, we found a focus on laboratory
experiments (57%) and design science research (23%), predominantly with context-specific
architectural descriptions of the designed CA artifacts (e.g., Anabuki et al. (2000), Hsu et al.
(2017), and Jain et al. (2018)). Less common research methods in the reviewed studies include
qualitative research, such as conceptual literature reviews (e.g., Li (2015)), interviews (4%),
for example concerning use cases of CAs (Laumer et al., 2019), the use of secondary data
(4%) like user reviews for natural language applications (Nguyen & Sidorova, 2017), and
surveys (3%). Research in the field using field studies (2%) or experiments (2%) only account
for a fraction of the used methods. Concerning the unit of analysis, most strongly focus on the
individual level (73%), considering the topic from a purely technological perspective (26%).
Regarding the theoretical grounding of the studies in our sample, we found that about one-
third of the studies (30%) explicitly draw on different theories or paradigms to understand
human interaction with CAs or guide their design. In particular, researchers have referred to
the Computers Are Social Actors (CASA) paradigm that Nass & Moon (2000) formulated.
Based on a review of three sets of experimental studies, they have shown that individuals
mindlessly apply social rules and expectations to computers exhibiting human characteristics
or behavior, such as communication via natural language. CA researchers draw on the CASA
paradigm for different kinds of study: for example to investigate the extent to which knowledge
gained through human-to-human interaction can be applied to human-CA interaction, as in
gender stereotyping (e.g., Cowell and Stanney (2005), Pfeuffer et al. (2019), Qiu and Benbasat
(2010)) or to explore how users perceive different anthropomorphic agent designs (e.g., Gong,
2008; Kim et al., 2013; Lee and Choi, 2017)).
Additionally, several researchers, such as Seeger et al. (2018) or Tinwell & Sloan (2014), drew
on the so-called “Uncanny Valley” (Mori, 1970; Mori et al., 2012) to understand adverse
emotional reactions to anthropomorphic CAs and to propose how they can be overcome. The
Uncanny Valley, in short, postulates that there is no monotonically increasing relationship
between the human-likeness of a technological artifact and a human affinity toward it, but that
there is a point (i.e., the Uncanny Valley) where “a person’s response to a human-like robot
would abruptly shift from empathy to revulsion as it approached, but failed to attain, a lifelike
appearance” (Mori et al. 2012, p. 98). On this theme, for example, Seeger et al. (2018) found
empirical evidence for an adverse emotional reaction to a CA that exhibits only partial human-
like characteristics, and Seymour et al. (2017) suggested that interactivity, realized through an
interactive 3D avatar that matches common human non-verbal cues, can contribute to
overcoming the Uncanny Valley sensation. In addition to the CASA paradigm and the Uncanny
Valley theory, we found that researchers draw on the Three-Factor Theory of
Anthropomorphism (Epley et al., 2007), which explains psychological determinants of humans
anthropomorphizing inanimate objects, or not. For example, Wagner and Schramm-Klein
(2019) rely on this theory in discussing the anthropomorphism perception in interaction with
digital assistants like Alexa. Furthermore, several researchers have adapted theories and
concepts originally from human-to-human interaction for human-CA interaction. For example,
Qiu and Benbasat (2010) and Vugt et al. (2010) draw on Similarity-Attraction Theory (Byrne,
1971; Byrne et al., 1967) to investigate the impact of demographic similarity and facial similarity
between a user and an agent. Similarly, Kim et al. (2013) and Kim and Mutlu (2014) draw on
the concept of social distance (Bogardus, 1947), manifested in physical proximity,
organizational status, and task structure, to understand user responses to humanoid robots.
Additionally, Gnewuch et al. (2017) draw on Grice’s Maxims (Grice, 1975) for effective
conversations to derive design principles for CAs in a customer service context.
Further theories and concepts adapted to human-CA interaction include social presence (e.g.,
Schuetzler et al. (2018) or Sohn (2019)), rapport (e.g., Krämer et al., 2018), reciprocity (e.g,.
Chattaraman et al., 2018), and self-determination theory (e.g., Lechler et al., 2019).
Finally, researchers have drawn on established models to investigate the acceptance of CAs,
such as the TAM (Davis, 1989) or the UTAUT (Venkatesh et al., 2003). For example, Wang &
Benbasat (2005) adapt the TAM to study trust in recommendation agents, and Laumer et al.
(2019) draw on the UTAUT to understand CA acceptance in healthcare.
Table 8 summarizes the theoretical grounding of the studies.
Computers Are Social
Nass and Moon (2000)
Cowell and Stanney (2005),
Mori et al. (2012)
Strait et al. (2015),
Tinwell and Sloan (2014)
Epley et al. (2007)
Seeger et al. (2018),
Wagner and Schramm-Klein (2019)
Byrne et al. (1967)
Qiu and Benbasat (2010),
Vugt et al. (2010)
Kim & Mutlu (2014)
Gnewuch et al. (2017)
Gefen and Straub (2003)
Schuetzler et al. (2018)
Tickle-Degnen and Rosenthal (1990)
Krämer et al. (2018)
Chattaraman et al. (2018)
Ryan and Deci (2000)
Quynh and Sidorova (2018),
Lechler et al. (2019)
Wang & Benbasat (2005)
Unified Theory of
Acceptance and Use
Venkatesh et al. (2003)
Laumer et al. (2019)
Table 8. Theoretical Grounding used in CA Research
4.2 Research Streams
By means of a cluster analysis we identified four streams in IS and CS research. Specifically,
these streams differ in the type of investigated CA regarding communication mode and
embodiment; they also differ in the design dimensions on which the studies focus, attending
to the agent’s identity, and to verbal and non-verbal communication. As shown in Table 9, we
term the streams “Text-based CAs,” “Virtual CAs,” “Speech-based CAs,” and “Physical CAs.”
Research on CAs with interaction via written text and no embodiment or
virtual static representation.
Research on CAs with interaction via written text and spoken natural
language, and with embodiment through virtual interactive avatars.
Research on CAs with interaction via spoken natural language and
without any virtual or physical embodiment.
Research on CAs with interaction via spoken natural language and with
Table 9. Research Streams for Conversational Agents
Table 10 shows the characteristics’ distribution in the framework for each research stream.
Note that multiple selections were used for coding human characteristics (e.g., studies that
investigate the effects of age users’ CA perception), design dimensions (e.g., studies that focus
on CAs’ verbal and non-verbal communication), and perception and outcome (e.g., studies
measuring CA performance and perception), which lead to sum rows exceeding 100%.
Number of studies
Private task support
Professional task support
Perception and Outcome
Table 10. Cross Tab Analysis
4.2.1 Research Stream 1: Text-based CAs
Studies in the first research stream focus on CAs with which users interact via written text and
that either have no embodiment or only a virtual static representation (e.g., with an image).
Thus, we refer to this stream as “Text-based CAs,” which as a type of CA has attracted the
research community’s interest, particularly since 2016, which saw more CA studies published
than virtual CAs, speech-based CAs, or physical CAs (see Figure 5 in Appendix C).
Research in this stream particularly investigates questions related to verbal communication
(55%), such as how to repair conversation breakdown (Ashktorab et al., 2019; Corti &
Gillespie, 2016), how to design effective agent communication and its user impact (Adler et
al., 2016; Derrick & Ligon, 2014; Hu et al., 2018; Van Der Meij, 2013), or how to gauge
message interactivity’s influence on users’ perception of an agent (Adam & Klumpe, 2019; Go
& Sundar, 2019).
About one-third of the studies in this stream explore aspects related to the (human) identity of
an agent (30%), such as how the agent’s gender influences the user (Pfeuffer et al., 2019; Qiu
& Benbasat, 2010) or how the degree of human-likeness in the agent’s representation (e.g., a
comic image of a robot or photo of an actual human person) impacts user perception (Gong,
2008). Further studies do not focus on specific design dimensions but address overarching
topics, such as chatbot use cases (Laumer et al., 2019; Meyer von Wolff et al., 2019), or
affordances (Stoeckli et al., 2018).
Concerning the contexts, many text-based CA studies do not specify a particular application
area, or they focus on the customer interface of organizations (35%), such as online customer
services (e.g., Gnewuch et al. (2018), Hu et al., (2018), Xu et al. (2017)) or marketing and
sales (e.g., Al-Natour et al. (2009), Vaccaro et al., (2018), Van den Broeck et al. (2019)).
Further frequently studied contexts include education (15%) and health (10%). In the context
of education, text-based agents have been investigated for learning languages (Fryer et al.,
2017), collaborative problem solving (Hayashi, 2013; Herborn et al., 2018), and programming
education (Hobert, 2019). In the area of health, researchers have focused on, for example,
chatbots for therapy (Bell et al., 2019; Constantin et al., 2019), for raising individual health
awareness (Meier et al., 2019), or supporting people with allergies (Hsu et al., 2017).Research
on text-based CAs focuses on constructs related to perception (e.g., humanness), attitude
(e.g., attractiveness), performance (e.g., responsiveness), and acceptance (e.g., perceived
usefulness). Regarding text-based agents, studies mainly investigate how design variations
concerning the agent’s identity (e.g., Araujo (2018), Go & Sundar (2019)) and verbal
communication (e.g., Hu et al. (2018), Schuetzler et al. (2014)), influence how the user
perceives the agent’s anthropomorphism or humanness.
Complementary to the question of how to design human-like text-based agents, researchers
have discussed how an anthropomorphic design might trigger perceptions of uncanniness in
the interaction (e.g., Gnewuch et al. (2018), Wünderlich and Paluch (2017)). Finally, the
outcomes of anthropomorphism perception in chatbots have been studied, regarding for
example, service encounter satisfaction (Gnewuch et al., 2018), brand perception (Araujo,
2018), and learning results (Jin, 2010).
4.2.2 Research Stream 2: Virtual CAs
Research in the second stream, termed “Virtual CAs,” comprises work on agents represented
by virtual animated avatars with which users interact via written and spoken natural language.
The research interest in both disciplines regarding virtual agents as a type of CA has been
steady, particularly in comparison to studies on text-based agents and speech-based CAs (see
Figure 5 in Appendix C). In contrast to research focused on verbal communication in the first
stream, studies on virtual CAs, in particular, investigate non-verbal communication (65%) and
the (human) identity of an agent (59%). For example, Krämer et al. (2013) explored how an
agent’s smile impacts user perception and behavior, finding that humans reciprocate an
agent’s smile. Similarly, several studies in this stream explored the impact of an agent’s gaze,
such as catching the user’s eye, on the user (Vertegaal et al., 2000, 2001), the effects of facial
similarity between the agent and the user (Vugt et al., 2010), or the agent’s digital gesturing
behavior (Biancardi et al., 2017). Finally, Seymour et al. (2018) presented emerging natural
face technology for creating a realistic visual presence of agents and proposed a research
agenda which includes fundamental philosophical questions arising from such agent
representations becoming increasingly realistic.
Further, virtual agents are mostly studied without a specific context being described (67%).
However, where context was given, the studies mostly focused on their application in health
(10%) or education (7%). Regarding health, research, for example, explored virtual CAs to
change stigmatizing attitudes toward mental health (Sebastian & Richards, 2017) or potential
advantages such agents could have over clinical psychologists in mental health interviews
(Yokotani et al., 2018). In educational context, Carlotto and Jaques (2016), for example,
studied animated pedagogical agents and suggested that an agent’s movement and gestures
contribute less to learning outcomes than interaction via speech. Similarly, Gulz and Haake
(2006) explore the design of agent animations and student motivation to learn.
Lastly, studies on virtual CAs mainly concerned the user’s attitudes toward them, user
perception, and emotions, often related to the agent’s non-verbal communication behavior. For
example, Burgoon et al. (2016) studied how a match between user expectations and the
agent’s behavior influenced the individual attitude toward the agent, finding that in particular
positive deviations from user expectations have a substantial positive effect on perceived
connectedness, receptivity, and dependability. Regarding emotions, others investigated
interactive designs, for example, expressing politeness (Niewiadomski & Pelachaud, 2010) or
displaying empathy (McQuiggan & Lester, 2007; Yang et al., 2017) which are direct responses
to the user’s inputs. More recent studies further explored how an agent’s interactive
representation and non-verbal communication behavior impact such agents’ persuasiveness
(Harjunen et al., 2018; Hyde et al., 2015; Looije et al., 2010; Rosenthal-von der Pütten et al.,
2018), as in decision-making tasks.
4.2.3 Research Stream 3: Speech-based CAs
The third research stream, termed “Speech-based CAs,” includes primarily speech-based CAs
(85%) without any physical or virtual embodiment. Similar to research on text-based CAs, the
number of publications that focus on speech-based agents has substantially increased since
2016 (see Figure 5 in Appendix C). Research in this stream concentrates on verbal
communication with the agent (62%) or topics not directly related to technological aspects,
such as use cases (Baier et al., 2018), team settings with agents for collaborative work (Bittner
et al., 2019), or the psychological impact of interacting with such assistants in a commanding
voice (Burton & Gaskin, 2019). Researchers in this stream often draw on widely distributed
assistants, such as Amazon’s Alexa (Son & Wonseok, 2018; Winkler et al., 2019; Winkler &
Roos, 2019), Apple’s Siri (Burton & Gaskin, 2019), or Google Assistant (Cho, 2019).
Concerning verbal communication, research on speech-based CAs is concerned with
conversation design, such as for useful task guidance (Vtyurina & Fourney, 2018), for fostering
a positive user experience (Burmester et al., 2019), or for combining social and functional ways
of communication (Clark et al., 2019).
Similar to research on virtual CAs, studies in this stream often do not specify a particular
context (43%). However, in contrast to the two previously mentioned streams, several studies
on speech-based agents focus on individual task support in a private context, for example,
using Alexa and Google as smart home components (Kowalski et al., 2019; Porcheron et al.,
2018; Purington et al., 2017). Further studied contexts include digital assistants at the
customer interface (10%), such as for advisory services (Dolata et al., 2019) or advertising
(Kim et al., 2018), in education (7%), such as for learning languages (Morton et al., 2012) or
team collaboration (Winkler et al., 2019a).
The aspects this research stream investigates relate particularly to performance (26%),
acceptance (22%), and attitude (19%). Concerning performance, research on speech-based
CAs has, for example, explored the number of tasks completed in teams (Winkler et al., 2019a;
Winkler et al., 2019b), conversation turns and time required to retrieve information from the
assistant (Le Bigot et al., 2006), or the agents’ social dialogue capabilities (Ward & Tsukahara,
2003). With regard to acceptance, researchers have, for example, studied the influence of an
agent’s answers to user questions on perceived usefulness (Jung et al., 2019) or the impact
of the agent’s personification and social interaction capabilities on user satisfaction (Purington
et al., 2017). Finally, related to attitude, studies on speech-based agents have investigated
aspects such as gender stereotypes arising from an agent’s female voice (Hwang et al., 2019)
or the agent’s answers’ impact on perceived politeness and pleasantness (Jucks et al., 2018).
4.2.4 Research Stream 4: Physical CAs
The fourth research stream comprises research on CAs with a physical embodiment (91%),
which interact via spoken natural language. We refer to this stream as “Physical CAs.” The
research interest in CAs with a physical embodiment has been comparatively steady, similar
to the research stream of virtual agents (see Figure 5 in Appendix C). Studies in this stream
emphasize verbal (35%) and non-verbal communication (35%). Concerning verbal
communication, researchers, for example, investigated how speech style, including calling
users by their names, influenced the perceived social distance toward the physically embodied
agent (Kim et al., 2013) or how different sales strategies impacted the number of goods
conversational robots sold in a department store (Watanabe et al., 2015). Research focusing
on non-verbal communication has focused on designing different types of behaviors, such as
nodding or maintaining eye contact, and evaluating such behaviors’ impact on the user and
her or his perception of the physical CA (Lee et al., 2004; Saerbeck et al., 2010; Sakamoto et
al., 2005; Szafir & Mutlu, 2012; Yamada et al., 2013). Research on physical CAs rarely
specifies a particular context (35%), but if so, investigates them in education (22%), at the
customer interface (17%), or in health contexts (13%). In the context of education, these
studies have explored physical CAs as learning partners that support social and cognitive
aspects in the learning process (Huang, 2012; Saerbeck et al., 2010; Zhang et al., 2019). At
the customer interface, such physical CAs were investigated to support product sales
(Bertacchini et al., 2017; Watanabe et al., 2015) or to innovatively provide customer service
(Stock & Merkle, 2018b). Finally, physical CAs have been studied in health, for example, as
caregivers (Kim et al., 2013) or to promote regular physical exercise (Kanaoka & Mutlu, 2015).
About the perception as well as outcomes of the interaction with physical CAs, studies have
focused on the relationship between user and agent (26%), the CA’s performance (26%), or
the user’s perception of the physical agent (21%). Concerning the relationship between the
physically embodied agent and the user, Sangseok and Lionel (2019), for example,
investigated how team members identify with a physically embodied CA while completing a
collaborative task, and how this impacts subgroup formation. Lee et al.'s (2012) work provides
a further example, discussing how users assign social roles to physical CAs and how users’
relationship to the agent with a physical embodiment influences their behavior toward other
users (e.g., protecting the agent or jealousy). Concerning performance, scholars, for example,
have researched the number of products a sales agent sold (Watanabe et al., 2015) or learning
results associated with a physical CA’s support (Szafir & Mutlu, 2012). Regarding the
perception of a physical agent, researchers have studied aspects such as social presence
(Pereira et al., 2014), humanness (Kim et al., 2013), or social behavior (Xu et al., 2013).
To date, researchers interested in CAs have lacked an overarching framework, classification,
or suitable organizing device to compare and analyze existing CA research. Identifying
relevant future research topics or areas has been hindered by the variety and numbers of
extant studies and findings, so that an organizing structure is missing. Our framework, adapted
from established research by Zhang and Li (2005), supports researchers in the classification
and organization of existing and future CA research. In the following section, we discuss the
observations we made during the review process, and we suggest avenues and directions for
future CA research.
5.1 An Agenda for Conversational Agent Research in IS
Based on our analysis of the identified CA studies, we propose six avenues for future research
that address research gaps that have not been investigated yet and need to be studied from
an IS perspective. For each avenue we motivate and formulate specific directions for future
research activities to address the identified gaps in the body of IS knowledge. Table 11
summarizes the avenues and gives directions for further research that will advance our
understanding of the interaction between humans and CAs.
Directions for Future CA Studies
and Adaptive CA
D1.1: Investigate the impact of user characteristics in the interaction
between humans and CAs on human perception and
interaction outcomes across different contexts.
D1.2: Investigate how CAs can be designed to adapt themselves to
individual users and their characteristics during the interaction.
D1.3: Study the potential that CA configuration or CA co-creation has
for the user, and the resulting impact on user perception and
CAs on Group and
Organizational Levels of
D2.1: Investigate which parts of collaborative group work can be
fulfilled by CAs and the resulting impact on team behavior and
D2.2: Examine how CAs should be designed to efficiently support
human team collaboration and how these designs influence
team members’ perception of and interaction with CAs.
D2.3: Analyze which types of (organizational) tasks and processes are
suitable for innovation and automation with CAs.
D2.4: Investigate the positive and negative impact and the potential
mitigation strategies when automating human work with
CAs with Virtual
Interactive and Physical
D3.1: Explore the impact of rich virtual interactive or physically
embodied CAs on user perception combined with the currently
available conversational capabilities.
D3.2: Investigate designs of virtual interactive and physical CA
embodiment to increase agent acceptance, adoption, and
performance across different contexts.
Knowledge gained in
D4.1: Partially replicate experimental CA studies in investigating
whether existing prescriptive and descriptive knowledge can be
transferred between different CA instances (i.e. combinations of
human, agent, and context) and adapted accordingly.
Directions for Future CA Studies
Ethical Implications of
Interacting with CAs
D5.1: Explore when, where, and how applying persuasive CAs is
D5.2: Develop CA’s design elements to address the ethical
dimensions of the user interaction, and investigate how users
perceive these design elements.
D5.3: Investigate the unintended side-effects of CA design and study
how to prevent such negative side-effects.
Research in a Field
D6.1: Conduct field studies to verify, extend, or refute existing
knowledge gained from experimental research in controlled
D6.2: Investigate the interplay between CAs’ limited conversational
capabilities and the combination of multiple (anthropomorphic)
design features of CAs.
D6.3: Study how the relationship between users and CAs takes shape
during initial use of the CA, and how it develops in multiple
interactions over a longer period of time.
Table 11. Agenda for Conversational Agent Research in IS
5.1.1 User Characteristics and Adaptive CA Designs
We found that only a quarter of the reviewed studies investigated how individual human
characteristics influence the perception of and interaction with CAs. Of these articles, several
empirical studies found significant effects. For example, Schroeder and Schroeder (2018)
observed that younger and male users are more likely to trust a CA. Rosenthal-von der Pütten
et al. (2018) found that older users are more easily persuaded by a CA that shows dominance
than by one with submissive behavior. Vugt et al. (2010) discovered that female users are
more likely to use a CA with facial similarity, while male users are more likely to prefer a CA
with facial dissimilarity. In addition to age and gender, characteristics like experience with CAs
(e.g., Ashktorab et al. (2019), Otoo & Salam (2018)), personality traits (e.g., Krämer et al.
(2018), Seeger et al. (2020)), or cognitive style (e.g., Hubal et al. (2008)) have been found to
influence how users perceive and interact with CAs. These studies mostly considered human
characteristics as complementary to the key constructs they investigate. However, for
characteristics like age and gender, experience with CAs, the task at hand, and a user’s
personality, seemed to significantly influence how humans perceive and interact with CAs in
different contexts (i.e., the usage scenario of the CA, such as health, education, private task
support; see table 13 for further contexts). Based on these initial findings, we suggest there is
a promising research opportunity to explicitly investigate the impact of individual user
characteristics (e.g., gender, age, personality, cultural background, etc.) on the interaction
between humans and CAs. This applies to different contexts and to aspects of the agent’s
perception, as well as to the interaction’s outcomes. For example, questions on how cultural
differences between users influence the perception of features regarding the agent’s identity
(e.g., name, social role), verbal communication (e.g., formal or informal language, expression
of emotions), and non-verbal communication (e.g., use of gestures and facial expressions,
emoticons) arise. Similar to the various culturally modifiable interface components of graphical
user interfaces (Reinecke & Bernstein, 2013), we argue that a variety of CA features is likely
to influence how users perceive such agents, depending on the user’s cultural background. In
short, a sound understanding of individual user characteristics’ impact, considering the context
as well as agent’s design features, can allow us to better understand differences and
commonalities in the perception of CAs, and in outcomes of the interaction. In summary, we
propose the following direction (D):
D1.1: Investigate the impact of user characteristics on the interaction between humans and
CAs on human perception and interaction outcomes across different contexts.
With our improved understanding of user characteristics and their influence on the interaction
between CAs and their users, we propose investigating how CAs can be designed to adapt
themselves to individual users during the interaction. In practice, CAs are typically
implemented using a “one size fits all” approach in which all users receive the same agent and
set of responses, regardless of characteristics like age, experience with such agents, or
personality (Følstad & Brandtzæg, 2017). Therefore, investigating adaptive designs securely
founded on empirical data and theory (Kocaballi et al., 2019) represents a lucrative research
opportunity. We suggest that CAs with efficient adaptive designs for heterogenous user groups
are likely to increase such agents’ acceptance, adoption, and performance in line with studies
for other types of IT artifacts, such as recommendation agents with graphical user interfaces
(Al-Natour, Benbasat, & Cenfetelli, 2006). Researchers might be able to draw on established
theories and constructs from human-to-human interaction, such as communication
accommodation theory (Giles, Coupland, & Coupland, 2010), similarity attraction theory
(Byrne, 1971; Byrne et al., 1967), matching and mirroring (Burgoon, Stern, & Dillmann, 1995),
or mimicry (Kozlowski & Ilgen, 2006) to inform adaptive CA designs. For example, researchers
can explore how CAs can efficiently tailor their verbal communication (e.g. selection of words
or syntax) to different user groups in the same way as humans adjust their language style in
interactions (Pickering & Garrod, 2004). Because moving away from ‘one size fits all’
approaches is likely to substantially increase the implementation’s complexity, we further
suggest evaluating and comparing different adaptive CA designs in order to identify the most
efficient features drawing on the three design dimensions of such agents, namely (human-like)
identity, verbal- and non-verbal communication).
D1.2: Investigate how CAs should be designed in order to adapt themselves to individual
users and their characteristics during the interaction.
As an alternative approach to user-adaptive designs, researchers should investigate
configurable agents’ potential that allows users to co-create CAs that fit their preferences.
Studies on several types of technological artifacts, such as mobile devices (Carter, Grover, &
Thatcher, 2013) or avatars (Belk, 2013), have shown that humans can identify with certain
inanimate objects, thereby promoting emotional attachment. Individuals beginning to see
objects as a part of their identity fosters this identification and the resulting emotional
attachment, as the psychological process of self-extension also explains (Belk, 1988). Building
or configuring one’s own technology has been shown to trigger the process of self-extension
for different types of artifacts such as robots (Groom et al., 2008; Robert & Sangseok, 2018)
or avatars (Ducheneaut et al., 2009). Fostering identification with CAs through configurable
designs or co-creation is likely to have a positive impact on, for example, enjoyment of the
interaction (Li, Browne, & Chau, 2006) or team performance in collaborative settings (Robert
& Sangseok, 2018) as found in studies on other types of technological artifacts. Thus, we
propose that researchers investigate how CAs can offer the possibility of configuration or co-
creation, including shaping the agent’s identity (name, gender, appearance). They should also
study how this impacts users’ perception of the agent, as well as the interaction’s outcomes.
In considering such designs, we suggest researchers particularly investigate users’ social
responses to the agents and how this impacts the relationship between the user and the
D1.3: Study the potential of CA configuration or CA co-creation for the user and the resulting
impact this has on user perception and interaction outcomes.
5.1.2 CAs on Group and Organizational Levels of Analysis
Considering the unit of analysis, we observed that the majority of the studies in our sample
focus on individual interaction with CAs (73%) or on technological descriptions of CA designs
(26%). Notable exceptions within the group and on (inter-) organizational levels of analysis are
Bittner et al. (2019) who developed a taxonomy of design option combinations for CAs in
collaborative work, and Cardona et al. (2019) who studied adoption and diffusion of
conversational technology in the German insurance sector. Apart from these studies, the large
majority of CA research focuses on the individual level, i.e. the interaction between a single
user and a single CA.
In line with researchers in the area of computer-supported collaborative work, such as Seeber,
Bittner, et al., (2019) and Seeber, Waizenegger, et al. (2019), we suggest investigating CAs in
group settings. We argue that, driven by advances in natural language processing and
machine learning, CAs with significantly improved capabilities emerged (McTear, 2017). Also,
they have human-like characteristics that can alter the role of IT from one of providing tools
that enhance team performance to eventually becoming artificial teammates (Malone, 2018).
This technological progress has given rise to numerous questions related to the design of CAs
in team settings, as well as to group interaction with such agents. For example, which roles in
a team can an anthropomorphic CA assume, i.e., in what situations is the CA able to fulfill a
gap where a human team member is missing? What are the advantages and limitations of a
CA in this role? While a large body of collaboration research is available on team compositions
and roles of team members (e.g., Belbin (2010)), we lack an understanding on which of the
roles CAs can fulfill and what the implications are when technology assumes such roles. While
we expect research on CA roles in team settings initially to focus on operational tasks, such
as managing the task, gathering information, or scheduling meetings, we anticipate that even
more capable CAs will emerge, which are able to assume roles typically associated with
human team members. For example, CAs equipped with present-day sensing capabilities are
already able to recognize individuals’ sentiments (Bertacchini et al., 2017; Feine et al., 2019)
and could be able to take measures where appropriate, such as proposing a break to improve
the general mood within the team. Thus, we suggest investigating this and similar team roles
by drawing on conversational technology available in practice, to better understand which parts
of collaborative work CAs can fulfill and how an artificial teammate will impact team behavior
D2.1: Investigate which parts of collaborative group work can be fulfilled by CAs and the
resulting impact on team behavior and performance.
Further, future studies should investigate the impact different agent designs have, such as
text-based or speech-based communication in research streams 1 and 3, or embodiment as
in research streams 2 and 4, in collaboration settings. For example, do team members
perceive the same agent differently depending on the communication mode, i.e., on whether
the members-CA interaction takes place via spoken or written natural language? And, what is
the impact of a higher degree of agent anthropomorphism on the acceptance, adoption, and
use of a CA within a team? According to extant research on the impact CA anthropomorphism
has on the individual perception of such agents (e.g., Araujo (2018), Go & Sundar (2019),
Rosenthal-Von Der Pütten & Krämer (2014)) and on the understanding of computers as social
actors (Nass & Moon, 2000), we expect the agent’s increased human-likeness to strengthen
the social responses to an agent within a team. This can be both positive (e.g., regarding CA
acceptance in the team or collective enjoyment of the interaction) and negative (e.g., regarding
shared feelings of frustration when the agent is not able to fulfill high expectations fostered by
a human-like design). While some of these social responses might be similar to individual
interaction with and perception of CAs, we do not understand how group settings (i.e., multiple
humans interacting with one or more CAs at the same time) influence such responses.
Therefore, we propose studying how CAs should be designed to be efficient in team
collaboration, and how such designs influence the way team members perceive and interact
with (anthropomorphic) CAs.
D2.2: Examine how CAs should be designed to efficiently support human team collaboration
and how these designs influence team members’ perception of and interaction with
In addition to generating a better understanding of the potential and limitations of CAs in team
collaboration, we propose studying such agents on the organizational level. Currently, the
majority of CA studies in organizations focuses on the customer interface, investigating
contexts like customer service (e.g., Hu et al. (2018), Stock & Merkle (2018b), Wünderlich &
Paluch (2017)) or marketing and sales (e.g., Hanus & Fox (2015), Kim et al. (2018), Vaccaro
et al. (2018)). Further, we found single studies in other, quite specific contexts, such as for
onboarding new employees (Liao et al., 2018) or for assisting workshop moderation
(Strohmann et al., 2018). However, we still lack an understanding of the key characteristics
that determine whether introducing a CA makes sense in a specific context. At present, studies
in new organizational contexts emerge in a bottom-up approach, possibly driven by practical
interests. Thus, we posit research potential to identify the types of tasks for which CAs can be
useful in a more abstract, context-independent way. For example, such task types can be
characterized by their rather structured nature and by a high occurrence frequency. However,
we do not know whether these tasks necessarily comprise an interaction that usually takes
place between two humans, i.e., a task where an anthropomorphic agent can at least partially
substitute the human contact. Nor do we know whether such tasks typically involve interaction
with complex software where the CA is intended to increase ease-of-use. A sound
understanding of the task types suitable for automation and innovation by means of CAs could
assist us in moving from the practical, opportunity-driven identification of application contexts
to a top-down approach in which such contexts could be determined by systematically
reviewing tasks and processes within an organization.
D2.3: Analyze which types of (organizational) tasks and processes are suitable for
innovation and automation with CAs.
Additionally, there is an opportunity to investigate the consequences that the introduction of
CAs has on the human workforce. Davenport and Kirby (2016) discuss how cognitive
technology (or AI technology in general) can support humans in performing various tasks.
Similarly, Brynjolfsson and McAfee (2016) elaborate on how IT is facilitating automation, thus
leading to task performance shifting from the human to the computer and so announcing the
second machine age. They argue that with these advances, more and more tasks traditionally
performed by humans are being automated, replacing workers with intelligent IT that has
positive (e.g., less simple, repetitive tasks for people) and negative consequences (e.g.,
employees fearing job losses). Building on this, researchers should explore how human
workers perceive the CA take-over of tasks they previously performed. For example, in a
customer service context, more and more first level support is performed by CAs (Huang &
Rust, 2018) and human operators that answer basic service requests are no longer required.
While this can be interpreted positively (e.g. no need to answer the same simple question
multiple times a day) there are also potentially negative consequences (e.g., living in fear of
losing one’s job to a machine). There are more contexts in organizations where CAs can
support or substitute human workforce members, such as in sales or invoice processing. While
automating human tasks is an ongoing effort, in the context of CA, substituting and automating
previously human work has a new component. CAs, especially when designed to be human-
like, are perceived as social actors (Nass & Moon, 2000; Reeves & Nass, 1996). Therefore,
when CAs take over human tasks, it is not just automation with some form of intelligent IT, it
could be another social actor taking over a human’s task. Further research should investigate
the impact this ongoing change has on human work performance and investigate how the
positive effects can be utilized even further, while potentially negative effects can be mitigated.
Further, it is important to investigate whether or not the specific nature of the CA, i.e., that it is
perceived as a social actor, has an impact on automation.
D2.4: Investigate the positive and negative impact, as well as potential mitigation
strategies when automating human work with (anthropomorphic) CAs.
5.1.3 CAs with Virtual Interactive and Physical Embodiments
We disclosed that the recently increased research interest in CAs, in particular, comprises the
research streams of text-based CAs (research stream 1) and speech-based CAs without
embodiment (research stream 3). In contrast, the streams of virtual CAs (research stream 2)
and physical CAs (research stream 4) remain on a steady, comparably low level of studies per
year (see Figure 5 in Appendix C). We explain this observation by referring to the high
availability of text-based CAs and speech-based agents for (experimental) research and the
current interest in such types of CAs in practice. Nevertheless, we believe that there are
substantial research opportunities to study physical and virtual CAs.
Recent studies on virtual agents highlight the strong social reactions humans show to virtual
CAs. For example, Harjunen et al. (2018) found that participants in an experiment were
comparatively more likely to accept unfair offers by a CA that smiled and touched them through
a haptic glove. Krämer et al. (2018) discovered that socially responsive non-verbal agent
behavior in terms of nodding, smiling, and posture shifts can reduce participant’s need to
engage in social activities after the interaction. Further, Seymour et al. (2018) highlight the
versatile potential and the implications of natural face technology that creates a realistic visual
presence, calling for “blue ocean” research in this area. Current interactive CAs, such as
Amelia by IPsoft (2020), underline this potential of virtual CAs in practical application,
particularly in organizational contexts like customer service or IT service desk automation.
Similar to emerging research on virtual agents, recent studies on physically embodied CAs,
such as Desideri (2018), Stock & Merkle (2018b), or Stock et al. (2019), demonstrate CAs’
potential with a physical embodiment. For example, Stock & Merkle (2018b) investigated
customer responses to behavioral cues during customer service encounters and found positive
emotional reactions to the humanoid robot’s behavior. Desideri et al. (2018) investigated
whether humanoid robots can offer mental health assessment benefits that improve on clinical
In short, new forms of realistic virtual interactive or physically embodied CAs are likely to have
a substantial impact on user perception, complementing the verbal communication currently in
the focus of IS research on CAs. We expect these additional and rich design features
associated with such forms of embodiment to immediately attract users’ attention in the
interaction and to strengthen social responses as indicated by the aforementioned, early
studies. Thus, we propose systematic study of the impact virtual interactive and physical
embodiment have in combination with the advanced natural language interaction present-day
D.3.1: Explore the impact of rich virtual interactive or physically embodied CAs on user
perception combined with the currently available conversational capabilities.
In addition to better understanding the impact of these CA embodiment forms on user
perception, we propose research on how such embodiment should be designed for different
contexts in order to increase users’ acceptance, adoption, and performance of CAs. For
example, we could ask how a pleasant embodiment of a virtual interactive customer service
CA should look, or how a CA’s physical embodiment should be designed to efficiently support
product sales in stores. Currently available forms of CA embodiment, such as IPsoft’s Amelia
for virtual interactive CAs or SoftBank’s humanoid robot Pepper, offer unprecedented design
features that can be used, such as facial movement (e.g., eye blinking or smiling) or gestures
(e.g., waving or moving the head toward a speaking person) in combination with advanced
conversational capabilities. However, we lack a solid understanding of how these features
should be designed and combined with one another in order to influence user perceptions to
achieve specific goals (e.g. increase trust in the CA or foster the perception of
anthropomorphism) across different contexts. Thus, we propose:
D.3.2: Investigate virtual interactive and physical CA embodiment designs to increase agent
acceptance, adoption, and performance across different contexts.
5.1.4 Transferability of Knowledge gained in CA Studies
Besides new insight in the state of the art in CA research from a given
ex-ante perspective (i.e., we preselected dimensions for coding and all analyses depended on
this selection), different ex-post observations emerged from our analysis. We found several
studies transferring and combining knowledge from different contexts (e.g., Seeger et al.,
(2018), Gnewuch et al. (2017), Tavanapour et al. (2019)) and different types of CAs (e.g.,
Araujo, (2018), Jeong et al. (2019), Wagner & Schramm-Klein (2019)). Justifying design
decisions (Gregor et al., 2020) or proposing hypotheses using existing prescriptive and
descriptive knowledge is the appropriate scientific methodology (Bhattacherjee, 2012).
However, during our analysis, the question emerged as to when and under which
circumstances prescriptive and descriptive knowledge can be adapted from one instance to
another, as when a specific type of user interacts with a specific type of agent in a specific
context (see Figure 2).
In our sample of 262 studies, 148 articles investigated the interaction between humans and a
CA (or specific design variants of the CA) for a given context and measured specific outcomes
(the type of users, CA design, and context – referenced as “instance” in the following). We
found studies that successfully adapted knowledge from one instance to their research context
in another instance. For example, Adam and Klumpe (2019) investigated a text-based CA in
the context of human resources that facilitates onboarding processes and successfully drew
on Lee and Choi's (2017) findings of reciprocal behavior and self-disclosure by a speech-based
agent having a positive effect on user satisfaction in the context of marketing and sales. Their
study provides arguments for the transferability of knowledge from one instance to another in
the context of CAs.
However, there is also research on CAs that investigated similar aspects, but found mixed
results, which makes it difficult to adapt knowledge from one instance to another instance.
Nass and Moon (2000) found that users perceived a CA’s female voice as less friendly and
less competent than the corresponding male voice, thus supporting their assumption that
“individuals would mindlessly gender stereotype computers” (Nass & Moon, 2000, p. 85). In
contrast, Forlizzi et al. (2007) found that CAs with a female-looking avatars were preferred to
male-looking avatars. Moreover, female-looking avatars received higher satisfaction ratings
than male avatars. Although the two studies do not assess exactly the same outcomes, they
illustrate the problem regarding the transferability of existing prescriptive and descriptive CA
knowledge. While Nass and Moon's (2000) study supports selecting a male gendered CA to
receive positive outcomes, Forlizzi et al. (2007) provide arguments for selecting a female
gendered on to create similar positive outcomes.
Against this background, the practice of transferring implications from observations between
instances (e.g., from a physical CA with embodiment to an agent with a virtual embodiment) is
questionable and potentially risky. Currently, researchers should only cautiously adopt
knowledge from a single instance because there is insufficient literature on the transferability
and adaptability of CA knowledge. Considering existing research, we were unable to identify
any study empirically investigating how or how well knowledge can be transferred between
different CA instances, and what the prerequisites for a successful transfer might be. Similarly,
we found no research that addresses this aspect from a conceptual or theoretical point of view.
Therefore, it is still unknown how well, for example, observations regarding the effect of
designing the CA as gendered (e.g., a CA having a male or female name or an avatar
representing a specific gender) can be transferred between a text-based chatbot (research
stream 1) and a physical CA (research stream 4). The two CA types offer a different set of
potential design elements to be used in communicating and portraying the CA’s gender or,
more generally, the agent’s degree of humanness. Thus, one can expect differences in the
users’ perception of the resulting CAs. The same is true for other CA design features (e.g.,
non-verbal communication of positive emotions through emoticons in a text-based agent,
compared to a smiling interactive agent) and for different contexts (e.g., from a CA in education
to a CA in customer services). In essence, the question remains regarding the circumstances
under which existing prescriptive and descriptive knowledge can be transferred and adapted
between CA instances. Our research framework can be helpful in systematically analyzing the
commonalities and differences between user groups (i.e., human users and their
characteristics), contexts (e.g., customer service or education), CA types (i.e., agent), and
resulting perceptions and outcomes across different instances. We assume that some effects
are observable across several instances, while others might be specific to a particular group
of users, application context, or CA type. In short, we propose that experimental CA studies
be partially replicated by deliberately varying one dimension from the original study (human,
context, agent) and then investigate the effect this has on user perception of the agent and
outcome(s) of the interaction.
D4.1: Partially replicate experimental CA studies in investigating whether existing
prescriptive and descriptive knowledge can be transferred between different CA
instances (i.e. combinations of human, agent, and context) and adapted accordingly.
Eventually, by conducting such studies where one or two dimensions are deliberately changed
from the original work, our knowledge of CA design and interaction will mature from a plethora
of seemingly related observations into a systematic framework, consisting of generalized
statements on overarching phenomena, such as CA gender stereotyping.
5.1.5 Ethical Implications of Designing and Interacting with CAs
We found only a single study investigating ethical aspects related to human interaction with
CAs. Banks (2018) proposes and validates a scale to measure the agent’s behavior regarding
morality and dependency on its implementation. Considering this substantial lack of research
on ethical implications of the design of and interaction with CAs, we formulate three directions
for future research in the following section.
Clearly, CA can be a tool for persuasion (Lehto & Oinas-Kukkonen, 2017), also because they
have been able to influence user’s cognition, emotions, and behavior. They offer developers
options to achieve other goals besides supporting users in making decisions or completing
tasks. For example, researchers, such as Adler et al. (2016), Derrick and Ligon (2014), or
Harjunen et al. (2018) adapt established approaches for persuasion known from human-to-
human interaction for designing CAs, such as emotional persuasion strategies (Adler et al.,
2016). However, subconsciously manipulating users comes with great ethical implications.
Referring to the literature on digital nudging (Lembcke et al., 2019), various aspects have to
be considered and weighted against one another before interference with users’ free will can
be considered as ethically justified: (1) the individual’s freedom of choice should be preserved,
(2) the intention behind the design should be transparent, and (3) the goal-oriented intention
of the interference should be justified.
Similar considerations are presented in other seminal models and frameworks, such as the
principles of ethical and persuasive technology design. Berdichevsky and Neuenschwander
(2002) proposed or the rules for building trustworthy AI as Floridi (2019) put forward.
Nonetheless, to the best of our knowledge, we do not have a dedicated discourse on the ethical
design of CAs. Extending from the general considerations on the ethics of persuasion in other
disciplines, future research should systematically identify, analyze, and discuss CA design’s
unique aspects. For instance, the CA’s capabilities have drastically improved in recent years
(McTear, 2017), leading to the ability to display empathy via sentiment-analysis (Diederich et
al., 2019), which falls in an area of unique ethical challenges (e.g., justifying chatbots that
dynamically adapt to the emotional state of the user, making it increasingly more difficult for
the user to make a free decision). Similarly, we need to investigate other new means CAs have
to take on a human-like appearance and interact with users.
D5.1: Explore when, where, and how applying persuasive CAs is ethically justifiable.
Against this background, there is also a need for research on design elements that are
specifically intended to make the CA “more ethical.” For instance, a common feature is for CAs
to self-disclose that they are not human (Grudin & Jacques, 2019; O’Leary, 2019) because
users can find it difficult to distinguish increasingly human-like CAs from actual humans
(Welch, 2018). Similar features could be developed for specific aspects of the communication
between humans and CAs. For instance, a CA could let the user know that its social cues (e.g.,
having a name, avatar, using self-references) are intended to change the user’s perception of
the CA in a certain way (e.g., letting the CA appear more trustworthy) to achieve a certain goal
(e.g., riding a bike more often). Understanding how users perceive such features is an
important new area of research. For instance, referencing the previous example of
trustworthiness, a CA explaining its persuasive design to the user might not lead to the user
becoming more aware of such an intention, but could instead increase the perception of
trustworthiness (e.g., “the CA takes care of the ethical dimensions of our interaction, therefore,
I can trust it”).
D5.2: Develop CA’s design elements to address the ethical dimensions of the user
interaction, and investigate how users perceive these design elements.
Besides the CA’s clear design intention to be persuasive (or in the future, ethically aware),
there are also unintended effects that need to be considered. For example, children interacting
with voice-based CAs, such as Amazon Alexa, can by design be encouraged to say “please”
when issuing voice commands (BBC 2019), a behavior that is then potentially adapted to
human-to-human interaction as well. However, frequently formulating commands that are
fulfilled instantly can also lead to similar behavior of children in human-to-human interactions
(Truong, 2016). Further, a recent UNESCO (2019) report outlined that voice-based CAs can
reinforce gender stereotypes as users continuously interact with mostly female CA voices in a
commanding tone. Similarly, researchers established gender bias in the design of text-based
CAs, finding that developers show a clear preference for implementing CAs with obvious
female-gender traits (e.g., having a traditional female name) (Feine et al., 2019). Therefore,
expanding current research clearly focused on the expected and intended effects of certain
CA designs, we need research on the unintended side-effects of CAs and also on the
developers’ biases and assumptions, leading to these side-effects.
D5.3: Investigate the unintended side-effects of CA design and study how to prevent such
5.1.6 Longitudinal CA Research in a Field Study Setting
Most studies in our sample (84%) consist of empirical research, preferably in a controlled
setting (57%), both through on-site laboratory and online experiments. In contrast, only a few
studies (4%) were carried out in a field study setting, despite the high availability of CAs in
different contexts in practice. For example, Adam and Klumpe (2019), conducted a randomized
field experiment with 2,095 visitors of an e-commerce website to investigate the impact of
message interactivity and an agent’s information self-disclosure in onboarding new customers.
In agreement with studies in controlled experimental settings, on information disclosure (e.g.
Pickard et al. (2016), Saffarizadeh et al. (2017)) and on message interactivity (e.g. Schuetzler
et al. (2014)), scholars find a positive effect of both constructs on actual user behavior
regarding information disclosure. Thus, the work by Adam and Klumpe (2019) provides
empirical support for insights from different laboratory experiments with field data. Thereby,
the study validated existing findings with actual user information sharing behavior in a real-
world scenario. Toxtli et al. (2018) provided a further example of initial fieldwork in CA research
by implementing and deploying a text-based CA for task management. Based on data from
information workers’ interaction with the agent, and from a survey, the authors investigated the
human nature of interactions and discuss issues related to response failure or the handling of
These exemplary studies underline CA research potential in the field. First, field studies can
help to overcome shortcomings related to external validity of the rich body of knowledge on
CAs gained in controlled experimental settings (Dennis & Valacich, 2001; Karahanna et al.,
2018). As shown in Adam and Klumpe's (2019) study, knowledge gained in single or multiple
laboratory experiments can be applied in field study settings. By conducting field studies on
the design of and human interaction with CAs, existing knowledge can be validated (see Adam
and Klumpe (2019)), extended (see Toxtli et al. (2018) who identified new aspects for study),
or (partially) refuted. Thus, we formulate the following direction for future research studies:
D6.1: Conduct field studies to verify, extend, or refute existing knowledge gained from
experimental research in controlled settings.
While assuming that many insights gained from laboratory experiments remain valid in the
field, it will be particularly interesting to study how human perception of CAs changes when
insights from different kinds of studies are combined. For example, various studies, such as
Gnewuch et al. (2018), Go and Sundar (2019), and Araujo (2018), identify positive effects of
different social cues on user perception (e.g. response delays to simulate a CA typing,
message interactivity, or a human-like avatar), intended to make agents appear human-like. A
question now is what happens if these rich social cues, identified in separate experiments, are
combined in a single CA in the field. Will it be able to induce a high level of perceived
anthropomorphism and, if so, will users perceive this agent as appealing or uncanny?
Additionally, as indicated in Toxtli et al.'s (2018) study, we lack a solid understanding of the
complex interplay of different design aspects in the field. For example, does existing research
help us to know how the limited conversational capabilities of present-day CAs, often
manifested in the agent’s inability to provide a purposeful response, practically influence users’
perception of agents with a human-like design. Will they swiftly diminish the identified positive
effects of the social cues the various laboratory experiments disclosed as good in avoiding
failure? In short, current CA research offers rich knowledge gathered in controlled experimental
settings. However, we do not know whether these insights hold true in the field where various
aspects, both related to the design of the agent itself and to the application context, influence
users’ CA interaction and perception. To advance our overall understanding of CA’s design
and human interaction with them, it would be good to move from investigating single specific
aspects in controlled settings to combining existing knowledge from laboratory experiments
and applying it in the field. As initial field studies such as Toxtli et al. (2018), indicate, several
issues that substantially impact user perception are likely to remain in practice. Also, a complex
interplay of different design aspects, so far investigated separately in different studies, could
influence human-CA interaction in, as yet, unforeseeable ways.
D6.2: Investigate the interplay between CAs’ limited conversational capabilities and the
combination of multiple (anthropomorphic) design features of CAs.
Further, we propose that researchers conduct CA studies over an extended period of time by
means of longitudinal research. Many of the studies in our sample investigate research
questions in experimental settings where participants engage in single or a few interactions
with an agent. While this approach certainly has benefits for investigating the isolated impact
different design alterations have on human perception of an agent, we argue that observing
users’ perception of CAs over the course of multiple interactions can help us understand the
emerging relationships between human users and (human-like) agents. As CAs exhibit various
social cues, first and foremost interacting via natural language as opposed to graphical user
interfaces, they trigger social responses as shown in many empirical studies in our sample
(e.g. Hong & Williams (2019), Lee & Choi (2017), Xu & Lombard (2017)). Users are likely to
form relationships with CAs over the course of multiple interactions where such agents can
assume a variety of roles ranging from simple digital assistants to companions or even friends.
As Bickmore and Picard's (2005) early study shows, CAs can be designed to leverage human
relationship building approaches which support establishing a social connection. Such features
can induce higher levels of trust and likability. Similarly, adverse experiences, such as
misunderstandings or an agent’s failure to appropriately complete a user’s request, are likely
to negatively impact user perception of CAs. This will also influence the relationship between
the user and the CA. We need information on how users will react if their befriended agent is
suddenly unable to adequately answer a simple question. Will the reaction be similar to a
computer’s failure or will it trigger a stronger social reaction similar to being disappointed by a
Further, as the specific capabilities of a CA are at first, to some extent hidden from the user,
they can require deeper investigation and learning compared to graphical user interfaces
(Følstad & Brandtzæg, 2017). Then, it will be interesting to see how the adoption and use of
CAs changes over a longer period of time. Thus, we propose that researchers investigate the
emerging relationship between users and CAs over time, and we suggest applying an
interaction-centric approach (Al-Natour & Benbasat, 2009) that recognizes the different roles
an agent can assume for a specific user and in a specific context.
D6.3: Study how the relationship between users and CAs takes shape during initial use of
the CA, and how it develops in multiple interactions over a longer period of time.
5.2 A Springboard for CA Studies in the IS Discipline
Our review analyzes and discusses the findings of 262 publications on the CAs’ design.
However, given the selection criteria of our review approach, we were only able to investigate
a subset of the available CA research that has been published in IS and CS as well as other
disciplines, such as didactics and pedagogy, medicine, ethics, and psychology.
In the following table, we provide a non-exhaustive list of CA research that we consider as
important to serve researchers and practitioners alike, as a starting point for addressing the
Bickmore & Cassell
Social dialogue with embodied conversational agents
Embodied conversational interface agents
Gulz & Haake (2006)
Design of animated pedagogical agents
- A look at their look
Graesser, Hu, & Person
Teaching with the help of talking heads
Graesser, Li, & Forsyth
Learning by communicating in natural language with
P & D
Graesser & McNamara
Self-regulated learning in learning environments with
pedagogical agents that interact in natural language
Johnson, Rickel, & Lester
Animated pedagogical agents: Face-to-face interaction in
interactive learning environments
Laranjo et al. (2018)
Conversational agents in healthcare: a systematic review
McNamara, & Lu (2009)
Embodied conversational agents as conversational
Massaro, Cohen, Daniel,
& Cole (1999)
Developing and evaluating conversational agents
Montenegro, da Costa, &
da Rosa Righi (2019)
Survey of conversational agents in health
Multimedia Learning with Animated Pedagogical
Provoost, Lau, Ruwaard,
& Riper (2017)
Embodied conversational agents in clinical psychology: a
M & P
Halamka, Kashavan, &
Chatbots and conversational agents in mental health: a
review of the psychiatric landscape
M & P
Veletsianos & Russell
Wik & Hjalmarsson (2009)
Embodied conversational agents in computer assisted
Ethical implications of conversational agents in global
M & E
McGreevey, Hanson, &
Clinical, Legal, and Ethical Aspects of Artificial
Intelligence–Assisted Conversational Agents in Health
M & E
Ethics Guidelines for Trustworthy AI
Establishing the rules for building trustworthy AI
CS = computer science, D = didactics and pedagogy, M = medicine, P = psychology, E = ethics
Table 12. Springboard to Further CA Research
5.3 Practical Implications
Our overview of CA research’s status quo and of avenues to advance the field is primarily
targeted at (HCI) scholars in the IS and CS disciplines with an interest in this technological
phenomenon. However, this paper also offers three key implications for practitioners.
First, the adapted research framework (Figure 2) can support CA conceptualization and
implementation in practice by highlighting aspects that should be considered during the design
process. For example, the framework can enable designers to reflect the relevant
characteristics of the user group for whom the CA is intended (e.g., concerning age, gender,
cultural background, or experience with CAs and the task at hand); or it can reflect the key
aspects related to how users perceive the agent and the interaction outcomes (e.g., measures
to evaluate the agent).
Second, the coded literature can help practitioners to identify empirically grounded design
approaches to influence certain design aspects related to how CAs are perceived. For
example, designers can draw on the literature database in identifying conversational strategies
that build trust in a CA (e.g., Schroeder & Schroeder, 2018; Stock et al., 2019) or foster
enjoyment in the interaction (e.g., Beale & Creed, 2009; Liao et al., 2018). Similarly, the
literature database can be useful in identifying undesirable effects on user perception due to
an agent’s design. Such effects could relate to privacy concerns (Sohn, 2019) or feelings of
uncanniness (Seeger et al., 2018; Strait et al., 2015; Tinwell & Sloan, 2014).
Third and finally, our research contributes ideas for field studies through a collaboration
between CA researchers and practitioners. Particularly, longitudinal studies could be beneficial
in validating findings from controlled experimental research in the field, and helping to identify
and overcome CAs’ shortcomings in practice. In our opinion, the substantial potential for both
research and practice lies in such collaborative studies, because in practice CAs often do not
meet (high) user expectations (Luger & Sellen, 2016) and much of the insights gained in
research have not been transferred to their application in the field.
Although we conducted our organizing and assessing review and subsequent analysis
according to established guidelines, potential limitations should be considered.
First, in our review and analysis, we have a restricted view of the available literature because
of the applied review methodology. We followed established guidelines to be as rigorous as
possible, given our self-chosen review constraints (i.e., selecting keywords, timeframe, and
outlets). We believe our selection of keywords and outlets is representative and sufficient for
the scope of this review article. In total, we found 262 publications in various outlets that
allowed us to draw a holistic picture of state-of-the-art CA research. However, our selection of
the outlets could be open to criticism. We decided to focus on an IS perspective and selected
the Basket of Eight, as well as leading conferences as sources for our review. Also, we
selected a representative subset of CS outlets to identify research published outside of IS
research. Future research could include further non-IS outlets to broaden our rather IS-
centered impression of research on the human-CA interaction.
Second, a limitation of all review articles is the ongoing availability of new publications. We
conducted the search process in January 2019 and updated it in November 2019. Considering
the publications over time (see Figure 4 in Appendix C), we can assume that even more
publications have become available while writing this article; in the future such work could
enrich our CA research presentation. One way to tackle this problem could be implementing
an online database that enables authors to submit their studies and each’s classification
following our framework. This would allow the community to have a reasonably recent overview
of the available research, and to discover further trends and opportunities for future research.
Similar online databases exist, for example, to search for variables and items
(Larsen & Bong,
2016) or conversational agents’ social cues
(Feine et al., 2019).
Third, our analysis and discussion depend on our sample. As outlined, we followed a rigorous
process in our review and assumed our set of publications to be representative for discussing
the status quo of HCI-related CA research from an IS perspective. Moreover, including
research published in non-IS outlets provides an even more holistic picture; however, a
different sample of publications might result in different findings. Nevertheless, we perceive
our identified research streams as insightful, relatively stable, and consistent independent of
potential changes in the underlying data by, e.g., new publications appearing.
Fourth, our framework can be extended with more dimensions and more granular
characteristics. The framework allowed us to classify the studies sufficiently to provide a
holistic picture of CA’s research status quo. Even so, future work could, for instance, extend
the agent dimension with a more detailed set of characteristics that would provide a more
productive overview of research on this specific aspect. To illustrate, Feine et al. (2019)
differentiate CAs’ design features into four categories (i.e., verbal, visual, auditory, and
invisible) with multiple subcategories. Similarly, the research could enrich the human
dimensions and the context with more detailed characteristics. The resulting insights of the
interaction might enable us to further investigate the transferability and adaptability of
In this article, we have organized a rich body of knowledge on the interaction between humans
and CAs in the IS and CS disciplines. We contribute a framework adapted from established
research that allows for classifying the vast body of knowledge on CAs. The framework
enables the research community to understand the interaction between humans and CAs in
specific contexts and can guide future research on CAs. Based on the findings of our literature
review, we have assessed the status quo of CA research and now propose six avenues with
sixteen actionable directions to move CA research forward. We invite researchers to address
the outlined directions for future studies and contribute valuable knowledge to this exciting
We would like to thank our Senior Editor Dorothy Leidner and the anonymous reviewers for
their support, encouragement and valuable feedback that helped us to substantially improve
the paper throughout the review process.
Abul, M., Siddike, K., Spohrer, J., Demirkan, H., & Kohda, Y. (2018). People’s Interactions with
Cognitive Assistants for Enhanced Performances. In Proceedings of the Hawaii
International Conference on System Sciences (HICSS) (pp. 1640–1648). Waikoloa
Village, Hawaii, USA.
Adam, M., & Klumpe, J. (2019). Onboarding With a Chat – the Effects of Message Interactivity
and Platform Self-Disclosure on User Disclosure Propensity. In Proceedings of the
European Conference on Information Systems (ECIS) (pp. 0–17). Stockholm, Sweden.
Adler, R. F., Iacobelli, F., & Gutstein, Y. (2016). Are you convinced? A Wizard of Oz study to
test emotional vs. Rational persuasion strategies in dialogues. Computers in Human
Behavior, 57, 75–81.
Al-Natour, S., & Benbasat, I. (2009). The Adoption and Use of IT Artifacts: A New Interaction-
Centric Model for the Study of User-Artifact Relationships. Journal of the Association for
Information Systems, 10(9), 661–685.
Al-Natour, S., Benbasat, I., & Cenfetelli, R. T. (2006). The Role of Design Characteristics in
Shaping Perceptions of Similarity: The Case of Online Shopping Assistants. Journal of
the Association for Information Systems, 7(12), 821–861.
Al-Natour, S., Benbasat, I., & Cenfetelli, R. T. (2009). The Antecedents of Customer Self-
Disclosure to Online Virtual Advisors. In Proceedings of the International Conference on
Information Systems (ICIS) (pp. 1–18). Phoenix, USA.
Anabuki, M., Kakuta, H., Yamamoto, H., & Tamura, H. (2000). Welbo: An Embodied
Conversational Agent Living in Mixed Reality Spaces. In Proceedings of the ACM CHI
Conference on Human Factors in Computing Systems (pp. 10–11). The Hague,
Araujo, T. (2018). Living up to the chatbot hype: The influence of anthropomorphic design cues
and communicative agency framing on conversational agent and company perceptions.
Computers in Human Behavior, 85, 183–189.
Ashktorab, Z., Jain, M., Liao, V. Q., & Weisz, J. D. (2019). Resilient chatbots: Repair strategy
preferences for conversational breakdowns. In Proceedings of the ACM CHI Conference
on Human Factors in Computing Systems (pp. 1–12). Glasgow, Scotland.
Baier, D., Rese, A., & Röglinger, M. (2018). Conversational User Interfaces for Online Shops?
A Categorization of Use Cases. In Proceedings of the International Conference on
Information Systems (ICIS) (pp. 1–17). San Francisco, USA.
Bandara, W., Gorbacheva, E., Beekhuyzen, J., Furtmueller, E., & Miskon, S. (2015). Achieving
Rigor in Literature Reviews: Insights from Qualitative Data Analysis and Tool-Support.
Communications of the Association for Information Systems, 34(8), 154–204.
Banker, R. D., & Kauffman, R. J. (2004). 50th Anniversary Article: The Evolution of Research
on Information Systems: A Fiftieth-Year Survey of the Literature in Management Science.
Management Science, 50(3), 281–298.
Banks, J. (2018). Perceived Moral Agency Scale: Development and Validation of a Metric for
Humans and Social Machines. Computers in Human Behavior.
Bariff, M. L., & Ginzberg, M. J. (1982). MIS and the behavioral sciences: Research patterns
and prescriptions. ACM SIGMIS Database.
Beale, R., & Creed, C. (2009). Affective interaction: How emotional agents affect users.
International Journal of Human Computer Studies, 67(9), 755–776.
Belbin, R. M. (2010). Management Teams: Why they succeed or fail. Routledge.
Belk, R. W. (1988). Possessions and the Extended Self. Journal of Consumer Research.
Belk, R. W. (2013). Extended Self in a Digital World. Journal of Consumer Research.
Bell, S., Sarkar, A., & Wood, C. (2019). Perceptions of chatbots in therapy. In Proceedings of
the ACM CHI Conference on Human Factors in Computing Systems (pp. 1–6). Glasgow,
Ben Mimoun, M. S., Poncin, I., & Garnier, M. (2012). Case study-Embodied virtual agents: An
analysis on reasons for failure. Journal of Retailing and Consumer Services, 19(6), 605–
Benlian, A., Klumpe, J., & Hinz, O. (2019). Mitigating the Intrusive Effects of Smart Home
Assistants by using Anthropomorphic Design Features: A Multi-Method Investigation.
Information Systems Journal, (forthcoming).
Berdichevsky, D., & Neuenschwander, E. (2002). Toward an Ethics of persuasive technology.
Communications of the ACM, 42(5), 51–58.
Berg, M. (2015). NADIA: A Simplified Approach Towards the Development of Natural Dialogue
Systems. In Natural Language Processing and Information Systems (pp. 144–150).
Springer International Publishing.
Bertacchini, F., Bilotta, E., & Pantano, P. (2017). Shopping with a robotic companion.
Computers in Human Behavior, 77, 382–395.
Bhattacherjee, A. (2012). Social Science Research: Principles, Methods, and Practices. Book
3. Tampa, Florida, USA.
Biancardi, B., Cafaro, A., & Pelachaud, C. (2017). Could a virtual agent be warm and
competent? investigating user’s impressions of agent’s non-verbal behaviours. In
Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (pp.
22–24). Denver, USA.
Bickmore, T., & Cassell, J. (2005). Social Dialongue with Embodied Conversational Agents. In
Advances in natural multimodal dialogue systems (pp. 23–54).
Bickmore, T. W., & Picard, R. W. (2005). Establishing and Maintaining Long-term Human-
computer Relationships. ACM Transactions on Computer-Human Interaction, 12(2), 293–
Bittner, E. A. C., Oeste-Reiß, S., & Leimeister, J. M. (2019). Where is the Bot in our Team?
Toward a Taxonomy of Design Option Combinations for Conversational Agents in
Collaborative Work. In Proceedings of the Hawaii International Conference on System
Sciences (HICSS). Grand Wailea, Maui.
Bittner, E., & Shoury, O. (2019). Designing Automated Facilitation for Design Thinking: A
Chatbot for Supporting Teams in the Empathy Map Method. In Proceedings of the Hawaii
International Conference on System Sciences (HICSS) (pp. 227–236). Grand Wailea,
Bogardus, E. S. (1947). Measurement of Personal-Group Relations. Sociometry.
Braun, M., & Alt, F. (2019). Affective assistants: A matter of states and traits. In Proceedings
of the ACM CHI Conference on Human Factors in Computing Systems (pp. 1–6).
Brynjolfsson, E., & McAfee, A. (2016). The Second Machine Age: Work, Progress, and
Prosperity in a Time of Brilliant Technologies. New York, USA: Norton & Company.
Burgoon, J. K., Bonito, J. A., Lowry, P. B., Humpherys, S. L., Moody, G. D., Gaskin, J. E., &
Giboney, J. S. (2016). Application of Expectancy Violations Theory to communication with
and judgments about embodied agents during a decision-making task. International
Journal of Human Computer Studies, 91, 24–36.
Burgoon, Judee K., Stern, L. A., & Dillmann, L. (1995). Interpersonal adaptation: Dyadic
interaction patterns. Cambridge University Press.
Burmester, M., Schippert, K., Zeiner, K. M., & Platz, A. (2019). Creating positive experiences
with digital companions. In Proceedings of the ACM CHI Conference on Human Factors
in Computing Systems (pp. 1–6). Glasgow, Scotland.
Burton, N., & Gaskin, J. (2019). “Thank You , Siri ”: Politeness and Intelligent Digital Assistants.
In Proceedings of the Americas Conference on Information Systems (AMCIS) (pp. 1–10).
Byrne, D. (1971). The Attraction Paradigm. Michigan, USA: Academic Press.
Byrne, D., & Griffitt, W. (1969). Similarity and awareness of similarity of personality
characteristics as determinants of attraction. Journal Of Experimental Research In
Byrne, D., Griffitt, W., & Stefaniak, D. (1967). Attraction and similarity of personality
characteristics. Journal of Personality and Social Psychology.
Cafaro, A., Vilhjalmsson, H. H., & Bickmore, T. (2016). First impressions in human-agent virtual
encounters. ACM Transactions on Computer-Human Interaction, 24(4), 1–40.
Candello, H., Pinhanez, C., Pichiliani, M., Cavalin, P., Figueiredo, F., Vasconcelos, M., &
Carmo, H. Do. (2019). The effect of audiences on the user experience with conversational
interfaces in physical spaces. In Proceedings of the ACM CHI Conference on Human
Factors in Computing Systems (pp. 1–13). Glasgow, Scotland.
Card, S. K., Moran, T. P., & Newell, A. (1980). The Keystroke-Level Model for User
Performance Time with Interactive Systems. Communications of the ACM.
Cardona, D. R., Schönborn, S., Werth, O., & Breitner, M. H. (2019). A Mixed Methods Analysis
of the Adoption and Diffusion of Chatbot Technology in the German Insurance Sector. In
Proceedings of the Americas Conference on Information Systems (AMCIS) (pp. 1–10).
Carlotto, T., & Jaques, P. A. (2016). The effects of animated pedagogical agents in an English-
as-a-foreign-language learning environment. International Journal of Human Computer
Studies, 95, 15–26.
Carroll, J. M. (2020). Human Computer Interaction - brief intro. In The Encyclopedia of Human-
Computer Interaction (2nd editio).
Carter, M., Grover, V., & Thatcher, J. B. (2013). Mobile Devices and the Self: Developing the
Concept of Mobile Phone Identity. In Strategy, Adoption, and Competitive Advantage of
Mobile Services in the Global Economy. https://doi.org/10.4018/978-1-4666-1939-
Cassell, J., Bickmore, T., Billinghurst, M., Campbell, L., Chang, K., Vilhjálmsson, H., & Yan, H.
(1999). Embodiment in conversational interfaces. In Proceedings of the ACM CHI
Conference on Human Factors in Computing Systems (pp. 520–527). Pittsburgh, USA.
Cassell, Justine. (2000). Embodied conversational interface agents. Communications of the
ACM, 43(4), 70–78.
Chattaraman, V., Kwon, W.-S., Gilbert, J. E., & Ross, K. (2018). Should AI-Based,
Conversational Digital Assistants Employ Social- or Task-Oriented Interaction Style? A
Task-Competency and Reciprocity Perspective for Older Adults. Computers in Human
Behavior, 90, 315–330.
Chaves, A. P., & Gerosa, M. A. (2018). Single or multiple conversational agents? An
interactional coherence comparison. In Proceedings of the ACM CHI Conference on
Human Factors in Computing Systems (pp. 1–13). Montréal, Canada.
Cho, E. (2019). Hey Google, can i ask you something in private? The effects of modality and
device in sensitive health information acquisition from voice assistants. In Proceedings of
the ACM CHI Conference on Human Factors in Computing Systems (pp. 1–9). Glasgow,
Clark, L., Pantidi, N., Cooney, O., Doyle, P., Garaialde, D., Edwards, J., … Cowan, B. R.
(2019). What makes a good conversation? Challenges in designing truly conversational
agents. In Proceedings of the ACM CHI Conference on Human Factors in Computing
Systems (pp. 1–12). Glasgow, Scotland.
Comendador, B. E. V., Francisco, B. M. B., Medenilla, J. S., Nacion, S. M. T., & Serac, T. B.
E. (2015). Pharmabot: A Pediatric Generic Medicine Consultant Chatbot. Journal of
Automation and Control Engineering, 3(2), 137–140.
Constantin, A., Lai, C., Farrow, E., Alex, B., Jeuring, J., Pel-Littel, R., & Nap, H. H. (2019).
“Why is the doctor a man?” Reactions of older adults to a virtual training doctor. In
Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (pp.
1–6). Glasgow, Scotland.
Cooper, H. M. (1988). Organizing knowledge synthesis: a taxonomy of literature reviews.
Knowledge in Society, 1(1), 104–126.
Corti, K., & Gillespie, A. (2016). Co-constructing intersubjectivity with artificial conversational
agents: People are more likely to initiate repairs of misunderstandings with agents
represented as human. Computers in Human Behavior, 58, 431–442.
Cowan, B. R., Branigan, H. P., Obregón, M., Bugis, E., & Beale, R. (2015). Voice
anthropomorphism, interlocutor modelling and alignment effects on syntactic choices in
human-computer dialogue. International Journal of Human Computer Studies, 83, 27–42.
Cowell, A. J., & Stanney, K. M. (2005). Manipulation of non-verbal interaction style and
demographic embodiment to increase anthropomorphic computer character credibility.
International Journal of Human Computer Studies, 62(2), 281–306.
Crockett, K., Latham, A., & Whitton, N. (2017). On predicting learning styles in conversational
intelligent tutoring systems using fuzzy decision trees. International Journal of Human
Computer Studies, 97, 98–115.
Dale, R. (2016). The return of the chatbots. Natural Language Engineering, 22(5), 811–817.
Davenport, T. H., & Kirby, J. (2016). Just How Smart Are Smart Machines? MIT Sloan
Management Review, 57(3), 21–25.
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of
Information Technology. Management Information Systems Quarterly, 13(3), 319–340.
De Rosis, F., Pelachaud, C., Poggi, I., Carofiglio, V., & De Carolis, B. (2003). From Greta’s
mind to her face: Modelling the dynamics of affective states in a conversational embodied
agent. International Journal of Human Computer Studies, 59(1–2), 81–118.
Dennis, A. R., & Valacich, J. S. (2001). Conducting Experimental Research in Information
Systems. Communications of the Association for Information Systems, 7(5), 1–41.
Derrick, D. C., & Ligon, G. S. (2014). The affective outcomes of using influence tactics in
embodied conversational agents. Computers in Human Behavior, 33, 39–48.
Desideri, L., Ottaviani, C., Malavasi, M., di Marzio, R., & Bonifacci, P. (2018). Emotional
processes in human-robot interaction during brief cognitive testing. Computers in Human
Behavior, (August), 0–1.
Diederich, S., Brendel, A. B., & Kolbe, L. M. (2020). Designing Anthropomorphic Enterprise
Conversational Agents. Business & Information Systems Engineering, (62), 193–209.
Diederich, S., Brendel, A. B., Lichtenberg, S., & Kolbe, L. M. (2019). Design for Fast Request
Fulfillment or Natural Interaction? Insights from an Online Experiment with a
Conversational Agent. In Proceedings of the European Conference on Information
Systems (ECIS) (pp. 1–17). Stockholm, Sweden.
Diederich, S., Janßen-Müller, M., Brendel, A. B., & Morana, S. (2019). Emulating Empathetic
Behavior in Online Service Encounters with Sentiment-Adaptive Responses: Insights
from an Experiment with a Conversational Agent. In Proceedings of the International
Conference on Information Systems (ICIS) (pp. 1–17). Munich, Germany.
Diederich, S., Lembcke, T.-B., Brendel, A. B., & Kolbe, L. M. (2021). Understanding the Impact
that Response Failure has on How Users Perceive Anthropomorphic Conversational
Service Agents: Insights from an Online Experiment. AIS Transactions on Human-
Computer Interaction, 13(1), 82–103.
Diederich, S., Lichtenberg, S., Brendel, A. B., & Trang, S. (2019). Promoting Sustainable
Mobility Beliefs with Persuasive and Anthropomorphic Design: Insights from an
Experiment with a Conversational Agent. In Proceedings of the International Conference
on Information Systems (ICIS) (pp. 1–17). Munich, Germany.
Dolata, M., Kilic, M., & Schwabe, G. (2019). When a computer speaks institutional talk:
Exploring challenges and potentials of virtual assistants in face-to-face advisory services.
In Proceedings of the Hawaii International Conference on System Sciences (HICSS) (pp.
105–114). Grand Wailea, Maui.
Duan, W., Yamashita, N., Hwang, S. Y., & Fussell, S. R. (2018). “ Let Me Ask Them to Clarify
If You Don’t Want To ”— A Clarification Agent for Nonnative Speakers. In Proceedings of
the ACM CHI Conference on Human Factors in Computing Systems (pp. 1–6). Montréal,
Ducheneaut, N., Don Wen, M. H., Yee, N., & Wadley, G. (2009). Body and mind: A study of
avatar personalization in three virtual worlds. In Conference on Human Factors in
Computing Systems - Proceedings. https://doi.org/10.1145/1518701.1518877
Elson, J. S., Derrick, D. C., & Ligon, G. S. (2018). Examining Trust and Reliance in
Collaborations between Humans and Automated Agents. In Proceedings of the Hawaii
International Conference on System Sciences (HICSS) (pp. 430–439). Waikoloa Village,
Epley, N., Waytz, A., & Cacioppo, J. T. (2007). On Seeing Human: A Three-Factor Theory of
Anthropomorphism. Psychological Review, 114(4), 864–886.
Ethics Guidelines for Trustworthy AI. (2019).
Fadhil, A., & Villafiorita, A. (2017). An Adaptive Learning with Gamification & Conversational
UIs. In Proceedings of the ACM CHI Conference on Human Factors in Computing
Systems (pp. 408–412). Denver, USA.
Fast, E., Chen, B., Mendelsohn, J., Bassen, J., & Bernstein, M. (2017). Iris: A Conversational
Agent for Complex Tasks. In Proceedings of the ACM CHI Conference on Human Factors
in Computing Systems (pp. 1–12). Denver, USA.
Feine, Jasper;, Gnewuch, U., Morana, S., & Maedche, A. (2019). Gender Bias in Chatbot
Design. In Proceedings of CONVERSATIONS 2019 (pp. 79–93).
Feine, Jasper, Gnewuch, U., Morana, S., & Maedche, A. (2019). A Taxonomy of Social Cues
for Conversational Agents. International Journal of Human-Computer Studies,
Feine, Jasper, Morana, S., & Gnewuch, U. (2019). Measung Service Encounter Satisfaction
with Customer Service Chatbots using Sentiment Analysis. In Proceedings of the
International Conference on Wirtschaftsinformatik (pp. 0–11).
Floridi, L. (2019). Establishing the rules for building trustworthy AI. Nature Machine
Intelligence, 1(6), 261–262. https://doi.org/10.1038/s42256-019-0055-y
Følstad, A., & Brandtzæg, P. B. (2017). Chatbots and the new world of HCI. Interactions, 24(4),
Forlizzi, J., Zimmerman, J., Mancuso, V., & Kwak, S. (2007). How interface agents affect
interaction between humans and computers. Proceedings of the 2007 Conference on
Designing Pleasurable Products and Interfaces, DPPI’07, (August), 209–221.
Fryer, L. K., Ainley, M., Thompson, A., Gibson, A., & Sherlock, Z. (2017). Stimulating and
sustaining interest in a language course: An experimental comparison of Chatbot and
Human task partners. Computers in Human Behavior, 75, 461–468.
Gambino, A., Shyam Sundar, S., & Kim, J. (2019). Digital doctors and robot receptionists: User
attributes that predict acceptance of automation in healthcare facilities. In Proceedings of
the ACM CHI Conference on Human Factors in Computing Systems (pp. 1–6). Glasgow,
Gefen, D., & Straub, D. (2003). Managing User Trust in B2C e-Services. E-Service Journal,
Gerlach, J. H., & Kuo, F. Y. (1991). Understanding human-computer interaction for information
systems design. Management Information Systems Quarterly.
Giles, H., Coupland, N., & Coupland, J. (2010). Accommodation theory: Communication,
context, and consequence. In Contexts of Accommodation.
Gnewuch, U., Morana, S., Adam, M. T. P., & Maedche, A. (2018). Faster Is Not Always Better:
Understanding the Effect of Dynamic Response Delays in Human-Chatbot Interaction. In
Proceedings of the European Conference on Information Systems (ECIS) (pp. 1–17).
Portsmouth, United Kingdom.
Gnewuch, U., Morana, S., & Maedche, A. (2017). Towards Designing Cooperative and Social
Conversational Agents for Customer Service. In Proceedings of the International
Conference on Information Systems (ICIS) (pp. 1–13). Seoul, Korea.
Go, E., & Sundar, S. S. (2019). Humanizing chatbots: The effects of visual, identity and
conversational cues on humanness perceptions. Computers in Human Behavior, 97,
Goasduff, L. (2019). Chatbots Will Appeal to Modern Workers. Retrieved June 19, 2020, from
Gong, L. (2008). How social is social responses to computers? The function of the degree of
anthropomorphism in computer representations. Computers in Human Behavior, 24(4),
Goodhue, D. L., & Thompson, R. L. (1995). Task-Technology Fit and Individual Performance.
Management Information Systems Quarterly, 19(2), 213–236.
Graesser, A. C., Cai, Z., Morgan, B., & Wang, L. (2017). Assessment with computer agents
that engage in conversational dialogues and trialogues with learners. Computers in
Human Behavior, 76, 607–616.
Graesser, A. C., Hu, X., & Person, N. (2001). Teaching with the help of talking heads. In
Proceedings - IEEE International Conference on Advanced Learning Technologies,
Graesser, A. C., Li, H., & Forsyth, C. (2014). Learning by Communicating in Natural Language
With Conversational Agents. Current Directions in Psychological Science, 23(5), 374–
Graesser, A., & McNamara, D. (2010). Self-regulated learning in learning environments with
pedagogical agents that interact in natural language. Educational Psychologist, 45(4),
Gregor, S., Kruse, L. C., & Seidel, S. (2020). The Anatomy of a Design Principle. Journal of
the Association for Information Systems, 1–49.
Grice, H. P. (1975). Logic and Conversation. Syntax and Semantics, Vol 3.
Groom, V., Takayama, L., Ochi, P., & Nass, C. (2008). I am my robot: The impact of robot-
building and robot form on operators. In Proceedings of the 4th ACM/IEEE International
Conference on Human-Robot Interaction, HRI’09.
Grudin, J., & Jacques, R. (2019). Chatbots, humbots, and the quest for artificial general
intelligence. In Proceedings of the ACM CHI Conference on Human Factors in Computing
Systems (pp. 1–11). Glasgow, Scotland.
Gulz, A., & Haake, M. (2006). Design of animated pedagogical agents - A look at their look.
International Journal of Human Computer Studies, 64(4), 322–339.
Hanus, M. D., & Fox, J. (2015). Persuasive avatars: The effects of customizing a virtual
salespersons appearance on brand liking and purchase intentions. International Journal
of Human Computer Studies, 84, 33–40.
Harjunen, V. J., Spapé, M., Ahmed, I., Jacucci, G., & Ravaja, N. (2018). Persuaded by the
machine: The effect of virtual nonverbal cues and individual differences on compliance in
economic bargaining. Computers in Human Behavior, 87, 384–394.
Hayashi, Y. (2013). Pedagogical conversational agents for supporting collaborative learning.
In Proceedings of the ACM CHI Conference on Human Factors in Computing Systems
(p. 655). Paris, France.
Heinrich, L. J., Heinzl, A., & Riedl, R. (2011). Wirtschaftsinformatik: Einführung und
Herborn, K., Stadler, M., Mustafić, M., & Greiff, S. (2018). The Assessment of Collaborative
Problem Solving in PISA 2015: Can Computer Agents Replace Humans? Computers in
Hill, J., Randolph Ford, W., & Farreras, I. G. (2015). Real conversations with artificial
intelligence: A comparison between human-human online conversations and human-
chatbot conversations. Computers in Human Behavior, 49, 245–250.
Hobert, S. (2019). Say Hello to ‘ Coding Tutor ’! Design and Evaluation of a Chatbot-based
Learning System Supporting Students to Learn to Program. In Proceedings of the
International Conference on Information Systems (pp. 1–17). Munich, Germany.
Hobert, S., & Wolff, R. M. Von. (2019). Say Hello to Your New Automated Tutor – A Structured
Literature Review on Pedagogical Conversational Agents. In Proceedings of International
Conference on Wirtchaftsinformatik (pp. 301–314). Siegen, Germany.
Hong, J. W., & Williams, D. (2019). Racism, responsibility and autonomy in HCI: Testing
perceptions of an AI agent. Computers in Human Behavior, 100, 79–84.
Hsu, P. (Pei-T., Zhao, J., Liao, K., Liu, T., & Wang, C. (2017). AllergyBot. In Proceedings of
the ACM CHI Conference on Human Factors in Computing Systems (pp. 74–79). Denver,
Hu, T., Xu, A., Liu, Z., You, Q., Guo, Y., Sinha, V., … Akkiraju, R. (2018). Touch Your Heart:
A Tone-aware Chatbot for Customer Care on Social Media. In Proceedings of the ACM
CHI Conference on Human Factors in Computing Systems (pp. 1–12). Montréal, Canada.
Huang, C.-M. (2012). Designing effective behaviors for educational embodied agents. In
Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (p.
931). Austin, Texas.
Huang, M. H., & Rust, R. T. (2018). Artificial Intelligence in Service. Journal of Service
Research, 21(2), 155–172.
Hubal, R. C., Fishbein, D. H., Sheppard, M. S., Paschall, M. J., Eldreth, D. L., & Hyde, C. T.
(2008). How do varied populations interact with embodied conversational agents?
Findings from inner-city adolescents and prisoners. Computers in Human Behavior,
Hwang, G., Oh, C. Y., Lee, J., & Lee, J. (2019). It sounds like a woman: Exploring gender
stereotypes in South Korean voice assistants. In Proceedings of the ACM CHI
Conference on Human Factors in Computing Systems (pp. 1–6). Glasgow, Scotland.
Hyde, J., Carter, E. J., Kiesler, S., & Hodgins, J. K. (2015). Using an Interactive Avatar’s Facial
Expressiveness to Increase Persuasiveness and Socialness. In Proceedings of the ACM
CHI Conference on Human Factors in Computing Systems (pp. 1719–1728). Seoul,
Interaction, A. S. I. G. on C.-H. (2020). ACM CHI Conference on Human Factors in Computing
Systems. Retrieved from https://sigchi.org/conferences/conference-history/CHI/
Ipsoft. (2020). Amelia in Action: A Selection of Stories from Organizations adopting IPsoft’c
cognitive agent. Retrieved from http://www.ipsoft.com/amelia/
Jain, M., Kota, R., Kumar, P., & Patel, S. (2018). Convey: Exploring the Use of a Context View
for Chatbots. In Proceedings of the ACM CHI Conference on Human Factors in
Computing Systems (pp. 1–6). Montréal, Canada. Retrieved from
Jeong, Y., Kang, Y., & Lee, J. (2019). Exploring effects of conversational fillers on user
perception of conversational agents. In Proceedings of the ACM CHI Conference on
Human Factors in Computing Systems (pp. 1–6). Glasgow, Scotland.
Jin, S. A. A. (2010). The effects of incorporating a virtual agent in a computer-aided test
designed for stress management education: The mediating role of enjoyment. Computers
in Human Behavior, 26(3), 443–451.
Johnson, K. (2018). Facebook Messenger hits 100,000 bots. Retrieved June 5, 2018, from
Johnson, W., Rickel, J., & Lester, J. (2000). Animated pedagogical agents: Face-to-face
interaction in interactive learning environments. International Journal of Artificial
Intelligence in Education, 11(1), 47–78.
Jucks, R., Linnemann, G. A., & Brummernhenrich, B. (2018). Student Evaluations of a (Rude)
Spoken Dialogue System Insights from an Experimental Study. Advances in Human-
Computer Interaction, 2018, 1–10.
Jung, H., Hwang, G., Lee, J., Oh, C., Oh, C. Y., & Suh, B. (2019). Tell me more: Understanding
user interaction of smart speaker news powered by conversational search. In
Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (pp.
1–6). Glasgow, Scotland.
Kanaoka, T., & Mutlu, B. (2015). Designing a Motivational Agent for Behavior Change in
Physical Activity. In Proceedings of the ACM CHI Conference on Human Factors in
Computing Systems (pp. 1445–1450). Seoul, Korea.
Karahanna, E., Benbasat, I., Bapna, R., & Rai, A. (2018). Opportunities and Challenges for
Different Types of Online Experiments. Management Information Systems Quarterly,
Kim, D., Park, K., & Park, Y. (2018). Alexa, Tell Me More: The Effect of Advertisements on
Memory Accuracy from Smart Speakers. In Proceedings of the Pacific Asia Conference
on Information Systems (PACIS). Yokohama, Japan.
Kim, K. J., Park, E., & Shyam Sundar, S. (2013). Caregiving role in human-robot interaction:
A study of the mediating effects of perceived benefit and social presence. Computers in
Human Behavior, 29(4), 1799–1806.
Kim, Y., Kwak, S. S., & Kim, M. S. (2013). Am I acceptable to you? Effect of a robot’s verbal
language forms on people’s social distance from robots. Computers in Human Behavior,
Kim, Y., & Mutlu, B. (2014). How social distance shapes human-robot interaction. International
Journal of Human Computer Studies, 72(12), 783–795.
Knijnenburg, B. P., & Willemsen, M. C. (2016). Inferring Capabilities of Intelligent Agents from
Their External Traits. ACM Transactions on Interactive Intelligent Systems, 6(4), 1–25.
Kocaballi, A. B., Berkovsky, S., Quiroz, J. C., Laranjo, L., Tong, H. L., Rezazadegan, D., …
Coiera, E. (2019). The personalization of conversational agents in health care: Systematic
review. Journal of Medical Internet Research.
Kowalski, J., Skorupska, K., Kopeć, W., Jaskulska, A., Abramczuk, K., Biele, C., & Marasek,
K. (2019). Older adults and voice interaction: A pilot study with google home. In
Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (pp.
1–6). Glasgow, Scotland.
Kozlowski, S. W. J., & Ilgen, D. R. (2006). Enhancing the Effectiveness of Work Groups and
Teams: A Reflection. Perspectives on Psychological Science, 13(2), 205–212.
Krämer, N. C., Lucas, G., Schmitt, L., & Gratch, J. (2018). Social snacking with a virtual agent
– On the interrelation of need to belong and effects of social responsiveness when
interacting with artificial entities. International Journal of Human Computer Studies, 109,
Krämer, N., Kopp, S., Becker-Asano, C., & Sommer, N. (2013). Smile and the world will smile
with you - The effects of a virtual agent’s smile on users’ evaluation and behavior.
International Journal of Human Computer Studies, 71(3), 335–349.
Lahoual, D., & Fréjus, M. (2019). When users assist the voice assistants: From supervision to
failure resolution. In Proceedings of the ACM CHI Conference on Human Factors in
Computing Systems (pp. 1–8). Glasgow, Scotland.
Laranjo, L., Dunn, A. G., Tong, H. L., Kocaballi, A. B., Chen, J., Bashir, R., … Coiera, E. (2018).
Conversational Agents in Healthcare: A Systematic Review. Journal of the American
Medical Informatics Association, 25(9), 1248–1258.
Larsen, K. R., & Bong, C. H. (2016). A tool for addressing construct identity in literature reviews
and meta-analyses. Management Information Systems Quarterly, 40(3), 529–551.
Laumer, S., Maier, C., & Gubler, F. T. (2019). Chatbot Acceptance in Healthcare: Explaining
User Adoption of Conversational Agents for Disease Diagnosis. In Proceedings of the
European Conference on Information Systems (ECIS) (pp. 0–18). Stockholm, Sweden.
Retrieved from https://aisel.aisnet.org/ecis2019_rp/88
Laumer, S., Racheva, A., Gubler, F., & Maier, C. (2019). Use Cases for Conversational
Agents : An Interview-based Study. In Proceedings of the Americas Conference on
Information Systems (AMCIS) (pp. 1–10). Cancun, Mexico.
Le Bigot, L., Jamet, E., Rouet, J. F., & Amiel, V. (2006). Mode and modal transfer effects on
performance and discourse organization with an information retrieval dialogue system in
natural language. Computers in Human Behavior, 22(3), 467–500.
Lechler, R., Stoeckli, E., Rietsche, R., & Uebernickel, F. (2019). Looking Beneath the Tip of
the Iceberg: The Two-Sided Nature of Chatbots and Their Roles for Digital Feedback
Exchange. In Proceedings of the European Conference on Information Systems (ECIS)
(pp. 0–17). Stockholm, Sweden. Retrieved from https://aisel.aisnet.org/ecis2019_rp/119
Lee, C., Lesh, N., Sidner, C. L., Morency, L.-P., Kapoor, A., & Darrell, T. (2004). Nodding in
conversations with a robot. In Proceedings of the ACM CHI Conference on Human
Factors in Computing Systems (p. 785). Vienna, Austria.
Lee, K. M., Jung, Y., Kim, J., & Kim, S. R. (2006). Are physically embodied social agents better
than disembodied social agents? The effects of physical embodiment, tactile interaction,
and people’s loneliness in human-robot interaction. International Journal of Human
Computer Studies, 64(10), 962–973.
Lee, M. K., Kiesler, S., Forlizzi, J., & Rybski, P. (2012). Ripple effects of an embedded social
agent. In Proceedings of the ACM CHI Conference on Human Factors in Computing
Systems (p. 695). Austin, Texas.
Lee, S. Y., & Choi, J. (2017). Enhancing user experience with conversational agent for movie
recommendation: Effects of self-disclosure and reciprocity. International Journal of
Human Computer Studies, 103, 95–105.
Lehto, T., & Oinas-Kukkonen, H. (2017). Examining the Persuasive Potential of Web-based
Health Behavior Change Support Systems. AIS Transactions on Human-Computer
Interaction, 7(3), 126–140.
Leidner, D. (2018). Review and Theory Symbiosis: An Introspective Retrospective. Journal of
the Association for Information Systems, 19(06), 552–567.
Leite, I., Pereira, A., Mascarenhas, S., Martinho, C., Prada, R., & Paiva, A. (2013). The
influence of empathy in human-robot relations. International Journal of Human Computer
Studies, 71(3), 250–260.
Lembcke, T. B., Engelbrecht, N., Brendel, A. B., & Kolbe, L. M. (2019). To nudge or not to
nudge: Ethical considerations of digital nudging based on its behavioral economics roots.
In Proceedings of the European Conference on Information Systems (ECIS) (pp. 0–17).
Li, D., Browne, G. J., & Chau, P. Y. K. (2006). An empirical investigation of web site use using
a commitment-based model. Decision Sciences, 37(3), 427–444.
Li, J. (2015). The benefit of being physically present: A survey of experimental works
comparing copresent robots, telepresent robots and virtual agents. International Journal
of Human Computer Studies, 77, 23–27.
Liao, Q. V., Hussain, M. M., Chandar, P., Davis, M., Crasso, M., Wang, D., … Geyer, W.
(2018). All Work and no Play? Conversations with a Question-and-Answer Chatbot in the
Wild. In Proceedings of the ACM CHI Conference on Human Factors in Computing
Systems (pp. 1–13). Montréal, Canada.
Looije, R., Neerincx, M. A., & Cnossen, F. (2010). Persuasive robotic assistant for health self-
management of older adults: Design and evaluation of social behaviors. International
Journal of Human Computer Studies, 68(6), 386–397.
Louwerse, M. M., Graesser, A. C., McNamara, D. S., & Lu, S. (2009). Embodied conversational
agents as conversational partners. Applied Cognitive Psychology, 23(9), 1244–1255.
Luger, E., & Sellen, A. (2016). “Like Having a Really Bad PA”: The Gulf between User
Expectation and Experience of Conversational Agents. In Proceedings of the ACM CHI
Conference on Human Factors in Computing Systems (pp. 5286–5297). San José, USA.
Luxton, D. D. (2020). Ethical implications of conversational agents in global public health.
Maedche, A., Legner, C., Benlian, A., Berger, B., Gimpel, H., Hess, T., … Söllner, M. (2019).
AI-Based Digital Assistants. Business & Information Systems Engineering, (4), 1–28.
Malone, T. W. (2018). How human-computer “superminds” are redefining the future of work.
MIT Sloan Management Review.
Massaro, D. W., Cohen, M. M., Daniel, S., & Cole, R. A. (1999). Developing and Evaluating
conversational Agents. In Human Performance and Ergonomics (pp. 173–194).
Academic Press. https://doi.org/10.1016/b978-012322735-5/50008-7
Matsushita, M., Maeda, E., & Kato, T. (2004). An interactive visualization method of numerical
data based on natural language requirements. International Journal of Human Computer
Studies, 60(4), 469–488.
Mavridis, P., Huang, O., Qiu, S., Gadiraju, U., & Bozzon, A. (2019). Chatterbox: Conversational
interfaces for microtask crowdsourcing. In Proceedings of the ACM CHI Conference on
Human Factors in Computing Systems (pp. 243–251). Glasgow, Scotland.
McAfee, A., & Brynjolfsson, E. (2017). Machine, Platform, Crowd: Harnessing Our Digital
Future. Norton & Company.
McGreevey, J. D., Hanson, W., & Koppel, R. (2020). Clinical, Legal, and Ethical Aspects of
Artificial Intelligence–Assisted Conversational Agents in Health Care. Journal of the
American Medical Association, 324(6).
McQuiggan, S. W., & Lester, J. C. (2007). Modeling and evaluating empathy in embodied
companion agents. International Journal of Human Computer Studies, 65(4), 348–360.
McTear, M. (2017). The rise of the conversational interface: A new kid on the block? Lecture
Notes in Computer Science.
McTear, M., Callejas, Z., & Griol, D. (2016). The Conversational Interface: Talking to Smart
Devices. Basel, Switzerland: Springer Publishing Company.
Meier, P., Beinke, J. H., Fitte, C., Behne, A., & Teuteberg, F. (2019). FeelFit – Design and
Evaluation of a Conversational Agent to Enhance Health Awareness. In Proceedings of
the International Conference on Information Systems (pp. 1–17). Munich, Germany.
Meyer von Wolff, R., Hobert, S., Masuch, K., & Schumann, M. (2019). What Do You Need
Today ? – An Empirical Systematization of Application Areas for Chatbots at Digital
Workplaces. In Proceedings of the Americas Conference on Information Systems
(AMCIS) (pp. 1–10). Cancun, Mexico.
Miner, A., Chow, A., Adler, S., Zaitsev, I., Tero, P., Darcy, A., & Paepcke, A. (2016).
Conversational Agents and Mental Health: Theory-Informed Assessment of Language
and Affect. In Proceedings of the International Conference on Human Agent Interaction.
Miner, A. S., Milstein, A., Schueller, S., Hegde, R., Mangurian, C., & Linos, E. (2016).
Smartphone-based conversational agents and responses to questions about mental
health, interpersonal violence, and physical health. JAMA Internal Medicine, 176(5), 619–
Montenegro, J. L. Z., da Costa, C. A., & da Rosa Righi, R. (2019). Survey of conversational
agents in health. Expert Systems with Applications, 129, 56–67.
Moon, Y. (2000). Intimate Exchanges: Using Computers to Elicit Self‐Disclosure From
Consumers. Journal of Consumer Research, 26(4), 323–339.
Morana, S., Gnewuch, U., Jung, D., & Granig, C. (2020). The Effect of Anthropomorphism on
Investment Decision-Making with Robo-Advisor Chatbots. In Proceedings of the
European Conference on Information Systems (ECIS) (pp. 0–18). Marrakech, Morocco.
Moreno, R. (2012). Multimedia Learning with Animated Pedagogical Agents. In The Cambridge
Handbook of Multimedia Learning (pp. 507–523). Cambridge University Press.
Mori, M. (1970). The Uncanny Valley. Energy.
Mori, M., MacDorman, K. F., & Kageki, N. (2012). The Uncanny Valley. IEEE Robotics and
Automation Magazine, 19(2), 98–100.
Morton, H., Gunson, N., & Jack, M. (2012). Interactive language learning through speech-
enabled virtual scenarios. Advances in Human-Computer Interaction.
Mou, Y., & Xu, K. (2017). The media inequality: Comparing the initial human-human and
human-AI social interactions. Computers in Human Behavior, 72, 432–440.
Nass, C., & Moon, Y. (2000). Machines and mindlessness: Social responses to computers.
Journal of Social Issues, 56(1), 81–103.
NextIT. (2018). Helping a railroad service conduct business. Retrieved November 1, 2018,
Nguyen, Q. N., & Sidorova, A. (2017). AI capabilities and user experiences: a comparative
study of user reviews for assistant and non-assistant mobile apps. In Proceedings of the
Americas Conference on Information Systems (AMCIS) (pp. 1–10). Boston, USA.
Niewiadomski, R., & Pelachaud, C. (2010). Affect expression in ECAs: Application to
politeness displays. International Journal of Human Computer Studies, 68(11), 851–871.
Nunamaker, J. F., Derrick, D. C., Elkins, A. C., Burgoon, J. K., & Patton, M. W. (2011).
Embodied Conversational Agent-Based Kiosk for Automated Interviewing. Journal of
Management Information Systems, 28(1), 17–48.
O’Leary, D. E. (2019). GOOGLE’S Duplex: Pretending to be human. Intelligent Systems in
Accounting, Finance and Management, 26(1), 46–53.
Olson, G. M., & Olson, J. S. (2003). Human-Computer Interaction: Psychological Aspects of
the Human Use of Computing. Annual Review of Psychology.
Oracle. (2016). Can Virtual Experiences Replace Reality? The future role for humans in
delivering customer experience, 19.
Otoo, B. A., & Salam, A. F. (2018). Mediating Effect of Intelligent Voice Assistant (IVA), User
Experience and Effective Use on Service Quality and Service Satisfaction and Loyalty. In
Proceedings of the International Conference on Information Systems (ICIS) (pp. 1–9).
San Francisco, USA.
Pereira, A. T., Prada, R., & Paiva, A. (2014). Improving social presence in human-agent
interaction. In Proceedings of the ACM CHI Conference on Human Factors in Computing
Systems (pp. 1449–1458). Toronto, Canada.
Perez, S. (2016). Starbucks unveils a virtual assistant that takes your order via messaging or
voice. Retrieved November 23, 2018, from https://techcrunch.com/2017/01/30/starbucks-
Pfeuffer, N., Toutaoui, J., Adam, M., Hinz, O., & Benlian, A. (2019). Mr. and Mrs.
Conversational Agent - Gender Stereotyping in Judge-Advisor Systems and the Role of
Egocentric Bias. In Proceedings of the International Conference on Information Systems
(pp. 1–17). Munich, Germany.
Pickard, M. D., Roster, C. A., & Chen, Y. (2016). Revealing sensitive information in personal
interviews: Is self-disclosure easier with humans or avatars and under what conditions?
Computers in Human Behavior, 65, 23–30.
Pickering, M. J., & Garrod, S. (2004). Toward a Mechanistic Psychology of Dialogue.
Behavioral and Brain Sciences, 27(2), 169–190.
Porcheron, M., Fischer, J. E., Reeves, S., & Sharples, S. (2018). Voice Interfaces in Everyday
Life. In Proceedings of the ACM CHI Conference on Human Factors in Computing
Systems (pp. 1–12). Montréal, Canada.
Powers, A., & Kiesler, S. (2006). The advisor robot: Tracing people’s mental model from a
robot’s physical attributes. In Proceedings of the 2006 ACM Conference on Human-Robot
Interaction (pp. 1–8). Salt Lake City, USA.
Provoost, S., Lau, H. M., Ruwaard, J., & Riper, H. (2017). Embodied conversational agents in
clinical psychology: A scoping review. Journal of Medical Internet Research, 19(5).
Punj, G., & Stewart, D. W. (1983). Cluster Analysis in Marketing Research: Review and
Suggestions for Application. Journal of Marketing Research, 20(2), 134–148.
Purington, A., Taft, J. G., Sannon, S., Bazarova, N. N., & Taylor, S. H. (2017). “Alexa is my
new BFF”: Social roles, user satisfaction, and personification of the Amazon Echo. In
Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (Vol.
Part F1276, pp. 2853–2859). Denver, USA.
Qiu, L., & Benbasat, I. (2009). Evaluating Anthropomorphic Product Recommendation Agents:
A Social Relationship Perspective to Designing Information Systems. Journal of
Management Information Systems, 25(4), 145–182.
Qiu, L., & Benbasat, I. (2010). A study of demographic embodiments of product
recommendation agents in electronic commerce. International Journal of Human
Computer Studies, 68(10), 669–688.
Quynh, N., & Sidorova, A. (2018). Understanding User Interactions with a Chatbot: A Self-
determination Theory Approach. In Proceedings of the Americas Conference on
Information Systems (AMCIS) (pp. 1–5). New Orleans, USA.
Reeves, B., & Nass, C. (1996). The Media Equation: How People Treat Computers, Television
and New Media Like Real People and Places. The Center for the Study of Language and
Reinecke, K., & Bernstein, A. (2013). Knowing what a user likes: A design science approach
to interfaces that automatically adapt to culture. MIS Quarterly: Management Information
Systems, 37(2), 427–453. https://doi.org/10.25300/MISQ/2013/37.2.06
Robert, J. L. R., & Sangseok, Y. (2018). Emotional Attachment, Performance, and Viability in
Teams Collaborating with Embodied Physical Action (EPA) Robots. Journal of the
Association for Information Systems, 17, 267–307.
Rosenberg-Kima, R. B., Baylor, A. L., Plant, E. A., & Doerr, C. E. (2008). Interface agents as
social models for female students: The effects of agent visual presence and appearance
on female students’ attitudes and beliefs. Computers in Human Behavior, 24(6), 2741–
Rosenthal-Von Der Pütten, A. M., & Krämer, N. C. (2014). How design characteristics of robots
determine evaluation and uncanny valley related responses. Computers in Human
Behavior, 36, 422–439.
Rosenthal-von der Pütten, A. M., Straßmann, C., & Krämer, N. C. (2018). Dominant and
submissive nonverbal behavior of virtual agents and its effects on evaluation and
negotiation outcome in different age groups. Computers in Human Behavior.
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic
motivation, social development, and well-being. The American Psychologist.
Rzepka, C., & Berger, B. (2018). User Interaction with AI-enabled Systems: A Systematic
Review of IS Research. In Proceedings of the International Conference on Information
Systems (ICIS) (pp. 1–16). San Francisco, USA.
Saerbeck, M., Schut, T., Bartneck, C., & Janse, M. D. (2010). Expressive robots in education:
varying the degree of social supportive behavior of a robotic tutor. In Proceedings of the
ACM CHI Conference on Human Factors in Computing Systems (pp. 1613–1622).
Saffarizadeh, K., Boodraj, M., & Alashoor, T. M. (2017). Conversational Assistants:
Investigating Privacy Concerns, Trust, and Self-Disclosure. In Proceedings of the
International Conference on Information Systems (ICIS) (pp. 0–12). Seoul, Korea.
Sakamoto, D., Kanda, T., Ono, T., Kamashima, M., Imai, M., & Ishiguro, H. (2005).
Cooperative embodied communication emerged by interactive humanoid robots.
International Journal of Human Computer Studies, 62(2), 247–265.
Sangseok, Y., & Lionel, P. R. J. (2019). Subgroup Formation in Human – Robot Teams. In
Proceedings of the International Conference on Information Systems (pp. 1–17). Munich,
Schlesinger, A., Hara, K. P. O., & Taylor, A. S. (2018). Let’s Talk About Race: Identity,
Chatbots, and AI. In Proceedings of the ACM CHI Conference on Human Factors in
Computing Systems (pp. 1–14). Montréal, Canada.
Schroeder, J., & Schroeder, M. (2018). Trusting in Machines: How Mode of Interaction Affects
Willingness to Share Personal Information with Machines. In Proceedings of the Hawaii
International Conference on System Sciences (HICSS) (Vol. 9, pp. 472–480). Waikoloa
Village, Hawaii, USA.
Schuetz, S., & Venkatesh, V. (2020). The rise of human machines: How cognitive computing
systems challenge assumptions of user-system interaction. Journal of the Association for
Information Systems, 1–42.
Schuetzler, R. M., Giboney, J. S., Grimes, G. M., & Nunamaker, J. F. (2018). The Influence of
Conversational Agents on Socially Desirable Responding. In Proceedings of the Hawaii
International Conference on System Sciences (HICSS) (Vol. 9, pp. 283–292). Waikoloa
Village, Hawaii, USA.
Schuetzler, R. M., Grimes, G. M., Giboney, J. S., & Buckman, J. (2014). Facilitating Natural
Conversational Agent Interactions: Lessons from a Deception Experiment. In
Proceedings of the International Conference on Information Systems (ICIS) (pp. 1–16).
Auckland, New Zealand.
Sebastian, J., & Richards, D. (2017). Changing