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Why do Chatbots fail? A Critical Success Factors Analysis



Chatbots gain more and more attention, both in research and in practice, and enter several application areas. While much research addresses technical or human-centered aspects, development, and adoption, little is known about Critical Success Factors (CSFs) and failure reasons of chatbots in practice. Design Science Research (DSR) oriented, we first analyze 103 real-world chatbots to examine the discontinuation rate of chatbots in 15 months. With a literature review and 20 expert interviews, we derive 12 specific CSFs and identify failure reasons which are evaluated in a focus group discussion with chatbot experts, afterwards. We explain chatbots' failure in practice, improve chatbot knowledge in Information Systems (IS) and Human Computer Interaction (HCI), and finally deduce recommendations and further research opportunities.
A Critical Success Factors Analysis for Chatbots
Forty-Second International Conference on Information Systems, Austin 2021
Why do Chatbots fail?
A Critical Success Factors Analysis
Completed Research Paper
Antje Janssen
Leibniz Universitt Hannover
Knigsworther Platz 1
30167 Hannover, Germany
Lukas Grützner
Leibniz Universitt Hannover
Knigsworther Platz 1
30167 Hannover, Germany
Michael H. Breitner
Leibniz Universitt Hannover
Knigsworther Platz 1
30167 Hannover, Germany
Chatbots gain more and more attention, both in research and in practice, and enter
several application areas. While much research addresses technical or human-centered
aspects, development, and adoption, little is known about Critical Success Factors (CSFs)
and failure reasons of chatbots in practice. Design Science Research (DSR) oriented, we
first analyze 103 real-world chatbots to examine the discontinuation rate of chatbots in
15 months. With a literature review and 20 expert interviews, we derive 12 specific CSFs
and identify failure reasons which are evaluated in a focus group discussion with chatbot
experts, afterwards. We explain chatbots’ failure in practice, improve chatbot knowledge
in Information Systems (IS) and Human Computer Interaction (HCI), and finally deduce
recommendations and further research opportunities.
Keywords: Chatbot, conversational agent, failure reasons, critical success factors,
design science research
Due to technological advances in Artificial Intelligence (AI) and Natural Language Processing (NLP) as well
as increasing user acceptance, chatbots have gained tremendous popularity in research and practice over
the last years (Adamopoulou and Moussiades 2020; Diederich et al. 2021). Practitioners see the chatbot
market growing from $17.17 billion in 2020 to $102.29 billion in 2026, indicating the high relevance of the
field (Mordor Intelligence 2021). This progress is also visible within an enormous increase of scientific
publications about chatbots (Zierau et al. 2020; Adamopoulou and Moussiades 2020). Chatbots also known
under the term conversational agents (Zierau et al. 2020) are mostly internet-based software systems that
interact with humans within a simulated conversation to perform tasks (Brandtzaeg and Følstad 2018).
These assistants are used to automate redundant processes in a wide variety of areas, such as education,
health or customer support, e.g., to ensure 24/7 availability, to increase efficiency or to minimize customer
support costs (Adamopoulou and Moussiades 2020; Janssen et al. 2020) and can be found on websites,
social networks or apps (Janssen et al. 2020). Chatbots are seen as typical examples of HCI, as they are
constantly changing due to further technological developments whereas the interaction with the user is
A Critical Success Factors Analysis for Chatbots
Forty-Second International Conference on Information Systems, Austin 2021
crucial for chatbot adoption and success (Adam et al. 2021). Although chatbots are not a new technology
(Schumaker et al. 2007) and the general availability of technology and tools is increasing significantly
(Galitsky 2019), from the end-user side, there still is a high failure rate of developed chatbots that are unable
to understand user input, do not react (Seeger and Heinzl 2021; Brandtzaeg and Følstad 2018, Følstad et
al. 2018; Filipczyk et al. 2016) or cannot longer be found on their previous used communication channels.
These negative and frustrating experiences in interacting with chatbots are still seen as one of the key
challenges in the deployment of chatbots (van der Goot et al. 2021; Følstad et al. 2018). Despite this, there
has been minimal qualitative research to date exploring the exact reasons why organizations are taking their
chatbots offline permanently or no longer maintaining them. Chatbot failure is annoying not only from the
user's point of view, but also from the chatbot provider's perspective, who has put a lot of work, time and
money into the development, as well as from a global perspective, as single negative chatbot experiences
can damage the reputation of chatbots in general (van der Goot et al. 2021). Scientific literature considers
various individual perspectives, such as single-case field reports from practitioners about challenges in
deploying chatbots (Fiore et al. 2019), characteristics relevant to failure or success based on user surveys
(Rodríguez Cardona et al. 2021; Diederich et al. 2021; Mozafari et al. 2021), and guidelines for improving
chatbot elements (Brandtzaeg and Følstad 2018). Seeger and Heinzl (2021) proved that high failure rates
among customer service chatbots may harm customer trust and stimulate negative word of mouth. These
authors recommend using ‘anthropomorphic design elements’ to avoid these effects (Seeger and Heinzl
2021). An aggregation of all these technical, behavioral, and institutional aspects and perspectives on failure
and success of chatbots is not yet available. A deeper understanding of the various factors from different
stakeholders that impact chatbots’ failures and success would increase the probability of a chatbot achieving
success. For this reason, it is desirable to investigate the critical success factors (CSFs) affecting the
development, deployment and usage of chatbots. We address these two research needs, which lead to our
following research questions:
RQ1: What are reasons for chatbots’ failure in practice?
RQ2: What are critical success factors for chatbots?
We use a DSR process model as a structural guide (Baskerville et al. 2018) to identify reasons for chatbot
failure and the key factors that determine chatbot success. In four main steps (Doyle et al. 2019) we
determine our research problem by analyzing 103 real-world chatbots and identifying reasons for chatbot
failure from 154 academic papers and 20 expert interviews. We further develop and evaluate within a focus
group discussion (FGD) twelve domain superior chatbot CSFs. We discuss our results and findings, and
present implications, limitations and strategies for research and practice before concluding.
Research Design and Methodology
Design Science Research
To answer our research questions, we follow the DSR principles to address a design problem experienced
by many service providers and service users, in particular to provide the foundations for the successful
development and deployment of chatbots (Gregor and Hevner 2013). DSR is a problem-solving paradigm
used to generate design knowledge and theoretical insights based on the formation of a theory-based
artifact and/or the implementation of empirical design principles in form of constructs, methods, models,
prototypes or design theories (Hevner et al. 2004; Baskerville et al. 2018; vom Brocke et al. 2020a). Within
this study, the artifact in form of the CSFs can be categorized into the design theory. As structural guidance
(Baskerville et al. 2018), we refer to a high level DSR process model. Doyle et al. (2019) compared multiple
DSR process models, e.g., Peffers et al. (2007) six-step model, and identified four core steps. We adopt these
generic process steps depicted by Doyle et al. (2019).
Following the DSR steps outlined by Doyle et al. (2019), the first step is to identify the problem and the
necessity for research by analyzing 103 chatbots. The second step consists of designing and building our
artifact, starting with the identification of a solution. We gain failure reasons and gather requirements for
CSFs for chatbots based on the analysis of 154 scientific papers. We further conduct 20 expert interviews,
to learn about their experiences regarding chatbots’ failures and gather further requirements to verify and
enhance our theory derived set of CSFs, which are then evaluated through an independent FGD in step
three. In step four, reasons for failure of chatbots and CSFs are presented in our results section.
A Critical Success Factors Analysis for Chatbots
Forty-Second International Conference on Information Systems, Austin 2021
Step 1: Problem Identification by Chatbot Taxonomy Analysis
While the development, release, and deployment of chatbots is largely reported in scientific literature and
practitioner reports, there are only isolated reports of failed chatbots, such as the Microsoft chatbot Tay
(Brandtzaeg and Følstad 2018). From a user perspective, several researchers define chatbot failure within
a human-to-chatbot interaction in the form of conversation irregularities, i.e., the chatbot's inability to
perform tasks for the user through its inability to either interpret the intent or to provide proper responses
(Filipczyk et al. 2016; Seeger and Heinzl 2021; Mozafari et al. 2021). From a more global perspective, we
expand the chatbot failure definition by also including chatbots that have been discontinued by being taken
To explore the extent to which chatbot discontinuation is an issue in practice, we revisited the sample of
Janssen et al. (2020) who classified 103 real-world chatbots within a taxonomy development process
according to Nickerson et al. (2013). The sample contains 103 chatbots from the widely used application
domains customer service, daily life, e-commerce, finance and work & career (Janssen et al. 2020, Appendix
pp. 8-10). In May 2019, with the aim of classifying a set of chatbots along several application domains, the
authors first classified a set of 12 most popular chatbots on the database “”, before selecting 10%
of the chatbots within each of 28 application areas on the chatbot database “” (Janssen et al.
2020). The sample includes chatbots from 35 different countries within six continents, like the USA,
Belarus, Argentina, Nigeria, India and United Arab Emirates. We decided to reapply this dataset, because
it enables us to identify chatbots from a broad spectrum of application domains that were taken offline
within a relatively small time-horizon of 15 months, as well as allows us to utilize the design elements
classification of the taxonomy to take first interpretations. Therefore, in September and October 2020, two
of the authors revisited both all 103 chatbots to start a conversation and engage with them. Using the
classification results, we calculated inter-coder reliability to measure the quality of agreement. This was
done by applying Cohens’ (1968) weighted kappa coefficient resulting in 0.882 which means almost perfect
agreement for this value (Landis and Koch 1977). This leads us to the assumption that a bias caused by two
coders can be precluded. In the context of an external user perspective, chatbots failed in our analysis if
they were no longer available or did not react appropriately anymore in a human-to-chatbot dialogue. We
focused on first insights engaging with chatbots instead of testing all primary tasks of a chatbot. If we could
not find a chatbot via the original URL we used Google Search to search for the chatbot and company name.
15 months after the initial screening by Janssen et al. (2020), 53 out of 103 chatbots proved to be failed.
Some chatbots were undetectable on the websites (e.g., Annemiek), other chatbots were converted to live
chats with human agents (e.g., Amanda), some websites were taken offline (e.g., Soa Seeks Check) or a
chatbot did not respond anymore within the conversation (e.g., Jaquelina). Considering the application
domains, it is noticeable that all e-learning chatbots (n=4) are no longer operational, as well as 69% of the
finance chatbots (n=13) and 57% of the work & career chatbots (n=7). If we take a look on failure rate at the
daily life (48%, n=48), e-commerce (44%, n=9) and customer service area (41%, n=22), a slight majority of
chatbots is still running.
To get a first understanding of the reasons why chatbots failed, we used the chatbot design-elements
taxonomy developed by Janssen et al. (2020) and their classification of chatbots to compare the two groups
of operating (n=50) and discontinued (n=53) chatbots. The two groups are very similar in terms of e.g.,
collaboration orientation, duration of relation, or socio-emotional behavior. The greatest difference can be
discovered in the dimension front-end user interface channel. While the group of failed chatbots consists
of 40% social media chatbots and 28% website chatbots, the group of still operating chatbots contains 50%
website chatbots and 28% social media chatbots. In addition, a distinction can be seen in the system
architecture dimension, while the group of failed chatbots contains 72% reactive chatbots but also 28%
proactive ones, the group of chatbots that are still operating contains only 14% proactive chatbots. Among
the customer service chatbots, it is noticeable that the group of failed chatbots consists of 89% chatbots that
did not offer additional human support, while 87% of chatbots are still operating that offer users the option
to get in touch with a human. Differences also exist regarding the chatbot role within the dialogue, while
85% of the facilitator customer service chatbots still exist, 53% of the customer service chatbots that claimed
to be experts failed.
A Critical Success Factors Analysis for Chatbots
Forty-Second International Conference on Information Systems, Austin 2021
Step 2.1: Solution Identification through Critical Success Factors
Considering that 53% of chatbots being no longer accessible after 15 months, we deduce that there is a high
chatbot discontinuation rate. Based on the analysis of the distinctive characteristics of design elements, first
clues for reasons, such as absence of additional human support, can be guessed why chatbots failed. The
causality between the characteristics and the unavailability of a chatbot cannot be shown only analyzing the
design-elements of chatbots externally. Our research goal is to explore the reasons why chatbots fail from a
chatbot providers’ and a scientific perspective. To manage chatbot failure risks, we identify chatbot CSFs
on a global level that should be considered both from a scientific and a practical perspective.
CSFs can be seen as one of the oldest and widely researched subjects in IS research (Lee and Ahn 2008;
Hawking and Sellitto 2010). We refer to the widely known and used CSF definition of Rockard (1979) and
Bullen and Rockard (1981). According to these authors, CSFs are defined as “[…] the limited number of
areas in which satisfactory results will ensure successful competitive performance for the individual,
department or organization. CSFs are the few key areas where “things must go right” for the business to
flourish and for the manager’s goals to be attained” (Rockard 1979 p. 84-85; Bullen and Rockard 1981 p. 7).
CSFs have been widely applied in various IS research fields, such as business process management (e.g.,
Trkman 2010), information technology (IT) projects (e.g., Iriate and Bayona 2020), agile analytic projects
(e.g., Tsoy and Staples 2020) and software development (e.g., Ahimbisibwe et al. 2015). Trckman (2010)
developed a theoretical framework based on theories of contingency, dynamic capabilities, and task
technology fit to appropriately combine business environment and business processes. As part of a case
study, 12 CSFs were identified, e.g., strategic alignment, and employee training and empowerment
(Trckman 2010). Based on a literature review, Iriate and Bayona (2020) compiled and summarized the
most cited and overlapping IT project CSFs, such as system quality, project management, and time. For
agile analytic projects, Tsoy and Staples (2020) updated 25 attributes of potential CSFs previously
identified by Chow and Cao (2008). Chow and Cao (2008) identified 12 CSFs with 25 associated attributes,
e.g., ability to work in a team or a high customer involvement. From related agile project literature, Tsoy
and Staples (2020) identified 10 additional attributes, e.g., for the CSF team capability, the new attribute
of a sufficient team diversity corresponding to a high task complexity. Ahimbisibwe et al. (2015) identified
28 frequently cited CSFs for software development and implied that the importance of CSFs vary for agile
and traditional projects. CSFs such as technological uncertainty or specification changes are much more
frequently cited in an agile context while CSFs such as project planning, vision, and mission are more of
concern in traditional projects (Ahimbisibwe et al. 2015).
Chatbots are mostly complex internet-based software systems (Brandtzaeg and Følstad 2018) and
accordingly fall within the scope of IT projects (Karlsen et al. 2005). However, chatbots differ from other
IS technologies because of their interaction and intelligence capabilities (Maedche et al. 2019). For example,
the natural language interface of chatbots differs from other types of user interfaces. The design of chatbot
interfaces, unlike graphical user interfaces for example, focuses not only on visual elements but also on
communicative behaviors (Araujo 2018). Consequently, we decided not to compile existing CSFs from
higher-level domains such as software development, but to identify specific CSFs for chatbots from scratch
based on chatbot-related literature and expert interviews.
Before identifying CSFs, it has to be defined what is understood under the term success in the chatbot
context. A human-chatbot interaction can be seen as successful if the intended task is completed
appropriately by the chatbot (Seeger and Heinzl 2021). Feine et al. (2019) emphasize that a chatbot is from
the user perspective successful when it is able to efficiently and satisfactorily perform longer conversations
with a user while the user experiences the consumption-related fulfilment as enjoyable. We define success
of chatbots from the organizational and main responsible managers’ point of view by the fact that a chatbot
is functioning and available, performs the tasks for which it is designed, and satisfies the users of a chatbot,
whereas CSFs are the few essential requirements that must run perfectly to be successful with the chatbot
(Rockard 1979; Bullen and Rockard 1981). Conversely, in our context, this means that a chatbot will fail if
these key factors are not properly fulfilled. Consequently, we moved away from the previous definition that
a chatbot fails when it is not responding appropriately or becomes unavailable within a human-to-chatbot
dialogue to a broader definition with differentiated perceptions by not considering solely an external user
perspective. In the following, using a concept-centric literature analysis and conducting expert interviews,
we will first explore reasons for failure before identifying chatbot CSFs according to our definition.
A Critical Success Factors Analysis for Chatbots
Forty-Second International Conference on Information Systems, Austin 2021
Step 2.2: Gathering Knowledge from Scientific Literature
To derive requirements from theory, we conducted a systematic literature review following Webster and
Watson (2002), Watson and Webster (2020) and vom Brocke et al. (2015). The review scope was structured
in line with our research questions and focused on research and theories on failure and success of chatbots.
We centered on the identification of CSFs and necessary requirements for chatbots. Reasons for chatbots’
failure and CSFs for chatbots are consequently interconnected and considered together in our literature
review, leading to the search string: (“chatbot” OR “chat bot” OR “conversational agent”) AND (“unsuccess”
OR “fail” OR “failure” OR “success” OR “success factor” OR “critical success factor”). From October to
December 2020, five databases were searched to identify publications from established journals and
conferences in IS, HCI and other relevant fields. After three screening phases in which we examined the
(total hits/relevant by title/relevant by abstract) (i.e., ACM Digital Library (601/27/13), AIS Electronic
Library (299/103/54), ScienceDirect (664/36/15), SpringerLink (2522/46/30), and Google Scholar
(12000/68/38)), and deleted duplications, we gathered 126 papers. Using forward search (10), backward
search (12), and similarity search (6), we completed our list, resulting in a total of 154 relevant papers.
Starting from this point, we explored reasons for chatbots’ failure in practice. We summarized the various
perspectives of failure, the current limitations, and shortcomings of chatbots, the various lessons learned,
and important elements and guidelines for chatbot success into a comprehensive set of very basic CSFs for
chatbots at the industry level. A concept matrix was built iteratively analyzing the 154 identified papers for
general content and factors relevant to success or failure for chatbots. After approximately 100 papers, no
new categories were discovered and existing ones were only slightly expanded, so we concluded our review
as exhaustive. The papers were classified in 32 categories. Similar categories were then clustered, resulting
in 10 potential CSFs for chatbots. The CSFs and associated categories from theory are shown in Table 2.
Step 2.3: Gathering Knowledge from Experts
To explore the underlying reasons for chatbot discontinuation within expert interviews, we first contacted
the 53 companies who initially deployed the not operational or not accessible chatbots in our sample in
November 2020. Unfortunately, it resulted in 53 negative or unanswered messages. We changed our
approach and contacted individual chatbot experts directly. To identify those experts, renowned chatbot
conferences, e.g., Chatbot Summit, were screened for keynote speakers. In addition, the LinkedIn network
was used to search for experts based on their job descriptions. A total of 60 individuals were contacted via
LinkedIn, which led to 20 semi-structured interviews with experts from five different countries (USA,
Germany, the Netherlands, Switzerland and Israel). The chatbot experts come from different work domains,
such as research, systems engineering, consulting, process ownership, or business leadership (Table 1). The
organizations and companies affiliated with the experts are of different sizes, such as universities, startups,
medium-sized companies, and large international corporations, which can be, e.g., determined by the
number of employees. This focus allowed us to include a broad range of different company types. The
organizations and companies analyze, develop, and distribute chatbots, provide chatbot-related services
and infrastructure, and/or deploy chatbots.
To guide the interview process to some extent, we followed the recommendation of Myers and Newman
(2007) and designed an interview guide. This guide was in advance also sent to the experts so that they
could prepare for the interview. The content of the interview guide was based on the recommendations on
the interview process by Bullen and Rockard (1981) and Caralli et al. (2004). The interview guide consisted
of seven sections. First, the topic of chatbots’ failure and the relevance CSFs was briefly introduced, followed
by a description of the interviewee’s tasks and goals related to chatbots. Then, an introduction to the method
of identifying CSFs was given and the need for research was outlined. In the fourth step, the experts’
personal experiences regarding the failure of chatbots was discussed. Starting on this, CSFs of chatbots were
elaborated. These factors were then prioritized in the sixth step. Methods for measuring the CSFs were
discussed in the final step. The 20 expert interviews took place in January and February 2021 and lasted
between 23 minutes and 52 minutes, with an average of 35 minutes. All interviews were conducted via video
chat or telephone and followed the interview process outlined in the previously described interview
guideline. After completing the expert interviews, the interviews were denaturalized transcribed (Oliver et
al. 2005) and coded within MAXQDA. The code system was based on the 32 categories previously identified
in our literature review and the assigned potential CSFs and consisted therefore of two levels. The use of
open coding (Wiesche et al. 2017) enabled us to iteratively expand and modify the initial list of coding tags
A Critical Success Factors Analysis for Chatbots
Forty-Second International Conference on Information Systems, Austin 2021
according to our findings, resulting in 40 categories clustered into 12 CSFs (Table 2). In addition, the
personal experiences of the experts regarding the failure of chatbots were highlighted by a coding tag.
Job description
Company description
PhD candidate and researcher
PhD candidate and researcher
Consulting and chatbot development
System engineer
Consulting and chatbot development
Conversational interface designer
Consulting and chatbot development
CEO and company founder
Consulting, training, and certification
CEO and technical leader
Mortgage financing
CEO and leader of development
Chatbot product owner
Business process owner chat
Global head of AI
Global product owner for chatbot and chat
Chatbot product owner
Head of e-commerce and sales innovation
Public transport
Sales engineer
Product owner conversational AI
Leader digital assistance program
Chemical and pharmaceuticals
Strategy and innovation architect for AI
Hardware and software
Manager digital transformation and change
Automotive supplier
PhD candidate and researcher
PhD candidate and researcher
PhD candidate and researcher
Post Doc and researcher
Chatbot, and AI consultant EMEA
Consulting and chatbot development
Table 1. Expert Profiles
Step 2.4: Extracting Failure Reasons in Practice & Developing an Initial Set of CSFs
We first extracted reasons for chatbots’ failure in practice based on literature and expert interviews to
answer RQ1. This led us to a set of six reasons, all of which have already been experienced by at least three
experts. RQ2 was related to the CSFs of chatbots. The CSFs found in literature review in Step 2.2 and by
expert interview conduction in Step 2.3 were synthesized into an initial set of CSFs. All ten potential CSFs
previously identified in our literature review were confirmed by the 20 experts. Two additional CSFs were
found, namely a chatbot development team and the factor developmental strategy. In addition, the
categories of CSFs were adjusted by identifying new ones and expanding or deleting previous ones. The
categories of the following CSFs, top management support, project resources, chatbot progress, chatbot
design, user-centric use case, and technology and tool availability, were modified. In selecting the names to
identify each cluster, i.e., the CSFs, an attempt was made to make the name descriptive enough for the
reader to recognize the reference. The names chosen are more abstract than the concepts they represent. In
some cases, the chosen category name was selected from the pool of concepts. In other cases, the chosen
name was borrowed from terminology commonly used in literature (e.g., “Trust”) (Følstad et al. 2018).
Step 3: Evaluation and Adjustment
To determine whether our artifact, i.e., the initial set of CSFs (Table 2), is comprehensible, understandable,
and useful (Gregor and Hevner, 2013), we conducted an evaluation considering “what”, “who” and “how”
(Pries-Heje et al. 2008) which is a crucial step within DSR (vom Brocke et al. 2020b). Regarding “what”,
the object of evaluation, we decided to evaluate the design process as well as our design product (CSFs).
Regarding the evaluation subject (“who”), we selected five individuals from three countries (Switzerland,
Germany, Luxembourg) who were not previously involved in the development of the CSFs (Table 1). To test
a broad applicability and understandability, we brought together people from research (Exp21, Exp22,
Exp23, Exp24) and practice (Exp24, Exp25) with chatbot and/or CSF methodology experience. Concerning
the “how”, we decided to conduct a FGD to get as many different perspectives on the CSFs within a joint
discussion as possible. In April 2021, the virtual FGD took place and lasted 92 minutes. In preparation,
participants received a worksheet with our questions, an explanation of our research process and the CSFs
A Critical Success Factors Analysis for Chatbots
Forty-Second International Conference on Information Systems, Austin 2021
table with definitions. After all participants introduced themselves, the first two authors explained the
problem statement, gave an overview of DSR, results from our literature review and the interviews on
chatbots’ failure, before presenting the CSFs. Questions of process understanding were clarified and the
definition of chatbot failure and the analysis of 103 chatbots were discussed. Overall, our DSR to identify
reasons for chatbots’ failure and CSFs appeared to be comprehensible and coherent. The reasons identified
in practice were confirmed by the participants. Exp25 emphasized that for already developed chatbots, it is
often necessary to tease out the real reasons for failure, as companies do not like to talk about what they
have done wrong. Exp25 noted that the reasons for chatbot failure identified within this study are very well
differentiated from each other, which the expert notes is very helpful, and reports that in reality it is usually
a combination of several reasons. Exp24 remarked with regard to the interviewed experts that there may
be significant geographical and cultural differences, e.g., due to legal regulations, and brings up an example
from Belarus, where it is common to change the cell phone tariff with two messages to a chatbot.
The CSFs were perceived by participants as comprehensible and extensive. Exp 25: “I would like to see
companies sometimes simply take three steps at the beginning and think about: what I actually want to do
in the project and what do I have to consider? And I could imagine the CSFs list being very helpful for this
in practice, especially for companies that don’t yet have so much experience.” But it was noted that the CSFs
will continue to evolve. Exp23 claimed: “If the same study were conducted 10 years from now, I guarantee
we would see different success factors.” Exp22 described that while the CSFs are understandable, it would
be helpful to have a brief description text for each of the twelve CSFs. We used these results of the evaluation
to improve the CSFs. While most categories were perceived as understandable and clearly delineated, we
renamed the category “adjustability and extensibility” to “simple editing and extensibility of design
elements”. We also merged the categories “access to database” and “connection to backend systems” which
was addressed by Exp21.
Results and Findings
Chatbots’ Failures Reasons in Practice
To answer RQ1, we present reasons for failure in practice extracted from scientific literature and expert
interviews. Our findings in Step 2.2 indicate that scientific literature provides many single-case field reports
from practitioners about challenges in creating chatbots, elements relevant to failure or success based on
user responses, and guidelines for improving these chatbot elements. But to date, there has been little
qualitative research examining the exact reasons why organizations take their chatbots offline permanently
or stop maintaining them. In a brief attempt, Brandtzaeg and Følstad (2018) cite two real-world examples
of failure. Ikea’s chatbot Anna failed because it could not balance robotic and human aspects, resulting in
customers abusing the chatbot (Brandtzaeg and Følstad 2018). Microsoft’s chatbot Tay, in turn, gave
unethical responses shortly after its release and was taken offline as a result (Brandtzaeg and Følstad 2018;
Zemčík 2020). Chatbots often fail in part because they do not meet user expectations and companies tend
to focus on business-centric use cases rather than user-centric use cases (Zamora 2017; Grudin and Jacques
2019). Based on user interviews and questionnaires, user trust and privacy concerns as well as perceived
ease of use and usefulness have also been identified as reasons that can lead to chatbots’ failures (Rodríguez
Cardona et al. 2021; Følstad et al. 2018; Mozafari et al. 2021).
Based on the experts’ personal experiences narrated within the interviews, we present six generic reasons
for the failure of chatbots in practice, identified in Step 2.3. Each of these reasons is based on the experts'
experience with an actual failed chatbot. Hypothetical reasons for chatbots’ failures were not incorporated.
Not enough resources: Six experts have come across the lack of resources as a reason for the failure of a
chatbot. A person primarily responsible for a chatbot leaves the company, not enough money is made
available for a sufficient technical infrastructure, or a third party providing relevant services spontaneously
breaks away. One interview partner expressed firmly: “Okay, look, that doesn't make sense like that. We're
going to redo the planning and we’re going to put everything back to square one, because the effort [...] is
significantly greater than what we want to achieve, and our core competence is simply elsewhere [...]. One
aspect, why chatbot projects fail is that the effort behind it is underestimated and [...] too few resources are
provided” (Exp16).
A Critical Success Factors Analysis for Chatbots
Forty-Second International Conference on Information Systems, Austin 2021
No business case: The second reason for the failure of chatbots in practice encountered by six experts is the
missing of a business case. The potential value creation for the company is significantly less than the effort
involved. A chatbot project is set up without properly evaluating the benefits or cost. One expert described
the situation as followed: “So SMS based chatbots can be pretty costly as far as your return on investment.
So when you're sending out several text messages a day to people [...] you might be spending [...] a couple
of dollars a day, whereas you might not be getting any return, which is one of the experiences I had
developing a covid-19 chatbot, so that one, we had to take offline after a few months” (Exp01).
Wrong use case: Five experts witnessed how a chatbot failed because of a wrong use case. A chatbot was
deployed for a task where the basic chatbot technology did not match the required task. One interviewee
described: “Basically, the idea was that you could get a complete construction financing via a chatbot [...].
But if the process doesn’t fit what you want to map with a chatbot [...] to process a complete construction
financing via the chatbot, then we fail. That simply doesn’t fit together. It is simply very difficult to map a
deeper complexity only via a chatbot” (Exp07).
Law regulations, data security, and liability concerns: The fourth identified reason for the actual failure
of chatbots in practice, mentioned by five experts, is legal regulations and privacy and liability concerns of
the organizations using a chatbot. One participant described the problematic situation encountered as
below: “We actually developed a chatbot for a major bank at the end of last year [...] it was about reporting,
how to report business numbers [...] and it actually failed because the EU has terminated the Privacy Shield
agreement with the USA and as a result there is now legal uncertainty, in a sense” (Exp05).
Ignorance of user expectation and bad conversation design: Four experts stated that they have witnessed
chatbots fail due to ignored user requirements and poor conversation design. An interviewee described how
chatbots have failed because of a disregard for good conversational design: “So usually, companies reach
out to us when their chatbots are not performing the way they ought to do [...]. They focus too much on the
technology [...] on knowledge management, or [...] on the business process. So what you see a lot of times
is that they [...] sort of create a flow chart of what the business process looks like. And then they pretty much
add some words to it [...] that doesn’t work, because people don’t talk the way your business is organized”
Poor content: Last identified reason for chatbots’ failures in practice is poor content. Three participants
experienced that a chatbot failed because the content requested by users was wrong, not complete, or not
up-to-date. Exp10 said that the lack of relevant data, regarding to current conversational topics of users,
leads to failure. While another expert stated his experience regarding incomplete data as followed: “So the
chatbot itself, from the technology, it’s possible that you tap into external sources [...]. You need a lot of
sources [...]. So, the questions that came in were of course all over the place. One wanted to know whether
Corona could be dangerous during pregnancy. The other wanted to know something, whether one can still
play soccer [...] that has blown up the information pool of the Robert-Koch-Institute” (Exp07).
Final Critical Success Factors for Chatbots
To answer RQ2, our final cross-domain set consists of 12 CSFs for chatbots (Table 2). 10 CSFs were
identified based on an extensive literature review of 154 papers. These CSFs were confirmed by the 20
expert interviews, and an additional two CSFs were found. Subsequently, the set was evaluated and adjusted
by five more experts. In addition, the 12 CSFs were divided into three variables describing the interrelation
of the CSFs.
Technology and tool availability: This CSF addresses the limitations of available underlying technology
and infrastructure for chatbot applications which was addressed by 73 scientific papers. Five experts
mentioned that the availability of basic chatbot technology and related tools is important. Exp01 asserts
that the increase in the availability of software tools for conversational design, conversational AI, tools for
testing chatbot frameworks, and project management tools in recent years have led to chatbot projects
being more successful. The development process of chatbot technology started back in the last century, but
it is still ongoing (Exp19). Chatbots have evolved from single-line, text-based chat systems that initially
supported human-to-human conversations to modern, complex, knowledge-based models that began to
emulate dialogue systems using natural language understanding and dialogue management (Galitsky 2019;
Kepuska and Bohouta 2018). Exp06 noted that chatbot technology still has many limitations, and Exp18
described it as a “pretty immature technology” and that “things are always kind of a bit buggy”.
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Forty-Second International Conference on Information Systems, Austin 2021
Associated category from coding
Example authors
Technology & tool availability
Galitsky 2019; Schumaker et al. 2007
User centric
use cases
Adequate use case
Rodríguez Cardona et al. 2019; Zamora 2017
User requirements
Følstad & Brandtzaeg 2020; Meyer von Wolff et
al. 2020
Acceptance to change operation methods
General chatbot technology acceptance
Weber & Ludwig 2020; Mesbah & Pumplun
Communicating the intention to introduce/use a chatbot
Aoki 2020
Data security
Lai et al. 2018; Følstad et al. 2018
Technical design elements
Janssen et al. 2020; Yuan et al. 2019
Conversational design elements
Kvale et al. 2019; Gnewuch et al. 2020
Design elements’ simple editing and extensibility
Koetter et al. 2019
Databases & backend systems accessibility
Kruse et al. 2019; Johannsen et al. 2020
Word sensitivity
Yu et al. 2016; Canhoto & Clear 2020
Level of intent & content understanding
Følstad & Brandtzaeg 2020; AbuShawar &
Atwell 2016
Technical robustness & chatbot efficiency
Nguyen & Sidorova 2017; Weber & Ludwig
Testing & training
Johannsen et al. 2018; Vijayaraghavan &
Cooper 2020
Continuous monitoring, updating and improvement
Jonke & Volkwein 2018; Brandtzaeg & Følstad
Chatbot self-development
Zemčík 2020; Hancock et al. 2019
Changing company structure and workflows
Manage top management expectations in short & long term
Top management support
Benbya et al. 2020; Pumplun et al. 2019
Transparent cost management
External resources
Human resources
Galitsky 2019; Kruse et al. 2019
Technical resources
Desouza et al. 2020; Winkler & Roos 2019
Highly dynamic long-term process (instead of a classic project)
Multidisciplinary process (not a pure IT and engineering based
and driven)
Start small, go big (quick wins)
Team composition
Team building
Clear definition of used success and performance metrics to
evaluate chatbot
Content management core team
User expectation
Følstad & Brandtzaeg 2020; Weber & Ludwig
User understanding of chatbot capabilities
Følstad et al. 2018; Aoki 2020
Perceived usefulness (based, e.g., on TAM)
Wuenderlich & Paluch 2017; Følstad & Skjuve
Unexperienced user guidance
Weber & Ludwig 2020; Piccolo et al. 2018
Seamless chatbot integration in customer journeys
Kuligowska 2015
Ease of use (based, e.g., on TAM)
Rodríguez Cardona et al. 2021; Rese et al. 2020
Trust in chatbot and operating company
Følstad et al. 2018; Sanny et al. 2020
Trust in chatbot technology
Fiore et al. 2019; Nordheim et al. 2019
Privacy concerns
Rodríguez Cardona et al. 2021; Kim et al. 2020
V* = Variable classification (Exo* = Exogenous, Endo* = Endogenous, Mod* = Moderator)
P* = Number of papers, E* = Number of experts
Table 2. Critical Success Factors for Chatbots
User-centric use cases: A suitable use case for chatbot deployment is a valid business case with reasonable
scope that adds value to both the business (Exp18) and the customer (Exp14, Exp02). Similarly, Rodríguez
Cardona et al. (2021) and Zamora (2017) recommend a user-centric approach, where different
requirements of potential users should be considered throughout the development phase to ensure that
value-added chatbot elements are prioritized. Exp03, Exp07, and Exp12 note that it must also be a logical
use case suitable for the use of chatbot technology. In addition, legal frameworks such as privacy regulations
and ethical considerations that distinguish between acceptable and unacceptable practices must also be
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Forty-Second International Conference on Information Systems, Austin 2021
considered (Rodríguez Cardona et al. 2019). The general acceptance of potential users to use a chatbot
represents another characteristic. Two interviewees elaborated that some people do not want to
communicate with a chatbot or are afraid that this technology leads to the loss of their job and therefore
have a negative stance on it (Exp14, Exp20).
Chatbot promotion: Establishing a clear communication and integration of a chatbot is important (Exp08,
Exp15). While eight experts emphasized the relevance of chatbot promotion, this was mentioned only once
in literature (Aoki 2020). Users need to be aware of and become familiar with the chatbot (Exp05, Exp17).
Similar, Aoki (2020) highlights about the use of chatbots in public institutions, that communicating the
intention to utilize a chatbot is an inexpensive step to raise user’s awareness and trust regarding the bot.
Exp20 firmly stated that “of course you have to actively promote it”.
Chatbot design: This CSF describes all the elements and capabilities of a chatbot that need to be considered
during its development. Technical design elements like the intelligence quotient or an avatar (Janssen et al.
2020) have to be considered next to conversation relevant elements, e.g., a structured conversation flow,
which must be as natural as possible regarding dialogue flow and formulations (Kvale et al. 2019; Koetter
et al. 2019). The development of a knowledge base, access to relevant databases and the connection of
different backend systems increases the capabilities of a chatbot and ensures a higher usefulness to the user
(Exp09, Exp14, Exp15, Exp20). Exp05 and Exp20 said that the option to escalate to a human if the
conversation stagnates must be considered to handle damage control and prevent the conversation from
getting stuck (Weber and Ludwig 2020). Data protection measurements must be clarified in advance
(Exp11, Exp14) and sufficient IT security must be guaranteed (Exp16, Exp20). Exp13 described the
consequences of disregarding this aspect as followed “If something goes wrong with it, you don't want to be
in charge of the project”. In addition, Exp12 and Exp19 note that the overall framework of a chatbot must
be easily adjustable without a lot of coding to implement dynamic requirements.
Chatbot progress: Chatbot progress describes the initial training of a chatbot, testing functionality and
content, as well as refining design elements and updating the knowledge base during live deployment. To
prevent a chatbot from failing during its use, it must be tested and validated (Vijayaraghavan and Cooper
2020; Ruane et al. 2018). Exp18 and Exp19 recommend iteratively testing the content and language model
of a chatbot changing test groups to test different content and different ways of asking questions. Many
experts emphasize that a chatbot must be continuously monitored and improved based on the data collected
and user feedback. Exp12 and Exp18 point out that it is generally impossible to predict how users will
interact with a chatbot and what information will be requested long-term, so a good monitoring and
updating process is needed. “There’s a shift in terms of what people are asking over time. And then you need
to be on top of that case” (Exp12). These statements confirm the recommendations of Brandtzaeg and
Følstad (2018), and Jonke and Volkwein (2018) to maintain and enhance the content of a chatbot. The
category of self-development of a chatbot identified in our literature review (e.g., Zemčík 2020) was not
confirmed by the experts and was dropped. The unsupervised evolvement of chatbot systems is not
desirable and, in the worst case, leads not only to linguistic hostility but also to psychological or physical
damage, e.g., through the incorrect instruction of treatment methods in areas such as healthcare (Exp01,
Top management support: This CSF describes the needed support of projects and changes of business
structures within an organization by the organization’s upper management. Exp05 mentions that a chatbot
needs a defined status in the company’s communication strategy. Exp11 and Exp13 further add that a
chatbot needs to be integrated into the corporate structure, which requires an adaptation of the workflows.
These changes have to be backed up by the top management to deal with conflicts of interest (Pumplun et
al. 2019). This also includes a shift in authority. Different project members and their tasks must be equally
valued and given voice by the top management (Exp06). New job roles that challenge existing structures
will emerge and chatbot developing teams will gain more influence on internal decision-making processes
(Benbya et al. 2020). In addition to the findings from our literature review, the experts underline that top
management and the chatbot project team must be in frequent contact with each other (Exp08, Exp10,
Exp17). Varying short- and long-term expectations as well as different understanding of success criteria for
chatbots must be synchronized and the added value offered by a chatbot clearly communicated (Exp12).
Project resources: This CSF specifies the resources and skills required to develop, implement, and maintain
a chatbot, forcing companies to adjust their technical, human, and financial resources (Kruse et al. 2019).
Exp11 and Exp14 mention that sufficient human resources on a full-time basis must be allocated. People
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Forty-Second International Conference on Information Systems, Austin 2021
with different skill sets such as system engineers, AI trainers, conversational designers or copyrighters are
required (Exp06). These developers require in-depth knowledge of a broad range, including areas such as
AI and SQL databases (Galitsky 2019; Kruse et al. 2019). In cases where the core competence of one’s own
organization does not lie in the development of chatbots, it is recommended to outsource some of the tasks
and work together with specialists (Exp15, Exp16). Exp15 and Exp18 specify that the partners must be
economically stable and that they must also exist in a few years, as a chatbot usually is operated for many
years. Since a chatbot requires many different resources over a long period of time and can lead to ambiguity
and resentment among stakeholders, proper and comprehensive cost management must be upheld (Exp11,
Developmental strategy: Seven experts mentioned that a basic understanding of the characteristics of a
chatbot must be created in the organization while this CSF has not received any attention in scientific
literature so far. Chatbots are not projects that are finished at a specific point, but an evolving process
(Exp06, Exp13, Exp19). The process of a chatbot project does not correspond to a waterfall-like or agile
method process with fixed target dates, where a chatbot can be launched as a finished product at the end
(Exp19). A chatbot is supported by a “highly agile project” and is developed in a longer term with many
individual tests and constantly changing test groups (Exp18) and should rather be treated “like an entire
eco system” (Exp06). Exp01 and Exp06 further add that this is not a simple engineering project, but a
balancing act between engineers, conversational designers, copyrights, and other professionals involved.
Initiating a chatbot with a small scope helps to get qualitative user feedback as quickly as possible allowing
to easier developing and scaling a chatbot (Exp08, Exp13, Exp19).
Chatbot developing team: The second CSF solely identified by the expert interviews consists of four
categories. The responsible development team must be composed of different work domains with different
responsibilities, since varying expertise is needed from areas such as systems engineering, user experience
design, psychology, or copyrighting (Exp01, Exp06, Exp19). To ensure a seamless development process,
these experts with completely different skill sets need to be synthesized and get to work as a cohesive team
(Exp02, Exp06). Exp06 and Exp14 point out that due to the differences in skills and perspectives, it is
important to have common clarity on the definitions of the evaluation metrics used. In the long term, there
must be a core team responsible exclusively for content management (Exp11, Exp18).
Usefulness: This CSF comprises categories dealing with the ability of a chatbot to efficiently perform the
tasks desired by the user, considering the user’s preferred workflow. Exp08 summarized this factor as
follows, “The most important thing is of course the subject, does the chatbot satisfy the customer’s need in
the end?!”. A chatbot must also be able to provide almost completely coverage of what the user expects
(Exp12). Similarly, Weber and Ludwig (2020) and Wuenderlich and Paluch (2017) outline in their studies
that a chatbot must be able to perform the tasks expected of it faster and more accurately than the user
himself or another human counterpart, e.g., call centers, could. Exp07, Exp11 and Exp12 further asserted
that users are required to understand how a chatbot works.
Usability: Usability refers to the current use of a chatbot and is composed of the three identified categories
usability, user guidance, and seamless integration of a chatbot into the customer journey. Users ought to
perceive the chatbot as an easy-to-use and smoothly functioning system (Rodríguez Cardona et al. 2021;
Rese et al. 2020). Exp12 describes this as, “Obviously, the interface needs to be intuitive”. Exp05 said that
the structure of the dialogue flow and its comprehensibility are important, too. A chatbot must be located
at the point in the customer journey where the customer needs or expects it (Exp12). In line with Weber
and Ludwig (2020) and Piccolo et al. (2018), Exp09 and Exp11 commented that inexperienced users must
be initially guided to avoid getting lost in the interaction process, e.g., by a short introduction.
Trust: End users trust towards general chatbot technology regarding reliability and goodwill of the chatbot
itself must be given (Fiore et al. 2019; Kim et al. 2020). Exp02 states that the confidence of the user in a
carefree use of a chatbot, i.e., trusting that a chatbot technology will work as intended, that it will not harm
the user, is critical for success, especially in application areas such as healthcare. Accordingly, a good
reputation of the brand or the associated organization is important, as trust in a chatbot depends on the
users’ previously established trust in the service provider (Følstad et al. 2018; Sanny et al. 2020). Exp02
commented it as follows, “There has to be a basic trust in a chatbot and its background”. Users trust in
guaranteed data and privacy protection must be assured, since low perceived risks, e.g., not having to
provide sensitive information, increases trust (Nordheim et al. 2019; Rodríguez Cardona et al. 2021). Exp20
described this as users want to be sure that their data will not be misused.
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Forty-Second International Conference on Information Systems, Austin 2021
Discussion, Implications, Limitations, and Outlook
To investigate chatbot failure and identify CSFs for chatbots, we analyzed 103 real-world chatbots,
performed an extensive literature review, and conducted 20 expert interviews within our DSR based
process. The analysis of a chatbot user was indirectly possible through the analysis of user-centered HCI
and IS literature (e.g., chatbot technology acceptance (e.g., Mesbah and Pumplun 2020) or user expectation
(e.g., van der Goot et al. 2021)) which allowed us to get an all-encompassing overview of chatbot CSFs.
We have examined chatbot failure from two different angles, from the chatbot user perspective and from
the chatbot provider perspective, which required us to focus on different chatbot definitions. In analyzing
103 real-world chatbots, we defined chatbots as having failed externally if they no longer answered us, could
no longer be found, or were transformed into a live chat. Since we unsuccessfully contacted all 53
discontinued chatbot providers, we cannot make definitive statements whether some of these chatbots may
have been successful and were taken offline for other reasons. But our analysis gives a general insight into
the high discontinuation rate within the global chatbot market. Further, within the sample, it is difficult to
assess the extent to which further chatbots failed from the company’s point of view, e.g., because of too few
hits, too few leads (Janssen et al. 2021), or from the user’s point of view, e.g., due to not executing the
desired request (e.g., successful request for travel reimbursement (Exp24)). Failure is a sensitive topic that
people do not like to talk about, which was a barrier in the search for interview partners. This could also
have been a reason why no company or chatbot developer of the 53 no longer functioning chatbots of the
dataset of Janssen et al. (2020) wanted to talk to us about the reasons. But our twenty interviewees
confirmed that failure is very relevant, even if it still for them receives very little attention in literature.
Conducting expert interviews (Table 1), we address this research need describing six reasons why chatbots
failed in practice from the chatbot provider side which is a novel perspective on failure compared to other
articles focusing only on the chatbot user perspective (e.g., Filipczyk et al. 2016; Seeger and Heinzl 2021;
Mozafari et al. 2021). As confirmed by the experts within our FGD, reasons for chatbots’ failures and our
CSFs can help practitioners to continuously manage risks. As discussed in the evaluation, multiple reasons
of failure often coincide (Exp25). In further research, it is worth to investigate in a survey which
combinations often occur and to what extent geographical, cultural and application domain-specific
differences exist.
From our literature review and the expert interviews, we identified a total of 12 CSFs and 40 categories that
are relevant to the success of a chatbot (Table 2). In terms of the number of papers addressing the items, it
is noticeable that the focus of scientific research is in line with expert opinions on what is important for the
success of chatbots in many areas. 73 papers focus on elements of conversational design, named by 13
experts. Similarly, 68 papers focus on technical design elements, which were identified as critical to success
by 15 of the 20 respondents. In contrast to scientific research, there is a much higher focus on the promotion
of a chatbot and the correct design of use cases in practice, e.g., CSF chatbot promotion and the associated
category are mentioned by 8 experts but only by one paper. In addition, we were able to identify two CSFs
in practice, respectively the chatbot development team and the development strategy, that were not
mentioned in the literature, which could also reveal research opportunities.
The majority of identified CSFs from experts and scientific research in the chatbot area show parallels to
other HCI and IS research areas, but some CSFs are chatbot specific. Exp24 and Exp25 highlight that CSFs
such as top management support or project resources are known in other IS research areas. The CSF top
management support has been identified in the context of IT projects (Ahimbisibwe et al. 2015) and
business systems implementation (Shapouri and Najjar 2020). Similarly, the importance of continuous
improvement of systems described by the CSF chatbot progress has been observed in the context of business
process management. Continuous improvement is critical to ensure long-term benefits (Trckman 2010).
Within a DevOps context (van Belzen et al. 2019), the CSFs technology and tool availability as well as the
need for adequate resources, e.g., technical infrastructure, have also been identified. Taking a closer look at
the CSFs mentioned exclusively in the interviews, e.g., team composition, it is noticeable that several
categories relate to project management issues which were mentioned in other HCI and IS topics, such as
for agile project management (Ahimbisibwe et al. 2015; Tsoy and Staples 2020) and ERP implementation
(Ahmad and Cuenca 2013). However, categories of the CSF chatbot design, such as technical design
elements, e.g., additional support for escalating to a human when the conversation stagnates (Janssen et
al. 2020), and conversational design elements, e.g., additional anthropomorphism features (Gnewuch et al.
2020), also highlight differences from previous CSFs from other IS and HCI research areas. It becomes
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Forty-Second International Conference on Information Systems, Austin 2021
apparent that in practice not only chatbot specific aspects, such as the design elements are important for
success but also project related aspects, such as top management support and the development team.
Developing and deploying chatbots is a highly complex interplay of factors, where a broader view is
necessary. We see a great added value in the all-encompassing consideration of CSFs in chatbot deployment
since all factors that one has to consider for successful chatbots are listed. In recent years, the discussion of
how much chatbots differ from other technologies has arisen (e.g., Zierau et al. 2020). Here, our list of CSFs
can be a starting point for comparing chatbots with other technologies based on CSFs. But, even if the
individual aspects may not differ, the interrelation and interaction of CSFs is crucial for chatbots’ success
which should be further examined in future studies. Therefore, e.g., Hilberts’ (2005) framework could help
to structure the identified CSFs into endogenous, exogenous and moderator variables and to identify
interrelations between CSFs.
Our focus was to get a chatbot application domain superior overview of failure reasons and CSFs, which is
why we emphasized on broad insights within the real-world chatbot analysis, interview partner selection,
and within our literature analysis. In our chatbot analysis, we discovered that many customer service
chatbots that did not offer additional human support no longer exist, which belongs to the technical design
elements category mentioned in practice and literature. Whether these specific chatbots really failed
because of this reason is speculation. But the current application areas, such as education (e.g., Winkler and
Roos 2019), health (e.g., Kim et al. 2020) or customer service (e.g., Følstad et al. 2018) differ greatly from
each other which is why the category importance for chatbot success may also vary across the application
domains. It is worth to examine the application domains in isolation and break down from our CSFs list
crucial application domain-specific CSFs. As diverse as chatbots are considering design elements (Janssen
et al. 2020), chatbot implementation processes are equally diverse (Exp23, Exp25). We give an all-
encompassing overview of the reasons for failure and chatbot CSFs, since in practice a chatbot is often
developed and deployed in diverse countries (Exp17). Also, from the degree of how much of a chatbot is
developed and maintained internally (e.g., by company employees) or externally (e.g., by consultancies and
chatbot developing firms), in practice, all combinations exist which should be further examined in future.
While our literature review, chatbot sample analysis from 35 countries and search for interviewees were on
a global scope, Exp24 in the evaluation mentioned that we may have a certain geographic and cultural bias
as we interviewed experts from Europe, Middle East and USA. Since there are also geographically and
culturally varying degrees of use and market penetration of chatbots (Diederich et al. 2021), it makes sense
to also involve interview partners from other regions, such as Asia and Africa (Exp24, Exp25). Regarding
the aforementioned real experienced reasons of failure, five experts mentioned law regulations and data
security aspects that depend very much on the government. In this context, it is worth to examine the
reasons for failure and the CSFs based on, e.g., geographical borders or across cultural dimensions. To
identify these cultural factors within a survey, a large number of diverse participants is needed to have any
significance. By following the four core DSR steps outlined by Doyle et al. (2019), based e.g., on Peffers et
al. (2007), we focused on developing and evaluating CSFs. Future research could address how to put these
CSFs into practice as part of a case study to avoid chatbot failure.
While we found that chatbots’ failures have been addressed very little in scientific literature, through an
analysis of 103 chatbots that we revisited after 15 months, we were able to identify that there is a high
discontinuation rate in the chatbot field and captured experienced reasons of chatbots’ failures through 20
qualitative expert interviews. To manage the chatbot failure risks, our research goal was to identify
fundamental CSFs for chatbots based on our extensive literature review as well as 25 experts within
interviews and a FGD for evaluation. For further researchers and practitioners being aware of these reasons
of failure as well as CSFs of chatbots, we contribute to managing risks while deploying and maintaining
chatbots. In addition, our CSFs analysis shows some deviations between chatbot-related topics addressed
in practice and research which can be used to identify further research needs. The list of reasons for
chatbots’ failures and the CSFs represents the current state of research and technology. As the field is
evolving rapidly in terms of e.g., AI, it is necessary to repeat this analysis and update the failure and CSFs
A Critical Success Factors Analysis for Chatbots
Forty-Second International Conference on Information Systems, Austin 2021
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... First, a primary reason for the limited success of CAs is their premature deployment, often driven by high expectations and management pressure, usually combined with little knowledge of the CA development process in general and of CA quality in particular. This practice often leads to nonuse, dissent, or complete failure, as highlighted by Janssen et al. (2021) and Lewandowski et al. (2022b). Unsatisfactory CA design and limited capabilities can result in a frustrating user experience that triggers resistance and a loss of trust in the CA, further hindering its successful adoption in realworld environments as organizations (Weiler et al., 2022). ...
... Unsatisfactory CA design and limited capabilities can result in a frustrating user experience that triggers resistance and a loss of trust in the CA, further hindering its successful adoption in realworld environments as organizations (Weiler et al., 2022). The failure of CAs is frustrating not only for employees, but also for the CA vendor, who has invested significant effort, time, and money in developing the CA (Janssen et al., 2021;van der Goot et al., 2021). ...
... Second, CAs are only marginally or not continuously evaluated to ensure their improvement, successful operation, and overall progress in organizations (Janssen et al., 2021;Meyer von Wolff et al., 2021). Therefore, previous research has proposed continuous evaluation (e.g., via monitoring (Corea et al., 2020) or chatlog data (Kvale et al., 2019)) and operation and improvement processes (Lewandowski et al., 2022b;Meyer von Wolff et al., 2022) to regularly assess their use, quality, and added value (Brandtzaeg & Følstad, 2018;Meyer von Wolff et al., 2022). ...
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Contemporary organizations increasingly adopt conversational agents (CAs) as intelligent and natural language-based solutions for providing services and information. CAs offer new forms of personalization, speed, (cost-)effectiveness, and automation. However, despite their hype in research and practice, many organizations still fail to seize CAs’ potential because they lack knowledge of how to evaluate and improve the quality of CAs to sustain them in organizational operations. We aim to fill this knowledge gap by conducting a design science research project in which we aggregate insights from the literature and practice to derive an applicable set of quality criteria for CAs. Our article contributes to CA research and guides practitioners by providing a blueprint to structure the evaluation of CAs and to discover areas for systematic improvement.
... Driven by the trend to adopt artificial intelligence (AI), PCAs can comprehend user input and respond to it adequately, which can help learners overcome specific challenges (Khosrawi-Rad et al., 2022b). Despite these advantages, PCAs often fail in practice (Følstad et al., 2018;Janssen et al., 2021;van der Goot et al., 2020). For example, in their recent practical analysis, Janssen et al. (2021) found that providers no longer actively operate any PCAs that the authors examined. ...
... Despite these advantages, PCAs often fail in practice (Følstad et al., 2018;Janssen et al., 2021;van der Goot et al., 2020). For example, in their recent practical analysis, Janssen et al. (2021) found that providers no longer actively operate any PCAs that the authors examined. PCAs' challenges include insufficiently stimulating conversations (Benner et al., 2022) and their often machine-like rather than natural and human-like nature (Seeger et al., 2018). ...
... With the motivational effect that game elements have, one might be able to enable this long-term commitment to PCAs (Nißen et al., 2021). With an AI-based PCA, establishing regular user interaction with the PCA also leads to the PCA being able to learn along with the acquired training data, improve its interaction behavior, and, thus, avoid negative experiences with PCAs that have low language-comprehension abilities (Inaba et al., 2015;Janssen et al., 2021). For example, Inaba et al. (2015) used game elements such as points to encourage users to interact with a conversational agent on a crowdsourcing platform with the overall goal of obtaining new training data. ...
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Pedagogical conversational agents (PCAs) are an innovative way to help learners improve their academic performance via intelligent dialog systems. However, PCAs have not yet reached their full potential. They often fail because users perceive conversations with them as not engaging. Enriching them with game-based approaches could contribute to mitigating this issue. One could enrich a PCA with game-based approaches by gamifying it to foster positive effects, such as fun and motivation, or by integrating it into a game-based learning (GBL) environment to promote effects such as social presence and enable individual learning support. We summarize PCAs that are combined with game-based approaches under the novel term "game-inspired PCAs". We conducted a systematic literature review on this topic, as previous literature reviews on PCAs either have not combined the topics of PCAs and GBL or have done so to a limited extent only. We analyzed the literature regarding the existing design knowledge base, the game elements used, the thematic areas and target groups, the PCA roles and types, the extent of artificial intelligence (AI) usage, and opportunities for adaptation. We reduced the initial 3,034 records to 50 fully coded papers, from which we derived a morphological box and revealed current research streams and future research recommendations. Overall, our results show that the topic offers promising application potential but that scholars and practitioners have not yet considered it holistically. For instance, we found that researchers have rarely provided prescriptive design knowledge, have not sufficiently combined game elements, and have seldom used AI algorithms as well as intelligent possibilities of user adaptation in PCA development. Furthermore, researchers have scarcely considered certain target groups, thematic areas, and PCA roles. Consequently, our paper contributes to research and practice by addressing research gaps and structuring the existing knowledge base.
... Through a detailed analysis, 144 sub-themes were identified and collated into 6 main themes pertaining to UX key principles for CUIs shown in Table 1. [40,44,73] Natural spoken language characteristics [2,25,28,32,48,74] Engaging dialogue [3,27,39,44,56,70,73,75] Main Theme Sub-theme References Allow for synonyms [12,54] Language clarity [17,33,59] Short and simple dialogue [2,4,42,76] Multimodality dialogue [2,9,69] Ethical design Data privacy [3,5,37,39,42,43,75] Self-disclosure and transparency [12, 24-30, 36, 37, 40, 43, 45, 51-53, 59, 66, 68, 73] Trustworthy environment [3, 5, 24, 27-30, 33, 36, 55, 59, 64, 67] Data security [5,33,37,43] Informed user consent [39,77] The usability theme describes features that attribute to the overall practicality and functionality of a CUI such as ease of use and robustness to unexpected input, the research suggested that these factors are often overlooked therefore creating significant usability issues [24]. Furthermore, CUI's should make use of subthemes such as using universal navigational terms in order to allow users to navigate the CUI effectively and efficiently. ...
... Through a detailed analysis, 144 sub-themes were identified and collated into 6 main themes pertaining to UX key principles for CUIs shown in Table 1. [40,44,73] Natural spoken language characteristics [2,25,28,32,48,74] Engaging dialogue [3,27,39,44,56,70,73,75] Main Theme Sub-theme References Allow for synonyms [12,54] Language clarity [17,33,59] Short and simple dialogue [2,4,42,76] Multimodality dialogue [2,9,69] Ethical design Data privacy [3,5,37,39,42,43,75] Self-disclosure and transparency [12, 24-30, 36, 37, 40, 43, 45, 51-53, 59, 66, 68, 73] Trustworthy environment [3, 5, 24, 27-30, 33, 36, 55, 59, 64, 67] Data security [5,33,37,43] Informed user consent [39,77] The usability theme describes features that attribute to the overall practicality and functionality of a CUI such as ease of use and robustness to unexpected input, the research suggested that these factors are often overlooked therefore creating significant usability issues [24]. Furthermore, CUI's should make use of subthemes such as using universal navigational terms in order to allow users to navigate the CUI effectively and efficiently. ...
The evolution of digital technologies enables a pervasive or ubiquitous computing environment in which the processing of information is linked with how we engage in society (e.g. e-government), with service providers (e.g. banking) and with friends and family (e.g. social media). In addition, our interface with technology has changed, with features such as conversational interfaces (e.g. chatbots), natural language processing and voice recognition. Despite the potential of the application of conversational interfaces, it is recognised that methods from Human-Computer Interaction (HCI) and User Experience (UX) design mostly associated with web or digital interfaces, are directly applied to conversational interface design, rather than recognising the unique design requirements of such interfaces. The purpose of this study was to investigate the key principles of user experience design pertaining to conversational interface design. Through the systematic analysis of 106 academic papers, 144 key principles were identified and structured into 6 themes collated into a conceptual learning model. By considering these 6 themes, conversational user interface (CUI) designers and developers may apply the conceptual learning model to design improved, fit-for-purpose conversational interfaces.KeywordsKey PrinciplesConversational User InterfaceUser ExperienceUser Experience DesignConceptual Learning Model
... Instead of providing prefabricated answers, LCs adapt their support to the needs and current knowledge level of the learners, facilitating the learning process. Despite extensive research in this field, the majority of conversational AI, including LCs, often struggle to sustain their market presence (Janssen, Grützner, & Breitner, 2021). A recent study conducted at our university (under review) has also shown a preference for ChatGPT over the pedagogically carefully designed LC. ...
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Digital transformation has disrupted learning in educational institutions, with conversational AI, such as ChatGPT, playing a significant role. Conversational AI varies from knowledgable fact-based dialogue systems to bonding learning companions (LC) that have gained attention in research and practice, aiming to establish long-term relationships with learners. This study conceptualizes the subjective value-in-use (ViU) of conversational AI, employing the framework of service-dominant logic. By conducting a systematic literature review and examining qualitative experiences of university students using conversational AI in a multi-week context, we explore ViU dimensions and discuss implications for the application of conversational AI in education. Our research highlights the multifaceted ViU of LCs, emphasizing aspects like social and hedonic value, autonomy, and inspiration. Considering perspectives of both learners and educators is crucial for integrating conversational AI effectively. This study provides insights from a value-centered perspective and encourages further exploration of the teachers' viewpoint and the ethical use of conversational AI in education.
... However, users often perceive PCAs as not motivating (Nißen et al., 2021;Wellnhammer, Dolata, Steigler, & Schwabe, 2020). According to a recent practice analysis by Janssen, Grützner, & Breitner (2021), this is a common reason why learners do not use them, and PCAs fail. Combining PCAs with gamebased approaches is one way to counteract this issue (Benner, Schöbel, Süess, Baechle, & Janson, 2022;Schöbel, Schmidt-Kraepelin, Janson, & Sunyaev, 2021). ...
... AI Chatbots have been one of the main focus areas of research on AI in education and technology-enhanced learning (Bahja, Hammad, and Hassouna 2019), often associated with self-regulated learning (Maldonado-Mahauad et al. 2022) and intelligent tutoring systems (Mirzababaei and Pammer-Schindler 2022). Previous studies examining AI chatbots' strengths and weaknesses report that availability and usefulness are among the most critical success factors, while poor content, lack of legal frameworks and poor conversation design are the main reasons for the failures of the systems in practice (Janssen, Grützner, and Breitner 2021;Kasneci et al. 2023). Further research has examined the potential of AI chatbots to enhance education and the strategies education stakeholders need to develop to tackle the risks of possible harm and disruptions (Tlili et al. 2023). ...
AI chatbots have recently fuelled debate regarding education practices in higher education institutions worldwide. Focusing on Generative AI and ChatGPT in particular, our study examines how AI chatbots impact university teachers' assessment practices, exploring teachers' perceptions about how ChatGPT performs in response to home examination prompts in undergraduate contexts. University teachers (n = 24) from four different departments in humanities and social sciences participated in Turing Test-inspired experiments, where they blindly assessed student and ChatGPT-written responses to home examination questions. Additionally, we conducted semi-structured interviews in focus groups with the same teachers examining their reflections about the quality of the texts they assessed. Regarding chatbot-generated texts, we found a passing rate range across the cohort (37.5 − 85.7%) and a chatbot-written suspicion range (14-23%). Regarding the student-written texts, we identified patterns of downgrading, suggesting that teachers were more critical when grading student-written texts. Drawing on post-phenomenology and mediation theory, we discuss AI chatbots as a potentially disruptive technology in higher education practices.
Business Analysis enables organizations to articulate needs and the rationale for change, and to design and describe solutions that can deliver value to the organization. A business analyst (BA) is the role that identifies business problems, understands its underlying causes, and ensures that these problems are addressed with effective solutions. BAs must operate in the new world of work and the objective of this paper was to consider the application of experiential learning to deliver industry-ready BAs. Firstly, we defined the world of work capabilities, followed by the profile of an industry-ready BA. We then considered the role of experiential learning in delivering industry-ready BAs and illustrated its application with a mapping to an HEI final year capstone project module. By considering the interaction and interrelationship among HEIs, industry organizations and BA graduates, HEIs can ensure that curricula focus on what is required of BAs to lead and thrive in the world of work.KeywordsBA capabilitiesIndustry-ready graduateIndustry-ready BA graduate capabilitiesexperiential learning
Conversational agents (CAs) are increasingly used as an additional convenient and innovative customer service channel to relieve service employees, as in the studied organization. In the process of analyzing and maintaining the present AI-based agent, however, user satisfaction is low as the CA lacks understanding and offers unsatisfactory solutions to users. Nonetheless, solving the requests and providing a positive user experience is crucial to relieve the service employees’ workload permanently. For CAs’ improvement, this study followed action design research (ADR) and used design thinking. We identified the central interaction problems (findability, welcome message, dialog control and fallback issues) with a monitoring process and analysis. Afterward, we interviewed users about their expectations and requirements and addressed these problems by creating user-centric mock-ups. Through a quantitative survey, the most popular solutions were implemented in a prototype. Finally, the resulting CA prototype was evaluated, showing a significantly improved user experience afterward, and design guidelines were discovered.Keywordsconversational agentschatbot user experience (UX)fallback strategyinteraction designartificial intelligence (AI)
Using conversational agents (CAs) has become increasingly popular for organizations for various applications, such as customer service and healthcare. However, user satisfaction and engagement with CA adoption stay behind expectations. In this context, quality criteria (e.g., regarding dialog flow or representation) can serve as a basis to evaluate CAs and improve their effectiveness to consequently affect user satisfaction and engagement. Hereby, we contribute to emerging research of quality criteria by proposing a prototype-based approach to facilitate the evaluation and improvement of CAs design and effectiveness. Our approach involves deriving scenarios from synthesized CA criteria from preliminary work, creating prototypes, and evaluating them in expert interviews. By comparing prototypes against each other, the influence on CA effectiveness can be measured. Our results demonstrate an approach for applying the criteria and offer a promising direction towards designing, developing, and operating CAs.Keywordsconversational agentchatbot effectivenessprototypingevaluationartificial intelligence (AI)
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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.
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Chatbots have become popular in recent years as a means of supporting a company’s external communication with customers, but they are also increasingly being used for internal purposes, especially to improve and accelerate workflows. Along these lines, recent studies have suggested that chatbots can also be applied to other internal business practices, such as innovation management. Nevertheless, the use of chatbots for innovation management is still an under-researched topic, and practical experiences are largely missing. We address this gap by identifying value propositions of chatbots to support a company’s innovation management process. Furthermore, we link the value propositions to particular process steps and success dimensions. To do so, we perform a literature review and complement the findings with expert interviews. This study contributes to a better understanding of the benefits of chatbot usage for the innovation management process.
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Design Science Research (DSR) is a problem-solving paradigm that seeks to enhance human knowledge via the creation of innovative artifacts. Simply stated, DSR seeks to enhance technology and science knowledge bases via the creation of innovative artifacts that solve problems and improve the environment in which they are instantiated. The results of DSR include both the newly designed artifacts and design knowledge (DK) that provides a fuller understanding via design theories of why the artifacts enhance (or, disrupt) the relevant application contexts. The goal of this introduction chapter is to provide a brief survey of DSR concepts for better understanding of the following chapters that present DSR case studies.
Due to advancements in artificial intelligence, chatbots are often indistinguishable from humans. Regarding the question whether firms should disclose their chatbots' nonhuman identity or not, previous studies find negative consumer reactions to chatbot disclosure. By considering the role of trust and service-related context factors, this study explores how negative effects of chatbot disclosure for customer retention can be prevented. Results show that chatbot disclosure has a negative indirect effect on customer retention through mitigated trust for services with high criticality. In cases where a chatbot fails to handle the customer's service issue, disclosing the chatbot identity not only lacks negative impact but even elicits a positive effect on retention. These findings demonstrate that disclosing the chatbots' machine-like identity not only has undesirable consequences but can lead to positive reactions as well.
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Critical success factors such as trust and privacy concerns have been recognized as grand challenges for research of intelligent interactive technologies. Not only their ethical, legal, and social implications, but also their role in the intention to use these technologies within high risk and uncertainty contexts must be investigated. Nonetheless, there is a lack of empirical evidence about the factors influencing user's intention to use insurance chatbots (ICB). To close this gap, we analyze (i) the effect of trust and privacy concerns on the intention to use ICB and (ii) the importance of these factors in comparison with the widely studied technology acceptance variables of perceived usefulness and perceived ease of use. Based on the results of our online survey with 215 respondents and partial least squares structural equation modelling (PLS-SEM), our findings indicate that although trust is important, other factors, such as the perceived usefulness, are most critical for ICB usage.
Chatbots are predicted to play a key role in customer service. Users’ trust in such chatbots is critical for their uptake. However, there is a lack of knowledge concerning users’ trust in chatbots. To bridge this knowledge gap, we present a questionnaire study (N = 154) that investigated factors of relevance for trust in customer service chatbots. The study included two parts: an explanatory investigation of the relative importance of factors known to predict trust from the general literature on interactive systems and an exploratory identification of other factors of particular relevance for trust in chatbots. The participants were recruited as part of their dialogue with one of four chatbots for customer service. Based on the findings, we propose an initial model of trust in chatbots for customer service, including chatbot-related factors (perceived expertise and responsiveness), environment-related factors (risk and brand perceptions) and user-related factors (propensity to trust technology). RESEARCH HIGHLIGHTS We extend the current knowledge base on natural language interfaces by investigating factors affecting users’ trust in chatbots for customer service. Chatbot-related factors, specifically perceived expertise and responsiveness, are found particularly important to users’ trust in such chatbots, but also environment-related factors such as brand perception and user-related factors such as propensity to trust technology. On the basis of the findings, we propose an initial model of users’ trust chatbots for customer service.