Trust in chatbots for customer service
findings from a questionnaire study

Cecilie Bertinussen Nordheim

Master thesis at the Department of Psychology
UNIVERSITY OF OSLO
15.05.2018

IRunning head: TRUST IN CHATBOTS FOR CUSTOMER SERVICE Cecilie Bertinussen Nordheim

2018
Trust in chatbots for customer service – findings from a questionnaire study

Cecilie Bertinussen Nordheim

http://www.duo.uio.no

IITRUST IN CHATBOTS FOR CUSTOMER SERVICE
Author: Cecilie Bertinussen Nordheim
Title: Trust in chatbots for customer service – findings from a questionnaire study
Supervisors: Cato Bjørkli (UIO) and Asbjørn Følstad (SINTEF)
Abstract
Recently, there has been an increased interest in chatbots. Chatbots are software systems
which interact with humans through natural language. This technology is utilised andimplemented in a variety of sectors, in particular for customer support purposes. Thoughchatbots’ capabilities for efficient interaction has increased, user uptake has been lower than
anticipated. A critical success factor for such uptake is user trust. However, there is aknowledge gap concerning the factors that affect trust in chatbots. This thesis aims to cover
this gap by presenting a questionnaire study, including the response of 154 users of customer
service chatbots. The study consisted of two parts. First, an explanatory part mainly based on
the Corritore et al. (2003) framework on trust in websites (including users’ perceptions ofease of use, risk, expertise, predictability and reputation). This framework was extended on
the basis of related literature to include users’ perception of anthropomorphism andpropensity to trust technology. The studied factors were structured according to three highlevel dimensions; chatbot-related, environment-related and user-related factors. The
explanatory analysis showed that users’ perceptions of expertise and risk, as well as users’
propensity to trust technology, explained the most variance in trust. Second, an exploratory
part where the respondents in their own words reported on what they considered to beimportant for their trust in the chatbot. Their responses made subject to thematic analysis. The
categories identified in the exploratory analysis suggested users’ trust to be affected by factors
partly reflecting those of the explanatory analysis (such as expertise, anthropomorphism, lowrisk and not trust relevant/no trust), and partly new factors (such as fast response, absence ofmarketing, brand and access to human operator). Based on the findings from the explanatory
and exploratory analysis, a new model of trust in chatbots for customer service is proposed.
The model includes chatbot-related factors (expertise, fast response, anthropomorphism,
absence of marketing), environment-related factors (brand, low risk, access to humanoperator) and user-related factors (propensity to trust technology).
Keywords: trust, chatbots, customer service, questionnaire study

IIITRUST IN CHATBOTS FOR CUSTOMER SERVICE
Acknowledgment
The choice of theme for this master thesis was based on my curiosity for automation
and artificial intelligence. I became fascinated by this technological revolution throughreading several articles published in DN and other newspapers. The interest increased evenmore after I went through the course “Human, Technology and Organization” at the
University of Oslo. To find a suitable organization to collaborate with, I addressed a numberof relevant companies. The response was overwhelming, and it was quite challenging todecide which direction to go for. After discussions with the different interests and carefulconsideration, I was convinced that SINTEF, with their huge knowledge and researchexperience, would be the absolute right choice of collaboration.
I would like to thank my incredible supervisor Asbjørn Følstad at SINTEF. Thanks foryour wise mentoring, your patience, keeping up with my many frustrations, sharing yourknowledge, and last but not least - always being available. You pushed me to overcome big
struggles and always to challenge myself. I would also like to thank my supervisor Cato
Bjørkli at the University of Oslo for his good advising, and Knut Inge Fostervold who helped
me with methodological challenges.
In addition to my supervisors, I would also like to thank the four companies who
participated in this study. Your acceptance to link my questionnaire to your customer service
chatbot was crucial for the recruitment of users that were willing to share their experiences.
Huge thanks to my family for motivating me, and my cohabitant for being patient
through my frustrations. Special thanks to my dad for the many good discussions and advices.
Finally, I would like to express my gratitude for the opportunity this master work has
provided, to investigate a subject that really fascinates me. Through the work I have been
inspired to dig deeper, and gain more knowledge and understanding of the challenges andimplications of this technological revolution.

Oslo, May 2018
Cecilie Bertinussen Nordheim

IVTRUST IN CHATBOTS FOR CUSTOMER SERVICE

Table of contents
INTRODUCTION .................................................................................................................................. 1
CHATBOTS AND RELATED TECHNOLOGY ............................................................................... 3
AUTOMATION AND HUMAN TECHNOLOGY RELATIONS ....................................................................... 3
THE CHANGES BROUGHT ABOUT BY MACHINE LEARNING AND AI ...................................................... 4
THE EMERGENCE OF CHATBOTS ........................................................................................................... 5
CHATBOT APPLICATIONS ..................................................................................................................... 6
USER PERCEPTION AND EXPERIENCE WITH CHATBOTS ........................................................................ 7
TRUST .................................................................................................................................................... 7
THE TRUST CONSTRUCT ....................................................................................................................... 7
TRUST IN INTERPERSONAL RELATIONS ................................................................................................ 9
TRUST IN AN ORGANIZATIONAL CONTEXT......................................................................................... 10
TRUST IN AN ONLINE CONTEXT .......................................................................................................... 11
TRUST IN TECHNOLOGY ..................................................................................................................... 13
SUMMARISING THE LITERATURE REVIEW ........................................................................... 14
RESEARCH QUESTION AND HYPOTHESIS ............................................................................... 16
METHOD .............................................................................................................................................. 16
THE PROJECT ...................................................................................................................................... 16
RESEARCH DESIGN ............................................................................................................................. 17
STUDY CONTEXT ................................................................................................................................ 17
RESPONDENT RECRUITMENT.............................................................................................................. 17
MATERIAL AND MEASURES ................................................................................................................ 18
Trust. .............................................................................................................................................. 18
Expertise. ....................................................................................................................................... 18
Predictability. ................................................................................................................................ 19
Anthropomorphism. ....................................................................................................................... 19
Ease of use. .................................................................................................................................... 19
Risk. ............................................................................................................................................... 19
Reputation...................................................................................................................................... 19
Propensity to trust technology. ...................................................................................................... 19
Intention to use. ............................................................................................................................. 19
Open-ended question. .................................................................................................................... 20
Measurement instruments for three elements not included in the analysis. .................................. 20
Development of the questionnaire. ................................................................................................ 20
ANALYSIS ........................................................................................................................................... 21
Preparing for analysis ................................................................................................................... 21
Validation ...................................................................................................................................... 21
Quantitative analysis ..................................................................................................................... 22
Qualitative analysis ....................................................................................................................... 23
ETHICAL CONSIDERATION .................................................................................................................. 24
RESULTS.............................................................................................................................................. 25
ABOUT THE RESPONDENTS................................................................................................................. 25
Respondent demographics. ............................................................................................................ 25
The respondents’ previous experiences ......................................................................................... 25
RESULTS FROM THE EXPLANATORY PART OF THE STUDY ................................................................. 26
An overview of the studied variables ............................................................................................. 26
Correlation between the variables. ............................................................................................... 26
Investigating the effect of gender and age..................................................................................... 27
Multiple regression analysis – explaining variation in trust......................................................... 27
Simple linear regression between trust and intention to use. ........................................................ 28
RESULTS FROM THE EXPLORATORY PART OF THE STUDY .................................................................. 28VTRUST IN CHATBOTS FOR CUSTOMER SERVICE
Chatbot-related categories ............................................................................................................ 29
Expertise – Correct answer (28,1%). ........................................................................................ 29
Expertise – Interpretation (9,6%). ............................................................................................. 30
Expertise – Concrete answer (16,4%) ....................................................................................... 30
Expertise - Eloquent answer (9,6%).......................................................................................... 30
Fast response (18,5%). .............................................................................................................. 31
Anthropomorphism (6.2%). ...................................................................................................... 31
Absence of marketing (4,1%) ................................................................................................... 31
Environment-related categories. ................................................................................................... 32
Brand (17,1%). .......................................................................................................................... 32
Low risk (6,2%). ....................................................................................................................... 32
Access to human operator (4,8%). ............................................................................................ 33
User-related categories. ................................................................................................................ 33
Not trust relevant/ no trust (7.6%)............................................................................................. 33
DISCUSSION ....................................................................................................................................... 33
SUMMARY OF THE RESULTS ............................................................................................................... 33
FINDINGS FROM THE EXPLANATORY PART ........................................................................................ 34
Trust as important for use. ............................................................................................................ 34
Chatbot-related factors. ................................................................................................................ 35
Environment-related factors.......................................................................................................... 36
User-related factors....................................................................................................................... 36
Factors with limited effect on users’ trust. .................................................................................... 36
The effect of age and gender. ........................................................................................................ 37
FINDINGS FROM THE EXPLORATORY PART ........................................................................................ 38
Chatbot-related factors. ................................................................................................................ 38
Environment-related factors.......................................................................................................... 40
User-related factors....................................................................................................................... 41
TOWARDS A MODEL OF FACTORS AFFECTING TRUST IN CUSTOMER SERVICE CHATBOTS ................. 42
LIMITATIONS AND FUTURE RESEARCH............................................................................................... 44
CONCLUSIONS .................................................................................................................................. 45
REFERENCES ..................................................................................................................................... 47
APPENDIX A – THE QUESTIONNAIRE ........................................................................................ 55
APPENDIX B – THE MEASUREMENT INSTRUMENTS ............................................................ 60
APPENDIX C – THEMATIC ANALYSIS FROM THE OPEN-ENDED QUESTION................ 63

VI

TRUST IN CHATBOTS FOR CUSTOMER SERVICE

Introduction
In today’s society, most if not all sectors digitize and automate in order to become
more efficient. According to Frey and Osborne (2013), the increasing availability andsophistication of software technologies disrupt labour markets by making workers redundant.
Within this context, a significant change is companies’ introduction of chatbots as asupplement to human customer support. Chatbots are computer programs interacting withhumans through natural language (Abu Shawar & Atwell, 2007). The purpose of chatbots is
to simulate a human conversation in response to natural language input through text or voice(Dale, 2016). In Norway, several banks have introduced text-based chatbots. Here, customers
are invited to address their customer service enquiries directly to a chatbot. For example, acustomer of a bank may ask the customer service chatbot about mortgage for young adults,
and immediately be answered through chat.
Although chatbots are the object of a recent surge of interest, chatbot research anddevelopment dates back to the 1960’s (Weizenbaum, 1966). The renewed interest can largely
be attributed to two developments. The developments within machine learning and artificial
intelligence (AI) have made chatbots easier to train and implement, due to strengthened
capabilities for identifying users’ intents and sentiment, and improved natural language
processing (Brandtzaeg & Følstad, 2017). Furthermore, chatbots have gained renewed interest
due to changes in the availability and popular uptake of messaging platforms. This channel
enables businesses to reach their target audience anytime and anywhere through platform suchas Facebook Messenger, Slack, WhatsApp or WeChat (Zumstein & Hundertmark, 2017).
As they proliferate on messaging platforms, and increasingly are implemented asdigital assistants by large technology companies, the use of chatbots is gradually becoming apart of people’s everyday life (Accenture, 2016). Chatbot technology has been introduced in avariety of online environments such as e-commerce, daily news and delivery services. Lately,
there has been a substantial growth in the development of chatbots for customer service andmarketing (Zumstein & Hundertmark, 2017). Servion (2017) has recently predicted 95% of
all customer interactions to be handled by AI-applications within 2025, including live
telephone and online conversations.
The current and predicted impact of chatbots and AI-applications in customer service
illustrates the fast growing change that makes it essential to gain knowledge about how
chatbots are used and perceived by users. In spite the early optimism concerning the launch of
chatbot technologies, by e.g. Facebook and Microsoft, theorists have noted users’ adoption of1

TRUST IN CHATBOTS FOR CUSTOMER SERVICE
chatbots to be less substantial than hoped (Simonite, 2017). The potential of chatbots haveseemingly not yet been realized as expected by the technology industry. One reason might bethat many chatbots currently on the market have failed to fulfil user’s needs due to a relatively
high frequency of meaningless responses, unclear purposes or insufficient usability (Coniam,2014). Lack of user focus by developers seems to be prominent.
For a new technology as chatbots to be taken up and used as intended by technology
companies and service providers, it is essential that users have trust in the service. Trust has
been argued to be a critical success factor in an online environment (Corritore, Kracher, &Wiedenbeck, 2003). Furthermore, the humanlike qualities of chatbots, such as their natural
language interaction, may make trust particularly important (Holtgraves, Ross, Weywadt, &Han, 2007).
The concept of trust is widely applied when discussing individuals and society. Morerecently, trust has been established as an important concept in studies of machines andtechnology. The idea of discussing trust in relation to technology may seem controversial(Chopra & Wallace, 2003). Nevertheless, researchers increasingly consider it relevant toinvestigate users’ trust in different technologies (e.g. Hancock et al., 2011). Trust has beenshown to be important for users’ uptake of new technologies (e.g. Corritore et al., 2003).
Following this, the uptake of chatbots among consumers depends on users trust. Without trust,the potential in chatbots may not be realized.
Although a considerable amount of research has been conducted on trust in theinterpersonal and societal domain, and also on users trust in various technologies, studies
addressing trust in chatbots are scarce. The lack of research implies that there is a knowledge
gap concerning the factors that affect humans’ use of chatbots. As basis for a framework oftrust in customer service chatbots, a relevant related model on trust in websites was presented
by Corritore et al. (2003). In their model, drawing upon the preceding trust literature, ease ofuse, risk, and four credibility factors (honesty, expertise, reputation, predictability) wereargued as the main factors affecting users’ trust in websites.
As a first step towards the needed knowledge on users’ trust in chatbots, it was in thisstudy considered beneficial to target chatbots within one particular domain. It was decided toscope it to customer service chatbots mainly for three reasons: (1) customer service is an
important domain for use of chatbots, (2) customer service represents a domain were humans
potentially will be replaced by machines, and (3) it is a requested knowledge for practitioners.
This made the study feasible, due to narrowing it down to one application where chatbots
have been used in a relatively uniform manner.
2TRUST IN CHATBOTS FOR CUSTOMER SERVICE
The thesis aims to contribute some of the knowledge needed to understand users’ trustin customer service chatbots. This knowledge is considered important both to strengthen our
understanding of chatbots and chatbot use, to help companies to make good strategies forchatbot design and implementation, and in enabling them to make customers satisfied in an
increasingly digitized world. This motivates the following research question, which will bepresented in more detail in the section Research question and hypothesis:
Which factors affect users’ trust in chatbots for customer service?
The next chapter provides an overview of relevant background literature. The
background presentation is divided into two main parts; one about automation and chatbots,
the other about the trust construct. In the first part, the topic of automation and recent
developments within AI and machine learning will be presented. Thereafter, review of theorigin of chatbots and current applications is provided, complemented by a short review ofliterature about user perception and experience with chatbots. In the second part, an overview
of the trust construct is provided, through exploration of trust in the interpersonal domain,organizations, online, and in relation to technology. Lastly, factors that on the basis of theliterature seem most promising to affect users’ trust in chatbots will be summed up in a table.
Chatbots and related technology
Automation and human technology relations
To better understand the role chatbots have achieved today, it is useful to reflect upon
automation and humans’ relation to technology. We are moving into a new time age, where
the technology options are enriched (Brynjolfsson & McAfee, 2017). This means that a lot of
our daily interactions depend on complex and autonomous technology. A lot of tasks andskills humans used to be responsible for have been replaced by automated systems (Hoff &Bashir, 2014). Automation has been described as “the execution by a machine agent (usually
a computer) of a function that was previously carried out by a human (Parasuraman & Riley,
1997, p. 231) and “technology that actively selects data, transforms information, makes
decisions, or control processes” (Lee & See, 2004, p. 50).
Automation entails a wide range of benefits, such as improved safety, comfort and job
satisfaction (Wickens, Lee, Liu, & Gordon-Becker, 2013). In contradiction, humans may trust
automation at times it’s not appropriate (Lee & See, 2004). By carefully designing andconsidering the human’s role, this failure can be avoided (Wickens et al., 2013). If you ask thequestion “Do you trust the machines advice?” and the response is no, this can give serious
consequences for the user, all depending on the performing task (Muir, 1987). The right3

TRUST IN CHATBOTS FOR CUSTOMER SERVICE
investment in technology can give rise for enhanced productivity, while machines that fail canlead to undesirable consequences. For large companies, a worst-case scenario can besubstantial financial losses (Venkatesh, 2000).
In their book “Machine, Platform, Crowd” which is an continuation of their muchreferenced book “The Second Machine Age”, Brynjolfsson and McAfee (2017) have provided
several examples of how computerisation have taken a big step from being confined to routine
manufacturing tasks. Google’s driverless cars provide an example of possible automated tasks
in the transport and logistic sector. Frey and Osborne (2013) discussed how the expected
technological changes will affect human’s work structure. Their analysis estimated 47% of
US employments as belonging to the high risk category, which means that associatedoccupations are highly susceptible to be automated in just a few decades.
The past section has shown the increasing potential automation serve, and how thiscan give rise to a range of benefits. However, automation can also entail challenges if notdesigned with the user in focus. The next section will take a deeper look at the concepts ofmachine learning and AI, and how developments in this domain have enriched the potential
for technological entities.
The changes brought about by machine learning and AI
An important premise for the current development of chatbots is the advances within
machine learning and AI. These advances are explicitly dedicated to the progress ofalgorithms that permit cognitive tasks to be automated (Frey & Osborne, 2013). Machines canthrough machine learning and AI be capable of performing tasks that earlier were considered
to require human judgement (Brynjolfsson & McAfee, 2017). Use of AI is seen in a range ofsectors, it has for instance made it possible to identify rare and devastating side effects ofmedications (Dietterich & Horvitz, 2015).
Machine learning is an approach to AI where systems learn by gradually improving
own ability to analyse and predict, through exposure to large amounts of data. Areas that haveseen the great impact includes speech recognition, image classification and machinetranslation (Brynjolfsson & McAfee, 2017). The big advances in machine learning ispredicted over the longer-term to have substantial beneficial influences on healthcare,education, transportation, commerce and general science (Dietterich & Horvitz, 2015). It isclearly proved that whenever the option is available, relying on data and algorithms alone
usually lead to a better decision than human experts (Brynjolfsson & McAfee, 2017).
Machine learning is quite similar to humans way of learning (Brynjolfsson & McAfee,2017). That’s why performance for systems based on machine learning often is compared to4TRUST IN CHATBOTS FOR CUSTOMER SERVICE
performance for humans. Liu, Shi, and Liu (2017) have provided an example of this. They
presented IQ of different AI systems, such as Google and Apple’s “Siri”. The winner of thistest was Google, not far away from passing the average IQ of a six-year-old human. If thisdevelopment continues, it will not be long before AI systems will reach and also pass theaverage IQ of a human.
In sum, this section has shown how the developments within machine learning and AI
have provided increased technological options. The renewed interest in chatbots is strongly
driven by this development, which makes chatbots capable of handling more tasks. The
following section will elaborate on chatbots and its origin.
The emergence of chatbots
Murgia, Janssens, Demeyer, and Vasilescu (2016) suggest that human-chatbot
interaction soon will be important in domain specific knowledge sharing, like question andanswer websites. The introduction of AI and machine learning has increased the potential andcapabilities chatbots can serve. Chatbots are gradually standard feature embedded insmartphones and web interfaces (Portela & Granell-Canut, 2017). The substantial uptake ofmessaging platforms such as Facebook Messenger and WeChat motivate service providers toreach out to customers through chatbots (Dale, 2016).
Conversational systems such as chatbots have been referred to with a variety of names(Ciechanowski, Przegalinska, Magnuski, & Gloor, 2018). In this thesis they are referred to as
chatbots, and are investigated in the context of text-based chatbots for customer support
purposes. Though chatbots for customer service typically are text based, voice-based chatbots
are also available on the customer market. Apple’s “Siri” is an example of a leading voicebased chatbot (Dale, 2016).
Chatbots as a technology is not new. The roots of conversational systems go back to
Weizenbaum’s computer programme “Eliza”; a computer programme that made natural
language conversation with a computer possible through text-based interaction. Eliza
simulated a therapist that users could chat with (Weizenbaum, 1966). Since then, there has
been substantial progress in developing natural language processing tools that understandboth spoken and written language (Baron, 2015). Shah, Warwick, Vallverdú, and Wu (2016)
compared current chatbots to Eliza, and found these to have superior conversational abilities
than their predecessor.
A way of testing chatbots’ ability to appear intelligent is the well-known “Turing test”.
Here, human judges converse with a partner that is either human or chatbot, without knowing
its true nature. The test criterion is whether the human judges are able to distinguish between
5

TRUST IN CHATBOTS FOR CUSTOMER SERVICE
a human and a chatbot (Turing, 1950). This test is implemented yearly at the Loebner prize,
where the chatbot appearing most humanlike win. To date, no computer program has
managed to convince the judges at the Loebner prize that they were human, not a robot(Baron, 2015). The fact that chatbots continuously improve, has raised optimism aboutbuilding chatbots that one day might pass the Turing test (Dale, 2016).
This section has shown that chatbot technology already have a broad history. The next
section will dig deeper into the context of this study, customer service chatbots and briefly
describe other chatbot applications.
Chatbot applications
The main reason for humans to use chatbots is productivity, meaning quicker answer
with less effort. Moreover, chatbots have been implemented with a variety of purposes, suchas provide information, social and emotional support, entertainment or link users to otherhumans or machines (Brandtzaeg & Følstad, 2017).
Customer service is a domain where chatbots have achieved strong and growinginterest (Accenture, 2016). The renewed interest in chatbots is also partly driven by thedevelopment within e-commerce and e-service to incorporate natural language interfaces(Holtgraves et al., 2007). In Norway we are witnessing a change in how customers are offeredassistance. Chatbots are gradually becoming a regular function in customer service platforms
in banks, insurance, consulting and industry. The humanlike conversation of chatbots gives
customers the opportunity to type questions, and in return get meaningful answers to thosequestions in everyday language (Crutzen, Peters, Portugal, Fisser, & Grolleman, 2011).
Chatbots can thereby be used to deal with many of the routine queries that typically make up
most service request (Accenture, 2016). Furthermore, chatbots never require vacation, getgrumpy or tired. According to Brynjolfsson and McAfee (2017), the initial step of listening
and understanding will be the hardest part of automating customer service.
Chatbots has also been proven useful in other domains. Within the health domain,
chatbots have been evaluated favourably in comparison to information lines and searchengines in answering adolescent’s questions to sex, drugs and alcohol (Crutzen et al., 2011).
A study in the educational domain found that students were overwhelmingly positive to use
chatbots as a mean for learning and practise of a foreign language (Fryer & Carpenter, 2006)
Recently, the use of chatbots have been discussed in the domain of human resources services,
as a way of recruiting candidates for jobs, supposedly facilitating the recruitment process(Monsen, 2018).

6

TRUST IN CHATBOTS FOR CUSTOMER SERVICEThis section has shortly addressed some of the application forms where chatbots havebeen implemented. Currently, chatbot technology is implemented mainly as a supplement tohuman customer support in an attempt to digitize companies. The next section will elaborate
on some of the studies which have highlighted the users’ perception and experiences with
chatbots.
User perception and experience with chatbots
A successful implementation of new technology is mainly determined by the users’
perception and experience. Research in the field of chatbots has established interestingfindings about the user perception. Hill, Ford, and Farreras (2015) compared human-humaninteraction online with a human-chatbot interaction. The results revealed human-chatbot
interactions to have longer duration and involve shorter messages than human-humaninteractions. Also, the human-chatbot interaction lacked the richness in vocabulary compared
to a conversation with a human. Corti and Gillespie (2016) found users to invest a higher
effort to repair misunderstandings when the chatbot was perceived as human, compared to theperception of the chatbot being automated.
Several studies have investigated users’ experiences with chatbots. For example,
Murgia et al. (2016) studied the human-chatbot interaction in the context of a question-answer
website. In their experiment, the preliminary results indicated that humans either don’t fully
trust suggestions given by a chatbot, or they expected chatbots to provide better answers thanhumans.
In sum, this section has shortly demonstrated some of the available research on
people’s perceptions and experiences with chatbots. However, the current literature has notshed light on the factors contributing to users trust in customer service chatbots. The
following pages will discuss the trust construct, and how this can be decisive for people’s useof chatbots. The second part of the background is structured into five sections; general
information about the trust construct, trust in the interpersonal domain, organizational, online,
and technology. First, trust will be discussed as a construct and how it can be a decisive factor
for use.
Trust
The trust construct
Trust has for decades been a subject of ongoing research, so far without any
universally accepted definition. It extends as important across a wide field, ranging frompsychology to human computer interaction (HCI) (Corritore et al., 2003). According to7

TRUST IN CHATBOTS FOR CUSTOMER SERVICE
Mayer, Davis, and Schoorman (1995), trust is “the willingness of a party to be vulnerable tothe actions of another party based on the expectation that the other will perform a particularaction important to the trustor, irrespective of the ability to monitor or control that other part”(p. 712). In the area of automation, Gregor and Madsen (2000) see trust as “the extent towhich a user is confident in, and willing to act on the basis of the recommendations, actions,and decisions of an artificially intelligent decision aid” (p. 1). Generally, both definitions
seem to state the importance of an individual who willingly put oneself in the hands ofanother party.
Trust can be viewed as an important component in the development and maintenanceof happy and well-functioning relationships (Simpson, 2007). Wang and Emurian (2005)
stress a trusting relationship to consist of two parties; a trustee and a trustor. In this study, thetrustor is the user/customer, and the trustee is the chatbot. Trust emanates from a person and isan act of a trustor. Trustworthiness is on the other hand characteristic of the object of trust,here stated as the chatbot (Corritore et al., 2003). On the basis of value in different domains,trust has been viewed as an attitude, an intention or a behaviour (Gregor & Madsen, 2000;
Mayer et al., 1995; Moray, Inagaki, & Itoh, 2000).
Lately, the topic of trust has generated increased attention in the domain of automationand technology. Lee and See (2004) are one of many researchers appraising trust as
significant to the understanding of human and automation partnership. Trust is in generaldescribed to belong to interactions among conscious beings. For many, the notion of trust
implies both the involved parties to be able to be vulnerable, experience betray and extend
goodwill (Friedman, Khan, & Howe, 2000). However, according to Wickens et al. (2013),
trust is important when dealing with any entity, no matter if it is a salesperson or an
automated device.
With regard to automation, trust has been perceived as a dependent factor for people’s
decision about monitoring and use (Merritt & Ilgen, 2008). Different studies have shown thatpeople tend to rely on automation they trust and keep distance to automation they don’t trust(Lee & Moray, 1992; Lewandowsky, Mundy, & Tan, 2000; Muir & Moray, 1996). Distrust is
not always severe, but can make people reject good and effective assistance (Wickens et al.,2013). Corbitt, Thanasankit, and Yi (2003) investigated consumers’ perception of trust buildon the internet. They showed how people having higher levels of trust in e-commerce weremore likely to use e-commerce. The same pattern is conceivable related to use of chatbots incustomer service, where low usage can be attributed a low trust.

8

TRUST IN CHATBOTS FOR CUSTOMER SERVICE
In the context of chatbots, including chatbots for customer service, a broader uptake
arguably depends on users’ trust in this technology. In particular, as chatbots possess
humanlike qualities such as the ability to convers. Holtgraves et al. (2007) found chatbots tobe viewed as having humanlike personalities, and that respondents kept much of the samesocial convention when talking to a chatbot as when to a human. As shown in the previous
section, there exist several studies on how user perceive or experience chatbots, but not on
trust in chatbots.
This section has described how researchers are connecting trust to automated systems
and also intention to use. To gain knowledge of factors affecting trust in chatbots for customer
service, definitions of trust in other domains will be explored. The next section will present
literature about trust in the interpersonal domain, an extensively studied research area.
Trust in interpersonal relations
Theories of interpersonal relationships have established trust as a social glue inrelationships, groups and societies (Van Lange, 2015). In the interpersonal perspective, trust
is a psychological state of an actor against a partner, where the actor to some extent isinterdependent (Simpson, 2007). For many, trust involves three components: (1) the
properties of the self/the person, (2) the specific partner in dialogue, and (3) the specific goalin the situation. If one of the three components changes, an individual’s perceptions, thoughts,
actions and feelings of trusting another will possibly also change (Hardin, 2003).
One of the big contributors to research of trust in the interpersonal domain was Rotter.
Rotter (1967) defined trust as “an expectancy held by an individual or group that the word,
promise, verbal or written statement of another individual or group can be relied upon” (p.
651). For him, trust was regarded as a personality trait. General work in the interpersonaldomain has confirmed that trust typically operates higher when people believe their partners
are more committed to the relationship, and have more motivations and benevolent intensions(Simpson, 2007). This shows how interests by one part can’t be achieved without reliance
upon another. Interdependence is one of two conditions with mostly agreement in the trust
literature (Rousseau, Sitkin, Burt, & Camerer, 1998).
This section has briefly presented conceptualizations of trust in the interpersonaldomain, where interdependence between two parties is highlighted in accordance to trust. The
next segment will cover trust in an organizational context, particularly by looking at Mayer etal. (1995) model.

9TRUST IN CHATBOTS FOR CUSTOMER SERVICE
Trust in an organizational context
Mayer et al. (1995) have made one of the most influential and accepted definitions oftrust (Rousseau et al., 1998). In their definition, vulnerability is a critical element. They did
especially stress humans to willingly put themselves at risk or in a vulnerable position. Thismeans they transfer responsibility for actions to someone else, otherwise trust won’t be an
important part of a relationship. The level of risk has been highlighted as dependent for trust,a factor which is also stated important in their model of organizational trust (Mayer et al.,1995). Risk is one of the two conditions with partly agreement in the literature, and has bypsychological, sociological and economic conceptualizations been defined as important fortrust (Rousseau et al., 1998).
In Mayer et al. (1995) model of factors contributing to trust in an organizational
context, three factors of perceived trustworthiness were defined: ability, benevolence andintegrity. The three characteristics of the trustee will thereby determine trustworthiness, andhelp building a foundation in the development of trust. The group of skills, competencies andcharacteristics that makes the trustor influence the domain is defined under ability.
Benevolence is the degree to which the motivations and intents of the trustee are in line withthose of the trustor. The extent to which the trustee hold on to a set of principles the trustor
finds acceptable, is the one Mayer et al. (1995) call integrity. Ability and integrity are factorsthat appear promising to transfer to a human-chatbot interaction Benevolence is on the other
hand a factor that is not conceivable important in relation to chatbots.
Mayer et al. (1995) model did also include peoples’ propensity to trust, which isevaluated as a stable within-part factor. This was regarded as the general willingness to trust
others. Applied to technology, propensity means one is willing to depend on technology
across situations and technologies (Mcknight, Carter, Thatcher, & Clay, 2011). Jian, Bisantz,
and Drury (2000) indicated humans’ general propensity to trust automated system as abaseline measure for predicting trust. Moreover, Merritt and Ilgen (2008) showed how
humans with high level of trust propensity were more likely to put a greater trust in an
automated system. It can thereby be reasonable to think that humans trust in chatbots is
affected by human’s general tendency to trust technology.
In sum, this section has provided a short overview of a much cited model of trust. Fourof the factors in the model; risk, ability, integrity and propensity to trust, seem to bepromising factors for trust in customer service chatbots. The next section will elaborate ontrust in an online context.

10

TRUST IN CHATBOTS FOR CUSTOMER SERVICE
Trust in an online context
The framework of Corritore et al. (2003) is a regularly cited framework addressing
trust in an interactive system as websites. Chatbots does also represent an interactive system,
where both systems are depending of the important online user interfaces. Corritore et al.
(2003) framework posit similarities with the above mentioned Mayer et al. (1995), and may
seem to be motivated by that model. Furthermore, Corritore et al. (2003) model is identified
by extensive offline literature deemed applicable to an online context. In the model, there are
two categories of factors impacting an individual’s degree of trust in a website: the perceived
and the external factors. The external factors exist implicitly and explicitly in a trust context.
The perceived factors are the individuals’ perception of the external factors. These perceived
factors will be emphasised in this study. Corritore et al. (2003) proposed that the perception of
the three factors, i.e. ease of use, risk and credibility, impact the user’s decision to trust in an
online environment.
First, Corritore et al. (2003) argue ease of use as one of the three perceived factors
impacting users’ trust. This is incorporated from the Technology acceptance model (TAM), a
model that describes the factors that need to be present for humans to use technology (Davis,
1989). Corritore et al. (2003) framework defines perceived ease of use as a reflection of how
simple the website is to use. Different e-commerce studies, for example Gefen, Karahanna,and Straub (2003) found perceived ease of use to be associated with increased trust. Similarly,
Li and Yeh (2010) argued ease of use to have a significant explanatory power in building trust
for vendors in mobile commerce. Ease of use appears prominent for trust in nearby context,
and will also be a reasonable factor to affect users’ trust in chatbots for customer service.
Second, risk is included in the trust model of Corritore et al. (2003) due to its
prevalence as a key factor of trust in the offline literature, as well as indications of being
important in online trust. They defined risk as the likelihood of an undesirable outcome. Thisfactor also seems to be motivated by the preceding literature on trust, e.g. Mayer et al. (1995)
who defined risk as necessarily for trust. In the context of customer service chatbots,
perceived risk arguably is low, though issues pertaining to getting hacked, fooled, waste time,or getting an incorrect answer may be relevant. When chatbots become more advanced andinvolve a higher engagement of the user, the risk will undoubtedly get higher. As risk, bymany researchers have been viewed as important for trust, there are reasons to believe thisfactor will play a role in the context of trust in chatbots for customer service.
Third, credibility is the last of the three perceived factors argued by Corritore et al.
(2003) to influence users’ trust in an online environment. Credibility gives a reason to trust
11TRUST IN CHATBOTS FOR CUSTOMER SERVICE
and function as a positive signal of the trustworthiness in the object. The perceived factor
credibility is further divided into four: honesty, expertise, predictability and reputation.
Honesty concerns well-intentioned and truthful action, and shares much of the samecharacteristics as Mayer et al. (1995) factor benevolence. This factor will not be regarded as apossible factor explaining trust in chatbots.
Expertise, as one of the four credibility factors is highlighted by Corritore et al. (2003)
to affect the users’ decision to trust. They defined expertise as the perceived knowledge orcompetence a website has. This is corresponding to Mayer et al. (1995) ability. Transferred tochatbots, expertise will be the perceived knowledge or competence, a factor conceivable toimpact users trust in chatbots for customer service.
Predictability is also by Corritore et al. (2003) stated as one of the four credibility
factors impacting users’ decision to trust. This covers the trustors expectation that the website
will act consistently, and that future transactions will continue to be successfully completed.
Predictability have similarities with Mayer et al. (1995) integrity factor, the extent to which
the actions are congruent with words. In the field of human-computer interaction, trust can beaffected by the consistency of the machines output given the same input (Merritt & Ilgen,2008). Muir and Moray (1996) explored predictability to be significantly related to subjective
trust. Here, a chatbots way of being predictable and acting consistently can be assumed toplay a role for the users trust.
Reputation is the last of four credibility factors suggested to be included in the modelon online trust. Reputation of a website defines the quality of recognized past performance(Corritore et al., 2003). Hoff and Bashir (2014) claimed an operators trust in an automatedsystem to be biased by the systems established reputation. For example, trust is said to be
influenced by e-commerce reputation in general (Corbitt et al., 2003). Others have shown
humans to display a higher tendency to trust automation when it is portrayed as a reputablesystem, by other words having a good, positive evaluation (De Vries & Midden, 2008). In thiscontext, it can be reasonable to believe that a chatbots’ reputation will impact humans trust in
chatbots for customer service.
This section has provided strong reasons for including Corritore et al. (2003)
framework as a starting point for the perceived factors influencing trust in chatbots forcustomer service. Ease of use, risk, expertise, predictability and reputation appears aspromising factors. However, as also stated by the authors, the model does not cover all
possible scenarios where humans interact with internet technologies. The next section will

12

TRUST IN CHATBOTS FOR CUSTOMER SERVICE
thereby explore how trust in technology has been investigated and if other factors appears
important.
Trust in technology
Trust is not limited to the interpersonal domain and can in many ways define how
people interact with technology. There even exist parallels between interpersonal trust andtrust in automation (Hoff & Bashir, 2014). What makes trust in humans to differ from trust in
machines is by Lee and See (2004) said to be machines lack of intentionality, traits likeloyalty, benevolence and values critical to the development of trust in human partners.
To consider trust in the context of robots, Hancock et al. (2011) conducted acomprehensive meta-analysis of the existing literature. They formulated a triadic model oftrust, where the influencing factors were categorized in human-related, robot-related andenvironmental-related. The result revealed robot-related characteristics, especially its
performance, as having the greatest influence on trust. The environmental-relatedcharacteristics had a moderate effect in this relationship. Chatbot-related factors, hence,characteristics with the chatbot is also conceivable to play a dominate role for users trust in
customer service chatbots.
Individual differences have in some cases also been proven central in explaining
differences in the perception of technological entities. Shah et al. (2016) made a study on theevaluation of conversational systems, where differences were found between age groups andthe genders. Younger age groups evaluated the systems higher than older age groups. The
same pattern was evident for females; they also evaluated the system higher than men. Basedon this, it is reasonable to investigate whether age and gender will influence users’ trust in
customer service chatbots.
The rapid technological developments and natural language processing has blurred thedistinction between humans and machines (De Visser et al., 2016). Some type of humanness
is seen in chatbots, which makes it important to evaluate anthropomorphism in this context.
Anthropomorphism defines the degree to which an intelligent agent like a chatbot is displayedwith human characteristics (De Visser et al., 2016). Reeves and Nass (1996) concluded intheir study that humans treat new technologies as real people, prolonged as trust entities.
With chatbots increasing linguistic capabilities, it is expected that the users are likely
to ascribe human traits to chatbots. This will be to the point where simply looking at a
generated dialog or interacting with a chatbot, and then distinguish this from a human will bea difficult and frightening task (Candello, Pinhanez, & Figueiredo, 2017). During a ten year
perspective, Nass and Moon (2000) performed a set of experiments showing that humans
13

TRUST IN CHATBOTS FOR CUSTOMER SERVICE
behaved in the same way with computers, as they did with real people. The authors identifiedsocial categories as something people relied on when interacting with computers. Apple, as
one of the big technology companies launched a major upgrade of their voice-based chatbot“Siri” summer 2017. This was not just an upgrade based on smaller technological attributes,
but a main focus of making “Siri” more humanlike (Matney, 2017).
This section has indicated the importance of evaluating trust in relation to technology.
It has also shown that individual differences in age and gender can affect human’s perception.
The most prominent finding is that humans to some degree view technological entities, as
entities displaying humanlike qualities. Furthermore, the big companies’ effort to makemachines more humanlike indicates the importance of considering its role in relation to users’
trust. The next chapter will give a summary of the factors that this literature review points out
as the most promising for evaluating the perceived factors influencing trust in chatbots forcustomer service.
Summarising the literature review
As a summary of this literature review, factors appearing most promising forunderstanding trust in customer service chatbots are presented in table 1. The summary isdone with a starting point in Corritore et al. (2003) framework. However, based on the rest of
the literature, other factors are also included.
The factors are structured according to three high-level dimensions: chatbot-related,environment-related and user-related factors. This structuring of the factors are motivated by
Hardin (2003) who viewed trust as three factors: the self, the partner and the goal in thesituation. A resembling structure in three levels is found in Hancock et al. (2011) metaanalysis, structured into human-related, robot-related and environmental-related factors.

14

TRUST IN CHATBOTS FOR CUSTOMER SERVICE
Table 1.
Descriptions of factors
Dimension

FactorContent

Reference

Chatbotrelated factors

Expertise

Expertise is seen as a factor associated with credibility, a cue fortrusthworthiness. In the context of an automated system, trust has beenargued to be mainly based on users' perceptions of the systems's expertise.
Here, the chatbots expertise is assumed to impact users' trust.

Corritore et al. (2003)
Muir & Moray (1996)

Predictability

Predictability is also seen as a factor associated with credibility, and concerns
the trustors' expectation that an object of trust will act consistently in linewith past experience. If the user perceives the chatbots as predictable, thismay lead to a feeling of trust in the chatbot.

Corritore et al. (2003)
Fogg et al. (2001)

Environmentrelated factors

User - related
factors

Anthropomorphism Humans have been found to apply social rules, such as politeness, in theirinteractions with computers. Furthermore, users have been found toperceive chatbots as having humanlike personalities. Because of this, it may
be assumed that anthropomorphism potentially may impact trust in chatbots.

Nass et al. (1999)
Nass et al. (1994)
Holtgraves et al.
(2007)

Ease of use

Ease of use has been seen as an antecedent to trust. It has been found tohave a positive relationship with consumers' trust in interactive systems suchas websites.This gives reason to assume that ease of use may be a factor
potentially affecting trust in chatbots.

Corritore et al. (2003)
Gefen et al. (2003)

Risk

Peoples' perception of risk has been shown to affect online trust. Some trust
theorists have also argued that trust is more relevant in contexts
characterized by risk. This higlighting of the relation between risk and trust
suggest that trust in chatbots may be dependent on risk perceptions.

Corritore et al. (2003)
Rousseau et al. (1998)

Reputation

Reputation, like expertise and predictability, is also seen as a factor
associated with credibility. Reputation of websites defines the quality ofrecognized past behaviour. It is reasons to believe that other peoples'
assessment of the chatbot may affect its reputation, and in this way affecting
how users experience trust in the chatbot.

Ganesan (1994)
Corritore et al. (2003)

Propensity totrust technology

There is substantial individual variation in humans' general willingness totrust others, referred to as propensity to trust. In this context, this propensity
will be seen as a general propensity to trust in technology. It seems
reasonable that propensity to trust technology affects trust in chatbots.

Mayer et al. (1995)
McKnight et al. (2011)

The literature also suggests a positive relationship between peoples trust in atechnology and intention to use (Lee & Moray, 1992; Merritt & Ilgen, 2008). Other factors,
such as age and gender has by some researchers been claimed as contributing to differences inthe evaluation of automated systems (e.g. Shah et al., 2016).
The literature review has mentioned other factors of relevance to trust, which do notseem particularly relevant for trust in chatbots and hence not included in the above overview.
An example of such a factor is benevolence, a factor highlighted by Mayer et al. (1995).
Another example is honesty (Corritore et al., 2003), which seems to be motivated by theMayer et al. (1995) benevolence factor. These two factors were not found to be promising for
explaining trust in chatbots as they are considered incapable of lying or serving an egocentric
profit motive.

15

TRUST IN CHATBOTS FOR CUSTOMER SERVICE
The summary in table 1 provides an overview of factors standing out in the literature
as most promising for explaining users trust in customer service chatbots. The next chapter
describes the research question and a hypothesis for this thesis.
Research question and hypothesis
User uptake of chatbots is less extensive than anticipated. For users to take advantageof this technology, they should trust it. There are currently no available studies thatinvestigate trust in the context of chatbots. It is especially important to gain knowledge
regarding trust in chatbots within customer service, since this is rapidly growing area ofchatbot use. Hence, the following research question was formulated for this study:
Which factors affect users’ trust in chatbots for customer service?
This research question requires a two-folded investigation, including an explanatory
and an exploratory part. The explanatory part is needed to investigate promising factors fromthe trust literature. The exploratory part is needed since the available trust literature doesn’t
explore trust in chatbots, which means that it is essential to check whether there are otherfactors relevant for trust in chatbots than those identified in the literature on trust in othercomparable technology. Based on the literature, the following hypothesis is stated for the
explanatory part:
H: Trust in chatbots is hypothesized to be affected by expertise, predictability,
anthropomorphism, ease of use, risk, reputation and users’ propensity to trust
technology.
MethodThe projectThis master project is conducted in cooperation with SINTEF and their ongoing
research program on chatbots. The goal of this research was to identify the factors
contributing to users’ trust in chatbots for customer service. Four companies were, by theauthor of this thesis onboarded as collaborators for recruiting study respondents. The
construction of the questionnaire and the analysis was mainly done by the author, withassistance and feedback from the two supervisors; Associate Professor Cato Bjørkli from the
University of Oslo, and Senior Scientist Asbjørn Følstad, Department of Software and Service
Innovation at SINTEF. Analysis of data was also supported by Knut Inge Fostervold,
Associate Professor at the University of Oslo.

16

TRUST IN CHATBOTS FOR CUSTOMER SERVICE
Research design
The research design comprised of an explanatory part and an exploratory part. The
explanatory part followed a correlational design (Svartdal, 2009), where relationships
between trust and related factors from the literature were studied. The exploratory part was set
up as a qualitative study, to investigate whether other factors were relevant beyond those
identified from the general trust literature.
The data collection was implemented through an online questionnaire study containing
two parts, respectively reflecting the explanatory and exploratory aims of the study; in thefollowing, these are referred to as the explanatory part and the exploratory part. In the
explanatory part, the identified factors were investigated through the respondents’ answers todifferent measurement instruments. The purpose of this was to study the degree to whichthese factors explain variation in trust, but not to make claims on causal relations. The
qualitative analysis of the exploratory part was conducted on data gathered from therespondents’ answers to an open-ended question.
Study context
Four Norwegian companies from the finance and energy sector, providing customer
service through chatbots, were onboarded as collaborators for recruiting study respondents.
Collaborating with four companies was useful to minimize the risk of getting inadequate data.
It was also unproblematic to analyse data from these on an aggregated level, as the companies
mainly had implemented their chatbots in the same way and for the same purpose.
The onboarding process started by contacting 12 different Norwegian companies, allhaving implemented chatbots. Of the contacted companies, four declined the invitation. From
the remaining eight invited companies, the companies were onboarded if they complied withthe following three criteria: (1) should have tested their chatbot for some time, (2) should be achatbot used for customer support purposes, and (3) the chatbot should be constructed in a
way that the customer could experience some potential risk of using it. Four of the companies
were found to comply with these criteria, and hence, onboarded. Two of the initially invitedcompanies that responded positive were not included in the study, since their chatbot weren’t
used for customer support purposes. Finally, two invited companies were not included in spitethat they complied with the criteria, because they were not able to start up data collection
sufficiently early.
Respondent recruitment
It was in this study seen as desirable that respondents had some experience fromchatbot interactions. The respondents were thereby invited to join as part of the dialogue with17

TRUST IN CHATBOTS FOR CUSTOMER SERVICE
the customer service chatbot supported by one of the four onboarded companies. The
invitation was presented immediately after the respondents had finished the dialogue with thechatbot. This enabled the respondents to report on their experience with the chatbot, while thiswas still fresh in their memory. The respondents were sampled by an invitation, with an
incentive of winning an iPad as part of the chat.
The four different companies presented the invitation to the questionnaire in the chatdialogue with some variation. Three had the questionnaire triggered by selected words. Thisrequired users of the customer service chatbot to write predefined words as “ok”, “thanks”,“bye”, “see you” for triggering the pop up of the questionnaire. The fourth had a person thatmanually established a link to the questionnaire in the chat dialogue after users had finishedtheir requests.
Material and measures
There is to date no established measurement instruments accommodated for the studyof trust in chatbots and associated factors. The measurement instruments for this study were
therefore established by adopting measurement instruments from the literature. The
questionnaire included one dependent variable (trust) and seven factors from the literature,assumed to explain variation in trust (expertise, predictability, anthropomorphism, ease ofuse, risk, reputation, and propensity to trust technology)1. The questionnaire also included ameasure on “intention to use”, which was included to check for its relation to trust. Possible
gender and age differences were also included in the questionnaire, as well as the two
background variables, “level of education” and “amount of earlier use”. In the following, thevariables and measurement instruments are briefly described. The entire questionnaire and acomplete overview of the measurement instruments and their basis in the literature isprovided respectively in Appendix A and B.
Trust. The dependent variable trust was measured by a combination of items fromtwo scales. Three items were adopted from Corritore, Marble, Wiedenbeck, Kracher, andChandran (2005) who looked at trust in websites, detail the measurement instrument appliedby Corritore et al. (2003). Two items were adopted from Jian et al. (2000) who looked at trustin automated systems. Cronbach’s alpha was .76.
Expertise. This factor was measured by a combination of items from two scales, aswell as a self-composed item. Two of the items were adopted from a scale to measure

1 For purposes of readability, the factors from the explanatory analysis and the categories identified in the exploratory

analysis are written in italic in the thesis.

18

TRUST IN CHATBOTS FOR CUSTOMER SERVICE
expertise in the context of websites (Corritore et al., 2005) and two from a scale to measureability in an organizational context (Mayer et al., 1995). Cronbach’s alpha was .96.
Predictability. This factor was measured by five items adopted from a scale tomeasure predictability of a website (Corritore et al., 2005). Cronbach’s alpha was .87.
Anthropomorphism. This factor was measured by five items adopted from a scale onanthropomorphism (Ho & MacDorman, 2010). Cronbach’s alpha was .95.
Ease of use. This factor was measured by a combination of items from two scales.
Three of the items were adopted from a scale to measure ease of use of a website (Corritore etal., 2005) and two were adopted from a scale to measure perceived ease of use by using chartmasters (Davis, 1989). Cronbach’s alpha was .86.
Risk. This factor was measured by five items adopted from a scale to measure risk ofusing a website (Corritore et al., 2005). Cronbach’s alpha was .92.
Reputation. This factor was measured by a combination of items from two scales.
Three of the item were adopted from a scale to measure reputation of a website (Corritore etal., 2005) and two items were adopted from a scale to measure reputation of a store(Jarvenpaa, Tractinsky, & Saarinen, 1999). Cronbach’s alpha was .87.
Propensity to trust technology. This factor was measured by a combination of items
from two scales. Two of the items were adopted from a scale to measure propensity to trust(Cheung & Lee, 2001) and three items were adopted from a scale to measure trusting stance –general technology (Mcknight et al., 2011). Cronbach’s alpha was .91.
Intention to use. Intention to use chatbots was measured by a combination of threescales. Two items were adopted from a scale to measure behavioural intention (Zarmpou,
Saprikis, Markos, & Vlachopoulou, 2012), two items were adopted from a scale to measureintention to use (Venkatesh & Davis, 2000), and one were adopted from a scale to measureintention to use a system (Venkatesh, Morris, Davis, & Davis, 2003). Cronbach’s alpha was.96.
All of the scales were originally in English, which required a translation into
Norwegian. To make sure the meaning of the original items was captured in the translation,these were checked by one of the thesis supervisors. A 7-point scale was chosen due to wishof having a neutral midpoint, as well as it was desired to have more variation than expectedfrom a 5-point scale. All the variables appeared with high inter-item reliability of Cronbach’s
alpha above .70. Values that are higher than .70 is regarded as reflecting respondents answers
with adequate internal consistency (Svartdal, 2009).

19

TRUST IN CHATBOTS FOR CUSTOMER SERVICE
Open-ended question. The questionnaire also included an open-ended question,
which was the basis for the exploratory part of study. Here, the respondents were encouraged
to report in free text on their thought on trust in the chatbot they had used for customer
service. The question was “What makes you experience trust in this chatbot” 2.
Measurement instruments for three elements not included in the analysis. The
questionnaire also included measurements for three elements which is not analysed in thisstudy. First, likeability was measured, but excluded from further analysis due to its loading on
the same factor as anthropomorphism. The two variables appeared as measuring the samelatent variable and indicated a lack of strong distinction for the respondent’s perception.
Second, usefulness was also tested in the questionnaire. Initially it was discussed to have amore complex model in which usefulness could be relevant. However, as it during theanalysis was found that a simpler model would be more adequate, usefulness was notincluded in subsequent analysis. Third, the questionnaire did also include a second openended question: “What could be changed for you to gain more trust in this chatbot?” 3. The
intended recipients of the findings generated from this question were the collaboratingcompanies, and the data was intended as a basis for reflection on possible changes in chatbot
design. Due to space limitations, and to reduce complexity in presentation, the analysis anddiscussion of the data from this question were not included in this thesis.
Development of the questionnaire. The questionnaire was piloted on eight people
with a variation in background and age. This gave useful feedback for adjusting thequestionnaire, clarifying and correcting what was difficult to understand for respondents.
Most of the pilot feedback concerned structure and layout. An example of a pilot
feedback was that the open-ended question contained an insufficient amount of available lines
where users could write. The expansion of lines made more space for respondents. Onechange that was important to clarify was that some of the items in the pilot included
questions, and not statements. As an example, “To what extent did you experience thischatbot as natural?” 4, which was changed to “The chatbot appears as natural”5. This made iteasier and more meaningful for respondents to evaluate from strongly disagree to strongly
agree.

2“Hva gjør at du opplever tillit til denne chatboten?”3“Hva kunne vært forandret for at du kunne fått mer tillit til denne chatboten?”
4“I hvilken grad opplevde du chatboten som naturlig?”
5 “Chatboten fremstår som naturlig”

20TRUST IN CHATBOTS FOR CUSTOMER SERVICE
Analysis
Preparing for analysis. Before the analysis could start, the data was quality checked.
Specifically, respondents not satisfying a set of predefined criteria were filtered out. There
were a total of five criteria: (1) minimum 18 years old, (2) not give the same score for morethan 90% of the questionnaire items, (3) completed more than 50% of the questionnaire, (4)answers reflecting that the respondents have recognized the three reversed questions as suchand (5) provide a meaningful answer in the open-ended question.
The raw data included the responses from 175 chatbot users. Of these, 21 were excluded
from subsequent analysis in line with the criteria above. Four were under the age of 18. One
had no variation in the answers. Seven had completed less than 50% of the questionnaire.
Five had answers indicating that they had misunderstood the three reversed questions; giving
the same score regardless of items belonging to the same factor being reversed or not. Three
had nonsense (joke) answers in the open-ended question. Finally, one had no answers. The
answers of the remaining 154 respondents were included in the subsequent analysis.
Validation. An exploratory factor analysis was carried out to check whether the items
intended to measure one factor loaded on the same latent factor, and also if it was necessary to
exclude some items before continuing on with analysis. Each measurement instruments had
consciously been constructed with five items. This was to make option for deleting items if
some of them made trouble in the measurement instruments. Overall, the exploratory factor
analysis revealed a relatively good factor structure for most of the factors, but with some
exceptions. As illustrated in table 2, there appeared some cross-loadings and low factor
loadings. Items intended to measure one factor also loaded on other factors. Such crossloadings may indicate that some factors to some extent have overlapping characteristics. In
addition, some of the items belonging to the variables were having weak factor loading, justbelow .30. This cross-loadings and low factor-loadings was not unexpected due theconstruction of the measurement instruments.
The dependent variable trust did appear with cross-loadings, as well as some weakfactor loadings. As trust is a broad construct, this was not unexpected. Items intended tomeasure trust were thereby constructed to make sure the whole spectre of the construct wascaptured in the items. Reputation had also a reversed item showing cross-loadings. This item
was thereby deleted before calculating the mean score of the factor. Ease of use did
surprisingly also have items not behaving as expected. Two items did not load on the same
factor as the other items from ease of use and were therefore excluded from the analysis. The
adjusted changes made a better factor structure, with not too many cross-loadings and low
21TRUST IN CHATBOTS FOR CUSTOMER SERVICE
factor loadings. Moreover, all the factors had high Cronbach’s alpha (α = > .70). It wasnevertheless a limitation in the measurement instruments, this will be discussed in the section
Limitations and future research.
Table 2.
Explorative factor analysis – Pattern Matrix
Factor1

Trust1
Trust2Trust3_RE
Trust4_RE
Trust5

0,43

2

3

4

5

6

7

8

9

-0,29

-0,35
-0,34
-0,42
-0,73
-0,27

-0,29

0,33

Intention to use1
Intention to use2
Intention to use3
Intention to use4
Intention to use5
Expertise1
Expertise2
Expertise3
Expertise4
Expertise5
Predictability1
Predictability2
Predictability3
Predictability4
Predictability5
Anthropomorphism1
Anthropomorphism2
Anthropomorphism3
Anthropomorphism4
Anthropomorphism5
Ease of use1
Ease of use3
Ease of use4
Risk1
Risk2
Risk3
Risk4
Risk5
Reputation1
Reputation2
Reputation3
Reputation4
Propensity to trust technology1
Propensity to trust technology2
Propensity to trust technology3
Propensity to trust technology4
Propensity to trust technology5

-0,74
-0,85
-0,86
-0,73
-0,86
0,89
0,72
0,74
0,64
0,75

0,26
0,29

0,79
0,79
0,67
0,61
0,37

0,33
0,59

-0,33

0,69
0,83
0,76
0,75
0,81
0,30

0,62
0,64
0,54

-0,26
0,37
0,71
0,76
0,86
0,89
0,86
-0,91
-0,85
-0,69
-0,56

0,28

0,33

-0,82
-0,84
-0,75
-0,86
-0,88

Note . Rotation method: Oblimin with Kaiser Normalization. Factor loading under .25 is deleted from the matrix. _RE means the items are reversed

Quantitative analysis. In the subsequent analysis, the data from the verified
measurement instruments from the explanatory part were used. Analysis were conducted by
the use of SPSS (Statistical Package for the Social Sciences), version 25. First, backgroundinformation of the sample was explored. Then, the items connected to each variable were
calculated for mean. Thereafter, descriptive statistics were calculated for all of the variables,
to check mean, standard deviation and skewness. A correlation analysis was performed to
explore the relationship between the variables. Since trust is regarded as the dependent
22

TRUST IN CHATBOTS FOR CUSTOMER SERVICE
variable, a multiple regression analysis was conducted to investigate the degree to which theseven factors predict trust.
Furthermore, the relationship between trust and intention to use were investigated. For
this purpose, a simple linear regression model was used. Finally, a t-test was used to check forgender differences, and a best line of fit was applied to check for linear, quadratic, or cubicalrelations between age and trust.
Qualitative analysis. The qualitative data concerned responses about the users`
experience of trust in chatbots. These data were made subject to a thematic analysis.
Following Ezzy (2002), the thematic analysis was carried out in three steps. Ezzy is using thesteps from grounded theory to support a thematic analysis. He claimed that the three steps
with motivation from grounded theory could be used for conducting a thematic analysis. A
thematic analysis allows themes to emerge from the data, rather than applying predefined
themes. In the course of the analysis process, each theme is presented as a category used forcoding the text. In the following text, to simplify reading, themes presented as categories arereferred to by the term categories. Table 3 provides an example of a theme emerging andcategorized by the use of the three steps described by Ezzy.
Table 3.
Example of a three step coding based on Ezzy’s steps for thematic analysis
The three steps

The aim with the coding

ExampleStep 1 - Open Coding

Explore the data, identify initialcategories and associated initial codes.

In step one, there was identified a number of categories covering the width of respondents'
feedback. This initial identification gave a total of 42 categories, with a great width and someoverlap in the categories. For example, relevant, understanding, precise, direct, logical andprofessional answers were among the categories identified in the initial coding.

Step 2 - Axial codingSpecifying the conditions that give rise to In step two, the initially identified categories in step one were reviewed. It was found thata category, review data to confirm
many of the categories concerned similar content. For example, the initial categories relevant,associations and new categories.
understanding, precise, direct, logical, and professional answers were found to reflect four
higher-level categories: correct answer, interpretation, concrete answer and eloquent answer.

Step 3: Selective coding

Identify the core categories, examine the In step three, the categories identified in step two were now explored relative to the previous
relationship between the core categories literature, specifically the factors from the explanatory part of the study. For example, it wasand other categories, and compare with found that the four higher-level categories in the example of step two reflected much of thepre-existing theory.
same content from the explanatory factor expertise. At the same time, the expertise category
from the exploratory part appeared with more nuance and depth than the expertise factor
from the explanatory part. Moreover, the initially identified categories correct answer,
interpretation, concrete answer and eloquent answer were now seen as sub-categories of thecategory expertise.

To validate the coding categories which represented the themes, the author of thisthesis and another analyst coded the data independently to check for inter-coder agreement.
Inter-coding agreement was checked by calculating Cohens kappa, a measure for calculating
the agreement between two analysts (Svartdal, 2009). Both analysts coded each respondent’s
answer in accordance with the coding categories. Thereafter, the coding conducted by each23

TRUST IN CHATBOTS FOR CUSTOMER SERVICE
analyst was compared. Detailed descriptions of each category are provided in Appendix C.
The calculation of Cohens kappa revealed that four of the identified categories in thedata had an inter-rater agreement around .60. Since the original distinction made by Cohen, it
has been disagreements about the right and acceptable distinction for agreement. According to
McHugh (2012), agreement around .60 can be too low. She understood Cohens kappa of .40.59 as weak, .60-.79 as moderate, .80-.90 as strong and >.90 as almost perfect. Cohen (1960)
had .40-.60 as moderate, .61-.80 as substantial and .81-1 as almost perfect. The goal was toget it closer to .80 than .60. By redefining the explanation given to the categories and have thetwo analysts to code the four categories again, all got a high Cohens kappa. A few was around>.70, but most of them around >.80, and thereby defined as strong agreement. See results
table 8, page 29 for detail of the Cohens kappa.
Some of the respondents’ answers touched upon more than one category. This is forexample seen in this response “It responds quickly, concise and persistent. In addition, I amaware that all correspondence is without personal information and is therefore completely
comfortable with everything being saved. (P3)”6 This response fits the three identified
categories fast response, low risk and expertise - concrete answer. The examples of thecategories will in the result section only be explained by examples containing one category.
All examples are presented in English, and the original answers in Norwegian are provided infootnotes.
Ethical consideration
The study was reported and approved by Norwegian Social Science Data Services(56727). The completion of the study is consciously done to minimize the effort required of
the respondents. The collected data was not containing health sensitive information, and it
was no assumed any reasonably negative effect by participating in the study. All respondents
were given an informed consent that had to be approved before starting the questionnaire.
Here, the information about the study was presented, clearly telling the respondents what was
demanded. The respondents could at any point leave the questionnaire, without expressing
their reason to do so. No personal data was collected, apart from the gathering of the
respondents’ email address. This was collected in a separate form, with no connection to thequestionnaire. The collection of email address was done to enable a participant lottery as
incentive to participate, with an iPad as the prize, and also for the purpose of gathering

6 “Den svarer raskt, konsist og presist. I tillegg er jeg klar over at all korrespondanse er uten personopplysninger og er derfor

helt komfortabel med at alt blir lagret”

24

TRUST IN CHATBOTS FOR CUSTOMER SERVICE
respondents that were willing to participate in an interview study followed up by SINTEF
research program on chatbots.
Results
The following chapter describes the results from the analysis, organized in three parts.
First, the sample will be presented. Second the results from the explanatory part of the study,
including presentation of the quantitative data are outlined. Last, the results from the
exploratory part, containing the qualitative data from the study will be presented.
About the respondents
Respondent demographics. The sample consisted of a quite even gender distribution,out of the 154 included respondents, 48% were women and 52% were men. One person did
not report gender. The youngest in the sample was 18, and the oldest 83 years. Mean age was
41 (SD = 13.89). Twenty did not report their age. The majority of respondents reported tohave higher education. 43% reported four or more years of higher education; 36% reported
one to three years of higher education.
The respondents’ previous experiences. As illustrated in figure 1, the respondents inthe study were fairly new to the use of chatbots within customer service. In total, 56.9%
reported to have used chatbots one to three times before. At the end of the scale, 19% had
used a chatbot more than 10 times before.

Figure 1. Histogram of the respondents’ previous use of chatbots.

25

TRUST IN CHATBOTS FOR CUSTOMER SERVICE
Results from the explanatory part of the study
This section presents the results from the explanatory part of the questionnaire, where
respondents answered different measurement instruments of factors standing out as promisingfrom the trust literature.
An overview of the studied variables. Table 4 presents a descriptive overview of thedependent variable trust, the seven factors assumed to affect trust in customer service chatbots
and intention to use. The highest mean was seen in ease of use with 6,29 of 7 (SD = 1,10).
This finding indicated that respondents experienced the chatbot as easy to use. Ease of use
also had a notable skew. This was the only factor with a skew above two, which indicates thatits distribution is not normal (West, Finch, & Curran, 1995). The second highest mean was
found in trust (M = 5.57, SD = 1.19) and intention to use (M = 5.62, SD = 1,47).
Table 4.
Sample size (N), mean (M), standard deviation (SD) and skewness (skew) for the ninevariables
Variable

N

M

SD

Skew

Trust

154

5,57

1,19

-0,95

Intention to use

150

5,62

1,47

-1,25

Expertise
Predictability
Anthropomorphism
Ease of use
Risk
Reputation
Propensity to trust technology

154
153
154
154
154
149
154

5,32
5,54
4,78
6,29
2,12
4,25
5,12

1,72
1,27
1,66
1,10
1,34
1,40
1,28

-1,06
-0,67
-0,58
-2,21
1,42
-0,05
-0,56

Correlation between the variables. A correlation analysis was conducted to explore
the relationship between the measured variables. As illustrated in table 5, there wereconsistently high and significant inter-correlation between the variables. The highest
correlation was found between expertise and trust, which means that high scores on expertise
also gives high scores on trust, r (154) =.66, p < .001. The correlation analysis also revealed ahigh positive relationship between anthropomorphism and trust, r (154) = .61, p < .001, andease of use and trust, r (154) =.60, p < .001.

26

TRUST IN CHATBOTS FOR CUSTOMER SERVICE
Table 5.
Correlation between the variables
Variable

1

2

3

4

5

6

7

8

9

1. Trust
2. Intention to use.58***

3. Expertise.66***.57***

4. Predictability.58***.43***.61***

5. Anthropomorphism.61***.58***.73***.51***

6. Ease of use.60***.51***.56***.57***.52***

7. Risk.-52***.-47***.-34***.-42***.-34***.-55***

8. Reputation.45***.58***.50***.35***.61***.41***.-37***

9. Propensity to trust technology
Note. ** p < .05, *** p < .001.

.33***.52***

.14

.20**

.22**

.32***.-30*** .37***

Investigating the effect of gender and age. An independent samples t-test was
conducted to explore gender differences in trust. The results indicated that there were notsignificant differences on trust between woman and men, t (151) = -.28, p = .78. A linear
regression analysis was used for testing the effect of age. This confirmed age to not be
important for trust; a line of best fit was established to investigate linear (R2 = .006), quadratic(R2 = .0005) and cubical (R2 = .011) relations.
Multiple regression analysis – explaining variation in trust. Multiple regression
analysis was used to test if the seven chatbot-, environment- and user-related factors
significantly predicted respondents’ ratings of trust. The results of the regression indicated theseven predictors explained 58% of the variance in trust (adjusted R2 = .58, F (7,141) = 30.28,
p < .001). Of the seven predictors, the analysis revealed three to be particularly important to
explain the variation in trust: expertise (β = .33, p < .001), risk (β = -.21, p < .05) andpropensity to trust technology (β = .14, p < .05). Expertise had the highest standardized
regression coefficient, see table 6.
Table 6.
Standardized regression coefficient (β) and t-values (t) for seven variables predicting trust
Trust
Independent variable

βtExpertise
Predictability
Anthropomorphism
Ease of use
Risk
Reputation
Propensity to trust technology

.33
.13
.17
.11
-.21
.03
.14

3.75 ***
1.77
1.96
1.43
-3.22 **-.44
2.28 **

Note. ** p < .05, *** p < .001.

27

TRUST IN CHATBOTS FOR CUSTOMER SERVICE
Simple linear regression between trust and intention to use. A simple liner
regression analysis was used to test if trust in chatbots predicts intention to use. The result of
the regression indicated that trust explained 34% of the variance in intention to use chatbots(adjusted R2 = .34, F (1,148) = 75.91, p < .001). See table 7.
Table 7.
Standardized regression and t-value for trust predicting intention to use
Independent variable
Trust

Intention to use
β

t

.58

8.71***

Note. *** p < .001.

Results from the exploratory part of the study
This section presents the results from the exploratory part in the questionnaire where
respondents freely could write their thoughts in response to an open-ended question: “Whatmakes you experience trust in this chatbot?”. Table 8 presents the final set of categories
identified in the thematic analysis. The table also shows which of the three high-level
dimensions each category belongs to, as well as a short description, frequency, and Cohens
kappa for each category.

28

TRUST IN CHATBOTS FOR CUSTOMER SERVICE
Table 8.
Results from the open-ended question: What makes you experience trust in this chatbot?
Dimension

Category

Explanation

Chatbot-related
categories

Expertise - Correct answer

Customers reporting that the chatbot providing accurate 41 (28,1%)
and relevant information is important for trust.

0,83

Expertise - Interpretation

Customers reporting that the chatbot correctly interpretsand understands the question, as well expressing clear
when they do not understand, is important for trust.

14 (9,6%)0,96

Experise - Concrete answer

Cusomers reporting that the chatbot providing concrete, 24 (16,4%)
clear and easily understandable answers is important fortrust.

0,77

Expertise - Eloquent answer

Customers reporting that the chatbot providing logical,
reasonable and professional answers is important fortrust.

14 (9,6%)

0,84

Fast response

Customers reporting trust to be dependent on a quick
response from the chatbot.

27 (18,5%)

0,89Anthropomorphism

Customers reporting trust to be dependent on thechatbot's humanlike characteristics, such as being niceand polite.

9 (6,2%)

1

Absence of marketing

Customers reporting to feel trust because of the absenceof marketing, and that the chatbot seems to put thecustomers first.

6 (4,1%)

0,83

Low risk

Customers reporting to feel trust on the basis of notneeding to specify personal or sensitive information in
the chat.

9 (6,2%)

1

Brand

Customers reporting to feel trust in the chatbot inconsequence of their trust in the company.

25 (17,1%)

0,83

Access to human operator

Customers reporting that having an opportunity to betransfered to a human operator is important for trust.

7 (4,8%)0,92

Not trust-relevant/ No trust

Customers reporting not to consider trust as relevant, or 11 (7,6%)
reporting not to have trust in chatbots due to their limited
capabilities.

0,78

Miscellaneous

Various answers with no direct fit to the othercategories, and not forming distinct categories.

0,76

Environment-related
categories

User-related
categories

Frequency

15 (10,3%)

Cohens kappa

Note. Frequency is calculated on the basis of the 146 respondens answering this question.

Chatbot-related categories. Results from the thematic analysis revealed chatbotrelated categories as important for users trust in chatbots for customer service. This is shownin the four categories: expertise, fast response, anthropomorphism and absence of marketing.
Expertise is further divided into four sub-categories, specifically correct answer,
interpretation, concrete answer and eloquent answer; all of these reflecting important aspectsof the expertise category.
Expertise – Correct answer (28,1%). The user reports strongly suggested that thecorrectness and relevance of the chatbots’ answers were important for trust. This sub-categoryof expertise was the one with the absolutely highest frequency; 28,1% of the respondents’answers were interpreted as reflecting this category. It seems like respondents were highly
sensitive to answers being correct for developing trust. A correct answer was important for29

TRUST IN CHATBOTS FOR CUSTOMER SERVICE
saving users’ time, as well as not wasting their time if they not got the requested information.
The sub-category correct answer is reflected in the following statements:“I experience trust when the chatbot gives a correct answer to my question” (P126)7“Because I get answers to my questions” (P37)8Expertise – Interpretation (9,6%). The user reports indicated that the chatbots’interpretation of the question was important in the development of trust. This sub-category ofexpertise illustrated the users wish of perceiving the chatbot to understand the asked question
and thereby manage to help. It did also mean that the chatbot was honest about the questions
it not was capable of answering. The chatbot interpretation seemed important for not wasting
the time of the user, as well as avoiding the need for contacting human customers support.
The sub-category interpretation is exemplified in the statements below:“ ....it perceives the problem and has several solutions” (P29)9
“It is honest about saying what it can and cannot do” (P74)10
Expertise – Concrete answer (16,4%). The user reports strongly indicated thatwhether the chatbot formulate the answers concrete was important for users trust. This was
also a sub-category of expertise and concerns users’ appreciation of concrete, short, precise
and clear answers from the chatbot. A concrete answer seemed to be important due to users
wish for answers that were not misleading or could easily be misinterpreted. Furthermore,
concrete answers have also been compared with the appreciated concrete answer from ahuman operator. The sub-category concrete answer is exemplified below:“It answers short and precise to my question” (P48)11“Clearly answers that does not allow for misinterpretations.…” (P59)12
Expertise - Eloquent answer (9,6%). The user reports suggested that users trust wasdependent on whether the chatbot formulated the answers in a professional way. Commentsfrom this sub-category of expertise revealed that users appreciated answers that appeared as
professional, credible and reasonable. Moreover, that the answer appeared logical and notstupid. The sub-category eloquent answer is exemplified below:

7 “Opplever tillit når det chatboten svarer er korrekt info på det jeg spør om”
8“Fordi jeg får svar på mine spørsmål”

9 “....oppfatter problemet og har flere løsninger”
10 “Den er erlig til å siga ifra vad den kan og vad den ikke kan”
11 “Den svarade kort och precis på min fråga”
12 “Tydelig svar som ikke gir rom for feiltolkning....”

30

TRUST IN CHATBOTS FOR CUSTOMER SERVICE“I got good answers to my questions. The way the robot formulates seems professional
and knowledgeable” (P121)13“....It is logic behind the answers I get, even if I don’t ask bank-related questions”(P91)14
Fast response (18,5%). The user reports strongly indicated that users trust wasdependent on a fast response from the chatbot. The fast response appears to be appreciated, as
it makes the chatbot an efficient way to get help. Some respondents have compared thechatbot used in customer service with human customer support, and noted that the chatbot canbe a quicker way to get the requested support. The fast response has by some also been
mentioned to clearly indicate that it was a robot the users were talking to. Fast response is
exemplified in the following statements:“It did right away understand what I wanted and could quick help me” (P133)15“Quick answer. Do not have to wait in que” (P1)16
Anthropomorphism (6.2%). The user reports indicated that some users wished thechatbots with humanlike characteristic, and that this can be important for trust. For example,
in the form of getting polite answers, or using colloquial expressions. A humanlike answer
seemed important because this is what customers are used to when chatting with customer
service, and therefore experienced as a more familiar form of conversation. Some evenreported they hadn’t noticed it wasn’t a human if the chatbot hadn’t stated this by itself.
Examples of the category anthropomorphism are provided below:“….and was polite” (P72)17“….and the chatbot did also say thanks when I said thanks for the answer. This makes
it more human and trust engaging” (P51)18
Absence of marketing (4,1%). The user reports indicated that some of the respondents
highlighted the perception of chatbots as being objective and not selling, and in this way
considered as an important factor for users’ trust. Users seemed to value answers directly
related to the question, and not leading the users on to other thoughts. Absence of marketing isreflected in the following statements:

13 “Jeg fikk gode svar på det jeg lurte på. Måten roboten formulerer seg virker profesjonell og kunskapsrik”
14 “ .... Der er logik bag de svar jeg får på mine spørsmål, også når jeg stiller "ikke-bank orienteret" spørsmål”
15“Den forstod hva jeg ville frem til med en gang og kunne hjelpe meg raskt”

16 “Raskt svar. Slipper å vente i kø”
17 “....og var høflig”
18 “.... chatboten sa også takk, når jeg skrev takk for svaret. Noe som gjør den mer "menneskeaktig" og tillitsinnbydende”

31

TRUST IN CHATBOTS FOR CUSTOMER SERVICE“….I trust that the chatbot answers me objective. No buying pressure☺ ”(P10)19“Provides information that is experienced fact-based, not normative or selling”(P104)20
Environment-related categories. Three categories emerged in this dimension after
conducting a thematic analysis: brand, low risk and access to human operator.
Brand (17,1%). The user reports strongly suggested that their trust in chatbots wasdependent on their trust in the company, hence, the perceived brand. That meant that thecustomers’ previously established positive relationship with the company was crucial.
Respondents will trust that the company has done a good job in developing the chatbot. It wasalso mentioned that they thought their companies appeared as serious and only provided
secure solutions. Furthermore, some mentioned that they trust the developers of chatbot tomake sure “human error” was not apparent. Examples of answers from this category are:“I’m trusting the company I contact, and then I also expect the chatbot to reflect thatcredibility” (P154)21“I’m trusting the ones who has programmed the chatbot to gives it good input, and inthat way give good answers” (P105) 22
Low risk (6,2%). The user reports suggested that for some users, trust was dependent
on the perceived low risk in the situation. The users noted that when they didn’t need toprovide any personal or sensitive information in the conversation, the level of risk wasperceived as low. Some users reported that for more personal questions, risk would beperceived as higher, which negatively affected their willingness to trust the chatbot. It seemed
like the users found it important to know how the security was covered. In addition, someusers expressed how they felt happy about being reminded to not specify sensitive
information when they were not in the private chat. This category is reflected in the following
statements:“When I contacted the chatbot, I only had a general question, which did not concern
me personally. The trust was by this high since the case wasn’t directly personal, thecase could have been different if my need was of a more personal character” (P60)23

19 “...Jeg stoler på at den svarer meg objektivt på hva som er mulig. Ingen kjøpepress :)”20 “Gir informasjon som oppleves faktabasert, ikke normgivende eller selgende”
21 “Jeg stoler på selskapet jeg kontakter og da regner jeg også med at chatboten gjenspeiler den troverdigheten”
22 “Jeg har tillit til at de som har programmert den har fått god input slik at chatboten gir gode svar”

32TRUST IN CHATBOTS FOR CUSTOMER SERVICE“Because I do not specify any personal information” (P57) 24
Access to human operator (4,8%). The user reports indicated that trust for some weredependent on the access to a human operator. This means that some valued the opportunity to
be transferred to a human operator if the chatbot couldn’t help, and that this was important forusers trust in chatbots for customer service. Below are statements exemplifying this category:“….And that the answers refer to relevant URLs, and customer service if necessarily”(P90)25“….and transferred to a human when required” (P65) 26
User-related categories. User-related categories represent individual differences infactors impacting users trust in chatbots for customer service. The only category appearing inthis dimension was not trust relevant/no trust.
Not trust relevant/ no trust (7.6%). The user reports indicated that some users
expressed scepticism about relating the concept of trust to the use of chatbot technology.
These respondents didn’t consider trust as relevant or they didn’t feel trust the way chatbots
are constructed right now. That meant that some of the respondents regarded chatbots as
having limited function and rather would have human contact. Examples of answers coded tothis category are:“Trust is a concept I will not use on not-living things. But I think that “easy” tasks,
like in my case was fining an IBAN-number can be well suited for chatbots” (P8)27“I do not have trust in this chatbot. I want to talk to humans. I do not like robots, andwant to have human contact” (P21)28
Discussion
Summary of the results
The aim and research question of this study was to explore which factors that affectusers’ trust in chatbots for customer service. The results of the study have provided insight tothe perceived factors that predict users’ trust in chatbots for customer service.

23 “Denne gangen jeg hevende meg til deg hadde jeg kun et generelt spørsmål, og angikk ikke personlig informasjon knyttet

til meg som person. Tilliten var dermed stor fordi saken ikke var direkte personlig, saken kunne stilt seg annerledes dersom
mitt behov var av mer personlig karakter”
24 “Fordi eg oppgjer ingen hemmelig informasjon”
25 “....Og at svarene viser til relevante nettsider, og til kundeservice når det er nødvendig”
26 “....og henviste til et menneske ved behov”
27 “Tillit er et begrep jeg neppe vil bruke om ikke-levende ting. Men jeg tror at "enkle" oppgaver - som i mitt tilfelle å finne
et IBAN-nummer - er velegnet for bruk av chatbot”
28 “Jeg har ikke tillit til chatboten. Jeg vil snakke med mennesker. Jeg liker ikke roboter og vil ha menneskekontakt”

33

TRUST IN CHATBOTS FOR CUSTOMER SERVICE
In the explanatory part, seven factors identified in the background literature, inparticular on the basis of the framework of Corritore et al. (2003) were investigated. These
were: expertise, predictability, ease of use, reputation, risk, as well as anthropomorphism andpropensity to trust technology. All the seven factors correlated with trust. The hypothesis forthe explanatory part was only partly supported, not all of the seven factors seemed to explain
trust similarly. Expertise, risk and propensity to trust technology were the factors explaining
most variance in trust. In the exploratory part, which consisted of an open-ended question, theintention was to investigate whether other factors than those analysed in the explanatory partwere perceived as relevant for trust. The intention was also to see whether the factors from theexplanatory part were reflected in the exploratory part, and possibly expanded or detailed.
The reoccurring and reflected factors were: expertise (correct answer, interpretation,
concrete answer, eloquent answer), anthropomorphism, low risk and not trust relevant/notrust. New categories were fast response, absence of marketing, brand and access to humanoperator. The two different part of the questionnaire study provided new and complementary
insight, which motivated a suggestion of a new three high-level dimensions’ model presented
at the end of the discussion.
In the below section, the results will be discussed in deeper detail. First, the resultsfrom the explanatory part, then those from the exploratory part. Thereafter, the factors
appearing as important from the explanatory part will be compared to, and extended with, thefindings from the exploratory part. On this basis, an initial model of factors affecting users’
trust in chatbots for customer service is proposed.
Findings from the explanatory part
Murgia et al. (2016) argued that human-chatbot interaction may soon becommonplace. Within customer service, the use of chatbots is taken up as a promising
supplement. Van Doorn et al. (2017) recently predicted technology to radically and rapidlychange the nature of customer service experiences by 2025. The major advancement heclaimed, is that technologies in the future can engage customers at a social level and facilitate
relationships between service robots and humans.
Trust as important for use. Despite the major developments in chatbots,
Ciechanowski et al. (2018) claimed that the user has largely been neglected. They considered
the understanding of the user as key to designing better chatbots. The use of chatbots withincustomer service has not been as high as anticipated. Wickens et al. (2013) argued for theneed to be considerate of trust issues when users are dealing with any entity, whether thisentity is a customer service person or an automated agent. In the context of chatbots,
34

TRUST IN CHATBOTS FOR CUSTOMER SERVICE
including chatbots for customer service, a broader uptake arguably depends on users’ trust in
this technology.
The results from this study have suggested support for connecting trust toward users’
intention to use. That is, users who feel trust in chatbots, also have an intention of using
chatbots for customer service. Similar findings have also been made in earlier studies, e.g.
Lee and Moray (1992) who stated an operators’ trust to an automated system as influencing
the operators intention for use. Within the field of HCI, it is widely acknowledged thathumans will use machines they trust more than machines they don’t trust (Merritt & Ilgen,2008).
Promising factors affecting users’ trust in chatbots for customer service were
established through the analysis of the conducted questionnaire. The hypothesis was partly
supported, although not all the initially proposed factors played a similar role in explaining
variances in trust. All the factors were significantly correlated with trust, but not all seemed to
explain trust equally. The chatbot-related factor expertise, environment-related factor risk anduser-related factor propensity to trust technology were in the multiple regression analysis seen
as significantly predicting trust.
Chatbot-related factors. The defined chatbot-related factor expertise was the factor
with the highest positive correlation with trust. Expertise did also appear as the factor withhighest standardised coefficient in the multiple regression analysis. That users’ perception ofexpertise is important for trust is not unexpected. Expertise, and related constructs such as
ability, competence and knowledge has extensively been discussed as an important factor forperceived trustworthiness, see for example Butler (1991). Muir and Moray (1996) similarly
argued trust in automated machinery mainly as a consequence of the users’ perception of themachines expertise. Corritore el al. 2003 highlighted expertise as one of the perceived
credibility factors impacting users’ decision to trust in websites. They viewed expertise as acue for trustworthiness, in the same way perceived expertise can be a cue for decision to trustin customer service chatbots.
Another chatbot-related factor from the explanatory part, with partly support forpredicting users’ trust, was anthropomorphism. Anthropomorphism concerns users’ tendency
to attribute humanlike qualities to non-human things. Previous studies have suggested thathumans apply socially learned roles, such as politeness, when interacting with machines(Nass, Moon, & Carney, 1999). There is currently a continuous concern for making
computers, robots or for example voice-based personal assistants to be perceived as morehumanlike and natural (Luthra, Sethia, & Ghosh, 2016). The fact that humans apply
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TRUST IN CHATBOTS FOR CUSTOMER SERVICE
humanlike qualities to machines, makes it possible that humans also prefer getting responses
from chatbots that appear humanlike. In this study, anthropomorphism approached asignificant contribution in the multiple regression analysis with an p-value just above thecriteria <.05. Although not significant at the criteria, it appeared with a high correlation withtrust. This show some support for users’ perception of anthropomorphism as an importantfactor affecting trust in chatbots for customer service.
Environment-related factors. The chatbot-related factors were not alone in affecting
users trust in chatbots. The environment-related factor risk, as motivated from Corritore et al.
(2003) model did also appear significant in predicting trust. Users’ perceived risk had asignificant negative correlation with trust. That is, users seeing the interaction with thechatbot as less risky also reported higher levels on trust in the chatbot. The multiple
regression analysis did as well identify risk as a significant predictor for the users trust in
chatbots for customer service. Risk has been one of the two conditions with most agreement
in relation to factors important for trust (Rousseau et al., 1998). According to the obtained
analysis, when users consider the situation as involving higher risk, the trust declines. The
same customer service situation can apparently be perceived different in regard to risk. In the
future, when the chatbot supposedly should help a customer with a huge mortgage involving
higher risk, users trust may appear different. Then it might be even more important to ensurethat other factors affecting users’ trust is present, in addition the operating of security.
User-related factors. As a user-related factor, peoples’ propensity to trust technology
did in the multiple regression analysis appear important in predicting users’ trust. In thecurrent literature, it is suggested that just as people may differ in their general tendency totrust, they may differ in their propensity to trust technology (Atoyan, Duquet, & Robert, 2006;
Muir & Moray, 1996). Merritt and Ilgen (2008) showed how humans with high level ofpropensity to trust were more likely to put a greater trust in an automated system. Similarly,
Chopra and Wallace (2003) argued that people with high propensity to trust were more likely
to trust in a particular instance. Based on the conducted analysis, users with higher propensity
to trust technology, also have a higher trust in chatbots.
Factors with limited effect on users’ trust. Of the initially hypothesized factors to
influence trust, three factors (predictability, ease of use and reputation) did not contributesignificantly to explain trust in the multiple regression model, in spite of being significantly
correlated with trust. The chatbot-related factor ease of use was overall evaluated high by
respondents, showing that users evaluate the chatbot for customer service as easy to use.
Moreover, ease of use did not appear with an accepted normal distribution. That the users
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TRUST IN CHATBOTS FOR CUSTOMER SERVICE
experienced the chatbots as so easy to use may be a possible reason for why it did not predict
trust. Ease of use was neither mentioned in the freely open-ended question, which partly
support the factor to not have any especially impact on trust.
The chatbot-related factor predictability was not perceived as important for users trustin chatbots for customer service as hypothesized. Predictability was substantially correlatedwith expertise (r = .61, p < .001). This might be the reason to why predictability did notcontribute significantly in the multiple regression analysis. Predictability can in this context
seem redundant. It can be speculated that the construct of predictability may possibly be seen
as an aspect of the expertise construct, and, hence, not contribute independently to a users’
trust perceptions.
The last limited supported relationship in the explanatory analysis is between theenvironment-related factor reputation and trust. In this study, reputation was expressed as theoverall impression, hence, the reputation users’ have established of the chatbot. As chatbots
still is an emerging technology, with relatively low uptake in the intended user population,
this might explain why this factor not appeared relevant. In addition, the mean of the factor
was close to four, which in the questionnaire was stated as a neutral response. Although
reputation did not appear important here, a category with substantial similarity to thereputation-construct of Corritore et al. (2003) was established in the exploratory part of thestudy. At a later stage, were people are more familiar with chatbots, and have a more frequent
use of them, the stated factor reputation might be more relevant.
The effect of age and gender. There were not found any gender or age differences inthis study. However, such differences have been identified in other studies. Shah et al. (2016)
did in their comparison of the conversational ability in new chatbots versus the old Eliza findfemales and younger user to rate the conversation more favourably than men and older age
groups. The reason to absence of individual differences might be that the respondents in thisstudy are more than average assertive when it comes to use of technology, thereby shadowing
possible gender and age effects. In addition, respondents had limited use and experience with
chatbot technology. It can be speculated that this initial test phase of chatbots in customer
service outweighs possible gender and age differences.
To sum up, this section has addressed the factors identified through the explanatory
part, to affect users’ trust in chatbots for customer service. The conducted analysis revealed
the perceived chatbot-related factors expertise and anthropomorphism, environment-related
factor risk and user-related factor propensity to trust technology as factors predicting users
trust. The next section will discuss the result from the exploratory part of the questionnaire.
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TRUST IN CHATBOTS FOR CUSTOMER SERVICE
Findings from the exploratory part
The aim of also including an exploratory part in the questionnaire was to identify anyother factors that not were covered by the hypothesis grounded in this studies` literature
review. Respondents answered the open-ended question “What makes you experience trust in
this chatbot?”. Some of the categories identified through the answers reflected the factors
from the explanatory part and also gave a more nuanced understanding of these in the context
of chatbots for customer service. Most of the occurring categories were chatbot-related
factors. This is in accordance with the results obtained in the meta-analysis of Hancock et al.
(2011), where factors related to the robot were suggested as having greatest influence onusers’ trust.
Chatbot-related factors. In the exploratory part, the category expertise was identified
as reflecting and complementing the factor expertise from the explanatory part of the study.
Expertise was identified as a much mentioned category in the thematic analysis, suggesting it
can be a possible relevant factor for trust. Moreover, the thematic analysis gave a morenuanced understanding of expertise in the context of chatbots for customer service. It showedthat perceived expertise was a more complex and comprehensive category than initiallyestablished in the explanatory part. Through the exploration of categories emerging from therespondents freely written comments, different aspects of expertise were identified. The four
identified sub-categories of expertise were correct answer, interpretation, concrete answer
and eloquent answer. Expertise, as motivated by Corritore et al. (2003) was then indicated as
an important factor for users’ trust. The importance of expertise has similarly been identified
in an online context of intelligent software agents. Detweiler and Broekens (2009) found astrong correlation between users trust in a software agent and the perceived ability of thesoftware agent. The exploratory part of the study has by this enabled us to understand whichaspects of expertise that really matters for users’ trust in chatbots for customer service.
Correct answer is the sub-category of expertise with the highest frequency from thethematic analysis. This sub-category explained that respondents are highly sensitive toanswers being correct, for developing trust. If the chatbot cannot answer, respondents havementioned it to be waste of time. Research on trust and HCI has argued how automation
errors negatively affect trust, and that this is one of two mainly supported conclusions (Merritt& Ilgen, 2008). For chatbots, wrong answers can make people decline its offers for assistancein the future.
Interpretation, another sub-category of expertise was by some users highlighted asimportant for trust. This sub-category defines the chatbots understanding of the question, what
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TRUST IN CHATBOTS FOR CUSTOMER SERVICE
means that the chatbot is correctly responding according to the request, and are honest if they
do not understand. Interpretation shows similarities with another sub-category of expertise,correct answer. For both the sub-categories, it is important that the users perceive the chatbot
to understand the asked question, and thereby manage to help. Dietvorst, Simmons, andMassey (2015) found users to have less trust in machine advises after witnessing machines
doing mistakes. This can partly relate to the chatbot providing a correct answer and having agood interpretation of the asked question.
Concrete answer and eloquent answer are indicated also as two important subcategories of expertise. Users seem to value a concrete answer from the chatbot, what meansthat the answer is perceived as precise and clear. Eloquent answer concerns users’ wish foranswers that appears as professional, logical and reasonable. In consequence, it may be an
idea for designers and developers of chatbots to put effort into the content given by the
chatbot. According to Shelat and Egger (2002), information content has been identified as astrong cue for trustworthiness. Similar, Corbitt et al. (2003) found perceived site quality to bea strong predictor of trust in e-commerce.
A new category emerging from the thematic analysis was users wish of getting a fast
response. This is also a highly mentioned chatbot-related category, which can be conceivableas a relevant factor for users’ trust. The highlighting of this factor might be a results of users’
wish of perceiving use of chatbot as an effective way of getting help. This echoes Brandtzaeg
and Følstad (2017), who found productivity to be the most expressed reason for humans totake advantage of chatbot technology. They argued that the majority of chatbot users seek
quick and consistent feedback when they need assistance or help. In the customer service
domain, this makes sense. Some of the statements also showed a comparison of chatbot
service as faster option than human customer support. The fast response has also been
mentioned as a too clear indication of the chatbot being perceived as a robot. It can be
speculated that a short delay in the answer can be important for users who wish the chatbots
to behave more humanlike.
It was in the exploratory part found a category reflecting and complementing thefactor anthropomorphism from the explanatory part. This category illustrates user wish ofperceiving the chatbot with humanlike qualities, such as nice and polite. Anthropomorphism
has gained increased focus in establishing successfully human and machines interactions. De
Visser et al. (2016) argued anthropomorphism as a critical variable that should be carefully
incorporated into any general theory of human-agent trust. Although anthropomorphism was
not frequently mentioned by respondents as a category, the fact that it was reflected also in
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TRUST IN CHATBOTS FOR CUSTOMER SERVICE
the exploratory part indicates it as a relevant factor. Moreover, research on anthropomorphism
has suggested that people prefer to interact with humanlike robot, with the capability of
expressing emotion, have eye contact and have a humanlike voice (Dautenhahn, Ogden, &Quick, 2002). However, in contrast to these findings, De Angeli, Johnson, and Coventry(2001) claim anthropomorphism in HCI to be a complex phenomenon which by the user
possibly can elicit strong negative reactions. In robotics, too excessive similarities can giverise to negative emotions, known as the “Uncanny Valley” (Mori, MacDorman, & Kageki,2012). A critical issue for chatbot developers will obviously be how humanlike a chatbot
should be. However, this study has indicated that users’ perception of anthropomorphism is
an important factor affecting trust for customer service chatbots.
Absence of marketing has in the thematic analysis been identified by a few
respondents as an important category. This chatbot-related category indicates how users feeltrust when the chatbot is perceived as objective and not selling in the chat. As this category
was mentioned as important for trust by some respondents, this can conceivable be an
important factor. Absence of marketing show similarities with honesty and benevolence,factors that initially was stated as not promising from the literature review. The benevolence
factor outlined by Mayer et al. (1995) concerns the extent to which a trustee is believed tohave good wishes for the trustor, apart from any egocentric profit motives. High benevolence
is inversely related to motivation to lie. Honesty is by Corritore et al. (2003) characterized aswell-intentioned and truthful actions. Absence of marketing can in this way seem to reflect
much of the same content as benevolence and honesty. An answer which sets the user first,
provides valuable and not selling information, can in this way be speculated as an importantfactor for users trust in chatbots for customer service.
Environment-related factors. The environment-related category low risk has been
seen to reflect and give a more nuanced understanding of the risk factor from the explanatory
part of the study. Low risk means that users’ feel trust as a result of not being forced to sharepersonal or sensitive information in the chat. Corritore et al. (2005) found in their tested
model of factor impacting trust in a website that high risk were associated with low trust. In
contradiction to this, Mayer et al. (1995) explained trust as not relevant unless there were risk
involved. However, in this study context, there are reasons to believe users trust is dependent
on not having risk in the situation. That means they feel trust in the absence of requests toshare personal information. Some even said their trust perceptions would be different if they
have to share more personal information. McKnight, Choudhury, and Kacmar (2002)
discussed trust as having a central role in helping consumers to overcome perception of risk.
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TRUST IN CHATBOTS FOR CUSTOMER SERVICE
Consumers who feel trust are comfortable with sharing personal information, acting on Web
vendor advices and making purchases. Detweiler and Broekens (2009) investigated an online
context where user had to depend on intelligent software agent to accomplish a task. Theirresearch revealed a strong negative correlation between perceived risk and trust. Although
there seem to be different opinions of the relevance of perceived risk, trust in chatbots forcustomer service does in this study indicate to be dependent on perceived low risk. In the
future, chatbots are likely to become more advanced and demand bigger involvement of theusers. For example, a user might have to specify personal information in the chat. Then it will
be important that other factors affecting users’ trust are present.
Brand is a partly new category emerging from the thematic analysis. This
environment-related category was frequently mentioned by respondents and will conceivablebe a relevant factor for users’ trust in chatbots. Brand show similarities with the reputation
factor from the explanatory part of the study. However, reputation from the explanatory partwas only testing the reputation of the chatbot, here brand is the reputation of the company
behind the chatbot. Respondents did in general claim their trust in chatbots for customer
service to be dependent on the perception of the brand. Brand is actually quite similar
Corritore et al. (2003) reputation factor, defined as the website reputation to influence
decision to trust. Different studies have highlighted the perception of brand as important fortrust. Jarvenpaa et al. (1999) tested factors contributing consumers trust in an internet store.
The finding of their study showed that the established reputation in the internet store affected
users trust. Similarly, Corbitt et al. (2003) derived reputation in general to influence trust in ecommerce.
The thematic analysis also revealed another environment-related category as importantfor users’ trust, that is, access to human operator. This category was by some respondents
evaluated important and can thereby be a relevant factor for users’ trust in chatbots forcustomer service. Access to human operator concerned users’ wish of getting transferred to ahuman if necessary. It can be speculated that access to human operator is echoing the brandcategory. That means that customers’ already established relationship with the company,
where customers are familiar with the way help is provided, may still need to be available
when chatbots are implemented.
User-related factors. Only one category emerged as user-related, that was not trust
relevant/not trust. This category is seen in users who don’t see trust as relevant or don’t
experience trust in chatbots. The category seems to have similarities with propensity to trustfrom the exploratory part, and thereby reflecting this factor. Users who initially are positive
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TRUST IN CHATBOTS FOR CUSTOMER SERVICE
about technology, will probably easier experience trust in chatbots. The ones who are more
sceptical and may find trust controversial to connect with use of technology, will not see trust
as decisive for use of chatbots. Humans’ general propensity to trust automated systems has
been mentioned by Jian et al. (2000) to provide an anchor in the development of trust in an
automated system. Moreover, they stated humans’ general propensity to trust automatedsystem as providing a baseline measure to predict trust. Future research should test whether
this factor only directly affect trust in chatbots, or if it functions as a moderator between tovariables, like it did in Mayer et al. (1995) framework for organizational trust.
Altogether, this section has discussed the main findings from the exploratory part of
the questionnaire study. Based on the thematic analysis, some categories were reflecting the
explanatory part and some new categories occurred. This gave a more nuanced understandingof the factors from the explanatory part, and thereby indicating which categories being
relevant to include as important factors impacting trust. The next section will include findings
from both the explanatory and exploratory part, and suggest a possible model of factors
affecting trust in chatbots for customer service.
Towards a model of factors affecting trust in customer service chatbots
The factors initially hypothesized as affecting trust in the explanatory part wereexpertise, predictability, anthropomorphism, ease of use, risk, reputation and propensity totrust technology. Following the analysis of the data from the explanatory part of thequestionnaire, the factors indicated to be of especially relevance were: expertise, risk,
propensity to trust and partly anthropomorphism. Ease of use, predictability and reputation
were not suggested as important factor for a proposed model. The categories identified in the
exploratory analysis suggested users’ trust to be affected by factors in part reflecting those of
the explanatory analysis (such as expertise, anthropomorphism, low risk and not trust
relevant/no trust), and in part other factors (such as fast response, absence of marketing,brand and access to human operator). Expertise was a key factor in explaining trust in the
explanatory part, and did in the exploratory part give a more expanded understanding of thefactor. Hence, different aspect of the expertise factor emerged as important for users’ trust:correct answer, interpretation, concrete answer and eloquent answer.
Based on the findings from the explanatory and exploratory analysis, a new model oftrust in chatbots for customer service will be proposed (see figure 2). The factors werestructured according to a three high-level dimensions’ model: chatbot-related, environmentrelated and user-related factors. Chatbot-related factors included expertise, fast response,
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TRUST IN CHATBOTS FOR CUSTOMER SERVICE
anthropomorphism, and absence of marketing. Environment-related factor included low risk,
brand and access to human operator. The only user-related factor of relevance was propensity
to trust technology. As some of the proposed factors emerging from the thematic analysis had
relatively low frequency, future research particular need to investigate whether these reallyshould be considered important in affecting users’ trust in chatbots for customer service.

Figure 2. A proposed model of factors affecting users’ trust in chatbots for customer service.
The proposed trust model includes factors that users were found to perceive asimportant for their trust in chatbots, following their relatively limited experience with
chatbots for customer services. However, trust over time is also dependent on how atechnology like chatbot actually works, not just how it is perceived following limited
experience. Whether trust is maintained over time is presupposed of chatbots being
trustworthy. As argued by Corritore et al. (2003) framework for trust in websites, factors
impacting trust are both perceptual and external. External factors exist explicitly or implicitly
in a particular trust context, and perceived factor are an individuals’ perception of the external
factors. External factors refer to conditions that are objectively observable, and not just based
upon subjective measures. In the short term, perceived expertise and actual expertise in acustomer service chatbot may possibly be diverging. In a long term, however, these will likely
converge, as the user gain experience on whether the help and suggestions provided by the
chatbot actually is good. This indicates that future studies should account for the external
factor, and see how this is impacting users’ trust in chatbots for customer service.
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TRUST IN CHATBOTS FOR CUSTOMER SERVICE
In sum, the proposed model is a product of the findings from both the explanatory andexploratory part of the questionnaire. The model serves as a suggestion to what the designers
and developers should highlight when they are constructing new chatbots, and how various
changes can influence users trust in chatbots for customer service. In the next section,
limitations and suggestion for future research are addressed.
Limitations and future researchThis study discusses and presents factors assumed to influence users trust in chatbots
for customer service. The findings and conclusion are however subject to several limitationsin the study. Four key limitations are elaborated.
First, a questionnaire study will never be free for problems or potential source errors.
As argued by Svartdal (2009), a source error in a questionnaire may occur when users answer
the questions in accordance to what they think they should mean, and not how they actuallyperceives it. Use of questionnaire is also limited due to users’ tendency to almost be
consistent in agreement (or disagreement). Also, a correlational design, as in the explanatory
part of the study, only implies opportunities for explaining and predicting variation in adependent variable, not claims regarding causal relations (Svartdal, 2009). Future studies isrecommended to use field studies and experiments that enable observation of behaviour that
requires trust under different conditions. Currently, SINTEF are doing an interview study intheir research program on chatbots to explore trust in chatbots from another angel, which also
can provide more in-depth knowledge.
Second, the conducted analysis had generality issues. All the respondents had limited
experience with chatbot as a technology. The majority of users had only tested chatbots 1-3
times before. Who can the findings generalize to? As the implementation of chatbots as asupplement to human customer support is new, there might be a risk that there are the moreengaged and curious users who seek use of chatbots. It will be interesting for future research
to check if the obtained results are prominent for all categories of chatbot users. Especially astime pass, and this technology no longer figures as new.
Third, the measurement instruments used in this study were not perfect. As it to date
are no established measurement instruments accommodated for the study of trust in chatbots
and associated factors, the measurement instruments were made by combining and adjusting
scales from other measures. An exploratory factor analysis revealed that some of the items
intended to measure one factor, were having cross-loadings and low factor-loading. This was
especially the case for the dependent variable trust. As mention by Simpson (2007), trust is a44

TRUST IN CHATBOTS FOR CUSTOMER SERVICE
complex and multidimensional construct, making it difficult to measure and interpret.
However, trust items were consciously made to capture the broad of the construct, but did by
this made some challenges. Future research is recommended to test and verify themeasurement instruments, in particular for the trust variable before conducting a new study. A
new study should be conducted with an increase in respondents. Moreover, it can beinteresting to check how the different variables is behaving in accordance to each other byconducting a path analysis, and as well explore if the propensity to trust variable is rather
having an important moderating role between variables.
Fourth, this study is only conducted in the Norwegian marked, and it will be of value
for future research to investigate if the proposed factors are valid for other countries as well.
In Norway, people have a quite strong trust in the community, which can be an extra
motivation for respondents to also trust in chatbots.
This study proposes and present a set of factors important for users trust in chatbots
for customer service. Chatbot used for customer support purposes is an area of substantial
growth and development, and ever more companies are implementing this technology. The
study provides a first step towards a needed understanding of trust in chatbots. Though thisstudy concerned chatbots in the domain of customer service, this gives indications of factorsthat may be important when chatbots are implemented also in other domains. Hopefully, thestudy can motivate and serve as basis for general research about trust in chatbots, and also tochatbots applied in domains such as education, health information and recruitment assistant.
This section has described some of the potential limitations in the study. Future
research is encouraged to replicate this study some years in the future, to investigate if thesame factors reoccur as important when chatbots are more advanced and users have gained
more experience.
Conclusions
This study has provided a contribution, in response to the current gap in research
literature about user’s perception of factors affecting trust in chatbots for customer service.
The results partly support the research hypothesis for the study, although the analysis showedthat not all of the seven factors seem to explain trust similarly. However, through the
exploratory analysis new factors emerged as important for users trust. The main findings arethat users’ trust in customer service chatbots can be affected by factors related to the chatbot,
the environment, and the user. Four different chatbots-related factors appeared to influence
trust, specifically the perception of expertise, fast response, anthropomorphism and absence
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TRUST IN CHATBOTS FOR CUSTOMER SERVICE
of marketing. The perception of low risk, brand, and access to human operator appeared asimportant environment-related factors. The only identified user-related factor was users’
propensity to trust technology. Although chatbots used in customer service currently serve asa supplement, many theorists claim this technology soon will be replacing the humanoperator. Hopefully, the study may motivate future research in the field of chatbots. A main
goal for chatbot developers and designers is to build platforms that can help users, facilitate
their work and their interaction with computers by the use of natural language. To reach sucha goal, it is crucial to build knowledge and be sufficient aware of the factors that affect users
trust in chatbots.

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TRUST IN CHATBOTS FOR CUSTOMER SERVICE
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Appendix A – The questionnaire

Chat bot - undersøkelse – Vis - Net t skjema

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Chatbot-undersøkelse
Side 1Du inviteres med dette til å delta i en undersøkelse om brukeres opplevelse av chatbots; særlig hva som gjør at vi som
brukere bli fortrolige, stoler på og ønsker å henvende oss til chatbots for å få svar og råd. Undersøkelsen gjennomføres som
en del av et masterprosjekt ved Universitetet i Oslo, og er tilknyttet et større forskningsprosjekt om chatbots ledet av SINTEF.
Du er invitert til å delta siden du har brukt en chatbot hos en av bedriftene som er tilknyttet prosjektet. Du må være 18 år eller
eldre for å delta.
Spørreundersøkelsen tar 5-10 minutter. Du vil besvare spørsmål om din opplevelse av chatbots. Undersøkelsen er anonym.
Svarene du gir vil ikke kunne spores tilbake til deg.
Alle deltagere i undersøkelsen kan være med i trekningen av en iPad 32GB. For å bli med i trekningen, må du legge igjen dinepostadresse i et skjema etter at du er ferdig med undersøkelsen. Det er ingen kobling mellom skjemaet med epostadressenog skjemaet der du avga svarene dine.
Dersom du vil, kan du også registrere deg for å bli invitert til et eventuelt oppfølgingsintervju pr. telefon. Dette kan du gjøre isamme skjema hvor du registrer din epostadresse for trekning av iPad. Din epostadresse vil ikke brukes til andre formål enn
denne undersøkelsen, og vil slettes så snart undersøkelsen er ferdig – ikke senere enn juni 2018. Frem til sletting lagres
epostadressen på et filområde der kun ansvarlig for undersøkelsen har tilgang.
Deltagelse i undersøkelsen er frivillig. Du kan avslutte undersøkelsen når du ønsker. Du kan også trekke ditt samtykket for
deltagelse på et hvilket som helst tidspunkt. I så fall vil epostadressen din umiddelbart slettes fra deltagerlisten.
Dersom du har spørsmål eller kommentarer til undersøkelsen kan du henvende deg til Cecilie Bertinussen Nordheim(ansvarlig for undersøkelsen), epost cecilbno@student.sv.uio.no, eller Cato Bjørkli (intern veileder av masterprosjektet), epost
cato.bjorkli@psykologi.uio.no.

Jeg er informert med innholdet i, og formålet med undersøkelsen og samtykker i å delta.

Ja
Side 2
Sideskift

Din erfaring med chatbots
Chatbots er automatiserte digitale tjenester som brukere kan interagere med gjennom chattedialoger fremfor å
kommunisere med et menneske. Denne tjenesten er bygget opp av regler og kunstig intelligens for å forstå dine
spørsmål og gi deg et passende pre-definert svar. Chatbots har blitt et supplment innen kundeservice, der du som
kunde kan få svar på generelle spørsmål døgnet rundt.
Hvor ofte har du brukt en chatbot?
1-3 ganger
4-6 ganger
7-9 ganger
10 eller flere ganger
Side 3
Sideskift

Tillit til chatboten du nettopp har brukt - 1/2
Tenk på chatboten du nettopp har brukt. Beskriv i hvilken grad du føler tillit til denne chatboten i et
kundeserviceperspektiv. Bruk din egen forståelse av hva tillit er ved besvarelse av spørsmålene under. Angi ditt svar
på en skala fra 1 (helt uenig) til 7 (helt enig).

1 (helt
uenig)

ht t ps://net t skjema.uio.no/user /for m/preview.ht ml?id=9 0 4 14

2

3

4

5

6

7 (helt
enig)

Side 1 av 5

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Chat bot - undersøkelse – Vis - Net t skjema

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Jeg opplever denne chatboten som troverdig
Jeg tror ikke denne chatboten vil handle på en måte som erufordelaktig for meg
Jeg er skeptisk til denne chatboten
Chatboten fremstår som villedendeJeg opplever å ha tillit til denne chatboten

Side 4
Sideskift

Tillit til chatboten du nettopp har brukt - 2/2
Tenk på chatboten du nettopp har brukt. Beskriv i hvilken grad du føler tillit til denne chatboten i et kundeserviceperspektiv. Brukdin egen forståelse av hva tillit er ved besvarelse av spørsmålene under. Dette er den viktigste delen av spørreundersøkelsen, såvi håper du kan skrive litt i de to åpne spørsmålene under.

Hva gjør at du opplever tillit til denne chatboten? Fortell med egne ord.

Hva kunne vært forandret for at du kunne fått mer tillit til denne chatboten? Fortell med egne ord.

Side 5
Sideskift

Din opplevelse av denne chatboten - 1/3
Tenk på chatboten du nettopp har brukt. Basert på din erfaring, vis hvor enig eller uenig du er i disse påstandene
ved å krysse av det tallet som du synes stemmer best for deg. Angi ditt svar på en skala fra 1 (helt uenig) til 7 (heltenig).
1 (helt
uenig)

2

3

4

5

6

7 (heltenig)

Jeg opplevde å få svar på det jeg lurte på
Chatboten fremstår som kunnskapsrik
Innholdet i denne chatboten reflekterer ekspertise
Jeg føler meg svært sikker på chatboten sin kompetanse
Chatboten er godt rustet til den oppgaven den er satt til å gjøre
Chatboten oppførte seg forutsigbart
Det var ingen overraskelser i måten chatboten svarte meg på
Chatboten oppførte seg som forventet
Jeg synes det er forutsigbart at chatboten har det innholdet den
harInnholdet i chatboten var i henhold til min forventning

Side 6
Sideskift

ht t ps://net t skjema.uio.no/user/for m/preview.ht ml?id=9 0 4 14Side 2 av 5

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Chat bot - undersøkelse – Vis - Net t skjema

0 5.0 5.20 18, 0 0.17

Din opplevelse av denne chatboten - 2/3
Tenk på chatboten du nettopp har brukt. Basert på din erfaring, vis hvor enig eller uenig du er i disse påstandene ved å krysse av
det tallet som du synes stemmer best for deg. Angi ditt svar på en skala fra 1 (helt uenig) til 7 (helt enig).

1 (helt
uenig)

2

3

4

5

6

7 (heltenig)

Det var enkelt for meg å lære hvordan jeg skal bruke denne
chatboten
Jeg føler det er enkelt å få chatboten til å gjøre det jeg vil den skal
gjøre
Chatboten er enkel å bruke
Min dialog med denne chatboten var klar og forståelig
Denne chatboten vil være fleksibel å samhandle med
Jeg føler meg sårbar når jeg snakker med denne chatboten
Jeg tror det kan være negative konsekvenser ved å bruke denne
chatboten
Jeg føler det er usikkert å snakke med denne chatboten
Jeg føler jeg må være forsiktig når jeg bruker denne chatboten
Jeg føler det er risiko involvert ved å snakke med denne
chatboten

Side 7
Sideskift

Din opplevelse av denne chatboten - 3/3
Basert på din erfaring, vis hvor enig eller uenig du er i disse påstandene ved å krysse av det tallet som du synes
stemmer best for deg. Angi ditt svar på en skala fra 1 (helt uenig) til 7 (helt enig).
1 (helt uenig)

2

3

4

5

6

7 (helt enig)

Chatboten er vel ansett av andre
Chatboten har et godt omdømme
Chatboten er respektert av andre
Jeg har hørt andre snakke positivt om denne chatboten
Jeg har hørt andre være kritisk til denne chatboten

Side 8Sideskift

Ditt inntrykk av chatboten
Tenk på chatboten du nettopp har brukt. Basert på din erfaring, vis hvor enig eller uenig du er i disse påstandene ved å krysse av
det tallet som du synes stemmer best for deg. Angi ditt svar på en skala fra 1 (helt uenig) til 7 (helt enig).

1 (helt uenig)

2

3

4

5

6

7 (helt enig)

Chatboten fremstår som naturlig
Chatboten fremstår som menneskelig
Chatboten fremstår som tilstedeværende

ht t ps://net t skjema.uio.no/user/for m/preview.ht ml?id=9 0 4 14Side 3 av 5

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Chat bot - undersøkelse – Vis - Net t skjema

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Chatboten fremstår som realistisk
Chatboten fremstår som autentisk
Jeg liker denne chatboten
Chatboten fremstår som vennlig
Chatboten har en hyggelig fremtoning
Chatboten er behagelig å snakke med
Chatboten er imøtekommendeSide 9Sideskift

Ditt syn på teknologi
I hvilken grad er du enig eller uenig med utsagnene nedenfor. Angi ditt svar på en skala fra 1 (helt uenig) til 7 (heltenig).
1 (helt
uenig)

2

3

4

5

6

7 (helt
enig)

Min typiske tilnærming er å stole på ny teknologi
Generelt stoler jeg på ny teknologi inntil det gir meg en grunn til åikke gjøre det
Selv under tvil vil jeg velge å stole på ny teknologi
Det er lett for meg å stole på ny teknologi
Min tendens til å stole på ny teknologi er høy

Ditt syn på chatbots
I hvilken grad er du enig eller uenig med utsagnene nedenfor. Angi ditt svar på en skala fra 1 (helt uenig) til 7 (heltenig).
1 (helt
uenig)

2

3

4

5

6

7 (helt
enig)

Ved å bruke chatbots som denne vil jeg få raskere svar på mine
spørsmål
Ved å bruke chatbots som denne vil jeg få svar på mine spørsmål
mer effektivt
Å bruke chatbots som denne øker min produktivitet
Chatbots som denne vil gjøre det enklere for meg å få svar på
mine spørsmål
Jeg synes chatbots som denne er nyttig innen kundeservice

Side 10
Sideskift

Din videre bruk av chatbots
Vis hvor enig eller uenig du er i disse påstandene ved å krysse av det tallet som du synes stemmer best for deg.
Angi ditt svar på en skala fra 1 (helt uenig) til 7 (helt enig).
1 (helt

ht t ps://net t skjema.uio.no/user/for m/preview.ht ml?id=9 0 4 14

2

3

4

5

6

7 (helt

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Chat bot - undersøkelse – Vis - Net t skjema

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uenig)

enig)

Hvis jeg har tilgang på chatbots som denne tenker jeg å brukedet
Jeg tror min interesse for chatbots som denne vil øke ifremtiden
Jeg vil bruke chatbots som denne så mye som mulig
Jeg vil anbefale andre å bruke chatbots som denne
Jeg planlegger å bruke chatbots som denne fremoverSide 11
Sideskift

Om deg
Til slutt vil vi gjerne ha noen få bakgrunnsopplysninger
Kjønn
Mann
KvinneDin alder

Hvor mange år med utdanning har du fullført?
Ungdomsskolen
Videregående skole
Høyere utdanning (1-3 år etter videregående skole)
Høyere utdanning (4 år eller mer etter videregående skole)
Side 12
Sideskift

Takk for at du tok deg tid til å svare på denne spørreundersøkelsen. Hvis du ønsker å være med i trekningen av en iPad blant deltagerne trykker dudeg inn på linken som dukker opp etter at du har sendt inn ditt svar på spørreundersøkelsen. Inne på den nye siden skriver du ned dinepostadresse.
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TRUST IN CHATBOTS FOR CUSTOMER SERVICE
Appendix B – The measurement instruments
Variable

Initial source

My measurments intstruments

Trust

I believe this website is trustworthy(Corritore et al., 2005)

Jeg opplever denne chatboten som troverdig I experience this chatbot as trustworthy

I believe this website will not act in a waythat harms me (Corritore et al., 2005)

Jeg tror ikke denne chatboten vil handle påen måte som er ufordelaktig for meg

Intention to use

Expertise

My measurements instruments in english

I do not think this chatbot will act in a waythat is disadvantageous for me

I am suspicious of the system's intent, action, Jeg er skeptisk til denne chatboten
or outputs (Jian et al., 2000)

I’m suspicious to this chatbot

The system is deceptive (Jian et al., 2000)

Chatboten fremstår som villedende

The chatbot appears deceptive

I trust this website (Corritore et al., 2005)

Jeg opplever å ha tillit til denne chatboten

I trust this chatbot

Assuming I have access to the system, I
Hvis jeg har tilgang til chatbots som denne
intend to use it (Venkatesh and Davis, 2000) tenker jeg å bruke det

If I have access to chatbots like this I willuse itI believe my interest towards m-services will Jeg tror min interesse for chatbots som
increase in the future (Zarmpou et al., 2012) denne vil øke i fremtiden

I think my interest for chatbots like this will
increase in the future

I intend to use m-services as much as
possible Zarmpou et al., 2012)

Jeg vil bruke chatbots som denne så mye
som mulig

I will use chatbots like this as much as
possible

I recommend others to use m-services(Zarmpou et al., 2012)

Jeg vil anbefale andre å bruke chatbots som I will recommend others to use chatbots
denne

I plan to use the system in the next <n>months (Venkatetesh et al., 2003)

Jeg planlegger å bruke chatbots som denne
fremoverI plan to use chatbots like this in the futureSelf-composed

Jeg opplevde å få svar på det jeg lurte på

I experienced to get my question answered

The website content reflects mastery ofknowledge (Corritore et al., 2005)

Chatboten fremstår som kunnskapsrik

The chatbot appears knowledgeable

The website content reflects expertise(Corritore et al., 2005)

Innholdet i chatboten reflekterer ekspertise

The content of the chatbot reflects expertise

I feel very confident about top management's Jeg føler meg svært sikker på chatboten sin I feel very sure about the chatbots
skills (Mayer et al., 1995)
kompetansecompetencePredictability

Top management is very capable ofperforming its job (Mayer et al., 1995)

Chatboten er godt rustet til den oppgavenden er satt til å gjøre

The chatbot is well equipped for the task it is
set to do

The website content is what I expected(Corritore et al., 2005)

Chatboten oppførte seg forutsigbart

The chatbot behaved predictable

There were no surprises in how the website Det var ingen overraskelser i måten
responded to my actions (Corritore et al.,chatboten svarte meg på
2005)

There were no surprises in how the chatbot
answered me

The website is what I anticipated (Corritore Chatboten oppførte seg som forventet
et al., 2005)

The chatbot behaved as predicted

I find it predictable that the website has thetype of content it does (Corritore et al.,2005)

Jeg synes det er forutsigbart at chatboten har I think it is predictable that the chatbot has
det innholdet den har
the type of content is does

The website content is predictable (Corritore Innholdet i chatboten var i henhold til min
et al., 2005)
forventning

The content of the chatbot was as expected

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TRUST IN CHATBOTS FOR CUSTOMER SERVICE
Anthropomorphisme

Ease of use

Risk

Fake – Natural (Ho & MacDorman, 2010) Chatboten fremstår som naturlig

The chatbot is natural

Machinelike – Humanlike (Ho &MacDorman, 2010)

Chatboten fremstår som menneskelig

The chatbot is humanlike

Artificial – Lifelike (Ho & MacDorman,
2010)

Chatboten fremstår som realistisk

The chatbot is realistic

Unconscious – Conscious (Ho & MacD
orman, 2010)

Chatboten fremstår som tilstedeværende

The chatbot is present

Artificial – Lifelike (Ho & MacDorman,
2010)

Chatboten fremstår som autentisk

The chatbot is authentic

Learning to operate this website was easy
for me (Corritore et al., 2005)

Det var enkelt for meg å lære hvordan jeg
skal bruke denne chatboten

It was easy for me to learn how to use thischatbot

I found it easy to get this website to dowhat I wanted it to do (Corritore et al.,2005)

Jeg føler det er enkelt å få chatboten til å I feel it is easy to get the chatbot to do
gjøre det jeg vil den skal gjøre
what I want it to do

I found the website easy to use (Corritore et Chatboten er enkel å brukeal., 2005)

The chatbot is easy to use

My interaction with chart-master would beclear and understandable (Davis, 1989)

Min dialog med chatboten var klar og
forståelig

My dialogue with the chatbot was clear andunderstandable

I would find chart-master to be flexible
to interact with (Davis, 1989)

Denne chatboten vil være fleksibel åsamhandle med

This chatbot is flexible to interact with

I feel vulnerable when I interact with thiswebsite (Corritore et al., 2005)

Jeg føler meg sårbar når jeg snakker med
denne chatboten

I feel vulnerable when I interact with thischatbot

I believe that there could be negative
consequences from using this website(Corritore et al., 2005)

Jeg tror det kan være negative konsekvenser I think it can be negative consequences whenved å bruke med denne chatboten
using this chatbot

I feel it is unsafe to interact with this website Jeg føler det er usikkert å snakke med denne I feel it is unsure to talk to this chatbot(Corritore et al., 2005)
chatboten

Reputation

I feel I must be cautious when using thiswebsite (Corritore et al., 2005)

Jeg føler jeg må være forsiktig når jeg bruker I feel I must be caution when I use thisdenne chatboten
chatbot

It is risky to interact with this website(Corritore et al., 2005)

Jeg føler det er risiko involvert ved å snakke I feel there is risk involved by talking to thismed denne chatboten
chatbot

This store is well known (Jarvenpaa et al.,1999)

Chatboten er vel ansett av andre

The chatbot is well known by others

The website is highly regarded (Corritore et Chatboten har et godt omdømmeal., 2005)

The chatbot has a good reputation

The website is respected (Corritore et al.,(2005)

Chatboten er respektert av andre

The chatbot is respected by others

The website has a good reputation(Corritore et al., 2005)

Jeg har hørt andre snakke positivt om denne I have heard other talking positive about thischatboten
chatbot

This store has a bad reputation in themarket (Jarvenpaa et al., 1999)

Jeg har hørt andre være kritisk til denne I have heard other being sceptic to thischatboten
chatbot

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TRUST IN CHATBOTS FOR CUSTOMER SERVICE
Propensity to trust
technology

My typical approach is to trust newtechnologies until they prove to me that I
shouldn’t trust them (McKnight et al.,
2011)

Min typiske tilnærming er å stole på ny
teknologi

My typical approach is to trust newtechnology

I usually trust a technology until it gives me a Generelt stoler jeg på ny teknologi inntil det I generally trust new technology until theyreason not to trust it (McKnight et al.,gir meg en grunn til å ikke gjøre det
give me a reason not to2011)

Likeability

Usefulness

I generally give a technology the benefit of Selv under tvil vil jeg velge å stole på ny
the doubt when I first use it (McKnight et al., teknologi
2011)

Even under doubt I will chose to trust newtechnology

It is easy for me to trust a person/thing(Cheung & Lee, 2001)

Det er lett for meg å stole på ny teknologi

It is easy for me to trust new technology

My tendency to trust a person/thing is high(Cheung & Lee, 2001)

Min tendens til å stole på ny teknologi er høy My tendency to trust new technology is highDislike – Like (Ho & MacDorman, 2010) Jeg liker denne chatboten

I like this chatbot

Unfriendly – Friendly (Ho &MacDorman, 2010)

Chatboten fremstår som vennlig

The chatbot is friendly

Unkind – Kind (Ho & MacDorman,
2010)

Chatboten har hyggelig fremtoning

The chatbot has a nice appearance

Unpleasant – Pleasant (Ho &MacDorman, 2010)(Chatboten er behagelig å snakke med

The chatbot is comfortable to talk to

Awful – Nice (Ho & MacDorman, 2010)

Chatboten er imøtekommende

The chatbot is accommodating

Using chart-master in my job would
enable me to accomplish tasks more
quickly (Davis, 1989)

Ved å bruke chatbots som denne vil jeg
få raskere svar på mine spørsmål

By using chatbots like this I will get
answers more quickly

Using chart-master would enhance myeffectiveness on the job (Davis, 1989)

Ved å bruke chatbots som denne vil jeg
få svar på mine spørsmål mer effektivt

By using chatbots like this I will get
answers to my questions more effectively

Using chart-master would improve myproductivity (Davis, 1989)

Å bruke chatbots som denne vil øke minproduktivitet

Using chatbots like this will increase myproductivity

Using chart-master would make it easier Chatbots som denne vil gjøre det enklere Chatbots like this will make it easier for
to do my job (Davis, 1989)
for meg å få svar på mine spørsmål
me to get answers to my questions
I would find chart-master useful in myjob (Davis, 1989)

Jeg synes chatbots som denne er nyttig
innen kundeservice

I think chatbots like this is useful incustomer service

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TRUST IN CHATBOTS FOR CUSTOMER SERVICE
Appendix C – Thematic analysis from the open-ended question
Dimensjon
Kategori
Chatbot-relaterte kategorier Ekspertise - Riktig svar
(Expertise - Correct answer)

Situasjon-relatertekategorier

Forklaring
Dette handler om at kunden understreker riktige svar som viktig fortillit. Chatboten må derfor gi riktige opplysninger som både er relevantog faktabasert. Det handler også om at chatboten innehar nok
kunnskap, og tilgang til å søke etter den informasjonen det blir spurtom.

Ekspertise - Forståelse hoschatboten (Expertise Interpretation)

Dette handler om at tillit til chatboten er avhengig chatboten sin
forståelse av spørsmålet. Det viser at det er viktig at chatboten
oppfatter problemet og har flere løsninger. Det vektlegges også at
chatboten må være ærlig når den ikke forstår spørsmålet.

Ekspertise - Konkret svar
(Expertise -Concrete answer)

Dette handler om at brukere ønsker å få et presist og enkelt svar.
Dette er noe som går igjen med andre beskrivelser som konsist,eksakt, direkte, klar, ikke generelt, ikke misledende og tydelig. En
vektlegger også at samme svar må gis til alle.

Ekspertise - Velformulert svar
(Expertise - Eloquent answer)

Kommentaren peker på at kundens tillit er avhengig velformulerte
formuleringer. Ord som benyttes er logisk/fornuftig/troverdig/
profesjonelt/rådgivende/tillitsvekkende, at svaret er gjennomtenkt og
gir god/bra informasjon. Det handler også om at chatboten ikke siernoe som er dumt eller nedverdigende.

Raskt svar (fast response)

Kommentaren viser at det å få et raskt svar har betydning for tillit.
Andre ord som brukes er hurtig, kjapt og det å få svaret med en gang.
Noen kommenterer at et raskt svar kan indikere at det er en robot.

Antropomorfisme(Anthropomorphism)

Denne kommentaren handler om at brukeren vektlegger menneskelige
egenskaper som viktig for tillit til chatboten. Dette er gjennom
beskrivelser som hyggelig, høflig og bruk av et naturlig språk.

Fravær av markedsføring
(Absence of marketing)

Dette peker på brukere som mener tilliten kommer av at chatboten
ikke forsøker å selge noe, eller setter bedriftens ønske fremst. Dette
handler også om at chatboten setter kunden først og er saklig.

Lav risiko (Low risk)

Dette viser til at kunden opplever tillit siden de ikke trenger å oppgi
hemmelig eller personlig informasjon. Noen mener det kan stille seg
annerledes om brukeren må dele mer. En mente det var viktig å vite atsamtalen forblir privat.

Bedriften (Brand)

Kommentaren viser at tillit til bedriften er avgjørende for tillit til
chatboten. Dette kommer frem ved at det er bedriften bak de stolen
på. Noen vektlegger også at de stoler på programmeringen som er
gjort, og da også sørget for at chatboten svarer riktig og kommer med
god informasjon.

Tilgang til menneskelig aktør(Access to human operator)

Dette handler om at chatboten skal kunne overføre kunden til enmenneskelig operatør hvis det er behov.

Bruker-relaterte kategorier Ikke tillitsrelevant (Not trust
relevant/no trust)Diverse (Miscellaneous)

Kommentaren viser at tillit ikke er relevant i denne konteksten. Dette
kommer frem ved kommentarer som mener chatboten er mer
underholdning enn nyttig. Andre mener det kun er en maskin som
svarer på enkle spørsmål og med en begrenset funksjonalitet. Det
handler også om at brukere ikke føler tillit til chatboten, og heller vil
snakke med mennesker.
Kommentarer som ikke hører hjemme i de overnevnte kategorier.

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