2. Industrial Marketing Management 101 (2022) 45–56
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Lehmann, 2019).
This study is an attempt to provide further insights into how to
heighten employees' positive psychosocial perceptions (i.e., attitudes)
about chatbots in B2B marketing by studying the impacts of techno
logical features on their beliefs regarding chatbots. It is important to do
so because marketing employees may be reluctant to use chatbots if they
do not have positive attitudes. For example, a recent study indicated that
when users feel positive about chatbots, they are more likely to believe
that using chatbots produces good value (Lalicic & Weismayer, 2021).
By understanding which chatbot features help improve employee per
ceptions, companies can identify and focus on specific key features when
selecting and implementing chatbots. These enhanced features can then
facilitate employees' acceptance and the integration of chatbots into
customer services. In accordance with Castelo et al. (2019), we captured
people's beliefs by using two terms: the perceived effectiveness of
chatbots and discomfort with using chatbots (please refer to the section
on “Employees' Psychological Perceptions of Chatbots” for an explana
tion). To capture technology features, our study draws upon the concept
of affordance and disaffordance (Gibson, 1977; Strong et al., 2014;
Wittkower, 2016) to develop the relevant chatbot features (please refer
to the section on “Technology Affordance” for an explanation). There
fore, our research questions are 1) what chatbot features influence per
ceptions of chatbot effectiveness? and 2) what chatbot features influence
discomfort with using chatbots?
Our study makes two main contributions. First, we apply the concept
of technology affordance and propose three types of chatbot affordance:
automatability, personalization, and availability. Our study thus pro
vides a deeper understanding of chatbot features. Second, our study
contextualizes the concept of disaffordance and proposes three types of
chatbot disaffordance: limited understanding, lack of emotion, and null
decision-making. Our study can thus enhance our understanding
regarding the limitations of chatbots. Alternatively, we uncover new
knowledge by demonstrating the relationships between the features of
chatbot technology and people's psychological perceptions from a
technology affordance perspective. The results provide useful guidelines
for companies to encourage marketing employees to use chatbots and
offer valuable suggestions for the future development of chatbots.
The rest of the paper is organized as follows. We first review previous
studies on chatbots. We then identify the features of chatbots following
the concepts of affordance and disaffordance upon which the research
model is based. We then describe our methodologies, including data
collection, measures, data analysis, and results. Finally, implications for
theory and practice, limitations, and suggestions for future research are
discussed.
2. Literature review and theoretical background
2.1. Related studies on chatbots
There are two main streams in the existing literature on chatbots.
One stream of chatbot research is predominantly focused on chatbot
design and feature optimization (Dale, 2016; Gnewuch, Morana, &
Maedche, 2017; Landis, 2014; Shah, Warwick, Vallverdú, & Wu, 2016;
Shawar & Atwell, 2007; Xu, Liu, Guo, Sinha, & Akkiraju, 2017). How
ever, most of the discussions concerning chatbot design and optimiza
tion revolve around psychology, linguistics, computer science, and
engineering, but there is an evident lack of academic research on chat
bots from a business perspective. Therefore, it is meaningful to consider
business practices to provide insight into which chatbot design features
can help companies improve performance.
In another stream of present work, scholars examine user behavior
and experiences with chatbots (Brandtzaeg & Følstad, 2017; Hill, Ford,
& Farreras, 2015; Jain, Kumar, Kota, & Patel, 2018; Mimoun, Poncin, &
Garnier, 2012; Schuetzler, Grimes, Giboney, & Buckman, 2014). When
human–chatbot communication was compared to human–human
communication, there were notable differences in the content and level
of quality of such interactions. In human–chatbot communication,
people used a greater number of messages, but the average length of the
messages was shorter than in human–human communications. The
employed vocabulary was more restricted overall, yet there was an in
crease in the amount of profanity used (Hill et al., 2015).
In addition, the existing literature has also studied how customers
perceive the use of chatbots. Those studies are conducted and analyzed
using a technological or anthropomorphic lens (Murtarelli et al., 2021).
In particular, the technological lens deals with functionalities for
effective chatbots. For example, Brandtzaeg and Følstad (2017)
concluded that productivity is the top motivation for customers to use
chatbots. Chung, Ko, Joung, and Kim (2020) found that chatbots' mar
keting efforts (e.g., customization and problem solving) can improve
communication accuracy and credibility, which in turn increases
customer satisfaction. Balakrishnan and Dwivedi (2021b) also demon
strated how human-to-machine interaction could increase cognitive
absorption. More recently, Lalicic and Weismayer (2021) identified
personalization, convenience, ubiquity, and super-functionality as the
four main reasons for chatbot adoption, and usage barrier, technology
anxiety, privacy concern, and need for personal interaction as the four
main reasons against it. These studies can help identify effective design
principles and appropriate functionalities for the development of chat
bots, thus leading to the adoption and use of chatbots.
Alternatively, the anthropomorphic lens emphasizes the social-actor
role that chatbots play when interacting with customers (Balakrishnan &
Dwivedi, 2021a; Murtarelli et al., 2021). Users (e.g., customers) may
favor social features (e.g., human likeness) that enable chatbots to
provide humanized conversations and help improve social relationships
between customers and firms (Sowa, Przegalinska, & Ciechanowski,
2021). For example, Brandtzaeg and Følstad (2017) reported that
entertainment was the second most frequent motivation for using
chatbots, a response given by 20% of participants, and that it is neces
sary that chatbots have a sense of humor to provide a pleasant user
experience. They argued it is crucial for chatbots to be “fun” even if the
primary purpose of chatbot technology is to enhance productivity. This
is consistent with other findings about users' first experiences conversing
with chatbots (Jain et al., 2018). Additionally, participants reported
heightened enthusiasm pertaining to the novelty of chatbots and the use
of chatbots for social and relational purposes. More recently, Pizzi,
Scarpi, and Pantano (2021) found that customers show a lower level of
psychological reactance when using anthropomorphic chatbots, espe
cially when the customers themselves can choose to initiate conversa
tion. Along this stream, Balakrishnan, Dwivedi, Hughes, and Boy (2021)
found that psychological commitment (e.g., regret avoidance) can in
crease resistance toward the adoption of AI voice assistants. Further
more, Roy and Naidoo (2021) determined that the effectiveness of
human-like conversation styles depends on consumers' time orienta
tion: while present-oriented consumers prefer warm styles, future-
oriented consumers prefer competent styles.
Recent literature has also begun to examine chatbot adoption among
employees. For example, Brachten, Kissmer, and Stieglitz (2021) indi
cated both intrinsic and extrinsic motivations increase employees'
intention to use Enterprise Bots. Regardless, the most prevalent and
familiar use of chatbots is in the context of customer service. Although
consumers appreciate the consistent availability and “responsiveness” of
technological auto-service (Meuter, Bitner, Ostrom, & Brown, 2005),
they also desire the personalized attention provided by traditional
human–human communication (Scherer, Wünderlich, & Wangenheim,
2015). Chatbots have the potential to combine the best of both. For
example, Chung et al. (2020) demonstrated that luxury brands can and
do deliver personalized care to their customers by using chatbots instead
of traditional human–human interactions. Shumanov and Johnson
(2021) additionally found that matching the consumer's personality
with a congruent chatbot personality can encourage the consumer to
interact more with the chatbot and bolster purchasing outcomes.
Additionally, Sheehan, Jin, and Gottlieb (2020) observed that when a
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chatbot is able to effectively resolve miscommunication by seeking
clarification, it is perceived as being qualitatively identical to a chatbot
that avoids errors altogether, and that an anthropomorphic chatbot may
satisfy customers who demand more humanized interaction.
2.2. Technology affordance
The literature has applied the concept of affordance to improve un
derstanding of the role of technology (in particular, modern technolo
gies) in various contexts (Grgecic, Holten, & Rosenkranz, 2015; Markus
& Silver, 2008). This leads to the term technology affordance, which re
fers to “the mutuality of actor intentions and technology capabilities that
provide the potential for a particular action” (Majchrzak, Faraj, Kane, &
Azad, 2013, p. 39). Thus, technology affordance arises as individuals
perceive how features of certain technologies can be used to support
their goals (Grgecic et al., 2015). Because individuals can have different
goals, they can interpret the features of a technology in different ways
and perceive that the technology can afford their corresponding goal-
oriented activities (Leonardi, 2013; Treem & Leonardi, 2013).
Technology affordance is a useful theoretical lens through which to
investigate how advanced technologies are used by individuals in
various phenomena such as cyberbullying and big data marketing. For
example, Chan, Cheung, and Wong (2019) proposed four types of social
media affordances relevant to cyberbullying and big data marketing.
They showed that information retrieval affordance enhances the pres
ence of suitable targets, whereas editability and association affordance
increase the absence of capable guardians. De Luca, Herhausen, Troilo,
and Rossi (2021) proposed three types of big data marketing affordances
including customer behavior pattern spotting, real-time market
responsiveness, and data-driven market ambidexterity. They found that
real-time market responsiveness and data-driven market ambidexterity
affordance are positively related to service innovation. Thus, technology
affordance can facilitate the investigation of how chatbots can be used
for improving customer services. In other words, goal-oriented actors (e.
g., marketing employees) interpret and learn about certain artifacts (e.
g., chatbots) regarding the possibilities for activities provided by these
artifacts (i.e., affordance) to support their goals (e.g., provide customer
services).
Although other theoretical lenses have been used to understand the
use of technology, technology affordance was chosen as the most
appropriate perspective as it is consistent with the objective of this
study. As a contrasting example, task-technology fit theory (TTF) has
been developed to understand how task and technology characteristics
jointly influence individual performance (Goodhue & Thompson, 1995).
The basic argument is that technology has a positive effect on perfor
mance when it is a good fit for the task. Since we are centering this study
on understanding how chatbots' technical features influence employees'
general perceptions, TTF is less appropriate for our study. Nevertheless,
TTF can be a useful theoretical foundation for future investigations into
how certain technical features of chatbots can support employees in
completing various tasks.
2.2.1. Affordances of chatbots
In the context of B2B marketing, with the materiality of chatbots,
affordances can be jointly facilitated by social and technical aspects
(Volkoff & Strong, 2013, 2017) and can be interpreted based on the
goals of marketing employees. In this way, the affordances of chatbots
emerge from the interactions between employees and chatbots such that
employees use certain features of chatbots for achieving marketing goals
via such activities as interacting with customers and providing services.
The rationale is that employees may use chatbots to maximize their
opportunities to enhance customer interactions for supporting customer
service. Accordingly, based upon the literature on technology afford
ance (Chan et al., 2019; De Luca et al., 2021) and chatbots (Schuetzler,
Grimes, & Scott Giboney, 2020), we propose three types of chatbot
affordance including automatability, personalization, and availability
(see Table 1). First, chatbots can manage customer service without the
intervention of marketing employees. Specifically, chatbots can auto
mate routine tasks during customer services such as answering common
questions (Paschen, Kietzmann, & Kietzmann, 2019), because historical
structured and unstructured data allows the integrated AI capabilities to
produce logical responses (Paschen et al., 2019), therefore reinforcing
automatability as the first affordance. Second, chatbots can adapt
products/services to meet a customer's specific needs and preferences,
thus improving their relations with customers (Chung et al., 2020).
Chatbots can recommend different products/services based upon each
customer's background and interests instead of suggesting the same
products/services to every customer. In fact, Chung et al. (2020) pro
posed personalization as one of the main marketing efforts of chatbots.
Therefore, we select personalization as the second type of chatbot
affordance. Finally, chatbots can be available 24/7, and availability (i.e.,
ubiquity) is one of the main reasons that businesses adopt chatbots
(Lalicic & Weismayer, 2021). Therefore, we propose availability as the
third type of chatbot affordance.
2.2.2. Disaffordance
Notwithstanding that technology affordances have gained much
attention (Leonardi, 2013; Strong et al., 2014; Treem & Leonardi, 2013),
researchers have also begun to pay attention to disaffordance. In one of
the first studies, Wittkower (2016) provided an initial understanding of
disaffordance in a systematic and theorized manner. In general, dis
affordance generally means the lack of affordance in design, which
suggests that the artifacts cannot provide the potential for behaviors
associated with achieving specific outcomes. In other words, a tech
nology cannot empower the users through their capacities for acting and
achieving goals because of unsuccessful or nonexistent design features.
Thus, disaffordance refers to artifacts failing to facilitate certain ob
jectives of goal-oriented actors (Wittkower, 2016). Disaffordance can be
caused by non-affordance or poor affordance. Non-affordance results in
a scenario in which a certain affordance does not appear within the
actors' experiences. It is possible that the artifacts lack a set of functions
to support the actors' goals. On the other hand, poor affordance refers to
Table 1
Chatbot affordances.
Chatbot
Affordance
Definition How the Affordance
Relates to Marketing
References
Automatability The extent to which
marketing
employees believe
that chatbots offer
the opportunity to
respond to
customers' questions
automatically.
This affordance
allows chatbots to
deal with simple
questions
automatically, so
that marketing
employees can focus
on handling complex
questions from
customers.
(Paschen et al.,
2019)
Personalization The extent to which
marketing
employees believe
that chatbots offer
the opportunity to
provide
personalized
responses to
customers.
This affordance
allows chatbots to
support personalized
responses and
customized services,
so that marketing
employees can
reduce their
involvement during
interactions with
customers.
Chung et al.
(2020); Lalicic
and
Weismayer
(2021)
Availability The extent to which
marketing
employees believe
that chatbots offer
the opportunity to
provide customer
services at any time
of the day.
This affordance
allows chatbots to
support customer
services at any time,
even when
marketing employees
are not available.
Lalicic and
Weismayer
(2021)
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the scenario where a certain intended affordance does not provide the
actual affordance. The artifacts may not have clear and unobstructed
interfaces to allow actors to achieve their goals, often as a consequence
of poor design. Accordingly, we focus on the non-affordance aspects of
disaffordance, and in this study, we propose three types of chatbot dis
affordances including limited understanding, lack of emotion, and null
decision-making (see Table 2). Fore mostly, because chatbots conduct
predictive analytics based upon data collected from customers (Mur
tarelli et al., 2021), they cannot understand a new scenario that they had
not previously encountered, creating the disaffordance of limited un
derstanding. Further, chatbots lack important human qualities, such as
the ability to express emotions (e.g., empathy) and judgement marking
(Murtarelli et al., 2021). Therefore, lack of emotion and null decision-
making are proposed as additional types of disaffordances.
2.3. Employees' psychological perceptions of chatbots
Castelo et al. (2019) suggested that people's psychological percep
tions of a technology (i.e., algorithms) include two primary variables:
the effectiveness of a technology and discomfort with using the tech
nology. More specifically, technology effectiveness is a cognitive factor
that captures people's cognitive beliefs about the technology's compe
tency, and discomfort with use is an affective factor that captures peo
ple's subjective feeling of discomfort with using the technology.
Discomfort represents an unhealthy state of psychological well-being
(Albarracin & Shavitt, 2018), so individuals try to avoid objects or ac
tivities resulting in discomfort.
The existing literature has applied several theoretical lenses to
investigate both positive and negative aspects of technology. For
example, Lalicic and Weismayer (2021) used the behavioral reasoning
theory to identify reasons for/against adopting chatbots. Here, “reasons
for” and “reasons against” are used to justify users' future behaviors and
represent their distinct cognitive routes leading to intention and support
of use (Westaby, 2005). In the case of smart home devices, Wang,
McGill, and Klobas (2018) adopted the net valence model to examine
how various benefits and risks influence the adoption intention of
different devices. Perceived benefits and risks each represented the
users' overall perceptions of benefits and risks. These approaches are
similar to Castelo et al. (2019), who captured individuals' positive and
negative feelings toward technology. While behavioral reasoning theory
and the net valence model only deal with individuals' cognitive beliefs,
effectiveness and discomfort cover both cognitive beliefs and affective
feelings. Therefore, we chose to use the approach from Castelo et al.
(2019).
Following Castelo et al. (2019), we propose that employees' attitudes
toward chatbots are influenced by their beliefs about the technology,
including effectiveness and discomfort with use. Effectiveness represents
the employees' positive cognitive perceptions toward chatbots and refers
to the degree to which the latter are competent to provide customer
services. Discomfort with using chatbots reflects employees' negative
affective perceptions when they are uneasy about using chatbots.
3. Research model and hypotheses development
Following Castelo et al. (2019), we argue that chatbot effectiveness
and discomfort with using chatbots can represent marketing employees'
perceptions of the technology, which influence their attitudes. In addi
tion, we illustrate how chatbot affordances and disaffordances are
related to employees' perceptions. Specifically, we propose that three
types of chatbot affordances (i.e., automatability, personalization, and
availability) are positively related to chatbot effectiveness and that three
types of chatbot disaffordance (i.e., limited understanding, lack of
emotion, and null decision-making) are positively related to discomfort
with use. In the following we discuss each hypothesis in more detail.
3.1. Impact of effectiveness and discomfort on attitudes
Attitude refers to an individual's general summary affective feeling of
(un)favorableness toward a behavior (Ajzen & Fishbein, 1980). An
employee's attitude toward chatbots thus represents his or her overall
subjective feeling about using chatbots within organizations. The liter
ature suggests that attitudes can be affected by both technological fea
tures and social aspects—more specifically, people's beliefs (e.g., Kwok
& Gao, 2005; Lin, Featherman, Brooks, & Hajli, 2019). In particular, it is
well understood that people's beliefs can form their attitudes (Ajzen &
Fishbein, 1980). For example, employees' beliefs about information
sharing have been proven to play a significant role in forming their at
titudes toward sharing information within organizations (Kolekofski Jr
& Heminger, 2003). Similarly, it is suggested that people's beliefs about
information and interpersonal relationships are related to their attitudes
about online behaviors such as online shopping (Lin, Wang, & Hajli,
2019). Likewise, people's beliefs in the effectiveness of an algorithm
have been found to be positively associated with their psychological
states regarding their use of the algorithm, that is, their reliance on the
algorithm (Castelo et al., 2019). In our study, chatbot effectiveness
captures employees' beliefs about the performance of chatbots based on
their own experiences. When one believes that the performance of
chatbot is high, one is likely to favor using chatbots. Therefore, it is safe
to expect that a higher level of effectiveness leads to a more positive
attitude toward using chatbots within organizations.
H1. Chatbot effectiveness is positively related to attitudes toward chatbots.
3.2. The impact of discomfort with using on attitudes
On the other hand, we argue that marketing employees' discomfort
with using chatbots can have a negative effect on their attitude. Whereas
chatbot effectiveness represents employees' positive cognitive percep
tions of chatbots, discomfort with using chatbots reflects their negative
affective perceptions. Cognitive dissonance theory identifies discomfort
as a component of dissonance (Festinger, 1957; Hinojosa, Gardner,
Walker, Cogliser, & Gullifor, 2017). Specifically, discomfort can moti
vate or drive the individual's attitude change process to reduce disso
nance (Fazio & Cooper, 1983). In our study, discomfort with using
Table 2
Chatbot disaffordance.
Chatbot
Affordance
Definition How the Affordance Relates to Marketing Reference
Limited
understanding
The extent to which marketing employees believe that chatbots
do NOT offer the opportunity to understand new questions from
customers.
This disaffordance does not allow chatbots to deal with new contexts, so
marketing employees need to be involved in these new contexts.
(Murtarelli
et al., 2021);
(Paschen et al.,
2019)
Lack of emotion The extent to which marketing employees believe that chatbots
do NOT offer the opportunity to express emotions during
interactions with customers.
This disaffordance does not allow chatbots to express emotions, so
marketing employees may need to deal with customers' emotions (e.g.,
to provide emotional support).
(Murtarelli
et al., 2021)
Null decision-
making
The extent to which marketing employees believe that chatbots
do NOT offer the opportunity to make decisions.
This disaffordance does not allow chatbots to make decisions, so
marketing employees need to be involved when certain marketing
decisions need to be made.
(Murtarelli
et al., 2021)
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chatbots represents employees' mental uneasiness about using chatbots.
Employees may hold neutral attitudes toward chatbots before adopting
them. However, if and when employees do not feel comfortable with
chatbots after using them, their negative affective feelings become
inconsistent with their previous attitudes toward chatbots. Such disso
nance then changes their attitudes (Hinojosa et al., 2017), and em
ployees probably develop a negative attitude toward them. As a result,
the employees' discomfort may make them reluctant to use the tech
nology. Castelo et al. (2019) also argued that discomfort is negatively
related to reliance on algorithms. Therefore, we hypothesize that:
H2. Discomfort with using chatbots is negatively related to attitudes toward
chatbots.
3.3. The impact of technology affordances on effectiveness
The literature suggests that affordances can affect people's beliefs
and psychological states in a variety of contexts (e.g, Karahanna, Xu, Xu,
& Zhang, 2018; Leidner, Gonzalez, & Koch, 2018). More specifically, in
the context of social media, Karahanna et al. (2018) summarized a va
riety of social media affordances and proposed a link between social
media affordances and people's psychological needs. In the context of
enterprise social media, Leidner et al. (2018) provided an enhanced
understanding of how the affordances of enterprise social media can
result in various outcomes such as stress and a sense of social support.
Thus, it is clear that affordances can affect people's beliefs.
In the context of chatbots, automatability offers the possibility of
answering customers and interacting with them automatically. This
affordance enables companies to serve customers via automatically
generated responses and to provide quick responses based on customer
requests (Xu et al., 2017). Taking advantage of this affordance, em
ployees may reduce their workloads accordingly because they do not
have to answer so many phone calls from customers. In addition, with
this affordance, employees have more opportunities to focus on other
things and can thus maximize the possibility of improving their per
formance. In this way, employees may believe that chatbots can better
satisfy their needs for performing the job and providing customer ser
vices with less effort, thus leading to a sense of effectiveness.
H3a. Automatability is positively related to chatbot effectiveness.
Personalization offers the opportunity to recall the history of con
versations with customers, which can enable chatbots to produce
personalized responses (e.g., Gomez, 2018; Ritter, Cherry, & Dolan,
2011). In this way, companies can provide dedicated customer services
because personalization offers the ability to respond to individual cus
tomers' requests and needs (Xu et al., 2017). With this affordance,
chatbots enable the companies to facilitate favorable interactions be
tween customers and businesses through the possibility of building
conversation skills (Schuetzler et al., 2020). As a result, employees have
confidence in providing customer services through the use of chatbots
because of the affordance of personalization. Therefore, a higher level of
perceived affordance of personalization is likely to be related to a higher
level of effectiveness of chatbots.
H3b. Personalization is positively related to chatbot effectiveness.
The affordance of availability enables companies to be accessible to
customers and respond to their requests at any time. This affordance
offers customers the possibility of interacting with businesses and get
ting some help anytime and anywhere. With this affordance, chatbots
offer the opportunity for businesses and customers to communicate
conveniently (Putri, Meidia, & Gunawan, 2019), thus supporting
customer needs and improving customer service (Pizzi et al., 2021). In
addition, the affordance of availability can build customers' satisfaction
because it can maximize the service quality provided by companies
(Ashfaq, Yun, Yu, & Loureiro, 2020). All these factors can increase the
likelihood that employees will believe that chatbots can be effective for
improving customer services. Therefore, employees' perceptions of the
effectiveness of chatbots can increase as the affordance of availability
increases.
H3c. Availability is positively related to chatbot effectiveness.
3.4. Impact of technology disaffordances on discomfort with using
chatbots
In our study we propose three types of chatbot disaffordance leading
to discomfort. Limited understanding is defined as the extent to which
marketing employees believe that chatbots do not offer the opportunity
to understand new questions from customers. Because the AI of chatbots
is developed based upon historical conversations with customers, chat
bots cannot handle completely new queries from customers (Gomez,
2018). In these situations, customers can feel frustrated because they do
not get the responses they need, and sales may be lost. As a result,
marketing employees may not feel comfortable letting chatbots
completely handle interactions with customers. Therefore, we hypoth
esize that:
H4a. Limited understanding is positively related to discomfort with using
chatbots.
In this context, the lack of emotion refers to the extent to which
marketing employees believe that chatbots do not offer the opportunity
to express emotions during interactions with customers. Unlike people,
chatbots have no emotions. Although chatbots can be taught to simulate
certain emotions (e.g., empathy) based upon certain messages (Aivo,
2019), marketing employees can understand customer emotions better
and respond more effectively. Customers can easily identify messages
created by chatbots and often appreciate revisions by marketing em
ployees that respond to their emotions (CoSource, 2018). Often this is
essential to keeping interactions going in the right direction and to
providing good service. Therefore, marketing employees may perceive
that they need to maintain a certain level of involvement to effectively
deal with customers' emotions. Therefore, we posit:
H4b. The lack of emotion is positively related to discomfort with using
chatbots.
Last, null decision-making refers to the extent to which marketing
employees believe that chatbots do not offer the opportunity to make
decisions. Chatbots cannot make decisions, which causes problems. For
example, Microsoft initiated a chatbot for Twitter. However, it was
turned into a racist account by content from users in less than 24 h
(Gomez, 2018). The chatbot did not have the decision-making capacity
to prevent this outcome. Therefore, marketing employees may not feel
comfortable letting chatbots run by themselves and may feel they must
be involved when decisions need to be made. Therefore, we hypothesize
that:
H4c. Null decision-making is positively related to discomfort with using
chatbots.
To conclude, our research model is shown in Fig. 1.
4. Research methodology
4.1. Data collection and samples
We hired a survey company to recruit American participants by
systematic sampling. The survey company maintains national panels
and can access people with different backgrounds. Our study is focused
on marketing employees who use chatbots to interact with their cus
tomers in B2B companies. The data were collected in January and
February of 2021 through a survey link sent to participants. During the
data collection process, we recruited participants from different back
grounds (e.g., age, gender, education, firm sizes, industry) to increase
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the generalizability. Participants were qualified for our study if they 1)
worked in the marketing department and used chatbots to provide
customer services, and 2) worked in B2B companies. Qualified partici
pants then proceeded to the main survey and completed the
questionnaire.
In total, we received 228 valid responses, and their demographic
background is shown in Table 3. In our sample, 218 participants were
full-time employees. On average, they had worked for their current
companies for 8.08 years (SD: 5.14). Participants used a variety of
chatbots such as Twilio, Aivo, and Bold360.
4.2. Measures
Measures are listed in the Appendix. All items were measured with a
7-point Likert scale. Items pertaining to chatbot effectiveness and
discomfort with using chatbots were adapted from Castelo et al. (2019);
items pertaining to attitudes toward chatbots were adapted from Klobas,
McGill, and Wang (2019). Items referring to chatbot affordances
(namely, automatability, personalization, and availability) and chatbot
disaffordances (namely, limited understanding, lack of emotion, and
null decision-making) were newly developed in accordance with the
work of Moore and Benbasat (1991). First, we defined each construct by
reviewing the relevant literature (Tables 1 and 2). Second, we developed
measures based on the definitions, and they were also assessed by two
experienced researchers to ensure the face and content validity of all
items. Lastly, we conducted a card-sorting exercise with a group of
students. The overall placement ratio of items within the target di
mensions was acceptable, indicating that the items were sorted into the
intended variables.
4.3. Data analysis and results
We designed our survey to exclude personally-identifying informa
tion and reduced evaluation apprehension. All the variables were
collected in one survey, so both procedural and statistical approaches
were adopted to alleviate the impact of common method bias (CMB)
(Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Then two statistical
analyses were conducted. First, Harman's single factor analysis was
conducted. The results revealed six factors, with the greatest factor
explaining 25.24% of the total variance. Second, a common method
factor that included all items was created (Podsakoff et al., 2003). We
then calculated the variance explained by the focal factor and by the
method for each item. The average variance explained by the focal factor
was 0.77, while the average variance explained by the method factor
was 0.005. The ratio was approximately 145:1, and all method factor
loadings were nonsignificant. Therefore, CMB was unlikely to be a
serious issue.
Mplus (Muthén & Muthen, 2017), a covariance-based structural
equation modeling technique, was then used to validate our items.
Confirmatory factor analysis was conducted, and the model had a good
fit (χ2
(341) = 576.81, CFI = 0.94, SRMR = 0.05). Furthermore, all items
loaded significantly on their focal constructs, and all loadings were
above 0.60 (Table 4). Cronbach's alpha and composite reliabilities (CRs)
were more than 0.70, and the average variance extracted (AVE) was
more than 0.50 (Table 4). These results support the convergent validity
of our items. Finally, as shown in Table 5, the correlations between the
constructs were less than 0.85 (Brown, 2015), and the square root of
each factor's AVE exceeded all correlations between that factor and any
other construct, supporting the discriminant validity. Overall, our items
showed good psychometric properties.
We then tested our hypotheses (Fig. 2). H1, stating that chatbot
H3c
H3a
Attitudes
toward Chatbots
Chatbot
Effectiveness
Discomfort with
Using chatbots
Personalization
Automatability
Availability
Chatbot Affordance
Lack of
Emotion
Limited
Understanding
Null Decision-
Making
Chatbot
Disaffordance
H1
H2
H3b
H4a
H4b
H4c
Fig. 1. Research mode.
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7. Industrial Marketing Management 101 (2022) 45–56
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effectiveness is positively related to attitudes toward chatbots, is sup
ported (β = 0.65, p < .001). H2 states that discomfort with using chat
bots is negatively related to attitudes toward chatbots. This hypothesis is
supported (β = − 0.16, p < .01). H3a argues that automatability is
positively related to chatbot effectiveness. This hypothesis is also sup
ported (β = 0.29, p < .001). H3b proposes that personalization is posi
tively related to chatbot effectiveness. This hypothesis is also supported
(β = 0.49, p < .001). H3c, arguing that availability is positively related
to chatbot effectiveness, is not supported (β = 0.10, p = .21). Limited
understanding is not significantly related to discomfort with using
chatbots and does not support H4a (β = 0.07, p = .23). Lack of emotion is
not significantly related to discomfort with using chatbots either and
does not support H4b (β = − 0.05, p = .33). H4c proposes that null
decision-making is positively related to discomfort with using chatbots.
This hypothesis is supported (β = 0.56, p < .001). Three dimensions of
chatbot affordance explain 49.07% of the variance from chatbot effec
tiveness, whereas three dimensions of chatbot disaffordance explain
32.74% of the variance from discomfort with using chatbots. Chatbot
effectiveness and discomfort with using chatbots together explain
47.66% of the variance in attitudes toward chatbots. The effects of
several other control variables were also tested, including age (β = 0.03,
p = .65), gender (β = 0.03, p = .60), education (β = 0.09, p = .14), firm
size (β = 0.05, p = .37) and tenure (β = 0.04, p = .53), though none were
significant. Overall, these results provide strong support for our model.
4.4. Post-hoc analysis
The literature has shown that differently sized companies typically
follow different managerial approaches that lead to different outcomes
(Qiu & Yang, 2018; Visentin & Scarpi, 2012). For example, it is possible
that smaller sellers may develop closer and more personal relationships
with buyers (Visentin & Scarpi, 2012). Therefore, it is possible that
marketing employees from firms of various sizes follow different ap
proaches to the use of chatbots for supporting customer services. In this
post-hoc analysis, we examine the moderating effect of firm size by
comparing the path coefficients between small firms and medium-to-
large firms following Keil et al. (2000). According to American Small
Business Administration, small companies usually have no more than
500 employees.1
Therefore, we classified firms with 500 employees or
fewer as small firms, and those with more than 500 employees as
medium-to-large firms. We then divided our sample into these two
subsamples (i.e., small firms and medium-to-large firms) accordingly.
The results show (Table 6) that the effects of automatability and null
decision-making are stronger for small firms, while personalization has a
larger effect for medium-to-large firms.
5. Discussion
In this study we aimed to examine how different types of chatbot
affordance and disaffordance influence employees' psychological per
ceptions of chatbot effectiveness and discomfort with using chatbots,
which in turn affect their attitudes toward chatbots in the B2B context.
Our results show that both chatbot effectiveness (H1) and discomfort
with using chatbots (H2) significantly affect employees' attitudes toward
chatbots. These results are consistent with Castelo et al. (2019), who
showed that both effectiveness and discomfort significantly influence
users' reliance on algorithms.
Our results further reveal that automatability (H3a) and personali
zation (H3b) can enhance chatbot effectiveness. These results highlight
the importance of automatability in driving chatbot effectiveness, as
suggested by Paschen et al. (2019). The results are also consistent with
Chung et al. (2020), who pushed personalization as a marketing point.
The effect of automatability is stronger for small firms, as demon
strated by post-hoc analysis. It is possible that small firms have fewer
marketing employees, who need chatbots' help to answer customers'
standard questions. On the other hand, post-hoc analysis shows
personalization has a stronger effect for medium-to-large firms. Since
Table 3
Participants' demographic background.
Category Sample (N = 228)
Gender
Female 36.4%
Male 63.6%
Age
18–24 6.6%
25–34 39.0%
35–44 39.9%
45–54 12.3%
55 or older 2.2%
Education
High school or below 11.8%
Some college education or bachelor's degree 58.3%
Graduate degree 29.8%
Company Size
1–100 11.4%
101–200 9.6%
201–500 18.4%
501–1000 22.4%
1001–3000 18.4%
> 3000 19.7%
Industry
Health care 5.7%
Manufacturing 15.8%
Education 4.8%
Higher education 1.3%
Banking/Finance 16.2%
Insurance 3.1%
Wholesale and Distribution 3.9%
Transportation 5.7%
Government 2.2%
Retail 11.4%
Hospitality 1.3%
Other 28.5%
Table 4
Item descriptive statistics.
Construct Item Mean SD Loading Alpha CR AVE
Automatability
AUT1 5.72 1.21 0.78 0.77 0.77 0.53
AUT2 5.76 1.19 0.72
AUT3 5.63 1.25 0.69
Personalization
PER1 5.00 1.57 0.81 0.89 0.89 0.66
PER2 5.10 1.45 0.78
PER3 4.76 1.78 0.84
PER4 5.18 1.61 0.83
Availability
AVA1 5.89 1.31 0.84 0.85 0.85 0.66
AVA2 5.90 1.23 0.81
AVA3 5.96 1.19 0.79
Limited
Understanding
LU1 4.76 1.76 0.80 0.88 0.88 0.70
LU2 4.73 1.76 0.87
LU3 4.65 1.83 0.85
Lack of Emotion
LE1 4.90 1.75 0.81 0.87 0.87 0.69
LE2 5.00 1.78 0.83
LE3 4.91 1.91 0.84
Null Decision-
Making
ND1 4.20 1.85 0.72 0.88 0.88 0.72
ND2 3.97 1.87 0.90
ND3 4.09 1.89 0.90
Chatbot
Effectiveness
EFF1 5.14 1.52 0.72 0.76 0.77 0.53
EFF2 5.56 1.22 0.65
EFF3 5.46 1.26 0.81
Discomfort With
Using Chatbots
DC1 3.58 1.90 0.83 0.90 0.90 0.75
DC2 3.33 1.93 0.86
DC3 3.16 1.91 0.91
Attitudes Toward
Chatbots
ATT1 5.58 1.19 0.85 0.87 0.87 0.63
ATT2 5.47 1.30 0.79
ATT3 5.43 1.33 0.73
ATT4 5.64 1.23 0.80
1
https://www.sba.gov/document/support–table-size-standards
X. Lin et al.
8. Industrial Marketing Management 101 (2022) 45–56
52
medium-to-large firms deal with more clients and interactions can be
less personal (Visentin & Scarpi, 2012), they probably prefer chatbots
that support personalized interactions with customers.
We also find that null decision-making (H4c) can result in discomfort
with using chatbots. This finding further emphasizes that employees feel
concerned that chatbots lack judgement marking, an important human
quality (Murtarelli et al., 2021). This effect is stronger for small firms as
seen in post-hoc analysis. Since smaller firms deal with fewer clients and
manage less customer data, they may lack deeper knowledge regarding
customer profiles. Therefore, marketing employees may desire tools
which can help them make decisions, and thus feel more concerned
about null decision-making.
It is worth noting that availability is not significantly related to
chatbot effectiveness, which is not consistent with previous arguments
from the literature (Lalicic & Weismayer, 2021). One possibility is,
because chatbots lack decision-making capabilities (i.e., null decision-
making), marketing employees still need to maintain some level of
involvement and supervision when dealing with customer inquiries and
fulfilling customer needs. Therefore, even if a chatbot is available 24/7,
employees may feel that this availability is ineffective during the hours
they themselves are not also available. An alternative possibility is that
Table 5
Correlation between constructs and square root of AVEs (on diagonal).
1 2 3 4 5 6 7 8 9
1 Automatability 0.73
2 Personalization 0.41 0.81
3 Availability 0.58 0.12 0.81
4 Limited Understanding 0.15 0.04 0.13 0.84
5 Lack of Emotion 0.13 − 0.08 0.16 0.40 0.83
6 Null Decision-Making − 0.05 − 0.07 0.01 0.41 0.39 0.85
7 Chatbot Effectiveness 0.54 0.62 0.33 0.03 0.03 − 0.06 0.73
8 Discomfort Using Chatbots − 0.10 − 0.04 − 0.02 0.28 0.20 0.57 − 0.13 0.86
9 Attitudes Toward Chatbots 0.55 0.56 0.23 0.05 − 0.11 − 0.16 0.67 − 0.24 0.79
H3c (n.s).
H3a (.29***)
Attitudes
toward Chatbots
Chatbot
Effectiveness
Discomfort with
Using Chatbots
Personalization
Automatability
Availability
Lack of
Emotion
Limited
Understanding
Null Decision-
Making
H1 (.65***)
H2 (-.16**)
H3b (.49***)
H4a (n.s.)
H4b (n.s.)
H4c (.56***)
R2
= .48
R2
= .49
R2
= .33
** p < .01
*** p < .001
Fig. 2. Results of model testing.
Table 6
The moderating effect of firm size.
Small firms
(N = 90)
Medium-to-large
firms (N = 138)
Sig.
Diff.?
H1: Effectiveness → Attitudes
toward Chatbots
0.59*** 0.70*** <***
H2: Discomfort with Using
Chatbots → Attitudes toward
Chatbots
− 0.22* − 0.13* <***
H3a: Automatability → Chatbot
Effectiveness
0.33* 0.28** >**
H3b: Personalization → Chatbot
Effectiveness
0.45*** 0.54*** <***
H3c: Availability → Chatbot
Effectiveness
0.08 0.07 bns
H4a: Limited Understanding →
Discomfort with Using Chatbots
0.14 0.05 bns
H4b: Lack of Emotion →
Discomfort with Using Chatbots
− 0.13 0.01 bns
H4c: Null Decision-Making →
Discomfort with Using Chatbots
0.63*** 0.51*** >***
* p < .05; ** p < .01; *** p < .001; Sig. Diff. = Significant difference; bns = both
are non-significant.
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9. Industrial Marketing Management 101 (2022) 45–56
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employees expect chatbots to function 24/7 anyway and therefore
consider constant availability as the minimum requirement.
It is also worth noting that limited understanding and lack of
emotion do not have significant impacts on discomfort with using
chatbots. Marketing employees have access to information about the
current progress of chatbot development and its limitations. Thus, they
do not feel too concerned about these two disaffordances since they can
always intervene when chatbots are interacting with customers. In the
following, we summarize our implications for both theory and
practitioners.
5.1. Implications for theory
Our study makes two main theoretical contributions. First, we extend
the technology affordance theory to the context of chatbots and identify
three relevant types of chatbot affordances: automatability, personali
zation, and availability. Chatbots have been actively used to support
customer services in many companies. As customers move from human-
to-human interactions to human-to-technology interactions, it is
important to understand which aspects of chatbots can facilitate effec
tive interactions with customers in this new setting. Our conceptuali
zation can thus shed light on the question of how technology affordances
can be facilitated based on the unique features of a technology. This can
also help researchers understand how users may use a technology to
achieve their goals. Our results show that availability does not have any
impact on chatbot effectiveness. On the other hand, automatability and
personalization can enhance marketing employees' perceptions of
chatbot effectiveness. Furthermore, the effect of personalization is
greater than that of automatability, indicating that marketing em
ployees focus more on how to use chatbots to provide customized ser
vices to their customers. These findings provide further evidence that
the affordance theory can play a key role in explaining how a technology
can build its effectiveness and generate outcomes (e.g., Leidner et al.,
2018; Lin & Kishore, 2021; Sæbø, Federici, & Braccini, 2020). By
adopting the technology affordance theory, our study provides empirical
evidence to develop and validate the scales for chatbot affordances. It
further enriches the technology affordance theory and extends it to the
most recently developed technologies. Thus, we contribute to the liter
ature by identifying the technology affordances of the emerging chatbot
technology and examining their impacts.
Although the concepts of affordance in general and technology
affordances in particular have gained increasing attention in the litera
ture (e.g., Leonardi, 2013; Strong et al., 2014; Treem & Leonardi, 2013),
few scholars have attempted to explore disaffordance, which is consid
ered an underexplored concept (Wittkower, 2016). Thus, our second
contribution is to provide some understanding of disaffordance theory
by contextualizing technology disaffordance and empirically testing its
impacts in the context of chatbots. More specifically, we identify three
types of chatbot disaffordances—limited understanding, lack of
emotion, and null decision-making—and then investigate their effects
on discomfort with using chatbots. Our analysis shows that limited un
derstanding and lack of emotion do not significantly influence discom
fort. On the other hand, null decision-making can increase discomfort.
These findings indicate that technology disaffordance can serve as a
theoretical lens for revealing what causes users' negative perceptions of
a technology. Thus, our study contributes to the literature by offering a
novel perspective on the underexamined effects of technology dis
affordance as the potential disabler of chatbot adoption.
Additionally, we confirm the roles of chatbot effectiveness and
discomfort with chatbots in developing employees' psychological per
ceptions of chatbots (e.g., Castelo et al., 2019). These are valuable
findings that can offer empirical evidence to support how companies
may effectively implement chatbots based on growing understandings of
employees' psychological statuses. Our study also provides evidence
from post-hoc analysis that the firm size moderates the respective effects
of chatbot affordances and chatbot disaffordances on chatbot
effectiveness and discomfort with using chatbots. Specifically, auto
matability more strongly affects chatbot effectiveness in small firms,
while personalization more strongly affects chatbot effectiveness in
medium-to-large firms. In addition, null decision-making more strongly
affects discomfort with using chatbots in small firms. These findings
offer additional insights into understanding how affordances and dis
affordances influence employees' perceptions of chatbots across firms of
different sizes. This study thereby contributes to the literature of psy
chological perceptions of technology through extending it to chatbots
and empirically testing the moderating effects of firm size.
5.2. Implications for practice
Our study also has important practical implications. Our results
signify that chatbot effectiveness is essential for marketing employees to
form more positive attitudes about the use of chatbots in their organi
zation. Companies using chatbots to support interactions with their
customers need to enhance chatbot effectiveness, so that their em
ployees can perceive chatbots more favorably and become willing to
adopt them. In addition, our post-hoc analysis places the importance of
effectiveness higher for medium-to-large firms than small firms.
Medium-to-large firms typically have more resources to adopt advanced
information technology tools to support their customer services, which
mitigate cost concerns and possibly isolate the effectiveness of the tools
as the sole key in building employees' attitudes. As such, medium-to-
large firms need to pay additional attention to the effectiveness of
chatbots when adopting chatbots.
Our study indicates that automatability and personalization are
important types of chatbot affordances that enhance chatbot effective
ness. Therefore, companies need to collaborate with chatbot vendors to
ensure that these two types of chatbot affordances are supported.
Moreover, our post-hoc analysis outlines which affordance should be
emphasized for small versus medium-to-large firms.
Regarding automatability, companies could provide frequently
asked questions and answers to vendors so that they can configure
chatbots to answer these questions automatically. Further, our post-hoc
analysis evinces a greater effect of automatability for small firms. Since
small firms likely have fewer marketing employees to provide customer
services, automatability is an important affordance so chatbots can
resolve customers' routine questions and concerns.
Regarding personalization, data from historical transactions and
customers profiles are also needed to let chatbots run analysis and
provide customized responses to certain customers. Further, our post-
hoc analysis reveals that the effect of personalization is larger for
medium-to-large firms. Personalization is a useful tool in providing
tailored services to the larger customer base of these firms.
The effect of availability is not significant, suggesting that marketing
employees do not perceive availability as an important affordance to
improve chatbot effectiveness. Therefore, availability may be less
emphasized during chatbot adoption, especially when a certain level of
involvement from marketing employees is needed for customized
interactions.
On the other hand, firms need to reduce employee discomfort with
using chatbots. Our results negatively relate discomfort with using
chatbots to employees' attitudes toward chatbots. Further, our results
highlight null decision-making as a vital type of disaffordance leading to
discomfort. In other words, employees do feel uncomfortable with the
chatbots' inability to make decisions. This can be an important direction
for the future development of chatbots. Although important decisions
are still made by marketing employees and managers, chatbots may be
developed to make simple decisions related to low-value and low-risk
purchases (e.g., office supplies). Additionally, our post-hoc analysis
measures higher impacts of null decision-marking for small firms.
Smaller firms may not have enough customers to develop detailed
customer profiles. Therefore, marketing employees need to rely more on
other tools, such as chatbots, to help them make decisions, and managers
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10. Industrial Marketing Management 101 (2022) 45–56
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from small firms need to pay more attention to this feature during
chatbot adoption. Employees seem to feel less concerned about a chat
bot's limited understanding and lack of emotion. Nevertheless, because
these two types of chatbot disaffordance are still positively related to
discomfort, vendors also need to make an effort to resolve them in the
long term.
6. Conclusion
Chatbots have been increasingly used in B2B marketing. In our study
we examined how marketing employees develop their attitudes toward
the use of chatbots within organizations. We argued that chatbot
affordances and disaffordances influence employees' perceptions (i.e.,
effectiveness and discomfort with using chatbots), which in turn affect
their attitudes. Survey data were collected from 132 B2B marketing
employees, and the results provided strong support for our model.
Our study has a few limitations. First, we recruited our sample using
a survey company. Although our participants are B2B marketing em
ployees from a variety of backgrounds, our sample may still be biased.
Second, we focused on the context of B2B, and the results may not hold
in the context of B2C. For example, B2C deals with individual con
sumers, and it is possible that certain types of affordances (e.g., avail
ability) and disaffordances (e.g., the lack of emotion) are more
important in the context of B2C. Third, we focused on American em
ployees. It is possible that employees from other cultures may emphasize
different types of affordance and disaffordance.
Future studies can extend our research in several ways. First, addi
tional types of chatbot affordance and disaffordance can be proposed in
B2B as well as in other contexts. Our study did not differentiate chatbots
by type, so while we attempt to increase the generalizability of this
study, it is still possible that certain types (e.g., human-like versus not
human-like) of chatbots may have specific affordances or disaffordances
that are not captured in this study. The stated effects of chatbot affor
dances and disaffordances may also vary across different types of chat
bots. Second, future researchers can examine how different types of
chatbot affordances and disaffordances influence marketing employees'
work performance and well-being. Third, our study examined the
moderating effect of firm size. Future studies can examine how other
relevant moderators, such as individual characteristics and industry
types (e.g., relationship-intensive industries versus non-intensive ones),
moderate the relationships between chatbot affordances and dis
affordances and employees' perceptions and adoptions.
While we focus on chatbots to conceptualize different types of
affordances and disaffordances, certain affordances and/or dis
affordances proposed in this study can be relevant to other types of AI.
Virtual reality (VR), for example, can simulate products (Courtney &
Van Doren, 1996) and generate utilitarianism and hedonism (Pizzi,
Scarpi, Pichierri, & Vannucci, 2019; Sung, Bae, Han, & Kwon, 2021). It
is possible that the affordance of availability may play a more important
role in the context of VR, as consumers will no longer need to go to
physical stores to view products (Pizzi et al., 2019). The disaffordance of
null decision-making can be relevant for VR as well. Although VR allows
users to view 3-D virtual data, it cannot make decisions for users
(Courtney & Van Doren, 1996). Moreover, VR has its own distinct ca
pabilities, such as eliciting certain emotions from users (Shin, 2018).
Future studies are needed to determine how the affordances and/or
disaffordances proposed in this study may influence employees' per
ceptions, and develop affordances and disaffordances in other contexts
of AI such as VR.
Acknowledgements
This study is supported by the internal grant from West Texas A & M
University.
Appendix: Measures
Automatability (self-development)
AUT1 Chatbots can automate answers to customers' most-asked questions.
AUT2 Chatbots can automate repeated tasks when interacting with customers.
AUT3 Chatbots can handle more questions from customers automatically.
Personalization (self-development)
PER1 Chatbots can provide personalized experiences to customers.
PER2 Chatbots can deal with customers' specific needs.
PER3 Chatbots can take into account customers' unique circumstances.
PER4 Chatbots can deliver immediate one-on-one responses based upon exactly what customers demand.
Availability (self-development)
AVA1 Chatbots can interact with customers 24 h a day and 7 days a week.
AVA2 Chatbots can interact with customers whenever they want to.
AVA3 Chatbots can interact with customers at any time of the day.
Limited understanding (self-development)
LU 1 Chatbots cannot understand a new context that was not previously taught during customer interactions.
LU2 Chatbots cannot understand new queries of customers not previously taught.
LU3 Chatbots cannot understand new questions of customers that were not asked previously.
Lack of emotion (self-development)
LE1 Chatbots cannot express emotions during customer interactions.
LE2 Chatbots cannot create emotional connections with customers.
LE3 Chatbots cannot feel customers' emotions during customer interactions.
Null decision-making (self-development)
ND1 Chatbots cannot make decisions during customer interactions.
ND2 Chatbots have poor decision-making capability during customer interactions.
ND3 Chatbots lack in decision-making during customer interactions.
Chatbot Effectiveness (Castelo et al., 2019)
EFF1 I can see the benefits in chatbots that can deal with customers' requests better than humans.
(continued on next page)
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(continued)
Automatability (self-development)
AUT1 Chatbots can automate answers to customers' most-asked questions.
EFF2 Chatbots that can deal with customers' requests could be useful.
EFF3 I believe chatbots can perform well during customer interactions.
Discomfort with using Chatbots (Castelo et al., 2019)
DC1 Using chatbots to interact with customers makes me uncomfortable.
DC2 Using chatbots to interact with customers goes against how I believe computers should be used.
DC3 Using chatbots to interact with customers is unsettling.
Attitude toward Chatbots (Klobas et al., 2019)
ATT1 Using chatbots is a (good/bad) idea.
ATT2 Using chatbots is a (wise/foolish) idea.
ATT3 Using chatbots is a (pleasant/unpleasant) idea.
ATT4 Using chatbots is a (positive/negative) idea.
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Xiaolin Lin is an assistant professor of computer information systems in the Department of
Computer Information and Decision Management, Paul and Virginia Engler College of
Business, West Texas A&M University. He received his Ph.D. in information systems from
Washington State University. His interests are the impacts of IT on e-commerce and
healthcare, cyber security, and gender differences in IT behavioral research. Dr. Lin has
published or has forthcoming papers in premier journals including Journal of Business
Ethics, Decision Sciences, Information & Management, International Journal of Electronic
Commerce, Industrial Marketing Management, and International Journal of Information
Management, among others. He has also presented numerous papers at international and
national conferences.
Bin Shao is a professor of decision management & Terry Professor of Business in the
Department of Computer Information and Decision Management, Paul and Virginia Engler
College of Business, West Texas A&M University. She earned her Ph.D. from University of
Illinois at Urbana-Champaign. Her current research interests are mainly in the interdis
ciplinary areas of information systems, decision management, and marketing. Her works
have been published in International Advances in Economic Research, International Journal of
Applied Management Science, Journal of Supply Chain and Operations Management, Journal of
Management Information and Decision Sciences, and many others.
Xuequn (Alex) Wang is a Senior Lecturer in Edith Cowan University. He received his Ph.
D. in Information Systems from Washington State University. His research interests
include social media, privacy, e-commerce, and human-computer interaction. His research
has appeared in MIS Quarterly, Information Systems Journal, Information & Management,
Communications of the ACM, ACM Transactions, and Communications of the Association for
Information Systems, among others.
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