Perception and Adoption of Customer Service Chatbots among

Millennials: An Empirical Validation in the Indian Context

Himanshu Joshi

International Management Institute, New Delhi, India

Keywords: Chatbots, UTAUT, Trust, Security, Adoption, Satisfaction, Millennials, India.

Abstract: The last decade is witness to several successful automation efforts like customers service chatbots. Besides

reducing costs for companies, chatbots saves time, effort, and enhances customer experience. Millennials

being aspirational, educated and technology savvy find chatbots suited to the way they seek information.

While there are several studies on technology adoption, work on chatbot adoption among millennials is scanty.

The purpose of this study is to examine the factors which influence user intention, adoption and satisfaction

related to chatbots. Hence, the objective is to develop a conceptual model through the extension of the Unified

Theory of Acceptance and Use of Technology (UTAUT) in the context of chatbot adoption. A mixed method

approach was employed characterized by qualitative data collection through five personal interviews followed

by a quantitative web-based survey. The data was collected from 60 users of chatbot applications. The

proposed model depicting 13 hypothesized relationships was estimated using the partial least squares-

structural equation modelling (PLS-SEM) approach. The results show that performance expectancy and social

influence significantly influence behavioural intention. Trust and facilitating conditions were found to impact

satisfaction significantly. With respect to adoption, facilitating conditions, satisfaction and behavioural

intention were found to have a positive but insignificant impact.

1 INTRODUCTION

The last decade is witness to the increasing popularity

of chatbots due to advancements in technology

innovations like artificial intelligence and natural

language processing. Gone are the days when

organizations used to route their consumer concerns

or complaints directly to their call centers executives.

Chatbots have emerged as an intermediary layer

between the user and the customer care executives

which filters and redirects the concerns depending on

its intelligence. A chatbot also known as

conversational agents is a software program that

simulates and mimics human conversations through a

website or an application and helps users in finding

relevant answers to their concerns. These programs

continuously learn, evolve, and adapt to user

requirements and offer high degree of personalized

experience which makes it appear as highly personal,

smart, useful, and responsive. As per BusinessInsider

(2019), the chatbot market size is projected to grow at

https://orcid.org/0000-0002-4774-7983

a CAGR of 29.7% from USD 2.6 billion in 2019 to

USD 9.4 billion by 2024.

Due to the advantages associated with chatbots, it

is emerging as a preferred medium in the customer

service domain. Factors like technology

advancements, demand for self-service and the

convenience of 24/7 assistance are fuelling the

growth of chatbots. According to the Chatbots

Magazine (2018) State of Chatbots Report 2018, the

most common frustrations reported by consumers

included hard to navigate websites (34%), inability to

get answers to simple questions (31%), and difficulty

in finding essential details about a business (28%).

Due to the inherent benefits, numerous customer

service chatbot applications have come up catering to

various industries like banking, insurance, food

delivery, online retail, hospitality, education,

healthcare, ticket bookings to name a few. However,

there are various factors which restrict the growth of

this market. These include lack of awareness about

chatbot applications, low technology skillset, access

Joshi, H.

Perception and Adoption of Customer Service Chatbots among Millennials: An Empirical Validation in the Indian Context.

DOI: 10.5220/0010718400003058

In Proceedings of the 17th International Conference on Web Information Systems and Technologies (WEBIST 2021), pages 197-208

ISBN: 978-989-758-536-4; ISSN: 2184-3252

197

to affordable internet, fear about data privacy and

confidentiality etc. According to a US study by

EMarketer (2018), the challenges of using chatbot

included too many unhelpful responses, redirect to

self-serve FAQs, bad suggestions, pop-up chatbot

prompts, unnecessary pleasantries, too long to

respond and lack of personalization. As per Chatbots

Magazine (2019), according to Spiceworks,

respondents reported the following about chatbots:

chatbots often misunderstood the nuances of human

communication (59 percent), chatbots performed

commands inaccurately (30 percent), chatbots had

difficulties in understanding accents (20 percent).

According to Mantra Labs (2019), both businesses

and consumers in India consider telephone and email

as most preferred channels even though average time-

to-resolution through email was 2 hours and 17

minutes. The survey also found that majority (59

percent) of them prefer to talk to an actual person for

customer service needs.

The paper is structured as follows. In the next

section, a brief overview of relevant literature is

provided before detailing the conceptual model and

research hypotheses development, research

objectives and the methodology. Then, the analysis of

data and findings are presented. Conclusion

discussing the managerial and academic implications

are discussed next. The last section presents the

limitations and future research directions.

2 LITERATURE REVIEW

There are various theories and models which explain

the acceptance and adoption of new technologies.

2.1 TAM

The Technology Acceptance Model (TAM) is one of

the most discussed and cited models of technology

adoption which explains why users accept and use a

technology. The model has two key constructs -

perceived ease of use (PEOU) and perceived

usefulness (PU) which explains user attitude,

intention, and actual usage. PEOU is defined this as

"the degree to which a prospective user believes that

using a particular system would be free from effort"

while PU is defined as "the degree to which a

prospective user believes that using a particular

system would enhance his or her job performance"

(Davis et al., 1989). Prior research work (Autry et al.,

2010; Gangwar et al., 2014) have consistently shown

that PEOU and PU explain 40% of the variance in

individuals’ intention to use and subsequent adoption

of a technology. Despite its frequent use by

researchers, TAM is often criticized for diverting

researchers' attention away from other important

research issues and creating an illusion of progress in

knowledge accumulation (Benbasat and Barki, 2007).

2.2 UTAUT

The unified theory of acceptance and use of

technology (UTAUT) is another well cited model to

explain user intention and behaviour associated with

a technology adoption. Proposed by Venkatesh et al.

(2003) it comprises of four constructs: performance

expectancy (PE), effort expectancy (EE), social

influence (SI) and facilitating conditions (FC). EE of

the UTAUT model can be considered as PEOU of the

TAM model as both focus on the ease-of-use aspect.

Similarly, PE is similar PU as both focus on

improving business performance. The UTAUT model

is a result of the synthesis of eight different theories

of technology acceptance: innovation diffusion

theory (IDT), theory of reasoned action (TRA),

theory of planned behaviour (TPB), the social

cognitive theory (SCT), the motivational model

(MM), the model of perceived credibility (PC)

utilisation, technology acceptance models (TAM) and

a hybrid model combining constructs from TPB and

TAM (C-TPB-TAM). A meta-analysis of 74

empirical studies on UTAUT from 2003 to 2013

revealed how parsimonious, accurate, and robust

UTAUT is at predicting acceptance and use of

technology (Khechine and Lakhal, 2016) with

behavioural intention emerging as the most often

measured dependent variable operationalized as a

proxy for system use.

2.3 Cognitive Model of Satisfaction

Oliver (1980) proposed this model which expresses

consumer satisfaction as a function of expectation and

expectancy disconfirmation. In other words,

satisfaction can be viewed as the difference between

user expectations and perceived performance.

According to Liao et al. (2009) system characteristics

of an information system create outcome expectations

which results in positive or negative feelings and in

turn determines user acceptance. The pre- and post-

usage experience results in satisfaction or

dissatisfaction, which is believed to influence attitude

change and purchase intention.

The two primary constructs of TAM and UTAUT,

that is, PEOU/EE and PU/PE can be viewed as

characteristics associated with a chatbot platform

which determines the acceptance, adoption and

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subsequent satisfaction. In the present study, the

UTAUT model along with trust and security has been

used.

Table 1: Summary of Recent Studies on Application of

Chatbots.

Author/

Model

PEOU

/EE

PU/

SI FC TR PR

Araujo and

Casais

(2020)/TAM

✓ ✓

Pillai and

Sivathanu

(2020)/TAM

✓ ✓

✓

Chatterjee

and

Bhattacharjee

(2020)/UTA

✓ ✓

✓

Kasilingam

(2020)/UTA

UT2

✓ ✓

Gansser and

Reich

(2021)/UTA

UT2

✓ ✓ ✓

Notes: PEOU/EE: Perceived Ease of Use/Effort

Expectancy, PU/PE: Perceived Usefulness/Performance

Expectancy, SI: Social Influence, FC: Facilitating

Conditions, TR: Trust, PR: Perceived Risk

3 RESEARCH HYPOTHESES

AND CONCEPTUAL MODEL

To explain the intention and adoption of chatbots

among millennials in India, the UTAUT model is

used as the theoretical basis. The following sub-

sections discussed the development of the hypotheses

to explain user intention, adoption, and satisfaction

with chatbots.

3.1 Effort Expectancy (EE)

Effort Expectancy can be defined as “the degree of

ease associated with the use of the system”

(Venkatesh et al., 2003). Araujo and Casais (2020)

conducted a study involving Portuguese respondents

and used the TAM model to determine customer

acceptance of shopping-assistant chatbots. They

found that PEOU use significantly influences attitude

toward chatbots which further has a positive influence

on behavioural intention. The factor PEOU is

identical to effort expectancy which is defined as the

expected effort required in doing using a chatbot. As

per prior research, PEOU/EE has been reported to

play an important factor in influencing the

behavioural intention for chatbot adoption in

hospitality and tourism (Pillai and Sivathanu, 2020);

higher education (Chatterjee and Bhattacharjee,

2020); online shopping (Kasilingam, 2020). More

recent studies (Nguyen et al.., 2021; Seo and Lee,

2021) have used a similar construct - system quality,

which focuses on the reliability, ease of use, response

time, and availability of chatbot systems. Based on

the literature, the following hypothesis is proposed:

H1: Effort expectancy has a positive influence on the

behavioural intention to use chatbots

3.2 Performance Expectancy (PE)

Performance expectancy is defined as “the degree to

which the user expects that using the system will help

him or her to attain gains in job performance”

(Venkatesh et al., 2003). When users perceive chatbot

services to be helpful (seeking information, online

transactions, prompt responses, practical solutions) it

creates a perception of improved experience resulting

in continuance intention (Nguyen et al., 2021).

Gansser and Reich (2021) employed the constructs of

UTAUT2 model and conducted a study involving

three segments of German chatbot users (mobility,

household and health). They found that performance

expectancy played a significant role in explaining

behavioural intention and use behaviour towards

artificial intelligence products.

Further, if the users feel that the chatbot system is

too complex and requires extensive mental effort, the

effort in learning to use the system may outweigh the

relative benefits associated with it. In other words,

effort expectancy determines the extent to which the

chatbot system would enable the user to better

perform the job and enhance the performance. This

savings in terms of time and effort can be used by the

user for some other job-related activity and enhance

productivity. Davis (1989) provides the justification

and the linkage between PEOU and PU.

Based on the above justification, we propose the

following two hypotheses:

H2: Performance expectancy has a positive influence

on the behavioural intention to use chatbots

H5: Effort expectancy has a positive influence on the

Performance Expectancy to use chatbots

Perception and Adoption of Customer Service Chatbots among Millennials: An Empirical Validation in the Indian Context

199

3.3 Social Influence (SI)

Social Influence can be defined as “the degree to

which an individual perceives that important others

believe he or she should use the new system”

(Venkatesh et al., 2003). As per the theory of

reasoned action (TRA), the behavioural intention is

influenced by an individual positive or negative

feeling which are developed because of the influence

of other individuals known to the subject (Fishbein

and Ajzen, 1975). In technology adoption, this is

referred to as subjective norm which is the degree to

which a user believes that his/her peer group (friends,

superiors) influences the use and adoption behaviour

(Taylor and Todd, 1995). Subjective norms or social

influence can be viewed as informal agreed norms

between the user and social influencers where the

user is expected to comply with the same. It is

believed that stronger is the social influence from the

peer group, the stronger would be the behavioural

intention.

Therefore, this reasoning leads to hypothesize the

following:

H3: Social Influence has a positive influence on the

behavioural intention to use chatbots

3.4 Facilitating Conditions (SI)

Facilitating conditions can be defined as the degree to

which an individual believes that an organizational

and technical infrastructure exists to support use of

the system. It comprises of external factors in the

environment that make an act easy to accomplish

(Thompson et al., 1991) and that exerts an influence

over a person desire to perform a task (Teo et al.,

2007). According to Kasilingam (2020), consumers

are more likely to adopt smartphone chatbots if the

technical infrastructure for it already exists. In

information technology context, it consists of

organizational and technical infrastructure to support

use of the system (Agarwal et al., 2009). Prior

researchers (Lin, 2011; Shaw, 2014) have reported

that facilitating conditions like individual skillset,

availability of affordable internet, smartphones, legal

institutions etc. can influence the intentions of users

in adopting chatbots. According to Chatterjee and

Bhattacharjee (2020), the existence of good quality

technical infrastructure and availability of requisite

user training can facilitate the intention to adopt a new

technology. Based on these arguments we propose the

following hypotheses:

H4: Facilitating Conditions has a positive influence

on the behavioural intention to use chatbots

H6: Facilitating Conditions has a positive influence

on the chatbot adoption

H7: Facilitating Conditions has a positive influence

on the satisfaction with chatbots

3.5 Perceived Risk (PR)

Perceived risk (PR) is commonly thought of as an

uncertainty regarding possible negative consequences

of using a product or service. It can be defined as the

potential for loss in the pursuit of a desired outcome

of using an online service.

Chatbots being a relatively new technology and

users having limited exposure to it may often result

into it being perceived as risky. Since chatbots

simulate conversations with humans over the Internet,

it can be used by hackers to use social engineering

techniques to impersonate themselves and capture

confidential, private and sensitive data. In areas

where there is a need for limited interactivity in terms

of predefined well-structured queries and responses,

a chatbot creates good engagement. However, in

situation where the communication is unstructured,

complex and uncertain, discrepancies in responses

may create confusion in the minds of the user. Hence,

the degree of perceived risk towards chatbot can

influence the trust and the intention to adopt it. It is

logical to believe that a greater perceived risk would

negatively influence the trust and intention towards

chatbots. Thus, we propose that:

H8: Perceived risk has a negative influence on the

behavioural intention to use chatbots

H9: Perceived risk has a negative influence on the

trust associated with chatbots

3.6 Trust (TR)

Baier (1986) considers trust as "the belief that others

will, so far as they can, look after our interests, that

they will not take advantage or harm us”. Trust in

technology can be defined as “a belief that a specific

technology has the attributes necessary to perform as

expected in a given situation”. (McKnight et al.,

2011). It can also be defined as the degree to which

users are confident in the reliability and quality of the

chatbot systems (Caceres and Paparoidamis, 2007).

According to Komiak (2003), trust comprises of two

dimensions: cognitive and emotional. While

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cognitive trust expects that a chatbot service provider

will have the necessary competence, benevolence and

integrity while emotional trust is the feeling of

security and comfort with the service provider.

In this study, we have examined consumer

rationality for examining trust by including

statements which capture the competence,

benevolence, and integrity with chatbots. Eren (2020)

in a study involving bank chatbots users from Turkey

found perceived trust in chatbots to significantly

influence customer satisfaction. While trust in the

context of online customer centric services like online

banking, mobile banking, social media etc. has been

extensively researched, its inclusion in chatbot

adoption studies is scanty. The present study

combines trust and perceived risk with the UTAUT

model and hypothesizes it to be one of the key

antecedents of user satisfaction. Hence, the following

hypothesis is formulated with respect to trust and

satisfaction:

H10: Trust has a positive influence on the satisfaction

with chatbots

3.7 Satisfaction (ST)

According to Nguyen et al. (2021), if users’

expectations from chatbot services are fulfilled and

they feel satisfied after experiencing the same, those

experiences will not only shape their intention but

will push them to continue using chatbots in the

future. Eren (2020) found that if customer

expectations from chatbots are met, it results in a

positive and significant impact on customer

satisfaction. In another study involving using chatbot

services for luxury brand, it was found that perceived

communication accuracy, credibility and competence

positively influences satisfaction (Chung et al., 2020).

Based on the above discission, the following

hypotheses are proposed:

H11: Satisfaction has a positive influence on the

behavioural intention to use chatbots

H12: Satisfaction has a positive influence on the

chatbots adoption

3.8 Behavioural Intention (BI)

Behavioural intention (BI) is defined as "a person's

subjective probability that he will perform intention

some behaviour" (Fishbein and Azjen, 1975). If there

is a strong intention, then the likelihood of that

converting or resulting in an action or behaviour is

very high. In other words, the existence of BI is

critical in shaping a technology usage behaviour.

Prior studies have provided considerable evidence of

the significant effect of BI on actual usage or adoption

in technology acceptance studies (Venkatesh et al.,

2003; Tarhini et al., 2015)>

H13: Behavioural intention has a positive influence

on the chatbot adoption

3.9 Adoption (AD)

The actual system usage or the adoption is the final

stage where a user starts using a technology. It can

also be defined as a user’s initial acceptance of a

technology.

Based on the review of literature various

hypotheses were derived. Figure 1 shows the

proposed conceptual model and the related research

hypotheses. The model comprises of six factors of the

UTAUT model (performance expectancy, effort

expectancy, social influence, behavioural intention,

and adoption). An extension of the model has been

proposed by the inclusion of three additional factors

in the chatbot context (perceived risk, trust and

satisfaction). The conceptual model depicts the

relationships between various antecedents of

behavioural intention, adoption and satisfaction.

Insights from the five personal interviews also

strengthened our conceptual model development.

Figure 1: Conceptual Model and Hypotheses.

4 OBJECTIVES OF THE STUDY

The objectives of the study are: 1) To carry out a

systematic review of literature on chatbot adoption

and examine the underlying models 2) To identify the

most frequently discussed constructs by previous

studies and supplement the same with the findings of

the qualitative analysis 3) To propose a conceptual

model and validate the hypothesized relationships

using a quantitative survey carried out among a

sample of Indian chatbot users.

Perception and Adoption of Customer Service Chatbots among Millennials: An Empirical Validation in the Indian Context

201

5 METHODOLOGY

To fulfil the research objectives of the study, a three-

step process was employed. In the first step, an

exploratory study was carried out by reviewing the

existing literature on Chatbot adoption. An extensive

search was carried out in bibliographic databases

using relevant keywords like “chatbot adoption”,

“chatbot intention and satisfaction”, “factors

influencing chatbot adoption”, “antecedents to

chatbot adoption”, “Unified Theory of Acceptance

and Use of Technology”, “UTAUT” etc. The

obtained results were examined for their recency,

appropriateness, and popularity in terms of citations.

After obtaining the list of studies on chatbots, a cross-

table was prepared with authors arranged along the

rows and constructs along the columns. The

underlying model used in the studies was also

documented. Based on the mapping between the two

(see Table 1), the constructs were clustered to

determine the most frequently used constructs.

In the second step, five personal interviews were

conducted with chatbot users labelled as R1 to R5. A

screening question was used to gauge whether the

chatbots users were aware of the application. Six

questions were framed some of which were incorrect

and those respondents who correctly answered all the

questions were shortlisted for personal interview. An

interview template was prepared which included

open-ended questions related to frequently used

chatbot applications; reasons for using chatbots;

positives and negatives about chatbot applications;

factors which influence the chatbot adoption

behaviour; experience using chatbots; and

satisfaction. Each interview was recorded and

transcribed verbatim. Content analysis was

performed on the qualitative data and broad themes

along with statements justifying the same were

extracted. The content analysis resulted in the

generation of few statements which were added to the

established constructs as given in the review of

literature.

In the final step, the hypothesized relationships

were represented in the form of conceptual model and

a survey instrument was designed. The questionnaire

comprised of three sections. Section one comprised

of questions on frequency of chatbot usage and

frequently used chatbot applications. Section two

comprised of perception-based questions on factors

influencing behavioural intention, satisfaction and

adoption measured on a five-point Likert scale

ranging from strongly disagree (1) to strongly agree

(5). The last section captured the user demographic

details. Due to pandemic restrictions, only online

surveys were used for data collection. A convenience

sample along with purposive sample was considered

as most appropriate in the study. Convenience

sampling was deemed appropriate as the authors

personally knew the respondents. Purposive sampling

was utilised to select respondents who extensively

used the internet and chatbots. The idea behind using

these sampling techniques was to get a representative

sample. A total of 250 respondents were mailed the

online survey out of which 70 respondents filled the

survey. Ten responses were omitted as there were no

standard deviation found in their responses pertaining

to the Likert scale questions. 60 responses were

finally considered for subsequent data analysis

indicating a response rate of 24 percent. The

minimum sample size for a PLS model should be at

least ten times the largest number of inner model

paths which in our case is six. Thus, the study meets

the minimum sample requirement of 60 (Hair et al.,

2017).

The sample comprised of more males (35, 58.3%)

than females (25, 41.7%). The minimum and

maximum age of the respondents was 26 and 40

respectively with 31.5 as the median age.

The distribution of all statements in the instrument

was checked for normality distribution. The kurtosis

range was found to be -1.487 to 2.974 and skewness

range was found to be -1.288 and 0.498. Thus, for

majority of statements (35 out of 48) the skewness

and kurtosis values lie between −1 and +1 acceptable

interval. The results show that there was no major

deviation from normal distribution (Hair et al., 2017).

To examine and validate the efficacy of the

conceptual model in explaining user intention, the

current study used the Partial Least Squares Structural

Equation Modelling (PLS-SEM) technique. This

technique employs a two-stage process starting with

the assessment of the measurement model (reliability

and validity) and the estimation of the structural

model (testing the hypothesized relationships).

6 ANALYSIS AND FINDINGS

Smart-PLS 3.0 was used to estimate the measurement

and the structural model.

6.1 Estimation of the Measurement

Model

The measurement model was assessed using

discriminant validity and convergent validity. The

internal consistency was examined using Cronbach

Alpha. As evident from Table 2, the Cronbach’s alpha

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value for all constructs was found to exceed 0.70,

which indicate that the measurement is reliable (Lin

and Huang, 2008). Convergent validity refers to how

closely the statements of a multi-item construct are

related to each other. In other words it is the extent to

which a measure relates to other measures of the same

phenomenon (Hair et al., 2017). For convergent

validity, the values of composite reliability (CR)

should be at least 0.7 and the average variance

extracted (AVE) must be greater than the threshold

value of 0.5. As evident from Table 2, the composite

reliability for all constructs was found to be greater

than 0.7 and the AVE was greater than 0.50 thereby

fulfilling the conditions of convergent validity.

Table 2: Cronbach’s Alpha (CA), Composite Reliability

(CR) and Average Variance Extracted (AVE).

Constructs

CA CR AVE

EE 0.718 0.843 0.649

PE 0.868 0.904 0.655

SI 0.875 0.914 0.726

FC 0.731 0.831 0.552

PR 0.920 0.936 0.675

TR 0.887 0.915 0.644

BI 0.855 0.892 0.580

ST 0.913 0.928 0.591

AD 0.668 0.798 0.500

Discriminant validity of the constructs was assessed

using three methods a) cross-loadings b) Fornell and

Larcker criterion, and c) Heterotrait-Monotrait Ratios

(HTMT). For the first method, the indicator loading

on its own construct should be higher than the loading

on any other construct (Chin, 1998). This condition

was found to be satisfied. The discriminant validity is

satisfied if the square root of the AVE for each

construct is higher than the correlation coefficient

with other constructs (Fornell and Larcker, 1981). In

our case, all the diagonal elements which are the

square root of the AVE are more than the inter-item

correlations reported below the diagonal for the

corresponding constructs (refer Table 3). Further, it is

seen that the HTMT value is below 0.9 (range 0.156

and 0.895) between any two reflective constructs.

Since all the three conditions are satisfied,

discriminating validity is established.

Since the conditions for both convergent and

discriminant validity were met, the measurement

model was considered satisfactory.

Table 3: Discriminant Validity.

AD BI EE FC PE PR ST SI TR

AD .71

BI .57 .76

EE .64 .61 .81

FC .53 .58 .59 .74

PE .62 .75 .76 .54 .81

PR .08 .27 .29 .29 .19 .82

ST .55 .68 .60 .58 .70 .22 .77

SI .59 .67 .68 .52 .71 .13 .69 .85

TR .31 .53 .44 .51 .53 .45 .64 .50 .80

AD: Adoption; BI: Behavioural Intention; EE: Effort

Expectancy; PE: Performance Expectancy; SI: Social

Influence: FC: Facilitating Conditions; PR: Perceived

Risk; TR: Trust: ST: Satisfaction. Diagonal values are

squared roots of AVE; off-diagonal values are the estimates

of the inter-correlation between the latent constructs

6.2 Assessment of the Structural Model

To examine the problem of multi-collinearity of the

inner model, the VIF (Variance Inflation Factor) was

computed for the five endogenous constructs. It was

found that VIF varied from 1.15 to 3.25, 1.68 to 2.08

and 1.35, 1 and 1 for intention, adoption, satisfaction,

performance expectancy and trust respectively. These

values are below the threshold value 3.33

(Diamantopoulos and Siguaw, 2006). Therefore, no

evidence of multicollinearity was found in the present

research.

Since the respondents were asked to answer

questions pertaining to both independent and

dependent variables, common method bias could be a

concern. To check for the presence of common

Method Bias, Harman’s single factor test was

conducted which involves examining the unrotated

factor solution to determine if a single factor accounts

for more than 50 percent of the variance (Podsakoff

et al. 2003). The results indicate that eleven different

factors accounted for 78.59 percent of the variance.

The single largest factor accounted for 35.65 percent,

which is below the threshold, common method bias

doesn’t seem to a problem.

The structural model was estimated by applying

bootstrapping technique, which is a resampling

technique that draws many subsamples, say 5000

from the original data (Vinzi et al., 2010). The

standardized path coefficients (refer Table 4) indicate

the estimates and significance of the hypothesized

relationships between the constructs. Hypothesis H1

Perception and Adoption of Customer Service Chatbots among Millennials: An Empirical Validation in the Indian Context

203

which examines the influence of EE on BI was found

be insignificant with an opposite sign (β = - 0.109, p

=0.427). One of the plausible reasons could be that

the respondents have not explored the full capabilities

of chatbots or have not been able to interpret the

question correctly. Hypothesis H2 relating to

performance expectancy to intention was found to

have a strongest and significant relationship with

respect to intention (β= 0.479, p=0.000). As expected

and consistent with prior research on chatbots (Eren,

2020; Chatterjee and Bhattacharjee, 2020;

Kasilingam, 2020; Melián-González et al, 2021;

Gansser and Reich, 2021), the results show that

performance expectancy is the main predictor of

intention.

Hypotheses H3 (β= 0.195, p=0.068) and H4 (β=

0.163, p=0.195). pertaining to social influence and

facilitating conditions, respectively, with intention

were found to be in the hypothesized direction.

However, only social influence was found to have a

significant influence at 10 percent level of

significance. Hypothesis H5 (β= 0.764, p=0.000),

which examines the influence of effort expectancy on

performance expectancy was found to be strongest

and significant in the entire conceptual model. Thus,

greater is the degree of ease associated with a chatbot

system, greater are the perceived improvements in

personal and professional activities. Hypothesis H6

(β= 0.245, p=0.167), H12 (β= 0.213, p=0.213). and

H13 (β= 0.278, p=0.114) depicting the influence of

facilitating conditions, satisfaction and intention on

adoption were found to be positive but insignificant

indicating that the existence of facilitating conditions,

satisfaction with chatbots and intention influence

adoption although not significantly.

Hypotheses H7 (β= 0.348, p=0.001) and H10 (β=

0.458, p=0.000). which examine the influence of

facilitating condition and trust on satisfaction found

that both the constructs were significant in explaining

satisfaction with chatbots. With respect to hypotheses

H8 (β= -0.107, p=0.219) and H11 (β= 0.160,

p=0.333) which looks at the relationship between

perceived risk and satisfaction on intention it is

evident that higher is the risk, lower is the intention,

and higher the satisfaction higher is the intention.

While both hypotheses are in the right direction, the

influence on intention is insignificant. Further, H9

(β= -0.450, p=0.000) explaining the influence of

perceived risk on trust is found to be significant. In

other words, higher the risk lower would be the trust

with chatbots.

The SmartPLS tool computes the coefficient of

determination (R square) which represents a measure

of predictive power that explains the degree to which

the antecedents explain the variance in an endogenous

construct in the model. In our model, there are five

endogenous constructs namely behavioural intention,

adoption, satisfaction, performance expectancy and

trust. The R square values of these endogenous

constructs are 0.658, 0.403, 0.494, 0.583 and 0.203 in

that order. The proposed model can explain 65.8

percent of the variation in behavioural intention, 40.3

percent in adoption, 49.4 percent in satisfaction, 58.3

percent in performance expectancy and 20.3 percent

in trust.

The cross-validated predictive relevance of

structural model was estimated by calculating Stone

Geisser Q

value with an omission distance of 7

(Geisser, 1974; Stone, 1974). Higher is the value of

, higher is the predictive accuracy of the model. In

our case, the values of Q

are found to be 0.370, 0.343,

0.271, 0.162 and 0.125 respectively for the

endogenous constructs: performance expectancy,

intention, satisfaction, adoption, and trust. Since all

values are greater than zero for the endogenous

constructs, it indicates that the values are well

reconstructed, and the model has predictive

relevance.

Lastly, the effect size f² was computed for each

endogenous construct. The f² values of 0.02, 0.15, and

0.35 present small, medium, and large effects (Cohen,

1988). For the endogenous construct intention, PE

had a medium effect size, FC, SI, ST and PR had a

small effect size whereas EE has an insignificant

Table 4: Structural Model Estimates.

Hypothesis Relationship

Path

Coefficient

p- value

H1 EE -> BI (+) -0.109 0.427

H2 PE -> BI (+) 0.479 0.000*

H3 SI -> BI (+) 0.195 0.068**

H4 FC -> BI (+) 0.163 0.194

H5 EE -> PE (+) 0.764 0.000*

H6 FC -> AD (+) 0.245 0.167

H7 FC -> ST (+) 0.348 0.001*

H8 PR -> BI (-) -0.107 0.219

H9 PR -> TR (-) -0.450 0.000*

H10 TR -> ST (+) 0.458 0.000*

H11 ST -> BI (+) 0.160 0.333

H12 ST -> AD (+) 0.213 0.213

H13 BI -> AD (+) 0.278 0.114

* indicates significance at 1 percent ** indicates

significance at 10 percent

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effect size since the f² value was below 0.02.

Regarding the endogenous construct, adoption, the

exogenous constructs FC, ST and BI had a small

effect size as the f² values were 0.06, 0.037 and 0.062

respectively. For the endogenous construct,

satisfaction, FC and TR were reported to have a

medium effect size as the values obtained were 0.177

and 0.307. With respect to trust, PR had a medium

effect size of 0.254. Lastly, with respect to

performance expectancy, EE had a large effect size as

the f² value was found to be 1.4 which is greater than

0.35.

7 CONCLUSIONS AND

IMPLICATIONS

The results of the structural model indicate that of 17

the proposed 13 hypotheses, six were supported.

Further, out of the remaining seven, six were not

supported though they had the desired hypothesized

direction. In case of H2 (relationship between effort

expectancy and intention), a contrary insignificant

relationship was found.

Performance expectancy seems to be the most

important factor explaining behavioural intention.

Thus, unless a user perceives that using a chatbot will

result in superior performance and enhanced

efficiency and productivity, their intention to use it

would be limited. Use of chatbots is in terms of

queries, doubts, searches and finding relevant results.

Organizations providing chatbot services should keep

in mind that users expect instant responses and short

answers which are simple to comprehend and can

guide users to follow-up questions. These chatbots

should be able to train and re-train themselves to

evolve into intelligent conversational agents. Another

important aspect is the timing of escalating a problem

which cannot be resolved by a chatbot. Unnecessary

inundating the user with back and forth questions can

be irritating. Technology experts can build in these

expectations to enhance user ability to derive better

performance.

Although performance expectancy emerged as an

important determinant, social influence was also

perceived by users as significantly influencing

chatbot intention. Depending on the context, whether

the user is using it in personal capacity or in an

organizational context, the social influence would

vary. In personal communications or transactions

involving chatbots, the influencers could be the

friends and family. In an organization, the

management or the peer community could influence

technology adoption. Since are sample comprise of

millennials, companies offering chatbots services can

target this group through social media and mobile

advertisement to create awareness about chatbot

capabilities.

Facilitating conditions and trust emerged as key

determinants which influence user satisfaction.

Chatbot providers should create chatbot services

which can run on any basic smartphone with decent

internet connectivity. Further, the availability of the

chatbot to communicate and engage in local language

is important. Since a chatbot simulates a human

conversation through artificial intelligence, the user

expectation is that their queries would result in

relevant suggestions which would help in developing

trust with the platform. Chatbot providers must

ensure that service and information quality is good as

poor initial experiences can create doubts resulting in

loss of trust. Professional interactions, quality of

request and advice, ensuring privacy etc. can help in

building trust. Managerial implications for chatbot

providers can be drawn from the findings related to

perceived risk and trust associated with chatbots. To

ensure that user expectation of safe and secure

transaction besides privacy and confidentiality of data

is in place, awareness sessions to educate users about

what user data is collected, how its stored and

analysed must be conducted. Rewards in the forms of

coupons and cashbacks could be a way to introduce

and encourage users to validate the security of the

platform. The managerial implication of this research

is that chatbot providers must pay attention to

perceived usefulness, perceived risk, trust, social

influence and facilitating conditions so as to increase

the satisfaction, intention and adoption of chatbots.

Besides managerial applications, the research

presents an extension of the UTUAT model. The

explanatory power for the model to explain intention

is good.

8 LIMITATIONS AND FUTURE

RESEARCH DIRECTIONS

Like all studies, this study also has few limitations,

which provide directions for future research. First and

foremost, is the small sample size. While the study

meets the minimum sample size criteria, considering

the size and importance of the millennial population,

future study can be carried out with a larger sample

size. A comparison between the perceptions of the

millennial user with Gen Z could add to the existing

body of knowledge on chatbots.

Perception and Adoption of Customer Service Chatbots among Millennials: An Empirical Validation in the Indian Context

205

Secondly, the study was restricted to four

constructs adopted from UTAUT model. Since

UTAUT 2 model has additional constructs like

hedonic motivation, habit, price value etc. future

examinations with these additional constructs could

help in improving our understanding of intention and

usage of chatbots.

Thirdly, we have considered chatbot application

as a broad category. It would be worth exploring how

the hypothesized relationships in the structural model

besides the predictive power compare with respect to

different chatbot applications (for e.g. Online

Shopping, banking, healthcare, tourism to name a

few).

Lastly, we have collected demographic details

like gender, age, income, education etc. Prior

researchers have examined the moderating effect of

these demographic variables. Future studies may be

carried out in this direction.

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APPENDIX

Appendix – 1 Please indicate your agreement or

disagreement on the following statements

Performance Expectancy (PE)

(Source: Venkatesh et al., 2012)

Chatbots:

• help me accomplish things more quickly

• are useful in my daily life (N)

• enable me to complete the task efficiently

Perception and Adoption of Customer Service Chatbots among Millennials: An Empirical Validation in the Indian Context

207

• enhances my task effectiveness

• give relevant suggestions (N)

Effort Expectancy (EE)

(Source: Gefen et al., 2003; Venkatesh et al, 2012)

Chatbots:

• interaction is clear and understandable.

• are ﬂexible to interact with

• are anticipative and intuitive in nature (N)

Social Influence (SI)

(Source: Venkatesh et al, 2012)

• Peers who influence my behaviour think that

I should use chatbots

• Peers important to me think that I should use

chatbots

• Organization peers promotes and supports

the use of chatbots

• Peers whose opinion I value prefer that I use

chatbots

Facilitating Conditions (FC)

(Source: Venkatesh et al, 2012)

• I have the resources needed to use chatbots

• I have the knowledge needed to use chatbots

• Chatbots are compatible with other

technologies I use

• I know whom to seek help when I face

difficulties in using chatbots

Perceived Risk (PR)

Chatbots:

• makes me vulnerable to potential fraud (N)

• makes me feel unsafe (N)

• appear to be suspicious (N)

• can misuse your personal information (N)

• are risky (N)

• puts my privacy at risk (N)

• exposes me to an overall risk (N)

Trust (TR)

(Source: Gefen et al., 2003)

• I don’t think chatbots are harmful

• Chatbots are trustworthy

• I do not doubt the honesty of chatbots

• I feel there are adequate legal provisions for

problems with chatbots

• Chatbots do not involve any user monitoring

• Overall, I trust chatbot transactions

Behavioural Intention (BI)

(Source: Venkatesh et al., 2012)

In the next one year

• I intend to use chatbots

• I predict to use chatbots

• I plan to continue using chatbots

• I will use chatbots in my daily life (N)

• I will prefer chatbots over human interaction

(N)

Adoption (AD)

I use chatbots to:

• generate product purchase suggestions (N)

• order product online (N)

• make online reservations (N)

• to get the latest news updates (N)

Satisfaction (ST)

Bargas-Avila et al. (2009)

Suggestions made by chatbots are:

• complete

• easy to understand

• personalized (N)

• relevant

• secure (N)

• reliable (N)

• flexible

• integrated (N)

• accessible

Notes: N means new statements

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