J of Consumer Behaviour - 2023 - Sattarapu - Tomeito or Tomahto Exploring consumer s accent and their engagement with (1).pdf

S P E C I A L I S S U E A R T I C L E
Tomeito or Tomahto: Exploring consumer's accent and their
engagement with artificially intelligent interactive voice
assistants
Praveen Kumar Sattarapu1
| Deepti Wadera1
| Nguyen Phong Nguyen2
|
Jaspreet Kaur3
| Sumeet Kaur4
| Emmanuel Mogaji5
1
School of Management, GD Goenka
University, Gurgaon, India
2
School of Accounting, University of
Economics Ho Chi Minh City, Ho Chi Minh
City, Vietnam
3
VIPS, New Delhi, India
4
FORE School of Management,
New Delhi, India
5
Keele Business School, Keele University,
Staffordshire, UK
Correspondence
Emmanuel Mogaji, Keele Business School,
Keele University, Staffordshire, UK.
Email: e.mogaji@keele.ac.uk
Abstract
Artificially intelligent interactive voice assistants (AIIVAs) are developed to understand
language, but there is limited insight into their ability to understand accents. While
there have been substantial advancements in understanding multiple languages by AII-
VAs, having an understanding of variety of accents is an emerging concern. To address
these concerns, we contextualised our study in India, one of the world's most popu-
lated and diverse countries with varying accents and dialects. Study 1 collected qualita-
tive data through semi structured interviews with participants, data was subsequently
thematically analysed, and a typology was developed with respect to the context of
use and consumers' emotional and rational reactions towards AIIVAs when interacting
with accents. For Study 2, we implemented the quantitative research method. This
was done to reiterate the conceptual model formulated from the qualitative research
findings. Findings suggest that positive emotional action has emerged as the most sig-
nificant factor, followed by rational action and negative emotional action. This study
contributes significantly to the theoretical understanding of future consumer behaviour
and human-computer interaction trends. It provides practical implications for
managers, tech developers, and other companies working and using speech-to-text
automatic speech recognition to know that while they train their algorithms with
languages, they should be mindful of the diverse accents of their consumers.
1 | INTRODUCTION
English may be considered the most widely spoken language world-
wide, with 1.5 billion speakers (Siegers, 2022). However, there are
many variations as people develop and speak different accents, which
are a distinctive way of pronunciation, often associated with a particu-
lar country, area, or social class. In this digital age, speech is becoming
an integral part of our lives and it is influencing the customer journey
and the decision-making process (Nebreda et al., 2021). For example,
speaking to self-service machines at drive-through, using speech-
to-text and subtitling conversations during online meetings or using
virtual assistants to get information (Ukpabi et al., 2018). It is impera-
tive to understand how consumers with various accents engage with
these technologies and deal with their consumption-related problems
(Zolfagharian & Yazdanparast, 2017, 2018).
To gain a better understanding of accents and consumers' interac-
tion with technology, this study focuses on artificially intelligent inter-
active voice assistants (AIIVAs), also known as voice-activated
Received: 20 June 2022 Revised: 12 April 2023 Accepted: 21 May 2023
DOI: 10.1002/cb.2195
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,
provided the original work is properly cited.
© 2023 The Authors. Journal of Consumer Behaviour published by John Wiley & Sons Ltd.
J Consumer Behav. 2023;1–21. wileyonlinelibrary.com/journal/cb 1

personal assistants or smart-home personal assistants (Dellaert
et al., 2020), which are technological innovations changing the way
consumers interact with the world. AIIVAs ‘triggers intelligence
through vocal interaction’, allowing voice commands backed by artifi-
cial intelligence to have a verbal end-user interaction and be used for
performing various tasks. AIIVAs are not only used independently but
are now integrated into products like speakers, smartphones, car navi-
gation systems, and other appliances commonly used for household
and commercial purposes. ‘Google Assistant’ from Google, ‘Siri’ from
Apple, ‘Cortana’ from Microsoft, ‘Alexa’ from Amazon, and ‘Bixby’
from Samsung are the five significant AIIVAs commercially available in
the market at this point (Novak & Hoffman, 2019).
Previous studies have recognised the enormous prospects of AII-
VAs. Dellaert et al. (2020) explored how consumers make dynamic
dialogues with an AIIVA. Moriuchi (2019) presented the role of voice
technology in the engagement and loyalty of users of such devices. At
the same time, McLean and Osei-Frimpong (2019) established social
benefits like attractiveness which motivates consumers to use AIIVAs.
While much is known about the hedonic benefits, para-friendship
relationships, and perceived privacy risks of using AIIVAs, far less is
known about the impact of consumers' accents on the engagement
with AIIVAs. While most AIIVAs are developed to understand
language, there is limited insight into their ability to understand native
accents and dialects. Considering the diverse number of accents and
dialects worldwide and consumers engaging with these AIIVAs in their
accents, we propose this has consequences for consumer interaction
and the adoption of AIIVA technologies (Dellaert et al., 2020). This
thus present a future trend in consumer behaviour with practical
implications for the stakeholders of brands investing into modern day
voice assistants.
Therefore, we aim to examine these consequences for consumer
behaviour towards the engagement and adoption of AIIVAs and pro-
vide much-needed theoretical insight into voice technology reliance.
Our research addresses following questions:
1. How do consumers with different English accents engage with their
AIIVAs?
2. How do consumers react towards the accent issues during their inter-
action with their AIIVAs?
To address these research questions, data was collected using
semi structured interviews in a qualitative approach and justified using
structural equation modelling in a quantitative approach. Millennial
consumers in India were chosen as a sample as India is a country with
diverse subcultural backgrounds. The stimulus, organism, response (S-
O-R) model was used as the theoretical framework for exploring how
participants engage with their AIIVAs despite having an English
accent.
The findings of this study help us understand how millennial
Indian consumers are engaging with their AIIVAs and reacting to the
accent issues that they face during their interaction. This study con-
tributes significantly to the theoretical understanding of future con-
sumer behaviour such as usability, pattern of usage, overall user
satisfaction levels and human-computer interaction trends in engage-
ment with their AIIVAs. The study reveals consumers' emotional and
rational reactions as they manage their interactions. These insights
provide practical implications for brand managers, tech developers
and other companies working and using speech-to-text automatic
speech recognition to know that while they train their algorithms with
languages, they should be mindful of the diverse accents of their
consumers.
The significant sections of the paper are as follows. Section 1 lists
the introduction to research problem, rationale for conducting a study
on the topic of accent problems with customers using AIIVAs, motiva-
tion of the research and the gap in past literature. Section 2 discusses
all past quantitative and qualitative empirical studies conducted in the
context of voice assistants. This section aims to list the gap in past
research and introduce the use of the two theories used for the study,
namely the SOR and the dialect theory. Section 3 explains the
research methodology for the study where a mixed method approach
has been taken. This section uses voice assistants and lists the article's
data collection, sample profile, and research process. Section 4 lists
the significant findings of the research. This section discusses the
major findings from the qualitative and quantitative data analysis.
Section 5 lists the conclusion of the research. The same has been
divided into theoretical and managerial implications for the study.
2 | LITERATURE REVIEW
2.1 | Artificially intelligent interactive voice
assistant
AIIVA is crucial in enhancing the users' perceptions of the machine's
intelligence (Dwivedi et al., 2021), and they are based on natural lan-
guage processing abilities, automated reasoning, knowledge representa-
tion, and machine learning capabilities (Knote et al., 2019). The AIIVA
also could function based on the social and emotional capacities of the
consumers and is not limited to the physical features of the VA. Thus,
the AIIVA possesses anthropomorphic features like a voice, making the
consumer feel that it is a human or a companion rather than a device
(Moussawi & Koufaris, 2019). It generates a feeling of autonomy and
pro-activeness in consumers. This autonomy further enhances the abil-
ity to communicate with the AIIVA with the help of natural language
and forms a perception of the AIIVA. Further, it increases the expecta-
tion of the consumers to understand their dialect and accent with
increased accuracy, and this is where the challenge comes in; these AII-
VAs are unique systems with the input being the consumer's voice
(Knote et al., 2019). This interaction, combined with factors of accent
from the consumers, could lead to varied inputs for the same voice
assistant device, which could be challenging to understand. Research in
the past has studied the AIIVAs from varied angles. Some of the primary
studies have proven the consumer problems and expectations of con-
sumers with the AIIVAs (Tulshan & Dhage, 2019), gender roles of AII-
VAs (Woods, 2018) and self-learning among students (Moussalli &
Cardoso, 2020).
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Past studies have used many known theories to understand the
gap between the consumers' expectations and the VA when it comes
to the satisfaction of the consumers (Porcheron et al., 2017; Zeng
et al., 2017). Most researches have studied the VA in contexts includ-
ing the home, in groups, and with different demographically seg-
mented consumers like the elderly, consumers with disabilities (Hugo
et al., 2021; Pradhan et al., 2018) and children (Druga et al., 2017;
Melodia et al., 2022). Further, some studies have examined the aspect
of users' personification with VA and how the consumer gets satisfied
with the same (Purington et al., 2017; Sannon et al., 2018), and the
significant problems consumers are facing with the issues of security
and privacy (Zeng et al., 2017).
Major studies in the past (Kumar et al., 2019; Moriuchi, 2019)
have concentrated on the quantitative approach of listing the signifi-
cant factors that lead to satisfaction, privacy, trust, user experience,
and adoption. Moriuchi's (2019) study improves the effectiveness of
using AIIVA by investigating the factors that could influence the use
of AIIVA, which could affect engagement and loyalty. The study's
results point out a need for localisation of the voice assistants for
forming a relationship between consumer engagement through
technology and attitude. The AIIVA must be helpful and localised for
non-transactional activities to benefit new technologies like voice
assistants. One form of localisation is understanding the customer's
local accent and adapting accordingly for better customer satisfaction.
Acikgoz and Vega (2021) studied the drivers of usage habits of AIIVA.
They empirically explain the concept of privacy, cynicism and find the
impact on trust when using voice assistants. Hernandez-Ortega et al.
(2021) prove in their study that smart experiences could impact the
consumers' love and passion for technology which could further
increase their intimacy and commitment towards voice assistants. This
could further enhance their service loyalty. The authors have consid-
ered the interaction of AIIVAs with regional customers worldwide and
their satisfying experience as a precedent to increase service loyalty
with the voice assistant. However, there is no empirical evidence of
the same in past literature, and this is a gap in past research con-
ducted on AIIVA. Pham Thi and Duong (2022) studied the impact of
voice cues and messages on the voice assistants' social presence and
perceived expertise (anthropomorphism). They found a positive
impact of these factors on the use intention of voice assistants. In
another empirical quantitative study by Whang and Im (2021), the
results suggested that consumers could imagine the AIIVAs as
pseudo-human agents and see them detached from the service pro-
vider. This leads to a more positive perception and evaluation of the
voice assistants' results. The study proved a positive relationship
between human likeness and parasocial relationships.
Some previous qualitative studies (Buhalis & Moldavska, 2021;
Nallam et al., 2020) have been limited to a general understanding of
users' perspectives. Woods (2018) explains the relevance of feminine
personas in the voices of Google and Alexa as a form of ‘digital
domesticity’ used by the AIIVA. This is used to engage consumers and
reply to queries with the calm feminine voices of Siri and Alexa. Pecu-
liar problems faced by the consumer while using voice assistants have
been studied, like the problems with the accents which act as stimuli
(Gulshan & Dhage, 2019). Thus, this study contributes to the past lit-
erature by investigating the impact of different accents and dialects
on perceptions towards the AIIVAs.
2.2 | Voice and accents
Consumers perceive the voice assistants based on their accents and
dialects, which further impact the participants' behaviour (Amrita &
Dhage, 2018). Past research has shown that the Australian standard
accent by a VA was perceived as being more educated and trustwor-
thy, along with being polite and severe. At the same time, consumers
perceived the dialectal accent as more natural, relaxed, and emotional.
Dahlbäck et al. (1993) examined the matched accents and their impact
on disclosures of a socially undesirable nature with the perceptions of
sociality in the voice assistants. Studies have shown that participants
rated the information quality of voice assistants higher when their
accents matched that of the AIIVAs. Sandygulova and O'Hare (2015)
reported that Irish children preferred the VA, which was set for a UK
accent. Tamagawa et al. (2011) also reported that New Zealanders
perceived the AIIVAs with US accents as robotic as they preferred the
UK accents. Further, this same set of customers rated New Zealand
accents as the highest with respect to their perceived robot ability
and the effectiveness of the VA. The accents of AIIVAs were more
trustworthy when they matched local accents of participants.
Yilmazyildiz et al. (2016) and Andrist et al. (2015) further con-
firmed that the speech of AIIVAs was preferred if it was related to the
standard or local dialect. Thus, we see a dearth of studies when it
comes to the problems of the AIIVAs concerning accents and dialects.
Rare studies have identified and explored New Zealand and UK
accents. There is still no study that explores the problems that con-
sumers face in diversified regions like India for the AIIVAs. Table 1
presents a summary of critical studies, highlighting the gap in the
knowledge of AIIVAs.
2.3 | SOR and dialect theory
The SOR model (Mehrabian & Russell, 1974) explores the reactions of a
customer, and dialect theory (Johar, 2016; Linn, 2014) speaks about how
consumers vary in their accents or dialects. The SOR model explains that
a human receives various stimuli from the environment (S), such as audi-
tory or visual stimuli. These stimuli can impact the state of internal emo-
tion and further the cognitive mechanisms (O) of humans. These
cognitive mechanisms elicit a response (R) (Russell & Mehrabian, 1974).
The SOR model explains that an external factor (like AIIVAs in this case)
can impact the internal state of the consumer.
The SOR model has been frequently applied to many streams of
consumer behaviour (Xu et al., 2019; Zhu et al., 2016). The context of
the application of the SOR theory in this study is based on the fact
that in a voice assistant, the voice will act as an external stimulus for
the respondent, which will lead to a cognitive process in the consumer
and generate a response. This explains the interactions of the voice
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TABLE 1 Summary of critical literature on artificially intelligent interactive voice assistants.
S/n Study Aims of the paper Methodology Key findings
1 Tulshan and
Dhage (2018)
Consumers' experiences with Virtual
assistants like Siri, Google Assistant,
Cortana, and Alexa.
Quantitative survey The paper found that consumers'
experiences with VA are not satisfying,
and some crucial improvements are
required in voice recognition,
contextual understanding, and hand-
free interaction.
2 Woods (2018) Examined the role of gendered
stereotypes among consumers in
leveraging the anxieties of virtual
assistants.
Case study method The research portrays the role of Gender
in AI in normative gender roles that of a
feminine voice where the consumer
could relate the same to that of a
caretaker, mother, and wife. The paper
elaborated on mobilising essentialist
feminine personas like ‘digital
domesticity’, so the consumer could
engage efficiently with surveillance
capitalism. The same was studied with
a case study method for Apple's Siri
and Amazon's Alexa and reported that
these feminine voices could intimate
forms of data exchange leading to
surveillance capitalism.
3 Moussalli and
Cardoso
(2020)
Examined the role of Amazon Echo and
other voice-controlled assistants in
the classroom.
Mixed methods: Survey
and interview
Alexa was judged to be a motivating
practice and encouraging self-learning
among students. The study proved that
VA provided stress-free input exposure
and output practice. The study showed
that beginner learners depended on
higher levels of non-native accents and
could be understood by the VAs when
it came to foreign language learning.
The VA seemed to adapt well to their
accented speech, although participants
reported some communication
breakdowns for the VA.
4 Kumar et al.
(2019)
examined customer engagement in
service (CES) based on the service-
dominant (SD) logic when it comes to
the VA.
Mixed
methods―Qualitative
and quantitative
The study reports interaction orientation
to create a positive service experience
among consumers. Moderators of
service experience like offering-related,
value-related, enabler-related, and
market-related factors were also
studied in the paper.
5 Acikgoz and
Vega (2021)
Examined the drivers behind the usage
habits of voice assistants (VAs). The
role of privacy cynicism was studied.
Quantitative The results reported that the ease of use
and perceived usefulness could impact
the consumers' attitudes toward VAs,
but privacy cynicism harmed the same.
6 Tyutelova et al.
(2020)
The paper examined the efficiency of
using the VA assistants by the brands
for communication with the
consumers.
Quantitative The findings report that VAs have been
efficient voice-activated digital
assistants, but the communication is
restrictedly successful with consumers.
7 Nallam et al.
(2020)
This study examined the use of VAs in
health information and resources for
older adult consumers.
Qualitative The findings report that adult consumers
found potential VAs to improve their
search experiences and efficiently
support them in their health tasks.
Access barriers, confidentiality risks,
and trusted information were the
primary concerns listed by consumers
while using VAs.
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agents with the consumer and their reaction to the same. Here the
organism reflects the Indian consumer's internal states and responses,
including the consumer's perceptions of this stimulus (the voice char-
acteristics of the VA). Response in the study refers to the user's deci-
sions, which could be an avoidance of the VA or behaviour of
enhanced use of the VA by Indian consumers. The SOR model has
been used in this study to support the theoretical model's underpin-
nings and explain how the consumers' perceptions relate to their
emotion- and cognitive-based utility of AIIVAs when it comes to the
problems related to the accents of the varied consumers in India.
Consumers in India vary according to their culture. Thus, the SOR
model applied to a diverse set of cultures is a unique study that has
never been done in the past. The more significant challenge of the
AIIVA is to be able to adapt to the dialect of the Indians. Dialects
could be defined as the variants or varieties of a language that can be
used by different speakers who have been separated by geographic or
social boundaries (Romaine, 1994). The dialect theory of communicat-
ing emotions considers language a universal emotion (Linn, 2014). Dif-
ferent cultures indeed have different accents (Johar, 2016). This
condition becomes crucial for the study as India is a country with sev-
eral dialects. The theory's second proposition is that dialects could
make emotion recognition less accurate among cultural boundaries.
Thus, small language changes could confuse the machine (Tamagawa
et al., 2011). Past research has reported that there are challenges with
different dialects and accents as they act as obstacles to the use of
voice search in the case of voice assistants (Johar, 2016; Sandygu-
lova & O'Hare, 2015). This is a bigger problem in a country like India,
with a population of 1652 ‘mother tongues’ and 103 foreign mother
tongues (Ministry of Education, Government of India, 2022). This
research will be able to list the major problems of the AIIVAs in as
diverse a population as India, which has never been done before.
2.4 | Conceptual model
Numerous social contexts could be less informative as a social cue
than accent in speech (Hansen, 2020; Hansen et al., 2017). People's
accents do influence their perception of others. Differences in accents
could be due to different dialectal or social and regional variations. In
a global world like today, voice assistants interacting with people with
a foreign accent is a widespread phenomenon. There is evidence in
past literature that foreign accents could impact many cognitive and
social contexts. This is further related to the positive or negative emo-
tions of the customer too. For example, users judge the foreign accent
of the voice assistants positively in terms of social status, education,
professional success even if it is due to the machine's inability to
TABLE 1 (Continued)
S/n Study Aims of the paper Methodology Key findings
8 Moriuchi (2019) Examined the use of VA in E-commerce.
The paper used technology
acceptance model constructs mainly
perceived ease of use and perceived
usefulness to study its impact on
engagement and loyalty between VA
and consumers.
Quantitative The paper listed perceived ease of use
and usefulness impacting the
engagement and loyalty between VA
and consumers. The localising VA
between transactional and
nontransactional-based online activities
was also seen to be an effective
moderator in this study on
E-commerce.
9 Hernandez-
Ortega et al.
(2021)
The paper examines the feelings of love
consumers develop for VAs when
interacting. This further acts as a
psychological mechanism to enhance
the experiences with the technology
and the consumer's service loyalty.
Quantitative The results show that VAs influence
consumers' passion for technology,
increasing their intimacy and
commitment.
10 Hsiao and
Chang (2019)
The paper studied the role of VAs in the
logistics industry for the growth of
e-commerce. This reduces the time of
communication.
Quantitative The results indicate common problem and
expectations of current operators with
the VAs regarding the delivery of
goods. The study also emphasises the
role of innovative operations and
planning with information technology-
enabled logistic services.
11 Buhalis and
Moldavska
(2021)
This paper examines the role of VAs for
hotels and guests in the hospitality
context. VAs were seen to increase
the effortless value cocreation for
guests cost-effectively. The study
examines the consumers' perceptions
and expectations of VAs in hospitality
sector.
Qualitative The research reports that VAs could help
hotels to improve customer service,
expand operational capability and
reduce costs, thus helping in
attainment of Strategic
competitiveness.
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understand the native accent of the consumer (Fuertes et al., 2012;
Pantos & Perkins, 2013). Osking and Doucette (2019) found that
voice control with virtual reality (VR) impacted the gamer's emotional
reaction significantly. The study also proved that the voice control in
an interactive VR narrative had improved the psychological appeal
towards AI-based applications by generating positive emotions about
the product. Hatzidaki et al.'s (2015) study showed that foreign-
accented speech could impact, to some extent, the higher-order pro-
cesses related to emotionally loaded semantic information. Thus,
there is a connection between accent strength (AS) and the con-
sumer's positive and negative emotional actions (NEA).
Thus, it can be hypothesised that:
Hypothesis 1. Accent strength impacts the positive
emotional actions of a consumer.
Hypothesis 2. Accent strength impacts the negative
emotional actions of a consumer.
It has been noticed that AS does lead to intense rational actions
(RA) and inference. In a study by Sobel and Kushnir (2013), it was
noticed that the student participants could make generic knowledge
inferences rationally by hearing the trainer's accent, which then
guided their RA of inferences about the reliability of testimony. Past
research has also suggested that AS could influence consumers' ratio-
nal judgements and actions toward a product, like the competence,
social attractiveness, and personal integrity of the device being mar-
keted (Mai & Hoffmann, 2014).
Hypothesis 3. Accent strength impacts the rational
actions of the consumer.
Past studies have shown a relationship between consumers' emo-
tional states and continuance use intention with the uses and gratifi-
cations theory. Studies have tried to combine the uses and
gratification theory with the SOR theory and investigated the effect
of different gratifications on using different media and voice assis-
tants. This has been related to the adoption or user continuance
intention (CI) with the help of the SOR theory, where the element of
emotion was found to be crucial (Gogan et al., 2018).
The basis of this hypothesis goes back to the studies on the uni-
fied model of information technology (IT) continuance, which proves
that the influences on a continuance behaviour or erratic behaviour
are based on reasoned action, experiential response and habitual
response. This reasoned action is a RA by the consumer which
impacts his decision to continue using or discontinue using the prod-
uct (Jung et al., 2018).
Hypothesis 4. Positive emotional actions impact the
continuance intention of the consumer.
Hypothesis 5. Negative emotional actions impact the
continuance intention of the consumer.
Hypothesis 6. Rational actions impact the continuance
intention of the consumer.
Hypothesis 7. Rational actions impact the discontinu-
ance intention of the consumer.
Hypothesis 8. Positive emotional actions impact the
discontinuance intention of the consumer.
Hypothesis 9. Negative emotional actions impact the
discontinuance intention of the consumer.
Figure 1 presents the conceptual model for these study.
3 | STUDY 1: QUALITATIVE STUDY
3.1 | Methodology
3.1.1 | Qualitative methodology
We adopted a qualitative research methodology through semi-
structured interviews to gather qualitative data. This method was
considered suitable for many reasons. First, it allows an in depth
understanding of the real time experience of the consumer using the
AIIVAs. Second, accents are personal to individuals and this methodol-
ogy allows for a better demonstration and expression of the
challenges which the consumers face while engaging with AIIVAs.
Third, the focus was on individuals' experiences and reflections on
using the AIIVAs, and a qualitative method allows for honest and
detailed answers from participants as they share the detailed insights
about their experiences while using AIIVAs.
3.1.2 | Data collection
Data was collected through interview questions; efforts were made to
ensure the suitability of the interview guide. First, the interview guide
was discussed within the team; second, the guide was shared with
H4
H1 H5
H2 H8
H3 H6 H9
H7
AS
PEA
RA
NEA
DCI
CI
FIGURE 1 Conceptual model. AS, accent strength; CI,
continuance intention, DCI, discontinuance intention, NEA, negative
emotional actions, PEA, positive emotional actions, RA, rational
actions
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senior colleagues to make further comments and adjustments; third,
we ran a pilot study with eight participants who were not members of
the final sample. Based on the result of pilot study, the questionnaire
was modified and questions were reduced to avoid repetition and
redundancy. The revised interview guide had open-ended questions
regarding consumers' experiences, attitudes, and behaviours toward
their AIIVAs. We arranged face to face interviews as per a suitable
time and location for each participant.
Praveen Kumar Sattarapu (first author) and Deepti Wadera (second
author) conducted the interviews between March and April 2022, and
the structured questionnaire was rolled out in September 2022. Partici-
pants in this study were assured that their information would be kept
confidential and that personal details would not be shared. Participants
were also informed that they were free to share the information at their
convenience. The interviews were recorded after explicit permission
from the participants (see Appendix A for interview guide) The inter-
views lasted between 45 and 72 min, with a median of 56 min. Table 2
presents the demographic information of the participants.
3.1.3 | Data analysis
The interviews were transcribed, generating a 152 pages double-
spaced word document, which was subsequently exported into
NVIVO for further analysis (Farinloye et al., 2019). King (2004, p. 263)
endorsed NVIVO as ‘powerful tools to aid the researcher in examining
possible relationships amongst themes’. The survey questions were
adapted from established scales and analysed using AMOS 23 and
IBM SPSS 23 to check causal relationships. The structured equation
modelling was used to analyse the proposed model for the research
study. The qualitative data were thematically analysed using NVIVO
following the six steps provided by Braun and Clark.
Jaspreet Kaur (fourth author) and Emmanuel Mogaji (sixth author)
carried out the data analysis. The six analysis stages include familiarisa-
tion with the transcript, reading to identify child nodes, and combining
these child nodes into sub-themes and themes to identify consumer
behaviours with the AIIVAs. These key themes were discussed regularly
with the team. There were critical reflections about the teams and their
suitability, one of which was changing the first theme from ‘the back-
ground of use’ to ‘context of use’ and separating the actions into two
distinct themes of ‘emotional actions’ and ‘RA’.
3.1.4 | Ethical considerations
Considerable effort was made to ensure this study's credibility and
authenticity. Nguyen Phong Nguyen (third author)'s University Ethics
Committee granted ethical approval for this research with decision
number 907/QD-DHKT-QLKHHTQT on 27 April 2021. All other
ethical considerations were implemented during the data collection.
Participants were made aware of the purpose of the research, and
they were not coerced to engage and offer these insights. There was
an ongoing iteration between the team to discuss the emerging
themes and key findings. The data credibility is enhanced by this exact
debriefing procedure, check-coding, and continuous data comparison
between the Research Team. In addition, a team member check
involved sending the transcript back to the participants to confirm if
their true thoughts were captured during the interview. None of the
participants corrected their transcript as it was an accurate collection
of their thoughts. In addition, following Shenton's (2004) advice, we
provided a detailed ‘audit trail’ documentation of procedures in
conducting this study.
3.2 | Findings and analysis
The data analysis revealed three key themes around consumers'
behaviour and engagement with their AIIVAs, explicitly focusing on
their accents. These three key themes are: (1) Context of use, (2) The
emotional reaction to the engagement, and (3) The rational reaction
to manage the situation. These themes and subthemes are subse-
quently discussed and buttressed with relevant anonymous quotes
from the participant.
3.2.1 | Context of use
Our study recognised that participants use AIIVAs for many reasons,
including getting news headlines, searching, playing songs, videos, and
jokes, setting up reminders, navigating, getting weather updates, and
even requesting recipes when cooking. The data analysis highlights a
typology of use: private, public with close associates, and public with
non-close associates.
Private use
This involves an individual engaging with the AIIVAs when no one is
around. They often seek private and sensitive information like infor-
mation pertaining to bank accounts, their schedule or reading mes-
sages. These are conversations that many people do not want others
to hear. Engagements with AIIVAs during private use were predomi-
nantly on the phone (n = 40, 71.4%), where people felt they could
speak closer to the mic to clarify their commands. This was closely fol-
lowed by using the AIIVAs in the car (n = 32. 57.1%). People seldom
used it in a private mode when they were at home in the lounge
(n = 38, 67.8%), in the bedroom (n = 30, 53.5%), and in the kitchen
(n = 24, 42.8%). One participant said: ‘I do not use it for banking pur-
poses as it is risky’. This notion was also corroborated by another say-
ing, ‘I never use it for checking bank balance as it is risky and involves too
much of dependence’. The key feature here is the ability for consumers
to speak repeatedly in their accent. They can repeatedly ask questions
because no one else is around to judge them or witness an embarras-
sing moment.
Public use with close associates
This is when the individuals engage with the AIIVA in the presence of
others. The difference here is often the closeness between the
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individual and those around them. Often this is with family and friends
who possibly understand the accents and will possibly intervene when
the engagement is not going on as expected. This occurs when the
individuals are requesting to view a movie, asking questions for fun,
or requesting directions for their journey. This public use is predomi-
nantly at home (n = 48, 85.7%) and the car (n = 31, 55.3%), in the
presence of other family members like children or siblings. One partic-
ipant shared her experience:
it is fun using Alexa in the house with the children, it is
like an entertainer, and we can ask different questions;
the children will sometimes use different accents to
get the correct response.
Regarding the hardware, participants often use the smart
speakers in the house for public use (n = 52, 92.8%) and sometimes
use their mobile phones (n = 33, 59%) for public use. Participants
noted that they could manage their emotions easier in the presence
of their close associates, as they were often assisted by them with dif-
ferent accents that AIIVA could easily understand.
Public use with non-close associates
The difference in this use is that this consists of using AIIVA when
people around are not close associates. The individuals were more
conscious about their accents when engaging with AIIVAs in their
presence. Situations included when an individual was at workplace
requesting a song and AIIVA would not respond due to the accent
used, or when the individual was in a car with others and AIIVA did
not respond to commands given during driving. Participants noted
that they were under much more scrutiny and emotional tension
when they had to engage with the AIIVAs in the presence of others.
One participant shared her experience during a party, saying:
We invited some friends and neighbours for my hus-
band's birthday party, and I wanted to request a song,
but Siri kept saying it did not understand, it was
becoming embarrassing and someone else asked and
Siri played the song.
3.2.2 | Emotional action
The context of AIIVAs' use in this study highlights the inter-
section between the motivation for use (to read out private messages
or call someone), the available devices (on a mobile phone or smart
speaker), and the locations of use (at home in front of family members
or work). As the individual is not alone he/she has to manage these
embarrassing situations which arouses emotional feelings, which
could be positive or negative.
Positive emotional actions
Consumers find their interaction with the AIIVAs funny and try to
repeat their commands in different accents and intonations. Partici-
pants also noted that family members even joined them in making the
AIIVAs further confused by asking the same question repeatedly. A
total of 37 participants (66%) noted that there is often a form of
excitement as they engage with AIIVAs; they recognise that the
machine is learning and hope they can help the AIIVAs learn different
TABLE 2 Demographic information
of the participants.
Demographics Frequency n = 56 %
Gender Female 32 57.1
Male 24 42.9
Age 26–30 23 41.1
31–35 21 37.5
36–41 12 21.4
Education First degree 31 55.4
Second degree 21 37.5
Third degree 4 7.1
Employment Self-employed 17 30.4
Public employed 21 37.5
Private company employed 18 32.1
VAs Ownership 1 8 14.3
2 29 51.8
3+ 18 32.1
VAs Experience Several times a day 36 64.3
Nearly everyday 13 23.2
At least once a week 5 8.9
Less than once a month 2 3.6
Abbreviation: VAs, voice assistants.
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accents. The office use of AIIVA was shared and was shown to possi-
bly arouse some positive emotions. Participants shared their experi-
ences as to how they found it funny when the AIIVA did not
understand their accent and gave an odd reply instead. This could lead
to ‘continuance’ or ‘discontinuance’ of using the AIIVA.
Negative emotional actions
Consumers noted that they were angry and frustrated with a lack of
response from the AIIVA. Nineteen participants (34%) shared how
they got frustrated with the AIIVAs because they were not under-
standing and responding to them correctly. Participants had high
hopes from AIIVAs and got angry and unsatisfied when they were not
able to understand their accent. One participant said: ‘I do expect VA
to understand and respond to my command in Indian accent of English, I
am speaking English and not vernacular, so what is the problem?’.
Another participant feared for her safety using AIIVA in the car as she
had to interfere with the phone directory when driving because the
AIIVA in the car could not understand the name of whom she was try-
ing to call. Nine participants (i.e., 16%) shared that they were ashamed
at the negative emotional reaction they experienced, mainly in the
office when non-close associates made a request, and the AIIVAs
were unable to understand their accents.
Even at home with close associates, participants had negative
emotional reactions when their children tried to correct them on their
accent while using AIIVA. This could lead to AIIVAs' ‘continuance’ and
‘discontinuance’.
3.2.3 | Rational action
Irrespective of the emotional reaction to these engagements, partici-
pants demonstrate some rational reactions as an indication of their
evaluation of their experience with the AIIVA.
Acceptance
Consumers accept that the AIIVA cannot process the request and use a
manual approach. Twenty-seven participants (48.2%) noted that they
accept that their accents cannot be understood, so they move on and
ignore the AIIVA. Participants believed that it was useless to keep trying
different accents as AIIVA was not trained enough to understand. One
participant said: ‘I cannot change my Accent; I will use the AIIVAs when
they get better; for now, I am done using my voice with a computer’.
Seek alternative prompt with AIIVA
Thirteen participants (23.2%) said they often have to keep repeating the
commands till they get a response, and 12 participants (21.4%) said they
had to rephrase the questions and use a different phrase. In some cases,
especially in the public context use, participants noted they had to use a
different meaning of words to request things and that they often tried
to find words and examples that were easy to pronounce and under-
stand with a small accent. Ten participants (17.8%) said that they tried
to use a British or American accent to engage with the AIIVAs. Those
operating the AIIVAs in public can seek an alternative from people
around them to help prompt the AIIVAs with the right accents. This can
often mean having a family member or children repeat the command at
home or in the car. Twenty-one participants (37.5%) said their children
had assisted them in prompting the AIIVAs at home. One mum said:
‘Google (smart speaker) does respond differently, Specially I feel kids know
how to use it better; it just listens and gets their voice at once’. This experi-
ence was also corroborated by another participant saying: ‘sometimes in
language understanding, [Alexa] understands younger generation better, like
my younger sister, the speaker understands her more clearly, and I do tell
her to help request songs’. The public use with non-close associates also
provides an opportunity to seek alternative prompts. Six participants
(10.7%) shared their experiences at work, saying that if the situation is
important, a colleague at work can help prompt the AIIVAs. Though
they found it embarrassing in some cases, participants felt that this was
the best way they could manage the situation if they needed a
response. This will further lead to a ‘continuance’ of using the AIIVA.
Seek alternative prompt without AIIVA
This is when individuals choose not to use their voice to engage with the
AIIVA because it has not been responsive. The majority of the participants
(n = 29, 51.7%) said they end up typing their request on the phone if the
AIIVAs are not responding to their accent. This, however, depends on the
context of use. This RA is more prevalent in private use (on a mobile
phones) since no one else can use a different accent. One participant said:
‘At least I do not type in any Indian accent, so it is easier to get my response
when I type into Google’. This action was also reported when using AIIVAs
in cars when people tried to make phone calls. The AIIVA does not recog-
nise the name, so participants have to type or manually search for the
phone number. This will lead to the ‘continuance’ of using the AIIVA.
Seeking alternative AIIVAs
Twenty-one participants (37.5%) shared their experience of how they
had to seek an alternative to AIIVAs. This has been described as a ‘dis-
continuance’ of the usage of the AIIVA. This decision is for many rea-
sons; often because they are not able to manage the embarrassment
(n = 11, 19.6%), no other family member is using the AIIVAs (n = 10,
17.8%), and they have the ability to afford another AIIVA (n = 8, 14.2%).
These participants are different from those who have accepted their fate
and moved on; they are aware of the prospects of AIIVAs and are willing
to explore other hardware to enjoy the benefits of AIIVAs. Ten partici-
pants (17.8%) noted that they had to stop using a particular AIIVA and
buy another one. One participant shared how they stopped using Google
and decided to use Alexa, though they found it challenging to integrate.
Another participant shared how they had to scrutinise the car they
wanted to buy because of the available AIIVAs. At the same time,
another said they had to instal Android Auto instead of Apple CarPlay
because of their experience. They noted it was confusing to set up but
easier to engage with. Managing the account set-up appeared to be a
significant concern for those who may be seeking an alternative. One
participant alluded that being an Apple user, it was challenging to explore
other AIIVAs because they had to set up a different account.
The intersectionality of the use context and the emotional and
rational reaction results in either using the AIIVAs or not using them.
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Participants report that they want to use AIIVAs, but the ability of
the AIIVAs to understand and respond to their accents is a signifi-
cant determining factor. Figure 2 below illustrates the graphical
summary of key themes from the study. It highlights various con-
texts of use and the emerging emotional reactions but shows how,
ultimately, consumers manage these emotional reactions by taking
actions, which may advertently include them stopping using
AIIVAs.
4 | STUDY 2: QUANTITATIVE STUDY
4.1 | Methodology
We adopted a mixed method approach to gain a better under-
standing of the consumers' experiences when using AIIVAs with
their accents. Mixed method combines the inductive and deductive
process of research analysis and offsets the limitations of the two
approaches to research namely quantitative and qualitative
research exclusively. Study 2 builds on the exploratory research in
Study 1. This mixed method approach aligns with previous man-
agement research that has combined two methods to provide a
robust description and interpretation of the data (Mogaji et al.,
2023; Oda
g Mittelstädt, 2023). The conceptual model which
emerged from Study 1 is further tested in this study with a pro-
posed model estimated with structural equation modelling, using a
structured questionnaire with standardised scales which was used
to measure the constructs, namely AS, positive emotional action
(PEA), NEA, RA, CI and discontinuance intention (DCI).
The model examined the impact of the AS (magnitude of ascent
stimulus to AIIVA as an outcome of exploratory research) on the
PEA, NEA and RA (the magnitude of the emotional reaction and
FIGURE 2 Graphical illustration of key themes and findings from the study.
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rational reaction of the consumer as was found in the outcome of
the exploratory research). Further the PEA, NEA and the RA, which
is the organism in the model, leads to a response which is the con-
tinuance or non-continuance of use of the AIIVA (which was seen as
an outcome response for the use of AIIVA or not using the AIIVA in
the exploratory model outcome). The AS scale was adapted from
Hansen (2020). The scale consisted of four items. The positive and
negative emotional action scale was adapted from Kiefer and Bar-
clay (2012). Each of the constructs, namely PEA and negative emo-
tional action, had five items each. The scales of rational action had
been adapted from the study of Çamlı et al. (2021). The standard
scale items of discontinuance of intention to use the VA were
adopted from the study of Zhang et al. (2016) and the scale for the
continuance of intention to use were adopted by Agarwal and Kara-
hanna (2000). See Appendix B for items of all constructs.
Data was collected through a structured questionnaire based
on reviewing relevant literature and using the SOR model and dia-
lect theoretical framework. Efforts were made to ensure the suit-
ability of the interview guide. First, the structured questionnaire
was discussed within the team; second, it was shared with senior
colleagues to make further comments and adjustments; third, we
ran a pilot study with 50 participants who were not members of the
final sample. Based on the result of pilot study, the questionnaire
was modified, and questions were reduced to avoid repetition and
redundancy. The study has used stratified sampling and partici-
pants were recruited from all over India through invitations and
calls which were shared via email, social media, and personal con-
tacts. A total of 312 participants filled the questionnaire, and
298 samples were deemed suitable for analysis. See Table 3 for
demographic information.
TABLE 3 Demographic information
of the participants (Study 2).
Demographics Frequency n = 298 %
Gender Female 172 57.7
Male 126 42.3
Age 26–30 119 39.9
31–35 113 37.9
36–41 66 22.1
Education First degree 164 55.0
Second degree 114 38.3
Third degree 20 6.7
Employment Self-employed 89 29.9
Public employed 115 38.6
Private company employed 94 31.5
AIIVAs Ownership 1 47 15.8
2 156 52.3
3+ 95 31.9
AIIVAs Experience Several times a day 189 63.4
Nearly everyday 71 23.8
At least once a week 27 9.1
Less than once a month 11 3.7
Abbreviation: AIIVAs, Artificially intelligent interactive voice assistants.
TABLE 4 Descriptive statistics analysis.
Mean SD N
AS1 3.987 0.8283 298
AS2 3.889 0.9222 298
AS3 3.928 0.9190 298
AS4 3.932 0.9728 298
NEA1 3.433 1.0897 298
NEA2 3.332 1.0543 298
NEA3 3.261 1.0371 298
NEA4 3.127 1.1232 298
PEA1 3.824 0.9710 298
PEA2 3.919 0.9514 298
PEA3 3.840 0.9132 298
PEA4 3.919 0.9514 298
PEA5 3.850 0.9653 298
RA1 3.919 0.9851 298
RA2 3.746 0.9362 298
RA3 3.606 0.9989 298
CI1 3.541 1.1293 298
CI2 3.678 0.9237 298
CI3 3.782 0.9504 298
DCI1 2.752 1.0403 298
DCI2 2.593 1.3359 298
DCI3 2.446 1.0814 298
DCI4 2.687 1.2367 298
Abbreviations: AS, accent strength; CI, continuance intention; DCI,
discontinuance intention; NEA, negative emotional actions; PEA, positive
emotional actions; RA, rational actions.
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4.2 | Analysis and results
The relation among various constructs was examined precisely in the
conceptual model, as seen in Study 1. Study 2 examined the
Hypotheses 1–9. AS, PEA, negative emotional action, rational action,
the continuance of use, and discontinuance of use were examined
and found significant. The survey questions were adapted from estab-
lished scales and analysed using AMOS 23 and IBM SPSS 23 to check
causal relationships. The details on the mean and SD of items are pro-
vided in Table 4.
Confirmatory factor analysis was used to test the reliability and
validity of the measurement model using SPSS 23. Primarily, we
checked the internal consistency reliability using Cronbach's α. Cron-
bach's α for each construct was between .72 and .86, which exceeds
.70 (Hair et al., 2012). Furthermore, all the standardised factor load-
ings were greater than the threshold of .70 (Fornell Larcker, 1981).
Also, each construct's average variance extracted (AVE) value was
above .5, and all the composite reliability scores were more significant
than the threshold value of .70, suggesting a satisfactory convergent
validity (Hair et al., 2012). Details are shared in Table 5.
Additionally, Table 5 shows the square roots of AVE presented by
the diagonal number, and off-diagonal numbers present the inter-
construct correlations. As indicated in Table 6, the correlation
between each variable and the other variables was lower than the
square root of the AVE, thereby suggesting satisfactory discriminant
validity (Fornell Larcker, 1981). All the constructs showed good reli-
ability, and the validity of the constructs was also established. The
standards for the acceptability of the measurement model are satis-
fied (Bagozzi Yi, 1988; Hair et al., 1992). Furthermore, as we used
one single source for the data collection, we conducted Harman's sin-
gle factor test to detect common method bias. The result showed that
TABLE 5 Constructs internal reliability and convergent validity.
Constructs Items Loadings Cronbach's alpha (α) Composite reliability (CR) AVE
Accent strength AS1 0.812 .863 .867 .620
AS2 0.847
AS3 0.773
AS4 0.701
Negative emotional action NEA1 0.799 .783 .869 .624
NEA2 0.773
NEA3 0.858
NEA4 0.816
Positive emotional action PEA1 0.744 .877 .841 .670
PEA2 0.780
PEA3 0.784
PEA4 0.754
Continuance intention CI1 0.833 .723 .764 .523
CI2 0.702
CI3 0.705
Discontinuance intention DCI1 0.855 .783 .818 .533
DCI2 0.897
DCI3 0.755
DCI4 0.916
Rational action RA1 0.701 .849 .853 .660
RA2 0.740
RA3 0.761
Abbreviations: AS, accent strength; CI, continuance intention; DCI, discontinuance intention; NEA, negative emotional actions; PEA, positive emotional
actions; RA, rational actions.
TABLE 6 Factor correlation matrix.
CI AS NEA DCI PEA RA
CI 0.723
AS 0.584 0.788
NEA 0.579 0.450 0.790
DCI 0.070 0.037 0.052 0.730
PEA 0.707 0.568 0.387 0.029 0.819
RA 0.712 0.538 0.416 0.009 0.761 0.812
Abbreviations: AS, accent strength; CI, continuance intention; DCI,
discontinuance intention; NEA, negative emotional actions; PEA, positive
emotional actions; RA, rational actions.
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the most significant variance explained by an individual factor was
32.45%, below the 50% threshold (Podsakoff Organ, 1986), thereby
implying that the problem of common method variance did not exist
in our study. All hypotheses were tested for the significant path coef-
ficients and to check the validity of the data. The results of the struc-
tural model, along with their coefficients and significance levels, are
shown in Table 7. The results proved the relationship between each
pair of constructs and supported most hypotheses (see Figure 3).
Overall, the findings indicate that positive emotion action has
emerged as the most significant factor, followed by rational action
and negative emotional action. Finally, Hypotheses 7–9 were not sup-
ported. The analysis indicates that the positive emotion action has a
non-significant negative relationship with the individual's DCI to use
AIIVAs. Similarly, negative emotion action has a non-significant posi-
tive relationship with the individual's DCI and the rational action has a
non-significant negative relationship with the individual's DCI to use
AIIVA.
5 | DISCUSSION
The research provides insights into the consumer's problems when
engaging with AIIVAs in English using their accents. Two separate stu-
dies―a qualitative and quantitative study―were mixed to gain a
holistic understanding of consumers' experiences when speaking with
AIIVAs. It is imperative to recognise that it is not customers' fault that
they have an accent, but the AIIVAs struggle to understand their
accents. While we contextualised this study in India, we must recog-
nise that even native English speakers in England with different
accents will have similar experiences. Consumers will keep engaging
with AIIVAS in their varied accents, and this is a growing consumer
behaviour trend (Buhalis Moldavska, 2021; Hsiao Chang, 2019). It
is, therefore, essential to recognise this conundrum and possibly find a
balance in developing technologies that understand accents.
While previous studies on AIIVAs have explored their technical
capabilities (Acikgoz Vega, 2021; Gulshan Dhage, 2019;
Moussalli Cardoso, 2020; Tyutelova et al., 2020), we present a dif-
ferent perspective that has not been explored. Researchers and
TABLE 7 Hypothesis testing results for the model.
Estimate SE CR p Decision
PEA AS 0.713 0.041 17.390 *** Accept
NEA AS 0.548 0.058 9.448 *** Accept
RA AS 0.682 0.046 14.826 *** Accept
CI PEA 0.391 0.03 13.033 *** Accept
CI NEA 0.35 0.024 14.583 *** Accept
CI RA 0.394 0.027 14.593 *** Accept
DCI RA 0.032 0.071 0.451 .646 Reject
DCI PEA 0.079 0.078 1.013 .314 Reject
DCI NEA 0.054 0.062 0.871 .385 Reject
Note: * p 0.001.
Abbreviations: AS, accent strength; CI, continuance intention; CR,
composite reliability; DCI, discontinuance intention; NEA, negative
emotional actions; PEA, positive emotional actions; RA, rational actions.
FIGURE 3 Structural model and
analysis.
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practitioners agree that AIIVAs understand the English language, but
we posit that there is a different dimension to this customer behav-
iour through challenges with accents. Our study highlights how con-
sumers manage this situation, balancing their emotional reaction of
embarrassment and frustration with a rational action of asking for
help or ignoring the AIIVAs. These virtual assistants are here to stay
(Hernandez-Ortega et al., 2021; Moriuchi, 2019; Nallam et al., 2020).
The context of use may vary, but our findings have clearly shown that
accent-related problems could lead to the consumer taking some dire
steps like refusal to use the device again. These limitations could be
detrimental to the business of companies providing technologies for
such services.
Our study recognises that we can connect more with customers
through their accents if we think beyond language. This emotional
connection through accents highlights a practical implication in con-
sumer behaviours―would consumers keep patronising a brand that
does not understand what they are saying? They will possibly seek
alternatives (Mogaji et al., 2020). Solving these business challenges
will be in the best interest of tech developers and business owners as
AIIVAs have a high potential in a growing market like in Asia and
Africa, where they speak English, albeit in different accents.
The findings of the quantitative research have shown that the
positive emotion action has emerged as the most significant factor,
followed by rational action and negative emotional action. The posi-
tive emotion action was found to impact AS, CI and DCI. The same
has been found in past literature (Mai Hoffmann, 2014; Sobel
Kushnir, 2013). NEA was also found to impact AS and CI but not DCI.
The NEA did not seem to impact the DCI. These findings are not in
synchronisation with past research studies (Hatzidaki et al., 2015;
Osking Doucette, 2019). Also, RA was found to significantly impact
the AS and CI, but not DCI. The reported impact of RA on DCI was a
new finding not evidently proven by any past research studies (Gogan
et al., 2018; Jung et al., 2018).
5.1 | Theoretical contributions
The study used two significant theories, the SOR and dialect theory,
pertinent in this context of consumer engagement with technology to
understand consumers' problems using AIIVAs. Our findings on
consumers' challenges with accents when engaging with AIIVAs offer
significant theoretical contributions to consumer behaviour and human-
computer interaction. Our study identifies the behaviours of consumers
when they engage with their accent, which is often a challenge for
AIIVAs beyond the language they have been trained to respond to, a
product attribute that can influence consumer engagement (Xu
Jin, 2022). Although some findings of the study are in sync with previ-
ous research on AIIVAs (Acikgoz Vega, 2021; Gulshan Dhage,
2019; Moussalli Cardoso, 2020; Tyutelova et al., 2020), our study is
the first of its kind exploring accents in the field of research on AIIVAs.
The past theories used in the study were the SOR and the dialect
theory. Where SOR has been used very often to explain the reaction
of a consumer (here the Indian consumer) to a stimulus (here the AII-
VAs), the same has never been studied in the context of a varied
stimulus that comes from different dialects of the Indian studied in
the study. Linn (2014) has explained in past literature that consumers
exhibit some processing benefits for their native regional dialect when
it comes to an expectation of the range of tasks from the AIIVAs. This
could include speech intelligibility, lexical decision, and higher-level
semantic processing, which the consumer expects from the voice
assistants (Sharma et al., 2022). Thus, from the outputs of the dialect
theory, we extend this variable to a new frame of ‘accent,’ which goes
forward to extend the SOR theory. The study adds new constructs to
the ‘stimuli in the SOR theory, which is now the “AS” of the con-
sumer. The same leads to a new construct of ‘organism,’ which is the
‘emotional and rational feelings’ of the consumer. Further, the same
has been related to the extended construct of ‘response’ which is
continuance or discontinuance of using the VA. This construct has
been studied in three types: private use and public use with closed or
non-close associates. These new constructs resulted from the qualita-
tive study, which examined ‘Acceptance, rejection or repetitive trials
of different words from AIIVA by the consumer after the voice assis-
tant did not understand the regional accent and did not generate the
needed results’.
Past research has studied voice assistants and consumer behav-
iour through studies like the one done by Lee and Park (2022) through
theories like parasocial relationships which explore the consumer
engaging with a chatbot leading to anticipation for quality communi-
cation and interaction. We have gone a step further to study the same
in the consumers by studying the problems they face with the voice
assistants and have analysed this using the dialect theory. Although
some consumers started practicing acceptance and moving on by get-
ting used to the problems of accent and dialect with voice assistants,
some decided to seek an alternative prompt (another response) and
tried different words and dialects to get the job done from AIIVAs,
which highlights their desire for value creation with the technology
(Abid et al., 2022; Jiang et al., 2022). Unsurprisingly, some went fur-
ther to converse with family and friends to try communicating with
the voice assistants to get the required output. This reaction assumed
that different people have different accents, and that maybe AIIVAs
understand another person's dialect and give the required output.
Thus, users attempt to change their dialect to offer various inputs as
they require complete functionality from the device. Still, other con-
sumers were seen to have a very severe reaction to the problems of
AIIVAs when it came to their accent and stopped using the same
accent or switched to other devices. Thus, the responses to the prob-
lems of varied dialects or AS of consumers (stimulus) were different in
varied situations when using the AIIVA. These constructs have never
been studied or listed in past literature. Thus, we contribute to the
past literature with a unique dialect-based SOR amalgamation and
application of the theories of SOR and dialect theory for the use of
AIIVAS, which is novel.
5.2 | Managerial implications
We have identified the inherent challenges with accents and AIIVAs.
We noted that it is beyond language that AIIVAs are doing well; they
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can understand commands in the English language, but there is a need
to recognise that the English language is spoken differently (different
accents in many places around the world); therefore, tech developers
and other companies working and using speech-to-text automatic
speech recognition needs to be aware of this and improve on their
technology to recognise and understand accents (Mogaji et al., 2021).
We recognise that consumers' experience will be further enhanced
when AIIVAs understand the accents of regions, and this will open
markets of interest and a high potential for developers.
The applicability of the findings could be for AIIVAs like Alexa, Siri
and Google, who have a mass market all around the world with people
having different pronunciations and dialects. Customisation of the voice
assistants could be crucial as the ease with accents for the regional people
could help them access the appropriate regional relevant content with
the help of the voice assistants. The application of the same is not limited
to smartphones over Siri but also to OTT, which today has content from
hundreds of countries worldwide. Thus, a regional movie played by Alexa
needs customisation to the local dialect of the region of people being tar-
geted. Further, if Alexa has to play a local Punjabi song in India, it will have
to be trained as per the dialects of Punjabi words used in India to do the
best search for customers and for getting effective results.
Further, it has to be understood by the brand developing Siri that the
names in the smartphone will be saved as per the cultural names or
meaning of words for Indians. Thus, when Siri has to recall the name, it
has to be programmed to understand the speaker's accent and the
regional name (e.g., Brahmpal Rajorki). At the same time, ‘Hey google’ is a
crucial device for searching content on Google. However, the local people
might try to search local content like a shop in India called ‘Banarasi
Sarees’. This is very localised, and the customer will ask the voice assis-
tants the best words they understand, or which are easy for them to
speak. It is thus the company's responsibility to customise the Google
search engine AIIVAs as per the local dialect needs of the consumers.
As Mogaji and Nguyen (2021) noted, managers might not be con-
versant with the enormous prospects of AI. It is imperative that busi-
nesses using AIIVAs and chatbots in customer service are aware of
these challenges with accents and subsequently bring this idea up with
their developers to explore opportunities to develop AI that can under-
stand accents and enhance engagement with their customers. This digi-
tal transformation approach applies to global brands with different
branches across regions and businesses operating in a local market.
More customers will keep using AIIVAs and chatbots (Abdulquadri
et al., 2021); this is a future trend in consumer behaviour as the
consumer-company interface is rapidly evolving towards a technology-
dominant logic where intelligent assistants act as the service interface
(Lariviere et al., 2017) and enable quicker and more effective decision
processes by consumers. Therefore, businesses should recognise the
diversity within the customer base and provide technology that eases
customer engagement.
6 | CONCLUSION
We sought to understand how consumers with English accents
engage with their AIIVAs and how they manage their interaction. We
achieved our aims and established consumers' emotional and rational
reactions to accent issues using AIIVAs. Research on this topic is lim-
ited and has been able to contribute to this limited body of work,
offering significant theoretical and managerial implications for stake-
holders. We acknowledge that this study may not be generalised as it
has been undertaken on millennial consumers in India; however, we
anticipate it will inspire other researchers, managers, and tech devel-
opers to investigate this topic further. Smart speakers and voice assis-
tants will be at the centre of interest in the coming years as they
enter the everyday life of households. Therefore, future research will
be needed to better understand the impact of accents, exploring this
in other countries.
ACKNOWLEDGEMENTS
The authors would like to thank the participants who generously
shared their time and experience for the purposes of this project. The
findings and recommendations are a collaborative interpretation of
the collective wisdom of the participants and would not be possible
without their support and participation. The authors thank the Guest
Editors and the two anonymous reviewers for their continuous efforts
to help improve the quality of the manuscript. Especially reviewer
2 for suggesting the need for a quantitative study to further
strengthens our research. Sumeet Kaur acknowledges and greatly
appreciated the infrastructure support provided by FORE School of
Management, New Delhi, India.
FUNDING INFORMATION
All authors declare that they have not received any funding for this
research.
CONFLICT OF INTEREST STATEMENT
All authors declare no conflicts of interest.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the
first author upon reasonable request. The data are not publicly avail-
able due containing information that could compromise the privacy of
research participants.
ORCID
Jaspreet Kaur https://orcid.org/0000-0001-8358-7334
Emmanuel Mogaji https://orcid.org/0000-0003-0544-4842
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How to cite this article: Sattarapu, P. K., Wadera, D., Nguyen,
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APPENDIX A
INTERVIEW QUESTIONS
Background
• How would you describe VAs?
• How often do you use a VA?
• On which platform(s) do you use your VAs are used? (mobile, car,
smart tv/kindle, smart speaker et al.)
• Which are the most often task you ask your VAs to do? (e.g., make
a call, set up reminder, weather, news headlines).
• Which Specific Tasks are performed using VAs? For example,
Check bank balance, book an ola/uber, order a pizza/food, guided
Yoga etc.
• Has the need for VAs made you upgrade any of your appliances to
Smart (IoT) devices like smart lighting, geyser, a/c et al?
Experience
• What has been your experience using VAs?
• How responsive have you VAs
• Have you noticed the VAs has a different response if you speak
compared to others?
Accent experience
• Do you think the VAs understands your Indian accent of English?
• Do you think the VAs understands your Vernacular language?
• Would you expect VAs to understand and respond to your com-
mand in your Indian accent of English?
• Do you feel VAs do not understand the regional ascent of India
even when given a command in English?
• Do you use VAs in presence of people of you use it privately to
manage the reception to your assent?
• Have you tried requested a film or song on a VAs and there was no
response?
• What did you do?
• Where there people around when you gave the command?
• Did they try to ask the VA again?
• Do you think VAs respond different for different gender?
• Do you think VAs respond different for age? Your daughter or your
mother?
Satisfaction
• How would you describe your level of satisfaction with VAs recog-
nising your accents?
• What would you be doing differently to ensure the VAs under-
stand your command?
• Does it bother you when the VAs does not recognise your
command?
• Any experience of when the VAs did not respond to your request?
• What did you do?
• Would you want to use VA again?
Alternative
• Do you think a particular type of VAs did not understand your
accents?
• Do you think VAs in different locations may not understand your
accent―in the car or in a friend's house?
• Do you think it's the type of VAs that you have?
• Have you considered buying a new VAs?
• Have you tried giving a command on another VAs?
• How was the experience?
Action
• Do you know you can change the settings of the VAs?
• Is that something you have done?
• Is that something you will consider?
• What do you think can be done to improve VAs understanding our
local accent?
• Would you buy a VAs for your parents?
• Would you consider buying VAs for a friend with a strong Indian
accent?
Closing
• Considering VAs will continue to be integrated into our life style,
what do you think is the best way to use it?
• Should Indians create their own VAs, we have the huge numbers/
• Any closing remark on VAs?
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APPENDIX B
ITEMS OF ALL CONSTRUCTS OF STUDY 2
AUTHOR BIOGRAPHIES
Praveen Kumar Sattarapu is a PhD Scholar demystifying con-
sumer attitudes in conversational AI (Voice assistants), includ-
ing speaking at Voice 22, a global conference on
conversational AI. He has over two decades of MarCommTech
experience with two global giants (WPP and Omnicom) with
senior leadership roles in India, Indonesia and Asia Pacific,
including managing PL for Marketing ROI division. In his last
stint as Managing Partner with Omnicom Media Group India,
he oversaw strategic planning, Marketing ROI (analytics/
econometrics) and helped many established businesses
embrace digital marketing and transformation. He currently
mentors start-ups and provides high-touch consulting to
investment firms in digital and new age media start-ups.
Accent strength
The strength of an accent in one's speech is a sign of their personality.
One can infer many things about the speaker from someone's solid or weak Accent.
It is possible to tell how someone will act by hearing their Accent.
The type of Accent in a person's speech is an important trait.
If someone were raised in one language, they would have its Accent their whole life.
Everyone has an accent and cannot change it, even if they try.
Negative emotional action
I am Bored with this VA
The content of the VA irritates me
I get angry with this VA
The operational activities of the VA are not interesting
I feel annoyed when using the VA
Positive emotional action
I have continuous positive emotional experiences with the VA
I feel sustained positive emotions while using the VA
My positive emotions keep reappearing while using the VA
My positive emotions re-surfaces in different situations of use with VA
My positive emotions overcome the negative emotions of using a VA
Rational action
I realize all my decisions are following my planned strategy of using of VA
I try to determine my decisions and actions on the VA considering the principle of maximum profit
I use intelligence in all my decisions and actions on VAs.
Continuance intention
I will frequently use my voice assistant in the future.
I intend to continue using my voice assistant rather than discontinue use.
I will use my voice assistant regularly in the future.
Discontinuance intention
In the future, I will use my VA much lesser than today
In the future, I will use another personal assistant.
I sometimes feel like taking a short break from the VA and returning later
If I could, I would discontinue the use of the VA.
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