Do những tiến bộ công nghệ gần đây, robot xã hội đang ngày càng trở nên phổ biến trong cuộc sống của người tiêu dùng. không gian. ChatGPT, một robot xã hội ảo, đã thu hút được sự chú ý đáng kể từ các phương tiện truyền thông đại chúng và giới học thuật những người sử dụng như nhau kể từ khi phát hành vào tháng 11 năm 2022. Sự chú ý này xuất phát từ những khả năng vượt trội của nó, như cũng như những thách thức tiềm ẩn mà nó đặt ra cho xã hội và các lĩnh vực kinh doanh khác nhau. Trước những diễn biến này, chúng tôi đã phát triển một mô hình lý thuyết dựa trên Lý thuyết thống nhất về chấp nhận và sử dụng công nghệ và người tiêu dùng loại hình giá trị tập trung vào trải nghiệm của người tiêu dùng để kiểm tra ảnh hưởng của các yếu tố kinh nghiệm đến có ý định sử dụng ChatGPT và sau đó cộng tác với nó để cùng tạo nội dung giữa các nhà quản lý doanh nghiệp.
Trang 1Available online 18 January 2024
0040-1625/© 2024 Elsevier Inc All rights reserved
A shared journey: Experiential perspective and empirical evidence of
virtual social robot ChatGPT’s priori acceptance
Amelie Abadiea, Soumyadeb Chowdhuryb, Sachin Kumar Manglac,*
aMarketing Department, TBS Business School, Lot La Colline II, Route de Nouasseur, Casablanca, Morocco
bInformation, Operations and Management Sciences Department, TBS Business School, 1 Place Alphonse Jourdain, 31068 Toulouse, France
cResearch Centre - Digital Circular Economy for Sustainable Development Goals (DCE-SDG), Jindal Global Business School, O P Jindal Global University, Sonepat, India
Consumer value creation framework
Managerial usage intention
A B S T R A C T Due to recent technological advancements, social robots are becoming increasingly prevalent in the consumer space ChatGPT, a virtual social robot, has captured significant attention from the mass media and academic practitioners alike since its release in November 2022 This attention arises from its remarkable capabilities, as well as potential challenges it poses to society and various business sectors In light of these developments, we developed a theoretical model based on the Unified Theory of Acceptance and Use of Technology and a consumer value typology centred around consumer experiences to examine the influence of experiential factors on the intention to use ChatGPT and subsequently collaborating with it for co-creating content among business man-agers To test this model, we conducted a survey of 195 business managers in the UK and employed partial PLS- structural equation modelling for analysis Our findings indicate that the efficiency, excellence, meaningfulness
of recommendations, and conversational ability of ChatGPT will influence the behavioural intention to use it during the priori acceptance stage Based on these findings, we suggest that organisations should thoughtfully consider and strategize the deployment of ChatGPT applications to ensure their acceptance, eventual adoption, and subsequent collaboration between ChatGPT and managers for content creation or problem-solving
1 Introduction
Since the inception of robotic applications, such as factory robots in
product assembly lines, scientific exploration robots in space and under
the ocean, military robots for surveillance and diffusing bombs, and
transportation drones, which were primarily operated by human
con-trollers, these have been transformed into autonomous or semi-
autonomous robots that are designed to alleviate pressing challenges
of society (Sheridan, 2020) In this context, the term ‘social robots’ has
been coined in scientific literature While there is no consensus over the
definition of social robots, they are usually defined as autonomous
agents that can communicate with humans and can act in a socially
appropriate manner Ideally, the existing definitions consider the
char-acteristics of social robots, with the assumption that they will operate in
public spaces and therefore should have a physical body (Fong et al.,
2003) The current definitions have yet to consider social robots in
virtual space, such as the metaverse or any online platform
Amidst the COVID-19 outbreak, social robots were deployed with
varied uses in actual scenarios (Aymerich-Franch, 2020) The pandemic-
induced restrictions, such as social distancing and quarantine, led to the use of social robots as supportive aids in healthcare services (Aymerich-
transmission of COVID-19 by performing specific tasks, such as toring patients and aiding healthcare personnel, thus minimizing the spread of the disease (Javaid et al., 2020) Additionally, studies have shown that quarantine measures and isolation have had adverse effects
moni-on people’s mental health and overall well-being (Violant-Holz et al.,
2020) Hence, social robots could potentially assist in promoting well- being during a pandemic (Yang et al., 2020) This led to the popu-larity of these applications in the mass media as well as in academic literature
OpenAI’s ChatGPT was released in November 2022 and raised considerable interest for its groundbreaking approach to AI-generated content, which produced complex original texts in response to a user’s question ChatGPT is a cutting-edge AI language model that leverages generative AI techniques to provide algorithm-generated conversational responses to question prompts (van Dis et al., 2023) The outputs from generative AI models are almost indistinguishable from human-
* Corresponding author
E-mail addresses: sachinmangl@gmail.com, smangla@jgu.edu.in (S.K Mangla)
Contents lists available at ScienceDirect Technological Forecasting & Social Change
journal homepage: www.elsevier.com/locate/techfore
https://doi.org/10.1016/j.techfore.2023.123202
Received 1 March 2023; Received in revised form 19 October 2023; Accepted 27 December 2023
Trang 2generated content, as they are trained using nearly everything available
on the web (e.g., around 45 terabytes of text data in the case of
ChatGPT) The model can be trained to perform specific tasks, such as
preparing slides in a specific style, writing marketing campaigns for a
specific demographic, online gaming commentary, and generating high-
resolution images (Chui et al., 2022a) The recent widespread global
adoption of ChatGPT has demonstrated the tremendous range of use
cases for the technology, including software development and testing,
poetry, essays, business letters, and contracts (Metz, 2022; Reed, 2022;
impact over organisations, ranging from enabling scalable and
auto-mated marketing personalisation, to generating optimized operation
sequences, coding information systems, quickening and scaling up
simulations and experiments for research and development, or simply
answering complex risk and legal interrogations of managers (Chui
et al., 2022b) ChatGPT prospects take a wide space of applications
across industries and businesses (Table 1) while demonstrating the
transformative potential of globally increasing the efficiency and
di-versity of human value production (Zhang et al., 2023; Shaji et al., 2023;
Frederico, 2023)
The launch of ChatGPT has caught the attention of all scholars,
regardless of discipline The popular press has also engaged in
discus-sions around the implications of ChatGPT, and, more broadly, on
generative AI, highlighting the many potential promises and pitfalls of
these systems The definitions of social robots found in most academic
articles appear to lack consistency, leaving the theoretical framework
within this context somewhat ambigous (Sarrica et al., 2020)
Predom-inantly, social robots are defined as autonomous agents that can engage
in significant social interactions with humans Their engagement style
depends on their applications, roles within specific environments, and
adherence to established social and cultural norms These definitions
portray social robots as complex machines, with analytical and
computational abilities surpassing those of humans, designed to
emotionally engage with people through communication, play, and
facial cues ChatGPT is a form of virtual social robot which possesses
autonomy, can sense and respond to environmental prompts (like
answering questions), can interact with humans (through virtual
con-versations), and understands and adheres to societal norms (due to its
programming that incorporates, to a certain extent, societal values,
barring instances of security breaches or algorithmic failures)
Conse-quently, ChatGPT fulfills four aspects of social robots encapsulated in
most foundational definitions (Fong et al., 2003; Duffy, 2003; Sarrica
current definitions overlook the concept of social robots in a virtual space, and they are neither comprehensive nor unequivocal For instance, the prevailing definition neglects to consider the cultural background and context in which social robots will be deployed For instance, the prevailing definition neglects to consider the cul-tural background and context in which social robots will be deployed Given the exponential popularity of virtual social robots like ChatGPT, which can be extensively used for natural language processing tasks such as text generation, language translation, and generating answers to
a plethora of questions, and can disrupt various sectors in business such
as the service industry, managerial decision making in different texts, marketing and sales, and human resource management, it is necessary to understand the perception of managers regarding this new technology For the technology to successfully operate in and be inte-grated into the business environment, it has to be accepted by managers, because they will be responsible for developing strategies to facilitate ChatGPT–employee collaboration based on their own perception Existing studies have shown that social robots can transform the service encounter of a consumer through novel and emotionally charged interactive experiences (Larivi`ere et al., 2017), and such is the case with ChatGPT Therefore, ChatGPT, like virtual social robots leveraging generative AI capabilities, will be a crucial technology that can be potentially considered the workforce of the future in a wide range of business settings and operations For example, existing studies have discussed compelling cases where social robots will find their way within organisations as AI technology evolves (Henkel et al., 2020), and may improve the working conditions of employees (Goeldner et al.,
con-2015), which will significantly enhance employee productivity, business productivity and the competitive advantage of firms (Kopp et al., 2021) Considering the virtual nature and generative AI capabilities of ChatGPT, its diffusion is likely to be faster within business organisations because of its superior analytical and computational capabilities compared to humans, interactive features, and ability to solve problems Since ChatGPT is a very new social robot, we have not yet come across any studies in the literature on its adoption by managers within orga-nisations, which leads us to the following research question:
RQ: Which factors will drive and inhibit the intention to use ChatGPT
by business managers that will facilitate ChatGPT-manager tion for content creation?
collabora-Grounded in the bodies of literature dealing with technology tion and consumer value creation, we integrate the unified theory of
adop-Table 1
Sectoral applications of ChatGPT (Derived from Richey et al., 2023; Dwivedi et al., 2023 and Budhwar et al., 2023)
Education Support to teaching and learning activities as ChatGPT fosters more
engaging and interactive educational tools and spaces Incorrect information taught and learnt due to data-driven biases
Gaming Unsupervised generation and personalization of sandbox game
scenarios and spaces Higher unpredictability of game use and less controllable gaming outcomes and purposes Media Diversification of media content and format and increasing
productivity in content creation Lower monitoring of information quality and truthfulness
Advertising Automation of ad generation (synthetic advertising) and
personalization of ads, supporting and extending the capabilities of
E-commerce Increasing easiness of platform and website development and
responsivity of e-services and chatbots to customer requests Increasing risks of e-commerce misuse and fraud
Healthcare Increase in accuracy and efficiency of electronic health record
systems and access to medical services (from AI caregivers) Increasing responsibility of machines over human patients’ health
Finance Increase in responsiveness and reliability of customer service
More accurate identification of aberrant transactions and fraud Reduction in human contact and monitoring of financial services
Logistics Increase in data-driven supply chain management efficiency and
Trang 3acceptance and use of technology (UTAUT) (Venkatesh et al., 2003) and
the marketing paradigm of experiential consumer value (Holbrook,
1999) to develop a model This model aims at classifying antecedents to
the acceptance of social robots into experiential features and viewing
value as co-created, respectively, to (1) provide the experiential
expla-nation and make sense of the relevant interpersonal factors discussed in
the existing literature on the use of social robots and (2) anchor the view
that humans and AI collaborate with the intention of co-creating value
By doing so, we aspire to bring a realistic perspective of the acceptance
of social robots closer to the current context and provide future research
with a comprehensive framework we believe to be consistent with the
development of a society where robots and humans would associate as
counterparts and neighbours (Kanda et al., 2004) In this study, we
examine the a priori acceptability phase, when users form their first
judgments about the technology after first/initial interactions (Bobillier-
acceptance (Terrade et al., 2009) and appropriation phases (Barcenilla
yet to see full-fledged business applications In this context, it is worth
pointing out that a review on the acceptability of social robots
con-ducted by David et al (2022) found that around 74 % of articles in their
database examined social robots’ a priori acceptance phase, despite
these robots (such as NAO, Era-Robot, Pepper) already being
opera-tional for several years in health, education, and service domains (Al-
2020)
This study broadens research on social robots by first building on and
expanding the theoretical developments from Duffy et al (1999)
introducing AI self-awareness, DiSalvo and Gemperle (2003)
consid-ering their anthropomorphia, Kreijns et al., 2007 demonstrating their
usefulness to collective goals or Lee and Lee (2020) presenting social
robots as service providers as efficient as humans to integrating social
robots into the context of an experience of co-creation of consumption
between an artificially intelligent service staff and a human customer In
this line, we synthesize findings about social robots to incorporate them
into a comprehensive and versatile framework, consumer experience
(Holbrook, 1999) Secondly, with this theoretical lens, the present
research enlarges studies of social robots to include the influence of
symbolic individual and social goals of human users as hedonic
moti-vations, seeking recognition of how they interact with robots In this
matter, our research extends research on the ethics of social robots to
their co-existence with other intrinsically human ideals such as esteem,
spirituality, or status Third, we presently show the intersections of the
narrative that “computers are social actors” (Nass and Moon, 2000) and
the experiential value of their social role, considering robots as co-
creators of social and inner experiences with a user This strengthens
research into shopping experience marketing through the scrutiny of
symbols and values shared by AI and humans (Bolton et al., 2018), for
instance making room for thought on the emergence of feelings of the
‘uncanny valley’ in the context of the shopping experience
The article is structured as follows First, we present the background
literature and identify gaps in knowledge in Section 2 Next, we propose
and discuss the theoretical model of our research in Section 3 Section 4
presents the methodology of our study, followed by the findings in
Section 5 The discussion is presented in Section 6 and the research
implications in Section 7 Section 8 outlines the conclusions and future
work
2 Literature review
2.1 Social robots
categorises human postures to find the appropriate action for a given set
of observed inputs before interacting with a human By searching for
postures when observing a human, AI demonstrates higher social
intelligence Breazeal and Scassellati (1999) advanced the view that if robots have the computing abilities to build complex human behaviours, they require a system to consistently project themselves into the next behaviour and account for past ones, based on their perception of a human user This “attention system” therefore increases a robot’s ability
to socially interact with a human user by collecting cues to what behavioural strategies, such as avoidance or engagement, and what emotions and motivations it should display to the user This follows
are defined as not only interacting with third parties but also as having developed an awareness of their own mental state and the mental state observed by those third parties In the words of Duffy et al (1999), social robots “behave in ways that are conducive to their own goals and those
of their community” in the complex and variable environment of a social group According to Wirtz et al (2018), service robots are “system- based, autonomous, and adaptive interfaces that interact, communicate, and provide services to an organisation’s customers” Social robots exhibit heuristics similar to those of humans and are accorded person-alities from users (Banks, 2020) Within the existing literature, commonly agreed properties associated with social robots range from social consciousness to empathy and sociability (Table 2) Duffy et al.’s
having capabilities to react and deliberately act while sensing their environment For Fong et al (2003), social robots are socially interac-tive in the sense that they assess other entities, such as humans or ma-chines, in a heterogeneous group, and interpret their position in society
in a personal way based on their recorded past experience For Breazeal
rooted in sociability, as is the ability to mirror the social context of human interactions through anthropomorphism and non-verbal signals added to AI
focusing on anthropomorphism in robots, which continues to gain mentum with technological advances in AI related to emotional and social intelligence Shin and Choo (2011) discussed socially interactive robots that act autonomously and communicate in constant interaction with humans and other machines, which means that they need to
mo-Table 2
ChatGPT social robot properties
Property General capabilities implied ChatGPT capabilities Social consciousness
( Duffy et al.,
1999 )
Embodied AI agent, Autonomous or semi- autonomous Reactive and deliberate
Embodied into a screen interface and the identity of ChatGPT
Autonomous Reactive and deliberatively advising
Social interactivity (
Fong et al., 2003 ) Personal perception and interpretation of the world
according to a recorded past experience
Identification of other agents, human, animal or machine, and
communication within a heterogeneous group Enactment according to a social role identified within
a group
Possesses the view of the world from Web Data, framed into ethical rules programmed by Open AI developers
Can identify the language of the user but needs the user
to disclose personal information to identify them comprehensively
Acts as a subordinate oriented towards the user with the central objective to serve them
Sociability (
Bartneck and Forlizzi, 2004 ;
Breazeal, 2002 )
Understanding and relatedness to humans:
empathy Mirroring of human social context through anthropomorphy and lifelike features Verbal and non-verbal signalling to humans
Displays signals of empathy and support to the user Mirrors verbal anthropomorphic only Communicates verbal signals only, under a textual form
Trang 4understand the emotions of humans to function efficiently Such social
skills are important for user learning towards collective and individual
goals (Kreijns et al., 2007) Social bots that can help users improve their
skills and show responsiveness lead to higher adoption by people, thus
embedding into the fundamentals of the service-dominant logic (SDL)
theory, refocusing the value of goods and services purchases towards the
experience and capacity a consumer can derive from them (Vargo and
competitive advantage; such centrism is based on the emergence of a
service as a process of co-creation, where consumption and provision of
the service continuously interact to raise the value of the experience,
transcending the acquisition of the service itself Research in this area
has primarily focused on assistance robots used in healthcare, mobility,
disability care, or education (Kelly et al., 2022), focusing on the
inter-personal aspect of social robots Even though this topic has a long history
in management research, it is currently being reinforced by academics
and follows a favourable context For example, Lee and Lee (2020) point
out that customers increasingly prefer minimal human contact during
their shopping experience, with AI robots providing higher customer
satisfaction than their human counterparts (Bolton et al., 2018)
However, social robots are perceived with greater distrust than their
human counterparts, with this phenomenon decreasing for
anthropo-morphic robots (Edwards et al., 2019) Adult users prefer human-like
computers, while children are more comfortable with cartoon-like
ro-bots (Tung, 2016) In this matter, other scholars draw attention to the
‘uncanny valley’ effect, which emerges from a cognitive dissonance
sensed by a human user between the features they instinctively expect
from a human likeness and what they perceive in a human-like robot,
thus raising a feeling of discomfort or disgust (K¨atsyri et al., 2015) This,
therefore, plays an important role in reducing the positive impact of
anthropomorphism on the acceptance of social robots, as argued by Yam
human-likeness expectations from users and increase social robot
acceptance in the Japanese service industry Humans confronted with
the contradiction of a realistic humanoid robot suffer from a discrepancy
between their anticipation of a human and their perception of something
that is not fully human (Mori et al., 2012) Indeed, the anthropomorphic
dimensions of social robots imply that a user could unconsciously
attribute this gap between what should resemble a human and what they
see of a robot to illness, death, or zombification (Diel et al., 2021),
leading to less trust and engagement from the user and overall, the
reversal of perceived usefulness into the intuition of impairment (
interfaces has shown that users with autism need less human help to
interact with a social robot than with a computer (Pop et al., 2013)
If we were to place the uncanny valley on a continuum, it would start
with industrial robots at one end, completely non-humanoid (eliciting
neutral emotional responses from humans), continuing along, we reach
a point where robots are designed to look very human-like, but just
enough to be detectable (emotional response becomes negative), and as
we move out of the valley and approach the far end of the spectrum, we
find entities that are virtually indistinguishable from real humans These
indistinguishable ones might be sophisticated androids or computer-
generated humans that look and move just like real people (for e.g.,
deepfakes) At this point, the emotional response becomes positive
because our brains are no longer able to detect the imperfections, and we
accept them as fully human (Cheetham et al., 2011) The discomfort
caused by human-like entities in the uncanny valley stems from these
entities violating our innate social norms and expectations, i.e., humans
have ingrained rules and expectations for social interactions, and
human-like entities are expected to follow these norms (Moore, 2012)
Moreover, human-like entities lack signs of life or consciousness which
can trigger fears about human identity, the uniqueness of human
con-sciousness, and may elicit the idea of being replaced, causing fear and
scepticism (MacDorman and Ishiguro, 2006) The variations in response
to human likeness of digitally created faces can be also attributed to
factors like individual differences and cultural backgrounds (Burleigh
et al., 2013)
Social robots can “respond to and trigger human emotions” (schel et al., 2020) to build interpersonal relationships and participate in communities on a larger scale Consumers demand more pleasure from intelligent agents and a kind of “fool’s licence” from social intelligent systems, as Dodgson et al (2013) showed Reciprocity and honesty are also expected from social robots (McEneaney, 2013) Social robots have trust, affection (Picard, 1999) or the ability to love (Samani et al., 2010),
Hen-or detect weak signals (Heylen et al., 2009) For example, most nized work on social robots concerns highly sensitive social situations, such as autism (Mejia and Kajikawa, 2017; Robins et al., 2005) Per-sonalisation, connectivity, and reliability give social robots the ability to innovate their services to support vulnerable users (Khaksar et al.,
recog-2016) Within the singular context of the recent Covid-19 pandemic, social robots have played a key role in restoring mimicked social in-teractions to support the successful enforcement of the social distancing necessary in such context, and to manage the monitoring and inhibition
of the associated danger of infection, especially for healing practitioners (Yang et al., 2020) Aymerich-Franch (2020) studied 240 use cases during this pandemic and classified social robots into three care func-tions: linking people with caregivers, protecting, and supporting humans’ well-being Therefore, social robots mirror and automatize a 24/7 social presence for answering health and well-being considerations
of individuals Further to healthcare, they mitigate the feelings of loneliness and boredom of families during the lockdowns of the Covid-
19 pandemic
social robots The first problem arises from the long-term habituation of users and the potential dependency that consumers might build on social robots If such AI robots are used in the care of disabled people, they could gradually lose their autonomy by relying on a social robot Social robots can also control decisions about impressionable users, infantilise them, or isolate them from other humans In these first cases, human integrity is challenged by the democratization of social robots, while a second problem arises from the integrity of the AI itself Social bots have social functions, behaviours, and appearance, but they lack authenticity and morality, leading to a preference for insentient machines over humans to meet social needs (Tan et al., 2021) However, users tend to cling to stereotypes when it comes to placing a social robot in a social position They prefer extraverted social robots for healthcare and masculine-looking robots for security tasks (Tay et al., 2014)
Research on robot acceptance is often embedded in the technology acceptance model (TAM) (Venkatesh et al., 2003) or its further devel-opment, the unified theory of acceptance and use of technology (UTAUT) (Venkatesh et al., 2003) The technology acceptance model (TAM) developed by Davis (1989) bases users’ intention to use a tech-nology on the utilitarian logic of the cost-benefit ratio of that technol-ogy Social robots fall within the framework of TAM (Shin and Choo,
2011) Robots that are seen as useful and easy to use attract demand and consumption Saari et al (2022) applied TAM 3 to various market seg-ments: the proactive early AI adopter is contrasted with the mass mar-ket The authors focused on AI functionalities to distinguish AI designs that are suitable for both segments They showed that perceived ease of use does not influence the intention to use social AI, while perceived usefulness is an important factor in a sample that is largely representa-tive of the mass market This leads to the perspective that users accept AI not because of its accessibility but as a way to advance themselves Mass users see social robots as a benefit that they enjoy In contrast, early adopters look for reliable and transferable outcomes However, utili-tarian expectations related to simplicity and usefulness have been complemented by expectations of enjoyment (Pillai et al., 2020), attractiveness (Thongsri et al., 2018), or well-being (Meyer-Waarden
existing evidence divides the factors influencing the intention to use social robots into utilitarian and hedonistic antecedents (De Graaf and
Trang 5Allouch, 2013)
The field of social robots also has its roots in the ‘computers are social
actors’ (CASA) perspective advocated by Nass and Moon (2000) The
authors assumed that users unconsciously confuse humans and
com-puters and tend to ascribe a psychological nature to a computer, leading
them to display social biases and form interpersonal relationships with
computers For example, users attributed action risks to AI collaborators
and anticipated moral hazards, lying, and problems in choosing robots
(McEneaney, 2013) Atkinson et al (2012) and Kanda et al (2004)
pioneered the CASA view of AI by articulating interpersonal trust and its
impact on the success of AI-human collaboration or human
vulnera-bility The matching person technology theory developed by Scherer and
the acceptance of social robots Using a grounded theory approach, the
authors qualitatively analysed the relationships established by users
with assistive technologies and emphasised that applying generic rules
to user acceptance of social robots does not do justice to the reality of
users Khaksar et al (2016) also used grounded theory to analyse the
degree of innovation of social robots Shin and Choo (2011) showed that
the adaptability and sociability of a social robot complements its
use-fulness and usability, as it leads users to develop a positive attitude
to-wards a social robot, while the perception of being in the company of a
psychologically aware robot directly increases the intention to use it
The authors distinguished between adaptivity and adaptability, which
describes how robots dynamically adapt to new solutions in changing
environments They showed that robots with the ability to dynamically
adapt to a Rousseauian social contract are more valued by their human-
like counterparts
As an interpersonal partner or social actor, the social robot is
incorporated into a cohort or individual’s life through trust (Kim et al.,
by presenting their social skills, ethical integrity, and goodwill (Kim
et al., 2020) In this way, the social robot’s intelligence, autonomy,
anthropomorphism, and empathy play a key role in developing trust
among users Gursoy et al (2019) introduced the concept of social robot
integration for consumers as a long-term use of a social robot and
pro-posed individual findings that encourage reflection on the development
of a relationship between a robot and a human, showing that trust in
social robots may not last as long as predicted According to the authors,
the emotions and motivation of the user are the most important factors
for the integration of a robot into the household The appearance of a
social robot that resembles a human increases the intention to use it but
decreases the willingness to integrate it For users, anthropomorphism is
an advantage when using intelligent social robots without worrying
about attachment, but it evokes anxiety in users that challenges their
cognitive integrity in the long run when it comes to integrating them
into everyday life The findings of Gursoy et al (2019) showed the
myopia of consumers in relation to the current increase in the demand
for humanoid social robots In this sense, they accept social robots but
lose their enthusiasm when they realise the potential future loss of
control or competition in interactions According to Gaudiello et al
de-velops more efficiently when users consider the functionalities of social
robots and make an abstraction from the whole social robot entity
interaction should be viewed through the lens of neuroscience Indeed,
even though social robots are becoming more capable of exhibiting a
social presence, the remaining gap between human and robot cognition
prevents social robots from fully meeting user expectations Because
social robots appear alien to humans, as latter mainly use the frontal
cortex, a part of the brain responsible for cognitive reasoning rather than
intuitive emotions Users develop engagement and empathy towards
social robots, but these arise from the mental perception of the social
robot’s state rather than spontaneous interaction with it Currently,
humans’ unconscious intuition prevents them from fully accepting a
social robot, as deep parts of the brain react and confront the user with
inexplicable feelings of rejection The sight of an inanimate, human- looking object neurologically explains the uncanny valley phenome-non (Rosenthal-von der Pütten et al., 2019)
2.2 ChatGPT (GPT-3)
One of the most remarkable specimens of social robots, GPT-3, known as ChatGPT, is currently gaining more and more public and scientific attention as a “cultural sensation” (Thorp and H H., 2023) Launched by the organisation Open AI, this conversational agent uses natural language processing and machine learning from data circulating
on the internet to engage in discussions with around 1 million users around the globe (Mollman, 2022) ChatGPT can be assimilated into a social robot as it presents the consciousness of a social being (Duffy
et al., 1999), possesses one-to-one interactivity (Fong et al., 2003), and sociability in its textual interface (Breazeal, 2002), as presented in
together with ChatGPT, such as The Guardian or BuzzFeed (Pavlik, 2023) GPT-3 shows high abilities to answer mathematical problems, find and synthesize information, understand business stakes (Kecht et al., 2023), recommend decisions (Phillips et al., 2022), or even write poetry (K¨obis
GPT-3 is a type of generative AI, which can generate content autonomously as text, plan, or programme from the analysis of massive data amounts Such generative AI presents unprecedented capabilities to create and provide responses in a human-like manner (Pavlik, 2023) GPT-3 also learns from its own interactions with users to improve future answers and personalise suggestions (Phillips et al., 2022) For instance, organisations can train such robots to deal with customers’ requests and assist customer relationship management (Kecht et al., 2023) Man-agers’ perceptions and attitudes towards ChatGPT have been presented
already supports research, idea creation, or writing messages and ports, and is seen as helping to improve their communication and effi-ciency at work, needing less time to yield outcomes of higher quality However, ChatGPT is only moderately appreciated by teachers, as it raises concerns about student dishonesty or even its value for the learning process, but it is perceived as bringing benefits in terms of students’ motivation and engagement, or of teachers’ responsivity and productivity, helping them focus on high-level tasks (Iqbal et al., 2022) Finally, academics perceive ChatGPT as a high-potential and disruptive technology for economies and humanity, as developed by the con-sortium of academics in the recent research of Dwivedi et al (2023) ChatGPT would help thoroughly and relevantly managers in their daily tasks but would increase risks of poor reputation, offensive content, plagiarism, loss of privacy, or inaccurate information Therefore, the democratization of ChatGPT should be framed with policy regulations and academic research
re-Many studies about GPT-3 express concerns about the ethical plications of such tools, regarding the human value of future creations (Else, 2023) or the social aspects of interacting with GPT-3 (Krügel et al.,
im-2023) For instance, Gao et al (2022) demonstrated that researchers were not able to distinguish a scientific text written by GPT-3 from a text written by a peer, and that artificial intelligence proves useful to detect artificially written documents Even users tend to underestimate how GPT-3 influences their decisions and may follow even amoral sugges-tions (Krügel et al., 2023) O’Connor (2022) also discussed whether GPT-3 augments or replaces the learning process of students, and Cas-
hand, K¨obis and Mossink (2021) also demonstrated that human readers would still have a preference for poems written by humans (even novice poets) rather than poems written by GPT-2, linking the ChatGPT tech-nology to unconscious rejections of robots highlighted by Rosenthal-von
a “tortured” writing style on occasions (Cabanac et al., 2021) Regarding
Trang 6the social implications of GPT-3, Henrickson (2023), for instance,
studied the case of thanabots, applications of GPT-3 to recreate the
interaction with a deceased relative to assist mourning journeys The
emotions built through a thanabot are based on the deceased person’s
rhetorical style and on mimicking shared past experiences If GPT-3 is
not present under an anthropomorphic physique (voice and humanoid
body), it is one the best depictions of social intelligence and acts as a rich
platform for future transformations into highly realistic social robots
for nurses or a fellow student who shares the care of patients (Aydın and
conversational robot, raising ontological and ethical questions about it
ChatGPT remain scarce (Krügel et al., 2023; K¨obis and Mossink, 2021)
As regards social robots, ChatGPT is useful to complete social robots’
conversational capabilities, yet currently involves diverse risks in the
form of sporadic AI failures (Balagopalan et al., 2023) but shows
pros-pects of long-term negative impact on organisations’ performance and
on individuals’ well-being (Thongsri et al., 2018; Lee and Moon, 2015)
For instance, as the global web nurtures social robots, this could lead to
the dissemination of hate speech or false information to the human
subjects of social interactions (Cao et al., 2021) Since ChatGPT
perpetually evolves through interactions, its application in social
ro-botics could inadvertently expose private user communications to the
public, indiscriminately merging this sensitive data with its broader pool
of learned information ChatGPT doesn’t have the ability to understand
or judge what’s morally right or recognize private information, which
could drastically impair service quality or consumer trust in service
quality in the case of social robots (Prakash and Das, 2021) If ChatGPT,
is unable to distinguish between private and public interactions, there’s
an inherent risk that personal information shared in confidence could be
inadvertently disclosed in other, unrelated contexts This loss of privacy
is not just a personal issue but can also have legal implications,
partic-ularly if the information shared involves health, financial data, or other
sensitive subjects protected under privacy laws like the GDPR or HIPAA
The learning process of generative AI applications is continuously
evolving and doesn’t inherently discriminate between what it should
and shouldn’t remember or share Without strict data governance
pro-tocols, the AI might unknowingly share private conversations, thinking
it’s providing helpful or relevant information based on previous
in-teractions This scenario could lead to uncomfortable situations, or
worse, harm someone if sensitive information about personal struggles,
identity, or private relationships is revealed to the wrong audience In
scenarios where private information is disclosed publicly, there’s a risk
of that data being used unethically or maliciously This misuse could
range from targeted advertising based on private information, to more
nefarious outcomes like blackmail or identity theft Using ChatGPT for
social robot applications demonstrates a risk to service performance
it-self (Lee and Moon, 2015) as the capability to push the technicity of a
process and to project its technologic outcome into transcendent
story-telling remains specific to humans (Jonas, 1982) In other words,
aca-demics cannot currently determine whether social robots would seek
constant progress in designing services or products, and overall bring a
growing social value, or more generally whether generative AI would
relate to the rather human endless desire for betterment Rather,
ChatGPT’s performance shows risks of harming individuals’ well-being
or the potential reinforcement of cases of certain users in affective need
or even dependence on conversations with social robots, or the
increasing delegation of social responsibilities to generative AI, thereby
preventing human users from developing personal competences to
navigate throughout our society by themselves (Xian, 2021) As users
become aware of potential privacy infringements, their trust in social
robotics could diminish This erosion of trust is harmful not only to the
user experience but also to the reputation and reliability of the
com-panies developing and utilizing these technologies
2.3 UTAUT and Holbrook’s experience-based typology of consumer value
The UTAUT remains one of the most commonly used models for the analysis of AI acceptance (Kelly et al., 2022) Extensions of the TAM, such as the theory of reasoned action, the theory of planned behaviours, and other frameworks such as the motivation model or the innovation diffusion theory (Oye et al., 2014), argue that the intention to use a particular technology is influenced by the performance and effort ex-pectancies, and by the two parameters from the environment of the user: social influence and facilitating conditions (Venkatesh et al., 2003) Social influence and facilitating conditions place the potential user in a context where technology acceptance receives the appraisal of sur-rounding peers and where the potential user’s means and environment support the acceptance of this technology In this model of technology acceptance, gender, age, experience with technologies, and voluntari-ness of use act as moderators of the relationship between the precedent factors and intention to use technology In healthcare, social robots’ acceptance integrates the concepts of trust and risk as antecedents (Prakash and Das, 2021; Fan et al., 2020) Regarding acceptance of AI in the field of GPT-3 services for the public, Kuberkar and Singhal (2020) advanced anthropomorphism as a factor of intention to use, while Cao
develop-ment concerns as players in the process of accepting AI assistance donism, security, and sustainability are also raised as arguments for consumers to accept AI (Gansser and Reich, 2021) However, De Graaf
that the UTAUT lacks consideration for hedonic factors of technology acceptance, such as attractiveness and enjoyment Such variables arise from the experience during the use of a robot and imply that robots are mistaken for social actors by the user
In consumer marketing, Holbrook (1999) developed a typology of dimensions that determine the value a consumer anticipates and re-trieves from the consumption experience Holbrook claimed that mar-keting frameworks focusing on product development for the average target person, without consideration of a consumer’s experience, lack comprehensiveness in a world where markets become increasingly fragmented, and he argued for the progression towards more interpre-tative models of customer satisfaction, experience and expectations In this line, the author presented eight value dimensions (see Table 3) that include consumer experience and can emerge simultaneously or sepa-rately: efficiency, excellence, status, esteem, play, aesthetics, ethics, and spirituality (Holbrook, 1999) Consumer value has extrinsic and intrinsic roots and can be oriented towards the self or others, while it comes from an active or a reactive reflection of the consumer In this sense, efficiency is extrinsic, active, and self-oriented, and it relates to the usefulness for efforts, time, and money engaged; excellence is extrinsic and self-oriented, but reactive to the reliability and core quality features of the consumed product or service Play, on the other hand, is intrinsic, self-oriented and active, depending on how enjoyable the consumption experience is, while aesthetics, intrinsic, self-oriented but reactive, states how pleasant the consumption is to the consumer’s senses (Holbrook, 1999)
Oriented towards others and active, status and ethics are extrinsic and intrinsic, respectively, the first depicting how others see the social value of the consumption of one product or service and the second how the consumer sees the moral value of their own consumption The latter,
Table 3
Consumer value typology based on consumer experience, from Holbrook (1999)
Extrinsic Intrinsic
Trang 7the ethical dimension of experience, can therefore be defined positively
as one’s adjustment to the moral values shared in one’s social group or
normatively, as the adjustment of one individual to a set of universal
moral standards, for instance, transparency, trustworthiness, and
re-sponsibility (Laczniak and Murphy, 2019) Therefore, the ethical value
of an experience represents the level of attention given to ethical
obli-gations, personal values and moral beliefs and the moral identity of
receivers within a service (Sun, 2020) Esteem and spirituality
di-mensions are also other-oriented but reactive, extrinsic and intrinsic,
respectively Esteem relates to how others treat the consumer who uses a
certain product or service, while spirituality relates to how the consumer
respects their own faith and personal well-being by consuming a product
or service (Holbrook, 1999) The author derives the spiritual dimension
of experience from an individual’s search for meaning and values to feel
connected to the complete self and the surrounding environment
(McKee, 2003) In an increasingly volatile, uncertain, complex, and
ambiguous world, the transformational and inclusion capabilities
materialized within experienced spirituality gain increasing attention
from businesses and academics (Husemann and Eckhardt, 2019; Santana
dimensions of the consumer experience share common orientations
about the objective of the experience and the actions in the experience
the others in the construction of an experience value in consumption
For instance, the values of efficiency, excellence, and aesthetics have in
common that they are not socially rooted but individually assessed
Similarly, ethics and spirituality come from the objective of gaining
intrinsic value as a consumer
The variation in moral perspectives among individuals is a complex
phenomenon, deeply rooted in cultural, societal, psychological, and
personal factors One person’s view of an immoral act might differ
substantially from another’s based on these influences This concept
becomes particularly evident when we examine cultural variations
across different societies (Hofstede, 2011) In this context, Hofstede’s
cultural dimensions theory helps to understand the impact of a society’s
culture on the values of its members and how these values relate to
behaviour It implies that people’s beliefs, values, and behavioural
norms can vary significantly between cultures For instance, in highly
individualistic societies, people are more likely to make moral
judg-ments based on personal beliefs, rights, and freedoms, sometimes
emphasizing the importance of standing up for one’s convictions even if
it goes against societal norms Conversely, in collectivist cultures,
mo-rality is often framed in terms of social harmony, and community
wel-fare Therefore, disrupting these elements, might be deemed highly
immoral Similarly, in cultures with high uncertainty avoidance,
devi-ating from established norms or engaging in behaviour perceived as
unpredictable may be considered immoral, whereas societies with low
uncertainty avoidance might be more tolerant of such actions This
variation underscores the importance of cultural sensitivity and
awareness, in the increasingly turbulent digital landscape
Academics have demonstrated that some dimensions can influence
others in certain contexts without global consensus, for example, play
can support ethical values by incentivizing the goodwill of the co-
creators of an experience (Sheetal et al., 2022) Lemke et al (2011)
also underlined the variability of the impact of status or esteem on
perceived excellence, while Gentile et al (2007) reminded us that
sensorial cues of the experience as aesthetical value will have a
fluctu-ating influence on other features of the consumption experience
depending on the context In sum, Holbrook (1999) provided a set of
modalities for the experience of consuming a service that can combine to
create a large diversity of different experiential situations Overall, the
theoretical narrative developed by Holbrook (1999) is based on the
service-dominant logic, which considers service as co-created by
cus-tomers towards a transformative value into capacities and extends it
from its utilitarian aspects to dimensions of hedonism and sense-making
Sense-making defines, according to Weick (1995), the continuous
process of mapping a comprehensive meaning or symbol of their actions and social interactions from the inner interpretation of context into their own values In this line, as customers enact service co-creation, they incorporate the purchasing experience into the construction of sense as a stimulus interpreted in coherence with life-long integrity (Holbrook,
1999) In parallel, service providers shall therefore guide the experience
to orient its value to the offer of a constructive and positive sense for the customer
Regarding AI use, Chen et al (2021) showed that experiential value influences the intention to buy an AI service and to cooperate for the service to result in the best quality possible, hence to co-create the value
of a service with AI Autonomous social robots also result in higher levels
of hedonic values (play and aesthetics) and symbolic values (status and ethics) for consumers (Frank et al., 2021) This typology of values also frames the acceptance of online AI chatbots and online purchases (Yin
useful to denote affective and symbolic cues behind AI acceptance in the completion of cognitive influences; AI aims at increasingly socialize and share experiences with consumers (Puntoni et al., 2021)
This study aims to understand the influence of the experiential pects anticipated from the use of social robots on their acceptance The choice of integrating UTAUT is justified by the willingness to provide a panoptic framework to social robots that neutralizes the social value of
as-AI, as opposed to the computers as social actors paradigm (Nass and
unmistakably even when they are not looked for On the other hand, the integration of this research into the Typology of Experiential Consumer Value (Holbrook, 1999) aims at proposing a comprehensive framework for the development of stimuli that influence social robots’ acceptance This theoretical framework will help understand and classify anteced-ents of social robots’ use into symbolic and hedonic cues from the experience anticipated by users Finally, we show the experiential meaning of each antecedent and provide a reasoning coherent with the perspective that users’ experience shapes their collaboration with a so-cial robot and therefore leads to a value co-created between AI and humans
2.4 Knowledge gaps
Recently, Puntoni et al (2021) stated that although AI technologies for the consumer are considered with objectivity as neutral objects provided to the public, they convey social specificities and interactional experiences, as in human-to-human services In this sense, the authors advocate the undertaking of research questions about feelings behind the experience of AI use for consumers; feelings of exploitation through personal data collection; the well-being felt from personalisation; the fear of self-integrity when delegating to AI assistants; the alienation felt from being categorized by an AI; and the dilemma between felt companionship and fear of vulnerability when developing interpersonal relationships with AI Academics undertaking the aforementioned pro-posals have focused on perceived data risks (Dinev et al., 2016), per-sonalisation (Liu and Tao, 2022), and fears of self-integrity, error, or discrimination for social robots (Cao et al., 2021) However, existing findings emerge in isolation, and no study overlaps felt experiences and attitudes associated with a unified perspective Furthermore, although semantics of collaboration and socio-technical value appear frequently
in such studies (Chowdhury et al., 2022), demonstrations of AI value as a co-creation between users and robots remain nascent and scarce (Huang
acceptance currently encompasses perspectives of a one-sided view of AI use values defined by the user (Krügel et al., 2023; Cao et al., 2021) Antecedents stemming from the existing findings fall into the spec-trum of AI design characteristics (Gansser and Reich, 2021) and inter-personal specificities (Kim et al., 2020; Gaudiello et al., 2016) However,
we argue that interpersonal relationships start with shared experiences encompassing both human and AI characteristics, being the result of a
Trang 8co-created story between a human through attitude and a robot through
learning from data Siegel (1999) explained the interpersonal
experi-ences footprint into neurobiological repercussions Cetin and Dincer
recognition, willingness to help, and shared expertise, on customer
loyalty Chen and Lin (2015) demonstrated that the experience felt by
users of online blogs positively influenced their intention to continue to
participate, which lasted in a sustainable relationship between blog
members Consumers feeling a social exchange with service providers,
feeling supported by them, showed intention to repurchase such services
and therefore sustain a recurrent contact Overall, common reasoning
explains experience as a factor of commitment and engagement (Roy
et al., 2021)
Since ChatGPT was publicly released, AI entered the field of
con-sumer marketing with a business-to-concon-sumer approach (Mollman,
2022) Consumers would, however, interact with GPT-3 aiming at
ful-filling a task or objective, such as students to support their homework
(O’Connor, 2023), users interested in learning about topics, or
jour-nalists and bloggers (Pavlik, 2023), making managers and students the
main user groups of GPT-3 nowadays Consequently, consumers of this
free service come to GPT-3 with the idea of finding help with a project,
personal, or oriented towards others They enact and react within the
conversation with GPT-3 and attribute shortcut meanings to value GPT-
3 and yield an outcome co-created with humans and GPT-3 as sources
3 Model development and hypotheses
According to the UTAUT, users build positive attitudes and intention
to use AI from the expectation of performance and effort, and from the
perception of facilitating conditions and social influences (Venkatesh
et al., 2003) For instance, users show higher intention to interact with a
social robot because they expect specific design features such as the
ability to provide a personalized performance (Gao and Huang, 2019) or
feel subjective norms valorise the use of AI (Taylor and Todd, 1995) On
the other hand, consumers are now increasingly exposed to social robots
as service providers and retrieve experiences from them (Puntoni et al.,
2021) Experiences bring a co-created service value between customers
and providers, in eight dimensions which simultaneously come from
efficiency, excellence, play, aesthetics, status, ethics, esteem, and
spiri-tuality (Holbrook, 1999) Experience dimensions will enhance the
intention to engage in repeated interactions and engagement in a
rela-tionship between the service provider and the service user (Roy et al.,
under-stand and explain how acceptance of social robots arises in interpersonal
relationships between social robots and humans The association of
these theories also offers the perspective that value is raised in co-
creation between both parts and embodies the concept of
collabora-tion in the value brought by AI (Chowdhury et al., 2022) In this regard,
academics confirmed that concepts that echo experiential value
signif-icantly influence the intention to interact with social robots: perceived
efficiency and excellence (Davis, 1989), enjoyment (Xian, 2021),
anthropomorphic aesthetics (Liu and Tao, 2022), social recognition
Asked, for instance, for spiritual guidance to find meaning in one’s
own life, ChatGPT would provide explicit knowledge about self-
realization practices as reflecting one’s own values, goals, and
capabil-ities, or self-care If it offers guidelines and satiates the desire for
in-formation about spirituality, it cannot play the role of a spiritual leader
without influencing a user’s free will, therefore requiring scrutiny on
how such generative AI can offer a spiritual impact on users On the
other hand, the ethical dimension in the experience of using ChatGPT
would first entail a normative framework of what is moral or not around
the globe regardless of cultural variation, and this normative framework
under the supervision of developers Second, as ChatGPT touches
transversal applications within a nation, the ethical feature of its swers would require constant dialogue with institutional regulators In this sense, questions remain on what ethical value the experience of interacting with ChatGPT can bring, between a normative view behind the design of international conversational technology, and the positive view of national regulation, and what potential caveats could appear
an-On the other hand, similarly to the human-to-human applications of
ChatGPT could interact with each other, first because they intrinsically include intersectional aspects and second because users apply the complexity of social interactions to intelligent technology (Nass and
to the efficiency or excellence of the robot Arsenyan and Mirowska
media influencers could repulse users, preventing them from enjoying other values of the experience of an AI Efficiency, under the parameter
of perceived usefulness, is impacted by a user’s privacy concerns about a robot, hence the excellence dimension of its user experience (Ho and Lin,
2010) Perceived organizational support for AI, leveraging the status value of using AI, can increase trust in its reliability, such as excellence, and in its performance, such as efficiency, felt by an employee (Park and
raising a user’s self-esteem with appraisal messages could increase the perceived ethical value of interacting with the AI, implying that the robot displays exclusive support to the individual as a rule Overall, in line with the CASA argumentation (Nass and Moon, 2000), users of ChatGPT would intersect utilitarian dimensions with hedonic, social, and self-oriented aspects when interacting with it as they would in the context of a service experience with a human Following Puntoni et al (2021), we built a theoretical model that studies and discusses such factors under the comprehensive lens of Holbrook’s experience frame-work (Holbrook, 1999) This model will enhance the ability of AI de-signers to build social robots as sustainable collaborators of humans by integrating bases that create value and relationships for social AI and letting them acknowledge a holistic view of the feelings experienced by users to ensure responsible ways to incorporate social robots in our society
In this line of arguments, the present model hypothesises that ciency, excellence, status, esteem, play, aesthetics, ethics, and spiritu-ality, as anticipated aspects of the ChatGPT experience, enhance the intention to use ChatGPT, which has a significant impact on ChatGPT–human co-creation The model harmonizes the dimensions of ChatGPT use by instrumenting with AI use antecedents underlined in the existing literature (Tables 4 and 5) It instruments efficiency with perceived usefulness and ease of use and excellence with ChatGPT assurance, arguing that precedent factors contribute highly to making a social robot look efficient and performant (Ho and Lin, 2010; Davis,
effi-1989) Status and esteem are, respectively proxied by ChatGPT social recognition, subjective norms and ChatGPT personalisation On the one hand, subjective norms and social recognition of using ChatGPT make a consumer expect to reach a certain social status for their use of ChatGPT
other hand, personalisation makes a user feel respected and recognized for their self-worth (Gao and Huang, 2019) The model also covers play and aesthetics with hedonic motivation and ChatGPT enjoyment and with ChatGPT anthropomorphism The theoretical argument advances that enjoyment and hedonic motivation are bases for play (Xian, 2021;
due to its ability to please the senses of users (Liu and Tao, 2022) Finally, the present model instruments ethics with concepts of ChatGPT procedural and interactional justice and spirituality with ChatGPT empowerment and ChatGPT well-being The last instruments are justi-fied with the narrative that justice increases expectations of ethical collaboration from a user facing the prospect of using ChatGPT (Del Río-
Trang 9adjusting to the spiritual goals of a user (Meyer-Waarden and Cloarec,
2022; Naranjo-Zolotov et al., 2019)
The considerable impact of efficiency, here in the form of perceived
usefulness and perceived simplicity of use, on self-reported utilization
among 120 managers was developed and confirmed by Davis in Davis,
1989, enhancing research on technology acceptance By delivering
utilitarian value, efficiency in retail also raises consumers’ intentions to
use AI while shopping (Pillai et al., 2020) In education, Huprich (2016)
showed that if AI applications are shown to improve students’ learning
processes, colleges would be willing to integrate them By demonstrating
that effort expectancy and usage convenience work in conjunction with
performance to impact the intention to use AI, Xian (2021) and Gansser
the intention to use AI Efficiency favourably influences the intention to
use for both early adopters and mass consumers, according to Saari et al
flexibility and sociability However, Kuciapski (2017) reminds us that a
technology’s usefulness depends on the context, task, or purpose that the
user has in mind The idea of compatibility between technology and the
user, as well as their surroundings, as a moderator adversely affecting
the impact of efficiency on the intention to use, was presented by
perceived cognitive demand (Thongsri et al., 2018) The impact of
ChatGPT’s efficiency on how users intend to use it depends on user-
specific contextual elements, even if it exhibits great capabilities to
respond to requests and produce content For instance, ChatGPT could
first be seen as helpful, but later appear to be unsuited to societal
per-formance (Krügel et al., 2023) As a result, we question the impact of
ChatGPT on the intention to use it in hypothesis(H) 1
H1 : ChatGPT use efficiency will have a significant effect on its
intention to use
The excellence of a social robot, being a matter of assurance, reduced
risk and concern about the quality of the outcome offered to the user,
increasing the intention to use intelligent banking services online (Ho
banking apps also positively influences the intention to use (Yu, 2012)
In the fashion industry, Lee and Moon (2015) questioned the willingness
Table 4
Model constructs definitions
ChatGPT use
efficiency Cost benefit – The productivity a user expects when using ChatGPT, in
terms of outcomes for efforts
Davis (1989)
ChatGPT use
excellence Reliability – The service quality a user expects from using ChatGPT,
relative to risk, hence reliability
Ho and Lin (2010) Johnson and Grayson (2005)
ChatGPT use
status Human appraisal – The appraisal a user expects from other humans
when using ChatGPT
Taylor and Todd (1995) Meyer-Waarden and Cloarec (2022)
ethics Morality – The moral standards and ethical obligation a user expects
when using ChatGPT
Del Río-Lanza et al
(2009)
ChatGPT use
spirituality Self-connection – How connected to themselves and to the
environment and how belonging to
something bigger a user expects to
feel when using ChatGPT
Naranjo-Zolotov et al
(2019) Meyer-Waarden and Cloarec (2022)
Table 5
Model constructs, associated items, and references
Efficiency • Using Chat GPT will improve my
• My interaction with Chat GPT will
be clear and understandable
• I will find it easy to get Chat GPT to
do what I want it to do
• Interacting with Chat GPT won’t require a lot of mental effort
• Interaction data will be protected
• I feel relieved to interact with Chat GPT
• Given by the Chat GPT’s track record, I have no reservations about acting on its advice
• Given the Chat GPT’s track record, I have good reason to doubt his or her competence
• I can rely on the Chat GPT to undertake a thorough analysis of the situation before advising me
• I have to be cautious about acting
on the advice of Chat GPT because its opinions are questionable
• I cannot confidently depend on Chat GPT since it may complicate
my affairs by careless work
(reversed)
• Interaction with ChatGPT will be favourable
Ho and Lin (2010) Johnson and Grayson (2005)
Status • People whose opinions I value will
encourage me to use Chat GPT
• People who are important to me will support me to use Chat GPT
• The senior management in my organisation will encourage using ChatGPT
• People who influence my behaviour think that I should use ChatGPT
• It would give me a more acceptable image of myself
• It would improve how my friends and family perceive me
• It would give me better social recognition
• Using ChatGPT will increase my profile in the organisation
• Using ChatGPT will be a status symbol in the organisation
Taylor and Todd (1995) Meyer-Waarden and Cloarec (2022)
Esteem • I feel that the Chat GPT system
recommendations are tailored to
• I feel that the Chat GPT system recommendations are delivered in
a timely way
• I would feel a sense of personal loss
if I could no longer use a specific Chat GPT system
• If I share my problems with the Chat GPT system, I feel he or she would respond caringly
Gao and Huang (2019) Johnson and Grayson (2005)
(continued on next page)
Trang 10to use online clothing personalisation software and confirmed sumers’ preference on performance and the risks of contrasts between expected quality and purchased quality Mcknight et al (2011) showed that excellence increased trust in the robot, which secured the intention
con-to use it For Cao et al (2021), excellence lies in susceptibility, and fear that the robot will develop suggestions with negative effects regarding the user’s goals On the other hand, the complexity of the tasks sur-rounding the use of a robot decreases its perceived excellence, first in terms of explainability and transparency, which appear lower for com-plex tasks fulfilled with AI, and also as users tend to lower their ex-pectations of AI assistants for goals that appear complex to them
performance of a purchase based on past observation of performance and can lead excellence to present an even higher impact than expected
on the intention to use ChatGPT This is demonstrated by the frequency with which robots are mentioned in the media (Thorp and H H., 2023)
In addition, ChatGPT weaknesses have recently been brought up by academics and journalists (Thorp and H H., 2023), potentially harming the expected excellence that consumers view in ChatGPT Therefore, we interrogate the influence of the excellence of ChatGPT use on the intention to use ChatGPT:
H2 : ChatGPT use excellence will have a significant effect on the
intention to use it
ChatGPT’s use status is presently instrumented with subjective norms (Venkatesh et al., 2003; Taylor and Todd, 1995) and social recognition (Meyer-Waarden and Cloarec, 2022) The UTAUT integrates the normative value of a technology as an antecedent of intention to use
transgress social norms in its suggestions and allegations decreases the intention to use it (Cao et al., 2021) The success of the use of AI in an organisation is pushed by leadership, organizational support and col-leagues sharing knowledge about it, proving that the social context regarding the use of a robot influences its success to effectively socially integrate into the organisation (Chowdhury et al., 2022) If the use of
Table 5 (continued)
• The Chat GPT system displays a
warm and caring attitude towards
me
• I can talk freely with the Chat GPT
system about my problems at work
and know that he or she will want
to listen
Play • Using Chat GPT is fun for me
• Using Chat GPT is very enjoyable
• Using Chat GPT is very
entertaining
• Using ChatGPT is a joy
• Using ChatGPT is an adventure
• Using ChatGPT is a thrill
• Using ChatGPT will be rewarding
• Using ChatGPT will be a pleasant
Liu and Tao (2022)
Ethics • I think my problem was resolved by
the Chat GPT in the right way
• I think the Chat GPT has been
guided with good policies and
practices for dealing with
problems
• Despite the trouble caused by the
problem, the Chat GPT was able to
respond adequately
• The Chat GPT proved flexible in
solving the problem
• The Chat GPT tried to solve the
problem as quickly
• The Chat GPT showed interest in
my problem
• The Chat GPT did everything
possible to solve my problem
• The Chat GPT was honest when
dealing with my problem
• The Chat GPT proved able and to
have enough authority to solve the
problem
• The Chat GPT dealt with me
courteously when solving the
problem
• The Chat GPT showed interest in
being fair when solving the
problem
• The treatment and communication
with the Chat GPT to solve the
problem were acceptable
Del Río-Lanza et al
(2009)
Spirituality • Chat GPT use is very important to
me
• Chat GPT I use is meaningful to me
• Chat GPT activities are personally
meaningful to me
• Based on Chat GPT usage, my
impact on what happens in the
community is large
• Based on Chat GPT usage, I have
significant influence over what
happens in the community
• Based on Chat GPT usage, I have a
great deal of control over what
happens in the community
• If I used ChatGPT my life quality
would be improved to ideal
• If I used this ChatGPT my well-
being would improve
• If I used this ChatGPT, I would feel
happier
Naranjo-Zolotov et al
(2019) Meyer-Waarden and Cloarec (2022)
Table 5 (continued)
B´ehavioral Intention
• I intend to use ChatGPT in the next
Co-creation • I will feel comfortable co-creating
content with ChatGPT
• I will feel comfortable to solve problems with ChatGPT
• The ChatGPT service will allow me
to have my say to co-create
• My ChatGPT experience is enhanced as a result of co-creation ability and capability
• I will enjoy collaborating with ChatGPT to solve problems/
• I think ChatGPT as an assistant/
workmate will be easy to get along with
Gao and Huang, 2019 Chowdhury et al., 2022
Trang 11such innovative technologies is felt as vital for organisations, managers
demonstrate a higher willingness to integrate robots (Ochmann and
their shift to this channel through the “demonetization effect”, the
democratization of mobile banking in their social environment (Sobti,
2019) The use of AI robots is caused by a social need to connect with
other humans (Thongsri et al., 2018) For example, the ability of an AI
application to benefit networking in education increases the intention to
use it (Kashive et al., 2020) Social influence increases the intention to
use social robots (Xian, 2021); recognition retrieved by users from their
peers using a robot positively influences their intention to use it (Meyer-
a robot also depends on individual user habits, such as addiction,
independently of social influence (Xian, 2021) and on a personal
mindset about new technologies, as resistance to change prevents
con-sumers from using robots regardless of mandates to interact with them
that is highly discussed and popular currently (Thorp and H H., 2023)
Intention to use it might be a matter of individual attitude towards
trends: optimism (Pillai et al., 2020), attraction, or rejection and
scep-ticism (Krügel et al., 2023) This leads us to formulate Hypothesis 3:
H3 : ChatGPT use status will have a significant effect on intention to
use it
In this study, personalization and affective trust proxy ChatGPT
users’ esteem as an experiential value in using ChatGPT For instance,
the fact that a robot suits the individual requirements of a user increases
the intention to interact with it (Pillai et al., 2020) A robot that
cour-teously obeys and responds to a user’s demands (Del Río-Lanza et al.,
2009) and behaves politely is more likely to be adopted by a consumer
to support user self-development also cause the intention to use it
en-ables users to express themselves through two-way communication (Gao
attractive to consumers Moreover, robots offering users a feeling of
control over decisions present higher user acceptance rates (Zarifis et al.,
2021) Johnson and Grayson’s (2005) concept of affective trust also
links to self-esteem, leading consumers to form an attachment to an offer
and trust it out of confirmation bias, rationalizing their own affections
with biased positive expectations of the offer For social robots, we infer
similar reasoning is followed by AI users (Abadie et al., 2019), and
supports the esteem value of interactions with ChatGPT to impact the
intention to use it Yet, within the hierarchy of consumers’ basic needs
are priorities relative to the need to raise self-esteem In this sense,
ChatGPT users’ esteem may not impact the intention to use it as much as
other experiential value dimensions In addition, as ChatGPT’s purpose
is oriented towards cognitive support, esteem might play a minor
cosmetic role in increasing the intention to use this service Finally,
within the field of Service Marketing, Li et al (2022) and Li et al (2019)
have demonstrated that perceived courteousness, service
responsive-ness, and esteem are influenced by the physical attractiveness of service
staff, as a “beauty premium” helping to forgive service failures Since
ChatGPT only shows behavioural anthropomorphism and no physical
appearance; it could show a low esteem value to ChatGPT users Based
on this debate, we challenge the influence of ChatGPT user esteem
through Hypothesis 4:
H4 : ChatGPT user esteem will have a significant effect on the
inten-tion to use it
In this study, we present the experiential dimension of play value
with perceived enjoyment (Pillai et al., 2020) and hedonic motivation
partly based on the fact that consumers enjoy interactions with the
robot In retail, similar findings confirm that collaborating with a service
robot is a “joy”, “thrill”, or “adventure” and relieves stress, which will significantly boost the intention to use it (Pillai et al., 2020) Moreover, social robots, being engaging and dynamic, are more popularly accepted among shoppers (Pillai et al., 2020) For Hui et al., 2007, positive atti-tudes towards robots anchor into AI, making tasks more interesting and fun for students The educative environment that frames acceptance and successful AI use should appear enjoyable (Kashive et al., 2020) Meyer-
con-sumers to use autonomous cars Likewise, Xian (2021) considered donic motivation to be a key factor of the intention to use robots in the leisure services sector However, the capabilities observed in ChatGPT appear majorly utilitarian, computation-based, and generate mainly textual data (Kecht et al., 2023) In this sense, the gamified aspect of ChatGPT emerges as low, questioning the representativity of past find-ings about AI and social robots in their ability to offer play to users of ChatGPT Therefore, testing whether ChatGPT also brings the play value and whether it has a role in the intention to use this robot arises as a critical task for research We thus develop Hypothesis 5, which suggests ChatGPT’s use of play significantly influences the intention to use it
he-H5 : ChatGPT’s use of play will have a significant effect on the
intention to use it
ChatGPT’s use of aesthetics refers in the present study to behavioural anthropomorphism developed by Liu and Tao (2022) The aesthetical value of an experience implies pleasure from the perceived beauty of a consumer product, action, environment, or interaction (with staff) offered (Holbrook, 1999) As regards social robots, research findings place anthropomorphism at the centre of perceived robot beauty or the pleasure to interact with it (Kanda et al., 2004; Duffy, 2003) Physical human likeness leads to a higher intention to use social robots (Blut
and reduces the perceived threat of a robot (Lee and Liang, 2016) through the formation of positive emotions with the anthropomorphic robot (Chiang et al., 2022) In addition, Seo (2022) showed that female robots increase the satisfaction of customers with hospitality services, as gender stereotyping represents females as appealing Liu and Tao (2022) showed that anthropomorphism appears attractive to users in its behavioural aspects: the robot that seems to develop autonomously its own decisions, own opinions and emotions, and overall is perceived as conscious, is more engaging to consumers For Esmaeilzadeh and Vaezi
Consciousness is defined as the robot’s ability to perceive its internal states, innovate, and communicate while agreeing on a specific set of symbols and behaviours to conform to This increases the empathy of the robot felt by a consumer, and overall increases the propensity to adopt the AI Robot anthropomorphism also leverages the perceived quality of
a service through its human-like behavioural or physical appearance, and fosters adoptions and even loyalty intentions of robots (Noor et al.,
2022) Similarly, the perceived humanity of a robot increases the acceptance of virtual assistants (Zhang et al., 2021) If Anthropomor-phism has been demonstrated as a positive influence to accept robots, it shows a diverging impact on users, who might feel repulsed by anthropomorphic robots if they felt that the robots’ intelligence and capabilities could threaten their own human intelligence (Gursoy et al.,
2019) Moreover, ChatGPT is still a recent phenomenon; not enough research has been conducted yet to confirm with confidence whether ChatGPT also falls into the case where human likeness has an attractive power on the user In addition, the present study focuses on behavioural anthropomorphism, as ChatGPT presents no physical appearance or voice, but only interacts through text In this sense, research on physical, psychological, and behavioural links between dimensions of anthropo-morphism remains scarce In this line, physical anthropomorphism could support or even activate the perception of behavioural anthro-pomorphism, allowing interrogations on ChatGPT’s ability to raise the intention to use it from human-like behaviour Consequently, we ques-tion the impact of ChatGPT’s anthropomorphism on the intention to use