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Digital servitization value
co-creation framework for AI
services: a research agenda for
digital transformation in financial
service ecosystems
Elizabeth H. Manser Payne
Department of Marketing, University of South Dakota, Vermillion,
South Dakota, USA, and
Andrew J. Dahl and James Peltier
Department of Marketing, College of Business and Economics,
University of Wisconsin-Whitewater, Whitewater, Wisconsin, USA
Abstract
Purpose –Innovative firmshaverapidlydevelopedartificial intelligence (AI) capabilities into their service
ecosystems, essentially changing perceptions of what is service quality and service delivery in their respective
industries. Nonetheless, the issues surrounding AI services remain relatively unknown. The purpose for this paper
is to offer a digital servitization framework for understanding how AI services impact value perceptions, consumer
engagement and firm performance measures. The authors use the financial service ecosystem to explore this topic.
Design/methodology/approach –The authors explore relevant literature on digital servitization,
service-dominant logic and AI/disruptive innovation. Next, a conceptual framework, organized by AI-Service
Exchange Antecedents, Context of AI Usage and Digital Servitization Consequences, is developed. The
authors conceptualize consequences for consumers and firms.
Findings –The main findings suggest that the linkages between consumers, financial institutions and
fintech companies with AI usage in a service ecosystem should be identified; how value is created among
multiple SD Logic-AI network actors should be analyzed; and the effects of AI-consumer interactions (lower-
level and higher levels of engagement) on firm performance measures should be explored.
Research limitations/implications –The conceptual framework identifies gaps in the literature and
suggests research questions for future studies.
Practical implications –This paper may assist practitioners with the development of AI-enabled
banking activities that involve direct consumer engagement.
Originality/value –To the authors’best knowledge, this research agenda is the first comprehensive
framework for understanding value co-creation in the context of AI in financial services, linking antecedents,
usage and consequences.
Keywords Online consumer behavior, Customer value, Services marketing, Financial services,
e-commerce, Consumer behaviour internet, Service quality, Information technology, Eservice quality,
Blogs, Digitalizations
Paper type Conceptual paper
Introduction
Digital technologies are revolutionizing service ecosystems (Dahl et al., 2019;Kucharska,
2019), changing how services are created, delivered and assessed (Chandler et al.,2019). The
Digital
servitization
Received 3 December2020
Revised 29 December2020
Accepted 30 December2020
Journal of Research in Interactive
Marketing
© Emerald Publishing Limited
2040-7122
DOI 10.1108/JRIM-12-2020-0252
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/2040-7122.htm
proliferation of digital technologies is placing increased emphasis on service innovations,
business models and interactive customer relationships (Sklyar et al.,2019;Sjödin et al.,
2020), and especially since the COVID-19 pandemic has impacted consumers’digitally-
driven relationship expectations (Sheth, 2020). An extension of the servitization literature,
which focuses on the transformation from a product to a service-centric approach, digital
servitization uses digital service technologies to create customer value (cf. Sklyar et al.,
2019). Digital servitization is thus transforming processes, capabilities and offerings to
capture increased service value arising from a broad range of enabling technologies (Sjödin
et al.,2020). Despite the potential benefits of co-created value through digital servitization,
research exploring how service ecosystems evolve in response to new technologies and
relationship platforms is limited (Manser Payne et al., 2021;Patrício et al., 2018;Sklyar et al.,
2019). Accordingly, a primary aim of this paper is to examine how digital servitization
impacts value co-creation and value configurations, which are critical components of
profitable service relationships (Kucharska, 2019;Peltier et al., 2020).
Digital servitization is especially prominent in the financial services industry (Belanche
et al.,2019;Jakši
c and Marin
c, 2019;Königstorfer and Thalmann, 2020). The rise of mobile
banking has moved customer relationships and service exchange from primarily brick-and-
mortar to the use of digital service technologies when and where customers choose
(Laukkanen, 2016;Rahi et al.,2019). The increased utilization of digital technologies linked
to customer data has given rise to artificial intelligence (AI) capabilities designed to
augment other banking services (Khrais and Shidwan, 2020). Examples of AI range from
auditory or textual chatbots, to advanced digital services including fraud detection and
personalized investment advice. Firms successful in their digital servitization efforts co-
create value with customers through market efficiencies and data assimilation (Huang and
Rust, 2018). Although there is growing recognition that AI and digital servitization in
banking have considerable potential, conceptual and empirical research investigating how
AI impacts the value co-creation process and how service innovations are configured and
protected by firms is scant (Kaplan and Haenlein, 2019;Manser Payne et al., 2021). A second
purpose of our paper is to advance understanding of the relationship between service
digitization and digital transformation, and specifically for the value-in-use of AI services
(Manser Payne et al.,2018).
Value co-creation for digital transformations in service ecosystems has increasingly been
investigated through the lens of Service-Dominant (SD) logic theory (Swan et al.,2019;
Peltier et al.,2020). SD logic contends that value co-creation for AI services is at its highest
point when consumers, banks and digital technologies intersect to realize the goals across
these key financial services publics (Vargo and Lusch, 2016). For example, how does AI
benefit both financial service consumers and providers, and how does technology enhance
value creation? This joint goal-realization is especially true when there is an alignment
between consumers’service expectations and actual service delivery (Kristensson, 2019). In
this sense, consumers create value through application-in-use of AI services, while banks
create value through enhanced service delivery driven by AI analytics. In this regard, AI in
banking represents a digital service platform for facilitating the identification and exchange
of resources (Lusch and Nambisan, 2015). Despite recent research interest in understanding
value creation in service ecosystems, there is a paucity of studies that investigate how
digital transformation and AI co-create consumer-firm value (Barney-McNamara et al.,2021;
Kohtamäkia et al.,2019;Raddats et al.,2019;Sjödin et al.,2019). As a disruptive innovation,
AI has the potential to positively or negatively impact service ecosystems (Dedehayir et al.,
2017;Reinhardt and Gurtner, 2018). Given this dearth, a final research purpose is to examine
how SD logic may enhance our understanding of how value is co-created for disruptive
JRIM
technologies, and particularly for AI-enabled services. We use SD logic as our primary
theoretical construct to understand service digitization, service innovations and outcomes of
AI value co-creation.
In this paper, we offer a multi-actor digital servitization value co-creation framework for
AI services. As shown in Figure 2, our framework has three dimensions: AI-service
exchange antecedents for value co-creation among multi-actors in the financial industry;
Context of AI Usage; and digital servitization consequences. We also provide insights for
understanding customer-facing and back-office integration of AI in financial services
ecosystems, and consumer interactions with an AI agent. To accomplish this, we review the
digital servitization, SD logic and AI/disruptive innovation literature and offer a future
research agenda. Our framework responds to calls for conceptual frameworks for advancing
SD logic theory for emerging digital transformations (Jaakkola et al., 2020).
Theoretical foundations
We first review the general servitization literature, and literature specifically related to
digital servitization and disruptive innovations. We then present a discussion of SD logic as
our unifying theory of value co-creation as AI is integrated into service ecosystems. We
close with an examination of how AI represents a disruptive innovations in the context of
the financial services and banking industry. While this review focuses on AI in banking, it is
applicable to other service contexts and disruptive innovations as well.
Servitization
Over the past decade, firms have increasingly shifted from product-oriented to service-
oriented business models (Cusumano et al., 2015). This transition to a servitization
perspective is driven by an understanding that service ecosystems are not static and thus
evolve through systematic changes and disruptions (Raddats et al.,2019). Servitization
involves a strategic transformation in which a business adopts a service-centric approach
that places services, instead of product-only solutions, as the primary growth engine for
meeting consumer demand (Banoun et al.,2016). For example, hardware sales of Apple’s
core products such as the iPhone, MacBook, iMac and iPad have traditionally been Apple’s
dominate revenue contributors. Recently however, Apple’s service and subscription
offerings such as Apple TVþ, Apple Music, Apple Pay, iTunes, the App Store, iCloud and
Apple Care are increasing dramatically and has contributed over $51bn from June 2019-June
2020 (Richter, 2020).
Servitization often results in value enhancements for customers and firms by offering
customized solutions that jointly impact need satisfaction and result in higher customer life-
time value (Coreynen et al., 2017). To date, the transition to the servitization literature and
underlying service ecosystems has been in the context of institutional literature (c.f. Banoun
et al.,2016). While viewing servitization from a firm perspective offers opportunities for
internal efficiencies and cost efficiencies, an outward orientation is needed whereby all
service ecosystem actors work together to create value through inter-actor coordination
activities (Lusch and Nambisan, 2015). Inter-actor servitization thus offers the potential to
create unique and differentiated relationships that enhance consumer engagement and firm
profitability (Sjödin et al., 2019).
Digital servitization
Servitization is increasingly viewed through the lens of digital transformations. Holmström
and Partanen (2014) define digital servitization in terms of the provision of digital to support
and/or act as a substitute for physical goods. Specific to transformation processes,
Digital
servitization
Kowalkowski et al. (2017, p. 8) frame digital servitization as the utilization of digital tools for
“the transformational processes whereby a company shifts from a product-centric to a
service-centric business model and logic”Lastly, Sjödin et al. (2020,p.479)define digital
servitization as the:
[...] transformation in processes, capabilities, and offerings within industrial firms and their
associate ecosystems to progressively create, deliver, and capture increased service value arising
from a broad range of enabling digital technologies.
Digital servitization thus uses information technology as a mechanism for developing new
value-creating revenue streams (Parida et al.,2019), which in turn go “hand in hand with
adopting a servitization strategy”(Parida et al.,2015, p. 41). The digital servitization
literature has been particularly interested in investigating how digital technology facilitates
the delivery of new services needed for competing in today’s increasingly complex markets
(Coreynen et al., 2017). The ability to do so is contingent in great part on a firm’s analytic
capabilities for turning customer and firm level data into actionable value creation strategies
and tactics (Sklyar et al., 2019).
Disruptive innovations
Disruptive innovations have the potential to positively or negatively impact service
ecosystems (Dedehayir et al.,2017;Reinhardt and Gurtner, 2018). Early work by Christensen
(1997) and Christensen and Bower (1996), viewed disruptive innovations as those that create
a new set of features that differ from incumbent technologies. More recently, disruptive
innovations have been defined as those that change performance metrics or market
expectations by offering radical functionality and/or discontinuous technical standards
(Nagy et al.,2016). Disruptive service innovations are found in multiple industries, including
mail delivery (FedEx), hospitality (Airbnb), retail (Amazon) and entertainment (Netflix).
Academic researchers are particularly interested in disruptive innovations that focus on the
digital transformation of services (Larivière et al., 2017;Manser Payne et al., 2021). For
example, researchers have investigated digital service delivery in the context of mobile
banking (Manser Payne et al., 2018), social media (Letaifa et al., 2016), and health care (Dahl
et al.,2019;Swan et al.,2019).
An innovation’s disruptive potential within a service ecosystem is multidimensional,
connecting various actors through technological features and marketplace dynamics that
radically alter service relationships (Guo et al.,2019). Disruptive service innovations thus
have the potential to transform service delivery via new processes, technologies and
deliverables (Chandler et al., 2019). A common thread across this evolving research stream is
that disruptive innovations impact how employees interface with customers and the extent
to which consumers benefit from these digital service encounters (Larivière et al.,2017).
Importantly, firms that fail to recognize the market’s acceptance of new, though not well-
adopted, service innovations (Bower and Christensen, 1995), face potentially severe
economic consequences (Obal, 2017). Research that addresses how firms launch service
innovations, either as pioneers or responders, and how these innovations generate
acceptance in service ecosystems is warranted (Chandler et al.,2019).
Service-dominant logic perspective on value co-creation in digitalized service ecosystems
Service-dominant (SD) logic describes service exchange as inherently beneficiary-oriented
and relational (Vargo and Lusch, 2008;Edvardsson et al., 2011), reflecting a paradigm shift
of how value creation occurs (Vargo and Lusch, 2016). SD logic emphasizes consumers are at
the center of the value chain, and outlines that value is co-created and experienced via
JRIM
consumers’co-production role and their concept of value-in-use (Edvardsson et al.,2014;
Ranjan and Read, 2016). Specific to service digitalization, consumers contribute to value co-
creation through coproduction of the service experience by using self-service technologies
(SST) (Bendapudi and Leone, 2003). SD logic also contends that consumers’co-create value
that they experience and is reflected in their unique evaluations of the service’s propositions
(Chandler and Lusch 2011;Edvardsson et al.,2011). Service providers may exert influence
on consumers’value-in-use concept by integrating personalization and relational factors into
the self-service experience (Ranjan and Read, 2016). SD logic implies that while service
providers propose value propositions, consumers ultimately define and realize value co-
creation (Chandler and Lusch, 2011;Vargo and Lusch, 2016).
Similar to Lusch and Nambisan (2015, p. 162), we define value co-creation as “the
processes and activities that underlie resource integration and incorporate different actor
roles in the service ecosystem.”The evolving ecosystems perspective of SD logic involves
considering how multiple stakeholders beyond the traditional dyadic service exchange
(service provider-consumer) influence value co-creation (Chen et al.,2020). Traditionally,
service exchange occurs in-person between two primary actors –the consumer and service
provider. The emerging perspective of SD logic is that there are multiple actors (i.e.
consumers, service providers and other stakeholders) and institutional norms that affect
resource integration and value co-creation processes in the service ecosystem (Edvardsson
et al.,2014; Vargo and Lusch. 2016). The rise of digital servitization implies that the service
exchange and experience extends beyond human-to-human interactions (Kleinaltenkamp et
al.,2012;Caridà et al.,2019), and may involve the consumer interacting with AI technology-
enabled actors (i.e. mobile banking apps, virtual assistants, chatbots). Even in
predominantly human-to-human service exchanges, the marketer or service provider may
integrate AI-enabled information systems to enhance the service process and thus impact
the consumer experience. Service providers employing digitalization innovations may thus
increase consumers’value co-creation and in turn benefit the firm via enhanced consumer
satisfaction, increased consumer loyalty and lower service delivery costs (Cossío-Silva et al.,
2016). Research is thus needed that explores antecedents and consequences of value co-
creation processes that involve non-human actors (i.e. AI-enabled bots) and integration of
operant resources (i.e. AI-enabled information systems) in these digitalized service
ecosystems (Manser Payne et al.,2021;Sklyar et al.,2019).
Vargo and Lusch (2016) updated and organized SD logic’s foundational premises (FP)
around five axioms. The five axioms offer valuable insights for researchers examining value
co-creation in digitalized service ecosystems. Table 1 presents our propositions of how these
axioms may inform future research on value co-creation in AI-enabled service digitalization
contexts. First, Axiom 1 (FP1) contends that AI may offer specialized knowledge that will
transform the fundamental nature of service exchange (Edvardsson et al., 2011;Edvardsson
et al., 2014). Research is thus necessary that investigates how the service experience evolves
as AI is integrated into service value propositions. Second, Axiom 2 (FP6) suggests that
consumers need to be active participants to maximize value co-creation (Storbacka et al.,
2016;Dahl et al.,2019). Research that examines how service providers can increase
consumer participation and usage intent of AI-enabled service technologies is thus
warranted. Third, Axiom 3 (FP9) implies that resource integration from a variety of
ecosystem actors needs to be considered (Chen et al., 2020;van Tonder et al., 2020).
Specifictofinancial services, these actors may include consumers (including consumer-
to-consumer interactions), traditional financial services organizations (i.e. banks/credit
unions), fintechs (i.e. digital-only direct-to-consumer providers, b2b technology service
providers), financial or tax advisors, government or other regulators, among others.
Digital
servitization
Moreover, should AI itself be considered as an ecosystem actor (Huang and Rust, 2018)?
Research that explores the interdependent roles of these actors will offer valuable
insights on how AI affects the value chains of this increasingly complex service
ecosystem. Fourth, Axiom 4 (FP10) requires that “value”outcomes be measured from
the beneficiary’s perspective (Vargo and Lusch, 2008). While our framework proposes a
variety of consumer and service provider (firm) outcomes, future research may consider
other beneficiary perspectives (i.e. frontline service provider). Finally, Axiom 5 (FP11)
outlines that research must consider the ecosystem’s institutions (i.e. norms, underlying
mechanisms) and institutional arrangements that may facilitate or hinder value co-
creation as AI-enabled technologies are introduced and leveraged within service
ecosystems. Although marketing scholars have shown increased interest in exploring
the role of institutional norms and logic embedded in service systems, much remains
unknown (Caridà et al., 2019;van Tonder et al., 2020). Future research should thus also
address the interrelated effects of any commonly accepted cognitive, affective or
behavioral norms.
Artificial intelligence as a disruptive innovation in the financial services ecosystem
Consumers are increasingly demanding more advanced and automated self-service
technologies that are AI-enabled (De Keyser et al., 2019). Unlike other SST, AI-enabled
service technologies are capable of sensing consumer needs, thereby potentially
increasing the ease of consumers’participation in value co-creation (Vargo and Lusch,
2008). In response, organizations employing digital servitization strategies increasingly
view AI-enabled technologies as efficient ways to replace human-to-human interactions
of frontline service providers (Huang and Rust, 2018), or to automate various processes
(Hoyer et al., 2020). Haenlein and Kaplan (2019,p.5)define AI as “asystem’s ability to
interpret external data correctly, to learn from such data, and to use those learnings to
Table 1.
Research
implications of SD
logic axioms for AI-
enabled service
ecosystems
Axiom Axiom description Research implications
Axiom 1/FP1 Service is the fundamental basis of
exchange
How does the service experience evolve as
AI is integrated?
Axiom 2/FP6 Value is co-created by multiple actors,
always including the beneficiary
How do service providers increase
consumer participation and usage intent of
AI-enabled service technologies?
Axiom 3/FP9 All social and economic actors are resource
integrators
Who are the actors involved? Is AI an actor?
What are the interdependent roles of these
actors? How do these roles affect the
resource integration process of the value
chain?
Axiom 4/FP10 Value is always uniquely and
phenomenologically determined by the
beneficiary
What is value in AI-enabled service
ecosystems? How is value measured? What
other perspectives exist for measuring AI-
enabled value?
Axiom 5/FP 11 Value cocreation is coordinated through
actor-generated institutions and
institutional arrangements
What norms or underlying mechanisms
exist? How do these facilitate or hinder
value co-creation? How does this differ
based on the service context, AI interaction
level, etc.?
Note: Axiom definitions as stated by Vargo and Lusch (2016, p. 18)
JRIM
achieve specific goals and tasks through flexible adaptation.”Accordingly, AI-enabled
technologies have human-like cognitive capabilities (Huang and Rust, 2018), including
knowing, learning, perceiving, sensing, acting, planning, communicating and
reasoning. The result is service encounters that now include human-AI interactions
(Larivière et al., 2017), with disruptive innovation implications for various service
ecosystem actors (i.e. consumers, frontline service providers, and other stakeholders)
(Fernandes and Oliveira, 2021), and across the customer lifecycle (Hoyer et al., 2020).
Research is thus necessary that explores AI’s impact on these actors’roles,
relationships, experiences and other outcomes (De Keyser et al.,2019).
The integration of AI-enabled technologies in services ecosystems creates multiple
questions regarding AI’s impact on service design and exchange (Huang and Rust, 2018;
Fernandes and Oliveira, 2021). For example, how does AI influence the roles andexperiences
of various ecosystem actors (i.e. consumers, service providers, among others)? How does AI
contribute to and change the value co-creation process in a service context? Central to these
questions is the debate within the SD logic literature on whether AI is an operand or operant
resource (Akaka and Vargo, 2014). SD logic defines operand resources as tangible assets,
while operant resources reflect intangible elements such as knowledge, skills or other
capacities (Madhavaram and Hunt, 2008). AI’s ability to sense, learn and predict appears to
align with the notion of operant resources (Akaka and Vargo, 2014). However, Lusch and
Nambisan (2015) suggest AI-enabled technologies may be considered as both an operand
and operant resource. As AI becomes more capable of human cognition and emotions
(Huang and Rust, 2018), marketers might consider these conversational bots as actors
within the value co-creation process (De Keyser et al.,2019). Research is thus necessary that
delineates how AI in consumer-facing contexts is changing the dynamics of service
exchange (Hoyer et al., 2020;Fernandes and Oliveira, 2021).
Similar to Fernandes and Oliveira (2021), we acknowledge AI’s application within a
service context may be diverse and span both customer-facing service and marketing
activities as well as back-office operations. Specifictothefinancial services ecosystem, we
outline some of the ways the financial industry is applying AI-enabled technologies in
Figure 1. As service providers integrate AI technologies in customer-facing activities, it not
only is likely to transform the consumers’service experience (Hoyer et al.,2020), AI may also
help create more efficient back-office operations. Likewise, AI integrated in back-office
processes will provide inputs that help inform and personalize customer-facing service and
marketing activities. Combined, the integration of AI is likely to enhance value co-creation
efforts across the service ecosystem value chain. Despite these clear benefits, research is
lacking on consumer acceptance of AI in consumer-facing contexts (De Keyser et al.,2019),
including virtual assistants and other AI technologies that offer different value-in-use
contexts (Fernandes and Oliveira, 2021). Comprehensive research is thus necessary to
examine the motivational drivers, institutional norms and other factors that influence
utilization, value co-creation processes and service perceptions (Vargo and Lusch, 2016).
Given that consumer-facing applications of AI offer the greatest potential to affect the value
co-creation chain, we focus our conceptual framework on customer-facing services and
marketing applications of AI.
Multi-actor digital servitization value co-creation framework for artificial
intelligence services
In Figure 2, we propose a theoretical framework to guide the direction of digital
servitization research for AI services within the financial services industry. We
organize the discussion of the framework by its essential core components: AI-service
Digital
servitization
exchange antecedents to AI-value co-creation from the multi-actors prevalent in
the financial industry, context of AI usage and digital servitization consequences.
While the framework is not exhaustive, it provides a starting point for understanding
the AI-enabled services value co-creation field of research.
Artificial intelligence-service exchange actors in the financial services ecosystem
(antecedents)
SD logic implies the success of value co-creation in a digital service ecosystem is reliant
on a network of various resource-integration actors, underlying mechanisms and systems
(Baraldi et al., 2012;Barney-McNamara et al., 2021;Storbacka et al., 2016;Razmdoost
et al., 2019). Each actor plays an influential role in AI value co-creation via integration of
their unique experiences (Razmdoost et al., 2019), heteronomous resources (Baraldi et al.,
2012) and other characteristics that shape the value-in-use of the AI usage context
(Akaka and Vargo, 2014). Our framework conceptualizes the financial services ecosystem
AI-service exchange network to primarily consist of three primary network actors –
consumers, traditional financial industry organizations (i.e. banks) and supporting
fintech institutions.
Growing adoption of mobile banking apps places the consumer in an active value co-
creation role via SST-based service exchange (Manser Payne et al., 2018). Consistent with SD
logic, we propose the consumer is not only a critical participating actor, but the central
beneficiary of the AI value co-creation process (Vargo and Lusch, 2016). Accordingly,
consumer characteristics such as previous banking experiences, trust in structural
systems, or comfort in interacting with AI technologies (i.e. chatbots or virtual
assistants) may influence their AI usage intentions (Razmdoost et al.,2019)andvalue-
in-use (Sandström et al., 2008;Edvardsson et al.,2014). Due to the disruptive nature of
AI, traditional financial industry actors increasingly rely on fintech companies
to support the implementation of AI-enabled services (Drasch et al., 2018). Therefore,
these service ecosystem actors need to establish coordinated efforts across the AI-
Figure 1.
Customer-facing and
back-office
integration of AI in
the financial services
ecosystem
JRIM
service exchange to first enhance consumers’value-in-use and then to increase
thepositiveoutcomesofAIusage(Sklyar et al., 2019). Below, we briefly discuss some of
the specific network actor characteristics outlined in our framework that are likely to be
pivotal in the AI value co-creation process.
Consumer characteristics. SD logic implies that consumer characteristics will have an
influential role in the resource integration process, and for assessing the value-in-use
experienced from AI exchanges (Barney-McNamara et al.,2021;Vargo and Lusch, 2016).
Sandström et al. (2008) suggest that consumers’evaluation of the service experience is likely
to extend beyond their demographic characteristics and include various emotional, cognitive
and normative factors. Accordingly, we further categorize consumer characteristics by their
relationship to utilitarian or hedonic value potential.
Utilitarian value. Research on mobile banking identifies a number of utilitarian value
characteristics that are likely to impact the consumer’s role in an AI service digitalization
context (Manser Payne et al., 2018). Consumers exhibiting utilitarian value-seeking attitudes
reflect the prioritization of service innovations that offer fulfillment via solving a financial
problem or accomplishing a financial task (Shi et al.,2017). The disruptive innovation
literature has long investigated consumers’perceptions of the relative advantages (costs vs
benefits such as time savings and convenience) they expect to realize if adopting the
innovation (Rodgers, 1995). Choudhury and Karahanna (2008) suggest convenience, trust
and information efficacy comprise three core dimensions of the relative advantage of digital
channels. Moreover, Collier and Kimes (2013) suggest value perceptions of the convenience
of SST may positively impact other relative advantage perceptions such as speed and
accuracy. Specific to AI and banking, Shankar and Rishi (2020) suggest the rise of service
digitization in banking requires assessing the relative advantage of convenience as a
multidimensional construct that encompasses access convenience, transactional
convenience and possession/post-possession convenience.
Figure 2.
Multi-actor digital
servitization value co-
creation framework
for AI services
Hedonic Value
•Need for human
interaction
•Enjoyment/Social
feelings/Social norms
•Comfort with
technology
•Feeling connected
•Perceptions of AI
interactions
Consumer Characteristics
AI-Service Exchange Antecedents
Utilitarian Value
•Security/Privacy of data
•Trust/Credibility
•Dimensions of relative
advantage/Innovation
decision
•Convenience
•Speed/Efficiency
•Risk tolerance
•Technology adoption
attitudes
•Technology readiness
•AI channel usefulness/ease
of use/service quality
•Service expectations
•Customer-centric,
platform-based orientation
•AI strategy embeddedness
in firm goals
•System quality/Network
quality/Information quality
Financial Industry Characteristics
•AI technology infrastructure
•Data sharing among network actors
•Security/Privacy of data
AI
h
l
i
f
Fintechs-- Supporting Institutional Actor
Characteristics
•Omni-channel
•Digital servitization-
orientation
•Organization culture
•Level of AI
technology
infrastructure
Context of AI Usage
Firm Performance Outcomes
•Customer satisfaction
•Service delivery
•Relationship banking
•Customer loyalty
•Profits
•Customer lifetime value
•Online reviews/WOM
sentiment
Digital Servitization Consequences
Lower Value-in-use
AI Contexts
Higher Value-in-use
AI Contexts
Consumer Outcomes
•Service experience
•Financial decision-making
•Financial well-being
•Overall well-being
Digital
servitization
Consumers are also likely to assess the utilitarian value of mobile banking and AI in light
of consumers’perceived trust (Zhou, 2012) and data security (Yousafzai et al.,2010) of the AI
digital service exchange. For example, consumers may experience greater preference and
increased usage for digital exchanges if AI is used for tasks or activities they view as lower
risk, easier to use or when they feel they have control over the exchange (Haridasan and
Fernando, 2018;Davenport et al.,2020). Conversely, consumers’trust and security concerns
may decrease willingness to use the digital service exchanges (Laukkanen, 2016;Veríssimo,
2018), or refuse to share complete information as part of AI value co-creation process
(Kristensson, 2019).
Recent research suggests factors such as technology readiness and digital orientation
may contribute to understanding of how consumers interact with technologies from both a
utilitarian and hedonic perspective (Pizzi et al.,2019;Blut and Wang, 2020). Parasuraman
(2000,p.308)defines technology readiness as “people’s propensity to embrace and use new
technologies for accomplishing goals in home life and at work.”Specific to the integration of
AI into financial services ecosystem, Belanche et al. (2019) indicate that consumers’
familiarity with AI moderates the relationship between perceived usefulness and intention
to use AI-enabled financial advisors, suggesting technology readiness, digital orientation
and other knowledge factors may have particularly strong effects from a utilitarian value
perspective.
Hedonic value. Hedonic value characteristics reflect consumers’emotional gratification
from the service exchange experience (Akbar and Hoffmann, 2020). Research examining the
hedonic value of AI from a consumer perspective considers value perceptions associated
with the emotional or “human-side”of the disruptive innovation. Like other service
digitization contexts, human interaction has played a key role in consumers’resource
integration and banking value co-creation activities. Social institutions and norms
established by these typical interactions may thus impact value co-creation in disruptive
innovations (Vargo and Lusch, 2016).
Human interactions help maintain strong emotional bonds (Fyrberg and Jüriado, 2009),
that help develop long-term relationships and determine personalized financial products and
services (Guo et al.,2019). However, digital channels and disruptive technology are
restructuring traditional consumer–employee interactions (Sklyar et al., 2019), requiring
research that examines how a consumer’s need for human interaction may impact AI-based
service exchange (Dabholkar and Bagozzi, 2002). Mixed results exist regarding need for
human interaction’s impact, with some research showing no impact on digital banking
(Curran and Meuter, 2005), and others determining it is a critical factor that impacts SST
usage (Dabholkar and Bagozzi, 2002;Collier and Kimes, 2013). Likewise, consumers’
connectedness and perceptions of enjoyment during digital service encounters may be
important hedonic constructs to investigate, with researchers suggesting that AI-based
service exchanges will not appeal to consumers with a high need for social connection
(Huarng et al., 2015;Boateng, 2019;Jakši
c and Marin
c, 2019). Comfort with technology
reflects consumers being at ease with their ability to use technology (Kim and Choi, 2019).
Comfort with technology has been linked to other hedonic factors including, risk and
enjoyment (Akhter, 2015), technology readiness (Hallikainen et al., 2019;Parasuraman,
2000), confidence (Kim and Choi, 2019), need for personal human interaction (Dabholkar and
Bagozzi, 2002), among other factors that impact co-creation (Chepurna and Rialp Criado.,
2018). Consequently, this factor may be critical to value co-creation as consumers will need
to feel comfortable interacting with an AI agent for AI services to be effective and to
enhance the service experience. Given different AI usage contexts and AI’s expanding
capabilities to exhibit more “human-like”interactions, cognitions and emotions (Huang and
JRIM
Rust, 2018), hedonic characteristic appear particularly important to examine in the AI value
co-creation framework (De Keyser et al.,2019).
Research needs. While there is research on consumer utilitarian and hedonic value
characteristics in digital channels, relatively little is known about how consumer
characteristics influence value co-creation when AI is introduced into service delivery
systems (Manser Payne et al., 2018). Given the significant commitment by financial
institutions and fintech companies into AI investments, it is highly likely that consumers
will engage with an AI agent at some level whether they want to (Veríssimo, 2018).
Consumer issues that are especially critical for research are listed below:
What role does data security and privacy play in AI interactions? How do
consumers security and privacy expectations differ for lower vs higher levels of AI
interactions? What dimensions of security and privacy are important? What
dimensions of risk do consumers perceive of AI?
How do perceptions of speed and efficiency impact AI services? What (if any)
advantages (ease of use, accuracy, time-savings, etc.,) do consumers see in AI-
enabled digital servitization? What specific banking activities or tasks do
consumers find more value in in an AI context? How do consumers’service
expectations differ for AI agents?
How does consumer trust impact AI-enabled digital touchpoints? How does
consumer trust differ for AI agents if the AI agent is embedded within traditional
banks (bank branches and digital touchpoints) vs digital-only banks?
How does a consumer’s need for human interaction influence AI usage? In what
ways would consumers perceive AI interactions when interacting with a bank
employee? What (if any) influence do demographic differences have on how
consumers perceive consumer–AI interactions?
What factors influence consumers’level of comfort interacting with AI? What
variances in enjoyment exist when interacting with AI-services? Would the
variances be moderated based on the specific banking activities or tasks? How
would this positively or negatively impact relationship banking?
What social norms may play an influencing role in consumer–AI interactions?
Financial industry characteristics. SD logic implies that marketers for service organizations
contribute to value co-creation by establishing and communicating the value propositions of
AI-enabled services (Chandler and Lusch, 2011). Value co-creation therefore relies on the
institutional norms and logic that may exist within specificfinancial organizations or be
embedded across the traditional financial services ecosystem (Barney-McNamara et al.,
2021;Edvardsson et al.,2014; Vargo and Lusch. 2016). The evolution to an AI digital
servitization-orientation requires shifts in banking strategies, service offerings and firm-
level orientations to remain competitive (Davenport et al.,2020). Similar to the adoption and
integration of other disruptive innovations, AI’s integration into the service exchange
process is likely to rely on organizational leadership establishing a culture that
demonstrates a commitment to AI investments not only into automating workplace
processes but also across consumer-facing elements (Fountaine et al.,2019).
The firm’s IT infrastructure and overall system quality is also likely to be critical to
enabling the firm to integrate AI in new ways to create value (Albashrawi and Motiwalla, 2020;
Mbama et al., 2018). For example, digital servitization is likely to require that traditional
financial industry actors such as banks adopt more of an omni-channel orientation to ensure AI
meets consumers’service expectations (Shi et al., 2020). Similarly, a firm’s digital orientation is
Digital
servitization
likely to contribute to value co-creation by establishing an organizational commitment to
service innovation through integrating digital technology like AI into more service usage
contexts (Khin and Ho, 2019;Sklyar et al., 2019). Finally, research suggests that how firms
embed AI into the service exchange is likely to affect consumers’value expectations and
influence usage intentions (Davenport et al., 2020). While current integration of AI
infrastructure in the banking industry tends to occur for more low-value applications (i.e.
monitoring accounts and transactional elements), higher-value personalized AI services are
becoming more commonplace (Donepudi, 2017). Firm-level processes and strategic orientations
are thus likely to impact how AI is integrated into the traditional human-to-human service
delivery and decision-making processes (Jarrahi, 2018). Greater AI investment by firms may
thus reflect a more customer-centric approach to their servitization strategy (Chung et al.,2018),
which in turn impacts consumers’intentions to use AI services (Purdy and Daugherty, 2016).
Combined, these firm orientations may impact not only the firm’s willingness to employ AI, but
consistent with SD logic, will also influence the value co-creation experienced by other actors
(Vargo and Lusch, 2016).
Research needs. Research on the financial industry’s role in the network is still in its
infancy. Empirical research is thus needed to better understand the role of traditional
financial organizations, industry institutions and logic in the value co-creation process and
how their characteristics impact consumer–AI interactions. We propose the following
agenda areas:
What dimensions of organizational culture have the most positive/negative
influence on AI investments? How does organizational culture that encourages AI
interactions impact consumers who have a higher need for human interactions?
What impact does that have on relationship marketing?
To what degree does the financial institution’s digital orientation drive AI
investments? What AI strategies can the financial industry incorporate in an omni-
channel environment to best service consumer needs? How do AI investments
enhance service quality and responsiveness to consumer needs?
How does AI investments change consumer banking behaviors? What differences
exist in consumer banking behavior between lower and higher levels of AI
interactions?
Fintechs: supporting institutional characteristics. An emerging perspective of SD logic
suggests that a variety of ecosystem actors beyond the consumer-service provider dyad
may influence AI resource integration and value co-creation efforts (Chen et al.,2020).
Fintechs are companies that offer innovative technology-based solutions and provide more
cost effective financial products and services for aligning consumers’needs in today’s
digital era (Drasch et al., 2018). Although fintech innovation is still in its early stage of
disruptive innovation, fintechs are an interesting ecosystem actor to consider as the
relationship between fintechs and banks may be both competitive and collaborative (Jakši
c
and Marin
c, 2019). Traditional banking firms may collaborate with fintechs to provide
services that support the organization’s digitization strategy (i.e. payment transfers through
Zelle or similar fintechs) (Belanche et al.,2019). Fintechs may also serve as direct
competition by offering consumers financial services that encourage consumers to forego
traditional banking relationships (Drasch et al.,2018). Fintechs thus serve as supporting
value co-creation actors when they provide traditional banks with AI-embedded
technologies and infrastructure to assist with the restructure of bank technologies (Drasch
et al., 2018), address security and privacy issues (Saksonova and Kuzmina-Merlino, 2017)
JRIM
and comply with regulatory requirements such as uncovering money laundering (Banwo,
2018) or other fraud detection (Ryman-Tubb et al.,2018). Questions also exist related to the
potential dark-side of algorithmic biases (Davenport et al., 2020), which may originate with
software engineers or other actors that design the AI systems (Königstorfer and Thalmann,
2020). Given the competitive nature of fintechs in relationship to traditional banks (Drasch
et al., 2018), there may be potential conflict in the institutional norms that create conflict in
how AI is embedded in the value co-creation process (Königstorfer and Thalmann, 2020),
requiring research that examines the supporting role of these actors in the ecosystem.
Finally, although not depicted in our framework, future research may consider supporting
network actors beyond fintechs that may impact AI’s integration into the financial services
ecosystem. These actors may include government/regulatory agencies, internet and
smartphone providers, among other actors.
Research needs. To date, the collaborative-competitive relationship between traditional
banks and fintechs has received little attention in the academic literature (Drasch et al.,
2018). Given the importance of inter-actor relationships and activities to value co-creation
(Vargo and Lusch, 2016), research on the roles of these third-party ecosystem actors is
warranted. To advance knowledge in this area, we suggest the following research questions:
RQ1. How are operant resources pertaining to data security, privacy, and fraud
detection shared among multiple actors that may be collaborative or competitive
actors? How do consumers perceptions of AI services different if offered directly
by fintech companies vs traditional financial industry firms?
RQ2. What impact does disruptive innovation play in collaboration within digital
service ecosystems? Or do disruptive innovations, such as AI, ultimately result in
competition and a reduction in value co-creation?
RQ3. When does regulation designed to protect data privacy and security inhibit
innovation? What data privacy and security regulation is needed for consumers
to trust AI-services?
Context of artificial intelligence usage intentions
The resource integration process embedded within SST is attracting increased research
attention (van Tonder et al.,2020). SD logic implies that consumers’unique usage contexts
will influence their value-in-use perceptions of these technologies (Edvardsson et al.,2014;
Lusch and Nambisan, 2015), and are thus critical to explaining the resource integration
process for SSTs like AI (Vargo and Lusch, 2016). In our framework, AI usage refers to the
extent to which consumers currently use or intend to use customer-facing banking services
embedded with AI. We also postulate that consumers’usage intentions may differ across
lower and higher value service contexts in which the AI resources are embedded. Consistent
with SD logic and our framework, Table 2 shows a categorization of AI characteristics
based on lower and higher levels of value-in-use context (Edvardsson et al., 2014;Vargo and
Lusch, 2016). Increased communication, personalization and other relational factors that
service providers integrate into the AI service exchange are likely to shape consumers’
value-in-use contexts (Ranjan and Read, 2016). Lower-value usage contexts refer to AI-
enabled transactional services that frontline service providers (i.e. bank tellers, personal
bankers) traditionally perform in a physical interaction and that require more basic levels of
human cognition (Huang and Rust, 2018). Personalization and the extent of the AI
communication in these usage contexts tend to be low as these value creating activities are
limited to account set-up, simple transactions of deposits, fund transfers and similar aspects.
Digital
servitization
On the other end of the spectrum are higher-valued AI services where communication with a
virtual financial assistant is advice-driven, conversational and more human-like (De Keyser
et al., 2019;Fernandes and Oliveira, 2021). Although still in the developmental stages,
higher-value AI usage contexts entail service experiences that are similar to two-way
human-to-human communication (Luo et al.,2019), and may include more higher-order
cognitive and emotional capabilities (Huang and Rust, 2018). For example, while chatbots
may perform more basic transactional elements, AI virtual financial assistants also offer
more advanced, real-time personalized value co-creation activities that include investment
planning, insurance planning, debt consolidation and other advice.
Research needs. The existing literature on AI usage intentions primarily focuses on back-
office AI applications, including big data (Vives, 2019;Rambocas and Pacheco, 2018), fraud
detection (Ryman-Tubb et al.,2018) or customer relationship management (Kietzmann et al.,
2018). Literature pertaining to value co-created in customer-facing AI interactions is sparse
(De Keyser et al.,2019;Hoyer et al.,2020;Fernandes and Oliveira, 2021), suggesting there is
considerable opportunity to extend understanding (Manser Payne et al.,2021). Research in
the area of how consumer behavior may be altered when interacting with conversational
bots is promising (Davenport et al., 2020). More research is needed to better understand how
consumers respond to different value-in-use contexts of AI usage in the banking and other
service industries. Research is also needed pertaining to barriers to AI usage and negative
perceptions and myths of AI in general service settings (Atkinson, 2018). We offer the
following issues for consideration:
How do consumers’AI usage intentions differ from other SST intentions? How will
AI with natural voice applications be perceived and used by consumers? As AI
Table 2.
Value co-creation
characteristics of
customer-facing AI
service agents
AI chatbots AI virtual assistants
Level of Digital Servitization Basic, transactional-driven,
human-machine contact
Advanced/complex, advice-driven,
human-machine collaboration
Degree of Communication Alerts, Q&A exchanges Conversational, real-time, more
reflective of human-to-human exchanges
Degree of Personalization Low High
Examples of Value Co-
Creating Activities
Lower Value-in-use contexts
Account set-up
Deposits
Bill payment
Fund transfers
Transaction reminders
Higher Value-in-use contexts
Same as lower value-in-use
plus...
Real-time personalized advice on
bank accounts
Real-time personalized
investment portfolios
Real-time personalized
retirement planning
Real-time personalized debt
consolidation
Real-time personalized financial
goal planning
Real-time personalized insurance
planning
JRIM
services become more common place and advanced, how can the advice from a
virtual financial assistant seem as trustworthy or credible as a human financial
advisor?
What role do different actors play in shaping the AI usage contexts? Specificto
consumers, what personality traits might influence AI usage? What other factors
will impact usage intentions?
How do privacy and data security constructs need to evolve to address AI-specific
concerns? What new measures might be necessary for AI usage contexts given the
importance of personalization to high value-in-use AI contexts?
Digital servitization consequences
The SD logic perspective outlines that AI utilization is likely to have digital servitization
consequences for consumers, service provider firms and other ecosystem actors (Vargo and
Lusch, 2016). In our framework, we identify consumer and service firm outcomes that may be
impacted by AI interactions. Consistent with SD logic, consumers represent a key beneficiary of
digital servitization (Chandler and Lusch, 2011;Edvardsson et al.,2011). Although research is
emerging that examines consumers’acceptance of AI-enabled service technologies (Adapa et al.,
2020;Fernandes and Oliveira, 2021),theliteratureremainssparseonconsumers’evaluation of
AI-enabled experiences (Hoyer et al.,2020;Shams et al.,2020). AI’s impact on consumers’
financial behaviors is also of interest (Königstorfer and Thalmann, 2020). Research on
digitalization outcomes in health care (Dahl et al.,2018;Dahl et al.,2019) suggests consumers’
financial decision-making and goal achievement may be enhanced as a result of integrating AI
into the service exchange. The SD logic and transformative service literature is also increasingly
interested in consumer well-being outcomes (Dahl et al.,2018;Chen et al.,2020), including
consumers’financial well-being and its relationship to overall well-being (Brüggen et al., 2018;
Netemeyer et al., 2018). Research is thus needed that investigates these and other consumer
outcomes of AI-enabled services (De Keyser et al., 2019).
Critical to on-going service innovation is AI’s potential impact on firm performance
measures (Sklyar et al.,2019;Sjödin et al.,2019). As AI becomes embedded in service
delivery (Luo et al.,2019), it has potential to transform business models and create new value
co-creation strategies (Kristensson, 2019;Sjödin et al., 2020). Despite research pertaining to
customer satisfaction and loyalty in non-AI contexts (Fraering and Minor, 2013;Bock et al.,
2016), little is known on the extent to which AI interactions may impact customer-service
firm relationships in the financial industry. Research is necessary that explores how AI
service innovations affect service relationships (Guo et al., 2019), customer satisfaction
(Chung et al.,2018), loyalty and profits (Cossío-Silva et al.,2016;Mbama et al.,2018), among
other potential firm performance outcomes (Albashrawi and Motiwalla, 2020).
Research needs. As AI innovation continues to disrupt the industry, value-creating
interactions are shifting to digital channels with no direct contact between consumers and
service employees (Gummerus et al.,2019). SD logic indicates various ecosystem actors are
likely to benefit from AI integration to the service exchange (Vargo and Lusch, 2016).
However, there is much that is unknown as far as the value co-created from these AI-enabled
interactions (Chepurna and Rialp Criado, 2018;Kohtamäkia et al.,2019;Manser Payne et al.,
2021;Raddats et al.,2019). We propose the following questions as it relates to the
consequences of AI-based service digitalization:
How have AI interactions influenced and reshaped service delivery and value
expectations? What new measures are needed to assess consumers’perceptions of
Digital
servitization
satisfaction, loyalty or service quality? How do consumers with a high need for
human interaction perceive interactions with a virtual AI financial assistant vs
interactions with a human employee? How do consumers perceive service quality
for interactions that include both a bank employee and an AI agent? Consequently,
how does AI change the role of a front-line service employee (i.e. customer service
representative) in relationship banking?
How does AI enhance or hinder consumer problem-solving skills or capabilities?
What effects do AI service interactions have on consumers’financial literacy and
decision-making? What are the resulting effects on financial well-being or carryover
effects to other aspects of well-being?
Are customer satisfaction and loyalty easier or more difficult to obtain when
consumers interact with an AI agent? How does the value-in-use AI context (higher
vs lower) impact the degree of loyalty? How do AI interactions impact profits and
creating sustainable competitive advantages?
Conclusion
AI is making radical changes to digital servitization, fundamentally altering service delivery
expectations, performance metrics and changing consumer behavior (Kohtamäkia et al.,2019).
New network actors are emerging and traditional consumer–firm relationships are being
disrupted (Larivière et al., 2017). Given the substantial growth of AI in digitalized service
ecosystems, it is critical that we gain a better understanding of how value is co-created and how
value configurations are being transformed in today’s digital era. We contribute to general
digital servitization literature by examining how service ecosystems may be transformed when
introduced to new disruptive technologies. While there has been attention given to this topic
(Sjӧdin et al., 2020), a strong understanding of how the disruptive influence of AI reconfigures
the value co-creation process is still missing from the literature (Manser Payne et al., 2021)We
take an SD logic perspective and explore possibilities for how AI services are evolving the
value co-creation process. We encourage research that examines the digital servitization
literature, and specifically with regard to SD logic and other useful theories.
We also present a digital servitization framework for AI services in financial services and
propose a number of research questions. We identify network actor characteristics that may
impact AI usage that need further research. We suggest decoupling customer-facing AI
services by the level of digitalization and personalization to better understand if consumers
have different value-in-use contexts for AI service. We also offer measures for assessing the
consequences of AI-embedded services. Traditional service ecosystems approach value
creation as a dyadic service exchange between a consumer and the service provider. The digital
servicescape with AI capabilities generates opportunities for multiple actors, such as fintech
companies, to participate in service ecosystems, creating an environment that is both
competitive and collaborative (Drasch et al., 2018). The dyadic service exchange between the
consumer and the financial organization may no longer offer the most optimal value creating
arrangement (Chen et al., 2020). The complexity to embed AI into service exchanges encourage
cooperation between traditional banking organizations with more tech-savvy fintech
companies (Drasch et al., 2018). Essentially, the AI context of digital servitization may create an
interdependency (Vargo and Lusch, 2016) where the actors integrate their skills and knowledge
of AI technology and consumer data and needs to offer superior AI-enabled value propositions
to the consumer. Ultimately, it is the consumer who will decide the value of AI services.
Consumers may need to embrace new skills to interact with a virtual financial agent. Some
consumers may feel more comfortable with AI and seek out its interactions (Lee and Lee, 2020).
JRIM
Based on this review andproposed research agenda, we offer four research propositions:
P1. Consumer perceptions will be positively related to greater future use of AI; greater
levels of AI utilitarian values and AI hedonic values will be needed as consumers
transition from lower value-in-use to higher value-in-use AI contexts.
P2. Industry characteristics related to customer centricity, platform-based orientation,
AI strategy embeddedness, technology infrastructure, organizational culture and
digital servitization-orientation will be positively related to greater future use of AI;
greater levels will be needed as consumers transition from lower value-in-use to
higher value-in-use AI contexts.
P3. Supporting institutional actor (i.e. Fintech) characteristics related to AI technology
infrastructure, data sharing, security/privacy of data will be positively related to
greater future use of AI; greater levels will be needed as consumers transition from
lower value-in-use to higher value-in-use AI contexts.
P4. Lower value-in-use and higher value-in-use AI contexts will be positively related to
customer and firm performance outcomes; the strength of association for each level
is contingent on high quality delivery of AI services.
The financial ecosystem has already experienced the disruptive power of innovation with
the mobile banking channel. Mobile banking has transformed value creating processes to
reflect changing consumer values of convenience and anytime/anywhere banking
(Choudhury and Karahanna, 2008). AI is in the early stages of disrupting the service
ecosystem. Much is yet to be learned about how consumers define value for AI services,
which factors will impact AI usage, and how consumer experiences of service delivery may
change in an AI context. Although we contextualize our framework in the financial services
ecosystem, the outlined research questions and propositions may apply to other service
ecosystems where service digitalization is occurring (i.e. health care). Future research
examining these other service ecosystems should identify any unique value-creating AI
activities across customer-facing or back office operations. Finally, diverse service
ecosystems may also include other supporting network actors (i.e. regulatory agencies,
government) that influence AI usage and require further investigation.
References
Adapa, S., Fazal-e-Hasan, S.M., Makam, S.B., Azeem, M.M. and Mortimer, G. (2020), “Examining the
antecedents and consequences of perceived shopping value through smart retail technology”,
Journal of Retailing and Consumer Services, Vol. 52 No. 1, pp. 1-11.
Akaka, M.A. and Vargo, S.L. (2014), “Technology as an operant resource in service (eco)systems”,
Information Systems and e-Business Management, Vol. 12 No. 3, pp. 367-384.
Akbar, P. and Hoffmann, S. (2020), “Creating value in product service systems through sharing”,
Journal of Business Research, Vol. 121 No. 12, pp. 495-505.
Akhter, S.H. (2015), “Impact of internet usage comfort and internet technical comfort on online
shopping and online banking”,Journal of International Consumer Marketing, Vol. 27 No. 3,
pp. 207-219.
Albashrawi, M. and Motiwalla, L. (2020), “An integrative framework on mobile banking success”,
Information Systems Management, Vol. 37 No. 1, pp. 16-32.
Atkinson, R.D. (2018), “‘It is going to kill us!’” and other myths about the future of artificial
intelligence”,IUP Journal of Computer Sciences, Vol. 12 No. 4, pp. 7-56.
Digital
servitization
Banoun, A., Dufour, L. and Andiappan, M. (2016), “Evolution of a service ecosystem: longitudinal
evidence from multiple shared services centers based on the economies of worth framework”,
Journal of Business Research, Vol. 69 No. 8, pp. 2990-2998.
Banwo, A. (2018), “Artificial intelligence and financial services: regulatory tracking and change
management”,Journal of Securities Operations and Custody, Vol. 10 No.4, pp. 354-365.
Baraldi, E., Gressetvold, E. and Harrison, D. (2012), “Resource interaction in inter-organizational
networks: foundations, comparison, and a research agenda”,Journal of Business Research,
Vol. 65 No. 2, pp. 266-276.
Barney-McNamara, B., Peltier, J.W., Chennamaneni, P.R. and Niedermeier, K.E. (2021), “A conceptual
framework for understanding the antecedents and consequences of social selling: a theoretical
perspective and research agenda”,Journal of Research in Interactive Marketing,
Belanche, D., Casal
o, L.V. and Flavi
an, C. (2019), “Artificial intelligence in fintech: understanding robo-
advisors adoption among customers”,Industrial Management and Data Systems, Vol. 119 No. 7,
pp. 1411-1430.
Bendapudi, N. and Leone, R.P. (2003), “Psychological implications of customer participation in co-
production”,Journal of Marketing, Vol. 67 No. 1, pp. 14-28.
Blut, M. and Wang, C. (2020), “Technology readiness: a meta-analysis of conceptualizations of the
construct and its impact on technology usage”,Journal of the Academy of Marketing Science,
Vol. 48 No. 4, pp. 649-669.
Boateng, S.L. (2019), “Online relationship marketing and customer loyalty: a signaling theory
perspective”,International Journal of Bank Marketing, Vol. 37 No. 1, pp. 226-240.
Bock, D.E., Mangus, S.M. and Folse, J.A.G. (2016), “The road to customer loyalty paved with service
customization”,Journal of Business Research, Vol. 69 No. 10, pp. 3923-3932.
Bower, J.L. and Christensen, C.M. (1995), “Disruptive technologies: catching the wave”,Harvard
Business Review, Vol. 73 No. 1, pp. 43-53.
Brüggen, E.C., Hogreve, J., Holmlund, M., Kabadayi, S. and Löfgren, M. (2018), “Financial well-
being: a conceptualization and research agenda”,Journal of Business Research,Vol.79
No. 10, pp. 228-237.
Caridà,A.,Edvardsson,B.andColurcio,M.(2019),“Conceptualizing resource integration as an embedded
process: matching, resourcing and valuing”,Marketing Theory, Vol. 19 No. 1, pp. 65-84.
Chandler, J.D. and Lusch, R.F. (2011), “Contextualization and value-in-context: how context frames
exchange”,Marketing Theory, Vol. 11 No. 1, pp. 35-49.
Chandler, J.D., Danatzis, I., Wernicke, C., Akaka, M.A. and Reynolds, D. (2019), “How does innovation
emerge in a service ecosystem?”,Journal of Service Research, Vol. 22 No. 1, pp. 75-89.
Chen, T., Dodds, S., Finsterwalder, J., Witell, L., Cheung, L., Falter, M., Garry, T.,Snyder, H. and McColl-
Kennedy, J.R. (2020), “Dynamics of wellbeing co-creation: a psychological ownership
perspective”,Journal of Service Management, Vol. ahead-of-print No. ahead-of-print,
Chepurna, M. and Rialp Criado, J. (2018), “Identification of barriers to co-create on-line: the
perspectives of customers and companies”,Journal of Research in Interactive Marketing,
Vol. 12 No. 4, pp. 452-471.
Choudhury, V. and Karahanna, E. (2008), “The relative advantage of electronic channels: a multidimensional
view”,MIS Quarterly: Management Information Systems, Vol. 32 No. 1, pp. 179-200.
Christensen, C.M. (1997), The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail,
Harvard Business School Press, Boston, MA.
Christensen, C.M. and Bower, J.L. (1996), “Customer power, strategic investment, and the failure of
leading firms”,Strategic Management Journal, Vol. 17 No. 3, pp. 197-218.
Chung, M., Ko, E., Joung, H. and Kim, S.J. (2018), “Chatbot e-service and customer satisfaction regarding
luxury brands”,Journal of Business Research, Vol. 117 No. 9, pp. 1-9.
JRIM
Collier, J.E. and Kimes, S.E. (2013), “Only if it is convenient: understanding how convenience influences
self-service technology evaluation”,Journal of Service Research, Vol. 16 No. 1, pp. 39-51.
Coreynen, W., Matthyssens, P. and Van Bockhaven, W. (2017), “Boosting servitization through
digitization: pathways and dynamic resource configurations for manufacturers”,Industrial
Marketing Management, Vol. 60, pp. 42-53.
Cossío-Silva, F.-J., Revilla-Camacho, M.-R., Vega-V
azquez, M. and Palacios-Florencio, B. (2016), “Value
co-creation and customer loyalty”,Journal of Business Research, Vol. 69 No. 5, pp. 1621-1625.
Curran, J.M. and Meuter, M.L. (2005), “Self-service technology adoption: comparing three technologies”,
Journal of Services Marketing, Vol. 19 No. 2,pp. 103-113.
Cusumano, M.A., Kahl, S.J. and Suarez, F.F. (2015), “Services, industry evolution, and the competitive
strategies of product firms”,Strategic Management Journal, Vol. 36 No.4, pp. 559-575.
Dabholkar, P.A. and Bagozzi, R.P. (2002), “An attitudinal model of technology-based self-service”,
Journal of the Academy of Marketing Science, Vol. 30 No. 3, pp. 184-201.
Dahl, A.J., Milne, G.R. and Peltier, J.W. (2019), “Digital health information seeking in an omni-channel
environment: a shared decision-making and service-dominant logic perspective”,Journal of
Business Research, pp. 1-11.
Dahl, A.J., Peltier, J.W. and Milne, G.R. (2018), “Development of a value co-creation wellness model: the
role of physicians and digital information seeking on health behaviors and health outcomes”,
Journal of Consumer Affairs, Vol. 52 No. 3, pp. 562-594. No
Davenport, T., Guha, A., Grewal, D. and Bressgott, T. (2020), “How artificial intelligence will change the
future of marketing”,Journal of the Academy of Marketing Science, Vol. 48 No. 1, pp. 24-42.
De Keyser, A., Köcher, S., Alkire (Née Nasr), L., Verbeeck, C. and Kandampully, J. (2019), “Frontline
service technology infusion: conceptual archetypes and future research directions”,Journal of
Service Management, Vol. 30 No. 1, pp. 156-183.
Dedehayir, O., Ortt, J.R. and Seppänen, M. (2017), “Disruptive change and the reconfiguration of innovation
ecosystems”,Journal of Technology Management and Innovation, Vol. 12 No. 3, pp. 9-20.
Donepudi, P.K. (2017), “Machine learning and artificial intelligence in banking”,Engineering
International, Vol. 5 No. 2, pp. 83-86.
Drasch, B.J., Schweizer, A. and Urbach, N. (2018), “Integrating the ‘troublemakers’: a taxonomy
for cooperation between banks and fintechs”,Journal of Economics and Business,Vol.100
No. 3, pp. 26-42.
Edvardsson, B., Kleinaltenkamp, M., Tronvoll, B., McHugh, P. and Windahl, C. (2014),
“Institutional logics matter when coordinating resource integration”,Marketing Theory,
Vol. 14 No. 3, pp. 291-309.
Edvardsson, B., Tronvoll, B. and Gruber, T. (2011), “Expanding understanding of service exchange and
value co-creation: a social construction approach”,Journal of the Academy of Marketing Science,
Vol. 39 No. 2, pp. 327-339.
Fernandes, T. and Oliveira, E. (2021), “Understanding consumers’acceptance of automated
technologies in service encounters: drivers of digital voice assistants adoption”,Journal of
Business Research, Vol. 122 No. 1, pp. 180-191.
Fountaine, T., Saleh, T. and McCarthy, B. (2019), “Building the AI-powered organization”,Harvard
Business Review, Vol. 97 No. 4, pp. 62-63.
Fraering, M. and Minor, M.S. (2013), “Beyond loyalty: customer satisfaction, loyalty, and fortitude”,
Journal of Services Marketing, Vol. 27 No. 4,pp. 334-344.
Fyrberg, A. and Jüriado, R. (2009), “What about interaction?: networks and brands as integrators within
service-dominant logic”,Journal of Service Management, Vol. 20 No. 4,pp. 420-432.
Gummerus, J., Lipkin, M., Dube, A. and Heinonen, K. (2019), “Technology in use –characterizing
customer self-service devices (SSDS)”,Journal of Services Marketing, Vol. 33 No. 1, pp. 44-56.
Digital
servitization
Guo, J., Pan, J., Guo, J., Gu,F. and Kuusisto, J. (2019), “Measurement framework for assessing disruptive
innovations”,Technological Forecasting and Social Change, Vol. 139,pp. 250-265.
Haenlein, M. and Kaplan, A. (2019), “A brief history of artificial intelligence: on the past, present, and
future of artificial intelligence”,California Management Review, Vol. 61 No. 4, pp. 5-14.
Hallikainen, H., Alamäki, A. and Laukkanen, T. (2019), “Individual preferences of digital touchpoints: a
latent class analysis”,Journal of Retailing and Consumer Services, Vol. 50 No. 9, pp. 386-393.
Haridasan, A.C. and Fernando, A.G. (2018), “Online or in-store: unravelling consumer’s channel choice
motives”,Journal of Research in Interactive Marketing, Vol. 12 No. 2,pp. 215-230.
Holmström, J. and Partanen, J. (2014), “Digital manufacturing-driven transformations of service
supply chains for complex products”,Supply Chain Management: An International Journal,
Vol. 19 No. 4, pp. 421-430.
Hoyer, W.D., Kroschke, M., Schmitt, B., Kraume, K. and Shankar, V. (2020), “Transforming the
customer experience through new technologies”,Journal of Interactive Marketing, Vol. 51,
pp. 57-71.
Huang, M.H. and Rust, R.T. (2018), “Artificial intelligence in service”,Journal of Service Research,
Vol. 21 No. 2, pp. 155-172.
Huarng, K.H., Yu, T.H.K. and Lai, W. (2015), “Innovation and diffusion of high-tech products, services,
and systems”,Journal of Business Research, Vol. 68 No. 11, pp. 2223-2226.
Jaakkola, E. Vargo, S.L. Kaartemo, V. and Siltaloppi, J. (2020), “Call for papers, advancing service-
dominant logic: institutions, service ecosystems and emergence”, available at: www.journals.
elsevier.com/journal-of-business-research/call-for-papers/institutions-service-ecosystems-
and-emergence (accessed 1 November 2020).
Jakši
c, M. and Marin
c, M. (2019), “Relationship banking and information technology: the role of
artificial intelligence and fintech”,Risk Management, Vol. 21 No. 1, pp. 1-18.
Jarrahi, M.H. (2018), “Artificial intelligence and the future of work: human-AI symbiosis in
organizational decision making”,Business Horizons, Vol. 61 No. 4, pp. 577-586.
Kaplan, A. and Haenlein, M. (2019), “Siri, Siri, in my hand: who’s the fairest in the land? On the
interpretations, illustrations, and implications of artificial intelligence”,Business Horizons,
Vol. 62 No. 1, pp. 15-25.
Khin, S. and Ho, T.C.F. (2019), “Digital technology, digital capability and organizational performance: a
mediating role of digital innovation”,International Journal of Innovation Science, Vol. 11 No. 2,
pp. 177-195.
Khrais, L.T. and Shidwan, O.S. (2020), “Mobile commerce and its changing use in relevant applicable
areas in the face of disruptive technologies”,International Journal of Applied Engineering
Research, Vol. 15 No 1, pp. 12-23.
Kietzmann, J., Paschen, J. and Treen, E. (2018), “Artificial intelligence in advertising: how marketers can
leverage artificial intelligence along the consumer journey”,Journal of Advertising Research,
Vol. 58 No. 3, pp. 263-267.
Kim, T.K. and Choi, M. (2019), “Older adults’willingness to share their personal and health information
when adopting healthcare technology and services”,International Journal of Medical
Informatics, Vol. 126 No.5, pp. 86-94.
Kleinaltenkamp, M., Brodie, R., Frow, P. and Hughes, T. (2012), “Resource integration”,Marketing
Theory, Vol. 12 No. 2, pp. 201-205.
Kohtamäkia, M., Parida, V., Oghazi, P., Gebauer, H. and Baines, T. (2019), “Digital servitization
business models in ecosystems: a theory of the firm”,Journal of Business Research, Vol. 104
No. 11, pp. 380-392.
Königstorfer, F. and Thalmann, S. (2020), “Applications of artificial intelligence in commercial banks –
a research agenda for behavioral finance”,Journal of Behavioral and Experimental Finance,
Vol. 27.
JRIM
Kowalkowski, C., Gebauer, H., Kamp, B. and Parry, G. (2017), “Servitization and deservitization:
overview, concepts, and definitions”,Industrial Marketing Management, Vol. 60, pp. 4-10.
Kristensson, P. (2019), “Future service technologies and value creation”,Journal of Services Marketing,
Vol. 33 No. 4, pp. 502-506.
Kucharska, W. (2019), “Online Brand communities’contribution to digital business models: social
drivers and mediators”,Journal of Research in Interactive Marketing, Vol. 13 No.4, pp. 437-463.
Larivière, B., Bowen, D., Andreassen, T.W., Kunz, W., Sirianni, N., Voss, C., Wunderlich, N.V. and De
Keyser, A. (2017), “‘Service encounter 2.0’”: an investigation into the roles of technology,
employees and customers”,Journal of Business Research, Vol. 79 No. 10, pp. 238-246.
Laukkanen, T. (2016), “Consumer adoption versus rejection decisions in seemingly similar service
innovations: the case of the internet and mobile banking”,Journal of Business Research, Vol. 69
No. 7, pp. 2432-2439.
Lee, S.M. and Lee, D.H. (2020), “‘Untact’”: a new customer service strategy in the digital age”,Service
Business, Vol. 14 No. 1, pp.1-22.
Letaifa, S.B., Edvardsson, B. and Tronvoll, B. (2016), “The role of social platforms in transforming
service ecosystems”,Journal of Business Research, Vol. 69 No. 5, pp. 1933-1938.
Luo, X., Tong, S., Fang, Z. and Qu, Z. (2019), “Frontiers: machines vs humans: the impact of artificial
intelligence chatbot disclosure on customer purchases”,Marketing Science, Vol. 38 No. 6, pp. 937-947.
Lusch, R.F. and Nambisan, S. (2015), “Service innovation: a service-dominant logic perspective”,MIS
Quarterly, Vol. 39 No. 1, pp.155-175.
Madhavaram, S. and Hunt, S.D. (2008), “The service-dominant logic and a hierarchy of operant
resources: developing masterful operant resources and implications for marketing strategy”,
Journal of the Academy of Marketing Science, Vol. 36 No. 1, pp. 67-82.
Manser Payne, E.H., Peltier, J.W. and Barger, V.A. (2018), “Mobile banking and AI-enabled mobile
banking: the differential effects of technological and non-technological factors on digital natives’
perceptions and behavior”,Journal of Research in Interactive Marketing, Vol. 12 No. 3, pp. 328-346.
Manser Payne, E.H., Peltier, J.W. and Barger, V.A. (2021), “Enhancing the value co-creation process: artificial
intelligence and mobile banking service platforms”,Journal of Research in Interactive Marketing.
Mbama, C.I., Ezepue, P., Alboul, L. and Beer, M. (2018), “Digital banking, customer experience and
financial performance: UK bank managers’perceptions”,Journal of Research in Interactive
Marketing, Vol. 12 No. 4, pp. 432-451.
Nagy, D., Schuessler, J.H. and Dubinsky, A. (2016), “Defining and identifying disruptive innovations”,
Industrial Marketing Management, Vol. 57, pp. 119-126.
Netemeyer, R.G., Warmath, D., Fernandes, D. and Lynch, J.G. Jr, (2018), “How am I doing? Perceived
financial well-being, its potential antecedents, and its relation to overall well-being”,Journal of
Consumer Research, Vol. 45 No. 1, pp. 68-89.
Obal, M. (2017), “What drives post-adoption usage? Investigating the negative and positive antecedents
of disruptive technology continuous adoption intentions”,Industrial Marketing Management,
Vol. 63, pp. 42-52.
Parasuraman, A. (2000), “Technology readiness index (TRI): a multiple-item scale to measure readiness
to embrace new technologies”,Journal of Service Research, Vol. 2 No. 4, pp. 307-320.
Parida, V., Sjödin, D. and Reim, W. (2019), “Reviewing literature on digitalization, business model innovation,
and sustainable industry: past achievements and future promises”,Sustainability,Vol.11No.2,p.391
Parida, V., Sjödin, D.R., Lenka, S. and Wincent, J. (2015), “Developing global service innovation
capabilities: how global manufacturers address the challenges of market heterogeneity”,
Research-Technology Management, Vol. 58 No. 5, pp. 35-44.
Patrício, L., Gustafsson, A. and Fisk, R. (2018), “Upframing service design and innovation for research
impact”,Journal of Service Research, Vol. 21 No.1, pp. 3-16.
Digital
servitization
Peltier, J.W., Dahl, A.J. and Swan, E. (2020), “Digital information flows across a B2C/C2C continuum
and technological innovations in service ecosystems: a service-dominant logic perspective”,
Journal of Business Research, Vol. 121 December, pp. 724-234.
Pizzi, G., Scarpi, D., Pichierri, M. and Vannucci, V. (2019), “Virtual reality, real reactions? Comparing
consumers’perceptions and shopping orientation across physical and virtual-reality retail
stores”,Computers in Human Behavior, Vol. 96 No. 7, pp. 1-12.
Purdy, M. and Daugherty, P. (2016), “Why artificial intelligence is the future of growth”, available at:
www.accenture.com/t20170524T055435__w__/ca-en/_acnmedia/PDF-52/Accenture-Why-AI-is-
the-Future-of-Growth.pdf (accessed 7 October 2020).
Raddats, C., Kowalkowski, C., Benedettini, O., Burton, J. and Gebauer, H. (2019), “Servitization: a
contemporary thematic review of four major research streams”,Industrial Marketing
Management, Vol. 83, pp. 207-223.
Rahi, S., Mansour, M.M.O., Alghizzawi, M. and Alnaser, F.M. (2019), “Integration of UTAUT model in internet
banking adoption context”,Journal of Research in Interactive Marketing, Vol. 13 No. 3, pp. 411-435.
Rambocas, M. and Pacheco, B.G. (2018), “Online sentiment analysis in marketing research: a review”,
Journal of Research in Interactive Marketing, Vol. 12 No. 2, pp. 146-163.
Ranjan, K.R. and Read, S. (2016), “Value co-creation: concept and measurement”,Journal of the
Academy of Marketing Science, Vol. 44 No. 3, pp. 290-315.
Razmdoost, K., Alinaghian, L. and Smyth, H.J. (2019), “Multiplex value cocreation in unique service
exchanges”,Journal of Business Research, Vol. 96 No. 3, pp. 277-286.
Reinhardt, R. and Gurtner, S. (2018), “The overlooked role of embeddedness in disruptive innovation
theory”,Technological Forecasting and Social Change, Vol. 132, pp. 268-283.
Richter, F. (2020), “Apple’s $50 billion side business”, available at: www.statista.com/chart/9218/
apples-services-revenue (accessed 15 November 2020).
Rodgers, E.M. (1995), The Diffusion of Innovations, Free Press, New York, NY.
Ryman-Tubb, N.F., Krause, P. and Garn, W. (2018), “How artificial intelligence and machine learning
research impacts payment card fraud detection: a survey and industry benchmark”,Engineering
Applications of Artificial Intelligence, Vol. 76, pp. 130-157.
Saksonova, S. and Kuzmina-Merlino, I. (2017), “Fintech as financial innovation –the possibilities and
problems of implementation”,European Research Studies Journal, Vol. XX No. Issue 3A, pp. 961-973.
Sandström, S., Edvardsson, B., Kristensson,P. and Magnusson, P. (2008), “Value in use through service
experience”,Managing Service Quality: An International Journal, Vol. 18 No. 2, pp. 112-126.
Shams, G., Rehman, M.A., Samad, S. and Oikarinen, E.L. (2020), “Exploring customer’s mobile banking
experiences and expectations among generations X, Y and Z”,Journal of Financial Services
Marketing, Vol. 25 Nos 1/2, pp. 1-13.
Shankar, A. and Rishi, B. (2020), “Convenience matter in mobile banking adoption intention?”,
Australasian Marketing Journal (Amj), Vol. 28 No. 4, pp. 273-285.
Sheth, J. (2020), “Impact of Covid-19 on consumer behavior: will the old habits return or die?”,Journal of
Business Research, Vol. 17 September, pp. 280-283.
Shi, S., Wang, Y., Chen, X. and Zhang, Q. (2020), “Conceptualization of omnichannel customer
experience and its impact on shopping intention: a mixed-method approach”,International
Journal of Information Management, Vol. 50,pp. 325-336.
Shi, V.G., Baines, T., Baldwin, J., Ridgeway, K., Petridis, P., Bigdeli, A.Z., Uren, V. and Andrews, D.
(2017), “Using gamification to transform the adoption of servitization”,Industrial Marketing
Management, Vol. 63, pp. 82-91.
Sjödin, D., Parida, V. and Kohtamäki, M. (2019), “Relational governance strategies for advanced service
provision: multiple paths to superior financial performance in servitization”,Journal of Business
Research, Vol. 101 No. 8, pp. 906-915.
JRIM
Sjödin, D., Parida, V., Jovanovic, M. and Visjnic, I. (2020), “Value creation and value capture alignment
in business model innovation: a process view on outcome-based business models”,Journal of
Product Innovation Management, Vol. 37 No.2, pp. 158-183.
Sklyar, A., Kowalkowski, C., Tronvoll, B. and Sörhammar, D. (2019), “Organizing for digital
servitization: a service ecosystem perspective”,Journal of Business Research, Vol. 104 No. 11,
pp. 450-460.
Storbacka, K., Brodie, R.J., Böhmann, T., Maglio, P.P. and Nenonen, S. (2016), “Actor engagement as a
microfoundation for value co-creation”,Journal of Business Research, Vol. 69 No. 8, pp. 3008-3017.
Swan, E.L., Dahl, A.J. and Peltier, J.W. (2019), “Health care marketing in an omni-channel environment:
exploring telemedicine and other digital touchpoints”,Journal of Research in Interactive
Marketing, Vol. 13 No. 4, pp. 602-618.
van Tonder, E., Saunders, S.G. and Dawes Farquhar, J. (2020), “Explicating the resource integration
process during self-service socialisation: conceptual framework and research propositions”,
Journal of Business Research, Vol. 121 No. 12, pp. 516-523.
Vargo, S.L. and Lusch, R.F. (2008), “Service-dominant logic: continuing the evolution”,Journal of the
Academy of Marketing Science, Vol. 36 No. 1, pp. 1-10.
Vargo, S.L. and Lusch, R.F. (2016), “Institutions and axioms: an extension and update of service-
dominant logic”,Journal of the Academy of Marketing Science, Vol. 44 No. 1, pp. 5-23.
Veríssimo, J.M.C. (2018), “Usage intensity of mobile medical apps: a tale of two methods”,Journal of
Business Research, Vol. 89 No. 8, pp. 442-447.
Vives, X. (2019), “Digital disruption in banking”,Annual Review of Financial Economics, Vol. 11 No. 1,
pp. 243-272.
Yousafzai, S.Y., Foxall, G.R. and Foxall, G.R. (2010), “Explaining internet banking behavior: theory of
reasoned action, theory of planned behavior, or technology acceptance model?”,Journal of
Applied Social Psychology, Vol. 40 No. 5, pp. 1172-1202.
Zhou, T. (2012), “Examining mobile banking user adoption from the perspectives of trust and flow
experience”,Information Technology and Management, Vol. 13 No. 1, pp. 27-37.
Further reading
Davis, F.D. (1989), “Perceived usefulness, perceived ease of use and user acceptance of information
technology”,MIS Quarterly, Vol. 13 No. 3, pp. 319-339.
Shankar, A. and Kumari, P. (2016), “Factors affecting mobile banking adoption behavior in India”,
Journal of Internet Banking and Commerce, Vol. 21 No. 1, pp. 1-25.
Shankar, A., Jebarajakirthy, C. and Ashaduzzaman, M. (2020), “How do electronic word of mouth
practices contribute to mobile banking adoption?”,Journal of Retailing and Consumer Services,
Vol. 52.
Stone, M.D. and Woodcock, N.D. (2014), “Interactive, direct and digital marketing: a future that depends
on better use of business intelligence”,Journal of Research in Interactive Marketing, Vol. 8 No. 1,
pp. 4-17.
Corresponding author
James Peltier can be contacted at: peltierj@uww.edu
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