Innovative converged service and its adoption, use and diffusion: a holistic approach to diffusion of innovations, combining adoption-diffusion and use-diffusion paradigms.
Motohashi, Kazuyuki ; Lee, Deog-Ro ; Sawng, Yeong-Wha 等
1. Research objective
The accelerating pace of progress in digital convergence is
resulting in an increasing intersection between voice and data
communications, and broadcasting and computer technologies, with the
broadband network as their point of convergence (Baldwin et al. 1996).
One of the consequences of this rapidly-expanding phenomenon of
convergence for users is the changing spectrum of choice in new media.
With converged services and devices, offering more than one function,
becoming the market norm, the choice for today's consumers is about
which product combines features that best suit their various needs and
addresses the most complete array of those needs.
IPTV (Internet Protocol TV) is a leading example of this new
generation of converged media. IPTV, a superior, interactive alternative
to one-way media, like traditional TV services, allows viewers to send
and receive voice and data traffic via the television set, connected to
the internet. The introduction of IPTV, as a full-triple play solution,
pushing the horizon of digital convergence further out, has also had the
effect of taking the already intense competition in the TV broadcasting
market, pitting digital CATV and digital satellite TV against
traditional terrestrial TV, to a new level of complexity.
Meanwhile, the accelerating innovation in the field of convergence
technology is triggering active research on the process of diffusion of
innovative converged media and devices. As a general rule, the speed of
diffusion of an innovative product is influenced both by the
characteristics of the new product and the characteristics of consumers
(Shih, Venkatesh 2004). Attempts to understand the process of diffusion
of new products or new technologies among consumers have been so far
made principally in the field of innovation diffusion. Traditional
theories on innovation diffusion were focused mainly on adoption. In
recent years, however, the focus has been gradually shifting toward the
use aspect of innovations.
Existing studies in innovation diffusion draw mostly upon
Rogers' (1995) innovation-adoption theory and acceptance diffusion
models such as the technology acceptance model (TAM). The framework of
adoption diffusion, proposed by Rogers, in which the focus is placed on
the process of consumers' adoption of new products or services has
later become the root of newer theories such as the theory of reasoned
action (TRA), the theory of planned behavior (TPB)--a theory expanding
on TRA--and the technology acceptance model (TAM)--a modified TRA.
Meanwhile, limitations inherent in this adoption-centered approach to
the diffusion of innovations have been pointed out by several
researchers (Gatignon, Robertson 1985).
Shih and Venkatesh (2004) recently proposed a new innovation
diffusion model they baptized "use-diffusion model", as
opposed to "adoption-diffusion models". Shih and Venkatesh
(2004) argued for the need to shift the focus of diffusion research from
'adoption' to 'use' as a solution to overcome the
limitations of traditional approaches in diffusion research while having
TAM or other adoption-centered theoretical frameworks. In this
alternative approach aimed at moving beyond the adoption-centered
paradigm of diffusion, consumers' usage patterns with products or
services they are currently using are important predictors for the
diffusion process of new products or services. More specifically, how
frequently a consumer uses a technology product or service and how
varied his / her use of the same product or service may be can explain
and determine his / her behavior adopted toward new products or
services, and thereby, also the process of their diffusion, according to
Shih and Venkatesh (2004).
Also of note is that in this new model, user categories, segmented
by usage patterns, plays a similarly prominent role as the innovation
adopter groups in traditional adoption-diffusion models.
In this study, a use-diffusion model was combined with a
traditional adoption-diffusion model for a more holistic approach to the
diffusion of IPTV services, a representative converged media service.
Based on the results of analysis, we derived implications for the
marketing of IPTV services that have practical importance for both
market entry strategy and the acceleration of diffusion.
2. Literature review
2.1. Characteristics of IPTV services
The concept of IPTV is far from static and is still evolving. Best
known as "IPTV", a name coined in the United States, this
application is known in Europe as "ADSL TV" and in Japan as
"broadband broadcasting". According to the ITU-T Focus Group,
IPTV is a two-way, interactive multimedia data service, coming with a
degree of QoS and QoE (Quality of Experience) guarantees, provided
through IP-based networks. OVUM, meanwhile, defined IPTV as a service
delivering broadcast content and TV programs and videos in the form of
VoD, over IP networks. Finally, according to FCC (2005), IPTV is a kind
of internet video service for direct downloads of films or TV shows
using broadband internet (VoD over the Internet) and advanced TV
functions such as DVR (Digital Video Recorder) or PVR (Personal Video
Recorder).
When describing IPTV, consumer-perceived characteristics are
criteria that are as important as technical characteristics. In
technical aspects, one can discern the following four characteristics
for IPTV: First, IPTV uses IP (Internet Protocol)-based broadband
internet networks as the transmission medium. It is thanks to the use of
internet protocol, crucial to enabling broad-ranging communications
services, that IPTV can combine voice, data and video services. This is
also the key feature that distinguishes IPTV from traditional non-IP
network-based TV services such as cable TV and satellite TV, which are
limited in the variety of services they can offer due to this very
reason. Second, TV is the user interface of IPTV (Kerschbaumer 2000). In
Korea, a prototype of IPTV has existed since 1997. This video service,
known as "webcasting", is also provided via broadband
internet. One crucial difference between webcasting and IPTV, however,
is that in the case of the former, the service platform is the computer
and not the TV. Third, IPTV provides multi-channel services, enabled by
multicasting streaming technology. Webcasting, although it uses
broadband internet as transmission means, as does IPTV, is distinct from
the latter in that webcasting videos are streamed through unicasting
technology and not multicasting technology. Fourth, IPTV enables a wide
variety of interactive services such as VoD, TV banking, information
search, e-mail and PVR (or DVR). Many next-generation features, such as
VoD, T-commerce or T-communication, that set IPTV apart from traditional
TV services, are made possible thanks to the two-way communications
capability of this IP-based medium (Kim 2004; Kim, C-h 2005).
Meanwhile, there are four main consumer-perceived characteristics
of IPTV: multichannel broadcasting, high-definition video, two-way data
transfers, and convergence, as shown in Table 1 below. Of these four,
two-way data transfers may be considered the foremost characteristic of
IPTV, insofar as it is a feature offered exclusively by digital media
such as digital satellite TV or digital CATV, and not by analog
satellite or CATV. Further, not all digital satellite TV services enable
two-way data transfers, as this requires broadband return channels as
well as real-time interactivity, which can be provided only through
bundling with telephone or internet services. Convergence is another
characteristic absent in analog CATV services, which, however, is an
option available for digital CATV. On the other hand, once fully
digitalized Terrestrial TV can provide multi-channel broadcasts and
two-way data transfer services. But, for terrestrial TV to enable
two-way data transfer, it must also resort to bundling with telecom
services, as is the case with digital satellite TV, to gain access to
broadband return channels and achieve real-time interactivity.
Consumer-perceived performance characteristics of terrestrial TV, upon
full digitalization, are, therefore, quite similar to those of digital
satellite (Ju, Han 2001; Kim, D-y 2005).
2.2. The innovation adoption-diffusion model and the use-diffusion
model
Research investigating the market acceptance of innovations has
long focused on consumers' choice behavior, namely, their
resistance to innovation and how continuously they use innovative new
products or services once they overcome their initial resistance and
adopt them. The central postulate of the adoption-centered approach to
the diffusion of innovations is that of the consumers' psychology
and their choice behavior in-play in their decision-making purchases
that have a determining influence on their final acceptance of a new
product or service (Hyori Jeon et al. 2011). For this reason, the object
of the inquiry in acceptance-centered studies is to determine what
factors influence the choice of information on which individual
consumers base their decision to adopt or reject an innovative product
or service, and the extent of that influence.
Studies espousing the technology acceptance perspective, further,
rest on the following four basic assumptions (Rogers 1983): First, the
level of recognition of innovativeness, when an innovative product is
released, varies among sellers as well as the marketing scheme they use
to distribute the product. Second, early adopters are individuals with
definable characteristics and are distinct from late adopters. Third,
there exist effective communications channels allowing the interaction
between innovation adopters of different categories (Park, G-s 2004).
Fourth, early adopters tend to be opinion leaders and have an influence
on the adoption of innovative products or services by others belonging
to their social group.
Studies adhering to an adoption-centered approach to diffusion also
view the process of adoption as a decision-making process toward
adopting or rejecting an innovation, and it consists of a series of
psychological phases, including the initial awareness of a new product
or service, development of an attitude toward it and the final stage of
acceptance /rejection decision-making. The process of acceptance of
innovations, insofar as it is the process through which a consumer
decides to adopt or reject a new product or service, constitutes the
centerpiece of the diffusion of innovations theory. One of the
theoretical and methodological consequences of this focus on adoption in
diffusion research has largely neglected information processing and
behavioral determinants in favor of hierarchy of effects models.
The main limitation of the adoption-centered approach to diffusion,
according to Shih and Venkatesh (2004), is the failure to concretely
explain the process of diffusion of an innovation as a whole, and in
particular, why the speed of diffusion varies and how the personal
characteristics of consumers influence this process. As an alternative
to the adoption-centered approach, providing a more in-depth
understanding of the process of diffusion of innovations, they proposed
a new model baptized "use-diffusion model".
One of the key assumptions underlying this model is that
consumers' adoption of a converged digital product, at a time when
the progress in information technology is rapidly shortening the
lifecycle of technologies, can be explained to a great extent by their
experience with products they are currently using. Table 2 below
provides a summary comparison of the use-diffusion model of Shih and
Venkatesh (2004) and the adoption-diffusion model: The first and
foremost difference that sets the use-diffusion model apart from the
adoption-diffusion model is the usage paradigm. In this model, the
consumers' usage pattern with products they currently use, measured
in two dimensions, 'rate of use' and 'variety of
use', is ascribed predictive capabilities for the diffusion of
future technology products and services.
Secondly, in an adoption-diffusion model, the process of diffusion
is viewed as a two-stage process, consisting of a phase of innovation
and a phase of imitation, and this same two-stage conception of
diffusion underlies other measures and constructs used in this model,
such as the S-shaped diffusion curve, penetration rate and adopter
categories. In the use-diffusion model, on the other hand, the focus is
on the continuous use of a technology, as well as the degree of
technological convergence of a product or service, as perceived with the
need for a technology and its influence, and users' adoption
behavior vis-a-vis new technologies in general (Kim, Yi 2007).
Thirdly, in adoption-diffusion models, the market is segmented into
five categories of innovation adopters, proposed by Rogers (1995),
according to the timing of adoption; namely, innovators, early adopters,
early majority, late majority and laggards. In the use-diffusion model,
meanwhile, the market is segmented according to the type of users and
use capabilities, into four user categories: intense users, specialized
users, non-specialized users and limited users.
These two models, although they differ in their focus and way of
segmenting the market as well as the criteria they use for the
segmentation, they also overlap concerning their basic conception of the
process of diffusion of innovations (Park, J-m 2005). Concretely, the
two models use the same basic variables of influence, such as individual
innovativeness, social communication, complexity, media influence and
relative advantage. These common variables, however, are not always
defined or understood exactly the same way in the two models (Kim, Lee
2005). For example, the innovativeness of a user is a concept distinct
from the innovativeness of an adopter. Further, unlike in the
adoption-diffusion model, in which observability, compatibility and
trialability constitute the key characteristics of the diffusion process
of innovations (Rogers 1995), in the use-diffusion model the same
process is explained through product experience, competition for use,
sophistication of technology and satisfaction (Shih, Venkatesh 2004).
3. Empirical research design
3.1. Research model
The objective of this study is to empirically investigate the
process of diffusion of converged media services by looking at the case
of IPTV. By combining the use-diffusion model proposed by Shih and
Venkatesh (2004) with a traditional TAM-based adoption-diffusion model,
we designed a conceptual research model, as shown in Figure 1 below. The
research model consists in applying a TAM-based, adoption-diffusion
model and the use-diffusion model to IPTV services so as to identify a
comprehensive range of factors influencing their diffusion process. The
variables selected for the research model are wide-ranging and include
major Tam variables, and some of the key variables used in the
use-diffusion model by Shih and Venkatesh (2004), the media substitution
theory and the total consumer experience (TCE) approach.
[FIGURE 1 OMITTED]
The adoption-diffusion model used in this study is a structural
model having perceived ease-of-use and perceived usefulness as mediating
variables. For the use-diffusion model, we chose the rate of use and
variety of use as mediating variables, following Shih and Venkatesh
(2004). The variables used in the latter model include substitution
effect and complementarity, constructs in the media substitution theory
proposed by Li (2004). Meanwhile, drawing on Sandstrom et al.
(2008)'s TCE model, we also included product experience among the
variables. Satisfaction, intention to re-use, and intention to subscribe
were selected as outcome variables for both the acceptance-diffusion and
use-diffusion models.
Common factors applicable to both the adoption-diffusion model and
the use-diffusion model include household innovativeness, communication,
complexity, relative advantage, perceived risk, and service quality. In
addition, key TAM factors such as perceived ease-of-use and perceived
usefulness, and compatibility, observability and trialability were
classified as factors uniquely applying to the adoption-diffusion model.
Factors specific to the use-diffusion model include three constructs
proposed by Shih and Venkatesh (2004), namely the rate of use, variety
of use and sophistication of technology, as well as product experience
from the TCE framework, and substitution effect, complementarity and
similarity from the media substitution theory as the variable of
competition for use.
3.2. Research hypotheses
3.2.1. Hypotheses on the adoption-diffusion of IPTV services
TAM (Technology Acceptance Model) and other adoption-centered
theories argue that the diffusion of an innovation within a social
network begins when an individual member comes into the awareness of it
and communicates his / her knowledge to others within the network
(Chatman 1986).
According to this postulate, for an effective diffusion of a new
product, a marketer must promote the product in such a way so as to
create as many early adopters as possible, who would, in turn, convert
more consumers into adopters through inter-consumer communication.
Factors believed to influence consumers' adoption of innovations
vary depending on the researcher, even though many share basic premises
on the diffusion process, which have been formulated by influential
adoption-diffusion theories such as the theory of diffusion of
innovations of Rogers (1995), TRA (Theory of Reasoned Action) by
Fishbein and Ajzen (1975), TPB (Theory of Planned Behavior) by Ajzen
(1991) and TAM (Technology Acceptance Model) by Davis (1989).
Rogers (1995) saw the diffusion of innovations as a progressive
process in which the adoption of an innovation, initially only by a
small number of people, gradually increases to eventually lead to a
mass-market take-up. In other words, Rogers believed that the early
phase of innovation adoption was closely linked to the personal
innovative tendencies of early users. Cai (2001), drawing on TRA, argued
that consumers' attitudes toward innovations and social norms were
important determinants of purchase behavior. A consumer's
behavioral intention with regard to the adoption of innovative
technology, he argues, is directly influenced by his / her personal
conviction and evaluation of this technology, and social norm-related
factors such as trust and conformity. Meanwhile, according to Malhotra
et al. (1999), in their study conducted from a TAM perspective, a
consumer's attitude toward a new technology product is formed by a
combination of social influence factors and perceived technical
characteristics of the product, such as its perceived ease-of-use and
usefulness.
Among early adoption-diffusion studies, Rogers and Shoemaker (1971)
proposed five factors that influence the rate of adoption and diffusion
of a new technology product: relative advantage, compatibility,
simplicity, observability and trialability. According to Rogers and
Sheomaker (1971), the driving factor of the diffusion process is the
relative advantage presented by a new technology product over the
existing product and the more the innovative product corresponds to
consumers' desires, beliefs, values and personal experience, the
greater the extent and speed of its diffusion. They further claimed that
as the ease-of-use of a new technology facilitates its broad take-up
and, in some cases, even trumps price considerations the simpler and
more innovative a product, the faster the rate of diffusion. An
eye-catching new product, they also maintained, has a better chance of
being quickly adopted by the mass-market and odds for market success are
greater for products that can be tried in advance of purchase without
financial risk to consumers (Kim et al. 2003; Rim et al. 2005).
Robertson and Gatignon (1986), in a study on the adoption of
innovative cutting-edge products among industrial buyers, advanced the
view that a competitive supplier-side environment influenced the
demand-side competition environment thereby also influencing the
diffusion of new technology products. Here, the competitive environment
has as its key components: industrial competitiveness, company
reputation, technology standardization and vertical inter-firm
cooperation.
Ram, Jung (1990) stated that the main barrier and resistance factor
in consumers' adoption of an innovative product was perceived risk.
They argued that when a consumer feels that purchasing a new product
carries risk, this perceived risk plays the role of a functional or
psychological barrier, producing the effect of delaying the adoption or
causing a downright rejection of the same product. In the case of an
innovative technology product, the role of perceived risk is mostly that
of a functional barrier, they further claimed. In TAM, a dominant
paradigm in today's adoption-diffusion research, attitude and
behavioral intention-related variables are believed to influence
individuals' actual adoption of innovations, through the
intermediary of perceived ease-of-use and perceived usefulness (Davis
1989).
Lee et al. (2002) report, in their study on the banking
industry's adoption of new technology products, that communication
was an important predictor of the actual acceptance of new products. In
other words, perceived ease-of-use and perceived usefulness are
determinants of consumers' behavioral intentions concerning
technology acceptance, and the influence of external variables on
technology acceptance is mediated by perceived ease-of-use and
usefulness (Agarwal, Karahanna 2000). Lee et al. (2003) validate TAM
through their meta-analysis of 101 empirical studies. Joo and Kim
(2004), meanwhile, found, in an investigation of technology acceptance
in the internet market, that innovativeness, external environment and
organizational characteristics were the three most important
determinants of acceptance.
In this study, we draw on the adoption-diffusion model and related
theoretical research, as well as the use-diffusion model by Shih and
Venkatesh's (2004), which expands on the latter, and TAM. Factors
considered include those unique to the adoption-diffusion model, such as
perceived ease-of-use, perceived usefulness, compatibility and
accessibility, and those common to both models such as household
innovativeness, communication, complexity, relative advantage, perceived
risk and service quality. In the adoption-diffusion model, we set the
two key influence factors for technology acceptance in TAM, namely,
perceived ease-of-use and perceived usefulness, as mediating variables,
and other variables, including compatibility, observability,
trialability, household innovativeness, communication, complexity,
relative advantage, perceived risk and service quality, as influence
factors. Intention to re-use and intention to subscribe and use were
considered the adoption outcomes for IPTV, as the former is an existing
service and the latter, a service new to the market. Using these
variables, we formulated the following hypotheses on the
adoption-diffusion of IPTV.
H1: Factors such as compatibility, observability, trialability,
household innovativeness, communication, complexity, relative advantage
and service
quality have a positive influence on the perceived ease-of-use and
perceived usefulness of IPTV and the intention to subscribe to IPTV
while the perceived risk of IPTV has a negative influence on its
perceived ease-of-use, perceived usefulness, and the intention to
subscribe to this service.
H2: The perceived ease-of-use and perceived usefulness of IPTV will
have positive effects on IPTV subscribers. In other words, if users
perceive IPTV to be ea, their purchase potential of IPTV will increase
because of the conviction through in advance IPTV service experience.
3.2.2. Hypotheses on the Use-diffusion of IPTV Services
Shih and Venkatesh (2004) argued that for a more complete
understanding of the process of consumers' acceptance of new
products, one needs to move beyond adoption factors and also examine
use-diffusion patterns. They developed an alternative model which takes
into consideration use-related aspects of innovation diffusion,
baptizing it the "use-diffusion model", and successfully
tested the model's validity by applying it to the diffusion process
of home electronics and technology products. Park J-m (2005), in a study
on the diffusion process of cutting-edge technology products, found that
product experience was a valid influence factor for the use-diffusion of
new technology products and that variety of use effectively influenced
the use-diffusion of innovative technologies.
Experiential marketing and TCE (Total Customer Experience) have
stressed that product experience influences the use and diffusion of new
products. Hahn et al. (1994), for instance, reported that product
experience had a direct and determining influence on the conversion of
an adopter into a repeater. The basic idea is that as a consumers'
experience of a new technology increases, they develop a better
understanding of its benefits and come to perceive it as an
indispensable part of their everyday life. In other words, the more
experienced and familiar a consumer is with a technology, the more
varied and frequent his / her use of this technology becomes. There have
been also studies in technology fields reporting the positive effect of
product experience on the use-diffusion of information systems and
services.
Cognitiative (1999), for example, reported that users'
experiences on the website of an online company not only shapes the
users' image of this company, but it also has a decisive influence
on their intention to revisit the same website in the future. Huberman
et al. (1998) noted that the amount of time spent by users on a website
is directly correlated with the value they perceive in the same website.
Gillespie et al. (1999) found evidence confirming that users'
experience of a website is a major determinant of their loyalty toward
the same website. The view that these studies share is that the more
relevant a website is to a user, the greater the amount of time he or
she spends browsing it. The relevance of a website, in other words,
incites users to more thoroughly explore its pages as well as make
repeat visits. Bucklin and Sismeiro (2000) reported similar findings
indicating that the amount of time spent on a website and number of page
views are largely determined by its perceived relevance to users.
As for usage behavior patterns, they are considered important
influence factor for product acceptance and diffusion not just within
the use-diffusion model by Shih and Venkatesh (2004), but also in many
other studies. Kahneman and Lovallo (1988) argued that the greater the
capabilities of the individual user to successfully use a new technology
product, the stronger the satisfaction they experience with the same
product.
In sum, individual users' technology proficiency, as it leads
to a higher rate of use as well as a more varied use of a new product,
ultimately results in greater satisfaction with the product. Huberman et
al. (1998), meanwhile, claimed that the amount of time users spend at a
website is directly linked to its value as well as the revenue generated
by it. Shih and Venkatesh (2004), pointing out the importance of a
household's usage pattern with regard to general technology
products as a predictor of their adoption behavior vis-a-vis new
technology products, stated that the adoption of an innovative product
is largely determined by the extent to which it improves existing
products in terms of technological sophistication. Technological
sophistication, here, refers to the versatility and capabilities of a
technology. The current state of information technology makes it
possible for a technology to be sophisticated without being difficult to
use. New products with a higher level of technological sophistication,
according to Shih and Venkatesh (2004), lead to a higher rate of use and
broader, more varied use.
In this study, using the theoretical framework of Shih and
Venkatesh (2004) and taking inspirations from the media substitution and
TCE theories, we selected the following influence factors for hypotheses
related to the use-diffusion of IPTV: product experience, sophistication
of technology, similarity, complementarity, substitution effect,
household innovativeness, communication, complexity, relative advantage,
perceived risk and service quality. Rate of use and variety of use, the
two main constructs, related to the usage pattern proposed by Shih and
Venkatesh (2004), were used as mediating variables. Three outcome
variables were selected, namely, satisfaction with IPTV, intention to
re-use it and attitude towards subscribing to an IPTV service. Two
hypotheses were formulated, as follows, on the relationship between
these use-diffusion variables:
H3: Factors, such as the product experience, sophistication of
technology, similarity, complementarity, substitution, household
innovativeness, communication, complexity, relative advantage, and
service quality will have positive effects on the rate of use and the
variety of use of IPTV, the satisfaction felt about and intention to
re-use IPTV while perceived risk will have negative effects on the rate
of use and the variety of use of the IPTV product as well as the
satisfaction felt about and intention to re-use IPTV.
H4: The rate of use and diversity of the IPTV product will have
positive effects on the satisfaction felt about and intention to re-use
IPTV.
3.3. Measurement
This study considers variables that are common to the
adoption-diffusion model and the use-diffusion model and those that are
specific to each of them. Variables specific to the adoption-diffusion
model considered in this study include compatibility, trialability,
observability, perceived ease-of-use and perceived usefulness.
Compatibility refers to the extent to which a new product
corresponds to the desire, trust, values and the past experience of a
consumer (Rogers 1995). In this study, we measured compatibility through
the compatibility of a product to the current purchase behavior,
cultural background and the lifestyle of a consumer (Jang, Cho 2000). As
for observability, this variable was measured through the observability
of benefits resulting from the use of a product, overall usefulness of
the product and information communication (Jang, Cho 2000). Trialability
was measured through the limited period of use, capabilities to use the
functions of a product, ability to use the product when needed and
performance enhancement resulting from the use of the product (Alexandra
2007). Perceived usefulness was measured through the ability to quickly
access information useful to a user, usefulness of a product for
conducting purchases and the variety of information made accessible
through the product (Davis 1989). Perceived ease-of-use was measured
through the ease of using desired functions and convenient methods for
accessing the internet and other manipulations (Davis 1989; Venkatesh
2000).
Variables specific to the use-diffusion model that are included in
this study are product experience, technological sophistication,
similarity, complementarity, substitution effect, and the rate and
variety of use.
We measured product experience through the length of use of a
product, from the initial purchase of the product to the current point
in time (Noyes, Garland 2006). In this study, adopting the perspective
of TCE, we measured emotional experience-related factors, such as
pleasures derived from IPTV, the experience of smooth integration of
functionalities and overall usefulness (Sandstrom 2008). Specifically,
we measured the experience of using the five main functions of IPTV
(VoD, personalized features, two way communication, value-added
services, entertainment and media content), which may be translated into
pleasure, smooth functional integration, temporal flexibility, increased
convenience for everyday activities and general usefulness.
Technological sophistication, meanwhile, was measured through the
versatility and capabilities of the product/service (Shih, Venkatesh
2004). As for functional similarity, we took into account the extent to
which a consumer, based on his/her personal experience, perceives a
product/service as functionally similar to existing ones (Martin,
Stewart 2001). Complementarity, which describes the relationship between
two products/services that simultaneously benefit by helping the other
maximize its usefulness, was measured with regard to the ability to
increase the existing media's entertainment potential and
usefulness for information access and as a communication tool (Jeffrey,
Atkin 1996). Substitution effect, corresponding to the relationship
between two products/services in which the use of one dispenses the use
of the other--in other words, one replaces the other--was measured with
regard to TV, internet, mobile phone, DMB (Digital Multimedia
Broadcasting) and other existing media in terms of IPTV's potential
to substitute their functionalities, the content they provide and the
time spent using this media (Li 2004). Variety of use was measured by
the variety of content types for which a product/service is used as the
means of access (Ram, Jung 1990). The uses considered in this study
range from communication with family members, family entertainment, care
and support of family members, home shopping, education and information
(Tinnell 1985). Rate or frequency of use was measured by number of hours
spent using a product/service (Shin, Venkatesh 2004).
Variables common to the adoption-diffusion model and use-diffusion
model included in this study are household innovativeness,
communication, complexity, relative advantage, perceived risk and
service quality.
Household innovativeness refers to how willing a household is to
adopt an innovation (Gatignon, Robertson 1985) or, in other words, how
rapid a household adopts an innovation, and this was measured by
curiosity/creativity, risk preference, voluntary simplicity, creative
re-use, and multiple use potential (Price, Ridgway 1983). Communication
was measured by whether there are helpers to provide assistance with
regard to the use of a product/service, the possibility to acquire
knowledge needed for its use, and how large or complete the related
information source is (Venkatesh 2000). Items used to measure complexity
were difficulty of use, complexity of manipulation, understanding of
advanced functions, and the need for explanations on advanced functions
(Rogers 1983). Relative advantage was measured by the comparative price
advantage of a product/service, the ease and speed of manipulation, and
portability (Moore, Benbasat 1991). Perceived risk was measured through
performance risk, financial risk, social risk, and psychological risk
(Hirunyawipada, Paswan 2006). Quality of service was measured by the
accuracy, relevance, completeness and comprehensiveness of information
provided through a product/service, and the variety of information made
accessible by it (Parasuraman et al. 1985).
The outcome variables chosen for this study were satisfaction,
intention to re-use, and intention to subscribe. Items used to measure
satisfaction were positive feelings and overall satisfaction with a
product/service, and favorable quality assessment (Shih, Venkatesh 2004;
Kim, Y-j 2005). Intention to re-use was measured by the intention to
continue to use the current product/service and the intention to
recommend it to others (Hellier et al. 2003). Finally, the intention to
subscribe/use by applying to IPTV was measured by the intention to
subscribe to an IPTV service, as indicated by households. Households
were provided with a basic description of IPTV and were asked to decide
whether they would subscribe to the service, within a short period of
time (Eastlick 1993).
3.4. Gathering and sampling of data
The data were gathered from both users and non-users of IPTV, in
accordance with the objectives of the study. The data from non-user
households were used in the analysis of the IPTV adoption-diffusion
model while the data from user households were used in the analysis of
the IPTV use-diffusion model.
The data were collected through a face-to-face interview conducted
at the homes or workplaces of the respondents, using two different
questionnaires for households currently using an IPTV service and those
that were not. The respondents were selected among households residing
in four major Korean cities, namely, Seoul, Incheon, Busan and Daegu.
500 total copies of the adoption-diffusion analysis survey questionnaire
were distributed to households that are not currently using IPTV, which
broke down by region to 200 copies to Seoul-based households and 100
copies each in Incheon, Busan and Daegu-based households; 414 valid
responses were returned. As for the use-diffusion analysis questionnaire
designed for households that are currently using IPTV, 250 copies were
distributed--100 copies in Seoul and 50 copies each in Busan, Incheon
and Daegu--and 160 valid responses were returned. The survey was
conducted over a period of one month, between October 15, 2008 and
November 14, 2008.
As for the geographical distribution of respondents who returned
valid responses, Seoul accounted for 44.2% of the total households that
are currently not using IPTV (183 out of 414 total households that
returned a valid response) followed by Busan (85 households), Daegu and
Incheon (73 households for both cities). As for households that are
currently using IPTV, 90 of them resided in Seoul, corresponding to
56.3% of total households that returned a valid response, while 29
others resided in Busan, 23 in Daegu and 18 in Incheon (Appendix).
4. Results
To test the reliability and validity of the variables used in the
two diffusion models, we calculated Cronbach's [alpha] and
performed a factor analysis. The results suggest that all variables had
a sufficient level of reliability and validity. Those of the constructs
used in the adoption-diffusion model that proved to have an acceptable
level of validity through the factor analysis were, then, tested for
internal consistency reliability. Cronbach's [alpha] was 0.660 or
greater for all factors tested. The internal consistency reliability
analysis, performed on the constructs used in the use-diffusion model,
resulted in a Cronbach's [alpha] of 0.8 or greater for all of the
factors.
4.1. The adoption-diffusion model
We performed a structural analysis of the adoption-diffusion models
predicting the diffusion of IPTV, using AMOS v.70. The resulting
goodness-of-fit measures are provided in Table 3 below, listing the
values separately for IPTV:
The goodness of fit of the overall model proved to be highly
adequate.
The structural analysis of the adoption-diffusion model on IPTV was
based on TAM. The results of the structural analysis of the
adoption-diffusion models on IPTV are listed in Table 3 below.
The results reveal that, trialability, perceived risk, quality of
service, household innovativeness and perceived ease-of-use had a direct
influence on potential users' intention to subscribe to IPTV. Also,
these variables had indirect mediation effects through its perceived
ease-of-use. What this implies in practical terms is that in order to
promote the adoption-diffusion of IPTV, marketers must try to enhance
consumer-perceived ease-of-use of this application. Thus, hypotheses 1
and 2 on the adoption-diffusion model on IPTV are generally well
supported.
4.2. The use-diffusion model
To test hypotheses 3 and 4 on the use-adoption of IPTV, we
performed a structural analysis of the use-adoption model using the
survey response data collected from IPTV users. The goodness-of-fit
measures obtained from the analysis are provided in Table 4 below.
Goodness-of-fit values from all indices, from [chi square] to GFI,
AGFI, RMR and NFI, were within a very satisfactory range. The
goodness-of-fit of the use-adoption model on IPTV was, notably, superior
to that of the two adoption-diffusion models on IPTV, respectively,
analyzed earlier. Based strictly on the goodness-of-fit values, the
use-diffusion model on IPTV may be considered to possess more
explanatory power than the two other models for the diffusion process in
real-world settings.
The results of structural analysis revealed that complementarity, a
construct specific to the use-diffusion model, and communication, a
construct common to the adoption-diffusion and use-diffusion models, had
a direct influence on the rate of use of IPTV and the satisfaction felt
by users. The intention to re-use IPTV, meanwhile, was influenced by
perceived risk and relative advantage. This is an indication that in
order to enhance consumers' satisfaction with IPTV, in the context
of promoting the use and diffusion of this service, providers must look
to increase its complementarity vis-a-vis existing media and strengthen
its communication-related functions. Meanwhile, to incite current users
to re-subscribe to IPTV, providers need to adopt a strategy which
reduces perceived risk associated with this service while simultaneously
increasing its relative advantage.
Specifically, the rate of use of IPTV was directly influenced by
variables from the media substitution theory, such as complementarity
and the substitution effect, and communication. The variety of use was
likewise positively influenced by the media substitution variables such
as similarity and complementarity, and the sophistication of technology.
These results imply that media substitution variables constitute
important determinants for the rate of use and the variety of use of
innovative products like converged media services. Also, satisfaction
with IPTV was positively influenced by complementarity, the substitution
effect and communication--variables which proved to also influence the
rate of use. In the meantime, the intention to re-use IPTV was
positively influenced by complexity and relative advantage, but
negatively influenced by perceived risk. However, the study found that
the rate of use and the variety of use of IPTV did not significantly
influence consumers' intentions to re-use this service. Hypotheses
3 and 4 on use-diffusion of IPTV were, therefore, partially supported.
5. Conclusions
5.1. Recapitulation of results
The results of the analysis of factors influencing the diffusion of
IPTV services, using the adoption-diffusion model and the use-diffusion
model, are summarized in Table 5.
First, in the case of the adoption-diffusion model, measuring
non-users' intentions to adopt IPTV, the trialability and perceived
ease-of-use proved to be determinants effectively influencing the
adoption behavior of potential subscribers, among endogenous factors,
specific to this approach. Among common factors, we found that household
innovativeness, perceived risk, and service quality were determinants of
adoption of IPTV.
Our results also indicate that for innovative, converged media
services like IPTV, perceived ease-of-use is an important mediator of
the relationship between factors influencing adoption-use behavior and
actual adoption-use. What this finding suggests in practical terms is
that enhancing ease-of-use is crucial to promote the adoption-diffusion
of IPTV. Our results also show that in order to enhance the perceived
ease-of-use of IPTV, service providers must emphasize observability,
trialability, communication, relative advantage, and service quality, at
the level of the product and technology, and try to capitalize on
household innovativeness at the level of consumers.
Second, the use-diffusion model on IPTV proved to have high
explanatory power for the diffusion process of converged media services.
Our analysis, based on the results of a survey of consumers currently
subscribed to an IPTV service, found that the use diffusion model far
exceeded the adoption-diffusion model in terms of explanatory power with
regard to satisfaction felt with the service and the intention to re-use
the service. The use-diffusion model also received very high
goodness-of-fit scores from all indices. The satisfaction that consumers
experience with IPTV was influenced by complementarity, the substitution
effect and the variety of use, among endogenous factors specific to the
use-diffusion model, and communication and relative advantage, among
common factors.
The intention to re-use IPTV was significantly influenced by
factors common to the two diffusion models, such as complexity, relative
advantage and perceived risk, rather than factors uniquely associated
with the use-diffusion model. These results indicate that in order to
enhance users' satisfaction with IPTV and, thereby, incite them to
continuously use the service, service providers must increase both its
complementarity and substitution effect vis-a-vis existing media and
induce more varied use of it. Consumers' intention to re-use IPTV,
as we have said earlier, is more greatly influenced by factors common to
the two diffusion models, namely, relative advantage and perceived risk,
rather than those that are specific to the use-diffusion model.
5.2. Implications
As a holistic approach to understanding the diffusion process of
IPTV, a converged media service that is rapidly rising as an alternative
to CATV, the traditional TV service with the highest household
penetration, this study empirically analyzed both an adoption-diffusion
model of innovation and a use-diffusion model. The main theoretical and
practical implications of this study are as follows:
At a theoretical level, this study is significant in four ways:
First, it is an empirical evaluation of the use-diffusion model proposed
by Shih and Venkatesh (2004) that compares it to a traditional
adoption-diffusion model. Although there have been some attempts, in
recent years, to explain the use-diffusion model, as an alternative to
the adoption-diffusion model, these attempts fall short of establishing
whether and to what degree the former exceeds the latter in terms of
explanatory power. The findings of this study provide concrete evidence
that the use-diffusion model surpasses the adoption-diffusion model in
its ability to explain the diffusion of innovations in real-world
situations.
Second, this study proposed a set of adoption factors and use
factors to consider from the adoption-diffusion perspective and
use-diffusion perspective, which influence the prospect and process of
diffusion for IPTV. In identifying adoption factors for IPTV, this study
distinguished common factors from model-specific factors, thereby
providing a theoretical basis for designing a strategic marketing
framework.
Third, this study found evidence that the rate and variety of use,
the two key mediators in the use-diffusion model by Shih and Venkatesh
(2004), do not have as determining or conclusive an influence on the
diffusion outcome as perceived ease-of-use or perceived usefulness with
constructs used in TAM, a model based on the traditional
adoption-diffusion paradigm. Shih and Venkatesh (2004), who focused on
different use patterns displayed by users of innovative technology
products or services, divided the market into user segments, derived
factors influencing the use-diffusion of new products and services from
the use patterns of these user segments, and accordingly predicted the
outcome of diffusion. Our results, however, revealed that usage patterns
based on rate of use and variety of use, alone, cannot adequately
explain the use-diffusion of innovations.
Fourth, our study found that product experience and technological
sophistication, two constructs proposed by Shih and Venkatesh (2004) as
important influence factors for the diffusion of a new product, had no
significant influence or predictive power on the diffusion outcome.
Product experience, being a central concept in experiential marketing
and the TCE paradigm, provides an ample, theoretical basis justifying
its importance as a variable in the diffusion process of new products or
services. We found, however, that concerning IPTV and, by extension,
converged digital media in general, product experience and technological
sophistication determine less satisfaction felt by consumers and their
intention to re-use the service than what is contributed to the process
in which traditional influence factors such as relative advantage or
perceived risk influence the intention to newly subscribe to the service
or continuously use it. In sum, the experience of an innovative product
or its technological sophistication alone proved to be insufficient to
explain its diffusion process or outcome. Our results, therefore,
suggest that product experience and technological sophistication, rather
than being independent predictors of the use-diffusion process of
innovations, are links mediating the influence of other factors.
This study offer a number of practical implications for the
marketing of IPTV, now set to begin commercial service in Korea,
concerning both market entry and market penetration strategies. When
designing a market entry and marketing strategy for IPTV, service
providers must try to create a positive perception among its potential
users, especially with regard to its trialability and ease-of-use.
Customer relationship management is to be perceived as significant
approach impacting the success of business company in the long run
(Korsakiene 2009). Our results, furthermore, suggest that service
providers need a strategy to reduce the opportunity costs arising from
the adoption of IPTV as well as the learning curve required before
consumers become proficient with its use. Meanwhile, to solidify the
grip on the early adopter market, the marketing strategy must stress
IPTV's complementarity vis-a-vis other media and its substitution
effect, so as to enhance satisfaction among this adopter segment and,
thereby, magnify the word-of-mouth, marketing effect.
The two-way, interactive data transfer capability of IPTV must be
also emphasized so as to increase consumers' awareness of this
medium as a communications solution and not just as a media delivery
service. Underlining the fact that IPTV provides customized content is
also a valid market entry strategy. Finally, in order to prevent the
desertion of IPTV by early adopters, for instance, at the end of a
promotional, free trial period, providers must emphasize the relative
advantage of this service to incite them to continue to use it. Our
findings also point to the need to reduce perceived risk associated with
subscribing to an IPTV service.
doi: 10.3846/16111699.2011.620147
APPENDIX
Survey Results
Surveyed Household Locations
Region IPTV Non-user IPTV User
Frequency Ratio (%) Frequency Ratio (%)
Seoul 183 44.2% 90 56.3%
Inchoen 85 20.5% 29 18.1%
Busan 73 17.6% 23 14.4%
Daegu 73 17.6% 18 11.3%
Total 414 100% 160 100%
Respondent characteristics
Adoption-diffusion Use-diffusion
Variables Category (IPTV Non-user) (IPTV User)
Frequency Ratio (%) Frequency
(persons) (persons)
Sex Male 187 45.2 90
Female 227 54.8 70
Age 20-29 62 15.0 42
Age Age 30-39 126 30.4 83
Age 40-49 157 37.9 27
Age over 50 69 16.7 8
Marital Married 352 85.0 87
Status Single 62 15.0 73
Under middle 3 0.7 19
school
graduate
High school 214 51.7 11
graduate
Education In college 10 2.4 113
College graduate 183 44.2 17
Master's degree 4 1.0 160
or higher
Self-employed 92 22.2 23
Sales 61 14.7 6
Production 16 3.9 2
Office Worker 109 26.3 76
Technician 44 10.6 13
Job Management 15 3.6 3
Specialized Job 15 3.6 17
House maker 55 13.3 8
Student 6 1.4 6
Unemployed 1 0.2 5
Other 0 0 1
Under KRW 70 16.9 28
2.5 million
Average KRW 2.5-5 million 277 66.9 76
income KWR 5-7.5 million 54 13.0 37
Above KRW 13 3.1 19
7.5 million
Total 414 100.0 160
Use-diffusion
(IPTV User)
Variables Category Frequency
(persons) Ratio (%)
Sex Male 90 56.3
Female 70 43.8
Age 20-29 42 26.3
Age Age 30-39 83 51.9
Age 40-49 27 16.9
Age over 50 8 5.0
Marital Married 87 54.4
Status Single 73 45.6
Under middle 19 11.9
school
graduate
High school 11 6.9
graduate
Education In college 113 70.6
College graduate 17 10.6
Master's degree 160 100.0
or higher
Self-employed 23 14.4
Sales 6 3.8
Production 2 1.3
Office Worker 76 47.5
Technician 13 8.1
Job Management 3 1.9
Specialized Job 17 10.6
House maker 8 5.0
Student 6 3.8
Unemployed 5 3.1
Other 1 0.6
Under KRW 28 17.5
2.5 million
Average KRW 2.5-5 million 76 47.5
income KWR 5-7.5 million 37 23.1
Above KRW 19 11.9
7.5 million
Total 160 100.0
Received 01 February 2011; accepted 28 April 2011
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Kazuyuki MOTOHASHI is a professor in the Department of Technology
Management for Innovation, at the University of Tokyo's Graduate
School of Engineering in Japan. Until early 2011, he had held various
positions at the Ministry of Economy, Trade and Industry of the Japanese
Government, and was an economist at the OECD and an associate professor
at Hitotsubashi University. His research interest covers a broad range
of issues in economic and statistical analysis of innovation, including
the economic impacts of information technology, the international
comparison of productivity, the national innovation system focusing on
science and industry linkages and SME innovation, and entrepreneurship
policy. He has published several papers and books on the above issues.
Mr. Motohashi was awarded a Master of Engineering degree from the
University of Tokyo, an MBA from Cornell University and a PhD in
business and commerce from Keio University. URL:
http://www.mo.tu-tokyo.ac.jp/.
Deog-Ro LEE (Ph.D., Yonsei University, Seoul, Korea) is a professor
at Seowon University's Business School in Korea. His current
interests in the field include creativity, leadership, humor, and
labor-management partnership. He has written 26 professional books
including 6 translation books and 4 book chapters, and has published
numerous articles in academic and professional journals such as the
Journal of Organizational Behavior, Personnel Review, Creativity
Research Journal, Korean Management Review, and the Korean Journal of
Sociology.
Yeong-Wha SAWNG is a director of the Technology Foresight Research
Team at the Electronics and Telecommunications Research Institute (ETRI)
in Korea. He received an MIM in Business Management from Whitworth
University in the USA and a PhD in Technology Management from Hanyang
University in Seoul, Korea in 1995 and 2006, respectively. He joined
ETRI in 2000, and has been working in the areas of digital convergence,
IT policy, mobile application, technology management, and business
strategy. His research interests also include high-tech. marketing,
technology management strategy, the e/m-Biz model, and consumer
behavior. He has been published in several international and Korean
journals. Since 2008, he has been working on a second PhD (Dissertation
PhD in Technology Management for Innovation) at the University of Tokyo
in Japan.
Seung-Ho KIM is a professor in the School of Health Service
Management at Kyungwoon University in Gumi City, Korea. He received his
MS degree in Business Administration at Seoul National University in
Korea in 1998. Before he joined as a faculty member of Kyungwoon
University, he had been with KINE as a vice president and a professor of
Daegu Hanny University. His main interests include technology
innovation, technology convergence and the evolution of industrial
ecology, strategic management of technology, and the R&D project
management.
Kazuyuki Motohashi [1], Deog-Ro Lee [2], Yeong-Wha Sawng [3],
Seung-Ho Kim [4]
[1] Department of Technology Management for Innovation, University
of Tokyo, Japan
[2] School of Business Management, Seowon University, Korea
[3] 218 Gajeong-ro, Yuseong-gu, Technoloy Foresight Research Team,
ETRI 305-700, Korea
[4] School of Health Service Management at Kyungwoon University,
Korea
E-mails: [1]
[email protected]; [2]
[email protected];
[3]
[email protected] (corresponding author); [4]
[email protected]
Table 1. Consumer-perceived performance characteristics of IPTV
Characteristics Description
Multi-channel broadcasting Dozens of broadcasting
channels
High-definition video High-definition video, Hifi
sound, 5.1 or better
Two-way data transfer Broadband, real-time return
channels, for data transfer
from users to the broadcaster.
Convergence Bundled services, other than
TV, provided through the set-
top box, home gateway or the
platform (TV set), which may
vary depending on the service
mix (i.e., double play, triple
play or quadruple play).
Table 2. Adoption-diffusion model vs. Use-diffusion model
Differences
Key Market Segmentation
difference segments criteria
Adoption- Adoption --Innovators --Timing or rate
diffusion --Early Adopters of adoption
model --Early Majority
(ADM) --Late Majority
--Laggards
Use- Use --Intense Users --Rate of use
diffusion --Specialized Users --Variety of use
model --Nonspecialized
(UDM) Users
--Limited Users
Differences
Model- Theoretical
specific factors consideration
Adoption- --Observability --S-shaped curve
diffusion --Compatibility of diffusion
model --Trialability --Speed of penetration
(ADM) and critical mass
--Two-step model
of diffusion
Use- --Product experience --Evolving nature of use
diffusion --Competition --Technology Integration
model for use --Sustained / continuous
(UDM) --Sophistication use
of technology --Disadoption
--Satisfaction --Essentialness of
Technology
--Impact of technology
Differences
Elements common to Both Models
Adoption- --Innovativeness
diffusion --Social
model communication
(ADM) --Complexity
--Influence of media
--Relative advantage
Use-
diffusion
model
(UDM)
Table 3. Structural analysis of use-diffusion model on IPTV
Chi GFI AGFI RMR NFI
Square
Indices
[chi square] df [chi square] GFI AGFI RMR NFI
/df(p)
5.865 1 5.865(.015) 0.998 0.917 0.004 0.997
Exogenous variable Path
Compatibility
Observability
Trialability
Communication
Complexity [right arrow]
Relative advantage
Perceived risk
Quality of service
Household innovativeness
Compatibility
Observability
Trialability
Communication
Complexity [right arrow]
Relative advantage
Perceived risk
Quality of service
Household innovativeness
Compatibility
Observability
Trialability
Communication
Complexity [right arrow]
Relative advantage
Perceived risk
Quality of service
Household innovativeness
Perceived ease-of-use [right arrow]
Perceived usefulness
Exogenous variable Endogenous variable IPTV
Compatibility 0.053
Observability 0.108 +
Trialability 0.178 **
Communication 0.097 *
Complexity Perceived ease-of-use -0.080 *
Relative advantage 0.266 **
Perceived risk 0.075 +
Quality of service 0.183 **
Household innovativeness 0.073 *
Compatibility 0.108 *
Observability 0.381 **
Trialability 0.082 *
Communication 0.121 **
Complexity Perceived usefulness 0.037
Relative advantage -0.037
Perceived risk 0.096 **
Quality of service 0.085 +
Household innovativeness -0.037
Compatibility -0.078
Observability 0.059
Trialability 0.252 **
Communication 0.013
Complexity Intention to subscribe to IPTV 0.046
Relative advantage 0.138
Perceived risk -0.144 *
Quality of service 0.219 *
Household innovativeness 0.233 **
Perceived ease-of-use Intention to subscribe to IPTV 0.301 **
Perceived usefulness 0.116
Notes: + p <. 1; * p <. 05; ** p <. 01
Table 4. Results of structural analysis of the use-diffusion model
on IPTV
[chi square] df [chi square] GFI AGFI RMR NFI
/df
1.822 1 1.822 0.998 0.918 0.0008 0.998
Exogenous variable Path Endogenous Estimate
variable
Product experience 0.088
Sophistication
of technology 0.060
Similarity -0.020
Complementarity 0.362 **
Substitution effect 0.191 *
Household Rate of use -0.125
innovativeness
Communication 0.260 **
Complexity 0.051
Relative advantage 0.197+
Perceived risk 0.015
Service quality 0.104
Product experience 0.107
Sophistication of 0.169 *
technology
Similarity 0.451 **
Complementarity -0.008
Substitution effect 0.329 **
Household innovativeness [right Variety of use -0.100
arrow]
Communication -0.116
Complexity 0.135
Relative advantage 0.144
Perceived risk 0.007
Service quality -0.069
Exogenous variable Exogenous Path
variables
Product experience Product experience
Sophistication Sophistica-
of technology tion of tech-
nology
Similarity Similarity
Complementarity Complementarity
Substitution effect Substitution effect
Household Household [right
innovativeness innovativeness arrow]
Communication Communication
Complexity Complexity
Relative advantage Relative ad-
vantage
Perceived risk Perceived risk
Service quality Service
quality
Rate of use
Variety of use [right
arrow]
Product experience Product experience
Sophistication of Sophistication of
technology technology
Similarity Similarity
Complementarity Complementarity
Substitution effect Substitution effect
Household innovativeness Household
innovativeness
Communication Communication
Complexity Complexity
Relative advantage Relative advantage
Perceived risk Perceived risk
Service quality Service quality
Rate of use
Variety of use
Exogenous variable Endogenous Estimate
variable
Product experience 0.005
Sophistication
of technology 0.060
Similarity -0.020
Complementarity 0.362 **
Substitution effect 0.191 *
Household Satisfaction -0.125
innovativeness
Communication 0.260 **
Complexity 0.051
Relative advantage 0.197+
Perceived risk -0.135
Service quality 0.144
0.045
Satisfaction 0.301 **
Product experience 0.133
Sophistication of 0.169
technology
Similarity -0.059
Complementarity 0.084
Substitution effect 0.098
Household innovativeness Intention to re-use -0.081
Communication 0.035
Complexity 0.178 *
Relative advantage 0.288 **
Perceived risk -0.300 **
Service quality -0.050
Intention to re-use 0.102
0.122
Notes: + p <. 1; * p <. 05; ** p <. 01
Table 5. Summary of results
Variables Adoption-diffusion
(Non-users)
Intention to subscribe
Compatibility
Observability
Trialability **
Perceived ease-of-use **
Perceived usefulness
Model- Product experience
specific factors Sophistication of
technology
Similarity
Complementarity
Substitution effect
Rate of use
Variety of use
Household **
Innovativeness
Communication
Common factors Complexity
Relative advantage
Perceived risk **
Service quality *
Variables Use-diffusion (Users)
Satisfaction
Compatibility
Observability
Trialability
Perceived ease-of-use
Perceived usefulness
Model- Product experience
specific factors Sophistication of
technology
Similarity
Complementarity **
Substitution effect *
Rate of use
Variety of use **
Household
Innovativeness
Communication **
Common factors Complexity
Relative advantage +
Perceived risk
Service quality
Variables Use-diffusion (Users)
Intention to re-use
Compatibility
Observability
Trialability
Perceived ease-of-use
Perceived usefulness
Model- Product experience
specific factors Sophistication of
technology
Similarity
Complementarity
Substitution effect
Rate of use
Variety of use
Household
Innovativeness
Communication
Common factors Complexity *
Relative advantage **
Perceived risk **
Service quality
Notes: + Moderate influence; * Strong influence; ** Very strong
influence
