Three-Dimensional Trends Superposition in Digital Innovation Life Cycle Model
Published at : 07 Dec 2020
Volume : IJtech
Vol 11, No 6 (2020)
DOI : https://doi.org/10.14716/ijtech.v11i6.4444
Minakov, V.F., Lobanov, O.S., Dyatlov, S.A. 2020. Three-Dimensional Trends Superposition in Digital Innovation Life Cycle Model. International Journal of Technology. Volume 11(6), pp. 1201-1212
| Vladimir F. Minakov | St. Petersburg State University of Economics, 21, Sadovaya Street, 191023 St. Petersburg, Russian Federation |
| Oleg S. Lobanov | St. Petersburg State University of Economics, 21, Sadovaya Street, 191023 St. Petersburg, Russian Federation |
| Sergey A. Dyatlov | St. Petersburg State University of Economics, 21, Sadovaya Street, 191023 St. Petersburg, Russian Federation |
The concept of innovative products’ life cycle as a
superposition of growing interest in their consumption, as well as processes of
falling demand, is substantiated in this study. This study found that such
trends in the life cycle of innovations are unified. The development of the
economic and mathematical model is based on the method of these trends’
superposition. The result of this superposition is the unity of the mathematical
representation of simultaneously occurring processes in the rise in demand for
innovative products, consumer disappointment or crowding out by competitive
innovative solutions. The mathematical description of each process is an
original mathematical form that reflects the diffusion phenomena, which differs
in the expression of the mathematical model parameters in the indicators form
of real-time series of innovative products consumption. The principle of
superposition and the unity of trends in the growth and decline of consumer
interest in innovative products makes it possible to proactively establish the
formation towards changing trends in the dynamics of innovative product
consumption. These distinctive features of the model ensure transition from a
qualitative to a quantitative representation of the Gartner cycle. This allows
the use of the developed model to solve the problems of forecasting the
consumption of innovations. In the growth phase, the processes of transition to
the plateau of innovation success, the reversal of the growth trend and the
onset of recession were identified. In the phase of movement to recession of
the hype, the model makes it possible to predict the time of transition to an
increase in consumption and to a plateau of innovation success. A special case
of the proposed model is the cycle curve developed by Gartner in a descriptive
form. The mathematical representation of such a case expands the applicability
of the model to quantitative indicators of innovation processes.
Change management; Cycles; Digital economy; Economic and mathematical model; Innovation management
The manifestation of these processes is especially noticeable in the
automotive market (Borisov and Pochukaeva, 2019)
(suffice it to recall the filing of a bankruptcy petition for the world capital
of the automotive industry of Detroit in 2013). In addition, there are problems
of effective management of innovation and investment processes (Sklyarova et al., 2019). The changeable stages of
success and problems in Elon Musk's companies are indicative.
Consequently, there is a need for the evolution of modeling methods in
innovation management (Minakov et al., 2017;
Glinskiy et al., 2018b). Indeed, Rogers' models and similar ones (Rogers, 2004; Rogers, 2015) reflected only the
main processes and phases of the life cycle (the spread of innovations among
“innovators, early adopters, early majority, late majority, lagging behind”).
The lack of such models led to the formalization of local processes (including
short-term) declines in interest in innovation (Rivera
et al., 2006), followed by a partial or complete restoration of their
spread (1995, model of hype cycles proposed by Gartner).
It is also important to note that the role of innovation has grown
significantly in connection with the digitalization of the economy (Barykin et al., 2020). First, solutions in the
field of information technology (IT solutions) have gone beyond the framework
of auxiliary tools used by the personnel of enterprises to improve the
efficiency of their activities (Karlik et al.,
2019). They have long and successfully performed the functions of
automation and control systems. ERP systems have expanded the boundaries of the
use of information and communication systems and technologies (ICT) to business
processes that go beyond enterprises – manufacturers of goods and services (Silkina et al., 2019). A large cluster of smart
solutions has separated employees from functional processes implemented by
automated IT solutions with elements of artificial intelligence. In addition,
intelligent systems and technologies have put individuals in a position of dependence
on capabilities and, for example, “smart proposals” of information systems
based on intellectual analysis of data on the needs and preferences of a person
based on his digital prints and traces (Ignatiev et
al., 2019; Karlik et al., 2020).
Digital resources have played a significant role in the global economy
during the COVID-19 pandemic. They have shown substitute properties in relation
to traditional production factors. The most ambitious innovative role of ICT
manifested itself as infrastructure (Dyatlov et
al., 2018; Glinskiy et al., 2020), when its previous form did not meet
the requirements of limiting physical interaction in the population to prevent
the spread of coronavirus.
At the same time, to date, no economic and mathematical model has been
created for detailing the life cycle of digital innovations, which would
include a cycle of excitement. In this regard, the scientific task of verifying
such a model is relevant. Based on this, the aim of this study is to develop
economic and mathematical models of the innovation diffusion dynamics by phases
of declining demand. In this regard, tasks of analyzing economic and
mathematical models of innovative changes life cycle that have been completed
to date should be solved, as well as the development of an approach to verify
such models in the phase of decline in the innovation diffusion rate.
The concept of innovation processes represented as a
superposition of diffusion trends made it possible to verify the economic and
mathematical models of innovation diffusion, which is distinguished through an
adequate mathematical description of the complete life cycle of innovation. In
this case, a special model is the model of agiotage cycles proposed by Gartner.
In this model, manifestation and diffusion result in the processes of rush
demand. An increase in consumption due to an increased consumer value of
innovative products compared to existing and traditional products and a
decrease in demand due to the formation of opposing factors. These opposing
factors include a lack of financial resources and an avalanche effect on the
company, which includes manufacturing of innovative products, risks arising
from the use of innovation by consumers (for example, information, in
information and communication systems), the influence of competitive
environment, the development of alternative innovative solutions and many
others. In the economic and mathematical model developed, the superposition of
growth and decline processes in the consumption of innovative products not only
increases the accuracy of modelling innovative processes but also obtains new
predictive indicators (leading indicators). Therefore, by identifying the
parameters of this model, the presence or absence of a decline trend and,
consequently, the subsequent breakdown of the general trend in innovative
products sales dynamics are identified. The authors carried out modelling and
obtained quantitative results for the rush cycle of innovations spread for the
following parameters: to = 200; t_ = 233; k=0.7; T = 44; Tn =
11. In addition, a calculation was performed and a three-dimensional
representation of demand indicators for innovative products was obtained when
the ratio of the growth parameters of demand diffusion and its inhibition (k in the range from 0.1 to 0.8) were changed.
The results obtained (demand level V)
in three-dimensional representation are the basis of managerial decisions in
innovation management systems, which include activating advertising, increasing
production volumes, attracting and distributing financial, labor and other
resources (or vice versa) and modernizing or disposing outgoing innovative
solutions. Thus, the developed model, as well as new indicators of innovation
processes obtained on its basis, will increase the efficiency of digital innovation
management invariant to the type of digital innovative product.
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