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God Helps Those Who Help Themselves! A Study of User-Innovation in Russia

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Konstantin Fursov, Thomas Thurner
GOD HELPS THOSE WHO HELP
THEMSELVES! A STUDY OF
USER-INNOVATION IN RUSSIA
BASIC RESEARCH PROGRAM
WORKING PAPERS
SERIES: SCIENCE, TECHNOLOGY AND INNOVATION
WP BRP 59/STI/2016
This Working Paper is an output of a research project implemented within NRU HSE’s Annual Thematic Plan for
Basic and Applied Research. Any opinions or claims contained in this Working Paper do not necessarily reflect the
views of HSE.
Konstantin Fursov
1
and Thomas Thurner
2
GOD HELPS THOSE WHO HELP THEMSELVES!
A STUDY OF USER-INNOVATION IN RUSSIA
This paper studies the specificities of Russian user-innovators on a sample of 1670 home
interviews. The percentage of end users who innovate in their daily life in the Russian population
and the willingness to share one’s ideas and developments is much higher in comparison to
western countries and probably historically rooted in long-standing community activities which
spread during soviet times. Our data suggests the existence of two different groups of user-
innovators: one group of urban, male, well educated, and financially better-situated individuals
who innovate for career reasons (or for fun) vs. a much more diverse group of small town folks
who innovate out of necessity. While the first group confirms findings well described in the
literature, the second group seems to be unique to developing markets and to Russia in particular.
User-innovation happens also in remote areas, and among user groups outside of the working
age. As these user-innovators are reluctant to share their innovations with others and would
rather keep them for themselves, a great source of ideas and commercial opportunity remains
untouched. Russia’s innovation system has so far concentrated on the classical innovation modes
around major cities or big companies. Given Russia’s extensive presence of user-innovators, it
might be a promising policy move to provide greater support to existing and emerging amateur
communities. We believe that our study adds insights into the informal and totally neglected
viewpoint on Russia’s innovation.
Keywords: user innovation, innovation community, Russia
JEL classification: H00, O31, O32, O33
1
Konstantin S. Fursov PhD, Senior Research Fellow, Laboratory for Economics of Innovation,
Institute of Statistical Studies and Economics of Knowledge, National Research University
Higher School of Economics.
9/11 Myasnitskaya street, 101000 Moscow, Russia. E-mail: ksfursov@hse.ru
2
Thomas Wolfgang Thurner PhD, Research Professor, Laboratory for Economics of
Innovation, Institute of Statistical Studies and Economics of Knowledge, National Research
University Higher School of Economics.
9/11 Myasnitskaya street, 101000 Moscow, Russia. E-mail: tthurner@hse.ru
3
Introduction
Over centuries, households produced goods for own consumption and for trade, mostly in
connection with a merchant who arranged for distribution. This ended when steam-engines
summoned the workers in the factory, which became the primary workplace. Still, households
continued to produce goods for own everyday use in parallel to industrial mass production. And
while mass production did not allow for tailoring a product to certain customers’ needs, home-
production was doing exactly that. Often centered on the larger family, available resources were
used to meet a great variety of needs. The economic importance of such activities attracted
attention already in the 1960s, especially for food production and child care (Ghez and Becker,
1975; Berk, 1987). In this tradition, Wu and Pretty (2004) demonstrate innovation in poor
farming households in rural areas of China.
Still, with increasing technological penetration into most parts of life, households could not
provide any such alternatives. Instead, users engaged with available products from mass
production and adopted them to their needs. Some users showed remarkable levels of
sophistication in this adaption exercise. Von Hippel (1978, 1986) suggested firms to identify
very active individuals who are ahead of the market and integrate these lead users into the firm’s
innovation process. The term usersis not limited to individuals but also include other firms.
Especially when an industry is characterized by the presence of few but very powerful
customers, a strong presence of user-innovation is likely. In a study of innovations in oil refining,
Enos (1962) showed the central role that user firms play. Historical studies furthermore
demonstrated that user innovation drove technological development for whole industries, like in
eighteenth-century iron smelting (Allen, 1983) or in the development of mine pumping engines
(Nuvolari, 2004). More recent studies on industries with user innovation activities thematize the
development of medical equipment (von Hippel and Finkelstein, 1979), semiconductor process
equipment (Lim, 2000), library information systems (Morrison et al.; 2000), embedded Linux
software (Henkel, 2003) or sporting equipment (Franke and Shah, 2003). Especially in the latter
field, well-researched examples of user innovation are innovations in skateboarding,
windsurfing, or snowboarding (Shah, 2000; von Hippel, 2001).
User-innovation has been facilitated by cheap computer hardware and software. The arrival of
the C64 home computer, which triggered interest among kids to engage in videogame
development and code, was shared in journals. These days, programmable computers are readily
available, like raspberry Pi or Arduino. Also, freely accessible and very powerful computer-
based design tools help to integrate users into aesthetic decisions (Baldwin and von Hippel,
2011).
For the longest time the innovative user was living a lonely life, only sporadically connecting
locally with like-minded individuals. This, however, has changed over the last decade due to
broadband Internet connections and the emergence of Web 2.0 technologies (e.g. Facebook,
Instagram, Flickr) through online forums, bulletin boards and online communities (Kietzmann et
4
al., 2011; Ritzer and Jurgenson; 2010; Franke and Shah, 2003). User innovators though take
benefits out of the use of products, which implies non-competition. Intellectual property rights
have very little importance for users, especially at the development stage and therefore such
rights are voluntarily given up to make the information a public good (Harhoff et al. 2003; Allen,
1983; von Hippel, 2005). It has been found that willingness to freely reveal an innovation
decreases if the agents compete with one another, for example, if they are individuals competing
in a sport (Franke and Shah 2003; Baldwin et al. 2006). Firms struggle to properly engage the
user-innovator into traditional production processes. Difficulties around consumer generated
intellectual property and the emotional property that comes with user innovation still remain
(Berthon et al., 2015).
Motivation of research and central question
The development of user-innovation has been conceptualized by academic research now for
quite some time, and previous studies have revealed great insights into the phenomenon. This
paper studies people in urban and rural community environments that modify or develop goods
or services for their own benefit. This approach is similar to Von Hippel (2005) but stands in
contrast to official statistics which require a connection to the market (OECD/Eurostat, 2005).
There is an ongoing debate on whether the OECD/Eurostat (2005) definition is suitable to
accommodate users that share knowledge with a peer group or community of practice.
Considering the user innovation phenomenon may not only help to clarify existing definitions,
but also supports including users (not only individuals) to the measurement framework (Gault
2012).
Today’s contributions have long moved beyond the original demographic studies of end-user (or
consumer) innovators by von Hippel et al. (2011) and now discuss how users can be motivated
and their creative potential be harvested to pursue commercial opportunities. Still, we are of the
believe that there is yet a lot to learn about who the user innovator actually is. This is especially
true as large-scale studies build on data from the UK, the US, Japan and recently Finland.
Although these nations surely count for a significant portion of the world’s economy, we believe
that the reality of the user innovator in these nations is entirely different to many others. Taking
this potential bias of the literature into account, this paper studies the characteristics of user-
innovators in Russia and how the Russian user-innovator is distinctive to the mere user. We
propose Russia as a well-suited case for direct comparison with findings out of the
aforementioned countries. Russians still face harsh living conditions due to the country’s
geographical location, and making ends meet was often difficult. Here, especially the vast
distances between settlements made supplies difficult to arrange, and survival often relied on the
ability to adjust.
5
Furthermore, the literature took great interest in market-oriented viewpoints towards user
innovation. Franke and von Hippel (2003) already stressed the commercial potential of user
innovations, and Shah and Tripsas (2007) introduced the term “user entrepreneurship” for such
commercialization activities. Block et al (2016) stressed the role of user-manufacturers for
commercialization success of user innovations. Still, for the vast majority of people living on this
planet, commercialization channels are not accessible. User innovators might live in rural areas,
lack the knowledge to approach the right actors, have no access to capital or live in countries
without the necessary institutional environment. Hence, this paper provides very valuable
insights into the user-innovation phenomenon in the absence of such commercialization
opportunities and an entrepreneurial culture. Again here, Russia is a particularly interesting
example as the country underwent a very distinctive economic development in comparison to the
western world. It is common knowledge that Russia, despite 20 years of reforms and attempts of
modernization, struggles to leave the legacy of the Soviet Union behind. Russia’s economy
suffers from poor framework conditions: political environment and stability, regulation quality,
rule of law and general quality of institutions (Polischuk 2013), wrong incentives and stimuli
resulting from flaws in Russia’s corporate governance models (Enikolopov & Stepanov 2013).
Most contributions culminate in stressing negative examples of the institutional environment in
which entrepreneurship in Russia attempts to flourish (Bruton et al. 2010; Puffer et al. 2010).
Also, enterprises pursue rents through various forms of vertical integration or close connections
with state authorities (Yakovlev 2014). Russia’s attempt to shift the economy from low-tech,
resource-intensive economic model to a knowledge-based, innovative logic is showing little
success. Especially during the first years of transition (1991 99), public spending in R&D went
down significantly, and private companies reduced their already minimal engagements even
further (HSE, 2015a). Russia lost most of its scientific potential, and her finest talent seeked
employment in universities in western countries. In the early 2000s, expenditures on R&D rose
again, largely due to military related R&D activities. To this day, contribution of industry to
R&D sits at 0.3% of GDP, against 1.4% average for the OECD countries (OECD, 2013). Only
9% of Russia’s firms do engage in technological innovation (HSE, 2015b).
Another aspect proposes Russia an interesting case to study user-innovation. Russia has
historically stood out in the provision of an excellent schooling system, but struggled to make
proper use of the talent. When in 1957 Sputnik sent the first signals from orbit and in 1961 Juri
Gagarin safely returned from an expedition into space, the Soviet Union was seen as a worthy
competitor in scientific and technological development. At the end of the 1960s though, this
development took a sudden turn and productivity together with the number of intellectual
workers - started to decline (Volkov 1999; Bergson, 1978; Gomulka, 1986). After the removal of
Nikita Khruschev in 1964, the inefficiencies in the economic planning system could no longer be
ignored as the central planning system failed to cope with the increasing complexity of economic
activity. The Kosygin or Liberman reforms in 1965 were inspired by suggestions from economic
"optimizers", who advocated a greater use of the country’s computational abilities that stemmed
from technological advancements. The reform pushed towards decentralization of planned
6
economy, highly computerized decision-making processes and introduced profitability and sales
as key performance indicators. Although the reform was cancelled in the 1970s again, the share
of semiskilled work positions increased up to the 1980s. In the early years of 2000s, the trend
reversed and demand for managerial positions grew rapidly but turned around again in the mid of
the first decade. Still, even today Russia struggles to make efficient use of her degree-holding
specialists (Anikin, 2013).
To this day, most studies discuss innovation in Russia through the lens of the National
Innovation System and criticize weaknesses in the necessary institutional environment to support
economic growth and firms’ competitiveness (e.g. Puffer and McCarthy, 2011). Some mention
domination of “technological imitators” in national innovation system (Gokhberg et. al. 2010),
unreliable enforcement of property rights, transparency in political governance, a weak and
inefficient judiciary (Chadee and Roxas, 2013; Estrin and Prevezer, 2011) or a dysfunctional
governance system (Guriev and Rachinsky, 2005; OPORA Russia, 2006). Others identified the
over boarding bureaucracy as a central problem (Sah and Stiglitz, 1986)
3
. However, academic
research has to this date not paid attention to Russia’s informal lines of innovation resting on the
creative potential of her people. We believe that our study adds insights into the informal and
totally neglected viewpoint on Russia’s innovation.
This paper continues with an overview of previous findings on user innovators. Subsequently,
we discuss the innovation environment in Russia and develop our hypothesis. This is followed
by the presentation of our findings and a discussion of these results in the light of previous
contributions.
Demographics Who is the user innovator?
Defining the user innovator is a tricky business. The literature suffers from a general lack of
robust large-scale studies on the characteristics of user-innovators, as most studies build on
small-sized samples or case studies (Bogers et al.,2010). Two large-scale studies analyzed the
presence of user innovation in the UK. Von Hippel et al (2012) estimated that 6.1% of the UK
consumer population aged 18 and older are in fact innovating consumers. These users made eight
innovations (creations and/or modifications) during the period of three years. A second large-
scale survey by NESTA suggested that 8 per cent of UK consumers create or modify one or
more products to better suit their needs. Around half of these innovators see their contribution as
new or modified in such a manner that the outcome qualifies as original innovations (Flowers et
al., 2010). Findings from Japan estimate the share of innovators among users at 3,7%, in the US
at 5,2% (Ogawa and Pongtanalert, 2011) and in Finland at 5,4% (de Jong et al., 2015). The UK
3
For interesting insights into the centralized organization of science and technology in the Soviet Union, we refer readers to the
Chulkov (2014).
7
surveys, though, covered user innovation at both individual and firm level, while the others focus
only on individual’s user-innovations.
The sample size of user innovators increases though when the subject shifts to sports. About a
quarter of enthusiasts work on improving the equipment they use (Franke and Shah 2003 in four
extreme sports; Lüthje et al., 2005 in mountain biking; Tietz et al., 2005 in kitesurfing, Raasch et
al., 2008 on Moth class sailing). Similarly high percentages have been reported from other
hobbyist communities, like the Lego model building community (Antorini, 2007). Von Hippel
(2004) gave an overview of the empirical studies at the time and suggested a range of user-
innovator base (varying in size and focus) from 10 % to nearly 40 % of users.
Previous studies showed that characteristics of user-innovators are heterogeneous, depending on
the industries or the role of the user. Hence, information needs, required skills and knowledge
show a great variance and are task-depending (von Hippel, 2005). Generally speaking, user-
innovators are largely male and highly (technically) educated (von Hippel et al., 2011). Jong et al
(2015) showed a much higher likelihood to innovate among people with at least a bachelor
degree and particularly among those who perform a technical job. If users innovate in technical
aspects (vs aesthetic aspects), these individuals do have a higher level of knowledge e.g. of how
bikes work (Lüthje et al., 2005). Compared with non-innovating users, user-innovators tend to be
at the leading edge of an important market trend (von Hippel, 2005). Also, they are sophisticated
in the use of technologies and related products (Morrison et al., 2000; Luthje et al., 2005 Tietz et
al., 2005).
De Jong et al. (2015) show that the share of innovators was greater among the male subgroup
than among the general consumer population. Also K. Pongtanalert, S. Ogawa (2015) show the
same slight overhang towards men. The gender bias towards male users is also repeated by
small-scale studies. In a study on the online gaming community, 14 were males and three were
females (Bryman and Bell, 2015). The suggested difference in the gender of innovators might
very well be a matter of interest. If electronic components were easy to build into clothing, it
attracted a great number of fashionistas mostly women (Buechley and Hill, 2010).
Quite some insights have been provided on the motivation of users. Especially for volunteer
users, there is a drive to develop and improve their own skills (Lakhani and Wolf, 2003; Lerner
and Tirole, 2002; von Hippel and von Krogh, 2003). Studies on programming communities have
shown that these individuals are often strongly motivated by personal learning opportunities
(Hertel et al. 2003; Lakhani and Wolf 2005). Also, once members have received help from the
community, there is a good chance that they want to give something back to the community
(Lakhani and von Hippel, 2003). And strangely enough, some find activities like fixing bugs
joyful. Sure, engagement in such groups might very well raise one’s own visibility and
recognition from other users might well attract potential employers (Lerner and Tirole, 2002;
Hertel et al.,2003; Jeppesen and Frederiksen, 2006).
8
With the increasing complexity of survey tools to capture user-innovation behavior, studies have
started to put a price tag to user innovation activities. Most of these studies suggest an aggregate
spending of tens of billions of dollars annually (e.g., de Jong et al., 2015). Internationally
competing moth sailors spent 435 Euro on equipment innovations per year (Raasch et al.; 2008),
and top notch whitewater kayak riders spent an average of $707 and 27 days per year (Hienerth
et al.; 2011). Each respondent spent 4.8 person-days and £101 on their most recent consumer
innovation. The aggregated value of innovation in the UK was estimated at £3.2 billion.
Methodology
This research builds on a large-scale survey conducted in September 2014 within the framework
of the Monitoring Survey of Innovative Behaviour of the Population
(http://www.hse.ru/en/monitoring/innpeople/). The overall stratified sample included 1670 home
interviews with respondents of 16 years of age and above. The sample is representative for the
Russian population by age, sex, education level, region (at federal district level), and city size.
The sample has been proportionally distributed among 97 urban and 37 rural areas, including 12
large cities with a population of over 1-mln residents. The sample excludes the Chechen and
Ingush republics as well as five sparsely populated and hard-to reach regions (mostly Far North)
with the overall number of residents not exceeding 3% of the total adult population. Citizens of
very small settlements (less than 50 inhabitants), military, imprisoned and homeless people
around 4% of the total adult population are not covered either.
Table 1: Survey summary
Addresses visited, total
8526
Non-living premises
263
Out of reach
2721
Total number of contacts
5528
Did not agree
1670
Did not fit
1650
Did not speak Russian
35
Could not respond
38
Ceased interviews
519
Successful interviews Included to the initial dataset
1670
To ensure a consistently high quality, 30% of the interviews were follow-up by phone calls as
well as through logical controls of the final dataset. Our study targets user-innovation on an
individual level. We did not ask for ‘household sector innovators’ by any of the individuals in all
residents in a household or unincorporated businesses (Ferran, 2000).
9
The questionnaire included three parts. In the first part, participants were asked about their
general perception of and attitudes towards science and technology. We asked respondents about
their familiarity with the term ‘innovation’ and requested to give examples. The second part
covered questions about the respondents’ experience in user innovation. Following the approach
initially developed by (von Hippel et al., 2010; von Hippel et al., 2012) we asked participants to
give a short description of their proclaimed user innovation (creation of new things or
modification of existing products adopting them to respondents’ needs) over the last three years.
Unlike de Jong and von Hippel (2009) or Pongtanalert and Ogawa (2015), we did not distinguish
between the creation of new things and the modification of existing products. Neither did we
give any hints or examples of the areas of application for innovations in the initial question. If
the interviewees responded to the first question positively, we invited them to specify their
motivation for innovation and the areas of use. The third part of the survey collected data on
sharing with others (incl. use of social networks) and whether the innovators protected these
innovations through intellectual property rights. Finally, we asked respondents if they belong to
any amateur community or club.
In a first step, we describe in detail which social and demographic characteristics constitute the
Russian user-innovators. The correlation matrix (in the annex) demonstrates weak correlations
between variables except income and social status of respondents as well as level of education
and knowledge of innovations. In a second step, we apply a discriminatory analysis to study
statistical differences between the group of mere users versus user-innovators. For the analysis
we apply three different models: The first run includes all observations unrestricted by age. In a
second model we limit our observations to Russia’s working age of 16-72.
4
Thereby, the sample
is more comparable to international practice. To match the sample in size, in model 3 we
randomly selected a similar sized sub-sample of non-innovators and compared it with our sub-
sample of user-innovators. For a survey summary and the weighting results (see tables 1 and 2).
Table 2: Weighting results
Statistical data (%)
Non-weighted cases (%)
Weighted cases (%)
SEX
Male
45.3
45.3
45.3
Female
54.7
54.7
54.7
AGE GROUP
16-24
16.0
16.4
16.0
25-39
27.7
27.5
27.7
40-54
26.2
26.4
26.2
55 and older
30.1
29.8
30.1
EDUCATION LEVEL
Higher
28.5
29.0
28.6
Secondary
50.1
50.4
50.1
Primary
21.4
20.6
21.3
4
In accordance with national statistical standards economically active population include those aged from 15 to 72
(Ekonomicheskaya aktivnost’ naseleniya Rossii. Statisticheskiy sbornik. Moscow: Rosstat, 2014).
10
Selection bias for controlled social groups is not exceeding 0.03%. Range of weight coefficients:
from 0.295 to 2.224. Total sum of weight coefficients is equal to 1670 (overall sample size).
Description of the variables
So far, only a few studies give insights into the innovation attitudes and skills in the Russian
population (e.g. Gokhberg and Poliakova, 2014; Gokhberg and Shuvalova, 2004). These
contributions are valuable indicators for variables that help describe the user innovators in Russia
and help to separate them from mere users.
We start with our choice of variables with a specificity in the Russian population: while over a
third of respondents believe innovation is a source of economic growth, only 17% see
innovations having an impact on their daily lives. Also knowledge of the economic value of
innovation is much lower than the EU average (Gokhberg and Poliakova, 2014). Many Russians
see no direct link to their own quality of life, we first ask whether our respondents are in fact
familiar with the term innovation. Especially among people who have a very positive approach
to new technology so called “technology admirers” the percentage of men is high, while
among older women a negative approach to technology dominates. Hence, we check whether
gender of the respondent is indeed a good predictor of the likelihood that a person will innovate.
It should be noted that the Soviet Union has historically placed great emphasis on its educational
system. Advanced skills were in dire need to drive the country’s industrialization processes after
WWII.
5
The aforementioned group of technology admirers has a much higher share of university
graduates then other users. Also, the same study shows that successful innovators have well
elaborated e-skills. Here, especially an efficient use of search engines is worth mentioning.
However, analysis of innovator teams showed that innovators are often efficient technology
users with - less frequently - university diploma (e.g. see Pongtanalert and Ogawa, 2015). Hence,
we include education into our list of variables. Around one third of technology admirers enjoy a
high income. Furthermore, such an attitude can be considered as an attribute of a specific
lifestyle. So, we include income into our list of variables. Former studies showed that many
Russians get acquainted with advanced technologies in their jobs as these technologies are
necessary to perform their work more efficiently. To take this into account we include
occupation in our list of variables.
Although Russians are rather skeptical about the living and working conditions of Russian
scientists, research activities are very positively regarded. A scientific career is still seen as
admirable and prestigious. The high social value of scientific work, and the complexity that
comes with R&D activities, are distinctive features of the “image” of science and were
mentioned by every second respondent (Gokhberg and Shuvalova, 2004). As such a lifestyle can
5
This is clearly seen in the change of school curriculum in the post-war period towards increasing the number of hours related to
practical work (Hans, 2012).
11
very well be achieved without a higher education, we include status into our list of variables.
Technology users can be further separated between active and passive ones. Active users are
those who have upgraded one’s own competencies in these technologies over the last five years.
In Russia, and probably in many parts of the world, the group of active users corresponds with
younger ages, while passive users are mostly older people. Age has also been connected to
different views on science in general. Especially younger people view scientific research as a
socially important occupation. Taking these findings into account, we include age into our list of
variables.
About a quarter of Russia’s population lives in villages and settlements spread out over the
countryside (Rosstat, 2010). Many of these villages are in fact hundreds of kilometers away from
the next bigger towns, and supply of daily goods can be a challenge let alone technology. It is
possible that in such settings, people are more incentivized to innovate than in the well-supplied
cities. Furthermore, surveys showed that inhabitants of big cities had a rather critical view on the
social importance of scientific activities. Hence, we include community size to our list of
variables. We use Moscow as an own category due to her size of over 12 million inhabitants.
Findings
In a first step, we set out to describe the characteristics of the Russian user innovator. Our first
observation rests on the size of user innovators. The sample consists of 160 user-innovators and
1510 mere users, which results in a surprisingly large size of 9,6%. The biggest share of user-
innovators has never heard of the word before (32,5 %), and over a quarter is familiar with the
meaning, but hardly uses the term (27,5%). Like in many other studies, we find a slight bias
towards gender in the group of user-innovators (48.8% to 51.2%). There is also a difference in
motivational reasons for innovation. While men indicate that they like doing it, women search
for cheaper subsidies. Also, there is a difference in the areas of innovation. Men are widely
engaged in computer and IT, also in housekeeping, while women are likely to innovate around
arts, craft or garden.
The user-innovator in Russia does not belong to a specific age group. Besides the very young
and very old cohorts, the age distribution is fairly well balanced. The age group of 25 to 34 was
the most innovative, indicate that they want to learn new skills or develop existing ones, because
friends were in need of help. Among the older group of 55 to 64 were mostly people who were
enthusiastic about their activities. The age group of 25 to 34 and 35 to 44 are those that share
their innovation most openly, while the oldest group are less likely to share. The latter might be
the consequence of lesser inclusion of older population to social networking and lacking of other
channels for sharing knowledge.
The Russian user innovator is financially not very well situated. The majority indicated that there
is money for food and new clothes, but new home appliances (TV set, washing machine, etc.) are
12
out of reach. The second largest group indicates that new home appliances are affordable for
them, but not a car. The majority of user innovators see themselves as part of Russia’s lower and
middle class. High-income earners innovate because they are enthusiastically about it and
develop new ideas in housekeeping and arts and crafts. Low-income earners focus on
innovations around kids.
The biggest group of user innovators enjoyed an upper-secondary (regular school) or post-
secondary (technical school or college) education (40,3%), followed by individuals with tertiary
(higher) education (24,5%) and secondary education (11.9%). In education, the noticeable
difference is lying between tertiary and secondary levels. Innovators with university degrees
explain their willingness to create new things because of personal interest or their friends or
relatives need of assistance, while innovators who finished a regular or technical school enjoyed
doing something new, due to a personal need or because of their wish to develop (learn) new
skills. Interestingly, people with secondary levels of education have a much higher likelihood to
share their innovations with others. While the overall share of those who seek protection of their
intellectual property is low, there is an increased interest in sharing such user-innovations among
others on a reciprocal basis. A total of 49% of user-innovators actually share their findings with
others. In contrast, the older age group between 55 to 64 innovate out of curiosity and interest,
but keep their ideas for themselves.
It seems there is no linear dependence between readiness to innovate and professional status.
CEOs or individual entrepreneurs are as active in user innovations as farmers and unemployed
people. The difference lies in their motivation. While the representatives of the first groups
enjoyed doing something new or assisted their friends, unemployed user innovators or
respondents engaged in agriculture share their enthusiasm, but relatively often mention a need
for some new developments. Moreover, their innovations are mostly related to home computers,
housekeeping and kids.
Most user-innovators live in relatively small cities with less than a 100.000 inhabitants (35,6%),
followed by villagers (23,8%). The number of user-innovators declines with the increase of city-
size, which is fairly remarkable. Especially Moscow Russia’s economic and political hub is
the home for less than 10% of user innovators in our sample. Community size shows a difference
in which areas innovation happens. Especially in small towns, people innovate with relatively
greater likelihood in the areas of cars and transport or housekeeping. Moscow is relatively strong
in computer and IT innovations. In the same time, these innovators in small towns don’t share
their developments. There is, however, another noteworthy difference. Only 11% of all user-
innovators are members of a local association or club, which corresponds to other studies
(Ogawa and Pongtanalert, 2011).
In a second step we study factors that separate the user-innovator from the mere user. In our first
model, we apply a discriminatory analysis to the full sample of user innovators and compare
them to the other users that did not report any innovations. As mentioned earlier, we see that
13
gender plays a significant role to separate between the two groups. The group of user innovators
has a much higher percentage of men. Secondly, user innovators have a greater income then the
average user, but also believe to have a higher status. Also, the user innovator is higher educated.
The word innovation is better known, and the user-innovator is better organized in local
associations. We were surprised to see no difference in the age group. Consequently, we
sharpened our sample size and reduced our observations to Russia’s working age group between
16 to 72 years. The resulting model shows only a significant discriminant factor in status and city
size. The importance of city size was finally showing statistical relevance. However, the
reduction of the age group was probably too restrictive as also the percentage of correctly
classified observations in fact declines. We conclude from this observation that the 25% of user
innovators we excluded are in fact of relevance as the young ones and the old-age pensioners are
a source of user innovation. In Model 3, we adjust the sample size of the non-user innovators to
the sample size of user innovators through a randomized selection. This statistical model now
shows that the user innovator in Russia is slightly younger, thinks of himself as of a higher status
and lives in a smaller city than the average Russian user. The Model 3 has the highest number of
correctly classified cases.
Table 3: Tests of equality of group means
Model 1
Model 2
Model 3
Variable
Wilks' Lambda
F
Sig.
Wilks' Lambda
F
Sig.
Wilks' Lambda
F
Sig.
gender
0,998
3,089
0,079*
0,999
1,858
0,173
0,999
0,384
0,536
age group
0,999
1,678
0,195
1,000
0,115
0,735
0,986
4,270
0,04*
income group
0,998
3,504
0,061*
0,999
1,709
0,191
0,996
1,121
0,291
status group
0,99
17,161
0,00*
0,991
13,254
0,00*
0,969
9,999
0,002*
education
0,998
3,105
0,078*
0,999
1,459
0,227
0,996
1,178
0,279
community
0,999
2,430
0,119
0,998
2,854
0,091*
0,986
4,318
0,039*
knowledge of innovation
0,997
4,780
0,029*
0,999
1,426
0,233
0,969
9,851
0,002*
member in association
0,997
4,744
0,03*
0,998
2,315
0,128
0,994
1,937
0,165
Number of valid cases (% of sample)
1622 (97,1%)
1519 (96,9%)
313 (96,9%)
Cases correctly classified:
59%
58%
62%
Discussion and conclusion
This paper studies the specificities of the Russian user-innovator. While also in Russia, male
user-innovators are over-represented, we see differences in the area of innovation and the
motivation. The male user-innovator is engaged in innovations in computing and IT, lives in
Moscow and has a high income. The female user-innovator engages in innovation around arts,
crafts and gardens, which corresponds to living in smaller towns or villages and often point out
to the need for cheaper substitutes for existing products. Similar differences appear in the age
14
groups. The cohort of 25 to 34 year olds see innovations as a way to improve one’s skills, which
corresponds with fairly recently received education. Being at an early career stage, innovation
activities might well boost their career perspectives or open the way into entrepreneurship.
Previous studies have not looked into such nuanced differences, but we find them rather
revealing.
When trying to track the specificities of Russian user-innovators vs mere users, we were pleased
by the high percentage of correctly classified observations of our models. Reducing the user-
innovators to individuals in the working age lowers the predictability of the model. We take this
as a hint of the importance of user-innovators outside this age group. In a second observation, we
struggled to reach a certain consistency among the variables with statistical significance.
Variables like income showed significance initially, but lost this attribute in our most accurate
model 3. The only variable that remained significant was the social value of status, an estimation
of one’s own belonging to a social status. This was even more surprising as income and social
status has shown an (expected) cross-correlation. We explain these observations as follows.
What our data suggests is the existence of two different groups of user-innovators: one group of
urban, male, well educated, financially better-situated individuals who innovate for career
reasons (or for fun) vs a much more diverse group of small town folks who innovate out of a
necessity. While the first group confirms findings, the second group seems to be a fairly unique
specificity of developing markets and of Russia in particular. Life in small towns in Russia is
exceptionally challenging due to long supply routes, and the ability to make ends meet still
remains a requirement in today’s time. There, these user-innovators are not well off, work in jobs
lower than their actual qualification. However, they think highly of themselves and their
activities - in line with Russia’s tradition as a country that is home to exceptional thinkers and
scientists. Such high social standing of innovation activities in Russia has been confirmed by
previous studies. We notice the consistently significant contribution of the value social status.
Russia’s user-innovators are driven both by egocentric (hedonic or personal development) and
altruistic (help others or fun, interest) motives rather than by goal oriented (career opportunities
or desire for a new market product) ones. This might also explain very low percentage of
respondents, protecting their innovations with patents or contacting manufacturers in comparison
with previous findings.
This research was motived by comparing the data out of Russia to studies from western
economies with a much stronger market drive. The differences of Russia’s user innovators to
others are actually stunning. Firstly, the high percentage (9,6%) of user-innovators in the Russian
population by far exceeds numbers suggested in other studies, where numbers range between 3-
6%. We suggest this high level of user-innovators to be linked with a long-standing history of
user-innovation and sharing of information in the country. Already during Soviet times, popular
journals like “Do It Yourself” (http://zhurnalko.net/journal-85), “Young Technician”
(http://zhurnalko.net/journal-204) and others were well-suited communication channels for the
sharing of user-innovations. Elements of DIY activities were also promoted through popular
15
science journals, like “Science and Life” (https://www.nkj.ru/archive/) that connected existing
amateur communities and supported reciprocity and a knowledge sharing culture. This might
explain the relatively high number of user-innovators compared to other countries conducted
similar surveys. Furthermore, such practices are also indicative of the very high willingness to
share one’s own innovative ideas. Almost 50% of the user innovators engage in such sharing
activities. If the older cohort is taken out, the number would be even higher. These numbers are
much higher than those found in western studies of around 20% of user-innovators who actually
share their ideas.
Historically, touristic clubs at schools, universities, research institutes and factories provided
opportunities for creative work and different types of inventory activities. The presence of
similar institutions and their importance for knowledge adaption and sharing have been
demonstrated by Wu and Pretty (2004) in rural China. User-innovation in Russia as probably in
many emerging economies - can be considered as a compensatory mechanism of a non-market
economy. As user-innovation happens also in remote areas, and among user groups outside of
the working age and these user-innovators are reluctant to share their innovations with others and
rather keep them for themselves, a great source of ideas and commercial opportunity remains
untouched.
Our findings provided new insights into the activities related to innovation practiced by the
Russian people. These insights can also inform policy makers to better support Russia’s creative
potential. As Russia’s innovation system has so far concentrated on the classical innovation
modes around major cities or big companies, our findings suggest that a growing group is
actually not properly supported in their innovative potential. Given Russia’s extensive presence
of user-innovators, it might be a promising policy move to provide greater support to amateur
communities instead of continuous investments to innovation clusters and/or business incubators.
These user-driven innovations can play a crucial role in developing economies as a
compensation of non-developed mass markets. If the required infrastructure is available and
accessible, growth through user innovations might foster transition to a post-industrial stage. One
vital step towards this goal is the establishment of a local infrastructure for innovation and
leading to better communication channels between users and manufacturers and, in time, giving
rise to a culture of innovation.
Our study is based on a fairly small number of user-innovators which again is due to the limited
presence of user-innovators in a population. Hence, further statistical analysis within the group
of user-innovators was not possible. It would of course be desirable to get more robust material
about differences within user-innovators and how a country’s social and economic factors
encourage such behavior. This would also require more insights out of less-developed parts of
the world. Further research should extend the research to innovative behavior of total populations
which might include a range of individual consumer practices (lifelong learning) and collective
actions (car sharing, crowd funding, etc.).
16
Acknowledgements
The nancial support from the Government of the Russian Federation within the framework of
the Basic Research Program at the National Research University Higher School of Economics
and within the framework of implementation of the 5-100 Programme Roadmap of the National
Research University Higher School of Economics is acknowledged.
17
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Appendix
Table 4: full questionnaire and responses
Question
Freq.
%
Gender
Male
914
54,7
Female
756
45,3
Age of the respondent
16 24
267
16,0
25 34
325
19,5
35 44
287
17,2
45 54
288
17,2
55 64
284
17,0
65 +
219
13,1
Income group
1 We barely make end meet, sometimes we can hardly buy food
46
2,8
2 We have money for food, but new clothes is too expensive for us
221
13,3
3 We have money for food and new clothes, but we can hardly afford new home
appliances (TV set, washing machine, etc.)
801
48,0
4 We can easily buy new home appliances, but cannot buy a car
520
31,2
5 We can buy a car, but can hardly afford a country house or good apartment
73
4,4
6 There is nothing we lack in
7
,4
Status
1 lowest
219
13,1
2 lower than average
502
30,0
3 middle layer
849
50,8
4 above average
85
5,1
5 highest
15
,9
Level of education
1 Pre-primary education
21
1,3
2 Primary education
103
6,3
3 Lower secondary education
77
4,7
4 Secondary education
294
18,1
5 Upper-secondary education
114
7,0
6 Post-secondary non-tertiary education
543
33,3
7 Not finished tertiary education
51
3,1
8 Full tertiary education
413
25,4
9 Post-graduate or second tertiary education, MBA
9
,6
10 Doctorate degree
3
,2
City (community) size
1 village
426
25,5
2 small city (population below 100 thousands)
407
24,4
3 medium city (100-500 thousands of population)
309
18,5
4 big cities (over 500 thousands of opulation)
389
23,3
5 Moscow-city
140
8,4
Are you familiar with the term «innovation»?
1 No, I hear it for the first time
504
30,2
2 I've heard this term, but not sure about its meaning
409
24,5
3 I am familiar with the term, but hardly use it
549
32,9
4 I know the meaning of the term and actively use it in my professional or daily life
207
12,4
Are you a member of any local amateur community, club or association
0 No
1543
92,8
1 Yes
120
7,2
24
Table 4: Correlation matrix of key variables used for modelling
Mean
SD
Age
Education
Income group
Status group
Community
Knowledge of
innovations
Membership in
association
Age
43,9
17,3
1,000
0,169**
0,244**
0,213**
0,043
0,226**
0,078**
Education
5,7
1,9
0,169**
1,000
0,289**
0,173**
0,285**
0,372**
0,076**
Income group
3,2
0,8
0,244**
0,289**
1,000
0,398**
0,198**
0,287**
0,106**
Status group
3,5
0,8
0,213**
0,173**
0,398**
1,000
0,017
0,147**
0,029
Community
2,6
1,3
0,043
0,285**
0,198**
0,017
1,000
0,230**
0,031
Knowledge of
innovations
2,3
1,0
0,226**
0,372**
0,287**
0,147**
0,230**
1,000
0,091**
Membership in
association
0,1
0,3
0,078**
0,076**
0,106**
0,029
0,031
0,091**
1,000
**. Correlation is significant at the 0.01 level (2-tailed); N=1670
Konstantin S. Fursov PhD, Senior Research Fellow, Laboratory for Economics of Innovation,
Institute of Statistical Studies and Economics of Knowledge, National Research University
Higher School of Economics.
9/11 Myasnitskaya street, 101000 Moscow, Russia. E-mail: ksfursov@hse.ru
Thomas Wolfgang Thurner PhD, Research Professor, Laboratory for Economics of Innovation,
Institute of Statistical Studies and Economics of Knowledge, National Research University
Higher School of Economics.
9/11 Myasnitskaya street, 101000 Moscow, Russia. E-mail: tthurner@hse.ru
Any opinions or claims contained in this Working Paper do not necessarily
reflect the views of HSE.
© Fursov, Thurner 2016
... Others suggested that personality characteristics also have an influence on knowledge sharing (Matzler et al., 2008). Contrary to these findings, data out of Russia revealed a much higher rate of sharing (Fursov and Thurner, 2016). These findings were argued to root in long-established practices in the day-to-day lives during the late Soviet Union, when goods supply in large parts of the country was at a sub-optimal level and user-innovation activities could play a role of a compensatory mechanism for non-market economic relations. ...
... Previous studies on the demographics of user-innovators have already revealed striking differences between user-innovators in western countries vs Russia. For example, data out of a Russian context suggest the presence of 9.6% of user-innovators, which far exceeds findings from other countries (Fursov and Thurner, 2016). This has been argued to be a consequence of the country's recent history and its geographic conditions. ...
... Almost 50% of the user innovators engage in such sharing activities. If the older cohort is taken out, the number would be even higher (Fursov and Thurner, 2016). Russia is also an interesting case as its user-innovators act largely outside classical commercialization channels. ...
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