We empirically test our model using a novel individual data set from the online gaming industry on daily content consumption, product innovation,
with approximately $25. 1 billion spent on video games, consoles, and accessories (Entertainment Software Association 2011) and $30 billion on crafts and hobbies in 2010 (Craft and Hobby Association, 2011;
Luo, Ratchford, and Yang, 2013. In terms of digital and online consumption, the numbers are also noteworthy.
Individuals can now access software, games, and social communities through smartphones, computers, and their gaming consoles (Williams, Yee,
and Caplan, 2008), and according to Nielsen, 81 billion minutes were spent on social networks and blogs in 2011 and 42%of tablet owners use them daily while watching TV (Nielsen, 2011).
Closely related to the empirical application in this paper, the worldwide market for online games surpassed $15 billion in 2010 with additional sales of virtual goods likely to exceed $1 billion (Playlogic Entertainment
Inc, 2010. With online connectivity and the presence of the Internet, online or network games have been growing exponentially,
with a recent study showing that about 67%of teenagers regularly play some game online (Playlogic Entertainment Inc, 2010).
Although product usage and content consumption are ubiquitous consumer decisions and largely explain repeat-purchase or product replacement (Huh and Kim, 2008),
but research on post-purchase behavior and product usage has been limited because of the lack of revealed preferences data on consumption;
data collection mostly focuses on transactional information. As an alternative, until recently, surveys or self-reported questionnaires have been used to study usage behavior, especially regarding technology products (Ram and Jung, 1990;
Shih and Venkatesh, 2004; Huh and Kim, 2008. To completely understand the interaction of consumers and products,
we make use of a unique data set-from the popular online video gameworld ofwarcraft-that tracks product usage, content consumption choices,
To the best of our knowledge, this is one of the first empirical studies to examine how different usage drivers specified by theoretical work influence consumption decisions using revealedpreferences data on product usage.
and consumers alike was provided in May of 2011 at the earnings call of Activision Blizzard, one of the major developers of computer games.
For example, using purchase data that mimics usage patterns closely, Hartmann and Viard (2008) and Koppalle et al.
and Yang (2013) use leisure activities data to investigate the relation between consumption usage decisions and consumer lifestyles.
A recent example by Huang Khwaja and Sudhir (2012) studies the consumption decisions of drinks using intra-day data,
Section 3 provides details about the novel data set on post-purchase decisions used in the paper.
The estimation algorithm and a discussion about identification are presented in Section 4. The results and managerial implications are described in Section 5,
For example, in video games, product updates are common and demand expertise from users to enjoy newly introduced content.
a Facebook group about a product, or a guild or clan of players in a video game, as it is the case in our application.
At each time t, consumer i decides on their status regarding communities of users of the product:
or remain without any connections to user communities. 2 We assume that consumers can make this decision at any 2we opt to model the consumer decision to join any group, instead of a specific group, in part due to data limitations and
but we found that the one-day state dependence combined with content aging to explain the data well. 9 the value of the highest level of any content enjoyed before time t by individual
For example, in computer games, a player's level is defined usually as a function of most complex completed content;
and lt are discretized for the estimation algorithm. 10 Finally, we assume that"ijt are unobserved shocks that are independently
in our application, users of a video game can choose content that is hard to complete and have to repeat it in some cases multiple times before proceeding with the game storyline.
We provide more details about Wit in the data section. The state variable pt denotes the index of the most recently introduced product update, pt 2 11 {0,,
and using historic data on update releases. The probability of an update occurrence-and hence the transition probability for-after pt time periods since the introduction of the previous update p is defined as Pr (pt+1=pt+1 ept)= 1/1+e(!
, hired programmers) to support a fairly stable schedule of content introduction; according to announcements from the firm, new content is launched
when server maintenance is performed, which makes Pr (pt+1=pt+1 ept, Xt)= 0 except when XMONDAY t=1. For the estimation of the duration model,
Therefore, Equation 12 is a dynamic programming problem for consumer i. Let V) Sdit*be the value function of being in state Sdit:
while Ishihara and Ching (2012) estimates a discount rate of 0. 885 per week for video games.
and Data The proposed approach can be used to obtain insights about the relation between product usage, sociability of actions,
We demonstrate its application with the study of consumer demand in the online computer gaming industry. 3. 1 An Online Game We use data from the online game World of Warcraft developed by Blizzard Entertainment, a division
According to the game's website, World of Warcraft is a Massively Multiplayer Online Role-playing game (MMORPG
/15 dividuals explore the environment developed by programmers. The game was introduced originally in 2004 and became the best-selling PC game of 2005 and 2006 worldwide.
By 2011, Blizzard had launched three full-fledged expansions and dozens of patches that added new content.
Our data is related to the second expansion of the game which sold more than 4 million copies in the first month alone (Blizzard Entertainment, 2008.
The game environment and related data are particularly suitable to the study of product usage for a number of reasons.
These data take the form of dates of first time completion of specific content consumption or a task performed in the game.
Several independent websites process this information into databases that allow cross-player comparisons and provide recommendations on how to progress in the game.
In this paper, we use a publicly available data set on product usage collected from such a site called Wowhead. 11 We complement these data with information about product updates, their content, firm's actions,
and other announcements from the official game website. Although purchase decisions are not the focus of the paper
but the majority of revenues comes from additional fees paid by users to access the online game server and consumer content.
and abstract from price response. 3. 2 Choice Sets and Product Updates Our data set includes daily information about the game from November of 2008 to December of 2010.
we use the beginning periods in our data set to create a starting state for each player,
the data includes only content related to the game main storyline. There are other unrelated tasks that we do not include in our analysis. 17 Patch Release time Age at t=1 Size#Tasks Task level,
similar to the schedule observed in our data. Although there was not a predefined schedule, the time interval between updates varied by only a few weeks.
and most additional content is introduced in a test server available to users, giving an almost perfect knowledge about the quality of content before it goes live
and timing of future updates. 13 3. 3 Player Participation and Progression The product usage data include actions of 206 users from one of the game servers, for
Our data set does not include new players for two reasons. First, the website used as a source of the data provides information about experienced users only.
Second the content introduced by the firm during our analysis was dedicated almost entirely to increasing participation of experienced players.
We use these data to create an empirical distribution used to define consumer expectations about the schedule of product updates. 18 in our sample completed 44 tasks out of a total of 440 tasks, with a standard deviation of 32.
and can grow 14we note that our data is more detailed than the patterns presented in the figure,
The data set contains the dates when an individual decides to join or leave a game community,
In our application, the firm published ability measures for each user before the product launch, called badges (a term also used in gamification:
with a clear break in the data, with a group of users significantly below 500 badges and another group clearly above that number.
We use data from the website World of Logs18 about the success rates for different content.
This website provides aggregate statistics about the number of times that users attempted and successfully completed tasks in the game.
%to reflect the availability to players of more advanced abilities that make old content easy. 4 Estimation 4. 1 Identification Before we describe the estimation algorithm,
we discuss the data patterns that identify the parameters in our model. Starting with the content utility function, its intercept is identified by the average observed rates of participation for each content alternative.
the individual data on decisions of joining, remaining, or leaving a group identify the overall costs of joining
through the observed frequency in the data of remaining in groups versus the frequency of using the product while in groups.
In the data, we observe consumers attempting higher level tasks more frequently when a product update is about to be released,
Our data show that users play more and at higher levels just before and once they are part of a community,
when making the decision to join the group. 4. 2 Estimation Algorithm The structure of the consumer choice process within one time period involves making two consecutive decisions:
and w (j Sdit) is the content success rate that is available as data. We note that for the community membership decision
We employ the iterative Expectation-Maximization (EM) algorithm procedure (Arcidiacono and Jones, 2003; Chung et al.
We combine the EM algorithm with the use of the constrained optimization approach (Su and Judd, 2012;
Luo, Pang and Ralph, 1996) to reduce the typical computational burden of a dynamic structural model associated with finding the solution to consumer dynamic programming problem.
Here, instead of using the nested fixed point algorithm (NFXP) on the Bellman equation to solve for the value function,
the log-likelihood of observing the data Y is LL (Y,, V)= XN i=1 0@XG g=1"Pr) i 2 g ai;,,
V) is the conditional probability that individual i belongs to segment g given her complete history of observed choices ai,
and use linear approximation of the value function in the objective function. 27 The estimation algorithm proceeds as follows:
First, we describe fit statistics that show evidence that the model explains the data well.
and consumers given the parameter values and data for each time period. Figure 3 shows actual and estimated participation over all tasks of the game.
A similar pattern is observed if we disaggregate content consumption by product update in our data. For our model, the hit-rate across all consumer choices
we assign each gamer to a particular segment based on the highest estimated individual probability of segment membership Pr (i 2 g). 24 In the four panels of Figure 4,
and the evolution of the average experience level of the user population in our data set.
The panels in Figure 5 show the results by segment and content choices j=1,..20.
as managers attempt to lead users to social media platforms to generate content and connect with other users with similar preferences. 5. 4. 1 An Alternative Innovation Schedule:
In our data, we observe most updates concentrated in the first half of the product lifecycle,
for example due to server capacity. 5. 4. 2 Change in Product Difficulty: Should Managers Make Content More or less Challenging?
The darker line displays participation in the lower complexity setting, the lighter line reflects higher complexity,
or websites in social platforms. In the case of our application, the firm has over the years implemented a number of tools that allowed easier access to groups,
or facilitate the transmission of knowledge to beginners-through changes in the parameter 2. The results are presented in the two panels of Figure 9. On the top panel,
while the lower panel reveals the percentage of participating users, for both the actual and counterfactual cases.
as the bottom panel shows, consumers are more active with the content. This engagement is especially important in later stages of the game
such as video games, TV SHOWS, mobile applications, book series, and, in more general terms, any durable good where practice is essential to develop the expertise necessary to use advanced product features.
Using data from the popular online game World of Warcraft, we find that motivations for product usage vary for different content.
Arcidiacono, P. and J. B. Jones (2003), Finite Mixture Distributions, Sequential Likelihood and the EM Algorithm, Econometrica, Econometric Society, Vol. 71 (3), pp
Entertainment Software Association (2011), Top 10 industry facts, October 10th. 12. Golder, P. N. and G. J. Tellis (2004), Growing, Growing, Gone:
The Case of Japanese Video games, Working paper. 21. Kopalle, Praveen K.,Scott A. Neslin, Baohong Sun, Yacheng Sun,
Debunking the stereotypical gamer profile, Journal of Computer-Mediated Communication, 13, pp. 993-1018.44. Yao, S.,C. F. Mela, J. Chiang,
Scientific Papers (www. scientificpapers. org) Journal of Knowledge management, Economics and Information technology 409 Special Issue December 2013 The Impact of Innovation in Romanian Small and Medium-Sized
Oncioiu Ionica, Titu Maiorescu University Bucharest, ionicaoncoiu@yahoo. ro, Romania Small firms are big business in the aid of economic development.
At the same time, much less is written about the majority of small and micro size firms that constitute the core of the economy.
The substance of this upgrade though is situated in a great range of differences from country to country or integration groups,
First of all SMES in the sectors of high-tech"and the media, characterized by a high affinity for the activities via the Internet,
At the same time, most small businesses that constitute the core of the economy do not innovate. Most SME innovations are marginal improvements of already existing products,
and has a total of 45 questions To collect data from interviewees a number of 730 companies were contacted by phone or email between January 2013 and June 2013.
The constituents of the scoring variables was undertaken a factor analysis, and the resulting factors would the input of a cluster analysis.
The present analysis also had the aim to investigate the state of planning and how innovation process of Romanian SMES is linked with it.
Such a research project would require investing considerable time in collecting data. We certainly have many casualties among SMES due to the incorrect application of innovation process
Monitor, Rapport voor Bel en Vlaanderen, 2006.17 Wickham, Philip A.,Strategic Entrepreneurship, Pearson, Fourth Edition, 2006.18 Williamson, O. E.,The Institutions of Governance
Data from 430 small and medium-sized enterprises were analyzed through hierarchical regression analysis, and innovation was found to be a significant factor in both family and non-family samples.
and long-term outlook (Anderson and Reeb 2003). These differences might be explained partly that family involvement affects activities and processes differently,
The cross-sectional nature of the data collection limits potential findings and it is unclear if similar results would be found in a comparison of large companies.
a longitudinal approach would provide more reliable data. While this research combined two samples from different countries, evidence of how this process can enhance the study was presented.
Support of these variables in both sets of data in this study is consistent with prior research,
Finally, objective performance measures may yield different results with performance data that could be verified independently.
whether the two samples varied from the findings of the combined data sets in terms of nationality and industry.
In order to test for country effects, the data were broken into two subsets:(1) US family and non-family respondents and (2) Australian family and nonfamily respondents.
The Australian data had less explanation in the family sample, and the adjusted R2 was 0. 15 but significant at the 1%level (ß=0. 39, t=4. 42, p<.01).
parallel testing was conducted for all non-family samples with comparable results for each data set.
For example, the hierarchical model with the innovation variable in the US family data set explained 42%of the variance
As the data were collected from various industries, they were tested in further regression analysis for possible industry effects.
letter was dispersed in Australia and the USA via email and in person. Two versions of the survey instrument:
The online survey was administered through three emails. The personal delivery method is acknowledged to increase response rates as completed responses can help (a) establish rapport with respondents
and factor analysis was used to reduce the number of items in some scales. Hierarchical linear regression analysis was utilized to analyze the relationships between the variables in the final model.
gathered the data, and drafted the manuscript. MS contributed to the research design and performed the statistical analysis.
RJB contributed to the data analysis coordination, and final editing. All authors have read and approved the final manuscript.
Global entrepreneurship monitor. Report on high-expectation entrepreneurship. Wellesley: London Business school/Mazars/Babson. Barney, J. 1991.
Expert systems with Applications, 27,459 465. Cohen, WM, & Levinthal, DA. 1990). ) Absorptive capacity: a new perspective on learning and innovation.
Journal of Management Information systems, 18,185 214. Grant, RM. 1996). ) Toward a knowledge-based theory of the firm.
software of the mind. London: Mcgraw-hill. Holt, DT, Rutherford, MW, & Kuratko, DF. 2010). ) Advancing the field of family business research:
) Information technology and the U s. economy. The American Economic Review, 91 (1), 1 32. Katz, N,
an empirical examination of internet firms. Journal of Small Business Management, 47 (3), 263 286.
Petrakis, PE, & Kostis, PC. 2012). ) The role of knowledge and trust in SMES. Journal of the Knowledge Economy, 1 20. doi:
Global Entrepreneurship Monitor (GEM) 2003 global report. Wellesley: Babson/London Business school/Kauffman Foundation. Reynolds, PD, & White, SB.
Business intelligence. Smith, WK, & Lewis, MW. 2011). ) Toward a theory of paradox: a dynamic equilibrium model of organizing.
+34 9544 88318 Fax:++34 9544 88300 http://ipts. jrc. ec. europa. eu/http://www. jrc. ec. europa. eu/This publication is a Technical Report by the Joint Research Centre of the European commission.
and more financial resources will be made available over the 2014-2020 programming period. However, despite this considerable financial effort, the returns to research and innovation policy in the periphery of Europe has been far from satisfactory.
where the core actors and suitable investment priorities and to allocate resources efficiently are more easily identifiable.
The analysis is reproduced also by focusing exclusively on a number of core and peripheral EU regions,
was subsequently transform into a panel variable by combining it with the World bank Governance Indicators (WBGI)( Kauffmann et al.,
R&d expenditures and R&d spillovers both display a positive and significant correlation with innovation. And finally
78 are classified as‘periphery'and 147 as‘core'.'A relevant characteristic of the regions in the‘periphery'group is a much lower average score for quality of government with respect to the‘core,
'both for the composite Qog index and for all of its four categories. 3 The regression results of the fixed effects model estimated for the two subgroups are presented in Table 3 in the appendix.
while columns (6)-(10) report the same specification for the regions in the core of Europe.
The estimates confirm the presence of significant differences in the factors that affect innovation in the core and the periphery of Europe.
By contrast, core regions whose quality of government institutions is generally much higher, benefit little in terms of innovation from further increases in quality of government.
Patents application Peripheral regions Core regions (1)( 2)( 3)( 4)( 5)( 6)( 7)( 8)( 9)( 10) Patents application (t-1
A great deal of additional information on the European union is available on the Internet. It can be accessed through the Europa server http://europa. eu/.How to obtain EU publications Our priced publications are available from EU Bookshop (http://bookshop. europa. eu),
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dacian. coita@yahoo. com Sorin Teodor Constantin Unicredit Tiriac Bank S. A. 2-4 Unirii Square, RO-410072, Oradea, Romania E-mail:
Existing research on developing economies has shown that SMES typically act as catalysts of economic growth and the scarce literature on OI in SMES indicates that small firms engaging in OI practices are more innovative
and resulting innovation capacities serve as catalysts to (developing) economies (Benácek, 1995; Peng, 2001; Wachtel, 1999.
whose representatives have prioritized contacting participating SMES personally for data collection whenever possible. Last but not least, the surveyed SMES were given the possibility to answer the questionnaire in their native language (with subsequent translation by the first author),
In the following sections we describe our data in relation to these topics. 3 A Characterization of Hungarian
the primary data for our explorative research was acquired through collaboration with well-established institutions as well as individual experts and consultants in two Eastern European countries:
In collecting data on SME innovativeness in terms of their new product/service introductionsvi, we have followed the prescriptions of the Oslo Manualvii (2005.
The remaining data has produced a realistic overview of SME innovativeness in our sample and is summarized in Figure 4. Table 1 Innovations
the owner/manager's deep understanding of the industry and market contribute to creating a‘realistic'outlook:
e g. taking photos of the artwork, uploading the images onto the website, establishing payment solutions,
Another reported disadvantage that is very typical of SMES is the difficulty concerning the development of a long-term strategic outlook that is necessary for OI to be successful.
and interpreting innovation data, Publications de l'OCDE. Fletcher, D.,Helienek, E. & Zafirova, Z. 2009.
easier access to innovation management best practice via various online and offline media, a maturing venture capital scene as well as higher workforce mobility. v Each participating SME's core area of operation was standardized using the Standard Industrial Classification (SIC) system whereby‘Finance,
Insurance, and Real estate'represents a stand-alone category and does not fall under‘Services'.'vi Although the Oslo Manual differentiates between four major categories of innovations,
as well as direct dialogue (between the authors and their partner organizations and the SMES during data collection) have stimulated participating SMES from Hungary
accurate data for further analysis. Participants were very positive about this‘educational'aspect of the study,
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In summary, a set of core issues and recommendations were agreed, as follows: recognition of the role of universities as a key partner in taking forward successful Smart Specialisation Strategies in partnership with other stakeholders in the region;
The above set of core issues and recommendations could valuably feed into the assessment of RIS3 and monitoring and evaluation of Structural Fund Operational Programmes that fund research and innovation activities.
4) Have local universities been involved in planning for the future programming period of EU Structural Funds,
The core question is how to design calls to meet common interests aligning regional development strategies with university research expertise and interests.
There were many examples of such agencies operating across European countries in previous programming periods of the Structural Funds.
especially SMES, for example, intensive computing facilities, experimental platforms (e g. agro-materials platform, chemical and physical analysis services.
which were aimed at implementing new facilities concerning intensive computing with SMES in the region. The project was successful in meeting its scientific goals (through establishing an Interuniversity Computation Centre involving all universities in the region)
but the targeted level of involvement of SMES was achieved not fully. The latter problem was assisted not by the requirement that a different and separate administrative body was involved in management of the SMES'participation in the project.
aerospace and embedded systems, health (ageing, cancer and use of ITCS) and agriculture and agronomics, which has had strong structuring effects.
e g. software engineering, renewable energies. The National University of Ireland, Maynooth, had much experience of use of EU Structural and Social Funds across its teaching
and research in computer science and information and communication technologies, and including dedicated incubation space. Project financed at circa 30%with ERDF funds,
Development of multi-institutional structured Phd programmes in areas designated as nationally important (e g. telecommunications, bioanalysis and therapeutics.
Obstacles relate to the situation that universities are considered not by regional authorities as key partners that can bring their contribution to the programming and implementation of the EU funds,
The university has accessed not European Social Fund grants in this current programming period. Within the 2007-2013 Structural Funds programme
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