The survey was sent to 35 Icelandic and international companies with the offices on
Iceland. The managers were asked to get 5-7 employees from their company,
participating in the front end of innovation. Two companies refused to participate in
research. Managers of the both companies argued that their organization was providing
IT service as representatives of the much bigger groups and don't involve the front end
of innovation activities. One senior manager confirmed his and his company participation
but after five weeks he refused his conformations. He explained it that his employees
were overloaded with the work. Thus, the researcher supposed to become between 175
and 245 responses. The responses rate at the end was 111. Between the 111 answers
were 18 missing data values (16%): 4 missing answers in the culture construct, 4 missing
answers in the team construct and 10 missing answers in the senior management
Among the total number of respondents, 78 were employees of small sized companies
(˂50 employees) with the total rate of (69, 4 %). Where 56 participants were from the
companies with the employees number between 10-29 (50, 5 % from the total rate), and
21 (18, 9 %) responses were from the organizations with the 30-49 employees each (see
Number of employees
250- and more
The responses from the Medium sized companies (˂ 250 employees) were 18 (16, 2
employees more than 250 (see Table 1).
50, 5 % (56 participants) of the responses are employed by organization between 1-3
years; 27, 9 % (31 members) are working for the companies longer than 7 years, and 17,
1% (19 responses) are employees of the given organizations between 4- 6 years. Data
contains 5 missing values that could affect total results. The majority or 83 participants
(75%) are male. The female are 27 (25%).
The first analysing the data concerning the age of respondents demonstrated, that 49,
5 % of participants were between 23 and 37 years old. 50, 5 % of the participants were in
the age 38-58 years old. For more detailed examination, the following data was recorded
for smaller age groups (see Table 2). Thus, recorded data showed, that the majority of
participants, 41% or 46 respondents were between 31-38 age years old. 28, 3% or 32
responses were between 39-46 years old. The youngest and oldest members were in
minority: 18 (16, 2%) and 15 (13, 5%) respectively.
Age of respondents
64 respondents or 57, 7 % of the total responses were given by participants with BS/BA
degree, 32, and 4 % (36) respondents were with MS/MA/MBA degree. Others, such as
associated degree in business management, multimedia diploma, media technology were
11 or 9, 9 % of the total sample.
Participants presented a wide range of nationalities. Even the majority of the
respondents, 102 or 91, 9 % of the total responses were Icelandic, six another nations
were between participants. Among them British (1), German (1), Italian (1), Polish (39,
Swedish (1) and 2 respondents from the U.S.
The aim of the research was to find out if there is a positive relationship between some
organisational factors, namely Innovation strategy, innovation culture, senior
management commitment, team factor and FEI performance. Regression analysis was
applied to examine the research’s hypotheses.
Regression analysis is commonly known and extensively used for expression
relationships between given variables (Montgomery, Peck, & Vining, 2012; Neter, Kutner,
Nachtsheim, & Wasserman, 1996). The key issue for successful regression analysis is to
collect proper data (Bates & Watts, 1988). Appropriately collected and managed data
make both analysing and interpretation easier. In turn, poorly received data could cause
misleading and misunderstandings (Montgomery et al., 2012; Neter et al., 1996)
Furthermore, regression could help to describe and analyse response data.
Montgomery et al. (2014) determine that Regression analysis could be applied to achieve
various goals, namely
description of the collected data
to estimate parameters
to estimate and to predict
program. SPSS program was used to explain and describe the data collection.
Before evaluating data, it was examined if there linear relationships between dependent
and independent variables. This assumption was tested with the run of the simple linear
regression analysis with the help of SPSS program. Thus, scatter plot demonstrated a
positive linear relationship between dependent variable Performance and independent
variables culture, strategy, senior management commitment and team (test for
Independence of Residuals was examined with the Durbin-Watson statistic (see
Appendix 1), which in our case is 1,824, approximately 2. The Durbin- Watson statistic
ranges from 0 to 4. Thus, the Durbin- Watson test means, that there is no correlation
among residuals. Value 1,824 is close to 2 that also indicates as independence of errors
The Casewise diagnostic aimed to determine, that cases are outliers. The data has
standardized residuals less then ±3. Consequently, Caseweis diagnostic table as a part of
SPSS output was not produced.
Testing of residuals' (errors) normality demonstrated that the standardized residuals
are nearly normally distributed (see Appendix 2). This assumption was also confirmed by
the Normal P- P Plot (see Appendix 3). Normal P-P Plot demonstrates a practically perfect
alignment of the points along the diagonal line.
In this chapter collected data will be analysed. First, cumulative analysis (one
dependent- FEI performance and four independent: innovation strategy, innovation
culture, senior management commitment and team constructs) will be examine. Multiply
regression method will be used with the help of SPSS program. Second, research
hypotheses will be analysed and reported. Each of four hypotheses proposed for this
research will be studied separately in order to confirmed or refuse the proposed
First of all, correlation analysis was conducted to find out if there is a correlation between
dependent variable performance of the FEI and independent variables: strategy, culture,
senior management commitment and team. Pearson's correlation coefficient measures
the level of linear dependence between dependent and independent variables (Lawrence
& Lin, 1989). The data correlation values ideally should indicate results below .7.
However, above .3. If the correlation between variables will be below .3, it will specify
the absence of sufficient correlation between dependent and independent variable.
Respectively, if the correlation between two or more variables is above.7, it could
signalise about the bivariate correlation between the variables (Pallant, 2007). The Table
3 demonstrates that the results for the linear correlation between dependent variables
construct performance and independent variables constructs strategy, culture, senior
management commitment and team constructs are within acceptable norms. There is a
strong positive correlation between the innovation strategy and innovation performance
in the FEI r (111) =.662, p<.05 and positive moderate correlation between the innovation
culture r (107) =.317, p<.05, dedicated team r (107) =.398, p<.05 and senior management
commitment r (101) =.385, p<.05. Furthermore, this table demonstrates strong positive
correlation between senior management commitment and innovation strategy (r=.507,
p<.05), senior management commitment and innovation culture (r=.578, p<.05) and
senior management commitment and team (r=.564, p<.05). There is also the strong
positive relationship between the innovation strategy (r=.509, p<.05) and the moderate
positive correlation between the team and innovation culture (r=.424, p<.05).
Table 3 Correlations
**. Correlation is significant at the 0.01 level (2-tailed).
The mean score for the total performance variable (M = 3.05, S = 0, 71) were 3.05, that
is not very high, but not low. In order to find out the difference between the efficiency
and effectiveness both variables were studied separately. Descriptive statistic was
applied to answer this question. The results demonstrated, that the mean score for
effectiveness was higher (M = 3.3, S = 0.92) then the mean score for the efficiency variable
(M=2.8, S=0.85). The descriptive statistic was applied to find out mean scores for the each
variable construct: innovation strategy, innovation culture, senior management
commitment and team (see Table 4). The collected data provides information, that all
variable constructs have rather high results. Thus, the highest mean score was identified
for the team variables construct (M=4,08, S=0,712), the next highest mean score was for
the innovation strategy variables construct (M=3,72, S=0,80), the senior management
commitment variables construct (M=3,59, S=0,89) and innovation culture variables
construct (M=3,31, S=0,83) were with the mean scores 3,59 and 3,31 respectively.
Table 4 Descriptive statistics
independent variables together), a multiple regression model was conducted. The
regression analysis was performed with the SPSS REGRESSION method between
dependent variable performance and independent variables strategy, culture, senior
management commitment and team. All the necessary variables were presented in
continuous ratings on the 5-points Likert scale.
The next Table 5 (Model summery) provides information how proper the model
suitable to R, R² and adjusted R -square. The results of these values could make clear, to
what extend the variance of the dependent variable FEI Performance is explained by the
given research model. Thus, R -value (multiple correlation coefficient), measuring the
dependent variable's FEI Performance prediction quality indicates R=, 689 relatively good
level of prediction. The coefficient of determination R² indicates the magnitude of
variance in the dependent variable FEI Performance that can be explained by the
independent variables: strategy, culture, team and senior management commitment. For
the given study R²=, 475. Adjusted R-Square is aimed to rectify this value for smaller data
collections and identify, 451, that means 45, 1% of the variance in FEI performance could
be explained by the model with four independent variables: innovation strategy.
Innovation culture, senior management commitment and team. In conformity with J.
Cohen's classification, Adjusted R² is an estimation of the data effect size, and 45, 1%
indicates a medium effect size (Cohen, 1992).
Table 5 Model summary
a. Predictors: (Constant), Leadership, Strategy, Team, Culture
Data analysis confirmed, that innovation strategy, innovation culture, senior
Four hypotheses were proposed for given research. These hypotheses aimed to provide
more detailed information to answer the research question. The cumulative analysis
offers information concerning summarized model: regression analysis between
dependent variable FEI performance and four organizational components together.
Simple Linear Regression was used to test each hypothesis separately and to provide
addition information about each component contribution. Thus, we proposed with the
This hypothesis was approved by Linear regression. Innovation strategy could statistically
significantly predict FEI activities performance (F (1,109) 85,175; p˂ .05) and accounted
for 43, 4% of the explained variability in the FEI performance contribution.
The second hypothesis was proposed to find out if any positive relationship between
the innovation culture and FEI performance:
The results from the research showed that there is a positive relationship between the
Innovation culture and FEI performance. Therefore, the second hypothesis was also
statistically verified (F (1,105) =11,699; p˂ .05).
Testing the third hypothesis also found out positive relationship between senior
management commitment and FEI performance (F (1, 99) =17,253; p˂ .05). In this way,
this hypothesis was also confirmed:
The results from a linear regression also established, that properly organized team
could statistically significantly predict FEI Performance (F (1,105) =19,804; ˂ .05).
H4. Properly organized FEI Tam is positively associated with the FEI Performance.
Thus, the results confirmed positive relationship between FEI team organization and
Discussion and conclusions
Contemporary academics identify innovation process as a complex mix of organizational
components and tools, which could be of critical consequence in achieving competitive
advantage on the global market (Florén & Frishammar, 2012; Koen et al., 2002; Krieger
Mytelka; Tidd, Pavitt, & Bessant, 2001). In turn, the front end of innovation as the first
stage of the Innovation process could have a crucial role in the implementing the whole
Innovation process within the organization (Poskela & Martinsuo, 2009; Verworn, 2009).
Taking into account that proper managing of the front end of innovation activities has a
positive impact on the front end of innovation performance, managers are facing the
challenges to manage and implement the front end activities with highest level of
efficiency and effectiveness. Different factors and organizational attributes could
contribute to FEI performance. Thus, Koen et al. (2014) has found out that such
organizational attributes as senior management involvement shared vision, corporative
strategy, resources, and innovation culture are significant for FEI activities and explain 53
percent of the total outcome of the FEI performance. Other academics support the
importance of such organizational attributes and factors as team, knowledge sharing and
utilization of internal and external informational sources (Jongbae Kim & Wilemon,
2002a; Óskarsson, 2005; Verworn, 2009).
The purpose of the following research was to find out if there are positive relationships
between such organizational attributes as innovation strategy, innovation culture, senior
management commitment and properly organized FEI team. The research was made
between the Icelandic companies developing and providing services in the information
technology sector on Icelandic and international market. These companies were
presenting both new products developers and providing IT services organizations. This
collected data demonstrated that most of the companies' participants were small-and-
medium sized organizations (with the total rate of 69, 4%).
The front end of innovation performance has been measured by its effectiveness,
where the centres of interest were process and resources utilization along with
effectiveness measurement, which focused on the outcome. The data analysis provides
information, that there were rather low results in the efficiency with the mean score of
2.8 (see Fig. 10), where just 22,5% of respondents were agree and strongly agree, that
the last project undertaken in the company was the fastest one. Only 21,6% of
respondents answered that the development costs did not exceed the budget. Taking
into account rather unpredictable FFE character in resource planning (L. Sanders Jones &
Linderman, 2014), these results demonstrate the importance of the right managerial
tools in order to enhance the efficiency of the FEI.
Figure 9 Efficiency of the FEI
The mean score for the effectiveness variables was 3.3 and showed rather good
results. Thus, 72% of respondents answered that they are agree and strongly agree, that
the last product concept was clear and in the line with the customers' needs (see Fig.11).
Capability to innovate, to produce a product, which in the line of the customer needs
could be crucial for company's sustainable competitiveness in the market (Edison et al.,
2013). Especially for the IT solutions and software developing organizations ability to
create new customer-oriented products is an essential factor for the long -term
competitive advantage (Hanson et al., 2011).
The attribute constructs consisted of three questions each were made to answer the
research questions. Regression analysis was applied statistically to support the research
hypotheses. Regression analysis statistically demonstrated positive relationships
between each of given organizational attributes and their positive effect on the front end
of innovation performance. The regression coefficients for all the independent variables
were significant (p<0.05). The ranges of the percentage variables explained were
different for each dependent variable. From approximately 10 percent for Innovation
culture construct to 43 percent for the innovation strategy. As many academics underline,
the success of the product on the market strictly depends on the organizational strategy
(Akman & Yilmaz, 2008; Cronin, 2014; Terziovski, 2010). Our data also demonstrate a
strong linkage between the front end of innovation performance and the innovation
strategy. Moreover, taking into account that 5-point Likert scale was used, the average
score for the innovation strategy construct was M=3, 7, that is rather a good result. In
order to achieve the highest performance within the front end of innovation activities,
organizations should be able to determine and allocate financial and human resources,
define clear goals, share the corporate vision, utilize knowledge and adapt market
changes (Goffin & Mitchell, 2005). The average score for the innovation culture construct
was 3, 31, the lowest between the all other results. The data also demonstrated, that the
most problematic questions for the participants were time and rewarding issues. Thus,
the research data demonstrated, that teams participating in the front end of innovation
activities are often struggling with the lack of time (39,4 % of the total respondents
negatively answered the question: "Employees of our firm have time to consider and test
new ideas". At the same time, many scholars stress that especially in the front end of
innovation phase the teams have to become enough time for idea developing, concept
and decision-making process (Ho & Tsai, 2011). The rewarding system is another
important issue that has to be considered by the organizations. Almost 30% of the
respondents answered that they strongly disagree or disagree, that their company
reward project members for their innovativeness (see Fig. 12). Many academics point out
that innovation culture has a positive contribution to the FEI performance (Angel, 2006;
Hafiza et al., 2011; Jaruzelski et al., 2011). The innovative culture consists of many
attributes and aims to create an atmosphere where open-mindedness, creativity, willing
to risk are strongly supported by an organization (Ahmed, 1998). Enough time to consider
new ideas create a possibility of innovating and rewarded innovativeness in turn could
stimulate this process (Chandy, 2003; Hafiza et al., 2011). Organizations, that reward and
support could encourage their employees to be innovative and believe in their creative
capabilities (Chandy, 2003).