Idea Management System Application Type Impact on Idea Quantity

Idea management system application considers idea quantity as the key to idea management success. The aim of this paper is to examine how different idea management system application types impact idea quantity. The authors conducted empirical research by conducting a survey based on adaptive structuration theory framework. In the research paper, an analysis of 447 responses was included. The study shows how to separate idea management system application types impact by idea quantity. The target group consisting of commercially available web-based idea management system applied enterprises bias present in the survey research may limit the generalisability of the results. The study contributes to the discussion about the idea management system application type impact on the idea management results by showing that different idea management system application types lead to different idea management results.


BA School of Business and Finance
Innovation management and application of information technologies in organisations has become increasingly more relevant over the last few decades. Web-based idea management systems (IMS) fall in line with the current developments (e.g. growing importance of ICT, the spread of open innovation and co-innovation, etc.) in all previously mentioned considerations, IMS is a manageable systematic tool to generate and evaluate ideas. The use of web-based IMS has become a part of the organizational culture in various enterprises and Web-based IMS are used by many well-known organizations such as Boeing, P&G, Volkswagen, Xerox, Pentax, Heineken, Panasonic, Sony, Fujitsu, Electrolux, Volvo, etc. The authors expect that throughout the following years the role of web-based IMS will grow as even more organizations will start to apply them. Many good examples show positive effects on organizations performance that use web-based IMS. For example, BT Group is using its IMS Webstorm which helped the company acquiring 10 000 new ideas in the seven years between 2005 and 2012. Realization of these ideas has helped the company to increase its revenue by 100 million pounds and improve customer loyalty (Bright Idea, 2010 Power, the only privately owned nuclear power station in Canada. In two years since it started using IMS Idealink Open, it has acquired more than 2700 new ideas and more than 10 000 participants have participated in their IMS process (generation, development). The use of IMS can lead to both a decrease in costs and an increase in revenue (Brain Bank, 2014). Application cases show that this tool gives the possibility to connect internal and external idea creators and evaluators in the idea management (IM) process and these systems could connect different entrepreneurship areas, for example, intrapreneurship and innovation management, opportunity identification and creation. But there is a lack of research on the web-based IMS application types and their respective results. Authors of this paper aim to explore web-based IMS application type impact on its application results. To fill the gap, authors apply theoretical and empirical approach with the main aim to examine how different IMS application types impact IMS results. Applegate (1986) is the first researcher mentioning IM and to begin IM and IMS research. Since then there have been several academic perspectives on how to research IM and IMS. A majority of researches focuses on systematic aspects of IM and IMS (e.g. Bailey and Horvitz, 2010;Barczak et al., 2009;Bjork and Magnusson, 2009;Coughlan and Jahanson, 2008;Flynn et al., 2003;Galbrait, 1982;Gish, 2011;Green et al., 1983;Korde and Paulus, 2016;Vandenbosch et al., 2006) and structural (e.g. Bassiti and Ajhoun, 2013;Bergendah and Magnusson, 2014;Divakaran, 2016;Luo and Tobia, 2015;Narvaez and Gardoni, 2015;Poveda et al., 2012;Summa, 2004;Voigt and Brem, 2006;Wooten and Ulrich, 2015). Structural literature sources focus on design and the process, but systematic literature sources focus on social capital, creativity, cognition, etc. (Rose and Jensen, 2012).
Authors have revealed in the previous researches that there are multiple types of research available with a structural perspective that provide a theoretical base for IMS concept exploration. Literature about IMS overviews mostly focus on existing IMS and their application and potential improvements (e.g. Summa, 2004;Bakker et al., 2006;Coughlan et al., 2008;Bothos et al., 2008;Bjork et al., 2009;Barczak et al., 2009;Beretta, 2015;Tung et al., 2009;Bailey et al., 2010;Hrastinski et al., 2010;Holzblatt et al.,2011), but some researches also aim to research development of new IMS (e.g. Flynn et al., 2003;Vandenbosch et al., 2006;Bothos, et al, 2009;Iversen, et al., 2009;Bansemir et al., 2009;Bettoni et al., 2010;Xie et al.,2010;Bothos et al., 2012;Lowe and Heller, 2014). This paper aims to be part of the first type of papers which explores existing systems focusing on commercially available systems. Most researches that explore existing IMS research focus on one or a few IMS but this research is based on a survey across multiple different IMS users.
IMS has not been given sufficient scientific attention and it should be researched how different IMS types impact its application results (van den Ende et al., 2015). This research is aiming at providing a contribution to fill this gap. First, the paper will help researchers and IMS users to understand the basic IMS application types and their potential results. Second, the exploration of different IMS types and their results could motivate entrepreneurs to re-evaluate their current approach to IM. Third, developers and users of web-based IMS see the potential of these systems but positive outcomes often do not occur and that is one of the reasons why organizations do not use them in the long term (DeSanctis and Poole, 1994). Due to these reasons, it is important to explore web-based IMS application types and their results, to explain what results companies could expect based on different application types.
In this paper, IM is defined as a systematic, manageable process of idea generation, evaluation, and repeated idea generation and evaluation. The IMS is defined as a tool, tool kit or complex system which provides systematic, manageable process in IM (Mikelsone and Liela, 2015). The authors use 2 IMS classifications: based on involved idea sources (internal, external, mixed) and based on the application focus (active, passive). The research aims to answer the research question: How different IMS application types impact idea quantity? To answer these questions 4 Figure 1 summarizes the motivation for this paper.

Aim
Gap to fill (academic topicality) Overall topicality (trends and practical topicality) Use theoretical and empirical approach to examine how different IMS application types impact IMS application idea quantity.
Main literature gap -no research on how IMS application types impact idea quantity.
IT application and innovation management in organizations is more relevant now than ever before. Trends to match -(1) in the age of knowledge tools that provide means for acquiring, evaluation and development of knowledge and ideas are extremely important; (2) the growing role of ICT increases the importance of web-based tools that support the innovation process; (3) web-based IMS is becoming more important in the context of open innovation and co-innovation, giving them access to both internal and external sources of ideas and knowledge.

Figure 1
Introduction Theoretical background

Idea management system application types
For this research the authors have applied two categories for classifying IMS application: (1) based on the involved IM source; (2) based on the IM application focus.
There are other possible categories for IMS classification: based on the provided process functions (limited, full, extra) and based on the IMS price type (monthly payment, yearly payments). The last two types of classifications will not be investigated further as they focus on systems, not on their application type.
Authors based on the ideas divide all IMS application cases as follows: _ internal IMS by involvement internal idea creators and evaluators; _ external IMS by involvement external idea creators and evaluators; _ mixed IMS by involvement internal and external idea creators and evaluators.
Based on the application focus all systems could be divided as "active" and "passive'', therefore, there are passive and active IMS. Passive IMS collect all ideas in an unfocused manner, but active IMS provide functions to collect ideas in a focused manner and most cases includes idea evaluation possibilities. IMS type descriptions are provided in Table 2.

Idea Management System Application Results
Quality and quantity of ideas are most often used as measurements for IM and IMS application, and as a result, should be considered as the main elements of the web-based IMS application outputs. Denis and Garfield (2003) have revealed that decision support system processes may encourage more participation that also provides a challenge to research this element in the IMS context. Wooten and Ulrich (2015) had researched feedback importance in the idea management process, based on the conclusion that managers face a decision about if and how to provide in-process feedback to the idea generators about the quality of submissions. Their research revealed that directed feedback benefits the average quality of entries submitted. The stimulus that impacts web-based IMS application and its results can also be researched. The authors have concluded that there is no common view on IMS output elements, except idea quality and quantity, and involvement. It would be advisable to create IMS effectiveness evaluation tool that would include the most important output elements. In this research, authors will apply the most frequent researched IMS output variables -idea quantity, idea quality and involvement. Idea quality could be defined as the average quality of generated ideas (idea creativity) (Selart & Johansen, 2011;Deichmann, 2012;Bjork & Magnusson, 2009). Idea quantity could be defined as a number of ideas generated (MacCrimmon & Wagner, 1994;Korde & Paulus, 2016;Girotra & Ulrich, 2010;Deichmann, 2012). There is an additional variable chosen to research results -involvement or number of involved people (Dennis & Garfield, 2003;Deichmann, 2012). In this research, authors focus on idea quantity.

Research instrument for measuring web-based IMS application results
A questionnaire was created for web-based IMS applied companies. The survey was conducted in the summer/autumn of 2018. Methods for obtaining primary data are described in Table 3.
A total of 186 elements are summarized in 8 question blocks. In this paper, the applied survey block is IMS results. The questionnaire was created and distributed in English, as the dominant language of the IMS and its use in English. All criteria were based on literature analysis and updated scales were based on the results of the case studies as used to describe the results of the application of IMS.

Data collection
The survey was conducted on the survey platform 'The QuestBack' (https://www.unipark.com/) created by UNIPARK. This platform was chosen because it is: (1) focused on academic surveys; (2) widely recommended by world-class researchers; (3) provides data security required by IMS representatives -BSI-certified data centre in accordance with ISO 27001; (4) complies with the requirements of the EU General Data Protection Regulation.
To test the questionnaire, it was sent to 9 companies that conducted a survey and were able to comment on any question. The test was done in 3 rounds, the questionnaire was sent to 3 companies using the IMS when comments were received and based on the feedback the questionnaire was improved. In the third round, comments on the structure or clarity of the questions were no longer provided. Based on the tests, the time of completing the questionnaire was determined (20-30 minutes). After the test, the survey results were deleted.
It should be noted that to reach the target audience more accurately, the authors asked IMS developers to distribute the survey to their clients. It was stipulated that the survey should only be sent to companies using the system in question to the person in charge of the IMS (mostly think-tanks, innovation managers or business managers). In the authors' private communication with 107 IMS

Data analysis
To validate data for the further analysis the pre-analysis was conducted by using the following methods: _ Point estimation and interval estimation -''the process of providing a numerical value for a population parameter based on information collected from a sample. If a single figure is calculated for the unknown parameter, the process is called point estimation. The process of providing a numerical value for a population parameter based on information collected from a sample. If an interval is calculated which is likely to contain the parameter, then the procedure is called interval estimation" (Everitt & Skrondal, 2010); _ Frequency distribution -"the division of a sample of observations into a number of classes, together with the number of observations in each class. Acts as a useful summary of the main features of the data such as location, shape, and spread" (Everitt & Skrondal, 2010); _ Mean of the group -to get the average value of the group; _ Standard deviation -to measure the spread of a set of observations; _ Modal and medial class (interval) -to observe the most frequent and ''the value in a set of ranked observations that divides the data into two parts of equal size" (Everitt & Skrondal, 2010); _ Coefficient of variation -to measure "the spread for a set of data defined" (Everitt & Skrondal, 2010); _ Confidence interval -to range the values, calculated from the sample observations, that is believed, with a particular probability, to contain the true parameter value (of the population); _ Sampling error -to observe "the difference between the sample result and the population characteristic being estimated" (Everitt & Skrondal, 2010). _ To test the hypothesis the following data analysis methods were applied: _ Significance tests for a population mean number for the result variable.
_ The t-test was used to measure statistically significant variations between IMS types. It was applied to test the hypothesis. _ Calculating p-values for the given test statistics and the degrees of freedom.

Basic data characteristics -idea quantity
Respondents frequency distribution based on survey data is shown in Figure 3.
The further detailed analysis consists of the arithmetic mean of the group, standard deviation, modal and medial class (group), coefficient of variation.
Arithmetic mean of grouped data is calculated as follows: for the unknown parameter, the process is called point estimation. The process of providing a numerical value for a population parameter based on information collected from a sample. If an interval is calculated which is likely to contain the parameter, then the procedure is called interval estimation" (Everitt & Skrondal, 2010);  Frequency distribution -"the division of a sample of observations into a number of classes, together with the number of observations in each class. Acts as a useful summary of the main features of the data such as location, shape, and spread" (Everitt & Skrondal, 2010);  Mean of the group -to get the average value of the group;  Standard deviation -to measure the spread of a set of observations;  Modal and medial class (interval) -to observe the most frequent and ''the value in a set of ranked observations that divides the data into two parts of equal size" (Everitt & Skrondal, 2010);  Coefficient of variation -to measure "the spread for a set of data defined" (Everitt & Skrondal, 2010);  Confidence interval -to range the values, calculated from the sample observations, that is believed, with a particular probability, to contain the true parameter value (of the population);  Sampling error -to observe "the difference between the sample result and the population characteristic being estimated" (Everitt & Skrondal, 2010). To test the hypothesis the following data analysis methods were applied:  Significance tests for a population mean number for the result variable.  The t-test was used to measure statistically significant variations between IMS types. It was applied to test the hypothesis.  Calculating p-values for the given test statistics and the degrees of freedom.

4.1.Basic data characteristics -idea quantity
Respondents frequency distribution based on survey data is shown in Figure 3. The further detailed analysis consists of the arithmetic mean of the group, standard deviation, modal and medial class (group), coefficient of variation. Arithmetic mean of grouped data is calculated as follows: where � -ith class (group) midpoint, � -frequency of the ith class (interval), -sample size, = ∑ � � � . The standard deviation of grouped data is calculated as follows: where: m i -ith class (group) midpoint, ƒ i -frequency of the ith class (interval), n -sample size, for the unknown parameter, the process is calle numerical value for a population parameter base interval is calculated which is likely to contain th estimation" (Everitt & Skrondal, 2010);  Frequency distribution -"the division of a sample with the number of observations in each class. Act data such as location, shape, and spread" (Everitt  Mean of the group -to get the average value of th  Standard deviation -to measure the spread of a se  Modal and medial class (interval) -to observe the observations that divides the data into two parts o  Coefficient of variation -to measure "the spread 2010);  Confidence interval -to range the values, calculat with a particular probability, to contain the true pa  Sampling error -to observe "the difference betwe characteristic being estimated" (Everitt & Skrond To test the hypothesis the following data analysis met  Significance tests for a population mean number f  The t-test was used to measure statistically signific to test the hypothesis.    The standard deviation of grouped data is calculated as follows: The further detailed analysis consists of the arithmetic mean of the group, standard deviation, modal and medial class (group), coefficient of variation. Arithmetic mean of grouped data is calculated as follows: where � -ith class (group) midpoint, � -frequency of the ith class (interval), -sample size, = ∑ � � � . The standard deviation of grouped data is calculated as follows: (2) where: The further detailed analysis consists of the arithmetic mean of the group, standard deviation, modal and medial class (group), coefficient of variation. Arithmetic mean of grouped data is calculated as follows: where � -ith class (group) midpoint, � -frequency of the ith class (interval), -sample size, = ∑ � � � . The standard deviation of grouped data is calculated as follows: , m iith class (group) midpoint.
The median (Me) of grouped data is calculated as follows: The median ( ) of grouped data is calculated as follows: where ��,� -lower class boundary of the interval containing the median, ���� -cumulative frequency of the interval before the median interval, �� -frequency of the median interval, ∆ �� -the median interval width. Medial interval is interval for which accumulated frequencies first time is equal or larger than half of the sample size. Coefficient of variation (CV) is calculated as follows: = � �̅ *100% (4) Point estimates were aggregated and are provided in Table 5.  Table 5, the medians for all IMS types are less than the means of the generated ideas. These differences indicate some asymmetry in the distribution of respondents -more often a smaller number of ideas are generated, but less often -a large number of ideas. There is a particularly large difference between these indicators for passive IMS as well as for internal IMS -as frequency distributions are skewed mean values does not give a good idea of a typical value that can be expected in case of using these types of IMS. The calculated coefficients of variation also indicate similar -passive and internal IMS has more variation, relative to its arithmetic means than other IMS application types. Further described is the interval estimation for the population mean. The confidence interval for the population means µ is calculated as follows:

As shown in
̅ ± where -margin error, = ���,�/� * � √� (6) ���,�/� -value of t distribution for the selected confidence level and sample size, -level of significance and 100*(1-)% -confidence interval. The upper confidence limit (UCL) is calculated as follows: UCL = ̅ + The lower confidence limit (LCL) is calculated as follows: Confidence intervals (CI) provide the lower confidence limit (LCL) and the upper confidence limit (UCL) that are likely to contain the true parameter value (of the population). The value 95% refers to the probability that the interval will capture the parameter being estimated (Tan & Tan, 2010). 95% confidence interval estimates are aggregated in Table 6.
where: x Me,l -lower class boundary of the interval containing the median, cf Me-1 -cumulative frequency of the interval before the median interval, f Me -frequency of the median interval, Δ Me -the median interval width.
Medial interval is interval for which accumulated frequencies first time is equal or larger than half of the sample size.

Coefficient of variation (CV) is calculated as follows:
The median ( ) of grouped data is calculated as follows: where ��,� -lower class boundary of the interval containing the median, ���� -cumulative frequency of the interval before the median interval, �� -frequency of the median interval, ∆ �� -the median interval width. Medial interval is interval for which accumulated frequencies first time is equal or larger than half of the sample size. Coefficient of variation (CV) is calculated as follows: = � �̅ *100% (4) Point estimates were aggregated and are provided in Table 5.  Table 5, the medians for all IMS types are less than the means of the generated ideas. These differences indicate some asymmetry in the distribution of respondents -more often a smaller number of ideas are generated, but less often -a large number of ideas. There is a particularly large difference between these indicators for passive IMS as well as for internal IMS -as frequency distributions are skewed mean values does not give a good idea of a typical value that can be expected in case of using these types of IMS. The calculated coefficients of variation also indicate similar -passive and internal IMS has more variation, relative to its arithmetic means than other IMS application types. Further described is the interval estimation for the population mean. The confidence interval for the population means µ is calculated as follows:

As shown in
̅ ± where -margin error, = ���,�/� * � √� (6) ���,�/� -value of t distribution for the selected confidence level and sample size, -level of significance and 100*(1-)% -confidence interval. The upper confidence limit (UCL) is calculated as follows: UCL = ̅ + The lower confidence limit (LCL) is calculated as follows: Confidence intervals (CI) provide the lower confidence limit (LCL) and the upper confidence limit (UCL) that are likely to contain the true parameter value (of the population). The value 95% refers to the probability that the interval will capture the parameter being estimated (Tan & Tan, 2010). 95% confidence interval estimates are aggregated in Table 6. Point estimates were aggregated and are provided in Table 5. As shown in Table 5, the medians for all IMS types are less than the means of the generated ideas. These differences indicate some asymmetry in the distribution of respondents -more often a smaller number of ideas are generated, but less often -a large number of ideas. There is a particularly large difference between these indicators for passive IMS as well as for internal IMS -as frequency distributions are skewed mean values does not give a good idea of a typical value that can be expected in case of using these types of IMS. The calculated coefficients of variation also indicate similar -passive and internal IMS has more variation, relative to its arithmetic means than other IMS application types.
Further described is the interval estimation for the population mean. The confidence interval for the population means µ is calculated as follows: The median ( ) of grouped data is calculated as follows: where ��,� -lower class boundary of the interval containing the median, ���� -cumulative frequency of the interval before the median interval, �� -frequency of the median interval, ∆ �� -the median interval width. Medial interval is interval for which accumulated frequencies first time is equal or larger than half of the sample size. Coefficient of variation (CV) is calculated as follows: = � �̅ *100% (4) Point estimates were aggregated and are provided in Table 5.  Table 5, the medians for all IMS types are less than the means of the generated ideas. These differences indicate some asymmetry in the distribution of respondents -more often a smaller number of ideas are generated, but less often -a large number of ideas. There is a particularly large difference between these indicators for passive IMS as well as for internal IMS -as frequency distributions are skewed mean values does not give a good idea of a typical value that can be expected in case of using these types of IMS. The calculated coefficients of variation also indicate similar -passive and internal IMS has more variation, relative to its arithmetic means than other IMS application types. Further described is the interval estimation for the population mean. The confidence interval for the population means µ is calculated as follows:

As shown in
̅ ± where -margin error, = ���,�/� * � √� (6) ���,�/� -value of t distribution for the selected confidence level and sample size, -level of significance and 100*(1-)% -confidence interval. The upper confidence limit (UCL) is calculated as follows: The lower confidence limit (LCL) is calculated as follows: where: ME -margin error, European Integration Studies 2020/14 200 between these indicators for passive IMS as well as for internal IMS -as frequency distributions are skewed mean values does not give a good idea of a typical value that can be expected in case of using these types of IMS. The calculated coefficients of variation also indicate similar -passive and internal IMS has more variation, relative to its arithmetic means than other IMS application types. Further described is the interval estimation for the population mean. The confidence interval for the population means µ is calculated as follows: ̅ ± where -margin error, = ���,�/� * � √� (6) ���,�/� -value of t distribution for the selected confidence level and sample size, -level of significance and 100*(1-)% -confidence interval. The upper confidence limit (UCL) is calculated as follows: The lower confidence limit (LCL) is calculated as follows: Confidence intervals (CI) provide the lower confidence limit (LCL) and the upper confidence limit (UCL) that are likely to contain the true parameter value (of the population). The value 95% refers to the probability that the interval will capture the parameter being estimated (Tan & Tan, 2010). 95% confidence interval estimates are aggregated in Table 6. where: t n -1,α/2value of t distribution for the selected confidence level and sample size, α -level of significance and 100*(1α)% -confidence interval.
The upper confidence limit (UCL) is calculated as follows: These differences indicate some asymmetry in the distribution of respondents -more often a smaller number of ideas are generated, but less often -a large number of ideas. There is a particularly large difference between these indicators for passive IMS as well as for internal IMS -as frequency distributions are skewed mean values does not give a good idea of a typical value that can be expected in case of using these types of IMS. The calculated coefficients of variation also indicate similar -passive and internal IMS has more variation, relative to its arithmetic means than other IMS application types. Further described is the interval estimation for the population mean. The confidence interval for the population means µ is calculated as follows: ̅ ± where -margin error, = ���,�/� * � √� (6) ���,�/� -value of t distribution for the selected confidence level and sample size, -level of significance and 100*(1-)% -confidence interval. The upper confidence limit (UCL) is calculated as follows: The lower confidence limit (LCL) is calculated as follows: Confidence intervals (CI) provide the lower confidence limit (LCL) and the upper confidence limit (UCL) that are likely to contain the true parameter value (of the population). The value 95% refers to the probability that the interval will capture the parameter being estimated (Tan & Tan, 2010). 95% confidence interval estimates are aggregated in Table 6.
The lower confidence limit (LCL) is calculated as follows: As shown in Table 5, the medians for all IMS types are less than the means of the generated ideas. These differences indicate some asymmetry in the distribution of respondents -more often a smaller number of ideas are generated, but less often -a large number of ideas. There is a particularly large difference between these indicators for passive IMS as well as for internal IMS -as frequency distributions are skewed mean values does not give a good idea of a typical value that can be expected in case of using these types of IMS. The calculated coefficients of variation also indicate similar -passive and internal IMS has more variation, relative to its arithmetic means than other IMS application types. Further described is the interval estimation for the population mean. The confidence interval for the population means µ is calculated as follows: ̅ ± where -margin error, = ���,�/� * � √� (6) ���,�/� -value of t distribution for the selected confidence level and sample size, -level of significance and 100*(1-)% -confidence interval. The upper confidence limit (UCL) is calculated as follows: The lower confidence limit (LCL) is calculated as follows: Confidence intervals (CI) provide the lower confidence limit (LCL) and the upper confidence limit (UCL) that are likely to contain the true parameter value (of the population). The value 95% refers to the probability that the interval will capture the parameter being estimated (Tan & Tan, 2010). 95% confidence interval estimates are aggregated in Table 6.
Confidence intervals (CI) provide the lower confidence limit (LCL) and the upper confidence limit (UCL) that are likely to contain the true parameter value (of the population). The value 95% refers to the probability that the interval will capture the parameter being estimated (Tan & Tan, 2010). 95% confidence interval estimates are aggregated in Table 6. A 95% CI means that if the study will be conducted multiple times with corresponding 95% CI for the mean constructed, author's expect 95% of these CI's to contain the true population mean (Tan & Tan, 2010) and it could be between 3810 to 4467 ideas generated in active IMS, for passive IMS between 881 to 1401, for internal IMS between 1061 to 1507, external IMS between 4015 to 4739 and mixed IMS between 4016 to 4824.

Hypothesis testing -idea quantity
Basic data set analysis showed that it is possible to test the hypothesis on the gathered data. That is the reason why further in this paper the authors conduct significance tests for population mean number of ideas created (idea quantity). A respondent's frequency distribution shows the main trends that will be tested: (1) active IMS provides higher idea quantity than passive IMS; (2) external IMS provides higher idea quantity than internal IMS; (3) mixed IMS provides higher idea quantity than internal and external IMS. See in Figure 8. Hypothesis tested: (H1) Active IMS provide higher idea quantity than passive:

Source: created by author's
A 95% CI means that if the study will be conducted multiple times with corresponding 95% CI for mean constructed, author's expect 95% of these CI's to contain the true population mean (Tan & Tan,20 and it could be between 3810 to 4467 ideas generated in active IMS, for passive IMS between 881 to 1 for internal IMS between 1061 to 1507, external IMS between 4015 to 4739 and mixed IMS between 4 to 4824.

4.2.Hypothesis testing -idea quantity
Basic data set analysis showed that it is possible to test the hypothesis on the gathered data. That is reason why further in this paper the authors conduct significance tests for population mean number of i created (idea quantity). A respondent's frequency distribution shows the main trends that will be tested active IMS provides higher idea quantity than passive IMS; (2) external IMS provides higher idea quan than internal IMS; (3) mixed IMS provides higher idea quantity than internal and external IMS. Se Figure 8. A 95% CI means that if the study will be conducted multiple times with corresponding 95% CI for mean constructed, author's expect 95% of these CI's to contain the true population mean (Tan & Tan,20 and it could be between 3810 to 4467 ideas generated in active IMS, for passive IMS between 881 to 1 for internal IMS between 1061 to 1507, external IMS between 4015 to 4739 and mixed IMS between 4 to 4824.

4.2.Hypothesis testing -idea quantity
Basic data set analysis showed that it is possible to test the hypothesis on the gathered data. That is reason why further in this paper the authors conduct significance tests for population mean number of id created (idea quantity). A respondent's frequency distribution shows the main trends that will be tested active IMS provides higher idea quantity than passive IMS; (2) external IMS provides higher idea quan than internal IMS; (3) mixed IMS provides higher idea quantity than internal and external IMS. Se Figure 8. A 95% CI means that if the study will be conducted multiple times with corresponding 95% CI fo mean constructed, author's expect 95% of these CI's to contain the true population mean (Tan & Tan,20 and it could be between 3810 to 4467 ideas generated in active IMS, for passive IMS between 881 to 1 for internal IMS between 1061 to 1507, external IMS between 4015 to 4739 and mixed IMS between 4 to 4824.

4.2.Hypothesis testing -idea quantity
Basic data set analysis showed that it is possible to test the hypothesis on the gathered data. That is reason why further in this paper the authors conduct significance tests for population mean number of i created (idea quantity). A respondent's frequency distribution shows the main trends that will be tested active IMS provides higher idea quantity than passive IMS; (2) external IMS provides higher idea quan than internal IMS; (3) mixed IMS provides higher idea quantity than internal and external IMS. Se Figure 8. A 95% CI means that if the study will be conducted multiple times with corresponding 95% CI fo mean constructed, author's expect 95% of these CI's to contain the true population mean (Tan & Tan,2 and it could be between 3810 to 4467 ideas generated in active IMS, for passive IMS between 881 to 1 for internal IMS between 1061 to 1507, external IMS between 4015 to 4739 and mixed IMS between 4 to 4824.

4.2.Hypothesis testing -idea quantity
Basic data set analysis showed that it is possible to test the hypothesis on the gathered data. That is reason why further in this paper the authors conduct significance tests for population mean number of i created (idea quantity). A respondent's frequency distribution shows the main trends that will be tested active IMS provides higher idea quantity than passive IMS; (2) external IMS provides higher idea quan than internal IMS; (3) mixed IMS provides higher idea quantity than internal and external IMS. Se Figure 8. Respondents frequency distribution for idea quantity based on sample data 0 1-10 11-100 101-1000 1001-5000 5001-10000 >10000 where ̅ � and ̅ � means of comparable sample variables, Also, p-values were calculated for given test statistics and the degrees of freedom. The p-value is the probability of obtaining a value of the test statistic as extreme as or more extreme than the actual value obtained when the null hypothesis is true. Thus, the p-value is the smallest significance level at which a null hypothesis can be rejected, given the observed sample statistic. Calculated t-statistics, degrees of freedom (df), critical values (tc) and p-values are aggregated in following Table 7.