Governance of web-based idea management system rewards: From the perspective of open innovation

Mikelsone, Elina; Segers, Jean-Pierre; Spilbergs, Aivars Article Governance of web-based idea management system rewards: From the perspective of open innovation Journal of Open Innovation: Technology, Market, and Complexity Provided in Cooperation with: Society of Open Innovation: Technology, Market, and Complexity (SOItmC) Suggested Citation: Mikelsone, Elina; Segers, Jean-Pierre; Spilbergs, Aivars (2022) : Governance of web-based idea management system rewards: From the perspective of open innovation, Journal of Open Innovation: Technology, Market, and Complexity, ISSN 2199-8531, MDPI, Basel, Vol. 8, Iss. 2, pp. 1-28, https://doi.org/10.3390/joitmc8020097 This Version is available at: https://hdl.handle.net/10419/274398 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Citation: Mikelsone, E.; Segers, J.-P.; Spilbergs, A. Governance of WebBased Idea Management System Rewards: From the Perspective of Open Innovation. J. Open Innov. Technol. Mark. Complex. 2022,8, 97. https://doi.org/10.3390/ joitmc8020097 Received: 17 April 2022 Accepted: 26 May 2022 Published: 1 June 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Journal of Open Innovation: Technology, Market, and Complexity Article Governance of Web-Based Idea Management System Rewards: From the Perspective of Open Innovation Elina Mikelsone 1,2,3,* , Jean-Pierre Segers 2and Aivars Spilbergs 3 1PostDoc, Idea Innovation Institute, Ltd., LV-3018 Jelgava Parish, Latvia 2Faculty of Engineering Economics and Management, Riga Technical University, LV-1048 Riga, Latvia; [email protected] 3Economics and Finance Department, BA School of Business and Finance, LV-1013 Riga, Latvia; [email protected] *Correspondence: [email protected] Abstract: Digitisation of business processes has attracted practitioner attention across a wide range of industries as it enables enterprises to better manage their processes and improve results. The rate of digitisation has grown over the last decade and has become essential for enterprises to handle unforeseen disruptions and promote better management of resources. The COVID-19 pandemic is one example of a recent major disruption that caught enterprises off-guard and had a major impact on their value chains. The adoption of digitisation of business processes has significantly sped up to improve enterprise and their value chain resilience and enable enterprises to survive and potentially thrive in today’s uncertain environment. Through this movement, innovative companies have embraced idea management to develop new business models and achieve competitive advantage. One of the major digital tools that support enterprises in their idea management efforts is web-based idea management systems. This study aims to show how enterprises can manage the rewards of platforms, such as web-based idea management systems, to achieve a first-in-class quality and quantity of ideas sourced from the stakeholders involved in these platform-based ecosystems. To reach this aim, a global survey study was conducted involving over 400 responses from enterprises that use web-based idea management systems and consequent results were analysed. The impact of intrinsic and extrinsic rewards on the results of enterprises is a well-researched area. To further verify the results, the authors have conducted ten expert interviews and a qualitative analysis of a data set sourced from a database that contained 129 company entries that utilise web-based idea management systems and was available for this research that was conducted over a two-year period. Results strongly indicate that mixed and financial reward type tasks result in a higher quantity of ideas created, however, higher idea quality—i.e., ideas selected—is higher for financial reward tasks. The research highlights that there could be differences between different web-based idea management systems application groups. Keywords: idea management systems; idea management; rewards; innovation; innovation management; ideation; open innovation 1. Introduction Open innovation helps companies to manage knowledge across organisations’ boundaries [ 1 ] due to the continuous inflows and outflows of knowledge [ 2 , 3 ], including creativity [ 4 ]. This makes open innovation an essential part of modern-day innovation ecosystems [ 5 , 6 ]. Furthermore, the growing popularity of open innovation, digitalisation and increasing remote work (accelerated by COVID-19) has led to the application of different virtual tools that help organisations to manage their innovation ecosystems and facilitate open innovation. Knowledge sharing is one of the key elements in rewards-based idea management (IM), which could be defined as outside-in open innovation. According to J. Open Innov. Technol. Mark. Complex. 2022,8, 97. https://doi.org/10.3390/joitmc8020097 https://www.mdpi.com/journal/joitmc J. Open Innov. Technol. Mark. Complex. 2022,8, 97 2 of 28 Markovic et al. [ 7 ], continuous improvement of employee and dedicated team knowledge and skills are likely to bring behavioural changes, increasing the level of outside-in open innovation. Digitalisation changes the nature of the information flow, therefore digital convergence becomes imperative [ 8 ], including open innovations, technology-related readiness [ 9 ] and web-based communication aspects [ 10 ]. There are different information management systems, such as electronic messaging systems [ 11 ], collaborative systems [ 12 ], group decision support systems, etc. By leveraging the capabilities of information management systems, innovative companies have embraced IM to develop new growing businesses and web-based idea management systems (IMS). Web-based IMS is one of the digital tools that can facilitate enterprises in their IM efforts [ 13 ]. IMS provides organisations with a systematic and manageable process of IM, whereas IM [ 14 ] is one of the first steps in the innovation process [ 15 , 16 ], which could be defined as a two-step process—idea generation and evaluation. There are several business models that can boost innovations. Figure 1[ 17 ] presents a typology of ten business model families for innovations. While each business model family is unique, it does not prevent businesses from combining and mixing these models. In this paper, the focus is on the IM business model that provides potential benefits and opportunities when combined with any of the other models, as shown in Figure 1. 1 Figure 1. Business Model Families [17]. J. Open Innov. Technol. Mark. Complex. 2022,8, 97 3 of 28 There are different types of IMS, for example, an internal type (mainly, the involvement of employees and internal stakeholders), an external type (mainly, the involvement of external parties—clients, crowds, academics, etc.) and a mixed type, which combines both the internal and external type characteristics. In general, IMS is a multi-stakeholder (platform-based) ecosystem for innovations. It is advantageous, as nowadays there is a growing trend toward multi-stakeholder—platform-based—collaborative ecosystems [ 18 ]. Web-based IMS are applied by many well-known and globally recognised organisations, such as Panasonic, Virgin, Sony, Fujitsu, Volvo, NASA, European Commission, etc. There is an abundance of cases that prove the positive effects a web-based IMS can have on an organisation’s performance [ 19 ]. In practice, during the IM process, enterprises often face several challenges when it comes to managing idea generation, evaluation and other management aspects of IM [ 20 , 21 ]. Although there is a great variety of web-based IMS and has shown they are successfully applied by well-known organisations, the situation that was described in a legendary publication Capturing the Complexity in Advanced Technology Use: Adaptive Structuration Theory by DeSanctis and Poole (1994) is still topical, as both the developers and the users of these information management systems see the potential of these systems. However, positive results rarely occur consistently because of the way organisations adopt these systems [ 22 ]. In this research, the authors will contribute to closing this gap by answering the following question: What types of rewards have to be applied to achieve better results, and in this case—what rewards lead to a higher idea quality and quantity? See Figure 2. J. Open Innov. Technol. Mark. Complex. 2022, 8, x FOR PEER REVIEW 3 of 29 involvement of external parties—clients, crowds, academics, etc.) and a mixed type, which combines both the internal and external type characteristics. In general, IMS is a multistakeholder (platform-based) ecosystem for innovations. It is advantageous, as nowadays there is a growing trend toward multi-stakeholder—platform-based—collaborative ecosystems [18]. Web-based IMS are applied by many well-known and globally recognised organisations, such as Panasonic, Virgin, Sony, Fujitsu, Volvo, NASA, European Commission, etc. There is an abundance of cases that prove the positive effects a webbased IMS can have on an organisation’s performance [19]. In practice, during the IM process, enterprises often face several challenges when it comes to managing idea generation, evaluation and other management aspects of IM [20,21]. Although there is a great variety of web-based IMS and has shown they are successfully applied by wellknown organisations, the situation that was described in a legendary publication Capturing the Complexity in Advanced Technology Use: Adaptive Structuration Theory by DeSanctis and Poole (1994) is still topical, as both the developers and the users of these information management systems see the potential of these systems. However, positive results rarely occur consistently because of the way organisations adopt these systems [22]. In this research, the authors will contribute to closing this gap by answering the following question: What types of rewards have to be applied to achieve better results, and in this case—what rewards lead to a higher idea quality and quantity? See Figure 2. Figure 2. Research topicality and gap. Source: created by the authors. This research will have both academic and practical contributions by filling the following gaps: Knowledge gap: No research looks at intrinsic and extrinsic rewards leading to better results in a web-based IMS application type context. There is existing research on rewards in IM within innovation and descriptive articles, however, in this research, authors specifically look at web-based (digital) IMS and IMS application types and therefore this research has a novelty factor. Figure 2. Research topicality and gap. Source: created by the authors. This research will have both academic and practical contributions by filling the following gaps: Knowledge gap: No research looks at intrinsic and extrinsic rewards leading to better results in a web-based IMS application type context. There is existing research on rewards in IM within innovation and descriptive articles, however, in this research, authors specifically J. Open Innov. Technol. Mark. Complex. 2022,8, 97 4 of 28 look at web-based (digital) IMS and IMS application types and therefore this research has a novelty factor. Practical knowledge gap: Web-based IMS application types in professional practices are not covered in existing research and there are only a few articles where researchers look at how different application types influence IM results. For companies, it is important to understand how they can apply web-based IMS more effectively and the key aspects they need to pay attention to in the IMS application. Within this research, the authors focus on the rewarding mechanisms to facilitate better results. Thus, the authors will provide practitioners with an answer to the following question: What rewards should be used based on the web-based IMS application type? By answering this question, practitioners will be able to make better-informed decisions based on the IMS application type and desired IM outcome. Empirical Gap: There are literature reviews that include descriptions of the importance of rewards in web-based IMS; however, there is no further focus or elaboration through empirical research. Theory Gap: There are theories about rewards and the adaptation of different information management systems, but in this paper, the authors will try to fill a gap identified in the literature review—how the adaptation of different reward mechanisms influences results based on the IMS application type (see in Figure 3. An adaptive structuration theory (AST) is considered an appropriate theoretical framework for this study, as it could reveal how structures and systems interact and present the results. AST states that the technology application by an organisation is determined by several forces: the technology, the organisational systems, the task and the way structures emerge and update in the appropriation process [ 23 ]. From the start, AST has been used to study decision systems, and it is a strong theoretical framework that can be used to study other more advanced information technologies [ 24 ], such as web-based IMS. Although AST provides the framework to understand the interaction between systems and structures, there is little evidence on how specific ICT tool applications may affect results associated with structures [ 25 ]. Here, the structures will be web-based IMS application types, focusing on outputs related to idea quality and idea quantity, while the rewards will be the systems that will interact with the structure (See Figure 3). J. Open Innov. Technol. Mark. Complex. 2022, 8, x FOR PEER REVIEW 4 of 29 Practical knowledge gap: Web-based IMS application types in professional practices are not covered in existing research and there are only a few articles where researchers look at how different application types influence IM results. For companies, it is important to understand how they can apply web-based IMS more effectively and the key aspects they need to pay attention to in the IMS application. Within this research, the authors focus on the rewarding mechanisms to facilitate better results. Thus, the authors will provide practitioners with an answer to the following question: What rewards should be used based on the web-based IMS application type? By answering this question, practitioners will be able to make better-informed decisions based on the IMS application type and desired IM outcome. Empirical Gap: There are literature reviews that include descriptions of the importance of rewards in web-based IMS; however, there is no further focus or elaboration through empirical research. Theory Gap: There are theories about rewards and the adaptation of different information management systems, but in this paper, the authors will try to fill a gap identified in the literature review—how the adaptation of different reward mechanisms influences results based on the IMS application type (see in Figure 3. An adaptive structuration theory (AST) is considered an appropriate theoretical framework for this study, as it could reveal how structures and systems interact and present the results. AST states that the technology application by an organisation is determined by several forces: the technology, the organisational systems, the task and the way structures emerge and update in the appropriation process [23]. From the start, AST has been used to study decision systems, and it is a strong theoretical framework that can be used to study other more advanced information technologies [24], such as web-based IMS. Although AST provides the framework to understand the interaction between systems and structures, there is little evidence on how specific ICT tool applications may affect results associated with structures [25]. Here, the structures will be web-based IMS application types, focusing on outputs related to idea quality and idea quantity, while the rewards will be the systems that will interact with the structure (See Figure 3). Figure 3. AST and web-based IMS. Despite the practical experience and authors’ prior research base for this topic, the existing literature on IM lacks the evaluation of different reward system results. In the literature, there is no common view of what is the best kind of motivation for encouraging people to provide idea suggestions [26]. Some researchers have based their assumptions on that intrinsic motivation is very important, such as the possibility to express ideas, apply and improve creativity, learn something new, see the ideas being applied, or Figure 3. AST and web-based IMS. Despite the practical experience and authors’ prior research base for this topic, the existing literature on IM lacks the evaluation of different reward system results. In the literature, there is no common view of what is the best kind of motivation for encouraging people to provide idea suggestions [ 26 ]. Some researchers have based their assumptions J. Open Innov. Technol. Mark. Complex. 2022,8, 97 5 of 28 on that intrinsic motivation is very important, such as the possibility to express ideas, apply and improve creativity, learn something new, see the ideas being applied, or improve conditions through creating new ideas [ 27 , 28 ]. In contrast, other researchers state the importance of an extrinsic motivation that is based on monetary rewards [ 29 , 30 ]. This study aims to show how enterprises can manage the rewards of such platforms as web-based IMS to achieve a first-in-class quality and quantity of ideas sourced from the stakeholders involved in these platform-based ecosystems. To reach this aim, a global survey study was conducted of over 400 responses from enterprises that use web-based IMS and the subsequent results were analysed. To verify the results, the authors have conducted ten expert interviews and a qualitative analysis of a data set sourced from a database that contained 129 company entries that utilise web-based IMS and was available for this research that was conducted over a two-year period. The focus of this study is on an organisational level, researching web-based IMS applications within the AST framework. In the next sub-section, the authors outline the main IMS and reward literature trends and verify the research novelty. Figure 4shows the general steps of the research design. The research is based on a systematic literature review that supports the research of existing theories and identifies research gaps. Based on the systematic literature review, the authors have defined the research question and selected the appropriate research methods. From the survey results, the authors defined sub-questions to be further researched. The triangulation approach was used by authors to verify results by using three different data collection methods. J. Open Innov. Technol. Mark. Complex. 2022, 8, x FOR PEER REVIEW 5 of 29 improve conditions through creating new ideas [27,28]. In contrast, other researchers state the importance of an extrinsic motivation that is based on monetary rewards [29,30]. This study aims to show how enterprises can manage the rewards of such platforms as webbased IMS to achieve a first-in-class quality and quantity of ideas sourced from the stakeholders involved in these platform-based ecosystems. To reach this aim, a global survey study was conducted of over 400 responses from enterprises that use web-based IMS and the subsequent results were analysed. To verify the results, the authors have conducted ten expert interviews and a qualitative analysis of a data set sourced from a database that contained 129 company entries that utilise web-based IMS and was available for this research that was conducted over a two-year period. The focus of this study is on an organisational level, researching web-based IMS applications within the AST framework. In the next sub-section, the authors outline the main IMS and reward literature trends and verify the research novelty. Figure 4 shows the general steps of the research design. The research is based on a systematic literature review that supports the research of existing theories and identifies research gaps. Based on the systematic literature review, the authors have defined the research question and selected the appropriate research methods. From the survey results, the authors defined sub-questions to be further researched. The triangulation approach was used by authors to verify results by using three different data collection methods. Figure 4. Research design steps. Source: created by the authors. Figure 4. Research design steps. Source: created by the authors. 2. Theoretical Framework Many researchers not only concentrate on exploring the different information technologies but also their applications and impact. According to the basic scanning of seven J. Open Innov. Technol. Mark. Complex. 2022,8, 97 6 of 28 scientific databases, there are over 100 papers, proceedings and notes that aim to explain it. There are two main trends in the literature. The first one is to explore the overall IT management and the different use aspects, for example, malleable IT uses [ 31 ], a technology adaption in groups [ 32 ], IT management responsibilities [ 33 ], an information system project control [ 34 ], social inclusion and IT [ 35 ], ICT and the wellbeing of nations [ 36 ], IT impact on performing companies [ 37 ], etc. The aim of these studies is to explore specific aspects of ICT. The second trend is to explore the specific type of IT, for example, group support systems [ 38 ], crowdsourcing [ 39 ], gaming platforms [ 40 ], knowledge management systems [ 41 ], etc. The aim of these studies is to research the specific ICT types and related aspects. In this paper, the authors rely on the second construct—exploring the specific aspect related to the specific ICT because it is a more focused view on the issue. The growing number of publications on IM has shown that there is an interest in the academic community about IM, however, still, there is a lack of academic research papers on many crucial aspects of IM. IM and IMS have been researched by various researchers (e.g., Brem, Bothos, Bjork, Barczak, Westerski, Bakker) and the gained results are quite similar, emphasising the potential of IM and IMS. Most researchers examine IM and its features, while the focus on IMS, and especially on web-based IMS, is rare in the scientific literature. The scarcely available literature forms a theoretical basis for future research. However, it must be noted that there is little theoretical guidance on how the application of different rewards influences the results of different IMS application types. 2.1. IMS Application Types and Outputs The paper is built on the assumption that IMS is a tool, a tool kit or a complex system which facilitates IM, while IM is a systematic and manageable process of idea generation, evaluation and the repeated idea generation and evaluation if it is needed [ 42 ]. There are different web-based IMSs available on the market. The authors apply two classifications of IMS as IMS application types: based on a task focus and on an involvement focus. According to the task focus, all IMSs could be divided into active or passive IMS. The passive IMS are without a task focus (participants are welcome to submit all kinds of ideas without a specific task); however, in an active IMS, the application process provides the opportunity to create separate tasks (participants generate ideas for the specific task or tasks). Based on the involvement focus, all IMS could be divided into internal, external or mixed focus. Internal focus provides only the involvement of idea creators from within the organisation’s bounds (mostly employees, separate departments, etc.); external IMS implies the possibility to involve external idea creators (e.g., crowds, clients, experts, etc.) and the mixed focus combines both approaches by involving both internal and external stakeholders in the IM process. IM results can be measured in two ways: based on idea quality (ideas selected for further development/implementation), and idea quantity (ideas created). The quality of ideas is the average amount of selected ideas for further development, while idea quantity is the number of ideas created [ 43 – 47 ]. Rewards are an important part of IMS as IM is a suggestion process, thus by involving rewards [ 48 ] the enterprises are more capable of motivating idea submissions and better results [49,50]. In previous research, authors have conducted a systematic literature review on the available IM literature. The theoretical framework development is based on a theoretical research method, namely, by use of a critical literature review. Data collection was conducted in 4 stages: (1) Scientific database research was conducted to explore the literature using the following terms (conducted in Q4, 2022): “idea management”, “idea management systems”; each of these terms was looked up in four different scientific databases; (2) only the literature directly about IM, IMS was selected; (3) we excluded all articles that were duplicates and/or articles that did not have full-text availability; (4) the selected literature was applied in a further systematic literature analysis to find the research gaps. Detailed literature source counts at the different stages are reflected in Table 1. J. Open Innov. Technol. Mark. Complex. 2022,8, 97 7 of 28 Table 1. Idea management literature review. Stages: Stage 1—in Article Title and/or Keywords in the Article: Stage 2—Directly about (Full Text Available): Stage 3—Unique Sources: IM IMS IM IMS IM IMS Scopus 66,459 28,860 126 42 234 86 Google Scholar 5,200,000 5,080,000 82 29 Ebsco 94 4 12 4 Web of Science 406 38 62 13 Sum: 5,266,959 5,108,902 282 88 Every term was searched for in the different databases, based on the scientific database availability. By analysing the content and future research directions of the articles, it was concluded that there are only a few papers where rewards are described; however, no paper focuses on web-based IMS types and the impact of rewards on idea quality and/or quantity. 2.2. Rewards The role of reward systems in innovation is a well-researched field according to the number of papers in the scientific database Scopus—with over 1427 articles at the time of this research. Authors have started the analysis of publications with an initial data collection through the search of pre-defined terms: TITLE-ABS-KEY (Innovation rewards) AND (LIMIT-TO (SUBJAREA, “BUSI”) OR LIMIT-TO (SUBJAREA, “SOCI”)). It means the author limited the literature research to the social sciences and business management journals. The search results were filtered and then the refined results were used for further network analysis. The network analysis provides qualitative scientific explorations with a focus on publication correlations and their impact on the publisher network. In the network analysis, keywords in the titles were appraised. The initial search was conducted in the Scopus database to collect the publications for network analysis. For the network analysis, the visualisation tool VOSViewer was applied. It is effective at clustering and visualising data, and therefore, it is used for the network presentation of keyword correlations [ 51 ]. The whole procedure is visible in Figure 5. J. Open Innov. Technol. Mark. Complex. 2022, 8, x FOR PEER REVIEW 7 of 29 articles that were duplicates and/or articles that did not have full-text availability; (4) the selected literature was applied in a further systematic literature analysis to find the research gaps. Detailed literature source counts at the different stages are reflected in Table 1. Table 1. Idea management literature review. Stages: Stage 1—in Article Title and/or Keywords in the Article: Stage 2—Directly about (Full Text Available): Stage 3—Unique Sources: IM IMS IM IMS IM IMS Scopus 66,459 28,860 126 42 234 86 Google Scholar 5,200,000 5,080,000 82 29 Ebsco 94 4 12 4 Web of Science 406 38 62 13 Sum: 5 , 266 , 959 5 , 108 , 902 282 88 Every term was searched for in the different databases, based on the scientific database availability. By analysing the content and future research directions of the articles, it was concluded that there are only a few papers where rewards are described; however, no paper focuses on web-based IMS types and the impact of rewards on idea quality and/or quantity. 2.2. Rewards The role of reward systems in innovation is a well-researched field according to the number of papers in the scientific database Scopus—with over 1427 articles at the time of this research. Authors have started the analysis of publications with an initial data collection through the search of pre-defined terms: TITLE-ABS-KEY (Innovation rewards) AND (LIMIT-TO (SUBJAREA, “BUSI”) OR LIMIT-TO (SUBJAREA, “SOCI”)). It means the author limited the literature research to the social sciences and business management journals. The search results were filtered and then the refined results were used for further network analysis. The network analysis provides qualitative scientific explorations with a focus on publication correlations and their impact on the publisher network. In the network analysis, keywords in the titles were appraised. The initial search was conducted in the Scopus database to collect the publications for network analysis. For the network analysis, the visualisation tool VOSViewer was applied. It is effective at clustering and visualising data, and therefore, it is used for the network presentation of keyword correlations [51]. The whole procedure is visible in Figure 5. Figure 5. The procedure of network analysis. Source: created by the authors. Figure 5. The procedure of network analysis. Source: created by the authors. To identify new trends in the field network, an analysis of the keywords was executed, thus, keywords in the title, abstract and keywords by using the following function: “(TITLEABS-KEY (“innovation rewards”))”. This was limited to the social sciences, business, management and accounting. This led to identifying 1427 documents. For further investigation, “.RIS” and “.CVS” files were exported from the Scopus database for further analysis. There is no doubt that the question about rewards in innovation processes is gaining traction year by year, based on the growing number of papers. See the Scopus database publication number by the years in Figure 6. J. Open Innov. Technol. Mark. Complex. 2022,8, 97 8 of 28 J. Open Innov. Technol. Mark. Complex. 2022, 8, x FOR PEER REVIEW 8 of 29 To identify new trends in the field network, an analysis of the keywords was executed, thus, keywords in the title, abstract and keywords by using the following function: “(TITLE-ABS-KEY (“innovation rewards”))”. This was limited to the social sciences, business, management and accounting. This led to identifying 1427 documents. For further investigation, “.RIS” and “.CVS” files were exported from the Scopus database for further analysis. There is no doubt that the question about rewards in innovation processes is gaining traction year by year, based on the growing number of papers. See the Scopus database publication number by the years in Figure 6. Figure 6. Number of publications in the Scopus. Source: created by the authors. According to the Scopus database, the most influential authors are Griffin, A., Chandy, R.K., Ford, E.W., Mazzucato, M., Prabhu, J.C., Sergeeva, N., Vossen, A. This differs from the results of the VosViewer network analysis where the most cited articles and linkages are created by Hauser J., Lin, H.J., West, J. A total of 238 works were clustered in 27 clusters and 316 links (see Figure 7). Figure 7. Network analysis based on citation. Source: created by the authors. The identified keywords were investigated using the tool VOSviewer to detect their interconnections. It is possible to observe the development of the topic and deduce new perspectives. The results can be seen in Figure 8. 0 20406080100120 2022 2016 2010 2004 1998 1992 1986 1980 1974 1947 Figure 6. Number of publications in the Scopus. Source: created by the authors. According to the Scopus database, the most influential authors are Griffin, A., Chandy, R.K., Ford, E.W., Mazzucato, M., Prabhu, J.C., Sergeeva, N., Vossen, A. This differs from the results of the VosViewer network analysis where the most cited articles and linkages are created by Hauser J., Lin, H.J., West, J. A total of 238 works were clustered in 27 clusters and 316 links (see Figure 7). J. Open Innov. Technol. Mark. Complex. 2022, 8, x FOR PEER REVIEW 8 of 29 To identify new trends in the field network, an analysis of the keywords was executed, thus, keywords in the title, abstract and keywords by using the following function: “(TITLE-ABS-KEY (“innovation rewards”))”. This was limited to the social sciences, business, management and accounting. This led to identifying 1427 documents. For further investigation, “.RIS” and “.CVS” files were exported from the Scopus database for further analysis. There is no doubt that the question about rewards in innovation processes is gaining traction year by year, based on the growing number of papers. See the Scopus database publication number by the years in Figure 6. Figure 6. Number of publications in the Scopus. Source: created by the authors. According to the Scopus database, the most influential authors are Griffin, A., Chandy, R.K., Ford, E.W., Mazzucato, M., Prabhu, J.C., Sergeeva, N., Vossen, A. This differs from the results of the VosViewer network analysis where the most cited articles and linkages are created by Hauser J., Lin, H.J., West, J. A total of 238 works were clustered in 27 clusters and 316 links (see Figure 7). Figure 7. Network analysis based on citation. Source: created by the authors. The identified keywords were investigated using the tool VOSviewer to detect their interconnections. It is possible to observe the development of the topic and deduce new perspectives. The results can be seen in Figure 8. 0 20406080100120 2022 2016 2010 2004 1998 1992 1986 1980 1974 1947 Figure 7. Network analysis based on citation. Source: created by the authors. The identified keywords were investigated using the tool VOSviewer to detect their interconnections. It is possible to observe the development of the topic and deduce new perspectives. The results can be seen in Figure 8. The system of linked keywords is represented as a network. The colours point to the clusters in which most of the keywords of a group occur together. The size of the node shows the connection degree of the keywords. A total of 509 keywords included three clusters, 55,492 links found, and a total link strength of 142,980. As seen, the most affiliating keyword is “employee”. This is also the most connected keyword in its cluster with the terms “team”, and “dimension”. The second-largest node is the keyword “incentive”. The third is “platform” and “community”. For the Scopus database, the main keywords show other words (see Figure 9) for example: “innovation”, “human”, “reward”, “motivation”, and “knowledge management” can be explained by the meaning of the network analysis— not the frequency but the weighting of the connections is emphasised. J. Open Innov. Technol. Mark. Complex. 2022,8, 97 15 of 28 of comparable groups and are compared with t-statistics critical values tν,α , where the degrees of freedom (d f )for the t-test statistics were calculated as follows: d f =s2 i ni+s2 j nj2 s2 i ni2 (ni−1)+ s2 j nj!2 (nj−1) (4) Table 4. Granger causality test statistics by IMS and reward types. IMS Type Reward Ideas F-Stat p-Value Significance Active Financial Created 12.963 0.0004 *** Passive Financial Created 4.0121 0.0452 * Internal Financial Created 3.4522 0.0638 . External Financial Created 51.776 0.0000 *** Mixed Financial Created 6.0445 0.0143 * Active Financial Selected 7.8796 0.0052 ** Passive Financial Selected 3.6451 0.0553 . Internal Financial Selected 8.7913 0.0032 ** External Financial Selected 10.210 0.0015 ** Mixed Financial Selected 5.5665 0.0187 * Active Non-financial Created 9.3584 0.0023 ** Passive Non-financial Created 13.103 0.0003 *** Internal Non-financial Created 6.8891 0.0090 ** External Non-financial Created 4.6263 0.0323 * Mixed Non-financial Created 14.291 0.0002 *** Active Non-financial Selected 10.882 0.0010 ** Passive Non-financial Selected 6.8975 0.0089 ** Internal Non-financial Selected 6.4158 0.0116 * External Non-financial Selected 3.7024 0.0549 . Mixed Non-financial Selected 4.3474 0.0376 * Active Mixed Created 14.219 0.0002 *** Passive Mixed Created 4.1253 0.0425 * Internal Mixed Created 3.7841 0.0512 . External Mixed Created 25.217 0.0000 *** Mixed Mixed Created 17.287 0.0000 *** Active Mixed Selected 7.0893 0.0081 ** Passive Mixed Selected 3.8753 0.0487 * Internal Mixed Selected 3.0621 0.0760 . External Mixed Selected 3.4441 0.0641 . Mixed Mixed Selected 7.4953 0.0064 ** Significance codes: *** = 0.001; ** = 0.01; * = 0.05; . = 0.1. Source: created by the authors. Additionally, p-values were calculated for the given test statistics and the degrees of freedom. The p-value is the probability of obtaining a value of the test statistic as extreme or as 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. The decision rule is to reject H 0 in favour of H A , if the t-statistic (t stat ) is larger than the t-critical (tα)values for (α=0.05): tstat >tα(5) In the next chapter, the authors of the article apply these methods to reveal the secrets about the rewards of web-based IMS. J. Open Innov. Technol. Mark. Complex. 2022,8, 97 16 of 28 3.3. Triangulation For triangulation to verify the results, the authors have conducted 10 expert interviews with IM and design thinking experts. Five questions were included related to the main five IMS application types, and each question had two sub-questions about what rewards create greater idea quality and quantity from their experience. The last question was looking at whether they agree with the statistical data analysis results from this research, or they do not think that it matches with reality. All interview data were anonymised. Expert criteria: (1) A person with over 3-years of practical experience in IM as a professional that provides these services to enterprises or is the responsible person for it in the enterprise; (2) highest education in related fields. In Table 5, see the information about informants. Table 5. Summary of informants. Identifier Sector Position Experience (Years) Education INT1 Academical, private Innovation consultant 10 Business administration INT2 Private Innovation Lead 4 Economics INT3 Academical, private Innovation consultant 5 Business administration INT4 Public, academical Innovation consultant 6 Economics INT5 Private Innovation Lead 4 Business administration INT6 Academical, private Innovation consultant 7 Business administration INT7 Private, public Innovation Lead 8 Economics INT8 Private Innovation consultant 9 Business administration INT9 Private Innovation Lead 15 Business administration INT10 Academical, private Innovation consultant 11 Business administration/Economics Source: created by the authors. The main question fields are represented in Table 6. According to these questions, data analysis was made—it was used as the code map. Table 6. Expert interview question fields. Main Question Field by IMS Type Sub-Questions about Rewards in Relation to the Results Active IMS application Best rewards to increase idea quantity Best rewards to increase idea quality Passive IMS application Internal IMS application External IMS application Mixed IMS application Source: created by the authors. Interviews were conducted in a one-on-one and Zoom settings. The interviews ranged from 45 min to 65 min long and the data were transcribed to prepare for analysis. The authors used a semi-structured interview guide. The semi-structured design was selected as it provides consistency between interviews while allowing a natural flow conducive to an open discussion and the emergence of unanticipated topics. The authors gained access to one web-based IMS entry of 129 company tasks in the period of 2018–2020, with the main limitation that data could be shown only as summary results about rewards, and that the results in the five IMS application types, name of IMS and their clients could not be shared publicly. The main analysis fields are represented in Table 7. J. Open Innov. Technol. Mark. Complex. 2022,8, 97 17 of 28 Table 7. Databases analysis. Main Analysis Fields Sub-Questions about Rewards in Relation to Results Active IMS application Type of rewards Idea quality Idea quantity Passive IMS application Internal IMS application External IMS application Mixed IMS application Source: created by the authors. 4. Results How to reward better? The answer is provided in the results. Based on the survey results, the main descriptive statistics were calculated (See Table 8) to characterise the average number of ideas created and ideas selected in the tasks by the different reward types. Table 8. Descriptive statistics on ideas created and selected by the rewards’ types. Reward’s Type Observations Ideas Created Ideas Selected Mean Standard Deviation Mean Standard Deviation Non-financial 207 1616.3 2569.5 9.4 14.5 Financial 51 3171.2 4048.0 19.2 31.3 Mixed 171 3202.8 3270.2 12.6 14.9 Source: created by the authors. The data represents that the higher the number of ideas is created in the mixed and financial reward type tasks, the idea of quality (ideas selected) is higher in the financial reward tasks. It also gives the statistical grounds for testing the pre-defined hypothesis. The next question is whether there are differences according to the IMS application type. Figure 15 demonstrates that there are differences in the average number of ideas created per task depending on the reward and IMS types. J. Open Innov. Technol. Mark. Complex. 2022, 8, x FOR PEER REVIEW 18 of 29 Table 8. Descriptive statistics on ideas created and selected by the rewards’ types. Reward’s Type Observations Ideas Created Ideas Selected Mean Standard Deviation Mean Standard Deviation Non-financial 207 1616.3 2569.5 9.4 14.5 Financial 51 3171.2 4048.0 19.2 31.3 Mixed 171 3202.8 3270.2 12.6 14.9 Source: created by the authors. The data represents that the higher the number of ideas is created in the mixed and financial reward type tasks, the idea of quality (ideas selected) is higher in the financial reward tasks. It also gives the statistical grounds for testing the pre-defined hypothesis. The next question is whether there are differences according to the IMS application type. Figure 15 demonstrates that there are differences in the average number of ideas created per task depending on the reward and IMS types. Figure 15. The average number of ideas created per task depends on the reward and IMS types. Source: created by the authors. Figure 16 demonstrates that there are differences in the average number of ideas selected per task depending on the reward and IMS types. Figure 16. The average number of ideas selected per task, depending on the reward and IMS types. Source: created by the authors. Figure 15. The average number of ideas created per task depends on the reward and IMS types. Source: created by the authors. Figure 16 demonstrates that there are differences in the average number of ideas selected per task depending on the reward and IMS types. J. Open Innov. Technol. Mark. Complex. 2022,8, 97 18 of 28 J. Open Innov. Technol. Mark. Complex. 2022, 8, x FOR PEER REVIEW 18 of 29 Table 8. Descriptive statistics on ideas created and selected by the rewards’ types. Reward’s Type Observations Ideas Created Ideas Selected Mean Standard Deviation Mean Standard Deviation Non-financial 207 1616.3 2569.5 9.4 14.5 Financial 51 3171.2 4048.0 19.2 31.3 Mixed 171 3202.8 3270.2 12.6 14.9 Source: created by the authors. The data represents that the higher the number of ideas is created in the mixed and financial reward type tasks, the idea of quality (ideas selected) is higher in the financial reward tasks. It also gives the statistical grounds for testing the pre-defined hypothesis. The next question is whether there are differences according to the IMS application type. Figure 15 demonstrates that there are differences in the average number of ideas created per task depending on the reward and IMS types. Figure 15. The average number of ideas created per task depends on the reward and IMS types. Source: created by the authors. Figure 16 demonstrates that there are differences in the average number of ideas selected per task depending on the reward and IMS types. Figure 16. The average number of ideas selected per task, depending on the reward and IMS types. Source: created by the authors. Figure 16. The average number of ideas selected per task, depending on the reward and IMS types. Source: created by the authors. 4.1. Do Just Non-Financial Rewards Provide a Larger Number of Ideas Created/Selected per Task Than Only Financial Ones? The answer to this question is provided by testing the first two hypotheses: 1. The use of just non-financial rewards provides a larger number of ideas created per task than the use of only financial ones. 2. The use of just non-financial rewards provides a larger number of ideas selected per task than the use of only financial ones. Calculated t-statistics, degrees of freedom (df), critical values (t c ) and p-values are aggregated in the following Table 9. Table 9. Hypothesis test statistics for non-financial and financial rewards comparison. Impact Type Non-Financial Reward Financial Reward t-Statistic df t-Critical p-Value MeaniniMeanjnj Ideas created 1616.3 207 3171.2 51 −2.6162 60.28 2.0003 0.9944 Ideas selected 9.4 207 19.2 51 −2.1720 55.37 2.0040 0.9829 Source: created by the authors. As the calculated t-statistics are not larger than the critical values for the comparable reward types pairs, the authors cannot reject the corresponding null hypothesis and conclude that the sample data does not provide enough evidence to support the alternative hypothesis—there is little chance that the use of non-financial rewards alone will provide a larger number of ideas created and a larger number of ideas selected than the use of financial rewards alone (ca. 0.6% and 1.7%, respectively). 4.2. Do Mixed Rewards Provide a Larger Number of Ideas Created/Selected per Task Than Only Financial/Non-Financial Ones? The answer to this question is provided by testing the second two hypotheses: 3. The use of mixed (financial and non-financial) rewards provides a larger number of ideas created per task than the use of only financial or non-financial rewards. 4. The use of mixed (financial and non-financial) rewards provides a larger number of ideas selected per task than the use of only financial or non-financial rewards. The following Table 10 summarises the statistics to test the third and fourth hypotheses. As can be seen, the use of mixed rewards increases the number of ideas generated and selected. However, it is not clear whether these increases are statistically significant, or if they can be explained by a sampling error. J. Open Innov. Technol. Mark. Complex. 2022,8, 97 19 of 28 Table 10. Hypothesis test statistics for mixed rewards comparison with non-financial and financial rewards. Impact Type Comparable Rewards Pairs MeaniniMeanjnjt-Statistic df t-Critical p-Value Ideas created F&N vs. FR 3202.8 171 3171.2 51 0.0511 70.57 1.9944 0.5203 F&N vs. NR 3202.8 171 1616.3 207 5.1626 319.1 1.9674 0.0000 Ideas selected F&N vs. FR 12.6 171 19.2 51 −1.4509 56.94 2.0032 0.9238 F&N vs. NR 12.6 171 9.4 207 2.1001 358.3 1.9666 0.0182 Source: created by the authors. As the calculated t-statistics are not larger than the critical values for the mixed and financial rewards, the authors cannot reject the corresponding null hypothesis and conclude that the sample data does not provide enough evidence to support the alternative hypothesis—the chance that the use of mixed rewards will provide a higher number of ideas created is only slightly higher than with a financial reward alone, and there is a little chance that the use of mixed rewards will provide a larger number of ideas selected than the use of financial rewards alone (ca. 7.6%). As the calculated t-statistics are larger than the critical values for the mixed and non-financial rewards, the authors would reject the corresponding null hypothesis and conclude that the sample data provide strong evidence to support the alternative hypothesis. The conclusions imply that the application of mixed rewards has resulted in more ideas created than the application of only non-financial rewards and that the application of mixed rewards has resulted in more ideas selected than the application of only non-financial rewards is supported also by low p-values (<0.02). 4.3. Does the Impact of the Mixed (Financial and Non-Financial) Rewards on the Number of Ideas Generated and Selected Differ Depending on the Different Types of IMS? The following Table 11 summarises the statistics on the impact of different rewards types on the number of ideas created and selected for different types of IMS. As can be seen, the most significant effect of the mixed remuneration is on the average number of ideas created in cases of the application of mixed, external and active IMS. The mixed remuneration does not affect the number of ideas created in cases of the application of passive and internal IMS. In terms of the number of ideas selected, it is less than with ideas created, however, it still has a significant impact in the application’s case of mixed and active IMS. Table 11. Mean values of ideas created and selected by the rewards’ types. IMS Type Means of Ideas Created Means of Ideas Selected F&N FR NR Total F&N FR NR Total Active 5240.6 4948.7 2628.5 3694.9 21.4 24.5 15.6 17.7 Passive 386.1 2119.4 385.2 633.3 3.4 14.4 4.4 5.3 Internal 821.2 1710.5 1083.9 1016.9 11.5 13.9 10.8 10.9 External 4841.9 5993.8 1572.5 3268.5 7.9 21.2 4.6 7.7 Mixed 4724.3 1083.3 2411.5 2930.8 18.8 22.0 11.6 14.6 Source: created by the authors. Dominating reward style is highlighted. The answer to this question is provided by testing the fifth hypothesis: 5. The impact of mixed (financial and non-financial) rewards on the number of ideas generated and selected is different depending on the different types of IMS. The following Table 12 summarises the statistics to test the fifth hypothesis. Though it is not clear whether these differences in mean values of ideas are created and selected and are statistically significant, or if they can be explained by a sampling error. J. Open Innov. Technol. Mark. Complex. 2022,8, 97 20 of 28 Table 12. Hypothesis test statistics for mixed rewards impact by IMS types. Impact Type Comparable IMS Type Pairs MeaniMeanjt-Statistic df t-Critical p-Value Ideas created Active vs. External 5240.6 4841.9 1.1994 338.7 1.9670 0.1156 Active vs. Mixed 5240.6 4724.3 1.4978 340.0 1.9670 0.0676 Ideas selected Active vs. Mixed 21.4 18.8 1.4140 324.3 1.9673 0.0792 Source: created by the authors. As the calculated t-statistics are not larger than the critical value for the comparable IMS types pairs, the authors cannot reject the corresponding null hypothesis and conclude that the sample data does not provide enough evidence to support the alternative hypothesis—the difference in the mean values of ideas created when mixed rewards are applied in the cases of active, external or mixed IMS types usage, and the difference in the mean values of ideas selected when active or mixed IMS types are applied, are not statistically significant at a 95% confidence level. However, the possibility that in the case of a mixed reward usage with active IMS applications will be generated and selected, a larger number of ideas is high enough (>92%). 4.4. Expert Interview Results Expert interviews verified statistical data analysis results that different IMS should use different rewards to obtain better results and also that financial and mixed rewards more often lead to better results than just non-financials alone. Detailed interview results are in Table 13. Table 13. Summary of interview results. Identifier Active IMS Application Passive IMS Application Internal IMS Application External IMS Application Mixed IMS Application Created Selected Created Selected Created Selected Created Selected Created Selected INT1 F&N F&N FR FR F&N OR FR FR F&N FR F&N F&N or FR INT2 F&N F&N F&N F&N F&N F&N F&N F&N F&N F&N INT3 F&N FR FR or F&N FR F&N ON FR FR F&N FR F&N F&N or FR INT4 FR OR F&N FR FR FR FR FR OR F&N FR OR F&N FR F&N F&N INT5 FR OR F&N FR F&N FR NR OR FR FR OR F&N FR OR F&N FR OR F&N F&N or FR F&N or FR INT6 F&N F&N FR FR F&N FR FR FR F&N F&N or FR INT7 FR OR F&N FR OR F&N F&N or FN FR NR FR OR F&N FR FR OR F&N F&N or FR F&N or FR INT8 FR OR F&N FR OR F&N F&N or FN FR FR FR OR F&N FR FR F&N F&N INT9 F&N FR F&N FR NR or FR FR OR F&N F FR OR F&N FR OR F&N F&N or FR F&N or FR INT10 F&N F&N OR FR FR FR F&N F&N F&N FR F&N F&R Source: created by the authors. According to the interview results, it was proved that financial or mixed rewards will lead to higher idea generation and selection results, and when looking at ideas created and selected, idea selection is stimulated by financial rewards more than mixed or non-financial alone. J. Open Innov. Technol. Mark. Complex. 2022,8, 97 21 of 28 Just one of the interview subjects has highlighted that overall, in all cases, the best results are with mixed rewards; however, they explain that there will be differences between what will be the proportion of financial and non-financial rewards in different web-based IMS applications. For example, they explain that in passive and external IMS types, financial reward portions should be bigger and the non-financial part is used just as a marketing element to attract the attention to the IM process. Thus, this additional explanation resonates with other expert perspectives because all other experts expressed that there are differences in what kind of IMS type organisation applies and rewards impact on results. According to the interviews, the dominating opinion is that passive and external IMS applications should be followed by financial rewards more than other IMS application types, both to create greater idea quality and quantity. However, in mixed IMS application types more mixed rewards are suggested to create more ideas. Experts have also mentioned some examples of more specific, interesting rewards for mixed, financial, and non-financial reward types: • Financial rewards: for each generated idea, EUR 1 and a bonus to the next salary is given (EUR 5000 for the best idea); a prize of a EUR 500 gift card in-game shop; a 10% salary increase for the best idea author; The top ten authors receive each EUR 100, but the top three receive a EUR 1000 gift card in different shops. • Non-financial rewards: one day as the boss; special badges for the number of generated ideas; one free day for the top ten contributors; a special concern for the person’s department, the potential to choose the food for the next office party. • Mixed rewards: a surprise box from the company goods (value: EUR 75) and the title “Idea guru”; EUR 50 cents for each generated idea and badges in the IMS system; EUR 500 for the best idea creators and hockey tickets with the opportunity to meet the hockey team after the game. According to the interviews, rewards should be appropriate and resonate with the tasks inactive IMS application cases (should be a good story about the reward), however, in passive IMS applications, these rewards should be something that will lead to interest in the long term and something that will not lose motivation. 4.5. Databases Qualitative Analysis Results The web-based IMS experience of 129 companies was analysed by counting the dominating IMS application types. In these 129 cases, the successful idea generation results were defined based on application. Thus, the authors counted the dominating rewards based on the system application. The main results are represented in Table 14, and these results are consistent with the survey results. Table 14. Databases analyses of the ideas created. N F&N FR NR Active IMS application 100 60% 29% 11% Passive IMS application 29 31% 69% 0% Internal IMS application 68 10% 87% 3% External IMS application 51 24% 75% 2% Mixed IMS application 10 70% 20% 10% Source: created by the authors. Dominating reward style is highlighted. To analyse the best idea selection results, the authors have filtered and selected cases where at least 10% of the ideas were selected for development. See Table 15. J. Open Innov. Technol. Mark. Complex. 2022,8, 97 22 of 28 Table 15. Databases analyses of the ideas selected. N F&N FR NR Active IMS application 90 57% 32% ↑11% Passive IMS application 23 17% 83% ↑0% Internal IMS application 67 10% 87% 3% External IMS application 51 24% 75% 2% Mixed IMS application 7 57% 29% ↑14% Source: created by the authors. “↑” means in comparison with ideas created. According to this analysis, the dominating types for good cases do not change, however, it is important to see if the proportion is increasing or decreasing. Based on these analysis results, selected ideas are stimulated by financial rewards in active, passive and mixed IMS application types. Statistically, these differences are not tested because in some groups, the sample size is of insufficient size. 5. Discussion: Web-Based Idea Management System Rewards and Open Innovation 5.1. How to Reward Better? The answer based on the descriptive statistics makes us believe that there is a higher number of ideas created for the mixed and financial reward type tasks, however, idea quality (ideas selected) is higher for the financial reward tasks. It is also highlighted that there could be differences between different web-based IMS application groups. The impact of intrinsic and extrinsic rewards on the results related to enterprises is a well-researched area. Some research proves that intrinsic rewards have a higher impact than extrinsic ones [ 77 ]; this research has proved that in a web-based IMS context, financial and mixed approaches provide better idea quality and idea quantity results. These results provide the following further research directions: (1) What are the differences based on the enterprise’s characteristics (e.g., size, industry etc.); (2) what is the impact of rewards on other results of IMS (e.g., different innovations, involvement, etc.) Do just non-financial rewards provide a larger number of ideas created/selected per task than only financial ones? Using non-financial rewards alone will provide a larger number of ideas created and a larger number of ideas selected than the use of only financial rewards. Do mixed rewards provide a larger number of ideas created/selected per task than only financial/non-financial? The chance that the use of mixed rewards will provide a higher number of ideas created is only slightly higher than with financial rewards alone, and the use of mixed rewards will provide a larger number of ideas selected than the use of only financial rewards. However, the application of mixed rewards will cause more ideas created than the application of non-financial rewards alone, and the application of mixed rewards has resulted in more ideas being selected than the application of non-financial rewards alone. These results consist of the self-determination theory that extrinsic rewards can enhance an intrinsic motivation [ 68 ] and the general interest theory, which extracts rewards, where that can enhance an intrinsic motivation [70]. Does the impact of mixed (financial and non-financial) rewards on the number of ideas generated and selected differ depending on the different types of IMS? The most significant effect of mixed remuneration is on the average number of ideas created in cases of the application of mixed, external, and active IMS. The mixed remuneration does not affect the number of ideas created in cases of the application of passive and internal IMS. In terms of the number of ideas selected, it is less than with ideas created, but it still has a significant impact in the application cases of mixed and active IMS. 5.2. Web-Based Idea Rewards System, and Its Way to Motivate Open Innovation Mixed and external IMS relate to open innovation, which is an important approach to collecting globally distributed knowledge and for enterprises increasingly searching for external ideas. The obvious issue is how to ensure the high-quality contribution of all J. Open Innov. Technol. Mark. Complex. 2022,8, 97 23 of 28 participants [ 78 – 80 ]. The response to this problem, from a web-based IMS perspective, is to select the most appropriate rewards to increase the involvement and end results. Webbased IMSs are tools that could help to boost open innovations with external and mixed IMS application types. Open innovation highlights that competitive advantage often comes from inbound connections [ 81 ] where external knowledge flows inside the firm [ 82 ]. From the research results, it is clear that the highest idea quality and quantity could be stimulated by financial rewards or financial rewards in combination with non-financial rewards. Open innovation via crowdsourcing [ 83 ] is a frequently applied method in organisations and crowdsourcing is one of the IMS sub-types. Community crowdsourcing, where involved actors are preselected on the basis of specific resources or required knowledge [ 84 ], could be defined as an active IMS application. Thus, to boost this type of crowdsourcing enterprise, the use of rewards that suit best for active IMS should be adopted. Open crowdsourcing, on the other hand, is a passive IMS where there is no pre-selection of contributors. Crowdsourcing via innovation intermediaries, where the external broker connects the company with potential contributors [ 85 ]—in this case, it could be either active or passive IMS—depends on the pre-selection process that the broker selects. External and mixed IMS are great for boosting open innovation across enterprises’ boundaries [ 2 , 3 ], and creating the innovation ecosystem [ 5 , 6 ]. In future research, authors should explore in detail how different crowdsourcing groups could be influenced by different types of rewards, because there could be different crowdsourcing types, such as scientific crowdsourcing [ 86 ]. The combination of internal and external IMS as mixed IMSs become more popular with the trend to move away from delivering innovative activities primarily internally, to outsourcing innovative activities externally. IMS could help organisations to support “multidexterity” from a perspective that it helps the organisation to simultaneously carry out a multiple ideas search and selection activities based on diverse strategic logic and levels of knowledge to generate innovative outcomes [ 87 ], thus bringing to life new business models [88]. 6. Conclusions 6.1. Theoretical and Practical Implications This paper offers several theoretical implications for scholars and researchers. First, rewards have been researched in a variety of research contexts, however, they have not been extensively researched in a web-based IMS context. Second, the results provide some insights that may help in designing future studies. They highlight the importance of empirical and theoretical research to select detailed financial and non-financial rewards. The results also show that there are a lot of possible elements to research in the future. In future studies, researchers should evaluate and select the most appropriate reward methods. The main theoretical implication is the answer to the composite research question: What types of rewards have to be applied that could lead to better results in different IMS application types, and in this case—what rewards lead to a higher idea quality and quantity? The paper filled several gaps: (1) knowledge gap—research focuses on intrinsic and extrinsic rewards leading to better results in a web-based IMS application type context. There was existing research on rewards in IM within the innovation and descriptive articles, however, this research looks at web-based (digital) IMS and IMS application types that provide this research with novel elements; (2) theory gap—there are theories about rewards and the adaptation of different information management systems; however, in this paper, authors filled a gap that was identified in the literature review—how the adaptation of different reward mechanisms influences results based on the IMS application type; (3) practical knowledge gap—web-based IMS application types in professional practices are not covered in existing research and there are only a few articles where it was discovered how different application types influence IM results. Thus, the authors provide practitioners with an answer to the following question: What rewards to use based on the web-based IMS application type? J. Open Innov. Technol. Mark. Complex. 2022,8, 97 24 of 28 This research contribution is part of a series of publications about the types of IMS and the use of IM methodology in different industries and business ecosystems. For example, in previous and outgoing research studies, the authors have discovered a relationship between web-based IMS and creativity based on the managerial survey results [ 74 ]; the IMS outcome’s influence on IMS creates benefits [ 75 ] and the results of the IMS application in goal setting and achieving, and decision making [ 89 ] and tasks of IMS [ 74 ]. In these papers, the authors analyse the outcomes and benefits of web-based IMS; however, in this paper, the authors analyse elements of the rewards that have led to such elements. Web-based IMS fall in line with the growing importance of information communication technologies, and the spread of open innovation and co-innovation; therefore, indirectly, this paper creates a contribution to open innovation and co-innovation approaches. For practical implications, the results proved that mixed reward usage with active IMS applications will provide higher idea quality and quantity; therefore, managers should focus on this combination of approaches. The practical contribution of the research results helps to understand what kind of results enterprises could be expected from different IMS application types and rewards. The research results highlight the benefits/implications of adopting different IMS for organisations. This paper provides managers with a richer set of theoretical tools, letting them make better decisions regarding the selection of IMS that is the best for achieving the results in a context. Web-based IMS types and their impact on the IMS results could help to understand the potential application of these systems in different application scenarios. 6.2. Limitations These results must be interpreted with caution and several limitations should be taken into account. To deal with many of these limitations, the authors applied the triangulation method to check the same areas of questions with other methods—expert interviews and databases analyses. The main limitations based on the basic research approach are discussed below. Issues with samples and selection, whereby only commercially available web-based IMS clients were invited into the surveys because of the approach to reach them through IMS developers. IMS developers distributed the survey to their selected clients, who approved the survey reception. In future studies, also non-commercial IMS application cases should be included. There was an insufficient sample size for statistical measurements to research the data by industry. Industry perspectives could bring some interesting exploration based on the industry approach differences. Additionally, database analysis provided only descriptive results because of the small amount of data. Regarding the lack of previous research studies on rewards in different web-based IMS, depending on the specific research topic, prior research studies that apply to the paper are limited. Here, it can be considered an important opportunity; presenting a gap with the need for further development in the study. The methods/instruments used to collect the data included the survey instrument which provided limited data about rewards. In future research, authors could use detailed database analysis and case studies to verify and test results. Authors have to realise that they could also create more detailed survey questions from another viable perspective of rewards. For example, the value of rewards could be considered because of the already extensive survey with over 186 variables; however, the authors could not include an important question about the values and sizes of the rewards in the survey. Regarding the limited access to data, the limited access to these respondents was only through IMS providers. Because of this limited access, authors needed to redesign and/or restructure their research depending on the circumstance. Findings are still reliable and valid despite this limitation.