How collective intelligence fosters incremental innovation

Lee, Jung-Yong; Jin, ChangHyun Article How collective intelligence fosters incremental innovation Journal of Open Innovation: Technology, Market, and Complexity Provided in Cooperation with: Society of Open Innovation: Technology, Market, and Complexity (SOItmC) Suggested Citation: Lee, Jung-Yong; Jin, ChangHyun (2019) : How collective intelligence fosters incremental innovation, Journal of Open Innovation: Technology, Market, and Complexity, ISSN 2199-8531, MDPI, Basel, Vol. 5, Iss. 3, pp. 1-17, https://doi.org/10.3390/joitmc5030053 This Version is available at: https://hdl.handle.net/10419/241346 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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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/ Journal of Open Innovation: Technology, Market, and Complexity Article How Collective Intelligence Fosters Incremental Innovation Jung-Yong Lee 1and Chang-Hyun Jin 2,* 1Department of Business Administration, SungKyunKwan University, Seoul 03063, Korea 2Department of Business Administration, Kyonggi University, Suwon 16227, Korea *Correspondence: [email protected] Received: 2 July 2019; Accepted: 7 August 2019; Published: 8 August 2019   Abstract: The study aims to identify motivational factors that lead to collective intelligence and understand how these factors relate to each other and to innovation in enterprises. The study used the convenience sampling of corporate employees who use collective intelligence from corporate panel members (n=1500). Collective intelligence was found to affect work process, operations, and service innovation. When corporate employees work in an environment where collective intelligence (CI) is highly developed, work procedures or efficiency may differ depending on the onset of CI. This raises the importance of CI within an organization and implies the importance of finding means to vitalize CI. This study provides significant implications for corporations utilizing collective intelligence services, such as online communities. Firstly, such corporations vitalize their services by raising the quality of information and knowledge shared in their workplaces; and secondly, contribution motivations that consider the characteristics of knowledge and information contributors require further development. Keywords: collective intelligence; social contribution motivation; personal contribution motivation; work process; operation; service innovation; incremental innovation 1. Introduction The ubiquity of information communication technologies—increasingly brought on by recent breakthrough developments—is apparent as a means of inducing intrinsic yet innovative change across corporate management practices. Competition is becoming among corporations that fight for survival amidst a rapidly changing global economic environment. As a result, corporations are keen on finding ways to build innovative capacity, which requires the introduction of collective intelligence as a means of solving problems through exchange and cooperation, with groups that are both internal and external to them. Several researchers haveraised the idea of launching a community focused on collective intelligence (hereafter CI) for the common good over recent years [ 1 ]. The challenges and opportunities in applying CI in enterprises are necessary to improve the effectiveness of decision-making or to solve various business issues [ 1 , 2 ]. The possibility of combining CI with existing corporate perspectives, such as rapid decision-making and efficient use of internal resources, has become a key factor in determining future sustainable development. “Collective intelligence increases the capacity for effective action in pursuit of common aims and finding emergent and sustainable solutions to the complex problems.” [3] The internal capacities of a corporation have perhaps reached its limit, and a new source of competitiveness is desperately needed to develop sustainable management practices. However, research on identifying cause-and-effect relationships associated with CI is lacking. Moreover, previous studies J. Open Innov. Technol. Mark. Complex. 2019,5, 53; doi:10.3390/joitmc5030053 www.mdpi.com/journal/joitmc J. Open Innov. Technol. Mark. Complex. 2019,5, 53 2 of 17 do not build a theoretical model that explains the relationship between CI and incremental innovation. Therefore, this study aims to explore motivational factors that lead to CI and understand how these factors relate to each other and to innovation in enterprises. The results will provide valuable data to support effective decision-making and various organizational strategies. 2. Literature Review 2.1. Collective Intelligence (CI) Research on CI has explored the capabilities of a collective [ 4 – 6 ]. CI as a topic has typically been studied and applied in the fields of sociology, business management, and computer science; however, today it is being applied across all social phenomena. CI, also known as swarm intelligence, has been a major interdisciplinary topic of research in the fields of entomology, sociology, computer science, and others [ 7 ]. CI enables an idea to have a greater impact than any individual’s idea on its own as a result of a process whereby the individual idea is combined with and evolves with others, creating a synergy effect. Wikipedia, a free encyclopedia created by the public where thousands of web users contribute written and edited articles based on their knowledge and information, was the first example of a website showcasing the existence of CI. Wikipedia is considered by some to be the most successful case of public CI in history. The definition of CI varies. It has been defined as the ability of a group to arrive at a solution that is better than what any of the members has achieved individually; it has also been defined as groups of individuals doing things collectively that seem intelligent [8,9]. Some scholars have defined “CI as the capacity of human communities to evolve towards higher-order complexity and harmony through such innovation mechanisms as variation-feedback-selection, differentiation-integration-transformation, and competition”[10] CI entails a process whereby individuals share, cooperate, and integrate their knowledge, information, and ideas, resulting in an intellectual capability that surpasses the intellectual capability of individuals; this process also results in the capabilities of a collective formed by the participation in and sharing of information and knowledge of many individuals [ 9 ]. Thus, CI characteristics include the creation of new knowledge through co-creation with others, as well as leading participation. CI is assumed to be a collection of rational judgments made by individuals and is considered to be a better source of judgment than a small group of specialized professionals or the combined capabilities of an individual. CI is a form of intelligence that results from the mutual engagement between people, which is thought to result in better solutions to problems than would otherwise be possible, because of the synergistic effects that result from the collective handling of tasks and the aggregation of advice and criticism [ 11 ]. CI is especially suited to work that demands innovation or creativity [ 5 ]. The CI approach adds a new perspective to technology in supporting energy awareness and eco-friendly choices [ 12 , 13 ]. Surowiceki argued that CI can move markets and society and that a collective always produces wiser judgments than an individual [4]. CI tools are conceived for situations of uncertainty about the impact of actions; it can also be a valuable marketing tool [10]. Based on methods of mutual engagement and cooperation level, Dutton classified CI into three types: (1) The sharing type; (2) the contributing type; and (3) the co-creating type [ 14 ]. These three classifications can be distinguished by their methods of mutual engagement. Sharing CI is typified by activities, such as a simple release of information and individual and one-on-one forms of mutual engagement. The second aspect is building CI through sharing, which includes tools to seek solutions to their problems and share and acquire new knowledge or information. Contributing CI is typified by activities, such as dialog among participants and active responses regarding opinions, knowledge, and information. It reshapes those who contribute information to the collective group [ 14 ]. The active responses considered in this instance involve the evaluation of opinions, information, and knowledge of one’s counterpart that serve as the basis for developing opinions, information, and knowledge. J. Open Innov. Technol. Mark. Complex. 2019,5, 53 3 of 17 From this perspective, CI-building through participants includes users’ positive participation in space to develop their personal knowledge, career, and capability recognition. Co-creating CI is typified by various active methods of mutual engagement, such as updating, editing, and debating, which seek to fine-tune opinions that lead to the production of knowledge or information. This includes the development of users’ operation or management knowledge or information through CI. Thus, three types of CI-building through participation, sharing, and co-creation are applied to this study. The general idea behind the collaboration is the notion that it elicits a self-driven response among individuals vying for a common goal [ 15 ]. In the case of Wikipedia, this involves active corrections and editing activities [ 16 ]. Leadbeater explains CI on the web-based on Web 2.0 characteristics, such as participation, sharing, and openness. CI requires participation [ 17 ]. Individuals must be able to easily express their opinions, register new viewpoints, and solicit opinions from other sources. Thus, CI emerges through continual voluntary participation of individuals. Leadbeater emphasizes the importance of collaborative creativity and mentions participation, cooperation, openness, and sharing as necessary factors for CI. He argues that collaborative creativity results in the active formulation of ideas through the process of sharing and opening up of ideas between individuals that are combined later [ 15 ]. Sulis perceives CI as a probability-based aggregation of multiple semi-independent participants who communicate their ideas and freely engage in discussions within their environments [ 17 ]. Corporations explore various means of applying CI to business practices and are especially keen on utilizing it to establish creative problem-solving methods [18]. 2.2. Motivation Theory Motivation theory has long been employed in academic areas, such as social science and management. This study explores the motivational factors that lead to CI by examining the Theory of Planned Behavior (TPB) and the Theory of Reasoned Action (TRA) proposed by Fishein and Ajzen [ 19 ]. These theories explain that the psychological mechanisms which were affecting attitudes lead to certain behaviors. The motivation behind knowledge sharing in online communities can be explained as an organized civil movement not influenced by any direct or clear perceptions of compensation carried out at the individuals’ discretion [20]. Other scholars also attempt to explain user motivations for knowledge sharing by applying the expected compensation from economic exchange theory, expected association from social exchange theory, and expected contribution from social cognitive theory as variables that affect knowledge sharing [ 21 ]. The subjective norms and perceived behavioral controls that underlie knowledge sharing are closely related to knowledge donation and knowledge collection [ 22 ]. Liao, To, and Hsu attempt to explain the knowledge-sharing attitudes of knowledge users by dividing them into groups motivated by utilitarian or hedonic motivations [ 23 ]. Wasko and Faraj add external rewards—a type of individual motivator—as a variable to explain the knowledge-sharing process [24]. The dispositions of people who contribute to CI may not differ from those who do not. Nov provides a pioneering insight into this notion in his study, exploring motivational factors among Wikipedians [ 25 ]. He adds two categories of motivation, behind the behavior of volunteers, to six others, posited in earlier studies: Protective, values-based, career, social, understanding, and enhancement—namely fun and ideology. This makes a total of eight contribution motivations. Through a series of surveys, Rafaeli et al. uncovered—in order of importance—cognitive needs (e.g., the desire for information acquisition or intellectual challenge), affective needs (e.g., the desire for emotional experiences), and integrative needs (e.g., the desire to share knowledge with others or contribute to others) as the contribution motivation behind Wikipedia. They propose exploring the relative importance of psychological, social, communal, economic, gratifying, and mutually engaging aspects among various motivational factors as additional topics of research related to motivation [ 26 ]. Based on previous discussions, this study attempts to explain the motivation behind the use of CI by dividing the motives into groups reflecting social or individual objectives. In other words, the contribution motivations behind CI are categorized into social contribution motivations and J. Open Innov. Technol. Mark. Complex. 2019,5, 53 4 of 17 individual contribution motivations. The former includes knowledge sharing, content correction, answering others’ questions, recognition of capability and acquisition of greater reputation, and a preference for knowledge cooperation. Individual contribution motivations include acquisition of new knowledge, expectations of responses from others, tangible and intangible compensations, an exhibition of knowledge, and benefits to career development. 2.3. Incremental Innovation What is incremental innovation? Some scholars have defined it as “not-radical” [ 27 ], which can be seen as an unsatisfying definition. Taruss, Boit, and Korir defined this as “incremental improvements to existing products, services, and organizational routines that can enhance performance, quality, and usefulness, and are vital to making more competitively advanced products and services.” [28–31] Incremental innovation vies for improvements in the status quo and is continual while improving on existing assets. Previous research divides incremental innovation into three categories: Work process and procedure innovation, operational innovation, and service innovation [ 8 , 32 ]. First, the work process and procedure innovation are related to the recycling and partial substitution of items associated with the production or specifications of a product. For instance, in terms of the field of service, this implies innovation associated with the simplification of management processes, work processes, or existing internal regulations; reduction of costs; and enhanced work efficiency [33,34]. Second, operational innovation includes innovations associated with redesigning methods and work plans for manufacturing products, and changes in methods, numbers of operations, and services related to reliability and quality. Third, service innovation includes innovations associated with the integration of lower-tier systems aimed at reconfiguring production systems that provide flexibility in operations and facilities. In the service sector, this entails innovations associated with improved services, customer satisfaction, and rapid customer service response to complaints [ 34 ]. In light of the above, this study attempts to understand how CI is related to incremental innovation, such as work processes, operations, and services innovation. 3. Hypotheses 3.1. The Relationship between Two Contribution Motivations and CI Nam, Ackerman, and Adamic include altruism and the ability to learn as individual motivators of knowledge sharing [ 35 ]. Wasko and Faraj include external rewards—a type of individual motivator—as a variable to explain the knowledge-sharing process [ 24 ]. The process of sharing knowledge produces joy as it entails the thought of helping others. According to social exchange theory, knowledge contribution motivation variables affect attitudes regarding knowledge sharing, which in turn affects the intent to share knowledge, and the intent to share knowledge is closely related to the act of sharing knowledge [ 21 ]. According to the expanded TPB, intent to share knowledge is affected by attitudes regarding knowledge sharing, subjective norms regarding knowledge sharing, technical norms regarding knowledge sharing, knowledge sharing itself, and limitations of knowledge sharing. Thereafter, the intent to share knowledge affects the act of sharing knowledge [ 36 ]. Each individual, as one adaptive agent, contributes knowledge, communicates with another, refers to another, votes for others, modifies other’s knowledge, and accomplishes the emergence of mass intelligence [ 37 ]. The notion of value co-creation described by service-dominant logic seems to be parallel to that of CI. Some scholars have mentioned that the ability to collectively harness intelligence represents a means of co-creating value in various aspects of modern business [ 38 ]. As shown in the above discussion, the assumptions that can be applied to motivation research regarding CI include categories of motivations, such as protective, value, career, social, understanding, and enhancement, as well as contribution motivations, such as cognitive needs, affective needs, and integrative needs [ 26 ]. J. Open Innov. Technol. Mark. Complex. 2019,5, 53 5 of 17 Thus, such individual motivators and social orientation relationships can be expected to affect the onset of CI. Therefore, this study intends to explore two contribution motivations for CI. Hypothesis 1 (H1). Social contribution motivations are positively related to sub-factors of CI-building through participation (H1-1), sharing (H1-2), and co-creation (H1-3). Hypothesis 2 (H2). Personal contribution motivations are positively related to sub-factors of CI-building through participation (H2-1), sharing (H2-2), and co-creation (H2-3). Evaluations of CI may vary depending on the level of active evaluation of information or knowledge. The degree to which knowledge or information is produced is also expected to become an important factor in evaluating CI [ 14 , 15 , 18 ]. Boder predicts that CI will play an essential role in corporate knowledge management. He notes that CI will bring new knowledge and innovation that will resolve core problems faced by corporations, and mentions the need to construct CI that includes the expertise of individuals associated with specialized knowledge in particular fields within an organization, as well as cultural norms, informal networks, and strategic market-related knowledge [39]. Engel et al. note that CI is a critical factor to improve decision-making in groups and to produce higher group performance [ 40 ]. CI was then shown to predict a team’s future performance on more complex tasks [ 41 ]. It can be assumed that CI is built upon some combinations of individual members’ attributes, group structures, processes, and norms. Innovation is in demand not only from corporations that are keen on adapting to globalization and rapidly changing domestic and international environments, but also from governments and all other sectors that strive to bring changes to organizations and processes for enhancing competitiveness and adaptability. In general, innovation entails incremental or radical change associated with objects, thoughts, and the status of progress or services [ 42 ]. As mentioned above, incremental innovation naturally occurs in the work environment or corporate work processes. Several studies clarify the relationship between a corporation’s organizational structure and culture with incremental innovation. This study predicts that CI—a form of intelligence that encompasses knowledge and information produced through user participation, sharing, and collaboration, and is created to improve individual work processes and procedures, product development, idea development, operations, and services—is closely related to incremental innovation. Most corporations emphasize process innovations that reduce costs, assist in product development, and make organizational management more efficient for satisfying customer needs. Some scholars observe that CI emerges from a combination of bottom-up and top-down processes within groups and predict future performance and learning in a wide range of environment [ 41 ]. Individual intelligence is recognized as a means of understanding job performance in an organization, as well as for understanding many aspects of group performance, such as service process, task, and innovation in the organization [ 41 ]. Apart from playing an important role in improving corporate work processes, management procedures, and customer satisfaction, CI-building is also expected to lead to the information entropy process at the organizational level. The relationship between CI and incremental innovation may produce different forms of disorders in the organizational system, based on information uncertainty. Thus, an entropy process emerges. Under these conditions, CI-building requires internal control elements to ensure that the organizational system evolves toward higher-order levels, in terms of the amount of information. Considering the scholarly definition of incremental innovation, CI will play a significant role in improving corporate work processes, management procedures, and customer satisfaction services, along with enhancing work efficiency associated with idea development, product development, and strategic thinking. From the perspective, it is expected that CI-building leads to incremental innovation. Therefore, our hypotheses are stated as follows: J. Open Innov. Technol. Mark. Complex. 2019,5, 53 6 of 17 Hypothesis 3 (H3). CI-building through participation positively affects work process innovation (H3-1), operation innovation (H3-2), and service innovation (H3-3) as sub-factors of incremental innovation. Hypothesis 4 (H4). CI-building through sharing positively affects work process innovation (H4-1), operation innovation (H4-2), and service innovation (H4-3) as sub-factors of incremental innovation. Hypothesis 5 (H5). CI-building through co-creation positively affects work process innovation (H5-1), operation innovation (H5-2), and service innovation (H5-3) as sub-factors of incremental innovation. 3.2. Suggested Research Model To guide the analysis of the data collected for this study, the study devised the following suggested research model to illustrate the relationships between the two contribution motivations, collective intelligence, and incremental innovation (Figure 1). J. Open Innov. Technol. Mark. Complex. 2019, 5, 53 5 of 13 3.2. Suggested Research Model To guide the analysis of the data collected for this study, the study devised the following suggested research model to illustrate the relationships between the two contribution motivations, collective intelligence, and incremental innovation (Figure 1). Figure 1. Suggested research model. 4. Method 4.1. Sampling and Data Collection A pretest was administered to 30 corporate employees who used CI in Korea. The measurement tools were based on the literature review related to participants’ motivations and were also measured as exogenous variables; CI, acceptance intention, and incremental innovation were also measured as endogenous variables. The study used the convenience sampling of corporate employees who use CI from about 2000 corporate panel members. Then, they were contacted by e-mail and telephone and asked to participate in the study. A description of CI was placed in the first section of the questionnaire. A total of 1500 corporate employees participated in this study and were given gift cards by the investigator. As seen in Table 1 below, over 75% of participants were 20–49 years of age and over 90% had received college-level instruction. On average, respondents occupied positions in accounting and management, strategic planning, production and research, and operations. Table 1. Demographic profiles. n = 1500 Frequency % Sex Male 760 50.7 Female 740 49.3 Education High School 102 6.8 College Student 367 24.5 Bachelor’s degree 678 45.2 Over M.A Degree 353 23.5 Ages 20–29 years 361 24.1 30–39 years 401 26.7 40–49 years 367 24.5 50–59 years 321 21.4 Over 60 years 50 3.3 Position Accounting and Management 455 30.3 Operations 242 16.1 Strategic Planning 318 21.2 Production and Research 269 17.9 Others 216 14.4 Firm Size 10 and fewer employees 219 14.6 10–30 employees 284 18.9 30–50 employees 138 9.2 50–100 employees 199 13.3 100–300 employees 272 18.1 Over 300 employees 389 25.9 4.2. Instrument Construction 4.2.1. Exogenous Variables Contribution motivations were measured by social and personal contribution motivations as exogenous variables. Social contribution motivations can be defined as embracing the objective of collective intelligence to share information, edit or modify the content, resolve curiosity, co-create Figure 1. Suggested research model. 4. Method 4.1. Sampling and Data Collection A pretest was administered to 30 corporate employees who used CI in Korea. The measurement tools were based on the literature review related to participants’ motivations and were also measured as exogenous variables; CI, acceptance intention, and incremental innovation were also measured as endogenous variables. The study used the convenience sampling of corporate employees who use CI from about 2000 corporate panel members. Then, they were contacted by e-mail and telephone and asked to participate in the study. A description of CI was placed in the first section of the questionnaire. A total of 1500 corporate employees participated in this study and were given gift cards by the investigator. As seen in Table 1below, over 75% of participants were 20–49 years of age and over 90% had received college-level instruction. On average, respondents occupied positions in accounting and management, strategic planning, production and research, and operations. J. Open Innov. Technol. Mark. Complex. 2019,5, 53 7 of 17 Table 1. Demographic profiles. n=1500 Frequency % Sex Male 760 50.7 Female 740 49.3 Education High School 102 6.8 College Student 367 24.5 Bachelor’s degree 678 45.2 Over M.A Degree 353 23.5 Ages 20–29 years 361 24.1 30–39 years 401 26.7 40–49 years 367 24.5 50–59 years 321 21.4 Over 60 years 50 3.3 Position Accounting and Management 455 30.3 Operations 242 16.1 Strategic Planning 318 21.2 Production and Research 269 17.9 Others 216 14.4 Firm Size 10 and fewer employees 219 14.6 10–30 employees 284 18.9 30–50 employees 138 9.2 50–100 employees 199 13.3 100–300 employees 272 18.1 Over 300 employees 389 25.9 4.2. Instrument Construction 4.2.1. Exogenous Variables Contribution motivations were measured by social and personal contribution motivations as exogenous variables. Social contribution motivations can be defined as embracing the objective of collective intelligence to share information, edit or modify the content, resolve curiosity, co-create information, and acquire fame. Questions about social contribution motivations used in this study consist of a total of five items developed in light of the existing literature [ 25 , 26 ]. Personal contribution motivations comprise a total of five items developed from existing research by defining the degree of desire to acquire new information, expectation about another’s responses, compensation, showing the ability to offer information, and work efficiency [20,21,23,24]. 4.2.2. Endogenous Variables: Collective Intelligence, Incremental Innovation Capability Collective intelligence was classified with collective intelligence building through participation, sharing, and co-creation. Collective intelligence building through participation included users’ positive participation in space to develop personal knowledge, their career and recognition of their capability. Collective intelligence building through sharing included tools to seek solutions to their problems and share and acquire their new knowledge or information. Collective intelligence building through co-creation included users’ operation or management forming knowledge, or information through collective intelligence. Collective intelligence is assumed to be a collection of rational judgments made by individuals and is considered to be a source of better judgment than is possible with a small group of specialized professionals or the combined capabilities of a single individual. Measurement tools for collective intelligence used in this study consist of a total of 13 items taken from the existing literature [15,18,24,39]. Scale items used in the study were adapted from previous studies. Twelve items for incremental innovation were developed or adopted from previous studies [ 8 , 32 – 34 ]. Incremental innovation was J. Open Innov. Technol. Mark. Complex. 2019,5, 53 8 of 17 measured by work process innovation, operation innovation, and service innovation. Work process innovation included the degree of improvement over the past, management process, work efficiency and cost reduction. Operation innovation represented the degree of improvement of welfare, work satisfaction, and relationships with others in the organization. Service innovation was defined as the degree of improvement in customer service, response to complaints, and satisfaction. All items used in this study were scored on 5-point Likert scales. 5. Data Analysis 5.1. Assessment of the Measurement Model To analyze the data, the study used EQS6b and SPSS. In order to verify the hypotheses proposed in this study, correlations, validity and reliability were examined. We conducted verification tests to determine tests for the measurement model’s validity using EQS6b. As shown in Tables 3 and 4 , the standard acceptance norm satisfies the requirement of reliability and validity by suggested by Bagozzi and Yi [ 43 ] and Hair et al. [ 44 ] and composite construct reliability and average variance extracted (AVE) following Fornell and Larker [ 45 ]. Discriminant validity was assessed by comparing the correlation of components to AVE. As seen in Table 2, the result of Bartlett’s test of sphericity was found to be significant ( χ2 =7959.3, df =741, p<0.001), while the Kaiser–Meyer–Olkin measure of sampling adequacy was 0.947 for the variables [ 44 ]. The results of the EFA (exploratory factor analysis) are described in Table 3. An exploratory factor analysis of all of our scale items revealed two factors explaining 56.08% of the variance in our study’s constructs, with the first factor explaining 30.42% and the last factor explaining 25.67% of the total variance for independent variables. Discriminant validity was assessed by comparing the correlation of components to AVE. Table 2. Statistics of construct items. Construct Survey Measures Social contribution motivation I wish to contribute my knowledge for the purpose of sharing it with others (sharing contribution) I wish to add or correct wrong information in the public based on my knowledge (addition ·correction) I wish to help others find answers to their questions (answer questions) I wish to exhibit my knowledge to large numbers of people (knowledge exhibition) I like to cooperate with others to create knowledge (knowledge collaboration) Personal contribution motivation I can acquire new knowledge and skills by contributing (skill acquisition) I find it helps my career (career development) I am curious about the responses of others regarding my knowledge (response of others) I like to be compensated for the provision of knowledge (tangible/intangible compensation) I wish to be recognized for my capability and seek a greater reputation (recognition and reputation) J. Open Innov. Technol. Mark. Complex. 2019,5, 53 15 of 17 is important in such a context. Leadership that lends an ear to even obnoxious or seemingly nonsensical thoughts is needed. 6.3. Future Direction and Limitations As an example, Procter and Gamble utilizes CI to reduce its share of research and development costs, while continuing with good performance. On the other hand, Monsanto spends a substantial amount of money on research and development without producing any major products and faces a crisis. Smooth mutual engagement, feedback, and active participation are methods that can raise CI. Additionally, corporate cultures must find efficient spaces in which employees can communicate. This study was subject to a significant limitation. It examined two different motivations for knowledge development: CI and incremental innovation. Thus, the causal relationships considered in this study might be weak. Future research on similar topics can consider individual goals or organizational goals as their objectives. Regarding the sample used in this study, the effect of CI might not be representative, because incremental innovation was drawn from only one outcome. The current limitations of the study are weak, and issues with business performance, etc., are not addressed. Therefore, additional studies with appropriate controls are needed to apply these results to theoretical models of online behavior. Another limitation is that the measurement tools to measure CI provided in this sample are not strong. The items to measure CI might cause certain problems when clarifying CI from individual and organizational standpoints. Thus, CI should be applied to similar topics to increase its statistical validity, such as content or face validities. The study did not address CI issues for different types of organizations. The relationship between the effectiveness of CI and its outcomes, in providing their size (e.g., number, industries they operate, technologies they use for CI, strategy with respect to idea generation, growth over the last years) should be developed. Finally, the cause-and-effect relationships are not considered clearly and strongly, as the present study is cross-sectional and non-experimental. Author Contributions: C.-H.J. wrote the paper and worked with J.-Y.L. to conceive and design the experiments J.-Y.L. and C.-H.J. performed the experiments and analyzed the data; J.-Y.L. and C.-H.J. contributed to parts of the experiments and the conclusions. Both authors made contributions to the work in this study. Funding: This research received no external funding. Conflicts of Interest: The authors declare no conflict of interest. References 1. Schuler, D.; De Liddo, A.; Smith, J.; De Cindio, F. 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