Content area
Purpose
Building on the socio-cultural theory and the climate literature, the purpose of this paper is to examine: how to effectively manage computer-mediated platforms to improve innovation performance, and which types of computer-mediated platforms firms should be more involved with.
Design/methodology/approachThe multivariate mediated regression method and relative effect analysis were employed to test the model.
FindingsAnalyses reveal that online creative climate mediates the effects of the perceived innovation policy on both novelty and meaningfulness of creative behaviors. In addition, online creative climate is positively related to both radical and incremental innovation performance. Further, the relative performance results of the four types of computer-mediated platforms are found to be unequal.
Practical implicationsThe results suggest to managers that establishing creative climates in computer-mediated platforms is a promising approach to improve firms’ innovation performance. The results further indicate that managers should acknowledge the advantages and limitations of each type of computer-mediated platform in order to increase innovation performance. Otherwise, firms may misallocate resources and investment efforts in computer-mediated platforms.
Originality/valueBy categorizing computer-mediated platforms into four types, this study provides the first synthesis of personal interactions that occur in computer-mediated environments. This study presents the first empirical assessment of how creative climate can be used as a facilitator for improving innovation performance and which type of computer-mediated platforms is more appropriate for radical or incremental innovations.
1. Introduction
As extensive research documents the importance of creative climate to innovation performance (e.g. Baer, 2012; Isaksen and Akkermans, 2011), it is no surprise that actively establishing creative climates in various environments is important for firms with the desire to generate superior innovations. Such arguments are of particular importance to the growing concept of computer-mediated platforms, referring to person-to-person communications and interactions over computer networks (Yadav and Pavlou, 2014). While research on computer-mediated platforms could be linked to non-online environments, this study focuses particularly on computer-mediated platforms in the online context that enable users to communicate and interact.
In theory, internet technology facilitates communication, interaction and exchange-related activities among users, consumers and firms (Yadav and Pavlou, 2014; Zhou et al., 2014). Building on this view, previous studies have tried to explore the effectiveness of interactions between consumers and firm activities in computer-mediated platforms. For example, a computer-mediated platform that stimulates user participation is generally believed to be beneficial to a firm’s new product development performance (Lee and Yang, 2015). In addition, computer-mediated platforms are often seen as rich sources of creative ideas that offer added value to firms (Hobbs et al., 2016; Zhou et al., 2014). In practice, a growing number of firms are developing and using computer-mediated platforms to support their innovation development. For example, Dell acquired the idea to sell laptops with the Linux operating system from its IdeaStorm website. Starbucks receives 100,000 ideas from its customers and business partners on MyStarbucksIdea.com to create new business opportunities. In total, 25 percent of Proctor & Gamble’s new products come from InnoCentive, a website where companies post challenges for which anyone can submit solutions (Dodgson et al., 2006). Another example is the Nearby Supernova Factory, an international astrophysics collaboration that is exploding stars in order to learn more about the expansion rate of the universe. About 30 members from around the world collaborate on scientific research through chat and virtual network computing on their own developed internet platform (Brandic and Raicu, 2011). The final example is YouTube, which provides an internet platform where individual creativity can thrive.
While more and more firms acknowledge the importance of computer-mediated platforms to innovation performance, very few studies have taken the firms’ perspectives to investigate the organizational mechanisms needed to facilitate user involvement in computer-mediated platforms (Cui and Wu, 2016). In addition, little is known about which types of computer-mediated platforms firms should be more involved with, in order to improve innovation performance. The literature is unable to offer better guidelines for managers’ decisions to adopt the right approach, because it lacks the theoretical development to explain the differences among various computer-mediated platforms (Yadav and Pavlou, 2014).
To address the above questions, we build on the socio-cultural theory (Lantolf, 2000) and the climate literature (Schneider et al., 2013) to develop a conceptual model that explains an organizational mechanism underlying the beneficial effects of computer-mediated platforms: perceived innovation policy – online creative climate – creative behavior – innovation performance. Specifically, we propose four types of computer-mediated platforms: consumer-consumer, firm-consumer, consumer-firm and firm-firm. We also propose an online creative climate, defined as the shared perceptions among users of the extent to which their computer-mediated platforms are encouraged to engage in creative behavior. To this end, we expect this study to advance the internet and innovation literature by offering the following contributions. First, the empirical results show that computer-mediated platforms can be categorized into four types that facilitate the development of a more impactful research area, therefore, contributing to not only academic discipline, but also to the internet practice. Second, this study empirically examines how creative climate can be used as a facilitator for improving innovation performance and which type of computer-mediated platforms is more effective. Therefore, this study provides new insights to researchers pursuing a deeper understanding of the computer-mediated platform – innovation performance relationship.
In the next section, we introduce four types of computer-mediated platforms, before moving on to the theoretical background and hypotheses development.
2. Four types of computer-mediated platforms
Since the existing literature seems to lack a framework to describe the structure of computer-mediated platforms, to address this question, we integrate the extant literature with the results of the exploratory study. Following the principles proposed by Yin (2013), we first generated a protocol for in-depth interviews. By a snowball sampling (Creswell, 2013), we then conducted the exploratory study that involved in-depth interviews with 18 users of computer-mediated platforms and 13 senior managers who had experience in the relevant knowledge domains, including creative climate and innovation development.
On average, each interview took 72 minutes (range=59-83 minutes). To capture all of the important points covered in the interviews, detailed notes were taken and the proceedings of the interviews were tape-recorded. With particular interviewees (five users of computer-mediated platforms and four senior managers), follow-up interviews were conducted to clarify issues or explore them more deeply. After carefully examining the transcripts, we and two other academics manually and electronically (NVivo 9) converted interviewees’ open-ended responses into four types of computer-mediated platforms.
To advance the results of the coding process, in terms of reliability and validity, we asked two other academics with backgrounds in qualitative research methods to analyze the interview results. We then checked inter-coder reliability based on both the total number of units and the total number of exact matches in units coded by the two academics. The value of inter-coder reliability was acceptable (Cohen’s κ=0.92). The results confirm that four types of computer-mediated platforms were identified:
Consumer-consumer platforms: users’ discussions in the context of consumers’ interactions with consumers, such as BrickBuilders, created by LEGO. Such platforms allow consumers to communicate with each other, which enhances a firm’s ability to create new products or services through acquiring valuable ideas and knowledge.
Firm-consumer platforms: users’ discussions in the context of firms’ interactions with consumers, such as InnoCentive, created by Proctor & Gamble. These platforms create a trustworthy environment where consumers are more willing to share and discuss their ideas and knowledge, and contribute their reviews and recommendations. Such platforms enable firms to create and share innovative knowledge effectively.
Consumer-firm platforms: users’ discussions in the context of consumers’ interactions with firms, such as the forum of the World of Warcraft online game, created by Blizzard Entertainment. These platforms are the environments where communication takes place from consumers to firms. For example, consumers propose their offers with a set budget online and firms review the consumers’ requirements and bid on the project within a limited time frame. The consumers review the bids and select the firms that will complete the offers. Such platforms empower consumers worldwide by providing an avenue for their knowledge of business. Contrary to firm-consumer platforms, users in consumer-firm platforms reverse discussion models, where the user determines and dominates the discussions, which increases consumers’ involvement in firms’ innovation projects.
Firm-firm platforms: users’ discussions in the context of firms’ interactions with firms, such as HP Labs Innovation Research Program, created by Hewlett-Packard. These platforms, with similar professional business consumers, interact on business-related issues and carry out business transactions. With such platforms, firms can efficiently acquire a comprehensive source of knowledge for particular markets, supplier experience, technical solutions and industry news, all of which enable firms to effectively create superior innovations.
Overall, the four types of computer-mediated platforms employ different approaches in which the firms and the customers play different roles. Most importantly, the differences among the four types of computer-mediated platforms suggest that each type may have a differential impact on innovation performance. In the following section, we discuss the socio-cultural theory and the climate literature, and explore their implications for the role of users in computer-mediated platforms.
3. Theoretical background
Organizational climate is defined as the shared perceptions among employees concerning the practices, procedures and behaviors that are expected, supported and rewarded in the workplace (Schneider et al., 2013). Essentially, organizational climate pertains to the shared perceptions of the way things are around here, which become social norms in a particular setting (Baer, 2012). A variety of climates have been introduced, including service climate (Bowen and Schneider, 2014), team climate (Somech and Drach-Zahavy, 2013) or climate change (Holmberg and Hellsten, 2015). Schneider et al. (2013) provide support for multiple climates and further argue that there should be a specific climate for a specific context. Of interest in this study is the existence and effects of online creative climate that shapes creative behavior in computer-mediated platforms.
According to Sundgren et al. (2005), creative climate refers to factors that stimulate or block creativity and innovations in everyday life. Creative climate thus concerns the perceptions of people regarding environment-related aspects of creative factors or policies (Isaksen and Akkermans, 2011). Regarding the differences between (offline) creative climate and online creative climate, online creative climate is developed in open and virtual communication settings, while creative climate is confined in a limited area (Baer, 2012; Schneider et al., 2013). Online creative climate emphasizes the value of networks for exploring the notion of collective creativity and creative solutions. Its intangible elements, such as many-to-many interactions through which user-generated contents are exchanged, enable users to stimulate creativity. Thus, online creative climate should be treated as a separate, unique form of creative climate, which usually thrives in a relatively flat organizational structure with a large variety of networks that can stimulate creativity.
However, creative climate has been less capable of explaining the internal process of learning among individuals (Hunter et al., 2007; Baer, 2012). The socio-cultural theory (Lantolf, 2000) suggests that the settings and experiences that individuals have in their lives influence what they learn and understand, and the process of learning is constructed through social interactions. Thus, the socio-cultural theory can offer insights into the process of individuals’ learning within a creative climate.
Taken together, in order to improve innovation performance, it appears viable for firms to use creative factors or policies to establish an online creative climate, in which users construct creative knowledge together within a social interaction context. Therefore, based on this reasoning, we argue that online creative climate in computer-mediated platforms could be created via firms’ innovation policies, in which users behave in a creative way through interactions. The outcomes of users’ creative behaviors thus contribute to innovation development, which, in turn, improves firms’ innovation performance (Cui and Wu, 2016; Somech and Drach-Zahavy, 2013). Figure 1 presents a conceptual model of the relationships among perceived innovation policy, online creative climate, creative behavior and innovation performance.
4. Hypotheses development
The creative climate literature (Baer, 2012; Isaksen and Akkermans, 2011; Sundgren et al., 2005) suggests that organizational climate captures employee perceptions regarding the organizational norms that pertain to innovation. Specifically, if employees perceive their organization as having an orientation toward innovation, then the norm would be that the organization approves of behavior that benefits the innovation. In this regard, we argue that users’ perceptions of innovation policy in computer-mediated platforms are positively related to online creative climate.
To be classified as an innovation policy, the policy must be perceived as intentional by users in computer-mediated platforms. Although an innovation policy in computer-mediated platforms may go undetected by users at the time it occurs, Goldstein et al. (2008) suggest that people who experience such a policy will engage in a process of trying to make sense of the satiation, which includes discussing the policy with other users. In addition, when members interact with an organizational policy, over time these interactions influence the perceptions of acceptable climate held by members of the organization (Schneider et al., 2013). This is especially true for the perception of online creative climate because it encourages users in computer-mediated platforms to engage in creative norms. As shared perceptions of an innovation policy increase, users perceive that the benefits of engaging in creative norms outperform the risks, which will positively impact the perception of online creative climate. On the other hand, if the shared perception among users of an innovation policy is lower, the engagement in creative norms will also be lower. Therefore, we hypothesize:
A positive relationship exists between perceived presence of an innovation policy and online creative climate in computer-mediated platforms.
We further argue that online creative climate in computer-mediated platforms mediates perceived innovation policy and creative behavior. The climate literature has shown that organizational climate is reliably related to employee behavior (Schneider et al., 2013). Along this line, we propose that online creative climate in computer-mediated platforms may also lead to positive creative behavior. In particular, following the studies of Im and Workman (2004) and Janssen and Huang (2008), we distinguish creative behavior into novelty and meaningfulness. Novelty of creative behavior refers to the users’ motivation to dedicate a discretionary effort toward goals of computer-mediated platforms in terms of originality and unique innovations. In contrast, meaningfulness of creative behavior refers to the users’ motivation to dedicate a discretionary effort toward goals of computer-mediated platforms in terms of appropriate and useful aspects of innovations.
Previous researchers (Zhou et al., 2014) have suggested that consumers in virtual environments are likely to express themselves freely and could generate insightful knowledge into the strengths and weaknesses of firms’ existing products or services. Based on this knowledge, firms are able to develop a new product that is superior to existing products in attributes or service offerings (Lee and Yang, 2015). In this line, users within an online creative climate in computer-mediated platforms are also likely to have a similar behavior to express themselves, with insightful knowledge regarding the strengths and weaknesses of existing products. This behavior is consistent with the meaningfulness of creative behavior. As such, perceived innovation policy, through an online creative climate in computer-mediated platforms eventually leads users to behave in a meaningful creative way.
In contrast, according to Slater et al. (2014), users’ breakthrough knowledge can be integrated into a new product design, by enhancing product attributes with the breakthrough ideas. In addition, users in virtual environments can offer firms knowledge about unique or breakthrough features of existing products (Zhou et al., 2014; Lee and Yang, 2015). Following these views, users within an online creative climate in computer-mediated platforms can also provide firms with information related to their products’ unique or breakthrough features. The action of providing unique or breakthrough information is the same as the action of novelty of creative behavior. As such, the perceived innovation policy, through the online creative climate, eventually also leads users in computer-mediated platforms to behave in a novel creative way. Therefore, we hypothesize:
Online creative climate mediates the relationship between the perceived innovation policy in computer-mediated platforms and (a) novelty and (b) meaningfulness of creative behaviors.
The degree of innovation ranges from a totally new, or discontinuous, innovation to an innovation involving a minor adaptation or improvement of an incremental nature (Han et al., 2012). While several innovation types have been proposed, this study adopts radical innovation and incremental innovation, because they provide fundamental functions in innovation activities (Han et al., 2012). Such a differentiation has been used in similar innovation research (Atuahene-Gima, 2005; Slater et al., 2014).
Incremental innovation is related to customer-led strategies that focus on manifest needs, and is posited to be the most common form of innovation (Atuahene-Gima, 2005; Han et al., 2012). In addition, the development of incremental innovation tends to limit the range of potential innovation, because it relies on customers’ current views of the market (Han et al., 2012). On the other hand, radical innovation is defined as fundamental changes in new products/services that represent revolutionary changes in product/service benefits (Atuahene-Gima, 2005; Slater et al., 2014). As such, incremental innovation performance describes a new value creation through the incremental addition of existing values, while radical innovation performance creates brand new values through innovative concepts.
Previous studies maintain that employees’ behavior has a positive effect on innovation outcomes. For example, drawing from relational identification theory, Yoshida et al. (2014) find that servant leadership promotes individual relational identification, which, in turn, fosters employee behavior to create innovations. Baron and Tang (2011) indicate that entrepreneurs are significantly related to their actions of creativity and that creativity, in turn, is positively related to firm-level innovation. Following this line, we expect that both novelty and meaningfulness of creative behaviors shaped by computer-mediated platforms within an online creative climate should be positively related to innovation performance.
Specifically, because an online creative climate in computer-mediated platforms encourages users to propose novel and new ideas, firms can acquire these novel ideas to create radical new products (Lee and Yang, 2015). According to Slater et al. (2014), novel ideas help firms develop breakthrough technologies or designs and, as a result, create radical innovations that are superior to competing ones. Novelty of creative behavior shaped by computer-mediated platforms within an online creative climate is also likely to create radical innovations. Similarly, this novelty of creative behavior can create incremental innovations as well. This is because, according to Baer (2012), users within a creative climate are likely to create novel ideas or concepts, which are the starting points for incrementally improving existing products. Overall, it is expected that novelty of creative behavior shaped by computer-mediated platforms within an online creative climate is likely to improve both radical and incremental innovation performance.
As for meaningfulness of creative behavior, because users in computer-mediated platforms within an online creative climate are willing to express their opinions regarding strengths and weaknesses of existing products (Zhou et al., 2014), firms can take advantage of users’ opinions or information to improve their existing products, which results in improving incremental innovation performance (O’Connor and Rice, 2013). On the other hand, users through meaningfulness of creative behavior could also provide firms with breakthrough ideas or information to develop brand new products (O’Connor and Rice, 2013), because radical innovations not only possess breakthrough technologies, but also present meaningfulness to customers. Following this line of thought, meaningfulness of creative behavior shaped by computer-mediated platforms within an online creative climate is expected to improve radical innovation performance as well. Therefore, we hypothesize:
The relationship between online creative climate and innovation performance is mediated by (a) novelty and (b) meaningfulness of creative behaviors.
Although the literature supports the positive impact of a creative climate on creative behavior and innovation performance, the effectiveness of each type of computer-mediated platform is not likely to be of similar magnitude. This is mostly because each computer-mediated platform has its own unique characteristics (Yadav and Pavlou, 2014), which may result in different outcomes. For example, consumer-consumer computer-mediated platforms focus mainly on consumers’ interactions (Cui and Wu, 2016), while firm-firm computer-mediated platforms focus on firms’ strategies and tactics (Lacka and Chong, 2016; Lin et al., 2011). In addition, each firm has different information needs and knowledge management processes, which, as a result, emphasize different aspects of innovation activities (Slater et al., 2014), such as the emphasis of novel or meaningful creativity. In addition, Schneider et al. (2013) indicate that despite the use of the same creative climate in one organization, different working environments of the organization produce different effects. Accordingly, the performance of the online creative climate is expected to be situational, depending on a specific type of computer-mediated platform. Therefore, we hypothesize:
The effect of online creative climate on (a) creative behavior and (b) innovation performance varies by different types of computer-mediated platforms.
5. Research method
5.1 Sample
The target population was defined as firms based in Taiwan that had used computer-mediated platforms in their new product/service development projects. These firms were selected for three reasons. First, Taiwan had a quite high internet usage rate of 66.4 percent in 2009. This 66.4 percent is comparable to the rates of Asian countries, such as 67.6 percent in Singapore, 66.8 percent in Hong Kong and 75.2 percent in Japan (Internet World Stats, 2010). Second, research has found that national culture could affect participant behavior (Hofstede et al., 2010). Particularly, the Taiwanese are raised in a relatively conservative cultural environment and, therefore, tend to be less expressive themselves. However, because the visual anonymity and psychological distance of the internet have caused Taiwanese participants to perceive the internet as being safer than face-to-face communication, they are more willing to express their opinions in computer-mediated platforms (Zhou et al., 2014). Finally, to survive the competition and maintain their competitive advantage, Taiwanese firms must continuously take advantage of the information from the internet context to develop new products/services that are specific to the Mainland China market, which has experienced rapid growth in the internet market over the past decade (Jiang, 2014).
To reduce the potential for common method bias and perceptual bias across the subjectively measured constructs for independent and dependent variables, we obtained different information from different sources (Podsakoff et al., 2012). Specifically, to capture the perceived innovation policy and online creative climate in computer-mediated platforms, we collected ten users from each type of computer-mediated platform as key respondents (namely, 40 users represented each firm). To avoid a selection of samples of active users only, after being given a list of users from participating firms we e-mailed all users on the list to notify them of what this study was doing and how we were conducting the survey. Focusing on the discussions related to quality of discussion content and professionalism, we randomly identified ten users who represented only one type of computer-mediated platform and did not overlap other types for the survey. Furthermore, we obtained data for both novelty and meaningfulness of creative behaviors from new product development managers, and for both radical and incremental innovation performance from finance managers (see Figure 1).
5.2 Data collection
5.2.1 Two pilot tests
We adapted established measures for some constructs (e.g. innovation performance) and created new measures for others (e.g. online creative climate and creative behavior). For the new measures, we followed the framework proposed by Churchill (1979), to develop new items from in-depth field interviews (52-74 minutes) with 33 senior managers. To finalize the measurement items, we and two other academics identified any ambiguous items, and then four senior managers evaluated the content, format and clarity of the items. For the established measures, we used the double-translation method to translate English items into Chinese ones (English-Chinese-English). This process included the authors initially translating the items into Chinese; another two academics then translating the Chinese version back into English; and this translation being checked by a third academic, to ensure conceptual equivalence.
When the initial items were specified, the first pilot test was performed to ensure the measurement was reliable and valid (Churchill, 1979). By way of a pencil-and-paper survey, this study used a convenience sample of 53 users of computer-mediated platforms and 47 senior managers, all of whom had been involved in creative climate, computer-mediated platforms and new product/service development projects. They were first asked to criticize the face validity of the items, which were then revised accordingly.
To enhance content validity, the second pilot test was conducted. Another convenience sample of 109 users of computer-mediated platforms and 68 senior managers were then asked to complete the questionnaire and to indicate any ambiguity or difficulty they experienced when responding to the items. The results show that very few wordings were modified for greater clarity and the Cronbach’s α values of all measures exceeded 0.93, suggesting the scales had a high degree of internal consistency. In addition, item discriminant validity was measured by comparison of the item-to-own scale correlation with the item-to-other scale correlation values. All items exhibited a higher correlation with their own scale than with the other scales. Overall, all scales were appropriate to this study.
5.2.2 Procedure
This study was designed to collect data longitudinally over four waves of data collection, early 2011, mid 2011, mid 2012 and mid 2013, through a separate survey. The main purpose was to allow time for the performance effects of online creative climate, creative behavior, and innovation performance to materialize.
In early 2011, we used a sample of the Top 5,000 companies operating in Taiwan, in terms of total revenue as published by the China Credit Information Service 2010. We first contacted all 5,000 firms by phone to request a manager who was in charge of computer-mediated platforms. We then limited the sample frame to firms which had implemented innovation policies in their computer-mediated platforms, referring to policies that explicitly aim at promoting the development of new products and services. In total, 657 firms were qualified and agreed to participate. A questionnaire for the measure of perceived innovation policy was sent to ten users representing each type of computer-mediated platform. Six months later, in mid 2011, the same users completed the online creative climate measure. This period was selected because it allowed time for the online creative climate to be established (Schneider et al., 2013). Subsequently, this study received useful responses from 594 firms (40 matched users for each firm).
In mid 2012, we conducted a follow-up survey to capture changes to creative behavior over time. We contacted new product development managers of the same 594 firms and asked them to rate creative behavior. In total, 16 of the original firms could not be reached, leading to 578 responses.
In mid 2013, we contacted the finance managers of the same 578 firms and asked them to rate their firms’ innovation performance. Three responses were excluded due to extreme values, resulting in 575 usable responses. The sample consisted of various industries: electronics (28.1 percent), telecommunications (26.4 percent), information technology (24.1 percent), web games (20.2 percent) and others (1.1 percent). On average, the firms had 738 users in each type of computer-mediated platform and launched 8.42 new products/services based on the contributions of their computer-mediated platforms over the period of survey.
To assess whether the third time sampling firms are representative of the second time sampling firms, we compared both samples and found no significant differences in terms of firm size, firm age or industries (F=1.52, p=0.61). We also compared the third sample with the first sample frame, and the results indicate that there were no significant differences with respect to firm size, firm age, or industries (F=0.52, p=0.93).
5.3 Measures
We developed a two-item scale to measure perceived innovation policy by asking users how often they perceived the presence of innovation policy in their computer-mediated platforms. Item responses ranged from never (0) to often (3). Responses to all items were summed (range 1-7) to create an overall score, where higher scores indicate greater perceived innovation policy.
We developed a six-item scale based on Sundgren et al. (2005) to measure online creative climate. We developed an eight-item scale based on suggestions by Im and Workman (2004) and Janssen and Huang (2008) to measure novelty and meaningfulness of creative behaviors, respectively. We used a four-item scale adapted from Atuahene-Gima (2005) to measure radical and incremental innovation performance, respectively.
We controlled for sources of heterogeneity in firm characteristics and industries. Specifically, we used the logarithm of the number of employees as an indicator of firm size, to control for the impact of the firms’ resources on innovation development (Im and Workman, 2004). We used the number of years the firms had been in operation to control for the impact of a firm’s age and experience in innovation development. Finally, dummy variables were used to control for the effects of industries. All multi-item measures were measured on seven-point Likert scales, and are presented in the Table AI.
6. Data analysis and results
6.1 Validation of measures
Following recommendations by Anderson and Gerbing (1988), the validation of the measures was tested by evaluating reliability, unidimensionality, convergent validity and discriminant validity. Since each type of computer-mediated platform from each firm was represented by ten users, a single score was created by a summated approach (Hair et al., 2010). Reliability was first assessed and the results indicate the Cronbach’s α values for all measures were well above the threshold recommended value of 0.7 (Nunnally, 1978).
Next, to assess the unidimensionality of constructs, we conducted an exploratory factor analysis (EFA) with a varimax rotation and a confirmatory factor analysis (CFA). The EFA results indicate that factor loadings of all the items ranged from 0.765 to 0.914, which were greater than 0.5, the conservative cut-off level (Hair et al., 2010). Factors emerged with eigenvalues ranging from 3.056 to 5.978, exceeding the acceptable level of 1.0 (Hair et al., 2010).
Further, we conducted CFA to assess convergent and discriminant validity. The results indicate that the items loaded as expected, and composite reliability was well above the recommended value of 0.6 (Hair et al., 2010), demonstrating adequate convergent validity (see Table AI).
Following a procedure suggested by Fornell and Larcker (1981), we computed the average variance extracted (AVE) by the indicators, and then compared it with the variance each factor shared with the other factors in the model. The value of the square root of each AVE was greater that the values of the inter-construct correlations (see Table AI), indicating the constructs were more strongly correlated with their own items. We also used χ2 difference tests to examine discriminant validity, as recommended by Anderson and Gerbing (1988). In the present study, the value of the unconstrained model was significantly lower than that of the constrained model in all cases. Overall, the measures in this study satisfied psychometric property requirements (Table I).
6.2 Hypotheses analysis
To test these hypotheses, we used the method provided by Hayes and Preacher (2011). This method allows calculation of direct effects and confidence intervals for the indirect effect of perceived innovation policy on creative behavior. The results shown in Table II present three regression models.
Model 1 shows that H1 is supported, because perceived innovation policy is positively related to online creative climate (β=0.78, p<0.01). Models 2 and 3 show the results of H2, in which perceived innovation policy is a significant predictor of both novelty (β=0.45, p<0.05) and meaningfulness (β=0.43, p<0.05) of creative behaviors. However, in Models 4 and 5, when online creative climate is added to the equation, perceived innovation policy is no longer a significant predictor, providing support for H2 and the mediating role of an online creative climate (ΔR2=0.12, 0.11). The indirect effect of perceived innovation policy (0.26 and 0.25) through online creative climate is significantly different from zero (95 percent).
To assess H3, we run methods again provided by Hayes and Preacher (2011). Table III shows the results of this analysis with three regression models. Models 1 and 2 show that online creative climate is a significant predictor of both novelty (β=0.44, p<0.05) and meaningfulness (β=0.42, p<0.05) of creative behaviors. Models 3 and 4 show that online creative climate is a significant predictor of both radical (β=0.26, p<0.01) and incremental (β=0.24, p<0.01) innovation performance. Finally, Models 5-8 show that when both novel and meaningfulness of creative behaviors are added to the regression equations, online creative climate continues to be a significant predictor of both radical (ΔR2=0.19 and 0.18) and incremental innovation performance (ΔR2=0.18 and 0.16). The indirect effect of online creative climate through novelty (0.10, 0.11) and meaningfulness (0.10, 0.12) of creative behaviors is significant from zero (95 percent). As a result, H3 is partially supported, but suggests both direct and indirect relationships between online creative climate and innovation performance.
6.3 Relative effect analysis
Two approaches were employed to test the different effectiveness among four different types of computer-mediated platforms. First, according to the Tukey HSD test (see Table IV), the analysis shows that, in terms of novelty of creative behavior, firm-firm computer-mediated platforms outperform consumer-consumer (difference=0.52, p<0.01), firm-consumer (difference=0.65, p<0.001) and consumer-firm (difference=0.78, p<0.001) computer-mediated platforms. Meanwhile, consumer-firm computer-mediated platforms have better performance in meaningfulness of creative behavior than consumer-consumer (difference=0.56, p<0.01), firm-consumer (difference=0.69, p<0.001) and firm-firm (difference=0.82, p<0.001) computer-mediated platforms do.
In terms of radical innovation, firm-firm computer-mediated platforms outperform their counterparts, consumer-consumer (difference=0.47, p<0.01), firm-consumer (difference=0.54, p<0.01) and consumer-firm (difference=0.70, p<0.001). On the other hand, consumer-consumer computer-mediated platforms have a better impact on incremental innovation than other types of computer-mediated platforms do, firm-consumer (difference=0.67, p<0.001), consumer-firm (difference=0.74, p<0.001) and firm-firm (difference=0.86, p<0.001).
Second, relative weight analysis (Tonidandel and LeBreton, 2011) was also conducted to more clearly reflect the contribution of each type of computer-mediated platform. Based on the relative weight analysis, shown in Table V, the results indicate that in terms of creative behavior, firm-firm computer-mediated platforms (relative weight (RW)=0.42) are the most critical for explaining the effectiveness of novelty of creative behavior, followed by consumer-consumer (RW=0.22), firm-consumer (RW=0.19) and consumer-firm (RW=0.16) computer-mediated platforms. Meanwhile, consumer-firm computer-mediated platforms (RW=0.45) are the most critical for explaining the effectiveness of meaningfulness of creative behavior, followed by consumer-consumer (RW=0.24), firm-consumer (RW=0.21) and firm-firm (RW=0.18) computer-mediated platforms.
As for innovation performance, firm-firm computer-mediated platforms (RW=0.38) are the most important for explaining the effectiveness of radical innovation, followed by consumer-consumer (RW=0.20), firm-consumer (RW=0.18) and consumer-firm (RW=0.14). On the other hand, consumer-consumer computer-mediated platforms (RW=0.43) are the most important for explaining the effectiveness of incremental innovation, followed by firm-consumer (RW=0.19), consumer-firm (RW=0.17) and firm-firm (RW=0.12). Both approaches have the same significant patterns of results, supporting H4a and H4b.
7. Discussion
This study sets out to better understand whether firms establishing online creative climates in computer-mediated platforms produce greater innovation performance, and whether any differences of these results lie in computer-mediated platforms. Results suggest that online creative climate positively impacts creative behavior. As users in computer-mediated platforms perceive that they are encouraged to make suggestions, they are more likely to do so. Online creative climate is positively related to innovation performance, suggesting that higher online creative climate has an influence on the effectiveness of firms’ innovations. In addition, online creative climate continues to be a significant predictor of innovation performance when included with creative behavior, suggesting that online creative climate has a direct impact on innovation performance beyond its impact through creative behavior.
Finally, the results demonstrate that the effectiveness of using online creative climate in each type of computer-mediated platform is not the same. In particular, compared with other types of computer-mediated platforms, firm-firm computer-mediated platforms perfectly shape novelty of creative behavior and produce the best radical innovation performance. A possible explanation for this difference is that users in firm-firm computer-mediated platforms may have more professional and expert experience than those in other types of computer-mediated platforms, and, thus, an increase in innovative and competing ideas and concepts occurs across users. This new insight suggests that firms engaging in computer-mediated platforms not only search for external sources of information, but also seek professional opinions. Our findings thus extend earlier research (e.g. O’Connor and Rice, 2013; Slater et al., 2014) demonstrating that external expertise plays a more critical role than consumers do in radical innovation development.
In contrast, consumer-firm computer-mediated platforms perfectly shape meaningfulness of creative behavior, while consumer-consumer computer-mediated platforms produce the best incremental innovation performance. This finding supports customer co-creation research focusing on involving customers in firms’ innovation process to design innovations (Cui and Wu, 2016), and indicates that customer experience plays an essential role in incremental innovation development (Baldwin and von Hippel, 2011). Research and managerial implications of these findings are discussed below.
7.1 Research implications
First, this study contributes to the internet literature by being the first to empirically categorize computer-mediated platforms into four different types of: consumer-consumer, firm-consumer, consumer-firm and firm-firm. In addition, extending previous research on computer-related environments (Yadav and Pavlou, 2014), this research provides empirical evidence that, as time progresses, in firm-firm computer-mediated platforms, firms could create better radical innovations, while in consumer-consumer mediated platforms, firms create better incremental innovations. This is mainly because user involvement in consumer-consumer mediated platforms typically results in minor modifications that do not fundamentally change the structural configuration of products/services, and the modifications are normally related to the satisfaction of expressed needs (Cui and Wu, 2016). In contrast, user involvement in firm-firm computer-mediated platforms can provide better professional insight, knowledge and experience that firms need to dramatically improve products/services. Most importantly, they can provide a better understanding of up to date technologies and bring about state-of-the-art knowledge early in the innovation development process, which in turn can contribute to the creation of radical new products/services (Slater et al., 2014). This new insight also provides additional support to the theoretical argument, in current innovation literature, that people with a higher level of professional experience in a firm are likely to have better visioning to generate radical innovations (Slater et al., 2014), and that radical innovation is a firm-centric theory of innovation (O’Connor and Rice, 2013). On the other hand, the abundant knowledge residing in the external consumers provides a wide range of ideas and concepts to support incremental innovation development (Baldwin and von Hippel, 2011).
Second, this research contributes to the climate research by recognizing the effectiveness of online creative climate in computer-mediated platforms. While previous studies have widely examined user engagement in innovation in various environments (Cui and Wu, 2016), discussions of an evolving role of creative climate in virtual environments remain limited. As the results indicate, online creative climate has an increasingly stronger impact on innovation performance over time. Our findings enrich this research stream by showing the existence of online creative climate in computer-mediated platforms and demonstrating its association with two types of innovation performance: radical and incremental. In addition to the direct effects of perceived innovation policy on users’ perceptions of online creative climate, tests of mediation indicate that such policy has a positive effect on creative behavior through its influence on online creative climate. This finding is consistent with previous climate research because the supervisor creates the context in which perceptions of climate are shaped, which then influences subsequent behavior (Schneider et al., 2013). The results of this study also begin to address the limited research on the role of supervisor behaviors in computer-mediated environments (Yadav and Pavlou, 2014).
Third, the findings suggest that computer-mediated platforms do develop and share beliefs relating to creative behavior and whether this behavior is encouraged in its respective platforms. We provide additional empirical support for the predictive validity of online creative climate in computer-mediated platforms through its influence on novelty and meaningfulness of creative behavior. From a broader perspective on organizational climate, this is consistent with previous research that examines a specific climate and its influence on a specific outcome (Schneider et al., 2013). In relation to the creative behavior literature, this study further strengthens theoretical work indicating that context will influence perceptions of the extent to which individuals are willing to behave in a creative way (e.g. Janssen and Huang, 2008). Given the benefits associated with creative behavior and the growing interest in the literature, we believe this study is timely and important.
Fourth, the findings suggest that, over time, online creative climate in computer-mediated platforms has an indirect impact on innovation performance, which is partially mediated by creative behavior. We extend earlier research on creative behavior by showing that an online creative climate associated with two types of creative behaviors (novelty and meaningfulness) depends on the specific computer-mediated platform. That is, the novelty of creative behavior in firm-firm computer-mediated platforms has the best innovation performance, while meaningfulness of creative behavior in consumer-firm computer-mediated platforms has the strongest impact on innovation performance. This is an important theoretical implication for open innovation researchers, as some open innovation studies assume that to collaborate with external third parties enables open innovation activities to have a direct impact on innovation performance (e.g. Cheng and Huizingh, 2014). However, the results reveal that this assumption may be inaccurate by showing that radical and incremental innovation performance also can be enhanced over time through creative behavior.
Finally, past research in this area has been undertaken mostly using US-based samples (e.g. Sundgren et al., 2005; Yadav and Pavlou, 2014), but less is known in non-Western countries. This study adds new data from computer-mediated environments in Taiwan to the existing, overwhelmingly US-based literature on creative climate and computer-mediated platforms. This calls for the importance of studying the development of other virtual climates in computer-mediated platforms.
7.2 Managerial implications
The findings provide some suggestions for managers. First, we classify computer-mediated platforms into four types. This categorization might be contrary to what many managers have in mind when they think about the internet platform, because the internet platform is often restricted to a medium for discussions. However, our research shows that this perspective is inadequate from both theoretical and empirical viewpoints. Although each of the four types has a positive impact on innovation performance, they do not have similar effects. This finding provides guidance for managers on the relative importance of computer-mediated platforms in achieving innovation performance. Managers should emphasize radical innovation development when managing firm-firm platforms. On the other hand, incremental innovation appears more important for managers engaging in consumer-consumer platforms. The results also suggest that managers need to look carefully at what type of consumer-consumer platform their firms are pursuing so that they can allocate their resources appropriately. When managers have determined which computer-mediated platform will likely yield the greatest benefits, they should develop and execute detailed creative climate action plans. This approach was used by our participant firms to manage their computer-mediated platforms. This resulted in a radically new innovation management process design which most firms believed to be superior to those of their competitors.
Second, given the increasing role of user involvement in innovation development (Cui and Wu, 2016), computer-mediated platforms have become extremely influential in offering insights that can be helpful in innovation development. While firms have tried to identify influential factors, our study shows that firms can improve their innovation performance through employing online creative climate in computer-mediated platforms. The results suggest to managers how an online creative climate can be used as a facilitator for improving innovation performance in computer-mediated platforms. Although we only examined perceptions of an online creative climate in computer-mediated platforms, we consider our results to be potentially relevant to or inspiring for other kinds of contexts as well. In addition, our model shows that online creative climate influences radical and incremental innovations both directly and indirectly through creative behavior. Thus, managers should align their facilitating creative climate decisions with developing creativity decisions. Practically, this suggestion implies that the marketing (responsible for computer-mediated platforms) and new product development (responsible for creativity) departments should work together closely.
Third, the results suggest that managers should not focus only on those who are directly involved in the innovation process (e.g. employees or suppliers), but also on cross-border users in computer-mediated platforms, where they are also able to directly and indirectly contribute to the innovation creation. In this view, managers should not be afraid to provide the required managerial and organizational support for their users to release their creative potential ideas. It is particularly important to attract the right expertise to the firm-firm computer-mediated platforms, in order to fuel the creative process of radical innovation.
Finally, from a strategic perspective, managers should be conscious of innovation development involving computer-mediated platforms that have begun to emerge. Several well-known firms, as noted earlier, along with this study’s samples, have been leveraging computer-mediated platforms to improve innovation performance by facilitating collaboration with consumers and other potential third parties. Thus, it has become increasingly important for managers to understand more complex processes between virtual climates and innovation activities in the internet context.
7.3 Limitations and future research
This study has a number of limitations, which also represent important directions for future research. First, the measure of online creative climate used in this study is specific to the context of computer-mediated platforms, which may not have captured the construct of the creative climate sufficiently, as the nature of climate is complex (Schneider et al., 2013). Future research could attempt to capture the domain of this construct with much richer and more detailed items.
Second, while the theoretical arguments in this study are developed for wide applicability, future studies, set in specific industries, may be required. For example, the retail industry, that has been strongly related to computer-related environments, such as showrooming and geofencing, has been quickly evolving. Study in this industry should be an important priority.
Finally, this study specifically divides computer-mediated platforms into four different types. Because computer-mediated platforms could be implemented in a hybrid approach (e.g. customer-customer along with customer-firm platforms), future studies could examine how such hybrid platforms coordinate to produce better innovation performance under online creative climate.
Figure 1
The conceptual model
[Figure omitted. See PDF]
Table IDescriptive statistics and correlations
| Variables | Mean | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 Perceived innovation policy | 5.61 | 1.48 | – | |||||||||||
| 2 Online creative climate | 5.87 | 1.14 | 0.28** | |||||||||||
| 3 Novelty | 5.38 | 0.78 | 0.20* | 0.27** | ||||||||||
| 4 Meaningfulness | 5.85 | 0.81 | 0.22* | 0.24* | 0.22* | |||||||||
| 5 Radical innovation | 5.92 | 0.89 | 0.24* | 0.22* | 0.2* | 0.19* | ||||||||
| 6 Incremental innovation | 5.24 | 1.12 | 0.26* | 0.23* | 0.21* | 0.17* | 0.25* | |||||||
| 7 Firm size (log) | 2.73 | 0.53 | 0.10 | 0.21* | 0.03 | 0.05 | 0.08 | 0.20* | ||||||
| 8 Firm age | 11.75 | 1.21 | 0.14 | 0.09 | 0.02 | 0.22* | 0.20* | 0.22* | 0.19* | |||||
| 9 Electronics | 0.22 | 0.28 | 0.12 | 0.07 | 0.08 | 0.09 | 0.17* | 0.25* | 0.06 | 0.18* | ||||
| 10 Telecommunication | 0.19 | 0.37 | 0.09 | 0.08 | 0.05 | 0.05 | 0.20* | 0.19* | 0.03 | 0.22* | 0.20* | |||
| 11 Information technology | 0.13 | 0.23 | 0.15 | 0.05 | 0.07 | 0.20* | 0.09 | 0.23* | 0.06 | 0.09 | 0.22* | 0.09 | ||
| 12 Web game | 0.10 | 0.32 | 0.13 | 0.02 | 0.01 | 0.06 | 0.02 | 0.21* | 0.09 | 0.16* | 0.03 | 0.07 | 0.21* | – |
Notes: *p<0.05; **p<0.01
Table IIRegression results for the mediating role of online creative climate
| β | SE | t | p | R2 | |
|---|---|---|---|---|---|
| Regression Model 1: outcome=online creative climate | |||||
| Constant | 6.74 | 0.76 | 9.27 | 0.00 | |
| Firm size | −0.12 | 0.10 | −0.52 | 0.61 | |
| Firm age | 0.05 | 0.02 | 0.30 | 0.76 | |
| Electronics | 0.06 | 0.03 | 0.32 | 0.79 | |
| Telecommunication | 0.11 | 0.02 | 0.28 | 0.73 | |
| Information technology | 0.02 | 0.09 | 0.51 | 0.69 | |
| Web game | 0.10 | 0.04 | 0.18 | 0.64 | |
| Perceived innovation policy | 0.78 | 0.23 | 3.24 | 0.01** | 0.37 |
| Regression Model 2: outcome=novelty creative behavior | |||||
| Constant | 2.17 | 0.86 | 6.17 | 0.00 | |
| Firm size | −0.02 | 0.08 | −0.88 | 0.35 | |
| Firm age | 0.11 | 0.09 | 0.47 | 0.56 | |
| Electronics | 0.02 | 0.07 | 1.36 | 0.17 | |
| Telecommunication | 0.09 | 0.04 | 0.82 | 0.36 | |
| Information technology | 0.06 | 0.11 | 0.45 | 0.61 | |
| Web game | 0.13 | 0.02 | 1.41 | 0.16 | |
| Perceived innovation policy | 0.45 | 0.35 | 2.19 | 0.03* | 0.22 |
| Regression Model 3: outcome=meaningfulness creative behavior | |||||
| Constant | 2.13 | 0.87 | 6.22 | 0.00 | |
| Firm size | −0.03 | 0.11 | −0.89 | 0.40 | |
| Firm age | 0.09 | 0.12 | 0.43 | 0.66 | |
| Electronics | 0.03 | 0.05 | 1.43 | 0.19 | |
| Telecommunication | 0.10 | 0.07 | 0.77 | 0.34 | |
| Information technology | 0.04 | 0.10 | 0.49 | 0.68 | |
| Web game | 0.09 | 0.03 | 1.37 | 0.17 | |
| Perceived innovation policy | 0.43 | 0.34 | 2.18 | 0.03* | 0.21 |
| Regression Model 4: outcome=novelty creative behavior | |||||
| Constant | 3.21 | 1.37 | 4.75 | 0.00 | |
| Firm size | −0.02 | 0.01 | −1.45 | 0.15 | |
| Firm age | 0.07 | 0.03 | 0.43 | 0.66 | |
| Electronics | 0.01 | 0.04 | 0.41 | 0.69 | |
| Telecommunication | 0.03 | 0.01 | 1.49 | 0.13 | |
| Information technology | 0.05 | 0.03 | 0.41 | 0.70 | |
| Web game | 0.10 | 0.02 | 0.48 | 0.60 | |
| Perceived innovation policy | 0.19 | 0.30 | 1.27 | 0.21 | |
| Online creative climate | 0.67 | 0.39 | 2.61 | 0.01** | 0.34 |
| Regression Model 5: outcome=meaningfulness creative behavior | |||||
| Constant | 3.37 | 1.43 | 4.85 | 0.00 | |
| Firm size | −0.03 | 0.01 | −1.48 | 0.14 | |
| Firm age | 0.09 | 0.02 | 0.44 | 0.67 | |
| Electronics | 0.02 | 0.01 | 0.40 | 0.72 | |
| Telecommunication | 0.08 | 0.04 | 1.52 | 0.13 | |
| Information technology | 0.07 | 0.03 | 0.39 | 0.83 | |
| Web game | 0.01 | 0.04 | 0.46 | 0.67 | |
| Perceived innovation policy | 0.18 | 0.29 | 1.26 | 0.20 | |
| Online creative climate | 0.63 | 0.37 | 2.56 | 0.01** | 0.32 |
Notes: β=unstandardized β coefficient. *p<0.05; **p<0.01
Table IIIRegression results for the mediating role of creative behavior
| β | SE | t | p | R2 | |
|---|---|---|---|---|---|
| Regression Model 1: outcome=novelty creative behavior | |||||
| Constant | 2.17 | 1.06 | 2.02 | 0.05 | |
| Firm size | −0.04 | 0.79 | −1.70 | 0.09 | |
| Firm age | 0.79 | 0.42 | 1.93 | 0.06 | |
| Electronics | 0.05 | 0.03 | 0.76 | 0.45 | |
| Telecommunication | 0.10 | 0.03 | 1.72 | 0.09 | |
| Information technology | 0.06 | 0.05 | 0.75 | 0.50 | |
| Web game | 0.03 | 0.02 | 0.52 | 0.81 | |
| Online creative climate | 0.44 | 0.39 | 2.58 | 0.02* | 0.31 |
| Regression Model 2: outcome=meaningfulness creative behavior | |||||
| Constant | 2.12 | 1.03 | 2.01 | 0.05 | |
| Firm size | −0.06 | 0.74 | −1.75 | 0.08 | |
| Firm age | 0.72 | 0.39 | 1.92 | 0.06 | |
| Electronics | 0.04 | 0.02 | 0.77 | 0.45 | |
| Telecommunication | 0.11 | 0.06 | 1.78 | 0.09 | |
| Information technology | 0.05 | 0.10 | 0.77 | 0.44 | |
| Web game | 0.01 | 0.01 | 0.58 | 0.79 | |
| Online creative climate | 0.42 | 0.36 | 2.60 | 0.02* | 0.29 |
| Regression Model 3: outcome=radical innovation | |||||
| Constant | 2.44 | 0.55 | 4.42 | 0.00 | |
| Firm size | −0.02 | 0.01 | −1.93 | 0.06 | |
| Firm age | 0.35 | 0.22 | 1.73 | 0.09 | |
| Electronics | 0.05 | 0.03 | 0.32 | 0.81 | |
| Telecommunication | 0.09 | 0.04 | 0.29 | 0.72 | |
| Information technology | 0.01 | 0.01 | 0.50 | 0.61 | |
| Web game | 0.09 | 0.02 | 0.55 | 0.86 | |
| Online creative climate | 0.26 | 0.08 | 3.50 | 0.00** | 0.40 |
| Regression Model 4: outcome=incremental innovation | |||||
| Constant | 2.43 | 0.54 | 4.43 | 0.00 | |
| Firm size | −0.03 | 0.04 | −1.90 | 0.06 | |
| Firm age | 0.33 | 0.19 | 1.72 | 0.09 | |
| Electronics | 0.07 | 0.09 | 0.30 | 0.79 | |
| Telecommunication | 0.10 | 0.01 | 0.26 | 0.77 | |
| Information technology | 0.08 | 0.11 | 0.49 | 0.63 | |
| Web game | 0.10 | 0.05 | 0.58 | 0.80 | |
| Online creative climate | 0.24 | 0.06 | 3.48 | 0.00** | 0.38 |
| Regression Model 5: outcome=radical innovation | |||||
| Constant | 1.87 | 0.51 | 3.76 | 0.01 | |
| Firm size | −0.01 | 0.01 | −1.25 | 0.24 | |
| Firm age | 0.15 | 0.12 | 0.38 | 0.89 | |
| Electronics | 0.13 | 0.07 | 0.33 | 0.78 | |
| Telecommunication | 0.03 | 0.01 | 0.22 | 0.93 | |
| Information technology | 0.02 | 0.03 | 0.45 | 0.64 | |
| Web game | 0.18 | 0.34 | 0.31 | 0.76 | |
| Online creative climate | 0.16 | 0.04 | 2.36 | 0.02* | |
| Novelty creative behavior | 0.39 | 0.07 | 3.96 | 0.00** | 0.59 |
| Regression Model 6: outcome=radical innovation | |||||
| Constant | 1.84 | 0.56 | 3.72 | 0.01 | |
| Firm size | −0.02 | 0.04 | −1.23 | 0.27 | |
| Firm age | 0.13 | 0.10 | 0.35 | 0.85 | |
| Electronics | 0.24 | 0.03 | 0.52 | 0.64 | |
| Telecommunication | 0.16 | 0.12 | 0.85 | 0.40 | |
| Information technology | 0.05 | 0.02 | 0.31 | 0.78 | |
| Web game | 0.12 | 0.14 | 0.48 | 0.68 | |
| Online creative climate | 0.15 | 0.03 | 2.34 | 0.02* | |
| Meaningfulness creative behavior | 0.37 | 0.05 | 3.89 | 0.00** | 0.58 |
| Regression Model 7: outcome=incremental innovation | |||||
| Constant | 1.77 | 0.54 | 3.70 | 0.01 | |
| Firm size | −0.04 | 0.03 | −1.23 | 0.25 | |
| Firm age | 0.12 | 0.10 | 0.36 | 0.82 | |
| Electronics | 0.16 | 0.03 | 0.29 | 0.70 | |
| Telecommunication | 0.08 | 0.01 | 0.19 | 0.95 | |
| Information technology | 0.10 | 0.09 | 0.48 | 0.69 | |
| Web game | 0.16 | 0.29 | 0.38 | 0.83 | |
| Online creative climate | 0.14 | 0.09 | 2.14 | 0.05* | |
| Novelty creative behavior | 0.35 | 0.10 | 3.92 | 0.00** | 0.56 |
| Regression Model 8: outcome=incremental innovation | |||||
| Constant | 1.73 | 0.49 | 3.69 | 0.01 | |
| Firm size | −0.03 | 0.02 | −1.24 | 0.26 | |
| Firm age | 0.10 | 0.09 | 0.32 | 0.85 | |
| Electronics | 0.22 | 0.03 | 0.55 | 0.69 | |
| Telecommunication | 0.23 | 0.19 | 0.81 | 0.44 | |
| Information technology | 0.09 | 0.11 | 0.35 | 0.84 | |
| Web game | 0.18 | 0.13 | 0.51 | 0.72 | |
| Online creative climate | 0.12 | 0.08 | 2.10 | 0.05* | |
| Meaningfulness creative behavior | 0.36 | 0.08 | 3.86 | 0.00** | 0.54 |
Notes: β=unstandardized β coefficient. *p<0.05; **p<0.01
Table IVResults of difference analyses (Tukey HSD test)
| Dependent variables | A | B | Difference (A-B) | Significance |
|---|---|---|---|---|
| Consumer-consumer | 0.52 | 0.014** | ||
| Novelty | Firm-firm | Firm-consumer | 0.65 | 0.008*** |
| Consumer-firm | 0.78 | 0.000*** | ||
| Consumer-consumer | 0.56 | 0.012** | ||
| Meaningfulness | Consumer-firm | Firm-consumer | 0.69 | 0.005*** |
| Firm-firm | 0.82 | 0.000*** | ||
| Consumer-consumer | 0.47 | 0.016** | ||
| Radical innovation | Firm-firm | Firm-consumer | 0.54 | 0.013** |
| Consumer-firm | 0.70 | 0.002*** | ||
| Firm-consumer | 0.67 | 0.004*** | ||
| Incremental innovation | Consumer-consumer | Consumer-firm | 0.74 | 0.000*** |
| Firm-firm | 0.86 | 0.000*** |
Notes: **p<0.01; ***p<0.001
Table VResults of difference analyses (relative weights analysis)
| β | SE | Relative weights | Relative weights as a % of R2 | |
|---|---|---|---|---|
| Novelty | ||||
| Consumer-consumer | 0.38 | 0.22 | 0.22 | 12.07 |
| Firm-consumer | 0.35 | 0.23 | 0.19 | 10.76 |
| Consumer-firm | 0.29 | 0.28 | 0.16 | 8.44 |
| Firm-firm | 1.02 | 0.19 | 0.42 | 68.74 |
| Total R2 | 0.49 | |||
| Meaningfulness | ||||
| Consumer-consumer | 0.40 | 0.29 | 0.24 | 11.08 |
| Firm-consumer | 0.36 | 0.27 | 0.21 | 9.83 |
| Consumer-firm | 1.18 | 0.18 | 0.45 | 72.04 |
| Firm-firm | 0.30 | 0.20 | 0.18 | 7.05 |
| Total R2 | 0.52 | |||
| Radical innovation | ||||
| Consumer-consumer | 0.29 | 0.21 | 0.20 | 12.85 |
| Firm-consumer | 0.25 | 0.27 | 0.18 | 11.64 |
| Consumer-firm | 0.32 | 0.24 | 0.14 | 9.94 |
| Firm-firm | 0.94 | 0.16 | 0.39 | 65.58 |
| Total R2 | 0.44 | |||
| Incremental innovation | ||||
| Consumer-consumer | 1.08 | 0.20 | 0.43 | 67.89 |
| Firm-consumer | 0.34 | 0.29 | 0.19 | 13.45 |
| Consumer-firm | 0.29 | 0.22 | 0.17 | 11.62 |
| Firm-firm | 0.23 | 0.21 | 0.12 | 8.04 |
| Total R2 | 0.46 | |||
Note: β=unstandardized β coefficient
Table AIMulti-item scales
| Items | Factor loading |
|---|---|
| Perceived innovation policy (new scale; α=0.83; CR=0.83; AVE=0.72) | |
| How often have you felt a policy toward innovation development in this platform? | 0.81 |
| How often have you felt that innovation development is a consistent policy in this platform? | 0.88 |
| Online creative climate (new scale; α=0.90; CR=0.92; AVE=0.66) | |
| The climate in this platform is basically positive and encourages new ideas | 0.82 |
| Users in this platform can bring up new ideas without quickly being criticized | 0.83 |
| This platform allows you to solve problems that you think are most suitable in a given situation | 0.74 |
| There is a free atmosphere in this platform, where the seriousness of the task can be mixed with unusual ideas | 0.89 |
| Different opinions, ideas, experience and knowledge can be discussed in this platform | 0.78 |
| This platform has a dynamic atmosphere | 0.80 |
| Creative behavior | |
| Novelty (new scale; α=0.89; CR=0.92; AVE=0.60) | |
| Users suggest novel ways to achieve goals of innovation projects | 0.83 |
| Users come up with novel ideas to improve products/services performance | 0.82 |
| Users search out novel technologies, processes, techniques, and product/service ideas | 0.78 |
| Users suggest novel ways to increase products/services quality | 0.75 |
| Users are good sources of novel creative ideas | 0.81 |
| Users promote novel ideas to other users | 0.77 |
| Users come up with novel solutions to problems | 0.72 |
| Users provide novel information on their needs | 0.70 |
| Meaningfulness (new scale; α=0.91; CR=0.92; AVE=0.61) | |
| Users suggest meaningful ways to achieve goals of innovation projects | 0.81 |
| Users come up with meaningful ideas to improve products/services performance | 0.78 |
| Users search out meaningful technologies, processes, techniques and product/service ideas | 0.73 |
| Users suggest meaningful ways to increase products/services quality | 0.84 |
| Users are good sources of meaningful creative ideas | 0.82 |
| Users promote meaningful ideas to other users | 0.79 |
| Users come up with meaningful solutions to problems | 0.71 |
| Users provide meaningful information on their needs | 0.74 |
| Innovation performance (Atuahene-Gima, 2005) | |
| Radical innovation (α=0.86; CR=0.88; AVE=0.64) | |
| Percentage of total sales from radical product/service introduced by your firm in the last three years (less than 5%, 5-10%, 11-15%, 16-20%, 21-25%, 26-30%, >30%) | 0.84 |
| Number of radical products/services introduced by your firm in the last three years (0-3, 4-6, 7-9, 10-12, 13-15, 16-18, >18) | 0.81 |
| Compared to the major competitor, your firm introduced more radical new products/services in the last three years (1=“strongly disagree,” 7=“strongly agree”) | 0.76 |
| Your firm frequently introduced radical new products/services into markets totally new to the firm in the last three years (1=“strongly disagree,” 7=“strongly agree”) | 0.78 |
| Incremental innovation (α=0.87; CR=0.87; AVE=0.63) | |
| Percentage of total sales from incremental product/service introduced by your firm in the last three years (less than 5%, 5-10%, 11-15%, 16-20%, 21-25%, 26-30%, >30%) | 0.81 |
| Your firm frequently introduced incremental new products/services into new markets in the last three years (1=“strongly disagree,” 7=“strongly agree”) | 0.77 |
| Compared to the major competitor, your firm introduced more incremental new products/services in the last three years (1=“strongly disagree,” 7=“strongly agree”) | 0.79 |
| Number of incremental products/services introduced by your firm in the last three years (0-3, 4-6, 7-9, 10-12, 13-15, 16-18, >18) | 0.83 |
Notes: α, Cronbach’s α; CR, composite reliability; AVE, average variance extracted
© Emerald Publishing Limited 2017