ONLINE USER INNOVATION COMMUNITIES, ABSORPTIVE
CAPACITY, AND FIRM FINANCIAL PERFORMANCE
Stefan Mulder S2045729
s.j.mulder@student.rug.nl
MSc. Business Administration – Strategic Innovation Management University of Groningen
January 2015
Abstract
Information technology (IT) have played an important role in changing firms’ innovation patterns from closed to open. In particular, the use of online innovation user
communities (OUICs), where users engage in value creation by submitting product reviews, providing feedback, suggesting ideas, commenting and voting on new ideas, and identifying new sources of innovation, is a new phenomenon of open innovation. However, firms may confront difficulties in benefiting from the external knowledge flows from OUICs and need to develop absorptive capacity (ACAP) to outweigh such deficiencies. Thus, this study theorizes and tests the moderating role of ACAP in strengthening the relationship between OUIC use and financial performance. Using a unique panel data set from the U.S., European, and Asian firms, the results corroborate the moderating role of ACAP in enhancing the effect of a firm’s OUIC use on its financial performance.
1. INTRODUCTION
Over the past decade, the numerous advances in information technology (IT), along with the surge in new IT-enabled production and circulation of knowledge, made access to external knowledge across firm boundaries a key source of innovation (Dong & Yang, 2015; Cohen & Levinthal, 1990; Kleis et al., 2012; Piva et al., 2012). IT facilitates the access and inflow of innovation inputs from distant customers, firms, or universities (Rossi-Lamastra et al., 2011). A relatively new way of producing knowledge with
external collaborations is through ‘online user innovation communities’ (OUICs), where users engage in value creation by submitting product reviews, providing feedback, suggesting ideas, commenting and voting on new ideas, and identifying new sources of innovation (Dong & Yang, 2015; Di Gangi & Wasko, 2009; Di Gangi et al., 2010).
The use of OUICs are not an entirely new phenomenon (e.g., Franke & Shah, 2003; Von Hippel, 1998), because the advances in IT have enabled end users of an
organization's products and services to organize and share innovations through the creation of these online communities (Di Gangi & Wasko, 2009). The knowledge that flows from the communities, can thereby be a possible opportunity to countervail for the limited financial and human capital that firms usually have to devote to research and development (Becker & Gordon, 1966; Stevenson & Gumpert, 1985). In addition, March and Simon (1958) suggested that most innovations result from borrowing rather than invention and in light of the phenomenon that firms are increasingly using IT to
complement internal innovation (Di Gangi & Wasko, 2009; Dong, 2010; Joshi et al., 2010; Tambe et al., 2012; Xue et al., 2012), it seems no surprise that more and more firms (e.g., Dell and Starbucks) have introduced OUICs for their end users. With utilizing OUICs, firms can capture value by increasing an organization's capacity to continuously renew its competencies and better align itself with a changing business environment (Di Gangi & Wasko, 2009; Teece & Pisano, 1997).
capacity (ACAP) to value, assimilate and apply the knowledge from outside sources (Cohen & Levinthal, 1990).
The concept of ACAP is a prominent topic of scientific inquiry (Kostopoulos et al., 2011) and the importance of the concept has been noted across all kinds of fields, like the strategic management (Lane & Lubatkin, 1998; Nahapiet & Ghoshal, 1998),
technology management (Schilling, 1998), international business (Kedia & Bhagat, 1988) and organizational economics (Glass & Saggi, 1998). Also Zahra and George (2002) suggest that most prior research show significant relationships between ACAP and innovate output and other outcomes that pertain a competitive advantage.
The positive effect of ACAP on other outcomes that pertain a competitive
advantage, such as on the financial performance that will be researched in this study, is also widely acknowledged in past and recent research. For example, Cohen and
Levinthal (1990) argue that a firm’s well developed ACAP could generate financial benefits over time, Kostopoulos et al. (2011) find that ACAP contributes both directly and indirectly, through innovation performance, to financial performance and Wales et al. (2013) find an curvilinear relationship between ACAP and the financial performance that is moderated by entrepreneurial orientation.
As discussed, users engage in value creation by submitting product reviews, providing feedback, suggesting ideas, and identifying new sources of innovation in OUICs. The primary input of ACAP is external knowledge inflows (Cohen & Levinthal, 1990; Zahra & George, 2002), so the degree to which the knowledge input from OUICs can realize its value for a firm depends on ACAP. In this study, I argue that a firm
requires ACAP to facilitate a firm’s OUIC use effect on its financial performance, because the knowledge input from OUICs needs to be valued, assimilated and applied before it can be financially beneficial. However, how ACAP facilitates the effect of a firm’s OUIC use on financial performance is unclear and remains unanswered in the literature. Thereby this study fills the gap in literature highlighted by Kostopoulos et al. (2011), that literature is scarce on explaining the role of different sources of external knowledge flows and whether ACAP intervenes to translate these flows into realized innovations.
This study contributes to the literature in different ways. The key contribution is to the open innovation literature, by theorizing a facilitating role for ACAP to value, assimilate, transform, and exploit knowledge to gain financial benefit from the
IT-enabled absorptive capacity (e.g., Joshi et al., 2010; Dong & Yang, 2015), this study reveals the facilitating role of ACAP to capture value from the OUICs. Thereby, this study enriches IS literature on the business value of IT by providing new findings on the performance impact of OUICs.
As argued before, ACAP may facilitate the effect of a firm’s OUIC use on its financial performance. Therefore the purpose of this study is to discover how ACAP facilitates this effect. This leads into the following research question:
How does ACAP facilitate the effect of a firm’s OUIC use on financial performance?
To assess this research question, the contributions of Zahra and George (2002) on the ACAP concept will mainly be used as a theoretical framework. Although one of the most cited definitions of ACAP was proposed by Cohen and Levinthal (1990), I choose Zahra and George’s (2002) definition of ACAP for my theoretical framework. Their definition is based on an extensive literature review and captures the rich theoretical arguments and the multidimensionality of the ACAP construct that other empirical studies do not always capture (Zahra & George, 2002). This makes it the most
comprehensive definition of ACAP and therefore I choose it for this study’s theoretical framework.
Zahra and George’s (2002) research will be elaborated on in chapter 2 ‘Theoretical background’, just as an elaboration on other crucial concepts and
definitions. Chapter 3 ‘Theory development’ is where the hypothesis will be developed, followed by the conceptual model. Chapter 4 ‘Methods’ is the discussion on how the data is collected and how this data will be analyzed. Chapter 5 ‘Results’ is the discussion of the data analysis. Chapter 6 ‘Discussion’ provides explanations and the interpretation of this study’s findings. And in the last chapter, chapter 7 ‘Conclusion’, this study’s
2. THEORETICAL BACKGROUND
In this chapter the theoretical background of this study will be discussed. First the OUIC phenomenon will be elaborated on, followed by an elaboration on the ACAP concept.
2.1 Online user innovation communities
As discussed in the introduction section, OUICs are online platforms where users engage in value creation by submitting product reviews, providing feedback, suggesting ideas, comment and vote on new ideas, and identifying new sources of innovation (Di Gangi & Wasko, 2009; Di Gangi et al., 2010). User communities have been increasingly used by firms in crowdsourcing as a way of open innovation for developing new products and services (Chesbrough, 2003; Nambisan 2002, 2013). In this form of open innovation customers take an active role and co-create innovations together with the company, instead of the firm creating innovations and exchanging it with their
customers (Kohler et al., 2011).
Firms that use OUICs can capture value by increasing an organization's capacity to continuously renew its competencies and better align itself with a changing business environment (Di Gangi & Wasko, 2009; Teece & Pisano, 1997). Furthermore, firms engage in OUICs because they feel that they can influence the direction of development, gain legitimacy to use the innovation, and benefit from the expertise of a large base of skilled users (Dahlander & Wallin, 2006). Strong ties to the communities allow firms to access important complementary assets (Teece, 1986), such as technological know-how and information on emerging user needs or interests that facilitate the appropriation of rents from internally developed innovations (Dahlender & Wallin, 2006; West, 2003).
cognitive or learning benefit, social integrative benefit, personal integrative benefit, and hedonic benefit.
The users typically freely reveal details of their innovations to other users and manufacturers (Harhoff et al., 2003), which is also the case in OUICs where they freely generate an extensively large volume of ideas for products and services. This can be illustrated with the number of ideas generated on the OUICs of Dell and Starbucks. On Dell’s OUIC, users have generated a total of 21,7861 ideas for products and services till
the beginning of December 2014, whereas they have generated 205,0512 ideas on
Starbucks’ OUIC.
2.2 Absorptive capacity
Past research have used the concept of ACAP to explain organizational phenomena that span multiple levels of analysis. Therefore it is no surprise that
numerous different definitions can be found in literature (e.g., Cohen & Levinthal, 1990; Kim, 1998; Mowery & Oxley, 1995). However, it is unclear if they converge to capture similar attributes of the same construct, indicating a much-needed dialogue on the definition and dimensions of ACAP (Zahra & George, 2002). As mentioned in the introduction section, one of the most widely cited definition of ACAP originates from Cohen and Levinthal (1990). They define ACAP as the firm’s ability to value, assimilate, and apply new knowledge. Zahra and George (2002) argue however, that Cohen and Levinthal (1990) and other empirical studies do not always capture the rich theoretical arguments and the multidimensionality of the ACAP construct, therefore Zahra and George’s (2002) definition is chosen for this study’s theoretical framework.
Building upon previous research, Zahra and George (2002) define ACAP as a set of organizational routines and processes by which firms acquire, assimilate, transform, and exploit knowledge to produce a dynamic organizational capability. The authors believe that these four capabilities represent the four dimensions of ACAP. In order to improve future measures, the authors reconceptualize the four dimensions of ACAP into the following capabilities: (1) Acquisition, (2) Assimilation, (3) Transformation and (4) Exploitation.
Data collected from:
2.1.1 Knowledge acquisition
Knowledge acquisition refers to a firm's capability to identify and acquire externally generated knowledge that is critical to its operations. A firm’s acquisition capability has three attributes that can influence ACAP: intensity, speed and direction. The intensity and speed can determine the quality of the acquisition, because the more quickly the acquisition, the more the firm will build requisite capabilities (Kim, 1997a, 1997b). The direction of accumulating knowledge can also influence the paths that the firm follows in obtaining external knowledge.
2.1.2 Knowledge assimilation
Knowledge assimilation refers to the firm's routines and processes that allow it to analyze, process, interpret, and understand the information obtained from external sources (Kim, 1997a, 1997b; Szulanski, 1996). The comprehension of this external knowledge might be difficult and this could lead to overlooking of ideas and discoveries that cannot easily be comprehended (Cyert & March, 1963; Rosenkopf & Nerkar, 2001), delays in comprehending the external knowledge because it embodies heuristics that differ significantly from those used by the firm (Leonard-Barton, 1995), and difficulties in comprehending due to context specificities that prevents outsiders from
understanding or replicating this knowledge (Szulanski, 1996). It becomes especially difficult when the value of knowledge depends on the existence of complementary assets that may not be available to the recipient firm. But, comprehension promotes knowledge assimilation that allows firms to process and internalize externally generated
knowledge (Teece, 1981).
2.1.3 Knowledge transformation
Knowledge transformation denotes a firm's capability to develop and refine the routines that facilitate combining existing knowledge and the newly acquired and assimilated knowledge. Thus, the ability of firms to recognize two apparently incongruous sets of information and then combine them to arrive at a new schema represents a transformation capability. It yields new insights, facilitates the recognition of opportunities, and, at the same time, alters the way the firm sees itself and its
2.1.4 Knowledge exploitation
Knowledge exploitation as an organizational capability is based on the routines that allow firms to refine, extend, and leverage existing competencies or to create new ones by incorporating acquired and transformed knowledge into its operations. The primary emphasis is on the routines that allow firms to exploit knowledge. Exploitation reflects a firm's ability to harvest and incorporate knowledge into its operations
(Tiemessen et al., 1997; Van den Bosch et al., 1999). The outcomes of systematic exploitation routines are the persistent creation of new goods, systems, processes, knowledge, or new organizational forms (Spender, 1996).
2.1.5 Potential and realized ACAP
Zahra and George (2002) make a distinction between potential and realized absorptive capacity. The potential absorptive capacity (PACAP) is a function of the acquiring and assimilating capabilities discussed above. PACAP makes the firm receptive to acquiring and assimilating external knowledge (Lane & Lubatkin, 1990), but does not guarantee the exploitation of this knowledge. Realized absorptive capacity (RACAP) on the other hand, is a function of the transformation and exploitation capabilities
discussed above and reflects the firm’s capacity to leverage the knowledge that has been absorbed. Zahra and George (2002) provide several arguments for this distinction, but in short, this distinction is made to evaluate the unique contributions of PACAP and RACAP to a firm’s competitive advantage. In this study this distinction will also be used to assess how ACAP and OUICs are related.
Now that the four dimensions of ACAP have been elaborated on, a graphical representation of this study’s theoretical framework can be presented. The original model also includes certain triggers that activate ACAP, but these triggers are not relevant for this study, so the adapted model is as follows:
Absorptive capacity Realized Transformation Exploitation Competitive advantage Strategic flexibility Innovation Performance Potential Acquisition Assimilation Figure 1:
3. THEORY DEVELOPMENT
Now that both the OUIC phenomenon and the ACAP concept are clearly defined and elaborated, it remains to assess how ACAP facilitates a firm’s OUIC use and the development of the hypothesis, followed by the conceptual model. This will be done according the potential and realized ACAP by Zahra and George (2002) discussed in the previous section.
3.1 OUIC use and PACAP
As discussed, users in OUICs suggest ideas and identify new sources of
innovation, which are critical to a firm’s open innovation activities. For PACAP, which is a function of the acquisition and assimilation capabilities, the primary input are external knowledge inflows (Cohen & Levinthal, 1990; Zahra & George, 2002). A firm’s
acquisition capability can support and facilitate the identification and acquiring of the external knowledge from OUICs. The market-facing knowledge that the large user base normally provides to the firm, can then be acquired in a relatively easy and in a shortly time manner. That in turn, may also improve the quality of the acquisition of external knowledge, since the quality can be determined on the speed and intensity of the acquisition, resulting in quicker building of the requisite capabilities (Kim, 1997a, 1997b). Thus, PACAP can support and facilitate the acquisition of knowledge from OUICs.
The users in OUICs provide feedback in the form of comments and votes on summited ideas. In this way ‘bad’ ideas are likely to be filtered out by the users (Dong & Wu, 2014) and that could save the firm from analyzing all the ideas. In addition, the ‘good’ ideas will be rated based on other users’ votes, feedback and popularity. Thereby, the users already do some analysis of the knowledge, but the firm still requires the capability to analyze, process, interpret, and understand the knowledge itself (Kim, 1997a, 1997b; Szulanski, 1996). Therefore it needs to develop capabilities to assimilate the knowledge (Zahra & George, 2002). Well-developed assimilation capabilities also prevent firms from overlooking of ideas and discoveries that cannot easily be
the OUIC, puts the firm in a good position to assimilate the new external knowledge (Kostopoulos et al., 2011). Thus, PACAP can facilitate and support the firm’s OUIC use in the assimilation of the knowledge from OUICs, which help to improve a firm’s financial performance.
In short, above arguments jointly suggest a facilitating role of PACAP in supporting the firm to acquire and assimilate external knowledge from OUICs.
3.2 OUIC use and RACAP
The knowledge from OUICs has to be combined with a firm’s existing knowledge base, in order to realize more value in its impact on financial performance. RACAP can support this process by transformation of knowledge. Knowledge transformation denotes a firm's capability to develop and refine the routines that facilitate combining existing knowledge and the newly acquired and assimilated knowledge (Zahra & George, 2002). So firms with high RACAP are better at recognizing two apparently incongruous sets of information, which is then combined to arrive at a new schema. This
transformation process also yields new insights, facilitates the recognition of opportunities, and, at the same time, alters the way the firm sees itself and its
competitive landscape. In this way RACAP facilitates and supports the transformation process of knowledge in such a way that firms with high RACAP have developed and refined routines to combine their existing knowledge with the knowledge from their OUICs.
Firms introduce OUICs to benefit from the expertise of a large base of skilled users and eventually to exploit this knowledge. The other capability of RACAP, exploitation of knowledge, is based on the routines that allow firms to refine, extend, and leverage existing competencies or to create new ones by incorporating acquired and transformed knowledge into its operations. Exploitation is evident, for example, in new ventures that capture knowledge from their market, competition, and customers, and then in which knowledge is used to create new competences (Zahra & George, 2002). The primary emphasis is on the routines that allow firms to exploit knowledge, so firms with high RACAP have well-developed capabilities to exploit new knowledge to its products, services, and other innovative activities. In this way RACAP can clearly
the knowledge transformation and knowledge exploitation, it is clear that the RACAP also facilitates a firm’s OUIC use.
3.3 Performance impact of OUIC use and ACAP
As explained earlier, ACAP (consisting of both PACAP and RACAP) facilitates acquisition, assimilation, transformation and exploitation of knowledge from OUIC use through the development of new cognitive schemas and the modification of existing practices (Kostopoulos et al., 2011). Kazanjian et al. (2012) argue that through such changes, firms are better able to pursue new product developments and product line extensions. This in turn, can promote financial performance and contribute to the achievement of competitive advantage (Lane et al., 2006; Zahra & George, 2002). As such, the mere acquiring new knowledge from OUICs, without effective processing, assimilation of knowledge and the introduction and commercialization of specific innovation outputs, cannot lead to tangible financial results for the organization over time. The effect of OUIC use by firms on financial performance will be thereby
strengthened by a firm’s ACAP.
The arguments provided in this chapter argue that a firm’s ACAP, both in terms of PACAP and RACAP, may enhance the impact of a firm’s OUIC use on its financial
performance. Thus, it leads to the following testable hypothesis. Figure 2 shows the research model:
H1: A firm’s ACAP strengthens the relationship between a firm’s use of OUIC and financial performance, such that firms with higher ACAP have a stronger
relationship between OUIC use and financial performance than that of firms with lower ACAP.
H1: + Use of online user
innovation community
Absorptive capacity
4. METHODS
This study will follow the design of the theory testing process. According to Van Aken et al. (2012), the driver of theory testing is a business phenomenon faced by many companies that has not been fully addressed by the academic literature. Also, the
evidence of the theoretical explanations of the business phenomenon is still
inconclusive. This is clearly the case for the literature on OUICs. Though prior literature suggests that using users’ knowledge in online communities is inflow of knowledge to innovate and provides some qualitative evidence, there is a paucity of studies that collects empirical data from multiple companies. Especially the interaction effect of inflow of external knowledge from OUICs and ACAP on financial performance has not been tested to date. Therefore the theory testing process is the appropriate design for this study.
The structure of this chapter will be as follows: first the data gathering will be elaborated on. Then, the measures, including the dependent, independent, moderator and control variable(s), will be discussed. And finally, the empirical model of this study we be discussed.
4.1 Data
The data for this study were gathered from multiple sources, including the
Compustat database and online websites. The Compustat database is owned by Standard and Poor and is one of the most comprehensive financial databases around. Data is available back to 1950 and nowadays it contains more than 24,000 publicly held companies3.
To find firms that have introduced OUICs over the past years for this study’s data sample, mainly Google was used with a number of keywords such as ‘submit user innovation’, ‘submit user ideas’, et cetera. The initial list of firms with OUICs consisted 15 firms with OUICs. This list had to be narrowed down to 10, because 5 firms (e.g., Lego and Spotify) were not publicly listed and therefore their financial data was not included in the Compustat database. BlackBerry was removed from the list because it had just
introduced its OUICs in 2014 and since the fiscal year of 2014 was not ended yet, no financial data for 2014 was available yet.
This list of firms was then supplemented with firms that have not introduced OUICs. For every firm with an OUIC, three major competitors without using OUICs have been selected. Matching is mainly based on the same industry the firms operate in and similar products on the market. For example, Dell is one of the firms with an OUIC and the three firms without OUICs that have been selected are Hewlett-Packard, Lenovo and ASUSTeK. These four firms are all comparable competitors, because they all produce computer hardware. OUIC use measure was then coded with dummy variables.
Ultimately, this process resulted in a final data sample of 40 firms across 5 different industry sectors. These and the number of firms within this sector are as follows:
Table 1
Industry distribution.
Industry Sector Number of firms
Manufacturing 8
Transportation, Communications, Electric, Gas
and Sanitary 4
Retail Trade 8
Finance, Insurance and Real Estate 4
Services 16
The fact that the firms from this data sample stem from five different industries could improve the generalizability of the findings of this study. What could further improve the generalizability is that the data sample includes firms from North-America (33), Europe (1) and Asia (6). The full list of the firm sample can be found in the table of the appendix.
As discussed, the financial data will be collected through Compustat. The firms will be looked up by using company names. I matched each company with its
All this data, applied formulas to calculate the ROS and the log transformation of the firm’s total assets were then merged into one Excel spreadsheet. The result was a panel data set with a total number of 573 firm-year observations that starts at the 2013 fiscal year and ranges back to the firm’s first notation in the Compustat database. All the firms have data that at least goes back to the fiscal year 2000, but some (e.g., IBM) have a very long history of data that goes back to 1950.
4.2 Measures
4.2.1 Dependent variable
The dependent variable in this study is the financial performance. Specifically, financial performance was employed by calculating return on sales (ROS)4. ROS
constitutes one of the most popular financial indicators of profitability that firms
consider in evaluating their strategic decisions and goals (Kostopoulos et al., 2011). ROS captures the profitability originating from the total sales (Barney, 1991). This measure was calculated by dividing a firm’s operating income before depreciation by its total sales (Needles et al., 2012).
4.2.2 Independent variable
The independent variable in this study is OUIC use. I coded with a dummy scale that equals 1 if a firm was using OUIC in a specific year and equals 0 if a firm was not using OUIC in a specific year. For the firms that have introduced an OUIC, the year that they have started their OUICs will be sought out by going to their OUIC websites and scroll back till the very first submission of an idea. This will be supplemented by using Internet Archive’s Wayback Machine to check whether the year of the first listing of the OUIC page corresponds with the year of the very first submitted idea.
4.2.3 Moderator variable
To measure a firm’s ACAP, previous academic literature has utilized either
qualitative approaches such as self-reports (e.g., Jansen et al., 2005; Lichtenthaler, 2009) or quantitative approaches as measuring the R&D intensity (e.g., Cohen & Levinthal, 2010; Tsai, 2001). This study takes a quantitative approach and uses R&D intensity to
4 In a robustness check, I used an alternative measure return on assets (ROA) to capture financial
measure the moderator of this study, ACAP. The R&D intensity is calculated by dividing a firm’s R&D expenditures by its sales.
4.2.4 Control variable
This study also includes a control variable that may influence the dependent variable of this study, the financial performance. It has been consistently found in literature that large firms in the U.S. tend to have better performance than small firms (Kurshev & Strebulaev, 2007). This effect needs to be controlled for and therefore the control variable in this study is firm size. This variable is operationalized as the log transformation of a firm’s total assets.
4.3 Empirical Model
To measure how ACAP facilitates the effect of a firm’s OUIC use on financial performance, regression analysis is used for hypothesis testing. Regression analysis is concerned with describing and evaluating the relationship between a given variable, often called the explained or dependent variable, and one or more other variables, often called the explanatory or independent variables (Maddala & Lahiri, 1992). This
technique makes it therefore applicable to study the moderating effect of ACAP on OUIC use and financial performance.
As discussed in the theory section, there is an expected moderating effect of ACAP use on a firm’s OUIC use and financial performance. Equation (1) shows the regression model with the dependent variable (ROS), independent variables (OUIC use), moderator (ACAP) and the control variable (Size), where ‘it’ denotes firm and fiscal year.
5. RESULTS
In this section the results of the data analysis will be discussed. First the
regression results will be elaborated on, followed by a graphical plot of the moderating effect.
5.1 Regression results
One of the most preferred regression techniques for evaluating moderating influences is the ordinary least squares (OLS) regression (Morris et al., 1986). Before using this technique for the regression analysis of the data, the distribution of the
variables was checked on whether they match the OLS assumptions. They were found to meet the assumptions, so the OLS technique was selected for this study’s data analysis. Standard errors were used in all analyses. Further, diagnostics were run to examine the multicollinearity by checking the variance inflation factors (VIFs) in the regression analysis. The VIFs were below the threshold of 10 (Dong & Yang, 2015), suggesting that multicollinearity was not a serious problem in the regression analysis.
Table 2 reports the descriptive statistics and table 3 reports the regression result for testing H1, with ROS as the dependent variable. The results showed that ACAP had a statistically significant and positive interaction effect with OUIC use (β = 0.661; p < 0.05). Given the support for this effect at a 0.05 significance level, H1 is supported.
H1 stated that a firm’s ACAP strengthens the positive relationship between OUIC use and financial performance, such that firms with higher ACAP have a stronger
relationship between OUIC use and financial performance than that of firms not using OUIC. Now this hypothesis is statistically supported. Next an interaction plot can be plotted to graphically depict the moderating effect.
Table 2
Descriptive statistics.
Mean SD Min. Max.
ROS 0.199 0.139 -0.49 0.57
OUIC 0.040 0.189 0 1
ACAP 0.041 0.068 0 0.68
Table 3
Regression results for return on sales (ROS).
Control Model Full Model
OUIC x ACAP H1: + (0.295) 0.661* OUIC (0.042) -0.114 ACAP (0.057) -0.075 Size 0.006*** (0.002) 0.0015*** (0.002) 𝑹𝟐 0.012 0.015 Adj. 𝑹𝟐 0.011 0.008
Note: n = 573. Standard errors are in parentheses. Dependent variable is the return on sales (ROS). * p < 0.05.
** p < 0.01. *** p < 0.001.
What stands out is the rather low R² of 0.015, however when the purpose of a study is to assess an interaction, then neither the significance of the main effects nor of the overall R² is relevant (Bedeian & Mossholder, 1994). Assessing an interaction effect is clearly the case in this study, although in this study it is termed a moderating effect, but is basically the same (Whisman & McClelland, 2005). Following the
recommendations of Bedeian and Mossholder (1994), I consider the R² of 0.015 not as a serious issue for the findings of this study.
5.2 Plot of moderating effect
Drawing upon the recommendations of Cohen et al. (2013) regarding the plotting of moderating effect, I graphically depicted the effect of ACAP on the relationship between use of OUIC and financial performance. Based on regression results, I calculated the financial performance for firms that do not use OUICs and ROS for firms that use OUICs at low (mean – 1SD) and high (mean + 1SD) levels of ACAP.
relationship is steeper when firms have high ACAP compared to the slope of the relationship when firms have low ACAP.
Figure 3
Moderating effect of ACAP.
6. DISCUSSION
Since the pioneering work Cohen and Levinthal (1990), an emerging body of literature has been studying the importance of a firm’s ACAP. However, despite the proliferation of studies, research still lacks empirical comprehension regarding key theoretical assertions on ACAP's antecedent conditions, such as knowledge inflows, and outcomes, such as innovation and financial performance (Kostopoulos et al., 2011; Lane et al., 2006). The study at hand addresses some of these deficits by studying how ACAP facilitates and moderates the effect of a firm’s OUIC use on financial performance. By using Zahra and George’s (2002) work on ACAP, it has been argued that ACAP facilitates OUICs by supporting a firm’s activities of acquiring, assimilating, transforming and exploiting knowledge from OUICs. In addition, it has been hypothesized that ACAP strengthens the positive relationship between a firm’s OUIC use and financial performance.
In OUICs, a large number of users suggest ideas, identify new sources of
innovation and provide their market-facing knowledge to the firm. It has been theorized that the acquisition capability of PACAP can facilitate this activity by supporting it to acquire this knowledge, by supporting the firm with the identification and acquiring of this external knowledge. In addition, this could enhance the quality of the acquisition, since the quality is determined on the speed and intensity of the acquisition (Kim, 1997a, 1997b).
Users already analyze some knowledge in the OUIC by providing feedback, comments and vote, however the firm still requires the capabilities to analyze, process, interpret, and understand the knowledge itself (Kim, 1997a, 1997b; Szulanski, 1996). Arguments have been provided that PACAP can facilitate this activity, by supporting the assimilation of this knowledge. This could also prevent the firm from overlooking of ideas that cannot easily be comprehended (Cyert & March, 1963) or overlooking of ideas that differ significantly in heuristics (Leonard-Barton, 1995).
routines that facilitate combining existing knowledge and the newly acquired and assimilated knowledge (Zahra & George, 2002).
Eventually, the firms want to financially benefit from the expertise and
knowledge of its OUIC, so they need to exploit this knowledge. It has been argued that RACAP can also facilitate this process, by supporting the firm’s exploitation of
knowledge. Firms with high exploitation capabilities can refine, extend, and leverage existing competencies by incorporating acquired and transformed knowledge into its operations.
Given the arguments that ACAP (consisting of both PACAP and RACAP) facilitates acquisition, assimilation, transformation and exploitation of knowledge from OUIC use, enables the firm to develop new cognitive schemas and the modification of existing practices (Kostopoulos et al., 2011). The mere acquiring of knowledge from OUICs can thereby not lead to enhanced financial results, without assimilation, transformation and exploitation of the knowledge. The effect of OUIC use by firms on financial performance, will be thereby moderated by a firm’s ACAP.
A panel dataset from U.S., European and Asian firms is used to test the
moderating effect of ACAP on OUIC use and financial performance. The results showed indeed that ACAP plays a significant moderating role by strengthening the positive relationship between a firm’s OUIC use and financial performance. In other words, ACAP facilitates a firm’s OUIC use as a mean to identify and translate external knowledge inflows into tangible benefits, as well as to mean to achieve superior financial results.
Given this finding, this study provides important theoretical implications to open innovation research by revealing how firms can financially benefit from the expertise and knowledge of a large user base. It reveals that firms require ACAP to acquire, assimilate, transform, and exploit to facilitate and strengthen the effect of OUIC use on financial performance. In that way the findings of this study also fill the gap in literature highlighted by Kostopoulos et al. (2011), that literature is scarce on explaining the role of different sources of external knowledge flows and whether ACAP intervenes to translate these flows into realized innovations.
ACAP intervenes and supports to financially benefit from the external knowledge of OUICs.
7. CONCLUSION
This study contributes to a better understanding of user innovation and firm performance. More specifically, it reveals a supporting role for ACAP by facilitating the effect of a firm’s OUIC use on financial performance. By theorizing that ACAP can facilitate the acquiring, assimilating, transforming and exploiting of knowledge from OUICs, it has been hypothesized that ACAP strengthens the positive relationship between a firm’s OUIC use and financial performance, such that firms with high ACAP have a stronger relationship between OUIC use and financial performance than that of firms with low ACAP. A panel data set consisting of U.S., European and Asian firms is used to statistically test this moderating effect of ACAP and the results provide strong statistical evidence for this effect.
This study has some limitations. The first limitation is related to the sample. The panel data set consists of U.S., European and Asian firms across 5 different industries, which should enhance the generalizability of the findings. However, the sample size of firms that use OUICs is relatively small (n = 10). This list is supplemented with firms that do not use OUIC for the regression analysis and that created a larger data sample (n = 40). On the one hand this can be explained because one selection criteria was that the company had to be publicly listed, otherwise it would not be listed in the Compustat database. So a few firms (e.g., Lego and Spotify) had to be removed from the data sample list. On the other hand lies the explanation that recent advances in IT have enabled users to share knowledge and ideas in online communities (Di Ganghi & Wasko, 2009).
Therefore this phenomenon is relatively new and not much firms have introduced such a community for their users. Future research directions could be to increase the data sample when more firms have introduced an OUIC for its users.
not as a process (Lane et al., 2006). Future research might therefore empirically test the moderating effect of ACAP on the relationship of OUIC use and financial performance in other measures of ACAP, such as a qualitative measure like self-reports (e.g., Jansen et al., 2005; Lichtenthaler, 2009). In addition, R&D spending measures ACAP as one all-encompassing activity, so the results of this study do not test how ACAP facilitates the acquiring, assimilating, transforming and exploitation of knowledge from OUICs
individually. Using the literature on user innovation communities and ACAP, it has been argued that ACAP can facilitate and support OUIC use, but future research might test the effect on the four activities individually.
The third limitation of this study is the model fit of the regression analysis, which was low. I am aware of this issue, but this study is not for the purpose of prediction and Bedeian and Mossholder (1994) argue that when assessing an interaction, the
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APPENDIX
Overview of the firms with(out) OUICs. Between the parentheses are the non U.S. firms
With OUIC Without OUIC
Dell Inc. Hewlett-Packard
IBM Lenovo
Starbucks Asus
Barclays Starwood Hotels
Marriott Host Hotels and Resorts
Salesforce Hilton Hotels
Sony RadioShack
SAP Adobe
Verizon Oracle
Best Buy Sprint
AT&T U.S. Cellular
HSBC Bank Royal Bank of Scotland
Cisco
