Innovation Capability Performance through Potential and Realized Absorptive Capacity: an Empirical Study in the West Java Mini Market

Article Information ________________ Article history: Accepted: September 2020 Approved: January 2021 Published: March 2021 ________________

However, innovation doesn't happen in vacuum circumstances. Innovation will only occur if the company has innovation capabilities (Chen, 2017;Laforet, 2011;Han et al., 1998;Lawson & Samson, 2001;Sözbilir, 2018;Teece et al., 1997;Yam et al., 2004;Cavusgil et al., 2003;Cohen & Levinthal, 1990;Zahra & George, 2002). Innovation capabilities provide the potential for effective innovation performance (Cavusgil et al., 2003). Innovation is a complex process and depends on a set of capabilities. These capabilities are scattered throughout the company structure, but all these capabilities can be aligned with a specific strategy. This set of capabilities forms a meta-capability known as innovation capability (Guan & Ma, 2003;Wang et al., 2008). So, innovation capabilities are the result of a complementary set of capabilities (Wonglimpiyarat, 2010), which is the embryo of innovation.
Capability refers to specific resources that help companies maintain a competitive advantage or know-how and knowledge about how to use resources efficiently (Stalk et al., 1992;Grant, 1991). To maintain competitive advantage, firms require adjustment to changes in the external environment and require ownership of internal competencies (Andrews, 1971;Chandler, 1962;Hofer & Schendel, 1978). Innovation capability refers to the internal ability to implement or apply new ideas, products, or processes (Burns & Stalker, 1961). If the company has innovation capabilities at a high level, then the company can integrate innovations into the company's activities and will have lasting success (Burgelman et al., 2004). This is following the opinion of Zheng et al. (2011) and Grant (1991) which state that company capability is a precursor to performance, including innovation performance.
Innovation capability is influenced by internal and external factors of the company (Iddris, 2016). One of these factors is the absorptive capacity (Cohen & Levinthal, 1990), which can be a lever of innovation capabilities (Lane et al., 2006;Medase & Barasa, 2019;Gallegos & Torner, 2018). The mechanism, recognizing the importance of external knowledge flows, is an important phenomenon in the process of building innovation capabilities (Zook & Rigby, 2002). Companies gradually began to abandon the idea that more new knowledge is generated in internal processes within the company itself (Gans & Stern, 2003). This can occur because the boundaries of an organization's knowledge reserves with external knowledge reserves are blurred (Teece, 1998). However, organizations do not automatically benefit from external knowledge flows even though the organization has exposed them (Cohen & Levinthal, 1989. Moreover, the organization must develop the ability to recognize the value of external knowledge, and then assimilate and utilize that external knowledge for commercial purposes. That is, the organization must develop absorptive capacity, which consists of the components of acquiring and assimilating knowledge (often referred to as the dimension of potential absorptive capacity); and the component of transforming and exploiting knowledge, or what is often referred to as the dimension of realized absorptive capacity (Zahra & George, 2002).
This study will explore the relationship between absorptive capacity and innovation capabilities in the modern grocery store business, better known as mini markets. Respondents in this study were mini market employees in West Java. Mini markets are a form of service business that is growing in Indonesia, especially West Java (Bella, 2019). Urgently, studies on innovation are more dominant in the manufacturing sector. Innovations in the service sector have received little attention (Johne & Storey, 1998), including mini markets. Service characteristics make innovation in this sector different from the manufacturing sector (Drejer, 2004;De Jong et al., 2003, Miles, 2008. To the best of researchers' knowledge, studies on the relationship between absorptive and innovation capabilities in mini markets are still very rare. The number of mini markets in Indonesia is the largest in Southeast Asia, reaching 43,826 stores in 2017, with a growth rate of around 3.2%, and each year around one thousand mini markets are added. Mini markets are also a significant contributor to the sales value of fast-moving consumer goods (FMCG), around 21%; grew by 8% in 2018, with 75% of consumers shopping in mini market formats, and visiting mini markets on average twice a week (Bella, 2019). This means that this situation is a challenge for mini market managers, if they do not understand what consumers want, then competitors will leave them (Hameli, 2018). Customers will easily move to another mini market if they are not satisfied.
However, the Covid-19 pandemic has weakened retail growth (Vader et al., 2020;Dinda, 2020;Alika, 2020). The Indonesian Retail Entrepreneurs Association (Aprindo) projects that in 2020 the retail industry will still grow, but will only reach around 1.5-2.0% or weaken compared to the growth in 2019 which ranges from 8% to 8.5% (Dinda, 2020;Alika, 2020). Sales of modern retail stores are still low, only about 40-60% of total sales before the pandemic. This situation causes cash flow for modern retail stores to experience difficulties, considering that sales are lacking, but store operations are still running (Dinda, 2020;Alika, 2020). Pandemics also change consumer behavior (Buck et al., 2020). So, for mini markets to survive, continuous innovation is a proactive action that must be taken (D'Aveni, 2010;D'Aveni et al., 2010;Buck et al., 2020;Vader et al., 2020). This can happen if mini market employees have innovative capabilities.
Also, the literature studying absorptive capacity is more dominant at the firm level (Lane et al., 2006;Volberda et al., 2010), or sometimes at the alliance level (Lane & Lubatkin, 1998;Lane et al., 2001) or the business unit level (Jansen et al., 2005;Tsai, 2001), ignoring the absorptive capacity at the individual level. Therefore, it is advisable to examine the concept at the individual level (Volberda et al., 2010;Lane et al., 2006). So, this study responds to the suggestions of these experts. Absorptive capacity at the individual level is determined by prior knowledge, cognition, and diversity of external networks (Jiménez-Castillo & Sánchez-Pérez, 2013;Lowik et al., 2012), and is associated with favorable outcomes such as task performance (Deng et al., 2008;Park et al., 2007), knowledge creation (Matusik & Heeley, 2005), and innovativeness (Lowik et al., 2012;Ter Wal et al., 2017;Tortoriello, 2014). Thus, the respondents in this study were individuals, mini market employees. This is in line with the statement of Cohen and Levinthal (1990) which states that individuals are at the forefront so that organizations can learn from external knowledge. The outcome, this study will add to the knowledge base of absorptive capacity from the perspective of individual roles.
Finally, this study also identifies the effect of the dimension of potential absorptive capacity on innovative capabilities, something other researchers rarely do. Existing empirical studies generally link absorptive capacity with innovative capabilities , or link potential absorptive capacity with realized absorptive capacity, and further link with innovative capabilities . This means that only the actual absorptive capacity directly affects innovation capability. One of the studies that we're able to prove the direct effect of potential absorptive capacity with innovation capability was conducted by Fosfuri and Tribó (2008) on 2,464 companies in Spain. So, this study will evaluate whether empirical studies of mini market employees in West Java support the relationship between potential absorptive capacity and innovative capabilities. Cohen and Levinthal (1989) are experts who promote the term absorptive capacity, which is defined as a firm's ability to identify, assimilate, and exploit knowledge of the environment. Furthermore, Zahra and George (2002) reconceptualize the capacity to absorb and define it as a set of routine and organizational processes to acquire, assimilate, transform, and exploit knowledge to produce dynamic capabilities. Kim (1997aKim ( , 1997b states that absorptive capacity is a firm's capacity to learn and solve problems. The idea of absorptive capacity was obtained by Cohen and Levinthal (1989) after summarizing the insights from individual learning theory and then translated into organizational contexts. In particular, they noted the principle that prior knowledge (including basic skills and learning experiences) tends to enhance later learning because memory is associative. At the organizational level, they see the acquisition of new knowledge as mediated by individuals who become "gatekeepers" and "boundary-spanners". The gatekeeper's job is to filter and transfer knowledge, whereas boundary-spanners are those that go beyond organizational silos and help spread knowledge across all areas and levels of the organization (Zahra & George, 2002;Daghfous, 2004a). Besides, the diversity of internal perspectives is also very important, because a very homogeneous internal view will result in resistance to external ideas (Cohen & Levinthal, 1990). Cohen and Levinthal (1990) stated that the basis of absorptive capacity is the individual ability of organizational members. However, Zahra and George (2002) and Duchek (2013) state that absorptive capacity is an organizational construct. Zahra and George (2002) state that absorptive capacity is a dynamic capability related to the creation and utilization of knowledge. Dynamic terminology refers to an organization's capability to update competencies continuously so that it is in line with the changing business environment (Teece et al., 1997). This capability can be realized through two routes. First, Teece et al. (1997) emphasized the role of organizational resources and processes in achieving competitive advantage. Second, Grant (1996) emphasizes the role of knowledge as the most strategic resource for companies to realize these capabilities.
There are four components of absorptive capacity and they are grouped into two categories or dimensions, potential absorptive capacity (consisting of components of knowledge acquisition and assimilation) and realized absorptive capacity, consisting of components of knowledge transformation and exploitation (Zahra & George, 2002). Knowledge acquisition refers to the company's capability in identifying and obtaining external knowledge that can support the company's operations. When new knowledge is identified and perceived as having value, this knowledge will be disseminated throughout the company boundaries (Daspit & D'Souza, 2013). Whereas knowledge assimilation is defined as routine services and company processes that allow analyzing, process, interpret and understand information obtained from external resources. This capability helps companies analyze and interpret new knowledge in the context of existing knowledge (Jansen et al., 2005). In this stage, the company assesses whether the structure of the new knowledge is following the existing knowledge structure in the company (Daspit & D'Souza, 2013). Furthermore, knowledge transformation is the capability of the company to develop and filter routines that facilitate combining existing knowledge with new knowledge and assimilating knowledge. This stage consists of modifying and adapting new knowledge and combining it with existing knowledge to explore new possibilities. This stage is considered the most relevant, which can be achieved by adding or removing knowledge, or by interpreting knowledge differently. Meanwhile, knowledge exploitation refers to routines that allow companies to filter, expand, and improve existing competencies or create new competencies by entering and transforming knowledge into company operations (Zahra & George, 2002). Operationally, this stage uses knowledge to create value that is beneficial to companies and customers (Daspit & D'Souza, 2013;Jansen et al., 2005;Zahra & George, 2002). The potential absorptive capacity helps the realized absorptive capacity in developing various organizational and strategic capabilities (Duchek, 2013;Lev et al., 2009). The potential absorptive capacity represents the capability of creating knowledge, while the realized absorptive capacity allows companies to make new offerings based on the knowledge base that has been created (Flor & Oltra, 2013).
Innovation capability is defined as the ability to continuously transform knowledge and ideas into new products, processes, and systems that provide benefits to the company and its interested parties (Lawson & Samson, 2001). With innovation capabilities, companies can utilize the resources they have to develop new products or services (Tang et al., 2015). Choudhury (2010) states innovation capability as the ability to create new and useful knowledge based on previous knowledge. From a business perspective, useful knowledge is the knowledge that can produce new products or services that are beneficial to all parties.
Innovation capability is the company's ability to develop new products and/or new markets, through aligning strategic innovation orientation with innovative behavior and processes (Wang & Ahmed, 2004), introducing new processes, products, or ideas in the organization to achieve success (Baregheh et al., 2009). For example, flexible organizational structure and design refer to organizational capabilities as dynamic capabilities, whereas individual creativity is the starting point for innovativeness (Amabile et al., 1996), and it is the firm capacity that constantly integrates, updates, builds, moves, and configures. Competencies and internal and external resources to seize and exploit opportunities (Teece, 2007). Lawson and Samson (2001) state that organizational innovation capabilities arise from the skills and abilities that allow the application of resources, and reflect an ability to continuously transform knowledge and ideas into new products, new processes, and new systems that are beneficial to the company and all interested parties. Koc (2007) considers innovation capacity as a continuous improvement of all capabilities and resources owned by the company to explore and exploit opportunities to develop new products under market needs.
Meanwhile, Chen (2009) states that innovation capability is a company's capability, which is inherent in processes, systems, and organizational structures, which can be applied to product and process innovation activities. A comprehensive definition from Ngo and O'Cass (2009) states that innovation capability is an integrative process of applying knowledge, skills, and collective company-owned resources to build innovation activities, in the form of technical innovations (products and/or services, and production process technology) as well as non-technical innovations (managerial, market, and marketing).
Innovation capability is a crucial factor in gaining a competitive advantage in turbulent market conditions. This capability directs the organization to develop continuous innovation, to respond to changes in the market environment (Slater et al., 2010). Innovation capabilities are embedded in all strategies, systems, and structures that support innovation in organizations (Gloet & Samson, 2016). Innovation capabilities are structured through the main processes within the company and cannot be separated from other practices (Lawson & Samson, 2001). Thus, the presence of this capability is considered a valuable asset for the company to provide and maintain a competitive advantage and is required in executing the company's strategy.
Innovation capabilities facilitate companies to rapidly introduce new products and adopt new systems and are also important as bait when the competition is ongoing. When hypercompetition is happening, this capability will provide a solution to win the competition. Innovation capability will be an asset to create innovative, competitive solutions. Sen and Egelhoff (2000) stated that innovation performance can be explained as a combination of assets and resources. This means that innovation requires resources, assets, and capabilities to direct the company to be successful in a turbulent environment. Therefore, Adler and Shenbar (1990) define innovation capability as the capacity to develop new products that satisfy market needs, the capacity to apply appropriate process technology to produce new products, the capacity to develop and adopt new products and process technologies to satisfy needs, in the future, and the capacity to respond to technological activities that can come at any time, and the capacity to respond to unexpected opportunities created by competitors.
Evidence states that innovation capability has a positive relationship with financial performance (Al-kalouti et al., 2020;Rajapathirana & Hui, 2018;Han et al., 1998;Calantone et al. 2002;Lau et al. 2010;Tuominen & Hyvönen, 2004;Hull & Rothenberg, 2008;Jansen et al., 2006), as well as market share and innovation (Calantone et al. 2002;Lau et al. 2010;Tuominen & Hyvönen, 2004). Companies with high innovation capabilities can develop innovative services and products and can cope with environmental changes during different phases of the business cycle (Matsuo, 2006). Experts argue that such companies will survive and succeed in various environmental conditions (Gao et al., 2007). Capability theory assumes that there are differences in capabilities that allow organizations to achieve lasting competitive advantage through a distinctive range of capabilities (Weerawardena & McColl-Kennedy, 2002). In summary, in general, all capabilities are considered to be precursors of better performance (Zheng et al. 2011). Therefore, all modern companies need innovation capabilities (Aragón et al., 2014).

Hypothesis Development Relationship between Potential and Realized Absorptive Capacity
Researchers argue that the ability to exploit external knowledge effectively is a critical factor for companies that want to improve innovation outcomes and company performance (Cohen & Levinthal, 1990;Zahra & George, 2002;Lane et al., 2006;Van Den Bosch et al., 2003;Lichtenthaler, 2009;Leal-Rodriguez et al., 2014). Some organizations must acquire knowledge from outside the organization if they are to maintain the rhythm of their activities (Zahra & George, 2002). The stages are recognizing the value of external knowledge, acquiring external knowledge, recognizing the benefits of external knowledge, and acquiring it. Thus, the level of absorptive capacity is the result of developing potential absorptive capacity; although the potential absorptive capacity and the realized absorptive capacity are independent, they both perform complementary functions in absorbing new knowledge acquired from outside the organization. Companies that have a high level of potential absorptive capacity do not always see an increase in performance (Zahra & George, 2002). Conversely, the realized absorptive capacity or the ability of the organization to transform and exploit knowledge will be the mediator between the two variables, the potential absorptive capacity, and performance. Without the acquisition and assimilation of previous knowledge, the process of transformation and exploitation of previous knowledge would not be possible (Zahra & George, 2002;Albort-Morant et al., 2018;Ali & Park, 2016;Ben-Oz & Greve, 2015;Leal-Rodrıguez et al., 2015). In summary, the ability to absorb looks like a funnel, where the potential dimension ensures the novelty and diversity of the required knowledge, while the realized absorptive capacity is a symbol of the operation of new knowledge (Harmaakorpi & Melkas, 2008).
Also, studies developed by Zahra and George (2002), Jansen et al. (2005), and Cepeda-Carrión et al. (2012) provide empirical evidence that the potential absorptive capacity is a precursor to the realized absorptive capacity. Concretely, they state that the organizational mechanism which refers to the potential absorptive capacity has a significant positive effect on the realized absorptive capacity. Zahra and George (2002) as well as Cohen and Levinthal (1990), state that the realized absorptive capacity is directed by the potential absorptive capacity. Therefore, based on the arguments previously described, the following hypothesis can be developed. Hypothesis 1: The potential absorptive capacity is positively related to the realized absorptive capacity.

Relationship between Realized Absorption Capacity and Innovation Capability
Cohen Levinthal (1990) proposed that the use of external knowledge acquired by an organization is a major determinant of innovation capabilities. The study of Zahra and George (2002) states that there is a significant positive relationship between absorptive capacity and innovation, and together these two concepts are the basic foundation of organizational competitive advantage. The results of the empirical study of Knudsen and Roman (2004) also found the ability of absorptive capacity to predict innovation capabilities. On the other hand, Caloghirou et al. (2004) investigated the extent to which the interaction of the organization's internal capabilities and external knowledge resources affects the level of organizational innovativeness. They found that absorptive capacity played a significant role.
Next, Nieto and Quevedo (2005) analyzed the effect of three variables related to industrial structure (capacity to absorb, technological opportunities, and abundance of knowledge) on innovation efforts developed by companies, carried out on 406 innovative companies in Spain. As a result, absorptive capacity determines innovation effort more than the other two industrial structure variables. Besides, absorptive capacity is also a moderating variable between technological opportunities and innovation efforts. Absorptive capacity facilitates the transfer of knowledge, which will be utilized to build technical and managerial innovation capabilities (Minbaeva et al., 2003). In other words, the study proved the positive effect of absorptive capacity on innovation capabilities.
Other empirical studies also provide support for the relationship between absorptive capacity with innovation capabilities and innovation performance (Hughes et al., 2014;Wu & Shanley, 2009;Subramaniam & Youndt, 2005;Daghfous, 2004b). One reason is that absorptive capacity promotes the speed, frequency, and magnitude of innovation (Kostopoulos et al., 2011). High absorptive capacity coupled with the advantages of first movers, rapid response to customers, and avoidance of the "locking" and "competency trap" effects (Zahra & George, 2002;Hamel, 1991), will facilitate companies in obtaining superior innovation performance. In fact, of the four components of absorptive capacity, the last two components, namely: transformation and exploitation, are components that directly affect innovation capabilities (Zahra & George, 2002). That is, the dimension of realized absorptive capacity has a relationship with innovation capability (Cepeda-Carrión et al., 2012;Xun & Xuehan, 2014). Therefore, a hypothesis can be made as follows. Hypothesis 2: Realized absorptive capacity is positively related to innovation capability.

Relationship between Potential Absorptive Capacity and Innovation Capability
Potential absorptive capacity is a source of competitive advantage over innovation, especially when an efficient internal knowledge flow exists to reduce the gap between potential and realized absorptive capacity. The potential absorptive capacity allows the company to identify and assimilate the external knowledge flows required in the innovation process. The potential absorptive capacity is conceptualized as a mapping from the flow of useful external knowledge to the information available internally (Fosfuri & Tribó, 2008). Zahra and George (2002) admit that the potential absorptive capacity gets less empirical studies than the realized absorptive capacity. Fosfuri and Tribó (2008) stated that companies with high potential absorptive capacity get more sales from new products or products that have been perfected. This influence will be greater when the internal information flow becomes more efficient. This study supports the argument of Zahra and George (2002) which states that the potential absorptive capacity is a necessary condition for achieving a competitive advantage in innovation, but companies also need to develop the ability to transform and exploit external knowledge to fully benefit from absorptive capacity.
The accumulation of potential absorptive capacity will be faster if it is supported by internal events. For example, major changes that include serious modifications in organizational design, strategy, and marketing can improve the potential level of absorptive capacity. Furthermore, proper internal reshaping will make the company more outward-looking and more "welcome" to internal information. So, when a company experiences a crisis, it is not always negative (Kim, 1998). Crises can trigger changes in attitudes towards external knowledge (Winter, 2000;Huber, 1991). This is especially useful for companies that have a "not found here" syndrome (Allen & Katz, 1982). Based on these studies, the following hypothesis can be developed. Hypothesis 3: Potential absorptive capacity is positively related to innovation capability

METHOD
This cross-sectional study is descriptive and associative, with the unit of analysis being mini market employees in West Java. The study population was all employees who worked in mini markets in West Java. The sample is determined based on the analysis technique used, namely: multiple regression (Hair et al., 1998;, with a minimum quota of 500 respondents. This amount has met the requirements for multivariate research (Hair et al., 1998;. Samples were obtained by the accidental sampling technique, by visiting each mini market and asking the employees' willingness at that time to participate in this research. A Likert scale questionnaire (Soedibjo, 2013) was used to obtain primary data, with a value of 1= strongly disagree and 5= strongly agree. The absorptive capacity (AC) variable is divided into four components, namely: acquisition and assimilation, referred to as potential AC; and transformation and exploitation, referred to as realized AC (Zahra & George, 2002). The acquisition component consists of four statement items, examples of items are: The employees of this mini market are happy to accept suggestions from consumers/the public. The assimilation component consists of three statement items, for example, Employees at this mini market participate in solving problems. Meanwhile, the transformation component consists of three statement items, for example, Employees in this mini market are trying to improve existing business processes. The exploitation component, consists of three statement items, for example, Employees take advantage of information/knowledge to get new opportunities. Finally, the variable innovation capability is mono-dimensional, modified from Lin (2007), consisting of seven statement items, for example, This mini market often generates new ideas.
To test the validity of the measuring instrument, two kinds of tests were made, validity, and reliability tests. Furthermore, the identification of relationship patterns between variables can be obtained from the empowerment of multiple regression analysis (Santoso, 2002;Hair et al., 1998;; useful for proving the three research hypotheses developed in this study.

RESULT AND DISCUSSION
This research was conducted on mini market employees in West Java, covering six districts and five cities. The total respondents were 597 people. The respondents work in Alfamart mini market as much as 47.7%; Indomaret 35.3%; Yomart 10.2%; and the rest in other mini markets. Based on gender, the number of male respondents did not differ much from female respondents, respectively 52.3% and 47.7%. Mini market employees who were respondents in this study were dominated by those who had a service period of <2 years, as much as 57.6%. Respondents with a work period of 2-5 years amounted to 37.5%, and the rest had a working period of more than five years. This means that almost half of the respondents have sufficient work experience. Also, respondents are part of the millennial generation, because they are dominated by those aged <30 years, as much as 93%; the rest was over thirty. Finally, based on education level, respondents generally graduated from SMA/SMK, as much as 92.8%; the rest are Diploma or Bachelor's graduates. Table 1 shows the three variables used in this study are reliable because they have a value of α> 0.70 (Sekaran, 2007;Robinson et al., 1991). Besides, the table shows the potential absorptive capacity (potential AC) has an average value of 4.04; realized absorptive capacity (realized AC) 3.98, and innovation capability 3.5; all are included in the "high" class if they are classified into five classes (included in the range 3. 41-4.20). Judging from the standard deviation, all variables has a standard deviation that is within a tolerable range, namely: a maximum of 20% of the average value (Santoso, 2002). This means that the answers of mini market employees in West Java tend to be homogeneous, not too diverse. Next, the correlation between variables is significant, this shows that this study meets the requirements of criterion-related validity or predictive validity; because all independent variables have a significant relationship with the dependent variable (Das et al., 2008). Potential AC has a positive and significant correlation with realized AC (0.625; p <0.01) and with innovation capability (0.548; p <0.01). Realized AC has a positive and significant correlation with innovation capability (0.666; p <0.01). The next step is to test the three hypotheses proposed in this study. Figure 1 shows the results of multiple regressions between variables made in the form of a structural model with path coefficients. It is seen that the potential AC significantly affects realized AC (β = 0.625; t = 19.544; p <0.05), and can explain 39.13% of the variation in realizing AC, the rest is explained by other variables. Potential AC also has a significant effect on innovation capability (β = 0.216; t = 5.663; p <0.05), and together with realized AC was able to explain 47.0% of the variation in innovation capability; the rest is explained by other variables. As hypothesized, realized AC also significantly affected innovation capability (β = 0.530; t = 13.883; p <0.05). In other words, all hypotheses are supported by empirical data.
The study developed by Jansen et al. (2005) also provides empirical evidence that the potential absorptive capacity is a precursor to the realized absorptive capacity. Concretely, they state that the organizational mechanism which refers to the potential absorptive capacity has a significant positive effect on the realized absorptive capacity. Zahra and George (2002) and Cohen and Levinthal (1990) stated that the realized absorptive capacity is directed by the potential absorptive capacity. Potential absorptive capacity is an organizational process of absorbing external knowledge through acquisition and assimilation, but the transformation and application of this knowledge will not automatically occur (Kang & Lee, 2017). Meanwhile, the realized absorptive capacity combines processes that utilize external knowledge to support organizational goals, which can be achieved through transformation and exploitation activities. Cassol et al. (2016) explained that the potential absorptive capacity is an instrument for obtaining and assimilating knowledge at a relatively dormant level until there is a reason to use it, then the knowledge will be at an active level, called the realized absorptive capacity.
This study proves that there is a positive and significant effect of the realized absorptive capacity on innovation capabilities (β = 0.530; t = 13.883; p <0.05), and together with the potential absorptive capacity, it can explain 47% of the variation in innovation capabilities. In line with the results of the study by Cepeda-Carrion et al. (2012), which proved that there was a significant and positive influence on innovativeness, with a path coefficient of 0.529 and was able to explain 28% of the variation in innovativeness. That is, the results of this study are similar to their results. The difference is that this study uses two dimensions of absorptive capacity to explain the existing variations in innovation capabilities.

Figure 1. Structural Model with Path Coefficients
The realized absorptive capacity is one of the two dimensions of absorptive capacity suggested by Zahra and George (2002). According to their model, the realized absorptive capacity is a precursor to innovation. So, because innovation will only occur if there is innovation capability, the realized absorptive capacity is a precursor to innovation capability. Thus, this research is in line with the original model from Zahra and George (2002), which assumes the process of absorptive capacity as a sequential or linear process, starting from acquisition, assimilation, transformation, and exploitation. Kostopoulos et al. (2011) stated that absorptive capacity determines the speed, frequency, and magnitude of innovation capability.
This empirical study was successful in proving the existence of a positive and significant effect of potential absorptive capacity on innovation capabilities in mini markets (β = 0.216; t = 5.663; p <0.05). This study supports the argument of Zahra and George (2002) which states that the potential absorptive capacity is a necessary condition for achieving a competitive advantage in innovation. In the context of this research, competitive advantage is not in the form of innovation but in innovation capability, which has been known as a precursor to innovation (Laforet, 2011;Chen, 2017). The difference is that the absorptive capacity model from Zahra and George (2002) does not explicitly explain the direct route from the potential absorptive capacity to innovation capability, but must go through the realized absorptive capacity mediator. The results of research by Fosfuri and Tribó (2008) identified companies with a high potential absorptive capacity that had a good performance in producing new products and enhanced products, and the outcome was better sales. The results of the study by Xun and Xuehan (2014) also prove that there is a positive and significant effect of the four components of Zahra and George's (2002) absorptive capacity on knowledge transfer efficiency and innovation performance.
This can happen because the absorbing capacity is not linear as suggested by Zahra and George (2002). This non-linear process is supported by Todorova and Durisin (2007), Volberda et al. (2010), as well as Aribi and Dupouët (2016). Todorova and Durisin (2007) state that the transformation process, which is a component in the realized absorptive capacity of Zahra and George (2002), is complementary to the assimilation process. When the assimilation process cannot be carried out because the cognitive structure does not change, or when new knowledge does not match the existing knowledge structure, it is not assimilation that occurs but transformation. That is, empirically what happens between mini market employees in West Java is knowledge exploration and exploitation (March 1991). In other words, the potential absorptive capacity of Zahra and George (2002) is also perceived to contain a transformation component (from Todorova & Durisin, 2007), so that it can significantly influence innovation capabilities. Although this effect is not as big as the effect of realized absorptive capacity on innovation capability (β = 0.216 compared to β = 0.530); however, it is in line with the statement of Zahra and George (2002) which states that the contribution of the realized absorptive capacity to innovation capability is greater than the potential absorptive capacity. However, they made no further explanation. According to Xun and Xuehan (2014), this can happen because the components of the potential and realized absorptive capacity have different effects on knowledge transfer activities.

CONCLUSION AND RECOMMENDATION
Empirically, the research has succeeded in proving all the hypotheses built to explore the relationship between the dimensions of absorptive capacity and innovation capabilities among mini market employees in west java. First, the potential absorptive capacity positively and significantly affects the realized absorptive capacity. Furthermore, the realized absorptive capacity also positively and significantly affects innovation capabilities. Third, this study shows empirical evidence of a positive and significant effect of potential absorptive capacity on innovation capabilities. This latter empirical evidence is scarce. However, this also has the consequence of providing a more robust database of absorptive capacity concepts. This includes exploring the absorptive capacity dimension of other researchers. Especially researchers who state that absorptive capacity is non-linear.


The Free-of-Charge Phenomena in the Network Economy—A Multi-Party Value Exchange Model

: The long-standing economic model is one where customers receive and pay for goods and services. However, in today’s modern network economy, why are vendors willing to provide free services and goods to free-riders at an apparent loss? The objective of this study is to provide a theoretical framework explaining why free network services emerge and how they work. This study adopted the multi-case study method, summarized 28 types of revenue model patterns from 51 indicative free network services, and inferred the causes for the antecedent conditions of each revenue model with Qualitative Comparative Analysis (QCA), in order to conﬁrm the causal relationship between the various antecedent conditions, conﬁgurations, and revenue models as conclusive evidence. In addition, this study established seven conditional propositions via their links with related theories, which were taken as the basis for providing in-depth explanations of the revenue model of the free network service, and expanded the demonstration of the original network economy.

Introduction
The Internet has changed the world. In 2013, the revenue from print advertising in the US newspaper industry dropped by 70%, and USD 1 value in the print media industry became USD 0.01 in digital media. In 2015, 44% of adults in the US no longer had a landline telephone connection, switching to mobile communication entirely. The value created by Google is "information," Facebook accumulates value from "friends," and Alibaba generates value by a large number of "commodities" [1]. People define the values of objects in their subject ways. For example, Kyle MacDonald was able to acquire a house worth about USD 50,000 using only a paperclip [2]. However, in the Internet era, the demand-side economy has become the focus, and provided it can attract enough users, almost everyone will be pulled in eventually [3]. In the battle between Google Android and Apple iOS, Apple's profit in 2011 was as high as USD 33 billion, which was the combined profit of Google and Microsoft [4]. The result of a platform war is usually a winner-take-all situation [5]. Even these free "digital public goods" allow a large number of free-riders to enjoy resources without paying. The greater the number of users, the more valuable the platform is to users, which in turn forms an inherent advantage, meaning that it is extremely difficult for competitors to overtake a popular platform.
The most important issue for the network economy is how to create profits without reducing the positive network effect. Any product or service is provided at certain costs. If the users are not charged, how can these free network services make profits? This study proposes that the reason why free network services succeed is that there must be valuable exchanges behind them. While "there is no free lunch in the world", a large number of users can indeed bring network effects and accumulate the value of network services, thus, a free network service cannot be sustainable if it fails to monetize the flow of a large number of free-riders.
Previous research regarding the profitability of free network services was mostly conducted with freemium or two-sided market subsidies, which lack a rigorous theoretical framework. This study inferred the revenue model of free network services from actual cases in order to attain a theoretical explanation. The next section offers the literature review for the new form of "digital public goods" that has subverted economic concepts from the past. The latter work aims at theoretical development. We adopt a holistic multi-case study method, observing individual cases and summarizing universal rules based on inductive reasoning [6]. The advantage of the multi-case study lies in the concept, which can provide an understanding of the free network services. Then, the pattern matching strategy is used to investigate how free network services can allow a large number of non-paying users and how they convert such free-riders into banknotes. Finally, we conduct a Qualitative Comparative Analysis (QCA) to confirm the causal condition regarding the inference of propositions which can be used as verification for future theory expansion.

Digital Public Goods
All goods can be classified based on the characteristics of rivalry and excludability [7]. "Non-rivalry" means that consumption by anyone will not reduce the consumption by others of a good; "non-excludability" means that all consumers can enjoy the good equally and cannot prevent others from using the good. Private goods are both rivalrous and exclusive, and they can only be used exclusively after acquisition (exclusive use); in relative terms, public goods are not rivalrous or exclusive and can be shared by all consumers without interference from each other. The marginal cost for service to an additional consumer is zero. In the case of a cable TV program, even if multiple viewers are added, the marginal cost will not be increased. Like public goods, common goods are not exclusive, which means that users can enjoy common goods without paying. The difference between public goods and common goods is that common goods are rivalrous, meaning they have a limited supply. There is often controversy regarding charging for public goods. Since serving one more consumer does not increase the marginal cost, public goods should not require payment. However, if there is no charge, who would be willing to provide services? Due to the characteristics of public goods, it is difficult to measure their value. It is also impossible to isolate non-paying users. Since people can enjoy the benefits without paying, everyone may prefer to be a free rider, thereby resulting in market failure. A solution is for the government to provide public goods using the funds from taxes in order to benefit everyone [7,8].
When the goods are available for free, such market power does not exist and cannot be used to allocate resources in the economic system [7]. Free goods result in a market no longer dominated by price, which is a challenge for economic analysis. When the price of any item falls, its demand will rise, which is the law of demand in traditional economics [8]. The zero price is one market, and any other price is another market [9]. No matter how low the price is, if users have difficulty in paying for it, it usually reduces their participation intention; however, when the price is zero, it can allow the demand curve to grow nonlinearly, thus, it is easier to achieve the so-called network effect. Since the value of the platform is created by the user community, it means that the biggest factor in the success of a platform is its ability to attract a large number of users, and users who are willing to use the platform must have a considerable incentive, that is, the platform can provide "free" services [4]. That free service is not necessarily meaningful, because even if you have tens of millions of users, you still cannot make money, thus, "free" should be regarded as a business model that helps generate revenue and achieve profitability [10]. As the value of a customer's goods is not related to the price they want to pay but is related to the "perceived value", which cannot be quantified [11], when the product price drops to zero, although the total demand will increase, it will not increase indefinitely, because even free products are not necessarily needed by everyone [12,13].
In the network economy, consumption involves not only the purchase of a single product but also choosing other products complementary to the product, which expands network effects [14]. For digital products, the 5% rule operates-5% of users paying for the product can support other free-riders [9]. Social and economic exchanges complement each other and determine individual behavior. Any actor first calculates the value and cost of an action before taking the action: the calculation considers both social and economic factors. If the exchange is mainly relational, the exchange is called a social exchange; if the exchange is transactional, the exchange is called an economic exchange [15]. It appears that editing Wikipedia is attractive to a small group of its users, to the enthusiastic welcome of its large reader group. A large number of free-riders is actually the best return for the minority contributors because the more prestige and attention capital is accumulated the greater their social-relational rewards. This is an example of the so-called "monetizing eyeballs" effect [9]. To sum up, the marginal benefits of "digital public goods" with network effects will increase when the public goods are used by more people. Meanwhile, a free monetization model can be established because the marginal cost of digital content and services provided is almost zero. Unlike traditional production factors, the network economy has increasing marginal benefits.

Methodology
In this multi-case study, multiple free network services are analyzed through the inductive approach, and their revenue model is summarized. We adopt grounded-theorybased partial-pattern-matching to infer general arguments, matching theoretical patterns with empirically found patterns from each case. In order to ensure that the research findings are consistent with the observed results, triangulation of data sources and methods is used in this study. Multiple data sources are adopted to repeatedly verify whether diverse evidence can yield consistent results, improving the validity of research [16,17]. After summarizing the revenue model of free network services one by one, this study adopted the Qualitative Comparative Analysis (QCA) method to verify the causal relationship between the premise and conclusion of the revenue model, and simultaneously completed "If-Then testing", which are presented in the form of statements, and "general rules" can be established by the relationship between p and q to connect to the theory [16]. Finally, seven conditional propositions were proposed according to the causality model. The iterative process of this research is illustrated in Figure 1.

Multi-Case Study
Evidence obtained from multiple cases is more robust [17]. The continuous replication of logical procedures from multiple cases can accumulate a certain foundation for theoretical construction. This can include literal replication, where the result meets prediction conditions, and theoretical replication, where the result does not meet prediction conditions. If the results of the analysis of practical cases are different from the prediction, it can be regarded as the starting point to revise the theory.
Case studies can be thought of as multi-case experiments. Different from the traditional sampling approach, case studies attempt to discover the logic that repeats in different cases. This can be used as an important step in building a theoretical framework [17]. We regard each case as an independent experiment to analyze the individual free network service and derive the related concepts. The conclusions from the different cases are used as individual evidence to support the discovery and reproduction of the revenue model of multiple network services. Then, different cases are compared to determine the distinctions between different types of revenue models. Each type of model had to include cases that were highly similar to each other but significantly different from the cases in other types of models. The research findings are summarized in the cross-case analysis report.

Pattern Matching
Finding "common variations" in multiple cases can substantiate research findings across different cases, and the distinctive characteristics of cases can be used to analyze the differences between types of models [16]. Case studies face significant challenges in analyzing the evidence presented by the cases, but pattern matching is one of the suitable analysis strategies [17]. The basic principle of pattern matching is to compare the observed pattern with the existing theoretical pattern to further confirm or expand existing theories.
In this study, the grounded-theory-based partial-pattern-matching [18] is adopted. It is a bottom-up induction method developed from the concept of grounded theory. Corresponding to existing theoretical patterns, observed patterns-summarized directly from the empirical data-are derived from the researchers' internal ideas and existing literature. This explanation-building procedure is a special type of pattern matching. Unlike traditional pattern matching that uses continuous comparative analysis, the theoretical pattern of constructive interpretation is not established at the beginning of the study. Generally, the evidence obtained by testing and comparing different types of cases is used to repeatedly revise the theoretical standings. The product of this step-by-step interpretation process can serve as the basis for not only analysis of each individual case but also for cross-case analysis. It also includes counter-interpretations of cross-case analysis, which can refine theoretical concepts [6,17].
In this study, we first collect secondary data for each case. Then, the content analysis, coding and classification of individual cases was performed to construct the initial theoretical concept. The data of the cases are compared, which entailed repeatedly comparing the case findings with the initial theory in order to revise the theoretical concept. As the theoretical concept keeps developing, it is continuously compared with the results of multiple cases, and finally, a revenue pattern that can explain the free network services is established.

Data Analysis
Secondary data are used in this study that include existing case studies, official statistics, official service terms or privacy policies, news reports, and online data. The data focuses on the exchange between parties and revenue sources of participants related to free network services. Descriptions including subjective user evaluations, operating profitability, investment strategies, internal allowances, and content data that cannot be objectively verified are not included in the analysis.
The granularity of this study is defined as one single network service. If one company provides multiple free network services, they will be considered separately. For example, apart from its primary social network, the data from Facebook also includes Facebook Messenger and Facebook Game. These three services are significantly different. Therefore, this study distinguishes Facebook into three network services, including Facebook, Facebook Messenger, and Facebook Game, and analyzes the revenue pattern by individual service. In addition, if the case was determined as providing a fee-free solution, it would be included and considered as a new case. For example, Google Maps provides API services with simultaneous free quotas and charging standards, thus, this study added Google Maps APIs, and independently explored its revenue model.

Collection and Processing of Case Data
The cases selected in this study must provide free network services to users. In order to summarize the different revenue models of free network services, different types of cases are considered to enable cross-case comparisons.
The cases are selected from the 12 industry categories above, and characteristic cases in each industry category are selected. A total of 36 free network service cases are initially selected. For case studies on theoretical construction, the number of sample cases can be increased based on the research situation and data collection process [21]. An example in this study is the Google Android issue: on July 18, 2018, the European Commission hit Google with an antitrust fine because its Android mobile operating system is free and open source for mobile device makers but it benefits from pre-installation of Google Mobile Services such as Google Play Store, Google Chrome, Search and so on. Google changed the licenses agreements of Android for the European Economic Area to comply with EU decision [22]. Therefore, we split the Android case into the EU zone-Android-EU-and the non-EU zone-Android-NonEU-and discuss the revenue model of the different regions. We also added cases that are often said to be able to compete with the free network services cases we chose. For example, the content analysis of free network services in the travel industry found that Airbnb is often mentioned. Therefore, this study added Airbnb as an alternative case. Similarly, when collecting data related to social media and social/messaging, we discover that TikTok, LinkedIn, and Twitter are also referred to frequently. They are also included as alternative cases to provide more sources of information for subsequent analysis and to fully understand the overall picture of revenue patterns in specific industries. In the end, this study selected a total of 51 cases. The background information for each case is summarized in Table A1 of Appendix A.

Coding of Case Data
In order to maintain the integrity of the cases, all the collected case data is written into a case memo, and the data of each case is checked to summarize the characteristics related to the revenue model. It is further marked by code notes to simplify the massive amount of data and classify it into a few concepts. As the platforms have a reciprocal exchange relationship with the consumers and the suppliers [23], the remuneration for value exchange is not only in the form of money. More likely, exchanges are compensated with increased reputational and attention capital. For example, the leverage of Facebook is friends, that of eBay is ratings by buyers and sellers, and that of Twitter is followers (1). We encode the data accordingly: participants in the value exchange (to identify the non-paying users), content providers, paying users, a description of the objects exchanged by both parties, the value that the free network service provides, and the requirements of participants. In the meanwhile, the possible monetization paths of the free models (1) and the platform economy [4,13] are adopted as the initial code for "revenue source", including freemium, the three-party market, transaction fees, and charging for access. With the increase in the number of cases analyzed, the types of revenue models will become increasingly convergent, and thus the coding will gradually converge. Finally, this study integrated the coding of the revenue sources into five categories, which are explained as follows. The data coding for the WeChat (Communication) case is shown in Table 1. 1. Freemium (UP+). A subset of non-paying users is lured to pay. First, users are attracted by free network services. After users become accustomed to the services, advanced services with a fee are introduced, which increases the revenue of free network service providers. For example, when the amount of content on a platform increases, consumers may be willing to pay for quality content or to block advertisements.

2.
Advertising (AdP). A type of third-party market, where a third-party pays for free commodity exchanges between the other two parties. Network advertising is the most common model. Advertisers buy ad impressions from free network service providers to publish advertisements to increase product exposure and attract potential customers to purchase their products [25].

3.
Transaction commission (2P). Payment is required only when two parties (the buyers and the sellers) on the platforms actually complete a transaction. Therefore, it does not hinder users from joining the platforms, but platforms must also beware of the buyers and sellers who might switch to offline transactions to avoid the fee. In order to prevent offline transactions, the platforms try their best to provide all information and standardized services so that consumers and producers have no direct contact.

4.
Payment as a content provider (CP). It is also known as charging for access. The platforms charge the content providers for the provision of a channel through which to contact the users, and there is usually a shelf fee for goods or services. 5.
Value-added services to content provider (CP+). A subset of content providers is lured to pay the advanced version of charging for access. The platforms provide advanced paid services for content providers, such as identifying more accurate and valuable users or displaying producers' content to users in more prominent positions. This brings more added value to the producers. 6.
Premium service (PP). Payment is from payers for a premium service. Network services that are mainly paid-for services are provided to enterprise users. 7.
Others (OP). Revenue sources other than the above sources, such as donations, interest, or other revenue sources.

Revenue Concept Maps
After the features related to the revenue models are extracted and coded, the revenue concept maps of the cases are drawn based on the case data coding table. This allows us to visualize the revenue pattern. When a new revenue concept map is being drawn, it is compared to the revenue concept maps of the cases in the same industry or from the same network service provider. This is done in order to search for new findings and to evaluate whether the original coding content and revenue concept maps should be modified. Based on the cross-case inductive analysis method, the revenue concept maps of multiple cases are compared to investigate the similarities and differences of each model and determine possible multi-exchange relationships. The repeated comparison process can result in gradually converging from which the theoretical logic can be constructed. The revenue concept map for the WeChat (Communication) case is referred to in Figure 2.

Pattern Matching
The results of the above process are the revenue concept maps of 51 cases. We then confirm the replication results of the revenue patterns by abstracting revenue concept maps in order to identify and summarize the types of revenue pattern diagrams for free network services. The principles for abstracting the revenue concept maps into revenue pattern diagrams are described as follows.

1.
One free network service only belongs to one kind of revenue pattern diagram. In the case of WeChat communication, the "Service Account" is designed for merchants in the hope that they will also use WeChat Pay. Although this brings in revenue from the transaction fees of the WeChat Pay platform, WeChat Pay is not included in the revenue pattern diagram of WeChat communication but is discussed separately.

2.
The revenue pattern is at least a dyad (two actors) relationship, which means that the free network services offer certain free services and have non-paying users. The other actors as payers or third-party content providers, whether existing depend on the revenue concept map. 3.
The exchange relationship between the free network service providers and all participants will be simplified for further model generation. In the case of free network services in the Google ecosystem, advertising service is controlled by the Google Ads service [26]. Based on the simplification principle, the free network service will use the fee charged to substitute the advertising service but will not be included in the detailed revenue model of Google Ads.
When the revenue concept map of the first case is abstracted into a revenue pattern diagram, the revenue concept maps of subsequent cases can be compared with the first revenue pattern diagram during the abstraction process. If the revenue concept maps of two cases are the same, the second case can be categorized into the first revenue pattern diagram; if different, a new revenue pattern diagram will be produced according to the abstraction principle. This process is repeated until all the cases have attributable revenue pattern diagrams. The descriptions of legends for the revenue pattern diagrams are illustrated in Table 2. If the participants of free network services are only non-paying users, the participant relationship is called a one-sided party that contains only one revenue model, as shown in Table 3; if there is another group of participants in addition to the non-paying users, such as content providers or payers, the participant relationship is called a two-sided party that concludes six revenue models from different content sources, as shown in Table 4. Take two-sided payment and user-generated content for example, in the cases of Dropbox, Google Drive, Microsoft OneDrive, Facebook, Gmail, LinkedIn, Outlook, WeChat, and Yahoo! Mail, non-paying users provide the source of all content, and one revenue source is the purchase of value-added services by users after they enjoy the free network services. Specifically, a revenue source of Dropbox, Google Drive, and Microsoft OneDrive is to charge service fees to enterprises for providing advanced services, as shown in Figure 3A. Facebook's revenue source is advertising billing, and its revenue pattern is shown in Figure 3B; the revenue source for Gmail, LinkedIn, Outlook, WeChat, and Yahoo! Mail is charging for access to and advertising on corporate services, and their revenue pattern is shown in Figure 3C.   Revenue pattern diagrams (A) revenue is from premium service, (B) revenue is from advertising, (C) revenue are both from premium service and advertising (Adapted from [24]).
If the free network services have all three types of participants-non-paying users, content providers, and payers-the participant relationship is called a multi-sided party that summarizes three revenue models from two revenue types, as shown in Table 5. The 51 cases are classified into 28 revenue pattern diagrams of free network services. For the free network services with a one-sided participant relationship, the revenue comes only from non-paying users; for the free network services with more than two types of participants, the revenue can come from non-paying users, content providers, or payers, or all of them. Based on the exchange model, the case data from this study is coded and patterns are classified, forming ten types of revenue models. After determining the antecedent cause conditions of each model, we officially entered the Qualitative Comparative Analysis (QCA) procedure.

Qualitative Comparative Analysis (QCA)
As it integrates qualitative case-orientation and quantitative condition variable-orientation, QCA can provide more in-depth analysis of cross-cases, meaning it can accurately match and compare the conditions of the cases, and produce different causal condition configurations for different results, which become a set theory that is transformed into a theoretical language [19]. Taking multiple free network services as the analysis cases, this study summarized the revenue model of free network services one by one to develop and construct conditional propositions and explanatory theories and used QCA to confirm the causal configuration of the theoretical model.
The study applied the most widely used csQCA technology, that is, the variable value can only be 0 or 1. The data encoding of each case can be regarded as a variable, that is, it can be converted into a truth table of 0 or 1 to construct a data matrix. If the variable existed in the encoding of the case, the value was set to 1, and if it did not exist, it was set to 0. Finally, the "participant relationship" was obtained by referring to the composition of the pattern of the individual case. At the same time, the original "content source" was coded as a "third-party", and then, subdivided into the "advertiser" category, and a total of 18 variables of free network services were obtained, as shown in Table 6.
QCA was used to identify the different causal condition configurations and correlation levels of the revenue model to verify 28 groups of revenue pattern categories. Based on the analysis of this study, in order for the data level of specific evidence to be abstracted to the theoretical concept level, it is necessary to simplify the multiple causal relationship model to develop appropriate conditional propositions and explanatory theories. We used the csQCA analysis method from Tosmana (Vers. 1.6) QCA software, as developed by [27], to explore the causal relationship of different case configurations with the revenue model. Since the variable value of csQCA must be dichotomized, that is, the value must be 0 or 1, this study used third-party payment (P) as the result variable to explore how the component conditions of each cause contribute to the result of third-party payment and made the preliminary classification of the case configuration, that is, the combination of necessary and sufficient conditions. The truth table after csQCA analysis is shown in Appendix A, Table A2. Is there a third-party payer. If there is a value of 1 in CP, CP+, 2P, PP, AdP, and OP, then this variable is 1, otherwise, it is 0. C = P Whether the third-party content provider is also a third-party payer. If there is 1 value in CP, CP+, and 2P, then this variable is 1, otherwise, it is 0 U = P = C Whether non-paying users are also third-party payers and content providers. If the values of U = C and U = P are 1, the variable is 1, and otherwise, it is 0.

Model Description
The theoretical concept we advocate for is "there is no such thing as a free lunch". The participants in the free network data services have economic or social returns to exchange so as to support a large number of non-paying users.
According to the QCA analysis results, this study simplified the content sources into two categories. If the content source variables (UC, CC, SC) of the case were all 0, it was regarded as a no content source and collectively referred to as "tool-based"; other than the no content source, there was at least one content source, which was marked as "content-based". Then, according to the content-based producer source, they can be divided into sub-categories, including "user-generated content (UC = 1)", "third-party content (CC = 1)", and "service provider content (SC = 1)".
Next, according to the configuration of the antecedent cause conditions in each model, the source of revenue was simplified into third-party payment and generalized advertising revenue, and then, the repeated similar and different pattern categories were re-analyzed for subsequent inferences of conclusions.

Third-Party Payment
Most "tool-based" services derive their revenue from freemium (UP+), except for the operating system type (Android-NonEU, iOS), as shown in Table 7. As operating systems are mostly bundled with devices or equipment for sale, it is not possible for them to earn extra income from users, and they can only charge third-parties to compensate a large number of non-paying users. Conversely, "content-based" services can all earn income from third-parties, as shown in Table 8. It is particularly worth mentioning that, if the content comes from a free network service, there is no freemium. It is inferred that such services usually provide additional APIs, such as Baidu Maps, Amap, Google Maps so that APIs tool-based services can obtain payment as freemium, which means they can have both kinds of income.

Generalized Advertising Services
At present, common advertising charging methods include cost per mile (CPM), cost per click (CPC), and cost per action (CPA). In addition, the cost per sales (CPS) pricing method is gradually becoming popular in recent years, that is, advertisers only pay after the product is sold [28][29][30], which is similar to the concept of the transaction commission. Therefore, this study argues that the content produced by the content provider is also for trading, and is broadly similar to advertisers, thus, the aforementioned "third-party payment" was further subdivided into two categories of "non-advertising revenue" and "generalized advertising revenue", in order to develop clearer conditional propositions.
Revenue types include advertising services (AdP), payment as a content provider (CP), payment for value-added content (CP+), transaction commission (2P), etc., all of which are regarded as "generalized advertising revenue", while freemium (UP+), paid services (PP), other channel income (OP), etc., are regarded as "non-advertising revenue". The source of revenue for most "tool-based" services is not generalized advertising revenue, as shown in Table 9; for "content-based" services, most types of user-generated content do not rely on generalized advertising revenue. Instead, the type of third-party content depends on the generalized advertising revenue. If the content comes from a free network service, its revenue source is only generalized advertising revenue. The classification matrix for "content-based" services is shown in Table 10.

Conditional Propositions
This study combined 51 cases to summarize in a revenue concept map of individual cases through the inductive method, and a total of 28 revenue pattern diagrams were collected using cross-case pattern comparison. At the same time, the multiple causal relationships between antecedent cause conditions and the revenue results of each cause were determined through QCA. In order to develop appropriate conditional propositions, this study repeatedly used QCA to simplify the conditional configuration to third-party payment and generalized advertising revenue. Finally, the classification results were compared with the QCA causal relationship configurations.
This study confirmed that the "revenue source" of the free network service was related to the "content source" dependent on different "participant relationships". Seven conditional propositions were proposed according to the causality model.

Unilateral Relationship
The primary characteristics of information products are free and perfect, which means that one more copy of the digital product is free, and the copy will be exactly the same as the original digital version. Through the Internet, the transmission distance becomes unlimited; therefore, the features of being free, complete, and having real-time replication makes the overall value of the network service much greater than the individual value of the product [13]. Another feature of an information product is that it is unbundled; in the past, multiple songs were presented on one CD, which is beneficial to record companies, and this is atomic economics. However, as the network makes the transmission cost almost zero, and selling singles will not cause cost increase, this is network economics [4]. Therefore, for a tool-based free network service that simply provides functions and no content, the functions are disassembled and sold separately, thus, the revenue path is often to pay for purchased advanced functions after the free content, such as Bing Search APIs, Google Maps APIs, Google Search APIs, Baidu Maps APIs, AMap APIs, and other cases.
Proposition 1. If a free network service is tool-based, it can charge non-paying users as freemium.

Bilateral Relationship
The biggest challenge in the two-sided market is the problem of "which comes first, the chicken or the egg". The users on one side and the market influence each other, meaning the users on one side will affect the users on the other side, thus, a platform with more friends will do better at attracting new users to form a network effect, and an effective feedback loop can increase value. If people think that the platform is valuable, they will be attracted to join the platform, thus, more users can promote more user interaction, and the platform will create more value and attract more tangible or intangible currency, which will feedback more value to users and attract more users, and continue as a virtuous cycle [4,31]. Compared with the unilateral relationship of only free users and free network services, the main feature of forming a bilateral relationship is that there are third-party content providers or payers. Although content providers or payers may also be derived from free users, there must be third-party participants in the bilateral relationship, and they are either the payers or the content providers, and only one role exists.

Tool-Based Free Network Service
Regarding tool-based network services, if there is no free value-added service provider, the main source of income is the "other" channel for third-party payment. Like Android-NonEU (non-EU region) and iOS, when more users use the operation system, the value it brings is that it can receive additional income from other services as a subsidy by carrying other services; for example, Google pays Apple every year to put Google software on iOS [5]; Android-NonEU (non-EU region) provides GMS free of charge in addition to Android, with the intention of earning a large amount of advertising revenue from GMS via the deployment by a large number of different mobile device vendors.
On the other hand, if the tool-based network service can provide free value-added services, such as Android-EU, Alipay, WeChat Pay, and PayPal, basically, the revenue model of Android-EU (European Union) should be the same as Android (non-EU region). As the European Union accused the Android operating system of monopolization, and then, derived a special charging situation, this study did not include it in the proposition discussions and only discussed the revenue model of Alipay, WeChat Pay, and PayPal. As the main revenue sources of these three are all "other" channels, they also receive transaction commissions for generalized advertising services.

Proposition 2.
If the free network service is tool-based, it can collect fees from third-parties, with income coming mainly from other channels; it can also charge non-paying users as freemium, and charge for generalized advertising, with income mainly coming from transaction commissions.

Content-Based Free Network Service: User Generated Content
More users represent more user feedback content and more user behavior information, which has the value of realization. At the same time, the information services used by more users will form the mainstream information services in the market, which encourages enterprises to pay for a more exclusive commercial version of the service, and thus, obtain paid corporate service revenue; a large number of users also represent more "eyeballs", which naturally attracts the traditional classified advertising model to earn more income. On the other hand, as content providers use the service for free, those users who want to exchange to gain social recognition or prestige will also be willing to switch to being paying users and purchase more exposure opportunities for the content they provide; such cases include WhatsApp, Facebook Messenger, Instagram, and Twitter, which do not offer value-added payment for free users, as well as Dropbox, Google Drive, Microsoft OneDrive, Facebook, Gmail, LinkedIn, Outlook, WeChat, and Yahoo! Mail, which do offer value-added payment for free users. Most revenue sources of these two types are from third-parties and are mainly for corporate services or fee-based services of traditional advertising.

Proposition 3.
If the free network service is content-based, and the content source is user-generated content, it can charge third-parties mainly for corporate services or narrowly defined advertising charges; it can also charge non-paying users as freemium.

Content-Based Free Network Service: Third Party Provided Content
There are three main sources of income that the media industry relies on: advertisements, transactions, and subscriptions [1], and content-based network services provided by third-parties can also be regarded as being part of the media industry, while free network services are regarded as large advertisement boards. The purpose of the boards is to gain more exposure for content providers to obtain transaction opportunities, and then, collect commissions from each transaction. In addition, the network has created another sales method, meaning the "subscription". The subscription streaming service model is more consumer-friendly due to its simplicity, as consumers only need to pay a fixed monthly fee instead of making decisions every time [4]. Therefore, most of the revenue sources are derived from transaction commissions, including Airbnb, Facebook Games, Google Play, Ctrip, Groupon, Microsoft Store, Booking.com, Vimeo, App Store (iOS), Lyft, Amazon e-commerce, and other cases; while GitHub, which is a free network service for open source software version management, is the exception due to its operating revenue from content providers who pay to purchase advanced services. In addition, Ctrip, Groupon, Microsoft Store, Booking.com, Vimeo, App Store (iOS), Lyft, Amazon e-commerce, and other cases, collect access fees from content parties besides the transaction commission. This fee is nothing more than giving the content provider the right to list their products or services, or provide paid advanced services, which allows content providers to have higher exposure or easier access to users to facilitate more trading opportunities. The search cost for the bilateral market of matchmaking is reduced due to the reduction of asymmetric information, and such networks should have the two-way effect [32]. Therefore, it can attract participants from the other side, and as the revenue sources are all from third-party content providers, they are all broad-based advertising revenue models; only Lyft, Didi Chuxing, Amazon e-commerce, and other cases provide paid premium services for free users.

Proposition 4.
If the free network service is content-based, and the content source is a third-party provider, it can charge fees from the generalized advertising, which are all paid by third-party content providers; it can also charge non-paying users as freemium.

Multilateral Relationship
When an actor has more opportunities to exchange, the actor has more power; such opportunities to exchange or control interests are directly related to the actor's structure in the network, and this can change depending on the actor's position change [33]. Therefore, a free network service that forms a multilateral relationship can obtain more resources and represent more revenue sources. Unlike bilateral relations, multilateral relationships have non-paying users, third-party content providers, and third-party payers, and all three sides can derive payers, thus, presenting multiple revenue sources.

Content-Based Free Network Service: Third Party Provided Content
While the main feature of information technology is disintermediation, via platform opens more new sources of supply, enables market aggregation, provides a centralized market, and creates re-intermediation [4]. Wikipedia, Baidu Search, Bing, Google Search, TikTok, Expedia.com, Taobao.com, YouTube, Uber, Grab, etc. are all platforms that connect multi-party participants so they can earn revenues from narrowly defined advertising services. Additionally, through platform expands the visibility of resources that brings benefit to more transaction commission as well as paid services from enterprises or nonpaying users.
Proposition 5. If a free network service is content-based and the content source is from a thirdparty, it can collect fees from the content provider and third-parties other than the content provider, which mainly refers to corporate services and generalized advertising; however, it can also charge non-paying users as freemium.

Content-Based Network Services: Multiple Parties Provided Content
The intermediary mechanism of the business model of the network economy has created many emerging channels that have connected a large number of users, generated unprecedented market power and efficiency, and changed many business ecologies [4]. Waze, Amap, Baidu Maps, Google Maps provide the content of network services at their own huge expense, and while the source of their revenue only comes from generalized advertising, the more important purpose is to facilitate a new service ecosystem.
Proposition 6a. If a free network service is content-based, and the main content source is the network service provider, it can charge a fee from the generalized advertising.
The platform should not charge users for revenue but should subsidize platform participants and charge users for the value obtained from the ecosystem. Although users decide the network effect, they may not be able to represent the platform's currency value, thus, the platform's promotion of multilateral interaction must create considerable excess value, in order that the platform can profit. If the purpose of the persistent loss of Market A is to let Market A grow to drive Market B, the premise must be that the profit of Market B is greater than the losses of Market A, and both sides of the market must grow at the same rate [4]. For example, Amap, Baidu Maps, and Google Maps all provide derived services such as Google Maps APIs, Baidu Maps APIs, and AMap APIs to bring more value to multi-sided users with complementary functions; that is, to provide complementary services to users, which were originally free, to use the service to increase the income of advanced services, while allowing multiple markets to profit by connecting a variety of services in own ecosystem.
Proposition 6b. If a free network service is content-based, and the main content source is a network service provider, most network services that can derive tool-based services can charge non-paying users as freemium.

Conclusions
Whether the network economy can make profits is a very complex issue. Nowadays, Google and Facebook users have exceeded 1/7 of the global population [4]. In addition, it took YouTube only one year after launch to reach more than 100 million views per day: no TV channels in history have ever created such audience ratings [9]. The unique advantages of the network economy promote interaction between external users. Only after links are established with a certain number of users will users consider the network services valuable, which is a "chicken-egg" problem [4]. Network services are similar to traditional public goods in that they are difficult to charge for. To charge users, barriers to user entry must be set up, which may reduce the number of users of the services and their network effects. They would lose their market value as a result. However, by simply providing free network services, providers may fail to make profits and be unable to sustain their operation.
The Internet, with its easy communication capabilities, allows the supply-and demandsides to see each other more easily, which promotes more transactions. This finding can be verified from the cases of bilateral relationships. In the network economy, the payment method of buyers and sellers may change between direct and indirect payments, and the transaction may not only be an economic exchange. Instead, it may involve reputation or attention as a social exchange; therefore, when the number of free-riders increases, the number of providers or users who are willing to pay may also increase. This gives the free network services more resources to monetize the free-riders.
Although users are not charged, the accumulated user base can drive positive network effects. This creates a multi-sided value exchange opportunity for service providers to convert a large "headcount" into a lot of money. In addition, with the social bridge, initially free information has more chances to be circulated to people who are willing to pay for it. Google Maps, which is a multilateral relationship case, absorbs huge content production costs and lets free-riders enjoy unlimited access to map information. Although they can only earn general advertising revenue, the related Google Maps APIs connect users of other markets, thereby driving different users to repurchase the same information. This creates a business model where both sides can profit.
This study summarized 28 types of revenue model and seven conditional propositions that have provided different interpretations of the business strategies of network services. For start-up companies that intend to provide network services, this study provides reference paths for different revenue models. "Free" is not the only means of survival. Business operators can obtain paid revenue patterns by comparing the "participant relationship" patterns with the "content source" conditions, and then design or plan corresponding service items. By attracting users to enjoy services for free, they can realize the network effects of positive expansion and make profits.
In the future, the "general rules" of the revenue model from this study can be future testing or confirming in the follow-up to develop hypotheses. Individually focus on an overall revenue model inside a large ecosystem such as Facebook, Apple, Amazon, and Google or Baidu, Alibaba, and Tencent can also be incorporated into future research recommendations.

Data Availability Statement:
The data that support the findings of this study are available from the first or corresponding author upon reasonable request.

Conflicts of Interest:
The authors declare no conflict of interest.