Organization learning, knowledge management and value co-creation: a fuzzy-set analysis.
The heterogeneity of resources characteristics, as knowledge resources are of great value and scarcity, difficult to imitate and difficult to replace, has become the base of competitive advantage (Hong et al., 2013). Usually, a new started firm with naturally disadvantage and the lack of knowledge resources has to implicate knowledge acquisition, integration, requiring managing knowledge more effectively. Organization learning is the basic learning value established by a firm. By regulating and instructing its learning behaviors, an organization improves and updates the inherited environment of knowledge system. Organization learning, as a crucial precondition for knowledge innovation, is the foundation of value co-creation activities and market performance (Sheng and Chien, 2015), contains three dimensions: leadership & authorization, open experiment, and a shared vision (Sinkula et al., 1997).
Existing organization learning research focuses primarily on contingent variable exploration in the relationship between organization learning and company performance. Rare studies concentrate on how organization learning motivates an organization's learning behaviors themselves and further improves corporate value co-creation and competition (Melton and Hartline, 2013). In addition, leadership & authorization, open experiment, and shared vision represent independent value propositions over the dynamic learning process respectively (Sinkula et al., 1997; Lino et al., 2016)). However, most present-stage empirical research into organization learning prefers to analyze it as a gestalt construct, and ignores the impact on corporate value co-creation activates by its complicatedly inter dimensions. In practice, establishing value co-creation is always a fruit of the equivalent effect of the strategy's different sets through multiple causal paths.
2. A configurationally approach to customer-oriented firms
The function of organizational learning is to ensure companies to access information and knowledge, understand the technical know-how, methods and practice of organizational processes and mechanisms (Lim, 2016). Organizational learning is a dynamic mechanism based on knowledge management, the mechanism to promote knowledge on different levels such as individual, team and organization. Individuals acquire external knowledge, teams focus on existing knowledge to compile and integrate, while organization aims at systemize knowledge and create new knowledge (Lyles, 2014). Organization learning is contingent on knowledge creation. It contains three elements: leadership & authorization, open experiment and a shared vision (Sinkula et al., 1997). The leadership & authorization reflects how greatly an organization weighs learning. By improving learning systems on organizational level, an organization promotes its members to comply with learning schedules so as to accomplish the learning mission that provides them with more skills (He and Feng, 2013). By open experiment, the organization encourages its members to break conventional hypotheses, instead of critically querying fixed intellectual modes. In a turbulent environment, original value-creating skills always have the corporation trapped in core rigidity due to path dependence (Barney, 2014). Given this, it is a necessity to break the norms or routines formed by knowledge extension accumulation over the course of learning, so that enhancing learning fitness. A shared vision reflects the degree that the decision-making level spurs the members to carry the organization's picture in their heads and hearts. The collective knowledge on organization level is derived from complete knowledge sharing among the individuals (Lin and Wu, 2014).
Knowledge management refers to the enterprise to acquire knowledge from outside, make the enterprise employee may at any time and any place apply knowledge in the process of knowledge integration, in order to create new knowledge and achieve organizational goals of corporate activities (Agha et al., 2016). Some scholars believe that knowledge management theory can explain the source of the enterprise competitive advantage in the market (Swift and Hwang, 2013), the reason for the existence of the enterprise is the creation, transformation and application of knowledge. There are mainly two aspects: one is the management of knowledge, the second is the process of knowledge management (He and Feng, 2013). Knowledge management process is mainly composed of three basic activities i.e. knowledge acquisition, knowledge integration and knowledge innovation (Baker and Sinkula. 1999). Knowledge acquisition is the process of transform the external enterprises tacit knowledge to search and assessment, as well as the process of acquiring new knowledge. Knowledge, showed through formal and informal processes in the enterprise, is structured to ensure knowledge sharing and integrating activities between different functional departments within the enterprise, such as analysis, compilation, etc. Knowledge creation is the process of the enterprise to create new knowledge (Wang and Wang, 2012). Knowledge acquisition, knowledge integration and knowledge creation is not a linear process with strict order. The resource set of organization learning is composed of three resources: leadership & authorization, open experiment, and a shared vision (Achcaoucaou et al., 2014). During the practice, a company applies different knowledge resource sets to the process of corporate value co-creation activities on the basis of its own endowment and learning mechanism. Through knowledge acquirement, integration and innovation, the company gains positive market performance.
Due to the discrepancy between knowledge creation paths, knowledge creation and knowledge integration differentiate from each other in the degree of employing knowledge resource sets. The former one lays more emphasis on open mind and shared visions in order to strengthen the force to search large-scale knowledge; whilst the latter one attaches more importance to leadership & authorization so as to render knowledge creation more efficient (Newbert, 2007). However, since knowledge features path dependence, both of the learning mechanisms share the same ultimate goal of establishing resource integration for value co-creation, which is to store new knowledge elements in organization memories as a way to vary tacit organization routines formed by knowledge extension (Agha et al., 2016).
Learning behaviors in an organization is always inserted in the link of value co-creation. Value co-creation is the process where "the consumer and the firm are intimately involved in jointly creating value that is unique to the individual consumer and sustainable to the firm" (Prahalad & Ramaswamy, 2004). value of a service or a product is not created by any actor solely, but co-created by the manufacturer/supplier and the consumer of the product or the service through engagement platform (Prahalad and Ramaswamy, 2014). Another stream of marketing research investigates value co-creation applying service-dominant logic including Foundational Propositions (FPs) (Vargo and Lusch, 2004). They recently updated the FPs into 5 axioms, stated that "Value is cocreated by multiple actors, always including the beneficiary." (Vargo and Lusch, 2016). This paper discusses corporate value co-creation activities from two aspects: resource integration and service exchange. The first one refers to operant resources (primarily knowledge and skills) enhancing human viability, especially through the creation of new resources; while the second one specifies service as the basis, rather than the unit of exchange (Peters, 2016). In order to retain competitive advantages in changeable markets, a firm needs to switch their marketing logic to co-create value with all the actors in the service ecosystem as a whole picture. Value co-creation plays a positive role in enhancing user satisfactions and company profitability as well and thus becomes the decisive variable for the company to achieve high performance returns.
Existing additive-approach-based regression equations or structural equations can be used to explain the uniqueness of variable relationships under specific circumstances, but fail to reflect the complexity (Fan et al, 2016). Moreover, they will lead to less prudent research findings if hidden influences of other variables are ignored, just as what they tend to do. fsQCA, founded on logical condition sets, builds up multi-element causal paths towards multiple conjunctional causations that a variable functions (Lisboa et al, 2016). fsQCA overcomes the shortages of existing research methods in that it effectively takes into account both the uniqueness and complexity of variable relationships. It is a set-theory-based research approach that explains sufficient and/or necessary relationships between conditions and results, which allows the construction of multiple sets of logical conditions (causal paths). These paths can be used to identify multiple conjunctional causes to the result while remains comparable under specific conditions. In this way, the uniqueness and complexity of variable relations are both reflected by fsQCA. It uses consistency and coverage to test the relationship between independent variables and dependent variables.
As a summary, by use of a theoretical framework of the knowledge resource-based view, this research conducted fuzzy-sets quantitative comparative analysis on 232 Chinese firm samples, in an attempt to discuss multiple conjunctional causation to value co-creation. This research intends to enrich research achievement pools with respect to organization learning theories, and provide reference and supports for corporate value co-creation. Figure 1 is the concept model in the paper.
3. Research design
3.1. variable measurements
This research involved the following variables: organization learning, knowledge management and value co-creation. All items were graded by respondents using 7-point Likert scales. "1" represents "totally disagree", and "7" is "totally agree". To ensure the credibility and validity of this measurement tool, we adopted an empirically verified scale that was co-developed by domestic and foreign scholars. After referring to reviews of related experts and corporation administrators, we conducted a preliminary survey among the EMBA groups in our university, and modified part of the items thereafter. With the final version of our questionnaire, we offered a formal investigation.
3.2. research sample and data collection
We surveyed the manufacturing industry and the service industry in five national hightech science parks (i.e. Zhongguancun Science Park in Beijing, Zhangjiang Hi-tech Park in Shanghai, Suzhou Industrial Park, Tianjin Economic-Technological Development Area, and Shenzhen High Tech Industrial Park). To ensure the diversity of sample sources, we collected research samples from online business directories of the said science parks as well as the business directory of EMBA members in a university. Among 400 questionnaires we issued, 232 were valid ones.
3.3. credibility and validity test
The paper first analyzed the credibility of the four variables, and found out that their Cronbach's a coefficients were 0.83, 0.8, 0.89 and 0.90, respectively. This result indicates good internal consistency. For validity test, we used the structural equation model to conduct confirmatory factor analysis on all variables. The result showed that the three organization learning factors had good fitness ([chi square]/df=1.22, GFI=0.971, RMSEA=0.033, CFI=0.990, TLI=0.983, AGFI=0.935), and that the two knowledge management factors had good fitness ([chi square]/df=1.913, GFI=0.945, RMSEA=0.068, CFI=0.961, TLI=0.932, AGFI=0.913). As the company performance factor is single, we produced a three-factor model by combining it with all items contained in the value co-creation. The fitness result was favorable ([chi square]/df=1.211, GFI=0.931, RMSEA=0.059, CFI=0.990, TLI=0.974, AGFI=0.911).
4. Data analysis
4.1. fs/QCA analysis
Calibrating is the first step of fsQCA. As the initial sample data failed to satisfy Boolean logic, we necessarily converted them into aggregate data falling in the interval of [0, 1]. With the scheme of continuous assignment (Ragin, 2008), we first defined [0, 1] continuous fuzzy sets. Then, sample data was mapped into [0, 1] interval by use of the linear contraction method. Numbers in the interval represented membership, for which "1"= "full membership" and "0"= "non-membership". Taking knowledge integration as an example, the original interval of its initial sample data was [1, 7]. We mapped it into the membership interval of [0, 1] using the approach of linear contraction, where the data whose membership was 1 represented the highest level of knowledge integration, and the data whose membership was 0 represented the lowest level of knowledge integration.
After calibrating data, in fsQCA2.5 software, we adopted the Quine-McCluskey algorithm to explore multiple causation paths for different variables under high company performance. The calculation result usually encompassed three logical condition sets: complex solution, parsimonious solution, and intermediate solution. The complex solution is commonly the subset of another two sets. In light of general practice of existing documents, on the premise of meeting the requirements of consistency and raw coverage, the paper adopted the logical condition set of complex solution. Table 1 is the calculation result.
As can be seen from table 1, the consistency of all logical condition sets exceeds 0.8, while the raw coverage ranges from 0.25 to 0.75. This shows that the data result is explanatory (Ragin, 2008). Please note that * denotes the existence of causality conditions, (r) is the non-existence of causality conditions, the blank indicates that the corresponding variable does not influence the result. *shows the existence of core-necessary conditions, while O shows non-existence. Consistency is the degree of empirical data sharing a given logical condition set that spawns the occurrence of the result; raw coverage shows to what extent the given set explains the occurrence of the result; unique coverage reflects the proportion of the empirical data to be explained and only can be explained by the given set.
There are two causation paths incurring high-level knowledge creation: Path 1 shows that the logical condition set of low-level open mindedness and high-level shared visions triggers high-level knowledge creation (consistency=0.81, raw coverage=0.47); Path 2 shows that the logical condition set of high-level leadership & authorization itself can trigger high-level knowledge creation (consistency=0.82, raw coverage=0.69), but not necessary.
There are two causation paths incurring high-level knowledge integration: Path 1 shows that the logical condition set of high-level open mindedness and high-level shared visions triggers high-levelknowledge integration (consistency=0.85, rawcoverage=0.71); Path 2 shows that the logical condition set of high-level open mindedness and low-level leadership & authorization can trigger high-level knowledge creation (consistency=0.88, raw coverage=0.58). As it occurs in both of the two paths, high-level open mindedness can be deemed as the necessary but not sufficient condition for the occurrence of high-level knowledge integration.
There are two causation paths incurring high-level value co-creation resource integration: Path 1 shows that the logical condition set of low-level leadership & authorization, high-level open mindedness and high-level knowledge integration triggers high-level value co-creation resource integration (consistency=0.89, raw coverage=0.30); Path 2 shows that the logical condition set of high-level open mindedness, high-level shared visions, low-level knowledge creation, and high-level knowledge integration can trigger high-level value co=creation resource integration (consistency=0.90, raw coverage=0.41). As it occurs in both of the two paths, high-level open mindedness and high-level knowledge integration can be deemed as the necessary condition for the occurrence of high-level value co-creation resource integration.
There are two causation paths incurring high-level value co-creation resource integration: Path 1 shows that the logical condition set of low-level leadership & authorization, high-level open mindedness and high-level knowledge integration triggers high-level value co-creation resource integration (consistency=0.8c, raw coverage=0.30); Path 2 shows that the logical condition set of high-level open mindedness, high-level shared visions, low-level knowledge creation, and high-level knowledge integration can trigger high-level value co=creation resource integration (consistency=0.90, raw coverage=0.41). As it occurs in both of the two paths, high-level open mindedness and high-level knowledge integration can be deemed as the necessary condition for the occurrence of high-level value co-creation resource integration.
There are two causation paths incurring high-level value co-creation service exchange: Path 1 shows that the logical condition set of low-level leadership & authorization, high-level open mindedness, high-level shared visions, low-level knowledge creation, and high-level knowledge integration triggers high-level value co-creation service exchange (consistency=0.87, raw coverage=0.35); Path 2 shows that the logical condition set of high-level leadership & authorization, low-level open mindedness, and high-level knowledge integration can trigger high-level value co-creation service exchange (consistency=0.91, raw coverage=0.30). As it occurs in both of the two paths, high-level knowledge integration can be deemed as the necessary condition for the occurrence of high-level value co-creation service exchange.
4.2. SEM analysis
Table 2 presents relevant results of a supplementary analysis of the proposed research model using SEM, as we can see, SEM results only shows the net effects among variables, and not all cases support an exclusive negative or positive relationship between the independent and the dependent variables
This paper regards organization learning as an enabler of knowledge resource set composed of three resource elements: leadership & authorization, open mindedness and a shared vision. Knowledge management is deemed as dynamic capability, which provides an access to corporate value co-creation activities. On this basis, the organization learning performance mechanism model is constructed in the paper. fsQCA is used to discuss multiple conjunctional causations to the occurrence of knowledge management, value co-creation, as well as company performance in that mechanism. We conclude that:
In terms of the construction conditions for high-level knowledge creation and high-level knowledge integration, (1) High-level leadership & authorization is its sufficient but not necessary condition. This means that the establishment of good learning systems in the organization facilitates knowledge accumulation and expansion for a company. And, members with high-level open mindedness may tend to have divergent thinking but instead fail to meet the demand for learning efficiency. In this connection, by building up a common development goal such that helping reach consensus, the organization enlarges its knowledge sharing space. Knowledge diffusion among organization members is accelerated accordingly, which improved the level of knowledge creation. (2) High-level open mindedness is its necessary but not sufficient condition. It is the integration of high-level open mindedness with either high-level shared visions or low-level leadership & authorization that realizes high-level knowledge integration. Leadership & authorization, with its emphasis on planning and regulations, will restrict divergent thinking required for pursuit of new knowledge, thus being adverse to the formation of high-level knowledge integration. Meanwhile, if the organization promotes its members to reach consensus, new knowledge will be diffused and created in the organization better than before, so that consolidating the fruits of knowledge integration. Therefore, at high-level open mindedness, it is high-level shared visions that facilitate the occurrence of high-level knowledge integration.
In terms of the construction condition for high-level value co-creation resource integration, high-level open mindedness and high-level knowledge integration are its necessary but not sufficient condition. The cultivation of members' open experiment will accelerate the integration and exploitation of knowledge resources in and out of the organization, which further promotes value co-creation. On this basis, knowledge integration helps speed up the collection, acquisition and integration of non-technology information. Please note that the action of knowledge creation on value co-creation resource integration may be influenced by the antecedent variables of leadership & authorization and shared visions. Specifically, path 1 shows that high-level leadership & authorization helps mitigate the effect of low-level knowledge creation on the formation of value co-creation resource integration; while knowledge creation plays an efficacious role in covering the loss of shared visions in path 2. Both of the two paths reflect that there is a nonlinear relation between knowledge creation and value co-creation resource integration which responses to leadership & authorization and/or shared visions. This summarization provides a new perspective for research into organization learning performance mechanism.
In terms of the construction condition for high-level value co-creation service exchange, different from the antecedent variables constituting value co-creation resource integration, high-level knowledge creation displaces high-level open mindedness and high-level knowledge integration and becomes the necessary condition for high-level value co-creation service exchange. With the feature of efficiency, extraction and execution, knowledge creation emphasizes the application of existing knowledge resources in order to maintain competitive advantages in the competitive market. Path 1 shows that at the low level of leadership & authorization, which corresponds to the lack of mature and planned learning systems at the organization level, value co-creation service exchange can be accelerated by increasing the level of open mindedness, shared visions, knowledge creation and knowledge integration. Path 2 shows that at the low level of open mindedness, value co-creation service exchange can be quickened by increasing the level of leadership & authorization and knowledge creation.
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School of Economics and Management, Beijing University of Posts and Telecommunications, No. 10, Xitucheng Road, 100876, Beijing, China
Table 1--Configurations for achieving high levels of the outcome conditions Knowledge creation Knowledge integration variables 1 2 1 2 Leadership & authorization * () (x) () Open mindedness (x) () * * * Shared visions * () * () Knowledge creation Knowledge integration consistency 0.81 0.82 0.85 0.88 raw coverage 0.47 0.69 0.71 0.58 unique coverage 0.05 0.39 0.08 0.04 solution coverage 0.84 0.76 solution consistency 0.88 0.85 Value co-creative Value co-creation resource integration service exchange variables 1 2 1 2 Leadership & authorization (x) () (x) * () Open mindedness * * * * (x) () Shared visions * () * O () Knowledge creation * (x) () * * * Knowledge integration * * * * () consistency 0.89 0.90 0.87 0.91 raw coverage 0.30 0.41 0.35 0.30 unique coverage 0.07 0.11 0.12 0.06 solution coverage 0.43 0.37 solution consistency 0.87 0.88 Table 2--SEM results Relationship (from x to y) Standardized estimate(t-value) x y Leadership & authorization Knowledge creation 0.36 (4.12) * Leadership & authorization Knowledge integration -0.04 (-0.83) Leadership & authorization Resource integration -0.00 (-0.02) Leadership & authorization Service exchange 0.40 (4.01) * Open mindedness Knowledge creation 0.13 (1.25) Open mindedness Knowledge integration 0.41 (5.01) * Open mindedness Resource integration 0.43 (2.66) * Open mindedness Service exchange 0.29 (2.24) * Shared visions Knowledge creation 0.15 (1.39) Shared visions Knowledge integration 0.08 (0.84) Shared visions Resource integration 0.37 (3.48) * Shared visions Service exchange 0.15 (1.65) Knowledge creation Resource integration 0.08 (1.79) Knowledge creation Service exchange 0.52 (4.96) * Knowledge integration Resource integration 0.39 (4.21) * Knowledge integration Service exchange -0.08 (-0.61)
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|Publication:||RISTI (Revista Iberica de Sistemas e Tecnologias de Informacao)|
|Date:||Oct 15, 2016|
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