Introduction

IB involves generating, disseminating, and applying ideas1. In today’s increasingly competitive society, IB is essential to the survival and competitiveness of businesses, including the innovation of products and services2. Innovation enables an organization to find a better method to create a product consumers desire or use its resources more efficiently3. Therefore, there must be an innovative culture within the organization so that IB permeates the organization’s development and every employee can innovate in generating, promoting, and applying knowledge4. An organization can effectively facilitate organizational knowledge sharing by integrating knowledge directly into its business plan and encouraging employee alignment. However, knowledge sharing in organizations frequently fails to function as intended. Some academics believe that knowledge is power, making it challenging for people to spread knowledge actively. In addition, the willingness of individuals to share knowledge with coworkers is the greatest challenge that drives IB5. Table 1 is added to clearly illustrate the abbreviations of all variables and their meanings.

Table 1 Demographic characteristics of the sample.
Full size table

Based on existing theoretical research, this paper aims to investigate employees of pharmaceutical logistics companies, attempting to uncover the underlying factors influencing employee knowledge sharing and the relationship between knowledge sharing and employee innovation behavior. The cultural dimensions theory was developed by Dutch management scholar Geert Hofstede (1980) based on extensive survey research conducted within IBM Corporation between the 1960s and 1970s6. Through factor analysis, Hofstede initially identified four fundamental cultural dimensions: (1) individualism-collectivism, (2) power distance, (3) uncertainty avoidance, and (4) masculinity-femininity. Subsequent research expanded this framework through the Chinese Value Survey, adding a fifth dimension: long-term versus short-term orientation7. The model’s validity was further confirmed through Hofstede’s replication study across 93 nations, which established a sixth dimension contrasting indulgence with restraint8. In comparison to previous studies, this research supplements and expands relevant theories primarily in the following aspects: This study integrates two theories, the hierarchy of needs theory and social exchange theory, to propose a five-dimensional model related to knowledge sharing.

A single theory is hard to explain a complex problem, so we need to use multiple theories to solve it. Cairney presented three approaches of multiple theories combination, this paper is based on one of Cainey’s approach, the “complementary” approach9. This method employs multiple concepts or theories to generate a series of viewpoints, which are used to explain empirical results.This paper integrates these two theories to establish a more comprehensive conceptual framework for hypothesis development. Each theory possesses distinct applicable domains and functional roles in elucidating the relationship between five independent variables and knowledge sharing. Maslow’s hierarchy of needs operates at the intrinsic motivation level, reflecting the driving forces behind employee behavior. It posits that human needs constitute the most fundamental determinants of knowledge-sharing behavior, thus serving as a guiding theoretical foundation. In contrast, social exchange theory functions at the practical implementation level, explaining employees’ decision-making processes regarding knowledge-sharing behavior based on principles of reciprocal exchange. Although these two theories adopt different perspectives, they complement each other effectively. Their systematic integration facilitates a more robust explanation of the relationships among five dimensions’ independent variables, knowledge sharing, and employee innovative behavior. Grounding our research in social exchange theory, we develop a benefit-cost analytical framework to examine organizational employees’ knowledge sharing behaviors. Specifically, this study investigates how employees’ perceived benefits and costs differentially influence their knowledge sharing propensities. Drawing upon Maslow’s hierarchy of needs theory, we operationalize benefit dimensions as SN, FC, and RB. Concurrently, we identify POK and EC as primary cost dimensions. Utilizing data obtained through questionnaire surveys, structural equation modeling was employed for examination. Existing research has primarily divided knowledge sharing dimensions based on aspects such as the direction, content or form, scope, and effects of sharing. This paper categorizes employee knowledge sharing into two dimensions, namely common knowledge sharing and key knowledge sharing, based on the content of sharing. The study explores the effects of the five dimensions’ independent variables on common and key knowledge sharing, as well as their impacts on innovation behavior. The conclusions drawn from this research contribute significantly to enriching relevant theories in knowledge management.

This study will assist managers in understanding how to encourage knowledge sharing to foster innovation by analyzing the impact of SN, RB, FC, POK, and EC on IB induced by CKS and KKS at work. Pharmaceutical logistics differs significantly from conventional logistics. The primary focus of pharmaceutical logistics is the distribution of medicines intended for disease treatment and life-saving purposes, which encompass a wide variety of medical types and are subject to stringent quality requirements. Unlike ordinary goods, pharmaceuticals must adhere to rigorously defined storage conditions and regulatory standards. Certain medications require specialized transport vehicles and corresponding cold chain measures. Consequently, pharmaceutical logistics companies and personnel demand a higher degree of professional expertise compared to general logistics. This case study is conducted at eight pharmaceutical logistics companies in the Chinese provinces of Henan and Jiangsu. The largest company has over 25,000 employees and generated 110 billion RMB in revenues in 2021. For many years, the company has been ranked among the best pharmaceutical companies in China. Fourth position of pharmaceutical commercial companies in China. Consequently, this study addresses the following three research questions.

  • RQ1: How do five dimensions’ independent variables (SN, RB, FC, POK, and EC) influence the CKS?

  • RQ2: How do five dimensions’ independent variables influence the KKS?

  • RQ3: How do CKS and KKS influence the IB?

The remaining sections of the study are organized as follows: Literature review provides literature reviews of hierarchy of needs theory, social exchange theory, knowledge sharing and innovative behavior, Research model and hypothesis explains the research technique employed to examine the proposed model, Research methodology explains the methodology applied in this study, Results presents the results of structural model, and Discussion and conclusion discusses the implications, originality, limitations, and future work.

Literature review

Hierarchy of needs theory

Psychologist Maslow believed that everyone has five levels of needs: physiological needs, safety needs, social needs, esteem needs and self-actualization needs10. These five levels of needs gradually rise from low to high. When one need is satisfied, the next level of needs will dominate and become the internal motivation to push people to continue their efforts. According to the needs hierarchy theory, individuals must be driven by their inner needs when making knowledge sharing decisions. Once an individual’s physiological and safety needs are met, higher-order needs such as collective belonging, power, harmonious interpersonal relationships, and recognition from others will surface, thereby motivating individuals to participate in knowledge sharing activities with colleagues through reciprocal interactions11. Therefore, based on the need hierarchy theory, employees’ pursuit of high-level needs becomes the internal driving force for their behavior, which in turn promotes employees’ knowledge sharing behavior. Maslow’s Hierarchy of Needs reflects a universal psychological and behavioral pattern inherent to human nature. To effectively motivate employees, organizations must first satisfy their lower-level needs (e.g., physiological and safety needs). Only after these foundational needs are met can employees pursue higher-level needs, such as belongingness, love, and esteem. Crucially, the motivational impact is maximized when the higher-level needs align appropriately with employees’ individual aspirations and organizational context.

Social exchange theory

Blau pointed out that social exchange is an exchange behavior based on rationality and reciprocity of interests12. One party will provide intrinsic rewards or extrinsic rewards to the other party and hope that the other party will make corresponding returns. It needs to be emphasized that intrinsic rewards are rewards obtained in interactions, including social approval, emotions, extrinsic rewards are rewards obtained outside interactions, including money, goods, etc. However, due to the uncertainty in the time of return and the possibility that one party will adopt opportunistic behavior during the exchange process, this exchange relationship has certain risks. At this time, it is particularly important to exchange the trust, commitment, communication, and other investments of both parties. A basic principle of social exchange is that social activities will be dominated by some exchange activities that can bring intrinsic or extrinsic rewards. The generation and sharing of knowledge, as an intrinsic reward, can provide opportunities for cooperation and learning. Opportunities to stimulate the generation of innovative knowledge and enhance the organization’s breakthrough innovation capabilities, thereby improving organizational innovation performance1. When employees decide whether to engage in knowledge sharing, they conduct a risk assessment by weighing the perceived benefits against potential losses. Knowledge sharing occurs only when the anticipated gains outweigh the perceived costs, thereby establishing a viable exchange under the principles of social exchange theory.

Knowledge sharing

Knowledge sharing constitutes a pivotal facet of proficient knowledge administration and constitutes an essential element within the knowledge management process13. Knowledge sharing within an organization is the process of sharing organizational knowledge among employees and between employees and the organization, at its core lies the transmission of knowledge between employees and the organizational conduct of knowledge exchange14.

Improving business capabilities through KS is crucial in the modern economy, as it fosters creativity and accelerates organizational innovation15. Serving as a pivotal determinant, knowledge sharing optimizes the organizational capacity for managing knowledge reservoirs and aids individuals in attaining business objectives with heightened efficacy16. Malik’s survey revealed that knowledge sharing through AI-mediated applications enhanced employees’ job satisfaction and organizational commitment while reducing turnover intention17. Hence, the advent of business revolutions and the presence of workplace diversity underscore the imperative of engaging in knowledge sharing endeavors to foster opportunities for augmenting staff self-efficacy, facilitating enhanced learning, and disseminating knowledge among diverse stakeholders.

Knowledge sharing occurs primarily at the individual level18. They further explain the process whereby individuals share work knowledge or skills with other individuals through various interactive methods within the organization and divide knowledge into general knowledge and key knowledge. General knowledge refers to information owned by individuals. It cannot affect their interests, whereas key knowledge is relative to general knowledge and possesses the qualities of high value, scarcity, and irreplaceability. Therefore, in this paper, CKS refers to the behavioral intention of employees within an organization to impart to other members knowledge that does not significantly affect their interests. KKS refers to employees’ behavioral intention to exchange knowledge that significantly affects their interests with other organizational members.

Innovative behavior

The term “innovation” was first introduced by Schumpeter (1934), who defined it as creative behaviors or activities conducted by humans. Innovation represents a relatively broad concept. Unlike passive innovation, employees’ innovative behavior constitutes a multi-stage complex process that evolves from innovative thinking to innovative actions. With the continuous development of social economy, theoretical research on innovative behavior has been progressively deepening. Creativity refers to novel yet useful ideas (or concepts) concerning products, services, or processes19.

Innovative behavior encompasses a series of activities centered around the generation of creativity, existing studies have primarily utilized the “production-execution” perspective to characterize IB. For instance, innovative behavior consists of three stages: defining the problem, generating ideas, seeking support for their ideas, productizing their ideas, and completing their innovative ideas20. In other words, IB is also the identification and implementation of new technologies or work strategies to enhance existing tasks5. Innovation is essential for the organization’s success in the market and the achievement of its goal of sustainable development. In this tumultuous market environment, a company’s production process and management must constantly innovate to ensure its survival and long-term viability21. Kleysen and Street developed and validated the five-dimensional structure of individual innovative behavior: searching for opportunities, generating ideas, evaluating ideas, advocating for ideas, and applying ideas22. This paper defines IB as discovering innovative opportunities at work, generating innovative ideas, then attempting to put them into practice, and lastly, generating innovative performance by referring to past accomplishments comprehensively.

Regarding research on the relationship between knowledge sharing and innovative behavior, Reagans argued that the integration of knowledge sharing with intra-team networks provides channels for information exchange among team members. They found that in heterogeneous teams with strong ties, the quality of knowledge sharing improves, leading to enhanced creativity23. Sparrowe examined network centrality and concluded that excessively high centrality hinders the full integration of knowledge and information among team members, which negatively impacts creativity24. In summary, while existing studies have extensively examined knowledge sharing and innovative behavior, there remains limited research on the distinct effects of common knowledge sharing and key knowledge sharing on innovative behavior. Therefore, this paper will further explore this gap.

Research model and hypothesis

Research conceptual model

Knowledge sharing behavior is related to employees’ willingness to share knowledge with others. This study investigates the factors influencing individuals’ common and key knowledge sharing, including SN, RB, FC, POK, and EC. The influence of CKS and KKS on IB is also studied. (See Fig. 1)

Fig. 1
figure 1

Research Model.

Full size image

Hypothetical development

Subjective norms and knowledge sharing

As a member of the group with its interests, to integrate into the group, the individual needs to accept the group’s influence and keep consistent with the group to avoid personal isolation. SN refers to the social pressure individuals perceive when considering specific behaviors25. In a collectivist situation, the respondents are more likely to be influenced by norms and collective values and to adopt behaviors that conform to cooperative values26. Drawing on these concepts, this article argues that SN refers to the degree to which employees are consistent with an atmosphere that encourages knowledge sharing. That is, important external relations such as leaders and colleagues will affect whether employees will take the knowledge sharing behavior. The factors that promote or hinder individual knowledge sharing behavior through research were proposed by Al-Kurdi, El-Haddadeh, and Eldabi (2020)27. The findings also show that SN is an important factor affecting knowledge sharing behavior. Furthermore, employees motivated by positive SN for specific behaviors were more motivated in knowledge sharing behavior than other related behavioral motivations. Based on this, we hypothesize the following:

  • H1a: SN have a positive impact on CKS behavior.

  • H1b: SN have a positive impact on KKS behavior.

Reciprocal benefit and knowledge sharing behavior

In social relations, reciprocity widely exists in the principles of human moral norms, it is the basis for establishing interpersonal social exchange relations and provides a stable mechanism for forming social systems. He believes that reciprocity is a moral norm that others give something of value to the other party, and the recipient should return28. “Reciprocity” is the core principle of social exchange theory, which reflects the embedded obligation in a specific situation through the exchange of benefits or favors, and is the behavior that individuals feel obliged to return to others. Perceived reciprocal benefits have a positive impact on individuals sharing knowledge19. Research by Adetia found that members of virtual communities expect to seek sharing and reciprocity from others in the community. When a strong sense of reciprocity accompanies individual knowledge sharing, it will further promote their activities and performance on the network29. This study believes that “reciprocity” refers to the perception that the costs and benefits related to knowledge sharing are fair in the cooperation of organizational personnel. Reciprocity works because individuals expect to exchange their kindness (sharing knowledge) for future kindness from others. On the contrary, they think that their bad behavior (hiding knowledge) will be hostile or even retaliated by others. Out of this psychology, individuals will share according to the expectations of others to maintain a friendly relationship between the two parties and ensure long-term cooperation between the two parties. Based on this, we hypothesize the following:

  • H2a: RB have a positive impact on CKS behavior.

  • H2b: RB have a positive impact on KKS behavior.

Face concern and knowledge sharing

In Chinese social life, face holds significant importance, as individuals strive to avoid losing face for themselves or others across all communicative contexts. Consequently, people adopt both preventive and remedial strategies to maintain face30. Hu (1944) was the first to conceptualize face as a form of social reputation established through demonstrating personal achievements in interpersonal interactions. Since then, face has emerged as a complex cultural phenomenon, garnering extensive scholarly attention. Researchers have subsequently conducted a multitude of studies examining face from diverse disciplinary perspectives. There are many definitions of face, which can be roughly classified into two categories: one is a self-image formed based on the evaluation of others, and the other is social respect, social prestige, or social value. Combining these two points of view, it can be concluded that face is a psychological experience produced in interpersonal communication31. As one of the dimensions of the face, many scholars have also researched and analyzed concerns about the face. When an individual’s face is threatened, he needs to fight for his face, prevent loss of face, and preserve his face32.

From the perspective of motivation theory, FC is a strong behavioral motivation that motivates individuals to work hard to gain face, such as by trying to do well and showing off their abilities11. Knowledge sharing means that the knowledge sharer shares and presents the information they know or unique knowledge to others. When knowledge receivers receive help, they will express their gratitude and praise to the other party, and knowledge sharers will thus win faces. Based on this, we hypothesize the following:

  • H3a: FC have a positive impact on CKS behavior.

  • H3b: FCs have a positive impact on KKS behavior.

Psychological ownership of knowledge and knowledge sharing

Pierce et al., (1991) proposed the concept of psychological ownership, which defines psychological ownership as an informal and conscious psychological possession that exists outside of formal ownership and legal ownership33. Psychologically perceived ownership arises from the process by which individuals often feel psychologically connected to different possessions (such as a house and a car) that play a vital role in an individual’s identity and exist as an extension of the self34. Hence, Psychological Ownership hinges upon an individual’s perceptual framework and is delineated as a condition wherein an individual perceives an object (whether tangible or intangible) or a portion thereof as “theirs”, central to psychological ownership are sensations of possession and psychological attachment to the objective. In the context where knowledge serves as the object of interest, owing to its inherent uniqueness and distinctive value, the process of individuals mastering knowledge transcends mere study, evolving into a deeply ingrained asset infused with personal attributes following internalized contemplation and reflection. Knowledge acquired through prolonged study and refinement readily fosters a sense of psychological ownership, with individuals perceiving the knowledge as their own, characterized by a sentiment of “this belongs to me,” coupled with a pronounced desire for control and exclusivity35. On the other hand, the knowledge acquired by a person through years of study and polishing will bring them growing economic value (such as high income and promotion opportunities), respected social status, and other benefits. When colleagues find themselves in a competitive environment characterized by a zero-sum game dynamic, they tend to anticipate potential harm to their own interests from others and consequently opt to withhold knowledge rather than accept assistance from their peers5. Based on this, we hypothesize the following:

  • H4a: POK negatively impacts CKS behavior.

  • H4b: POK negatively impacts KKS behavior.

Execution cost and knowledge sharing

Cost is what it takes to get something done. In the framework of social exchange theory, costs are delineated as the adverse outcomes stemming from an exchange behavior, consequently diminishing the frequency of said behavior11. Employees need to spend resources on the knowledge sharing process. The time and effort required to contribute knowledge affect knowledge sharing36. For example, when employees have new skills and methods, they believe sharing these with colleagues will greatly benefit their organizations. However the cost of sharing knowledge exists, and knowledge sharing takes time and consumes energy that could be invested in tasks that will bring an explicit return. Before taking action, a rational person considers its positive and negative consequences. Previous studies have shown that costs, like benefits, are important determinants of knowledge sharing37. Individuals engage in knowledge contribution solely when the anticipated advantages surpass the associated costs. It has been shown that sharing is often inhibited when knowledge contribution takes time38. Therefore, we assume:

  • H5a: EC have a negative impact on CKS behavior.

  • H5b: EC have a negative impact on KKS behavior.

Knowledge sharing and innovative behavior

One’s ability to transfer and utilize knowledge may motivate one’s innovative abilities, such as solving problems quickly and reacting quickly to new challenges. A proficient dissemination of knowledge can yield significant intellectual capital and intangible assets, thereby enhancing organizational efficacy3. Corporate innovation initiatives entail the contribution of tacit technologies and knowledge by members to innovate and validate novel products. Subsequently, the knowledge accrued through the innovation process is disseminated across various departments and organizations. This means that when the organization can better manage its knowledge assets, both the organization and the individual will get better development39.

Employees’ willingness to think beyond the box is crucial to any company’s long-term success40. If companies in this position cannot successfully incorporate innovation into their production plan, they will lose a significant competitive advantage and may be forced to shut down41. However, this development towards innovation depends on some factors, including knowledge sharing and transformation, practice, inventiveness, and personnel skill. Knowledge sharing is one of these factors that has been shown to significantly encourage employees to act creatively inside their workplace42. Based on the above discussion, this study hypothesizes that:

  • H6a: CKS behavior have a positive impact on IB.

  • H6b: KKS behavior has a positive impact on IB.

  • H6c: KKS behavior significantly influences employees’ IB more than CKS behavior.

Research methodology

The present study employs SPSS 23.0 and AMOS 24.0 software for data processing and analysis. Initially, descriptive analysis is conducted to comprehend the basic characteristics of the sample. Subsequently, reliability and validity analyses are performed to assess the internal consistency and discriminant validity among variables. Finally, correlation analysis, structural equation path analysis, and differential testing of influence magnitude are employed to investigate the interrelationships among variables. All items are rated on a five-point Likert scale, ranging from 1="strongly disagree” to 5="strongly agree.”

Sample and data collection

This study employed companies operating within the pharmaceutical logistics industry in China. This study adopts the questionnaire survey method and surveys 8 Chinese pharmaceutical logistics companies from January to June 2024. The HR or administration departments were directly contacted via telephone, email, and in-person visits to facilitate data collection. The research objectives were delineated, and assistance was solicited for disseminating and retrieving between 60 and 120 questionnaires per company. In total, 840 questionnaires were distributed, yielding 527 responses, resulting in a response rate of 62.73%. Sixty-two incomplete or invalid questionnaires were excluded, leaving 527 questionnaires deemed suitable for statistical analysis. The demographic characteristics of the 527 respondents, encompassing gender, education, age, and length of service, are delineated in Table 2. Overall, the sample distribution is relatively wide and has a certain representation.

Table 2 Demographic characteristics of the sample.
Full size table

Measurement item

The questionnaire comprised nine sections, with the initial segment encompassing demographic details provided by respondents. Parts two to five and seven to eight each contain 4 questions. Parts five and nine consist of 3 questions each. Table 3 presents information about the questionnaire and the sources used to achieve a higher study construct. The Questionnaire are presented in Appendix A, the details of Questionnaire are refered to Table A1, Table A2, Table A3, Table A4, Table A5, Table A6, Table A7 and Table A8.

Table 3 Measurement item.
Full size table

Common methods bias

To evaluate the risk of common method bias, the single-factor approach was employed. An unrotated exploratory factor analysis was conducted on all questionnaire measurement items to ascertain if the first factor elucidated could encapsulate over 40% of the total variance48. The results of the single-factor test can be found in Table B1 of Appendix B. The outcomes of the single-factor examination indicates the absence of a single factor accounting for the majority of data variance (i.e., exceeding 40% of the total variance), suggesting that the risk of common method bias does not pose a significant threat.

Reliability analysis and validity analysis

Reliability refers to the credibility of the sample data. The test methods used in this study are the internal consistency coefficient α and the combined reliability CR49. The test standard is that both α and CR are not less than 0.7. After importing the survey data of this study into SPSS23.0 and running it, the reliability analysis results of each variable are obtained, as shown in Table 3. Both α and CR surpass 0.7, signifying the robust reliability and stability of the measurement model. Therefore, the reliability of the questionnaire variables is good and meets the research requirements. Validity refers to the degree to which the research scale or the conceptualization of the research variable can be accurately assessed utilizing suitable measurement instruments and methodologies.

Employing the maximum variance method, specific outcomes are detailed in Table 4. The cumulative explained variance amounts to 74.229%, with the common degree of variables exceeding 0.5, suggesting minimal missing information and highlighting the high representativeness of the identified 8 factors. Each measurement item exhibits a maximum factor loading surpassing 0.5, indicative of sound construct validity for these variables.

Table 4 Exploratory factor analysis and reliability Test.
Full size table

Correlation analysis and discriminant validity

This study employed a rigorous AVE (Average Variance Extracted) method to assess discriminant validity. Specifically, the square root of the AVE for each variable should exceed the correlation coefficient between variables, indicating the presence of discriminant validity among the variables50. See Table 5 for the differences in variables in this paper.

Table 5 Inter-construct correlation table with √AVE scores.
Full size table

It can be seen from Table 4 that the correlation coefficients of SN, RB, and FC with CKS are: 0.461, 0.430, and 0.344, respectively, and the P values have reached a significant level of 0.05, indicating that SN, RB, FC, and CKS all have significant positive correlations. The correlation coefficients of SN, RB, FC, and KKS are 0.440, 0.442, 0.214, respectively, and the P values have reached a significant level of 0.05, indicating that there is a significant positive correlation between SN, RB, FC, and KKS. The correlation coefficients for POK, EC, and CKS were found to be -0.273 and − 0.321, with all P values reaching a significant level of 0.05, implying a notable negative correlation among POK, EC, and CKS. Similarly, the correlation coefficients for POK, EC, and KKS were − 0.446 and − 0.342, with P values also reaching a significant level of 0.05, indicating a significant negative correlation between POK, EC, and KKS. The correlation coefficients of CKS, KKS, and IB are: 0.382, 0.498, and the P values all reached a significant level of 0.05, indicating that there is a significant positive correlation between CKS, KKS, and IB.

Results

Structural model fitting

Figure 1 was fitted using AMOS software. The specific fitting indicators can be seen in Table 6. Chi-Square Minimum Discrepancy (CMIN)/Degrees of Freedom(DF) is 1.775, less than 3, and meets the judgment standard, while GFI and AGFI both reach the acceptable standard of 0.9. The indices IFI, TLI, and CFI all attain values of 0.9, and the RMSEA stands at 0.038, each falling below the threshold of 0.08. Moreover, both PGFI and PNFI exceed 0.5. Therefore these main fitting indicators selected in this paper are in line with the standards of general SEM research and are considered that the structural model has a good degree of fitness51.

Table 6 Model fit indices of the structural model.
Full size table

Structural model analysis results

It can be seen from Table 7; Fig. 2, that the standardized coefficients of SN on CKS and KKS are 0.370 and 0.336, respectively, and P < 0.001, indicating that SN has a significant positive impact on both CKS and KKS and that SN has a greater impact on KKS. Therefore, assumptions H1a and H1b are established.

The standardized coefficient of RB on CKS and KKS were 0.312 and 0.282, respectively, and P < 0.001, indicating that RB had a significant positive impact on both CKS and KKS, and RB had a greater impact on KKS. Therefore, assumptions H2a and H2b are established.

The standardized coefficient for the relationship between FC and CKS is 0.250, with a significance level of P < 0.001, suggesting a significant positive impact of FC on CKS. Conversely, the standardized coefficient for the relationship between FC and KKS is 0.069, with a significance level of P > 0.05, indicating that FC does not exert a significant effect on KKS. Consequently, hypothesis H3a is supportive, whereas H3b is not.

The standardized coefficient of POK on CKS is indicated as 0.069, and P > 0.05, indicating that POK had no significant effect on CKS, the normalized coefficient of POK on KKS was 0.250, and P < 0.001, indicating that POK had a significant negative effect on KKS. Therefore, hypothesis H4a is not supportive, but H4b is supportive.

The standardized coefficient of EC on CKS and KKS were − 0.209 and − 0.191, respectively, and P < 0.001, indicating that EC had a significant negative impact on both CKS and KKS and that EC had a more significant impact on KKS. Therefore, assumptions H5a and H5b are supportive.

The standardized coefficient of CKS and KKS on IB were 0.178 and 0.485, respectively, and P < 0.001, indicating that both CKS and KKS had a significant positive impact on IB, and KKS had a greater impact on IB. Therefore, assumptions H6a and H6b are supportive.

Table 7 Path coefficients for structural Model.
Full size table
Fig. 2
figure 2

Standardized Coefficients of the structure equitation model.

Full size image

Analysis of the difference in the influence of CKS and KKS on IB

In this study, unstandardized coefficients were used for comparative analysis of group differences, and the test formula is as follows:

$$Z_{1} = \frac{{b_{1} - b_{2} }}{{\sqrt {se_{{b1}} ^{2} + se_{{b2}} ^{2} } }}$$

In this context, b represents the unstandardized regression coefficient, while se denotes the standard error. When the absolute value of the Z value exceeds 1.96, it indicates a significant disparity between the two groups concerning the unstandardized regression coefficient. Otherwise, the difference is deemed insignificant.

After calculation, it can be concluded that there is a significant difference in the degree of influence of CKS and KKS on IB, and KKS has a more significant influence on IB than CKS. Therefore, hypothesis H6c is established. The specific values are shown in Table 8.

Table 8 Analysis of the difference in the influence of CKS and KKS on IB.
Full size table

In this study, the standard for measuring whether the hypothetical relationship is established is: whether the significance level of the structural equation path is above 0.05. If the significance level is lower than 0.05, the hypothesis is established. If the significance level is not lower than 0.05, the hypothesis test is considered insignificant, and the hypothesis is not established. The difference analysis of the influence degree of CKS and KKS on IB is judged according to the Z value. If Z > 1.96 or Z<-1.96, the hypothesis is established. Otherwise, it is not established.

Discussion and conclusion

Discussion

Empirical research was conducted on the proposed hypothesis, and the following conclusions were drawn, as shown in Table 9.

Table 9 Hypotheses Summary.
Full size table

In this paper, CKS refers to employees’ behavioral intention to pass on knowledge that does not significantly affect their interests to other members. In contrast, KKS is the behavior intention of employees in an organization to pass on their knowledge, which significantly impacts their interests to other members.

This paper hypothesizes that SN positively impacts knowledge sharing, and the result confirms the hypothesis. Based on the findings of this study, knowledge sharing serves as a driving force for the long-term development of organizations. Therefore, enhancing employees’ willingness to share knowledge should be regarded as a top priority. Organizations ought to foster a fair and supportive atmosphere that encourages mutual assistance among employees, thereby strengthening subjective norms related to knowledge sharing and ultimately increasing knowledge sharing intentions.

The validation of hypotheses H2a and H2b confirms the facilitating effect of RB on knowledge sharing. As an exogenous motivational mechanism, RB reduces individuals’ psychological costs by providing knowledge contributors with anticipated future benefits, expecting help when needed, gaining access to desired knowledge, and establishing positive interpersonal relationships. Consequently, this mechanism enhances individuals’ willingness to engage in knowledge sharing.

The study reveals that FC exerts a positive influence on CKS, and the result confirms the hypothesis. But FC has a negative impacts on KKS, which contradicts the initial hypotheses. Further analysis indicates that CKS does not jeopardize the core interests of knowledge contributors; instead, it enables them to gain face, thereby increasing their willingness to engage in CKS. However, individuals tend to protect their key knowledge due to the unique advantages it confers and the potential costs associated with losing such advantages. This protective behavior leads them to prioritize knowledge retention over face gain, ultimately hindering knowledge sharing. Therefore, in innovation management practices, organizations should carefully identify and clarify employees’ face-related concerns, adopting targeted strategies based on individual differences to effectively stimulate employees’ creative motivation and innovative behaviors.

According to H4a, we believe that POK has a negative effect on CKS, but the results indicate that this effect is not significant. Common knowledge consists of information that has no significant impact on one’s self-interest, so sharing common knowledge among colleagues is not in one’s self-interest. This information will not be detrimental to their career advancement or economic interests. It can reduce coworkers’ psychological and emotional distance, making them more willing to share. POK has a negative impact on KKS behaviors, which is the same as the original hypothesis. Key knowledge is acquired through years of learning and polishing, which can bring benefits such as competitiveness and respected social status, and knowledge owners are not willing to share.

The results show the negative relations between EC and knowledge sharing, fits the original hypothesis. The findings demonstrate that the effort and time required for knowledge sharing exert a significant negative impact on both the quality and quantity of shared knowledge. These results align with Connelly’s study, which established that individuals perceiving time constraints exhibit lower propensity for knowledge sharing behaviors38.

The results indicate that both CKS and KKS positively affect employees’ IB. This paper conducts additional research using unstandardized coefficients to compare and analyze the distinctions between CKS and KKS. It concludes that KKS is more influential than CKS. Common knowledge typically comprises information with negligible impact on personal interests, consequently, sharing such knowledge among colleagues poses minimal risk to career advancement or economic benefits. While CKS facilitates IB, KKS demonstrates significantly greater enhancement effects on IB. However, the dissemination of key knowledge entails substantial risks for contributors, including potential economic losses, threats to professional standing, and diminished career opportunities. These risks substantially diminish employees’ willingness to engage in KKS. Therefore, organizations should implement enhanced incentive mechanisms to promote the sharing of key knowledge.

Theoretical implications

Hofstede’s six cultural dimensions represent a well-established theoretical framework that has been widely adopted by scholars across disciplines(Hofstede & Minkov, 2010). Building upon this foundational model, the present study integrates Maslow’s hierarchy of needs theory and Social exchange theory to develop a five-dimensional framework (SN, RB, FC, POK, EC) specifically tailored to contemporary organizational systems. The corresponding measurement scale demonstrates satisfactory reliability and construct validity based on comprehensive analysis.

Building upon prior research, this study categorizes knowledge sharing into two distinct types: key knowledge sharing (KKS) and common knowledge sharing (CKS). This study conducts a comparative analysis of the operational mechanisms through which the five-dimensional model differentially influences CKS and KKS. This comparative analysis not only deepens current understanding of knowledge sharing but also provides a conceptual framework for future research in the field.

Building upon existing theoretical foundations, this study systematically investigates the relationship between knowledge sharing and employee innovative behavior, with particular emphasis on differentiating between two distinct dimensions of knowledge sharing. Through rigorous empirical analysis, we elucidate the differential impact mechanisms of various knowledge sharing types on innovative behavior. We demonstrate that knowledge sharing significantly enhances innovative behavior, with KKS exhibiting a more pronounced effect size compared to CKS. This research confirms the previous research conclusions and expands the research depth in this area to a certain extent, providing a reference for further refinement and in-depth follow-up research.

Practical implications

This study adopts employees’ knowledge sharing perspective within organizational settings to investigate its underlying mechanisms influencing innovative behaviors. Building upon the empirical findings discussed in the preceding section, the following managerial implications are derived:

First, organizations should establish a knowledge sharing network that integrates both common knowledge as the foundation and key knowledge as the supporting framework. Organizations should cultivate a knowledge sharing culture from routine operations, actively promoting the development of both online and offline platforms for knowledge exchange. This facilitates the sharing of internal knowledge as well as external heterogeneous knowledge, thereby enhancing the innovative behavior of new employees.

Second, organizations should establish institutional policies that provide rewards or recognition for employees who actively share knowledge—particularly key knowledge—and assist colleagues in problem-solving. Such positive reinforcement mechanisms can incentivize greater participation in knowledge sharing activities while strengthening collaborative inclinations among employees. Concurrently, corresponding disciplinary measures should be implemented to address knowledge hoarding or obstruction behaviors. This dual-track institutional design enhances employees’ awareness of knowledge sharing imperatives, thereby fostering more proactive and voluntary knowledge dissemination.

Lastly, from a cultural perspective, organizations should cultivate a knowledge sharing ethos and exemplify positive role models, while systematically integrating knowledge sharing behaviors into corporate values and codes of conduct.

Limitations and future research implications

Due to a lack of human and material resources and other pragmatic considerations, the pharmaceutical logistics industry and the regions of Henan and Jiangsu account for the vast majority of the research objects in this study. Although the sample data largely satisfy the research requirements, the generalizability of the conclusions may still require confirmation. In addition, the effect of the control variables has not been effectively verified due to the small sample size.

To further verify this study’s theoretical foundation, future research should expand the sampling range and increase the number of samples. In addition, future research may investigate differences between employees’ roles or disciplines in their experience with IB, including department leaders, organizational leaders, and employees from the social and technical domains. In the future research design, I may add moderating variables or mediating variables to my research model to enrich the research on employee innovation behavior.