Creating Sustainable Organizations through Knowledge Sharing and Organizational Agility: Empirical Evidence from China

Abstract

This study explores the relationships among knowledge sharing, adaptability, and performance, and furthermore seeks to address a gap in the existing literature on how those relationships may vary between organizational sectors. Economic sustainability for firms of all sizes and sectors is likely to depend to a large extent on the creation of a sustainable organizational culture built on collaboration, innovation, and adaptability. The importance of knowledge management in developing sustainable and higher functioning organizations is well accepted in the literature. Likewise, the ability of organizations to realize competitive advantage by adapting and responding in a timely manner to changes in the landscape is well supported. Building on previous research, this study further examines how organizations in different sectors may experience that interaction differently. Based on data gathered through 720 online surveys and subjected to empirical analysis, the findings suggest that work groups that are more agile can more readily realize the benefits of a knowledge sharing organization culture. Further, in contrast to the main body of existing literature, the findings indicate that there is little difference in these benefits among organizations operating in different sectors, notably, within the context of mainland China. These findings may be of interest to those with an interest in knowledge sharing, organizational agility, organizational behavior, sustainable organizations, collectivistic cultures, to practitioners with an interest in developing higher functioning organizations, and to social scientists in related research areas such as cultural studies and psychology.

1. Introduction

There is consensus in the existing literature that economic sustainability is a precursor to social sustainability and, in its absence, a constraint on ecological sustainability [1]. Likewise, there is consensus that among the factors that contribute to the success of organizations, knowledge sharing and the ability to adapt quickly to changes in circumstances are increasingly seen as essential areas of interest [2,3]. How those factors may affect performance differently across organizational sectors is not well explored in the existing literature. Likewise, research on knowledge sharing and organizational adaptability is China is underrepresented.

Increasingly, work groups and teams are used by companies to perform vital tasks, making group effectiveness strategically vital to organizations and essential for enterprises to survive and prosper in turbulent environments [4,5,6,7,8]. The existing literature on the relationships between knowledge sharing and organizational agility and organizational performance indicates that organizations in different sectors are likely to be affected differently by these factors. The examination of how knowledge sharing and adaptability can affect work group performance and how that effect may vary across organizational sectors is the aim of this study.

As researchers have sought to better understand factors which impact work group performance, some studies have focused on factors related to group dynamics such as cohesion [9], the influence of member behavior [10], basic processes and group composition [11], or the influence of perceived power imbalances [6]; while other studies have paid attention to the organizational context and climate [12]. Other studies meanwhile have focused on the influence of knowledge management systems [8], or on the impact of transactive memory systems [13,14].

There are immense variations among the different contexts that groups operate in, and a great diversity of functions that groups fulfill. This may partly explain the lack of a clear conceptual definition of work group performance in the literature and a lack of agreement among scholars about how to measure it. Some approaches taken to measure group work performance include having stakeholders evaluate their own team’s performance [10,13]; having managers, external to the teams, evaluate the performance of teams under their supervision [6]; having stakeholders (such as customers and clients) assess team performance [10]; and having a combination of team members, managers, and leaders evaluate the team’s performance [15]. Still other scholars contend that more objective data are required, such as sales commission [5,8].

Organizational agility (OA) has also been receiving increased attention from scholars. There is broad consensus in the literature that OA is considered a dynamic capability or a set of capabilities which allow organizations to respond rapidly to change and to realize superior performance in dynamic, turbulent, and competitive contexts [16,17,18,19]. The concept is broad and touches on multiple aspects of a firm’s strategy and operations. Various definitions of OA are complex, overlapping in some areas and diverging in others, which makes it challenging to establish the shared meaning of terms among different scholars and to identify clear common themes [19].

Recent studies have paid increasing attention to mechanisms to help managers evaluate needed levels of OA [18], practical methodologies to implement OA [20], the role of OA as a moderator impacting market performance [21], the influence of talent management on OA [22], the relationship between IT capability and OA [23], and factors that either enable or hinder agility [16,24]. Moreover, there is a significant amount of research on agile manufacturing [25,26,27,28,29,30,31,32] and enterprise agility [33]. The impact of OA on firm performance is a critical concern in much of the research; however, there appears to be a gap in the literature regarding assessing the impact of OA on work groups or teams within larger organizations.

It is well established among scholars that knowledge is a critical resource for organizations [34,35]. Recent research has also focused on knowledge as a crucial element to successfully formulating and implementing a firm’s purpose [36,37]. For decades, knowledge management has gained traction as a scholarly discipline and as an array of management activities to effectively manage and extract value from knowledge creation and integration [38,39,40,41]. Knowledge sharing processes are an important concern in knowledge management, and several recent studies have attempted to define the term according to specific knowledge sharing behaviors (KSBs) that can be measured and influenced [42,43,44,45,46]. One stream of research has attempted to deepen our understanding of how reward systems impact KSBs [42,43,47,48]. Another stream of research has looked more broadly at factors such as organizational culture [44], social norms [49,50,51], organizational climate [52], paths among organizational subunits and path dependency [53,54], and perceived enjoyment of participants [55].

Despite the growing interest in organizational agility, knowledge sharing, and group performance, little research has attempted to integrate these streams of research to examine the impact on group performance. More specifically, there appears to be a gap in the existing literature on how knowledge sharing behaviors are moderated by organizational agility, and how that effect may vary among organizational sectors. Based on this gap, the research questions put forward are:

RQ1:

What is the relationship between knowledge sharing behavior and work group performance?

RQ2:

Does organizational agility have an effect on the relationship between knowledge sharing behavior and work group performance?

RQ3:

Does the mediating effect of organizational agility between knowledge sharing behavior and work group performance vary by the sector of the organization?

Knowledge sharing and adaptability are important drivers of performance. How they may affect organizations in different operating sectors differently, particularly in China, could have substantial implications both for foreign organizations that operate in China, and for domestic Chinese organizations.

This study contributes to the existing literature by investigating the links between organizational agility, knowledge sharing, and work group performance and how they may vary among organizations in different sectors.

There exists a gap in the existing literature regarding how those factors may manifest different across organizational sectors. Additionally, research on knowledge sharing and organizational adaptability is China is underrepresented. This study seeks to address both of those gaps.

2. Literature Review

2.1. Economic Sustainability

Consideration of sustainability as a concept is not a recent phenomenon, but it has, in recent years, become increasingly relevant and has evolved into a fairly well developed theoretical paradigm, most often conceptualized by the use of the three pillars model [56] of ecological, economic, and social sustainability. Separate discussions on ecological, social, and economic areas is a simplistic view [57] and presents a challenge for those seeking empirical analysis of the three pillars due to the inseparable and interdependent nature of factors that comprise them [58,59]. There is however, a consensus on the relevance of concern for sustainability in all three areas. Examples include ecological sustainability as manifested by the discussion of climate change [60]; social sustainability viewed through the lens of the inequality of global wealth distribution and the problems that arise from poverty [61,62]; and, finally, economic sustainability, which is a necessary precursor to addressing both social and ecological sustainability related concerns [63,64,65,66].

Economic sustainability is complicated by the shifting backdrop of global economic interconnectivity resulting in increasingly competitive conditions for organizations operating in all organizational sectors. Economic sustainability for many firms has depended upon their ability to leverage their intellectual capital [2], both formal such as patents and copyrighted materials, and informal such as the knowledge and expertise accumulated by members of the organizations [38]. Likewise, sustainable operations for many firms has depended on their ability to adapt to changes in circumstances both internal and external to their organizations [21,67].

It is the nexus of these two topics, knowledge sharing and adaptability, and how they affect organizational performance and enable the sustainable operation of organizations in different operational sectors that this study will examine.

2.2. Knowledge Sharing Behavior

Knowledge sharing behavior (KSB) is a crucial element of knowledge management systems [42]. The origins of the concept of knowledge sharing can be found in the knowledge management literature [40,41] which is related to the knowledge-based theory of the firm [34]. Bartol and Srivastava [42] define knowledge sharing as “individuals sharing organizationally relevant information, ideas, suggestions, and expertise with one another” (p. 65), and Wilson et al. [68] consider sharing knowledge as one of the basic processes of group learning along with the storage and retrieval of knowledge. Drawing on the prior work of Nonaka [69] and Polyanyi [70], Bartol and Srivastava explain that knowledge can be shared explicitly or tacitly, and requires effort from the knowledge sharer. Because it involves exchanges among individuals, knowledge sharing is considered distinct from knowledge transfer, which generally describes intra-organizational exchanges of knowledge between entities such as departments, or describes inter organizational movements of knowledge [42]. Yang and Chen [46] provide one of the broadest definitions of knowledge sharing and emphasize the behavioral component, describing it as a “set of behaviors about knowledge exchange which involve the actors, knowledge content, organizational content, appropriate media, and societal environment” (p. 96). Due to its voluntary nature and its contribution to the effectiveness of the organization, KSB is related to organizational citizenship behavior [45].

Methods and approaches to operationalizing and measuring KSB can vary from study to study. In their investigation, Cabrera et al. [49] operationalized KSB according to two types: voluntarily seeking ideas and information from co-workers, and providing insights and ideas to co-workers. Citing the work of Davenport and Prusak [41] who proposed evaluating KSB based on knowledge sharing activities during meetings, Hung et al. [43] measured this behavior according to participant outcomes, which included the number of ideas generated, the usefulness and creativity of the ideas generated, and the perceived meeting satisfaction.

One recent stream of research has attempted to provide insights about organizational reward systems and motivational drivers to achieve effective knowledge sharing. Conclusions and findings, however, have been inconsistent, particularly when it comes to the role of extrinsic rewards. Bartol and Srivastava [42] for example, contended that monetary rewards could be effective for encouraging KSB and predicted that rewards based on collective performance would be an appropriate and effective way to encourage cooperation and engagement among employees. Lee and Ahn [48], however, investigated reward systems to encourage knowledge sharing within organizations and concluded that individual-based reward systems were more efficient than group-based ones. Hung et al. [43] evaluated the impact of intrinsic versus extrinsic motivation on knowledge sharing, which, as explained above, was defined as participant outcomes during meetings. They concluded that economic rewards did not lead to an increase in knowledge sharing, whereas reputational feedback, which results in employees feeling that knowledge sharing enhances their reputation, had a significant positive effect.

Bock et al. [47] studied motivational drivers which determined individual KSB and developed a framework that considered a number of factors impacting the intention to share knowledge, such as “anticipated extrinsic rewards, anticipated reciprocal relationships, sense of self-worth, and three facets of organizational climate’’ [47]. Their findings suggested that these factors, applied as antecedents to attitude and subjective norms, positively contributed to KSB with the exception of extrinsic rewards which appeared to negatively impact attitudes towards knowledge sharing. For example, Ipe [44] described four factors that influence KSB: (a) the nature of the organizational knowledge; (b) the motivation of the actors to share knowledge; (c) opportunities to share the knowledge; and (d) the culture of the work environment. The fourth factor, organizational culture, is the most critical because the other three factors are embedded in it. Ipe’s work built on the prior work of Schulz [51] who examined the relationship between the production and distribution of organizational knowledge among subunits and concluded that different learning processes were adopted according to the nature of the knowledge, and that this, in turn, affected how the knowledge was shared. Cabrera et al. [49] came to similar conclusions when they evaluated determinants of individual engagement in knowledge sharing between organizational subunits. Among the organizational variables assessed, normative pressures, described as “perceptions of support from colleagues and supervisors towards knowledge sharing” (p. 259), showed the greatest impact. This concurs with the findings of Ryu et al. [50] who attempted to better understand factors which impacted the knowledge sharing of hospital physicians and concluded that subjective norms (the internalization of outside influences) had the greatest influence on their intention to share knowledge followed by attitude.

Chen et al. [71] set out to study the impact of knowledge management systems, organizational climate, and attitude on the intention of employees to share knowledge. Their findings showed that attitude was the most significant factor but that knowledge management systems self-efficacy, and organizational climate, by positively contributing to attitude, indirectly affected knowledge sharing. Witherspoon et al. [72] assessed the antecedents of organizational knowledge sharing, including the intentions and attitude of the knowledge sharer, rewards for knowledge sharing, and the organizational culture. Their findings provided support for a positive relationship between all three areas studied and KSB; furthermore, their findings suggested it is easier to motivate employees to share knowledge in collectivist cultures than in individualist ones.

From a different perspective, the findings of Ton et al. [73] suggest that knowledge hiding, as opposed to knowledge sharing, also affects group performance, but in the opposite manner. Groups in which the members are prone to hoarding behavior when it comes to knowledge tend to perform at a lower level.

To conclude, many studies have been carried out which explore antecedents of KSB and assess factors which positively impact KSB, but few studies have attempted to measure the impact of KSB on other aspects of the organization or on work group performance. There seems to be an underlying assumption in much of the research that KSB is desirable and linked to positive organizational outcomes; however, there is little empirical research which provides support for this assumption. Therefore, this paper sets out to explore the extent to which organizations with a higher level of knowledge sharing behavior, are likely to experience a higher level of performance at the work group level. Thus, hypothesis one is put forward as

Hypothesis 1 (H1):

KSB has an effect on WGP.

Likewise, based on the prior literature, it seems likely that organizations that have heightened levels of KSB are likely to be more readily able to adapt to changes in circumstances. Thus, hypothesis two is put forward as

Hypothesis 2 (H2):

KSB has an effect on OA.

2.3. Organizational Agility

Interest in the topic of organizational agility has been gaining attention in recent scholarly research. While distinct from the concept of agile management practices well known in software development [74], OA does share many aspects in common with agile practices, including an emphasis on continuous, iterative improvement cycles; effectively meeting clients’ needs; rapid product development; and flexibility [26,74].

Based on her analysis of 75 scholarly papers published between 1994 and 2018, Walter [19] provides the following operational definition of OA: “Organizational Agility is a learned, permanently-available dynamic capability that can be performed to a necessary degree in a quick and efficient fashion, and whenever needed in order to increase business performance in a volatile market environment” (Walter, 2021, p. 379). Walter, furthermore, argues that OA should be viewed as a continuum, integrated into the context of the organization and its business environment, and “independent of the industry” (p. 381). Walter identified and described the following four categories of agility: drivers (environmental changes impacting the organization), capabilities (an organization’s ability to handle change), enablers (tools, practices, and technology), and dimensions (parts of the organization that must be agile to achieve OA such as management, technology, and the workforce).

As stated in the Introduction, a substantial amount of research exists that deals with agility in the manufacturing sector [13,33] Moreover, there have been studies which put forward practical management tools or de-scribe mechanism for achieving OA cost effectively and in a manner coherent with the organization’s strategy [18,26,30].

Another stream of OA research has attempted to evaluate the factors which influence OA such as company culture [16], talent management [22], and IT capability [23]. Van Oosterhout [24] analyzed change factors and assessed “agility gaps” (p. 132) which companies faced in four different industry sectors; they highlighted “the existence of inflexible legacy systems” (p. 132) as a significant perceived barrier to increased agility.

Finally, there is support in the literature for the impact of agility, including agile manufacturing on the performance of enterprises [75,76]. Vickery et al. [77], examining the roles of supply chain information technologies and supply chain operational initiatives in creating agility and encouraging performance, concluded that agility acted as a mediator related to firm performance. Another study [78] similarly showed that, particularly in turbulent contexts, agile manufacturing increased the competitiveness of firms and led to improved operational, financial, and market performance. Akkaya and Qaisar [21] studied OA and its influence on market performance related to dynamic capabilities and concluded that OA played an important moderating role.

To conclude, there is robust support in the scholarly literature for the influence of factors such as organizational culture and/or IT capabilities on OA, as well as support for the positive impact of OA on firm performance, including as a mediator of firm performance. There are few studies, however, which address the role that OA plays in influencing KSB or its impact on work group performance. Therefore, hypotheses three and four are put forward as

Hypothesis 3 (H3):

OA has an effect on WGP.

Hypothesis 4 (H4):

OA has a moderating effect on the relationship between KSB and WGP.

2.4. Work Group Performance

While researchers have identified many different types of work groups, Sundstrom et al. [79] identified basic defining characteristics of work groups which include “shared duties in an organization and interdependence in carrying them out” (p. 49). This is similar to Edmondson’s definition of work teams [80,81]. In this paper, we do not make a distinction between work group performance and team performance. The ability of teams to effectively communicate both their explicit and tacit knowledge to develop shared mental models is a key factor in determining their success [68]. As stated in the Introduction, there does not appear to be a shared conceptual understanding among different scholars as to exactly what work group performance (WGP) consists of; and, similar to KSB, researchers have adopted various approaches for measuring it.

Drawing on the earlier work of Janz et al. [10], which emphasizes that team performance is specific to tasks, Choi et al. [13] evaluated team performance of knowledge workers based on the perceptions of multiple stakeholders (such as clients and customers) of the quality of their deliverables, their effective time management, and their ability to meet deadlines. In their study, Chung et al. [6] measured group performance by having managers rate groups under their supervision according to four items: work quality, work quantity, group initiative, and overall performance; Frazier and Bowler [12] similarly used a survey tool completed by the work group supervisors who rated the overall performance of the group. Hoegle and Gemuenden [15] measured team performance using a survey instrument completed by team members, leaders, and managers who supervised the teams. Iyengar et al. [8] on the other hand, citing the advice of Argote and Miron-Spektor [82], used sales commissions as a measure of group performance, considering these data to be more objective than self-reported measures. This study assesses the perceptions of team members of the creativity, efficiency, effectiveness, and initiative of their own teams, as well as the quality of the work produced.

A significant number of studies have investigated factors that influence work group performance, such as characteristics of the group [9,11]. Janz et al. [10] asserted that process behaviors, such as helping, sharing, and innovating, positively impacted effectiveness but that the relationship was affected by contextual factors such as goals, feedback, and time pressure. Hoegle and Gemuenden [15], who developed a team work quality construct, analyzed six factors that they predicted would contribute to effective team performance such as communication, coordination, balanced contributions, mutual support, efforts made by members, and group cohesion. Their findings suggested that these factors were positively associated with team performance.

Other scholars have examined the extent to which transactive memory systems and IT support positively impacted team performance [13,14].

Still other studies have assessed the role of voice climate [12], the impact of power structures [6], and the effects of repository knowledge management systems [8].

The literature on this topic provides support for the impact of factors such as process behaviors positively impacting team performance and hence contributing to organizational sustainability. This provides a firm basis for H1 which posits a positive relationship between KSB and WGP.

2.5. Organizational Sectors

There are several ways to categorize organizations into different sectors, depending on the context of the discussion; categories can be based on size as measured either by revenue, sales, or the number of employees [83]. Other categorical systems may include the profit-nonprofit; Social Enterprise spectrum [84]; the public vs. private sector view; or the ownership structure view of sole proprietor, partnership, corporation [85,86,87,88]. Organizational sectors can also be differentiated based on the tenure of the organization, or start-up vs. established firms [89].

Previous research on organizational sectors has found differences between sectors in several areas, including organizational commitment [84,90] and how organizational effectiveness and performance are measured [91]. Other research has shown a difference in how organizations in different sectors are affected by their approach to intellectual capital, knowledge, and how it is used and shared [92,93]. Differences have also been reported between conflict management styles of organizations in different sectors [94]. Of particular interest are findings in the prior research that show differences in how organizations in different sectors can be transformed through innovation [87] as innovation based changes in organizational behavior are closely related to knowledge sharing driven organizational agility.

For purposes of this study, to examine the relationship between KSB, WGP, and the effect of OA on that relationship, organizations were divided into broad sectors based on the findings of previous research which indicated that they are likely some similarities within a country/culture, across certain sectors [86,95,96]. It follows, then, that a comparison of the KSB-WGP and OA relationships would likely be more meaningful between those broad sectors of public and private sectors [90], specifically, government, manufacturing-based private enterprise, services-based private enterprise, agriculture, healthcare, and education.

Private enterprise is divided into manufacturing and services sectors due to differing aspects that agility is factored into in these business models [97]. The time it takes a manufacturing firm to adapt to changes in market conditions is expected to be considerably greater than the expectation for services firms [98].

Likewise, the agriculture sector is of interest as a separate sector because it is a unique, large, and important part of the economic system in China [99]. Furthermore, the production cycle is considerably different from manufacturing or services based firms [100], meaning that both knowledge sharing, and particularly OA, may differ from other sectors of the economy [101,102]. This is of particular relevance to this study when considering the recent focus on the sustainability of the agriculture sector in China [103].

In the public sector, prior research indicates that the performance of public sector organizations tend to be affected differently than profit-making organizations by information sharing [104], and by employee learning practices which can be either formal, as structured training, or informal as knowledge sharing behavior between employees [105].

Within the broad category of the public sector, education is unique and of special interest as a critical factor of socioeconomic sustainability and its importance in poverty reduction [106] and socioeconomic mobility [107,108,109]. Education has also been found to play a key role as a driver of innovation-based economic development [110,111,112,113]. Education may shape the views of future business and public leaders and thereby provide insights into trends in leadership styles [114]. Further, education as a sector is unique because it often straddles the line between private and public sectors [115,116,117]. As such, it has been found to respond differently than other organizations to situations requiring OA [118,119], and is therefore appropriate to consider education as separate sector.

Healthcare is of special interest because of the importance of OA in the healthcare systems for social, and ultimately, economic sustainability during periods of health related-crises such as the COVID pandemic that began in 2020 [120,121]. Further, similar to the education sector, the healthcare sector may be in the public, or private sectors, and because there is evidence that OA in the healthcare sector faces unique challenges, unlike that of other organizational sectors [122,123].

Having established both the importance of education and healthcare, and the basis for consideration of them as separate factors, and with these five sectors as reference points based on the findings of prior research on related topics, it seems probable that one or more of the constructs of KSM, WGP, and OA, as well as the interactive relationships between them, are likely to differ between sectors thus becoming the Conceptual Framework as indicated in Figure 1. Therefore, hypotheses five and six are put forward for testing:

Hypothesis 5 (H5):

The effect of KSB on WGP will differ between OSs.

Hypothesis 6 (H6):

The moderating effect of OA between KSB and WGP will differ between OSs.

2.6. Conceptual Framework

To address the research questions, this study will test the hypotheses.

3. Methodology

The detailed methodology of empirical research, quantitative methods, including Cronbach’s alpha, Principal Component Analysis, and linear regression analysis were adopted to analyze the data. from an online survey. For this study, a regression analysis was employed as part of the methodology for the following reasons: firstly, means of factors were calculated to observe the performance of individual factors compared to the full scale, and also to see the overall level, to ultimately better identify the gap and where progress can be made in the future; secondly, Cronbach’s alpha was applied to measure internal consistency between a set of items, and to further investigate how closely they are related to each other as a group, as well as to measure the scale reliability. Researchers apply this method to assure the credibility of the data among multiple items measuring a single factor, to finally confirm the reliability of the dataset [124].

The rationale for considering the usage of linear regression is that, compared to some machine algorithms, it has a considerably lower time complexity [125]. Considering the causal effect, this research requires distinct methods to conduct analysis, thus producing precise and explicable results. An evident advantage of linear regression models is linearity, which makes the estimation procedure simple and able to be interpreted and understood in an accessible manner [126]. In this way, linear and similar models are widespread in academic and quantitative research fields, including psychology, social science, and medicine [127,128].

The three constructs of Knowledge Sharing Behavior, Organizational Agility, and Work Group Performance were tested for validity using Cronbach’s Alpha test, hypotheses 1–3 were tested separately using simple regression analysis for R², the unstandardized regression coefficients (beta), t-statistics, and associated p-values.

To test H1-H3, a simple regression analysis was performed to investigate the relationships between Organizational Agility (OA) and the relationship between Knowledge Sharing Behavior (KSB) and Work Group Performance (WGP). The dependent variable was WGP. The independent variable was KSB. The mediator variable for the analysis was OA.

Survey Instrument and Data Collection

The instrument was adapted from surveys previously used and validated in related research. The construct of Organizational Agility was measured through use of five questions adapted from the research of Lu and Ramamurthy [23]. The construct of Work Group Performance was measured with five questions adapted from an instrument previously used by Choi et al. [13], and Knowledge Sharing was measured with seven questions from a study carried out by Oliveira et al. [129]. All construct questions in the survey were presented with a 7-point Likert scale response.

An online survey as a data collection tool offers a variety of benefits, including wide geographic coverage, anonymity for respondents, a decreased bias level compared to the pressure given by in-person interviews, and, importantly, cost-saving [130]. Easily under-stood and neutral language was used during the survey translation to facilitate the participants’ comprehension of the material. Items in the survey from which the data were derived for this study were, in turn, adapted from previous studies and used with minor modification for context. The survey items used in the previous study were initially writ-ten in English, so the survey was translated from English to Chinese. To validate the question translation, the survey translation was checked by two native Mandarin speakers with PhDs from Western universities and then translated back into English. This translation and back-translation approach is a widely applied technique in cross-cultural social research to achieve translation accuracy and credibility with minimized differences [131]. Additionally, the survey was distributed to a pilot group of nine people for trial and adjusted according to their feedback to assure the translation accuracy, proper length and time taken, as well as the ease of understanding.

Previously, the survey link was shared through the social media platform WeChat, the most widely used social network application in China. The survey was distributed via the software application Wen Juan Xing where the data were compiled and downloaded. As is typical in reposting behavior in social media in China [132], a snowball sampling effect was observed.

Citizens from Zhejiang province, the emerging economic hub of southeast China, contributed most to the sample of the study. The Wechat social media platform is widely used in China for online surveys, customer feedback, and for posting product and service reviews. Due to the significant market penetration by the application, nearly all smartphone users are familiar with the app and were therefore qualified for inclusion in the survey. A total of 720 completed surveys were obtained through convenience sampling. The surveys were completed over a 21-day period and participation in the survey was voluntary and anonymous. After inspection, all surveys were determined to be valid. The survey instrument is Appendix A.

4. Results

4.1. Sampling

The demographic detail of the sample is shown in

Table 1. Demographics of the sample.

Variables	Subcategory	Frequency	Percent
Gender	Male	292	40.6
	Female	333	46.3
	Not disclosed	95	13.2
Age	Under 20	75	10.4
	20–29	120	16.7
	30–35	140	19.4
	36–40	131	18.2
	41–45	123	17.1
	45–50	65	9
	51–55	44	6.1
	Order than 55	22	3.1
Education	Under middle school diploma	81	11.3
	Middle school diploma	178	24.7
	High school diploma	117	16.3
	2–3 years vocational college	125	17.4
	4-year college degree	172	23.9
	Graduate college degree	47	6.5
Sector	Government	124	17.2
	Manufacturing	167	23.2
	Services	199	27.6
	Agriculture	123	17.1
	Healthcare	80	11.1
Position	Middle Manager	109	15.1
	Senior Manager	120	16.7
	Clerical	161	22.4
	Entry-level worker	109	15.1
	Agriculture worker	97	13.5
	Factory worker	88	12.2
	Teacher	36	5
Experience	Less than 1 year	104	14.4
	1–3 year	175	24.3
	4–7 year	184	25.6
	8–12 year	217	30.1
	More than 12 years	40	5.6

. Among the total number of respondents, more than half are female (n = 333, 46.3%) compared to the male respondents (n = 292, 40.6%) while N = 95, 13% preferred not to disclose their gender or chose “other.”

The most represented age group is 30–35 (n = 140, 19.4%) but the respondents are mostly between 20–45 which seems consistent with the working age demographic. The lowest number of participants belong to the age group older than 55 (n = 22, 3.1%), which makes sense as they are most likely to be retired, or not a heavily adopted group for smartphone use.

A breakdown of the educational level, while seemingly unremarkable, serves to confirm the applicability of the sample as representative of membership in organizations, and subsequently in work groups as defined for the purposes of this study. The vast majority of respondents fall into the middle school, high school, vocational, and four-year college categories. Most of the participants work in the services sector (n = 199, 27.6%), and 23.2% of participants work in the manufacturing sector.

The highest number of the participants work in the organization as clerical workers (n = 161, 22.4%), and most of the respondents reported their work experience is 8~12 years (n = 217, 30.1%). All in all, the demographic breakdown of the respondents seems as expected, unremarkable, and also well positioned to represent the target population of those who are part of work groups. Clarification of the work group as part of a larger organization, or perhaps as all members of a small organization was included in the introduction to the online survey. See Appendix A.

Additional demographic information was included in the survey but was not included in the hypothesis development and was discarded.

4.2. Reliability Analysis

The Cronbach alpha values shown in

Table 2. Cronbach Results.

Constructs	Cronbach Alpha	N of Items
Agility	0.97	5
Performance	0.98	5
KSB	0.98	7

indicate that all the three constructs are greater than 0.9, suggesting very good internal consistency reliability. According to Pallant [133], Cronbach alpha values above 0.7 are considered acceptable; however, values above 0.8 are preferable. This means that the constructs used in the research are reliable for further analysis.

The Principal Component Analysis with Promax rotation was used to explore the principal components of the KSB, WGB, and OA scales. The results of the initial analysis revealed three components with Eigenvalues over 1, explaining 72%, 11.12%, and 8.44% of the variance respectively. Seven items loaded on component 1(KSB), five items loaded on component 2 (Agility), and five items loaded on component 3 (Performance) as shown in

Table 3. PCA Analysis.

Items	Components
	Agility	Performance	KSB
Agility 1	0.931
Agility 2	0.936
Agility 3	0.924
Agility 4	0.970
Agility 5	0.922
Performance 1		0.947
Performance 2		0.958
Performance 3		0.847
Performance 4		0.966
Performance 5		0.948
KSB 1			0.822
KSB 2			0.962
KSB 3			0.953
KSB 4			0.951
KSB 5			0.955
KSB 6			0.961
KSB 7			0.944

.

Each of three main components or constructs (Agility, Performance, and KSB) are reliable. In other words, the questions used for each of the three constructs are measuring the same thing. For example, agility has five questions. All of the five questions are measuring agility. KSB has seven questions. All of the seven questions measure KSB. PCA analysis also confirmed that there are three main factors. It identified three main themes or components in the data set. Component loading table shows which items or questions are correlated with each of the three components. For example, each of the five questions measuring agility are highly correlated with the same component which is Agility.

4.3. Hypothesis Testing—Regression

The regression analysis was conducted for testing the formulated hypotheses H1–H3.

Table 4. Regression Analysis.

Independent Variables	Dependent Variable	R2	Unstandardized Coefficients	t	Sig.	Impact
KSB	OA	0.42	0.59	22.56	0.000	Significant
KSB	WGP	0.50	0.69	26.95	0.000	Significant
OA	WGP	0.45	0.71	24.15	0.000	Significant

shows the results of the three separate simple linear regression analyses including the value of R², the unstandardized regression coefficients (beta), t statistics, and associated p-value.

As shown in Table 4, the impact of KSB on WGP was significant, predicting 50% variance in the OGP. The coefficient of KSB indicates that a 1 unit increase in KSB is associated with a 0.69-unit increase in WGP.

Thus, H1 is supported.

Confirmation of H1 indicates that those organizations with a higher level of knowledge sharing behavior are likely to be more successful, at least to the extent that the individuals in those organizations perceive success, and at the workgroup level in the organizations.

For H2, the analysis indicates that there was a significant impact of KSB on OA. KSB accounted for 42% of the variability in OA. The coefficient of KSB indicates that, if KSB increases by 1 unit, the OA score will be increased by 0.59 units.

Thus, H2 is supported.

Confirmation of H2 indicates that those organizations that are more readily able to adapt to changes in circumstances and are more flexible in ways to react to unexpected events are likely to be more successful at the work group level.

There is a significant impact of OA on WGP. OA accounted for 45% of the variance in the WGP. The beta coefficient of OA suggests that a 1 unit increase in OA is associated with a 0.71 increase in WGP.

Thus, H3 is supported.

Confirmation of H3 indicates that those organizations that are more flexible and more readily able to adapt to changing circumstances are more like to experience a higher level of work group performance.

Thus, the hypotheses H1, H2, and H3 were supported and together served to combine, strengthen, and validate prior research on the individual topics of KSB, OA, and WGP and to present a foundation on which to build the analysis for H4 and H5.

4.4. Hypothesis Testing—Mediation

To investigate the possible mediation effect of Organizational Agility (OA) on the relationship between Knowledge Sharing Behavior (KSB) and Work Group Performance (WGP) as was put forth in hypothesis 4 (H4), a simple mediation analysis was performed. The dependent variable was WGP. The independent variable was KSB. The mediator variable for the analysis was OA. The indirect effect of KSB on WGP was statistically significant, E = 0.23, 95% CI (0.18, 0.28). Furthermore, 67% of the relationship operates directly and 33% of the relationship operates indirectly via OA, as shown in Table 4.

Thus, hypothesis H4 is supported.

4.5. Hypothesis Testing—Moderation

To test hypothesis 5 (H5), the possible difference in the effect of OA on the KSB-WGP relationship between different organizational sectors, or industries, a moderation analysis was performed. The outcome variable for the analysis was WGP. The predictor variable for the analysis was KSB. The moderator variable evaluated for the analysis was organizational sectors (OS).

As indicated in

Table 5. Moderation Analysis.

Interaction Terms	Coefficients	SE	T	p
KSB-Manufacturing	−0.01	0.08	−0.16	0.87
KSB-Service	−0.08	0.08	−1.06	0.29
KSB-Agriculture	0.03	0.09	0.43	0.66
KSB-Healthcare	−0.13	0.10	−1.28	0.19
KSB-Education	−0.05	0.15	−0.36	0.71

Note: “Government” was used as reference category for comparison.

, the different interactions between KSB and OA were not statistically significant. In other words, organizations in different business sectors do not show significant differences when it comes to the relationship between KSB and WGP which was tested by H1. As indicated in Table 5, all the p values are greater than 0.05. This relationship does not show significant variation among different organizational sectors.

Thus, hypothesis 5 (H5), the effect of KSB on WGP will differ between OSs, is not supported.

4.6. Hypothesis Testing—Moderated Mediation

To test H6, whether the mediation effect of OA on the relationships between KSB and WGP was different among organizations in different sectors, a moderated mediation analysis was performed. KSB was the predictor variable, agility as the mediator. The outcome variable was WGP and OS was the proposed moderator.

The result shown in

Table 6. Moderation Mediation Analysis.

	Index	BootSE	BootLLCI	BootULCI
Manufacturing	0.04	0.05	−0.06	0.15
Service	−0.02	0.05	−0.01	0.08
Agriculture	0.09	0.06	−0.03	0.21
Healthcare	−0.03	0.06	−0.16	0.09
Education	−0.002	0.10	−0.21	0.19

indicates that the mediating effect of agility on the relationship between KSB and OGP does not change significantly based on different organizational sectors (as shown in Table 6; zero is within the confidence interval).

Thus, the hypothesis (H6) that mediating effect of Agility between KSB and WGP will differ between OSs is not supported.

4.7. Hypothesis Summary

In summary, as shown in

Table 7. Hypothesis Summary.

Hypothesis	Status
H1: KSB has an effect on WGP	Accepted
H2: KSB has an effect on OA	Accepted
H3: OA has an effect on WGP.	Accepted
H4: OA has a mediating effect on the relationship between KSB and WGP.	Accepted
H5: The effect of KSB on WGP will differ between OSs.	Rejected
H6: The mediating effect of OA between KSB and WGP will differ between OSs.	Rejected

, H1–H4 are supported by the data, while H5 and H6 are not supported. The implications and possible factors that may be instrumental in these findings are discussed in Section 5.

5. Discussion

The results of this study contribute to the body of literature in two areas: the relationships between KSB, OA, and WGP, which serve to solidify, coalesce, and build on the findings of prior research by validating the effect of KSB on WGP and by demonstrating the mediating effect of OA on the relationship between KSB and WGP. Interestingly, this was achieved by providing evidence in contrast to the findings of much of the earlier work on the dissimilarities in organizational behavior between organizations of different sectors. It is important to note that nearly all of the preponderance of research done in these areas was not done in China, but rather, with few exceptions, in Western countries. It seems to follow then, that there is a greater similarity in organizational behaviors in different organizational sectors in China than in most other contexts. This is a major contribution of this study.

5.1. Theoretical Contribution

The contributions of this study are threefold. Firstly, by extending the previous work on connecting and validating the relationship between KSB and WGP, and addressing the gap in the existing literature by examining this relationship within the context of China.

Secondly, by demonstrating, empirically, the mediating effect of OA on the relationship between KSP and WGP. While there exists, as discussed above, some related literature, this study contributes with specificity how the effects of KSB on WGP can be increased when there exists a simultaneous ability of the organization to respond and adapt quickly to changes in the operational environment.

Thirdly, the rejection of H5 and H6 indicate a difference in the sample population of this study from the data sets of the majority of the previous literature. As part of the theoretical contribution of this study is the context of Chinese organizations, the difference in the results can most likely be found in the context of the study. If in fact, as seems likely, the basis of this difference is the sample, then this study presents empirical evidence that the influence of culture is the cause. It seems that cultural collectivity, as described by Hofstede [134], within China, is greater than expected and is a sufficiently powerful factor that it will overcome the differences in the relationships between KSB, WGP, and OA that have been established in the past research conducted in a variety of other contexts and cultures.

5.2. Managerial Implications

The practical, or managerial implications are straightforward. Those in managerial and leadership positions within organizations located in, or operating in China, can benefit from the implications of the findings of this study. For example, organizations can more readily apply benchmarking in certain areas of organizational operations, to organizations in different sectors than the one in which they operate, with confidence that the results are generalizable across organizational sectors. Another managerial implication would be recognizing the strength of the cultural influence on workers across all operational sectors.

Secondly, given the diverse sectors that some large organizations operate in China, a firm may wish to apply the same processes for KS and OA across subsidiaries and operations in different sectors, with the expectation of similar outcomes. This could add considerable insights for both Chinese and foreign managers and leaders in those organizations within China that have foreign managers and for Chinese organizations that operate outside of China.

5.3. Limitations and Future Directions

Although this study examined relevant and well validated constructs, there are some limitations that should be noted. Firstly, there was a need to examine work group performance rather than financial performance at the macro organizational level, which may not accurately reflect how the organizational behaviors influence differing views of performance. The strength of this study, within the context of China, is also a weakness as the findings on the different sectors are in contrast to much of the existing literature, which was not based in China. Therefore, the contribution of this study’s specific findings, within the context of China, could also be seen as a limitation as the results lack generalizability, while calling for additional research on the topic, both within China and in other contexts.

As with most survey data, there are ways to improve the reliability of the data, both with increased sample size and greater granularity in the sample demographics. Given the high likelihood that there is a collectivistic, cultural component that is influential in the findings, and given the consensus regarding the cultural changes that China has undergone in the last two generations, it seems that a similar study that included specific examination of the age of the respondents may reveal further insights into the effect of culture on the findings.

6. Conclusions

Development of the hypotheses H1–H6 is well grounded in the findings of the existing literature. As H1–H4 are supported by the data, this study adds to the literature connecting knowledge sharing behavior to performance, and provides new evidence of how organizational agility can increase the positive influence of knowledge sharing. Additionally, this study provides a new contribution with regard to the limitations of how knowledge sharing and agility affect performance across various organizational sectors. The rejection of H5 and H6 indicates the presence of a factor present in this study that is in contrast with most of the existing literature. The findings of this study present a compelling argument that the collectivist nature of organizational behavior in China is a more powerful influential factor than in other cultural settings, resulting in less variance in the way that organizations are affected by knowledge sharing behaviors and organizational agility than in other cultural settings.