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. 2023 Jan 19;18(1):e0278721. doi: 10.1371/journal.pone.0278721

Knowledge sharing behaviour among head nurses in online health communities: The moderating role of knowledge self-efficacy

Salah Shehab 1,*, Mohammad Al-Bsheish 2,3,*, Ahmed Meri 4, Mohammed Dauwed 5, Badr K Aldhmadi 6, Haitham Mohsin Kareem 7, Adi Alsyouf 8, Khalid Al-Mugheed 9, Mu’taman Jarrar 10,11,*
Editor: Supat Chupradit12
PMCID: PMC9851523  PMID: 36656899

Abstract

Background

Head nurses are vital in understanding and encouraging knowledge sharing among their followers. However, few empirical studies have highlighted their contribution to knowledge-sharing behaviour in Online Health Communities (OHCs). In addition, scant literature has examined the moderating role of knowledge self-efficacy in this regard.

Purposes

This study examines the moderating role of self-efficacy between the association of four selected individual factors of head nurses (i.e., Trust, Reciprocity, Reputation, and Ability to Share) and their knowledge-sharing behaviour in OHCs in Jordan.

Method

The data were obtained by using a self-reported survey from 283 head nurses in 22 private hospitals in Jordan. A moderation regression analysis using a structural equation modelling approach (i.e. Smart PLS-SEM, Version 3) was utilised to evaluate the study’s measurement and structural model.

Results

Knowledge self-efficacy moderates the relationship between the three individual factors (i.e., Trust, Reciprocity, and Reputation) and knowledge-sharing behaviours. However, self-efficacy did not moderate the relationship between the ability to share and knowledge-sharing behaviours.

Implications

This study contributes to understanding the moderating role of knowledge self-efficacy among head nurses in online healthcare communities. Moreover, this study provides guidelines for head nurses to become active members in knowledge sharing in OHCs. The findings of this study offer a basis for further research on knowledge sharing in the healthcare sector.

1. Introduction

Knowledge-sharing behaviour is becoming increasingly indispensable in today’s business environment [1, 2]. Knowledge sharing is an essential resource for effectively implementing essential business functions, and like other industries, healthcare organisations are beginning to use knowledge sharing as a new practice. Knowledge sharing is a conveyance behaviour wherein individuals disperse their knowledge, experiences, and skills to others [3]. Effective knowledge sharing is vital in healthcare organisations because it significantly enhances the quality of care and patient safety [4]. Healthcare workers can use knowledge sharing for their patients, making it easier to share information about their diagnoses and treatments [5]. Thus, knowledge sharing is a strong element for improvements and further development within the healthcare sector [6, 7].

The existence of Online Communities (OC) can facilitate knowledge sharing [8, 9]. In contrast to traditional knowledge sharing in real-world communities, members of OC are distributed across geographic locations. Difficulties related to face-to-face knowledge exchanges among OC members may weaken the bond among OC. Therefore, scholars have investigated knowledge-sharing behaviours in various online communities [10–12]. Online Health Communities (OHCs) are one kind of an OC, where maintaining health information is a public concern. OHCs through social media and other web-based forums, facilitate their members to participate in health topics, even those with sensitive considerations such as pregnancy, menstruation, and sexuality [13, 14].

OHCs have recently received substantial attention from health practitioners due to several considerations. Everyday users tend to be well-educated on disease causes, treatment advice and preventive actions by simply inputting personal health information into OHCs [15]. Individuals go as far as to opt for self-diagnosing through OHCs rather than the traditional way by physically visiting hospitals [16]. Besides, OHCs grew impressively after observable internet technology advancement and emerged as a powerful medium among healthcare providers to be active members in OHCs [17].

Participation in OHCs by healthcare workers can share their experiences, information, and feelings with each other and offer help and support [14]. One benefit of OHCs includes 24/7 access to information and assistance from individuals without any restrictions imposed by geographic location. The relatively free and less risk-oriented nature suggests that several opinions are always better for making decisions regarding health and medical concerns [18–20]. Previous studies show that OHCs are positively associated with a user’s treatment options, health outlook, and outcomes [21]. Participants who share knowledge within an OHC view the contribution as a perceived benefit as they may find happiness in enhancing their knowledge or social value in educating others [21, 22]. Other perceived benefits may include financial incentives from the communities (such as fees or donations), the joy of interacting with other community members, and/or the increased reputation within the community due to their contributions [23].

The process of knowledge sharing is less effective within an organisation without the involvement and engagement of the human element [24]. Several studies have identified the role of individual factors in knowledge-sharing behaviours [25–27]. For instance, Shehab et al. [28] reviewed 31 studies that investigated predictors of knowledge-sharing in different contexts; they found that the roles of individual factors are dominant. However, knowledge-sharing behaviours studies in Jordanian hospitals are scarce. As Alhalhouli [29] reported, “Variables that enhance or dissuade knowledge sharing behaviours in the Jordanian hospitals have not been poorly recognised.” Al-Dalaien et al. [30] established a conceptual model of motivational factors of knowledge transfer in Jordanian hospitals. Aldohyan’s et al. [31] study in the Saudi context emphasised that “Hospitals should always refer to efficient knowledge sharing and educational strategies that render beneficial outcomes to patients, healthcare workers, and the public community”. However, lack of studies exploring the role of knowledge self-efficacy between the associations of individual factors (trust, reciprocity, reputation, and ability to share) with knowledge-sharing behaviours.

Accordingly, the current study extends the previous literature and fills the gap by examining four individual factors (trust, reciprocity, reputation, and ability to share) with knowledge-sharing behaviours in OHCs. Additionally, this study examines the moderating effect of knowledge self-efficacy as it can change the strength of the direct effect between the above-mentioned individual factors and the knowledge-sharing behaviours of head nurses in Jordan.

This article is organised as follows. The first section discusses the study theories, hypotheses, and research model. This is followed by a section that presents the research methodology and analyses the results. Last, the implications and conclusions have been provided as well.

2. Literature review

2.1 Underpinning theory

Social Cognitive Theory (SCT) postulates that the mutually triangular interaction of individual factors like individual cognition, social factors such as social group (Online Community), environmental factors, and individual expectations and beliefs shape human behaviours [32, 33]. SCT primarily focuses on self-efficacy, considered as useful prescriptive and practical concepts formulated in modern psychology" [34]. Other authors also provided their opinions on self-efficacy. For example, Lent [35] states that self-efficacy refers to “people’s judgment of their abilities to organise and implement courses of action required to achieve certain types of performance”. The study moderator (i.e. self-efficacy) lays the foundation for personal achievement, personal well-being, and human motivation, human performance; Bandura [36] assumed that people’s level of motivation, emotional states, and actions depend more on what they believe than on what is objectively true. Self-efficacy reflects people’s beliefs about their competence or effectiveness in carrying out tasks and tends to be more self-confident [37].

Previous literature has also empirically confirmed this concept [38–40]. SCT suggests that individual motivation and action are apparent bounded, and an individual is more or less likely to undertake a specified behaviour [41]. Thus, the study model used social cognitive theory.

2.2 Hypotheses building

Over the last few decades, studies have emphasised the importance of individual self-efficacy and expectation in predicting individual health behaviours [42]. Self-efficacy refers to people’s judgment of their capabilities to organise and execute courses of action required to attain designated types of performance [35, 43]. It is “one of the most theoretically, heuristically and practically useful concepts formulated in modern psychology” [34]. Prior research has demonstrated that self-efficacy lays the foundation for personal achievements, personal well-being, and human motivation. Bandura [36] explained, “People’s level of motivation, affective states, and actions are based more on what they believe than on what is objectively true”.

Knowledge self-efficacy is important in influencing the process of knowledge sharing and the influencing factors that contribute to knowledge sharing among online communities. For example, Hsieh et al.’s study showed that knowledge self-efficacy could moderate the relationships between reputation and pleasure in helping others share knowledge [44]. Thus, the conclusion can be reached that self-efficacy strongly influences an individual’s behaviour [45]. Aligned with this, the current study investigates the moderating effect of self-efficacy on the relationship between individual factors and knowledge-sharing behaviour. The assumption is that when the level of knowledge and self-efficacy is high, head nurses are very confident in their ability to provide valuable knowledge.

Knowledge sharing in online communities has been given less attention to the relationship between knowledge self-efficacy and knowledge-sharing behaviour [21]. This may be an issue in knowledge sharing because complexity and knowledge barriers to exchanging knowledge among online communities may be seen as knowledge efficacy deficits [46, 47].

Knowledge self-efficacy suggests that people who think their knowledge is valuable would be more likely to share greater knowledge [48]. It is described as a function of self-beliefs with which individuals accomplish a particular work [49], and knowledge self-efficacy can lead to greater productivity and performance. Knowledge self-efficacy is a type of self-assessment affecting decisions on how an individual will behave and be motivated under tasks and the level of effort asserted in the face of challenges.

Past researchers have linked knowledge self-efficacy to motivation and behaviour [49, 50]. Those with higher levels of self-efficacy tend to perform better than those with lower levels [51]. Recently, researchers have concentrated on knowledge self-efficacy. This has been implemented in knowledge management to validate the effect of self-assessment, self-confidence, and motivation of individuals for knowledge sharing. Self-efficacy is highlighted as individual expectations of positive outcomes of behaviour since, if individuals doubt the capability to complete the behaviour successfully, pursuing an action would be perceived as worthless. According to Wasko and Faraj [52], an individual with high knowledge self-efficacy may feel happy answering questions easily, specifically questions from beginners. Consequently, such a person may develop a more positive behaviour towards sharing knowledge [53–55]. Additionally, their ethical commitment should strongly influence knowledge-sharing behaviour in online healthcare communities.

The current study anticipates that the influence of individual factors of trust, reciprocity, reputation, and ability to engage in knowledge-sharing behaviour will become stronger as head nurses gain more knowledge and self-efficacy in online healthcare communities. (See Fig 1).

Fig 1. Research model.

Fig 1

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Accordingly, the following hypotheses are posited:

H1: Knowledge self-efficacy moderates the relationship between trust and knowledge-sharing behaviour.

H2: Knowledge self-efficacy moderates the relationship between reciprocity and knowledge-sharing behaviour.

H3: Knowledge self-efficacy moderates the relationship between reputation and knowledge-sharing behaviour.

H4: Knowledge self-efficacy moderates the relationship between the ability to share and knowledge-sharing behaviour.

3. Methodology

3.1 Design, sampling, and settings

A quantitative cross-sectional study was conducted using self-reported booklet surveys targeting individual head nurses. The population of this study was private hospitals’ head nurses in Jordan (total private hospitals = 68). Head nurses were targeted in this study (Total number of head nurses = 510) to serve the study purposes as they are considered health leaders. Public and even followers’ nurses prefer contact with head nurses due to their managerial rank as first-line management; thus, they are knowledgeable, closer, and often participate in online communities. The researchers purposively selected the private hospitals in the capital (i.e. Amman) because these institutions have competitive advantages, technological capabilities, the highest capacity, diversity in terms of speciality, supportive research cultures, and the highest number of hospitals located in Amman. The research team sent the request to all private hospitals in Amman (n = 32 hospitals). Ten hospitals rejected participation in this study. Approval was received from 22 private hospitals with a total of 322 head nurses (study population.

The sample size was calculated based on the G*Power software package, effect size (f2 = 0.15); a significant level (α = 0.05), and power 1-β = 0.95, which calculated a minimum sample size of 74 was required with five independent variables, including the moderator. Beside G*Power software package, Krejcie and Morgan, (1970) sample size formula was used to get minimum number of respondents to be surveyed, which is 210” [56]. Therefore, this study reached data from 283 respondents, which was satisfactory.

3.2 Ethical considerations and data collection procedures

Ethical approval number UNITEN/COGS 23/2/1/PM20604 was attained from the College of Graduate Studies, Universiti Tenaga Nasional, Malaysia, on 25 April 2018. The 22 private hospitals approved their employees’ voluntary participation in the study and encouraged their head nurses to participate. The first page of the booklet survey was a cover page that provided the study purposes, the necessary definitions, and the approval sign to conduct the study. Written consent was obtained from all the respondents after they were informed regarding their right to withdraw from participation at any time, that data would be only for academic purposes, and that their responses would be confidential. Booklet surveys were distributed personally to all head nurses in the hospitals (n = 22) to be completed by their staff (n = 322). The consent form was gathered from participants; accordingly, the data collection process started in May 2018 and lasted until October 2018. Of the 322 surveys distributed, the total usable surveys received were 283, with an effective response rate of 84%.

3.3 Measures

The study questionnaire was revised several times before starting the collection data process (i.e. content validity). The last version of the questionnaire includes two parts; the first asked demographic questions such as gender, age, education, internet usage, and experience. The second part included six scales (i.e., trust, reciprocity, reputation, ability to share, knowledge self-efficacy, knowledge sharing behaviour) and used a 5-point Likert ranging from 1 = strongly disagree to 5 = strongly agree. The first scale was trust, defined as employees’ belief in good intent, competence, and reliability concerning contributing and reusing knowledge. The four items for the trust scale were adapted from previous literature [57, 58]. The Cronbach’s alpha of the items was 0.912. The second scale contained three items adapted from the reciprocity scale that Zhang et al. [21] modified and referred to a belief that current sharing behaviour would cause future requests for knowledge to be easily satisfied by others [21]. The Cronbach’s alpha of the items was 0.921. The third scale was reputation, which refers to a perception of improved reputation and image due to sharing knowledge in the online community. Four items were adapted from Kankanhalli et al.’s [54] study. The Cronbach’s alpha of the items was 0.931. The fourth scale was the ability to share, which refers to the capabilities of conceiving and sharing meaning in different situations. The scale was adapted from Radaelli et al. [59]. The Cronbach’s alpha of the items was 0.86. The fifth scale was self-efficacy, which means the degree of confidence in one’s ability to provide valuable knowledge to others. The four items were adapted from Bock and Kim [60] and Lu et al. [61]. The Cronbach’s alpha of the items was 0.902. The last scale was knowledge sharing behaviour, which refers to a process of knowledge exchange between individuals who disperse their obtained knowledge, experiences, and skills to others and groups [21]. Five items were used to measure knowledge-sharing behaviour from Bock and Kim [60] and Lu et al. [61] studies. The Cronbach’s alpha of the items was a = 0.85. The survey was written in the English language. S1 Appendix presents a list of items for each of the measures.

3.4 Data analysis techniques

Descriptive statistics and moderation regression analysis using SPSS and structural equation modelling approach (i.e. Smart PLS-SEM, Version 3) were the key statistics in this study. This study used Smart PLS3 to test the hypotheses posited. Smart PLS3 uses a bootstrapping technique to estimate path coefficients and standard errors [62]. The moderation impact of self-efficacy was evaluated considering a 5000-bootstrap sample, a 95% confidence interval (CI) and a significance level of 0.05. Before running Smart PLS3, descriptive results were performed using SPSS Version 18.0 (SPSS Inc., Chicago, IL, USA).

4. Results

4.1 Demographics characteristics

The descriptive and frequency analysis output of SPSS 18.0 showed the 283 nurses’ demographic characteristics. The majority were female head nurses (52.7%), 25–30 years old (30.4%), with Bachelor’s degrees (71.4%) and more than 10 years of experience (71%). Furthermore, the daily Internet usage among the head nurses was 1–3 hours (52.7%). (See Table 1).

Table 1. Demographic characteristics.

Characteristic Profile N %
Gender Male 134 47.3
Female 149 52.7
Age 25–30 years 86 30.4
31–35 years 73 25.8
36–40 years 58 20.5
> 40 years 66 23.3
Education Bachelor’s 202 71.4
High diploma 45 15.9
Masters 33 11.7
PhD 3 1.1
Internet usage < 1hour 27 9.5
1–3 hours 149 52.7
4–6 hours 66 23.3
>6 hours 41 14.5
Experience <5 years 17 6
5–10 years 65 23
>10 years 201 71
Total 283
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4.2 Validity and reliability

Smart PLS 3.0 measurement or outer models detect if the collected data are valid and reliable. In this study, Convergent validity was tested using Cronbach’s alpha (α), Composite reliability (CR), and Average Variance Extracted (AVE) and achieved an acceptable value. Cronbach’s alpha, Composite reliability, and Average Variance Extracted were more than .70, .70, and .50; respectively, of the study variables (trust (α = .89, CR = .92 and AVR = .75), reciprocity (α = .89, CR = .93 and AVR = .82), reputation (α = .90, CR = .93 and AVR = .77), and ability to share (α = .92, CR = .94 and AVR = .81), Knowledge Self-Efficacy (α = .93, CR = .95 and AVR = .82), and Knowledge Sharing Behaviour (α = .78, CR = .86 and AVR = .61). Fornell-Larcker criterion and Heterotrait-Monotrait (HTMT) were examined by Smart PLS 3.0 measurement model and indicated to valid data [63]. (See Table 2).

Table 2. Convergent and discriminant validity.

Convergent validity *Discriminant validity (Fornell-Larcker criterion) **Discriminant validity (HTMT Ratio)
Constr. α>.70 CR>.70 AVE>.50 TRU REC REP ABS KSB KSE TRU REC REP ABS KSB KSE
TRU .89 .92 .75 .89
REC .89 .93 .82 .19 .90 .84
REP .90 .93 .77 .72 .18 .79 .79 .82
ABS .92 .94 .81 .72 .26 .69 .91 .67 .72 .65
KSE .93 .95 .82 .57 .23 .61 .58 .88 .66 .77 .67 .63
KSB .78 .86 .61 .66 .34 .70 .29 .66 .90 .71 .62 .71 .52 .68
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Note: TRU: Trust, REC: Reciprocity, REP: Reputation, ABS: Ability to share, KSE: Knowledge Self-Efficacy, KSB: Knowledge Sharing Behaviour. α = Cronbach’s alpha, CR  = Composite reliability, AVE = Average variance extracted.

* Fornell-Larcker criterion: the value in bold is accepted if it is higher than the corresponding row and column values.

** HTMT Ratio < .85 is valid.

Discriminant validity could also be examined by assessing items’ cross-loading [63]. To achieve an acceptable level of cross loading, the indicators’ (items) loading of the constructs should be higher than the loading on another construct, which was achieved as Table 3 shows.

Table 3. Cross loading of constructs.

ABS KSB KSE REC REP TRU
ABS1 0.910 0.655 0.149 0.630 0.565 0.551
ABS2 0.911 0.643 0.129 0.590 0.488 0.543
ABS3 0.915 0.619 0.191 0.590 0.479 0.493
ABS4 0.854 0.654 0.195 0.754 0.519 0.571
KSB1 0.531 0.688 0.136 0.466 0.401 0.453
KSB3 0.575 0.785 0.161 0.585 0.504 0.508
KSB4 0.547 0.806 0.083 0.514 0.48 0.532
KSB5 0.584 0.836 0.18 0.583 0.513 0.518
KSE1 0.155 0.131 0.882 0.237 0.218 0.109
KSE2 0.202 0.184 0.897 0.258 0.238 0.149
KSE4 0.120 0.146 0.906 0.179 0.186 0.058
KSE5 0.180 0.179 0.929 0.257 0.210 0.122
REC1 0.673 0.623 0.256 0.906 0.560 0.570
REC2 0.627 0.640 0.240 0.908 0.513 0.567
REC3 0.649 0.617 0.213 0.909 0.509 0.554
REP1 0.453 0.468 0.179 0.46 0.842 0.471
REP2 0.559 0.575 0.245 0.536 0.908 0.520
REP3 0.508 0.539 0.185 0.537 0.887 0.518
REP4 0.483 0.553 0.216 0.500 0.873 0.480
TRU1 0.552 0.553 0.123 0.496 0.498 0.850
TRU2 0.537 0.584 0.130 0.554 0.462 0.868
TRU3 0.487 0.549 0.075 0.572 0.505 0.883
TRU4 0.509 0.547 0.101 0.528 0.502 0.865
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4.3 Construct cross-validated redundancy (Q2)

The blindfolding output of SmartPLS is calculated to measure the predictive relevance of the latent variables of a study. Table 4 shows that Stone-Geisser Q2 equal 1 –SSE/SSO. As a result of Henseler et al. [64] procedures, a research model with Q2 > 0 attained the accepted value of predictive relevance.

Table 4. Construct cross-validated redundancy.

Latent variable SSO SSE Q2 = 1- (SSE/SSO)
Knowledge Sharing Behaviour 1,632.00 1,112.16 0.318
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Note: SSE is the sum of Squares of Prediction Errors; SSO is the Sum of Squares Observations.

4.4 Coefficient of determination (R2)

The output of the PLS3 structure produced a coefficient of determination values (R2) of knowledge sharing behaviour (KSB) as 0.695. This means that ABS, KSE, REC, REP, and TRU together explained 69.5% of knowledge-sharing behaviour among head nurses in Jordan. A larger R2 value increases the predictive ability of the structural model. In the current study, R2 is substantial according to Chin’s [65] classification of R2 value.

4.5 Study hypotheses testing

This study investigated four hypotheses concerning the moderation effect of knowledge self-efficacy between trust, reputation, reciprocity, and ability to share with knowledge-sharing behaviour. The result of 5000 bootstrapping of 283 cases to measure the significance of the path coefficients with a 95% Confidence Interval showed a moderation effect of knowledge self-efficacy in the relationship between trust, reputation, reciprocity, and knowledge-sharing behaviour, as shown in Table 5 and Fig 2.

Table 5. Test of the moderating effect of knowledge self-efficacy.

# Path f 2 β SE T Value P (Sig)
H1 TRU*KSE---> KSB 0.026 0.149 0.069 2.166 0.03 **
H2 REC*KSE---> KSB 0.032 -0.167 0.071 2.358 0.018 **
H3 REP*KSE---> KSB 0.078 0.192 0.053 3.640 0.001 ***
H4 ABS*KSE---> KSB 0.015 -0.119 0.066 1.794 0.073(n.s)
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Note

***: p<0.01

**: p<0.05

Fig 2. SEM figure of knowledge sharing behaviour with moderating effect.

Fig 2

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In more detail, the moderating effect of knowledge self-efficacy (interaction between knowledge self-efficacy and trust, TRUST*KSE) exists in the relationship between reputation and knowledge-sharing behaviour. The results were also statistically significant (β = 0.142, p = 0.03) and positive, which revealed that knowledge self-efficacy was able to moderate the relationship between trust and knowledge-sharing behaviour positively. Based on these findings, trust was more positively effective on knowledge sharing behaviour when the knowledge self-efficacy is at a higher level; when the knowledge self-efficacy increases, this factor will increase; hence, trust will increase the knowledge sharing behaviour.

Concerning reciprocity, the results were statistically significant (β = -0.167, p = 0.018). It was also negative, meaning that knowledge self-efficacy negatively moderated the relationship between reciprocity and knowledge-sharing behaviour. This finding indicated that, at a high level of knowledge self-efficacy, reciprocity had a lower effect on knowledge-sharing behaviour and vice versa. In more detail, when the level of knowledge self-efficacy reduces, reciprocity would be more effective in knowledge-sharing behaviour.

Reputation was also found to be a moderator and statistically significant and positive (β = 0.192, p<0.001). This revealed that knowledge self-efficacy positively moderated the relationship between reputation and knowledge-sharing behaviour. Thus, it can be concluded that reputation was more positively effective on knowledge-sharing behaviour at a high level of knowledge self-efficacy. Likewise, if knowledge self-efficacy increases, the reputation factor will affect the level of knowledge-sharing behaviour.

Surprisingly, the bootstrapping calculation between the ability to share and sharing behaviour did not have a significant effect (β = -0.119, p = 0.073). This means that knowledge self-efficacy did not moderate the relationship between the ability to share and knowledge-sharing behaviour.

5. Discussion

This is the first study investigating the knowledge-sharing behaviour of OHC in the Jordanian context. The main objective of this study was to assess the moderating effect of knowledge self-efficacy on the relationship between four individual factors and knowledge-sharing behaviour among head nurses in online health communities in Jordan.

Lai and Hsieh [55] found that reciprocity was a critical motivator of continued knowledge-sharing behaviour for people with low knowledge self-efficacy. First, they found that knowledge self-efficacy moderates trust and knowledge-sharing behaviour in online health communities. If an individual has a strong sense of knowledge self-efficacy, he or she will have no problem sharing [55].

The current study found that knowledge self-efficacy among head nurses can increase the effect of trust on knowledge-sharing behaviour and higher knowledge self-efficacy. The effect of trust on the part of head nurses is more positive and effective regarding their knowledge-sharing behaviour. In line with Social Cognitive Theory, this finding suggests that nursing knowledge-sharing behaviour increases with their ability to control or behave.

Second, knowledge self-efficacy served as a moderator between reciprocity and knowledge-sharing behaviour; this finding was consistent with previous studies in different contexts [37]. More specifically, the moderating effect of knowledge self-efficacy between reciprocity and knowledge-sharing behaviour implies that an individual with low knowledge self-efficacy is more reciprocal in sharing knowledge than an individual with a high score of knowledge self-efficacy.

Third, the present study found that knowledge self-efficacy moderates reputation and knowledge-sharing behaviour. This implies that the effect of reputation on knowledge-sharing behaviour was high for the employee with a high level of self-efficacy. In other words, reputation strongly influenced knowledge contributors with high levels of self-knowledge efficacy [3, 55]. This significance of the moderating role of knowledge sharing between reputation and knowledge sharing is also in line with social cognitive theory. As stated, the theory asserts that behaviour is the product of an individual’s past experience and level of self-efficacy. Accordingly, knowledge self-efficacy increases the effectiveness of reputation in enhancing knowledge-sharing behaviours among head nurses. Head nurses who gain reputations from online communities and have higher knowledge self-efficacy will be more likely to share knowledge in OHCs. The present study extends the understanding of the moderating role of knowledge self-efficacy between reputation and knowledge-sharing behaviour. It also extends the understanding of the applicability of knowledge self-efficacy among head nurses working in online health communities, specifically in Jordan.

The results contradicted the proposed hypothesis, as knowledge self-efficacy did not mediate between the ability to share and knowledge-sharing behaviour. This result might be due to inadequate knowledge-sharing activities at private hospitals, which may have shown that knowledge self-efficacy does not support their ability to share in OHCs. In addition, this result is consistent with Sitharthan et al. [66] and Nguyen et al. [3] studies that reported that self-efficacy does not always moderate the relationship between two personal variables.

5.1 Implications and future research

This study expanded the literature regarding knowledge-sharing behaviour, individual factors, and knowledge self-efficacy. Examining the study model in the healthcare sector in Jordan is not only considered to offer an extension of the literature. However, it also fills the gap in the existing literature by providing a comprehensive understanding of the above moderating effect of knowledge self-efficacy, which could enrich knowledge-sharing behaviours.

The study’s findings could benefit policymakers in hospital settings to improve the knowledge-sharing behaviour of head nurses in OHCs and help them understand essential factors that could affect their knowledge-sharing behaviour in these communities. Moreover, it is helpful for the Jordanian context in obtaining a better understanding of the main factors increasing the knowledge-sharing behaviour of head nurses. The role of individual factors includes trust, reciprocity, and reputation, which have been shown to improve knowledge sharing in online health communities.

Furthermore, it provides practical contributions about the role of knowledge self-efficacy as a predictor of knowledge-sharing behaviour. Specifically, the results regarding the link between knowledge self-efficacy and knowledge-sharing behaviour offer clear insights for hospital management to avoid challenges affecting knowledge-sharing practices [29, 67].

The health policymakers can apply these findings in setting a plan for supporting knowledge self-efficacy. For example, head nurses should allocate specific time to share knowledge via online healthcare communities and connect sharing amounts to a “points system.” The knowledge-sharing behaviour among nurses depends on their willingness to acquire skills, knowledge, and experiences through online communities. Other contextual factors, such as hospital size, also affect the nurses’ knowledge; for instance, a large hospital would conduct effective training sessions, workshops, and seminars for nurses, while a small hospital would not. The management should encourage the knowledge-sharing behaviour culture through OHCs to increase the level of knowledge self-efficacy among head nurses. Topics in different disciplines, such as pain management, safety performance, quality care, and health digitalisation, should be considered in this regard. Accordingly, increasing knowledge-sharing behaviour among its communities. Overall, this study gives top management at private hospitals more understanding of how knowledge self-efficacy can encourage head nurses to share their knowledge in online health communities. Future research can explore new variables as independent, dependent, or moderating variables such as organisational and environmental factors [68, 69] or extend the investigation to more regions and sectors such as education and finance. Moreover, future research may investigate non-significant results in this study, such as the ability to share through the moderating effect of knowledge self-efficacy.

5.2 Study limitations

This study has limitations. First, the generalizability of the current study’s findings is limited in two aspects. In particular, the study involved one representative from among the head nurses of each department in the hospitals. However, other employees were not considered when making up the study sample. Second, the data collection was restricted to private hospitals in Amman city due to the ability to access data. Therefore, the findings may not be generalisable to other sectors in Jordan or other countries. This could extend to other hospitals in different regions or other healthcare sectors in the future. Hence, comparable studies could be conducted in other sectors to consider more employees during the survey. Third, the study was completed in 2019; the data reported here were dated. However, because the study variables are interpersonal interactions, they are less likely to be affected by time [68]. The last limitation of the study is that it is cross-sectional and cannot establish the causality of the study model.

6. Conclusion

As technology and social media become advanced, knowledge-sharing behaviour is in OHCs to enhance the health status of individuals and communities. This study focused on knowledge self-efficacy and reflection on individuals’ factors and knowledge-sharing behaviour. Head nurses with a high self-efficacy of knowledge can improve their knowledge-sharing behaviour. Knowledge self-efficacy moderates trust, reciprocity, and reputation with knowledge-sharing behaviour, while the ability to share did not. This study has several implications for private hospitals in Amman regarding the key roles of individual factors and knowledge self-efficacy in improving knowledge-sharing behaviour among head nurses in online health communities. Accordingly, recognising the links explained by the study model could add value to the theory and practice.

Supporting information

S1 Appendix

(DOCX)

Click here for additional data file. (21.9KB, docx)
S1 Data

(XLS)

Click here for additional data file. (171.7KB, xls)

Data Availability

All relevant data are within the article and its Supporting Information files.

Funding Statement

The authors received no specific funding for this work.

References

  • 1.Jinyang L. Knowledge sharing in virtual communities: A social exchange theory perspective. J Ind Eng Manag. 2015;8(1):170–83. [Google Scholar]
  • 2.Tran T. The effect of knowledge sharing and innovativeness on organisational performance: An empirical study in Vietnam. J Asian Financ. 2021;8(8):503–0511. [Google Scholar]
  • 3.Nguyen TM, Nham TP, Froese FJ, Malik A. Motivation and knowledge sharing: A meta-analysis of main and moderating effects. J Knowl Manag. 2019;23(5):998–1016. [Google Scholar]
  • 4.Mura M, Mura M, Lettieri E, Lettieri E, Radaelli G, Radaelli G, et al. Behavioural operations in healthcare: A knowledge sharing perspective. Int J Oper Prod Manag. 2016;36(10):1222–46. [Google Scholar]
  • 5.Bouma S. Smarter Knowledge sharing quantitative research into the perceived usefulness of Lotus Live as knowledge sharing system by Dutch healthcare professionals. Tilburg University; 2011. [Google Scholar]
  • 6.Mc Evoy PJ, Ragab MAF, Arisha A. The effectiveness of knowledge management in the public sector. Knowl Manag Res Pract [Internet]. 2019;17(1):39–51. Available from: 10.1080/14778238.2018.1538670 [DOI] [Google Scholar]
  • 7.Kim Y-M, Newby-Bennett D, Song H-J. Knowledge sharing and institutionalism in the healthcare industry. J Knowl Manag. 2012;16(3):480–94. [Google Scholar]
  • 8.Mozaffar H, Panteli N. The online community knowledge flows: distance and direction. Eur J Inf Syst [Internet]. 2022;31(2):227–40. Available from: 10.1080/0960085X.2020.1866442 [DOI] [Google Scholar]
  • 9.Hsu M, Ju TL, Yen C, Chang C. Knowledge sharing behavior in virtual communities: The relationship between trust, self-efficacy, and outcome expectations. 2007;65:153–69. [Google Scholar]
  • 10.Song P, Zhang C, Xu YC, Huang L. Brand extension of online technology products: Evidence from search engine to virtual communities and online news. Decis Support Syst. 2010;49(1):91–9. [Google Scholar]
  • 11.Cheung CMK, Lee MKO, Lee ZWY. Understanding the continuance intention of knowledge sharing in online communities of practice through the post‐knowledge‐sharing evaluation processes. J Assoc Inf Sci Technol. 2013;64(7):1357–74. [Google Scholar]
  • 12.Park JH, Gu B, Leung ACM, Konana P. An investigation of information sharing and seeking behaviors in online investment communities. Comput Human Behav. 2014;31:1–12. [Google Scholar]
  • 13.Rai P, Kumar PS, Oh S, Kwon H, Mathur GN, Varadan VK, et al. Smart healthcare textile sensor system for unhindered-pervasive health monitoring. In: Nanosensors, Biosensors, and Info-Tech Sensors and Systems 2012. International Society for Optics and Photonics; 2012. p. 83440E. [Google Scholar]
  • 14.Fan H, Lederman R, Smith SP, Chang S. How trust is formed in online health communities: A process perspective. Commun Assoc Inf Syst. 2014;34(1):531–60. [Google Scholar]
  • 15.Imlawi J, Gregg D. Understanding the satisfaction and continuance intention of knowledge contribution by health professionals in online health communities. Informatics Heal Soc Care [Internet]. 2020;45(2):151–67. Available from: doi: 10.1080/17538157.2019.1625053 [DOI] [PubMed] [Google Scholar]
  • 16.Tanis M, Hartmann T, te Poel F. Online health anxiety and consultation satisfaction: A quantitative exploratory study on their relations. Patient Educ Couns. 2016;99(7):1227–32. doi: 10.1016/j.pec.2016.01.021 [DOI] [PubMed] [Google Scholar]
  • 17.Zhao J, Ha S, Widdows R. Building trusting relationships in online health communities. Cyberpsychology, Behav Soc Netw. 2013;16(9):650–7. doi: 10.1089/cyber.2012.0348 [DOI] [PubMed] [Google Scholar]
  • 18.Cline RJW, Haynes KM. Consumer health information seeking on the Internet: the state of the art. Health Educ Res. 2001;16(6):671–92. doi: 10.1093/her/16.6.671 [DOI] [PubMed] [Google Scholar]
  • 19.Mein J, Gao G, Agarwal R. The creation of social value: Can an online health community reduce rural–urban health disparities? Mis Q. 2016;40(1). [Google Scholar]
  • 20.Lin TC, Lai MC, Yang SW. Factors influencing physicians’ knowledge sharing on web medical forums. Health Informatics J. 2016;22(3):594–607. doi: 10.1177/1460458215576229 [DOI] [PubMed] [Google Scholar]
  • 21.Zhang X, Liu S, Deng Z, Chen X. Knowledge sharing motivations in online health communities: A comparative study of health professionals and normal users. Comput Human Behav. 2017;75:797–810. [Google Scholar]
  • 22.Liu W, Fan X, Ji R, Jiang Y. Perceived community support, users’ interactions, and value co-creation in online health community: The moderating effect of social exclusion. Int J Environ Res Public Health. 2020;17(1):204. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Yan Z, Wang T, Chen Y, Zhang H. Knowledge sharing in online health communities: A social exchange theory perspective. Inf Manag. 2016;53(5):643–53. [Google Scholar]
  • 24.Ali AA, Dominic PDD. Organisational and individual factors impact on knowledge sharing practice: The association with cost reduction. 2016 3rd Int Conf Comput Inf Sci. 2016;536–41. [Google Scholar]
  • 25.Obrenovic B, Jianguo D, Tsoy D, Obrenovic S, Khan MAS, Anwar F. The enjoyment of knowledge sharing: Impact of altruism on tacit knowledge-sharing behavior. Front Psychol. 2020;11(July):1–16. doi: 10.3389/fpsyg.2020.01496 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Fullwood R, Rowley J, McLean J. Exploring the factors that influence knowledge sharing between academics. J Furth High Educ [Internet]. 2019;43(8):1051–63. Available from: 10.1080/0309877X.2018.1448928 [DOI] [Google Scholar]
  • 27.Abdel Fattah FAM, Mohamed AHHM, Bashir MIA, Al Alawi AMM. Determinants of knowledge-sharing behaviour among students at higher educational institutions in Oman: a planned behaviour theoretical perspective of knowledge sharing. Glob Knowledge, Mem Commun. 2020;70(6–7):611–36. [Google Scholar]
  • 28.Shehab S, Rahim REA, Daud S. A review of individual factors on knowledge sharing: Evidence from the empirical literature. Int J Eng Technol. 2018;7(4):186–94. [Google Scholar]
  • 29.Alhalhouli ZT. Factors Affecting Knowledge Sharing Behavior among Stakeholders in Jordanian Hospitals Using Social Networks. 2014;3(5):919–28. [Google Scholar]
  • 30.Al-Dalaien AAAH, Drus SM, Kasim H. A conceptual model of motivational factors of knowledge transfer for hospitals. Int J Eng Adv Technol. 2019;9(1):2313–9. [Google Scholar]
  • 31.Aldohyan M, Al-Rawashdeh N, Sakr FM, Rahman S, Alfarhan AI, Salam M. The perceived effectiveness of MERS-CoV educational programs and knowledge transfer among primary healthcare workers: A cross-sectional survey. BMC Infect Dis. 2019;19(1):1–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Bandura A. Social cognitive theory: An agentic perspective. Annu Rev Psychol. 2001;52(1):1–26. doi: 10.1146/annurev.psych.52.1.1 [DOI] [PubMed] [Google Scholar]
  • 33.Bandura A. Human agency in social cognitive theory. Am Psychol. 1989;44(9):1175. doi: 10.1037/0003-066x.44.9.1175 [DOI] [PubMed] [Google Scholar]
  • 34.Betz NE, Klein KL, Taylor KM. Evaluation of a short form of the career decision-making self-efficacy scale. J Career Assess. 1996;4(1):47–57. [Google Scholar]
  • 35.Lent RW. Career development from a social cognitive perspective. In Brown D., Brooks L., & Associates (eds.), Career choice and development (pp. 373–421). SanFrancisco: Jossey-Bass; 1996. [Google Scholar]
  • 36.Bandura A. Self-efficacy: Toward a unifying theory of behavioral change. Psychol Rev. 1977;84(2):191–215. doi: 10.1037//0033-295x.84.2.191 [DOI] [PubMed] [Google Scholar]
  • 37.Aslam I, Safdar T, Shafiq M, Khan SB. Does self efficacy moderate the relationship between knowledge sharing and organisational citizenship behavior? (A quantitative research in Civil Secretariat Quetta). 2018;7(3):232–8. [Google Scholar]
  • 38.Chen S, Chuang Y, Chen P. Knowledge-based systems behavioral intention formation in knowledge sharing: Examining the roles of KMS quality, KMS self-efficacy, and organisational climate. Knowledge-Based Syst. 2012;31:106–18. [Google Scholar]
  • 39.Tsai M-T, Cheng N-C. Programmer perceptions of knowledge-sharing behavior under social cognitive theory. Expert Syst Appl. 2010;37(12):8479–85. [Google Scholar]
  • 40.Alireza Bin Baba MS, Tamjid H, Gholipour R. Information security—Professional perceptions of knowledge-sharing intention under self-efficacy, trust, reciprocity, and shared-language. Comput Educ. 2013;68:223–32. [Google Scholar]
  • 41.Font X, Garay L, Jones S. A social cognitive theory of sustainability empathy. Ann Tour Res. 2016;58:65–80. [Google Scholar]
  • 42.Rodboonsong S, Sawasdee A. Fostering knowledge sharing behavior in educational institutes of Thailand. Int J Crime, Law Soc Issues. 2021;7(2):63–73. [Google Scholar]
  • 43.Jarinto K, Jermsittiparsert K, Chienwattanasook K. A theoretical and empirical framework for knowledge sharing: An auto industry case-study. Int J Innov Creat Chang. 2019;10(1):406–25. [Google Scholar]
  • 44.Hsieh PJ, Lai HM, Kuo PY. Physician acceptance behavior of the electronic medical records exchange: An extended decomposed theory of planned behavior. Proc—Pacific Asia Conf Inf Syst PACIS 2013. 2013; [Google Scholar]
  • 45.Cherian J, Jacob J. Impact of self efficacy on motivation and performance of employees. Int J Bus Manag. 2013;8(14):80–8. [Google Scholar]
  • 46.Lee CS, Ma L. News sharing in social media: The effect of gratifications and prior experience. Comput Human Behav. 2012;28(2):331–9. [Google Scholar]
  • 47.Memon MA, Nor KM, Salleh R. Personality traits influencing knowledge sharing in student–supervisor relationship: A structural equation modelling analysis. J Inf Knowl Manag. 2016;15(2):1650015. [Google Scholar]
  • 48.Shaari R, Bakri N, Rahman AA. Antecedents of knowledge sharing behavior among nurses: Towards research agenda. Procedia—Soc Behav Sci. 2015;171:635–41. [Google Scholar]
  • 49.Bandura A. Fearful expectations and avoidant actions as coeffects of perceived self-inefficacy. Am. Psych. 1986;41(12):1389–91. [Google Scholar]
  • 50.Igbaria M, Iivari J. The effects of self-efficacy on computer usage. Omega. 1995;23(6):587–605. [Google Scholar]
  • 51.Zhao H, Seibert SE, Hills GE. The mediating role of self-efficacy in the development of entrepreneurial intentions. J Appl Psychol. 2005;90(6):1265. doi: 10.1037/0021-9010.90.6.1265 [DOI] [PubMed] [Google Scholar]
  • 52.Wasko M, Faraj S. Why should I share? Examining social capital and knowledge contribution in electronic networks of practice. MIS Q. 2005;29(1): 35–57. [Google Scholar]
  • 53.Lin H-F. Knowledge sharing and firm innovation capability: An empirical study. Int J Manpow. 2007;28(3/4):315–32. [Google Scholar]
  • 54.Kankanhalli A, Tan BCY, Wei K-K. Contributing knowledge to electronic knowledge repositories: An empirical investigation. MIS Q. 2005;29(1):113–43. [Google Scholar]
  • 55.Lai H, Hsieh P-J. The decision to continue sharing knowledge in virtual communities: The moderating role of knowledge-sharing experience and knowledge self-efficacy. In: PACIS 2013 Proceedings. 2013. p. 6–18. [Google Scholar]
  • 56.Krejcie R.V.; Morgan D.W. Determining Sample Size for Research Activities. Educ. Psychol. Meas. 1970,30, 607. [Google Scholar]
  • 57.Jarvenpaa SL, Leidner DE. Communication and trust in global virtual teams. Organ Sci. 1999;10(6):791–815. [Google Scholar]
  • 58.Hassandoust F, Logeswaran R, Farzaneh Kazerouni M. Behavioral factors influencing virtual knowledge sharing: theory of reasoned action. J Appl Res High Educ. 2011;3(2):116–34. [Google Scholar]
  • 59.Radaelli G, Lettieri E, Mura M, Spiller N. Knowledge sharing and innovative work behaviour in healthcare: A micro-level investigation of direct and Indirect effects. Creat Innov Manag. 2014;23(4):400–14. [Google Scholar]
  • 60.Bock G, Kim Y. Breaking the myths of rewards: An exploratory study of attitudes about knowledge sharing. Info Res Manag Jour. 2001; 15(2):1–8/ [Google Scholar]
  • 61.Lu L, Leung K, Koch PT. Managerial knowledge sharing: The role of individual, interpersonal, and organisational factors. Manag Organ Rev. 2006;2(1):15–41. [Google Scholar]
  • 62.Awang Z, Wan Afthanorhan WMA, Asri MAM. Parametric and non parametric approach in structural equation modeling (SEM): The application of bootstrapping. Mod Appl Sci. 2015;9(9):58–67. [Google Scholar]
  • 63.Hair JF, Hult GTM, Ringle CM, Sarstedt M. A primer on partial least squares structural equation modeling (PLS-SEM). 2nd ed. SAGE Publications; 2016. [Google Scholar]
  • 64.Henseler J, Ringle Christian M., Sinkovics RR. The use of partial least squares path modeling in international marketing. In: New challenges to international marketing. Emerald Group Publishing Limited.; 2009. p. 277–319. [Google Scholar]
  • 65.Chin WW. Issues and opinion on structural equation modeling. MIS Q. 1998;22(1). [Google Scholar]
  • 66.Sitharthan G, Hough MJ, Sitharthan T, Kavanagh DJ. The alcohol helplessness scale and its prediction of depression among problem drinkers. J Clin Psychol. 2001;57(12):1445–57. doi: 10.1002/jclp.1108 [DOI] [PubMed] [Google Scholar]
  • 67.Dammaj A, Alawneh A, Sweis RJ. Investigating the relationship between knowledge sharing and service quality in private hospitals in Jordan Ayman Abu Hammad. Product Qual Manag. 2016;17(4):437–55. [Google Scholar]
  • 68.Jarrar M, Al-Bsheish M, Aldhmadi BK, Albaker W, Meri A, Dauwed M, et al. Effect of practice environment on nurse reported quality and patient safety: The mediation role of person-centeredness. Healthc. 2021;9(11):1–17. doi: 10.3390/healthcare9111578 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Al-Bsheish M, Jarrar M, Mustafa M, Azril M, Meri A, Dauwed M. ICU nurses’ safety performance related to respect for safety and management commitment: A cross-sectional study. Contemp Nurse [Internet]. 2022;1–23. Available from: doi: 10.1080/10376178.2022.2104740 [DOI] [PubMed] [Google Scholar]

Decision Letter 0

Supat Chupradit

20 Sep 2022

PONE-D-22-23050Knowledge Sharing Behaviour among Head Nurses in Online Health Communities: The Moderating Role of Knowledge Self-EfficacyPLOS ONE

Dear Dr. Jarrar,

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Reviewers' comments:

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: 1-Abstract part: the purpose fo this study is not related with study hypothesis. The purpose of this study might be examine or investigate associated between knowledge self efficacy and individual factors.

2-According to sampling design that is calculated sampling sizes by using G*Power the authors should describe the effect size, power of test.

3-Very well organize in result and discussion part.

Reviewer #2: Overall, this manuscript is quite well written. However, it is also found that some references, especially in literature reviews, are quite old. Authors should focus only on research in the past 5 years and consider citations to these relevant articles.

--> Sriyakul, T., & Jermsittiparsert, K. (2021). Factors effecting Preventive Health Behavior among the Students at Universities in Thailand: Mediating Role of Self Efficacy. Educational Sciences: Theory & Practice, 21(4), 223-233.

--> Rodboonsong, S., & Sawasdee, A. (2020). Fostering Knowledge Sharing Behavior in Educational Institutes of Thailand. International Journal of Crime, Law and Social Issues, 7(2), 63-73.

--> Jarinto, K., Jermsittiparsert, K., & Chienwattanasook, K. (2019). A Theoretical and Empirical Framework for Knowledge Sharing: An Auto Industry Case-study. International Journal of Innovation, Creativity and Change, 10(1), 406-425.

Reviewer #3: I have reviewed the paper title: Knowledge Sharing Behaviour among Head Nurses in Online Health Communities: The Moderating Role of Knowledge Self-Efficacy

This article is strongly recommended for publication after incorporating certain changes. This article needs thorough proofreading. The overall quality of the Language is good. Just major grammatical mistakes are found. All tables and figures are relevant. The research Methodology has been well defined. All data are aligned with the findings of the research. This article is a good attempt in field research and will be beneficial for future researchers.

Abstract

1. Understand, Clear

Introduction

1. Lack of Gap reflection to do clear research, please clarify the point. Why point to internet contexts?

Methods

1. Measuring of research by your research approaches that methods are there any references to reduce bias? Please explain, Is it a limitation in interpreting the results to populations/samples?

2. Should describe the population, random sampling and the sample size to be concise and clearer and add academic support description.

3. Do you have IRB approval in this research?, If it have please show number approve, If it not please explain how about your method to protect participants in this research.

4. Please add Data analysis section: The key statistics that they be used in hypothesis testing, should be described in detail to support your decision at the end of this section.

5. Recheck table / figure to quality standard for the journal.

6. Suggestion and Policy recommendation, please add and point it in your paper.

7. Limitation of your study?, Add recommendations about policy recommend.

8. References, the researcher should check and revise the format again. Check styles and recheck all.

Reviewer #4: Overall

The researcher writes and organizes the article content well, clearly and appropriately according to the academic context.

Literature review

Figure should be adjusted to make it more noticeable and clearly according to publication standards. please check resolution and figure standard again.

Method

Do you have IRB approval in this research?, If it have please show number approve, If it not please explain how about your method to protect participants in this research.

Result

4.2 Model Assessment

1) The researcher should state the statistical criteria which used to accept the outer and inner model.

2) The result should be show the SEM figure with important statistical values in the model: outer and inner model.

Reference

The researcher should check and revise the format again.

Reviewer #5: -Why do you choose head nurses?

-The random sampling should be clearly stated: which random sampling method is applied?

-Should be specified Validity and Reliability of questionnaire.

-Knowledge-sharing behaviour may be developed from skills, knowledge, and experiences of the nurses themselves. The more knowledge the nurses have, the more their knowledge-sharing behavior develop. In addition, the size of the hospital also affects the knowledge of the nurses, for example, a large hospital would conduct effective training sessions for nurses, while a small hospital would not.

-The research findings should be compared with other areas in Jordan.

**********

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Reviewer #1: No

Reviewer #2: Yes: Kittisak JERMSITTIPARSERT

Reviewer #3: No

Reviewer #4: No

Reviewer #5: No

**********

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Attachment

Submitted filename: PONE-D-SE.pdf

Click here for additional data file. (3MB, pdf)
PLoS One. 2023 Jan 19;18(1):e0278721. doi: 10.1371/journal.pone.0278721.r002

Author response to Decision Letter 0

9 Oct 2022

The following is our point-by-point response.

Reviewer #1:

Here are the responses to your concerns:

1. Abstract part: the purpose of this study is not related with study hypothesis. The purpose of this study might be examine or investigate associated between knowledge self-efficacy and individual factors.

Thank you for the comments. The study purpose adjusted accordingly.

2. According to sampling design that is calculated sampling sizes by using G*Power the authors should describe the effect size, power of test.

Thank you for the comment. Edited accordingly.

3. Very well organize in result and discussion part.

Thank you.

Reviewer #2:

1. Overall, this manuscript is quite well written. However, it is also found that some references, especially in literature reviews, are quite old. Authors should focus only on research in the past 5 years and consider citations to these relevant articles..

Thank you for the comment. Corrected accordingly and the following references added.

-Rodboonsong, S., & Sawasdee, A. (2020). Fostering Knowledge Sharing Behavior in Educational Institutes of Thailand. International Journal of Crime, Law and Social Issues, 7(2), 63-73.

Jarinto, K., Jermsittiparsert, K., & Chienwattanasook, K. (2019). A Theoretical and Empirical Framework for Knowledge Sharing: An Auto Industry Case-study. International Journal of Innovation, Creativity and Change, 10(1), 406-425.

Reviewer #3:

1. This article is strongly recommended for publication after incorporating certain changes. This article needs thorough proofreading. The overall quality of the Language is good. Just major grammatical mistakes are found. All tables and figures are relevant. The research Methodology has been well defined. All data are aligned with the findings of the research. This article is a good attempt in field research and will be beneficial for future researchers.

Thank you for the comments provided and efforts in optimizing our manuscript. The language corrected accordingly and sent again to native English speaker.

2. Abstract: Understand, Clear.

Thank you.

3. Introduction: Lack of Gap reflection to do clear research, please clarify the point. Why point to internet contexts?

Thank you for the comment. Corrected accordingly and become more reader friendly. Online Health Communities (OHCs) are one kind of an Online Communities, where maintaining health information is a public concern. OHCs through social media and other web-based forums, facilitate their members to participate in health topics, even those with sensitive considerations such as pregnancy, menstruation, and sexuality (Fan et al., 2014; Rai et al., 2012). Several studies have identified the role of individual factors in knowledge-sharing behaviours (Abdel Fattah et al., 2020; Fullwood et al., 2019; Obrenovic et al., 2020). However, lack of studies exploring the role of knowledge self-efficacy between the associations of individual factors (trust, reciprocity, reputation, and ability to share) with knowledge-sharing behaviours.

4. Measuring of research by your research approaches that methods are there any references to reduce bias? Please explain, Is it a limitation in interpreting the results to populations/samples?

Thank you for the comment. Corrected accordingly and study limitations improved.

5. Should describe the population, random sampling and the sample size to be concise and clearer and add academic support description.

Thank you for the comment. Corrected accordingly and become more reader friendly. The population of this study was private hospitals’ head nurses in Amman, Jordan. Private hospitals in the capital (i.e. Amman) because these institutions have competitive advantages, technological capabilities, highest capacity, diversity in terms of specialty, supportive research cultures, and the highest number of hospitals located in Amman (n= 32). The research team attained approval from 22 private hospitals with 322 head nurses (study population). The sample size was calculated based on the G*Power software package, effect size (f 2= 0.15); a significant level (α= 0.05) and power 1-β = 0.95, which calculated that a minimum sample size of 74 was required with five independent variables, including the moderator.

6. Do you have IRB approval in this research? If it have please show number approve, If it not please explain how about your method to protect participants in this research..

Thank you for the comment. Corrected accordingly and become more reader friendly. Ethical approval number UNITEN/COGS 23/2/1/PM20604 was attained from the College of Graduate Studies, Universiti Tenaga Nasional, Malaysia, on 25 April 2018. The hospitals approved their employees' voluntary participation in the study and encouraged their head nurses to participate, and informed consent was obtained from all head nurses agreed to be part of this survey.

7. Please add Data analysis section: The key statistics that they be used in hypothesis testing, should be described in detail to support your decision at the end of this section.

Thank you for the comment. Corrected accordingly and become more reader friendly. Descriptive statistics and moderation regression analysis using SPSS and structural equation modelling approach (i.e. Smart PLS-SEM, Version 3) were the key statistics in this study; respectively. This study used Smart PLS3 to test the hypotheses posited. Smart PLS3 uses a bootstrapping technique to estimate path coefficients and standard errors (Awang et al., 2015). The moderation impact of self-efficacy was evaluated using a 5000 bootstrap sample, a 95% confidence interval (CI) and significance level of 0.05. Before running Smart PLS3, descriptive results were performed using SPSS Version 18.0 (SPSS Inc., Chicago, IL, USA).

8. Recheck table / figure to quality standard for the journal.

Thank you for the comment. The table / figure has been redrawn to make it clearer

9. Suggestion and Policy recommendation, please add and point it in your paper.

Policy recommendations has improved and revised accordingly

10. Limitation of your study? Add recommendations about policy recommend.

Limitation and recommendations has improved and revised accordingly

11. References, the researcher should check and revise the format again. Check styles and recheck all.

Thank you for the comment. References and study format has revised accordingly.

Reviewer #4:

The researcher writes and organizes the article content well, clearly and appropriately according to the academic context.

1. Literature review: Figure should be adjusted to make it more noticeable and clearly according to publication standards. Please check resolution and figure standard again.

Thank you for the comment. The model (Figure 1) has been redrawn to make it clearer

2. Method: Do you have IRB approval in this research?, If it have please show number approve, If it not please explain how about your method to protect participants in this research.

Thank you for the comment. Corrected accordingly and become more reader friendly. Ethical approval number UNITEN/COGS 23/2/1/PM20604 was attained from the College of Graduate Studies, Universiti Tenaga Nasional, Malaysia, on 25 April 2018. The hospitals approved their employees' voluntary participation in the study and encouraged their head nurses to participate, and informed consent was obtained from all head nurses agreed to be part of this survey.

3. Result: 4.2 Model Assessment

a) The researcher should state the statistical criteria which used to accept the outer and inner model.

Thank you for the comment. Corrected accordingly and become more reader friendly. Descriptive statistics and moderation regression analysis using SPSS and structural equation modelling approach (i.e. Smart PLS-SEM, Version 3) were the key statistics in this study; respectively. This study used Smart PLS3 to test the hypotheses posited. Smart PLS3 uses a bootstrapping technique to estimate path coefficients and standard errors (Awang et al., 2015). The moderation impact of self-efficacy was evaluated using a 5000 bootstrap sample, a 95% confidence interval (CI) and significance level of 0.05. Before running Smart PLS3, descriptive results were performed using SPSS Version 18.0 (SPSS Inc., Chicago, IL, USA).

b) The result should be show the SEM figure with important statistical values in the model: outer and inner model.

Thank you for the comment. SEM figure has added as Figure 2 shown

4. Reference: The researcher should check and revise the format again.

Thank you for the comment. References and study format has revised accordingly.

Reviewer #5:

1. Why do you choose head nurses?

Thank you for the comment. Justification has added in methodology part accordingly.

“Head nurses were targeted in this study to serve the study purposes as they considered as one of health leaders. Public and even followers’ nurses prefer contact with head nurses due to their managerial rank as a first line management to them; thus, they are knowledgeable, closer, and often participating part in online communities”.

2. The random sampling should be clearly stated: which random sampling method is applied?

3. Should be specified Validity and Reliability of questionnaire.

Thank you for the comments. Reliability and validity reported accordingly (α= Cronbach's alpha, CR = Composite reliability)

4. Knowledge-sharing behaviour may be developed from skills, knowledge, and experiences of the nurses themselves. The more knowledge the nurses have, the more their knowledge-sharing behavior develop. In addition, the size of the hospital also affects the knowledge of the nurses, for example, a large hospital would conduct effective training sessions for nurses, while a small hospital would not.

Nice paragraph and insight the authors to consider it in the discussion part. Thank you

5. The research findings should be compared with other areas in Jordan.

Thanks your comments, authors did some text modifications. However, scarce research is available in the Jordanian context in this regard

We confirm that this work is non-funded and original and has not been published anywhere, nor is it currently under consideration for publication elsewhere.

Please address all correspondence concerning this manuscript to me at [mutaman.jarrar@yahoo.com, mkjarrar@iau.edu.sa].

Thank you for your consideration of this manuscript.

Sincerely,

Mu’taman Jarrar

Decision Letter 1

Supat Chupradit

31 Oct 2022

PONE-D-22-23050R1Knowledge Sharing Behaviour among Head Nurses in Online Health Communities: The Moderating Role of Knowledge Self-EfficacyPLOS ONE

Dear Dr. Jarrar,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process

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Kind regards,

Supat Chupradit, Ph.D., M.Ed., B.Sc.(OT), B.P.A., B.Ed., B.A.

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

Reviewer #4: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: 1-The social cognitive theory that is found since 1996, that might be questionable this theory still suitable for using at the present. The authors might illustrulate the evidences support.

2-It not nesssary show the hyphothesis setting process of this study. That might be shows in review literature part.

3-Due to using the part analysis. This statistic is the family of multivariate statistic. The authors should choose another method for calculate minimum sampling size.

4-There are some measure are established since 1999 the authors should examine the psychometric properties before using collect data.

5-In this study has found the lower Beta values (see in table 5), the authors should explain the reasons and evidences support why the knowledge self-efficacy is not mediate between the ablity.

Reviewer #2: The authors have made complete revisions to the paper to the satisfaction of all recommendations. The main strength of this paper is its strong literary references. However, the authors may also modify the Abstract composition to be a single paragraph without any subheading in this section.

Reviewer #3: The manuscript revision and response by author, Knowledge Sharing Behaviour among Head Nurses in Online Health Communities: The Moderating Role of Knowledge Self-Efficacy. I think you improve all comments that I reviewed this manuscript. Please recheck all references.

Regards,

Reviewer #4: Knowledge Sharing Behaviour among Head Nurses in Online Health Communities: The Moderating Role of Knowledge Self-Efficacy. Thank you for your work hard to revise manuscript. I follow your response referees. I think Its improve to valuable article to publish.

Best Regards,

**********

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Reviewer #1: No

Reviewer #2: Yes: Kittisak JERMSITTIPARSERT

Reviewer #3: No

Reviewer #4: No

**********

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PLoS One. 2023 Jan 19;18(1):e0278721. doi: 10.1371/journal.pone.0278721.r004

Author response to Decision Letter 1

3 Nov 2022

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Thank you for the comment.

References and study format has revised accordingly, and all unrelated references were removed accordingly.

Reviewer #1:

1- The social cognitive theory that is found since 1996, that might be questionable this theory still suitable for using at the present. The authors might illustrate the evidences support.

Thank you for the comment.

The social cognitive theory (SCT) is frequently used to guide behavior change interventions such as Knowledge Sharing Behaviour. It may be particularly useful for examining how individuals interact with their surroundings. The SCT can be used to understand the influence of social determinants of health and a person's experiences on behavior change. Comparing to other theories in this regard such as social exchange theory was developed in 1958, SCT is still current and many recent articles were used this theory.

for example:

Lin, H. C., & Chang, C. M. (2018). What motivates health information exchange in social media? The roles of the social cognitive theory and perceived interactivity. Information & Management, 55(6), 771-780.‏

Ahmed, Y. A., Ahmad, M. N., Ahmad, N., & Zakaria, N. H. (2019). Social media for knowledge-sharing: A systematic literature review. Telematics and informatics, 37, 72-112.‏

Lin, X., & Kishore, R. (2021). Social media-enabled healthcare: a conceptual model of social media affordances, online social support, and health behaviors and outcomes. Technological Forecasting and Social Change, 166, 120574.‏

2- It not necessary shows the hypotheses setting process of this study. That might be shows in review literature part.

Thank you. Hypotheses testing and study framework was established in the literature review part

3- Due to using the part analysis. This statistic is the family of multivariate statistic. The authors should choose another method for calculate minimum sampling size.

Thank you for this comment.

It was calculated by using another method and added to the manuscript accordingly. “Beside G*Power software package, Krejcie and Morgan, (1970) sample size formula was used to get minimum number of respondents to be surveyed, which is 210”.

4- There are some measure are established since 1999 the authors should examine the psychometric properties before using collect data.

The reviewer means “Jarvenpaa, S. L., & Leidner, D. E. (1999). Communication and trust in global virtual teams. Organization science, 10(6), 791-815”

Despite this paper being old to some extent, however, it strong and citable one (more than 4000 citations). Moreover, the psychometric properties of this measure such as validity and reliability were examined in this study and it was valid and reliable.

5- In this study has found the lower Beta values (see in table 5), the authors should explain the reasons and evidences support why the knowledge self-efficacy is not mediate between the ability.

Yes, the study empirical results show no moderation effect of knowledge self-efficacy between the ability and knowledge-sharing behaviors.

Justification in the text is “This result might be due to inadequate knowledge-sharing activities at private hospitals, which may have shown that knowledge self-efficacy does not support their ability to share in OHCs. In addition, this result is consistent with Sitharthan et al. (65) and Nguyen et al. (3) studies that reported that self-efficacy does not always moderate the relationship between two personal variables”

Reviewer #2:

The authors have made complete revisions to the paper to the satisfaction of all recommendations. The main strength of this paper is its strong literary references. However, the authors may also modify the Abstract composition to be a single paragraph without any subheading in this section

Thank you for this comment. Abstract become a single paragraph without any subheading

Reviewer #3:

The manuscript revision and response by author, Knowledge Sharing Behaviour among Head Nurses in Online Health Communities: The Moderating Role of Knowledge Self-Efficacy. I think you improve all comments that I reviewed this manuscript. Please recheck all references.

Thank you for your reviewing our work. References and study format has revised accordingly, and all unrelated references were removed accordingly.

Reviewer #4:

Knowledge Sharing Behaviour among Head Nurses in Online Health Communities: The Moderating Role of Knowledge Self-Efficacy. Thank you for your work hard to revise manuscript. I follow your response referees. I think Its improve to valuable article to publish.

Thank you for your reviewing our work

We confirm that this work is non-funded and original and has not been published anywhere, nor is it currently under consideration for publication elsewhere.

Note: The data uploaded in the system to confirm the availability for future researchers requesting access to the data from corresponding authors.

Please address all correspondence concerning this manuscript to me at [mutaman.jarrar@yahoo.com, mkjarrar@iau.edu.sa].

Thank you for your consideration of this manuscript.

Sincerely,

Mu’taman Jarrar

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file. (2MB, docx)

Decision Letter 2

Supat Chupradit

22 Nov 2022

Knowledge Sharing Behaviour among Head Nurses in Online Health Communities: The Moderating Role of Knowledge Self-Efficacy

PONE-D-22-23050R2

Dear Dr. Jarrar,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Supat Chupradit, Ph.D., M.Ed., B.Sc.(OT), B.P.A., B.Ed., B.A.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #3: All comments have been addressed

Reviewer #4: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors organized the revise article very well. The information from this article are rich and usuful.

Reviewer #3: Knowledge Sharing Behaviour among Head Nurses in Online Health Communities: The Moderating Role of Knowledge Self-Efficacy. Revision version base on comments by reviewers. Accept.

Reviewer #4: Thank you for considering reviewer comments and suggestions. I am satisfied with the responses.

All the best for your article.

**********

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If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #3: No

Reviewer #4: No

**********

Acceptance letter

Supat Chupradit

28 Nov 2022

PONE-D-22-23050R2

Knowledge Sharing Behaviour among Head Nurses in Online Health Communities: The Moderating Role of Knowledge Self-Efficacy

Dear Dr. Jarrar:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Assistant Professor Supat Chupradit

Academic Editor

PLOS ONE

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