Want to feel better, share what you know

ABSTRACT

In view of the mental health issues associated with the COVID-19 pandemic, this study draws on the theories of proactive coping and altruism to examine how knowledge sharing can be used to address employee performance and mental wellbeing. Emotional intelligence is modelled as a moderator in these relationships. Two studies were conducted in Australia and Vietnam to validate the proposed relationships. The results show that only knowledge donating has a positive effect on employee performance, whereas both types of knowledge sharing are significantly related to positive mental wellbeing. Emotional intelligence exerted significant moderation effects between knowledge donating and positive mental health in the case of Australia, and between collecting and performance in the Vietnam study. This study enriches knowledge sharing literature by integrating into position psychology. The findings have implications for practitioners to adopt a cost-effective means to address mental health and increase job performance.

KEYWORDS:

• Knowledge sharing
• COVID-19
• mental health
• emotional intelligence

1. Introduction

The COVID-19 pandemic has affected daily life worldwide and created an unprecedented challenge for individuals, and their mental health (S. X. Zhang et al., 2020). Mental health is a state of well-being that may affect individuals’ personal, social, and work life, and impact on associated parties. According to Deci and Ryan (2008), positive mental health refers to optimal psychological functioning and mental wellbeing, whereas, negative mental health refers to psychological distress. Negative mental health has also been defined as an individual’s emotional suffering, which is visible through the symptoms of anxiety (e.g., restlessness), depression (e.g., loss of interest and sadness) (Marchand, Drapeau, & Beaulieu-Prévost, 2012), and insomnia (Payton, 2009). Anxiety, depression, and insomnia contribute to reduced mood and interest, the long term sadness, and the reduced capacity of employees to cope with everyday responsibilities (Marchand et al., 2012; Rassanjani et al., 2021). Mental health has a significant impact on individuals and organisations, as well as the community at large (Marchand et al., 2012; Payton, 2009; S. X. Zhang et al., 2020).

Researchers have identified a wide range of antecedents to mental health including career and work satisfaction, job characteristics, supervisory style, optimism, perceived control, and self-esteem (e.g., Arnetz et al., 2011; Bronkhorst et al., 2015). Addressing mental health has primarily been approached from an examination of external factors (e.g., community-based approaches; leadership; e.g., Hoge et al., 2013). Personal attributes have received limited attention. Nonetheless, physical activities have long been regarded as a coping approach to positive mental wellbeing (Stathopoulou et al., 2006). Coping can be reactive to a post-event harm or a proactive response that is future-oriented and risk-averse rather than harm minimisation (Greenglass and Fiksenbaum, 2009). Proactive management from an individual perspective has received limited attention in the academic community.

COVID-19 is evidently a sustained stressor that has affected mental health (Etuka et al., 2021). Whilst reported cases of negative mental health have increased during this crisis, a larger portion have maintained positive mental health throughout the pandemic (Etuka et al., 2021). This may be attributed to effective coping behaviours. Consistent with the foregoing discussion, the current study, drawing on the Schwarzer and Luszczynska’s (2008) coping theory and Becker’s (1976) altruism conceptualisation, proposes that knowledge sharing as an altruistic coping strategy may be related to employee mental health (a direct relationship between knowledge sharing and mental health).

Research (e.g., Prentice, 2019) has shown that job performance can be an antecedent of employee mental wellbeing. Consistent with this view, the study proposes that job performance may intervene in the relationship between employee knowledge sharing behaviour and mental health (an indirect relationship between knowledge sharing and mental health). The rationale for this proposal is provided in the following section. The establishment of this relationship has financial and social implications for employees and organisations as knowledge sharing is often volitional and requires little, or no, organisational resources (Ng, 2022; Nguyen & Prentice, 2020).

Knowledge sharing is an altruistically connotated behaviour. Research has shown that emotionally intelligent individuals tend to engage in such behaviour (e.g., Ain et al., 2022; Carmeli, 2003; Charbonneau & Nicol, 2002). On this basis, this study also examines how emotional intelligence may affect employee knowledge sharing behaviour and moderate its influence on performance and mental health. Specifically, the study draws upon the theories of proactive coping and altruism to propose knowledge sharing as a coping strategy for employees and examines how different types of knowledge sharing can be used to address employee performance and mental wellbeing. Employee performance is modelled as a mediator, and emotional intelligence as a moderator of these relationships. Knowledge sharing is operationalised into knowledge donating and collecting. Mental health included positive and negative mental wellbeing. Two studies are intended for this research to assess these proposed relationships. The details of the methodology and findings of the study are provided in the method and discussion sections respectively.

The following section provides a theoretical foundation for the proposed relationships and presents hypothesis development. The methods for testing the hypotheses are outlined, followed by data analysis and the presentation of the findings. Discussion and implications of these findings conclude the paper.

2. Literature review and hypothesis development

2.1. Knowledge sharing and mental health

Knowledge sharing (KS) is a process of exchange behaviour related to thinking, learning, and knowledge between individuals within organisations (Bock et al., 2005). It is “a process through which individuals mutually exchange personal, subjective, and tacit knowledge, thereby creating new knowledge collectively” (Yun & Lee, 2017, 389). Wang and Noe (Wang et al., 2020, 117) defined knowledge sharing as “the provision of task information and know-how to help others and to collaborate with others to solve problems, develop new ideas, or implement policies and procedures”.

Van den Hooff and de Ridder (2004) maintain that knowledge sharing encompasses two dimensions: knowledge donating and collecting. The former refers to employees communicating personal intellectual capital (IC) to co-workers. The latter indicates employees gaining knowledge and IC from co-workers. Although different, donating and collecting often coexist, and the donor may also be a collector of knowledge. This can be explained by the theory of egoistic and egocentric altruism. Egoistic altruism refers to reciprocal cooperation, in that one shares to induce a reciprocal transfer. This concept can be used to interpret knowledge donating. The process of donating knowledge is concurrent with receiving from others and can be manifested in gratitude expressed by the recipients or through information exchange. The egocentric altruism concept evolved from Becker’s (1976) economic model of altruism, indicating that the donor would donate when the benefits of giving outperforms receiving. Knowledge collecting is reflected in egocentric altruistic behaviour. Therefore, the study opts to include the two dimensions of knowledge sharing within the discussion. These two dimensions have recently been adopted by a number of scholars (e.g., Al-husseini & Elbeltagi, 2018; Nham et al., 2020) to provide more insights into knowledge sharing behaviour.

Hobfoll’s (1989, 2011) conservation of resource (COR) theory, suggests that resources (knowledge) are deemed to be valuable to individuals. Hobfoll argues that when individuals lose resources, the replacement of these resources will be imperative and more important to the individual. From the motivation of people to endure, persist, and attain resources, Halbesleben et al. (2014) stressed the individuals view was subjective in relation to whether the resources did, or didn’t, help to achieve goals. Therefore, resources that are not believed to be exceptional may still actually be of importance to individuals in certain circumstances. Wang et al. (2020) argued that knowledge sharing could help enhance the growth of personal characteristic resources and eventually lead to better mental health. Zhang, Zou and Zhang (J. Zhang et al., 2016) found burnout to be negatively affected by knowledge sharing. Le et al. (2018) indicated employees’ emotions and mental wellbeing can result from sharing. Consistent with foregoing discussion, the following hypotheses are offered:

H1: Knowledge sharing (a, donating; b, collecting;) between co-workers is significantly related to employee mental health.

2.2. Knowledge sharing, job performance, and mental health

Aulawi et al. (2009) argued that knowledge sharing can promote critical thinking and creativity and may allow individuals to create new knowledge. Duke et al. (2022) maintained that both knowledge collecting and knowledge donating affect an organisation’s innovation capability positively. Islamy et al. (2020) also found that knowledge donating and collecting activities conducted by students had a substantial positive impact on academic performance. Wu and Lee (2017) found psychological capital to be positively and linked to both knowledge donating and collecting, and this in turn can lead to better performance. Akram et al. (2018) also found that both knowledge collecting and knowledge donating enhanced the innovative work behaviour of employees. Knowledge lurking is also important to develop knowledge (Nguyen et al., 2021) and is used as a learning strategy (Neelen and Fetter, 2010), and is therefore likely to lead to better performance. As such, we hypothesise:

H2: Knowledge sharing (a, donating; b, collecting) between employees is significantly related to perceived performance.

The extant literature also indicates that job performance is correlated with job satisfaction and happiness (Harris & Fleming, 2017; Prentice & Thaichon, 2019). More specifically, if employees perform better in an organisation, they will feel positive motivation (i.e., happy and confident). Conversely, if they do not perform well, they will experience negative motivation (i.e., stress, depression, and anxiety) and become unproductive and less useful to the organisation. Spector (1997) stated that job satisfaction is expressed by the feelings one has towards job performance. Valaei and Jiroudi () have indicated that job satisfaction is an emotional orientation based on performance in the workplace. For instance, job performance determines whether employees feel positive or negative motivation within the workplace (Harris & Fleming, 2017). Van Gordon, et al. (2014) have also demonstrated an association between job performance and employee work-related feelings, indicating a relationship between job performance and employee positive and negative mental health. Performance determines employee mental health, that is, if an employee performs better in an organisation, they will feel positive motivation (i.e., happy and confident). Where employees do not perform well, they will feel negative motivation (i.e., stress, depression, and anxiety), and will ultimately feel unproductive (Rosen et al., 2010). This discussion leads to the following hypothesis.

H3: Job performance is significantly related to mental health.

Knowledge sharing plays an important role in the formation of a complex social environment within an organisation. This occurs through the exchange of different types of knowledge and ideas in various forms (Cropanzano & Mitchell, 2005). A network can be formed between different actors working within an organisation. Based on social exchange theory and the norm of reciprocity (Gouldner, 1960), willingness to transfer knowledge increases when there is reciprocal balance. As trust between the parties increases, mutual exchanges take place and are advantageous to both employees and the organisation. Additionally, COR theory suggests that knowledge sharing is viewed as a resource that is deemed valuable for individual goal attainment, which may augment personal growth resources (Wang et al., 2020). An increase in work performance or perceived performance could have a positive impact on wellbeing. Van Gordon et al. (2014) found a relationship between work performance and work-related employee feelings, signalling a link between performance and mental well-being. Psychological well-being is also elevated for individuals who acquire and find new ways of solving problems in the course of attaining goals, accomplishing roles at work effectively, and resolving difficulties and problems (CY Kim, 2021). Consistent with the foregoing discussion, the following hypotheses are offered:

H4: Knowledge sharing (a, donating; b, collecting) has a significant indirect effect on mental health through job performance.

2.3. The moderating role of emotional intelligence (EI)

Emotional intelligence (EI) is defined as “the capacity for recognising our own feelings and those of others, for motivating ourselves, and for managing emotions well in our relationships” (Goleman, 1998, 317). This concept has been extensively discussed and debated in the literature in relation to its conceptualisations, measurement, and predictability (see, Prentice, 2019). EI is commonly acknowledged as a branch of social intelligence that can be used to predict individual wellbeing and personal success. It should be measured by objective performance but conceptualised as a trait EI when measured by self-ratings. Rather than modelling as a predictor, the current study proposes EI as a personal ability that can be utilised to moderate employee behaviour for better performance and wellbeing outcomes. This approach is sighted in numerous studies that position EI as a moderator (Jordan, Ashkanasy and Hartel, 2002; Görgens Ekermans and Brand, 2012; Prentice et al., 2013, 2020).

The extant literature confirms EI affects employees’ motivation and intention to participate in knowledge sharing (Jung & Yoon, 2016; Zulfadil, Hendriani, & Machasin, 2020).Individuals who are emotionally intelligent are more exposed to their inner feelings and experiences and are better at transmitting and sharing experiences with others (Ansari & Malik, 2017). According to Jung and Yoon (2016), individuals with high EI have better control of their emotions, thinking, and feelings. High EI individuals are also more capable of coping with new situations and environments than individuals with low EI (Jung & Yoon, 2016; Zulfadil et al., 2020). Employees within an organisation can be trained to develop emotional intelligence. Emotionally intelligent individuals are more eager to engage in social interactions and participate in knowledge sharing with colleagues and co-workers (Goh and Lim, 2014). This suggests that emotionally intelligent individuals help to increase company productivity by sharing experiences and knowledge with co-workers. Hence, the following hypotheses are offered:

H5: EI has a significant moderating effect on the relationship between knowledge sharing (a, donating; b, collecting) and job performance.

Many scholars have studied the impact of EI on health-related outcomes (e.g., Sy et al., 2006, Martins et al., 2010). Research has demonstrated that lower EI leads to higher stress due to the lack of ability to manage negative feelings instigated by a deficiency of self-awareness (Sy et al., 2006). EI is a resource that can be utilised to achieve valued outcomes by enabling the improvement, and use of, other resources such as networks (Winkel et al., 2011). This is in line with COR (Hobfoll, 1989, 516), which posits that “people strive to retain, protect and build resources and that what is threatening to them is the potential or actual loss of these valued resources”. As such, individuals with higher levels of emotional intelligence can use emotions to enable more knowledge sharing behaviours, to achieve better performance, and be mentally healthy. This is consistent with Salvoy et al.’s (1999) argument that people with higher EI are better able to manage stressful situations. Hough (2003) reasoned that individuals pursue accomplishments and success in the workplace in order to increase confidence levels. Fisher’s (2003) findings suggest that employees will obtain a sense of well-being from good performance in the workplace. In line with the preceding arguments, we hypothesise that:

H6: EI has a significant moderating effect on the relationship between job performance and mental health.

3. Method

3.1. Sample and data collection procedure

A longitudinal study would be optimal to understand the effects of knowledge sharing on employee job performance and mental wellbeing. As the research was undertaken during the pandemic, there were many uncertainties and unpredictable factors that may affect the outcomes of interest. To minimise these confounding effects, we opted for a cross-sectional study but conducted it in two countries (i.e., Australia and Vietnam) to test the hypotheses. This option was not intended to compare the results from the two countries but was undertaken to cross validate the proposed relationships. The choice of countries was determined by the authors’ capacity to access these countries.

The target respondents in both countries were those employed and working during the COVID-19 pandemic and had engaged in virtual or physical knowledge sharing. First, a pilot-test was performed with 25 participants to assess the validity and reliability of the questionnaire. Minor revisions were made to improve the clarity of the questionnaires and shorten the response time as a result of this pilot study. Second, for the survey conducted in Vietnam, the questionnaire was translated from English into Vietnamese then back translated to ensure accuracy by two professional editors for comparison. Third, the main survey in Australia was distributed through Qualtrics to Australian residents and working in Australia during the pandemic. The screening questions were included to ensure that respondents were 18 years and above and had participated in knowledge sharing within the workplace. In Vietnam, the survey was distributed via social media platforms including Facebook. Screening questions were included to ensure the eligibility of respondents. The data were collected in November 2020. After 4 weeks, 370 and 281 usable responses were generated from Australia and Vietnam, respectively, and used for data analysis. Table 1 presents the demographic data of the two samples.

Table 1. Demographic information for the respondents.

Variables	Categories	Australia (N = 370)		Vietnam (N = 281)
		Number	Percentage	Number	Percentage
Gender	Male	138	37.3	70	24.9
	Female	231	62.4	210	74.7
	Others	1	.3	1	.4
Age	18–25	47	12.7	61	21.7
	26–35	128	34.6	112	39.9
	36–45	81	21.9	100	35.6
	46–55	53	14.3	8	2.8
	56 or more	61	16.5	0	0
Education	Elementary school	2	.5	0	0
	High school	45	12.2	7	2.5
	Some college/diploma	77	20.8	8	2.8
	Bachelor’s degree	165	44.6	100	35.6
	Post-graduate	81	21.9	166	59.1
Marital status	Single	169	45.7	99	35.2
	Married without children	49	13.2	12	4.3
	Married with children	152	41.1	170	60.5

3.2. Measure

The construct measurements were adapted from existing studies with all items being measured on a 5-point Likert scale with 1 indicating strongly disagree, 5 being strongly agree. The items to measure knowledge donating and collecting were adapted from Akhavan and Mahdi Hosseini (2016). Job performance is often measured through multiple means (e.g., supervisor rating, objective indicators). This study opted for self-rating as we believe only the employee who had engaged in knowledge sharing would understand how this sharing had affected performance. Therefore, a self-reported scale from Chiang and Hsieh (2012) was used to measure job performance. The measure for mental health was adapted from Lukat et al., (2016), including positive and negative mental health. Emotional intelligence was measured by Law et al.’s (2004) emotional intelligence scale (WEIS). WEIS was based on four ability dimensions described in the ability EI model (see Brackett and Mayer, 2003) and has been widely used and cited in the literature. The WEIS contains 16 items (statements), and four dimensions. These four dimensions are self-emotion appraisal, other-emotion appraisal, use of emotion, and regulation of emotion. The Cronbach alpha values for all scales in both countries were above .70 (see, Table 2).

Table 2. Reliabilities and validities for the study variables.

Variables	Items	Australia				Vietnam
		Factor loading	α	CR	AVE	Factor loading	α	CR	AVE
Knowledge donating	I shared my information, skills and experiences with my colleagues	.87	.84	.91	.77	.88	.89	.93	.82
	When I knew any new information, I told my colleagues about it	.90				.92
	When I learned something new, I told my colleagues about it	.86				.92
Knowledge collecting	I asked my colleagues what they knew	.87	.82	.89	.73	.87	.83	.90	.75
	When I needed certain knowledge, I asked my colleagues	.83				.89
	I asked my colleagues about their abilities when I needed to learn something	.87				.84
	As a result of knowledge sharing
Job performance	you fulfiled my job responsibilities	.84	.91	.93	.73	.86	.91	.94	.76
	you met performance standards and expectations of the job	.90				.91
	Your performance level satisfied your manager	.84				.92
	You were effective in your job	.84				.90
	Your performance was still good as the time before the pandemic	.86				.76
Positive mental health	As a result of knowledge sharing, you
	felt happy	.80	.72	.90	.63	.70	.85	.90	.64
	Enjoyed the things you do	.77				.79
	COVID isolation has been a wonderful adventure for you	.73				.83
	Felt cheerful, light-hearted	.86				.85
	Full of things interesting to you during COVID 19 isolation	.81				.83
Negative mental health	As a result of knowledge sharing, you
	was nervous	.80	.95	.96	.71	.71	.92	.94	.63
	Felt tense or high strung	.86				.72
	Been anxious or worried	.79				.70
	Felt restless, fidgety, or impatient	.83				.84
	Felt downhearted and blue	.87				.85
	Been in low or very low spirit	.89				.87
	Feel depressed	.85				.82
	Been moody or brooded about things	.86				.81
	Felt so down in the dumps nothing could cheer you up	.83				.81
	Feel you had nothing to look forward to	.81				.78
Self-emotion appraisal	You have a good sense of why you have certain feelings most of the time	.80	.82	.89	.66	.80	.92	.94	.80
	You have a good understanding of your own emotions	.87				.94
	You really understand what you feel	.87				.93
	You always know whether or not you are happy	.70				.91
Others’ emotion appraisal	You always know your friends’ emotions from their behaviour	.77	.85	.90	.69	.86	.93	.95	.82
	You are a good observer of others’ emotions	.86				.93
	You are sensitive to the feelings and emotions of others	.83				.90
	You have a good understanding of the emotions of people around you	.85				.93
Use of emotion	You always set goals for yourself and then try your best to achieve them	.77	.81	.88	.64	.90	.93	.95	.82
	You always tell yourself I am a competent person	.79				.89
	You are a self-motivated person	.82				.92
	You would always encourage yourself to try your best	.82				.91
Regulation of emotion	You are able to control your temper and handle difficulties rationally	.81	.86	.91	.71	.87	.93	.95	.82
	You are quite capable of controlling your own emotions	.85				.92
	You can always calm down quickly when you are very angry	.84				.91
	You have good control of your own emotions	.86				.92

Note: α = Cronbach’s alpha, CR = composite reliability, AVE = average variance extracted.

4. Results

4.1. Measurement model

Two-step approach was undertaken in data analysis: 1) measurement model was assessed to examine reliabilities and validities of the study variables and model; 2) structural equation modelling was conducted to test the hypotheses.

Confirmatory factor analyses (CFA) with maximum likelihood estimation were performed to assess reliability and validity as emotional intelligence is a multidimensional construct. The results show that all items had significant loading (above .70) on corresponding constructs (Hair et al., 2018). The average variance extracted for each factor was over .50, demonstrating adequate convergent validity (Fornell & Larcker, 1981). The composite reliability for all variables were acceptable with values above .7. The square root of average variance extracted for each construct exceeded the correlation between constructs and the result indicated discriminant validity. Table 2 presents the factor loadings, composite reliability, Cronbach alpha values, and AVE for all study variables. Table 3 presents the correlations between the study variables.

Table 3. Correlation and validity results for this study.

	1	2	3	4	5	6	7	8	9	10
1.Knowledge donating	.88|.90
2.Knowledge collecting	.69*|.67*	.85|.87
3.Lurking	.58*|.60*	.56*|.58*	.88|.92
4.Job performance	.44*|.41*	.36*.33*	.35*|.31*	.85|.87
5.Positive mental health	.13*|.28*	.17*|.25*	.18*|.18*	.20*|.38*	.79|.80
6.Negative mental health	.01|.06	.06|.12*	.14*|.09	−.20*|-.08	−.20*|-.00	.84|.79
7.Self-emotion appraisal	.39*|.42*	.37*|.32*	.27*|.32*	.37*|.47*	.31*|.31*	−.11*|.01	.81|.89
8.Other-emotion appraisal	.38*|.42*	.32*|.38*	.33*|.36*	.29*|.37*	.17*|.18*	.15*|.15*	.60*|.51*	.83|.90
9.Use of emotion	.43*|.51*	.38*|.44*	.36*|.45*	.35*|.49*	.33*|.27*	−.05|.01	.55*|.65*	.46*|.59*	.80|.91
10. Regulation of emotion	.31*|.38*	.29*|.35*	.26*|.37*	.31*|.51*	.44*|.27*	−.21*|-.01	.56*|.55*	.48|.61**	.61*|.55*	.84|.91

Note: *p < .05; Australia|Vietnam; The bold numbers in the diagonal row are the square roots of the average variances extracted (AVE)

4.2. Hypotheses testing

Structural equation modelling was performed to test the hypotheses (Table 4). The model fit was acceptable: Australia: χ2 = 118.44, df = 37, χ2/df = 3.20, p < .001; CFI = .96; TLI = .98; RMSEA = .08; Vietnam: χ2 = 62.22, df = 37, χ2/df = 1.68, p < .01; CFI = .99; TLI = .93; RMSEA = .05. Path modelling was performed to test H1, H2 and H3. The results show that knowledge donating (Australia: ß = .17, p < .05; Vietnam: ß = .35, p < .01) and collecting (Australia: ß = .22, p < .05; Vietnam: ß = .31, p < .05) were positively related to positive mental health but not related to negative mental health. In the case of job performance, knowledge donating was positively related to job performance (Australia: ß = .53, p < .0005; Vietnam: ß = .34, p < .01) but knowledge collecting (Australia: ß = −.10, p > .05; Vietnam: ß = .06, p > .05) did not affect job performance. Nevertheless, job performance affected both positive mental health (Australia: ß = .25, p < .0005; Vietnam: ß = .36, p < .0005) and negative mental health (Australia: ß = −.31, p < .0005; Vietnam: ß = −.22, p < .01). The results are presented in Table 4.

Table 4. Results of the proposed relationships.

Path	Australia (N = 370)	Vietnam (N = 281)
Knowledge donating → Job performance	.53***	.34**
Knowledge donating → Positive mental health	.17*	.35**
Knowledge donating → Negative mental health	.05	−.10
Knowledge collecting → Job performance	−.10	.06
Knowledge collecting → Positive mental health	.22*	.31*
Knowledge collecting → Negative mental health	.13	.25
Job performance → Positive mental health	.25***	.36***
Job performance → Negative mental health	−.31***	−.22**
R^2
Job performance	.25	.20
Positive mental health	.10	.23
Negative mental health	.08	.06

Note: *p < .05; **p < 0.01; *** p < .001

To test the mediation role of job performance, the bias-corrected bootstrapping procedure was used with 2000 bootstrap samples and a 95% confidence level. As only knowledge donating and knowledge collecting significantly influenced positive mental health and job performance, and job performance significantly influenced positive mental health, the mediation role of job performance was only assessed on the impact of knowledge donating and knowledge collection on positive mental health. The results in Table 5 suggest that job performance exerted a significant partial mediation effect on the relationship between knowledge donating with positive mental health (Australia: ß = .09, p < .001; Vietnam: ß = .13, p < .001), and partial (Australia) and full mediation (Vietnam) with negative mental health (Australia: ß = −.15, p < .05; Vietnam: ß = – .04, p > .05)

Table 5. Mediation effect of job performance.

Path	Mediator	Australia				Vietnam
		Regression weight	Lower	Upper	p	Regression weight	Lower	Upper	p
Knowledge donating→Positive mental health	Job performance	.09	.03	.14	*	.13	.07	.20	*
Knowledge donating→Negative mental health	Job performance	−.15	−.24	−.07	*	−.04	−.11	.00	NS

Notes: *p < .05, NS = not significant

To test the moderation role of emotional intelligence, latent interactions between emotional intelligence and other independent variables were included in the structural model (see, Table 6). The results show that in Australia, emotional intelligence positively moderated the relationship between knowledge donating and positive mental health (ß = .22, p < .01). In Vietnam, emotional intelligence positively moderated the relationship between knowledge donating and job performance (ß = .08, p < .05) and knowledge collecting and job performance (ß = .12, p < .05). The results are presented in Table 6.

Table 6. Moderating effect of emotional intelligence (Australia/Vietnam).

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8
	DV: Job performance	DV: Positive mental health	DV: Negative mental health	DV: Job performance	DV: Positive mental health	DV: Negative mental health	DV: Positive mental health	DV: Negative mental health
Main effects Knowledge donating	.30/-.16	−.89**/-.05	−.42/-.38
Knowledge collecting				.13/-.36	−.26/-.29	.00/-.45
Job performance							−.16/.78**	.49/79**
EI	.30/.20	−.29/.05	−.68/-.42	.30/.11	.25/-.11	−.33/-.58*	.24/.70*	.84/-.58
Moderation effect
Knowledge donating x EI	.01/.08*	.22*/.06	.13/.12
Knowledge collecting x EI				.03/.12*	.07/.12	.04/.16
Job performance x EI							.06/-.13	−.20/.18

Notes: *p < .05; **p < .01, ***p < .001, DV = dependent variable, EI = emotional intelligence, Australia|Vietnam

5. Discussion

Given the rise of mental health issues during the COVID-19 pandemic, the study drew upon the theories of proactive coping and altruism to examine the relationships among knowledge sharing, employee performance, mental health and emotional intelligence. Knowledge sharing was operationalised into knowledge donating and collecting. Mental health included positive and negative mental wellbeing. Discussion of the study findings is as follows.

5.1. Knowledge sharing, performance, and mental health

The current research operationalises knowledge sharing into two dimensions and demonstrated that employee performance was significantly affected by knowledge donating but not by knowledge collecting. Previous studies (e.g., Obeidat et al., 2016) have provided evidence of a positive relationship between overall knowledge sharing and job performance without investigating the different aspects of knowledge sharing. This study suggests that knowledge donators tend to perform better. This finding indicates that those who share knowledge with colleagues may be more capable within their jobs and more confident and knowledgeable. Their confidence and capability enable them to accomplish job tasks more effectively. However, the finding present nuanced differences to those of Nguyen and Prentice (2020) which suggested a significant positive relationship between job performance and the knowledge collector. As this study was undertaken during the COVID-19 pandemic, those more likely to collect knowledge may not be aiming for task efficiency but may be preparing for job changes resulting from the impact of the pandemic on their organisation.

Nevertheless, both knowledge sharing behaviours were significantly related to positive mental health as shown in this study. Knowledge sharing behaviours are often volitional. However, this sharing process involves virtual and/or personal interactions between colleagues or co-workers. During the pandemic, employees may have to work at home or in the office, with minimal physical contact with colleagues to comply with health measures. Human beings are a social species that benefit from interaction and cooperation to survive and thrive. Knowledge sharing presents a means to interact with others, even if it is virtual. Knowledge donors share to help others solve work issues. These givers may find a sense of satisfaction in helping others, raising their self-esteem and value in the organisation. Recognition from colleagues for their contribution and communication may assist employees to feel better about themselves, leading to positive mental health. This is consistent with Becker’s (1976) concept of the altruist who is often a utility-arousing masochist act to experience enjoyment via the stimulus of watching the recipient’s pleasure. Knowledge collectors feel less stress due to the receipt of knowledge shared by others to address work difficulties, which leads to positive mental health. These results are consistent with those in Nguyen (2022), reporting that knowledge donation and collection affected job performance as the process of sharing knowledge facilitates connections and rapport among co-workers.

This study found that job performance was significantly related to both positive and negative mental health. Although most research shows that mental health is a precursor of job performance, this research suggests that performing well at work can lead to positive mental health. This finding is plausible and consistent with that of Prentice and Park (2019) where employee performance can be a determinant of job satisfaction, burnout, and other mental wellbeing outcomes. During the pandemic, with travel restrictions and other preventive measures undertaken to limit outdoor activities, working becomes a preferred option for working professionals who tend to dedicate themselves to work. As shown in a recent study (Sharif et al., 2021), too much free time may lead to poor subjective wellbeing. Work performance is an indicator of mental satisfaction, however, poor performance can result in being fired or retrenched within an organisation that is already negatively affected by the pandemic. A sense of job security is also related to mental health.

This research also shows that job performance partially mediates the relationship between knowledge donating and positive/negative mental health within the Australia case, and was partially mediated with positive mental health and fully with negative mental health in the case of Vietnam. Establishing significant mediation indicates that sharing knowledge may lead to employee wellbeing, with high performers benefiting from the sharing of information experiencing better mental health. Although this study was cross sectional, with the proposed predictors, mediators, and criterion variables being collected at the same time, the target respondents were advised to report their performance and mental health as a result of sharing knowledge to counter the effect of the cross-sectional study.

5.2. The moderating role of EI

EI was found to moderate the influence of knowledge donating and collecting on job performance in the case of Vietnam, but only on positive mental health in the case of Australia. These findings may indicate that employees in Australia are more expressive of their emotions and mental wellbeing, whereas those in Vietnam tend to focus more on performance. During the pandemic, employees may experience different emotions which influence the impact of cognitive learning in the knowledge sharing process on job performance and mental health. When employees have more interaction with others in the knowledge sharing process, emotionally intelligent employees tend to engage more with knowledge exchange to improve job performance and mental health.

EI is generally a predictor of job performance and mental health. However, this study showed that EI had no significant direct relationship with performance but did enhance the effect of knowledge sharing on employee mental health (Australian employees) and job performance (Vietnamese employees). Modelling EI as a significant moderator is consistent with Prentice et al. (2013) and Nguyen & Prentice (2020). This study demonstrates that employees who share knowledge have positive mental health. Those who have a high level of EI may be more likely to interact with co-workers through knowledge sharing, this behaviour leads to better wellbeing. Sharing knowledge within virtual environments can be frustrating without direct physical contact or a lack of body language, which may lead to miscommunication. Emotionally intelligent employees can be more empathetic and accommodating, which would facilitate sharing with colleagues. Similar findings are reported in Jung and Yoon (2016), Zulfadil et al. (2020), and Go & Lim (2014). These studies show that individuals with a high level of EI are better at coping with challenging situations than those with a low level of EI.

6. Implications

The study draws on coping and altruism theories to examine whether knowledge sharing, as a proactive coping behaviour, can be utilised to address employee performance and mental health. This examination has implications for knowledge sharing and public health research and relevant fields. The findings may also be useful for human resource practitioners and psychologists.

The finding that only knowledge donating and not collecting is significantly related to performance ratings, cautions researchers to investigate different aspects of knowledge sharing to understand their impact on individual and organisational outcomes. Especially, in some special situations such as organisational crises, knowledge donating and collecting become critical to connect employees together to improve job performance and reduce mental health (see Nguyen, 2020). The process of donating may concur with collecting, where the donor may also be the receiver of knowledge. Those who are more likely to share knowledge tend to rate their performance better. This finding suggests that researchers should separate donors from receivers to address job performance.

The significant relationships between two knowledge sharing behaviours and mental health provide a fresh perspective on mental health for public health research. Most research has attempted to seek external factors such as work environment and job stressors (Kobayashi et al., 2008; Stansfeld and Candy, 2006). This study was approached from an individual perspective and demonstrated that employees could address their own wellbeing by sharing or initiating contact or interactions with co-workers. Mental health research may benefit from further exploration of self-elicited initiatives to address individual wellbeing. The significant mediation effect of job performance showcases that performance is not necessarily an outcome but may be a precursor of employee mental health. However, the performance-mental health relationship may be reciprocal in that high performers tend to be happier with greater positive mental health which drives them to perform better.

EI has been acknowledged as a predictor of mental health (see Zeidner, Mathews and Roberts, 2012). The significant moderation effect exerted by EI on the relationship between knowledge donating and mental health shows that EI can be utilised to moderate individual’s behaviours for mental wellbeing. EI is recognised to be an ability, some may possess a higher level of this emotional ability, others lower (see Catherine et al, 2020, Moroń & Biolik-Moroń, 2021). The study shows that highly emotionally intelligent employees are not necessarily better performers with positive mental health as it does not exert significant direct effects on the outcomes of interest. However, those who share knowledge with a higher level of emotional intelligence do have enhanced positive mental health. These findings suggest that mental health research should be approached as individual behaviours not abilities. These abilities require organisational sources for training.

This study also has several implications for relevant practitioners. Given that knowledge sharing is positively related to performance and mental health, HR practitioners and management should identify the factors or motives that drive employees to share their knowledge. This initiative would be cost effective for the organisation as knowledge sharing behaviours are volitional and require minimal organisational resources. Sharing is not only beneficial to the organisations, but also conducive to individual wellbeing. This message could be conveyed to employees, especially those who are more likely to care for their own welfare.

The significant moderating role of EI suggests EI assessment should be included in the recruitment of new employees and training be incorporated into employee performance management. Emotionally intelligent employees tend to share knowledge rather than merely receiving. This sharing not only enhances subjective wellbeing but also organisational performance. Encouraging employees to share their knowledge could enhance organisational culture and employee morale. As such, incentives should be provided for those who share to instil employee proactivity towards co-workers and the organisation.

For mental health and HR practitioners, this study provides a new avenue to address employee wellbeing – volitional or self-elicited behaviours. Rather than introducing intervention programs that require organisational resources, practitioners could investigate the factors that prompt or motivate knowledge sharing behaviours. Research has shown that personality traits such as conscientiousness, agreeableness, and openness are related to knowledge sharing (see Matzler et al., 2008). Personality assessment incorporated into the candidate selection process may prove beneficial. Recruiting candidates who are conscientious, agreeable, and open to experience are more likely to share knowledge and connect with colleagues. This may lead to better organisational outcomes (job satisfaction and performance) and improved employee mental health.

7. Limitations and future research

The study has limitations to acknowledge and be addressed in future research. First, claiming a predictive relationship between knowledge sharing, performance, and mental health in the cross-sectional study can be problematic. A longitudinal study would be more appropriate to assess whether employees who engage in knowledge sharing perform better or have more positive mental health. The current research attempted to address this limitation by collecting data in two different countries with different cultures to validate the findings. The results were promising for the proposed predictive relationship. However, future research that endeavours to generate panel data to verify these relationships would be productive. Self-reporting ability EI was used in this study. Although previous meta-analysis shows that EI can be self-reported, based on the ability model, an ability test with objective indicators is preferred. Self-rated performance can be inflated. Objective assessment and/or other ratings (e.g., supervisor rating) are more reflective of accurate performance evaluation. This study was intended to examine how knowledge sharing behaviour was related to job performance, given that only employees themselves know whether they engaged in knowledge sharing, the self-rated performance was opted for. If the research purpose intended to model job performance as the study outcome, self-rating should only be a supplementary assessment. As the study was undertaken in two countries, cultural effects should also be taken into account in relation to the proposed relationships. Future research should address the limitations revealed in this study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Zayed University [R21108];Griffith Institute for Tourism;