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LINKING ENGAGEMENT FOR INNOVATION WITH INNOVATIVE PERFORMANCE: THE ROLE OF DISCRETIONARY EFFORTS AND KNOWLEDGE-SHARING BEHAVIOUR

ASIF NAWAZ

https://orcid.org/0000-0001-8775-0434

Department of Business Administration, Sukkur IBA University, Nisar Ahmed Siddiqui Road, Sukkur, Sindh, Pakistan

E-mail Address: asif.phdmgt18@iba-suk.edu.pk

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SHUAIB AHMED SOOMRO

https://orcid.org/0000-0003-0849-8942

Department of Business Administration, Sukkur IBA University, Nisar Ahmed Siddiqui Road, Sukkur, Sindh, Pakistan

E-mail Address: shuaib.ahmed@iba-suk.edu.pk

Corresponding author.

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, and

YASIR MANSOOR KUNDI

https://orcid.org/0000-0001-8962-2751

Department of Management, Institute of Business Administration Karachi, Karachi, Sindh, Pakistan

E-mail Address: ymkundi@iba.edu.pk

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https://doi.org/10.1142/S1363919623500275Cited by:4 (Source: Crossref)

Abstract

Drawing on the Conservation of Resources and Social Exchange Theories, this study examines the relationship between employee engagement for innovation (EEI) and innovative performance (IP) through discretionary efforts (DE) and knowledge sharing (KS). We collected data from 202 peer dyads of 15 universities in Pakistan and used SmartPLS 4 for hypotheses testing. Results indicated a significant positive relationship between EEI, DE, KS and IP. In contrast, mediation mechanism via DE and KS did not show the expected mediation effects. This outcome can be attributed to the unique socio-cultural context in which we collected the data. This work focussed on employees working in a collectivist culture within a patriarchal society, where traditional hierarchical structures and values influence the dynamics between EEI, DE, KS and IP. We also discuss the theoretical and practical implications and the directions for future research.

Keywords:

Introduction

Employee engagement is a crucial factor that strengthens employees’ mental and emotional connection with their work, team and organisation. Schaufeli et al. (2002) defined job engagement as “[…] a positive, rewarding state of mind relating to work that is marked by vitality, devotion, and immersion”. Employees who are fully engaged in their professional activities exhibit high energy, are fully excited about their jobs and what they do in life. They are passionate about their work and look for creative ways to improve performance; also, they are more likely to be engaged with the organisation (Reina-Tamayo et al., 2017). Many studies have shown that individuals’ engagement levels vary based on their working environment, personal characteristics and many other factors. For instance, employees who work in supportive and collaborative environments are likely to be engaged than those who work in a toxic or stressful environment (Sonnentag, 2003; Macey and Schneider, 2008). Similarly, employees with strong personal traits such as a high level of self-efficacy and an innovative mindset are more likely to be engaged than those without them (Bakker et al., 2014; Gupta et al., 2017). Although similar concepts, such as job satisfaction or involvement, are used to predict employee engagement that leads to creativity and explains innovative performance (IP), they are studied infrequently (Van and Nafukho, 2019).

Innovative behaviour means “new ideas and mechanisms developed by employees grounded on current situations”, including generating and executing ideas (Wang et al., 2021). It is a multilayer process that involves developing, evaluating, communicating and executing novel ideas. Employees must think innovatively and find ideas to improve the organisation’s products, services or processes. Once employees develop ideas, they then assess them to determine whether they are feasible and have the potential to be successful. After that, they share them with their colleagues within the company to gain support and to get feedback on how to improve them further from their circles. Once employees have communicated their ideas and have support from colleagues, they need to execute them. This involves taking the necessary steps to implement the ideas and to make them a reality.

There are a lot of hindrances that halt employees’ innovative performance (Anderson et al., 2014). The literature shows that innovative behaviour and performance are ways to achieve organisational goals effectively (Gupta et al., 2017). Innovative behaviour is vital for organisations because it can help them to stay ahead of the competition and improve their performance. This study investigates factors that impede employees’ innovative performance and how organisations can foster a culture that encourages and enhances innovative behaviour and performance to achieve their goals effectively. We say organisations can encourage innovative behaviour by creating a culture that supports creativity and risk-taking. They also provide employees with the necessary resources to generate and execute new ideas. Aligning with the previous literature, this study analyses innovative performance as the outcome variable, considering the research call of Bailey et al. (2017) and Nel and Linde (2019) to employ an objective measure to capture individuals’ innovative performance in academia.

Literature indicates that organisations go to tremendous lengths to foster innovation to remain successful (Dhir and Shukla, 2019; Gupta and Sharma, 2016). Also, studies indicate that allowing employees to succeed increases their sense of engagement (Compagnucci et al., 2021). Engagement outcomes are segregated into three groups, i.e., intra-role behaviours [discretionary efforts (DE), organisational citizenship behaviour, turnover intention, etc.], extra-role behaviour [in the form of knowledge sharing (KS), adaptivity, productivity and creativity] and personal growth and development (Van and Nafukho, 2019). While existing literature explores the relationship between employee engagement for innovation (EEI) and innovative performance, we argue that gaps exist in understanding the role of discretionary efforts and knowledge sharing as a mechanism resulting from engagement for innovation, especially in the context of employee innovative performance. Discretionary efforts are the extra work that employees voluntarily put above and beyond what their jobs actually require. It is crucial to look at how employee engagement for innovation encourages them to go above and beyond their assigned responsibilities, utilising their efforts to promote innovation (De Chiara, 2017). Furthermore, knowledge sharing is essential for promoting innovation within the firms. However, the said relationship to employee engagement for innovation is lacking and not thoroughly examined. Understanding how engagement influences knowledge-sharing behaviours and how this, in turn, affects innovative performance is crucial in comprehending the complete mechanism from engagement to tangible innovative outcomes. So, this study fills the gap by incorporating discretionary efforts (intra-role behaviour) and knowledge sharing (extra role behaviour) as an explanatory mechanism to foster employees’ innovative performance.

This study contributes to the nascent literature in various ways. First, it extends the application of the Conservation of Resources (COR) Theory (Hobfoll, 1989, 2011a) and Social Exchange Theory (SET) (Demerouti et al., 2001) by applying them in universities to explain how leaders in academia can foster innovative performance at an individual level. Understanding the nature, role and function of innovative performance within academia may explain how employee engagement can drive their performance. These theoretical frameworks also explain resource investment and return through discretionary efforts and innovative performance. Second, it unveils a rarely investigated construct of discretionary efforts and knowledge sharing as an underlying mechanism between employee engagement for innovation and innovative performance simultaneously, which is not found in the literature as a mediator between the said nexuses. Understanding how innovative performance is triggered by discretionary efforts and how it may relate to university employees’ performance is crucial. Further, from a university standpoint, this study stresses that leaders have both responsibility and potential to gear up employee engagement for innovation and involved employee behaviours. Therefore, this study investigates the mediating function of discretionary efforts and knowledge-sharing behaviour between engagement and innovative performance. In doing so, we look into knowledge-sharing behaviour and discretionary efforts as drivers for innovative performance and employees as a function of work engagement. Understanding this relationship could provide valuable information for university employees to support their retention and development strategies.

Theoretical Model and Hypotheses

The existing literature provides valuable insights into the relationship between employee engagement for innovation and discretionary efforts. The literature indicates the impact of employee engagement on innovative performance, but only fewer works have specifically examined the role of discretionary efforts resulting from engagement, particularly when the employees are engaged in innovation. Engagement fulfils the desire of human beings beyond the shackles of their job descriptions and does something extraordinary for themselves (Yuan et al., 2019; Eldor and Harpaz, 2016, p. 215). Studies have shown a positive relationship between employee engagement and innovative performance. Engaged employees, who exhibit higher levels of motivation and involvement, are more likely to contribute in generating ideas, experimenting with new approaches and implementing innovative solutions (Anjum et al., 2019). These intrinsic motivation and commitment foster a conducive environment for innovation within organisations.

Alternatively, the COR Theory (Hobfoll, 2001) posits that an engaged employee finds more resources to invest in in-role and extra-role efforts and reinvest in innovative behaviour. Moreover, engaged employees work with vigour, dedication and absorption and enjoy their work intrinsically and extrinsically (Karatepe et al., 2018). Engagement provides an opportunity to fulfil extrinsic goals and achieve the intrinsic goal of self-growth (Eldor and Harpaz, 2016). It also fosters positive emotions of interest, enthusiasm and challenge, which widen the thinking ability to seek “out-of-the-box” solutions (Eldor and Harpaz, 2016).

Discretionary efforts are non-transactional and enhanced with non-monetary factors such as job characteristics and commitment (Sharafizad and Redmond, 2019). Vigour, a dimension of engagement, is supposed to predict motivation, goal-oriented behaviour and organisational performance (Shirom, 2011); it leads to vitality and increased performance (Cangiano et al., 2019). A vigorous employee is less prone to exhaustion, enjoys challenges and has excellent recovery capabilities (Mäkikangas et al., 2014). Moreover, challenges predict excitement, which increases mastery in work, while threats shrink detachment and relaxation (Michel et al., 2016).

A critical aspect that has received limited attention in the literature is the role of discretionary efforts resulting from employee engagement for innovation in various activities in work settings (Shanmugam and Krishnaveni, 2016). Discretionary efforts refer to the additional attempts that employees voluntarily invest in their work beyond the minimum required. It encompasses going above and beyond the assigned tasks, taking initiatives and actively seeking opportunities to contribute to organisational success.

We argue that when employees are engaged in innovation activities, they are more likely to demonstrate discretionary efforts by actively engaging in behaviours that contribute to advancing innovative initiatives (Nel and Linde, 2019). These discretionary efforts may include conducting additional research, collaborating with colleagues, attending innovation-related training or taking on additional responsibilities to support innovation activities (Van and Nafukho, 2019). It implies that engagement in innovation catalyses employees to go beyond their regular job requirements and invest extra effort towards driving innovation. By voluntarily exerting additional discretionary efforts, employees demonstrate their commitment to innovation and contribute to the overall success of innovative endeavours within the organisation (Bailey et al., 2017). Hence, we hypothesise the following.

Hypothesis 1.Employee engagement for innovation has a positive relationship with discretionary efforts.

Literature has found a positive relationship between employee engagement for innovation and innovative performance (Van and Nafukho, 2019). Engaged employees are emotionally committed, motivated and dedicated to their work and actively contribute to achieving organisational goals. When these engaged employees direct their efforts towards innovation-related activities, it can substantially impact their innovative performance (Liu et al., 2022; Wang et al., 2022). Engaged employees exhibit higher levels of intrinsic motivation, which leads to a proactive approach to seeking innovative solutions and generating creative ideas. They are more likely to invest their time and energy in thinking critically, challenging existing norms and exploring new possibilities. This engagement-driven creativity enhances innovative performance and contributes to the organisation’s ability to develop novel products, processes and strategies.

Moreover, employee engagement for innovation could foster a culture of collaboration. Engaged employees are more likely to actively participate in idea exchange, provide feedback and collaborate across departments (Bailey et al., 2017). This collaborative environment facilitates the pooling of diverse perspectives, expertise and experiences, thus enhancing the quality and effectiveness of innovative performance (Jason and Geetha, 2019). Additionally, employee engagement for innovation positively impacts employees’ willingness to take risks and embrace uncertainty. Engaged employees feel a sense of psychological safety and trust within the organisation, thereby encouraging them to experiment with new ideas, challenge existing practices and embrace calculated risks associated with innovation (Kim and Park, 2017). Their engagement-driven confidence and autonomy contribute to their ability to implement innovative ideas and solutions effectively. So, we argue that employees actively engaged in innovation-related activities will demonstrate higher levels of innovative performance. In line with the above, we say that employees are likely to be creative, collaborate effectively and implement innovative solutions when they are emotionally connected, motivated and committed to innovation. The positive relationship between employee engagement for innovation and innovative performance implies that fostering employee engagement in innovation is crucial for organisations seeking to enhance their innovative capabilities (Gosselt et al., 2019; Hameed et al., 2016). Hence, we hypothesise the following.

Hypothesis 2.Employee engagement for innovation has a positive relationship with innovative performance.

Discretionary efforts are the voluntary, proactive efforts employees invest in their work beyond the required minimum to achieve organisational goals (Lloyd, 2008). It involves going above and beyond the assigned tasks, taking initiatives and actively seeking opportunities to contribute to organisational success (Frenkel and Bednall, 2016). According to the COR Theory, once the employees have more resources in the form of engagement for innovation, they invest these resources by putting extra efforts to move into the gain spiral. The discretionary efforts push them to invest more to materialise the efforts in the form of objective innovative performance (Hobfoll, 2011b). Literature indicates DE could mediate the relationship between EEI and IP. We say that engaged employees who exert positive discretionary attempts are more likely to engage in behaviours that contribute to innovative performance. Their additional efforts and proactive behaviours enhance their capacity to be creative, implement innovative solutions and contribute to the organisation’s innovation goals (Bailey et al., 2017; Lloyd, 2008).

Moreover, discretionary efforts enhance the effectiveness of engagement for innovation by facilitating the translation of engagement into tangible outcomes. Engaged employees put effort to demonstrate a more substantial commitment to innovation-related activities engagement (Hesketh et al., 2016; Shanmugam and Krishnaveni, 2016), which leads to increased innovative performance. Their willingness to invest additional efforts signals higher levels of dedication and intrinsic motivation, resulting in a more significant impact on innovative performance (Eldor and Harpaz, 2016). Accordingly, engagement for innovation could positively influence innovative performance by mediating the discretionary efforts. It posits that engaged employees who exert discretionary efforts are more likely to exhibit higher levels of innovative performance (Wang et al., 2019). Discretionary efforts act as a mechanism through which engagement for innovation translates into tangible outcomes, reinforcing the link between employee engagement for innovation and innovative performance (Raineri and Paillé, 2016). Hence, we hypothesise the following.

Hypothesis 3.Discretionary efforts mediate the positive relationship between engagement for innovation and innovative performance.

Knowledge sharing refers to the process of new knowledge generation through sharing information, expertise and feedback (Van Wijk et al., 2008). In today’s turbulent environment, achieving a competitive edge through innovation is the ultimate solution to reaching the target and knowledge sharing is a means to it (Kim and Park, 2017; Connelly et al., 2012). It is because sharing of knowledge results in the recurrence of tested practice (knowledge), avoidance of faults that others have committed (experience) or approval of action with feedback (Afsar et al., 2019). This reduces the costs of innovation (Phung et al., 2018).

The literature surrounding engagement for innovation and knowledge sharing provides valuable insights into the relationship between these two constructs. Existing literature supports an association between engagement for innovation and knowledge sharing. Employees who are engaged in innovation are more likely to share knowledge (Paillé and Meija-Morelos, 2019; Henseler et al., 2016). This relationship can be seen as engagement for innovation creates a culture that encourages collaboration and knowledge exchange among the employees. Similarly, engaged employees are more willing to share their knowledge, expertise and innovative ideas with colleagues, contributing to a collective learning and development environment (Wikhamn, 2019; Van and Nafukho, 2019). In addition, engagement for innovation promotes a sense of intrinsic motivation leading to a greater desire to share knowledge. And engaged employees are more likely to feel a sense of fulfilment and satisfaction from contributing to the growth and success of their peers through knowledge-sharing activities (Kim and Park, 2017). Furthermore, engagement for innovation fosters a supportive and open communication environment. Engaged faculty members perceive their colleagues as allies rather than competitors, encouraging them to freely share their knowledge, experiences and innovative practices without fear of judgement or negative repercussions (Nel and Linde, 2019; Kura et al., 2019).

Based on the existing literature, innovative employees will exhibit higher knowledge-sharing behaviours. Engagement for innovation acts as a catalyst, creating an environment that promotes knowledge sharing among employees. So, they are more likely to engage in active knowledge-sharing practices, including sharing experiences, best practices, resources and innovative ideas, which contribute to the collective growth and advancement of the organisations (De Chiara, 2017). Hence, we hypothesise the following.

Hypothesis 4.Employee engagement for innovation has a positive relationship with their knowledge sharing.

The literature on the relationship between engagement, knowledge sharing and innovative performance provides valuable insights into the interplay of these constructs in organisational studies. Existing literature supports the relationship between engagement and innovative performance. Engaged employees are more likely to demonstrate higher levels of innovative performance, including adopting innovative methods and generating novel research ideas. In addition, knowledge sharing has been identified as a vital factor that mediates the relationship between engagement and innovative performance. When employees engage in knowledge-sharing behaviours, they facilitate the exchange of innovative ideas, insights and experiences with their colleagues. This knowledge sharing provides an avenue for expanding innovative practices, stimulating creativity and enhancing the overall innovative performance within the organisations.

Employee knowledge sharing acts as a mediator by creating a pathway through which engagement influences innovative performance (Li et al., 2019). Engaged employees are more inclined to actively participate in knowledge-sharing activities, such as sharing research findings, collaborating on projects, providing feedback, and mentoring, and engaging in interdisciplinary discussions. Through these knowledge-sharing behaviours, engaged employees create a collaborative and dynamic environment that fosters innovation and enhances the overall performance (Gupta et al., 2017). We argue that knowledge sharing mediates the relationship between engagement for innovation and innovative performance. It posits that engaged employees who actively share knowledge will demonstrate higher levels of innovative performance. Employee engagement for innovation positively influences their propensity to engage in knowledge sharing, which, in turn, enhances their innovative performance within the organisational workplaces (Tan et al., 2019). Hence, we hypothesise the following.

Hypothesis 5.Knowledge sharing mediates the positive relationship between engagement and innovative performance.

Methodology

Research and innovation are integral to modern education, especially university education. The Higher Education Commission (HEC) of Pakistan annually releases the national university ranking. Among the other criterion variables, “research and innovation” holds the highest weightage in the process (HEC, 2020). Consistent with the requirements of the study, the top five universities from the respective general, science and business universities ( $n = 15$ ) were included as a sampling frame for data collection from HEC 2020 ranking. Respondents were randomly selected from the contact list of each institute and approached for data collection via their email and contacts.

A sample of $N = 600$ was randomly selected by picking 40 contacts of each university in the list. In the first phase, an invitation with a questionnaire was distributed among the research faculty and staff. The first part of the questionnaire asked about demographic information, i.e., gender, age, education, etc. In the second part of the questionnaire, in the first phase, respondents were asked to rate their engagement for innovation and discretionary efforts and knowledge-sharing behaviours of their peers. Respondents were also requested to provide the complete names, email ids and WhatsApp contacts of the peer they rated. The contact information was later on used for making peer dyads. A total of 219 responses with complete contact details were received after the first phase. All responses were complete, as we tagged every question as mandatory in the online form. In the second phase, after one month, the same questionnaires were sent to peers to rate themselves and the peers, i.e., respondents rated in the first phase. In response to the second-phase survey, 202 complete responses were received, resulting in 202 faculty peer dyads for further analysis. The final response rate was 34%. The response to the data regarding the innovative performance was objectively obtained from the Offices of Research, Innovation and Commercialization (ORIC) of universities and the Google Scholar profiles of the researchers. A full picture of profiles is provided in Table 1.

**Table 1. Respondent demographics.**
Characteristics	Frequency ( $f)$	Percentage
Gender
Male	130	64.4%
Female	72	35.6%
Education
Graduation (18 years of education)	82	40.6%
M.S./M.Phil.	62	30.7%
Ph.D.	54	26.7%
Post-doc	4	2%
Organisation size
$\leq 500$ Students	10	5%
500–1,500 Students	30	14.9%
1,600–3,600 Students	23	11.4%
$> 3, 600$ Students	139	68.8%
Job title
Research Assistant	6	3%
Lecturer	98	48.5%
Assistant Professor	68	33.7%
Associate Professor	3	1.5%
Professor	7	3.5%
Other Administrative Positions	20	9.9%
Organisation type
Public	165	81.7%
Private	32	15.8%
Public–Private Partnership	5	2.5%

Measures

All the scales were adopted from established scales. However, some minor changes were made to suit the context of the study. Unless otherwise mentioned, all of the items were measured on five-point Likert scale ranging from 1 (Strongly disagree) to 5 (Strongly agree).

Engagement for innovation was measured with five items from Saks Alan (2006). The scale has good reliability ( $CR = 0.81$ ). Sample item includes: “I really ‘throw’ myself into my job.” Discretionary efforts were measured with a six-item peer-rated scale developed by Hesketh et al. (2016). Sample item includes: “He/She feels it worthwhile to work hard for this organization.” Knowledge sharing is measured with a peer-rated five-item scale developed by Connelly et al. (2012). To better understand this behaviour, KS was measured from a peer’s perspective, i.e., how they feel about an individual’s knowledge-sharing behaviour. The scale is reliable, with an $α$ -value of 0.83 (Connelly et al., 2012). A sample item is: “This coworker explains everything very thoroughly.”Innovative performance is objectively measured from several variables, including publications, total impact factor, conference papers, research grants and the number of patents. Measuring performance objectively is preferred over subjective measures of innovative behaviour (Gupta et al., 2017).

Results

We used SPSS 25 for frequency distribution and correlational analysis (Table 2), and SmartPLS 4 for model assessment (Hair et al., 2016). A two-step approach is utilized to assess the measurement model, followed by structural model. Although the data was collected from different sources, there were chances of common method variance (CMV) (Podsakoff et al., 2003). Hence, before analysis, the CMV test was performed in SPSS. We converge all the items on a single factor yielding a total of 30.7% variance thus eliminating the chances of CMV (MacKenzie and Podsakoff, 2012).

**Table 2. Means, standard deviations (SDs) and correlations.**
	M	SD	1	2	3	4	5	6	7	8	9
1. Gender	0.36	0.48
2. Edu	1.90	0.86	0.05	—
3. Organisation size	3.44	0.92	0.01	0.21**	—
4. Job_T	2.84	1.28	$- 0.04$	0.06	$- 0.002$	—
5. Org_type	1.21	0.46	$- 0.02$	0.06	0.02	0.22**	—
6. Age_in_Years	40.66	8.32	$- 0.06$	$- 0.05$	0.05	0.12	$- 0.28$ **	—
7. EEI	10.83	3.48	$- 0.03$	0.08	0.07	$- 0.14$ *	$- 0.24$ **	0.01	—
8. DE	13.96	5.09	0.06	0.26**	$- 0.04$	0.12	$- 0.16$ *	$- 0.03$	0.37**	—
9. KS	12.05	4.75	0.02	0.21**	$- 0.03$	0.004	$- 0.15$ *	$- 0.05$	0.23**	0.62**	—
$N$ =202

Note: Edu: Education, DE: discretionary efforts, EEI: employee engagement for innovation, KS: knowledge sharing and IP: innovative performance. $* * p < 0.01$ and $* p < 0.05$ .

Reliability and validity assessment

We evaluated the measurement model prior to testing the hypotheses to ensure the validity and reliability of the measurement variables. All measurement variables were found to be valid and reliable (Table 3). The indicator’s reliability is assessed with outer loading. Indicators having 0.6 or higher loadings were retained for further analysis. However, some items (i.e., EEI_4, DE_1 and patents from EEI, DE and IP, respectively) were removed due to their lower loadings. The rest of the loadings were greater than 0.6. Average Variance Extracted (AVE) values were greater than 0.50, and Cronbach’s alpha, composite reliability and rho_A values were greater than 0.70, suggesting sufficient convergent validity and reliability (Hair et al., 2016).

**Table 3. Reliability and validity.**
Latent constructs and indicators	Loading	AVE	CR	Rho_A	$α$
Discretionary efforts		0.63	0.89	0.89	0.85
DE_2	0.787***
DE_3	0.834***
DE_4	0.815***
DE_5	0.694***
DE_6	0.835***
Employee engagement for innovation		0.61	0.86	0.93	0.80
EEI_1	0.775***
EEI_2	0.754***
EEI_3	0.705***
EEI_5	0.884***
Knowledge sharing		0.74	0.93	0.82	0.91
KS_1	0.818***
KS_2	0.879***
KS_3	0.803***
KS_4	0.870***
KS_5	0.923***
Innovative performance		0.61	0.86	0.89	0.79
Pub	0.787***
R_grant	0.832***
IF	0.826***
Conf_pap	0.664***

Note: AVE: Average Variance Extracted, CR: composite reliability, $α$ : Cronbach’s alpha, Pub: publication, R_grant: research grant, IF: impact factor and Conf_pap: conference paper. $* * * p < 0.01$ and $* * p < 0.05$ .

Furthermore, discriminant validity was ensured by using heterotrait–monotrait (HTMT) ratio (Henseler et al., 2015). All the values are with $HTMT < 0.85$ , ensuring discriminant validity (see Table 4).

**Table 4. Discriminant validity: HTMT.**
Latent variable	1	2	3	4
DE	—
IP	0.19	—
EEI	0.56	0.33	—
KS	0.79	0.17	0.32	—

Note: DE: Discretionary efforts, EEI: employee engagement for innovation, KS: knowledge sharing and IP: innovative performance.

Structural model

Once the measurement model qualifies, the assessment criteria signal the structural model’s assessment (Hair et al., 2019). We use path coefficients with 5,000 bootstrapping samples (Hair et al., 2016) to assess direct and indirect paths. Moreover, $f^{2}$ (effect size), $Q^{2}$ predictive relevance and $R^{2}$ (coefficient of determination) are used for further assessment (Hair et al., 2019) smartPLS.

Before assessing the structural links, collinearity needs to be examined to ensure it does not bias the regression results. The issue of multicollinearity was assessed from the value of the variance inflation factor (VIF) (Hair et al., 2016). A VIF score of higher than 3.3 at the factor level indicates the presence of both the collinearity and the common method bias problems. Table 5 displays the VIF values of the structural model, indicating that the model has no VIF values above that threshold, thereby confirming that it is free of both collinearity and bias problems (Umraniet al., 2019; Hair et al., 2019).

**Table 5. Path assessment.**
	Relationships	$β$	SE	$t$	95% CI		VIF	Support
					LL	UL
H₁	$EEI \to DE$	0.51	0.04	11.98***	0.42	0.56	1	Yes
H₂	$EEI \to IP$	0.33	0.06	5.85***	0.22	0.42	1.35	Yes
	$KS \to IP$	0.07	0.11	0.67	$- 0.13$	0.22	1.96	No
H₄	$EEI \to KS$	0.31	0.06	4.99***	0.20	0.41	1	Yes
	$DE \to IP$	$- 0.14$	0.09	1.49	$- 0.28$	0.02	2.38	—
	$QE(KS) \to IP$	$- 0.11$	$- 0.12$	2.52**	$- 0.21$	$- 0.03$	—	—
	$QE(DE) \to IP$	0.06	0.06	1.32	$- 0.03$	0.14	—	—
	$QE(EEI) \to DE$	$- 0.06$	$- 0.06$	1.001	$- 0.17$	0.05
	$QE(EEI) \to KS$	$- 0.02$	$- 0.02$	0.32	$- 0.14$	0.08
	$QE(EEI) \to IP$	0.05	0.06	1.23	$- 0.04$	0.13

Note: DE: Discretionary efforts, EEI: employee engagement for innovation, KS: knowledge sharing, IP: innovative performance, LL: lower level, UL: upper level, SE: standard error and QE: quadratic effect. $* * * p < 0.01$ and $* * p < 0.05$ .

Path coefficients

Table 5 summarises the results of path assessment. The path coefficients supported Hypothesis 1, the direct relationship between employee engagement and discretionary efforts ( $β = 0.51$ , $t = 11.9$ , $p < 0.001$ ). Similarly, Hypothesis 2, the direct relationship between engagement and innovative performance ( $β = 0.33$ , $t = 5.85$ , $p < 0.001$ ), is supported. Hypothesis 4, the direct relationship between engagement and knowledge sharing ( $β = 0.31$ , $t = 4.99$ , $p < 0.001$ ), also got support. Further, the results of quadratic effect assessment support the linear relationships except the KS $\to$ IP relationship. The quadratic effect ( $β = - 0.11$ , $t = 2.52$ , $p < 0.005$ ) is significant indicating an inverse U-shaped relationship between KS and IP.

Predictive power assessment

Coefficient of determination (i.e., $R^{2}$ ) is used to assess the variation explained in the endogenous variable and is regarded as explanatory or predictive power (Rigdon, 2012; Shmueli and Koppius, 2011). However, its values vary in the field of study (Hair et al., 2016). For social sciences, $R^{2}$ values up to 0.10 are also considered substantial (Hair et al., 2019). In this study, we got $R^{2}$ values of 0.25 for DE, 0.10 for KS and 0.09 for IP that are on the lower bound, however, acceptable in social sciences (Hair et al., 2019). The reason is that only the engagement explains all the variances in IP as (though not hypothesised) DE and KS almost explain nothing in innovative performance. Table 6 summarises the predictive power assessment of the model.

**Table 6. Predictive capability of the model.**
Constructs	$R^{2}$	$Q^{2}$	$Q^{2}$ -predict	RMSE	MAE	$f^{2}$	Effect size
DE	0.26	0.15	0.24	0.883	0.676	0.35	Large
KS	0.10	0.07	0.083	0.972	0.692	0.11	Moderate
IP	0.09	0.05	0.069	0.995	0.664	0.09	Moderate

Note: DE: Discretionary efforts, EEI: employee engagement for innovation, KS: knowledge sharing, IP: innovative performance, MAE: mean absolute error and root-mean-squared error (RMSE).

Assessment effect size indicates that engagement has a substantial effect on DE (0.35) and a moderate effect on IP (0.09) and KS (0.11), respectively (Hair et al., 2019; Chin, 1998). The effects of DE and KS on IP (0.008 and 0.003) are negligible (Hair et al., 2019; Chin, 1998).

We measure the predictive quality of the model by assessment of the Stone–Geisser $Q^{2}$ (Stone, 1974; Geisser, 1974) by using the blindfolding technique (Sarstedt et al., 2014). The process eliminates single data points from the data matrix and imputes them from the remaining data points (Hair et al., 2019). $Q^{2}$ should be above zero to claim predictive relevance of the model (Hair et al., 2019). Following the criteria, the result indicates that all the three endogenous variables (i.e., DE, KS and IP) have the $Q^{2}$ values of 0.15, 0.07 and 0.05, respectively, which are greater than zero indicating predictive relevance.

The $R^{2}$ evaluates the in-sample model fit of the dependent constructs’ composite scores, by using the model estimates to predict the case values of the total sample. But it gives no indication of its out-of-sample predictive power (ability to forecast the values of new cases not included in the estimation process). $Q^{2}$ calculation excludes single data points, but not in the entire case; it imputes the omitted data points therefore resulting in grouping of in-sample and out-of-sample predictions (Shmueli et al., 2019). Contrary to the standard conventional model evaluation metrics such as $R^{2}$ and $Q^{2}$ , PLSpredict offers a means to assess a model’s out-of-sample predictive power (Shmueli et al., 2019). A positive value of $Q_{predict}^{2}$ shows that the error of PLS path model is less than the prediction error of the (most) naive benchmark and indicates high predictive power (Shmueli et al., 2016). The analysis of graphs shows non-symmetric shape and $Q_{predict}^{2}$ is less than the mean absolute error (MAE) pointing to the high predictive power of the model (Shmueli et al., 2016, 2019).

Lastly, PLS-SEM does not have the established goodness-of-fit criteria; however, standardised root-mean-square residual (SRMR) is considered as a good criterion (Henseler et al., 2016). The SRMR value for the saturated model is 0.073 that is within the prescribed limit, suggesting a good model fit (Nejati and Shafaei, 2023).

Mediation analysis

We adopted Preacher and Hayes’s (2008) mediation assessment technique. Contrary to the hypothesis (H3) mediating role of DE between engagement and innovative performance got no support. The bias-corrected, confidence interval has zero ( $LL = - 0.14$ , $UL = 0.01$ ) included in between the lower and upper bounds (Hayes, 2017; Nitzl et al., 2016). Similarly, the results did not show the expected mediating effect of KS between employee engagement and innovative performance as the confidence interval includes zero ( $LL = - 0.48$ , $UL = 0.06$ ) in between the lower and upper limits (Nitzl et al., 2016). Table 7 provides these results in more detail.

**Table 7. Path coefficients for the mediating effect of discretionary efforts and knowledge sharing.**
	Relationship	$β$	SE	$t$	95% CI		Support
					LL	UL
H₃	$EEI \to DE \to IP$	$- 0.07$	0.05	1.45	$- 0.14$	0.01	No
H₅	$EEI \to KS \to IP$	0.02	0.03	0.64	$- 0.05$	0.07	No

Note: DE: Discretionary efforts, EEI: employee engagement for innovation, KS: knowledge sharing, IP: innovative performance, LL: lower level, UL: upper level and SE: standard error.

Assessment of endogeneity

Endogeneity undermines a study’s robustness and impairs its ability to provide a meaningful causal explanation for a phenomenon (Yıldız, 2022). When an independent variable is connected with the error term, endogeneity bias occurs, which can be induced by omitted variables. Given the non-experimental character of this work, we investigated endogeneity using SmartPLS 4 and Park and Gupta’s (2012) Gaussian copula method. The endogeneity evaluation results, shown in Table 8, show that none of the conceivable Gaussian copulas across all feasible models were significant ( $p > 0.05$ ). The results show that some of the Gaussian copulas are significant. The reason is that these variables are not exogenous variables that are dependent on EEI. Engagement for innovation is the only exogenous variable in all tested models hence the Gaussian copulas for EEI are insignificant in every model (e.g., Models 1, 4, 6 and 7) concluding the non-existence of endogeneity in the data and models (Nejati and Shafaei, 2023).

**Table 8. Endogeneity assessment using the Gaussian copulas approach.**
Model	Relationships	Coefficient	$p$ -Value
Model 1: $EEI \to IP$	$DE \to IP$	$- 0.139$	0.182
	$EEI \to IP$	0.33	0.052
	$KS \to IP$	0.066	0.58
	$GC(EEI) \to IP$	0.002	0.989
Model 1: $DE \to IP$	$DE \to IP$	0.205	0.265
	$EEI \to IP$	0.32	0
	$KS \to IP$	0.067	0.57
	$GC(DE) \to IP$	$- 0.332$	0.015
Model 3: $KS \to IP$	$DE \to IP$	$- 0.157$	0.107
	$EEI \to IP$	0.424	0
	$KS \to IP$	0.768	0.001
	$GC(KS) \to IP$	$- 0.744$	0.004
Model 4: EE, $KS \to IP$	$DE \to IP$	$- 0.146$	0.141
	$EEI \to IP$	0.314	0.059
	$KS \to IP$	0.797	0.001
	$GC(KS) \to IP$	$- 0.778$	0.005
	$GC(EEI) \to IP$	0.091	0.548
Model 5: DE, $KS \to IP$	$DE \to IP$	$- 0.161$	0.463
	$EEI \to IP$	0.425	0
	$KS \to IP$	0.77	0.005
	$GC(KS) \to IP$	$- 0.747$	0.013
	$GC(DE) \to IP$	0.005	0.979
Model 6: DE, $EEI \to IP$	$DE \to IP$	0.205	0.27
	$EEI \to IP$	0.322	0.059
	$KS \to IP$	0.067	0.574
	$GC(DE) \to IP$	$- 0.332$	0.015
	$GC(EEI) \to IP$	$- 0.002$	0.99
Model 7: DE, EEI, $KS \to IP$	$DE \to IP$	$- 0.177$	0.428
	$EEI \to IP$	0.315	0.06
	$KS \to IP$	0.815	0.005
	$GC(DE) \to IP$	0.03	0.868
	$GC(EEI) \to IP$	0.093	0.548
	$GC(KS) \to IP$	$- 0.798$	0.015

Note: DE: Discretionary efforts, EEI: employee engagement for innovation, KS: knowledge sharing and IP: innovative performance.

Discussion

Direct relationships

The study hypothesised a direct relationship between employee engagement for innovation and discretionary efforts (H₁), innovative performance (H₂) and knowledge sharing (H $_{4})$ . The findings are consistent with the extant literature that claims employee engagement as the driving antecedents that push positive outcomes (Karatepe et al., 2020; Saks Alan, 2019). However, a noticeable point is the low $R^{2}$ value of innovative performance, exhibiting that almost 90% of the variance is unexplained and the factors need to be investigated. Moreover, employee engagement also explained a trivial variance in the knowledge-sharing behaviour (0.10) with peers. The reason might be a lack of trust (Mohammed and Kamalanabhan, 2019) to transform knowledge through integrated efforts to achieve innovative performance due to task dependency (Gagné et al., 2019). Another reason might be using self-rated knowledge-sharing scales, and the response might be inflated due to social desirability bias in those studies (Fisher and Katz, 2000).

The study used peer-rated knowledge-sharing experience and objective measures of innovative performance. It proposed a direct link between knowledge sharing and innovative performance (H₄); however, contrary to the expectations, knowledge sharing has no significant effect on innovative performance. The results might be anomalous with the studies claiming knowledge sharing contributes to innovative behaviour and performance (Setini et al., 2020; Lu et al., 2012). The reason might be opportunistic and political objectives to share selective or obsolete knowledge (Willem and Scarbrough, 2006) that might not be effective in innovative performance.

The mediating role of discretionary efforts and knowledge sharing

The study hypothesised the mediating role of discretionary efforts and knowledge sharing between employee engagement and individual innovative performance (H₃ and H₅). However, the results did not show the expected mediation effects. The reason might be the cultural context that determines assignment (Kmec and Gorman, 2010) and behaviours. Further, this could be attached to a cultural context in which we collected data. The study focussed on participants from a Pakistani context, i.e., collectivist context and patriarchal society, where traditional hierarchical structures and values might influence the dynamics between engagement for innovation, discretionary efforts, knowledge sharing and innovative performance. In collectivist cultures, employees prioritise group harmony and goals over individual interests (Sharafizad et al., 2019). The cultural disposition could result in employees exhibiting engagement for innovation due to a desire to contribute to organisational performance. The collective approach might lead employees to invest discretionary efforts for the greater good, despite the individual engagement for innovation levels, possibly weakening the mediation effect. The results are inconclusive concerning the research claiming a significant mediating role of knowledge sharing in the engagement–performance relationship or engagement–innovative behaviour relationship (Kim and Park, 2017; Lu et al., 2012). As discussed earlier, opportunistic and political objectives might hinder sharing of beneficial or selective knowledge (Willem and Scarbrough, 2006). Since the Pakistani society is based on patriarchal norms, accordingly the country’s culture might introduce complexities (Soomro et al., 2023). The hierarchical nature of patriarchal societies could impact how discretionary efforts are perceived and distributed within the organisational hierarchy. In such settings, discretionary efforts might be directed more by leadership directives than individual engagement for innovation. This possibly may weaken the said mediation mechanism between the variables.

In the mediation mechanism of knowledge sharing, traditional values also influence the dynamics of innovative performance. Respect for authority could modify the mediation process. While employee engagement might create an atmosphere where they want to contribute, the societal fabric might influence how this engagement for innovation translates into knowledge sharing (Afsar et al., 2020; Schons and Steinmeier, 2016). The collectivist and patriarchal structures might introduce unique dynamics that shape how knowledge is shared and utilised within organisations. Hierarchical structures and a strong emphasis on communal goals impact how employees perceive the value of sharing knowledge and the subsequent impact on innovative performance (Donia et al., 2019). The intricate interplay between cultural values, power dynamics and innovative behaviours warrants further exploration to grasp the underlying mechanisms at play truly. Another explanation comes from the analysis of quadratic effect that supports the inverted U-shaped relationship between KS and IP.

Theoretical contribution

The study contributed to theory in the following ways. First, the study demonstrates the relationship between engagement for innovation and employee discretionary efforts as an outcome of engagement (Bailey et al., 2017). The findings suggest a significant positive relationship between employee engagement for innovation and discretionary efforts. It encourages engaged employees towards innovation, making them put extra effort in terms of time, intensity and direction to achieve organisational goals. The study contributes to the literature by focussing on innovative performance rather than creativity or innovative behaviour. The former is considered as the starting point of innovation, while behaviour may be adapted for image building by organisations and individuals (Gosselt et al., 2019; Hameed et al., 2016). Hence innovative performance is beyond image building (Gupta et al., 2017). We also utilised objective measures of innovative performance to capture the essence of innovative outcomes of individuals. By measuring innovation objectively, the study adds to the realm of research investigating the link between engagement and innovation (Wang et al., 2019; Ismail et al., 2019). The objective measure is also crucial since university ranking is based on research outcomes rather than research efforts. And behaviour may show only the efforts while performance exhibits the results of these efforts that come in tangible form.

The next possible contribution concerns the mediating effect of discretionary efforts and knowledge sharing. The study tries to understand how engagement affects innovative performance. Although the study found a positive association between engagement and knowledge sharing and discretionary efforts, the reason may be to share knowledge that is not supportive to innovative performance. On the other hand, the discretionary efforts resulting from engagement might not be sufficient to extract innovative performance out of it. The discretionary efforts might also be image-building strategies that have no real contribution to the actual innovative performance to show alignment with the organisational goals (Hewlin et al., 2017). For instance, an individual appears in every meeting, workshop and seminar related to research but produces no research output. This study is conducted in a collectivist country where image building has been used to impress others. So, the study adds to the existing literature and gives similar and differential findings.

Practical implications

The study provides some practical implications for management. Study findings suggest that organisations should prioritise creating a work environment that promotes employee engagement and encourages innovation. This can be achieved by providing opportunities for employees to participate in decision-making processes, fostering open communication channels and recognising and rewarding efforts. Organisations can improve employee innovative performance by using their diverse perspectives, ideas and expertise by building an atmosphere that promotes engagement for innovation (Qamar et al., 2023). Similarly, organisations must establish strategies to assess employee performance and a mechanism that links engagement for innovation with inventive performance. For instance, it entails conducting surveys and monitoring employee engagement activities that result in positive work culture and performance in organisations. Furthermore, organisations should set criteria to assess innovative performance, such as the number of activities carried out, their influence on performance and the speed with which they respond to market changes. By regularly measuring engagement for innovation and performance, organisations can identify areas for improvement and make data-driven decisions to strengthen the relationship between employee engagement for innovation and performance.

Encouraging employee engagement for innovation can have a positive impact on discretionary efforts. Organisations should prioritise fostering an environment that gives employees autonomy, growth opportunities and a sense of purpose (Hewlin et al., 2017). By nurturing an engaging culture, employees are more likely to feel motivated and empowered, hence contributing beyond their requirements, such as going above and beyond the job assigned, sharing ideas and collaborating with colleagues. This positive relationship between employee engagement for innovation and discretionary efforts can enhance innovative performance and organisational success.

Furthermore, organisations create a culture that encourages open communication, collaboration and exchanging ideas. When employees actively engage in innovative endeavours, they are likely to share knowledge, expertise and insights with their colleagues (Toma and Butera, 2015). This knowledge sharing contributes to collective intelligence and promotes a culture of continuous learning and improvement within the organisation. By fostering engagement for innovation and facilitating knowledge-sharing practices, organisations can harness the collective wisdom of their employees, enhance problem-solving capabilities and accelerate innovative ideas and processes. In this way, organisations achieve higher success and competitiveness in today’s dynamic and knowledge-driven business landscape.

Limitations and future research directions

The study tried to objectively verify employee innovative performance from their institutes and Google Scholar profiles. As a human effort, research is not far from limitations. Innovative performance was measured objectively and CMV test was conducted, so there is still chances of CMV (Podsakoff et al., 2003). Future studies can use longitudinal designs to collect data at different times to address the issue of common method variance (MacKenzie and Podsakoff, 2012). We collected cross-sectional data, therefore it raises questions on the causality of relationships. Future researchers can use experimental designs to address these limitations (Podsakoff and Podsakoff, 2019). Additionally, the findings of this research may be used with caution in other work settings. Because in other setups, the meaning of innovative performance might be different from research institutes.

Another limitation of this study is that it merely focussed on the positive outcomes of job engagement; however, research has found different forms of employee well-being (engaged but emotionally exhausted) (Inceoglu et al., 2018; Warr, 2013). Hence, future research may simultaneously investigate the differential effects of positive and negative engagements. The low coefficient of determination value points towards the existence of a mediating mechanism that needs to be studied in the future. Hence future studies should investigate the mediating role of people’s time management to harvest innovative performance.

Moreover, individuals see their leaders as role models (Bandura and Walters, 1977). Hence future research should investigate whether leaders translate their words into action by exhibiting innovative performance. The leader’s word–deed alignment (Kannan-Narasimhan and Lawrence, 2012) in terms of innovative performance may also act as a boundary condition to translate engagement, discretionary efforts and knowledge sharing (Park et al., 2018; Simons et al., 2015) into innovative performance (Gupta et al., 2017). Like other phenomena, innovation may also be influenced by institutional factors or an internal desire to change. Hence, the reason to pursue change may also limit or expand the outcomes of discretionary efforts to account them as symbolic or wholeheartedly. Hence, future research may also find the contingency role of motivation (i.e., ISO certification) (Heras-Saizarbitoria and Boiral, 2019) in adopting innovation policy to determine the conversion of efforts into outcomes. The indication of inverted U-shaped relationship between KS and IP also opens up avenues for future research to challenge the conventional wisdom of positive DE relationship between the two variables.

Conclusion

This study investigates faculty engagement for innovation and innovative performance via discretionary efforts and knowledge-sharing behaviours. The research proposed employee engagement as a driving force that pushes individuals to put in discretionary efforts and translate them into innovative performance. The results revealed a significant positive relationship between engagement for innovation, discretionary efforts, knowledge sharing and innovative performance. This study adds to the scant literature on the mediating role of discretionary efforts and knowledge sharing between employee engagement for innovation and innovative performance.

ORCID

Asif Nawaz https://orcid.org/0000-0001-8775-0434

Shuaib Ahmed Soomro https://orcid.org/0000-0003-0849-8942

Yasir Mansoor Kundi https://orcid.org/0000-0001-8962-2751

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