International Journal of Evidence Based Coaching and Mentoring
2021, Vol. 19(1), pp.90-104. DOI: 10.24384/nq43-ar60

Academic Paper

Effect of Mentoring on Job Performance among Indian Millennials: A Quantitative Study

S. Chatterjee (Birla Institute of Management Technology, India)
A.K. Dey (Birla Institute of Management Technology, India)
H. Chaturvedi (Birla Institute of Management Technology, India)

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Introduction

Comment or criticism from supervisors considerably influences the job satisfaction and performance of employees (Young & Perrewé, 2000). Employee mentoring has been known to influence employees’ performance. Research suggests that mentees who acknowledged mentoring support exhibited improved job performance and lower turnover intention, and they experienced better career growth (Allen & O’Brien, 2006). Accordingly, organizations and researchers are constantly exploring new methods to help employees maximize their performance, especially through supporting initiatives such as mentoring programs.

More recently, mentoring programs have targeted millennial employees in organizations. The millennial generation, or “millennials,” are individuals born between 1980 and 2000 and currently in the age group of 19 to 39 (Zemke, Raines, & Filipczak, 2000). Their professional ambitions, attitudes towards work, and understanding of innovative technologies will describe the customs of the 21st-century workplace. By the end of 2025, millennials will account for 75% of the global labour force (Economy, 2019). Therefore, attracting, nurturing, and managing the expectations of millennial employees is important for the future of any business. With globalization, the Indian market has created challenging job openings and tasks that attract the millennial generation. Millennials account for almost one-third of India’s total labour force, with 425 million individuals. Their proportion in the labour force is expected to increase to 45–50% by the end of 2020, and 75% by 2025 (Morgan Stanley, 2017; Narayan, 2019). However, despite their importance in the Indian market, there is little research on mentoring and job performance among Indian millennials. Accordingly, the present study aimed to examine the effect of mentoring on job performance among Indian millennials from different industrial sectors. In doing so, it aimed to identify practice insights for managers, to enable them to develop more effective mentoring programs for this population. The following sections of the paper first present the existing research on job performance and mentoring, which was used as the basis for the hypotheses tested in the present study.

Effect of mentoring on job performance

The concept of job performance has been discussed by various authors (Campbell, Gasser, & Oswald, 1996; Motowidlo, Borman, & Schmit, 1997; Viswesvaran et al., 1996). Motowidlo et al.’s (1997 p. 72) theory of individual differences in job performance defines performance as the “aggregated value to the organization of the discrete behavioral episodes that an individual performs over a standard interval of time”. They divided job performance into two dimensions, task performance and contextual performance. Technical skill and job knowledge (Van Scotter, Motowidlo, & Cross, 2000) are indicators of task performance. This “in role” behaviour is an essential part of the employee’s job description. In other words, it includes tasks that employees are actually paid for. On the other hand, contextual performance, also called as “extra-role” behaviour, includes activities that are not mandatory or are not an essential part of the job description. Contextual performance behaviours sustain the larger organizational, social, and psychological environment in which the technical core functions (Motowidlo et al., 1997).

The seminal work of Kram (1985) on the mentoring process and mentoring relationship increased researchers’ attention on this construct. She identified two categories of mentoring activities: a) career-related and b) psychosocial functions. The former is related to professional development, while the latter pertains to developing a relationship that encourages the improvement of a mentee’s feelings of skill (Kram, 1985). Some researchers have included role modelling as the third element of mentoring (Ragins & Scandura, 1994). In the Indian context, considering the “collectivist” and “power-distancing” culture, mentees prefer to have a one-to-one relationship with their mentor (Hofstede, Hofstede, & Minkov, 2010), who is substantially older than themselves, and who can provide career support as well as psychological support (Kumar, 2018; Ramaswami & Dreher, 2010).

Mentoring has been viewed as the most effective method to attract and retain millennial employees who have a higher learning and developmental agility, and higher achievement orientation (Sosik, Godshalk, & Yammarino, 2004). Their skill-development focus and need for immediate and constructive feedback make them perfect candidates for mentoring programs (Spiegel, 2011). Millennials seek to develop their skills constantly to provide them a competitive edge; therefore, they value training and development opportunities at the workplace, including on-the-job training, coaching, and mentoring (Brack & Kelly, 2012). Millennials value respecting elders and developing close relationships with them, which leads them to expect their superiors to build a personal relationship with them (Hershatter & Epstein, 2010). Additionally, they seek constant coaching and feedback from their superiors (Spiegel, 2011). A longitudinal study conducted by Google revealed that millennial employees welcomed career development advice that took their career aspirations into consideration (Garvin, 2013). Other studies focusing on millennials revealed that they value constructive performance-based feedback that aids their career development (Jawahar, 2006).

Despite the paucity of studies focusing on Indian millennials, the few existing studies reported similar findings. For instance, a study on 653 multi-generational executives working in Indian public and private sector companies found that Indian millennials expected their mentor/supervisor to show interest in their career aspirations by assuming an active role in conducting regular and timely career discussions focusing on their needs (Chawla, Dokadia, & Rai, 2017). Similarly, Dokadia, Rai, and Chawla (2015) found that Indian millennials crave for instant and constructive self-development feedback from their managers. Together, these studies acknowledge the uniqueness of millennials, which suggests the need for research focusing on this generation.

Internationally, substantial research has examined the impact of mentoring on job performance. For instance, in their study on employees from public universities in Kenya, Mundia and Iravo (2014) reported that mentoring programs were an essential method of employee growth in successful firms. The mentor’s ability, either formally or informally, affected the activities of the mentoring program, which in turn led to greater psychosocial assistance and career growth of individuals, and consequently, better overall productivity. Another recent study that used data on 572 employees from 61 companies in Korea found that mentoring and a significant influence on job performance through career development (Lee & Lee, 2018). These findings were reiterated in a study on 250 employees from public and private universities in Islamabad (Tanoli, 2016). Similarly, a mixed-method study on 367 construction employees in Nigeria found that mentoring had a positive influence on job performance (Ofobruku & Nwakoby, 2015). Another mixed-method study on 48 faculty members from Nigerian universities revealed that mentoring prepares mentees for delivering higher performance (Okurame, 2008). Several studies have confirmed that career and psychosocial support, as well as role modelling from the mentor, encourage mentees to maximize their potential and achieve their personal and the organization’s goals (Akarak & Ussahawanitchakit, 2008; Lo, Ramayah, & Kui, 2013).

Further, as explained earlier, in their seminal work on the elements of job performance, Borman and Motowidlo (1993) suggested the distinction between contextual and task performance. Subsequently, in their study on 421 US Air Force mechanics, Motowidlo and Van Scotter (1994) found that contextual and task performance contributed independently to overall performance. Therefore, they acknowledged the need to study both these constructs separately. Even in the context of the effects of mentoring on job performance, authors have separately studied the influence of the former on contextual and task performance. Specifically, in their study on 330 employee–supervisor dyads in China, Sun, Pan, & Chow (2014) found that mentoring had a direct influence on contextual performance alone, while Okurame and Ajayi (2017) observed the same in the case of task performance among university students.

Though these studies have been conducted outside India, it is important to note that most have been conducted in regions that have collectivist cultures and predominantly power-based work relationships, like Asia and Africa (Hofstede et al., 2010; Kuada, 2010). Similarly, in India too, the traditional culture and nature of social ties influences mentoring relationships (Kumar & Kumar, 2018). Therefore, they could be applied to the Indian context. Additionally, in a qualitative study, Ramaswami and Dreher (2010) acknowledged that participants’ expectations from mentors and perceived benefits remained the same across cultures. Furthermore, the few existing Indian studies on mentoring have corroborated the findings of international studies. For instance, a study on 151 mentees from one organization in eastern India found that mentoring influenced job performance, with traditional mentoring having a stronger effect as compared to relational mentoring (Srivastava & Jomon, 2013). Similarly, a study on 64 mentors and 88 mentees from two public sector organizations revealed that over 60% of the mentees, and 87% of the mentors reported that mentoring improved employee job performance (Buddhapriya, 2017). Another study on 276 employees from the banking sector in one state in India found that mentoring influenced job performance through self-efficacy, personal learning, communication satisfaction, and relationship quality (Jyoti & Sharma, 2017). As there is limited research on millennial mentoring in India, and the few existing studies have limited generalizability due to the nature of the sample chosen (generally from a single sector or geographical location), the present study aimed to fill this research gap by using a more nationally representative sample.

Therefore, the present study aimed to fill this research gap by first examining the relationship between mentoring and both these constructs separately, and job performance as a whole, by testing the following hypotheses:

H1: There is a significant relationship between Indian millennials’ mentoring and contextual performance scores.

H2: There is a significant relationship between Indian millennials’ mentoring and task performance scores.

H3: There is a significant relationship between Indian millennials’ mentoring and total performance scores.

Additionally, the extent of influence of mentoring on job performance and its components was estimated using regression analysis. In the context of research on job performance, while most studies rely on self-rated job performance assessments, one recent unique study considered line managers’ evaluation of their employees’ job performance. This study on 207 IT professionals from small and mid-size organizations in three European countries found evidence for a link between mentoring and job performance (Bozionelos et al., 2016). Similarly, in their review of over 47 studies on mentoring and employee outcomes, Dougherty and Dreher (2007) found that only a few studies had examined outcomes using mentor-mentee dyads or mentors alone. As the mentors’ perspective is rarely examined, the present study aimed to assess job performance from both the mentees’ and mentors’ perspectives. Additionally, it was considered desirable to assess job performance from the mentees’ and mentors’ perspective to avoid common method bias.

Methodology

To meet the aforementioned objectives, this study employed a quantitative approach by assessing perceived levels of mentoring and job performance in a sizeable sample of Indian millennials.

Sample

Multistage sampling was employed to select a sample that was representative of the target population, i.e., Indian millennial employees who receive mentoring in their organization. The first stage involved purposive sampling of organizations who declared that they have a mentoring program in place. To include different types of organizations, which would improve the generalizability of the findings in the Indian context, those from the automotive, electrical, chemical, energy, metallurgical, construction, food, glass, textile and clothing, and consumer goods industries were considered for this study. In the second stage, considering feasibility, economy, and reduced variability, each geographical area (four zones in India, namely East, West, North, and South) was considered as a cluster. Branches of organizations selected in Stage 1 were chosen such that an equal number of respondents could be recruited from the aforementioned four geographical zones in India. This was done to improve the generalizability of the study to the Indian context.

In the final stage, at the individual level, potential participants were identified using random sampling, following which, mentors and mentees agreeing to participate in the study were selected. As the study questionnaire included 23 items, using Hair, Black, Babin, and Anderson’s (2010) method, the sample drawn from the total population needed to be from 115 to 230 participants. This study was conducted with 122 millennial employees who received mentoring on the job.

In addition to millennial employees, data on their job performance were also collected from their mentors. Therefore, the 122 selected employees’ respective mentors were also included in the study. As number of certified mentors in the Indian context is not available, this information was collected from organizations. Since several mentors worked with more than one mentee in the organization, the final sample comprised 36 mentors.

Sample Characteristics

The final sample comprised 122 millennial mentees and their respective mentors. The primary participants’ (millennial mentees) characteristics have been summarized in Table 1.

Table 1: Characteristics of millennial mentees participating in the present study

Variablef%
Year of birth1980–199011493.4
1991–200086.6
QualificationsGraduate7964.8
Postgraduate4335.2
Professional qualificationsMBA/PGDM5645.9
Others6654.1
Years of service in the current organization<13629.5
1-36956.6
3-564.9
>5119
Total years of experience1-31411.5
3-52218
>58670.5
Hierarchical position in the current organizationFront Line Officers12098.4
First-line manager21.6

Data Collection

Data were collected using a 23-item self-report questionnaire. The questionnaire was sent to millennial employees and their mentors, in consultation with the Human Resource (HR) heads of the particular organization. In certain cases, the questionnaire was sent to the HR heads to help them facilitate the data collection process. It comprised two sections, one on mentoring (9 items) and the other on job performance (14 items). The participants required around 10 minutes to complete the questionnaire. Additionally, the questionnaire included items on the primary participants’ demographic characteristics.

Mentoring

Mentoring was assessed using the 9-item Mentoring Functions Questionnaire (MFQ-9) developed by Castro and Scandura (2004), which is a revised and validated version of the original 15-item Multidimensional Mentoring Measure developed by Scandura and Ragins (1993) This 9-item questionnaire has demonstrated superior validity and reliability (Castro, Scandura, & Williams, 2004). Mentoring was measured in terms of career support, psycho-social support, and role modelling. All items in this scale are rated on a 5-point Likert scale ranging from Strongly Agree to Strongly Disagree. Responses were scored 1 to 5, such that lower scores are indicative of higher perceived mentoring. The total score ranged from 9 to 45, while scores on each of the three components ranged from 9 to 15. Hu, Wang, Wang, Chen, & Jiang (2016) reported a Cronbach’s α of .93, while it was .85 in the present study. 

Job performance

The job performance scale utilized in this study was based on Borman and Motowidlo’s (1997) concept of task performance and contextual performance. The task performance scale comprises six items adapted from the tool developed by McAllister (1995). It includes Fisher’s (1980) viewpoint of attendance and coordination. A sample item is “I outperform my colleagues.” The contextual performance scale comprises eight items adapted from the works of Organ (1988) and Farh, Earley, and Lin (1997). A sample item is “I actively help my colleagues with their work.” All items are rated on a 5-point Likert scale ranging from Strongly Agree to Strongly Disagree. Responses were scored from 1 to 5, such that lower scores are indicative of higher perceived job performance. The total score ranged from 14 to 70, while scores on contextual and task performance ranged from 6 to 30 and 8 to 40, respectively. To avoid common method bias, participants’ job performance was also assessed by their mentors. The Cronbach’s α for the job performance questionnaire was .89 for mentees and .93 for mentors in the present sample.

Ethical considerations

This study was approved by the institutional review board of the authors’ organization. Additionally, the study procedure adhered to ethical requirements pertaining to informed consent, voluntary participation, and confidentiality (Lavrakas, 2008). Specifically, participants were informed about the purpose and procedure of the study; that their participation was voluntary and they could withdraw consent at any point during the study, without any penalty or negative consequences; that confidentiality of their responses would be maintained; and that their identity would be protected. No personal identification data were collected, and it was ensured that participants’ ratings mentoring were not shared with their mentors, who were also their managers.  

Data analysis

The data collected from 122 Indian millennial mentees and their respective mentors were first analyzed using descriptive statistics to understand trends in the present sample. Subsequently, self- and mentor-rated job performance were compared using the paired t-test. To understand the relationship between mentoring and job performance in millennial mentees, correlation coefficient was utilized. Subsequently, a regression analysis was conducted to estimate the extent to which mentoring influenced self-rated job performance and its components. First, group-wise differences in job performance scores were examined for all demographic variables using one-way ANOVA, to identify important variables to be included as covariates in the regression analysis. However, in the present sample, none of the demographic variables showed group-wise differences in job performance scores. Therefore, no covariates were included in the regression analysis. 

Finally, to confirm the findings of the regression analysis, structural equation modeling (SEM) was used to test different models on the relationship of mentoring with total job performance, and with task and contextual performance. Here, an initial exploratory factor analysis (EFA) was conducted to determine scale items that best measured the target variables. As explained in detail in the results section, a part of this SEM analysis was conducted considering the findings of the paired t-test that compared self- and mentor-rated job performance ratings. All analyses were conducted using IBM SPSS Statistics (Version 23) and IBM SPSS Amos (Version 22).

Results

Perceived mentoring and job performance in Indian millennials

Means and standard deviations on mentoring and job performance, and their components are reported in Table 2.

Table 2: Means and standard deviations on mentoring and job performance and their components

RespondentVariableTheoretical rangeMeanStandard Deviation
Millennial menteesMentoring9–4517.525.43
Career support3–155.82.4
Psychosocial support3–156.212.14
Role modelling3–155.521.82
Job performance14–7026.427.3
Contextual performance8–4013.934.39
Task performance6–3012.483.64
Mentors*Job performance14–7029.2610.34
Contextual performance8–4016.36.24
Task performance6–3012.974.49

Note. * Scores are for mentors’ evaluation of their mentees’ job performance and its components

In terms of mentoring, a lower score was indicative of a higher level of perceived mentoring. Note that, for millennial mentees, their mean scores suggested that they perceived a high level of mentoring in general and in all three areas of mentoring. Among the three areas of mentoring, their scores were the lowest on role modelling, which suggested that they perceived the highest level of mentoring in terms of role modelling, followed by career support and psychosocial support, respectively.

Job performance of the millennial mentees was evaluated by themselves and their mentors. For both, a lower score was indicative of a higher level of job performance. Regarding self-evaluated job performance, as evident from Table 2, mentees reported a high total score and a high score on both contextual and task performance. Their score on task performance was higher than that on contextual performance.

Similar results were observed for mentor-rated job performance. However, note that a paired t-test revealed that the self-rated and mentor-rated scores differed significantly for the total score (t = -2.65, p = 0.009) and for the contextual performance score (t = -3.67, p = 0.000), with mentees rating their own performance better (lower scores) than mentors did. Their ratings of task performance matched more closely.

Further, item-wise paired t-tests (Table 3) revealed that mentee-mentor responses matched statistically for seven out of the 14 items on job performance, with non-significant differences on five of the six items on task performance, and only two if the eight items on contextual performance. These findings were later used when conducting the SEM analyses, described in the next section.

Table 3: Findings of the item-wise paired t-test for self- and mentor-rated job performance 

Job performance typeItemsPaired differencestp
MeanSD
Contextual performanceI actively help my colleagues with their work-.301.29-2.607.010
I focus on team performance-.531.36-4.341.000
I am courteous at work.021.08.167.868*
I take measures to resolve conflict at work-.391.17-3.623.000
I actively make suggestions to improve my company-.381.30-3.202.002
I actively publicize my company's strength-.341.20-3.120.002
I manage to complete assigned work that is beyond my responsibility-.291.24-2.563.012
I actively coordinate with my colleague-.161.21-1.418.159*
Task performanceI outperform my colleagues-.041.31-.345.731*
I handle emergencies well.021.34.135.893*
I achieve objectives that are assigned to me.001.29.0001.000*
I am never late nor take off early from work-.091.31-.760.449*
I aim to attain perfection in my work-.341.28-2.961.004
I am prudent and seldom make mistakes-.031.24-.220.826*

Note. *Non-significant differences indicate that self- and mentor-rated job performance was similar. 

Relationship between mentoring and job performance of Indian millennials

Mentoring and job performance, and all their components were correlated positively and significantly. As evident from Table 4, all coefficients showed a moderately strong correlation, confirming the presence of a relationship between mentoring and job performance. Thus, Hypothesis 1, 2, and 3 were confirmed. To further substantiate these findings, regression analyses were conducted to estimate the extent to which mentoring predicted job performance. Before proceeding with the regression analyses, we checked for multicollinearity two ways: correlation coefficients and variance inflation factor (VIF) values. While the former was below the recommended cutoff of 1 (0.485 to 0.688), the VIF values were less than 2.10, which was highly acceptable. The relationship between all the independent variables of mentoring and the dependent variable job performance were found to be linear in the analysis of the partial regression plots. Normality was checked through the normality probability plots of the residuals. Since there was heteroscedasticity, the data were transformed into Z scores. The transformed standardized residual plot showed that was no pattern of increasing or decreasing residuals (plots have not been presented due to space limitations). Thus, homoscedasticity was achieved. Data were interpreted from original or untransformed variables as, according to Hair et al. (2009), “the transformations may change the interpretation of the variables.” Independence of residuals was not checked as this data set was cross sectional and not longitudinal.

Table 4: Correlations between mentoring and self-rated job performance, and their components

 Self-rated job performance total scoreSelf-rated task performanceSelf-rated contextual performance
Mentoring total score.56.55.48
Career support.54.48.5
Psychosocial support.43.45.33
Role modelling.46.48.37

Note: All p values were significant at p < 0.05. 

Findings of the regression analyses revealed that mentoring was a significant predictor of job performance (F(1,120) = 55.39, p = 0.000), with an R2 of 0.32. Specifically, participants’ self-rated job performance score increased by 0.76 points for each point increase in the mentoring score (95% confidence interval: 0.55–0.96). Considering that lower respective scores were indicative of higher mentoring and job performance in the present study, the regression findings suggest that lower perceived mentoring predicted lower self-rated job performance. The total mentoring score was also a significant predictor of contextual and task performance (Table 5).

Regarding the relationship between the components of mentoring and those of job performance, all findings were significant (Table 5). Note that, the variance in contextual performance was mostly explained by career support (25% total variance explained (TVE)), followed by role modelling and psychosocial support (14 and 11% TVE), respectively. On the other hand, all three types of mentoring explained around 20% of the variance in task performance. Again, in the present study, considering that lower respective scores on the variable components were indicative of higher mentoring and job performance related to those areas, the findings related to the regression coefficient suggest that lower levels of role modelling, psychosocial support, and career support predicted to lower self-rated contextual and task performance.

Table 5: Results of the regression analysis on the relationship between mentoring and self-rated job performance, and their components

VariablesFDfpR2Coefficient95% confidence interval
Mentoring and Self-rated contextual performance35.341, 1200.0000.230.390.26–0.51
Mentoring and Self-rated task performance52.261, 1200.0000.300.370.27–0.47
Career support and Self-rated contextual performance40.201, 1200.0000.250.920.63–1.20
Career support and Self-rated task performance36.221, 1200.0000.230.730.49–0.97
Psychosocial support and Self-rated contextual performance15.091, 1200.0000.110.690.34–1.03
Psychosocial support and Self-rated task performance30.411, 1200.0000.200.760.48–1.04
Role modelling and Self-rated contextual performance18.981, 1200.0000.140.890.49–1.29
Role modelling and Self-rated task performance35.821, 1200.0000.230.960.64–1.27

Findings of the structural equation modeling

Finally, SEM was used to confirm the findings on the impact of mentoring on job performance using only those items which contributed the most to measuring the corresponding variable (determined through an exploratory factor analysis). First, an EFA with varimax rotation was conducted to identify the specific items related to job performance and mentoring that contributed the most to measure the respective variables. The EFA was conducted after confirming appropriateness using the KMO measure of sampling adequacy and Bartlett’s test of sphericity. For the self-rated job performance tool, a final 4-factor model with 12 items was selected, with 72.23% Total Variance Explained (TVE). The Cronbach’s α for the 12-item self-rated job performance tool was high, at 0.87. For the mentoring tool a single-factor, 4-item model was found to have the highest % TVE (63.91%). The Cronbach’s α for the 4-item mentoring questionnaire was good, at 0.81. Further, its construct reliability was high, at. 0.88. Therefore, it was decided to use the 12-item, 4-factor self-rated job performance tool, and the 4-item single-factor mentoring tool for the SEM that tested the three models presented in Figure 1a, b, and c, respectively. For all models, the standardized direct effects have been presented along the arrows.

The chi-square statistics confirmed the veracity of Model 1 (Figure 1a) (χ2 = 171.273, p = 0.000, df = 99). The Goodness of Fit Index (GFI) was marginally below the recommended cutoff of 0.9 (at 0.86) and Comparative Fit Index (CFI) was above the recommended cutoff of 0.9 (at 0.913). The Root Mean Square Error of Approximation (RMSEA) was 0.058, which was only marginally over the cutoff of 0.05. Finally, the Standardized Root Mean Square Residual (SRMR) was below the recommended cutoff of 0.08 (at 0.0684). Thus, the model exhibited good or nearly-adequate fit on all indices.

Figure 1: Models tested using structural equation modelling

Model 2, which included the constituents of job performance (task and contextual performance) (Figure 1b), exhibited good or nearly-adequate model fit on all indices (χ2 = 173.906, p = 0.00, df = 98, GFI = 0.859, CFI = 0.908, RMSEA = 0.08, SRMR = 0.071), but the fit indices were not as good as those for Model 1. To further understand the constructs better, and to consider both self- and mentor-rated job performance in the analysis, the SEM was conducted again only using job performance items that did not show a significant mentee-mentor difference in the paired t-test (Table 3). This analysis using the 7-item job performance tool (Item 3, 8, 9, 10, 11, 12, and 14) to test Model 2 again (Figure 1b) showed an improvement in fit (χ2 = 71.595, p = 0.00, df = 43, GFI = 0.900, CFI = 0.937, RMSEA = 0.074, SRMR = 0.061) as compared to those for the preceding models. As most of these items pertained to task performance, these findings suggest that the relationship between mentoring and job performance may be understood better by examining self- and mentor-rated task performance.

Finally, another SEM was conducted to examine the influence of mentoring on contextual and task performance (excluding the total job performance score) (Model 3; presented in Figure 1c). For this analysis, the 12-item, 4-factor self-rated job performance tool, and the 4-item single-factor mentoring tool were used. Findings revealed much poorer and inadequate fit on all indices (χ2 = 193.745, p = 0.00, df = 98, GFI = 0.847, CFI = 0.884, RMSEA = 0.09, SRMR = 0.091). Therefore, this model was rejected and it was not retested using the 7-item job performance tool as done for Model 2.

Taken together, the results of the 4 SEMs confirmed that mentoring influenced total job performance (thus confirming Hypothesis 3 as well as contextual and task performance as constituents of job performance (partially supporting Hypotheses 1 and 2). However, the direct effect on contextual and task performance (excluding total job performance) was not confirmed. Accordingly, Hypothesis 1 and 2, which explored the influence of mentoring on contextual and task performance was partially rejected, in that the model testing the direct effect was not robust but the effect through total job performance was significant.

Discussion

This section discusses the present findings in light of the existing research on these topics. However, due to a lack of Indian studies in this area, it was not possible to make a direct relevant comparison. Nevertheless, the present findings add substantially to the existing research on job performance and mentoring. The present study aimed to examine the effect of mentoring on the job performance of Indian millennials. Findings revealed that Indian millennials had high self and mentor-rated job performance, and high perceived mentoring. Mentoring is emerging as a preferred intervention in several organizations globally (Naim & Lenka, 2017). Specifically, as suggested by the Social Exchange Theory, the high benefit-cost ratio perceived by employees who receive mentoring encourages them to exhibit a higher tendency to exert efforts to attain organizational goals (Rutti, Helms, & Rose, 2013). Thus, the present findings corroborate the notion that higher perceived mentoring may be related to better perceived job performance among Indian millennials. Additionally, in the present study, findings of the correlation analysis, regression analyses, and SEM revealed that mentoring had a positive influence on job performance. This finding is also supported by other previous studies that have reported several positive effects of mentoring, including higher job performance and career growth (Lee & Lee, 2018; Mundia & Iravo, 2014; Ofobruku & Nwakoby, 2015; Okurame, 2008; Tanoli, 2016). Though these studies were conducted in different sectors and countries, majority of them can be applied to the Indian context because countries like Korea, Pakistan, Kenya, and Nigeria have a similar collectivist tradition like India, which largely governs the nature of the mentor–mentee relationship (Hofstede et al., 2010; Kuada, 2010; Kumar & Kumar, 2018).

The SEM results obtained in the present confirmed that mentoring influenced total job performance, as well as contextual and task performance as constituents of job performance. However, the direct effect of mentoring on contextual and task performance alone (excluding total job performance) was not confirmed. These findings are surprising and provide some insight into the characteristics of Indian millennials. Borman and Motowidlo (1993) suggested the distinction between contextual and task performance and Motowidlo and Van Scotter (1994) added that contextual and task performance contributed independently to overall performance. Other studies confirmed that mentoring had a direct influence on contextual performance alone (Sun et al., 2014) and task performance (Okurame & Ajayi, 2017). However, in the present study, Model 3, which tested the influence of mentoring on contextual and task performance alone, did not show adequate goodness of fit despite having high standardized direct effect regression weights. This finding could suggest the unique characteristic of Indian millennials. Note that, Sun et al.’s (2014) study included employees of various age groups from China. They did not focus exclusively on millennials. On the other hand, Okurame and Ajayi (2017) only focused on Nigerian university students. Therefore, the present incongruent finding may be specific to the Indian context and needs to be explored further in future studies.

Implications for mentoring practice

The present study confirmed that mentoring influences Indian millennial employees’ job performance. This points to the importance of understanding the characteristics of Indian millennials and tailoring mentoring programs according to their needs and aspirations. Saunderson (2009) suggested that this could help managers improve the performance of their team members. For instance, he explained that millennials are motivated by immediate and frequent feedback, which could be provided by the manager in one-on-one meetings. Accordingly, organizations must design mentorship programs to cater to millennials needs. As pointed by Hershatter and Epstein (2010), having being brought up to respect elders and develop close relationships with them, millennials expect their superiors to build a personal relationship with them. Therefore, mentors should consider developing a close relationship with their mentees to create a sense of camaraderie. Additionally, the present study revealed that Indian millennials valued career-support the most, followed by psychosocial support, and finally role modelling. This is an important finding, because despite the historical existence of mentoring through the teacher–student (guru–shishya) relationship between managers and subordinates in India (Raina, 2002), there is little research on the nature of mentoring in Indian organizations (Ramaswami & Dreher, 2010). Therefore, mentors should focus on facilitating the career development of Indian millennials, as they are known to value personal growth over other achievements or incentives at the workplace. These findings were expected considering that, owing to the collectivist and power-distancing traditions of India, mentees seek older mentors who provide career support, as well as act as a counsellor who provides nurturing and psychosocial support (Kumar, 2018). Additionally, role modelling had the highest explanatory power for task performance, which confirms the already acknowledged potential of using role modelling as a learning tool. Therefore, mentors should consider “leading by example” by exhibiting behaviours and performance that they expect their mentees to exhibit.

Limitations and future directions

Despite its significant contributions, especially to the Indian literature on mentoring, job performance, and millennials, the present study had some limitations that need to be considered when interpreting and applying its findings. Firstly, though this was possibly the first Indian study to have examined job performance from mentees’ and mentors’ perspective, perceptions regarding mentoring were not studied from mentors’ perspective. Additionally, it is important to note that for all participants, their mentors were their managers. Therefore, their rating of mentoring that they received could have been influenced by social desirability. Though this was avoided, in part, by assuring them of the confidentiality of their responses, its effects on the findings cannot be discounted completed. As done for job performance, it is desirable to also assess mentoring from mentees’ and mentors’ perspective. Furthermore, the SEM analysis did not consider mentors’ responses per se (it was accounted for only indirectly in Model 3, by including items that took the results of the paired t-test into consideration). Therefore, as also acknowledged by Scandura and Pellegrini (2007), future research needs to conduct an in-depth study of these variables based on mentors’ responses.

Furthermore, in the sampling process, it was attempted to included organizations from a wide range of industrial sectors to improve the generalizability of the study. However, this may have prevented the examination of any industry-specific patterns. It is recommended that future studies explore if the relationship between mentoring and job performance varies across industrial sectors. The current regression analyses did not account for other factors that may influence job performance or perceived mentoring. Indeed, the present sample did not exhibit any group-wise differences in job performance according to the participants’ demographic characteristics. However, the role of other mediating and confounding variables cannot be ignored. Therefore, future studies need to assume a more well-rounded approach. Finally, as the present study employed a cross-sectional design, the findings are insufficient to make causal inferences or to determine the direction of causality. Accordingly, future longitudinal research is recommended.

Though these findings have several limitations, the present study is significant in that it is the first one to examine the relationship between mentoring and job performance among Indian millennials, who comprise a major proportion of the current workforce. Indeed, this study could be used as a launching point for further research with a more refined scope. Additionally, it was the first to assess job performance from mentees’ and mentors’ perspective. The importance of examining management concepts from employees’ and their managers’ perspectives is evident. This feature of the present study too could provide impetus for more holistic multi-informant studies in this field.

Conclusion

Using multiple analysis techniques on data collected from a sizeable sample of Indian millennials who received mentoring, the present study confirmed that mentoring influenced total job performance as well as contextual and task performance as constituents of the latter. Additionally, it revealed that while Indian millennials seemed to focus on their contextual and task performance while evaluating their own job performance, mentors tended to focus more on their contextual rather than task performance. While validating the components, perspectives of mentor and mentee matched more in case of task performance rather than contextual performance. Further, in terms of mentoring, career support emerged as the most significant predictor of job performance for the present sample of Indian millennial mentees, followed by psychosocial support and role modelling. Indeed, these findings have significant implications for mentoring practice.

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About the authors

S. Chatterjee is a CHRO, consultant, coach, and mentor with over 26 years’ experience in the field. He is currently a Professor of HR & OB at Sharda University, India. 

A.K. Dey is a Professor of Operations and Decision Sciences at BIMTECH, India and the Chairperson and Editor of South Asian Journal of Business. Chaturvedi, H. is the Director and a Professor at BIMTECH, India. 

Details

  • Owner: Hazel King
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