A Study of the Mentees’ Perspective of the Informal Mentors’ Characteristics Essential for Mentoring Success

Abstract

Researchers across the world have shown interest in studying organizational mentoring. This article attempts to identify informal mentor characteristics that are considered significant by mentees for the effectiveness of the mentoring experience. Data used for this research comprise 311 managerial-level employees, employed and residing in the city of Mumbai, Delhi and Kochi in India. First, an exploratory factor analysis is done to understand how the items representing the specific categories in the qualitative data would cluster. This results in six factors. A confirmatory factor analysis is then done to test this model, and it comes up with a not so adequate fit. Thereafter, the six factors are reorganized into four factors based on the canonical correlations between the factors. The resultant four-component model shows a good fit compared to the six-component model. The four mentoring characteristics that are identified through this study (in no specific order) are sincerity, commitment, skill and knowledge development and organizational ascendency and impact. The results obtained thus represent an attempt to describe the significant informal mentor characteristics from the mentees’ perspective in a contemporary business environment.

Keywords

Informal mentoring mentor traits mentor functions mentor characteristics mentor roles mentee’s perception

Introduction

Mentoring happens in all walks of life. The older generation guides the younger generation almost in all professions and occupations. This is applicable in many different business set-ups as well. Since business organizations are artificial social institutions, mentoring as a process has been formalized in many business set-ups, such as, health care (Perrone, 2003) and accounting (Weinstein & Schuele, 2003).

Mentoring is a developmental experience for a younger and less experienced individual by an older and more experienced individual (Savage, Karp & Logue, 2004). More often than not, the relationship creates a win-win situation for both the parties involved, that is, for the mentor (the older and more experienced person) and the mentee (the younger and the less experienced person). Practically speaking, mentoring is immensely useful to organizations as mentees pick up a lot of tacit knowledge from the mentors, who in turn prepare them for organizational responsibilities (Kram & Hall, 1996). Employee growth and advancement is positively affected by mentoring (Arifeen, 2010).

The term ‘mentoring’ is quite ambiguous with many definitions, some of which contradict each other (Jacobi, 1991). There is still no consensus amongst academicians and practitioners over a single definition on mentoring (Ehrich & Hansford, 1999). Girves, Zepeda and Gwathmey (2005) defined mentoring as ‘an intentional process that is supportive, nurturing and protective, providing orchestrated or structured experiences to facilitate growth’ (p. 453).

Mentoring is a process wherein a senior and more experienced person in the organizational hierarchy, that is, the mentor, takes keen interest in the personal and professional development of a junior and less experienced employee, that is, the mentee (Noe, Greenberger & Wang, 2002; Russell & Adams, 1997).

Formal mentoring implies developing a mentoring policy, identifying mentees against a set of predetermined objectives, selecting mentors on some predefined criteria, pairing of the mentors and mentees, documenting the process of mentoring through minutes of meetings or any other templates prepared by the facilitators, giving feedback to the mentors and mentees and closing the process at an agreed-upon timeline. Some organizations even award title to the most effective mentors as Mentoring Champions or with similar titles to acknowledge good work done.

There are some serious pitfalls to formal mentoring (Jacobi 1991; Kram & Bragar 1991; Noe, 1991; Tellez 1992; Wright & Werther, 1991; Wright & Wright 1987). Though drafting the mentoring policy may not be very complicated, there is still doubt whether the documented goals and the unwritten goals are complementary or otherwise. While identifying mentees might seem easier, identifying mentors who would be willing to spare their time to develop a junior employee without any tangible rewards may be quite a task. Further on, matching the mentors to the mentees is a daunting challenge. Many formal mentoring programmes fail because of inappropriate pairing of mentors and mentees. The most debatable aspect of formal mentoring is the imposition of a particular mentor on to mentees. Research suggests that the highest form of learning occurs when mentees exercise their free will and choose mentors from whom they wish to learn. This is a luxury that only informal mentoring can offer.

There is evidence to state that informal mentoring is more effective than formal mentoring for development of employees to take place (Eby & Allen, 2002; Eby et al., 2004). Informal mentoring encourages growth and learning. Informal mentoring allows people to choose their mentors to learn from. There are no formats, no reports, no accountability to some facilitator and no compulsion whatsoever. Thus, the present article chooses to focus only on informal mentoring in the context of Indian business organizations.

Literature Review

In recent times, researchers across the world have engaged themselves much in discussing informal mentoring in organizations, its various aspects and its importance. Some of these works are: Ragins and Cotton (1999) study the effects of the type of mentoring relationship and the gender composition of the relationship on mentoring functions and career outcomes. Based on responses of 352 female and 217 male protégés, they obtain that the protégés of informal mentors perceive their mentors to be more effective and they get greater compensation than the protégés of formal mentors. Protégés with informal mentors also have been found to have more career outcomes than the non-mentored individuals. However, no significant differences are found between non-mentored and formally mentored individuals. The gender composition of the relationship is found to affect the mentoring functions and outcomes, and the gender of the protégé who interacts and the type of relationship is also found to affect mentoring functions.

Singh, Bains and Vinnicombe (2002) do a survey of managers in the local government authority of United Kingdom to ascertain their views on the benefits of informal mentoring to the organization. Both mentors as well as the protégés perceive mentoring as an investment in a future pool of managers and a tool for management of change. Mentoring is also seen as a means to transfer knowledge, organizational learning and cross-departmental communication; that is, mentoring is viewed as nodes in an information network.

Inzer and Crawford (2005) review the literature of formal mentoring programmes in organizational settings and come up with the result that though formal mentoring is possible in organizations, it is less effective than informal mentoring. This is a descriptive study and draws largely from the studies of other researchers.

Okurame and Balogun (2005) study the role of informal mentoring in making successful career in an African work environment. Using data on bank managers from 10 banks located in four central business districts of Lagos State, Nigeria, they conduct a hierarchical regression analysis to reveal that informal mentoring accounts for a significant proportion of the variance in career success. Karkoulian, Halawi and McCarthy (2008) examine how both formal and informal mentoring helps to improve knowledge management in the Lebanese banking sector. Their results suggest that informal mentoring is highly correlated with knowledge management. But, there is little support found for formal mentoring.

Welsh, Bhave and Kim (2012) try to understand the extent to which potential mentors and protégés agree that an informal mentoring relationship exists. They also try to find out if gender affects this agreement. Based on individuals identifying their mentors and mentees, these are compared with each others’ responses and then the patterns of matches and mismatches are analyzed to understand the level of matching and gender differences. It is found that there is little agreement between mentoring partners: neither potential protégés nor potential mentors are much accurate at identifying reciprocal informal mentoring partners. Besides, gender is not found to be related to different levels of matching.

Though all these works discuss informal mentoring, not much research has been carried out in the area of informal mentor characteristics, particularly in the context of the Indian business organizations. The present article seeks to fulfil this gap.

The rest of the article is organized as follows: Section three defines the scope and the research objective of the study. Section four discusses the instrument and section five presents the data. The analytical framework and the results of the analysis are presented in section six. Section seven presents the discussion based on the findings. The article finally concludes in section eight.

Scope of the Research and Research Objective

After an informal mentoring relationship has been established, it is entirely the mentor’s and the mentee’s personal commitment and intrinsic motivation that drive the process. It may be assumed that typically the mentor is more senior, older and experienced than the mentee, hence (s)he may enjoy more formal and informal power in the relationship. The mentee usually looks up to the mentor as a role model. We may also assume that in a way the success of informal mentoring largely depends on the mentor and his/her personal characteristics. As cited in Niehoff (2006, p. 321), ‘While the value and quality of mentoring depends partly on the quality of the mentors, little research has focused on the characteristics of the mentors (Allen, 2003; Allen et al., 1997a, b).’ Understanding mentor traits will help us in understanding what mentors’ qualities do mentees look for in mentors (Wanberg et al., 2003).

This is the research idea behind this article. It would be interesting to find out what personal characteristics of mentors are important for informal mentoring success. Mentor characteristics are the mentor functions and mentor traits put together. There is evidence to state that not much research has been carried out in the area of mentor characteristics (Allen, 2007). To understand this, the employees who have chosen to have informal mentors are asked to rate the mentor characteristics on a given scale. The scale has been explained in the next section of the article. The informal mentors are not asked for self-evaluations for two reasons: (i) an experienced and senior member of an organization can claim to be an informal mentor, but there is no mechanism to validate this claim and (ii) self-evaluations are subjected to more perception errors than another person (mentee) rating the mentor.

It is true that the mentee’s opinions are also perceptions, but these are the views that really matter. The present study focuses on finding out from the mentees what characteristics of the informal mentors do they find as essential for success. This research will be useful to the middle and senior managers, particularly in Indian business organizations, who aspire to become mentors to the younger generation but do not know what it is that the younger generation is looking for. Understanding their needs will help the mentors to be more useful to the mentees, organizations and societies at large.

Instrument

Amongst the various research findings in the area of mentoring, one of the most cited works is that of Kram (1985). In her pioneering work, Kram developed a mentoring model in which the mentor has two distinct roles to play. One is where the mentor caters to the career development of the mentee and another one where the mentor caters to the psychosocial needs of the mentee. The career development functions include challenging assignments, coaching, exposure, protection and sponsorship. The psychosocial functions include acceptance, counselling, friendship and role modelling.

While there are many more, at least three instruments for measuring mentoring functions are well accepted (Ragins, 1999). These are the mentoring functions scale (Noe, 1988), the mentoring functions questionnaire (Scandura, 1992; Scandura & Ragins, 1993) and the mentoring role instrument (Ragins & Cotton, 1999; Ragins & Mcfarlin, 1990). One unmistakable implication of the striking differences between these three instruments is the ambiguity around the concept of mentoring functions (Wanberg et al., 2003).

It is an issue of academic debate as to how many mentoring functions does a mentor actually performs (Scandura & Williams, 2001; Tepper, Shaffer & Tepper, 1996). Mentoring functions/functions performed by a mentor can be assumed to be the same as mentoring roles/roles performed by a mentor (Ehrich & Hansford, 1999).

While many researchers have accepted Kram’s framework (Noe, 1988), some debate over a third mentor function, that is, role modelling (Scandura, 1992; Scandura & Ragins, 1993). Another study by Steinberg and Foley (1999) also concludes on three mentoring functions, namely, career sponsoring, job coaching and personal development. The highest numbers of mentor functions to be clearly spelt out are 21 (Smith, Jerusalesm & Vernard, 2005). They have established 38 mentor characteristics, that is, 21 mentor functions and 17 mentor traits. The 17 mentor traits are ability to teach, empathy, honesty, organizational savvy (ability to understand how company works), understands company’s core values, bearing/personal presence, willingness to share time, compassion/understanding, concern for effectiveness, confidentiality, dependability, genuine, high moral and ethical standards, integrity, knowledge, professional competence and trust. The 21 mentor functions are coach; provides support; provides vision and widens horizons; teamwork; acceptance; broaden experience; challenges; cooperation; discipline; exposure and visibility; follow-up; intervener; introduction to corporate culture; motivates; networking ability; provide cross-functional information; role model; share credit; sponsor; teacher; and transfer skills, leadership and technology.

The present study is based on these 38 mentor characteristics (21 mentor functions and 17 mentor traits). These 38 mentor characteristics (17 mentor traits and 21 mentor functions) have been used for developing the questionnaire. A statement for each of the 38 mentor characteristics has been developed, and thus, the instrument/questionnaire has 38 items (each representing one characteristic). These are on a six-point Likert scale where strongly disagree was given one point and strongly agree was accorded six points (disagree—two points, slightly disagree—three points, slightly agree—four points and agree—five points).

To understand the presence (or absence) of an informal mentor in the life of the mentee, three questions are asked to the respondent of which if two or more are answered positively, it is concluded that the individual had an informal mentor in the organization. The three questions asked are:

Do you have a senior and more experienced member in the organization who takes keen interest in your personal development?

Do you have a senior and more experienced member in the organization who takes keen interest in your professional development?

Do you have a senior and more experienced member in the organization who you consider as your friend, philosopher and guide?

These three questions were used to segregate mentored employees from the non-mentored employees, and then only the mentored employees (hereinafter referred to as mentees) were administered the questionnaire.

Data

The sample consisted of 311 executives, employed and residing in the city of Mumbai, Delhi and Kochi. Data are gathered from different sectors of the Indian business industry. This includes the banking sector, the oil and petroleum sector, logistics sector, hospitality sector, the financial sector, IT and telecommunications, pharmaceuticals, textiles, retail, fast-moving consumer goods (FMCG), shipping, construction, media and engineering. A convenience sampling technique is used. The employees are all managerial-level employees from varied departments varying in age from 21 to 40 years. There are no respondents below the age of 20 years, and the maximum number of mentees where in the age bracket of 25–30 years. Refer Table 1 for age-wise classification.

Table 1.

Age-wise Classification of the Sample

Sectors	Age
Sectors	1	2	3	4	5	6	7	Total
Aviation	0	1	0	0	0	0	0	1
Banking	0	1	7	13	2	20	20	63
BPO	1	6	13	3	0	0	0	23
Communications	0	1	0	0	0	0	0	1
Constructions	0	1	1	0	0	0	0	2
Consulting	0	2	4	0	0	0	0	6
Education	0	0	0	0	1	0	0	1
Engineering	0	0	2	0	1	0	0	3
Finance	0	0	1	0	0	0	0	1
FMCG	0	0	0	1	0	0	0	1
Freight forwarding	0	2	7	2	3	0	0	14
Hospitality	0	1	9	1	0	0	1	12
IT	0	6	7	2	0	0	0	15
ITES	0	0	2	0	0	0	0	2
Infrastructure development	0	0	2	1	0	0	0	3
Insurance	0	3	3	2	0	0	0	8
KPO	0	1	0	0	0	0	0	1
Manufacturing	0	1	4	1	1	0	0	7
Media	0	2	0	0	0	0	0	2
Operations	0	1	1	0	0	0	0	2
Pharmaceuticals	0	1	2	0	0	0	0	3
Petroleum and petro products	0	3	9	14	15	27	38	106
Placement	0	1	1	0	0	0	0	2
Power	0	1	0	0	0	0	0	1
R&D	0	0	1	0	0	0	0	1
Retail	0	1	0	0	0	0	0	1
Service	0	4	5	1	0	1	0	11
Shipping	0	2	0	0	0	1	1	4
Telecom	0	0	1	4	2	0	0	7
Trading	0	0	1	1	0	0	0	2
Travel and tourism	0	0	0	1	1	0	0	2
								308*

Source: Author’s own.

Note: *Remaining 03 samples do not have their ages mentioned.

Analytical Framework and Results

The underlying factor structure of the instrument is first analyzed using an exploratory factor analysis (EFA, version 19 of Statistical Package for the Social Sciences [SPSS]). This analysis examines the relationships between various variables without determining the extent to which the results fit a particular model. Hence, in the second phase, a confirmatory factor analysis (CFA, SPSS Amos version 21) is done to test the model that emerges from the EFA.

Exploratory Factor Analysis (EFA)

An EFA on the items (17 traits and 21 functions) as measured in the present article is conducted using principal axis factoring with promax rotation. This is done to understand how the items representing the specific categories in the qualitative data would cluster. To begin with, the correlation matrix for the 38 items that make up the mentor traits and mentor functions (as perceived by the mentees) is computed to understand the applicability of the EFA. The matrix shows all the 38 items are significantly correlated at less than 0.05 levels, which suggests that they constitute one or more factors and hence make a case for EFA. The Kaiser–Meyer–Olkin (KMO) and Bartlett’s test of sphericity measures are also obtained. The KMO, which measures the sampling adequacy, should be greater than 0.5 for a satisfactory factor analysis to proceed. The KMO measure obtained for present data is 0.95. The Bartlett’s test of sphericity is also significant (significance is 0.000), which means that the correlation matrix for the set of underlying measurable variables is not an identity matrix and hence the variables are not correlated and thereby each measurable variable is indeed a factor influencing response. Thus, the case for conducting an EFA on the data on mentee responses is checked. The descriptive statistics related to this data are also obtained and have been reported in Table 2.

Table 2.

Descriptive Statistics of the Sample Data

	N	Mean	Std. Dev.
Ability to teach	311	5.40	0.702
Ability to coach	311	5.17	0.806
Shows empathy	311	5.10	0.903
Honest person	311	5.24	0.879
Organizational savvy	311	5.59	0.600
Provides support	311	5.40	0.759
Provides vision and widens horizons	311	5.20	0.817
Teamwork	311	5.25	0.858
Understands company core values	311	5.42	0.718
Willingness to share time	311	4.93	1.066
Acceptance	311	5.01	0.933
Personal presence	311	5.28	0.747
Broaden experience	311	5.25	0.741
Challenges	311	4.98	0.910
Compassion	311	5.13	0.863
Concern for effectiveness	311	5.28	0.875
Confidentiality	311	5.16	0.933
Cooperation	311	5.22	0.852
Dependability	311	4.72	1.399
Discipline	311	5.26	0.842
Exposure and visibility	311	5.24	0.808
Follow-up	311	5.19	0.862
Genuine	311	5.28	0.867
High ethical standards	311	5.25	0.858
Integrity	311	5.08	0.885
Intervener	311	5.03	0.872
Introduction to corporate culture	311	4.92	1.064
Knowledge	311	5.47	0.656
Motivates	311	5.39	0.766
Networking ability	311	4.92	0.966
Professional competence	311	5.42	0.736
Provides cross functional info	311	4.81	1.120
Role model	311	5.17	0.883
Shares credit	311	5.21	0.858
Sponsor	311	5.03	0.987
Teacher	311	5.27	0.809
Transfer leadership skills	311	5.36	0.802
Trust	311	5.38	0.830
Valid N (list-wise)	311

Source: Author’s own.

These statistics give information about the descriptive qualities of the underlying data. The number of cases in the data set is recorded in column tilted ‘N’. As the purpose of the study is to find out the factors underlying a group of variables, it is essential that the sample should be sufficiently large to enable this to be done reliably. Thus, it is important to identify if there are any missing data and to determine the sample size on which the analysis is based. Here N equals 311, which indicate that there are no missing data, and the analysis is based on 311 samples on which data were originally collected. The third and fourth columns of Table 1 give the average and standard deviation of the variables used in the EFA.

The analysis comes up with six factors that have eigenvalues greater than 1, and they account for 52 per cent of the total variance. The scale items together with the factor loadings of the EFA are reported in Table 2.

On the basis of the item content, these factors are named as: sincerity, commitment, grooming, value-based thinking, perspective building, skill and knowledge development. The sets of items defining the six components have high internal consistencies as measured by Cronbach alpha (Nunnally & Bernstein, 1994). These ranged from 0.73 to 0.89 (refer Table 3).

Table 3.

Exploratory Factor Analysis for Identifying Mentor Characteristics

Factors	Variables	Factor Loadings	% of Variance	α coefficient
Sincerity	Honest personCompassionConfidentialityDependabilityDisciplineGenuineHigh ethical standardsIntegrityTrust	0.710.520.450.330.430.780.730.500.56	39.3	0.88
Commitment	Shows empathyProvides supportWillingness to share timeAcceptanceCooperationFollow-upIntervener	0.690.450.810.660.390.500.36	4.2	0.89
Grooming	Personal presenceExposure/visibilityIntroduction to cultureNetworking abilityShares credit	0.360.550.690.650.32	3.04	0.81
Value-based thinking	Organizational savvyProvides and widens horizonTeamworkUnderstands core company valuesConcern for effectivenessKnowledge	0.530.340.320.630.430.34	2.27	0.79
Perspective building	Broaden experienceChallengesMotivatesProfessional competenceProvides cross functional informationRole modelSponsorTransfers leadership skills	0.360.470.410.550.540.430.430.38	1.7	0.85
Skill and knowledge development	Ability to teachAbility to coachTeacher	0.690.470.57	1.6	0.73

Source: Author’s own.

Confirmatory Factor Analysis

A CFA is done on the data at the second phase to test the model that emerged when the EFA was done. In fact, a series of CFA is performed and compared in terms of goodness of fit to determine the most appropriate scenario. For each of these scenarios, the model fit is compared in terms of six indices: chi-square statistic, the goodness fit index (GFI), the adjusted goodness of fit Index (AGFI), the chi-square comparative fit index (CFI), Tucker–Lewis index (TLI) and the root mean square error of approximation (RMSEA).

The first scenario involves the six-component model that emerged from the EFA on the mentees’ responses. The results of this six-component model (refer Figure 1) did not show an adequate fit. The chi-square (χ²) statistic was 1,669.94 (degrees of freedom = 650, p = .001) with the χ²/df ratio having a value of 2.57, which is less than 3.0. Following Joreskog and Sorbom (1993), it should be between 0 and 3, with smaller values indicating better fit. However, problem with this measure of fit is that it generally tends to come up with conservative estimates for fit when many variables are analyzed and is sensitive to sample sizes. Thus, the chi-square CFI is also reported. This is a measure of fit that is not adversely affected by sample size and projects a good fit when it is close to 0.95 (Hu & Bentler, 1999). Here it is 0.840. The other fit indices reported are: GFI = 0.771, AGFI = 0.739 and TLI = 0.827. All these scores including that of CFI are less than 0.9, which is not satisfactory. These scores if closer to 1.0 indicate better fit with a value of 1.0 indicating a perfect fit (Bentler, 1992; Bentler & Bonett, 1987). The next set of fit statistics focus on the RMSEA. The RMSEA that measures discrepancy per degree of freedom is considered for values of 0.08 or lower (Browne & Cudeck, 1993). For this six-component model, it is 0.071 and hence indicates a good fit.

Figure 1.

Source: Author’s own.

The standardized coefficient estimates of the factor loadings are all positive and above the acceptable level (>0.3) but are all not very high (some are as low as 0.45, 0.51, etc.). However, the correlations between the six factors are not much on the lower side with the highest being 0.93.

Thus, in an attempt to improve the model fit, the items with low factor loadings are eliminated. There are nine such factors. The new model (path diagram in Figure 2) shows a slightly improved fit with χ²/df = 2.86, CFI = 0.87, TLI = 0.853, GFI = 0.805, AGFI = 0.765 and RMSEA = 0.077. But yet the fit is not good enough. Besides, as observed from Figure 2, there is one canonical correlation (r_c) value of more than 1, which is between the components grooming and value thinking. This shows there exists high multicollinearity (1.01). This means the items under these factors/components are duplicating between each other. This result also shows that the discriminant validity requirement is not met (Chinna, 2009).

To remedy a situation like that, either one of the factors could be removed or both factors could be combined. Based on the review of literature, both factors are considered important for mentor’s characteristics and hence it is chosen to combine the two factors and rename the new component as ‘organizational ascendency’. The fit of the resulting model shows an improvement over the earlier model (refer Figure 3). But still the fit does not seem to be satisfactory enough. Besides, some of the factor loadings are still found to be much low (0.59). Further, the canonical coefficient between factors grooming and value thinking and perspective building is as high as 0.94, indicating discriminant validity issues.

Thus, these variables (four of them with low factor loadings) are further removed and two variables (organizational ascendency and perspective building) are combined and renamed as ‘organizational ascendency’ and ‘impact’. The resultant four-factor model shows an adequate fit with χ²/df = 2.08, CFI = 0.917, TLI = 0.906, GFI = 0.85, AGFI = 0.82 and RMSEA = 0.065. All canonical correlations show values reasonably less than 1.0, implying discriminant validity issues are tested and acceptable. Though the canonical correlation between commitment and organizational ascendency is much high (0.91), we accept this model given its good fit. On the factor loadings, the standardized coefficient estimates are between 0.60 and 0.81 and hence are good as they are much above their acceptable level of 0.3 with p-value less than 0.001.

As against this four-component model (see Figure 4), two models with alternative scenarios are tried for fit. These are in line with previous literature that points to two broad mentoring functions, ‘psychosocial’ and ‘career’, as was originally identified by Kram (1980). The first model examined the two broad functions of ‘psychosocial’ and ‘career’ as a two-component model, while the second specified these two components as second-order factors to the six components originally obtained from the EFA. The results of these two models (refer Table 4) indicate a much poorer fit compared to the four-component model suggested by the present article.

Thus, the present article identifies four distinct characteristics of mentoring as perceived by the mentees, that is, sincerity, commitment, skill and knowledge development and organizational ascendency and impact. The results of the CFA showed that these four components that capture all functions and traits of mentors as perceived by mentees exhibit both convergent and discriminant validity.

Figure 2.

Source: Author’s own.

Figure 3.

Source: Author’s own.

Figure 4.

Source: Author’s own.

Table 4.

Results of Two-component and Second-order Two Component Models

Model	Goodness of Fit Indices
Two-component model	χ²/df = 2.99CFI = 0.793TLI = 0.781GFI = 0.707AGFI = 0.673RMSEA = 0.08
Two-component second-order model	χ²/df = 2.59CFI = 0.836TLI = 0.824GFI = 0.764AGFI = 0.734RMSEA = 0.072

Source: Author’s own.

Discussion

The present article identifies four distinct characteristics of informal mentoring that are perceived significant by the mentees for effectiveness of the mentoring experience. Unlike earlier researches, the present study uses samples drawn from a number of organizations across business sectors in India and worked from a base in qualitative to a quantitative analysis using EFA and CFA. The results obtained thus represent an attempt to describe the characteristics of informal mentors from the mentees’ perspective in a contemporary business environment.

The four informal mentoring characteristics that are identified through this study (in no specific order) are sincerity, commitment, skill and knowledge development and organizational ascendency and impact. The study defines a mentor as sincere if he is perceived to be an honest person who is genuine and holds high ethical standards, integrity and is trustworthy. The mentor can be said to have commitment to the mentoring process if he provides support, is willing to share time, is cooperative, shows empathy and support, shares credit, intervenes and is accepting towards the mentee. Skill and knowledge development here refers to the mentor’s ability to teach and coach. Organizational ascendency and impact refers to the personal presence of the mentor, giving exposure and visibility to the mentee, introducing the mentee to the corporate culture and networking in general. A mentor who widens horizons, has concern for effectiveness, instils teamwork and understands the company values can be said to be imparting value-based thinking to the mentee. Perspective building is also included in this mentor characteristic. It implies that the mentor provides cross-functional information, broadens experiences, challenges, motivates and transfers leadership skills. The mentor is also a sponsor and a role model and enhances the professional competence of the mentee under this characteristic.

There are a number of similarities between the final outcome of the study and the functions identified by Kram (1980) in her pioneering work. For example, Kram’s career development function of exposure parallels with grooming characteristic in our study. Also, coaching as a career development function of the mentor as developed by Kram is very similar to skill and knowledge development characteristic in our study.

In comparison to previous factor analytical studies of mentoring functions that identified two- or three-category models (e.g., Noe, 1988; Scandura & Katerberg, 1988; Schockett & Haring-Hidore, 1985; Tepper et al., 1996; Turban & Dougherty, 1994), the data of the present study were consistent with a model with four distinct functions. Indeed, results showed this four-component model to be having a much better fit than either a two-component (career and psychosocial) or a second-order two-component model.

Conclusion

There are a large number of studies identifying formal mentor characteristics that are important for mentoring success. But there seems to be little work in the area of informal mentors’ characteristics that are essential for mentoring success especially in the Indian context. The present article has attempted to identify such informal mentor characteristics that are considered significant by the mentees for the effectiveness of the mentoring experience. For this, a sample of 311 managerial-level employees is drawn from a number of organizations across business sectors in India. The study has worked from a base in qualitative to a quantitative analysis using EFA and CFA. The four mentoring characteristics that are identified are sincerity, commitment, skill and knowledge development and organizational ascendency and impact. This four-component model shows a good fit as compared to some existing models in literature, and the results obtained thus represent an attempt to describe the characteristics of informal mentoring from the mentees’ perspective in a contemporary business environment. The scope for further research in this area can include the mentor’s perspective. Combining the mentor’s and mentee’s perspective can give a holistic perspective to the theme. Mentoring functions as a variable largely gets influenced by the phase of the informal mentoring relationship (Wanberg et al., 2003). This has not been taken into consideration in our study. Also findings can always be strengthened if cross-validated on larger and more diverse samples.

Footnotes

Acknowledgements

The authors are grateful to the anonymous referees of the journal for their extremely useful suggestions to improve the quality of the article. Usual disclaimers apply.

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