Introduction: There has been considerable interest in e-mentoring in recent years (i.e., Bonnett, Wildermuth, & Sonnenwald, 2006; DiRenzo, Linnehan, Shao, & Rosenberg, 2010). E-mentoring helps eliminate many obstacles that otherwise limit the development of mentoring relationships, such as: geographical barriers, lack of suitable mentors in an area, and the incompatibility of a match based on various identity statuses (Bierema & Merriam, 2002; Ensher & Murphy, 1997; Ensher et al., 2003; Fagenson-Eland & Lu, 2004; Kasprisin et al., 2002; Single & Single, 2005; Stewart & McLoughlin, 2007). E-mentoring, however, comes with a set of its own challenges, including:
- the time and resources required to set-up and maintain e-mentoring websites
- supporting dynamic, and genuine two-way communication while overcoming the lack of body language and non-verbal cues (Ensher et al., 2003; Stewart & McLoughlin, 2007).
This study aims to identify the factors that influence the adoption of e-mentoring by mentors. The goal is to shed light onto the current state of e-mentoring initiatives.
Using mixed-methods, the researchers examined mentors’ perceptions of e-mentoring, including:
- perceptions of relative advantage
- compatibility – the degree to which mentors understand the similarities between traditional and e-mentoring
- opportunity to practice e-mentoring in an experimental environment
- significant changes due to the program.
The authors focused on five particular predictor variables
- relative advantage
- computer self-efficacy
- perceived pressures from mentees
- personal innovativeness
First, an extensive review of the innovation and e-mentoring literature was completed. Next, three mentoring experts and three e-mentoring mentors were interviewed and then their responses were content analyzed. The results were used to produce a questionnaire on experience in e-mentoring practices. The questionnaire was piloted with 12 e-mentoring mentors prior to being disseminated to mentors at Athens University of Economics and Business. Then, 234 mentors (46% male) completed the questionnaire. They averaged between 46 and 60 years old, with a bachelor’s degree, and average income of 2500-3000 euro/month.
– Perceived relative advantage of the e-mentoring program, personal innovativeness, computer self-efficacy and gender were all positively correlated with the adoption of e-mentoring, whereas, the more obstacles (i.e., lack of software/hardware, lack of IT support, low internet speed, lack of training) that were endorsed the lower the adoption rate of e-mentoring.
– Compatibility, complexity, opportunities to pand observability of effects all failed to have significant effects on e-mentoring adoption
– Relative advantage, however, was the only innovation attribute found to be statistically significant in predicting e-mentoring adoption.
– age and gender differences were tested and revealed that younger (<46 years) male mentors had higher average levels of personal innovativeness and computer self-efficacy, followed by younger female mentors, whereas older women reported the most problems, reluctance to innovate and felt less computer literate.
This study is the first study to examine the adoption of e-mentoring, and not merely the effects of an attempted implementation. Thus it provided important insights and observations into what aspects to focus on in mentor training (prior to actual program implementation) to increase the chances of successful e-mentoring adoption by mentors.
Mentors’ perception of the relative advantage of the e-mentoring program (over traditional mentoring) was found to be an important predictor in the successful adoption of e-mentoring programs.
Mentors’ computer self-efficacy and personal innovativeness should be considered when examining the adoption of a program that includes information technology, as is the case in e-mentoring.