Enthusiasts, conscripts or instrumentalists? The motivational profiles of event volunteers

Abstract

Recent research into volunteer motivation offers the possibility of improved recruitment and retention techniques for volunteer organizations by designing organizational recruitment messages to match volunteer motivations. Using a sample of event volunteers (N = 488) drawn from five event organizations, this paper tests for the existence of distinct groups of volunteers with similar bundles of motivations (‘motivational profiles’), using cluster analysis. This paper finds six distinct motivation profiles: three types of enthusiasts who love different aspects of the volunteering experience; two types of conscripts, who serve with varying degrees of reluctance; and instrumentalists, who choose to volunteer to obtain some form of material benefit. This finding has potentially great significance for volunteer managers – enabling them to recruit the type of volunteer they want, to be able to identify the volunteer type they do not want, and to design volunteer management practices that better meet the needs of the volunteers they want to retain.

Keywords:

• events
• recruitment
• volunteers
• motivation

Notes

† An earlier version of this paper was published as Treuren, G. (2007). A multiple motivational approach to classifying the reasons why people event volunteer. In R. Chapman (Ed.), Managing our intellectual and social capital, Proceedings of the Australia and New Zealand Academy of Management conference held in Sydney, December 2007.

1. In this paper, variables are described as having a mean (M) and a standard deviation (SD). The mean (M) is the average score of the variable, and is calculated as the sum of all of the scores of that variable divided by the number of scores. The standard deviation (SD) reflects how much the scores of a variable vary around the average. The larger the standard deviation, the more widely the scores of that variable are distributed around the mean. A low SD suggests that the scores for that variable are tightly packed around the mean. A high score means that scores are more widely dispersed. One of the properties of standard deviations is that 68.3% of scores in a normally distributed sample are within one standard deviation of the mean. As a result, a variable can be described in terms of its mean and its standard deviation. These scores can be used to neatly describe a case, or group of cases, such as a profile, in dimensionless terms using standardized scores. A case can be described in terms of how many standard deviations it is above or below the mean score. In Table 4, for example, Profile 1 has an EgoAlt score of 2.7, while the average EgoAlt score for all respondents was 3.49, with a Standard Deviation of 1.07. Profile 1 can thus be described as having a standardised score of −0.74 (2.7–3.49/1.07).