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Can Institutional Support Improve Volunteer Quality? An Analysis of Online Volunteer Mentors

Abstract

Volunteer management practices have been shown to have positive effects on employees in terms of skill development, job success, organizational identity, and morale in the public, nonprofit, and corporate sectors. Despite considerable research on volunteering, questions remain about how management practices of volunteer programs may affect volunteer performance. Leveraging data comparing self-enrolled and corporate-recruited volunteer mentors into a large-scale online program for entrepreneurs, this study measures the impact of institutional support on volunteer intensity, persistence, and quality. It also presents a novel way to measure volunteer quality through sentiment analysis to measure the tone of online messages, an emerging statistical technique. Findings suggest that a high level of institutional support leads to higher quality mentor engagement, compared to self-enrolled volunteers, while a low level of support leads to mentor quality much lower than self-enrolled volunteers.

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Fig. 1

Notes

  1. org.program represents the independent variable of interest, Organization Client Program (Yes) and the three levels of institutional support. This specification is the same across all models.

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Correspondence to Dyana P. Mason.

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Appendix 1—Validation of Sentiment Analysis

Appendix 1—Validation of Sentiment Analysis

The sentiment analysis package provided in TextBlob is trained from movie reviews. Therefore, there may be a concern that it cannot assess the sentiment expressed in messages exchanged between entrepreneurs and mentors. To address this potential concern, we validated the measures by comparing the outputs from the package with an evaluation done by human raters. We randomly picked 2,000 messages out of 126,037 conversation messages in English and asked three graduate research assistants to categorize the messages into four categories: positive, negative, neutral/objective, and ambiguous. Following the practice described in Baqapuri’s (2015) report, we instructed the research assistants to codify the messages with the following criteria.

  • Positive If the entire message has a positive/happy/excited/joyful attitude or if something is mentioned with positive connotations. Also, if more than one sentiment is expressed in the message, but the positive sentiment is more dominant. Example: “You have accomplished a lot so far. Good job!”

  • Negative If the entire message has a negative/sad/displeased attitude or if something is mentioned with negative connotations. Also, if more than one sentiment is expressed in the message, but the negative sentiment is more dominant. Example: “Sorry—I'm probably looking to help someone a bit more established right now and local to the area.”

  • Neutral/Objective If the creator of a message expresses no personal sentiment/opinion in the message and merely transmits information. Advertisements of different products would be labeled under this category. Example: “First I share with you some online pages/articles about Iran and travel to it: CNN's popular article: [PERSONAL WEBSITE].”

  • Ambiguous If more than one sentiment is expressed in the message which is equally potent with no one particular sentiment standing out and becoming more obvious. Also, if it is obvious that some personal opinion is being expressed here, but due to lack of reference to context, it is difficult/impossible to accurately decipher the sentiment expressed. Example: “I kind of like heroes and don’t like it at the same time…”.

The final category is determined by the consensus of the raters, defined as the category upon which two or more raters agree. We had 1,633 messages with two raters assigning the same category, an 82.98% consensus rate. We then reviewed the remaining messages that do not have an agreed-upon category and determine the category for the analysis. Finally, we use the package to generate the polarity and subjectivity scores. The average scores by category are presented in Table

Table 6 Sentiment analysis validation

6.

Table 6 shows that positive and neutral messages account for 91% of the sample. This finding is not surprising because we expect people to interact with each other politely when they seek advice or consult others. We then conduct a series of t tests to see whether the package is able to distinguish one category from the other. The results are summarized in the second half of Table 6.

In terms of polarity, the package is able to separate positive messages from the other three categories. The mean polarity score of positive messages is significantly higher than those of the other three. However, the scores for the remaining three categories are not statistically distinguishable. This finding is consistent with the observation that the majority of messages are either positive or neutral. It is rare to see people express strongly negative emotions in business conversations.

In terms of subjectivity, we observe a similar pattern except for that positive and ambiguous messages are not statistically different. This should not be a surprise either according to our coding instruction. A message is coded as ambiguous because it expresses positive and negative emotion in an equal weight. Therefore, it should score high in subjectivity. The finding that negative and neutral/objective messages are not statistically different from each other also suggests that negative messages we see from the sample are not really “negative” but more of “objective.”

Overall, this analysis provides face validity of our construct. While the package is not trained for this study specifically, we are confident that it is able to evaluate sentiments expressed in the message exchanged between mentors and entrepreneurs.

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Mason, D.P., Chen, LW. & Lall, S.A. Can Institutional Support Improve Volunteer Quality? An Analysis of Online Volunteer Mentors. Voluntas 33, 641–655 (2022). https://doi.org/10.1007/s11266-021-00351-9

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Keywords

  • Nonprofit
  • Volunteering
  • Mentoring
  • Entrepreneurs