Over the past decade, many studies have provided some understanding on what influences association members‟ decision to attend a meeting; however, more systematic and theoretical research is necessary. The decision making process of attendees is a complicated human behavior practice. The major contribution of this study is adopting the sense of community (SOC) model from psychology to gain a better understanding of the attendee behavior. By doing so, this study will add a theoretical foundation to the existing research in the meeting industry. Also, this study will contribute to the SOC research in psychology by applying the concept to a different setting. This study investigates whether annual conferences build a SOC among association members. First, the SOC of association members at the conference will be analyzed. Second, the influence of SOC on members‟ satisfaction with the conference will be studied. Lastly, the direct and indirect relationship between SOC and future intentions (i.e., return to next meeting, membership renewal, and membership recommendation) will be examined. Data was collected through an intercept survey approach at three annual conferences of national/international associations. The questionnaire consisted of four sections: sense of community, satisfaction with the conference, future intentions, and member profile. Confirmatory factor analysis was conducted to test the factor structure and structural equation modeling was used to examine the relationships, and Spearman‟s rank order correlation was used to see the strength of the relationship between respondent characteristics and sense of community. The results showed that sense of community was a strong predictor of future iv intentions. Implications, limitations, and suggestions for future research are discussed in the final chapter.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-3286 |
| Date | 01 January 2012 |
| Creators | Hahm, Jee Yeon |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
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