Factors that affect group generative collaborations in enterprise social media / Elisavet Averkiadi.
Innovation is closely linked to a company's ability to survive and thrive. As a result, companies are becoming increasingly interested in group generative collaborations - the conception of novel ideas and solutions through group exchanges. This exploratory study investigates the prevalence of gener...
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| Language: | English |
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2020.
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| Dissertation Note: |
Thesis M.A. Michigan State University. Media and Information 2020. |
| Physical Description: | 1 online resource (vii, 56 pages) : illustrations |
| Format: | Thesis Electronic eBook |
MARC
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| 245 | 1 | 0 | |a Factors that affect group generative collaborations in enterprise social media / |c Elisavet Averkiadi. |
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| 502 | |g Thesis |b M.A. |c Michigan State University. Media and Information |d 2020. | ||
| 504 | |a Includes bibliographical references (pages 53-56). | ||
| 520 | |a Innovation is closely linked to a company's ability to survive and thrive. As a result, companies are becoming increasingly interested in group generative collaborations - the conception of novel ideas and solutions through group exchanges. This exploratory study investigates the prevalence of generative collaborations in ESM-based groups and identifies antecedents of such collaborations in groups. Additionally, the nature (i.e. language) of these collaborations is explored. A mixed methods approach is employed for this study, comprised of a content analysis of text-based data from an ESM platform, building a machine learning classifier model, and a predictive regression model. The results of this exploratory study show that approximately 44% of exchanges on an ESM platform contain elements of generative collaborations. Furthermore, the predictive negative binomial regression model illustrates that a group's visibility (closed or open), the bonding and bridging behaviors of group members, and the size of a group are significant antecedents for group generative collaborations. These outcomes are valuable insights that help advance our theoretical understanding of ESM-based group generative collaborations and could also help improve these collaboration behaviors in the context of ESM platforms, and possibly beyond. | ||
| 588 | |a Description based on online resource; title from PDF title page (viewed on Jan. 14, 2021). | ||
| 650 | 0 | |a Business enterprises |x Computer networks. |0 http://id.loc.gov/authorities/subjects/sh95010396 | |
| 650 | 0 | |a Social media. |0 http://id.loc.gov/authorities/subjects/sh2006007023 | |
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