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Proactive Assessment for Collaboration Success

Author

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  • Teresa L. Ju
  • Yao Chin Lin
  • Nhu-Hang Ha

Abstract

This study describes a government–academia–industry joint training project that produces Vietnamese midlevel technical managers. To ensure collaboration success, a proactive assessment methodology was developed as a supplement to the conventional project management practices. In the postproject feedback, the funding agencies acknowledged that the project fulfilled its contractual obligations and achieved its objectives. The implementing university was pleased as it broke ground in this type of collaboration in Taiwan. The industrial partners, however, were not so sure about the effectiveness of this collaborative training endeavor because there were many skirmishes between company supervisors and Vietnamese interns caused by the interns’ self-interested perception and expectation. Consequently, a theoretical framework for predicting internship acceptance and preventing unfavorable perceptions was proposed to strengthen the proactive assessment methodology. Collaboration research, funding agencies, academia, and industry could all benefit from this study.

Suggested Citation

  • Teresa L. Ju & Yao Chin Lin & Nhu-Hang Ha, 2014. "Proactive Assessment for Collaboration Success," SAGE Open, , vol. 4(3), pages 21582440145, July.
    • Handle: RePEc:sae:sagope:v:4:y:2014:i:3:p:2158244014541780
    DOI: 10.1177/2158244014541780
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    References listed on IDEAS

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    2. Minjeong Kim & Han Woo Park, 2012. "Measuring Twitter-based political participation and deliberation in the South Korean context by using social network and Triple Helix indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(1), pages 121-140, January.
    3. Viswanath Venkatesh & Fred D. Davis, 2000. "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management Science, INFORMS, vol. 46(2), pages 186-204, February.
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    Cited by:

    1. Schnaubelt, Matthias & Fischer, Thomas G. & Krauss, Christopher, 2020. "Separating the signal from the noise – Financial machine learning for Twitter," Journal of Economic Dynamics and Control, Elsevier, vol. 114(C).

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