Building relationship innovation in global collaborative partnerships: big data analytics and traditional organizational powers. (8th December 2016)
- Record Type:
- Journal Article
- Title:
- Building relationship innovation in global collaborative partnerships: big data analytics and traditional organizational powers. (8th December 2016)
- Main Title:
- Building relationship innovation in global collaborative partnerships: big data analytics and traditional organizational powers
- Authors:
- Akhtar, Pervaiz
Khan, Zaheer
Rao‐Nicholson, Rekha
Zhang, Minhao - Abstract:
- Abstract : This study examines how relationship innovation can be developed in global collaborative partnerships (alliances, joint ventures, mergers, and acquisitions). The recently emerging theory of big data analytics linked with traditional organizational powers has attracted a growing interest, but surprisingly little research has been devoted to this important and complex topic. Therefore, after developing the theoretical foundations, our study empirically quantifies the links between the theoretical constructs based on the data collected from chief executive officers, managing directors, and heads of departments who work in contemporary global data‐and‐information driven collaborative partnerships. The results from structural equation modeling indicate that the relationship innovation depends on the power of big data analytics and non‐mediated powers (NMP, expert and referent). The power of big data analytics also mediates the correlation between NMP and relationship innovation. However, mediated powers (coercive and manipulative) negatively affect the power of big data analytics and relationship innovation. The interaction effects further depict that analytically powered partnerships have better relationship innovation compared with those which focus less on the analytical power. Consequently, the contributions of this study provide a deeper understanding of mechanisms of how modern collaborative partnerships can use big data analytics and traditional organizationalAbstract : This study examines how relationship innovation can be developed in global collaborative partnerships (alliances, joint ventures, mergers, and acquisitions). The recently emerging theory of big data analytics linked with traditional organizational powers has attracted a growing interest, but surprisingly little research has been devoted to this important and complex topic. Therefore, after developing the theoretical foundations, our study empirically quantifies the links between the theoretical constructs based on the data collected from chief executive officers, managing directors, and heads of departments who work in contemporary global data‐and‐information driven collaborative partnerships. The results from structural equation modeling indicate that the relationship innovation depends on the power of big data analytics and non‐mediated powers (NMP, expert and referent). The power of big data analytics also mediates the correlation between NMP and relationship innovation. However, mediated powers (coercive and manipulative) negatively affect the power of big data analytics and relationship innovation. The interaction effects further depict that analytically powered partnerships have better relationship innovation compared with those which focus less on the analytical power. Consequently, the contributions of this study provide a deeper understanding of mechanisms of how modern collaborative partnerships can use big data analytics and traditional organizational powers to co‐create relationship innovation. … (more)
- Is Part Of:
- R & D management. Volume 49:Number 1(2019)
- Journal:
- R & D management
- Issue:
- Volume 49:Number 1(2019)
- Issue Display:
- Volume 49, Issue 1 (2019)
- Year:
- 2019
- Volume:
- 49
- Issue:
- 1
- Issue Sort Value:
- 2019-0049-0001-0000
- Page Start:
- 7
- Page End:
- 20
- Publication Date:
- 2016-12-08
- Subjects:
- Research, Industrial -- Management -- Periodicals
658.57 - Journal URLs:
- http://www.blackwell-synergy.com/servlet/useragent?func=showIssues&code=radm&open=2001#C2001 ↗
http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467-9310 ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1111/radm.12253 ↗
- Languages:
- English
- ISSNs:
- 0033-6807
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 7218.400000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 9455.xml