Human Resource Management in Organizational Project Management
Bibliographic record
Abstract
It is increasingly common for work activities to take place in projects, and projects are therefore of growing importance as sites for career development, for leading and managing professional workers, and for individual and organizational development. Links between human resource management (HRM) activities that occur on projects, and their broader implications for project-based organizations in terms of knowledge, learning, and competence development, are therefore important foci for research. Projects are also important from the perspective of the well-being, ethical treatment, and motivation of workers. Projects are established within and between organizational functions (Bredin & Söderlund, 2011) but also span organizational boundaries (Lundin & Steinthórsson, 2003; Swart & Kinnie, 2014). Projects involve people from within and between organizational departments and also within and between disciplinary specialties. The implications of project-based organizing for managing human resources would appear to be significant (Huemann, 2015; Keegan, Huemann, & Turner, 2012; Palm & Lindahl, 2015; Söderlund & Bredin, 2006; Vicentini & Boccardelli, 2014), and yet traditional HRM models, where projects are not a key consideration, continue to dominate mainstream HRM theorizing (Swart & Kinnie, 2014). In mainstream HRM theorizing, traditional long-term and stable employment relationships are assumed and focal organizations are those with clearly defined internal and external boundaries. Project management literature has also traditionally downplayed what could be called the human factor – human capital or people aspects of project organization and management (Keegan & Turner, 2003). A shift from the mainly technical to increasingly people-focused aspects of project management has, however, been discernible in the past decade (Huemann, Keegan, & Turner, 2007). Project management researchers have started to explore more systematically HRM issues and their possible contribution to the performance of organizations that do most of their work in projects (Bredin & Söderlund, 2011). The systematic study of project professionals’ careers has developed recently, reflecting an increased appreciation of the importance of projects as a major part of many organizations (Crawford, French, & Lloyd-Walker, 2013; Hölzle, 2010) and the resulting increased importance of HRM issues and “people capabilities” (Bredin, 2008) required of project-based organizations is slowly increasing. Similarly, even though HRM theorists have not, to date, fully embraced the importance of the project context for practices, processes, and outcomes, this too appears to be changing as studies of HRM become more contextually sensitive.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".