Strategic alignment and leadership influence: The crucial role of senior stakeholder management in modern corporate real estate
Bibliographic record
Abstract
Corporate real estate (CRE) leaders operate at the intersection of business strategy, operational efficiency and, most importantly, financial stewardship. Their role extends far beyond managing physical spaces; they are instrumental in aligning all real estate decisions with the organisation’s long-term vision and performance goals. To succeed in this complex environment, particularly in the post-COVID-19 era, CRE professionals must collaborate with a diverse range of stakeholders, including C-suite executives who shape strategic direction, business unit (BU) leaders focused on functional outcomes, finance teams that scrutinise cost and investment decisions and facility managers and external service providers who are responsible for the day-to-day management of real estate assets. Navigating these relationships demands a deliberate and strategic approach to stakeholder management. It involves understanding and balancing often competing priorities, communicating the value of CRE initiatives clearly and building trust across all levels of the organisation. Stakeholder management is not merely a supporting function, but a core competency that influences the success of portfolio transformations, workplace strategies, capital investment and sustainability goals. This paper delves into the theories and practical applications of stakeholder engagement within the CRE context. It offers insights into identifying and prioritising stakeholders, managing diverse expectations and implementing structured engagement strategies that drive alignment between real estate actions and broader business objectives. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".