Investigating Sustainability Reporting Standards in Multinational Canadian Publicly Traded Companies Operating in Emerging Economies
Bibliographic record
Abstract
This thesis seeks to investigate how sustainability reporting standards can be used to assess strategic performance in emerging economies by multinational Canadian publicly traded companies. Interviews were conducted and took place between July 2020 and July 2021. The organizational participants comprised a total of 22 sustainability experts and executives from eleven different multinational Canadian publicly traded companies operating in emerging economies. The in-depth semi-structured interviews were the primary method utilized to tackle the principal and sub-research questions. A critical review of the literature showed that relatively little research is dedicated to the methods employed in integrating and evaluating sustainability activities and performance from a strategic viewpoint. Consequently, this thesis seeks to address a significant research gap. Building on the existing study, this study conducted a systematic review of sustainability or equivalent reports and semi-structured interview responses from the sustainability executives using qualitative content analysis. This thesis designed and proposed an original analytical framework that provides a systematic approach to the integration of sustainability with organizational processes and mainstream infrastructure of multinational Canadian publicly traded companies operating in emerging economies. The proposed analytical framework is reinforced by a sustainability integration strategy map and sustainability integration performance measures to help guide the implementation process. The proposed analytical framework, the supported strategy map, and performance measures are based on the “Balanced Scorecard Framework” and may help guide multinational Canadian publicly traded companies through the process of organizing and structuring sustainability into their critical business infrastructure. This study found that none of the organizational participants had utilized sustainability reporting standards to assess their strategic performance in emerging economies. The findings also illustrate how a ‘synergy’ and coherence can be created between strategic and sustainability performance measurements and sustainability reporting standards through an ‘aggregate measurement’ process.
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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 | 0.032 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".