The role of image and reputation as intangible resources in non-profit organisations: a relationship management perspective
Bibliographic record
Abstract
The current research on relationship management primarily focuses on enhancing customer relationships through image or reputation in organisations. The resource-based theory portrays image and reputation as important intangible resources that are derived from combinations of internal investments and external appraisals. With this in mind, the role of image and reputation in value creation needs to be carefully delineated. In non-profit settings, stronger image and reputation are likely associated with higher quality of goods and services, better delivery of those goods and services, improved management of donations and funds, and improved outcomes (e.g., higher capability to make a difference in societies). \n \nFollowing a critical analysis of current literature with relevant examples, this paper argues that image and reputation are the keystones of non-profit organisations’ differentiation strategy. The resource-based theory suggests that resource factors represent a stronger explanation of differences in firm performance. Organisations are more likely to grow and develop higher performance potential if more resources are invested in image and reputation. By integrating several disparate resources, image and reputation as intangible resources can become more difficult to imitate and provide a more sustainable source of competitive advantage in organisations. Thus both image and reputation are likely influential elements that assist non-profit organisations in developing and managing relationships with external stakeholders, and thereby aid organisations in attracting important resources such as donations and volunteer support. \n \nThe study findings contribute to the more general understanding of image and reputation from a relationship management perspective in the non-profit context. Thus, the paper adds a new dimension to the body of literature by arguing that image and reputation can be utilised as relationship management tools in non-profit organisations. However, image and reputation are external to organisations and volatile in nature. Non-profit managers must strategically develop relationship management activities in their organisations, with image and reputation being central.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".