Continuously Improving in Tough Times: Overcoming Resource Constraints with Psychological Capital
Bibliographic record
Abstract
Individuals and organizations must continuously improve to succeed in today’s competitive economic climate, yet a major dilemma in tough economic conditions is that the resources needed to support such improvement behaviors are limited. Existing theories on resources, continuous improvement, and organizational stressors are relevant yet insufficient for answering the important question of how individuals remain motivated to pursue continuous improvement and growth activities despite minimal resources to support them. Therefore, the goal of this research was to build and test theory on this phenomenon. We began this program of research with a phenomenological study of employees in a manufacturing environment to better understand their appraisals regarding continuous improvement under resource-constrained conditions. The results highlighted the ways in which employees interpret constraints as either a threat or a challenge, and how psychological capital guides these interpretations and subsequent continuous improvement. Informed by this rich data, we proposed a synthesized theoretical model which was tested in another resource-constrained environment that demands continuous improvement, namely entrepreneurs launching a new business. The results of a time-lagged survey study of nascent entrepreneurs largely supported the theoretical model, documenting the benefits of psychological capital as a way to reduce the perceived threat of resource constraints and promote continuous improvement.
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 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".