Examining the impetus for internal CSR Practices with digitalization strategy in the service industry during COVID‐19 pandemic
Bibliographic record
Abstract
Abstract The year 2020 began with a glimpse into the darkness with the onset of the worldwide COVID‐19 pandemic. An invisible, threatening virus has forced many countries to practice restricted movement and impose lockdowns for the sake of their citizens’ safety and well‐being. In response, many business organizations have implemented various remote‐work arrangements. These arrangements have spurred the use of digitalization strategies and have landed many employees in the vulnerable virtual workplace. With employees facing all these uncertainties and vulnerabilities, their commitment to their workplace could come into question. At the same time, organizations facing tremendous challenges are searching for committed employees to navigate through this turbulent time. From a strategic management perspective, organizations could revisit their internal core competencies to prevail through internal corporate social responsibility (CSR) practices. Meanwhile, the rapidly growing pace of digitalization could further augment organizations’ survival and resilience. This research paper showcases the empirical outcomes of the promising match between internal CSR practices and digitalization strategy; and employees’ organizational commitment during times of crisis. The results reveal that internal CSR practices positively stimulate employees’ organizational commitment, while digitalization strategy intercedes in the nexus between internal CSR practices and affective commitment. The empirical outcomes shed light on business organizations and their ability to take a frugal approach in turbulent times.
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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 | 0.004 | 0.012 |
| 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 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".