Taking Things Out of Context: A Configuration Analysis of the Persuasiveness of Digital Technologies for Sustainability Across Work-Home Boundaries
Bibliographic record
Abstract
As organizations strive to improve their environmental performance, some try to persuade their employees to engage in pro-environmental behaviors (PEBs), while others encourage PEBs among their consumers to reduce environmental harm. Digital technologies are used to further enhance these initiatives as part of digital sustainability, but there is limited understanding of whether and under what conditions the influence of persuasive digital technologies extends beyond the focal context to impact PEBs in non-focal contexts. We explore these questions by examining how interactions between individuals’ green identity, strength of work-home boundaries, perceived complexity of the behavior, and integration support provided by persuasive digital technology for sustainability affect PEBs at work and home. Qualitative comparative analysis of data from a survey of 403 individuals using employer and utility-sponsored applications reveals four configurations that lead to higher PEBs in the workplace and home and four configurations that lead to low levels of such behaviors. From these configurations, we develop five theoretical propositions. The research contributes to understanding the microfoundations of digital sustainability and illuminates the persuasive effects of digital technologies for sustainability within and outside their focal contexts. This work also offers practical guidance to organizations as they implement digital strategies for environmental sustainability.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".