Exploring the principle and competencies for phygital service inclusion
Bibliographic record
Abstract
Purpose This study proposes the concept of phygital service inclusion, exploring its core principle and identifying the key competencies required for its implementation, particularly for individuals experiencing vulnerabilities. Design/methodology/approach This paper adopts the term “individuals experiencing vulnerabilities” to highlight the situational and dynamic nature of vulnerability, thereby avoiding the stigmatizing effect of labeling individuals as "vulnerable" and instead promoting a perspective that affirms dignity and resilience. In line with this framing, the study employs an abductive qualitative approach, engaging with Deaf communities in an emerging country as a salient case of individuals experiencing vulnerabilities. To explore cultural and social identity, a netnographic analysis was conducted on user-generated content from YouTube, analyzing 22 videos with a combined total of approximately 7.8 million views and 12,000 comments. In addition, semistructured interviews were conducted with Deaf individuals, complemented by passive observation at a community center that facilitates phygital interactions, providing deeper insights into their lived experiences. Findings This study conceptualizes phygital service inclusion as the intentional integration of physical and digital service elements to create experiences that are inclusive, accessible and equitable, particularly for individuals experiencing vulnerabilities. Central to this concept is the principle of “Physically Informed – Digitally Enhanced,” highlighting the importance of anchoring digital innovations in the physical realities of individuals experiencing vulnerabilities. Furthermore, this study identifies four key competencies for service professionals – ambicultural, interpersonal, advocacy and digital – that are essential for designing and implementing inclusive phygital service systems. Research limitations/implications The study centers on a particular group of individuals experiencing vulnerabilities, which, while offering valuable insights, represents only one dimension of a broader spectrum of such individuals. The methodological scope is further shaped by the reliance on YouTube as the sole source of secondary data and by the limited geographic reach of interviews, which were conducted in just two regions of the country. Originality/value This study advances the theoretical discourse on inclusive service systems by bridging physical and digital service elements. It offers actionable frameworks and insights for designing culturally sensitive and contextually adaptive phygital service ecosystems, with a particular focus on empowering individuals experiencing vulnerabilities.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".