Mobile application adoption in business by the unorganized retailers and expanding the con-structs by using TAM, DOI, TOE theories
Bibliographic record
Abstract
In this paper, the authors proposed a new framework for Mobile application adoption by unorganized retailers. Unorganized retail is a renowned retail business in India. Unorganized retail is a so-called low-cost retail format where some of the retail stores are Kirana store, Grocery store, provisional store, ready to eat store, mom & pop store, peddlers, Hawkers, and stationery store. Retailers face stiff competition from omnichannel, multi-channel retailers, and e-tailers. To counter this competition, unorganized retailers adopt some Mobile applications which are relevant to their business. While some of the Mobile Applications are WhatsApp business, Facebook page, telegram, blogs, google maps Mandi app, Udaan app, Katha book app, OK credit, Dukan app, and just dial. This paper explains a deep discussion about unorganized retailers, unorganized retailers' contributions to the economy, the Digital India program, and Mobile Application. A new comprehensive framework has been proposed for mobile application adoption by the unorganized retailer after getting insights from theories like Technology Adoption Model (TAM), Extension of Technology Adoption Model (TAM), Technology, Organization and Environment (TOE), and Diffusion of Innovation (DOI). In this research, the author segments three major clusters which influence mobile technology adoption. The three major clusters are Technology, Organization, and Environment. These clusters had 10 constructs that influence mobile technology adoption. This research elaborately discussed the drivers of technology adoption in an unorganized retail context. We close by concluding that mobile application adoption by the unorganized retailer and reshaped the unorganized retail.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".