Online-To-Offline (O2O) Business: Empirically Examining the Adoption Vs. Non-adoption
Bibliographic record
Abstract
The growth of Online-to-Offline (O2O) business is accompanied by a number of failures.Compounding the problem, theoretically-informed knowledge about "why firms adopt O2O" remains limited.The purpose of this study is to address this gap and examine the reasons that influence organizational decisions to adopt (or do not adopt) O2O.Informed by synthesis of Diffusion of Innovation (DOI) and Technology-Organization-Environment (TOE) theories, the data collected from 24 qualitative semi-structured interviews were analyzed using content analysis techniques.The results show that firms adopt O2O to: (1) cope with the shift in marketplace (one from sellers to buyers) by adding flexibility and security into offerings, (2) improve engagement with customers through expansion of scope of relationship and reach, and (3) enlarge customer portfolio and profitability.Unexpectedly, the results show that one of the reasons firms not adopt O2O is because they believe that their existing engagement with customers is mature and stable, changes to which could be determinantal.Managerial inertia is another roadblock.Theoretically, the findings build new knowledge on the adoption/non-adoption decision of O2O and reasons thereof.The results equip managers with insights from industry and provide them an understanding of the challenges as well as reasons for adoption/non-adoption of O2O.
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 | 0.009 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".