A Revised Technology–Organisation–Environment Framework for Brick-and-Mortar Retailers Adopting M-Commerce
Bibliographic record
Abstract
This paper argues that brick-and-mortar retail Small and Medium Enterprises (SMEs) can benefit significantly from the capabilities of mobile commerce (m-commerce) to respond to the unpredictable changes in the business environment, accommodate new consumer experiences, boost sales of products/services, and achieve a competitive advantage. Consequently, this study explored the potential application of the Technology–Organisation–Environment (TOE) framework for m-commerce by brick-and-mortar retail SMEs. The study adopted the positivist paradigm and followed a cross-sectional study design. A structured questionnaire was used to collect data from a sample of 263 retail business personnel. The Analysis of Moment Structures (AMOS) software was used to analyse the data. The findings unveil that all the proposed constructs associated with the organisational context and technological context are critical for the use of m-commerce. The proposed framework provides a fresh set of contextual variables which align with brick-and-mortar retailer operations and mobile commerce practices. It is envisaged that the extended framework may help conventional businesses to understand and identify the requisite factors in the adoption and use of m-commerce and assist business supporters in the process of technological innovation transfer.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".