Bibliographic record
Abstract
Business relationships have been studied for decades (Wilkinson, 2001). However, the literature has been criticized of the lack of focus on information technology (IT) usage within business relationships (Reid & Plank, 2000). As managers have started to employ digital tools such as the Internet, intranets, and extranets, buyer-seller relationship scholars have realized the need to focus on IT deployment within relationships. There is a growing body of research that focuses on the different types of technologies being employed such as electronic data interchange (EDI) (Naudé, Holland, & Sudbury, 2000), Internet-based EDI (Angeles, 2000), and extranet (Vlosky, Fontenot, & Blalock, 2000) and their influence on business relationships. Nevertheless, mobile technology usage within business relationships is a nascent field of scientific inquiry. Besides buyer-seller relationship literature, mobile commerce (MC) (conducting commercial activities via mobile networks) literature also noticeably lacks academic research on business usage of mobile technology (Okazaki, 2005; Scornavacca, Barnes, & Huff, 2005). By combining these indications for further research from the buyer-seller relationship and MC fields it can be argued that there is a clear call for research in this area. Hence, I aim to bridge some aspects of the identified research gap. The research gap is filled in by discussing bonding within buyer-seller relationships to illustrate how mobile technologies create a novel bond in business relationships. It is acknowledged that some research on the adoption of mobile technology in the business context exists (see e.g., Kadyté, 2005). The paper is organized as follows: First, a brief discussion of the background of business relationships, mobile technologies, and bonding is provided. Then, I highlight how mobile technologies are used within relationships with a case study. After that, future trends in this pertinent area are presented. The paper finishes with a concluding discussion.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".