Analyzing informal value transfer networks through the lens of social exchanges
Bibliographic record
Abstract
Purpose This study aims to focus on how informal value transfer networks, Hawala business in particular, used social exchanges in their business dealings. More specifically, the conducted research looked into how social exchange theory was used in Hawala business relationship initiation and management. Design/methodology/approach Twenty-one depth interviews were conducted with Hawala Network members, and Hawala customers, in Qatar, Saudi Arabia and Pakistan. The collected qualitative data were analyzed through content analysis and NVivo 11 software. Findings The study outcome indicated that Social Exchange Theory was a principal relationship driver in Hawala Networks. Especially, trust had a pivotal role in evolvement and nurturing of Hawala Network business and social exchanges. Other relationship variables, namely, reciprocity, religious affiliation, reputation and information sharing had a significant part in relationship building as well. Results supported a prominent influence of time in carefully controlled and rigorously assessed transformation of Hawala relationships. This metamorphosis converted an exchange from short-term into a long-term orientation where limited amount transactions changed into large sum transactions and restricted information exchange moved to elaborate information sharing. In addition, findings revealed that monetary and non-monetary interactions between Hawala Network members took the form of a homogeneous club, with shared social, cultural, religious and ethnic values. In particular, financially constrained and illiterate social groups preferred Hawala services due to ease of servicing in the form of minimal bureaucracy, fast transfers and low service charges. These marginalized fractions of society had limited access to formal banking which made Hawala business their main (and in most cases only) source for sending and receiving financial remittances. Hawala Networks provided an effective alternative to formal banking for disadvantaged communities. Originality/value This study provided unique and useful insights into the nature of social exchanges within Hawala Networks. Especially, it provided clarification on how informal networked businesses used Social Exchange Theory to by-pass the need for legal protection and formal contracts. Furthermore, the study highlighted the role Hawala business played in providing essential banking services (e.g. transfer of money and micro-lending) to educationally and economically deprived individuals.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".