Understanding relationship marketing strategy in Ghana’s informal economy: a case of micro, small and medium enterprises
Bibliographic record
Abstract
Purpose Relationship marketing (RM) has rarely been applied to micro-, small- and medium-sized enterprises (MSMEs) in the informal economy (IE). Thus, this study aims to explore the RM strategy of service rendering micro-enterprises in the IE of a sub-Saharan African country like Ghana. Design/methodology/approach This study used a qualitative research approach using a multiple case study design, semi-structured interview and a random sampling technique to sample 15 micro-enterprises. Thereafter, the case was analysed thematically. Findings Results show that micro-enterprises in the IE engage in multiple dimensions of RM in their line of business. Also, micro-enterprises in the IE perceived RM as customer care and somewhat their standard of RM benefits measurement are different from those firms in the formal sector. Furthermore, micro-enterprises in the IE face challenges such as high resource commitment, harassment and the technological gap in practising their RM strategy. Practical implications MSMEs in the IE should develop a framework to minimise the shortfall of the challenges associated with RM implementation for business continuity and growth because customers are the lifeblood of the business. Originality/value To the best of the authors’ knowledge, this is the first known study that looks at RM practices of MSMEs in the IE. It has thrown light on the understudied subject of RM in MSMEs. For micro-enterprises operating in the Ghanaian IE, the benefits to be derived from practising RM are rewarding. The adopted qualitative methodology has provided an in-depth insight into a vital area for both academics and practitioners.
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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 | 0.028 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".