E-Branding and Customer Patronage in the International Market: Insights from Tantalizers Plc, Uyo
Bibliographic record
Abstract
In an increasingly digitized and globalized economy, the importance of e-branding in fostering customer engagement and expanding international market reach has become vital particularly for indigenous firms seeking to tap into diaspora markets. The study investigated the effect of e-branding on customer patronage in the international market, using Tantalizers Plc, Uyo as a case study. Specifically, the research examined how five components of e-branding—website quality, social media engagement, online customer experience, search engine optimization (SEO), and digital advertising consistency influence key patronage indicators such as purchase frequency, retention, referral rate, sales volume, and brand loyalty among diaspora customers in the United Kingdom, United States, and Canada. The study adopted a quantitative survey research design. Data were collected from 380 respondents through a structured online questionnaire and analyzed using descriptive statistics and multiple linear regression. Findings revealed that all five e-branding components had statistically significant positive effects on customer patronage. Online customer experience emerged as the strongest predictor of patronage behavior, followed by website quality and digital advertising consistency. The regression model showed that 59.1% of the variance in customer patronage could be explained by the e-branding variables. The study concludes that effective digital branding strategies are critical for indigenous firms aiming to build loyalty and visibility in the international marketplace. It recommends that Tantalizers PLC, Uyo invest in optimized web platforms, interactive social media campaigns, personalized online experiences, SEO strategies, and consistent digital advertising to sustain and grow its international customer base. The study contributes to knowledge by providing a diaspora-focused e-branding framework for African firms expanding beyond local market
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".