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Record W7126134367

Exploring Effective Strategies to Improve Financial Sustainability Among Microfinance Institutions in Delta State, Nigeria

2025· article· W7126134367 on OpenAlexaboutno aff
Alexander Sakharov

Bibliographic record

VenueScholarWorks (Walden University) · 2025
Typearticle
Language
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsnot available
Fundersnot available
KeywordsPurchasingThematic analysisFocus groupDigital marketingQualitative researchMarketing strategySocial mediaSustainabilityCustomer relationship management
DOInot available

Abstract

fetched live from OpenAlex

Many small business leaders struggle to tailor online strategies that drive demand, limiting growth and differentiation in competitive digital markets. Business leaders must adopt data-informed, customer-centered approaches that align digital marketing efforts with consumer behavior and market expectations. Grounded in social exchange theory, the purpose of this qualitative pragmatic inquiry was to explore the online shopping marketing strategies that some small business marketing leaders use to increase demand for products and services. Data were collected through semistructured interviews with six small business leaders from Vancouver, British Columbia; Toronto, Ontario; and Seattle, Washington. The four main themes identified using thematic analysis were (a) focus on marketing channel strategies; (b) personalized and trust-oriented customer engagement strategies; (c) goal-oriented innovation strategies; and (d) navigating barriers to strategic implementation. Recommendations included diversifying marketing channels, adopting new technologies, and maintaining a focus on target audiences to build customer trust, which plays a vital role in customer-vendor relationships. The implications for positive social change include potential for small business leaders to develop products that better align with consumer needs, promoting stress-free purchasing decisions, and strengthening consumer trust in vendors.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0010.000
Scholarly communication0.0010.009
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.228
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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