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Patronage of local footwear have not been encouraging in Nigeria despite recent investments. The purpose of the study is to evaluate celebrity endorsement and customer patronage of small and medium-scale enterprises (SMEs) products in African context, with focus on trustworthiness, expertise, attractiveness, respect and similarity (TEARS) model.

The research was designed as a descriptive survey. An online structured questionnaire was applied for data collection. Cronbach Alpha and content validity were used for reliability and validity, respectively. TEARS model was used to ascertain key dimensions, and Pearson correlation coefficient and logistic regression were applied into the analysis.

The findings reveal that celebrity endorsement is not associated with patronage of local footwears, though TEARS model analysis indicates the direction of consumers rating on celebrity endorsement. Factors such as recommendation and quality impact the consumer willingness to buy local footwear.

The small sample size calls for caution in generalization.

The study suggests that although the TEARs model is viable, all the dimensions are mutually exclusive. However, this depends on the characteristics of the brand. In driving patronage, managers must pay attention to personal and non-personal cues such as price, quality and source of information about their brand.

The originality is buttressed from the value it provides for local product production and patronage. The significant factors are indicated as key to addressing low patronage.

Gathering knowledge regarding personality traits has long been the interest of academics and researchers in the fields of psychology and in computer science. Analyzing profile data from personal social media accounts reduces data collection time, as this method does not require users to fill any questionnaires. A pure natural language processing (NLP) approach can give decent results, and its reliability can be improved by combining it with machine learning (as shown by previous studies).

In this, cleaning the dataset and extracting relevant potential features “as assessed by psychological experts” are essential, as Indonesians tend to mix formal words, non-formal words, slang and abbreviations when writing social media posts. For this article, raw data were derived from a predefined dominance, influence, stability and conscientious (DISC) quiz website, returning 316,967 tweets from 1,244 Twitter accounts “filtered to include only personal and Indonesian-language accounts”. Using a combination of NLP techniques and machine learning, the authors aim to develop a better approach and more robust model, especially for the Indonesian language.

The authors find that employing a SMOTETomek re-sampling technique and hyperparameter tuning boosts the model’s performance on formalized datasets by 57% (as measured through the F1-score).

The process of cleaning dataset and extracting relevant potential features assessed by psychological experts from it are essential because Indonesian people tend to mix formal words, non-formal words, slang words and abbreviations when writing tweets. Organic data derived from a predefined DISC quiz website resulting 1244 records of Twitter accounts and 316.967 tweets.