IPR

This study aims to fill the research gap factor between the ability of product innovation and enrich marketing performance. This study proposed Geo-cultural product attractiveness to bridge the gap between product innovation capabilities in improving marketing performance. Sample data from 250 SMEs in South Sulawesi Province in Indonesia was used to test our model. Data analyzed using descriptive statistics and Structural Equation Modeling. The result confirmed the model with variables: Innovation, Geo-cultural product attractiveness, Marketing Orientation, and Marketing performance indicated that Geo-cultural product attractiveness holds a strategic role in enhancing marketing performance.

Modul ini membahas tentang pelatihan model kelayakan usaha sutera Kabupaten Wajo

The purpose of this study to analyze the inconsistent effect of Green Marketing Strategy on Marketing Performance. This study offers the concept of Geo-cultural Product Attractiveness to bridge the research gap between Green Marketing Strategy in improving Marketing Performance. The data is taken from the SME population in the South Sulawesi Province as much as 115. The analysis is carried out inferentially using Structural Equation Modelling to test the research model consisting of 3 (three) proposed hypotheses. The results of the study show that green marketing strategy has a negative and insignificant effect on marketing performance directly, Geo-Cultural Product Attractiveness has a direct effect on marketing performance, and Green Marketing Strategy has an indirect effect on Marketing Performance through Geo-cultural Product Attractiveness positively. Geo-cultural Product Attractiveness is a full mediating variable in the relation of product Green Marketing with marketing performance.

Marketing Performance is one of the weaknesses of SMEs in many previous studies. This study aims to bridge the research gap between Social Media Marketing and improve marketing performance. This study offers the concept of Brand Resonance Capability to bridge the gap in increasing marketing performance and social media marketing. Three hypotheses were developed and tested in 121 SME samples in the region of South Sulawesi, Indonesia. Structural Equation Modelling analysis is using to test these hypotheses. The study results confirm the model with the following variables: Social Media Marketing, Brand Resonance Capability, and Marketing Performance have a significant effect on each other, which indicates that the Brand Resonance Capability variable has a strategic role in enriching marketing performance.

Small and Medium Enterprises (SMEs) have a central role in the Indonesian economy. SMEs are a driver of the Indonesian economy and non-oil exports and have a significant role in absorbing labor. The SME sector in Indonesia generally has several obstacles, one of which is marketing constraints. The ability to build and communicate brands to customers tends to be low, so it has not been able to bind customers and affect the marketing performance of SMEs. This ability during the pandemic has worsened, as can be seen from the deteriorating marketing performance of SMEs. This study attempts to fill the research gap between digital marketing and marketing performance. This study offers the concept of Brand Articulating Capability to bridge the gap between Digital Marketing in increasing Marketing Performance. Three hypotheses were developed and tested in a sample frame of 230 SMEs in South Sulawesi, Indonesia. The analysis was carried out using Structural Equation Modelling to test the research. The study\'s findings support the model using the following variables: Digital Marketing has a significant effect on Marketing Performance, Digital Marketing has a significant impact on Brand Articulating Capability, and the Brand Articulating Capability variable has a mediating and strategic role in improving marketing performance.

Algoritma ini menggunakan pendekatan clustering berbasis K-Means untuk mengelompokkan data penjualan berdasarkan pola permintaan, faktor wilayah, jenis produk, promosi, dan kondisi cuaca. Algoritma bekerja dengan cara mengidentifikasi kesamaan dalam data penjualan dan membaginya ke dalam beberapa klaster dengan karakteristik serupa. Setiap klaster mewakili segmen pasar tertentu, seperti wilayah dengan permintaan tinggi, produk yang paling diminati, atau wilayah yang kurang responsif terhadap promosi. Proses dimulai dengan mengubah data mentah menjadi representasi numerik melalui teknik preprocessing seperti one-hot encoding dan standarisasi. Selanjutnya, K-Means menentukan pusat klaster (centroids) secara iteratif hingga pola kelompok yang optimal ditemukan. Hasil dari algoritma ini digunakan untuk memahami perilaku pembelian konsumen dan memprediksi permintaan secara lebih spesifik berdasarkan klaster. Dengan hasil clustering, sistem ini mampu memberikan rekomendasi manajemen stok yang lebih efisien, mengurangi risiko limbah bahan, dan mendukung strategi distribusi berbasis data untuk memenuhi permintaan di berbagai wilayah secara tepat waktu.