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Shirkah: Journal of Economics and Business
Published by Institut Agama Islam Negeri Surakarta
ISSN : 2504235     EISSN : 25034243     DOI : 10.22515/shirkah.v7i1.403
Core Subject : Economy,
Economics, Econometrics & Finance
Syirkah: Jurnal Ekonomi dan Bisnis adalah jurnal peer-review yang diterbitkan tiga kali setahun (April, Agustus, dan Desember) oleh Fakultas Ekonomi Islam dan Bisnis Institut Agama Islam Negeri (IAIN) Surakarta Jawa Tengah Indonesia bekerja sama dengan Perhimpunan Indonesia Ekonom Islam (lihat naskah MoU ). Jurnal ini dimaksudkan untuk menjadi platform diseminasi artikel yang melaporkan hasil penyelidikan ilmiah tentang Ekonomi dan Bisnis Islam. Jurnal ini memfokuskan pembahasannya pada bidang keuangan Islam, filantropi Islam, pemikiran ekonomi Islam, dan pemasaran Islam (lihat Fokus & Ruang Lingkup ).
Arjuna Subject : Ilmu Ekonomi, Ekonometrika dan Keuangan (semua kategori) - Ilmu Ekonomi, Ekonometrika dan Keuangan (Lain-Lain)
Articles 311 Documents
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From Zero Sales to Survival: Forecast-triggered Decision-making in Ecotourism MSMEs Singgih Purnomo; Nurmalitasari Nurmalitasari; Nurchim Nurchim; Novemy Triyandari Nugroho
Shirkah: Journal of Economics and Business Vol. 11 No. 1 (2026)
Publisher : Universitas Islam Negeri Raden Mas Said Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22515/shirkah.v11i1.1108

Abstract

Ecotourism micro, small, and medium-sized enterprises (MSMEs) often face highly volatile demand characterized by frequent zero-sales days, strong seasonality, and exposure to external shocks. In such conditions, sustainability depends less on forecast accuracy and more on timely, low-cost operational decisions. This study examines how forecast-triggered decision-making supports short-run viability under intermittent, zero-heavy demand. Using manually recorded daily sales data from ecotourism MSMEs in Tawangmangu, Indonesia, a two-stage approach is applied that separates sale occurrence from sales magnitude. First, a logistic model estimates the probability of a sale to generate early-warning signals. Second, conditional sales magnitude is predicted to indicate readiness levels rather than precise revenue targets. Instead of focusing on accuracy alone, the analysis evaluates decision usefulness through time-ordered backtesting, emphasizing avoidable operating days and early-warning lead time. The results show that sale-occurrence signals effectively guide daily operating decisions, while magnitude forecasts support proportional readiness. The framework identifies a substantial share of avoidable operating days and provides several days of advance warning before prolonged zero-sales periods. This enables earlier cost control and capacity adjustment. The study contributes by offering a practical, human-in-the-loop decision framework that links demand uncertainty with adaptive actions using simple, manually recorded data.

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