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Artikel ini bertujuan untuk mengetahui faktor-faktor yang mempengaruhi adopsi cloud computing di rumah sakit Muhammadiyah di Indonesia dengan menggunakan Technology Acceptance Model (TAM). Sampel yang digunakan dalam penelitian ini adalah 51 sampel. Alat analisis yang digunakan Partial Least Square (PLS) pada program SmartPLS versi 2.0. Faktor pendukung dalam adopsi cloud computing : perceived ease of use, perceived usefulness, security effectiveness, reliabilty, needs, cost effectiveness, top management support, complexity, behavioral intention to use dan actual usage. Evaluasi goodness of fit dan resampling bootstraping digunakan untuk menganalisis kelayakan model. Evaluasi goodness of fit model struktural diukur menggunakan nilai predictive-relevance diperoleh nilai sebesar = 0,95 yang artinya model memiliki nilai prediktif yang sangat baik. Hasil uji hipotesis diperoleh dengan menggunakan resampling bootstraping dengan menghitung hubungan antar variabel memiliki nilai t- hitung yang lebih besar = 2.01 dalam adopsi cloud computing di rumah sakit Muhammadiyah, 8 (delapan) hipotesis diterima yaitu: Perceived usefulness, , Perceived ease of use , needs, top management support, complexity dan 3 (tiga) hipotesis ditolak yaitu: security, reliabilty dan cost effectivenes Kata kunci: cloud computing, perceived usefulness, needs, top management support, behavioral intention to use, partial leastsquare (PLS).

The study aims to prove whether good corporate governance (GCG) is able to predict the probability of companies experiencing financial difficulties. Financial ratios that traditionally used for predicting bankruptcy remains used in this study. Besides, this study also compares logit and probit regression models, which are widely used in research related accounting bankruptcy prediction. Both models will be compared to determine which model is more superior. The sample in this study is the infrastructure, transportation, utilities & trade, services and hotels companies experiencing financial distress in the period 2008-2011. The results show that GCG and other three variables control i.e DTA, CR and company category do not prove significantly to predict the probability of companies experiencing financial difficulties. NPM, the only variable that proved significantly distinguishing healthy firms and distress. In general, logit and probit models do not result in different conclusions. Both of the models confirm the goodness of fit of models and the results of hypothesis testing. In terms of classification accuracy, logit model proves more accurate predictions than the probit models.