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김기문,박희용 대한설비공학회 1999 설비공학 논문집 Vol.11 No.2
The application of hydrocarbon refrigerant mixtures in a hermetic reciprocating compressor for dehumidifier is investigated. The selected refrigerants are R12, R134a, HC-Blend (R290/R600a), CX(R152a/R600a) and OS-l2a. Both theoretical and experimental investigations have been performed for the selected refrigerants. The test results of hydrocarbon refrigerants have been compared to traditional refrigerant(R12) and R134a. The results show that hydrocarbon refrigerant mixtures(HC-Blend, CX and OS-l2a) are very good alternatives in the refrigeration system for R12 and R134a.
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김기문,박희용 대한설비공학회 1998 설비공학 논문집 Vol.10 No.6
The application of hydrocarbon refrigerants in a hermetic reciprocating compressor for refrigerator is investigated. The selected refrigerants are isobutane(R600a), propane(R290), R12, binary mixture of R600a/R290, and OS-21CII. Both theoretical and experimental investigations have been performed for the selected refrigerants. The test results of hydrocarbon refrigerants have been compared to the traditional refrigerant(R12). The results show that hydrocarbon refrigerants(HC-Blend, OS-21C II) are very good alternatives in the refrigeration system for R12.
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Call for an Open Discussion on Empirical Viability of Causal Indicators
김기문,신봉식,Varun Grover,Roy D. Howell,김기주 한국산업정보학회 2017 한국산업정보학회논문지 Vol.22 No.6
Over the past decade, we have witnessed Serious Debates in MISQ and Other Journals Between Two Camps that have Differing Views on the use of Causal Indicators to Measure Constructs. There is the Camp that advocates Causal Indicators (ADVOCATE) and the Camp that opposes Their Usage (OPPONENT). The Debates have been primarily centered on the OPPONENT’s Argument that the Meaning of a Latent Variable is determined by its Outcome Variables. However, Little Effort has been made to Validate the ADVOCATE’s Dispute (Against the OPPONENT’s Arguments) that the Meaning of a Latent Variable is decided by its Causal Indicators if there is no Misspecification. Our Study precisely examines the Integrity of the Argument. For this, we empirically examine how the two Primary Psychometric Properties-Comprehensiveness and Interrelationship-of Causal Indicators Influence Theory Testing between Latent Variables through Three Different Tests (i.e., Comprehensive Test, Interrelationship Test, and Mixed Test). Conducted on Two Different Datasets, Our Analysis Consistently Reveals that Structural Path Coefficients are Hardly Sensitive to the Changes (i.e., Misspecification) in the Properties of Causal Indicators. The Discovery offers Important Evidence that the Sound Theoretical Logic of a Causal Model is not in Sync with the Empirical Mechanism of Parameter Estimation. This Underscores that a Latent Variable Formed by Causal Indicators is empirically an elusive notion that is Difficult to Operationalize. As Our Results have Significant Implications on the Integrity of Numerous IS studies which have conducted Theory or Hypothesis Testing Using Causal Indicators, we strongly advocate Open Discussions among Methodologists regarding Our Findings and Their Implications for Both Published IS Research and Future Practices.
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