Market Value Of Services Research Articles

Classical and Deep Learning Time Series Prediction Techniques in the Case of Indonesian Economic Growth

Gross Domestic Growth (GDP) as a proxy of an economic growth in one country is defined as the market value of services and goods produced by the country for a year. As the GDP of one country gets higher and higher through the years, this indicator points out that in general the economic growth of the country is growing; specifically, the values of services and goods accumulated in the market are increasing. To determine what indicators of a country that affects one’s economic growth remains an open question. Therefore, this research attempts to study those indicators and particularly utilize them to predict the economic growth. To answer those questions, this research employs diverse time series techniques ranging from classic time series analysis to machine learning and deep learning. Subsequently, our dataset comprises World Development Indicators (WDI) of Indonesia from 1962 to 2016. By measuring Root-Mean-Square Error (RMSE), we show Seasonal Autoregressive Integrated Average (SARIMA) and Convolutional LSTM give the the best performance from classical and deep learning techniques respectively. Our analysis shows SARIMA’s performance is boosted by its ability to capture trend and seasonality of the dataset; equally important, Convolutional LSTM equipped with convolutions as part of reading input into LSTM units significantly boost the performance over either Convolutional or LSTM networks. Furthermore, our analysis points out that the indicator which most strongly contribute to predict GDP is CO₂ emission. This result agrees to the fact that some countries with high CO₂ emission also has high GDP as well; also this finding should warn Indonesian government of the increasing CO₂ pollution.

Federally Qualified Health Center Dental Program Finances: A Case Study

We analyzed the operation of one Connecticut federally qualified health center (FOHC) dental program with seven delivery sites. We assessed the financial operation of the different delivery sites and contrasted the overall performance of the FOHC with private practices. We obtained data from a pretested financial survey instrument, electronic patient visit records, and site visits. To assess clinic productivity, we used two output measures: patient visits and market value of services. For the latter, we estimated the implicit fee of each service provided in patient visits. On average, these clinics were running a modest deficit, mainly due to startup costs of two new clinics. The primary factor that impacted net revenues was low reimbursement rates, including privately insured patients. When FOHC dental revenues were adjusted to market rates, revenues were close to expenses. FOHC dental clinics are major components of the dental safety net system. This case study suggests that the established clinics use resources as effectively as private practices.

When Do Drilling Alliances Add Value? The Alliance-Value Model

Summary A recent Gas Research Institute report details three previously unstudied aspects of alliances: specific measurable factors that improve alliance success, how a successful alliance should be structured, and when an alliance makes economic sense. For the purposes of this report, an alliance is defined by "technical process integration," which includes joint planning, execution and evaluation. Technical process integration shifts the definition of an alliance beyond a simple focus on the commercial terms. The most innovative tool to emerge from the report, the Alliance-Value model, addresses those aspects surrounding when an alliance makes economic sense. The theory behind the Alliance-Value model is that the long-term viability of any drilling relationship hinges on its ability to create real value and achieve stability. Based upon the report's findings, the most effective way to form such an alliance is through a detailed description and integration of the technical processes involved. This new type of process-driven alliance is characterized by a value chain which links together a common set of technical processes, mutually defined bottom-line goals, and shared benefits. Building a process-driven alliance requires time and people and therefore has an associated cost. The real value generated by an alliance must exceed this startup cost. The Alliance-Value model computes the net present value of the cash flows for four different operating arrangements: (1) business as usual (conventional competitive bidding process), (2) process-driven alliance (linking technical processes to accelerate production and reduce expenses), (3) incentivized process-driven alliance (linked technical processes with performance incentives to promote stability), and (4) no drill case (primarily used to gauge the market value of services). These arrangements test different degrees of process integration between an operator and its suppliers. They can also help determine if the alliance can add enough value to exceed startup costs and if the relationship will be stable. Each partner can test the impact of the relational structure on its own profitability. When an alliance is warranted, all participants can benefit from real value generated in a stable relationship. However, relationships that focus on commercial terms, instead of on process linking, may be playing a zero-sum, no-win game.