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Smaller companies must continually review the pay‐per‐click (PPC) option or an organic listing on search engines. The purpose of this paper is to present a case study of a small manufacturing firm that is beginning to evaluate which search engine, Yahoo or Google, is more cost effective. Ultimately, management would like to identify if PPC advertising is worth the cost to a small company.

A one month's section of data from Yahoo and Google was examined. Patterns or indications as to which key word landed a better bid position was determined. Seven consecutive campaigns for click‐through rates (CTRs), average cost per click (CPC) and average position of keywords between the search engines Yahoo and Google were observed.

The average CPC was higher with Google. Kennedy Incorporated set a budget with Google and Yahoo to stay below a certain dollar limit, thus the total costs were the same. That would make the difference in the average CPC rather significant. Management also noticed a higher CTR on Yahoo than on Google. Thus it appears that Yahoo outperformed Google most of the time in the monthly samples.

Longer historical data need to be studied, to see if these patterns continue. Other statistics that would be interesting to examine are data that would reveal how CTR in Yahoo and Google are affecting conversions to sales.

It appears from this data that Yahoo is outperforming Google for Kennedy Incorporated with a better CTR and a lower variance in average position when listed on the screen by a return from a search. The company has the impression that Yahoo is a better company for PPC advertising when the marketing budget is small.

Forecasting the number of bed days (NBD) needed within a large hospital network is extremely challenging, but it is imperative that management find a predictive model that best estimates the calculation. This estimate is used by operational managers for logistical planning purposes. Furthermore, the finance staff of a hospital would require an expected NBD as input for estimating future expenses. Some hospital reimbursement contracts are on a per diem schedule, and expected NBD is useful in forecasting future revenue.

This chapter examines two ways of estimating the NBD for a large hospital system, and it builds from previous work comparing time regression and an autoregressive integrated moving average (ARIMA). The two approaches discussed in this chapter examine whether using the total or combined NBD for all the data is a better predictor than partitioning the data by different types of services. The four partitions are medical, maternity, surgery, and psychology. The partitioned time series would then be used to forecast future NBD by each type of service, but one could also sum the partitioned predictors for an alternative total forecaster. The question is whether one of these two approaches outperforms the other with a best fit for forecasting the NBD. The approaches presented in this chapter can be applied to a variety of time series data for business forecasting when a large database of information can be partitioned into smaller segments.

Bankruptcy prediction has attracted a great deal of research in the data mining/machine learning community, due to its significance in the world of accounting, finance, and investment. This chapter examines the influence of different dimension reduction techniques on decision tree model applied to the bankruptcy prediction problem. The studied techniques are principal component analysis (PCA), sliced inversed regression (SIR), sliced average variance estimation (SAVE), and factor analysis (FA). To focus on the impact of the dimension reduction techniques, we chose only to use decision tree as our predictive model and “undersampling” as the solution to the issue of data imbalance. Our computation shows that the choice of dimension reduction technique greatly affects the performances of predictive models and that one could use dimension reduction techniques to improve the predictive power of the decision tree model. Also, in this study, we propose a method to estimate the true dimension of the data.

Managing a large hospital network can be an extremely challenging task. Management must rely on numerous pieces of information when making business decisions. This chapter focuses on the number of bed days (NBD) which can be extremely valuable for operational managers to forecast for logistical planning purposes. In addition, the finance staff often requires an expected NBD as input for estimating future expenses. Some hospital reimbursement contracts are on a per diem schedule, and expected NBD is useful in forecasting future revenue.Two models, time regression and autoregressive integrated moving average (ARIMA), are applied to nine years of monthly counts of the NBD for the Rhode Island Hospital System. These two models are compared to see which gives the best fit for the forecasted NBD. Also, the question of summarizing the time data from monthly to quarterly time periods is addressed. The approaches presented in this chapter can be applied to a variety of time series data for business forecasting.

One aspect of the Clinton Healthcare Reform programme is to assist health maintenance organizations (HMOs) in collecting and analysing data for the purpose of continuous quality improvement. The HEDIS 2.0 quality performance measurement model is currently in use and endorsed by the National Committee for Quality Assurance (NCQA). Outlines a process using HEDIS 2.0 by which an HMO can identify crucial problem areas and track the success of the solution process. Discusses the use of other relevant statistical tools.

While individuals with an intellectual disability form a significant minority in the worldwide prison population, their healthcare needs require specialist attention. In Ireland, services for prisoners with intellectual disabilities need development. However, there is little substantive data estimating the prevalence of intellectual disabilities within the Irish prison system. The paper aims to discuss these issues.

The authors systematically review published data relating to the prevalence of intellectual disabilities in prisons in the Republic of Ireland. The authors searched four databases, governmental websites and corresponded with experts.

Little published data were elicited from searches except for one nationwide cross-sectional survey which reflected a higher prevalence than reported in international studies. Studies from forensic mental health populations are narrated to contextualise findings.

This study found that there is little data to accurately estimate the prevalence of intellectual disabilities in the Irish prison system and the limited data available suggests that this is likely to be higher than international estimates. The authors highlight the need for further research to accurately estimate prevalence in this jurisdiction, alongside the need to develop screening and care pathways for prisoners with an intellectual disability.

The purpose of this paper is to explore the consumer adoption literature on local food. This study discusses the applicability of traditional models of adoption and diffusion to understand new phenomena such as the development of local food networks.

A systematic review of the literature on the adoption and diffusion of local food systems was conducted.

A total of three main challenges within the literature on the adoption and diffusion of local food are identified: the lack of a clear definition of what constitutes local food, divergent market assumptions and divergent consumer assumptions. In addition, this study points to the need for new perspectives on consumer adoption and diffusion of local food practices.

This paper provides an overview of current local food research streams and contributes to the literature on consumer adoption and diffusion of local food consumption.