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Benchmarking is a tool for analysing a property or portfolio performance against its peers. This paper outlines some of the central components of benchmarking and demonstrates how benchmarking can bring perspective to operating expenses and ultimately positively affect the valuation of a property. Careful benchmarking of operating expenses can reveal much about the value of investment properties and help to identify opportunities to create value or enhance the value of an asset. Examples illustrate how the value of a given asset can vary dramatically, with even small percentage changes in operating expenses. Such information, in turn, provides the basis for facility and asset management decisions, ranging from estimating budgets to planning capital expenditures for upgrades or improvements. Benchmarking can also enable detailed comparative analysis, which in turn, can assist in identifying areas for improving operations and management by trimming costs or adjusting service levels.

The purpose of this chapter is to identify the most appropriate ways of defining the adoption and non-adoption of activity-based costing (ABC). This chapter uses the responses to a questionnaire survey of management accountants working in British manufacturing industry to test if there are differences across various definitions of adoption and non-adoption in the level of competition, product customization, manufacturing overhead cost percentage and operating unit size. When there are no significant differences between the groups making up each definition this indicates that the definition is appropriate and can be used to define adoption or non-adoption. The results of the research show that the only appropriate definition for ABC adoption is operating units that are currently using ABC. It is possible to define non-adoption in three ways as operating units that are not using ABC, but have considered it; those that are not using ABC, but have considered it except those intending to use it; and those that have rejected ABC, but have never adopted activity-based principles or have never previously used ABC. Comparisons between these two groups show that operating units that have adopted ABC are significantly larger than non-adopters, regardless of how non-adoption is defined. Prior research into the adoption of ABC has used a variety of definitions for the adoption and non-adoption of ABC without examining the appropriateness of these definitions. This chapter overcomes this deficiency by empirically testing the most appropriate definitions.

This research aims to explore the fine-grained relationships between nurse staffing and hospital operational performance with respect to care quality and operating costs. The authors also investigate the moderation effect of competition in local hospital markets on these relationships.

A six-year panel data is assembled from five separate sources to obtain information of 2,524 USA hospitals. Fixed-effect (FE) models are used to test the proposed hypotheses.

First, nurse staffing is initially associated with improved care quality until nurse staffing reaches a turning point, beyond which nurse staffing is associated with worse care quality. Second, a similar pattern applies to the relationship between nurse staffing and operating costs, although the turning point is at a much lower nurse staffing level. Third, market competition moderates the relationship between nurse staffing and care quality so that the turning point of nurse staffing will be higher when the degree of competition is higher. This shift of turning point is also observed in the relationship between nurse staffing and operating costs.

The study identifies three ranges of nurse staffing in which hospitals will likely experience simultaneous improvements, a tradeoff or simultaneous decline of care quality and operating costs when investing in more nursing capacity. Hospitals should adjust nurse staffing levels to the right directions to achieve better care or reduce operating costs.

Nurses constitute the largest provider group in hospitals and profoundly impact care quality and operating costs among all health care professionals. Optimizing the level of nurse staffing, therefore, can significantly impact the care quality and operating costs of hospitals.

Strategic cost structure choices determine how firms divide operating costs between fixed and variable components, and therefore have important implications for financial performance. The purpose of this paper is to examine the effect of operating leverage on equity Betas when managers have discretion over firms' cost structures.

Using panel data for publicly listed trucking firms over years 1994‐2006, market model Betas are regressed on controls and alternatively measured proxies for operating leverage: degree of operating leverage, assets in place and percentage of company employed drivers.

Results of this study generally show positively significant coefficients on all three operating leverage variables.

Operating characteristics of many industries require that firms make substantial investments in long‐lived assets that result in high fixed costs (e.g. depreciation), and for these firms cost structure is exogenously or technologically constrained leaving managers with little discretion. In contrast to these types of firms, the authors examine the effect of operating leverage (OL) on Betas when managers have discretion over firms' cost structures. Trucking firms are a particularly interesting industry group for analyzing the impact of operating OL choices on Beta because distinct strategic cost structure choices are available to the management of trucking firms that result in various degrees of OL throughout the industry.

As a popular supply chain finance (SCF) strategy, reverse factoring has been widely adopted by buyer firms. However, the extant literature provides scant empirical evidence on the performance effect of reverse factoring. The purpose of this study is to seek to narrow this gap by empirically examining the relationship between reverse factoring and operating performance and the contingency conditions of this relationship.

Based on a sample of 167 announcements of reverse factoring implementation made by publicly listed Chinese manufacturing firms between 2014 and 2018, this paper employs a long-term event study approach to analyze the operating performance effect of reverse factoring as well as the moderating effects of production and innovation capabilities.

The event study results indicate that reverse factoring has a positive effect on buyer firms' operating performance in terms of cost efficiency and operating margin. In addition, both production and innovation capabilities positively moderate the relationship between reverse factoring and operating margin. However, neither of them moderates the relationship between reverse factoring and cost efficiency.

This is the first study that empirically examines the impact of reverse factoring on operating performance based on secondary data. Furthermore, it sheds light on the SCF literature by providing insights into the contingency effects of production and innovation capabilities, which also extends our understanding of the application of extended resource-based view in SCF research.

Examines the way in which managers perceive the relative importance of the three costs as applied to professional service firms, financial services firms and retail distribution and manufacturing. Managers′ perceptions may be determined by the nature of the business, the operational focus and the opportunities for the control of costs. Examines the existing literature for indications of the importance attached to each of these costs. Concludes that managers have more discretion of the control of operating costs where they represent a high proportion of the three types and where supply and overhead costs form a high proportion of total costs, managers can exercise discretion by challenging existing industry recipes through restructuring their operations.

The purpose of this paper is to investigate how decoupled direct payments, paid to farm operators based on historical yields and base acreage, may lead to production distortions by altering a farmer's access to credit or enabling the farmer to receive more favorable credit terms. The authors estimate the impact of decoupled direct payments under the 2002 Farm Bill on the credit terms of farm operators, specifically the interest rate on short‐term operating loans. If farm operators are able to obtain more favorable credit terms and reduce their operating cost, then this offers an additional mechanism through which decoupled payments may distort current production.

The authors estimate the impact of decoupled direct payments on the interest rate on short‐term operating loans. In the analysis, the authors control for farm financial characteristics, farm operator characteristics, and other factors. Data from the Agricultural Resource Management Survey for the years 2005‐2007, are used in the weighted regression analysis. Jackknifed standard errors are also computed.

As the proportion of base acres to total operated acres increases it is found that interest rates decline by a small but statistically significant amount. This implies that direct payments lead to lower operating costs through better credit terms.

Lower operating costs may in turn allow some farmers to expand production or produce on land that would otherwise be unprofitable to operate and hence left idle. Ultimately, this distorts current production. However, the small magnitude of the authors' results suggests that the reduction in interest rates, though positive, may have limited distortionary impacts.

The paper provides evidence that decoupled payments alter a farm operator's credit terms and hence could lead to current production distortions. The paper contributes to the growing body of research investigating the mechanisms by which decoupled payments have the potential to distort current production.

This study aims to explore the influence of fuel price, electricity price, fuel consumption (FC) on operating cost, generation and operating income (OI), and how to get the optimized electricity generation (EG) through the operation plants mix economically.

This study is the kind of explanatory research that describes the influence of dependent variable on the independent variable through hypothesis testing. The unit of analysis in this study is PT PLN (Persero) data, and the data is represented by the company’s statistical data from 2004 to 2019. The inferential statistical method is used to analyse the variance in this study-based or component-based with partial least square using the software of SmartPLS 3.2.9.

The fuel price of generation has a positive significant effect on the average electricity price in both models, a negative significant effect on the FC in model A and a positive significant effect in model B, a positive significant effect on the EG in the model A and negative significant effect in the model B, a positive significant effect on the operating cost of generation in both models, and a positive significant effect on OI in both models also.

To the best of the author’s knowledge, this paper is the first to study the influence of generation fuel price, electricity price, FC on operating cost, EG and OI of the power company and using a complex research design with partial least square.

The purpose of this paper is to determine the effect that ownership and management structures have on ability to control operating expenses. For individual investors, intensity of management experience is also explored as a possible explanatory variable for operating expenses. For property management services that are contracted out, the level of the fee is investigated as a possible cause for movements in operating expenses as well. Finally, operating expenses are used as a possible explanatory variable for a property’s lease-up performance during the year.

The analysis consists of a series of regression models performed on data provided by the 2012 Rental Housing Finance Survey (RHFS) in the USA. The RHFS is a unique data set that covers a wide degree of information on multifamily properties. The RHFS represents 2,260 properties in total, and covers various aspects of the apartment industry, including financing and operational cost measures. Control variables used as independent variables include number of units, year of property acquisition, and age of building.

Individual ownership and self-management proved to be statistically significant drivers in driving down log operating expenses. Hours spent by individuals performing property management roles on their own properties had a slightly positive association with operating expenses. For professional managers, the fees devoted solely to the manager or management company had a highly significant and positive effect on other operating costs. Finally, when separating out the individual components of operating expenses, only two variables had significant effects on tenant lease-ups: management expenses (positive) and security expenses (negative).

The data set is potentially biased toward those properties with less than 100 units, and thus it would be problematic to assume that these findings are generalizable to the population at large. There are also no geographic coding indicators within the RHFS data set, which eliminates the potential to control for various market factors and rural/urban differences.

The research provides an understanding of some of the basic factors behind increases in operating expenses, which ultimately has implications for performance benchmarks such as net operating income and property market value.

The reasonable controlling of operating expenses ultimately has potentially positive implications for low- to moderate-income populations, who would ultimately experience lower rents as a result.

This research represents one of the first known uses of the RHFS database.