Search results

The subject area of the assignment is accounting education and testing techniques.

This paper details an effective method to create individualized assignments and testing materials. Using a spreadsheet (Microsoft Excel), the creation of the unique assignments and answer keys can be semi-automated to reduce the grading difficulties of unique assignments.

Because students are using a unique data set for each assignment, the students are able to more effectively engage in student to student teaching. This process of unique assignments allows students to collaborate without fear that a single student would provide the answers. As tax laws (e.g., credit and deduction phase-outs, tax rates, and dependents) change depending on the level of income and other factors, an individualized test is ideal in a taxation course.

The unique assignments allow instructors to create markedly different scenarios for each student. Using this testing method requires that the student thoroughly understands the conceptual processes as the questions cannot be predicted. A list of supplementary materials is included, covering sample questions, conversion to codes, and sample assignment questions.

This technique creates opportunities for students to have unique assignments encouraging student to student teaching and can be applied to assignments in any accounting course (undergraduate and graduate). This testing method has been used in Intermediate I and II, Individual Taxation, and Corporate Taxation.

Business schools are tasked with matching curriculum to techniques that industry practitioners rely on for profitability. Forecasting is a significant part of what many firms use to try to predict budgets and to provide guidance as to the direction the business is headed. This chapter focuses on forecasting and how well business schools match the requirements of industry professionals. Considering its importance to achieving successful business outcomes, forecasting is increasingly becoming a more complex endeavor. Firms must be able to forecast accurately to gain an understanding of the direction the business is taking and to prevent potential setbacks before they occur. Our results suggest that, although techniques vary, in large part business schools are introducing students to the forecasting tools that graduates will need to be successful in an industry setting. The balance of our chapter explores the forecasting tools used by business schools and firms, and the challenge of aligning the software learning curve between business school curriculum and industry expectations.

The relationship between electricity demand and weather in the United States has been studied as of late due to increased demand, de-regulation, and new pricing models. The influence of weather or seasonality in energy consumption, particularly electricity demand, has been widely researched. A significant scientific interest in the seasonality of energy consumption has led to an important number of papers exploring the role of weather variability and change on energy consumption. Most of these papers model demand as a function of seasonal climate factors.

The goal of this research is a broad examination of monthly residential electricity demand for a region of the mid-Atlantic using Excel and step-wise regression. This is achieved by using a sequence of models built in Excel in which different patterns are gradually introduced in the estimations. Data over a seven-year period is utilized. A backward elimination step-wise regression analysis is employed to determine which independent variables best model the data. Initial independent variables included high monthly temperature, low monthly temperature, time, year, month, seasonal quarter, and introduction of a “green” tax credit for solar and wind energy.

Models for forecasting the electricity demand and the predictive power of these models is assessed. The work is organized as follows: Data description and the methodology, trend and the seasonality of electricity usage in the mid-Atlantic region, the predictive power and seasonality of the models, and main conclusions drawn from the study.

This chapter presents an Excel-based regression analysis to forecast seasonal demand for U.S. Imported Beer sales data. The following seasonal regression models are presented and interpreted including a simple yearly model, a quarterly model, a semi-annual model, and a monthly model. The results of the models are compared and a discussion of each model's efficacy is provided. The yearly model does the best at forecasting U.S. Import Beer sales. However, the yearly does not provide a window into shorter-term (i.e., monthly) forecasting periods and subsequent peaks and valleys in demand. Although the monthly seasonal regression model does not explain as much variance in the data as the yearly model it fits the actual data very well. The monthly model is considered a good forecasting model based on the significance of the regression statistics and low mean absolute percentage error. Therefore, it can be concluded that the monthly seasonal model presented is doing an overall good job of forecasting U.S. Import Beer Sales and assisting managers in shorter time frame forecasting.

The reciprocal method for allocating support department costs is preferred over the direct and step-down methods because it captures all support services provided to other departments. However, even as business organizations increase the number of support departments and their costs, the adoption of the reciprocal method has been hindered by mathematical difficulties in solving simultaneous equations. This paper illustrates spreadsheet matrix functions that remove the difficulties associated with the reciprocal method. The algebraic expressions for reciprocated costs commonly presented in accounting textbooks are used to form an equivalent matrix relationship. Then spreadsheet matrix functions easily compute reciprocated costs for support departments from the matrix relationship, and also allocate the reciprocated costs to other departments.

In order to shed light on the implications of the business intelligence (BI) for management accounting (MA) and decision making, this study investigates as to how the use of the BI affects the production, transmission, and reception of performance measures (PM).

To investigate the issue at hand, a case study of an Italian company is carried out. The case study method is deemed suitable to explore the complex, penetrating, and unpredictable relationship between BI and PM.

The case analysis shows that the use of the BI can affect the production of PM by leading the organization to frame PM into an indicator setting. Moreover, the BI can affect the transmission by introducing a new, “visual” approach for presenting PM to decision makers, which is also relevant in the reception as a mobilizing factor.

This study contributes to improving the understanding of BI implications for MA and decision making, which is still limited in the accounting academia. Additionally, this research adds to extant knowledge about the relationship between measurement and management; more specifically, it contributes to understanding the “fate” of PM.

Furthermore, the findings illustrated in this chapter can be relevant from a practical point of view: by showing the role that BI solutions can play in producing and transmitting PM, the study shows the potential contribution of the use of the BI in managing and overcoming problems arising in these phases, favoring the use of these measures.

Prior research (Harrast, Olsen, & Sun, 2023) analyzes the eight emerging topics to be included in future CPA exams and discusses their importance to career success and appropriate teaching locus in light of survey evidence. They find that the general topic of data analytics is the most important of the eight emerging topics. To further understand the topics most important to career success, this study analyzes subtopics underlying the eight emerging topics. The results show that advanced Excel analysis tools, data visualization, and data extraction, transformation, and loading (ETL) are the most important data analytics subskills for career success according to professionals and that these topics should be both introduced and emphasized in the accounting curriculum. The results provide useful information to educators to prioritize general emerging topics and specific subtopics in the accounting curriculum by taking into account the most pressing needs of the profession.

The perception and communication of risk for organizations are highly topical and difficult to address in higher education (HE) due to its complexity and variety of structures, processes and identities. The omnipresence of managerialism in HE currently also impacts organizational innovation. This is interrogated in terms of the form and effect of innovation and improvization (Cunha, Neves, Clegg, & Rego, 2015). The development of tools to manage risk perception is discussed alongside perceptions of risk and their potential management through agile processes to enable a university-wide collaboration across services to enable a unified and streamlined proactive management of risk and its corollaries of loss.

The focus of this chapter is on the daily management of operational risks in higher education institutions (HEIs). It considers the causes and impact of drift and competitiveness in organizational processes and their impact on organizational efficiency. This chapter will consider risk perception and contract management across HEIs.