Search results

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.

Link prediction in social networks refers toward inferring the new interactions among the users in near future. Citation networks are constructed based on citing each other papers. Reciprocal link prediction in citations networks refers toward inferring about getting a citation from an author, whose work is already cited by you. The paper aims to discuss these issues.

In this paper, the authors study the extent to which the information of a two-way citation relationship (called reciprocal) is predictable. The authors propose seven different features based on papers, their authors and citations of each paper to predict reciprocal links.

Extensive experiments are performed on CiteSeer data set by using three classification algorithms (decision trees, Naive Bayes, and support vector machines) to analyze the impact of individual, category wise and combination of features. The results reveal that it is likely to precisely predict 96 percent of reciprocal links. The study delivers convincing evidence of presence of the underlying equilibrium amongst reciprocal links.

It is not a generic method for link prediction which can work for different networks with relevant features and parameters.

This paper predicts the reciprocal links to show who is citing your work to collaborate with them in future.

The proposed method will be helpful in finding collaborators and developing academic links.

The proposed method uses reciprocal link prediction for bibliographic networks in a novel way.

Multi-robots simultaneously coverage and tracking (SCAT) is the problem of simultaneously covering area and tracking targets, which is essential for many applications, such as delivery service, environment monitor, traffic surveillance, crime monitor, anti-terrorist mission and so on. The purpose of this paper is to improve the performance of detected target quantity, coverage rate and less deadweight loss by designing a self-organized method for multi-robots SCAT.

A self-organized reciprocal control method is proposed, coupling task assignment, tracking and covering, equipped with collision-avoiding ability naturally. First, SCAT problem is directly modeled as optimal reciprocal coverage velocity (ORCV) in velocity space. Second, the preferred velocity is generated by calculating the best velocity to the center of some robot detected targets. ORCV is given by adjusting the velocity relative to neighbor robots’ toward in optimal coverage velocity (OCV); it is proven that OCV is collision-free assembly. Third, some corresponding algorithms are designed for finding optimal velocity under two situations, such as no detected targets and empty ORCV.

The simulation results of two cases for security robots show that the proposed method has detected more targets with less deadweight loss and decision time and no collisions anytime.

In this paper, a self-organized reciprocal control method is proposed for multi-robots SCAT problem, which is modeled in velocity space directly, different to the traditional method modeling in configuration space. What is more, this method considers the reciprocal of robots that contributes to the better accomplishment of SCAT cooperatively.

This chapter takes a critical perspective on the conventional wisdom that more advanced cost allocation methods have the potential to provide a more accurate picture of “true” cost, inevitably leading to optimal product mix and pricing decisions, and ultimately to greater profitability.

Two concrete examples of the growing divergence between cost accounting theory and practice – the failures of activity-based costing and the reciprocal method of service department cost allocation to take root in practice – are examined through the lens of post-structuralist literary theory.

The findings suggest that economic truth has been devoured in an accounting simulation. The accounting model no longer reflects any profound economic reality; it precedes reality.

Much of the mainstream management accounting literature remains theoretically grounded in the belief that ‘true’ cost exists, as an object, which is revealed through our cost accounting systems. This chapter raises serious questions about this foundation, and therefore the practical applicability of a great deal of research.

Society has granted the accounting profession a great deal of responsibility and autonomy, largely on the confidence that it has historically provided an objective and truthful model of economic reality. The findings in this chapter suggest that the basis for the accounting profession’s preferential charter in society ought to be critically examined.

At a time where research has advanced toward an ever-narrower focus on self-referential tautologies and ever more complex modeling techniques, this chapter provides a new and stimulating, albeit provocative, perspective to yet unresolved issues in management accounting research and practice.

The purpose of this paper is to address and overcome the problem that a single prediction model cannot accurately fit a data sequence with large fluctuations.

Initially, the grey linear regression combination model was put forward. The Discrete Grey Model (DGM)(1,1) model and the multiple linear regression model were then combined using the entropy weight method. The grain yield from 2010 to 2015 was forecasted using DGM(1,1), a multiple linear regression model, the combined model and a GM(1,N) model. The predicted values were then compared against the actual values.

The results reveal that the combination model used in this paper offers greater simulation precision. The combination model can be applied to the series with fluctuations and the weights of influencing factors in the model can be objectively evaluated. The simulation accuracy of GM(1,N) model fluctuates greatly in this prediction.

The combined model adopted in this paper can be applied to grain forecasting to improve the accuracy of grain prediction. This is important as data on grain yield are typically characterised by large fluctuation and some information is often missed.

This paper puts the grey linear regression combination model which combines the DGM(1,1) model and the multiple linear regression model using the entropy weight method to determine the results weighting of the two models. It is intended that prediction accuracy can be improved through the combination of models used within this paper.

The purpose of this paper is to develop a new systematic method for a multi-unit organization to cope with the cost allocation problem, which is an extension of the reciprocal method. As uncertainty is the inherent characteristic of business environments, assuming changes in engaged parameters is almost necessary. The outputs of the model determine the total value of each unit/business lines or product.

In the proposed method, contrary to existing models, business units are able to transfer their costs to other units, and also, not necessarily transfer the total costs of support units completely. The DEMATEL approach, which finds all relationships between different parts of a system, is also applied for computing effects of the units’ expense paid to each other. Moreover, a fuzzification approach is used to capture linguistic experts’ judgments about related data.

Being closer to the real-world problem in comparison to the previous approach, the proposed systematic approach encompasses the other cost allocation models.

Applying the proposed model for a system like a multi-unit organization, the total price of each unit/business line can be obtained. Moreover, this cost allocation process guides the related decision-makers to better manage the expenses that each unit pays the others.

In the existing studies, business units cannot pay expense support units. However, in the proposed method, the business units are able to pay expenses for other units, and also, not necessarily pay total expenses for support unit completely. Moreover, considering engaged parameters as fuzzy numbers makes the proposed model closer to real-world problems.

The purpose of this paper is to focus on challenges faced by longitudinal quantitative analyses of school improvement processes and offers a systematic literature review of current papers that use longitudinal analyses. In this context, the authors assessed designs and methods that are used to analyze the relation between school improvement processes and student outcomes. Based on this the authors point out to what extent the papers consider different aspects of the complex nature of school improvement (e.g. multilevel structure, indirect and nonlinear effects, reciprocity). The choice of study designs and methods of analysis substantially determines which aspects of this complexity are taken into account.

The authors searched in four international high-impact journals and in ERIC for articles reporting longitudinal school improvement studies. The database of the review consisted of a total of 428 journal articles. In total, 13 of the 428 papers met the selection criteria and were analyzed in detail.

The analyzed papers use a wide range of designs and methodological approaches. They support the assumption that sophisticated quantitative longitudinal designs and methods can be applied effectively in school improvement research. However, considering the complexity of school improvement is accompanied by high demands on designs and methods. Due to this none of the papers met the standards applied in this review completely.

In particular, further research is needed to consider a long period of observation, reciprocal indirect and nonlinear processes in a multilevel structure. Moreover, research is required for a better and unambiguous theoretical foundation and empirical validation of the number of and intervals between measurement points.

If more consideration is given to the complex nature of school improvement in future studies, the broader knowledge base will allow a better understanding of the dynamic relation of school improvement and student learning. It would thus be possible to make more appropriate recommendations for the support of school improvement practice.

The original contribution of the paper is to show which aspects of the complexity of school improvement processes – and to what extent – are currently addressed in designs and methods of analysis applied in quantitative longitudinal studies that investigate the relation between schools’ capacity to managing change and student outcomes. Additionally the authors aim at deriving need for further research and giving guidelines how designs and methods in further studies can reflect the complexity appropriately. It is highly important to consider all aspects of this complexity to describe and understand the dynamic relation of school improvement processes and student outcomes.

The purpose of this paper is (1) to determine the factors that significantly influence the capital structure, (2) to determine the factors that significantly influence profitability, (3) to find the factors that significantly influence growth opportunities, (4) to find reciprocal influence between capital structure and profitability and (5) to find reciprocal influence between capital structure and growth opportunity.

The population of this research is a manufacturing company listed on the Indonesia Stock Exchange during the period of 2010–2016. The number registered in the manufacturing sector is 144 companies. The sampling technique applied is purposive sampling. The fulfillment criteria are companies that have been approved before 2010. Another criterion is that the company is not delisting during the observation period. From that total of population, companies that meet the requirements are 117 companies. This observation was conducted for seven years since 2010–2016, so the center of the analysis of this research was a total of 819. The inferential statistics method used to analyze the research data is generalized structural component analysis (GSCA).

The results of this study indicate that (1) the factors that influence the capital structure include effective tax rate, financial flexibility, growth, uniqueness, asset Utilization, firm size and tangibility; (2) factors that affect profitability include liquidity, growth, firm age, uniqueness, tangibility, volatility, advertising and asset turnover; (3) growth opportunity have a negative and significant influence on capital structure. This means an increase in growth opportunity can be defined as an increase in depreciation that will not be used as collateral for managers to increase debt. This increase in debt will have an impact on reducing growth opportunities; (4) profitability and capital structure have a two-way causality relationship, which means they influence each other and (5) capital structure and growth opportunities have a negative reciprocal relationship.

The authenticity of the study is implied in the following explanation: The authors try to examine the reciprocal effect of capital structure on profitability and capital structure on growth opportunities and the factors that influence these two endogenous variables that have never been done by previous researchers. This research is motivated by research conducted by (Chathoth and Olsen, 2007; Jian-Shen Chen et al., 2009; Yang et al., 2010) using the structural equation model (SEM). However, this study uses GSCA as a method of research analysis.

The purpose of this paper is the presentation of a learning model for a manager and employee working collaboratively to make advances in knowledge, skills, work performance and in the quality of their relationship. The model is called reciprocal action learning.

The approach was to examine concepts and research that could be linked to reciprocal learning. Desired, ultimately, was creation of a proposal that put forth an explanation of manager–employee learning and a means for placing the effort into practice. Theories and concepts are identified in support of the learning approach and its functioning. Action or experiential learning was identified as the vehicle for implementation.

Substantive, supportive information was identified in the expression of a practical action plan for a manager to use to spring reciprocal learning to life.

The action plan set forth can serve as a model or template for a manager, particularly those managers with little experience in guiding employee learning. Initial use of the concepts and action plan could be regarded as an experiment and could set the stage for additional, more informed efforts at reciprocal learning.

While much empirical and other research addresses employee learning and management/manager learning, there is very little research or material available regarding how a manager and an employee can directly learn together in working on an issue (problem, change, improvement, etc.) in a collaborative fashion that embraces equality.

In order to improve the estimation accuracy of soil organic matter, this paper aims to establish a modified model for hyperspectral estimation of soil organic matter content based on the positive and inverse grey relational degrees.

Based on 82 soil sample data collected in Daiyue District, Tai'an City, Shandong Province, firstly, the spectral data of soil samples are transformed by the first order differential and logarithmic reciprocal first order differential and so on, the correlation coefficients between the transformed spectral data and soil organic matter content are calculated, and the estimation factors are selected according to the principle of maximum correlation. Secondly, the positive and inverse grey relational degree model is used to identify the samples to be identified, and the initial estimated values of the organic matter content are obtained. Finally, based on the difference information between the samples to be identified and their corresponding known patterns, a modified model for the initial estimation of soil organic matter content is established, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.

The results show that the methods of logarithmic reciprocal first order differential and the first-order differential of the square root for transforming the original spectral data are more effective, which could significantly improve the correlation between soil organic matter content and spectral data. The modified model for hyperspectral estimation of soil organic matter has high estimation accuracy, the average relative error (MRE) of 11 test samples is 4.091%, and the determination coefficient (R²) is 0.936. The estimation precision is higher than that of linear regression model, BP neural network and support vector machine model. The application examples show that the modified model for hyperspectral estimation of soil organic matter content based on positive and inverse grey relational degree proposed in this article is feasible and effective.

The model in this paper has clear mathematical and physics meaning, simple calculation and easy programming. The model not only fully excavates and utilizes the internal information of known pattern samples with “insufficient and incomplete information”, but also effectively overcomes the randomness and grey uncertainty in the spectral estimation of soil organic matter. The research results not only enrich the grey system theory and methods, but also provide a new approach for hyperspectral estimation of soil properties such as soil organic matter content, water content and so on.

The paper succeeds in realizing both a modified model for hyperspectral estimation of soil organic matter based on the positive and inverse grey relational degrees and effectively dealing with the randomness and grey uncertainty in spectral estimation.