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1 – 10 of over 2000Marc Wouters, Susana Morales, Sven Grollmuss and Michael Scheer
The paper provides an overview of research published in the innovation and operations management (IOM) literature on 15 methods for cost management in new product development, and…
Abstract
Purpose
The paper provides an overview of research published in the innovation and operations management (IOM) literature on 15 methods for cost management in new product development, and it provides a comparison to an earlier review of the management accounting (MA) literature (Wouters & Morales, 2014).
Methodology/approach
This structured literature search covers papers published in 23 journals in IOM in the period 1990–2014.
Findings
The search yielded a sample of 208 unique papers with 275 results (one paper could refer to multiple cost management methods). The top 3 methods are modular design, component commonality, and product platforms, with 115 results (42%) together. In the MA literature, these three methods accounted for 29%, but target costing was the most researched cost management method by far (26%). Simulation is the most frequently used research method in the IOM literature, whereas this was averagely used in the MA literature; qualitative studies were the most frequently used research method in the MA literature, whereas this was averagely used in the IOM literature. We found a lot of papers presenting practical approaches or decision models as a further development of a particular cost management method, which is a clear difference from the MA literature.
Research limitations/implications
This review focused on the same cost management methods, and future research could also consider other cost management methods which are likely to be more important in the IOM literature compared to the MA literature. Future research could also investigate innovative cost management practices in more detail through longitudinal case studies.
Originality/value
This review of research on methods for cost management published outside the MA literature provides an overview for MA researchers. It highlights key differences between both literatures in their research of the same cost management methods.
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Wenchao Ma, Lina He, Zeng Dan, Guanyi Chen and Xuebin Lu
With the rapid development of China’s urbanisation and market economy, municipal solid waste (MSW) generation is increasing dramatically. In response to the threat of…
Abstract
With the rapid development of China’s urbanisation and market economy, municipal solid waste (MSW) generation is increasing dramatically. In response to the threat of environmental pollution and the potential value of converting waste into energy, both the government and the public are now paying more attention to MSW treatment and disposal methods. In 2014, 178.6 million tonnes of MSW was collected at a safe treatment rate of 84.8%. However, the treatment methods and the composition of MSW are influenced by the collection area, its gross domestic product, population, rainfall and living conditions. This chapter analysed the MSW composition properties of Lhasa, Tibet, compared with other cities, such as Beijing, Guangzhou and so forth. The research showed that the moisture content of MSW in Lhasa approaches 31%, which is much lower than the other cities mentioned previously. The proportion of paper and plastics (rubbers) collected was 25.67% and 19.1%, respectively. This was 1.00–3.17 times and 0.75–2.44 times more than those found in Beijing and Guangzhou, respectively. Non-combustibles can reach up to 22.5%, which was 4.03–9.11 times that of Beijing and Guangzhou, respectively. The net heating values could reach up to 6,616 kilojoule/kilogram. The food residue was only half the proportion found in other cities. Moreover, the disposal method applied in each city has also been studied and compared.
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Uglješa Stankov, Ulrike Gretzel and Viachaslau Filimonau
Vivian M. Evangelista and Rommel G. Regis
Machine learning methods have recently gained attention in business applications. We will explore the suitability of machine learning methods, particularly support vector…
Abstract
Machine learning methods have recently gained attention in business applications. We will explore the suitability of machine learning methods, particularly support vector regression (SVR) and radial basis function (RBF) approximation, in forecasting company sales. We compare the one-step-ahead forecast accuracy of these machine learning methods with traditional statistical forecasting techniques such as moving average (MA), exponential smoothing, and linear and quadratic trend regression on quarterly sales data of 43 Fortune 500 companies. Moreover, we implement an additive seasonal adjustment procedure on the quarterly sales data of 28 of the Fortune 500 companies whose time series exhibited seasonality, referred to as the seasonal group. Furthermore, we prove a mathematical property of this seasonal adjustment procedure that is useful in interpreting the resulting time series model. Our results show that the Gaussian form of a moving RBF model, with or without seasonal adjustment, is a promising method for forecasting company sales. In particular, the moving RBF-Gaussian model with seasonal adjustment yields generally better mean absolute percentage error (MAPE) values than the other methods on the sales data of 28 companies in the seasonal group. In addition, it is competitive with single exponential smoothing and better than the other methods on the sales data of the other 15 companies in the non-seasonal group.
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Weihao Li, Ying Chen and J. Ryan Lamare
This chapter aims to answer whether foreign multinational corporations (MNCs) operating within the Chinese context differ from indigenous firms on several essential labor…
Abstract
This chapter aims to answer whether foreign multinational corporations (MNCs) operating within the Chinese context differ from indigenous firms on several essential labor standards indicators: white- and blue-collar salaries, pension insurance, and working hours. In drawing upon neo-institutional and organizational imprinting theories and applying these to the Chinese context, the study addresses competing arguments regarding the expected effects of ownership type on these indicators. We employ seemingly unrelated regressions (SURs) to empirically examine a novel national survey of 1,268 firms in 12 Chinese cities. The regression results show that foreign MNCs do not provide uniquely beneficial labor practice packages to workers when compared with various indigenous firm types, including state-owned enterprises (SOEs), affiliate businesses of Hong Kong, Macau, and Taiwan, and domestic private enterprises (DPEs). Specifically, although MNCs provide relatively higher wage rates, they underperform relative to SOEs concerning social insurance. However, DPEs consistently underperform relative to MNCs across most indicators. The mixture of the results contributes important nuances to the application of neo-institutional and organizational imprinting theories to the Chinese context.
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Hui-Chu Shu, Jung-Hsien Chang, Chia-Fen Tsai and Cheng-Wen Yang
This study investigates the impacts of operational risks and corporate governance on bond yield spreads, examining their impacts on bond yield spreads during the COVID-19…
Abstract
This study investigates the impacts of operational risks and corporate governance on bond yield spreads, examining their impacts on bond yield spreads during the COVID-19 pandemic. The results indicate that operational risks significantly raise yield spreads, especially for high-leverage firms. Moreover, a higher independent director percentage reduces debt costs. Furthermore, the results reveal more pronounced effects of operational risks on yield spreads during the COVID-19 pandemic, with these risks increasing the financing costs for large firms. When the effect of the independent director percentage on the yield spreads increases, this consequently raises the debt costs for large firms.
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