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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).

This structured literature search covers papers published in 23 journals in IOM in the period 1990–2014.

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.

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.

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.

The ultimate objective of the any e-Learning system is to meet the specific need of the online learners and provide them with various features to have efficacious learning experiences by understanding their complexities. Any e-Learning system could be much more improved by tracking students commitment and disengagement on that course, in turn, would allow system to have personalized involvements at appropriate times in order to re-engage learners. Motivations play a important role to get back the learners on the track could be done by analyzing of several attributes of the log files. This paper aims to analyze the multiple attributes which cause the learners to disengage from an online learning environment.

For this improvisation, Web based learning system is researched using data mining techniques in education. There are various attributes characterized for the disengagement prediction using web log file analysis. Though, there have been several attempts to include motivating characteristics in e-Learning systems are adapted, presently influence on cognition is acknowledged mostly.

Classification is one of the predictive data mining technique which makes prediction about values of data using known results found from different data sets. To find out the optimal solution for identifying disengaged learners in the online learning systems, Naive Bayesian (NB) classifier with Particle Swarm Optimization (PSO) algorithm is used which will classify the data set and then perform the independent analysis.

The experimental results shows that the use of unrelated variables in the class attributes will reduce the accuracy and reliability of a any classification model. However, the hybrid PSO algorithm is clearly more apt to find minor subsets of attributes than the PSO with NB classifier. The NB classifier combined with hybrid PSO feature selection method proves to be the best feature selection capability without degrading the classification accuracy. It is further proved to be an effective method for mining large structural data in much less computation time.

The crude oil market has experienced an unprecedented overreaction in the first half of the pandemic year 2020. This study aims to show the performance of the global crude oil market amid Covid-19 and spillover relations with other asset classes.

The authors employ various pandemic outbreak indicators to show the overreaction of the crude oil market due to Covid-19 infection. The analysis also presents market connectedness and spillover relations between the crude oil market and other asset classes.

One of the essential findings the authors report is that the crude oil market remains more responsive to pandemic fake news. The shock of the global pandemic panic index and pandemic sentiment index appears to be more promising. It has also been noticed that the energy trader's sentiment (OVX and OIV) was measured at a too high level within the Covid-19 outbreak. Volatility spillover analysis shows that crude oil and other market are closely connected, and the total connectedness index directs on average 35% contribution from spillover. During the initial growth of the infection, other macroeconomic and political events remained to favor the market. The second phase amidst the pandemic outbreak harms the global crude oil market. The authors find that infectious diseases increase investor panic and anxiety.

The crude oil investors' sentiment index OVX indicates fear and panic due to infectious diseases and lack of hedge funds to protect energy investments. The unparalleled overreaction of the investors gauged in OVX indicates market participants have paid an excessive put option (protection) premium over the contagious outbreak of the infectious disease.

The empirical model and result reported amid Covid-19 are novel in terms of employing a news-based index of the pandemic, which are based on the content analysis and text search using natural processing language with the aid of computer algorithms.

原油市場在流行病肆虐的2020年的頭半年經歷史無前例的過度反應。本文旨在顯示全球原油市場在2019冠狀病毒病流行期間的表現及原油市場與其它資產類別之溢出關係.

我們使用各種大流行病爆發的指標，來顯示原油市場因2019冠狀病毒病的感染而過度反應。我們的分析亦涉及市場的關聯性及原油市場與其它資產類別之溢出關係.

我們其中一個基本的發現是: 原油市場仍對大流行病的虛假新聞有更迅速的反應。全球大流行病恐慌性指數及大流行病情緒指數所帶來的震驚似乎是有希望的。大家亦察覺，能源交易商的情緒(OVX及OIV) 在2019冠狀病毒病爆發期間被測量為處於太高的水平。波動溢出分析顯示、原油與其它市場有密切的關係，而總關聯度指數引導平均35%來自溢出量的作用。在感染傳播初期，其它的宏觀經濟和政治事件仍對市場有利。在大流行病爆發期間的第二階段則損害全球原油市場。我們發現，傳染病會增加投資者的恐慌和焦慮.

原油投資者的情緒指數OVX顯示因傳染病及因缺乏對沖基金來保障能源投資而帶來的懼怕和恐慌。於OVX測算到的投資者空前的過度反應顯示市場參與者就這傳染病的感染爆發付出過量的賣權(保障)權利金.

我們的經驗模型和在2019冠狀病毒病肆虐期間匯報的研究結果，從使用以新聞為基礎的流行病指數的角度而言是新穎的。而這些全以內容分析和正文搜尋為基礎、使用自然語言處理，並輔以計算機算法.

Women are, in increasing numbers, participating in the labour market and are an important part of an organisation’s human resource pool. Nevertheless, women still face inappropriate treatment at work. One cause of this is family‐related issues. In particular, pregnancy and child birth present special challenges for working women. Discrimination towards pregnant women is commonplace in work settings. Problems are often related to individual work relationships, for example, the one between the pregnant follower and her manager. It is important to understand problems that impact on women in working life that can disturb their job satisfaction, their performance and willingness to give their best for the organisation. Therefore, for the benefit of both employer and employee, existing practices in leader follower relationships during pregnancy are worth studying in more depth. In leadership studies, the Leader‐Member Exchange (LMX) theory is focused on dyadic leader‐follower relationships and is thus used here to understand this phenomenon. In the present article, the literature on pregnancy and work as well as on LMX is re viewed. On the basis of these reviews, a future research agenda is offered.

Traditional machining centre selection methods may not guarantee a cost effective solution. Properly trained back‐propagation artificial neural network (BPANN) tend to select reasonable machining centres when presented with machining parameters that they have never seen before. The aim of this paper is to demonstrate the applicability of artificial neural networks (ANNs) to machine centre selection problems.

A three‐layer feedforward back‐propagation supervised training approach is selected to address the machining centre selection problem and demonstrated its potential through an example. This is intended to help readers understand implications on manufacturing system design and future research.

Very limited studies attempted the machining centre selection problem. Feedforward ANN approach has been applied to a wide variety of manufacturing problems. Neural networks have training capability to solve problems that are difficult for conventional computers or human beings. The developed BPANN model has potential to solve the machine centre selection problem with notable consistency and reasonable accuracy.

The BPANN model is an innovative approach fundamentally based on artificial intelligence, which is not directly visible to the user, but is able to solve through a simpler and supervised feedforward back‐propagation training process. The model consists of an input layer, a hidden layer and an output layer. The 18 neurons fixed in the input layer are same as the set of machining centre parameters which are taken directly from the machine tool manufacturer's catalogues. Evidently the proposed three‐layer ANN model has the capability of solving the machine centre selection problem with three hidden neurons for threshold level of 0.9, noise level of 0.05 and tolerance of 0.01.

The work size, weight, travel range, spindle speed range, horse power, feed, accuracy, tool magazine and price are used as machining centre selection parameters. Machining centres' information in the form of 24 patterns along with the desired machining centres' were used to train and test the network.

The purpose of this paper is to examine whether and how the usage of regulatory fit in cost reduction announcements affects employees’ willingness to engage in the cost reduction program (CRP).

Based on the regulatory fit theory, a scenario-based experiment was conducted (n = 517) to investigate the effect of promotion- or prevention-oriented framing of the CRP on the acceptance and the motivation to actively contribute to the CRP using multiple ANOVAs.

The study results point out that the framing of the cost announcement messages should use feelings or emotions and ensure gains for promotion-focused employees to decrease the negative effects of regulatory nonfit. However, in the case of prevention-focused employees, facts and reasons should be used in combination with an assertion of nonlosses in the announcement message to prevent regulatory nonfit.

This research deepens the understanding on the decision-influencing role of managerial cost announcements on employee motivation and the impact of different regulatory orientations. By this, the authors enhance the current understanding of how employees can effectively be integrated into CRPs and expand previous research on how regulatory fit theory can be used by organizations dealing with negative events.

The study findings offer several opportunities and implications for managers engaged in corporate communication. More specifically, the study findings provide helpful guidelines for organizations to align their cost reduction announcement with the regulatory focus of their employees to reach regulatory fit and thus enhance employee willingness to participate in the CRP.

Despite the increasing attention of regulatory focus and regulatory fit theory and to the best of the authors’ knowledge, this is the first attempt to search for combined effects of emotions and facts versus potential gains and ensuring nonlosses, which both were shown to influence outcomes predicted by regulatory fit.

Multitasking machining (MTM) systems have become increasingly sophisticated and expensive capital equipment. The lack of practical guidelines for selection of these machines can lead to significant undesirable machine attributes, application mismatch, and longer return on investment. The purpose of this paper is to provide an insight to numerous features and configurations of MTM systems and to present several application‐based selection guidelines.

A taxonomy of MTM systems is developed based on the number of axes of motions, tooling and spindle systems. Practical guidelines for general and advance features are presented with special regard to multi‐axis and multi‐spindle features.

MTM systems are capable of meeting several production goals such as cycle time reduction, minimizing non‐value added times and concurrent processing of multiple parts. However, they possess inherent programming challenges due to their complex configuration and simultaneous machining functions.

The diversity of system configurations demand a decision support system, such as a rule‐based expert system to capture the many variations of MTM systems.

This paper should be useful to decision makers in industry or academia who are involved in selection of MTM systems.