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The flexibility of batch process enables its wide application in fine-chemical, pharmaceutical and semi-conductor industries, whilst its complexity necessitates control performance monitoring to ensure high operation efficiency. This paper proposes a data-driven approach to carry out controller performance monitoring within batch based on linear quadratic Gaussian (LQG) method.

A linear time-varying LQG method is proposed to obtain the joint covariance benchmark for the stochastic part of batch process input/output. From historical golden operation batch, linear time-varying (LTV) system and noise models are identified based on generalized observer Markov parameters realization.

Open/closed loop input and output data are applied to identify the process model as well as the disturbance model, both in Markov parameter form. Then the optimal covariance of joint input and output can be obtained by the LQG method. The Hotelling's Tˆ2 control chart can be established to monitor the controller.

(1) An observer Markov parameter approach to identify the time-varying process and noise models from both open and closed loop data, (2) a linear time-varying LQG optimal control law to obtain the optimal benchmark covariance of joint input and output and (3) a joint input and output multivariate control chart based on Hotelling's T² statistic for controller performance monitoring.

The monitoring and control of business processes and their variables have strategic importance in order to respond to the dynamics of the world of business. Many monitoring processes are focussed on controlling time and cost and the overall performance is evaluated through a standard set of key performance indicators. These passive approaches do not consider a holistic/system view and therefore ignore the interrelationships between various external and internal variables impacting a business process. This paper investigates an application of multivariate statistical process control techniques [mainly principal component analysis (PCA) and partial least squares (PLS)] which have been successfully used in process and chemical industries, to model, monitor, control and predict business process variables. A prototype, innovative managerial control system (IMCS), was developed to investigate the application of PCA and PLS techniques to monitor, control and predict business process performance. Data was collected and analysed using a case study in a precast concrete building products company. This study has proved that the PCA approach can be effectively used to control business processes. Also, the PLS approach is found to provide better forecasts as compared to commonly used decomposition method. The benefits and limitations of using multivariate statistical process control techniques as applied to business process control are highlighted.

The purpose of this study is to extend the classical noncentral F-distribution under normal settings to noncentral closed skew F-distribution for dealing with independent samples from multivariate skew normal (SN) distributions.

Based on generalized Hotelling's T² statistics, confidence regions are constructed for the difference between location parameters in two independent multivariate SN distributions. Simulation studies show that the confidence regions based on the closed SN model outperform the classical multivariate normal model if the vectors of skewness parameters are not zero. A real data analysis is given for illustrating the effectiveness of our proposed methods.

This study’s approach is the first one in literature for the inferences in difference of location parameters under multivariate SN settings. Real data analysis shows the preference of this new approach than the classical method.

For the real data applications, the authors need to remove outliers first before applying this approach.

This study’s approach may apply many multivariate skewed data using SN fittings instead of classical normal fittings.

This paper is the research paper and the authors’ new approach has many applications for analyzing the multivariate skewed data.

An onsite experimental study was conducted in order to observe conflict management styles of 90 middle‐level managers from a large Korean furniture manufacturing company. By using accomplices, conflict conditions were introduced in a controlled setting which simulated the features of a work environment. The experiment manipulated the relative status among the subjects and observed the influence of this treatment on the subjects' choices among different conflict management styles. Both structured observations and self‐report questionnaires converged to show that conflict management styles differed significantly when the managers interacted with superiors, peers, or subordinates: the managers were mainly avoiding with superiors, compromising with peers, and competing with subordinates. The current research suggests that the relative status among the parties in conflict determines the choice of conflict management styles.

This study examined responses from 58 food and chemical exporters in Saudi Arabia. Managerial perceptions on 24 export obstacles that were derived from the literature are analyzed and reported. The single most important obstacle perceived by the sample exporters is fierce competition in foreign markets. Competition is followed by high cost of imported raw materials, absence of information about foreign markets, wide fluctuations in the foreign exchange rate, and high overseas transportation costs. The eight categories of the obstacles are: market information, competition, shipping, government policy, foreign market risks, export procedures, production/marketing cost, and internal/technical problems. MANOVA analysis showed that chemical and food exporters are statistically different in their mean response to these obstacles. ANOVA pinpointed those variables that are different at the .05 level. They are: risks involved in selling abroad, language and cultural differences, complex export procedures, lack of adequate export revenue insurance program, and absence of an export management and consulting company. Managerial and policy implications are discussed. Further, recommendations for tackling the top export obstacles are presented.

This study aims to examine relationships between several key technology acceptance variables that predict workers’ wearable sensing devices (WSDs) acceptance in the construction industry by using technology acceptance model, theory of planned behavior and unified theory of acceptance and use of technology (UTAUT) model. The study proposes a hybrid conceptual model to measure construction field workers’ intentions to use WSDs and their usage behaviors. The study introduces variables that are instrumental in understanding and improving WSD acceptance in construction.

The study was carried out using a structured literature review, online survey and structural equation modeling. A total of 195 field workers across the USA, with experience in using WSDs, participated in the study.

Results indicate that all three theories predict WSD acceptance with variables explaining at least 89% of the variance in actual use, with the UTAUT outperforming other models (91%). However, the differences between the predictive power of these models were not statistically significant. A hybrid conceptual model is proposed using findings from the present study.

The study contributes to knowledge and practice by highlighting key variables that influence WSD acceptance. Findings from this study should provide stakeholders with critical insights needed to successfully drive WSD acceptance in the construction industry.

To the best of the authors’ knowledge, this is the first study that evaluates the predictive strength of multiple technology acceptance theories and models within the construction worker safety technology domain. Additionally, the study proposes a hybrid conceptual model which could provide practitioners and researchers with information pertinent to enhancing WSD acceptance.

A great deal of attention in physical distribution management has been raised by both practitioners and academicians as to what type of carrier service is desired by shippers and consignees. From the viewpoint of the buying system, the selection of an appropriate transport mode and the selection of a given carrier and particular services offered by the carrier is an important decision. Billions of dollars are spent annually by the shipping public. The sheer magnitude of the expenditure coupled with the limitation on customer service which can result from the carrier selection choice is making it an increasingly important decision. Purchasing systems need better information concerning carrier selection variables than they now have to enable them to design the organisation for buying carrier services, and to select, train, and supervise traffic people as well as to decide how much of the process can be computerised.

The purpose of this study was to investigate the risk taking behavior of school principals (the dependent variable) identified according to one of two leadership styles by Fielder's Esteem for Least Preferred Co‐worker instrument. Situation specific dilemmas were formulated for the study in a newly developed instrument which manipulated two situation specific independent variables (position power and group support). The findings support the contention that situation variables have a significant effect on the risk disposition of school leaders though no evidence was found to suggest that leader type (task oriented or relations oriented) may be determined by manifest differences in either observed risk disposition or computed risk shift.

Control charts are designed to be effective in detecting a shift in the distribution of a process. Typically, these charts assume that the data for these processes follow an approximately normal distribution or some known distribution. However, if a data-generating process has a large proportion of zeros, that is, the data is intermittent, then traditional control charts may not adequately monitor these processes. The purpose of this study is to examine proposed control chart methods designed for monitoring a process with intermittent data to determine if they have a sufficiently small percentage of false out-of-control signals. Forecasting techniques for slow-moving/intermittent product demand have been extensively explored as intermittent data is common to operational management applications (Syntetos & Boylan, 2001, 2005, 2011; Willemain, Smart, & Schwarz, 2004). Extensions and modifications of traditional forecasting models have been proposed to model intermittent or slow-moving demand, including the associated trends, correlated demand, seasonality and other characteristics (Altay, Litteral, & Rudisill, 2012). Croston’s (1972) method and its adaptations have been among the principal procedures used in these applications. This paper proposes adapting Croston’s methodology to design control charts, similar to Exponentially Weighted Moving Average (EWMA) control charts, to be effective in monitoring processes with intermittent data. A simulation study is conducted to assess the performance of these proposed control charts by evaluating their Average Run Lengths (ARLs), or equivalently, their percent of false positive signals.

This paper explores the effects of the adaptation of the learning organisation (LO) concept to teaching in a university setting. The literature in accounting education calls for a change in traditional teaching methods in order to produce accounting graduates whose skills are more appropriate for the contemporary business and professional environment. To address this deficiency accounting educators have started to explore and use teaching innovations. In this study, the LO concept has been adapted to a teaching environment, in contrast to most research which has focused on LO in business settings. The paper, through the examination of empirical evidence arising from a natural experiment, was able to determine some of the impacts arising from the adaptation of the LO concept to teaching in a university setting. The results of the paper suggest that the adaptation of the LO concept not only resulted in improved student learning outcomes, but also influenced life‐long learning. Further research could widen the scope of the operationalisation of the LO concept and explore its use in other undergraduate and postgraduate units.