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The purpose of this paper is to solve the problem of quality prediction in the equipment production process and provide a method to deal with abnormal data and solve the problem of data fluctuation.

The analytic hierarchy process-process failure mode and effect analysis (AHP-PFMEA) structure tree is established based on the analytic hierarchy process (AHP) and process failure mode and effect analysis (PFMEA). Through the failure mode analysis table of the production process, the weight of the failure process and stations is determined, and the ranking of risk failure stations is obtained so as to find out the serious failure process and stations. The spectrum analysis method is used to identify the fault data and judge the “abnormal” value in the fault data. Based on the analysis of the impact, an “offset operator” is designed to eliminate the impact. A new moving average denoise operator is constructed to eliminate the “noise” in the original random fluctuation data. Then, DGM (1,1) model is constructed to predict the production process quality.

It is discovered the “offset operator” can eliminate the impact of specific shocks effectively, moving average denoise operator can eliminate the “noise” in the original random fluctuation data and the practical application of the shown model is very effective for quality predicting in the equipment production process.

The proposed approach can help provide a good guidance and reference for enterprises to strengthen onsite equipment management and product quality management. The application on a real-world case showed that the DGM (1,1) grey discrete model is very effective for quality predicting in the equipment production process.

The offset operators, including an offset operator for a multiplicative effect and an offset operator for an additive effect, are proposed to eliminate the impact of specific shocks, and a new moving average denoise operator is constructed to eliminate the “noise” in the original random fluctuation data. Both the concepts of offset operator and denoise operator with their calculation formulas were first proposed in this paper.

The purpose of this longitudinal study is to qualitatively investigate the role of the general managers (GMs) and senior managers (SMs) in strategic talent management (STM) in hotels during COVID-19. Using upper echelon theory and the dynamic attention-based view, this paper explores the role of upper echelon theory cognitive characteristics (orientation towards STM and decision-making approach) and three dynamic attention-based view attention dimensions (communication, resource attention to the HR function and new configurations of STM) in influencing STM.

This study uses semi-structured interviews with hotel GMs and SMs at two time points over the duration of COVID-19 in six hotels (family-owned, boutique and international hotel chain) located in Ireland, the UK, Germany, Singapore and India.

The findings of this study reveal that GMs and SMs across the different hotels differed in their orientation towards STM and their decision-making approaches and this influenced cognitive and resource attention to STM. GMs and SMs remained cognitively attentive to STM through their communications around STM, and they revealed resource attention through resources to the HR function and new configurations of STM practices during COVID-19. The authors identify three distinct configurations of STM practices in operation in hotels during COVID-19.

This study’s findings reveal important practice implications in that GMs and SMs have a key role to play in the implementation of STM and the need to reconfigure how STM is undertaken during the crisis. This contrasts with the more espoused role suggested for these talent actors in the literature.

The authors used a longitudinal qualitative research design to surface the dynamic role of GMs’ and SMs’ cognitive and resource attention to STM in hotels during COVID-19 and the key role that orientation towards STM and decision-making approach affected both cognitive and resource attention dimensions.

Promoting interns’ organizational socialization has become an urgent concern for the hotel industry. Building on career construction theory, this study aims to use a time-lagged design to investigate the interrelationships among perceived organizational support (POS), psychological capital and organizational socialization and their consequent effects on interns’ intention to stay in the hotel industry.

Panel data were obtained in three waves from hotel interns from 21 upscale hotels located in 13 cities in China with a time lag of 10 weeks (N = 369). The structural equation modeling was used for data analysis.

POS has a significantly positive effect on interns' psychological capital. Additionally, both POS and psychological capital contribute to the intention to stay in the hotel industry through the mediation of organizational socialization.

Hotels should communicate with interns more explicitly, provide assistance programs to alleviate uncertainty and reward interns on their excellent service performance to improve POS. Moreover, setting up psychological capital programs and empowering interns to be involved in task development is beneficial for enhancing psychological capital. Hotels should also consider mentoring as a socialization approach. Further, career planning and counseling programs should be provided for interns’ long-term hospitality career development.

A time-lagged research method is adopted to provide a new approach to improve interns’ intention to stay in the hotel industry from the interactionist perspective. This study enriches research about psychological capital, POS and organizational socialization.

Three functions are identified and integrated into one unique control scheme for multivariate quality control. The control scheme will identify any out‐of‐control samples, select the subset of variables that are out of control, and diagnose the out‐of‐control variables. New control variable selection algorithm and diagnosis methods are proposed and a framework for the control scheme is developed based on simulation results.

This study aims to examine the effect of frontline managers’ green mindfulness on their green creativity directly and through green creative process engagement (GCPE). Furthermore, perceived corporate social responsibility (CSR) moderates the link between green mindfulness and GCPE.

The data were collected from 592 frontline managers from the hospitality industry of Pakistan. The data were analyzed using Hayes’ PROCESS macro.

The findings indicate that green mindfulness has a positive relationship with GCPE and green creativity. Moreover, GCPE mediates the relationship between green mindfulness and green creativity. Perceived CSR also moderates the link between green mindfulness and GCPE. The moderated-mediation effect of perceived CSR is also found to be significant.

The results imply that mindfully green frontline managers’ cognitive resources provide greater attention toward environmental problems and connectedness to nature, which encourages hospitality service firms’ frontline managers’ green creativity.

The novelty of the present study is the development and empirical testing of an integrated framework to investigate that when and how green mindfulness affects green creativity.

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.

This research proposes a multivariate control chart, whose parameters are optimized using genetic algorithms (GA) in order to accelerate the detection of a change in the vector of means.

This chart is based on a variation of the Hotelling T² chart using a sampling scheme called generalized multiple dependent state sampling. For the analysis of performances of this chart, the out-of-control average run length (ARL) values were used for different scenarios. In this comparison, it was considered the classic Hotelling T² chart and the T² chart using the scheme called multiple dependent state sampling.

It was observed that the new chart with its optimized parameters is more efficient to detect an out-of-control process. Additionally, a sensitivity analysis was performed, and it was concluded that the best yields are obtained when the change to be considered in the optimization is small. An application in the resolution of a real problem is given.

In this research, a multivariate control chart is proposed based on the Hotelling T² statistic but adding a sampling scheme. This makes this control chart more efficient than the classic T² chart because the new chart not only uses the current information of the T² statistic but also conditions the decision to consider a process as “in- control” on the statistic's previous information. The practitioner can obtain the optimal parameters of this new chart through a friendly program developed by the authors.

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 aims to design a multivariate control chart that improves the applicability of the traditional Hotelling T² chart. This new type of multivariate control chart displays sufficient information about the states and relationships of the variables in the production process. It is used to make better quality control decisions during the production process.

Multivariate data are collected at an equal time interval and are represented by nodes of the graph. The edges connecting the nodes represent the sequence of operation. Each node is plotted on the control chart based on their Hotelling T² statistical distance. The changing behavior of each pair of input and output nodes is studied by the neural network. A case study from the cement industry is conducted to validate the control chart.

The finding of this paper is that the points and lines in the classic Hotelling T² chart are effectively substituted by nodes and edges of the graph respectively. Nodes and edges have dimension and color and represent several attributes. As a result, this control chart displays much more information than the traditional Hotelling T² control chart. The pattern of the plot represents whether the process is normal or not. The effect of the sequence of operation is visible in the control chart. The frequency of the happening of nodes is recognized by the size of nodes. The decision to change the product feature is assisted by finding the shortest path between nodes. Moreover, consecutive nodes have different behaviors, and that behavior change is recognized by neural network.

Modifying the classical Hotelling T² control chart by integrating with the concept of graph theory and neural network is new of its kind.

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.