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The purpose of this paper is to explore the internal interaction mechanism of marine scientific research and education, industrial structure upgrading and marine economic growth from a systematic perspective, based on which this work forecasts their future development trends.

In this study, a multivariate grey model is applied to the prediction of marine scientific research and education, industrial structure upgrading and marine economic growth. Considering the impact of the COVID-19 on marine development, this paper introduces the weakening buffer operator into MGM(1,m) and constructs the AWBO-MGM(1,m) model. To verify the validity and accuracy of the new model, this paper uses AWBO-MGM(1,m), MGM(1,m), GM(1,N), GM(1,1), back propagation neural network and linear regression models for simulation and prediction based on the data from 2010 to 2021, respectively.

From the theoretical perspective, the development of marine scientific research and education can accelerate industrial upgrading and promote marine economic growth by providing high-quality talents, promoting marine science and technology progress and reducing transaction costs; while the upgrading of marine industrial structure and marine economic growth can promote the development of marine scientific research and education by guiding social capital, enhancing talent demand and stimulating market vitality. From the empirical analysis, the AWBO-MGM(1,m) model can effectively deal with epidemic shocks and has higher fitting and prediction accuracy than the other five comparative models.

The government should pay attention to the construction of marine scientific research and education, so as to provide high-quality talents and advanced scientific research results for the high-quality development of marine economy. On the basis of using science and technology to firmly build the primary and secondary marine industries, the government should actively guide the labor, capital and other factors of production to the tertiary industry, thereby promoting the optimization and upgrading of marine industrial structure.

On the one hand, the interplay mechanism of marine scientific research and education, industrial structure upgrading and marine economic growth is analyzed from a systematic perspective; on the other hand, the enhanced AWBO-MGM(1,m) possesses higher forecasting performance and is applicable to the systemic multivariate forecasting problem in the presence of outstanding external shocks.

This study aims to propose a tailored Lean Six Sigma framework providing an accessible Lean Six Sigma methodology for compound feed manufacturers with the aim of mitigating rising costs and increasingly complex demands from customers.

A Lean Six Sigma framework was designed combining Lean value stream mapping and Six Sigma structured problem-solving with a case study in an Irish compound feed manufacturer.

The study found that the Lean Six Sigma implementation framework provided a simplified approach, which fitted the resource availability within compound feed manufacturing.

The study is limited by the constraints of a sole case study in providing empirical evidence of the effectiveness of the framework. Nevertheless, a conceptual Lean Six Sigma model is proposed, which will assist compound feed manufacturers implementing a continuous improvement approach.

This paper proposes a simplified approach to the implementation of Lean Six Sigma in agricultural compound feed manufacturers and in small and medium-sized organisations. This is the first such study in Ireland and will add to the body of work on Lean in agriculture and aid other agri-businesses and compound feed manufacturers in understanding how Lean Six Sigma can benefit.

The purpose of this paper is to propose a concept of cloud auction robot (CAR) and its execution platform for transforming perishable food supply chain management. A new paradigm of goods-to-person auction execution model is proposed based on CARs. This paradigm can shift the management of traditional manual working to automated execution with great space and time saving. A scalable CAR-enabled execution system (CARES) is presented to manage logistics workflows, tasks and behavior of CAR-Agents in handling the real-time events and associated data.

An Internet of Things enabled auction environment is designed. The robot is used to pick up and deliver the auction products and commends are given to the robot in real-time. CARES architecture is proposed while integrating three core services from auction workflow management, auction task management, to auction execution control. A system prototype was developed to show its execution through physical emulations and experiments.

The CARES could well schedule the tasks for each robot to minimize their waiting time. The total execution time is reduced by 33 percent on average. Space utilization for each auction studio is improved by about 50 percent per day.

The CAR-enabled execution model and system is simulated and verified in a ubiquitous auction environment so as to upgrade the perishable food supply chain management into a new level which is automated and real-time. The proposed system is flexible to cope with different auction scenarios, such as different auction mechanisms and processes, with high reconfigurability and scalability.

This study evaluates the moderating role of work-based social supports (i.e. supervisor support and co-worker support) in the relationship between job insecurity and job burnout among hospitality employees in Malaysia. Besides, the direct effect between job insecurity and job burnout is examined.

The cross-sectional data of this study were based on a total of 220 self-administered questionnaires that have been completed by hospitality employees from three different states in Malaysia. Respondents were recruited based on a snowball sampling approach. The data were collected during the COVID-19 pandemic, which was from October 2020 to January 2021.

Partial least square-structural equation modeling (PLS-SEM) was performed via SmartPLS software. The finding confirmed that job insecurity significantly intensifies employees' job burnout. Supervisor support and co-worker support were found to moderate the link between job insecurity and burnout. As anticipated, the relationship between job insecurity and job burnout increased when supervisor support is low. But high co-worker support was found to strengthen the impact of job insecurity on job burnout instead of the reverse.

This study supplements the existing literature by clarifying which sources of work-based social support (i.e. co-worker support or supervisor) is more salient in alleviating the adverse impact of job insecurity on job burnout during the COVID-19 pandemic among hospitality employees in Malaysia.

The purpose of this study is to investigate the perceived effects between soft management practices, employee behaviours and the implementation of digital technologies in manufacturing plants, as well as how these relate to the emergence of digital waste.

This paper uses case-based research. Data was collected in two large manufacturing companies based in Norway and Sweden through semi-structured interviews with two management representatives and four shop-floor employees. The data was used to evaluate 29 variables describing lean- and total quality management (TQM)-associated employee behaviours and soft management practices, in light of digital transformation.

The results suggest that several variables were positively influenced by the digital transformation process. These were top management leadership, middle management involvement, employee education, corporate social responsibility focus, innovation, knowledge sharing, work-family balance, psychological capital, job satisfaction and career commitment. Training employees, creativity, discretionary effort, turnover intention and proactivity appear to be negatively influenced by digital transformation The findings also indicate that several soft management practices and employee behaviours were not only influenced by manufacturing digitalization but also themselves influenced the process. The potential for digital waste creation was also detected in several variables, including reward and recognition and training employees.

Managers, practitioners and academics may learn about the importance of certain managerial practices and employees’ behavioural needs during the digital transformation process. The findings may help in prioritizing TQM and soft lean management practices and certain employee behaviours during the digital transformation and in creating awareness of digital waste.

This study builds on several existing studies discussing the impact of digital transformation on soft management practices and employee behaviours. It provides insights from a lean and TQM angle and offers a means of prioritizing certain practices and behaviours during a digital transformation. This study also highlights the significance of digital waste.

The purpose of this paper is to identify the productivity improvement potentials from maintenance planning practices in manufacturing companies. In particular, the paper aims at understanding the connection between machine criticality assessment and maintenance prioritization in industrial practice, as well as providing the improvement potentials.

An explanatory mixed method research design was used in this study. Data from literature analysis, a web-based questionnaire survey, and semi-structured interviews were gathered and triangulated. Additionally, simulation experimentation was used to evaluate the productivity potential.

The connection between machine criticality and maintenance prioritization is assessed in an industrial set-up. The empirical findings show that maintenance prioritization is not based on machine criticality, as criticality assessment is non-factual, static, and lacks system view. It is with respect to these finding that the ways to increase system productivity and future directions are charted.

In addition to the empirical results showing productivity improvement potentials, the paper emphasizes on the need for a systems view for solving maintenance problems, i.e. solving maintenance problems for the whole factory. This contribution is equally important for both industry and academics, as the maintenance organization needs to solve this problem with the help of the right decision support.

The purpose of this paper is to move beyond individual level characteristics of founders to explain the performance gap between white and black majority owned new ventures. It specifically investigates three potential mediators: demographic characteristics of venture’s location, financial size of the venture and its credit riskiness.

The Kauffman Firm Survey, a longitudinal data set of 4,928 new ventures started in the USA in 2004, has been utilized in this paper. Pooled OLS and Logit regression models were employed for direct effects. Mediation effects were tested using two different approaches: the Baron and Kenny approach and decomposition analysis.

The paper finds that the financial size and credit riskiness mediate the relationship between majority race ownership and the performance of a venture.

The data were collected for a single cohort (2004) of nascent firms; furthermore, the sample draws from firms based in the USA. Future studies could replicate this research utilizing samples of different cohorts and from other parts of the world.

The paper provides important guidance to policy makers. In general, to reduce the performance gap between black and white owned ventures, providing access to subsidized assets, capital and credit could be very helpful.

Past research suggests that the majority race ownership of a new venture impacts its performance and attributes these differences to heterogeneous endowments, usually of the primary owner. In this paper, analyses are conducted at multiple levels and new mechanisms through which the internal resources and capabilities of a new venture mediate the relation are discovered.

Exploring the influencing factors on urban rail transit (URT) ridership is vital for travel demand estimation and urban resources planning. Among various existing ridership modeling methods, direct demand model with ordinary least square (OLS) multiple regression as a representative has considerable advantages over the traditional four-step model. Nevertheless, OLS multiple regression neglects spatial instability and spatial heterogeneity from the magnitude of the coefficients across the urban area. This paper aims to focus on modeling and analyzing the factors influencing metro ridership at the station level.

This paper constructs two novel direct demand models based on geographically weighted regression (GWR) for modeling influencing factors on metro ridership from a local perspective. One is GWR with globally implemented LASSO for feature selection, and the other one is geographically weighted LASSO (GWL) model, which is GWR with locally implemented LASSO for feature selection.

The results of real-world case study of Shenzhen Metro show that the two local models presented perform better than the traditional global model (OLS) in terms of estimation error of ridership and goodness-of-fit. Additionally, the GWL model results in a better fit than GWR with global LASSO model, indicating that the locally implemented LASSO is more effective for the accurate estimation of Shenzhen metro ridership than global LASSO does. Moreover, the information provided by both two local models regarding the spatial varied elasticities demonstrates the strong spatial interpretability of models and potentials in transport planning.

The main contributions are threefold: the approach is based on spatial models considering spatial autocorrelation of variables, which outperform the traditional global regression model – OLS – in terms of model fitting and spatial explanatory power. GWR with global feature selection using LASSO and GWL is compared through a real-world case study on Shenzhen Metro, that is, the difference between global feature selection and local feature selection is discussed. Network structures as a type of factors are quantified with the measurements in the field of complex network.

By examining a case study in Tyrol, Austria, the paper aims to demonstrate the role of farm diversification and the influence of the peasants’ habitus on social-ecological resilience.

Drawing on a field study conducted in two remote villages of the Ötztal valley, Austrian Alps, this study provides insights into the interplay of tourism and farming and its impact on farm resilience. Qualitative narrative interviews, the so-called farm biographies, served to investigate these issues. Interpretations of data are based on qualitative content analysis.

The results highlight that farming and tourism are highly enmeshed in the case study area and that the additional income creates room for manoeuvre for the farms to activate their adaptive capability. At the same time, peasant values guide the farming activities. The farms in this study demonstrate a strong farm resilience that is enabled by farm diversification and rooted in their peasant habitus. This positively affects the social-ecological resilience.

In contrast to other studies, which have mainly applied the concepts of social or community resilience to investigate the resilience and vulnerability of rural areas, this study highlights the resilience of farms in mountain areas.

This paper aims to present the findings from a European study on the digital skills gaps in tourism and hospitality companies.

Mixed methods research was adopted. The sample includes 1,668 respondents (1,404 survey respondents and 264 interviewees) in 5 tourism sectors (accommodation establishments, tour operators and travel agents, food and beverage, visitor attractions and destination management organisations) in 8 European countries (UK, Italy, Ireland, Spain, Hungary, Germany, the Netherlands and Bulgaria).

The most important future digital skills include online marketing and communication skills, social media skills, MS Office skills, operating systems use skills and skills to monitor online reviews. The largest gaps between the current and the future skill levels were identified for artificial intelligence and robotics skills and augmented reality and virtual reality skills, but these skills, together with computer programming skills, were considered also as the least important digital skills. Three clusters were identified on the basis of their reported gaps between the current level and the future needs of digital skills. The country of registration, sector and size shape respondents’ answers regarding the current and future skills levels and the skills gap between them.

The paper discusses the digital skills gap of tourism and hospitality employees and identifies the most important digital skills they would need in the future.