Search results

This chapter highlights the key areas for the leadership and innovation research agenda in 2035. This agenda will direct researchers’ focus to the core transversal skills that individuals must have amidst the shift toward a greener and digitalized economy. Such skills include leadership, management, creativity, communication, and adaptability. The role of macro governmental policies and micro organizational policies is of great significance to ensure the implementation (if any) of these changes and of core to the research agenda. This chapter will also guide researchers to the challenges at the higher education level that need to be addressed to ensure the balance between the skills and knowledge acquired by workers through education and the needs of businesses in order to increase productivity and innovation.

This research aims to analyse the perceptions of practitioners in three regions regarding the challenges faced by their firms during the pandemic, considered a black-swan event. It examines the strategies implemented to mitigate and recover from risks, evaluates the effectiveness of these strategies and assesses the difficulties encountered in their implementation.

In the summer of 2022, an online survey was conducted among supply chain (SC) practitioners in France, Poland and the St. Louis, Missouri region of the USA. The survey aimed to understand the impact of COVID-19 on their firms and the SC strategies employed to sustain operations. These regions were selected due to their varying levels of SC development, including infrastructure, economic resources and expertise. Moreover, they exhibited different responses in safeguarding the well-being of their citizens during the pandemic.

The study reveals consistent perceptions among practitioners from the three regions regarding the impact of COVID-19 on SCs. Their actions to enhance SC resilience primarily relied on strengthening collaborative efforts within their firms and SCs, thus validating the tenets of the relational view.

COVID-19 is (hopefully) our black-swan pandemic occurrence during our lifetime. Nevertheless, the lessons learned from it can inform future SC risk management practices, particularly in dealing with rare crises. During times of crisis, leveraging existing SC structures may prove more effective and efficient than developing new ones. These findings underscore the significance of relationships in ensuring SC resilience.

Society is critically dependent on an adequate supply of hospital doctors to ensure optimal health care. Voluntary turnover amongst hospital doctors is, however, an increasing problem for hospitals. The aim of this study was to systematically review the extant academic literature to obtain a comprehensive understanding of the current knowledge base on hospital doctor turnover and retention. In addition to this, we synthesise the most common methodological approaches used before then offering an agenda to guide future research.

Adopting the PRISMA methodology, we conducted a systematic literature search of four databases, namely CINAHL, MEDLINE, PsycINFO and Web of Science.

We identified 51 papers that empirically examined hospital doctor turnover and retention. Most of these papers were quantitative, cross-sectional studies focussed on meso-level predictors of doctor turnover.

Selection criteria concentrated on doctors who worked in hospitals, which limited knowledge of one area of the healthcare environment. The review could disregard relevant articles, such as those that discuss the turnover and retention of doctors in other specialities, including general practitioners. Additionally, being limited to peer-reviewed published journals eliminates grey literature such as dissertations, reports and case studies, which may bring impactful results.

Globally, hospital doctor turnover is a prevalent issue that is influenced by a variety of factors. However, a lack of focus on doctors who remain in their job hinders a comprehensive understanding of the issue. Conducting “stay interviews” with doctors could provide valuable insight into what motivates them to remain and what could be done to enhance their work conditions. In addition, hospital management and recruiters should consider aspects of job embeddedness that occur outside of the workplace, such as facilitating connections outside of work. By resolving these concerns, hospitals can retain physicians more effectively and enhance their overall retention efforts.

Focussing on the reasons why employees remain with an organisation can have significant social repercussions. When organisations invest in gaining an understanding of what motivates their employees to stay in the job, they are better able to establish a positive work environment that likely to promote employee well-being and job satisfaction. This can result in enhanced job performance, increased productivity and higher employee retention rates, all of which are advantageous to the organisation and its employees.

The review concludes that there has been little consideration of the retention, as opposed to the turnover, of hospital doctors. We argue that more expansive methodological approaches would be useful, with more qualitative approaches likely to be particularly useful. We also call on future researchers to consider focussing further on why doctors remain in posts when so many are leaving.

Precast wall lifting during prefabricated building construction faces multiple non-lean problems, such as inaccurate lifting-time estimation, unreasonable resource allocation and improper process design. This study aims to identify the pathways for improving lifting performance to advance lean construction of prefabricated buildings.

This study developed a methodological framework that integrates the discrete event simulation method, the elimination, combination, rearrangement and simplification (ECRS) technique and intelligent optimization tool. Two schemes of precast wall lifting, namely, the enterprise's business as usual (BAU) and enterprise-leading (EL) schemes, were set to benchmark lifting performance. Furthermore, a best-practice (BP) scheme was modeled from the perspective of lifting activity ECRS and resource allocation for performance optimization.

A real project was selected to test the effect of the methodological framework. The results showed that compared with the EL scheme, the BP scheme reduced the total lifting time (TLT) by 6.3% and mitigated the TLT uncertainty (the gap between the maximum and minimum time values) by 20.6%. Under the BP scheme, increasing the resource inputs produces an insignificant effect in reducing TLT, i.e. increasing the number of component operators in the caulking subprocess from one to two only shortened the TLT by 3.6%, and no further time reduction was achieved as more component operators were added.

To solve non-lean problems associated with prefabricated building construction, this study provides a methodological framework that can separate a typical precast wall lifting process into fine-level activities. Besides, it also identifies the pathways (including the learning effect mitigation, labor and machinery resource adjustment and activities’ improvement) to reducing TLT and its uncertainty.

This paper endeavors to create a predictive model for the energy consumption associated with the multi-material fused deposition modeling (FDM) printing process.

An online measurement system for monitoring power and temperature has been integrated into the dual-extruder FDM printer. This system enables a comprehensive study of energy consumption during the dual-material FDM printing process, achieved by breaking down the entire dual-material printing procedure into distinct operational modes. Concurrently, the analysis of the G-code related to the dual-material FDM printing process is carried out.

This work involves an investigation of the execution instructions that delineate the tooling plan for FDM. We measure and simulate the nozzle temperature distributions with varying filament materials. In our work, we capture intricate details of energy consumption accurately, enabling us to predict fluctuations in power demand across different operational phases of multi-material FDM 3D printing processes.

This work establishes a model for quantifying the energy consumption of the dual-material FDM printing process. This model carries significant implications for enhancing the design of 3D printers and advancing their sustainability in mobile manufacturing endeavors.

Personal influence is one of the most powerful strategies to influence publics’ behaviours. Yet, there is scant attention on how personal influence is leveraged for different public relations purposes in different cultural contexts. This study empirically investigates the presence and use of personal influence among Italian public relations professionals.

A survey was conducted through a self-administrated, web-based questionnaire and was developed from earlier studies investigating personal influence in public relations literature. Survey participants included public relations professionals across public, non-profit and private sectors.

The findings empirically show the presence and regular use of personal influence by professionals from all sectors to cultivate interpersonal relationships. Personal influence is considered a personal resource and used to leverage own influencing power. The findings also document four major manifestations of personal influence, which were named: relational closeness strategy, engagement strategy, expertise strategy and added value strategy.

This study enhances our understanding of personal influence in a specific cultural context and offers strategic insights for international professionals seeking to leverage influence in the socio-political environment of Italy. It also offers elements to improve public relations education and training.

The study offers some preliminary understandings of how Italian professionals leverage their personal influence in their daily public relations activities contributing with empirical evidence to the body of knowledge in public relations.

The purpose of this paper is to analyse museums and theme parks as a tourist package and how the proximity of airports to the city and public transport influence the development of this tourist package to stimulate tourism demand in cities.

Qualitative and quantitative indicators have been applied in our methodology to measure the most visited European theme parks and museums from 2012 to 2022. Moreover, the localisation of airports has allowed us to address the importance of theme parks and museums in cities and their regional economies.

The results suggest that the location of the city, entertainment complementary activity, airport proximity, intermodal passenger transport, air and train accessibility, tourism demand and supply, and a high concentration of population in cities have a high influence on the development of a tourist package that includes museums and theme parks to stimulate the tourism demand in European urban cities. London and Paris are two of the most visited cities in the world, and these are the most attractive European cities for tourists in terms of efficiency because tourists can optimize much better their space and time to visit the city’s tourist attractions during their holidays. Another important finding is that the public transport service plays an important role in museums and theme parks’ visits and the optimization of space-time for tourists when they are visiting a city and its tourist attractions on holidays, especially subways, trains and buses. Although time-space measures of accessibility in public transport in cities must be improved to optimize the time of the native population and tourists.

This research shows the complementary role of museums and theme parks as an attractive tourist package and an entertainment, cultural and educational activity to improve the quality of tourism supply and redistribute tourist flows in European countries. Moreover, there are limited studies that tackle the theme of parks and museums in a tourism context.

This paper aims to conduct an analysis of the influence of both the breadth and depth of outward foreign direct investment (OFDI) undertaken by Chinese high-tech listed companies during the period spanning 2010–2019. The data pertaining to these companies was used as a research sample to analyze the effects of OFDI on radical innovation performance.

Hierarchical regression analysis was used to test the proposed models, using survey data collected from 442 high-tech companies in China.

The findings of this study indicate a curvilinear (i.e. U-shaped) relationship between the breadth/depth of OFDI and radical innovation performance. Additional analysis reveals that OFDI plays a role in facilitating innovation breakthroughs by enhancing the internal dynamic capabilities of companies. Moreover, it is observed that a well-established institutional environment in the host country of investment can positively moderate the relationship between OFDI breadth/depth and radical innovation performance.

This study proffers a significant contribution to the understanding of the crucial role played by OFDI from emerging economy companies in enhancing radical innovation performance. Moreover, it offers theoretical guidance for multinational companies aiming to foster innovation breakthroughs.

As after-hours technology-mediated work (ATW) becomes common in organizations, the increased workload and interference to life caused by ATW has induced employee turnover. This research develops a mediated moderation model to explain how employees' intrinsic and extrinsic motivations for ATW affect their turnover intention through work–life conflict.

A survey was conducted to collect data of 484 employees from Chinese companies. Partial Least Square was used to perform data analysis.

The results show that intrinsic motivation for ATW has an indirect negative impact on turnover intention via work–life conflict, whereas extrinsic motivation for ATW has both a positive direct impact and a positive indirect impact (via work–life conflict) on turnover intention. This study also helps find that time spent on ATW can strengthen the positive impact of extrinsic motivation for ATW on turnover intention but has no moderation effect on the impact of intrinsic motivation for ATW. Furthermore, this study reveals that the interaction effect of time spent on ATW and extrinsic motivation on turnover intention is mediated by employees' perceived work–life conflict.

By discovering the distinct impact of employees' intrinsic and extrinsic motivations for ATW on turnover intention, this research provides a contingent view regarding the impact of ATW and offers guidance to managers regarding how to mitigate ATW-induced turnover intention through fostering different motivations.

Accurate and timely cost prediction is critical to the success of construction projects which is still facing challenges especially at the early stage. In the context of rapid development of machine learning technology and the massive cost data from historical projects, this paper aims to propose a novel cost prediction model based on historical data with improved performance when only limited information about the new project is available.

The proposed approach combines regression analysis (RA) and artificial neural network (ANN) to build a novel hybrid cost prediction model with the former as front-end prediction and the latter as back-end correction. Firstly, the main factors influencing the cost of building projects are identified through literature research and subsequently screened by principal component analysis (PCA). Secondly the optimal RA model is determined through multi-model comparison and used for front-end prediction. Finally, ANN is applied to construct the error correction model. The hybrid RA-ANN model was trained and tested with cost data from 128 completed construction projects in China.

The results show that the hybrid cost prediction model has the advantages of both RA and ANN whose prediction accuracy is higher than that of RA and ANN only with the information such as total floor area, height and number of floors.

(1) The most critical influencing factors of the buildings’ cost are found out by means of PCA on the historical data. (2) A novel hybrid RA-ANN model is proposed which proved to have the advantages of both RA and ANN with higher accuracy. (3) The comparison among different models has been carried out which is helpful to future model selection.