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Following the COVID-19 pandemic, the issues related to job insecurity became even more prominent than before. This paper sets out to identify the determinants of job insecurity in the context of the COVID-19 pandemic among Romanian workers, a topic than was scarcely addressed in previous studies.

Based on a representative sample at the national level (N = 744), the authors performed a multilinear regression analysis that pinpoints the factors predicting job insecurity.

This study findings indicate that high-skilled workers employed on permanent arrangements, having higher workloads, who had received organizational support, and managed to harmonize work demands with family responsibilities experience the lowest levels of job insecurity. In this study, teleworking does not influence the level of job insecurity. Gender (being male), working in the public sector and approving the political management of the COVID-19 pandemic also count in securing the feelings employees have about their job.

Besides filling a geographical gap in the literature, another innovative contribution of the paper is the emphasis on the importance for the employees of how public authorities manage the public health crisis. Also, this study explores the workload as a factor of job insecurity which was unaddressed previously. Implications for research and practice are emphasized.

Vertical urban settlements are becoming the predominant form of contemporary urban design in response to population increase and rapid urbanization. These developments are, however, perceived to be poorly designed and incongruent with the users’ needs. The purpose of this study was to present a resident satisfaction assessment of Hong Kong’s vertical settlements.

A review of the literature was conducted on the concept of vertical urbanism, residential satisfaction and postoccupancy evaluation. Fifty performance indicators were classified into three groups including indoor environment, safety and maintenance; furniture, utilities and spaces; and privacy, appearance and city life. These indicators were used to develop a Web-based questionnaire administered to residents in the three administrative regions of Hong Kong. Ninety-eight respondents participated in the study, and the results were analyzed using the resident satisfaction index and the overall satisfaction index. A multilinear regression analysis was also made to develop a model describing the most relevant performance indicators for determining the overall residential satisfaction.

The results revealed that residents expressed dissatisfaction with performance indicators, including “level of noise generated from outside the building (neighboring buildings, traffic, noise, etc.),” “variation and stability of indoor temperature,” “sustainable design of the building’s environment (cyclability, walkability, electric charging stations, etc.),” “availability and capacity of car parking,” “size and adequacy of spaces for social interaction” and “considerations for occupants with special needs (disabled, aged people, children, occupants with a medical condition, etc.).” The results also revealed that some indicators such as the maintenance of elevators, adequate interior space and surrounding areas were considered as significantly influencing residential satisfaction. Similarly, the building height and wind-induced motion were not significantly influencing residential satisfaction. The results also revealed that a multilinear Regression model with five variables and an adjusted R² value of 93% could estimate the overall residential satisfaction.

The concept of vertical urban design is the new paradigm in the shaping of future cities. The originality of this study is its adoption of post-occupancy evaluation to assess occupants’ residential satisfaction. As well as the determination of factors that should inform the planning, design and management of vertical urban settlements. Thus, the study has significant implications for research in vertical urban development, as well as the professional practice of building and urban planners, designers and managers.

The purpose of this paper is to empirically identify key factors of UK food supply chains (SCs) that significantly contribute to CO₂ emissions (CO2e) taking into account the life cycle assessment (LCA). The UK food supply chain includes imports from other countries.

This research develops a conceptual framework from extant literature. Secondary data obtained from ONS and FAOSTAT covering from 1990 to 2014 are analysed using Multilinear Regression (MLR) and Stochastic Frontier Analysis (SFA) to identify the factors relating to CO₂ emissions significance, and the efficient contributions that are being made to their reduction in the UK food supply chains.

The study results suggest that Transportation and Sales/Distribution are the two key factors of CO₂ emissions in UK food supply chains. This is confirmed by two multivariate methods, MLR and SFA. MLR results show that transportation increases UK CO₂ emissions by 10 tonnes of CO₂ emissions from one tonne of fruits and vegetables imports from overseas to the UK Sales and Distribution reduces the UK CO₂ emissions by 1.3 tonnes of CO₂ emissions due to improved, technological operation activities in the UK. In addition, the SFA results confirm that the key factors are sufficient to predict an increase or decrease in CO₂ emissions in the UK food supply chains.

This study has focused on the LCA of the UK food supply chain from limited data. Future studies should consider Sustainability Impact Assessment of the UK food supply chain, identifying the social, economic, regulatory and environmental impacts of the food supply chain using a re-defined LCA (all-inclusive assessment) tool.

This research suggests that food supply chain professionals should improve efficiency, e.g. the use of solar energy and biogas, and also integrate low-carbon policies and practices in food supply chain operations. Furthermore, governments should encourage policies such as mobility management programmes, urban redevelopment and privatisation to enhance better transportation systems and infrastructure to continuously reduce CO₂e from the food trade.

Although logistics play a major role in CO₂ emissions, all logistics CO₂ emissions for other countries are not included in the ONS data. This research reveals some important insights into the UK food supply chains. Logistics and other food supply chain processes of importing countries significantly contribute to CO₂ emissions which are yet to be considered in the UK food SCs.

Existing deterministic models that predict the capacity of corroded reinforced concrete (RC) beams have limited applicability because they were based on accelerated tests that induce general corrosion. This research gap was addressed by performing a combined numerical and statistical analysis on RC beams, subjected to natural corrosion, to achieve a much better forecast.

Data of 42 naturally corroded beams were collected from the literature and analyzed numerically. Four constitutive models and their combinations were considered: the elastic-semi-plastic and elastic-perfectly-plastic models for steel, and two tensile models for concrete with and without the post-cracking stresses. Meanwhile, Popovics’ model was used to describe the behavior of concrete under compression. Corrosion coefficients were developed as functions of corrosion degree and beam parameters through linear regression analysis to fit the theoretical moment capacities with test data. The performance of the coefficients derived from different combinations of constitutive laws was then compared and validated.

The results showed that the highest accuracy (R² = 0.90) was achieved when the tensile response of concrete was modeled without the residual stresses after cracking and the steel was analyzed as an elastic-perfectly-plastic material. The proposed procedure and regression model also showed reasonable agreement with experimental data, even performing better than the current models derived from accelerated tests and traditional procedures.

This study presents a simple but reliable approach for quantifying the capacity of RC beams under more realistic conditions than previously reported. This method is simple and requires only a few variables to be employed. Civil engineers can use it to obtain a quick and rough estimate of the structural condition of corroding RC beams.

A destination's ability to attract tourists is associated with the visitor experience and, in recent years, visitors have increasingly used virtual environments and digital innovation, such as social media platforms, to communicate their experience of tourist destinations. A positive well-communicated tourist experience improves the reputation of the destination and has relevant consequences for both the destination's attractiveness and its competitive advantage. On the contrary, when the destination's reputation is negatively affected by visitors' experiences, comments and reviews, such destination might compromise its ability to attract new visitors. Studies in this field agree alike that the tourist experience is negatively affected by overcrowding and overflows phenomena occurring around the visited city attractions. The present research, merging the aforementioned observations, investigates whether visitor density affects the online reputation of the Uffizi Gallery, estimated by extracting visitors' opinions and feedbacks on the city's main attractions from TripAdvisor ratings and from Twitter posts, by applying sentiment analysis to evaluate whether the text is positive, negative, or neutral. The city of Florence is an ideal case study, as the city records almost 16 million tourist overnight stays per year hence highly exposed to the risk of tourist overcrowding and overflows. The research findings reveal that Uffizi Gallery experiences and mood are influenced by the number of visitors insisting and if tourists live a negative experience, this is further exacerbated by the growing density of visitors themselves. We find that, if tourists have a negative experience, this is exacerbated by the density of visitors to the Uffizi Gallery. The results reveal also that tourists' experiences are even more influenced by any general dissatisfaction they experience in the city of Florence in a broader way. Practical implications and theoretical contributions are discussed.

Several factors influence the costs of buildings. Thus, identifying the cost significant factors can assist to improve the accuracy of project cost forecasts during the planning phase. This paper aims to identify the cost significant parameters and explore the potential for improving the accuracy of cost forecasts for buildings using machine learning techniques and large data sets.

The Australian State of Victoria Building Authority data sets, which comprise various parameters such as cost of the buildings, materials used, gross floor areas (GFA) and type of buildings, have been used. Five different machine learning regression models, such as decision tree, linear regression, random forest, gradient boosting and k-nearest neighbor were used.

The findings of the study showed that among the chosen models, linear regression provided the worst outcome (r² = 0.38) while decision tree (r² = 0.66) and gradient boosting (r² = 0.62) provided the best outcome. Among the analyzed features, the class of buildings explained about 34% of the variations, followed by GFA and walls, which both accounted for 26% of the variations.

The output of this research can provide important information regarding the factors that have major impacts on the costs of buildings in the Australian construction industry. The study revealed that the cost of buildings is highly influenced by their classes.

Several cross‐sectional studies reported that breakfast skipping and a poor consumption of fruits and vegetables are associated with overweight and obesity‐related morbidities. In this respect healthy eating habits such as regular breakfast and a sufficient intake of fruit and vegetables are two important items to prevent childhood and adolescent obesity. The purpose of this study is to determine the breakfast frequency and the fruit and vegetable consumption in Belgian adolescents.

A cross‐sectional study using pupils contacted by nurses in schools during an information session about female hygiene. On that occasion all pupils (boys and girls) completed a questionnaire concerning their eating habits. Subjects were boys (n = 1390) and girls (n = 3610) between 12 and 15 years of age from the main three educational programmes (general, technical and occupational programme).

Socio‐economic status significantly influenced breakfast frequency with differences between the highest educational programme (general) and the lower technical‐ and occupational programmes. The use of daily breakfast was respectively 67 versus 49 and 48 per cent for the boys and 60 versus 42 and 39 per cent for the girls). For both sexes breakfast frequency decreased when comparing age groups from 12 with 15 years of age respectively from 67 to 55 per cent for the boys and from 61 to 47 per cent for the girls. Half of the subjects who declared to have overweight (self‐image) did not take breakfast at a regular base. Twenty six per cent of the boys of the French speaking part and 19 per cent of the boys of the Flemish speaking part of Belgium declared to take only one or less than one portion of fruit and/or vegetables a day. For the girls the reported intake of one or less than one portion of fruit and/or vegetables was respectively 20 and 16 per cent. Only 13 per cent of the French speaking boys and 10 per cent of the Flemish speaking boys reported an intake of four or more fruits and/or vegetables a day. The percentage of girls reaching an intake of four or more a day was 15 per cent for the two language regions. Fruit and/or vegetable intake was influenced significantly by the educational programme and by the educational level of the mother. In conclusion, healthy eating habits among girls and boys in Belgium (as estimated by breakfast frequency and fruit and/or vegetable intake) are rather poor. These habits are influenced by age, educational programme, self‐image and educational level of the mother.

In the light of these findings action should be taken to improve the eating habits among Belgian children to prevent childhood obesity.

The study is carried out to analytically reconnoiter geotechnical index properties of subgrade soils as key variables that shape the cost profile of road infrastructure projects in a tropical geographic setting with starkly heterogenous ground conditions.

Using the Niger Delta region, as a point of reference, data on geotechnical index properties of subgrade soils at spatially dispersed locations for 61 completed highway projects are collated. Exploratory statistical tests were carried out to infer significant associations with final project costs before regression analysis. Regression analysis is principally deployed as an explanatory analytical tool, relevant to quantify the sensitivity of highway project costs to the individual and collective impact of geotechnical variables.

Several parameters of expansivity and compressibility exhibited significantly strong associations with the final costs recorded on the highway projects. The statistical analysis further established a cause-effect relationship, whereby small changes in the geotechnical properties of sub-grade soils at project locations, would result in disproportionately large changes in the cost of road construction.

The study findings provide insight into the sensitivity of road construction costs to geotechnical variables, which can serve as a useful input in financial risk analysis for development appraisal and the generation of location adjustment factors.

The study statistically demonstrates location-induced construction cost profiles, triggered in response to the spatial geotechnical variability and occurrence of problem subgrade soils in the humid tropics, which may be different from those traditionally established in studies of cold and temperate climate soils.

This study investigated the potential negative effects of a sponsored team's losing performance on audiences' trust and purchase intention toward the sponsoring brand. Shedding light on the moderating role of sponsoring brand familiarity among audiences and audience team identification regarding such negative effects, the study establishes when sports sponsorship may incur risk to a sponsoring brand.

Three experimental designs (audience as stimulus of a team's losing vs control condition) were used to indicate whether and when losing performance influences participants' trust and purchase intention toward the sponsoring brand.

The participants in the losing condition report lower brand trust and purchase intention. Brand trust mediates the relationship between losing results and decreased purchase intention. The negative effects of losing on brand trust and purchase intention only appear when the sponsoring brand has low familiarity among audiences and only for audiences with low identification.

The strategy of a brand with low familiarity sponsoring a team that frequently loses has risks and is not worth advocating. However, if an unknown brand has already sponsored a team that often loses, the efforts to cultivate audiences' identification with the team can reduce the potential risks.

The affirmed negative effects of losing performance on brand trust and purchase intention have value for firm sponsorship decisions. This study contributes to the sponsorship literature by revealing two boundary conditions (sponsoring brand familiarity and audiences' team identification) for those negative effects.

This research aims to investigate critical success factors, barriers and initiatives of total productive maintenance (TPM) implementation in selected manufacturing industries in Addis Ababa, Ethiopia.

This study built and looked into a conceptual research framework. The potential barriers and success factors to TPM implementation have been highlighted. The primary study techniques used to collect relevant data were a closed-ended questionnaire and semi-structured interview questions. With the use of SPSS version 23 and SmartPLS 3.0 software, the data were examined using descriptive statistics and the inferential Partial Least Square Structural Equation Modeling (PLS-SEM) techniques.

According to the results of descriptive statistics and multivariate analysis using PLS-SEM, the case manufacturing industries' TPM implementation initiative is in its infancy; break down maintenance is the most widely used maintenance policy; top managers are not dedicated to the implementation of TPM; and there are TPM pillars that have been weakly and strongly addressed by the case manufacturing companies.

The small sample size is a limitation to this study. It is therefore challenging to extrapolate the research findings to other industries. The only manufacturing KPI utilized in this study is overall equipment effectiveness (OEE). It is possible to add more parameters to the manufacturing performance measurement KPI. The relationships between TPM and other lean production methods may differ from those observed in this cross-sectional study. Longitudinal experimental studies and in-depth analyses of TPM implementations may shed further light on this.

Defining crucial success factors and barriers to TPM adoption, as well as identifying the weak and strong TPM pillars, will help companies in allocating their scarce resources exclusively to the most important areas. TPM is not a quick solution. It necessitates a change in both the company's and employees' attitude and their values, which takes time to bring about. Hence, it entails a long-term planning. The commitment of top managers is very important in the initiatives of TPM implementation.

This study is unique in that, it uses a new conceptual research model and the PLS-SEM technique to analyze relationships between TPM pillars and OEE in depth.