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The purpose of this review is to argue that the way that perceived employee misfit (PEM) has been measured in quantitative studies does not capture the construct identified in qualitative studies.

Through reverse citation analysis, this study reveals how low levels of value congruence became the currency of PEM in quantitative studies.

This study finds that in the absence of alternatives, researchers have taken low scores of value congruence as a measure of misfit. However, there is limited evidence to show that PEM relates to values, supplementary conceptualization or interactions with the organization (rather than interactions with other employees, tasks, etc.). In addition, the most commonly used instruments measure degrees of similarity, not disparity, making the interpretation of PEM-related data unclear. Combined, these factors raise construct validity concerns about most quantitative studies of PEM.

Given the upsurge of interest in PEM, there is an urgent need for greater clarification on the nature of the construct. From the analysis, this study identifies two key dimensions of studying PEM that create four distinctly different ways of conceptualizing the construct.

This study highlights a series of major methodological weaknesses in the study of PEM and reveal that almost all published quantitative studies of PEM are actually studying something else; something whose nature is very unclear.

According to relevant research, non-uniform speed has a significant impact on the vehicle-track systems. Up to now, research work on it is still very limited. In this paper, the PEM is adopted to further transform it into a deterministic process to solve the vehicle’s problem of running at a non-uniform speed.

The multi-body vehicle model has 10 degrees of freedom and the track is regarded as a finite long beam supported by lumped sleepers and ballast blocks. They are connected via linear Hertz springs. The vertical track irregularity is a Gaussian stationary process in the space domain. It is transformed into a uniformly modulated nonstationary random process in the time domain with respect to the non-uniform vehicle speed. By solving the equation of motion of the coupled vehicle-track system with the pseudo-excitation method, the pseudo-response and consequently the power spectral density and the standard deviation of the structural response can be obtained.

Two kinds of vehicle braking programs are taken in the numerical example and some beneficial conclusions are drawn.

The pseudo-excitation method (PEM) was used to perform the random vibration analysis of a coupled non-uniform speed vehicle-track system. Transforming the track irregularity into a uniformly modulated nonstationary random process in time domain with respect to the non-uniform vehicle speed was undertaken. The pseudo-response of the coupled system is solved by applying the Newmark algorithm with constant space integral steps. The random vibration transfer mechanism of the coupled system is fully discussed.

The study aims to propose an intelligent real-time traffic model to address the traffic congestion problem. The proposed model assists the urban population in their everyday lives by assessing the probability of road accidents and accurate traffic information prediction. It also helps in reducing overall carbon dioxide emissions in the environment and assists the urban population in their everyday lives by increasing overall transportation quality.

This study offered a real-time traffic model based on the analysis of numerous sensor data. Real-time traffic prediction systems can identify and visualize current traffic conditions on a particular lane. The proposed model incorporated data from road sensors as well as a variety of other sources. It is difficult to capture and process large amounts of sensor data in real time. Sensor data is consumed by streaming analytics platforms that use big data technologies, which is then processed using a range of deep learning and machine learning techniques.

The study provided in this paper would fill a gap in the data analytics sector by delivering a more accurate and trustworthy model that uses internet of things sensor data and other data sources. This method can also assist organizations such as transit agencies and public safety departments in making strategic decisions by incorporating it into their platforms.

The model has a big flaw in that it makes predictions for the period following January 2020 that are not particularly accurate. This, however, is not a flaw in the model; rather, it is a flaw in Covid-19, the global epidemic. The global pandemic has impacted the traffic scenario, resulting in erratic data for the period after February 2020. However, once the circumstance returns to normal, the authors are confident in their model’s ability to produce accurate forecasts.

To help users choose when to go, this study intended to pinpoint the causes of traffic congestion on the highways in the Bay Area as well as forecast real-time traffic speeds. To determine the best attributes that influence traffic speed in this study, the authors obtained data from the Caltrans performance measurement system (PeMS), reviewed it and used multiple models. The authors developed a model that can forecast traffic speed while accounting for outside variables like weather and incident data, with decent accuracy and generalizability. To assist users in determining traffic congestion at a certain location on a specific day, the forecast method uses a graphical user interface. This user interface has been designed to be readily expanded in the future as the project’s scope and usefulness increase. The authors’ Web-based traffic speed prediction platform is useful for both municipal planners and individual travellers. The authors were able to get excellent results by using five years of data (2015–2019) to train the models and forecast outcomes for 2020 data. The authors’ algorithm produced highly accurate predictions when tested using data from January 2020. The benefits of this model include accurate traffic speed forecasts for California’s four main freeways (Freeway 101, I-680, 880 and 280) for a specific place on a certain date. The scalable model performs better than the vast majority of earlier models created by other scholars in the field. The government would benefit from better planning and execution of new transportation projects if this programme were to be extended across the entire state of California. This initiative could be expanded to include the full state of California, assisting the government in better planning and implementing new transportation projects.

To estimate traffic congestion, the proposed model takes into account a variety of data sources, including weather and incident data. According to traffic congestion statistics, “bottlenecks” account for 40% of traffic congestion, “traffic incidents” account for 25% and “work zones” account for 10% (Traffic Congestion Statistics). As a result, incident data must be considered for analysis. The study uses traffic, weather and event data from the previous five years to estimate traffic congestion in any given area. As a result, the results predicted by the proposed model would be more accurate, and commuters who need to schedule ahead of time for work would benefit greatly.

The proposed work allows the user to choose the optimum time and mode of transportation for them. The underlying idea behind this model is that if a car spends more time on the road, it will cause traffic congestion. The proposed system encourages users to arrive at their location in a short period of time. Congestion is an indicator that public transportation needs to be expanded. The optimum route is compared to other kinds of public transit using this methodology (Greenfield, 2014). If the commute time is comparable to that of private car transportation during peak hours, consumers should take public transportation.

This paper investigates whether career capital (CC) development abroad, expatriate type, career type and career stage affect expatriates' career success in terms of perceived marketability and the number of promotions.

The study presents findings from a 2020 follow-up study among 327 expatriates, including assigned expatriates (AEs) (n = 117) and self-initiated expatriates (SIEs) (n = 220), who worked abroad in 2015 and 2016. Among that group, 186 had continued their international career, while 141 had repatriated. Structural equation modeling with robust maximum likelihood estimation was used to test this study's hypotheses. MPlus 8.6 software supported the analysis.

The study outlines that CC developed abroad positively impacts perceived marketability and the number of promotions. Second, repatriates reported a greater degree of perceived marketability than those continuing an international career. Career type did not predict the number of promotions. The expatriate type did not influence any of the career success measures. Finally, expatriates in their late-career stage did not achieve a similar level of career success as those in other career stages.

All the expatriates were university-educated Finnish engineers and business professionals, and the career benefits of expatriation could differ for different sample groups. The study calls for more context-sensitive global careers research. The findings have positive implications for self-guided career actors considering working abroad. Organizations could focus more of their global talent attraction, management and career efforts on SIEs.

To analyze the impacts of these four antecedents on the career success of expatriates, the authors cooperated with two Finnish labor unions in 2020 to explore the careers of 327 expatriates, having surveyed the same group in 2015/2016. Such follow-up studies are not very common in expatriation research since it is difficult to keep track of expatriates who change locations and employers.

This paper aims to investigate the impact of three different flow channel cross sections on the performance of the fuel cell.

A comprehensive three-dimensional polymer electrolyte membrane fuel cell model has been developed, and a set of conservation equations has been solved. The flow is assumed to be steady, fully developed, laminar and isothermal. The investigated cross sections are the commonly used square cross section, the increasingly used trapezoidal cross section and a novel hybrid configuration where the cross section is square at the inlet and trapezoidal at the outlet.

The results show that a slight gain is obtained when using the hybrid configuration and this is because of increased velocity, which improves the supply of the reactant gases to the catalyst layers (CLs) and removes heat and excess water more effectively compared to other configurations. Further, the reduction of the outlet height of the hybrid configuration leads to even better fuel cell performance and this is again because of increased velocity in the flow channel.

The data generated in this study will be highly valuable to engineers interested in studying the effect of fluid cross -sectional shape on fuel cell performance.

This study proposes a novel flow field with a variable cross section. This design can supply a higher amount of reactant gases to the CLs, dissipates heat and remove excess water more effectively.

Traffic flow prediction has always been a top priority of intelligent transportation systems. There are many mature methods for short-term traffic flow prediction. However, the existing methods are often insufficient in capturing long-term spatial-temporal dependencies. To predict long-term dependencies more accurately, in this paper, a new and more effective traffic flow prediction model is proposed.

This paper proposes a new and more effective traffic flow prediction model, named channel attention-based spatial-temporal graph neural networks. A graph convolutional network is used to extract local spatial-temporal correlations, a channel attention mechanism is used to enhance the influence of nearby spatial-temporal dependencies on decision-making and a transformer mechanism is used to capture long-term dependencies.

The proposed model is applied to two common highway datasets: METR-LA collected in Los Angeles and PEMS-BAY collected in the California Bay Area. This model outperforms the other five in terms of performance on three performance metrics a popular model.

(1) Based on the spatial-temporal synchronization graph convolution module, a spatial-temporal channel attention module is designed to increase the influence of proximity dependence on decision-making by enhancing or suppressing different channels. (2) To better capture long-term dependencies, the transformer module is introduced.

Effective performance of a direct ethanol fuel cell (FC) stack depends on the satisfactory operation of its individual cells where it is always challenging to manage the temperature gradient, water flow and distribution of reactants. In that, the design of the bipolar fuel flow path plate plays a vital role in achieving the aforementioned parameters. Further, the bipolar plates contribute 80% of the weight and 30%–40% of its total cost. Aim of this study is to enhance the efficiency of fuel to energy conversion and to minimize the overall cost of production.

The authors have specifically designed, simulated and fabricated a standard 2.5 × 2.5 cm² active area proton exchange membrane (PEM) FC flow path plate to study the performance by varying the flow fields in a single ladder, double ladder and interdigitated and varying channel geometries, namely, half curve, triangle and rectangle.

Using the 3D PEMFC model and visualizing the physical and electrochemical processes occurring during the operation of the FCs resulted in a better-performing flow path plate design. It is fabricated by using additive manufacturing technology. In addition, the assembly of the full cell with the designed flow path plate shows about an 11.44% reduction in total weight, which has a significant bearing on its total cost as well as specific energy density in the stack cell.

Simultaneous optimization of multiple flow path parameters being carried out for better performance is the hallmark of this study which resulted in enhanced energy density and reduced cost of device production.

The purpose of this study is to evaluate student visual literacy skills using the newly designed visual literacy framework and visual literacy (VL) scale.

It includes a newly designed framework, a self-reporting questionnaire and a scale to evaluate an individual's VL skills and overall competency. The self-reporting questionnaire consists of 13 items with a five-point Likert scale.

The newly developed VL skill scale assessed the Fiji students’ competency (i.e. identify, understand, evaluate and communicate using visuals). The mean for the 13 items on VL skills showed average results, but 46.33% recorded high visual literacy competencies. The multiple linear regression analysis outcomes showed all 13 skills demonstrated significant contributions to becoming visually literate.

The limitation of this study is that the questionnaire is self-reporting, so the evaluation can be highly rated. The implications are that relevant stakeholders will be able to devise strategies and content to improve visual literacy in Fiji.

Images are playing an important role today, especially after COVID-19, which forced the education system to go online. Online learning involves a lot of visuals, and as such, visual literacy is important to students so that they can successfully learn online. This paper brings out the important aspects of visual literacy, which needs to be understood by the students.

In society, everything involves visuals. This paper introduces a visual literacy scale and a visual literacy tool to measure the visual competencies of individuals. If people understand the components of visual literacy, then visual competencies of the people will also improve.

To the best of the authors’ knowledge, this paper is the first one on evaluating visual literacy competencies in Fiji and also in the South Pacific. The visual literacy tool is also new to the world.

Using the theoretical framework of social comparison theory (SCT), this study investigates the effects of employee envy on service sabotage behaviors in the hospitality industry. It further examines the complex dynamics of self-performance and job dissatisfaction in this context. Notably, this paper seeks to determine the potential moderating role of perceived employability in the interactions between service sabotage, employee envy, job dissatisfaction and self-performance.

Our research structure was divided into four distinct models. The findings of Model 1 highlight the significant impact of employee envy on service sabotage. The analysis in Model 2a shows that job dissatisfaction acts as a partial mediator in the employee envy and service sabotage linkage. On the other hand, Model 2b reveals self-performance as yet another partial mediator between envy-service sabotage relationships. In turn, Model 3 demonstrates that job dissatisfaction and self-performance play a serial mediation role in the envy-service sabotage relationship. In addition, our research shows that perceived employability effectively moderates the three proposed paths within these relationships.

Our research structure was divided into four distinct models. The findings of Model 1 highlight the significant impact of employee envy on service sabotage. The analysis in Model 2a shows that job dissatisfaction acts as a partial mediator in the employee envy and service sabotage linkage. On the other hand, Model 2b reveals self-performance as yet another partial mediator between envy-service sabotage relationships. In turn, Model 3 demonstrates that job dissatisfaction and self-performance play a serial mediation role in the envy-service sabotage relationship. In addition, our research shows that perceived employability effectively moderates the three proposed paths within these relationships.

Hotel managers must keep a close eye on their front-of-house staff to avoid any unintentional or direct interactions with customers. Equally important is the consistent and impartial treatment of all employees, which is an important consideration for managers to consider because it can help mitigate employee envy and job dissatisfaction.

This study seeks to enhance understanding of SCT by emphasizing perceived employability as a boundary influencing the relationships between these factors and desired outcomes in the hotel industry, such as job dissatisfaction, self-performance and service sabotage. This paper is an initial attempt to investigate the underlying mechanisms in the relationship between envy and service sabotage.

With over-the-top (OTT) streaming services rapidly transforming the media industry and saturating the market, the authors' study seeks to enrich the goal-directed behaviour model by exploring how perceived risks and descriptive norms influence OTT consumption.

Survey data from OTT subscribers were collected online to assess their risk behaviours. The 353 responses obtained were analysed with SmartPLS, validating the structural equation modelling (SEM) through structural and measurement model verification.

The authors' findings illustrate that descriptive norm, perceived behavioural control, as well as positive and negative anticipated emotion (NEM) and attitude, contribute positively to the desire to engage with OTT streaming services. Interestingly, the authors' study contradicts common assumptions, revealing that subjective norms do not significantly impact the propensity to utilise OTT services. This counterintuitive finding necessitates a reconsideration of prevalent theories and contributes to a nuanced understanding of OTT adoption determinants.

The data gathering for this study were conducted from the perspective of a single nation. Therefore, caution must be exercised when generalising this study's results.

The practical ramifications of this research are vast, providing OTT service providers and marketers with actionable insights to maximise user engagement and navigate perceived risks related to OTT service adoption and consumption.

This study's exploration of perceived risks and descriptive norms enhances the goal-directed behaviour model's breadth, facilitating a holistic comprehension of the constructs shaping OTT consumption behaviours. It would be the first attempt to combine perceptual, affective and behavioural factors and perceived risks to understand the user's predisposition to engage in OTT streaming services.