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This research investigates how consumers who are most active on Facebook during the day vs in the evening differ, differ in their ad consumption, and how advertising effects vary as a function of a key moderator: gender.

Using a survey of 281 people, the research identifies Facebook users who are more intensely using mobile social media during the day versus in the evening, and measures five Facebook mobile advertising outcomes: brand and product recall, clicking on ads, acting on ads and purchases.

The results show that women who are using social media more intensely during the day are more likely to use Facebook to seek information, hence, Facebook mobile ads tend to be more effective for these users compared to those in the evening.

This contributes to the literature by analyzing how the time of day affects social media behavior in relation to mobile advertising effectiveness, and broadening the scope of mobile advertising effectiveness research from other than just clicks on ads to include measures like brand and product recall.

By analyzing the effectiveness of mobile advertising on social media as a function of the time of day, advertisers can be more targeted in their media buys, and so better use their social media budgets, i.e. advertising is more effective for women who use social media (Facebook) more intensely during the day than for those who use social media more intensely in the evening as the former tend to seek more information than the latter.

This research extends media ecology theory by drawing on circadian rhythm research to provide a first demonstration of how the time of day relates to different uses of mobile social media, which in turn relate to social media mobile advertising consumption.

While research on social media advertising has been steadily increasing, little has been explored on how users consume ads when they engage with social media at different periods along the day. This paper extends media ecology theory by investigating time of day, drawing on the circadian rhythm literature, and how it relates to social media usage.

This study aims to identify an automated machine learning algorithm with high accuracy that sport practitioners can use to identify the specific factors for predicting Major League Baseball (MLB) attendance. Furthermore, by predicting spectators for each league (American League and National League) and division in MLB, the authors will identify the specific factors that increase accuracy, discuss them and provide implications for marketing strategies for academics and practitioners in sport.

This study used six years of daily MLB game data (2014–2019). All data were collected as predictors, such as game performance, weather and unemployment rate. Also, the attendance rate was obtained as an observation variable. The Random Forest, Lasso regression models and XGBoost were used to build the prediction model, and the analysis was conducted using Python 3.7.

The RMSE value was 0.14, and the R2 was 0.62 as a consequence of fine-tuning the tuning parameters of the XGBoost model, which had the best performance in forecasting the attendance rate. The most influential variables in the model are “Rank” of 0.247 and “Day of the week”, “Home team” and “Day/Night game” were shown as influential variables in order. The result was shown that the “Unemployment rate”, as a macroeconomic factor, has a value of 0.06 and weather factors were a total value of 0.147.

This research highlights unemployment rate as a determinant affecting MLB game attendance rates. Beyond contextual elements such as climate, the findings of this study underscore the significance of economic factors, particularly unemployment rates, necessitating further investigation into these factors to gain a more comprehensive understanding of game attendance.

This study aims to deepen the understanding of the challenges and implications entailed by deploying mobile clinics in conflict zones to reach populations affected by violence and cut off from health-care services.

This research combines an integrated literature review and an instrumental case study. The literature review comprises two targeted reviews to provide insights: one on conflict zones and one on mobile clinics. The case study describes the process and challenges faced throughout a mobile clinic deployment during and after the Iraq War. The data was gathered using mixed methods over a two-year period (2017–2018).

Armed conflicts directly impact the populations’ health and access to health care. Mobile clinic deployments are often used and recommended to provide health-care access to vulnerable populations cut off from health-care services. However, there is a dearth of peer-reviewed literature documenting decision support tools for mobile clinic deployments.

This study highlights the gaps in the literature and provides direction for future research to support the development of valuable insights and decision support tools for practitioners.

Meetings with colleagues are an essential activity in workplace collaboration. The iterative nature of collaborative work demands spaces that team members can access quickly and easily. Creating suitable meeting spaces will become more critical if the hybrid work model continues and the workplace environment becomes the hub for face-to-face collaborative time, learning and training. Workspace and fit-out is expensive so it is crucial that the investment in meeting spaces supports employees’ collaboration activities.

This paper presents a case study of a corporate organisation undertaken in New Zealand to investigate how employees from two business units use their workspace to collaborate within their own team and across other teams in their organisation. The study uses ethnographic techniques, including participant observation and in-depth face-to-face interviews.

The findings show that the frequency and nature of small group work in collaboration was underestimated in the initial planning of the new workspace. Although participants found the design and fit-out of the formal meeting rooms supportive of collaborative work, the meeting rooms were in high demand, and it was difficult to find a room at short notice. The breakout spaces were confusing because they lacked key design attributes identified by the participants as conducive to small group work. Design shortfalls together with fit-out features perceived as supportive of collaborative work are identified.

The research reports on employees’ perceptions and experiences across two functionally diverse business units, reflecting their different needs and concerns.

Untimely responses to emergency situations in urban areas contribute to a rising mortality rate and impact society's primary capital. The efficient dispatch and relocation of ambulances pose operational and momentary challenges, necessitating an optimal policy based on the system's real-time status. While previous studies have addressed these concerns, limited attention has been given to the optimal allocation of technicians to respond to emergency situation and minimize overall system costs.

In this paper, a bi-objective mathematical model is proposed to maximize system coverage and enable flexible movement across bases for location, dispatch and relocation of ambulances. Ambulances relocation involves two key decisions: (1) allocating ambulances to bases after completing services and (2) deciding to change the current ambulance location among existing bases to potentially improve response times to future emergencies. The model also considers the varying capabilities of technicians for proper allocation in emergency situations.

The Augmented Epsilon-Constrained (AEC) method is employed to solve the proposed model for small-sized problem. Due to the NP-Hardness of the model, the NSGA-II and MOPSO metaheuristic algorithms are utilized to obtain efficient solutions for large-sized problems. The findings demonstrate the superiority of the MOPSO algorithm.

This study can be useful for emergency medical centers and healthcare companies in providing more effective responses to emergency situations by sending technicians and ambulances.

In this study, a two-objective mathematical model is developed for ambulance location and dispatch and solved by using the AEC method as well as the NSGA-II and MOPSO metaheuristic algorithms. The mathematical model encompasses three primary types of decision-making: (1) Allocating ambulances to bases after completing their service, (2) deciding to relocate the current ambulance among existing bases to potentially enhance response times to future emergencies and (3) considering the diverse abilities of technicians for accurate allocation to emergency situations.

Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction.

To achieve this, the authors have set up an experiment with the control data set having only the AQ data set and meteorological (Met) data set, while the experimental data set is made up of the AQ data set, Met data set and traffic data set. Several ML models (such as extra trees regressor, eXtreme gradient boosting regressor, random forest regressor, K-neighbors regressor and two others) were trained, tested and compared on these individual combinations of data sets to predict the volume of PM_2.5, PM₁₀, NO₂ and O₃ in the atmosphere at various times of the day.

The result obtained showed that various ML algorithms react differently to the traffic data set despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%.

This research is limited in terms of the study area, and the result cannot be generalized outside of the UK as some of the inherent conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research, therefore, leaving out a few other ML algorithms.

This study reinforces the belief that the traffic data set has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form of traffic data set in the development of an AQ prediction model. This implies that developers and researchers in AQ prediction need to identify the ML algorithms that behave in their best interest before implementation.

The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.

This research sought to examine the differential effect of chatbot banking artificial intelligence (AI) on consumer experience in the banking industry. A positivist paradigm was adopted to sample 389 consumers who were previously exposed to chatbot banking in Zimbabwe. A causal research design was employed whilst a quantitative approach was followed. In analysing data, the research study applied the structural equation modelling (SEM) technique. The authors found that chatbot banking significantly improves customer experience (CX) in the banking industry. Reliability and responsiveness of the chatbot need to be enhanced for effective improvements in CX. A need was also identified to enhance CX through the development of an ease-to-use chatbot which is embedded in everyday messaging applications of consumers. A significant association was also found between perceived benefits of chatbot banking and CX. This study informs the development of competitive advantage by banks and other related companies through AI-based CX management strategies. In times of pandemics and beyond, chatbot banking can be very instrumental in improving CX.

This study aimed at investigating employee training, employee participation and organizational commitment (OC) and the moderating effect of workplace ostracism among bank employees.

The study used a descriptive and cross-sectional design with the aid of a standard scale constructed into a questionnaire. Cluster, convenience and simple random sampling techniques were used to select 1,067 respondents, of which 870 were deemed fit for the study. The theories underpinning the study were the social exchange theory (SET) and social identity theory (SIT). Four hypotheses were developed and tested using hierarchical multiple regression analysis, and moderation using PROCESS macro.

The study found that employee training and employee participation had a significant positive relationship with organizational commitment, while organizational ostracism had a significant but negative relationship with organizational commitment among bank employees. The study also found that workplace ostracism moderated the relationship between organizational climate and organizational commitment The study recommended that organizational commitment requires management training their workforce, allowing employee participation in decisions, and minimizing or outrightly eradicating the practice of organizational ostracism. It is, therefore, concluded that workers place great value on training and participation in decision-making and frown at organizational ostracism.

This paper fills in the gaps left by the paucity of empirical investigation of the moderating role that workplace ostracism plays between employee training, employee participation and organizational commitment – a feat that is lacking in developing countries. It serves as a reminder to management to prevent or entirely eliminate workplace ostracism to allay an employee's impression of being a threat to an organization when commitment is low.

COVID-19 disrupted the usual way of working for many people across the globe, making full-time work from home and hybrid models two popular work arrangements. Despite the proliferation and high acceptance of the hybrid model, very little research has focused on the same. This study aims to compare the impact of transitions caused by remote work on work disengagement under two settings: remote work and hybrid model.

The data is collected from three corporate hubs in India: Hyderabad, Gurgaon and Bangalore. This study’s respondents represent two working models: full-time work from home and a hybrid model. Responses were collected using Google forms-based questionnaire, which resulted in the following usable responses: 356 (hybrid) and 398 (work from home).

The findings reveal that the structural model for the hybrid sector explained 11% of the variance in work disengagement, while the same for work from home model accounted for 20% of the variance in work disengagement. The authors also tested for the moderation of individual resilience between work-home and home-to-work conflicts and home-to-work transitions and work-to-home conflict under full-time work-from and hybrid models. Based on 356 respondents from hybrid category and 398 from work from home, the study found that employees experience less work-to-home and home-to-work conflicts in the hybrid model and employees experience more work-to-home and home-to-work conflicts in the full-time work from home model.

The study is also the first to examine the moderating role of individual resilience as a tool to bounce back and handle conflicts. As the full-time work from home model leads to more work-to-home and home-to-work conflicts, individuals have more scope to exhibit resilience, and thus, the moderating relationship is stronger in the full-time work from home model. The paper offers theoretical and managerial implications.