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This research aims to evaluate the status of project management best practices in the road infrastructure sector of Ghana through the inquiry of project managers’ competency.

Based on a thorough literature review on competency and project management best practices, a criterion for measuring best practices based on the competencies was identified. Using closed-ended questionnaires as a survey instrument, data was collected and analyzed using a one-sample t-test, Kendall’s concordance coefficient and simple regression.

The key finding reveals that in achieving project management best practices for the Ghanaian road infrastructure projects, project managers should straighten and strengthen their capability in terms of skills, personal characteristics and knowledge as ranked. An appropriate blend of these characteristics would be a necessary requirement for project managers. Through this, project managers and project-based road infrastructure organizations will be able to win the confidence of their stakeholders and attain improvements in cost-effectiveness, quality and time management.

This study provides project managers in the road infrastructure sector with relevant information on which criteria and variables are critical and are frequently required to manage such projects. This will be helpful for training programs and professional development of project managers in the road infrastructure sector.

The study provides a new direction and focuses for project managers in the road infrastructure industry toward best project management practices in developing countries. It also complements existing studies in this area which deepen the understanding of the subject area.

Barriers to adequate healthcare in rural areas remain a grand challenge for local healthcare systems. In addition to patients' travel burdens, lack of health insurance, and lower health literacy, rural healthcare systems also experience significant resource shortages, as well as issues with recruitment and retention of healthcare providers, particularly specialists. These factors combined result in complex change management-focused challenges for rural healthcare systems. Change management initiatives are often resource intensive, and in rural health organizations already strapped for resources, it may be particularly risky to embark on change initiatives. One way to address these change management concerns is by leveraging socio-technical simulation models to estimate techno-economic feasibility (e.g., is it technologically feasible, and is it economical?) as well as socio-utility feasibility (e.g., how will the changes be utilized?). We present a framework for how healthcare systems can integrate modeling and simulation techniques from systems engineering into a change management process. Modeling and simulation are particularly useful for investigating the amount of uncertainty about potential outcomes, guiding decision-making that considers different scenarios, and validating theories to determine if they accurately reflect real-life processes. The results of these simulations can be integrated into critical change management recommendations related to developing readiness for change and addressing resistance to change. As part of our integration, we present a case study showcasing how simulation modeling has been used to determine feasibility and potential resistance to change considerations for implementing a mobile radiation oncology unit. Recommendations and implications are discussed.

In recent years, the relationship between Manchester United fans and their club has been put under the spotlight due to the contentious relationship between the fanbase and the club’s American owners, the Glazer family. However, the commercialisation of Manchester United and their ramping up of their associated brand accelerated massively during the 1990s, as a result of the coincident timing of the country’s glamour club returning to dominance during a period of ever-greater financial returns for top-flight success. As the undoubted commercial trailblazers in English football (and the first English club to be listed on the Stock Exchange), analysing their development during the 1990s is, arguably, the best way of understanding how and why top-flight football clubs operate the way they do and, in a knock-on effect on the league’s competitiveness, why the clubs below them can so easily fall away.

The case describes a hypothetical hedge fund manager who is examining whether to invest in bitcoin. The case discusses potential risks and rewards of investing in bitcoin, the role of bitcoin and digital currencies more broadly, and financial innovation in the space, such as ICOs. It can be taught as part of a second-year MBA elective course in investments, financial institutions/capital markets, or fintech.

The purpose of this study is to explore the relationship between Lean manufacturing and Industry 4.0 for small and medium size of enterprise in Japan and Brazil.

The authors conducted a quantitative survey (20 companies in Japan and 30 companies in Brazil) combined with a qualitative interview (2 companies in Japan and 15 companies in Brazil).

According to the quantitative study, 90% of them practice Lean manufacturing and 40% of them practice Industry 4.0. In the qualitative study in Brazil, four managers responded that the Lean manufacturing is a prerequisite for Industry 4.0 since any production process with waste cannot be productive, even with sophisticated digitalization technology.

The authors explored further the relationship between “defensive Digital Transformation (DX),” which is based mainly on Lean manufacturing, and “offensive DX,” which relates to customer value creation through Industry 4.0. This study clarifies the relationship and plays as a roadmap to develop better the manufacturing from current status to the vision of Industry 4.0.

The Australian Government has long used its international scholarship programs as an instrument of soft power in international diplomacy. The paper examines an international scholarship program and its role in Australia’s soft power efforts during a period in recent history.

The Australia in the Asian Century White Paper of 2012 is used as a lens to reveal how the Australian Government viewed the role of international scholarship programs in international diplomacy at a specific point in the recent past, and compares it with research revealing what was contemporaneously happening with one key government-funded scholarship program.

This paper is based on a comprehensive case study of the Australian Government’s Endeavour Scholarships and Fellowships program (2004–2019). Endeavour was an ambitious and expensive merit-based program with 6,600 recipients in numerous and diffuse sub-categories. The program was complex and cumbersome and lacked clear priorities, particularly in its lack of geographic focus. It missed opportunities to connect with the political zeitgeist, largely due to opaque priorities and inadequate evaluation regimes which focused entirely on outcomes for individual recipients rather than on relationships for Australia.

This research draws on the first academic study of the Endeavour program. Other scholarship programs (for example Australia Awards and the New Colombo Plan) have attracted considerable scholarly interest. The Endeavour research provides an additional counterpoint for studies of Australian scholarship programs and their contribution to international diplomacy. It is timely to consider this in 2024 when Australia is putting a new focus on its investment in international scholarships.

The global prevalence of social media and its potential to cause polarization are highly debated and impactful. The previous literature often assumes that the ideological bias of any media outlet remains static and exogenous to the polarization process. By studying polarization as a whole from an ecosystem approach, the authors aim to identify policies and strategies that can help mitigate the adverse effects of polarization and promote healthier online discourse.

To investigate online polarization, the authors perform a systematic review and analysis of approximately 400 research articles to explore the connection between cognitive bias and polarization, examining both causal and correlational evidence. The authors extensively evaluate and integrate existing research related to the correlation between online polarization and crucial factors such as public engagement, selective exposure and political democracy. From doing so, the authors then develop a PolarSphere ecosystem that captures and illustrates the process of online polarization formation.

The authors' review uncovers a wide range of associations, including ideological cognition, bias, public participation, misinformation and miscommunication, political democracy, echo chambers and selective exposure, heterogeneity and trust. Although the impact of bias on social media polarization depends on specific environments and internal/external conditions, certain variables exhibit strong associations across multiple contexts. The authors use these observations as a basis from which to construct PolarSphere, an ecosystem of bias-based polarization on social media, to theorize the process of polarization formation.

Based on the PolarSphere ecosystem, the authors argue that it is crucial for governments and civil societies to maintain vigilance and invest in further research to gain a deep comprehension of how cognitive bias affects online polarization, which could lead to ways to eliminate polarization.

This paper seeks to demonstrate the value of using social media to capture fashion trends, including the popularity of specific features of clothing, in order to improve the speed and accuracy of supply chain response in the era of fast fashion.

This study examines the role that text mining can play to improve trend recognition in the fashion industry. Researchers used n-gram analysis to design a social media trend detection tool referred to here as the Twitter Trend Tool (3Ts). This tool was applied to a Twitter dataset to identify trends whose validity was then checked against Google Trends.

The results suggest that Twitter data are trend representative and can be used to identify the apparel features that are most in demand in near real time.

The 3Ts introduced in this research contributes to the field of fashion analytics by offering a novel method for employing big data from social media to identify consumer preferences in fashion elements and analyzes consumer preferences to improve demand planning.

The 3Ts improves forecasting models and helps inform marketing campaigns in the apparel retail industry, especially in fast fashion.

The purpose of this paper is to examine Asian Americans' perceptions of the police, specifically how they construct support. Although such literature has been growing in recent years, research on Asian American interactions with the police remains limited. Additionally, this paper is situated within the theoretical framework of system justification theory to account for Asian Americans' views of the police.

This study relies on interview data collected from 20 Asian Americans residing in mid-Atlantic states. Participants were either recruited directly by the researchers or through the snowball-sampling method.

Police support is influenced by perception of neighborhood safety, personal police contact and empathetic feelings toward the police. Specifically, regarding the latter component, humanizing or empathizing with police officers is a form of rationalizing individual police misconduct that reinforced police legitimacy. Most participants had similar characteristics and displayed police justification. Additional research is needed regarding what characteristics or patterns are likely to lead to lower levels of police justification.

This article's findings improve our understanding of system justification among Asian Americans, particularly as it relates to policing.

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.