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PurposeThis paper aims to study the reaction of construction project managers in coping with workforce productivity challenges that arise from the COVID-19 pandemic.Design/methodology/approachQuantitative research via questionnaires targeting the project managers from G7 contractor companies in Malaysia was carried out in the data collection process. In total, 182 responses have been analysed descriptively and inferentially using SPSS software.FindingsThe research discovers that most of the construction projects and their workforce productivity were adversely impacted by the COVID-19 pandemic. Besides, the strategies adopted by project managers in Malaysia and the ranking of strategies based on their effectiveness were indicated.Originality/valueThe findings of this research have contributed to a new understanding of the construction workforce productivity during the COVID-19 era from the perspective of project managers in Malaysia. A guideline established from the ranking of strategies would be useful to ensure the survival of the industry in Malaysia aftermath of the COVID-19 pandemic.

Background Musculoskeletal diseases requiring surgical procedures in working-age adults impose a significant financial burden on many societies. This observational study focused on evaluating the seven most common elective orthopedic surgeries among the Finnish working-age employed population with an aim to assess the extent of sickness absence accumulation before and after the surgical procedure, along with the duration of hospital stay during treatment. Additionally, the study aimed to quantify productivity-adjusted life years (PALY) and assess the societal economic impact of PALY loss due to sickness absence. Methods Data from electronic health records from the Wellbeing Services County of Southwest Finland, Finland, in 2020–2022, compiled with information from national administrative registries, were used to identify working-age, employed patients from seven disease groups ( N  = 770). Lengths of pre- and post-operative sickness absences together with in-hospital days were calculated, and PALY were estimated over the corresponding periods. Point estimate of the productivity cost of PALY losses was calculated in total and on a per-patient basis based on a reported national average value of one sickness absence day (€344). Results The average hospital stay across all patient groups ranged from 1.0 to 5.5 days. The longest pre-operative sickness absence was in lumbar spine fusion (36.7 days), and the shortest in patients with knee ligament injuries (4.3 days). The longest post-operative average sickness absence was for shoulder procedures (114.0 days), and the shortest for carpal tunnel syndrome patients (37.2 days). In all seven patient groups together, on average, 17.4 PALY (27.5% of total PALY loss) were annually lost (corresponding to €2,192,683) in the year before hospitalization, 1.0 (1.6%) PALY annually lost (€194,773) during the hospitalization, and 44.8 (70.9%) PALY annually lost (€5,619,243) in the year after hospitalization. The highest pre-operative per-patient PALY loss in monetary terms was among patients with lumbar spine fusion (€16,693), while the highest post-operative per-patient PALY loss in monetary terms occurred among patients with knee osteoarthritis and knee replacement (€28,673). Conclusions The sickness absences were typically longer and PALY losses in monetary terms per patient were higher in the post-operative period compared to the pre-operative period. The seven patient groups examined produced considerable PALY losses and related total productivity costs to the Wellbeing Services County of Southwest Finland. The results of the study call for actions to support a more rapid return to work.

In this paper, \"AI-Powered Super-Workers,\" the revolutionary power of artificial intelligence (AI) on the workforce is empirically shown. Based on real data, the conclusions show significant shifts in work satisfaction and productivity. For example, up to 52% productivity benefits were seen in a variety of professions; one such function was that of a Sales Executive (John Smith, for example), whose productivity rose by 50% after AI integration. Job satisfaction soared, with a significant 46% improvement noted by Employee 1 (John Smith). The 20% boost in skill that Employee 2 (Sarah Johnson) demonstrated highlights the efficacy of AI-driven training. AI use patterns that highlight individual differences in AI adoption include Employee 4 (Emily Brown) using AI for 21 hours. This research may be summarized by the following keywords: AI use, workforce productivity, job satisfaction, skills advancement, and AI integration.

The role of employee behavior in organizations and their interaction with management is crucial in advancing the economic progress of workers. This study examines the impact of employee behavior and management practices on organizational performance and economic progress, using advanced artificial intelligence techniques to explore complex relationships and provide evidence-based strategies for sustainable workforce development. The research analyzes critical aspects such as job satisfaction, motivation, participation, and communication to uncover the underlying mechanisms that contribute to economic development. It recognizes the dynamic relationship between employees and management, confirming the central role of effective leadership, communication, and teamwork in achieving positive results. The study emphasizes that harmonious cooperation between employees and management is necessary to create a favorable work environment that contributes to the economic development of workers. It utilizes an artificial neural network (ANN) to better understand the interdependencies between different parameters and their effects within the framework of this ongoing project. The results contribute to the existing body of knowledge by providing practical implications for organizations seeking to optimize the employee–employer relationship and increase the overall workforce productivity. By understanding the complex dynamics between employee behavior and management practices, organizations can create a supportive environment that maximizes employee potential and contributes to sustainable economic growth. The findings demonstrate an accuracy of over 70%, indicating that enhancing job satisfaction and communication can significantly improve employee participation, productivity, and overall organizational performance.

This study investigates the impact of digital Human Resource Management (HRM) strategies on workforce productivity and organizational performance within Micro, Small, and Medium Enterprises (MSMEs) and startups in Indonesia. Anchored in the Resource-Based View (RBV) and Dynamic Capabilities Theory, the research conceptualizes digital HRM as a strategic asset that enhances organizational performance by enabling firms to develop, deploy, and reconfigure human capital capabilities in response to digital transformation. The conceptual model includes five constructs: Digital Technology Adoption in HRM, Digital Training and Development, Work Flexibility and Hybrid Management, Innovation in Recruitment and Retention, and Utilization of HR Analytics and Artificial Intelligence (AI). Data were collected through structured questionnaires distributed to MSME and startup leaders and analyzed using Structural Equation Modeling (SEM) with Smart PLS. The results indicate that Digital Technology Adoption, Digital Training and Development, and Work Flexibility do not significantly influence workforce productivity or organizational performance (p > 0.05). However, Innovation in Recruitment and Retention and the Utilization of HR Analytics and AI have a significant and positive impact on both workforce productivity and performance outcomes (p = 0.000). These findings highlight that while not all digital HR strategies yield immediate benefits, the ability to build dynamic capabilities through innovative talent practices and data-driven HR systems is essential to achieving competitive advantage and organizational success in the digital economy.

Purpose Artificial intelligence (AI) can assist in the worldwide shortage of cybersecurity workers in technical and managerial roles. Thus, the purpose of this study was to investigate the role of AI in automating many of the routine tasks associated with cybersecurity. As such, AI enables cybersecurity personnel to reduce their workloads and focus on more strategic aspects of their work. Design/methodology/approach This study is an exploratory field study. The authors started by conducting a literature review to assess the possibility that AI tools can provide and how they can improve cybersecurity efficacy. Following this, the authors identified the specific core tasks for two cybersecurity work roles (technical and managerial) and searched for specific commercial tools that can perform each of the tasks. Then, the authors used the free ChatGPT 3.5 to list the current cybersecurity systems that use AI for the associated tasks, which the authors then reviewed with the tools’ documentation and websites to confirm these tasks were conducted or assisted by AI. Findings Results indicated that all 14 cybersecurity tasks of the technical work role are currently noted to be performed by commercial cybersecurity systems with AI-integrated capabilities, while only 11 of the 17 managerial work role tasks currently appear to be performed by AI. Practical implications The rapid integration of AI capabilities into commercial cybersecurity systems may suggest that the cybersecurity workforce must be currently trained on how to use AI tools in their daily operations, especially as it pertains to technical cybersecurity work roles. Social implications The cybersecurity workforce shortage is reported to exceed four million cybersecurity workers worldwide in 2023. Thus, further understanding of the role of AI in improving the efficiency of technical and managerial cybersecurity tasks is significant. Originality/value The value of this research lies in the initial assessment of the current AI capabilities of commercial cybersecurity systems, which will ultimately provide the “super-human” performances resulting from human-AI teaming.

Background Obesity prevalence continues to rise in Canada, highlighting a growing public health concern. This study updates estimates of the societal cost of inaction in treating obesity, emphasizing the significant economic burden stemming from both direct healthcare costs and indirect productivity losses. Methods We combined data from national surveys and published literature to estimate the 2023 national economic implications of obesity. Comparing adults with obesity (BMI ≥ 30) to those with healthy weight (25 > BMI ≥ 18.5), we assessed healthcare costs, absenteeism, presenteeism, disability pensions, mortality-related costs, workforce participation, and earnings. Canadian data were used where possible, supplemented by U.S. data, standardized to 2023 CAD$. Results The cost of inaction in treating obesity in Canada was $27.6 billion in 2023, including $5.9 billion in direct healthcare and $21.7 billion in indirect costs. Excess healthcare costs are driven by higher utilization of medical services. Indirect costs include approximately $8.2 billion from reduced workforce participation, $6.8 billion from presenteeism, $3.8 billion in lower earnings among employed with obesity, $2.0 billion from lost wages due to premature mortality, $682 million from absenteeism, and $268 million from disability pensions. Conclusions The economic implications of not addressing obesity effectively are substantial, emphasizing the urgent need for utilizing effective chronic disease management strategies. Our findings highlight the disproportionate impact on women and the broader economic consequences, underscoring the imperative for tailored policy interventions. Investing in comprehensive, evidence-based obesity management not only enhances individual well-being but also yields significant societal and economic benefits.

The study examined a gender-based investigation of employment, workforce productivity and economic growth in West African countries. The empirical analysis is centred on panel consisting of 16 West African countries over the period 2007-2019 using Esteve-Volarts model. The method of estimation employed are panel system generalised method of moments. The major findings suggest that there exists a positive and major relationship between male and female wage and salaried workers to economic growth although male wage contribution to growth supersedes female contribution. Also, male and female labour force participation rate are directly linked to economic growth. Likewise, female workforce productivity contributed significantly to economic growth of the selected countries. This study recommends that programmes should be put in place to facilitate more female participation in well paid jobs which will help enhance productivity as well as increase the growth in the countries. These recommendations will help to narrow the inequality of gender inequality in employment and productivity of workforce amongst West African countries.

This study applies data envelopment analysis (DEA) to evaluate the economic sustainability of U.S. airlines by examining labor efficiency as a pivotal component of cost management and long-term sustainability. Focusing on five key employee categories—pilots, flight attendants, ground staff, maintenance staff, and management—the analysis uses data from the MIT Airline Data Project spanning 2007 to 2020 to calculate relative efficiency scores for fifteen major airlines. The findings reveal significant disparities in labor efficiency across different airline sectors, particularly highlighting challenges in managing cost-intensive roles, such as ground, maintenance, and management staff. Notably, Southwest Airlines consistently demonstrates strong economic sustainability through its efficient labor practices, while carriers including United, jetBlue, Alaska, and Hawaiian Airlines exhibited marked inefficiencies in 2020, indicating a critical need for operational improvements. This research contributes to the field of airline management by linking labor efficiency metrics with broader economic sustainability objectives, thereby offering strategic insights for enhancing cost-effectiveness and ensuring the long-term financial health of the industry.

Abstract—The article presents a forecast of the potential GDP of Kazakhstan performed using a structural method based on a production function that establishes relationships between the factors of production, total factor productivity, and output. Forecast estimations of trends of the key factors of production and the total factor productivity that form the long-term trend of the potential GDP of the country in the context of the global energy transition are presented. The baseline scenario of a long-term forecast of the potential GDP of Kazakhstan for the period until 2050 as adjusted for the objective of decarbonization of the economy is developed. Approaches to economic policy that could “nudge” the potential GDP up are proposed.