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Air pollution affects labour productivity and these effects arise in both indoor and outdoor environments and at varying levels of worker skill. They also arise at levels of air pollution generally considered to be within existing air quality standards and guidelines. Although the damage per individual is small when compared to more extreme events, such as mortality and hospitalisations, the effects are more widespread and may thus represent a significant cost to society. Labour is an essential element in every nation’s economy serving as one of the primary factors of production and India not an exception. Investing in human capital is viewed as a key source of sustained increase in labour productivity and economic growth. On the other hand, environmental regulations are typically considered to be a struggle on the economy. However, improved environmental quality may actually enhance productivity by creating a healthier workforce. At the same time, air pollution may affect labour productivity and can reduce the productivity of workers in physically demanding occupations. This chapter may be an attempt to provide comprehensive estimates of the major air pollutants in different states of India and also tries to identify the linkage between air pollution and labour productivity in case of Indian manufacturing sector.

Purpose – This study aims to determine the results of productivity index, profitability and improvement of company prices and to understand the relationship between partial input factors and productivity, profitability, and price fixing.

Design/Methodology/Approach – In this work, the productivity at the palm oil factory PT Sayaukath Sejahtera was measured and evaluated by using The American Productivity Center (APC) model approach.

Findings/Results – The results showed that each index that has been analyzed has a 5.143% decrease in the productivity index per year with a profitability equal to 0.286% per year and an increase in the price improvement index of 5.143% per year. Thus, it is concluded that from each index that has been analyzed, there is a decrease in the productivity index and profitability per year and there is an annual increase in the price improvement index.

Research Limitations/Implications (if applicable) –

Practical Implications (if applicable) –

Originality/Value –

This empirical study aims to explore how actors in specific human resource practices (HRPs) such as line managers (LMs) impact employee productivity measures in the context of financial institutions (FI) banks.

This cross-country study adopted a qualitative methodology. It employed semi-structured interviews to collect data from purposefully selected 12 business facing directors (BFDs) working in the top 10 banks in Nigeria and the UK. The data collected were analysed with the help of the trans-positional cognition approach (TPCA) phenomenological method.

The findings of a TPCA analytical process imply that in the UK and Nigeria’s FIs, the BFDs line managers’ human resources practices (LMHRPs) resulted in a highly regulated workplace, knowledge gap, service operations challenges and subjective quantitatively driven key performance indicators, considered service productivity paradoxical elements. Although the practices in the UK and Nigerian FIs had similar labels, their aggregates were underpinned by different contextual issues.

To support LMs in better understanding and managing FIs BFDs productivity measures and outcomes, we propose the Managerial Employee Productivity Operational Definition framework as part of their toolkit. This study will be helpful for banking sectors, their regulators, policymakers, other FIs’ industry stakeholders and future researchers in the field.

Within the context of the UK and Nigeria’s FIs, this study is the first attempt to understand how LMHRPs impact BFDs productivity in this manner. It confirms that LMHRPs result in service productivity paradoxical elements with perceived or lost productivity implications.

This study aims to examine the impact of financial inclusion and bank stability on agricultural productivity in Sub-Saharan Africa (SSA).

The study used 38 countries in the SSA with data spanning between 2004 and 2021. The data were analyzed using the two-step system generalized method of moments (GMM) and the panel-corrected standard error (PCSE) model.

The study found a positive effect of financial inclusion and bank stability on agricultural productivity. The study also discovered that while the access component of financial inclusion has a negative influence on agricultural productivity, the usage dimension has a positive impact.

The study suggests to policymakers that an inclusive and stable financial system improves agricultural productivity. The findings recommend that policymakers should empower farmers to leverage financial inclusion.

This study provides insightful discussion on the impact of financial inclusion and its various dimensions and bank stability on agricultural productivity in SSA.

This study aims to examine the enablers of productivity of enterprise-level Agile development process using modified total interpretative structural modeling (TISM). The two main objectives of the current study are to determine the variables influencing enterprise-level agile development productivity and to develop modified TISM for the corresponding components.

To identify enablers of the productivity of enterprise-level agile software development process a literature review and opinions of domain experts were collected. A hierarchical relationship among variables that show direct and indirect influence is created using the modified TISM (M-TISM) technique with Cross Impact Matrix-Multiplication Applied to Classification analysis. This study examined and analyzed the relationships between the determinants within the enterprise using a M-TISM technique.

With the literature review, the study could identify ten enabling factors of the productivity of Agile development process at the enterprise level. Results depict that program increment (PI) planning and scalable backlog management, continuous integration and continuous delivery (CI/CD), agile release trains (ART), agile work culture, delivery excellence, lean and DevOps practices, value stream mapping (VMS), team skills and expertise, collaborative culture, agile coaching, customer engagement have an impact on the productivity of enterprise-level Agile development process. The results show that team collaboration, agile ways of working and customer engagement have a greater impact on productivity improvement for enterprise-level Agile development process.

The developed model is useful for organizations employing scaled Agile development processes in software development. This study provides a recommended listing of key enablers, that may enable productivity improvements in the Agile development process at the enterprise level. Strategists should focus on team collaboration and Agile project management. This study offers a modified TISM model to academicians to help them understand the effects of numerous variables on maintaining the productivity of an enterprise-level Agile. The identified characteristics and their hierarchical structure can help project managers during the execution of Agile projects at the enterprise level, more effectively, increasing their success and productivity.

The study addresses the gap in the literature by interpretative relationships between the identified enabling factors. The model validation is carried out by a panel of nine experts from several information technology organizations deploying Agile software development at the enterprise level. This unique method broadens the knowledge base in Agile software development at scale and provides project managers and practitioners with a practical foundation.

Due to the rapid development of artificial intelligence (AI) technology, increasing the use of AI in healthcare is critical, but few studies have explored the extent to which physicians cooperate with AI in their work to achieve productive and innovative performance, which is a key issue in operations management (OM). We conducted empirical research to answer this question.

We developed a conceptual model based on the ambidextrous perspective. To test our model, we collected data from 200 Chinese hospitals. One senior and one junior physician from each hospital participated in this research so that we could get a more comprehensive view. Based on the sample of 400 participants and the conceptual model, we examined whether different types of AI use have distinct impacts on physicians’ productivity and innovation by conducting hierarchical regression and post hoc tests. We also introduced team psychological safety climate (TPSC) and AI technology uncertainty (AITU) as moderators to investigate this topic in further detail.

We found that augmentation AI use is positively related to overall productivity and innovative job performance, while automation AI use is negatively related to these two outcomes. Furthermore, we focused on the impacts of the ambidextrous use of AI on these two outcomes. The results highlight the positive impacts of complementary use on both outcomes and the negative impact of balance on innovative job performance. TPSC enhances the positive impacts of complementary use on productivity, whereas AITU inhibits the negative impacts of automation and balanced use on innovative job performance.

In the age of AI, organizations face greater trade-offs between performance and technology management. This study contributes to the OM literature from the perspectives of operational performance and technology management in three ways. First, it distinguishes among different AI implementations and their diverse impacts on productivity and innovative performance. Second, it identifies the different conditions under which automation AI use and augmentation are superior. Third, it extends the ambidextrous perspective by becoming an early adopter of this approach to explore the implications of different types of AI use in light of contingency factors.

This study aims to examine the impact of renewable energy consumption on agricultural productivity while accounting for the effect of financial inclusion and foreign direct investment in Brazil, Russia, India, China and South Africa (BRICS) countries during 2000–2020.

The study has used the latest data from World Bank and International Monetary Fund databases. The dependent variable in the study is agricultural productivity. Renewable energy consumption, carbon emissions, financial inclusion and foreign direct investment are independent variables. Autoregressive distributed lag (ARDL) approach was used to examine the short-run and long-run impact of renewable energy consumption, carbon emissions, foreign direct investment and financial inclusion on agricultural productivity.

The findings imply that consumption of renewable energy, carbon emissions and foreign direct investment have a positive impact on agricultural productivity while financial inclusion in terms of access does not seem to have any significant impact on agricultural productivity. Providing farmers, access to financial services can be beneficial, but its usage holds more importance in impacting rural outcomes. The problem lies in the fact that there is still a gap between access and usage of financial services.

Policymakers should encourage the increase in the usage of renewable energy and become less reliant on non-renewable energy sources which will eventually help in tackling the problems associated with climate change as well as enhance agricultural productivity.

Most of the earlier studies were based on tabular analysis without any empirical base to establish the causal relationship between determinants of agricultural productivity and renewable energy consumption. These studies were also limited to a few regions. The study is one of its kind in exploring the severity of various factors that determine agricultural productivity in the context of emerging economies like BRICS while accounting for the effect of financial inclusion and foreign direct investment.

Many manufacturers have attempted to improve the productivity of their facilities without much success. The Japanese, on the other hand, have recently achieved significant improvements in productivity by implementing JIT. There are several reasons for the Japanese success. First, JIT adheres to well‐established concepts and methods of production and operations management. Second, JIT is a holistic approach to productivity that directs attention to all resources involved in producing a product, and to all factors affecting their utilization. Third, JIT avoids many obstacles to productivity, in addition to utilizing resources efficiently. Fourth, JIT focuses on shopfloor design and operations where resources are primarily affected. Manufacturing managers can learn several useful lessons for improving productivity from the Japanese experience.

Productivity improvement is central to the growth and survival of an organisation. The results of a study carried out to measure productivity in a wire rope manufacturing company are described. Several models of productivity are developed and tested to draw out weak operational spots for the company. An illustration of how the results can be used to derive predictive models of company productivity and total unit cost is provided. Cause and effect diagrams are presented to establish the logical premises for taking action towards improvement of productivity in the operational areas.

The question of low productivity in Botswana is a cause of concern and a study has been carried out into perceptions about productivity in a sample of academic and public librarians. The main barriers to productivity were a lack of: job satisfaction, technological facilities and employee empowerment, together with poor management, working environment, relationship among staff, and inefficient use of human and material resources. The authors suggest that a serious culture change is required which would involve modifying both management and employees attitudes towards work, behaviour and commitment.