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This paper investigates the impact of bank credit on agricultural productivity in the Central African Economic and Monetary Community (CEMAC) from 1990 to 2019. Studies’ results on the impact of bank credit on agricultural productivity are not conclusive. The studies demonstrate diverse outcomes which are debatable. The results are conflicting.

Agricultural value added (AGRVA) to the gross domestic product (GDP) proxies agricultural productivity while domestic credit to the private sector by banks (DCPSB), broad money supply, land, inflation (INF), physical capital (PHKAP) and labour supply are explanatory variables. The autoregressive distributed lag technique is utilized.

The co-integration test results show a long-run co-integration among the variables. The findings disclose that DCPSB, land and PHKAP impact positively on the AGRVA. Broad money supply, INF and labour impact negatively on the AGRVA to the GDP.

The results suggest that the CEMAC governments should encourage effective ways to increase bank credit flow to private enterprises in the agricultural sector through efficient bank's intermediation.

The governments should create more agricultural banks and improve the operation of existing ones to ensure direct credit to agricultural activities. The Bank of Central African Economic and Monetary Community should apply aggressive policy which eliminates all the bottlenecks undermining credit flow to the private sector in mutualism with agricultural productivity.

The commercial banks should give more credit to private sector to mutually benefit the agricultural sector and the banking sector. The governments of the CEMAC economies should expand funding into the capital market which considerably boosts agricultural productivity.

Studies’ results on the impact of bank credit on agricultural productivity are not conclusive. The studies demonstrate diverse outcomes which are debatable. The results are conflicting; some reveal positive impacts, some show negative impacts and others indicate U-shape behaviour. Hence, research is required to fill the lacuna.

The purpose of the paper is to develop a method to integrate the schedule-based analysis with a productivity-based analysis to prove and support the result of the damages calculation.

In this paper, a “cost and schedule impact integration” (CSI₂) model is proposed to objectively show and estimate lost productivity due to changes in construction projects.

A schedule-based analysis to include separate tracking of change order costs can be used to predict productivity due to the delay and disruption; changes in construction projects almost always result in delay and disruption. However, the schedule-based analysis needs to be integrated with a productivity-based analysis to prove and support the result of the damages calculation.

The results of this study expand upon construction practices for proving and quantifying lost productivity due to changes in construction projects.

The contribution of the paper is summarized as the introduction of a “schedule impact analysis” into a “cost impact analysis” technique to assess the damages, as well as to demonstrate the labor productivity impact due to delay and disruption in construction projects.

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.

The study aims to empirically investigate the trends and determinants of labour productivity of the two broad sectors –industry and services – and their components, namely, manufacturing and market services sectors, in the case of major developing and developed economies of Asia-Pacific over the period 1980-2014 and make a comparison thereof.

The study uses econometric methodology of panel unit root tests, panel cointegration and group-mean full modified ordinary least squares (FMOLS).

The study finds that while capital deepening, government size, institutional quality, productivity of the other sector and financial openness affect productivity of all the sectors significantly, the impact of human capital and trade openness varies across sectors in the case of developing economies. Furthermore, the impact of technological progress becomes significant in the post-liberalization reforms period in the developing economies. The study further finds that capital deepening, human capital, government size, institutional quality, productivity of the other sector, government size and trade openness are significant determinants of productivity of all sectors of developed economies under consideration. However, the impact of technological progress is stronger for manufacturing sector than services and its components. Furthermore, while both equity and debt liabilities (as measures of financial openness) influence sectoral productivity of industry and manufacturing sectors positively and significantly in case of developed economies, only equity liabilities have a significant influence on the productivity of developing economies. This may indicate existence of more developed financial markets in the case of developed economies.

The study identifies important structural differences in determinants of productivity both across sectors and across developing and developed economies of Asia-Pacific.

Despite the great investments in information and communication technologies (ICT), research has not persuasively established corresponding productivity increases, while many studies have also found no significant relationships between productivity and ICT. However, several shortcomings have been identified in past studies, e.g. measurement errors, redistribution of impacts, ICT mismanagement. This study proposes a methodology for assessing the ICT productivity impact that overcomes these shortcomings. The methodology is tested in a dataset of three star hotels in the UK by using data envelopment analysis, a non‐parametric technique. Findings revealed that productivity gains do not accrue from ICT investments per se, but rather from the full exploitation of ICT networking and informalization capabilities. Suggestions regarding the effective and productive configuration and management of ICT applications are provided.

This paper aims to analyze the research productivity and impact of the finalists of the AIB best dissertation award, now titled the Buckley and Casson Award, but from 1987 to 2012 the Farmer Award. Specifically, this paper examines whether there is a relationship between winning the best dissertation award and subsequent publication productivity and impact. Relationships between academic institution and institutional geographic location and finalists are also examined.

The paper examines 25 years of citation counts and the number of publications in Google Scholar of Farmer Award winners and finalists of the AIB best dissertation award from inception in 1987 to 2009, with cited publications as a measure of productivity and citations as a measure of impact. Top performers in productivity and impact are identified, and the averages of winners and non-winners are analyzed in aggregate, over time and per year. Data on finalists' institution and geographic location of institution are analyzed to describe the importance of location and institution to the award.

It is found that the overall average citations of the winners of the award is less than that of the non-winners, and that in the large majority of years the non-winners have an average citation count higher than that of the winners. However, taking averages in five year increments shows more mixed results, with non-winners performing better in two periods and winners performing better in two periods, with the remaining period being split as to research productivity and impact.

Aggarwal et al. in this journal summarized a variety of data on Farmer Award finalists from the 1990s to gain insights on institutions represented by finalists, the publication record of finalists, and content of dissertations, among other characteristics. This paper updates some of the insights from that paper by examining data on award winners from 1987 to 2013, and adds further insight by examining for the first time cited publications and citation counts winners and non-winners for the same period excluding the last two years.

The aim of this article is to research on forestry green total factor productivity and explore the impact of financial support on forestry green total factor productivity.

The methods used in this study are super efficiency SBM model of undesired output and empirical model. SBM model is a kind of Data Envelopment Analysis (DEA). The SBM model with non-expected outputs (slacks-based measure) can be used to deal with the problem of efficiency measurement with multiple input and output variables and can be used to analyze the efficiency of green development of forestry economy.

First, the overall green total factor productivity of the authors’ country's forestry has shown a trend of first decline and then an increase from 2008 to 2018, and there are significant spatiotemporal differences; second, financial support has a significant positive impact on forestry green total factor productivity; third, environmental regulation has a significant threshold effect in the process of financial support on forestry green total factor productivity, and the role of financial support shows a trend of first increasing and then decreasing.

Secondly, taking the data of 30 provinces and cities in the authors’ country from 2008 to 2018 as the research object, using the super-efficiency SBM-Malmquist index to measure the country's forestry green total factor productivity and analyze its temporal and spatial changes; finally, a dynamic panel model was established to explore the impact of financial support on forestry green total factors quantitative impact on productivity, and adding environmental regulation as a threshold variable to establish a dynamic threshold regression, and found that financial support has a nonlinear impact on forestry green total factor productivity.

The inevitable change orders in construction projects have either direct or indirect impacts on a project’s duration. Reduced productivity is one of the indirect consequences that lead to major delays in the completion of the project. The purpose of this study is to develop a model that could quantify the impact of change orders on labor productivity and result in the establishment of policies to lessen their effects.

A model was developed to analyze the effects of change orders on labor productivity, then policies for managing their impacts on productivity and project duration were established. A water treatment case study was selected to serve as the scenario in which to implement and evaluate the model and policies.

The results of this study indicated that pressure to adhere to a schedule initially leads to an increase in labor productivity, but it is often followed by a significant drop that is a result of employee frustration. It was concluded that the pressure can be positive if it is applied for a short period of time; however, it continues for a significant length of time, the duration of the project will increase appreciably.

The proposed model can be implemented to identify the factors that affect labor productivity in a construction project. Its utilization will also help project managers assess when change orders occur and determine, which policies will be most effective in optimizing labor productivity.

Many attempts to justify the business value of increased investments in information technology (IT) have shown mixed results. While findings from earlier studies have been conflicting, recent firm level studies indicate that IT investments have a positive impact on productivity. However, whether IT adds value to organizations is an on going debating issue. Thus, thus it is worth of further investigation.

The paper employs multiple techniques – a regression, regression trees, and regression splines – and integrate the responses provided from each technique.

While IT investments have a positive impact on productivity, the impact is conditional and is not uniform but depends on the amounts invested in other related areas, such as non‐IT labor, non‐IT capital, and/or IT investments.

The IT impact on productivity can be maximized when investments in other related areas are considered together than when they are considered in isolation. Therefore, IT investment decisions should not be made without consideration of the levels of other investments within an organization to avoid any waste in additional investments in IT.

While most previous studies have studied in terms of its existence or non‐existence of the IT impact, we investigate the conditions under which the IT impact would or would not exist. Thus, our study provides with the opportunity for gaining a deeper understanding of the impact of IT investments on productivity.

The popular press often touts workforce demographic diversity as profit enhancing because it may reduce the firm's communication costs with particular segments of customers or yield greater team problem-solving abilities. On the other hand, diversity also may raise communication costs within teams, thereby retarding problem solving and lowering productivity. Unfortunately, there is little empirical research that disentangles the above countervailing effects. Diversity in ability enhances the team productivity if there is significant mutual learning and collaboration within the team, while demographic diversity may harm productivity by making learning and peer pressure less effective and increasing team-member turnover. We evaluate these propositions using a novel panel data from a garment plant that shifted from individual piece rate to group piece rate production over three years. Because we observe individual productivity data, we are able to econometrically distinguish between the impacts of diversity in worker abilities and demographic diversity. Teams with more heterogeneous worker abilities are more productive at the plant. Holding the distribution of team ability constant, teams composed of only one ethnicity (Hispanic workers in our case) are more productive, but this finding does not hold for marginal changes in team composition. We find little evidence that workers prefer to be segregated; demographically diverse teams are no more likely to dissolve, holding team productivity (and hence pay) constant, than homogeneous teams.