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This paper aims to focus on scrutinizing the economics of greenhouse gas (GHG) emissions in Vietnam's rice production sector.

Using surveyed data from household rice producers, the smallest available production scale, the author delves into the economics of GHG emissions, constructs a data-driven bottom-up marginal abatement cost curve for Vietnam’s rice production, and evaluates the impacts of carbon pricing on production outputs and GHG emissions.

The author’s estimates reveal that the average profit earned per tonne of GHG emissions is $240/tCO2. Notably, the profit earning per tonne of GHG emissions varies substantially across producers, indicating significant opportunities for improvement among low-efficiency producers. The analysis suggests that a reasonable carbon price would yield a modest impact on the national rice output. The quantitative analysis also reaffirms that the primary driver of GHG emissions in Vietnam’s rice production stems from non-energy inputs and industrial processes rather than the utilisation of energy inputs, emphasizing the importance of improving cultivation techniques.

This research is original.

This study aims to provide an overview and quality appraisal of the current scientific evidence concerning the prevalence and characteristics of mental and physical disorders among sentenced female prisoners.

A mixed-methods systematic literature review.

A total of 4 reviews and 39 single studies met the inclusion criteria for the review. Mental disorders were the main area of investigation in the majority of single studies, with substance abuse, particularly drug abuse, as the most consistently gender biased disorder, with higher prevalence among women than men in prison. The review identified a lack of updated systematic evidence on the presence of multi-morbidity.

This study provides an up-to-date overview and quality appraisal of the current scientific evidence concerning the prevalence and characteristics of mental and physical disorders among female prisoners.

This study examines the relationship between demand forecasting error and retail inventory management in an uncertain supplier yield context. Replenishment is segmented into off-season and peak-season, with the former characterized by longer lead times and higher supply uncertainty. In contrast, the latter incurs higher acquisition costs but ensures certain supply, with the retailer's purchase volume aligning with the acquired volume. Retailers can replenish in both phases, receiving goods before the sales season. This paper focuses on the impact of the retailer's demand forecasting bias on their sales period profits for both phases.

This study adopts a data-driven research approach by drawing inspiration from real data provided by a cooperating enterprise to address research problems. Mathematical modeling is employed to solve the problems, and the resulting optimal strategies are tested and validated in real-world scenarios. Furthermore, the applicability of the optimal strategies is enhanced by incorporating numerical simulations under other general distributions.

The study's findings reveal that a greater disparity between predicted and actual demand distributions can significantly reduce the profits that a retailer-supplier system can earn, with the optimal purchase volume also being affected. Moreover, the paper shows that the mean of the forecasting error has a more substantial impact on system revenue than the variance of the forecasting error. Specifically, the larger the absolute difference between the predicted and actual means, the lower the system revenue. As a result, managers should focus on improving the quality of demand forecasting, especially the accuracy of mean forecasting, when making replenishment decisions.

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

Images taken from construction site interiors often suffer from low illumination and poor natural colors, which restrict their application for high-level site management purposes. The state-of-the-art low-light image enhancement method provides promising image enhancement results. However, they generally require a longer execution time to complete the enhancement. This study aims to develop a refined image enhancement approach to improve execution efficiency and performance accuracy.

To develop the refined illumination enhancement algorithm named enhanced illumination quality (EIQ), a quadratic expression was first added to the initial illumination map. Subsequently, an adjusted weight matrix was added to improve the smoothness of the illumination map. A coordinated descent optimization algorithm was then applied to minimize the processing time. Gamma correction was also applied to further enhance the illumination map. Finally, a frame comparing and averaging method was used to identify interior site progress.

The proposed refined approach took around 4.36–4.52 s to achieve the expected results while outperforming the current low-light image enhancement method. EIQ demonstrated a lower lightness-order error and provided higher object resolution in enhanced images. EIQ also has a higher structural similarity index and peak-signal-to-noise ratio, which indicated better image reconstruction performance.

The proposed approach provides an alternative to shorten the execution time, improve equalization of the illumination map and provide a better image reconstruction. The approach could be applied to low-light video enhancement tasks and other dark or poor jobsite images for object detection processes.

Construction professionals are the major contributors to developing a sustainable construction industry, whereas architects, engineers and quantity surveyors are the key construction professionals who must play extraordinary roles in achieving better sustainable construction. Therefore, this study aims to identify the job attributes of key Sri Lankan construction professionals in addressing challenges associated with climate change.

The study adopted a mixed research approach. A literature review and preliminary semi-structured interviews were used to appraise the job roles of architects, engineers and quantity surveyors in addressing challenges associated with climate change. The data collected through the qualitative approach were used in an online questionnaire survey, and the findings were analysed using the relative index method.

The findings highlight that regardless of the knowledge of the professional category on green rating tools, carbon footprint, adaptation of renewable energies for the reduction of energy consumption, building information modelling-related applications and waste management concepts/practices are the foremost job attributes required for the key Sri Lankan construction professionals in addressing challenges associated with climate change.

The results from this study provide a handful of guidance to construction industry professionals, national and international professional institutions, non-governmental organisations and other relevant authorities to address climate change within the built environment by identifying ways for improving the relevant key job attributes of construction industry professionals.

To the best of the authors’ knowledge, this is the first study that explores the job attributes of key Sri Lankan construction professionals in addressing the challenges associated with climate change.

From a psychosocial perspective, this study aims to understand the impact of psychosocial safety climate (PSC) on the intent to stay of construction workers and provides practical recommendations for construction enterprises to retain construction workers.

This study proposes the conceptual framework explained by the conservation of resources (COR) theory and develops a mediation model of “PSC – job satisfaction – intent to stay” within the framework supported by the stimulus–organism–response (SOR) model. Then, a questionnaire survey of 489 construction workers in Guangzhou was conducted and structural equation modeling (SEM) analysis was performed on the data collected.

Results show that PSC has a significant and positive effect on job satisfaction and intent to stay. In addition, job satisfaction partially mediates the effect of PSC on intent to stay. Hence, the theoretical model of “PSC – job satisfaction – intent to stay” has been empirically tested and supported.

This study is the first to investigate the effect of PSC on intent to stay and enriches the research on the retention of construction workers. The COR theory explains well the mechanism of PSC influence on intent to stay, thus expanding its application to the construction field. Moreover, this study provides practical recommendations for construction enterprises to retain workers so as to build a stable and productive workforce.

Expanding on the research of the antecedents of abusive supervision, this study aims to explore supervisor role overload as a supervisor-level predictor of abusive supervision. Based on transactional stress theory, the authors investigate role overload that is appraised as a challenge or a hindrance stressor by supervisors, leading to pleasant or unpleasant feelings, respectively. The authors propose that, based on their appraisal, these feelings of supervisors act as a mediating mechanism that can facilitate or inhibit their abusive behaviour at work. Additionally, the authors posit emotional intelligence (EI) as a key moderator in helping supervisors manage the negative feelings arising from perceiving role overload as a hindrance and preventing them from demonstrating abusive supervision.

To test the proposed moderated mediation model, the authors collected two-wave data from middle-level supervisors or managers from several organisations located in the Eastern Province of Saudi Arabia (N = 990).

The results largely support the hypothesised relationships and show that depending on supervisor appraisal, role overload can generate pleasant or unpleasant feelings in supervisors and, consequently, impede or facilitate abusive supervision. They also shed light on the moderating effect of EI, in that supervisors scoring high on EI are better equipped to deal with unpleasant feelings arising from role overload and effectively manage their workplace behaviour, that is, to avoid abusive behaviours.

Role overload can have different impacts on employees: on the one hand, there is a potential for growth, which entails drive and enthusiasm; on the other hand, it could feel like an unsurmountable mountain for employees, leading to different forms of anxiety. Because what we feel is what we project onto others, supervisors experiencing unpleasant feelings cannot be the best leader they can be; even worse, they can become a source of negativity by displaying destructive behaviours such as abusive supervision. The corollary of something as minor as an interaction with a leader experiencing unpleasant feelings could have a ripple effect and lead to adverse outcomes for organisations and their employees. This study explores the different perceptions of role overload and the subsequent feelings coming from those perceptions as supervisor-level predictors of abusive supervision. While it is not possible to objectively put a different lens inside the minds of supervisors when they face stressors at work, to feel pleasant or unpleasant, they can be trained to manage their negative feelings and keep their behaviours in check. Particularly, training managers to be more emotionally intelligent can help them not only achieve growth by overcoming challenges at work but also acknowledge and adapt their feelings to keep their behaviours in the workplace positive. In practical terms, this research can provide organisations with the knowledge required to nip the problem of abusive supervision in the bud, as prevention is always better than cure.

The purpose of this study is to explore the information needs and behaviors of Chinese first-time mothers from the perspective of information journey theory.

This study uses the popular coding language Python to write a program to extract 366 posts published by first-time mothers in the Babytree community of China's largest maternal and infant platform. Content analysis is used to analyze and encode the collected data.

This study summarizes the information needs of first-time mothers in China specifically, which includes six aspects of health, pregnancy care, postpartum parenting, socialization, self-actualization, and entertainment. Then, based on its hierarchical and stage characteristics, more flexible information needs matrix for Chinese first-time mothers has been developed to reflect their changing 15 types of information needs more fully. Finally, it constructs the information journey model of Chinese first-time mothers including five stages.

This is the first time that information journey theory has been applied to the research on information needs and behaviors of first-time mothers. The authors have also further expanded the information seeking and acquisition phases and found new gaps in the phases to further refine the information journey model. This study can help us better understand the online information needs and behaviors of Chinese first-time mothers and provide support for organizations and online health community managers to develop new information services or improve the quality of existing services.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-10-2021-0538.

This study aims to examine the role of trust in management and state government in mitigating the relationships between individual- and state-level stressors and well-being during the COVID-19 pandemic.

Data were collected from a US sample still working during the first wave of infections (N = 437) and was supplemented with objective state-level data. Data were analyzed using hierarchical regression modeling with the PROC MIXED procedure with SAS software to incorporate both individual- and state-level variables.

Results indicated that individual-level stressors (work and family role overload) were positively associated with stress; however, the relationship between family role overload and stress was mitigated among those with high trust in state government. Results indicated that state-level stressors (infection rates and population density) were not associated with stress; however, the relationship between state population density and stress was positive among those with low trust in management and negative among those with high trust in management.

This study highlights the need for organization and government leaders to build trust before and during crisis situations, as well as engage in a collaborative approach to managing stressors in crisis situations.

This study highlights the importance of expanding the focus of employee trust across organizational boundaries for understanding employee well-being during a crisis situation. This study also demonstrates the cross-over effects of trust, such that organization leaders can protect workers from community stressors, while government leaders can protect workers from family stressors.

This study seeks to explicate how institutional disruptions impact multinational corporation (MNC) subsidiary control choices. It uses institutional theory to understand the influence of formal and informal institutions across countries on the type of control system employed in an MNC manufacturing subsidiary.

This study’s sample is based on a unique dataset from five trustworthy sources. We use multi-level models to account for the hierarchical nature of the sample of 1,630 multinational subsidiaries spread across 26 host countries by firms from 21 home countries.

The institutional distance between the host and the home country has a negative relationship with strategic control. In contrast, the home country’s power distance has a positive relationship with strategic control.

Study findings indicate the need to incorporate formal and informal institutional elements in the control system’s conceptual framing and design. This notion complements existing visualizations of optimizing MNC controls through extant articulations of minimizing governance costs through organizational design choices or strategic needs.