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The primary objective of this study is to understand the motivational process of the nurses who were engaged in COVID care and performed organizational citizenship behavior (OCB) without caring about their own lives. This study also aims to investigate the consequences of such OCB on life satisfaction when the situation continues for a longer period.

For the study’s first objective, an online cross-sectional survey was conducted on 236 critical care nurses from 18 states of India extending over the period from March 2021 to November 2021, when the country passed through the second wave of COVID-19 infection. For the second objective, a longitudinal study compared the life satisfaction level during and post-COVID by giving a one-year gap (November 2022).

The findings of this study reveal that during this COVID period, nurses were strongly performing OCB through their organizational and professional commitment, but one year later, they were identified with a low level of life satisfaction.

Research on nurses has already categorized the profession under high emotional labor. Research also reveals that an individual’s emotional labor strategy (deep acting or surface acting) determines job satisfaction. However, under the COVID situation, nurses have displayed an extraordinary level of OCB by placing their own life at risk. Analysis of this study indicates that the OCB within the nurses during the COVID period originated from their commitment, not from the emotional labor strategy, which they used to select as a coping mechanism. However, one year later, it has affected their mental health and lowered their life satisfaction.

The purpose of this study is to make property price forecasts for the Chinese housing market that has grown rapidly in the last 10 years, which is an important concern for both government and investors.

This study examines Gaussian process regressions with different kernels and basis functions for monthly pre-owned housing price index estimates for ten major Chinese cities from March 2012 to May 2020. The authors do this by using Bayesian optimizations and cross-validation.

The ten price indices from June 2019 to May 2020 are accurately predicted out-of-sample by the established models, which have relative root mean square errors ranging from 0.0458% to 0.3035% and correlation coefficients ranging from 93.9160% to 99.9653%.

The results might be applied separately or in conjunction with other forecasts to develop hypotheses regarding the patterns in the pre-owned residential real estate price index and conduct further policy research.

This study aims to undertake a review of how carbon trading contributes to a reduction in emission of greenhouse gases (CHGs).

A narrative literature review approach was adopted to identify and synthesise existing literature using the Scopus and Web of Science databases. Articles were limited to the past 10 years to obtain the most current literature. The various ways in which carbon trading leads to reductions in emissions were identified and discussed.

The results showed that the main ways in which carbon trading contributes to reductions in emissions are through innovation in low-carbon technologies, restoration of ecosystems through offset money, development of renewable and clean energy and providing information on investment related to emissions.

The value of this study is to contribute to the built environment’s climate change mitigation agenda by identifying the role of carbon trading.

The output of this research identifies and contextualises the role carbon trading plays in the reduction of CHG emissions.

This research aims to assess how well the e-governance initiative aligns with its design expectations and on-ground realities for property management to redevelop Delhi, the mega-city of India. Additionally, the study proposes strategic interventions to address any gaps identified, aiming to improve the project’s effectiveness and success.

The research employs a singular exploratory case study methodology to scrutinise the e-governance initiative undertaken by the Government of India to confer property rights. Specifically, the study utilises a qualitative research approach known as design-reality gap (DRG) analysis. The study draws from primary and secondary sources using a mixed-methods data collection strategy. Primary data are gathered through a purposeful and snowball sampling method involving direct engagement with respondents, whilst secondary data are sourced from the project portal.

The investigation reveals a substantial disparity between the planning and execution phases of e-governance projects. This incongruity predominantly manifests in the domains of process, staffing and skills, as well as objectives and values. The study further finds that strategic collaboration amongst stakeholders and the sustainability of decisions are the most critical factors in ensuring the success of e-governance initiatives and bridging the DRG of e-governance projects.

This research highlights the complex challenges faced by e-governance projects in technical, human and organisational aspects. The successful implementation and sustainability of these projects require effective strategies to overcome challenges, which management should proactively identify and act on. To improve services, beneficiaries should be encouraged to provide feedback and suggestions, as they play a crucial role in service enhancement. A dynamic feedback loop would be established by creating a two-way interaction between beneficiaries and service providers, leading to iterative service improvement. It is important to note that the study’s findings are more illustrative than conclusive due to the moderate sample size, reflecting its limitations.

The research contributes to the scholarly discourse on e-governance and policy implementation by comprehensively examining the alignment between conceptual design and real-world execution. It also introduces a normalised scale for the DRG framework, mapping its dimensions to deduce specific outcomes. This innovative approach enhances the research’s originality and value, offering insights applicable in both academic and practical domains.

The integration of social media and e-commerce has resulted in a rising phenomenon among individual content providers (ICPs), who used to offer free content, to provide consumers with paid content, such as online courses, Q&As or consultations. Despite the prevalence of ICPs’ content monetization, empirical research has rarely studied its underlying mechanism. This paper examines how the characteristics of free content contributed by ICPs on social media platforms influence their paid content sales, focusing on the perspective of human brand.

The empirical setting is an online knowledge exchange platform, where users are allowed to provide free content (e.g. answers) on the social media platform and launch paid content (e.g. lectures) on the e-commerce platform. A machine learning technique is employed to construct measures for the characteristics of free content, and fixed-effects estimation is presented to confirm which factors have a significant influence on the sales of paid content.

The empirical results show that the quality, diversity and expertness of free content have a significant positive impact on the sales of the ICP-paid content, with the brand popularity of ICP playing a mediating role.

This study is the first attempt to demystify the relationship between content contribution and ICPs’ content monetization from the perspective of human brand. The findings validate the effectiveness of the “Selling by Contribution” strategy and provide valuable insights for ICPs and social media platforms.

We investigate the impact of private credit expansion and contraction on the unemployment rate in Economic Community of West African States (ECOWAS) countries.

Credit expansion and contraction are measured using a three-level criterion. The fixed effect panel regression model was used to estimate the impact of private credit contraction and expansion on the unemployment rate in ECOWAS countries.

Private credit contraction significantly increases the unemployment rate in ECOWAS countries. Private credit expansion does not have a significant effect on the unemployment rate. Real GDP growth has a significant negative effect on the unemployment rate which supports the prediction of the Okun’s Law while the inflation rate has a positive and insignificant effect on the rate of unemployment in ECOWAS countries which contradicts the prediction of the Phillips curve.

Policymakers in ECOWAS countries need to be cautious when introducing policies that lead to private credit contraction as it could increase unemployment. Policymakers in ECOWAS countries should also find the “threshold” below which private credit contraction will worsen the unemployment rate and introduce policy measures to ensure that private credit contraction does not fall below the threshold.

The literature has not examined the factors leading to tight labor markets or unemployment in West African countries.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-12-2023-0939.

This study aims to deliver a new perspective on how the interaction of independent and cooperative innovation affects firm digitization. Based on resource constraint theory, this study argues that the aforementioned interaction negatively affects firm digitization. The moderating role of managerial discretion is also discussed in light of the principles of the awareness-motivation-capability (AMC) framework.

The proposed hypotheses are empirically tested using a negative binomial modeling approach. The data used are from A-share listed companies in China’s Shanghai and Shenzhen stock markets from 2006 to 2020.

This study suggests that the interaction of independent innovation and cooperative innovation negatively impacts digitization. In addition, this study argues that environmental discretion and organizational discretion weaken the negative impact of the mentioned interaction on digitization. However, additional discretion in the Chinese context has no effect on above relationships.

This study explores the impact of the interaction of independent and cooperative innovation on digitization and incorporates managerial discretion into this framework based on the AMC framework.

The study aims to optimize truck routes by minimizing social and economic costs. It introduces a strategy involving diverse drones and their potential for reusing at DNs based on flight range. In HTDRP-DC, trucks can select and transport various drones to LDs to reduce deprivation time. This study estimates the nonlinear deprivation cost function using a linear two-piece-wise function, leading to MILP formulations. A heuristic-based Benders Decomposition approach is implemented to address medium and large instances. Valid inequalities and a heuristic method enhance convergence boundaries, ensuring an efficient solution methodology.

Research has yet to address critical factors in disaster logistics: minimizing the social and economic costs simultaneously and using drones in relief distribution; deprivation as a social cost measures the human suffering from a shortage of relief supplies. The proposed hybrid truck-drone routing problem minimizing deprivation cost (HTDRP-DC) involves distributing relief supplies to dispersed demand nodes with undamaged (LDs) or damaged (DNs) access roads, utilizing multiple trucks and diverse drones. A Benders Decomposition approach is enhanced by accelerating techniques.

Incorporating deprivation and economic costs results in selecting optimal routes, effectively reducing the time required to assist affected areas. Additionally, employing various drone types and their reuse in damaged nodes reduces deprivation time and associated deprivation costs. The study employs valid inequalities and the heuristic method to solve the master problem, substantially reducing computational time and iterations compared to GAMS and classical Benders Decomposition Algorithm. The proposed heuristic-based Benders Decomposition approach is applied to a disaster in Tehran, demonstrating efficient solutions for the HTDRP-DC regarding computational time and convergence rate.

Current research introduces an HTDRP-DC problem that addresses minimizing deprivation costs considering the vehicle’s arrival time as the deprivation time, offering a unique solution to optimize route selection in relief distribution. Furthermore, integrating heuristic methods and valid inequalities into the Benders Decomposition approach enhances its effectiveness in solving complex routing challenges in disaster scenarios.

This paper studies the optimal decision-making and coordination problem of a dual-channel fresh agricultural product (FAP) supply chain. The purpose is to analyze the impact of information sharing on optimal decisions and propose a coordination mechanism to encourage supply chain members to share information.

The two-echelon dual-channel FAP supply chain includes a manufacturer and a retailer. By using the Stackelberg game theory and the backward induction method, the optimal decisions are obtained under information symmetry and asymmetry and the coordination contract is designed.

The results show that supply chain members should comprehensively evaluate the specific situation of product attributes, coefficient of freshness-keeping cost and network operating costs to make decisions. Asymmetric information can exacerbate the deviation of optimal decisions among supply chain members and information sharing is always beneficial to manufacturers but not to retailers. The improved revenue-sharing and cost-sharing contract is an effective coordination mechanism.

The conclusions can provide theoretical guidance for supply chain managers to deal with information asymmetry and improve the competitiveness of the supply chain.

This paper combines the three characteristics that are most closely related to the reality of supply chains, including horizontal and vertical competition of different channels, the perishable characteristics of FAPs and the uncertainty generated by asymmetric demand information.

Grounded in the framework of social learning theory (SLT), the current study explores the impact of leaders’ unethical proorganizational behavior (UPB) on their subordinates’ self-management and moral self-efficacy, which, in turn, affect knowledge hiding and sharing among followers. This study aims to examine how instrumental thinking influences the relationship between leader UPB and subordinate behaviors, shaping knowledge sharing and hiding.

Using a longitudinal approach, this research uses a two-wave data collection strategy with a one-month interval. The study cohort comprises 378 employees drawn from technology service firms situated in China.

Empirical findings confirm that leader UPB is linked to increased follower self-management and knowledge hiding, as well as reduced moral self-efficacy. Instrumental thinking moderates these effects, amplifying knowledge hiding and diminishing moral self-efficacy while reducing knowledge sharing.

The study contributes to the existing literature on UPB by offering insights into the distinct consequences of leader UPB on knowledge-related behaviors of followers. Furthermore, the exploration of employees’ instrumental thinking in the context of leader UPB underscores the strategic manipulation of knowledge to fulfill individual goals, thereby enriching the underpinnings of the SLT. The study underscores the imperative for organizations to grasp the implications of UPB and underscores the necessity for stringent ethical frameworks to mitigate its deleterious impact.

The study contributes to the existing literature on UPB by offering insights into the distinct consequences of leader UPB on knowledge-related behaviors of followers. Furthermore, the exploration of employees’ instrumental thinking in the context of leader UPB underscores the strategic manipulation of knowledge to fulfill individual goals, thereby enriching the underpinnings of the SLT. The study underscores the imperative for organizations to grasp the implications of UPB and underscores the necessity for stringent ethical frameworks to mitigate its deleterious impact.

The present study addresses a gap in the current literature by elucidating the multifaceted outcomes of leaders’ UPB on paradoxical behaviors related to knowledge sharing and hiding among followers. This nuanced examination underscores the need to comprehend the intricate contingencies that accentuate the effects of UPB, particularly in the realm of leadership dynamics. Grounded in SLT, this study delves into leadership dynamics and ethical decision-making.