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This article investigates the long-run impact of remittance inflows on agricultural productivity (AGP) in emerging Asian economies (Bangladesh, Sri Lanka, Malaysia, India, Nepal, Philippines, Pakistan, and Vietnam), employing a panel dataset from 2000 to 2018.

This study initially applies cross-sectional dependence (CSD), second-generation unit root, Pedroni, and Westerlund panel co-integration techniques. Next, it uses the augmented mean group (AMG) and common correlated effect mean group (CCEMG) methods to investigate the long-term impact of remittance inflows on AGP while controlling for several other important determinants of agricultural growth, such as cultivated area, fertilizers, temperature change, credit, and labor force.

The empirical findings are as follows: The results first revealed the existence of CSD and long-term co-integration between AGP and its determinants. Second, remittance inflows significantly boosted AGP, indicating that remittance inflows played a crucial role in improving AGP. Third, global warming (changes in temperature) negatively impacts AGP. Finally, additional critical elements, for instance, cultivated area, fertilizers, credit, and labor force, positively affect AGP.

This study suggests that policymakers of emerging Asian economies should develop an exclusive remittance-receiving system and introduce remittance investment products to utilize foreign funds and mitigate agricultural production risks effectively.

This is the first empirical examination of the long-term impact of remittance flows on agricultural output in emerging Asian economies. This study utilized robust estimation methods for panel data sets, such as the Pedroni, Westerlund, AMG, and CCEMG tests.

This research was initiated by motivation from a real business problem that delves into lean management practices in dairy farm operations. It investigates how lean management practices can be applied as an improvement strategy in the dairy business to evaluate its impact on performance, where profitability is a decisive factor.

Based on the qualitative design, a 5-phase action research methodology was used in this study, where multiple data collection sources were used, including focus group discussions, on-site observation or Gemba walks and process mapping. The impact is evaluated by comparing the key performance measures with the same period before and after research.

The research revealed that lean management practices can significantly improve dairy business performance. It explained vital aspects of lean management practices and their sequence with examples of first-hand applications. It explained, how lean management practices were applied in dairy farm operations. Furthermore, the research resulted in significant benefits, in terms of quality, cost and profitability.

This research was conducted in a real business setting in the field environment, to improve dairy business performance. It was a distinctive application of lean management practices to solve a national problem. This could be used as a road map to bring continuous improvement at the national level to improve the performance of food value chains.

This research is unique because it addresses the methodological, population and empirical gaps in dairy farm operations. It adds value to the existing knowledge base by sharing best practices, developed and implemented for the first time to the best of our knowledge, like high-level process mapping and performance measures at different levels. Furthermore, the solutions can be simulated in related farm operations to bring breakthrough improvements in dairy business performance.

As a result, the current study attempted to investigate the impact of green human resource (GHR) practices on long-term performance, and the path has been explained through organizational identification, which is supported by social identity theory.

To achieve the present study's primary goal, data were obtained from manufacturing businesses and analyzed using partial least square (Smart PLS) on the data of 284 Pakistani small and medium-sized enterprises (SMEs) registered with the small and medium-sized enterprises development authority (SMEDA).

As a result, the findings show that organizational identification explains the indirect relationship between sustainable performance and green human resource management (GHRM).

To limit the limited negative effect on the environment and society, the findings provide several suggestions for the government authorities and policymakers to adopt green practices and policies.

Green practices are essential for a company to limit its negative environmental effect. Environmental critical problems among shareholders put pressure on the firm to implement GHR practices and organizational identification with long-term success.

This study aims to determine the factors that influence investors’ behavioral intentions in investing in green retail sukuk through the constructs offered by the extended pro-environmental planned behavior (PEPB) theory and adding several other constructs such as perceived benefit (PB), perceived risk (PR) and religious value (RV).

Non-probability sampling was used to collect data from 460 Muslims living on Java who had invested in green sukuk retail and had a basic understanding of it as an alternative Islamic investment instrument. PLS-SEM was used to test the data with SmartPLS 3.0.

Perceived authority support (PAS) and perceived environmental concern (PEC) positively and significantly affect attitude (AT), subjective norm (SN) and perceived behavioral control (PBC). This study also shows that SN, PBC, PB, PR and RV boost INT significantly. AT has a positive but insignificant effect.

This study has limitations from the demographic aspect of respondents who only accommodate respondents who are Muslim and live in Java Island.

This research suggests ways to socialize green sukuk investment to the public as potential investors by describing environmental benefits and how retail green sukuk can benefit investors and the environment. Competent parties who understand Islamic finance, and muamalah contracts can socialize beginner voters who do not understand the risks and rewards of green sukuk investments.

This research suggests ways to socialize green sukuk investment to the public as potential investors by describing environmental benefits and how retail green sukuk can benefit investors and the environment.

This study introduces environmental-based constructs PAS and PEC, which are infrequently used in research models that measure the intention to invest in green investment instruments like green sukuk. Additional constructions like PB, PR and RV enhance research results.

The spectrum resources are becoming increasingly scarce and underutilized, and cooperative spectrum sensing (CSS) in cognitive wireless sensor networks (CWSNs) offers many solutions with good results, but this paper aims to address the significant issue of CSS in the context of low signal-to-noise ratio (SNR).

This study proposes Pearson Correlation Coefficient (PCC) to obtain value feature values under the Rayleigh channel model, which are then used for Memorial K-means Clustering (MKC) analysis of CSS in CWSNs at low SNR regimes. In addition, MKC algorithm is used for training and converted it into supervised model.

A series of numerical simulation results demonstrate that the correctness and effectiveness of the proposed MKC, especially the detection and false alarm probabilities in a low SNR condition. The detection probability is increased by 5%–12% at low SNR compared with other methods.

The MKC algorithm can reduce the impact of randomness on the clustering centers for multiple groups, which combined with PCC can effectively reduce the influence of noise at low SNR, and the unsupervised transformed model effectively reducing the complexity of re-discrimination.

This study employs the norm activation model (NAM) and the elaboration likelihood model (ELM) to investigate the drivers of consumers’ buying intent (BUI) for eco-friendly products (EFP). The primary emphasis is on eco-consciousness, which includes environmental literacy (ENL) and environmental concern (ENC). The research further examines the mediating role of ENC in the relationship between ENL and BUI while also considering the sequential mediation effects involving both ENC and eco-responsible practices (ERP). Additionally, the study explores the moderating influence of attitude (ATT) on the relationship between ENC and ERP and its effect on ERP and BUI.

Employing purposive sampling, 412 online survey responses were statistically analysed using partial least squares structural equation modelling (PLS-SEM).

The findings of this study demonstrate that ENL exerts a significant and positive influence on ENC, subsequently enhancing BUI. Moreover, there is a positive relationship between ENC and ERP and ERP and BUI. This research further indicates that ENC positively mediates the relationship between ENL and BUI. Additionally, ENC and ERP sequentially and positively mediate the relationships involving ENL and BUI. Furthermore, the results suggest that the strength of the association between ENC and ERP marginally decreases (trivial), and the strength of the relationship between ERP and BUI decreases (small).

This study advances the existing body of knowledge by integrating NAM and ELM to examine the drivers of consumers’ BUI toward EFP thoroughly. The research offers novel insights into the relationship between ENL and ENC and their effects on ERP and BUI, underscoring the significant role of consumers’ eco-consciousness. The findings have practical implications for businesses and policymakers who seek to formulate strategies that align with consumers’ psychological, cognitive, and behavioural processes in the context of Sustainable Development Goal 12 (SDG12), which can contribute to the global effort to foster more eco-friendly products and a sustainable future.

This research, grounded in the conservation of resources (COR) theory, investigates the impact of supervisor incivility on emotional exhaustion and nurses’ job neglect. It also examines the buffering roles of resilience and professional calling in shaping the relationship between supervisor incivility and emotional exhaustion, as well as between emotional exhaustion and nurses’ job neglect.

The study uses multisource and time lag data from registered nurses (N = 426) using self-administered questionnaires. At time T1, nurses self-report about resilience, supervisor incivility, emotional exhaustion and professional calling. At time T2, they rate each other’s job neglect in pairs (N = 213).

The findings indicate a significant positive relationship between supervisor incivility and nurses’ job neglect partially mediated by emotional exhaustion. Additionally, resilience and professional calling function as strong buffers, mitigating the effects of supervisor incivility on emotional exhaustion and job neglect, respectively.

The contribution of the study lies in its exploration of the underlying COR, thus connecting supervisor incivility to job neglect among nurses, offering valuable insights into the mediating role of emotional exhaustion and the moderating effects of resilience and professional calling. These findings extend the theoretical understanding of workplace incivility and provide actionable implications for fostering supportive environments in healthcare settings. However, the reliance on convenience sampling and a relatively small sample size (N = 426) limits the generalizability of the results. Future research should address these limitations by employing larger, more diverse samples to validate and expand upon these findings.

The study offers practical implications for healthcare organizations by highlighting the need to address supervisor incivility through training programs, resilience-building initiatives and fostering a sense of professional calling among nurses. These interventions can mitigate emotional exhaustion, reduce job neglect and promote a supportive work environment, ultimately enhancing nurse well-being and patient care quality.

This study from the perspective of COR theory contributes uniquely to the literature by bridging gaps in understanding how supervisor incivility impacts nurses’ job neglect via emotional exhaustion which remained a relatively underexplored area. It also advances knowledge by introducing resilience and professional calling as moderators, providing a comprehensive framework for addressing the adverse effects of workplace incivility in the healthcare sector.

This study examines the underlying relationships between the critical factors of building information modeling (BIM) implementation and the factors' groupings among architecture, engineering and construction (AEC) organizations in Saudi Arabia. The objectives of the study are to (1) identify the critical factors for BIM implementation, (2) analyze the interrelationships between the critical factors and (3) compare the critical factors between the different organizational characteristics.

First, potential factors were identified through a systematic literature review and interviews with AEC professionals. Then, a questionnaire survey was sent to AEC professionals and the collected data were analyzed using the following techniques and tests: mean score ranking, standard deviation, normalized value, factor analysis (FA), analysis of variance (ANOVA) and post-hoc Tukey test.

The analyses show that 14 factors are critical for BIM implementation in Saudi Arabia. The top critical factors include the existence of standard contracts on data security and user confidentiality, consistent views on BIM among stakeholders and the availability of guidelines for implementing BIM. Of the 14 critical factors, 9 can be grouped into 4 underlying factors: environmental, governmental, legal and organizational. The analysis shows that the criticality of the most critical factors grouped by the FA varies between different levels of BIM competency. Finally, the presence of public–private partnerships (PPPs) in realizing BIM projects is a new and emerging critical factor for BIM implementation in Saudi Arabia.

This study differs from prior works on BIM implementation in Saudi Arabia by using FA to explore the underlying relationships among factors of BIM implementation and the factors' groupings. Based on the FA results, a roadmap for implementing the BIM was developed. These findings will help to purposefully and efficiently customize BIM implementation strategies and initiatives to ensure successful BIM implementation in Saudi Arabia.

By addressing the dearth of literature on the subject of cybersecurity risks and artificial intelligence (AI), this study aims to close a research gap by concentrating on the ever-changing environment of online social networks (OSNs) and technology. The main goals are to classify cyberattacks into categories like malware, phishing/spam and network intrusion detection; to identify efficient algorithms for preventing cyber threats; to review relevant literature from 2019 to 2020; and to use machine learning algorithms to detect suspicious behavior related to malware. The study offers a novel framework that suggests particular machine learning algorithms for every kind of cyber threat, hence improving cybersecurity knowledge and reaction capacities. This makes the research useful for examining the impact of cybersecurity on smart cities.

Thirty papers have been examined on AI and machine learning algorithms, including K-nearest-neighbor (KNN), convolutional neural networks (CNN) and Random Forest (RF), that were published in 2019 and 2020. Using analytical software (NVivo), a qualitative approach is used to retrieve pertinent data from the chosen research. The researchers divide cyberattacks into three groups: network intrusion detection, phishing/spam and malware.

The study’s conclusions center on how AI and machine learning algorithms linked to cybersecurity are reviewed in the literature, how cyberattacks are classified and how an inventive framework for identifying and reducing risks is proposed. This makes the research useful for researching the implications of cybersecurity for smart cities.

The practical implications of this research are noteworthy, particularly in the realms of technology, AI, machine learning and innovation. The utilization of the NVivo technique enhances decision-making in uncertain situations, making the study’s results more reliable. The findings showcase the applicability of tools in analyzing malicious cyberattacks to address issues related to social media attacks, emphasizing their practical utility. The study’s relevance is further highlighted by a real-world example, where a Kuwaiti public sector fell victim to a malware attack, underlining the importance of cybersecurity measures aligned with the New Kuwait 2035 strategic development plan. The innovative framework presented in the research guides the selection of algorithms for detecting specific malicious attacks, offering practical insights for securing information technology (IT) infrastructure in Kuwait.

The rapid digitization in Kuwait, accelerated by the COVID-19 pandemic, underscores the pivotal role of technology in government services. Ma’murov et al. (2023) emphasize the significance of digitization, particularly in accessing and verifying COVID-19 information. The call for a dedicated digital library for preserving pandemic-related material aligns with the evolving digital landscape. Cybersecurity emerges as a critical concern in Kuwait and the Gulf Cooperation Council (GCC), necessitating transnational cooperation (Nasser Alshabib and Tiago Martins, 2022). In the local context, the inefficiency of information security systems and low awareness among government employees pose cybersecurity challenges (Abdulkareem et al., 2014). Social media’s role during the pandemic highlights its significance, yet the need for cybersecurity in this domain remains underexplored (Ma’murov et al., 2023; Safi et al., 2023).

The unique aspect of the paper is its in-depth investigation of the relationship between cybersecurity and AI in OSNs. It uses a special application of machine learning methods, including CNN, RF and KNN, to identify suspicious behavior patterns linked to malware. The detailed analysis of 30 research papers released between 2019 and 2020, which informs the choice of suitable algorithms for diverse cyber threats, further emphasizes the study’s uniqueness. The novel framework that has been suggested categorizes assaults and suggests certain machine learning techniques for identification, offering a useful instrument to improve comprehension and reactions to a variety of cybersecurity issues.

The current study used the social exchange theory to examine the dimensional impact of total quality management (TQM) on the organisational citizenship behaviour for the environment (OCBE) of employees through the mediatory role of environmental corporate social responsibility (ECSR).

The data were collected from 311 employees of ISO-14001-certified manufacturing firms in Pakistan. Both symmetrical partial least squares structural equation modelling (PLS-SEM) and asymmetrical fuzzy-set qualitative comparative analysis (fsQCA) methods were applied to test the proposed hypotheses to enhance the robustness of the results.

PLS-SEM results discovered that all dimensions of TQM, like process management, leadership, human resource management, customer focus, information and analysis and strategic planning, foster the OCBE through the mediation role of ECSR. The robustness of the findings was manifold when fsQCA results complemented the results by discovering that all six dimensions have been identified as sufficient conditions and some as necessary conditions to drive the OCBE.

The theoretical contribution of this study sheds light on TQM's function in boosting OCBE through the mediation of ECSR. Practically, the business managers may utilise TQM as a strategy to foster the OCBE in order to mitigate environmental damages of their organisations’ operations by instilling OCBE among the employees.