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This study aims to examine the effects of climate change and agricultural technologies on crop production in Vietnam for the period 1990–2018.

Several econometric techniques – such as the augmented Dickey–Fuller, Phillips–Perron, the autoregressive distributed lag (ARDL) bounds test, variance decomposition method (VDM) and impulse response function (IRF) are used for the empirical analysis.

The results of the ARDL bounds test confirm the significant dynamic relationship among the variables under consideration, with a significance level of 1%. The primary findings indicate that the average annual temperature exerts a negative influence on crop yield, both in the short term and in the long term. The utilization of fertilizer has been found to augment crop productivity, whereas the application of pesticides has demonstrated the potential to raise crop production in the short term. Moreover, both the expansion of cultivated land and the utilization of energy resources have played significant roles in enhancing agricultural output across both in the short term and in the long term. Furthermore, the robustness outcomes also validate the statistical importance of the factors examined in the context of Vietnam.

This study provides persuasive evidence for policymakers to emphasize advancements in intensive agriculture as a means to mitigate the impacts of climate change. In the research, the authors use average annual temperature as a surrogate measure for climate change, while using fertilizer and pesticide usage as surrogate indicators for agricultural technologies. Future research can concentrate on the impact of ICT, climate change (specifically pertaining to maximum temperature, minimum temperature and precipitation), and agricultural technological improvements that have an impact on cereal production.

To the best of the authors’ knowledge, this study is the first to examine how climate change and technology effect crop output in Vietnam from 1990 to 2018. Various econometrics tools, such as ARDL modeling, VDM and IRF, are used for estimation.

The energy generation process through photovoltaic (PV) panels is contingent upon uncontrollable variables such as wind patterns, cloud cover, temperatures, solar irradiance intensity and duration of exposure. Fluctuations in these variables can lead to interruptions in power generation and losses in output. This study aims to establish a measurement setup that enables monitoring, tracking and prediction of the generated energy in a PV energy system to ensure overall system security and stability. Toward this goal, data pertaining to the PV energy system is measured and recorded in real-time independently of location. Subsequently, the recorded data is used for power prediction.

Data obtained from the experimental setup include voltage and current values of the PV panel, battery and load; temperature readings of the solar panel surface, environment and the battery; and measurements of humidity, pressure and radiation values in the panel’s environment. These data were monitored and recorded in real-time through a computer interface and mobile interface enabling remote access. For prediction purposes, machine learning methods, including the gradient boosting regressor (GBR), support vector machine (SVM) and k-nearest neighbors (k-NN) algorithms, have been selected. The resulting outputs have been interpreted through graphical representations. For the numerical interpretation of the obtained predictive data, performance measurement criteria such as mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE) and R-squared (R²) have been used.

It has been determined that the most successful prediction model is k-NN, whereas the prediction model with the lowest performance is SVM. According to the accuracy performance comparison conducted on the test data, k-NN exhibits the highest accuracy rate of 82%, whereas the accuracy rate for the GBR algorithm is 80%, and the accuracy rate for the SVM algorithm is 72%.

The experimental setup used in this study, including the measurement and monitoring apparatus, has been specifically designed for this research. The system is capable of remote monitoring both through a computer interface and a custom-developed mobile application. Measurements were conducted on the Karabük University campus, thereby revealing the energy potential of the Karabük province. This system serves as an exemplary study and can be deployed to any desired location for remote monitoring. Numerous methods and techniques exist for power prediction. In this study, contemporary machine learning techniques, which are pertinent to power prediction, have been used, and their performances are presented comparatively.

The purpose of this study is to investigate the simultaneous effects of normal wall transpiration, stretching strength parameter, velocity slip and nanoparticles on the flow of a ternary hybrid nanofluid through an elastic surface. The goal is to understand the behavior of the flow field, temperature distribution, skin friction and temperature gradient under these conditions, and to explore the existence and nature of solutions under varying parameter values.

The analysis involves expressing the flow field, power-law temperature field, skin friction and temperature gradient in closed-form formulas. The study examines both stretching and shrinking surfaces, distinguishing between unique and dual solutions. The methodology includes deriving exact solutions for exponential and algebraic temperature and temperature rate formulas analytically by deriving the system of governing equations into ordinary differential equations.

The study reveals that for a stretching sheet, the solution is unique, whereas dual solutions are observed for a shrinking surface. Special solutions are provided for various parametric values, showing the behavior of the exponential and algebraic temperature and temperature rate, with a focus on identifying turning points that demarcate the existence and non-existence of single or multiple solutions. The solutions are represented through graphs and tables to facilitate a comprehensive qualitative analysis. The research identifies turning points that determine the presence or absence of single or multiple solutions, uncovering multiple solutions for different parameter sets. These findings are displayed graphically and in tabular form, highlighting the complex interplay between the parameters and the resulting flow behavior.

This analysis contributes to the field by providing new insights into the multiple solution phenomena in ternary hybrid nanofluid flows, particularly under the combined effects of normal wall transpiration, stretching strength, velocity slip and nanoparticle presence. The identification of turning points and the exact solutions for various temperature profiles are of significant value, offering a deeper understanding of the factors influencing the flow and thermal characteristics in such systems. The study’s findings have potential applications in optimizing fluid flow in engineering systems where such conditions are prevalent.

Design cost overrun is one of the prominent factor that can impact the sustainable delivery of the project. It can be encountered due to a lack of information flow, design variation, etc. thereby impacting the project budget, waste generation and schedule. An overarching impact of this is witnessed in the sustainability dimensions of the project, mainly in terms of economic and environmental aspects. This work, therefore, aims to assess the implications of a technological process, in the form of building information modelling (BIM), that can smoothen the design process and mitigate the risks, thus impacting the sustainability of the project holistically.

The identified design risks in construction projects from the literature were initially analysed using a fuzzy inference system (FIS). This was followed by the focus group discussion with the project experts to understand the role of BIM in mitigating the project risks and, in turn, fulfilling the sustainability dimensions.

The FIS-based risk assessment found seven risks under the intolerable category for which the BIM functionalities associated with the common data environment (CDE), data storage and exchange and improved project visualization were studied as mitigation approaches. The obtained benefits were then subsequently corroborated with the achievement of three sustainability dimensions.

The conducted study strengthens the argument for the adoption of technological tools in the construction industry as they can serve multifaceted advantages. This has been shown through the use of BIM in risk mitigation, which inherently impacts project sustainability holistically.

The impact of BIM on all three dimensions of sustainability, i.e. social, economic and environmental, through its use in the mitigation of critical risks was one of the important findings. It presented a different picture as opposed to other studies that have mainly been dominated by the use of BIM to achieve environmental sustainability.

The disconnect between producers and consumers is a fundamental issue causing irregularities, inefficiencies and leakages in the agricultural sector, leading to detrimental impacts on all stakeholders, particularly farmers. Despite the potential benefits of Metaverse technology, including enhanced virtual representations of physical reality and more efficient and sustainable crop and livestock management, research on its impact in agriculture remains scarce. This study aims to address this gap by identifying the critical success factors (CSFs) for adopting Metaverse technology in agriculture, thereby paving the way for further exploration and implementation of innovative technologies in the agricultural sector.

The research employed integrated methodology to identify and prioritise critical success criteria for Metaverse adoption in the agricultural sector. By adopting a mixed-method technique, the study identified a total of 15 CSFs through a literature survey and expert consultation, focusing on agricultural and technological professionals and categorising them into three categories, namely “Technological”, “User Experience” and “Intrinsic” using Kappa statistics. Further, the study uses grey systems theory and the Ordinal Priority Approach to prioritise the CSFs based on their weights.

The study identifies 15 CSFs essential for adopting Metaverse technology in the agricultural sector. These factors are categorised into Technological, User Experience-related and Intrinsic. The findings reveal that the most important CSFs for Metaverse adoption include market accessibility, monetisation support and integration with existing systems and processes.

Identifying CSFs is essential for successful implementation as a business strategy, and it requires a collaborative effort from all stakeholders in the agriculture sector. The study identifies and prioritises CSFs for Metaverse adoption in the agricultural sector. Therefore, this study would be helpful to practitioners in Metaverse adoption decision-making through a prioritised list of CSFs in the agricultural sector.

The study contributes to the theory by integrating two established theories to identify critical factors for sustainable agriculture through Metaverse adoption. It enriches existing literature with empirical evidence specific to agriculture, particularly in emerging economies and reveals three key factor categories: technological, user experience-related and intrinsic. These categories provide a foundational lens for exploring the impact, relevance and integration of emerging technologies in the agricultural sector. The findings of this research can help policymakers, farmers and technology providers encourage adopting Metaverse technology in agriculture, ultimately contributing to the development of environment-friendly agriculture practices.

The purpose of this study is to develop and assess the effects of unified theory of acceptance and use of technology (UTAUT 2) constructs – effort expectancy (EE), social influence (SI) and hedonic motivation (HM) – on behavioural intention (BI), as well as the impact of innovation resistance theory (IRT) constructs – usage barrier (UB) and tradition barrier (TB) – on innovation resistance (IR) behavior in the context of digital zakat payment in Aceh. In addition, this study also examines how knowledge of fiqh zakat influences both BI and IR.

This was a quantitative study including 350 Acehnese persons who paid zakat online. This research used a Likert scale, and the sampling technique was purposive sampling applied for the Acehnese people. The research respondents were civil servants, private employees, BUMN employees (employees of State-Owned Enterprises), merchants, restaurant owners, professionals and other occupations who had paid professional zakat through a digital system mechanism. The data were analysed using partial least squares structural equation modelling.

This research found that the constructs built through the theory of UTAUT 2 explained the position of the EE variable, which had a significant effect on BI. On the other hand, the variable of SI and HM did not significantly affect BI in digital zakat payment. This finding demonstrated that BI significantly influenced actual usage (AU). UB and TB had no impact on IR, according to the theoretical framework developed by IRT. Yet, the knowledge about the fiqh zakat (KFZ) significantly affected the AU. In terms of the moderation role, the KFZ variable moderated the relationship between BI and AU. However, the KFZ variable did not moderate the relationship between IR and AU.

This research had limitations and could still be investigated further by involving a larger sample. This study does not include all UTAUT 2 and IRT constructs, but only involves UTAUT 2 and IRT constructs based on the phenomenon of digital zakat paying behavior in the people of Aceh.

This research had a managerial contribution and an evaluation of the use of digital zakat collection services in Aceh and zakat management institutions in various countries. The existence of significant EE should be a reference for zakat institutions to produce continuous payment applications with a higher level of convenience in the future. In addition, the government should encourage more organised fiqh zakat education in society to plan a more optimal zakat collection. The reason for this is that KFZ has been shown to moderate zakat intentions towards actual digital zakat payment behaviour.

The results of this study were then accommodated by the government to design a digital zakat collection system so that it resulted in optimising the collected zakat funds. The greater the zakat funds collected, the greater the economic impact and social resilience of the community was in the midst of the post-covid and global crisis.

This research provided an essential value in the aspect of collecting zakat funds, especially in the study of the behaviour of paying zakat digitally. The theory of planned behaviour predominated in earlier studies that investigated zakat-paying behaviour. Yet, this research was even more focused as it used the constructs of UTAUT 2 and IRT theory and applied the involvement of a moderator variable like fiqh zakat knowledge that was barely discussed.

This article presents a methodological application of decision support with the purpose of identifying and better aligning sustainable banking strategies. Those strategies are based on best practices declared by employees and conducted during efficient periods affecting sustainable production, the health quality of clients, the organization’s profitability and social impact on the local community across different sectors.

The approach involves a two-phase process: first, it employs directional data envelopment analysis (DEA) to benchmark knowledge based on employee opinions gathered through interviews to evaluate strategies related to banking services; then, using the best-worst method and ELECTRE outranking incorporating elements of fuzzy set theory based on an experienced decision-maker’s input, sustainable banking strategies are ranked according the different perspectives for leveraging outputs from the first step.

The outcomes yield a ranking of strategies, emphasizing the crucial role of technology in banking services while highlighting the need for more agile services to ensure customer satisfaction. This underscores the necessity of aligning with the market perspective, as fintech companies are reshaping the socio-technological-environmental landscape of financial services.

The research combined DEA and multicriteria analysis in the context of the banking sector, providing a comprehensive and analytically robust approach translated as a decision-making framework for promoting sustainability by aligning operational efficiency and social responsibility. These tools can guide banks in adopting more sustainable practices that benefit the institution, society and the environment.

Decisions in the banking sector encompass a wide array of concepts, from internal technical factors to customer feedback on service processes and offerings. The proposed approach considers decision analysis in complex environments, and the application developed in this study considered not only internal banking activity-oriented concepts but also the preferences of human agents developing them and the managerial perspective focused on issues involving components associated with sustainability.

By integrating DEA with multicriteria analysis, this study paves the way for a more efficient, environmentally conscious and socially responsible management scenario in the Brazilian banking sector. This research assesses operational efficiency and offers a comprehensive framework for selecting and implementing sustainable practices in the banking sector.

“Know thyself” helps one to decide the career goal of his/her life and enables one to become self-concordant. In this context, the present work aims to discern the childhood interests (CI) of the HR professionals vis-à-vis their relevance.

The current study is grounded on two theoretical perspectives such as Socio-Cognitive Career Theory (SCCT) and Holland Theory of Career Choice (HTCC). In this regard, a mixed methodology has been applied. The research has been carried out in two phases. In the first phase, a focused group of experienced HR professionals was interviewed to understand their CI and their relevance to the HR profession through a qualitative analysis method such as narrative analysis. In the second phase, an Intuitionistic Fuzzy Number-based Full Consistency Method (FUCOM) is applied to find out the dominant CI based on the ratings given by 423 Indian HR professionals.

All professionals agreed that their CI have helped them perform in their profession. The research identifies five themes or main attributes of personality: Creative (Aesthetic-Non Verbal/Cultural), Communicative (Verbal/ Expressive Activities), People friendliness (Social Contribution), Socially Inclusive (Sociological leadership interest) and Physical Activity (Kinesthetic interests) with 24 subattributes (i.e. childhood interests). It is found that intrinsic interests in societal contributions have been a dominant feature since the childhood days of HR professionals. In all cases, FUCOM shows a very small DFC value <0.00005.

The research provides an important direction to the decision-makers for policy making and aspiring professionals an essential impetus to career planning.

This study is a rare one to discern CI using a mixed methodology for Indian HR professionals.