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The study aims to assess the effectiveness of prevailing methods for quantifying physical climate risks. Its goal is to evaluate their utility in guiding financial decision-making within the real estate industry. Whilst climate risk has become a pivotal consideration in transaction and regulatory compliance, the existing tools for risk quantification frequently encounter criticism for their perceived lack of transparency and comparability.

We utilise a sequential exploratory mixed-methods analysis to integrate qualitative aspects of underlying tool characteristics with quantitative result divergence. In our qualitative analysis, we conduct interviews with companies providing risk quantification tools. We task these providers with quantifying the physical risk of a fictive pan-European real estate portfolio. Our approach involves an in-depth comparative analysis, hypothesis tests and regression to discern patterns in the variability of the results.

We observe significant variations in the quantification of physical risk for the pan-European portfolio, indicating limited utility for decision-making. The results highlight that variability is influenced by both the location of assets and the hazard. Identified reasons for discrepancies include differences in regional databases and models, variations in downscaling and corresponding scope, disparities in the definition of scores and systematic uncertainties.

The study assists market participants in comprehending both the quantification process and the implications associated with using tools for financial decision-making.

To our knowledge, this study presents the initial robust empirical evidence of variability in quantification outputs for physical risk within the real estate industry, coupled with an exploration of their underlying reasons.

Though Murabaha financing experienced substantial growth in several majority-Muslim countries, its market share in the Moroccan banking industry is still very narrow than other conventional banks’ instruments. The current research investigated the ability of an extended theory of planned behavior (TPB) framework to explain the main drivers of attitude and intention to use Murabaha financing among Moroccan households. The moderating effect of Islamic religiosity was also scrutinized.

Data were collected via a survey of 512 Moroccan consumers and analyzed using the partial least squares (PLS) technique.

First, attitude toward Islamic banking products is a key predictor of consumer intention to use Murabaha financing. At the same time, consumers’ attitudes are influenced by Islamic financial literacy, subjective norms, behavioral control and profit and loss sharing. Islamic religiosity was also found to positively moderate the link between attitudes towards Islamic banking (IB) and intention to use Murabaha financing, e.g. positive attitudes toward IB are more likely to convert into an intention to use Murabaha financing among Muslim consumers with higher levels of religiosity.

To boost consumers’ intention to use Murabaha financing, Islamic bank managers should consider further investment in advertising to enhance consumers’ awareness about IB products. Islamic banks should also consider digital and social media marketing to increase consumers’ awareness about the products and spread a positive e-WOM with regards to their products. Our findings emphasize the importance of Islamic religiosity in shaping Muslim consumers’ intentions to use Murabaha financing. Islamic banks ought to make sure that Murabaha financing contracts are strictly adherent to and compliant with Shari’ah principles. They should also train their frontline employees on Islamic financing activities so that they can effectively respond to the queries and questions of Murabaha potential consumers.

The study findings contribute to the IB literature by demystifying the key factors shaping Muslim consumers’ intentions to use Murabaha financing. The study also extends the literature by emphasizing Islamic religiosity as a basis for Muslim consumers’ behavior in the context of IB. To the best of our knowledge, this study is among the first to empirically investigate Muslim consumers’ intention to use Murabaha financing in North Africa and the Arab countries.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-10-2022-0680

The global population has been experiencing an unprecedentedly rapid demographic transition as the populations have been growing older in many countries during the current decades. The purpose of this study is to introduce a Grey Exponential Smoothing model (GESM)-based mechanism for analyzing population aging.

To analyze the aging population of Sri Lanka, initially, three major indicators were considered, i.e. total population, aged population and proportion of the aged population to reflect the aging status of a country. Based on the latest development of computational intelligence with Grey techniques, this study aims to develop a new analytical model for the analysis of the challenge of disabled and frail older people in an aging society.

The results suggested that a well-defined exponential trend has been seen for the population ages 65 and above, a total of a million) during 1960–2022; especially, the aging population ages 65 and above has been rising rapidly since 2008. This will increase to 24.8% in 2040 and represents the third highest percentage of elderly citizens living in an Asian country. By 2041, one in every four Sri Lankans is expected to be elderly.

The study proposed a GESM-based mechanism for analyzing the population aging in Sri Lanka based on the data from 1960 to 2022 and forecast the aging demands in the next five years from 2024 to 2028.

This paper addresses the challenges associated with forecasting electricity consumption using limited data without making prior assumptions on normality. The study aims to enhance the predictive performance of grey models by proposing a novel grey multivariate convolution model incorporating residual modification and residual genetic programming sign estimation.

The research begins by constructing a novel grey multivariate convolution model and demonstrates the utilization of genetic programming to enhance prediction accuracy by exploiting the signs of forecast residuals. Various statistical criteria are employed to assess the predictive performance of the proposed model. The validation process involves applying the model to real datasets spanning from 2001 to 2019 for forecasting annual electricity consumption in Cameroon.

The novel hybrid model outperforms both grey and non-grey models in forecasting annual electricity consumption. The model's performance is evaluated using MAE, MSD, RMSE, and R², yielding values of 0.014, 101.01, 10.05, and 99% respectively. Results from validation cases and real-world scenarios demonstrate the feasibility and effectiveness of the proposed model. The combination of genetic programming and grey convolution model offers a significant improvement over competing models. Notably, the dynamic adaptability of genetic programming enhances the model's accuracy by mimicking expert systems' knowledge and decision-making, allowing for the identification of subtle changes in electricity demand patterns.

This paper introduces a novel grey multivariate convolution model that incorporates residual modification and genetic programming sign estimation. The application of genetic programming to enhance prediction accuracy by leveraging forecast residuals represents a unique approach. The study showcases the superiority of the proposed model over existing grey and non-grey models, emphasizing its adaptability and expert-like ability to learn and refine forecasting rules dynamically. The potential extension of the model to other forecasting fields is also highlighted, indicating its versatility and applicability beyond electricity consumption prediction in Cameroon.

Rigorous applications of analytical tools in information systems (IS) research are important for developing new knowledge and innovations in the field. Emerging tools provide building blocks for future inquiry, practice and innovation. This article summarizes the findings of an analysis of the adoption and reporting of partial least squares structural equation modeling (PLS-SEM) analytical tools by Industrial Management & Data Systems authors in the most recent five-year period.

Selected emerging advanced PLS-SEM analytical tools that have experienced limited adoption are highlighted to broaden awareness of their value to IS researchers.

PLS-SEM analytical tools that facilitate understanding increasingly complex theoretical models and deliver improved prediction assessment are now available. IS researchers should explore the opportunities to apply these new tools to more fully describe the contributions of their research.

Findings demonstrate the increasing acceptance of PLS-SEM as a useful alternative research methodology within IS. PLS-SEM is a preferred structural equation modeling (SEM) method in many research settings and will become even more widely applied when IS researchers are aware of and apply the new analytical tools.

Emerging PLS-SEM methodological developments will help IS researchers examine new theoretical concepts and relationships and publish their work. Researchers are encouraged to engage in more complete analyses by applying the applicable emerging tools.

Applications of PLS-SEM for prediction, theory testing and confirmation have increased in recent years. Information system scholars should continue to exercise sound practice by applying these new analytical tools where applicable. Recommended guidelines following Hair et al. (2019; 2022) are included.

This research addresses a need in early childhood education for evidence-based teaching strategies that build emotional self-regulation skills in young children. The intervention assessed in this study focused on increasing the emotion vocabulary of preschool-aged students.

This mixed-methods, quasi-experimental study evaluated the impact a dialogic reading approach combined with direct instruction of emotion words during a shared book-reading activity had on students' emotion vocabulary knowledge. The study was conducted in a licensed daycare center in a suburb of Chicago, Illinois, with ten four- and five-year-old students. Pre- and post-session surveys assessed the intervention's impact on the students' receptive and expressive vocabulary knowledge, and observation notes captured the students' responses to the intervention activities.

The results showed significant increases with small to medium effect sizes between the students’ pre- and post-session survey scores for both receptive and expressive emotion vocabulary knowledge, a strong positive correlation between the level of student engagement during the intervention and their emotion vocabulary assessment scores, and the impact other variables had on the intervention’s effectiveness.

This research provides information on a culturally adaptable and quickly learned teaching strategy that could be used to build emotional self-regulation skills in the early childhood classroom.

This research uniquely applies this intervention as a universal strategy with preschool-aged children.

This study aims to uncover the constraints of automation and the affordances of augmentation related to implementing artificial intelligence (AI)-powered systems across different task types: mechanical, thinking and feeling.

Qualitative study involving 45 interviews with various stakeholders in artistic gymnastics, for which AI-powered systems for the judging process are currently developed and tested. Stakeholders include judges, gymnasts, coaches and a technology vendor.

We identify perceived constraints of automation, such as too much mechanization, preciseness and inability of the system to evaluate artistry or to provide human interaction. Moreover, we find that the complexity and impreciseness of the rules prevent automation. In addition, we identify affordances of augmentation such as speedier, fault-less, more accurate and objective evaluation. Moreover, augmentation affords to provide an explanation, which in turn may decrease the number of decision disputes.

While the unique context of our study is revealing, the generalizability of our specific findings still needs to be established. However, the approach of considering task types is readily applicable in other contexts.

Our research provides useful insights for organizations that consider implementing AI for evaluation in terms of possible constraints, risks and implications of automation for the organizational practices and human agents while suggesting augmented AI-human work as a more beneficial approach in the long term.

Our granular approach provides a novel point of view on AI implementation, as our findings challenge the notion of full automation of mechanical and partial automation of thinking tasks. Therefore, we put forward augmentation as the most viable AI implementation approach. In addition, we developed a rich understanding of the perception of various stakeholders with a similar institutional background, which responds to recent calls in socio-technical research.

The purpose of this study is to test the hypotheses proposed by Konadu et al. (2023) for the first time and provide empirical insight on the subject. Corruption concerns affect all economies, but those attempting to avoid foreign grants are especially vulnerable. Stakeholders in these economies have pushed for more honest public sector (PS) workers and better oversight of public funds in an effort to build a more trustworthy and efficient government to improve PS performance. Just as the mechanisms through which employee integrity (EI) influences work performance (WP) have not been proven empirically, neither has the effect of EI on WP in African economies. Also, how purposeful leadership (PL) interacts with EI to boost WP is yet to be empirically examined in the integrity literature.

This paper surveyed and analysed the responses of 875 workers across the three most corrupt large PS organisations in Ghana using Smart PLS 4. Perceived organisational support and contract fulfilment functioned as control factors influencing job satisfaction (JS, a mediator). Psychological need satisfaction and perceived procedural justice serve as control factors for organisational identification (OI, an additional mediator). Education, tenure, job position, sex and age were used as control variables in WP. Product indicator and variance accounted for (VAF) methods were used to estimate the impacts of moderation and mediation, respectively. A 5% level of significance was determined.

As hypothesised, this study found that EI and WP had a significantly positive connection (ß = 0.119, p = 0.026), and both JS (VAF = 25.16%) and OI (VAF = 39.59%) partially mediated this connection. Moreover, PL positively moderated the EI–JS (ß = 0.155, p = 0.000) and EI–OI (ß = 0.095, p = 0.000) connections.

This paper affords empirical insight on the EI–WP relationship, how this relationship is mediated and how the EI–JS and EI–OI relationships are amplified. In this context, it sheds light on new ways in which EI and WP in the PS are improved. In addition, this paper provides a roadmap for forthcoming academics to test the hypotheses in diverse PS contexts globally to triangulate the results.

Leadership in PS organisations must maintain a “values-grounded approach” to all parts of human resource (HR) practices, including hiring, performance reviews, leadership enhancement programmes, training and promotions, if they are to attract, develop and retain employees who stand for the sector’s ethics and beliefs.

This research gives African nations proof that enhancing EI in the PS is important, and it lays out the many ways in which EI transforms into WP. It also draws attention to the challenges that purposeful leaders may help alleviate and the opportunities that they may present.

To the best of the authors’ knowledge, the hypotheses put forward in the conceptual research by Konadu et al. (2023) are tested empirically for the first time in this study. It also adds to the empirical literature that already exists on EI, JS, OI, WP and PL in the PS. This contributes to the disciplines of integrity, performance and leadership by enhancing theoretical frameworks and expanding upon existing knowledge.

The purpose of this conceptual paper is to increase the clarity and application of the original model and integration table. Specifically, the intent was to increase clarity by highlighting elements left out of the original article (sentence frames), adding additional information on the medium of leadership practice through the addition of Bolman and Deal's leadership lessons, and improving the flow and structure of leader steps as leaders move across the model in the leader sequence of events. Finally, the model was tweaked to adjust the political frame across both direction-giving and empathetic language based on participant feedback and a literature review.

The approach of this conceptual paper is to continue the evolution of the integration of the Four-Frames and Motivating Language Theory (MLT) first presented in DLO in 2019 using updated literature from Bolman and Deal (2019, 2021) and feedback from real-world implementation of the contents from the original article.

The findings of this conceptual paper are in two forms. First, the Version 2.0 integration model illustrates greater clarity and an improved sequence for implementation by leaders to address the pressing issues they face. Second, the illustration of the sentence frames and their evolution from the original article (2019) to the current paper highlights the power and integration of the Four-Frames and Motivating Language Theory regarding their versatility and utilization as a critical thinking and teaching tool for leaders and organizations.

For practitioners, this conceptual paper provides additional information on how to implement the Four-Frames as leaders through the power and influence of leadership communication and action.

The value of the Version 2.0 integrated model continues to leverage the combination of the Four Frames and Motivating Language Theory, which is still unique in the research. This evolution of the integration creates greater clarity in terms of understanding and utilizing leadership, connecting and sequencing the Four-Frames and MLT together, and applying the Four-Frame(s) accurately. This article continues to extend the field of Motivating Language Theory and addresses a gap in the work of the Four Frames – communication.

This study empirically evaluates the effect of China’s 2016 Green Financial System (GFS) framework on corporate green development, focusing on the role of green investment in achieving sustainability.

This study uses a quasinatural experiment design to combine difference-in-difference and propensity score matching methods for analysis. It examines 799 polluting and 1,130 nonpolluting firms from 2013 to 2020, enabling a comprehensive assessment of the GFS framework’s influence.

This study affirms a statistically significant positive influence of the GFS framework on escalating green investment levels in polluting firms. Robust sensitivity analyses, encompassing parallel trend assessment, entropy balancing test, and alternative proxies, corroborate these findings. A mediation analysis identifies the implementation of an environmental management system as the potential underlying mechanism. A cross-sectional analysis identifies high financial slack, high profitability, mandatory CSR regulations, and marketization level as the influencing factors.

The study’s findings have critical implications for policymakers, regulators, and companies. Demonstrating the effectiveness of the GFS framework in driving green investment underscores the importance of aligning financial systems with sustainability goals.

This study contributes novel empirical evidence on the positive effect of China’s GFS framework on corporate green development. The quasinatural experiment design, coupled with comprehensive sensitivity analyses, strengthens the robustness of the findings.