Environmental Decision Research Articles

An examination of the valuation process in a low transparent property market – perspectives of property valuers in Nigeria

PurposeThe finance sector and property market challenges in some global regions have been linked to inefficient property valuation practices. As a result, global valuation professional organisations have set up standards and norms to promote efficient and transparent operations in the property valuation industry. Despite these concerns, valuation industries in some countries still face challenges that threaten their smooth operations. One of such is Nigeria, which faces various problems attributable to its valuation process and regulatory system. Consequently, this paper aims to examine the valuation process in Nigeria with a bid to identify the weaknesses in its valuation process and how it contributes to problems identified in the literature.Design/methodology/approachA qualitative research approach was adopted, and semi-structured interviews were conducted with 12 valuers across different segments of the valuation industry in Nigeria. The data were subjected to thematic analysis using Nvivo 12 software.FindingsOur findings indicate a fundamentally weak valuation system and regulatory system marked by an opaque engagement process, underpricing of valuation services, inefficient domestication of international valuation standards, poor implementation and monitoring system and concerns about the training and certifications to meet global norms. These identified weaknesses contribute to and fuel problems such as client influence and valuation inaccuracy, among others.Practical implicationsThe study has some implications for the valuation professional organisations in Nigeria. The valuation professional organisations should devise systems and enact standards that go beyond solely replicating the IVS and RICS Red Book to effective domestication to suit local norms. Given the inefficient implementation and monitoring system, the use of proptech that supplements legal instruments needs to be adopted. Furthermore, the regulations should be strengthened in line with the trends of sustainability, duty of care and use of data as advocated by the IVSC. This will promote trust in the system and allow global stakeholders to transact more confidently with the Nigerian industry, as the current set-up does not evoke sufficient confidence in the system to deliver excellent and transparent valuation assignments.Originality/valueThis study provides perspective from an untransparent property market on the implications of a poor regulatory system and valuation process for valuers and stakeholders who may rely on valuations conducted in such an environment for decision-making. The findings from this study potentially provide input for the valuation professional organisation in Nigeria in identifying the gaps in their framework and current practices and providing some suggestions to promote improvements.

Conservation agriculture measures as a strategy to create sustainable social and psychological changes in agricultural communities

The main aim of this study was to investigate the potential of encouraging intention to use conservation agriculture technologies to create sustainable social and psychological changes in agricultural communities. The study was carried out using a cross-sectional survey using 384 Iranian landowner farmers as the sample. The research instrument was a researcher-made questionnaire. Furthermore, its validity and reliability were evaluated using different quantitative and qualitative indices. The results revealed that the basic variables of Theory of Planned Behavior including attitude towards conservation agriculture (ACA), subjective norms of conservation agriculture (SNCA), and perceived behavioral control on conservation agriculture (PBCCA) positively and significantly affected the intention towards application of conservation agriculture technologies and practices (IACATP). Comparison of the original and extended versions of Theory of Planned Behavior showed that the inclusion of the variables perceived risk conventional agriculture (PRCA), knowledge about conservation agriculture (KCA), level of education, farm size, and income level in the original Theory of Planned Behavior could increase its explanatory power from 66.4% to 77.3%. This result was one of the most important contributions of the present study that can be considered a turning point for social change interventions in rural and agricultural communities. The study also led to new insights into the mechanisms of encouraging IACATP and social-psychological changes in agricultural communities. These innovative insights can be used by different users such as governmental policy-makers, technology developers, social change interventionists, environmental decision-makers, and researchers.

Cognitive ability in matching with strategic uncertainty: An experimental study

Strategyproof mechanisms have become the predominant choice for educational institutions. However, both laboratory experiments and empirical evidence highlight the persistent occurrence of strategic misreporting, leading to adverse consequences. This underscores the need to examine the reporting strategies of students, particularly those with varying cognitive abilities, across different decision environments. We present an experimental comprehension test of reporting strategies using computerized opponents to precisely control the levels of strategic uncertainty. The results reveal that removing strategic uncertainty does not significantly affect truth-telling rates. However, subjects with median cognitive ability are more truthful under strategic uncertainty, especially when information is incomplete. Additionally, providing information about priorities has a negative and significant impact on truth-telling rates. The findings of this research demonstrate that constructing a simplified market environment using computerized agents and providing relevant feedback to students can effectively enhance their understanding of the matching mechanism and nudge them to make optimal strategy choices.

Economic irrigation water productivity of wheat and potato: An earth observation perspective on policy implications in the Litani Basin, Lebanon

Efficient water management is critical in addressing the challenges posed by water scarcity and resource sustainability in agriculture. However, the utilization of remote sensing technology to monitor and enhance water productivity in data-scarce environments remains an open question. This study underscores the importance of integrating earth observation systems into agricultural water management policies, elucidating their potential to shape crucial policy decisions and investment strategies, particularly in regions facing data limitations. To address this issue, we provide a comprehensive framework that blends remote sensing data with field-based economic information, to develop the concept of economic productivity of irrigation water. Leveraging FAO’s Water Productivity Open-access portal (WaPOR) data, we spatially assess yields, water use, crop water productivity, economic water productivity, and economic irrigation water productivity (EIWP) for wheat and potatoes in the Upper Litani Basin of Lebanon's Bekaa Valley. Early season potatoes outperform irrigated wheat significantly, providing up to ten times higher returns on irrigation water ($6/m3 vs. $0.6/m3). Spatial analysis shows that southern areas have 30 % lower potato EIWP than northern areas of the valley but 55 % higher wheat EIWP, mainly due to higher rainfall. Our findings reveal that merely considering crop water productivity is insufficient when deciding what to plant, as it fails to account for profitability. While transitioning to crops or practices with greater EIWP can enhance economic productivity on the short-term, it may simultaneously lead to increased water consumption. As a result, the shift towards highly water-productive systems may require measures such as controlling irrigated areas or implementing restrictions on water withdrawals to maintain stable water consumption levels. This study offers valuable insights for water resource management, agricultural policy, and sustainable water utilization, especially in semi-arid regions like the Litani Basin, providing a foundation for informed decision-making in these resource-constrained environments.

Impact of adjusted fertility policies on household consumption patterns in China

China's fertility policy adjustments have impacted family fertility decisions but have not reversed the declining birth rate, with economic pressures playing a significant role. This study utilized data from the China Household Finance Survey (CHFS) and treated the implementation of China's "two-child" policy as a natural experiment. Using a propensity score matching-difference-in-differences model, the study analyzed data from four periods before and after the policy change to examine how having underage children and additional children affects household consumption behavior. The results indicate that household consumption increases with the number of children, as families with newborns experience higher overall consumption levels. The addition of a second child amplifies this effect, but per capita consumption expenditure decreases as family size grows. Differences in consumption patterns were observed between families with an additional first child and those with a second child. Furthermore, the impact of additional children on household consumption varies significantly depending on the family's education level. The study suggests that the government should design differentiated fertility policies tailored to family characteristics to reduce economic and childcare pressures and create a more supportive environment for family decision-making.

A Novel TOPSIS Framework for Multi-Criteria Decision Making with Random Hypergraphs: Enhancing Decision Processes

In today’s complex decision-making landscape, multi-criteria decision-making (MCDM) frameworks play a crucial role in managing conflicting criteria. Traditional MCDM methods often face challenges due to uncertainty and interdependencies among criteria. This paper presents a novel framework that combines the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with random hypergraphs to enhance decision processes. In TOPSIS, asymmetry in criterion interactions is typically managed by assigning different weights, while for independent criteria, Euclidean distance introduces geometric symmetry, treating all dimensions (criteria) equally when calculating an alternative’s distance from ideal or negative-ideal solutions. Although assigning weights can partially address asymmetry caused by interdependencies and uncertainties among criteria, it cannot fully account for uncertainty in data and criteria interactions. Our approach integrates random hypergraphs to better capture these relationships, offering a more refined representation of decision problems and improving the robustness of the decision-making process. In this method, we first capture criteria interactions in a random hypergraph. Using properties of the graph and input data, the algorithm then generates weights for interacted groups of criteria. These weights, termed “dynamic weights”, adapt in response to changes in criteria interactions and data, forming the basis for a generalized TOPSIS algorithm. A comparative study with illustrative examples highlights the advantages of this enhanced TOPSIS framework, showing how random hypergraphs expand its analytical capabilities. This research advances the theoretical foundation of MCDM frameworks while offering practical insights for practitioners seeking robust solutions in complex and uncertain decision environments.

Causal Inference and AI: Elevating Decision-Making in Uncertain Environments

Artificial intelligence (AI) has changed the way decisions are made in industries; however, its impact may be limited in situations with uncertainty where cause and effect relationships play a crucial role. While conventional AI models are good at spotting connections between variables they may struggle when it comes to predicting results in fields such as healthcare finance and self-driving vehicles where understanding causation is key. Causal inference an emerging area in AI goes beyond finding correlations and enables systems to make stronger and dependable decisions, in intricate settings. By incorporating reasoning into AI systems operations can enhance their predictive abilities by comprehending the fundamental mechanisms governing cause and effect connections This piece delves into the amalgamation of causal deduction in AI by examining its theoretical groundwork as well as practical implementations and the obstacles and advantages of merging causal understanding with machine learning. Through real life examples and new methodologies such as networks and counterfactual logic establishment this paper underscores how causal inference can enhance AIs decision making skills by rendering them more flexible and efficient, in ambiguous scenarios. Keywords: Artificial Intelligence, Causal Inference, Machine Learning, Decision-Making, Bayesian Networks, Counterfactual Reasoning, Uncertainty, High-Stakes Environments, Predictive Models, Causality.

The value of information in predicting harmful algal blooms

Environmental decision-making is inherently subject to uncertainty. However, decisions are often urgent, and whether to take direct action or invest in collecting additional data beforehand is pervasive. To make this trade-off explicit, the value of information (VoI) theory offers a powerful decision analytic tool to quantify the expected benefit of resolving uncertainty in a decision context. Although it is mainly used in economic contexts, it can be applied to biodiversity conservation and management.In our approach, we evaluate the expected surplus in resolving uncertainty about the occurrence of harmful algal blooms (HABs) in the German North Sea coastal waters and the effect on decision-making. We use an established dynamic foodweb model (NPPZ) with two competing phytoplankton consortia (harmful, non-harmful) and regional monitoring data to analyse the prediction accuracy of different indicators. Our analysis revealed a prediction accuracy of a HAB occurrence of 0.65 % if additional information on zooplankton is included. We then evaluate the effect of reducing uncertainty about these indicators (e.g., through extended monitoring) on management decisions employing a VoI analysis. We find that additional information may lead to an expected welfare gain of up to 2.67 million Euro in our decision context. Our results highlight the significant potential for VoI analysis to enhance decision-making in fishery and ecosystem management and provide insights for future monitoring strategies to mitigate the adverse effects of HABs. This approach contributes valuable methodological insights for optimising management strategies and further emphasises the importance of considering uncertainty in decision-making processes.

Multi-criteria decision making with Hamacher aggregation operators based on multi-polar fuzzy Z-numbers

Multi-polar fuzzy sets are crucial for capturing and representing diverse opinions or conflicting criteria in decision-making processes with greater flexibility and precision. While, Z-numbers are important for effectively modeling uncertainty by incorporating both the reliability of information and its degree of fuzziness, enhancing decision-making in uncertain environments. To date, no model in the literature exhibits the properties of multi-polar fuzzy sets and Z-numbers. In this article, we introduce a new concept of multi-polar fuzzy Z-number and Hamacher operations for multi-polar fuzzy Z-numbers. Based on the Hamacher operations, we propose aggregation operators for multi-polar fuzzy Z-numbers, namely, multi-polar fuzzy Z-number Hamacher weighted averaging operator, multi-polar fuzzy Z-number Hamacher ordered weighted averaging operator, multi-polar fuzzy Z-number Hamacher weighted geometric operator and multi-polar fuzzy Z-number Hamacher ordered weighted geometric operator. Additionally, we develop a decision-making model based on the proposed Hamacher aggregation operators. Further, we apply the proposed technique to a couple of case studies to check the validity and authenticity of the proposed methodology. Finally, we compare the outcomes of the study with several existing techniques to assess the accuracy of the proposed model.

A Consensus Framework for Evaluating Dispute Resolution Alternatives in International Law Using an Interval-Valued Type-2 Fuzzy TOPSIS Approach

This research is motivated by the arbitrary nature of decision-making environments and the dynamic changes in decision patterns, particularly in international dispute resolution. These challenges introduce uncertainties that could be effectively managed by fuzzy logic, which provides a robust framework for evaluating alternatives under multiple criteria. In this study, an Interval-Valued Type-2 Fuzzy TOPSIS approach is proposed to assess various dispute resolution methods, including negotiation, good offices, mediation, international inquiry, conciliation, international organization, arbitration, and international jurisdiction. Common criteria are determined by examining academic literature and by interviewing relevant experts.—cost-efficiency, duration, impartiality, binding nature, and generalizability are considered essential in determining the best resolution method. The proposed method allows for a nuanced evaluation by incorporating both primary and secondary levels of uncertainty, enabling decision-makers to determine the best alternative solution more reliably. This method’s application extends not only to the international law field but also to industrial engineering, where complex, uncertain decision environments require similarly sophisticated multicriteria decision-making tools. By systematically analyzing these resolution methods, this study aims to provide a structured, quantifiable approach that enhances the decision-making process for both international legal practitioners and engineers working with uncertain and dynamic systems. The results of this study ultimately contribute to improved decision-making outcomes and greater efficiency in multidisciplinary problem solving. The assessments of experts in international law, international relations, and political science in their respective fields of expertise have been gathered to form a consensus. This study contributes to the literature as it is the pioneering application of fuzzy multicriteria decision-making techniques in the field of international law. The results of this study imply that the best option from the different decision-maker evaluations is international jurisdiction. Consequently, the utilization of multicriteria decision-making tools can result in more informed and effective decisions in complex and uncertain situations, which is advantageous to both legal practitioners and engineers. Additionally, incorporating different disciplines can help streamline the decision-making process and improve overall efficiency in solving multidisciplinary problems.

Assessing resilience measures in the Austrian dairy supply chain: a case-based simulation integrating strategic, tactical, and operational decisions

Given the increasing complexity of supply chains and the potential for disruptive environmental factors, such as the COVID-19 pandemic, decision-makers must increase their (operational) resilience. In particular, food supply chain actors must adopt proactive and reactive strategies and measures to mitigate disruptions and avoid negative consequences, especially for short-term food supply. We present a comprehensive approach to resilience-related decision-making based on empirical data from Austria's dairy supply chain. The research identifies actionable resilience measures across strategic, tactical, and operational dimensions alongside generic resilience strategies. Once done, we integrate them into a discrete-event simulation model that emulates Austria's dairy supply chain. Using a realistic decision-making environment, the model facilitates quantifying and evaluating the effectiveness of the measures in response to disruptions. Insights from the case study assessed in the simulation model emphasise the importance of preparing for different types of disruptions and impact magnitudes with a diverse set of resilience measures. Furthermore, their integration must be carefully assessed for potential unintended consequences in the post-disruption phase. Effective supply chain resilience-related decision-making requires a thoughtful selection of strategic initiatives combined with tactical and operational implementation across generic resilience strategies to prepare for and respond to disruptions.

Multi-Sensor Fusion and Deep Learning: A New Frontier in Stabilization Algorithm Optimization

The rapid development of automation technology has highlighted the importance of stabilization algorithms in ensuring the stability and functionality of smart devices. These algorithms are critical for maintaining balance and precision, especially in applications such as unmanned aerial vehicles (UAVs), handheld devices, and autonomous systems. This research focuses on the application of stabilization algorithms across various domains, analyzing their principles, advantages, limitations, and potential optimization strategies. The study employs a literature review to investigate the integration of multi-sensor fusion and deep learning techniques to improve system stability. It shows that multi-sensor fusion significantly enhances system robustness by mitigating external disturbances, while deep learning improves autonomous decision-making in dynamic environments, particularly in UAV path planning and task execution. The findings confirm the crucial role of stabilization algorithms in modern systems and provide insights into future optimization directions. By combining deep learning with sensor fusion, future systems are expected to achieve greater stability and autonomy, contributing to the advancement of UAVs and other intelligent systems.

Probabilistic Uncertain Linguistic VIKOR Method for Teaching Reform Plan Evaluation for the Core Course “Big Data Technology and Applications” in the Digital Economy Major

The reform of the teaching plan for the core course “big data technology and applications” in the digital economy major has become a globally recognized challenge. Qualitative data, fuzzy and uncertain information, a complex decision-making environment, and various impact factors create significant difficulties in formulating effective responses to teaching reform plans. Therefore, the objective of this paper is to develop a precision evaluation technology for teaching reform plans in the core course “big data technology and applications”, to address the challenges of uncertainty and fuzziness in complex decision-making environments. In this study, an extended VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) method based on a probabilistic uncertain linguistic term set (PULTS), which is presented for teaching reform plan evaluation. The extended probabilistic uncertain linguistic VIKOR method can effectively and accurately capture the fuzziness and uncertainty of complex decision-making processes. In addition, PULTS is integrated into the VIKOR method to express decision-makers’ fuzzy language preference information in terms of probability. A case study is conducted to verify and test the extended method, and the research results demonstrate that it is highly effective for decision-making regarding teaching reform plans to foster the high-quality development of education, especially in uncertain and fuzzy environments. Furthermore, parameter and comparative analyses verify the effectiveness of the extended method. Finally, the paper outlines directions for future research.

Navigating uncertainty with cybernetics principles: A scoping review of interdisciplinary resilience strategies for rail systems

AbstractCommon difficulties across industries are discovered in data management, where handling the volume, variety, and quality of data is crucial for informed decisions in uncertain environments. In this context, rail management must navigate complex decision‐making to ensure safety, service continuity, and cost‐effectiveness. The 2020 Stonehaven derailment is an example of the increasing vulnerability of rail infrastructure to environmental factors and systemic failures. It emphasizes the need for resilient systems, proficient at preventative maintenance and adaptable to escalating challenges. These matters further accentuate the need for context‐dependent strategies that bridge theoretical insights and practical applications. This scoping review explores strategies for decision‐making under uncertainty across sectors such as civil infrastructure, agriculture, water management, and emergency response. It unfolds a selection of procedures addressing the impacts of extreme weather and other unexpected disruptions. It also sets a foundation for future research to support rail infrastructure adaptation to climate change by advocating the use of cybernetic principles and artificial intelligence (AI) to enhance decision‐making processes. Cybernetics enables collaborative human‐AI methods, improving adaptability and resilience. However, balancing and incorporating diverse stakeholder viewpoints into decision chains remains difficult. While promising, substantial research and system improvements are needed to fully harness the potential of AI.

Youth engagement in global conservation governance.

Youth are increasingly recognized for their important role in shaping environmental decisions surrounding conservation. Regrettably, youth who are crucial decision-makers are often excluded from environmental governance spaces due to structural barriers, both economic and political. As highlighted by recent environmental justice literature, this marginalization hinders their active participation in the decision-making process. The recent publication of the International Union for Conservation of Nature (IUCN) Youth Strategy 2022-2030 has brought prominent environmental organizations into the debate. The IUCN World Conservation Congress (WCC) presents a useful example from which to understand how youth access and participate in decision-making at the highest level of governance in a prominent global conservation organization. We used event ethnography and participant observation methods to study the WCC Forum in Marseille, France (2021). We sought to examine the geopolitical intricacies of power and the underlying inequalities at the root of youth engagement, or lack thereof. We considered the IUCN's engagement with youth, outlining the process from previous resolutions and recommendations to the publication of the IUCN Youth Strategy in 2022. The results from the youth narratives we compiled showed that youth are not a monolith, that tokenism should be challenged, and that youth have agency but require support. We argue that when youth are mobilized in metalevel decision-making spaces, their engagement is stratified and unequal. We situated youth engagement in decision-making through the perspective of environmental organizations as a contribution to environmental governance and youth literature.

A Meta-Analysis of the Review Literature on Multiple-Criteria Decision Aids for Environmental Issues

Environmental decision making is a complex process that requires the consideration of multiple factors. Therefore, Multiple-Criteria Decision Aiding (MCDA) aims to address the challenges of environmental decision making. This paper analyses published review papers that discuss the use of MCDA in environmental problems, with the goal of drawing useful meta-level conclusions. The review papers were categorised by application field and sorted by various criteria. The main findings of each paper were also analysed. The analysis reveals that MCDA publications in the specific domain have shown a strong upward trend. Hybrid MCDA is increasingly being applied as it can cope with the multidimensional challenges of environmental decision making. AHP appears to be the most widespread method. The sustainable energy sector is particularly interested in the use of MCDA. However, while decision-makers extensively use MCDA in environmental problems, its corresponding application in real-world settings is not always satisfactory.

Blockchain’s role in operation strategy of power battery closed-loop supply chain

With the rapid development of the electric vehicle industry, the issue of recycling retired power batteries is becoming increasingly prominent. Considering the adoption of blockchain technology to enhance information traceability for retired power batteries, we construct three closed-loop supply chain decision-making models: a supply chain that does not adopt blockchain technology, a manufacturing enterprise that independently bears the input cost of blockchain technology, and a scenario which both manufacturing and selling enterprises jointly bear the blockchain technology input costs. Through the comparative analysis of the equilibrium solutions of these models, including the profits of each supply chain member, consumer surplus, environmental impact, and social welfare, it is concluded that the level of information traceability increases with the increase of the blockchain technology input cost-sharing ratio λ by the selling enterprise. However, there is a threshold for λ and λ cannot be infinitely increased. The profits of both manufacturing and selling enterprises increase with the adoption of blockchain technology, yet the ladder-utilizing enterprise achieves the highest profits when not adopting blockchain technology. Furthermore, the adoption of blockchain technology can increase consumer surplus and social welfare while reducing the impact on the environment. These studies provide theoretical guidance on both economic and social environment for the operation decision of each member in the closed-loop supply chain of power batteries.

Costly exploration produces stereotypes with dimensions of warmth and competence.

Traditional explanations for stereotypes assume that they result from deficits in humans (ingroup-favoring motives, cognitive biases) or their environments (majority advantages, real group differences). An alternative explanation recently proposed that stereotypes can emerge when exploration is costly. Even optimal decision makers in an ideal environment can inadvertently form incorrect impressions from arbitrary encounters. However, all these existing theories essentially describe shortcuts that fail to explain the multidimensionality of stereotypes. Stereotypes of social groups have a canonical multidimensional structure, organized along dimensions of warmth and competence. We show that these dimensions and the associated stereotypes can result from feature-based exploration: When individuals make self-interested decisions based on past experiences in an environment where exploring new options carries an implicit cost and when these options share similar attributes, they are more likely to separate groups along multiple dimensions. We formalize this theory via the contextual multiarmed bandit problem, use the resulting model to generate testable predictions, and evaluate those predictions against human behavior. We evaluate this process in incentivized decisions involving as many as 20 real jobs and successfully recover the classic dimensions of warmth and competence. Further experiments show that intervening on the cost of exploration effectively mitigates bias, further demonstrating that exploration cost per se is the operating variable. Future diversity interventions may consider how to reduce exploration cost, in ways that parallel our manipulations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Top 10+1 indicators for assessing forest ecosystem conditions: A five-decade fragmentation analysis

Globally, land use change has consistently resulted in greater losses than gains in aboveground biomass (AGB). Forest fragmentation is a primary driver of biodiversity loss and the depletion of natural capital. Measuring landscape characteristics and analyzing changes in forest landscape patterns are essential for accounting for the contributions of forest ecosystems to the economy and human well-being. This study predicts national forest distribution for 2036 and 2054 using a Cellular Automata (CA) system and assesses ecosystem conditions through landscape metrics at the patch, class, and landscape levels. We calculated 130 metrics and applied a Variance Threshold method to remove features with low variance, testing different thresholds. The first filtered-out metrics were further analysed through Principal Component Analysis combined with a Feature Importance technique to select and rank the top 10 indicators: effective mesh size, splitting index, mean radius of gyration, largest patch index, mean core area, core area percentage, Simpson's evenness index, mutual information, Simpson's diversity index, and mean contiguity index. The eleventh selected indicator is the AGB density, a structural measurement for ecosystem condition and a proxy for forest carbon storage and sequestration assessments. From 2000 to 2018, the national AGB forest carbon stock decreased from 131.5 to 91.3 Megatons (Mt) with expected values for 2036 and 2054 being 71.8 and 55.3 Mt., respectively. Landscape measurements quantitatively describe forest dynamics, providing insights into the structure, configuration, and changes characterizing landscape evolution. This research underscores the capability of CA models to map large-scale forest resources and predict future development scenarios, offering useful information for conservation and environmental management decisions. Additionally, it provides measurements to support Ecosystem Accounting by assessing forest extent and indicators of its conditions.

Exploring the impact of business analytics on strategic decision-making in uncertain environments

Business analytics presents significant opportunities for enhancing strategic decision-making (SDM), which is crucial for organizational competitiveness. However, there is a knowledge gap in understanding the interactions among environmental dynamism, business analytics use, environmental scanning, and rational and intuitive SDM. This paper addresses this gap by leveraging the information processing view and analyzing 218 survey responses using partial least squares (PLS) path modeling. It reveals that environmental dynamism influences business analytics use and environmental scanning. Business analytics use positively impacts rational SDM but negatively affects intuitive SDM. Environmental scanning partially mediates the relationship between business analytics use and rational SDM, and there is an inverse correlation between rational and intuitive SDM. This research introduces a novel theoretical framework, enriching the information processing view, and deepens understanding of how strategic information processing capabilities influence SDM. It also provides practical insights for organizations using business analytics to improve SDM processes in uncertain environments.