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Limited research exists on the role of information access as a key success factor for rural entrepreneurs. The purpose of this study is to examine the importance of information access, among other inequality forms, in shaping entrepreneurial outcomes.

The authors used a qualitative, inductive research methodology. They conducted interviews with rural entrepreneurs and gained insights into the role of information access and basic tangible resources in shaping their ventures.

The authors identified two critical factors that impact rural entrepreneurs: inequalities in access to information and basic tangible resources. They found that inequalities in rural entrepreneurship are often interrelated, rather than isolated. The authors demonstrated that the relationship between entrepreneurship and inequality is not solely characterized by a positive or negative correlation but is a dynamic interplay where certain inequality forms may create barriers to opportunities for some individuals while creating opportunities for others.

The authors uncovered key barriers in rural entrepreneurship and constructed a roadmap to effectively address these challenges, providing valuable insights to policymakers and support initiatives and enabling the target of high-impact resources. This research supports efforts to provide rural entrepreneurs with equal opportunities to grow and succeed.

This research significantly contributes to the field by examining the complexities of entrepreneurship in emerging economies, with a particular focus on rural areas. The authors introduced a comprehensive conceptual model linking inequality and entrepreneurship, explored the challenges faced by rural entrepreneurs and offered strategic policy recommendations for development initiatives.

Sustainable development goals (SDGs) are gaining significant importance in the current environment. Many businesses are keen to adopt SDGs to get a competitive edge. There are certain challenges in realigning the present working scenario for sustainable development, which is a primary concern for society. Various firms are adopting sustainable engineering (SE) practices to tackle such issues. Artificial intelligence (AI) is an emerging technology that can help the ineffective adoption of sustainable practices in an uncertain environment. In this regard, there is a need to review the current research practices in the field of SE in AI. The purpose of the present study is to comprehensive review the research trend in the field of SE in AI.

This work presents a review of AI applications in SE for decision-making in an uncertain environment. SCOPUS database was considered for shortlisting the articles. Specific keywords on AI, SE and decision-making were given, and a total of 127 articles were shortlisted after implying inclusion and exclusion criteria.

Bibliometric study and network analyses were performed to analyse the current research trends and to see the research collaboration between researchers and countries. Emerging research themes were identified by using structural topic modelling (STM) and were discussed further.

Research propositions corresponding to each research theme were presented for future research directions. Finally, the implications of the study were discussed.

This work presents a systematic review of articles in the field of AI applications in SE with the help of bibliometric study, network analyses and STM.

This study focused on exploring the performance factors (PFs) that impact Infrastructure Sanitation Projects (ISSPs) in the construction sector. The aim was twofold: firstly, to identify these crucial PFs and secondly, to develop a robust performance model capable of effectively measuring and assessing the intricate interdependencies and correlations within ISSPs. By achieving these objectives, the study aimed to provide valuable insights into and tools for enhancing the efficiency and effectiveness of sanitation projects in the construction industry.

To achieve the study's aim, the methodology for identifying the PFs for ISSPs involved several steps: extensive literature review, interviews with Egyptian industry experts, a questionnaire survey targeting industry practitioners and an analysis using the Relative Importance Index (RII), Pareto principle and analytic network process (ANP). The RII ranked factor importance,  and Pareto identified the top 20% for ANP, which determined connections and interdependencies among these factors.

The literature review identified 36 PFs, and an additional 13 were uncovered during interviews. The highest-ranked PF is PF5, while PF19 is the lowest-ranked. Pareto principle selected 11 PFs, representing the top 20% of factors. The ANP model produced an application for measuring ISSP effectiveness, validated through two case studies. Application results were 92.25% and 91.48%, compared to actual results of 95.77% and 97.37%, indicating its effectiveness and accuracy, respectively.

This study addresses a significant knowledge gap by identifying the critical PFs that influence ISSPs within the construction industry. Subsequently, it constructs a novel performance model, resulting in the development of a practical computer application aimed at measuring and evaluating the performance of these projects.

Despite the well-recognized importance of recycled water, the study of industry-peer pressure on recycled water is relatively new. This study investigates how organizations experience and react to industry-peer pressure to set recycled water targets. Additionally, this study investigates the role of board chairs involved in sustainability committees in contributing to responses to industry-peer pressure.

Using Eviews 12, this study employed a pooled logistic regression model to analyze data from 1,346 firms on Taiwan and Taipei exchanges (2017–2020).

The findings revealed that frequency-based imitation drives recycled water target-setting diffusion. However, there is no direct relationship between outcome-based imitation and recycled water target-setting. Notably, outcome-based imitation drives the adoption of recycled water target-setting of firms with board-chair membership in sustainability committees.

This study faces certain data limitations. First, this study primarily focuses on water recycling. Future research could explore other ways to reduce water usage, such as using water-efficient equipment. Second, this study gathered information solely on the presence or absence of a board chairperson on the sustainability committee. Future researchers could explore the impact of the composition of sustainability committee on recycled water target-setting. Lastly, the sample used in this study is restricted to Taiwan's corporations that existed during 2017–2020. Future researchers may consider adopting a longitudinal design in other economies to address this limitation.

The findings of this study offer several guidelines and implications for recycled water target-setting and the composition of sustainability committees. It responds to an urgent call for solutions to water shortages when pressure from governments and nongovernmental organizations is relatively absent. The number of industry peers that have already set recycled water targets is indispensable for motivating firms to set their own recycled water targets. In terms of insufficient water-related regulatory pressure and normative pressure, this study found evidence suggesting that the direct motivation for setting recycled water targets stems from mimetic pressures via frequency-based imitation. The evidence in this study suggests that policymakers should require companies to disclose their peers’ recycled water target information, as doing so serves as an alternative means to achieving SDG 6.3.

Recycled water target-setting might be challenging. Water recycling practices may face strong resistance and require substantial additional resources (Zhang and Tang, 2019; Gao et al., 2019; Gu et al., 2023). Therefore, this study suggests that firms should ensure the mindfulness of board members in promoting the welfare of the natural environment when making recycled water target-setting decisions. To reap the second-mover advantage, firms must consider the conditions in which board members can more effectively play their role. Corporations may help their chairpersons in setting recycled water targets by recruiting them as members of sustainability committees. Meanwhile, chairpersons tend to activate accurate mental models when the water conservation performance of pioneering industry peers is strong enough to indicate the potential benefits of adopting recycled water target-setting. Investors’ and stakeholders’ understanding of how the composition of sustainability committees is related to recycled water target-setting may help to identify the potential drivers of firms’ water responsibility. Investors and stakeholders should distinguish firms in terms of the board chair’s membership of their sustainability committee and focus on water-use reduction outcomes in the industry. This study provides insights into circumstances whereby chairpersons help to restore the water ecosystem.

This study explains how frequency-based and outcome-based imitation are two prominent mechanisms underlying the industry-peer pressure concerning recycled water target-setting. Moreover, this study fills literature gaps related to the moderating roles of board-chair membership in sustainability committees concerning industry-peer pressure on recycled water target-setting.

This research aims to analyse the perceptions of practitioners in three regions regarding the challenges faced by their firms during the pandemic, considered a black-swan event. It examines the strategies implemented to mitigate and recover from risks, evaluates the effectiveness of these strategies and assesses the difficulties encountered in their implementation.

In the summer of 2022, an online survey was conducted among supply chain (SC) practitioners in France, Poland and the St. Louis, Missouri region of the USA. The survey aimed to understand the impact of COVID-19 on their firms and the SC strategies employed to sustain operations. These regions were selected due to their varying levels of SC development, including infrastructure, economic resources and expertise. Moreover, they exhibited different responses in safeguarding the well-being of their citizens during the pandemic.

The study reveals consistent perceptions among practitioners from the three regions regarding the impact of COVID-19 on SCs. Their actions to enhance SC resilience primarily relied on strengthening collaborative efforts within their firms and SCs, thus validating the tenets of the relational view.

COVID-19 is (hopefully) our black-swan pandemic occurrence during our lifetime. Nevertheless, the lessons learned from it can inform future SC risk management practices, particularly in dealing with rare crises. During times of crisis, leveraging existing SC structures may prove more effective and efficient than developing new ones. These findings underscore the significance of relationships in ensuring SC resilience.

This study employs Stein Rokkan's methodological approach to analyse state formation in the Greater Middle East. It develops a conceptual framework distinguishing colonial, populist and democratic pacts, suitable for analysis of state formation and nation-building through to the present period. The framework relies on historical institutionalism. The methodology, however, is Rokkan's. The initial conceptual analysis also specifies differences between European and the Middle Eastern state formation processes. It is followed by a brief and selective discussion of historical preconditions. Next, the method of plotting singular cases into conceptual-typological maps is applied to 20 cases in the Greater Middle East (including Afghanistan, Iran and Turkey). For reasons of space, the empirical analysis is limited to the colonial period (1870s to the end of World War 1). Three typologies are combined into one conceptual-typological map of this period. The vertical left-hand axis provides a composite typology that clarifies cultural-territorial preconditions. The horizontal axis specifies transformations of the region's agrarian class structures since the mid-19th century reforms. The right-hand vertical axis provides a four-layered typology of processes of external intervention. A final section presents selected comparative case reconstructions. To the authors' knowledge, this is the first time such a Rokkan-style conceptual-typological map has been constructed for a non-European region.

Land value varies at a micro level depending on the location’s economic, geographical and political determinants. The purpose of this study is to present a comprehensive assessment of the determinants affecting land value in the Indian city of Thiruvananthapuram in the state of Kerala.

The global influence of the identified 20 explanatory variables on land value is measured using the traditional hedonic price modeling approach. The localized spatial variations of the influencing parameters are examined using the non-parametric regression method, geographically weighted regression. This study used advertised land value prices collected from Web sources and screened through field surveys.

Global regression results indicate that access to transportation facilities, commercial establishments, crime sources, wetland classification and disaster history has the strongest influence on land value in the study area. Local regression results demonstrate that the factors influencing land value are not stationary in the study area. Most variables have a different influence in Kazhakootam and the residential areas than in the central business district region.

This study confirms findings from previous studies and provides additional evidence in the spatial dynamics of land value creation. It is to be noted that advanced modeling approaches used in the research have not received much attention in Indian property valuation studies. The outcomes of this study have important implications for the property value fixation of urban Kerala. The regional variation of land value within an urban agglomeration shows the need for a localized method for land value calculation.

Conflict and violence are the main drivers of globally escalating humanitarian needs. Local grassroots initiatives are pivotal in distributing humanitarian supplies in the acute response phase until more established humanitarian aid organizations can enter. Nevertheless, scant research exists regarding the role of grassroots associations in providing humanitarian assistance during a military conflict. The purpose of this paper is to understand the role of grassroots associations and identify important themes for effective operations.

This paper adopts a case-study approach of three Ukrainian grassroots associations that began operating in the immediate days of the full-scale invasion of Ukraine. The findings are based on analyzing primary sources, including interviews with Ukrainian volunteers, and are supported by secondary sources.

Grassroots associations have local contacts and a contextual understanding of population needs and can respond more rapidly and effectively than large intergovernmental agencies. Four critical themes regarding the operations of grassroots associations emerged: information management, inventory management, coordination and performance measurement. Grassroots humanitarian response operations during conflict are challenged by personal security risks, the unpredictability of unsolicited supplies, emerging volunteer roles, dynamic transportation routes and shifting demands.

Grassroots responses are central to humanitarian responses during the acute phase of a military conflict. By examining the operations of grassroots associations in the early months of the 2022 war in Ukraine, the authors provide a unique perspective on humanitarian logistics. Nonetheless, more inclusive models of humanitarian responses are needed to harness the capacities and resilience of grassroots operations in practice.

Despite city authorities in Bangladesh being concerned about urban sustainability, they often face difficulties in addressing predominant urban challenges threatening urban sustainability, due to limited relevant literature. To reduce this gap, this study aims to address the predominant urban challenges and assess their severity levels in four city corporations of Bangladesh, e.g. Rajshahi, Sylhet, Barishal, and Gazipur.

Using a mixed-method approach, this study rigorously analyzed field-level data obtained from 1,200 residents across selected cities using diverse statistical techniques. The quantitative analysis included descriptive analysis, exploratory factor analysis, and chi-square tests, whereas qualitative insights were derived through thematic analysis.

The study uncovered nine predominant urban challenges under two crucial factors “Feeble Urban Management” and “Illicit Activities” that collectively explain 62.20% variance. “Feeble Urban Management” explains 44.17% variance, whereas “Illicit Activities” accounts for 18.13%. Within these challenges, uncontrolled urban sprawl, inadequate disaster management, congested roads, and shabby drainage and waste management pose significant threats to urban sustainability. Illicit activities, manifested by encroachment on water sources, grabbing roadside, destruction of natural properties, and activities undermining social security, compound the urban sustainability issue. Severity analysis reveals Sylhet (54.5%), Rajshahi (46.4%), and Barishal (31.2%) as highly impacted, whereas Gazipur exhibits moderate severity (66.7%).

The findings of this study reveal intrinsic insights into urban challenges in Bangladesh that will provide valuable guidance to city authorities, equipping them to implement integrated and effective initiatives and programs that overcome these predominant urban challenges, with a specific focus on Rajshahi, Sylhet, and Barishal city corporations.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.