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The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.

This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.

This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.

This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.

This study was carried out to analyze the importance of consumer preference data in forecasting demand in apparel retailing.

To collect preference data, 729 hypothetical stock keeping units (SKU) were derived using a full factorial design, from a combination of six attributes and three levels each. From the hypothetical SKU's, 63 practical SKU's were selected for further analysis. Two hundred two responses were collected from a store intercept survey. Respondents' utility scores for all 63 SKUs were calculated using conjoint analysis. In estimating aggregate demand, to allow for consumer substitution and to make the SKU available when a consumer wishes to buy more than one item in the same SKU, top three highly preferred SKU's utility scores of each individual were selected and classified using a decision tree and was aggregated. A choice rule was modeled to include substitution; by applying this choice rule, aggregate demand was estimated.

The respondents' utility scores were calculated. The value of Kendall's tau is 0.88, the value of Pearson's R is 0.98 and internal predictive validity using Kendall's tau is 1.00, and this shows the high quality of data obtained. The proposed model was used to estimate the demand for 63 SKUs. The demand was estimated at 6.04 per cent for the SKU cotton, regular style, half sleeve, medium priced, private label. The proposed model for estimating demand using consumer preference data gave better estimates close to actual sales than expert opinion data. The Spearman's rank correlation between actual sales and consumer preference data is 0.338 and is significant at 5 per cent level. The Spearman's rank correlation between actual sales and expert opinion is −0.059, and there is no significant relation between expert opinion data and actual sales. Thus, consumer preference model proves to be better in estimating demand than expert opinion data.

There has been a considerable amount of work done in choice-based models. There is a lot of scope in working in deterministic models.

The proposed consumer preference-based demand estimation model can be beneficial to the apparel retailers in increasing their profit by reducing stock-out and overstocking situations. Though conjoint analysis is used in demand estimation in other industries, it is not used in apparel for demand estimations and can be greater use in its simplest form.

This research is the first one to model consumer preferences-based data to estimate demand in apparel. This research was practically tested in an apparel retail store. It is original.

This article aims to evaluate the entry and exit of companies from local productive structures, with a specific focus on the sectoral complexity of these activities and the complexity of these portfolios. The study focuses on empirically demonstrating the thesis that related economic diversification exacerbates the development gap between more and less complex regions.

The article uses indicators formulated by the economic complexity approach. They allow a relevant descriptive analysis of the economic diversification process in Brazilian micro-regions and provide the foundation for the econometric tests conducted. Through three distinct estimation strategies (OLS, logit, probit), the influence of complexity and relatedness on the entry and exit events of firms from local portfolios is tested.

In all estimated models, the stronger relationship between an activity and a portfolio significantly increases its probability of entering the productive structure and, at the same time, acts as a significant factor in preventing its exit. Furthermore, the results reveal that the complexity of a sector reduces the probability of its specialization in less complex regions while increasing it in more complex regions. On the other hand, sectoral complexity significantly increases the probability of a sector leaving less complex local structures but has no significant effect in highly complex regions.

Due to the data used, the indicators are calculated considering only formal job numbers. Additionally, the tests do not detect the influence of spatial issues. These limitations should be addressed by future research.

The article characterizes a prevailing process of uneven development among Brazilian regions and brings relevant implications, primarily for policymakers. Specifically, for less complex regions, policies should focus on creating opportunities to improve their diversification capabilities in complex sectors that are not too distant from their portfolios.

The article makes an original contribution by proposing an evaluation of regional diversification in Brazil with a focus on complexity, introducing a more detailed differentiation of regions based on their complexity levels and examining the impact of sectoral complexity on diversification patterns within each group.

This chapter investigates whether, and if so, how particular firms in a transition economy are involved in bribery. Built on pressure theories, we explain how the direct effects of firm characteristics and contextual characteristics determine firm bribery behavior. Entrepreneurs make choices based on perceptions of a specific pressure due to organizational characteristics (internal pressures) or due to context (external pressures). The relationship between firm characteristics, context, and bribery was estimated using unique data from a survey of 606 Vietnamese entrepreneurs. We controlled for various entrepreneurial, organizational, and industrial characteristics. The exploratory findings support firm attributes hypotheses, which is a negative relationship between firm size and bribery and a nonmonotonic U-shaped relationship between firm age and bribery. Besides, the effects of context on bribery are also found. Specifically, the result supports a positive relationship between competition and bribery and a negative relationship between the quality of the government and bribery.

This study aims to examine the perceptions and opinions of relevant stakeholders regarding entrepreneurship opportunities and growth in the Gilgit-Baltistan (GB) province of Pakistan. Specifically, it focuses on the role of special economic zones (SEZs), such as Maqpondass SEZ and the China–Pakistan Economic Corridor (CPEC), in fostering nascent entrepreneurship (NE) and promoting regional development.

The study employs ordered logistic regression to estimate the relationship between various independent variables and nascent entrepreneurship (NE). The independent variables include awareness of CPEC (AAC), awareness of Maqpondass SEZ (AAMEZ), SEZ incentives (SEZInc), regional market competitiveness (RMC), loan availability (LA) and education and experience (EE).

The findings indicate a robust positive relationship between SEZ-based industries and the growth of local small businesses and enterprises in Gilgit-Baltistan. Furthermore, the study suggests that government incentives, access to finance, skill development, relevant knowledge, and connections with local businesses facilitate the establishment of new ventures.

The study underscores the importance of focusing on human capital development, providing financial assistance, and creating incentives for adopting advanced technology to foster the growth of local businesses in Gilgit-Baltistan through SEZs. It emphasizes the need for policymakers and stakeholders to prioritize initiatives that support entrepreneurship and innovation in the region.

This study contributes to the existing literature by providing novel insights into the perceptions of entrepreneurship development in Gilgit-Baltistan, particularly concerning the influence of natural resources and SEZs. It fills a gap in the research by offering valuable implications for policymakers, researchers, and practitioners seeking to promote sustainable economic development in the region.

The purpose of this paper is to critically examine the multinational oil companies’ (MOCs) corporate social responsibility (CSR) initiatives in Nigeria. Its special focus is to investigate the impact of the global memorandum of understanding (GMoU) on mainstreaming gender sensitivity in cash crop market supply chains in the Niger Delta region of Nigeria.

This paper adopts an explanatory research design with a mixed method to answer the research questions and test the hypotheses. A total of 1,200 rural women respondents were sampled across the Niger Delta region.

Results from the use of a combined logit model and propensity score matching indicate a significant relationship between the GMoU model and mainstreaming gender sensitivity in cash crop market supply chains in the Niger Delta.

This study implies that MOCs’ CSR interventions that improve women’s access to land and encourage better integration of food markets through improved roads and increased mobile networks would enable women to engage in cash crop production.

This implies that improving access to credit through GMoU cluster farming targeted at female farmers would improve access to finance and extension services for women in cash crop production in the Niger Delta.

This research contributes to the gender debate in the agricultural value chain from a CSR perspective in developing countries and is rational for demands for social projects by host communities. It concludes that businesses have an obligation to help solve problems of public concern.

This study aims to examine whether the newly available auditor tenure information is associated with non-GAAP earnings, as the recent requirement to disclose the initial year of auditor-client relationship in audit reports may give the impression that longer auditor tenure may be related to lower audit quality.

Using a sample of firm-quarters from 2017 to 2020, the authors conduct both univariate and regression analyses. We use hand-collected data for auditor tenure, SEC comment letters, and non-GAAP variables.

First, the authors find that the likelihood of disclosing non-GAAP earnings monotonically increases with auditor tenure on a univariate basis. Second, auditor tenure is negatively associated with aggressive non-GAAP reporting. Third, the authors document evidence of aggressive reporting in general; that is, items excluded in calculating non-GAAP earnings are associated with future performance. However, the association declines with longer auditor tenure. Finally, the authors report evidence that the likelihood of receiving an SEC comment letter that contains non-GAAP comments decreases with longer auditor tenure.

The results show that regulators need to consider both GAAP and non-GAAP disclosures’ costs and benefits when enacting auditor tenure regulation. Investors can benefit from the findings in evaluating the quality of non-GAAP earnings. The findings are also relevant to the SEC when allocating limited resources in monitoring non-GAAP reporting.

To the best of the authors’ knowledge, this is the first study showing that auditor tenure is associated with the quality of non-GAAP earnings. Given that financial reporting quality should be understood as a comprehensive system comprising both mandatory and voluntary disclosures, this study complements the literature that examines the effect of auditor tenure on financial reporting quality using GAAP reporting.

This paper investigates the ways digital media applications in rural areas have transformed the influence of social networks (SN) on farmers' adoption of various climate change mitigation measures (CCMM), and explores the key mechanisms behind this transformation.

The study analyzes data from 1,002 farmers’ surveys. First, a logit model is used to measure the impact of SN on the adoption of different types of CCMM. Then, the interaction term between digital media usage (DMU) and SN is introduced to analyze the moderating effect of digital media on the impact of SN. Finally, a conditional process model is used to explore the mediating mechanism of agricultural socialization services (ASS) and the validity of information acquisition (VIA).

The results reveal that: (1) SN significantly promotes the adoption of CCMM and the marginal effect of this impact varies with different kinds of technologies. (2) DMU reinforces the effectiveness of SN in promoting farmers' adoption of CCMM. (3) The key mechanisms of the process in (2) are the ASS and the VIA.

This study shows that in the context of DMU, SN’s promotion effect on farmers' adoption of CCMM is strengthened.

Financial inclusion in Peru has been addressed through coverage, quality of financial services, movement of transactions, and service points. The purpose of this chapter is to evaluate for the department of Ancash, Peru, the link between financial inclusion and its socioeconomic factors. Socioeconomic variables and financial inclusion of the Ancash department of the National Household Survey are taken as indicators, later contrasted through the logit model, with the financial inclusion variable being the explained variable.

There is evidence of positive and negative relationships between financial inclusion and socioeconomic variables; these are important components for planning financial inclusion. Raising the levels of formal employment, the educational level and considering the area of residence would be a strategy to generate a dynamic of inclusion in the department of Ancash.

This study aims to examine whether there exists any relationship between corporate biodiversity reporting decision (CBRD) and corporate environmental performance (CEP).

The primary sample contains 442 firm-year observations over a period of 13 years (2008–2020) for 34 listed Finnish companies. Based on both legitimacy theory and voluntary disclosure theory, 2 logit regression models are estimated to test the CBRD–CEP nexus. CBRD is a dichotomous variable. Three proxies for CEP, namely propensity to emit greenhouse gas (GHG), propensity to consume water and propensity to generate waste are employed.

This study finds that firms having higher propensity to consume water and generate waste are inclined to release biodiversity-related information. The findings support legitimacy theory suggesting that firms with inferior environmental performance may decide on reporting biodiversity information for legitimation purpose.

The study uses Finnish data and hence, the results may lack in generalizability to other national contexts.

The results of this study should be valuable to policy makers for formulating mandatory biodiversity reporting standards to ensure disclosure of standard, extensive and authentic biodiversity-related information by companies. The results should also be valuable to corporate managers and eco-friendly investors.

Corporate biodiversity reporting (CBR) is an under-researched area of environmental accounting literature. Using the Finnish context, this paper extends the existing literature by investigating whether any association exists between CBRD and CEP, which has not been examined before.