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Article
Publication date: 19 October 2023

Huaxiang Song

Classification of remote sensing images (RSI) is a challenging task in computer vision. Recently, researchers have proposed a variety of creative methods for automatic recognition…

Abstract

Purpose

Classification of remote sensing images (RSI) is a challenging task in computer vision. Recently, researchers have proposed a variety of creative methods for automatic recognition of RSI, and feature fusion is a research hotspot for its great potential to boost performance. However, RSI has a unique imaging condition and cluttered scenes with complicated backgrounds. This larger difference from nature images has made the previous feature fusion methods present insignificant performance improvements.

Design/methodology/approach

This work proposed a two-convolutional neural network (CNN) fusion method named main and branch CNN fusion network (MBC-Net) as an improved solution for classifying RSI. In detail, the MBC-Net employs an EfficientNet-B3 as its main CNN stream and an EfficientNet-B0 as a branch, named MC-B3 and BC-B0, respectively. In particular, MBC-Net includes a long-range derivation (LRD) module, which is specially designed to learn the dependence of different features. Meanwhile, MBC-Net also uses some unique ideas to tackle the problems coming from the two-CNN fusion and the inherent nature of RSI.

Findings

Extensive experiments on three RSI sets prove that MBC-Net outperforms the other 38 state-of-the-art (STOA) methods published from 2020 to 2023, with a noticeable increase in overall accuracy (OA) values. MBC-Net not only presents a 0.7% increased OA value on the most confusing NWPU set but also has 62% fewer parameters compared to the leading approach that ranks first in the literature.

Originality/value

MBC-Net is a more effective and efficient feature fusion approach compared to other STOA methods in the literature. Given the visualizations of grad class activation mapping (Grad-CAM), it reveals that MBC-Net can learn the long-range dependence of features that a single CNN cannot. Based on the tendency stochastic neighbor embedding (t-SNE) results, it demonstrates that the feature representation of MBC-Net is more effective than other methods. In addition, the ablation tests indicate that MBC-Net is effective and efficient for fusing features from two CNNs.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 17 no. 1
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 18 March 2024

Takeshi Sakai, Hideyuki Akai, Hiroki Ishizaka, Kazuyuki Tamura, Ban Heng Choy, Yew-Jin Lee and Hiroaki Ozawa

This study aims to develop a self-reflection scale useful for teachers to improve their skills and to clarify the Japanese teachers’ characteristics during mathematics lesson…

Abstract

Purpose

This study aims to develop a self-reflection scale useful for teachers to improve their skills and to clarify the Japanese teachers’ characteristics during mathematics lesson observation (MLO). In MLO, it is important to understand the lesson plan in advance to clarify observation points, and we aim to develop a scale including these points.

Design/methodology/approach

Based on the pre-questionnaire survey, nine perspectives and two situations for MLO were extracted. From these, a questionnaire for MLO was created. The results obtained from 161 teachers were examined, and exploratory factor analysis was conducted. ANOVA was conducted to analyze the effect of differences across the duration of teaching experience on the identified factors.

Findings

We developed a self-reflection scale consisting of 14 items with three factors: [B1] focus on instructional techniques and evaluation, [B2] focus on proactive problem-solving lesson development and [B3] focus on the mathematical background of the learning content. While duration of teaching experience showed no effect, three factors of the self-reflection scale for MLO showed a significant effect. Further multiple comparisons revealed the degree of focus was [B2]>[B1]>[B3].

Originality/value

Teachers who use this developed scale may grasp the strengths and weaknesses of their own MLO, which leads to self-improvement. The perspectives emphasized in lesson observation are the same when creating lesson plans and implementing lessons, leading to lesson improvement. Furthermore, based on the characteristics of teachers revealed, new training programs regarding MLO can lead to higher-quality lesson studies.

Details

International Journal for Lesson & Learning Studies, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2046-8253

Keywords

Article
Publication date: 19 August 2022

Karthik Bajar, Aditya Kamat, Saket Shanker and Akhilesh Barve

In recent times, reverse logistics (RL) is gaining significant traction in various automobile industries to recapture returned vehicles’ value. A good RL program can lower…

Abstract

Purpose

In recent times, reverse logistics (RL) is gaining significant traction in various automobile industries to recapture returned vehicles’ value. A good RL program can lower manufacturing costs, establish a green supply chain, enhance customer satisfaction and provide a competitive advantage. However, reducing disruptions and increasing operational efficiency in the automobile RL requires implementing innovative technology to improve information flow and security. Thus, this manuscript aims to examine the hurdles in automobile RL activities and how they can be effectively tackled by blockchain technology (BCT). Merging BCT and RL provides the entire automobile industry a chance to generate value for its consumers through effective vehicle return policies, manufacturing cost reduction, maintenance records tracking, administration of vehicle information and a clear payment record of insurance contracts.

Design/methodology/approach

This research is presented in three stages to accomplish the task. First, previous literature and experts' opinions are examined to highlight certain factors that are an aggravation to BCT implementation. Next, this study proposed an interval-valued intuitionistic fuzzy set (IVIFS) – decision-making trial and evaluation laboratory (DEMATEL) with Choquet integral framework for computing and analyzing the comparative results of factor interrelationships. Finally, the causal outline diagrams are plotted to determine the influence of factors on one another for BCT implementation in automobile RL.

Findings

This study has categorized the barriers to BCT implementation into five major factors – operational and strategical, technical, knowledge and behavioral, financial and infrastructural, and government rules and regulations. The results revealed that disreputable technology, low-bearing capacity of IT systems and operational inefficiency are the most significant factors to be dealt with by automobile industry professionals for finer and enhanced RL processes utilizing BCT. The most noticeable advantage of BCT is its enormous amount of data, permitting automobile RL to develop client experience through real-time data insights.

Practical implications

This study reveals several factors that are hindering the implementation of BCT in RL activities of the automobile industry. The results can assist experts and policymakers improve their existing decision-making systems while making an effort to implement BCT into the automobile industry's RL activities.

Originality/value

Although there are several studies on the benefits of BCT in RL and the adoption of BCT in the automobile industry, individually, none have explicated the use of BCT in automobile RL. This is also the first kind of study that has used IVIFS-DEMATEL with the Choquet integral framework for computing and analyzing the comparative results of factor interrelationships hindering BCT implementation in automobile RL activities.

Details

Smart and Sustainable Built Environment, vol. 13 no. 1
Type: Research Article
ISSN: 2046-6099

Keywords

Article
Publication date: 19 February 2024

Vinicius Elias Villabruna, Cleiton Hluszko, Daiane Rossi, Murillo Vetroni Barros, Jasmine Siu Lee Lam and Fernando Henrique Lermen

Seaports are vital in facilitating sustainable development, and environmental, social and governance (ESG) factors significantly impact an organization’s performance. Therefore…

Abstract

Purpose

Seaports are vital in facilitating sustainable development, and environmental, social and governance (ESG) factors significantly impact an organization’s performance. Therefore, this study aims to identify and evaluate barriers and strategies of green investments to promote ESG practices within the seaport sector.

Design/methodology/approach

To fulfill this aim, a systematic literature review, interpretive structural modeling and the matrix of cross-impact multiplications were applied to classification analysis.

Findings

12 barriers were prioritized and categorized by experts in a focus group to optimize efforts and define the materiality of these barriers in implementing ESG strategies within seaport companies.

Practical implications

The implications of this study provide an alternative approach for ESG management in the context of seaports that can be applied in different regions by experts' opinion assessment.

Originality/value

No prior studies assessed the barriers and strategies for green investments in ESG from the port sector perspective.

Details

Management of Environmental Quality: An International Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1477-7835

Keywords

Book part
Publication date: 5 April 2024

Taining Wang and Daniel J. Henderson

A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production…

Abstract

A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production frontier is considered without log-transformation to prevent induced non-negligible estimation bias. Second, the model flexibility is improved via semiparameterization, where the technology is an unknown function of a set of environment variables. The technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs, environment variables, and/or inefficiency determinants. Furthermore, the technology function incorporates a single-index structure to circumvent the curse of dimensionality. Third, distributional assumptions are eschewed on both stochastic noise and inefficiency for model identification. Instead, only the conditional mean of the inefficiency is assumed, which depends on related determinants with a wide range of choice, via a positive parametric function. As a result, technical efficiency is constructed without relying on an assumed distribution on composite error. The model provides flexible structures on both the production frontier and inefficiency, thereby alleviating the risk of model misspecification in production and efficiency analysis. The estimator involves a series based nonlinear least squares estimation for the unknown parameters and a kernel based local estimation for the technology function. Promising finite-sample performance is demonstrated through simulations, and the model is applied to investigate productive efficiency among OECD countries from 1970–2019.

Article
Publication date: 20 March 2024

Gopal Krushna Gouda and Binita Tiwari

The COVID-19 outbreak disrupted the business environment and severely affected the morale and performance of the employees. Further, the Indian automobile industry witnessed major…

Abstract

Purpose

The COVID-19 outbreak disrupted the business environment and severely affected the morale and performance of the employees. Further, the Indian automobile industry witnessed major setbacks and drastically impacted sector in COVID-19. Talent agility is an emerging concept in the field of HRM that will foster innovations and productivity in the automobile industry. Thus, this study aims to explore the barriers to building in-house agile talents in the Indian automobile industry in the new normal.

Design/methodology/approach

The barriers of talent agility were identified through a literature review and validated through experts’ opinions. This study used a hybrid approach, which combines Interpretive Structural Modelling-Polarity (ISM-P) and decision-making trial and evaluation laboratory (DEMATEL) to develop a hierarchical structural model of the barriers, followed by classification into cause and effect groups.

Findings

The result of the multi-method approach identified that shortage of skills and competencies, lack of IT infrastructure, lack of ambidextrous leaders, lack of smart HRM technologies and practices, lack of attractive reward system/career management, poor advanced T&D, poor industry, institute interface and financial constraints are the critical barriers.

Practical implications

It can provide a strategic roadmap for automobile manufacturers to promote talent agility in the current wave of digitalization (Industry 4.0). This study can help the managers to address and overcome the barrier and hurdles in building talent agility.

Originality/value

This study is unique in that it addresses the contemporary issues related to talent agility in the context of the Indian automobile industry in the current rapidly changing environment. This study developed a holistic integrated ISM(P)-DEMATEL hierarchical framework on the barriers of talent agility indicating inner dependency weights, i.e., the strength of interrelationship between the barriers.

Details

Journal of Modelling in Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1746-5664

Keywords

Article
Publication date: 9 February 2024

Alexandre Esteves and Pedro Piccoli

The purpose of this study is to investigate the influence of firm-specific investor sentiment on Brazilian companies’ accrual-based earnings management between 2010 and 2018. The…

Abstract

Purpose

The purpose of this study is to investigate the influence of firm-specific investor sentiment on Brazilian companies’ accrual-based earnings management between 2010 and 2018. The paper aims to bring deeper insight into the relationship between the investor expectations and managers’ decision-making in an emerging market.

Design/methodology/approach

The authors use the quantitative approach and apply a multiple linear regression model to test the relationship among the abnormal accruals, the firm-specific investor sentiment index and the control variables. The final sample includes data from 175 companies, between 2010 and 2018.

Findings

These results reveal a negative association between firm-specific investor sentiment and accrual-based earnings management, which could mean that the risk propensity of managers to manipulate earnings increases when they face known losses in the capital market.

Research limitations/implications

The research findings provide a valuable understanding of how emerging capital market expectations can influence managerial decisions, such as accrual-based earnings management. The geographical area of study was limited to only Brazil.

Originality/value

Previous studies on developed markets show that market-wide investor sentiment positively influences accrual-based earnings management. However, the present study shows that the firm-specific investor sentiment index has a significant and negative relationship with Brazilian companies’ earnings manipulation, whereas market sentiment indicates contradictory relationship in previous studies in the country.

Propósito

El propósito de este estudio es investigar la influencia del sentimiento de los inversionistas a nivel de empresa en la manipulación contable de las empresas brasileñas entre 2010 y 2018. El documento pretende aportar una visión más profunda sobre la relación entre las expectativas de los inversores y la toma de decisiones de los gestores en un mercado emergente.

Diseño/metodologia/enfoque

usamos el enfoque cuantitativo y aplicamos un modelo de regresión lineal múltiple para probar la relación entre las acumulaciones anormales, el índice de sentimiento de los inversores a nivel de empresa y las variables de control. La muestra final incluye datos de 175 empresas, entre 2010 y 2018.

Hallazgos

Los resultados revelan una asociación negativa entre el sentimiento de los inversores a nivel de empresa y la manipulación contable basada em acumulaciones, lo que podría significar que la propensión al riesgo de los administradores a manipular las ganancias aumenta cuando enfrentan pérdidas conocidas en el mercado de capitales.

Limitaciones/implicaciones de la investigación

los resultados de la investigación proporcionan una valiosa comprensión de cómo las expectativas de los mercados de capitales emergentes pueden influir en las decisiones de gestión, como la manipulación contable basada en acumulaciones. El área geográfica de estudio se limitó únicamente a Brasil y, en consecuencia, los hallazgos y conclusiones del estudio tuvieron sus límites.

Originalidad/valor

estudios anteriores sobre mercados desarrollados muestran que el sentimiento de los inversores a nivel de mercado influye positivamente en la manipulación contable. Sin embargo, el presente estudio muestra que el índice de sentimiento de los inversores a nivel de empresa tiene una relación significativa y negativa con la manipulación de las ganancias de las empresas brasileñas, mientras que el sentimiento del mercado indica una relación contradictoria en estudios anteriores en el país.

Details

Academia Revista Latinoamericana de Administración, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1012-8255

Keywords

Article
Publication date: 21 August 2023

Puja Singh, Vishal Suresh Pradhan and Yogesh B. Patil

The main purpose of this paper is to investigate drivers and barriers of climate change mitigation strategies (CCMS), their linkages and impact in Indian Iron and Steel Industry…

Abstract

Purpose

The main purpose of this paper is to investigate drivers and barriers of climate change mitigation strategies (CCMS), their linkages and impact in Indian Iron and Steel Industry (IISI) in light of ninth sustainable development goal (building resilient infrastructure, promote sustainable industrialization and foster innovation).

Design/methodology/approach

To identify relevant drivers and barriers, a thorough literature review and opinions of industry experts were obtained. Utilizing Total Interpretive Structural Modeling (TISM), the selected drivers and barriers were modeled separately along with Cross Impact Matrix-multiplication Applied to Classification (MICMAC).

Findings

Pragmatic and cost-effective technology, less supply chain complexity, robust policy and legal framework were found to have the highest driving power over all the other drivers. Findings suggest political pressure as the most critical barrier in this study. The results from TISM and MICMAC analysis have been used to elucidate a framework for the understanding of policymakers and achieve top management commitment.

Practical implications

This paper will help researchers, academicians, industry analysts and policymakers in developing a systems approach in prioritizing CCMS in energy-intensive (coal dependent) iron and steel plants. The model outcomes of this work will aid operational research to understand the working principles in other industries as well.

Originality/value

To the best of authors' knowledge, there is paucity of reported literature for the drivers and barriers of CCMS in iron and steel industry. This paper can be considered a unique, first attempt to use data from developing nations like India to develop a model and explain relationships of the existing drivers and barriers of CCMS.

Details

Management of Environmental Quality: An International Journal, vol. 35 no. 1
Type: Research Article
ISSN: 1477-7835

Keywords

Open Access
Article
Publication date: 28 June 2022

Sahar Jawad, Ann Ledwith and Rashid Khan

There is growing recognition that effective project control systems (PCS) are critical to the success of projects. The relationship between the individual elements of PCS and…

1506

Abstract

Purpose

There is growing recognition that effective project control systems (PCS) are critical to the success of projects. The relationship between the individual elements of PCS and successfully achieving project objectives has yet to be explored. This research investigates the enablers and barriers that influence the elements of PCS success and drive project objectives.

Design/methodology/approach

This study adopts a mixed approach of descriptive analysis and regression models to explore the impact of six PCS elements on project outcomes. Petroleum and chemical projects in Saudi Arabia were selected as a case study to validate the research model.

Findings

Data from a survey of 400 project managers in Saudi’s petroleum and chemical industry reveal that successful PCS are the key to achieving all project outcomes, but they are particularly critical for meeting project cost objectives. Project Governance was identified as the most important of the six PCS elements for meeting project objectives. A lack of standard processes emerged as the most significant barrier to achieving effective project governance, while having skilled and experienced project team members was the most significant enabler for implementing earned value.

Practical implications

The study offers a direction for implementing and developing PCS as a strategic tool and focuses on the PCS elements that can improve project outcomes.

Originality/value

This research contributes to project management knowledge and differs from previous attempts in two ways. Firstly, it investigates the elements of PCS that are critical to achieving project scope, schedule and cost objectives; secondly, enablers and barriers of PCS success are examined to see how they influence each element independently.

Details

Engineering, Construction and Architectural Management, vol. 31 no. 13
Type: Research Article
ISSN: 0969-9988

Keywords

Article
Publication date: 7 September 2023

Jeferson Carvalho, Paulo Vitor Jordão da Gama Silva and Marcelo Cabus Klotzle

This study investigates the presence of herding in the Brazilian stock market between 2012 and 2020 and associates it with the volume of searches on the Google platform.

Abstract

Purpose

This study investigates the presence of herding in the Brazilian stock market between 2012 and 2020 and associates it with the volume of searches on the Google platform.

Design/methodology/approach

Following methodologies are used to investigate the presence of herding: the Cross-Sectional Standard Deviation of Returns (CSSD), the Cross-Sectional Absolute Deviation (CSAD) and the Cross-Sectional Deviation of Asset Betas to the Market.

Findings

Most of the models detected herding. In addition, there was a causal relationship between peaks in Google search volumes and the incidence of herding across the whole period, especially in 2015 and 2019.

Originality/value

This study suggests that confirmation bias influences investors' decisions to buy or sell assets.

Details

Review of Behavioral Finance, vol. 16 no. 2
Type: Research Article
ISSN: 1940-5979

Keywords

1 – 10 of 139