The study focuses on the employees’ behavioral intentions towards the usage of disruptive technology in the industry. The digital technology application in consumer, retail, and hospitality, education and training, financial services, the health sector, infrastructure, government, and airports. The study objectives were to explore the possible adoption of innovation and creativity changes and their acceptance by the employees in the organization. To identify the variables impacting behavioral intention and analyze how these variables relate to perceived usefulness, attitude, perceived ease of use, facilitating conditions, and technology optimism. A structured questionnaire was used to collect data from 335 respondents, who were selected based on their relevance to the study objectives. The questionnaires were distributed through the Google Forms application, and the data were collected and analyzed periodically. The findings of the study provide valuable insights into the behavioral intention towards disruptive technologies in Kuala Lumpur and Putrajaya locations in Malaysia and highlight the significance of factors such as perceived usefulness, attitude, perceived ease of use, facilitating conditions, and technology optimism. The research contributes to the existing body of knowledge on Industry 4.0 by providing empirical evidence and practical implications for organizations seeking to leverage disruptive technologies in their operations management.

To study the environment of the Kipushi mining locality (LMK), the evolution of its landscape was observed using Landsat images from 2000 to 2020. The evolution of the landscape was generally modified by the unplanned expansion of human settlements, agricultural areas, associated with the increase in firewood collection, carbonization, and exploitation of quarry materials. The problem is that this area has never benefited from change detection studies and the LMK area is very heterogeneous. The objective of the study is to evaluate the performance of classification algorithms and apply change detection to highlight the degradation of the LMK. The first approach concerned the classifications based on the stacking of the analyzed Landsat image bands of 2000 and 2020. And the second method performed the classifications on neo-images derived from concatenations of the spectral indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Building Index (NDBI) and Normalized Difference Water Index (NDWI). In both cases, the study comparatively examined the performance of five variants of classification algorithms, namely, Maximum Likelihood (ML), Minimum Distance (MD), Neural Network (NN), Parallelepiped (Para) and Spectral Angle Mapper (SAM). The results of the controlled classifications on the stacking of Landsat image bands from 2000 and 2020 were less consistent than those obtained with the index concatenation approach. The Para and DM classification algorithms were less efficient. With their respective Kappa scores ranging from 0.27 (2000 image) to 0.43 (2020 image) for Para and from 0.64 (2000 image) to 0.84 (2020 image) for DM. The results of the SAM classifier were satisfactory for the Kappa score of 0.83 (2000) and 0.88 (2020). The ML and NN were more suitable for the study area. Their respective Kappa scores ranged between 0.91 (image 2000) and 0.99 (image 2020) for the LM algorithm and between 0.95 (image 2000) and 0.96 (image 2020) for the NN algorithm.

In today’s fast-moving, disrupted business environment, supply chain risk management is crucial. More critically, Industry 4.0 has conferred competitive advantages on supply chains through the integration of digital technologies into manufacturing and logistics, but it also implies several challenges and opportunities regarding the management of these risks. This paper looks at some ways emerging technologies, especially Artificial Intelligence (AI), help address pressing concerns about the management of risk and sustainability in logistics and supply chains. The study, using a systemic literature review (SLR) backed by a mapping study based on the Scopus database, reveals the main themes and gaps of prior studies. The findings indicate that AI can substantially enhance resilience through early risk identification, optimizing operations, enriching decision-making, and ensuring transparency throughout the value chain. The key message from the study is to bring out what technology contributes to rendering supply chains resilient against today’s uncertainties.

This article delves into the controversial practice of utilizing a student’s first language (L1) as a teaching resource in second language (L2) learning environments. Initially, strategies such as code-switching/code-mixing and translanguaging were considered signs of poor linguistic ability. There was a strong push towards using only the target language in foreign language education, aiming to limit the first language’s interference and foster a deeper immersion in the new language. However, later research has shown the benefits of incorporating the first language in bilingual education and language learning processes. It’s argued that a student’s knowledge in their native language can actually support their comprehension of a second language, suggesting that transferring certain linguistic or conceptual knowledge from L1 to L2 can be advantageous. This perspective encourages the strategic use of this knowledge transfer in teaching methods. Moreover, the text points to positive results from various studies on the positive impact of L1 usage in L2 classrooms. These insights pave the way for further exploration into the application of the first language in adult English as a Second Language (ESL)/English as a Foreign Language (EFL) education, particularly regarding providing corrective feedback.

Due to the bounded rationality of decision-makers and the substitution effect of non-green products, retailers are not always profitable when selling green products. To assist retailers who may be disadvantaged in the game, this study constructs a two-stage green supply chain game model, considering the bounded rationality of decision-makers and the substitution effect of non-green products, and analyzes the impacts of two operational strategies that retailers can adopt—price-cutting strategy and early replenishment strategy. The research reveals that retailers tend to lower prices in the second stage when price reductions stimulate consumer purchases, enhancing their profitability. However, strategic retailers may raise prices in the first stage to create room for discounts later, potentially harming consumer interests. Contrary to expectations, anticipating future demand does not always improve supply chain profitability in the early replenishment strategy, which mainly depends on the market environment. Early replenishment deprives retailers of negotiation leverage in the second stage, and bulk orders may lead manufacturers to over-invest in green innovation. Therefore, this strategy is effective only when green innovation costs are low, consumer environmental awareness is high, or price sensitivity is low.