درخت حوزه‌های تخصصی

The advancement of artificial intelligence (AI) and its growing societal importance are reshaping decision-making processes and policy analysis roles. This study examines the readiness of manufacturing companies in Malaysia to embrace AI technology, considering its potential to enhance decision-making, productivity, quality control, job automation, and data analysis. Focusing on the Technology, Organization, and Environment (T-O-E) readiness framework, the research investigates the relationship between these dimensions and AI adoption readiness among manufacturing companies in Shah Alam, Selangor, Malaysia. AI adoption readiness serves as the dependent variable, while technological, organizational, and environmental readiness dimensions act as independent variables. The study applies the T-O-E framework to AI readiness and proposes a framework for assessing AI readiness at the manufacturing level. It identifies factors influencing readiness within the technological, organizational, and environmental dimensions, including relative advantage, compatibility, resources, competitive pressure, top management support, and government regulations. Through rigorous analysis, patterns, trends, and correlations are revealed, highlighting a significant link between the T-O-E readiness dimensions and AI adoption readiness. Notably, organizational readiness emerges as a key driver of AI adoption in Malaysian manufacturing companies. The results of this investigation have broad implications, offering suggestions to improve organizational preparedness and unlock AI’s potential benefits for businesses in the industrial sector. Additionally, the research lays the groundwork for further studies on AI readiness across various industries and international contexts. As AI becomes increasingly integrated into manufacturing processes, adaptive businesses gain competitive advantages on a global scale. These advantages include increased productivity, informed decision-making, streamlined quality control, improved customer satisfaction, and potential contributions to economic growth. The study concludes by recommending strategies to reinforce organizational readiness and emphasizes the need for future research to deepen understanding of AI adoption readiness in the manufacturing industry. The integration of AI technology offers benefits such as enhanced productivity, decision-making, quality control, and customer satisfaction, granting businesses a competitive edge in the digital landscape and increasing stakeholder interest.

Eco-friendly product usage is a defining trend in consumer purchase intentions during this decade. Younger, tech-savvy consumers are often more sensitive to environmental concerns when purchasing products due to the abundance of information available on social media platforms. In the food and beverage industry, this trend has led many younger consumers to prefer purchasing eco-friendly, sustainable cutlery for food and beverage consumption. With online social commerce platforms selling eco-friendly utensils readily available, a larger group of consumers, primarily young adults, seem to purchase these products impulsively due to various factors. The four independent variables proposed in this study that seem to influence the online impulse buying behavior of young adults toward these eco-friendly products are fear, serendipity, electronic word of mouth (eWOM), and website quality. Convenience sampling was utilized to gain a greater understanding of the targeted consumer group, using a 5-item Likert scale to collect data from respondents. The validity and reliability of the questionnaire items were further confirmed using statistical methods, namely average variance extracted (AVE), composite reliability (CR), and Cronbach’s alpha. The hypotheses were tested using AMOS software (version 24) through confirmatory factor analysis (CFA) to tabulate the results. Based on the study conducted, it is concluded that fear and eWOM do influence the online impulse buying of eco-friendly food utensils among young adults in Kuala Lumpur. It is further suggested that elements such as fear and eWOM play a significant role in online impulse purchases of eco-friendly utensils, as young adults rely heavily on social media marketing. This reliance contributes to these consumers’ growing concern for their future, prompting them to be more environmentally conscious.

Few studies have addressed the interaction among reader and text variables in reading comprehension. This research probed the role of aspects of lexical knowledge to the TOEFL reading comprehension of passages with low and high lexical cohesion. The extent of this contribution was checked for five types of TOEFL reading test items: Main Idea, Stated Detail, Inference, Reference, and Lexical Inferencing. For this purpose, 60 Iranian English as a Foreign Language (EFL) took the New Vocabulary Levels Test (NVLT), Word Associates Test (WAT), and TOEFL reading comprehension tests with low and high lexical cohesion determined through the computational tool Coh-Metrix. Results of Wilcoxon signed-rank tests revealed that the students outperformed on three types of TOEFL reading items (Main Idea, Inference, and Lexical Inferencing) for texts with high lexical cohesion. Multiple linear regression analyses further showed that vocabulary depth was a stronger predictor of the participants’ scores on Lexical Inferencing item of TOEFL reading texts with high lexical cohesion, while vocabulary size predicted the same item performance for the low-lexical-cohesion subtest. Moreover, vocabulary depth could significantly predict performance on Main Idea test item of TOEFL high-lexical-cohesion subtest over and above the low-lexical-cohesion subtest. The implications for teachers, test designers, and materials writers are discussed.