This study conducts a systematic literature review to analyze the integration of artificial intelligence (AI) within business excellence frameworks. An analysis of the findings in the reviewed articles yielded five major themes: AI technologies and intelligent systems; impact of AI on business operations, strategies, and models; AI-driven decision-making in infrastructure and policy contexts; new forms of innovation and competitiveness; and the impact of AI on organizational performance and value creation in infrastructure projects. The findings provide a comprehensive understanding of how AI can be integrated into organizational excellence emerged frameworks to address challenges in infrastructure governance, and sustainable development. Key questions addressed include: how AI affects consumer behavior and marketing strategies. What AI’s capabilities for businesses, especially marketing and digital strategies? How can organizations address the drivers and barriers to help make better use of AI in these business operations? Should organizations even do anything with these insights? These questions and more will be tackled throughout this discussion. This paper attempts to derive a comprehensive conceptual framework from several fields of human resources, operational excellence, and digital transformation, that can help guide organizations and policymakers in embedding AI into infrastructure and development initiatives. This framework will help practitioners navigate the complexities of AI integration, ensuring profitability and sustainable growth in a highly competitive landscape. By bridging the gap between AI technologies and development-related policy initiatives, this research contributes to the advancement of infrastructure governance, public management, and sustainable development.

The food and beverage sector played a big part in contributing to the economic growth in Malaysia hence there was a major increase in the numbers of restaurants opening up for businesses. This study therefore examines factors with the aims of ensuring a sustainable development in full-service restaurants in West Malaysia. The results of this study have made a substantial contribution to restaurant owner’s’ comprehension of the fundamental components that underlie customer satisfaction and loyalty. By examining the moderating effect of the customer’s gender in full-service restaurants in West Malaysia, the objective of this study was to ascertain the relationships between the three variables (quality of the food served at the restaurant, service quality, and environment), as well as the degree to which each attribute was able to relate to diner satisfaction. The underpinning theory for this study was the Theory of Planned Behavior (TPB). Quantitative methods according to descriptive research and convenience sample strategy were utilized in this cross-sectional study. Questionnaires were distributed to 264 respondents through various online platforms such as WhatsApp, Telegram, Facebook, and email. Data collection was evaluated using the Statistical Program for Social Sciences (SPSS) version 27. In order to examine the connection between the three factors and diner’s satisfaction, various tests such as the multiple regression analysis, One-way ANOVA and Beta Coefficient test were carried out. The findings gave current restaurant owners and potential restaurant owners an overview of the different attributes influencing diner’s satisfaction at full-service restaurants in West Malaysia and also the extent of the moderating effect of diner’s gender had on each attribute. The outcome of this paper is expected to provide a sustainable growth in this industry.

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.

The bubble milk tea industry in Malaysia which was thought to have slowed down in the recent years since its first appearance in 2010 has made a comeback. At the point of conducting this research, there are almost 100 brands of bubble milk tea in Malaysia and it is not surprising that some of these shops are selling more than a thousand cups a day. However, there has been limited research conducted on factors influencing brand equity on bubble milk tea brands in Johor Bahru. This study is to investigate whether brand loyalty, perceived quality, brand awareness and brand association influence brand equity on bubble milk tea brands in Johor Bahru through distribution of online questionnaires. This study novelty is at the examining the factors influencing brand equity in the context of bubble milk tea in Johor Bahru, Malaysia. Data derived from responses of 400 respondents through sampling were analysed using SPSS v29. Hypotheses testing performed through simple linear regression revealed that brand loyalty, perceived quality, brand awareness and brand association have significant effect on brand equity of bubble milk tea brands in Johor Bahru, Malaysia. It was also demonstrated that perceived quality has the most significance influence on brand equity. Organizations in the bubble milk tea industries are able to benefit from these findings by prioritizing their marketing strategies to gain competitive edge over their competitors. With findings that perceived quality having the most significance influence, marketers with limited resources can narrow down their options and focus on this specific dimension to increase their brand value.

This study aims to elucidate the impact of marketing investment dimensions (MTS, MTOE, ROMI) on profitability indicators (ROA, ROE, GPM, OPM) and sustainable growth indicators (SGR, ARG) for service companies. The study population consisted of 135 service companies listed on the Amman Stock Exchange. A purposive sample of 55 companies was selected from this population. Financial reports and statements from 2018–2022 for these companies were analyzed to achieve the study objectives, employing appropriate statistical methods like multiple regression to test hypotheses. Previous literature shows conflicting results regarding the relationship between marketing investment dimensions and profitability/sustainable growth. Some studies found positive impacts, while others did not. This study contributes to this debate by providing statistical evidence. The results show that higher MTS, MTOE, and ROMI have a positive impact on SGR, OPM and ROA but a negative impact on GPM, ARG, and ROE. This underscores that marketing investments should be viewed in conjunction with overall operating expenses. Companies that control other expenses and increase the marketing investment proportion of total operating expenses may achieve better financial performance. Marketing investment metrics can serve as useful diagnostics and measures of effectiveness for improving marketing profitability, financial performance, and growth. In summary, this study statistically demonstrates the nuanced impacts of marketing investments on service company profitability and sustainable growth indicators. The results emphasize analyzing marketing spends in context of broader expenses and overall company financial health.

This research delves into the urgent requirement for innovative agricultural methodologies amid growing concerns over sustainable development and food security. By employing machine learning strategies, particularly focusing on non-parametric learning algorithms, we explore the assessment of soil suitability for agricultural use under conditions of drought stress. Through the detailed examination of varied datasets, which include parameters like soil toxicity, terrain characteristics, and quality scores, our study offers new insights into the complexities of predicting soil suitability for crops. Our findings underline the effectiveness of various machine learning models, with the decision tree approach standing out for its accuracy, despite the need for comprehensive data gathering. Moreover, the research emphasizes the promise of merging machine learning techniques with conventional practices in soil science, paving the way for novel contributions to agricultural studies and practical implementations.