Design and procurement integration strategies in construction projects play an important role and have an impact on the overall project cycle. Integrated design and procurement will increase productivity and reduce waste. This research aims to provide a guide to good design and procurement integration strategies in Design and Build (DB) projects in government projects. This research uses qualitative and quantitative methods in the form of a schematic literature review followed by a Focus Group Discussion (FGD) with the Delphi method to formulate integrated design and procurement that improve project performance. In-depth interviews were conducted with 90 respondents to explore the implementation of the design and procurement strategy on the project used as a case study. The results of this research are recommendations for an integrated design and procurement strategy which can be used as a Standard Operating Procedure (SOP) in DB projects on government projects so that it can provide added value from the start of the project being designed through tenders. This research can be utilized by project stakeholders, academics and anyone who will develop project performance through the integrated design and procurement in the long term.

Accurate demand forecasting is key for companies to optimize inventory management and satisfy customer demand efficiently. This paper aims to Investigate on the application of generative AI models in demand forecasting. Two models were used: Long Short-Term Memory (LSTM) networks and Variational Autoencoder (VAE), and results were compared to select the optimal model in terms of performance and forecasting accuracy. The difference of actual and predicted demand values also ascertain LSTM’s ability to identify latent features and basic trends in the data. Further, some of the research works were focused on computational efficiency and scalability of the proposed methods for providing the guidelines to the companies for the implementation of the complicated techniques in demand forecasting. Based on these results, LSTM networks have a promising application in enhancing the demand forecasting and consequently helpful for the decision-making process regarding inventory control and other resource allocation.

In recent times, there has been a surge of interest in the transformative potential of artificial intelligence (AI), particularly within the realm of online advertising. This research focuses on the critical examination of AI’s role in enhancing customer experience (CX) across diverse business applications. The aim is to identify key themes, assess the impact of AI-powered CX initiatives, and highlight directions for future research. Employing a systematic and comprehensive approach, the study analyzes academic publications, industry reports, and case studies to extract theoretical frameworks, empirical findings, and practical insights. The findings underscore a significant transformation catalyzed by AI integration into Customer Relationship Management (CRM). AI enables personalized interactions, fortifies customer engagement through interactive agents, provides data-driven insights, and empowers informed decision-making throughout the customer journey. Four central themes emerge: personalized service, enhanced engagement, data-driven strategy, and intelligent decision-making. However, challenges such as data privacy concerns, ethical considerations, and potential negative experiences with poorly implemented AI persist. This article contributes significantly to the discourse on AI in CRM by synthesizing the current state, exploring key themes, and suggesting research avenues. It advocates for responsible AI implementation, emphasizing ethical considerations and guiding organizations in navigating opportunities and challenges.

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 competition for financial support among non-profit organizations (NPOs) has been intense for quite some time. It is crucial for these organizations to boost their competitive edge by gaining a deep understanding of donor behavior and fostering ongoing interactions with them. In today’s world, where convenience and efficiency are highly valued, factors such as the timing and location of donations can deter potential donors from contributing. Rigid and inconvenient donation methods can also hinder the donation process. As a result, this study aims to explore the role of convenience within the donation process, specifically investigating whether the convenience of online donation platforms provided by non-profit organizations significantly influences donors’ propensity to make contributions. This research differentiates the range of services offered by non-profit organizations and employs a questionnaire survey to examine the websites of the NPOs. A total of 466 valid responses were gathered. The empirical findings indicate that donors prioritize simplicity and speed in the online donation process. Additionally, donors prefer websites where they can easily locate necessary information and various details about the donation process, with relevant links that minimize time waste and complexity in navigating the website. The study also reveals that the convenience factor significantly influences donation behavior. Based on these insights, the study offers recommendations for non-profit organizations on how to provide donor-centric services by focusing on the aspects of convenience that donors value most in the donation process.