Industrial Management
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Item Staphylococcus edaphicus KCB02A11 incorporated with natural adsorbents: first report on its tolerance and removal of hexavalent chromium [Cr(VI)](Springer Link, 2024) Aththanayake, A. M. K. C. B.; Rathnayake., I. V. N.; Deeyamulla, M. P.; Megharaj, MallavarapuDeteriorating the quality of different parts of the ecosystem due to toxic metals is a serious global issue. Hexavalent chromium is a metal that can cause adverse effects on all living beings, including plants, animals, and microorganisms, on exposure to high concentrations for prolonged periods. Removing hexavalent chromium from various types of wastes is challenging; hence the present study investigated the use of bacteria incorporated with selected natural substrates in removing hexavalent chromium from water. Isolated Staphylococcus edaphicus KCB02A11 has shown higher removal efficiency with a wide hexavalent chromium range (0.025-8.5 mg/L) within 96 h. Incorporating the isolated strain with natural substrates commonly found in the environment (hay and wood husk) showed high removal potential [100% removal with 8.5 mg/L of Cr(VI)], even within less than 72 h, with the formation of biofilms on the used substrates applied for metal removal on a large scale for prolonged periods. This study is the first report investigating hexavalent chromium tolerance and removal by Staphylococcus edaphicus KCB02A11.Item Impact of IoT Integration on Enterprise Resource Planning (ERP) Systems: A Comprehensive Literature Analysis(Institute of Electrical and Electronics Engineers (IEEE), 2024) Wijesinghe, Shalani; Nanayakkara, Imasha; Pathirana, Rashmi; Wickramarachchi, Ruwan; Fernando, IshenkaThe integration of Internet of Things (IoT) technology with Enterprise Resource Planning (ERP) systems has gained significant attention in recent years. This research study aims to provide a comprehensive analysis of the impact of IoT integration on ERP systems. The study explores the benefits, challenges, and potential solutions associated with combining IoT and ERP. The findings highlight that IoT integration with ERP offers several advantages, such as real-time data collection, improved supply chain visibility, enhanced asset tracking, and predictive maintenance capabilities. These benefits lead to increased operational efficiency, reduced costs, and better decision-making. The integration of IoT with ERP also presents challenges that need to be addressed. These challenges include data security and privacy concerns, IoT traffic, and data management. The research identifies potential solutions and best practices to overcome these challenges. Furthermore, the study discusses the implications of IoT integration on various functional areas of ERP systems, such as healthcare, manufacturing, logistics, inventory management, and customer relationship management. The research methodology includes an extensive review of existing literature and case studies. This research provides valuable insights into the impact of IoT integration on ERP systems, offering guidance for organizations considering already implemented IoT-enabled ERP solutions or currently implementing ERP solutions.Item Impact of AI-based predictive analytics on demand forecasting in ERP systems: A Systematic Literature Review(Institute of Electrical and Electronics Engineers (IEEE), 2024) Fathima, Fazaal; Inparaj, Rishani; Thuvarakan, Dushyanthan; Wickramarachchi, Ruwan; Fernando, IshenkaArtificial intelligence (AI) has revolutionized demand forecasting within Enterprise Resource Planning (ERP) systems, offering a powerful tool to enhance accuracy and efficiency in predicting future demand patterns. This literature review explores the impact of AI-based predictive analytics on demand forecasting in ERP systems by synthesizing and analyzing existing research. This paper provides a comprehensive examination of the transformative effects of AI-driven demand forecasting across diverse industries, including fashion retail, biopharmaceuticals, energy management, and transportation. We highlight the unique benefits and applications of AI-driven demand forecasting, such as anticipating customer needs, optimizing inventory levels, and making data-driven decisions, ultimately leading to a competitive edge in the marketplace. Our study emphasizes the importance of AI integration into ERP systems for businesses seeking to enhance decision-making and achieve organizational success in today's dynamic and competitive business landscape. By providing valuable insights and showcasing significant improvements in forecasting accuracy, real-time insights, supply chain efficiency, and risk management facilitated by AI-based predictive analytics, this research contributes to advancing knowledge in the field and offers practical guidance for businesses and researchers alike.Item Factors Influencing the Adoption of Agile Project Management Methodologies by Engineering Teams in the Telecommunications Industry(Institute of Electrical and Electronics Engineers (IEEE), 2024) Dilhara, Thamindu; Jayasinghe, Shan; Fernando, IshenkaThis study explores the dynamic landscape of Sri Lanka’s telecommunications industry, specifically examining the correlation between engineering teams and the adoption of agile project management methodologies. With the industry’s shift towards virtual content, cloud computing, and software-driven systems, it is essential to examine the factors that impact the adoption of agile practices. Through a comprehensive literature analysis, this study identifies and analyzes ten key factors that significantly influence the integration of agile methodologies within engineering teams. Afterwards by obtaining the expert opinions, research conducts a comprehensive data analysis by identifying four key factors: organizational culture, adaptability, communication, problem-solving, and employee engagement. The study surveyed 145 telecommunication engineering teams, incorporating vital demographic characteristics to enhance the validity of its findings. Key insights reveal the critical role of organizational culture in driving agile implementation, with effective problem-solving practices contributing positively. Surprisingly, superior communication exhibits limited direct impact. Interestingly, the moderation effect of employee engagement on the relationship between organizational culture and agile adoption is negative. In contrast, employee engagement significantly influences the relationship between effective problem-solving practices and agile adoption. The study concludes with practical recommendations for creating an agile-friendly environment, investing in adaptabilitytraining, enhancing communication tools, and cultivating effective problem-solving practices. These insights aim to guide the telecommunications industry in Sri Lanka towards agile practices, fostering increased productivity, improved quality, and accelerated time-to-market.Item Antecedents Of Driving Customer Purchase Intention Via AI Based Customer Engagement Strategies In The Post Pandemic Era(Institute of Electrical and Electronics Engineers (IEEE), 2024) Hensman, Sheramy; Jayasinghe, Shan; Fernando, IshenkaThis study explores the antecedents that affect customer purchase intention in the post-pandemic era, specifically through AI (Artificial Intelligence) based customer engagement strategies. By analyzing a sample size of at least 147 social media users in Sri Lanka and examining demographic profiles such as age, gender, occupation, and average monthly income, this research addresses a gap in the literature by investigating the positive impact of AI on conversion rate optimization. The study focuses on the factors of brand credibility, customer satisfaction, price sensitivity, brand attitude, and social influence, and their impact on consumer purchase intention in the context of AI-based customer engagement. This research rejects some hypotheses related to brand credibility, price sensitivity, and social influence, and accepts others related to customer satisfaction and brand attitude. It highlights the importance of customer satisfaction and brand attitude in driving consumer purchase intention in the context of AI-based customer engagement. The findings provide valuable insights for businesses and marketers seeking to optimize AI strategies for improved customer engagement and higher conversion rates.Item Analyzing the Impact of Student Engagement on Learning Outcomes in E-learning Platforms: Case on Programming Language Courses(Institute of Electrical and Electronics Engineers (IEEE), 2024) Lakshani, Erandika; Wijayanayake, Janaka; Prasadika, JinendriThis study explores how students engage with e-learning platforms and how it relates to their academic success, focusing on programming language learning among Information Technology (IT) undergraduates in Sri Lanka. This study employs the Community of Inquiry (CoI) framework in conjunction with the evaluation of academic pressure to measure student engagement. The research aims to understand the impact of cognitive presence, social presence, and teaching presence on learning outcomes in e-learning, while also examining how infrastructure plays a moderating role. The data were gathered through a structured questionnaire, and the gathered data were analyzed using SmartPLS 4 software. The findings highlight that cognitive presence, social presence, and teaching presence significantly predict better learning outcomes, emphasizing their crucial role in the effectiveness of e-learning in programming language courses. The study doesn't find a strong connection between the academic pressure felt by undergraduates and the learning outcomes. This suggests that success in e-learning is influenced by various factors beyond traditional academic pressures. Additionally, we investigate the moderating effect of infrastructure on these relationships. Unexpectedly, the study reveals that infrastructure doesn't significantly change the connections between cognitive presence, social presence, and teaching presence on learning outcomes. In simpler terms, the technical support provided doesn't seem to alter how these elements impact student success in this context. This study offers valuable insights into the intricate dynamics of student engagement, academic pressure, and infrastructure within the realm of e-learning. These findings hold significance for educators, policymakers, and researchers in the field of education, providing actionable knowledge to enhance support for students navigating e-learning environments.Item Drivers of Actual Usage of Building Information Modelling Tools by Civil Engineering Professionals in Construction Industry of Sri Lanka(Institute of Electrical and Electronics Engineers (IEEE), 2024) Fathima, Fazaal; Jayasinghe, Shan; Prasadika, Jinendri; Wijerathna, SujithThe construction industry in Sri Lanka is a key driver of the country’s economy, contributing significantly to GDP, employment, and infrastructure development. However, the industry faces challenges due to outdated design methods and antiquated technology, hindering efficient stakeholder communication and collaboration, particularly during crucial stages like design. Cloud-based Building Information Modeling (BIM) emerges as a solution, providing a centralized platform for real-time collaboration. BIM is widely recognized as an industry standard worldwide, but its implementation in Sri Lanka’s construction industry is still in its early stages. This research, guided by the Unified Theory of Acceptance Model and Use of Technology (UTAUT) and Technology Acceptance Model (TAM), explores BIM adoption factors. A systematic literature review was conducted to identify key drivers through a meticulous analysis of 50 studies: Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Social Influence (SI), Facilitating Conditions (FC), and Behavioral Intention (BI). The conceptual framework, based on TAM and UTAUT, was developed. Analyzing data from 131 respondents via PLS-SEM, the study found positive impacts of SI on BI, as well as impacts of BI and FC on Actual Usage (AU). Moreover, the impact of SI, PU, and PEOU on AU was fully mediated by BI. Results of this research underscore BIM’s significance, offering insights for effective adoption in Sri Lanka’s construction projects.Item Determinants of Employee Engagement in the Post-Pandemic Working-from-Home Contexts: Evidence from the Sri Lankan Software Industry(Institute of Electrical and Electronics Engineers (IEEE), 2024) Thasipan, Rajagopal; Jayasinghe, Shan; Prasadika, A. P. K. J.; Wijerathna, Sujith KasunThis study explores the dynamic landscape of employee engagement within the Sri Lankan Software Industry amid the post-pandemic surge in remote work. Recognizing the unique challenges and opportunities of this transition, the study highlights the necessity for a comprehensive comprehension of the factors that impact engagement when conventional workplace structures are not present. Framed within the context of the evolving work-from-home scenario, the study addresses the industry-specific gap in knowledge, contributing both theoretically and practically. Leveraging a comprehensive literature review and insights from industry professionals, the research identifies factors crucial to employee engagement, including employee well-being, small-group collaboration, job safety and security, job satisfaction, social interaction, and the supervisor's role. The hypotheses formulated and tested through a survey of 196 technical employees reveal significant associations between these factors and employee engagement. Notably, employee well-being emerges as a key contributor, with holistic well-being programs deemed essential in remote work settings. The study further explores the mediating role of small-group collaboration and the moderating impact of the supervisor's role, shedding light on the intricate relationships that shape engagement. While emphasizing the positive impact of transparent communication on job safety and security, the research underscores the importance of proactively addressing challenges to boost overall job satisfaction. The findings offer valuable insights for organizations navigating the complexities of remote work, emphasizing the multifaceted strategies required to enhance organizational performance in the evolving work landscape.Item Assessment of Human Emotional Responses to AI–Composed Music: A Systematic Literature Review(Institute of Electrical and Electronics Engineers (IEEE), 2024) Fernando, Poorna; Mahanama, Thilini V.; Wickramasinghe, ManyaIn the world of musical creation, the integration of artificial intelligence (AI) represents a significant paradigm shift in emotional engagement. This research investigates the human emotional responses evoked when listening to AI-composed music. Focused on figuring out the emotional impact of AI-composed music, the study explores the complex relationship between human emotional experiences and compositions crafted by AI algorithms. Through a comprehensive literature review, this paper examines existing methodologies, insights, and gaps in understanding the emotional dimensions of AI-composed music. Major findings reveal that while AI software like artificial intelligence virtual artist (AIVA) shows it can help explore emotional authenticity, ongoing doubt and preference for music made by humans highlight the need for more research. Attitudes of both listeners and music professionals toward AI-composed music are characterized by skepticism and negative perceptions, emphasizing the urgency to address reservations and investigate the unique emotional qualities of AI-composed music. Furthermore, the complex nature of music emotion recognition, influenced by factors such as music genre, cultural perspective, and age group, complicates understanding emotional responses to both human-created and AI-composed music. The paper supports the development of analytical methods, particularly through machine learning and deep learning approaches, to enhance understanding of the complexities of emotional responses and improve AI music composition. A human- experience-centered framework is proposed to address subjectivity in assessing emotional responses to music. This research aims to understand the details of emotional responses and find out if AI-composed music can really evoke emotions comparable to human-created compositions.Item Stream Count Predictive Analysis for Upcoming Songs on Spotify using Machine Learning: A Systematic Literature Review(Institute of Electrical and Electronics Engineers (IEEE), 2024) Ranidu, M.G. Yoshitha; Mahanama, Thilini V.; Wijenarayana, SankiniIn the era of evolving music consumption, this systematic literature review researches the realm of predictive analytics for music streaming, specifically targeting Spotify's stream count prediction in Sri Lanka through machine learning methodologies. With streaming platforms shaping the music industry landscape, accurately predicting song popularity becomes essential for artists, producers, and industry stakeholders. This review analyzes global studies on machine learning's application in forecasting stream counts while defining their methodologies and outcomes. It intricately examines diverse machine-learning methodologies employed in prior research endeavors. Ranging from regression models and ensemble techniques to deep learning architectures, the spectrum of methodologies used in forecasting stream counts on music streaming platforms is elucidated. Noteworthy techniques such as support vector machines (SVM), random forests, and recurrent neural networks (RNNs) have demonstrated efficacy in capturing intricate patterns within music data for predictive analysis. Our paper highlights the significance of feature engineering and selection methods, underscoring their pivotal role in enhancing the accuracy of predictive models. Through this comprehensive study, this review aims to expose specific gaps in stream count prediction models tailored to Sri Lanka's varied music preferences and consumption habits. By illuminating these gaps, it aspires to stimulate future research endeavors focused on refining predictive models, ultimately empowering the Sri Lankan music industry with more insights for better strategic decision-making.