The integration of digitalization and servitization has become a significant trend in transforming the manufacturing industry due to digital intelligence technology. This paper examines the impact of the integration of digitalization and servitization on the performance of manufacturing companies and how small-scale enterprises can promote digital transformation leading to servitization. The study involved surveying 331 manufacturing companies in China using a seven-point Likert scale questionnaire. Measurement scales were validated using confirmatory factor analysis and discriminant validity tests. Mediation analysis assessed digitalization’s impact on servitization and firm performance. The study’s findings emphasize the significant impact of digitalization and servitization on enterprises’ performance. Digitalization plays a crucial role in mediating this relationship. The study highlights three critical dimensions of digital variables, including digital technology, digital labor, and digital relationship resources, essential in enabling effective servitization. Manufacturing enterprises generally prefer aligning their technology investments and organizational changes within the digitalization framework to implement servitization successfully. The study suggests two integration strategies, namely conservative and aggressive. The finding emphasizes that the convergence of digitalization and servitization leads to a new manufacturing production mode called digital servitization.

The chemical reinforcement of sandy soils is usually carried out to improve their properties and meet specific engineering requirements. Nevertheless, conventional reinforcement agents are often expensive; the process is energy-intensive and causes serious environmental issues. Therefore, developing a cost-effective, room-temperature-based method that uses recyclable chemicals is necessary. In the current study, poly (styrene-co-methyl methacrylate) (PS-PMMA) is used as a stabilizer to reinforce sandy soil. The copolymer-reinforced sand samples were prepared using the one-step bulk polymerization method at room temperature. The mechanical strength of the copolymer-reinforced sand samples depends on the ratio of the PS-PMMA copolymer to the sand. The higher the copolymer-to-sand ratio, the higher the sample’s compressive strength. The sand (70 wt.%)-PS-PMMA (30 wt.%) sample exhibited the highest compressive strength of 1900 psi. The copolymer matrix enwraps the sand particles to form a stable structure with high compressive strengths.

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.

The experiments were carried out to validate an analytical method and to examine the impact of various decontaminating solutions on the removal of acephate residues from okra. Acephate analysis was performed using HPLC-UV, and sample extraction was done using the QuEChERS method. Method validation encompassed assessing specificity, linearity, precision, accuracy, as well as limits of detection (LOD) and quantification (LOQ). The method exhibited excellent linearity with R² values ≥ 0.99. LOD and LOQ were determined at 0.5 µg mL⁻¹ and 2 µg mL⁻¹, respectively. The results indicated average recoveries ranging from 80.2% to 83.3% with a % RSD below 5%. The decontamination procedures include rinsing with running tap water, soaking in lukewarm water, 2% CH₃COOH, 1% NaCl, 5% NaHCO₃, 0.01% KMnO₄, and in commercially available decontamination products such as nimwash, veggie clean, and arka herbiwash for a duration 10 minutes. Among all the treatments, soaking in nimwash solution showed remarkable effectiveness (96.75% removal), followed by veggie clean (94.97% removal) and arka herbiwash (95.80% removal). Washing okra samples in running tap water was found to be the least effective compared to other treatments.