In multiple biological processes, molecular recognition performs an integral role in detecting bio analytes. Molecular imprinted polymers (MIPs) are tailored sensing materials that can biomimic the biologic ligands and can detect specific target molecules selectively and sensitively. The formulation of molecularly imprinted polymers is followed by the formulation of a control termed as non-imprinted polymer (NIP), which, in the absence of a template, is commonly formulated to evaluate whether distinctive imprints have been produced for the template. Given the difficulties confronting bioanalytical researchers, it is inevitable that this strategy would come out as a central route of multidisciplinary studies to create extremely promising stable artificial receptors as a replacement or accelerate biological matrices. The ease of synthesis, low cost, capability to 'tailor' recognition element for analyte molecules, and stability under harsh environments make MIPs promising candidates as a recognition tool for biosensing. Compared to biological systems, molecular imprinting techniques have several advantages, including high recognition ability, long-term durability, low cost, and robustness, allowing molecularly imprinted polymers to be employed in drug delivery, biosensor technology, and nanotechnology. Molecular imprinted polymer-based sensors still have certain shortcomings in determining biomacromolecules (nucleic acid, protein, lipids, and carbohydrates), considering the vast volume of the latest literature on biomicromolecules. These potential materials are still required to address a few weaknesses until gaining their position in recognition of biomacromolecules. This review aims to highlight the current progress in molecularly imprinted polymers (MIPs)-based sensors for the determination of deoxyribonucleic acid (DNA) or nucleobases.
Computer-mediated communication has dramatically transformed the human communication landscape by enhancing speed, content and social presence. The world has been experiencing a sharp decline in using email and phone calls due to organizations' rapid adoption of instant messenger (IM) for their day-to-day communication with their stakeholders. Moreover, the world has been experiencing a sharp decline in using email and phone calls since the beginning of the IM era. Hence, the aim of this study is to comprehend the IM adoption process through the lens of three theories. A structured questionnaire was developed to collect data from the organizations and test hypotheses using consistent PLS-SEM (PLSc) in SMART PLS combined with bootstrapping. The results indicated that parallelism is the most dominating factor explaining IM adoption in organizations followed by transmission velocity, rehearsability and symbol set. The findings of this study also confirmed that team performance is not influenced by communication but by the quality of the communication and the level of knowledge within the group that can create using instant messenger. Indeed, this empirical study is one of the limited investigations that combine three theories to explain the IM adoption process and its effect on team performance. Moreover, this study contributes both theoretically and practically to comprehending the adoption process of IM. Lastly, this study reconfirmed the importance of Media Synchronicity Theory (MST) and Social Presence Theory (SPT) in predicting IM adoption; and the findings of this research extended the applications of the Adaptive Structuration Theory (AST) from the IM perspective, which is, indeed, rare. Finally, this study provides a great foundation for managers to understand the importance of IM in their day-to-day communication systems.
This study reframes the Service Robot Acceptance Model (sRAM) as a value-centred framework. It examines how functional, social, relational and individual factors shape customer value in robot-mediated restaurant encounters and how this value drives re-patronage intention, word-of-mouth intention and robot-human rapport across generational cohorts. On-site intercept surveys were conducted with 351 diners in Kuala Lumpur restaurants using service robots. Using a disjoint two-stage PLS-SEM, the four second-order antecedent blocks predicted customer value and three post-use outcomes. Multi-group analysis tested generational moderation (Baby Boomers, Generation X, Generation Y, Generation Z). Fuzzy-set qualitative comparative analysis (fsQCA) and necessary condition analysis (NCA) identified cohort-specific configurations leading to high customer value. Relational and functional factors are the strongest overall predictors of customer value, followed by social and individual factors. Customer value, in turn, consistently predicts re-patronage intention, word-of-mouth intention and robot-human rapport. Generational results show distinct value-formation logics: Baby Boomers combine functional, relational and social drivers, Generation X relies mainly on social cues, Millennials on individual and relational factors, and Generation Z primarily on functional performance. fsQCA reveals equifinal configurations, with different combinations of functional, social, relational and individual conditions producing high value by cohort. Managers should intentionally design robot encounters by aligning functional, social, relational, and individual value drivers with the expectations of different generational cohorts, moving beyond uniform interaction styles toward cohort-sensitive value orchestration. The study extends sRAM beyond adoption by positioning customer value as the central post-use mechanism, integrating four antecedent blocks with generational and configurational perspectives to explain how service robots generate loyalty, word of mouth and rapport in hospitality settings.