Punjabi University is a collegiate state public university located in Patiala, Punjab, India. It was established on 30 April 1962, and is only the second university in the world to be named after a language, after Hebrew University of Israel. Originally it was conceived as a unitary multi-faculty teaching and research university, primarily meant for the development and enrichment of Punjabi language and culture, but alive to the social and education requirements of the state.
The issues of intellectual capital (IC) and efficiency in developing countries' banking literature are contemporary. Scant empirical research is available that establishes the associations between these items. Thus, the present study explores the linkages between IC and banking efficiency in Ethiopia for the study of 2011-2022. The study applied a two-step system generalized method of moments (2SYS-GMM) to determine how IC affects the firm efficiency. To measure IC, the modified value-added intellectual coefficient (M-VAIC) and its three parts-human capital efficiency (HCE), structural capital efficiency (SCE), and relational capital efficiency (RCE)-were employed. The study used the data envelopment analysis (DEA) model to evaluate banks' technical, allocative, and cost-efficiency scores from 2011 to 2022. The study's findings show that Ethiopian commercial banks' overall mean technical, allocative, and cost efficiency is 89.1%, 75.7%, and 68%, respectively. The 2SYS-GMM regression results show that the overall IC, as measured by M-VAIC, has a strong positive contribution towards technical, allocative, and cost-efficiency scores. The SCE positively affects technical and cost efficiency. Moreover, RCE significantly improves allocative efficiency and cost efficiency but doesn't substantially affect banks' technical efficiency. Surprisingly, HCE hasn't made a substantial contribution to the efficiency of banks in Ethiopia. Theoretically, the results of this study support the resource-based theory perspective of utilizing bank IC as an intangible resource to enhance efficiency. Consequently, the findings of this paper might be helpful for policymakers, investors, practitioners, and researchers in putting in place a sound investment, measurement, and management of tangible and intangible assets that enhance intellectual capabilities and banks' efficiency.
PurposeThis study explores the impact of sponsorship disclosure on purchase intention, focusing on the serial mediation of brand attitude and source credibility and the moderating roles of generation (Gen Y and Gen Z) and parasocial relationships.Design/methodology/approachA survey tool was distributed to social media users of Facebook and Instagram who follow at least one social media influencer and have previously engaged in promotional content from the influencers. A total of 553 responses were analyzed through partial least squares (PLS) multigroup analysis using PLS-structural equation modeling (SEM) while moderating effects were examined using PROCESS macro in SPSS.FindingsResults indicate that sponsorship disclosure negatively affects brand attitude and source credibility, lowering purchase intention, while strong parasocial relationships mitigate this effect. Brand attitude and source credibility jointly mediate the relationship between disclosure and purchase intention. Gen Z perceives disclosures as transparent and authentic, enhancing receptiveness, whereas Gen Y is more skeptical.Practical implicationsThe study possesses valuable insights for brands, marketers and policymakers to optimize influencer campaigns. Findings suggest that brands should design transparent yet strategically framed disclosures, prioritize credible influencers and foster strong parasocial relationships to mitigate negative disclosure effects and enhance consumer engagement.Originality/valueThis study is among the first to integrate sponsorship disclosure, brand attitude, source credibility, generational differences and parasocial relationships within the framework of the elaboration likelihood model. By simultaneously examining serial mediation and moderation and integrating multiple theoretical perspectives, this study provides a comprehensive understanding of how sponsorship disclosure influences purchase intention and offers new insights for enhancing influencer marketing effectiveness.
Breast cancer (BC) remains one of the foremost reasons of mortality worldwide. Early detection of BC can significantly support timely diagnosis and helpful in treatment. This study presents a fresh improved version of Chernobyl disaster optimizer (CDO) known as ICDO method for classifying BC disease. CDO version has been merged with Dimension Learning Hunting (DLH) search strategy to reduce shortcomings of CDO method for instance diversity, poor local avoidance, slower and premature convergence, weak exploration ability and failure in trapping the goal respectively. The ICDO method benefits from DLH strategy to enhance the exploitation and exploration behaviour of particles in search area. Secondly, this work also presents the classification model based on a merged neural network, ICDO, along with exchange knowledge for classifying BC datasets. To evaluate the strength of ICDO method, 29-CEC’ 2017 benchmarks and two different datasets such as MIAS and CBIS-DDSM have been used in this study. Tabulated results reveal that ICDO model is superior to others in proving the best optimal solutions for CEC functions and classification results for datasets.
The present research work presents a novel fluorescence-based chemosensor (5R)-5-[4-(dimethylamino)phenyl]-3-(3-hydroxyphenyl)-4,5-dihydro-1H-pyrazole-1-carbothioamide (DHPC) for the specific detection of Mo6+ ions. DHPC demonstrates a strong "turn-off" fluorescence response upon interaction with Mo6+ ions, enhanced by paramagnetic quenching processes. The sensor exhibits remarkable selectivity towards different metal ions (Al3⁺, Co2⁺, Ni2⁺, Hg2⁺, Cd2⁺, Pb2⁺, Mg2⁺, Cu2⁺, Sn2⁺, Ba2⁺, Zn2⁺, K⁺, Na⁺, Ca2⁺, and Mo⁶⁺), as confirmed by fluorescence and UV–Vis absorption spectroscopy. The determined limit of detection (LOD) is 1.74 μM which confirmed pyrazoline sensitivity. Density functional theory (DFT) analyses provide crucial understanding regarding the electrical properties of sensor, chemical interactions, and fluorescence quenching phenomena. This study advances the understanding of pyrazoline based fluorescence sensors, providing the entrance for greater metal ion sensing applications. The practical application was evaluated using water samples, including tap, mineral, and river water, exhibiting high recovery rates with no interference.
Stainless steel is the most widely utilized material in various process-based sectors because of its remarkable properties such as resistance to corrosion, better performance and excellent mechanical strength. However, it is prone to acidic corrosive attacks due to various environmental conditions. The paper provides a detailed comparative assessment of corrosion behaviour of stainless steel in various concentrations of acidic environments such as sulphuric acid (H2SO4), nitric acid (HNO3), hydrochloric acid (HCl), etc. The distinctive features of various methods such as cathodic protection, selective alloying, coatings, and surface treatment drawn from the latest published literature are discussed, and suggest that the adaptation of these methods provides the potential to enhance corrosion protection in industrial environment. In nut shell, this review can be used by future researchers for different insights regarding the corrosion mitigation and to facilitate their translation into diverse industry applications.
