Customer loyalty: The case of mobile phone users in Universiti Utara Malaysia

This research attempts to examine the relationships between service quality, switching barriers and brand image and customer loyalty in the Universiti Utara Malaysia sector. Based on the theoretical model, a comprehensive set of hypotheses were formulated and a methodology for testing them was outlined.These hypotheses were tested empirically by means of questionnaires to demonstrate the applicability of the theoretical model.The results indicate that service quality, switching barriers, and brand image are separate constructs that combine to determine loyalty, with service quality and switching barriers exerting a stronger influence than brand image.Hypotheses H1, H2 were supported, while hypothesis H3 was rejected

How Good are LLMs in Generating Personalized Advertisements?

In this paper, we explore the potential of large language models (LLMs) in generating personalized online advertisements (ads) tailored to specific personality traits, focusing on openness and neuroticism. We conducted a user study involving two tasks to understand the performance of LLM-generated ads compared to human-written ads in different online environments. Task 1 simulates a social media environment where users encounter ads while scrolling through their feed. Task 2 mimics a shopping website environment where users are presented with multiple sponsored products side-by-side. Our results indicate that LLM-generated ads targeting the openness trait positively impact user engagement and preferences, with performance comparable to human-written ads. Furthermore, in both scenarios, the overall effectiveness of LLM-generated ads was found to be similar to that of human-written ads, highlighting the potential of LLM-generated personalised content to rival traditional advertising methods with the added advantage of scalability. This study underscores the need for cautious consideration in the deployment of LLM-generated content at scale. While our findings confirm the scalability and potential effectiveness of LLM-generated content, there is an equally pressing concern about the ease with which it can be misused

Automation of [18F]fluoroacetaldehyde synthesis: application to a recombinant human interleukin-1 receptor antagonist (rhIL-1RA)

[(18)F]Fluoroacetaldehyde is a biocompatible prosthetic group that has been implemented pre‐clinically using a semi‐automated remotely controlled system. Automation of radiosyntheses permits use of higher levels of [(18)F]fluoride whilst minimising radiochemist exposure and enhancing reproducibility. In order to achieve full‐automation of [(18)F]fluoroacetaldehyde peptide radiolabelling, a customised GE Tracerlab FX‐FN with fully programmed automated synthesis was developed. The automated synthesis of [(18)F]fluoroacetaldehyde is carried out using a commercially available precursor, with reproducible yields of 26% ± 3 (decay‐corrected, n = 10) within 45 min. Fully automated radiolabelling of a protein, recombinant human interleukin‐1 receptor antagonist (rhIL‐1RA), with [(18)F]fluoroacetaldehyde was achieved within 2 h. Radiolabelling efficiency of rhIL‐1RA with [(18)F]fluoroacetaldehyde was confirmed using HPLC and reached 20% ± 10 (n = 5). Overall RCY of [(18)F]rhIL‐1RA was 5% ± 2 (decay‐corrected, n = 5) within 2 h starting from 35 to 40 GBq of [(18)F]fluoride. Specific activity measurements of 8.11–13.5 GBq/µmol were attained (n = 5), a near three‐fold improvement of those achieved using the semi‐automated approach. The strategy can be applied to radiolabelling a range of peptides and proteins with [(18)F]fluoroacetaldehyde analogous to other aldehyde‐bearing prosthetic groups, yet automation of the method provides reproducibility thereby aiding translation to Good Manufacturing Practice manufacture and the transformation from pre‐clinical to clinical production