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The purpose of this paper is to describe the authors’ experience operationalizing the care delivery value chain (CDVC) as a management and continuous quality improvement (QI) approach to strengthen HIV/AIDS services provided in Northern Togo through addressing gaps across a care continuum.

The authors led a series of discussions to develop a CDVC specific to existing HIV/AIDS services in Northern Togo. Using the CDVC framework, 28 specific gaps in service delivery were identified and integrated into a strategic QI plan.

At 12 months, 92 percent of delivery gaps had demonstrated improvement. The CDVC framework proved to be valuable in the following ways. First, it facilitated the first comprehensive mapping of HIV/AIDS services in the Kara region of Togo. Second, it enabled the identification of gaps or insufficiencies in the currently available services across the full continuum of care. Third, it catalyzed the creation of a strategic QI plan based on identified gaps.

This case description is the authors’ experience in one setting and should not be considered comparative in nature. Furthermore, the approach described may not be applicable to all initiatives and/or organizations. As described, the lack of sophisticated and comprehensive data collection systems limited the authors’ ability to collect reliable data on some of the QI initiatives planned.

The operationalization of the CDVC framework is an effective approach to drive continuous QI.

Through the operationalization of the CDVC, the authors developed a new approach for assessing existing services, identifying gaps in service delivery and directing continuous QI initiatives in a strategic manner.

Research on training and/or L&D effectiveness is predominantly conducted in a traditional L&D context. Little research is conducted on training and/or L&D in the context of artificial intelligence (AI)-based learning. The present study aims to investigate the relationship between the adoption of AI-based learning systems and learners’ behavior. Drawing from the theory of planned behavior, the research examines the impact of attitude (ATT), subjective norm (SN) and perceived behavioral control (PBC) as AI-based learning intention (ALI) factors relate to changes in learners' behavior. Additionally, inspired by the self-determination theory by Deci and Ryan, the study further examines the mediating role of learner engagement between ALI and behavioral change.

Following a theoretical framework and using a systematic literature review method, the survey research has been planned by considering a sample from Indian industries. The collected data have been analyzed using SPSS-AMOS 27. While path analysis has been conducted to analyze the direct impact of ALI on learners' behavior, Hay’s PROCESS macro has been used to check the mediating impact of learner engagement between ALI and learners' behavior.

The results proved a significant and positive impact of all ALI factors such as ATT, SN and PBC on learners’ behavioral change. Further, the research found that learning engagement (LE) successfully mediates between AI learning intention and behavioral change.

In the absence of any empirical study in identifying the relationship among learning intention, LE and behavioral outcome, the result of this study may provide useful insights to researchers and practitioners.