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This paper focusses on effective teaching and learning methods in the context of a larger project that aims to align objectives in higher education with employer requirements in the field of purchasing and supply management (PSM). The reason is that little is known about which specific skills and competencies of PSM professionals are needed outside academia and which learning objective higher education should incorporate to meet the practical PSM requirements of firms and organisations. Practice as well as literature share the understanding that PSM professionals need a well-balanced mixture of knowledge and soft-skills: the merely explicit know-what (codified knowledge), know-why (theory), know-how (method) and inter- & intrapersonal soft skills.
Virtual reality (VR) is starting to realize some of its promise as a tool to improve training effectiveness. However, research on VR for training and development is limited. Existing theories and models relating to organizational training and learning are infrequently used in the VR literature. A greater understanding of why VR works in the training context would help training designers create effective programs that leverage this continuously developing technology. This paper provides a typology of VR technologies specifically relevant to HR and integrates HR training frameworks and theory into findings on VR training from these other literatures. We specifically focus on immersive VR technology and seek to better understand reasons for the effectiveness of VR technologies for both training and assessment. We review findings, integrate related streams of research, and offer guideposts for those contemplating VR implementation in four important areas: training reactions in a VR context, VR-specific learning outcomes, opportunities for assessment using VR, and the effect of VR on training transfer. We conclude the paper by identifying a VR-training agenda for HR researchers.
AI-based chatbots as enabler for efficient external knowledge management in public administration
(2024)
This study addresses the pressing issue of staff shortages in German public administrations through the lens of digitalization, focusing on the potential of AI-based chatbots to solve this problem by replacing human labour. Employing a Design Science Research Process (DSRP) methodology, the research synthesizes theoretical foundations and regulatory frameworks to develop a robust chatbot concept. The artifact presented is a comprehensive architectural framework integrating user-centric design, linguistic processing, and regulatory compliance. The proposed artifact navigates complex federal structures and diverse IT infrastructures, promoting accessibility and inclusivity. Implications suggest enhanced efficiency and accessibility in public service delivery for potentially increasing citizen satisfaction and decreasing employee workload. The study underscores the importance of legal compliance and the evolving regulatory landscape in AI deployment. Future research will involve prototyping and evaluating the artifact's performance and applicability throughout the course of the DSRP, thus contributing to the advancement of digital transformation in public administrations.
In-depth analysis of customer journeys to broaden the understanding of customer behaviors and expectations in order to improve the customer experience is considered highly relevant in modern business practices. Recent studies predominantly focus on retrospective analysis of customer data, whereas more forward-directed concepts, namely predictions, are rarely addressed. Additionally, the integration of robotic process automation (RPA) to potentially increase the efficiency of customer journey analysis is not discussed in the current field of research. To fill this research gap, this paper introduces “customer journey mining”. Process mining techniques are applied to leverage digital customer data for accurate prediction of customer movements through individual journeys, creating valuable insights for improving the customer experience. Striving for improved efficiency, the potential interplay of RPA and customer journey mining is examined accordingly. The research methodology followed is based on a design science research process. An initially defined customer journey mining artifact is operationalized through an illustrative case study. This operationalization is achieved by analyzing a log file of an online travel agency functioning as an orientation for researchers and practitioners while also evaluating the initially defined framework. The data is used to train seven distinct prediction models to forecast the touchpoint a customer is most likely to visit next. Gradient-boosted trees yield the highest prediction accuracy with 43.1%. The findings further indicate technical suitability for RPA implementation, while financial viability is unlikely.