Search results

The purpose of this study is to determine what technological strategies were used within each of the phases of the ADDIE framework when developing content for professional training. The study also examined the affordances of those technologies in training.

A PRISMA systematic review methodology (Moher et al., 2015) was utilized to answer the four questions guiding this study. Specifically, the PRISMA extension Preferred Reporting Items for Systematic Reviews and Meta-Analysis for Protocols (PRISMA-P, Moher et al., 2015) was used to direct each stage of the research, from the literature review to the conclusion. In addition, the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA principles; Liberati et al., 2009) are used to guide the article selection process.

The findings reveal that the majority of the studies were in healthcare (36%) and education (24%) and used an online format (65%). There was a wide distribution of ADDIE used with technology across the globe. The coding for the benefits of technology use in the development of the training solution revealed four trends: 1) usability, 2) learning approaches, 3) learner experience and 4) financial.

This systematic review only examined articles published in English, which may bias the findings to a Western understanding of how technology is used within the ADDIE framework. Furthermore, the study examined only peer-review academic articles from scholarly journals and conferences. While this provided a high level of assurance about the quality of the studies, it does not include other reports directly from training providers and other organizations.

These findings can be used as a springboard for training providers, scholars, funders and practitioners, providing rigorous insight into how technology has been used within the ADDIE framework, the types of technology, and the benefits of using technology. This insight can be used when designing future training solutions with a better understanding of how technology can support learning.

This study provides insight into the uses of technology in training. Many of these findings and uses of technology within ADDIE can also transfer to other aspects of society.

This study is unique in that it provides the scholarly community with the first systematic review to examine what technological strategies were used within each of the phases of the ADDIE structure and how these technologies provided benefits to developing a training solution.

While, supply chain resilience (SCRES) continues to be a dominant topic in both academic and business literature and has gained more attention recently, there is limited knowledge on SCRES capabilities specific to business functions. The purpose of this paper is to identify and investigate capabilities shared between supply, operations and logistics that are most important for SCRES.

To address this gap, the authors followed a multi-method research approach. First, the authors used the grounded theory method to generate a theoretical framework based on interviews with 51 managers from five companies in automotive SCs. Next, the authors empirically validated the framework using a survey of 340 SC professionals from the manufacturing industry.

Five significant capabilities emerged from the qualitative study; all were significant in empirical validation. This research advances the knowledge of SCRES as it informs managerial decision-making by identifying capabilities common to supply, logistics and operations that impact SCRES.

This research advances the knowledge of SCRES as it informs managerial decision-making by identifying capabilities common to supply, logistics and operations that impact SCRES. In addition, the findings of this research help managers better allocate resources among significant capabilities.

Managers and scholars alike claim that artificial intelligence (AI) represents a tool to enhance supply chain collaborations; however, existing research is limited in providing frameworks that categorise to what extent companies can apply AI capabilities and support existing collaborations. In response, this paper clarifies the various implications of AI applications on supply chain collaborations, focusing on the core elements of information sharing and trust. A five-stage AI collaboration framework for supply chains is presented, supporting managers to classify the supply chain collaboration stage in a company’s AI journey.

Using existing literature on AI technology and collaboration and its effects of information sharing and trust, we present two frameworks to clarify (a) the interrelationships between information sharing, trust and AI capabilities and (b) develop a model illustrating five AI application stages how AI can be used for supply chain collaborations.

We identify various levels of interdependency between trust and AI capabilities and subsequently divide AI collaboration into five stages, namely complementary AI applications, augmentative AI applications, collaborative AI applications, autonomous AI applications and AI applications replacing existing systems.

Similar to the five stages of autonomous driving, the categorisation of AI collaboration along the supply chain into five consecutive stages provides insight into collaborations practices and represents a practical management tool to better understand the utilisation of AI capabilities in a supply chain environment.