Understanding the Potential of Augmented Reality in Manufacturing Processes

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Increasing flexibility requirements and skill gaps resulting from today’s world of globalisation and digitisation pose constant challenges for manufacturing companies. Augmented Reality (AR) applications offer an efficient way to overcome these tensions by enhancing the interaction between people and technology. However, individual models in the scientific literature show ambiguous findings, and a statistically powerful empirical assessment is still missing. Hence, this research project aims to understand the potential of AR applications in manufacturing environments by aggregating the empirical findings. For this purpose, the following research question is posed: ’Can the use of AR solutions benefit manufacturing activities and if so, how?’. Following the media naturalness theory by Kock [2005], this research hypothesises that AR solutions in comparison to classical instructions have a reducing effect on processing times, errors rates, and cognitive load levels of workers during manufacturing activities. To answer the research question and prove the hypotheses, this research project conducts three meta-analyses in which several small studies are synthesised into one large study. Specifically, the meta-analyses address the evaluation criteria ’time’, ’errors’, and ’cognitive load’. The underlying systematic literature search to collect and evaluate relevant data follows the framework by Vom Brocke et al. [2009]. What is more, this research project examines the interrelationships between the evaluation criteria and moderating variables using meta-regressions. Finally, surveys with industrial experts in a consumer goods and chemical company support and expand the findings from the meta-analyses and the meta-regressions. The meta-analyses show that AR applications in comparison to classical instructions indeed have a reducing effect on the described evaluation criteria. In particular, based on the studies, a small, reducing effect can be achieved for ’time’, a medium, reducing effect for ’errors’, and a small to medium, reducing effect for ’cognitive load’. For this reason, all three previously formulated hypotheses are accepted. Furthermore, in line with the media naturalness hypothesis by Kock [2005, p. 122], the meta-regressions show that ’cognitive load’ moderates the evaluation criterion ’time’. The results are validated with the help of the expert surveys in the company context, with time savings being identified as the greatest potential and lack of proven profitable business models as the greatest challenge. Further research could, on the one hand, focus on repeating the meta-analyses as soon as new empirical studies are available and on the analysis of moderating variables. On the other hand, a long-term validation in manufacturing environments across industries is still missing and could show further scientific and practical relevance.