Meta-Analysis of the Unified Theory of Acceptance and Use of Technology (UTAUT): Challenging its Validity and Charting A Research Agenda in the Red Ocean

There are both formal and informal cries that UTAUT and by association the stream of research on technology adoption has reached its limit, with little or no opportunities for new knowledge creation. Such a conclusion is ironic because the theory has not been sufficiently and suitably replicated. It is possible that the misspecifications in the various replications, applications, and extensions led to the incorrect conclusion that UTAUT was more robust than it really was and opportunities for future work were limited. Although work on UTAUT has included important variables, predictors and moderators, absent a faithful use of the original specification, it is impossible to assess the true nature of the effects of the original and additional variables. The present meta-analysis uses 25,619 effect sizes reported by 737,112 users in 1,935 independent samples to address this issue. Consequently, we develop a clear current state-of-the-art and revised UTAUT that extends the original theory with new endogenous mechanisms from different, other theories (i.e., technology compatibility, user education, personal innovativeness, and costs of technology) and new moderating mechanisms to examine the generalizability of UTAUT in different contexts (e.g., technology type and national culture). Based on this revised UTAUT, we present a research agenda that can guide future research on the topic of technology adoption in general and UTAUT in particular

Automated computer-based detection of encounter behaviours in groups of honeybees.

Honeybees form societies in which thousands of members integrate their behaviours to act as a single functional unit. We have little knowledge on how the collaborative features are regulated by workers' activities because we lack methods that enable collection of simultaneous and continuous behavioural information for each worker bee. In this study, we introduce the Bee Behavioral Annotation System (BBAS), which enables the automated detection of bees' behaviours in small observation hives. Continuous information on position and orientation were obtained by marking worker bees with 2D barcodes in a small observation hive. We computed behavioural and social features from the tracking information to train a behaviour classifier for encounter behaviours (interaction of workers via antennation) using a machine learning-based system. The classifier correctly detected 93% of the encounter behaviours in a group of bees, whereas 13% of the falsely classified behaviours were unrelated to encounter behaviours. The possibility of building accurate classifiers for automatically annotating behaviours may allow for the examination of individual behaviours of worker bees in the social environments of small observation hives. We envisage that BBAS will be a powerful tool for detecting the effects of experimental manipulation of social attributes and sub-lethal effects of pesticides on behaviour

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp

Technology readiness: a meta-analysis of conceptualizations of the construct and its impact on technology usage

The technology readiness (TR) index aims to better understand people’s propensity to embrace and use cutting-edge technologies. The initial TR construct considers four dimensions—innovativeness, optimism, insecurity, and discomfort—that collectively explain technology usage. The present meta-analysis advances understanding of TR by reexamining its dimensionality, and investigating mediating mechanisms and moderating influences in the TR–technology usage relationship. Using data from 193 independent samples extracted from 163 articles reported by 69,263 individuals, we find that TR is best conceptualized as a two-dimensional construct differentiating between motivators (innovativeness, optimism) and inhibitors (insecurity, discomfort). We observe strong indirect effects of these dimensions on technology usage through mediators proposed by the quality–value–satisfaction chain and technology acceptance model. The results suggest stronger relationships for motivators than for inhibitors, but also that these TR dimensions exert influence through different mediators. Further, the moderator results suggest that the strength of TR–technology usage relationships depends on the technology type (hedonic/utilitarian), examined firm characteristics (voluntary/mandatory use; firm support), and country context (gross domestic product; human development). Finally, customer age, education, and experience are related to TR. These findings enhance managers’ understanding of how TR influences technology usage

Factors Influencing the Acceptance of Healthcare Information Technologies: A Meta-Analysis

Healthcare information technologies (HIT) can address several challenges faced by healthcare systems. To benefit from the advantages HIT offer, users must first accept them. This meta-analysis synthesizes previous research on HIT acceptance. It uses data from 214 independent samples reported in 193 articles and 83,619 technology users from 33 countries. The study contributes to the HIT literature by (1) synthesizing the empirical findings on technology acceptance factors and combining them in a comprehensive model, (2) testing the mediating mechanisms of health technology acceptance, and (3) examining contextual differences. The study finds that HIT acceptance depends on various predictors proposed by the technology acceptance model and the unified theory of acceptance and use of technology. These factors displayed strong indirect effects through effort expectancy, perceptions of the technology, performance expectancy, and attitudes toward using HIT. Studies overlooking these effects may underestimate the importance of various acceptance factors. Finally, the results suggest that technology acceptance varies across healthcare technologies (remote information systems [IS], wearables), users (staff/patients, age, voluntariness, experience), and locations (hospitals, healthcare systems, life expectancy in country). We also provide IS managers with guidance for improving technology acceptance in the healthcare industry to ensure efficient, high-quality services