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This paper considers agentic-activities relating to the Irish language on Twitter and who is engaged with them? To answer this question, this study draws on Twitter data and filters it using the linguistic identifiers, '#Gaeilge' and the word 'Gaeilge' (Irish language). Tweets with these identifiers are conceptually considered as formulations of agentic practice with respect to language (Sabatier, P. [2007]. "Theories of the Policy Process." Boulder, CO: Westview Press). Individuals who use the #Gaeilge identifier in particular are viewed within this study as micro-implementers. Their communications provide partial insight into agentic activities within the Twittersphere specifically, related to and about the Irish language. To explore this data, peak, network, content and sentiment analyses were conducted. The study's findings illustrate that there are a number of influential hubs in this network. They are in the main micro-level individuals as opposed to macro-level or governmental institutions promoting the Irish language. This paper demonstrates the potential for in-depth research into agentic practices of minority linguistic communities within Twitter, and outlines the limitations of a computational approach in conducting such research.

This paper explores the factors which influence the business location decisions of start-ups, focusing in particular on the role of personal factors. Established explanations of industry location emphasize proximity to firms in the same or related industries and proximity to a wider set of business services, though recent research suggests that personal factors may play an important role in explanations of industry location—particularly in technology-enabled sectors. A survey of 97 new firms, founded between 2008 and 2012, in the Irish software services sector, shows that the business location decision is influenced by the personal motivation of entrepreneurs to attain a desired quality of life, and that this outweighs economic factors such as proximity to firms within the same or related industries, proximity to a broader set of supporting business services, infrastructure or the availability of government support schemes. Personal factors are particularly important to firms located outside the Dublin metropolitan area and to home-based businesses. This has important policy implications for national and regional governments seeking to encourage entrepreneurship in technology-enabled service sectors. [ABSTRACT FROM PUBLISHER]

Commercial buildings are a significant consumer of energy worldwide. Logistics facilities, and specifically warehouses, are a common building type which remain under-researched in the demand-side energy forecasting literature. Warehouses have an idiosyncratic profile when compared to other commercial and industrial buildings with a significant reliance on a small number of energy systems. As such, warehouse owners and operators are increasingly entering energy performance contracts with energy service companies (ESCOs) to minimise environmental impact, reduce costs, and improve competitiveness. ESCOs and warehouse owners and operators require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. This paper explores the performance of three machine learning models (Support Vector Regression (SVR), Random Forest, and Extreme Gradient Boosting (XGBoost)), three deep learning models (Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)), and a classical time series model, Autoregressive Integrated Moving Average (ARIMA) for predicting daily energy consumption. The dataset comprises 8040 records generated over an 11-month period from January to November 2020 from a non-refrigerated logistics facility located in Ireland. The grid search method was used to identify the best configurations for each model. The proposed XGBoost models outperformed other models for both very short-term load forecasting (VSTLF) and short-term load forecasting (STLF); the ARIMA model performed the worst. [ABSTRACT FROM AUTHOR]

The continued proliferation of information technology in all aspects of our lives fosters benefits but also generates risks to individuals' privacy. In emerging contexts, such as government surveillance technologies, there is a dearth of research investigating the positive and negative drivers of citizens' acceptance. This is an important gap given the importance of citizen acceptance to the success of these technologies and the need to balance potentially wide-reaching benefits with any dilution of citizen privacy. We conduct a longitudinal examination of the competing influences of positive beliefs and privacy concerns on citizens' acceptance of a COVID-19 national contact tracing mobile application among 405 Irish citizens. Combining privacy calculus theory with social exchange theory, we find that citizens' initial acceptance is shaped by their perceptions of health benefits and social influence, with reciprocity exhibiting a sustained influence on acceptance over time and privacy concerns demonstrating a negative, albeit weak influence on willingness to rely on the application. The study offers important empirical and theoretical implications for the privacy literature in the government surveillance, location-based services, and mobile health application contexts, as well as practical implications for governments and developers introducing applications that rely on mass acceptance and reciprocal information disclosure. • Contact tracing applications offer both benefits and risks to individuals' privacy. • Perceived health benefits influence willingness to download and rely on these apps. • Social influence is an important factor in acceptance of contact tracing apps. • Reciprocity shapes acceptance of contact tracing apps prior to and post launch. [ABSTRACT FROM AUTHOR]