ABSTRACT
Concerns about the effects of global warming and predicted rising sea levels are radically changing government policies to lower carbon emissions using sustainable green technologies. The United Kingdom aims to reduce its carbon emissions by 78% by 2035 and achieve net zero by 2050. This is a major driver for energy management and is influencing development of buildings which use autonomous smart technologies to assist in lowering carbon footprints. These Smart Buildings use digital technologies by connecting sensor data with intelligent systems which can be monitored remotely to provide more efficient facilities management. The data harvested and transmitted from the IoT sensors provides a key component for Big Data Analytics using techniques such as Association rule mining for intelligent interpretation which can assist facilities management becoming more agile regarding office space utilization. The shift toward hybrid working particularly instigated by the COVID-19 pandemic and recent energy supply concerns caused by the Ukraine crisis presents facilities management with opportunities to optimize their space, reduce energy consumption, and allow them to identify commercial opportunities for the unused space throughout the building. This chapter discusses the use of association rules for data mining derived from a simulated dataset for an investigative analysis of office workflow patterns for facilities management operations, resource conservation, and sustainability. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Big data analytics has been widely adopted by large companies, enabling them to achieve competitive advantage. However, small and medium-sized enterprises (SMEs) are underutilising this technology due to a number of barriers including financial constraints and lack of skills. Previous studies have identified a total of 69 barriers to SMEs adoption of big data analytics, rationalised to 21 barriers categorised into five pillars (Willetts et al. in A strategic big data analytics framework to provide opportunities for SMEs. In: 14th International technology, education and development conference, 2020a, [Willetts M, Atkins AS, Stanier C (2020a) A strategic big data analytics framework to provide opportunities for SMEs. In: 14th International technology, education and development conference, pp 3033–3042. 10.21125/inted.2020.0893]). To verify the barriers identified from the literature, an electronic questionnaire was distributed to over 1000 SMEs based in the UK and Eire using the snowball sampling approach during the height of the COVID-19 pandemic. The intention of this paper is to provide an analysis of the questionnaire, specifically applying the Cronbach’s alpha test to ensure that the 21 barriers identified are positioned in the correct pillars, verifying that the framework is statistically valid. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Small and medium-sized enterprises (SMEs) (which includes micro companies employing < 10) make a significant contribution to the UK economy accounting for 99.9% of all businesses, employing 60% of the work force and generates £2,168 billion;this represents 52% of the turnover of all businesses in the UK [1]. Big Data Analytics is rapidly being utilised by large companies on a global scale to gain competitive advantage which is well documented in the literature. However, the evidence from the literature indicates that SMEs are underutilising this technology for a variety of reasons, for example lack of expertise and cost implications. The intention of this paper is to identify barriers to the adoption of Big Data Analytics by SMEs to help them overcome the challenges and to exploit the benefits of Big Data Analytics to improve their competitive advantage which will benefit the wealth of the country particularly in the aftermath of Covid-19. © 2020 IEEE.