Open AccessFactors Influencing the Adoption of Automated Data Collection Technologies by Building Contractors in Kenya
Author(s) -
Victor Maina,
Stephen Diang‘a
Publication year - 2022
Publication title -
international journal of research and scientific innovation
Language(s) - English
Resource type - Journals
eISSN - 2321-2705
pISSN - 2320-5520
DOI - 10.51244/ijrsi.2022.9303
Subject(s) - data collection , government (linguistics) , information and communications technology , business , radio frequency identification , population , stratified sampling , sample (material) , global positioning system , identification (biology) , knowledge management , marketing , process management , engineering , telecommunications , computer science , computer security , philosophy , linguistics , statistics , demography , mathematics , chemistry , botany , chromatography , sociology , world wide web , biology
Despite the fact that automated data collection (ADC) technologies come with new avenues of opportunities to reckon with that can be relevant for establishment of effective and efficient management approaches, studies indicate that construction industry has lagged behind in adopting and implementing these technologies. In the Kenyan construction industry, the current application of the information communication technology (ICT) platforms is on the conventional technologies like cameras, Smart phones & tablets applications and Radio Frequency Identification (RFID). However the use of more advanced ICT platforms like Global positioning systems (GPS) and wireless sensor networks remains highly unexploited in the construction industry. This paper seeks to establish factors which affect the adoption of automated data collection technologies by building contractors in Kenya. A Descriptive research survey design was used and structured questionnaires issued. The target population in this study comprised of Building works contractors in categories National Construction Authority (NCA) 1 to NCA3 operating within Nairobi County. Stratified systematic sampling was then used to draw the sample size from the population of 300 with a return of response rate of 63%. The study concluded that: the level of adoption of automated data collection (ADC) technologies by local building contractors in Kenya is significantly influenced by the cost of technology, availability of technology, management commitment, size of the firm and human resource capacity. The study recommends that construction firms should have competent planning and strategy teams to deal with innovation adoption. There is also the need for the government to improve the information communication technology infrastructure and through bodies like NCA introduce training programs on an industry level on the emerging technologies that can be applied in the construction sector.