AN AN EMPIRICAL ASSESSMENT OF INNOVATION PRACTICES OF QUANTITY SURVEYING FIRMS IN GHANA

Innovation is ascertained to be a major driver of growth of the productivity of a firm. This has spurred the interest of many researchers to study and harness the adoption of innovation. Extant literature indicates that some professional services offered by the quantity surveying (QS) firms are not needed by the client, or may be outdated. Consequently, the QS firms have to develop the stamina to challenge the existing unnecessary and unwanted or outdated practices and implement innovative practices. What is more alarming is that the QS firms are rated to have a low disposition towards the adoption of innovation. This established context propelled the need for empirically assessing the innovation practices amongst the QS firms in Ghana. A quantitative research approach was employed for this study and a census sampling technique was adopted. A total of 43 questionnaires were administered to the entire population and 24 were retrieved. The current level of innovation practices amongst the Ghanaian QS firms was interpreted using Rogers’ innovation diffusion theory. The results indicated that QS firms in Ghana are early adopters of process innovation, product/technological innovation and business system innovation. The study showed that QS firms adopt innovation practices in rendering their services and even though they do not initiate new ideas, they are the first to adopt the ideas initiated by the innovators. This study has drawn attention to the assessment of innovation practices and increasing the knowledge base of innovation practices in Ghanaian QS firms

Innovate to compete : an empirical assessment of measures to enhance innovation adoption in Ghanaian quantity surveying firms

Innovation in construction services is a source of competitive advantage; thus, firms are constantly innovating new ways of working and producing new productsin order tostay in competion. Regardless of this immeasurable benefitof innovation, the Ghanaian quantity surveying (QS) firms are very sluggish in adopting innovation. Also, there is a paucity of research work that will enable QS firms to maximize innovation adoption. This study was conducted to identifyand examinemeasures to enhance innovation adoption in Ghanaian QS firms. Quantitativeapproach and census sampling techniquewere employed in the study. The dependent variables retrieved from 24 out of 43 questionnaires administered to QS firms in Accra and Kumasi were analysed using mean score and Kendall’s coefficient of concordance test. The study concluded that leadership, information and communication technology, supportive work environment, education and training policy, collaboration with partners, and organisational resources are the mostsignificant measures to enhancinginnovation adoption in Ghanaian QS firms. It is recommended thatQS firmsconstantly put into practice large spectraof new ideas in rendering services in order not to be out of competition. This study could serve asbasis for management invarious QS firms in drawing up policies to enhance innovation adoption. Also, QS firms in other developing countries particularly those in sub-Saharan Africawhere thechallenges impeding innovation are likely to be similar can also benefit from the findings. Future research could be focusedon identifying the key attributes and managing the expectations of innovation champions in the QS firms

Minimization of high computational cost in data preprocessing and modeling using MPI4Py

Data preprocessing is a fundamental stage in deep learning modeling and serves as the cornerstone of reliable data analytics. These deep learning models require significant amounts of training data to be effective, with small datasets often resulting in overfitting and poor performance on large datasets. One solution to this problem is parallelization in data modeling, which allows the model to fit the training data more effectively, leading to higher accuracy on large data sets and higher performance overall. In this research, we developed a novel approach that effectively deployed tools such as MPI and MPI4Py from parallel computing to handle data preprocessing and deep learning modeling processes. As a case study, the technique is applied to COVID-19 data from state of Tennessee, USA. Finally, the effectiveness of our approach is demonstrated by comparing it with existing methods without parallel computing concepts like MPI4Py. Our results demonstrate promising outcome for the deployment of parallel computing in modeling to minimize high computational cost