Evaluation of Factors Affecting Road Maintenance in Kenyan Counties Using the Ordinal Priority Approach

Abstract

Improving Kenya's complete road network has been used to alleviate poverty and achieve the Vision 2030 goals. Roads enhance all areas of social development, including demand for and access to information, health, and education, in addition to poverty alleviation. However, the majority of Kenyan highways are plagued by a variety of maintenance concerns. This study aims to rank counties according to critical severity based on factors affecting road maintenance using an Ordinal Priority Approach (OPA). Five challenges that could have an impact on road maintenance were looked at from the views of three decision-makers. The results of the study show that road materials are the least challenging aspect affecting road maintenance performance, while procurement practices are the most problematic. The studies also revealed that Nairobi County is most affected by these factors, whereas Nakuru County is least affected. In light of the findings, recommendations were made to assist both the government and the road sectors in dealing with these difficulties.

A grey ordinal priority approach for healthcare waste disposal location selection

“…It can also effectively deal with those missing data when the concerned expert is not sure to assign a specific ordinal rank to a criterion or is in doubt to specify the relative performance of an alternative against a given criterion. Since the last few years, the researchers have endeavoured to explore its capability in solving diverse MCDM problems, like selection of healthcare supplier (Quartey-Papafio et al ., 2021), construction sub-contractor (Mahmoudi and Javed, 2022), project portfolio (Mahmoudi et al ., 2022a), transportation planning strategy (Pamucar et al ., 2022), road maintenance strategy (Bouraima et al ., 2022), distributed ledger technology (Sadeghi et al ., 2022a) and so on. Recently, it has also been successfully combined with TOPSIS for project selection (Mahmoudi et al ., 2021a), and shortlising of automotive parts suppliers (Bah and Tulkinov, 2022); data envelopment analysis (DEA) for supplier performance assessment (Mahmoudi et al ., 2022b); FST for selection of resilient suppliers selection (Mahmoudi et al ., 2022c), blockchain technology selection in construction organizations (Sadeghi et al ., 2022b) and appraising construction suppliers (Mahmoudi et al ., 2022d); neutrosophic fuzzy set for industrial robot selection (Abdel-Basset et al ., 2022); rough set theory for sustainable mining (Deveci et al ., 2022); and grey system theory to evaluate low-carbon sustainable technologies in agriculture (Shajedul, 2021), sustainable supplier selection for construction megaprojects (Mahmoudi et al ., 2021b) and identification of barriers to electric vehicle adoption (Candra, 2022).…”

“…It can also effectively deal with those missing data when the concerned expert is not sure to assign a specific ordinal rank to a criterion or is in doubt to specify the relative performance of an alternative against a given criterion. Since the last few years, the researchers have endeavoured to explore its capability in solving diverse MCDM problems, like selection of healthcare supplier (Quartey-Papafio et al, 2021), construction subcontractor (Mahmoudi and Javed, 2022), project portfolio (Mahmoudi et al, 2022a), transportation planning strategy (Pamucar et al, 2022), road maintenance strategy (Bouraima et al, 2022), distributed ledger technology (Sadeghi et al, 2022a) and so on.…”

Integrated Spherical Decision-Making Model for Managing Climate Change Risks in Africa

“…Considering the limitations of prior research, effectively addressing climate change risks and successfully implementing adaptation activities demands an approach that provides a holistic managerial perspective. This method should explicitly take into account multiple criteria to enhance decision outcomes [40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59]. Multi-criteria decision-making (MCDM) techniques, adept at structuring complex problems and accommodating various criteria, are well-suited for this purpose [55,[60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79].…”

Strategies for Enhancing Industry 4.0 Adoption in East Africa: An Integrated Spherical Fuzzy SWARA-WASPAS Approach

“…Conventional decision-making approaches centered around a single criterion are insufficient for addressing the inherent complexities of these challenges [24][25][26][27][28][29][30]. Consequently, multi-criteria decision-making (MCDM) approaches have gained traction, demonstrating promise in providing policymakers and managers with flexible and adaptable tools [31][32][33][34][35][36][37][38][39]. These approaches use predetermined parameters to classify and select one or more elements from a set of alternatives [40][41][42][43][44][45][46], and the chosen parameters are subsequently assessed based on their effectiveness in fulfilling their respective functions and determining the suitability of alternative options [47][48][49][50][51][52].…”