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Mitigating Risks of Corruption in Construction: A theoretical rationale for BIM adoption in Ethiopia
(2021)
This PhD thesis sets out to investigate the potentials of Building Information Modeling (BIM) to mitigate risks of corruption in the Ethiopian public construction sector. The wide-ranging capabilities and promises of BIM have led to the strong perception among researchers and practitioners that it is an indispensable technology. Consequently, it has become the frequent subject of science and research. Meanwhile, many countries, especially the developed ones, have committed themselves to applying the technology extensively. Increasing productivity is the most common and frequently cited reason for that.
However, both technology developers and adopters are oblivious to the potentials of BIM in addressing critical challenges in the construction sector, such as corruption. This particularly would be significant in developing countries like Ethiopia, where its problems and effects are acute. Studies reveal that bribery and corruption have long pervaded the construction industry worldwide. The complex and fragmented nature of the sector provides an environment for corruption. The Ethiopian construction sector is not immune from this epidemic reality. In fact, it is regarded as one of the most vulnerable sectors owing to varying socio-economic and political factors. Since 2015, Ethiopia has started adopting BIM, yet without clear goals and strategies. As a result, the potential of BIM for combating concrete problems of the sector remains untapped. To this end, this dissertation does pioneering work by showing how collaboration and coordination features of the technology contribute to minimizing the opportunities for corruption. Tracing loopholes, otherwise, would remain complex and ineffective in the traditional documentation processes.
Proceeding from this anticipation, this thesis brings up two primary questions: what are areas and risks of corruption in case of the Ethiopian public construction projects; and how could BIM be leveraged to mitigate these risks? To tackle these and other secondary questions, the research employs a mixed-method approach. The selected main research strategies are Survey, Grounded Theory (GT) and Archival Study. First, the author disseminates an online questionnaire among Ethiopian construction engineering professionals to pinpoint areas of vulnerability to corruption. 155 responses are compiled and scrutinized quantitatively. Then, a semi-structured in-depth interview is conducted with 20 senior professionals, primarily to comprehend opportunities for and risks of corruption in those identified highly vulnerable project stages and decision points. At the same time, open interviews (consultations) are held with 14 informants to be aware of state of the construction documentation, BIM and loopholes for corruption in the country. Consequently, these qualitative data are analyzed utilizing the principles of GT, heat/risk mapping and Social Network Analysis (SNA). The risk mapping assists the researcher in the course of prioritizing corruption risks; whilst through SNA, methodically, it is feasible to identify key actors/stakeholders in the corruption venture. Based on the generated research data, the author constructs a [substantive] grounded theory around the elements of corruption in the Ethiopian public construction sector. This theory, later, guides the subsequent strategic proposition of BIM. Finally, 85 public construction related cases are also analyzed systematically to substantiate and confirm previous findings.
By ways of these multiple research endeavors that is based, first and foremost, on the triangulation of qualitative and quantitative data analysis, the author conveys a number of key findings. First, estimations, tender document preparation and evaluation, construction material as well as quality control and additional work orders are found to be the most vulnerable stages in the design, tendering and construction phases respectively. Second, middle management personnel of contractors and clients, aided by brokers, play most critical roles in corrupt transactions within the prevalent corruption network. Third, grand corruption persists in the sector, attributed to the fact that top management and higher officials entertain their overriding power, supported by the lack of project audits and accountability. Contrarily, individuals at operation level utilize intentional and unintentional 'errors’ as an opportunity for corruption.
In light of these findings, two conceptual BIM-based risk mitigation strategies are prescribed: active and passive automation of project audits; and the monitoring of project information throughout projects’ value chain. These propositions are made in reliance on BIM’s present dimensional capabilities and the promises of Integrated Project Delivery (IPD). Moreover, BIM’s synchronous potentials with other technologies such as Information and Communication Technology (ICT), and Radio Frequency technologies are topics which received a treatment. All these arguments form the basis for the main thesis of this dissertation, that BIM is able to mitigate corruption risks in the Ethiopian public construction sector. The discourse on the skepticisms about BIM that would stem from the complex nature of corruption and strategic as well as technological limitations of BIM is also illuminated and complemented by this work. Thus, the thesis uncovers possible research gaps and lays the foundation for further studies.
Following restructuring of power industry, electricity supply to end-use customers has undergone fundamental changes. In the restructured power system, some of the responsibilities of the vertically integrated distribution companies have been assigned to network managers and retailers. Under the new situation, retailers are in charge of providing electrical energy to electricity consumers who have already signed contract with them. Retailers usually provide the required energy at a variable price, from wholesale electricity markets, forward contracts with energy producers, or distributed energy generators, and sell it at a fixed retail price to its clients. Different strategies are implemented by retailers to reduce the potential financial losses and risks associated with the uncertain nature of wholesale spot electricity market prices and electrical load of the consumers. In this paper, the strategic behavior of retailers in implementing forward contracts, distributed energy sources, and demand-response programs with the aim of increasing their profit and reducing their risk, while keeping their retail prices as low as possible, is investigated. For this purpose, risk management problem of the retailer companies collaborating with wholesale electricity markets, is modeled through bi-level programming approach and a comprehensive framework for retail electricity pricing, considering customers’ constraints, is provided in this paper. In the first level of the proposed bi-level optimization problem, the retailer maximizes its expected profit for a given risk level of profit variability, while in the second level, the customers minimize their consumption costs. The proposed programming problem is modeled as Mixed Integer programming (MIP) problem and can be efficiently solved using available commercial solvers. The simulation results on a test case approve the effectiveness of the proposed demand-response program based on dynamic pricing approach on reducing the retailer’s risk and increasing its profit.
In this paper, the decision-making problem of the retailers under dynamic pricing approach for demand response integration have been investigated. The retailer was supposed to rely on forward contracts, DGs, and spot electricity market to supply the required active and reactive power of its customers. To verify the effectiveness of the proposed model, four schemes for retailer’s scheduling problem are considered and the resulted profit under each scheme are analyzed and compared. The simulation results on a test case indicate that providing more options for the retailer to buy the required power of its customers and increase its flexibility in buying energy from spot electricity market reduces the retailers’ risk and increases its profit. From the customers’ perspective also the retailers’accesstodifferentpowersupplysourcesmayleadtoareductionintheretailelectricityprices. Since the retailer would be able to decrease its electricity selling price to the customers without losing its profitability, with the aim of attracting more customers. Inthiswork,theconditionalvalueatrisk(CVaR)measureisusedforconsideringandquantifying riskinthedecision-makingproblems. Amongallthepossibleoptioninfrontoftheretailertooptimize its profit and risk, demand response programs are the most beneficial option for both retailer and its customers. The simulation results on the case study prove that implementing dynamic pricing approach on retail electricity prices to integrate demand response programs can successfully provoke customers to shift their flexible demand from peak-load hours to mid-load and low-load hours. Comparing the simulation results of the third and fourth schemes evidences the impact of DRPs and customers’ load shifting on the reduction of retailer’s risk, as well as the reduction of retailer’s payment to contract holders, DG owners, and spot electricity market. Furthermore, the numerical results imply on the potential of reducing average retail prices up to 8%, under demand response activation. Consequently, it provides a win–win solution for both retailer and its customers.