Performance-Influencing Factors and Improvement Paths of Third-Party Governance Service Regarding Environmental Pollution—An Empirical Study of the SEM Based on Shanghai Data

By employing ecological economics involving performance-influencing factors and third-party governance services that have the aim of reducing environmental pollution, a theoretical framework which includes object, subject, process that measures the performance of such services is analyzed on the basis of the idea of sustainability science (2.0). The research hypotheses regarding the relationships among service performance, multi-stakeholder subjects’ satisfaction, and the effect of process management are put forward; then, a three-dimensional performance measurement model, which includes performance, stakeholders, and the process of providing third-party governance services for addressing environmental pollution, is constructed based on the pressure–state–response (PSR) driving force model. At the same time, based on empirical data of the Shanghai municipality, the structural equation model (SEM) is used to empirically test the nine proposed research hypotheses. The empirical test results show that, except for the research hypotheses in regard to regulating variables and controlling variables, all of the research hypotheses passed the test. It means the direct performance, single subjective satisfaction, process management effects are performance-influencing factors. However, the stakeholders’ cooperation satisfaction partially influences the performance of third-party governance services regarding environmental pollution. Finally, through theoretical and empirical research, this paper proposes countermeasures and suggestions for improving the performance of third-party governance services regarding environmental pollution in Shanghai focusing on two aspects: one is the market governance mechanism innovation, and the other is regulations and standards innovation.

Prediction of performance outcomes for procurement of public-sector construction consultants for property management

Purpose Public-sector construction clients in the UK and Australia have a clear objective to maximise potential and value for construction and infrastructure projects. Outcome-based performance predictive models, which link influencing factors to individual performance outcomes, were developed for the public-sector property management clients. The paper aims to discuss this issue. Design/methodology/approach Combined qualitative-quantitative methods were used to examine the causal relationships between performance outcomes and input economic and job performance factors. Hypotheses on individual relationships generated by a literature review were refined using the findings from a qualitative multiple-case study of three universities, and then tested by a quantitative hierarchical regression analysis using data from 60 consultancies collected from a questionnaire survey sent to the estate management offices of the universities, which form a unique public sector. Each performance project outcome was regressed against influencing factors. Performance predictive models were established in the form of regression equations. Findings Five performance outcomes are identified: time, cost, quality, innovations and working relationship with the client. These can be significantly predicted by regression models, based on performance influencing factors of project staff, competence of firm, execution approach, size of firm, consultant framework and competition level. Research limitations/implications The performance predictive models developed should be regarded as “conceptual”. Public-sector clients may have different organisation objectives and hence different requirements for performance outcomes, which may further vary according to specific project situations. The models should be adapted to suit individual needs. Adjustments can be made by using the combined qualitative-quantitative methods adopted in this research, thus creating customised models for property management and construction-related clients. Practical implications The client’s professional team should focus on the significant performance influencing factors and take advantage of the performance predictive models to select quality consultants. Construction consultants should address the factors in the tender proposals in order to add value to the project and benefit the client. Originality/value The existing input-based assessment approach applied at the tender stage cannot guarantee the strategic project objectives to be achieved. The performance predictive models are adaptable for property management and construction disciplines within the wider public sector, thus contributing to achievement of the government construction policy.

Application of Analytic Hierarchy Process to Identify the Most Influencing Human Factors (HFs) and Performance Influencing Factors (PIFs) in Process Safety Accidents

ABSTRACT Numerous studies suggest that 80% of accidents in hydrocarbon processing industries are as a result of human factors (HFs). While a lot is known of human factor influence to process accident, the effects of performance influencing factors (PIFs) on human factors in process accidents is not yet well understood. This study examined HFs and PIFs which influences the propagation of undesired occurrences in hydrocarbon processing industries. An illustrative case study of the Tesoro refinery accident was analysed. To affirm consistency in judgments, Analytic Hierarchy Process AHP – a multi-criteria decision-making method was used in identifying HFs and PIFs of critical events. BPMSG AHP computer program was also used to validate results obtained from the manual calculations. Three critical events namely non-routine operations (Event 1), manual manipulation of several isolation block valves (Event 2) and automated start-up operations of Naphtha Hydro-Treating unit (Event 3) were identified. The ranking of PIFs revealed that Procedures, Level of Supervision, Task Characteristics, and Skill Level were major influencing factors to the event. Analysis of human factors revealed that Job Factors had the most significant influence (41%), while the Individual Factors and Organisational Factors had (31%) and (28%) influence respectively. The consistency index (CI) and consistency ratios (CR) of the PIFs were 0.174 and 0.193 for critical Event 1; 0.170 and 0.120 for critical Event 2; and, 0.037 and 0.033 for critical Event 3 respectively. This showed consistency in judgments of the study on PIFs selected for the critical events identified. The Tesoro refinery accident was due to latent organisational and cultural failures.