Implementing systems thinking to manage risk in public private partnership projects

Identifying a Systems Thinker: Matching a Candidate’s Systems Thinking Abilities with the Job

Systems thinking is an evolving field, and there is growing demand to integrate systems thinking into many fields. The goal of the present study was to develop a new tool for identifying systems thinkers. The study was conducted in two stages. The first, a qualitative stage, consisted of interviews, which were followed by a quantitative factor analysis in the second stage. We interviewed industry executives, lecturers at a technological college for practical engineers, and college students in order to identify the needs for systems thinking. We found that interviewees from different disciplines and roles defined some common requirements for systems thinking, but they also specified some different requirements according to their current professional status. The second stage of the study involved 120 participants with varied professional characteristics. The participants attended a five-hour seminar on systems thinking. After learning about systems thinking, participants answered a questionnaire designed to identify systems thinkers. Factor analysis was then used by the researchers to divide the responses to the questionnaire into five factors that represent common characteristic of systems thinking. Analyzing the responses to the questionnaire according to the five factors facilitates identifying the respondents’ knowledge of systems thinking and their ability to use it. Such information is important both for the process of hiring employees and for employee training processes.

Risk sharing for PPP project in construction waste recycling industry in China

With the deepening urbanization, urban renewal, and reconstruction movement in China, a large amount of construction waste has been generated. In order to ease the financial pressure on the government to deal with construction waste, introducing the PPP model in the construction waste recycling industry has become popular. Based on the theory of system dynamics, this paper studies the risks of using the PPP model in construction waste recycling industry. First, a causal loop diagram was depicted according to the interaction of the main risk factors. Then, using the system dynamics software Vensim, a dynamic model of the risks of the PPP project was constructed. The results show the following: (1) the PPP project in the construction waste recycling industry is profitable; (2) For the PPP project in the construction waste recycling industry, the tendering risk will break out in 6~7 years, the greatest impact on tendering risk is the cooperation environment risk, where cooperation environment risk entropy is increased by 50% and tendering risk entropy is increased by 53.66%; (3) Construction risks will break out in 7~8 years. The construction cost, construction delay, and design defects have the greatest impact on construction risks, where entropy is increased by 50% and construction risk entropy is increased by 19.30%, 19.25%, and 19.08%, respectively; (4) The operational risk has not broken out in 10 years and is expected to break out in 10 to 20 years. Raw material power supply, production technology level, and operations management efficiency have the greatest impact on operational risk, where entropy is increased by 50% and operational risk entropy is increased by 21.52%, 20.84%, and 20.76%, respectively. These results can provide suggestions and references for the risk management of PPP projects in China's construction waste recycling industry.

Data-driven impact assessment of multidimensional project complexity on project performance

Purpose

The purpose of this paper is to propose a data-driven scheme for identifying critical project complexity dimensions and establishing the trade-off across multiple project performance criteria.

Design/methodology/approach

This paper adopts a hybrid approach using Bayesian Belief Networks (BBNs) and Artificial Neural Networks (ANNs). The output of the ANN model is used as input to the BBN model for prioritizing project complexity dimensions relative to multiple project performance criteria. The proposed process is demonstrated through a real application in the construction industry.

Findings

With a number of nonlinear interactions involved within and across project complexity and performance, it is not feasible to model and assess the strength of these interactions using conventional techniques. The proposed process helps in effectively mapping a “multidimensional complexity” space to a “multidimensional performance” space and makes use of data from past projects for operationalizing this mapping scheme by means of ANNs. This obviates the need for developing a parametric model that is both challenging and computationally cumbersome. The mapping function can be used for generating all possible scenarios required for the development of a data-driven BBN model.

Originality/value

This paper introduces a data-driven process for operationalizing the mapping of project complexity to project performance within a network setting of interacting complexity dimensions and performance criteria. The results of the application study manifest the importance of capturing the interdependency across project complexity and performance. Ignoring the underlying interdependencies and relying exclusively on conventional correlation-based techniques may lead to making suboptimal decisions.

An Assessment of Individuals’ Systems Thinking Skills via Immersive Virtual Reality Complex System Scenarios

This study utilized the application of authentic Virtual Reality (VR) to replicate the real-world complex system scenarios of a large retail supply chain. The proposed VR scenarios were developed based on an established systems thinking instrument that consists of seven dimensions: level of complexity, independence, interaction, change, uncertainty, systems’ worldview, and flexibility. However, in this study, we only developed the VR scenarios for the first dimension, level of complexity, to assess an individual’s Systems Thinking Skills (STS) when he or she engages in a turbulent virtual environment. The main objective of this study was to compare a student’s STS when using traditional ST instruments versus VR scenarios for the complexity dimension. The secondary aim was to investigate the efficacy of VR scenarios utilizing three measurements: Simulation Sickness Questionnaire (SSQ), System Usability Scale (SUS), and Presence Questionnaire (PQ). In addition to the three measures, NASA TLX assessment was also performed to assess the perceived workload with regards to performing the tasks in VR scenarios. The results show students’ preferences in the VR scenarios are not significantly different from their responses obtained using the traditional systems skills instrument. The efficacy measures confirmed that the developed VR scenarios are user friendly and lie in an acceptable region for users. Finally, the overall NASA TLX score suggests that users require 36% perceived work effort to perform the activities in VR scenarios.

Exploring Probabilistic Network-Based Modeling of Multidimensional Factors Associated with Country Risk

Assessment of country risk provides a vital source of information to organizations for expanding and globalizing their operations. Various rating agencies are involved in developing models for assessing country risk, which utilize different statistical techniques for establishing the overall impact of individual factors on country risk. The main limitation of existing studies on country risk is their limited focus on exploring the relative contribution of individual factors to country risk in a probabilistic network setting. Utilizing real data, we develop a probabilistic network model that captures dependencies among multidimensional factors associated with country risk. Further, we assess the network-wide vulnerability and resilience potential of individual factors to identify critical factors. The findings of this study provide policy-makers with some unique insights into prioritizing strategies to mitigate country risk. Further, this study provides the context for multinational enterprises to utilize the proposed methodology for prioritizing key factors associated with the relative variables of interest such as regional stability and business environment among others.

Approaches to Risk Identification in Public–Private Partnership Projects: Malaysian Private Partners’ Overview

The complexity of public–private partnership (PPP) projects ensures that risks could emerge and spread in unpredictable ways if they are not well identified and managed. The emergence of PPP projects has brought major changes in the construction industry, the most notable being in procurement methods influencing risk allocation to private parties. Thus, it is crucial to have an effective risk management for public and private partners to eliminate or minimise risks. Formulating an effective risk management system is a crucial challenge faced by both of parties in order to minimise or optimise risks. The aim of this study was to investigate the process of risk identification of private partners in Malaysian PPP projects. Data were collected throughout a 2-month period using a survey with a sample of nine Malaysian companies engaged in PPP projects, and the survey results were analysed using mean scores. The findings indicate that due to a lack of knowledge and experience of Malaysian private partners in the risk identification process, a comprehensive database for risk identification is highly necessary for the private sector. Another issue emerging from the findings is that it may be reasonable to use a combination of risk identification tools for PPP projects with a high level of complexity. The findings of the present study can greatly assist public and private partners to select the most appropriate tools for risk identification at the early stages of PPP projects.