Search results

This paper delves into the ex ante rates of return demanded by the private sector in Indonesian public–private partnership (PPP) infrastructure projects and the manifold factors emanating from project attributes that can influence these rates.

This paper analyzes feasibility studies of 37 PPP projects across different sectors. The studies were carefully selected based on relevance, completeness and validity of data. The analysis uses statistical techniques, including Levene’s tests, t-tests, ANOVA tests, Cohen’s effect size and Pearson correlations, to explore differences in cost of capital and excess returns across various attributes.

Based on the statistical analysis, no significant difference exists between the excess return of 200 basis points (bps) and the equity excess return of 0 bps. This suggests that the eligibility criteria for PPP projects require an internal rate of return (IRR) equal to the weighted average cost of capital plus 200 bps or an equity IRR equal to the cost of equity. The variations in the tested variables among diverse project attributes do not exhibit statistically significant disparities, even though specific attributes display moderate to high effect sizes.

This paper represents one of the first attempts to examine the rates of return demanded by the private sector in the context of Indonesian PPP projects. It comprehensively explores the factors that influence these rates, drawing on insights derived from feasibility studies.

The present paper aims to introduce a new methodology taking risk behavior of decision maker into account to fine‐tune the value of a risky public‐private‐partnership (PPP) project and the corresponding cost of capital based on the target rate of return set by the project sponsor and the degree of project risks.

The proposed methodology combines the cumulative prospect theory (CPT) to characterize the risk preference of the project sponsor and the Monte Carlo simulation to assess the project riskiness. The methodology requires a pre‐set target rate of return that will define the relative gains and losses for a prospect theory project sponsor. The application was illustrated using a build/operate/transfer toll road project as a case study.

As the project sponsor sets a greater target return, the probability of the project not meeting the target is accordingly greater. Given that losses have greater impact than gains on the decision, other things being equal, a higher target return leads to a higher value correction. It has also been demonstrated that the corresponding project's cost of capital can be up‐ or downadjusted depending on the project's riskiness which may result in a reverse preference to favor a higher risk scenario.

The methodology uses the CPT parameters that need to be further confirmed and validated if applied to value large risky projects like PPP investments.

The proposed methodology offers a different approach to correctly value a risky PPP project by extending the application of the cumulative prospect theory that well explains the irrationality of human decision behavior under risk into a financial decision‐making process. It takes the full benefit of simulation to understand project risks and also assists financial decision‐making.

The purpose of this paper is to identify macro-environmental critical success factors (CSFs) and key areas for improvement for public-private partnerships (PPP) in infrastructure development, using Indonesia as a case study.

The methodology includes the definition of CSFs based on the United Nations for Economic and Social Commission for Asia and the Pacific's self-assessment diagnostic tool and a survey on importance and performance attributes, the application of gap analysis (GA) and importance-performance analysis to prioritize areas needing urgent improvements, and the use of inter-rater agreement analysis to examine to what extent the ratings tend to converge on the same conclusions regarding importance and performance.

Out of 40 possible success factors, a total of 16 are identified as CSFs in the context of Indonesia. GA suggests that no performance ratings exceed importance ratings for the identified CSFs, indicating the need for remedial actions. The factors requiring immediate improvements are all associated with commitments: to policy continuity, financial transparency, and corruption eradication.

Although the paper discussing a specific country, the proposed approach is replicable and adaptable in different country contexts. Indonesia's experience can also be of value to governments facing similar problems in encouraging private investment in infrastructure.

The paper contributes to the body of knowledge on PPP in infrastructure development by focussing exclusively on macro-environmental CSFs and Indonesia's PPPs, which are both rarely discussed in the existing literature.

The purpose of this paper is to identify 30 government-led critical success factors (CSFs) from both the meso and micro levels in public-private partnership (PPP) infrastructure development, measured the importance of these factors, and evaluated the government performance within the Indonesian context.

The authors used weighted gap analysis, the Mann-Whitney test, and the Holland and Copenhaver procedure to support the analysis.

The agreement-adjusted mean scores suggest that the identified CSFs are essential, but that these CSFs underperform in Indonesia. The tests indicated that the gap between performance and importance was significant on both the individual and aggregate level, and no respondent-background bias was observed in the data sets.

This paper provides valuable information for prospective international investors who might be interested in alternative PPP investment opportunities in Indonesia.

This paper enriches the existing body of knowledge on Indonesia’s PPP activities. This is important as, despite the fact that Indonesia offers one of the largest opportunities in Asia for investment in the national infrastructure sector, studies on Indonesia’s PPPs are rarely reported in the literature. This paper also offers a simple, practical, and replicable approach with a sound theoretical basis that can assist governments in identifying and evaluating PPP-specific determinant factors under their control, as well as in measuring their performance on these factors.

The purpose of this paper is to present a yardstick efficiency comparison of 269 Indonesian municipal water utilities (MWUs) and measures the impact of exogenous environmental variables on efficiency scores.

Two-stage Stackelberg leader-follower data envelopment analysis (DEA) and artificial neural networks (ANN) were employed.

Given that serviceability was treated as the leader and profitability as the follower, the first and second stage DEA scores were 55 and 32 percent (0 percent = totally inefficient, 100 percent = perfectly efficient), respectively. This indicates sizeable opportunities for improvement, with 39 percent of the total sample facing serious problems in both first- and second-stage efficiencies. When profitability instead leads serviceability, this results in more decreased efficiency. The size of the population served was the most important exogenous environmental variable affecting DEA efficiency scores in both the first and second stages.

The present study was limited by the overly restrictive assumption that all MWUs operate at a constant-return-to-scale.

These research findings will enable better management of the MWUs in question, allowing their current level of performance to be objectively compared with that of their peers, both in terms of scale and area of operation. These findings will also help the government prioritize assistance measures for MWUs that are suffering from acute performance gaps, and to devise a strategic national plan to revitalize Indonesia’s water sector.

This paper enriches the body of knowledge by filling in knowledge gaps relating to benchmarking in Indonesia’s water industry, as well as in the application of ensemble two-stage DEA and ANN, which are still rare in the literature.

Host governments often provide guarantees in build‐operate‐transfer (BOT) infrastructure projects to attract private sector investors. Problems arise because the governments often do not know the full extent of contingent liabilities when issuing guarantees, and because they account and record guarantee costs only when guarantees come due. This paper discusses the guarantees' financial impact from the perspectives of the government and the project sponsor. A typical Indonesian BOT toll road project is taken as the case study. Stochastic simulation using Latin Hypercube technique is applied on the cash flow model with and without guarantees. Several types of guarantees including minimum revenue guarantee, maximum interest rate guarantee, debt guarantee, tariff guarantee and minimum traffic guarantee are discussed. Simulation results reveal that guarantees can reduce risk but are not free of cost. If compared with equivalent subsidies, however, some guarantees can be more effective in lessening the extent of project risk.

Faced with reduced budgets and rising service expectations, public authorities are increasingly cooperating with private businesses. This paper examines an alternative procurement- and service delivery concept, Performance-based Logistics (PBL). It has been introduced by the US and UK armed forces. However, other nations, such as Germany, are still reluctant to follow. This article has two aims: First, to identify the conceptual characteristics of PBL, and second, to analyze potential reasons why although PBL is popular in some nations, others are so reluctant to introduce it. This will be done using a mixed method approach. The concept of PBL will be introduced by deductively developing a conceptual model of PBL using a business model framework. The analysis of PBL application will be performed using an in-depth case study from the German defense sector. This will be framed by a literature review and concluded by managerial recommendations.

The post-COVID-19 scenario has presented significant learning challenges for university students worldwide. The swift shift from face-to-face to online classes posed greater difficulties because students were not mentally, financially, or physically prepared for this change, nor were they provided with adequate training to operate the learning management system (LMS). Online learning necessitates a school-like environment at home, which is challenging for students to replicate. This study aimed to determine the effect of online learning on students’ academic achievement and to explore the challenges they faced in adapting to this new mode of learning. A quantitative research approach was employed, gathering primary data from 230 respondents in the Faculty of Social Sciences at the National University of Modern Languages, Islamabad. This was done using a validated closed-ended questionnaire featuring a five-point Likert scale. The collected data underwent analysis via the Friedman test using SPSS 20v. The results revealed that online learning negatively impacted students’ academic achievement due to factors such as lack of internet accessibility, decreased motivation towards academics, low satisfaction levels, and difficulties in understanding academic concepts, particularly in the natural sciences. The study recommends a focus on implementing new teaching methods such as reciprocal teaching, digitalizing classrooms, offering remedial classes, and enhancing student motivation through teacher engagement.

This paper aims to provide and empirically test a conceptual model in which artificial intelligence (AI), knowledge management processes (KMPs) and supply chain resilience (SCR) are simultaneously considered in terms of their reciprocal relationships and impact on manufacturing firm performance (MFP).

In the study, six hypotheses have been developed and tested through an empirical survey administered to 120 senior executives of Italian manufacturing firms. The data analysis has been carried out via the partial least squares structural equation modelling approach, using the Advanced Analysis for Composites 2.0 variance-based software program.

Using a conceptual model validated using an empirical survey, the study sheds light on the relationships between AI, KMPs and SCR, as well as their impacts on MFP. In particular, the authors show the positive effects of the adoption of AI on KMPs, as well as the influence of KMPs on SCR and MFP. Finally, the authors demonstrate that KMPs act as a mediator through which AI affects SCR and MFP.

This study highlights the critical role of KMPs for manufacturing firms that can deploy AI to stimulate KMPs and through attaining a high level of the latter might succeed in enhancing both their SCR and MFP.

This study demonstrates that manufacturing firms interested in properly applying AI to ameliorate their performance and resilience must carefully consider KMPs as a mediator mechanism.