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The purpose of this paper is to examine factors affecting fundraisers’ voluntary information disclosure on crowdfunding platforms based on risk-perception theory (RPT).

Structural equation modeling was employed to test the hypothesized relationships using data collected from China.

The authors found that plagiarism risk and financing risk are two important variables that influence fundraisers’ voluntary information disclosure. Specifically, plagiarism risk has a negative effect on fundraisers’ voluntary information disclosure, while financing risk has a positive effect on fundraisers’ voluntary information disclosure. Plagiarism risk is affected by information concerns, perceived control, project innovativeness, and quality of alternatives, while financing risk is affected by protection policy and information norms.

This study enriches crowdfunding research by identifying factors influencing fundraisers’ voluntary information disclosure and contributes to RPT by applying it in a new crowdfunding context.

The purpose of the current study is to identify variables that, when integrated into the random effects parametric survival model, could be used to forecast the failure rate of small banks in the USA. A bank’s income production, efficiency and costs were taken into consideration when choosing the internal components. The breakout of the financial crisis, bank regulations that affect how the banking sector operates and the federal funds rate are the primary external variables.

This study uses the random effects parametric survival model to investigate the causes of small bank failures in the USA from 1996 to 2019. The study identifies several characteristics that failed banks frequently display. The main indications that may help to identify the elevated risk of small bank failures include the ROA, the cost of funds, the ratio of noninterest income to assets, the ratio of loan and lease losses to assets, noninterest expenses and core capital (leverage) ratio to assets. Economic disruptions, financial market distress and industry-based regulatory redress by the government exacerbate the financial distress borne by small banks.

The study revealed that a failed bank typically demonstrates a certain number of characteristics. The key factors that might assist identify which bank would be most likely to collapse include the cost of funding earning assets, the yield on earning assets, core Capital (leverage) ratio to assets, loan and lease loss provision to assets, noninterest expense and noninterest income to assets. Additionally, when a financial crisis occurs or the government changes regulations that could raise the cost of compliance for small banks, the likelihood that a bank will fail increases.

Models based on survival theories are more suitable when the authors examine bank failure as a unique event that happens gradually. The authors use a random effects parametric survival model to investigate the internal and external factors that may influence prospective small bank failure. This model has been developed and used in the medicinal research field. The authors do not choose the Cox proportional hazards model because it does not work well with panel data.

Nowadays, an increasing number of firms choose to develop proprietary software, instead of buying packaged software. What factors will affect different types of software investments? According to the environment-strategy alignment research, environment should be an influential factor. However, environment's role has received scarce attention in the literature. The authors' study addresses this research gap by investigating how industry environment affects different types of software investments. The study identifies three types of software investments (software insourcing, outsourcing, and buying) and examines how the characteristics of the industry environment (including industry munificence, dynamism, and concentration) influence each software investment.

The generalized least squares (GLS) model and the ordinary least squares with panel-corrected standard errors (OLS-PCSE) model are applied to test the hypotheses, based on industry-level panel data from the US Bureau of Economic Analysis (BEA).

The analysis shows that industry munificence, dynamism, and concentration have different impacts on software insourcing, outsourcing, and buying, respectively.

This study classifies software investment into three types – software insourcing, outsourcing, and buying and investigates how the industry environment affects them. The findings suggest that research should distinguish among software insourcing, outsourcing, and buying due to their different characteristics.

Prior IT productivity research usually assumes constant returns on IT investment. This study suggests that the impact of IT investment on productivity may not be constant but may change with the IT investment scale and over time. Specifically, we divide IT investment into commercial IT and in-house IT and investigate their changing impacts on industry labor productivity.

A model of the productivity impacts of commercial IT and in-house IT with changing effects of scale and over time is developed and empirically tested based on industry-level panel data from the US. Bureau of Economic Analysis (BEA).

The returns on commercial IT investment increase with scale but decrease over time, while the returns on in-house IT increase over time.

This study provides a new perspective for IT productivity research by investigating the changing productivity impacts of IT investment. It also suggests that commercial IT and in-house IT should be distinguished, as they have different impacts on productivity.

The purpose of this paper is to introduce a new method called simultaneous elimination and back-substitution method (SEBSM) to solve a system of linear equations as a new finite element method (FEM) solver.

In this paper, a new technique assembling the global stiffness matrix will be proposed and meanwhile the direct method SEBSM will be applied to solve the equations formed in FEM.

The SEBSM solver for FEM with the present assembling technique has distinct advantages in both computational time and memory space occupation over the conventional methods, such as the Gauss elimination and LU decomposition methods.

The developed solver requires less memory space no matter the coefficient matrix is a typical sparse matrix or not, and it is applicable to both symmetric and unsymmetrical linear systems of equations. The processes of assembling matrix and dealing with constraints are straightforward, so it is convenient for coding. Compared to the previous solvers, the proposed solver has favorable universality and good performances.

In China, engineering insurance has been questioned as not being beneficial as expected. This paper seeks to further understand how China's engineering insurance industry functions and to provide a macro perspective explanation for engineering insurance's underdevelopment.

Three industrial organization hypotheses were extended to the engineering insurance context: structure conduct performance (SCP), relative market power (RMP) and efficiency structure (ES) hypotheses. This paper employed the Generalized Method of Moments (GMM) and Data Envelopment Analysis (DEA) bootstrap to test the hypotheses using panel data from 2008 to 2017.

The results suggest that the SCP paradigm is validated in China's engineering insurance market, indicating a concentrated market where the welfare of consumers (e.g. owners, contractors and designers) may be eroded. Several factors are identified to have significant impacts on engineering insurers' performance, such as the investment return, percentage of engineering business, the ratio of outstanding claims, the number of large contractors, market rivalry and entry barriers.

Despite the sheer size of China's construction industry and the urgent need to improve risk management, the insurance industry that serves construction firms engineering insurance is underdeveloped. Engineering insurance is yet to be understood from a macro perspective, which may reveal the underlying reasons for engineering insurance's underdevelopment. The industrial organization theories provided a theoretical framework to test the functioning of this specific industry. The disaggregated data (engineering line specific) is employed to ensure effective regulation and policymaking.

The purpose of this paper is to provide additive manufacturing-based solutions for preparation of elastomeric foam with broaden compressive stress plateau.

Mechanic models are developed for obtaining designs of foam cell units with enhanced elastic buckling. An experimental approach is taken to fabricate the foams based on direct ink writing technique. Experimental and simulation data are collected to assist understanding of our proposals and solutions.

A simple tetragonal structured elastomeric foam is proposed and fabricated by direct ink writing, in which its cell unit is theoretically designed by repeating every four filament layers. The foam exhibits a broader stress plateau, because of the pronounced elastic buckling under compressive loading as predicted by the authors’ mechanic modeling. A two-stage stress plateaus as observed in the foam, being attributed to the dual elastic buckling of the cell units along two lateral directions of the XY plane during compression.

Future work should incorporate more microscopic parameters to tune the elastomeric foam for mechanic performance testing on linear elastic deformation and densification of polymer matrix.

Additive manufacturing offers an alternative to fabricate elastomeric foam with controlled cell unit design and therefore mechanics. Our results comment on its broad space for development such superior cushioning or damping material in the fields of vibration and energy absorption.

This work has contributed to new knowledge on preparation of high performance elastomeric foam by providing a better understanding on its cell structure, being printed using direct ink writing machines.

Public–Private Partnerships (PPPs) are crucial to the procurement of global infrastructure projects. Moreover, a price mode based on a cluster of core concessionary items is key to the delivery of value-for-money and successful project outcomes. However, existing research has yet to fully identify PPP concessionary items, nor yet described the range of practical price modes. This study provides taxonomy of core concessionary items impacting PPP projects, systematically classifies price modes, and assesses the applicability and risk impacts of those price modes on PPP projects.

This study adopts a comparative case study method in analyzing core concessionary items and alternative price modes. China is taken as the context, as it is one of the world’s largest PPP markets. In ensuring research validity and reliability, diverse data sources are utilized, with a graphic content analysis tool developed to capture the structure of price modes.

Eight PPP price modes are identified. These are: (1) UP (Unit Price) mode, (2) ALS (Annual Lump Sum) mode, (3) IRR (Internal Rate of Return) mode, (4) RP (Return for Investing Capital (RIC) - Profit Rate of O&M (PROM)) mode, (5) RFP (RIC - Financing Interest Rate (FR) - PROM) mode, (6) RFPL (RIC - FR - PROM - Lower Limit of User Charge (LLoUC)) mode, (7) RFL (RIC - FR - Lump Sum/Fixed Unit Price O&M Contract (LSOM/FUP)) mode, and (8) RFLL (RIC - FR - LSOM/FUP - LLoUC) mode. Other main findings are as follows: (1) Five risk allocation configurations can be achieved via these price modes. Yet while different price modes enable the allocation of specific risks, these do not always align with contracting parties’ original intentions. (2) IRR and RP modes may be less applicable in general because of their vulnerability in allocating critical risks and capacity for spurring opportunistic behavior.

By depicting the paths by which concessionary items in price modes affect cash flow, a systematic analysis of price modes was conducted exposing structural characteristics, along with risk allocation choice implications. The study is unique in: (1) Providing a systematic classification of PPP price modes used in PPP projects, (2) Presenting a comprehensive identification and streamlining of concessionary items in PPP practice, and (3) Analyzing the risk effects of different price modes. Together, these outcomes offer a hitherto unavailable perspective on PPP project risk management. The value of the study lies in the following: (1) Existing studies employ diverse concessionary items, but their applicability varies. This study offers an overarching framework facilitating decision-making in selecting appropriate PPP price modes and in determining concessionary items. (2) This study adds to the understanding of PPP price modes in significant ways that will aid local governments and potential sponsors in crafting and administrating more workable contract designs.

Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

The purpose of the study is to numerically investigate the relationship between the increase in transaction cost and prolongation of cooperative period acting as a nonmonetary incentive for municipal PPP projects.

A model that combines real option theory and the concept of prospect theory is proposed in the study. Three municipal road PPP projects published by China Public Private Partnerships Center are selected as cases. The data of these cases are analyzed based on the model established.

The prolongation of the cooperative period affects the increase in transaction cost, which gradually decreases when the prolonged cooperative period increases. Furthermore, the large-investment PPP projects own more transaction cost compared with less-investment projects. The decrease in transaction cost in the former is less than that in the latter. The increase in transaction cost is evidently alleviated in a project with less investment when the cooperative period is prolonged further.

The study systematically analyzes the relationship among transaction cost economics, real option theory and prospect theory and proposes a theoretical flowchart of the effect of nonmonetary incentive on the transaction cost. A model to quantify the effect of nonmonetary incentive (i.e. prolongation of cooperative period) on the transaction cost is proposed for the first time. The results of the study verify that the nonmonetary incentive affects transaction cost.