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Accurate and timely cost prediction is critical to the success of construction projects which is still facing challenges especially at the early stage. In the context of rapid development of machine learning technology and the massive cost data from historical projects, this paper aims to propose a novel cost prediction model based on historical data with improved performance when only limited information about the new project is available.

The proposed approach combines regression analysis (RA) and artificial neural network (ANN) to build a novel hybrid cost prediction model with the former as front-end prediction and the latter as back-end correction. Firstly, the main factors influencing the cost of building projects are identified through literature research and subsequently screened by principal component analysis (PCA). Secondly the optimal RA model is determined through multi-model comparison and used for front-end prediction. Finally, ANN is applied to construct the error correction model. The hybrid RA-ANN model was trained and tested with cost data from 128 completed construction projects in China.

The results show that the hybrid cost prediction model has the advantages of both RA and ANN whose prediction accuracy is higher than that of RA and ANN only with the information such as total floor area, height and number of floors.

(1) The most critical influencing factors of the buildings’ cost are found out by means of PCA on the historical data. (2) A novel hybrid RA-ANN model is proposed which proved to have the advantages of both RA and ANN with higher accuracy. (3) The comparison among different models has been carried out which is helpful to future model selection.

Purpose — To develop methodologies to evaluate search engines according to an individual's preference in an easy and reliable manner, and to formulate user-oriented metrics to compare freshness and duplication in search results.

Design/methodology/approach — A personalised evaluation model for comparing search engines is designed as a hierarchy of weighted parameters. These commonly found search engine features and performance measures are given quantitative and qualitative ratings by an individual user. Furthermore, three performance measurement metrics are formulated and presented as histograms for visual inspection. A methodology is introduced to quantitatively compare and recognise the different histogram patterns within the context of search engine performance.

Findings — Precision and recall are the fundamental measures used in many search engine evaluations due to their simplicity, fairness and reliability. Most recent evaluation models are user oriented and focus on relevance issues. Identifiable statistical patterns are found in performance measures of search engines.

Research limitations/implications — The specific parameters used in the evaluation model could be further refined. A larger scale user study would confirm the validity and usefulness of the model. The three performance measures presented give a reasonably informative overview of the characteristics of a search engine. However, additional performance parameters and their resulting statistical patterns would make the methodology more valuable to the users.

Practical implications — The easy-to-use personalised search engine evaluation model can be tailored to an individual's preference and needs simply by changing the weights and modifying the features considered. A user is able to get an idea of the characteristics of a search engine quickly using the quantitative measure of histogram patterns that represent the search performance metrics introduced.

Originality/value — The presented work is considered original as one of the first search engine evaluation models that can be personalised. This enables a Web searcher to choose an appropriate search engine for his/her needs and hence finding the right information in the shortest time with the least effort.

Dirk Ahlers studied computer science at the Carl-von-Ossietzky-University Oldenburg, Germany. In 2005, he started working as a research assistant at the OFFIS Institute for Information Technology, Oldenburg where he conducted projects in mobility and geospatial retrieval. While working at OFFIS, he also pursued his PhD with a topic in geographic information retrieval. He is currently working at UNITEC, a private university in Tegucigalpa, Honduras where he researches potential for local search for the case of a country with little Web coverage, challenging informal address schemes, and uncertain location data. His research interests are geospatial Web information retrieval, search engines, location-based services, Web technology, mobility, and everything geo. E-mail: dirk@dhere.de

This paper aims to investigate the impact of relationship quality among team members in the project team on knowledge transfer effectiveness and analyze the role of organizational structure in the influencing process.

The hypotheses are verified by the Structural Equation Modeling (SEM) analysis using Smart PLS 3 software with the data collected from 236 questionnaire samples in Chinese construction industry.

The results indicate that relationship quality has a direct impact on knowledge transfer in project teams and centralization has a negative impact on relationship quality. Moreover, relationship quality plays a mediating role between centralization and knowledge transfer effectiveness and formalization plays a negative moderating role in the effect of relationship quality on knowledge transfer effectiveness.

This paper studies intra-project knowledge transfer from the perspective of relationship quality of project teams and explores the antecedent and moderating role of organizational structure in the influence of relationship quality on knowledge transfer.

This paper aims to report the influence of hexamethylenetetramine (HMTA) on phosphate coatings formed on AZ31 magnesium alloys.

These phosphate coatings were obtained by immersing magnesium alloys in phosphate baths with HMTA. The morphology and composition of the phosphate coatings were investigated via scanning electron microscopy, energy dispersive spectrometry and X-ray diffraction.

The phosphate coatings were mainly composed of CaHPO₄·2H₂O. The HMTA concentration in the phosphate bath influenced the crystallization and corrosion resistance of the phosphate coating.

The polarization curve shows that the anti-corrosion qualities of the phosphate coating were optimal when the HMTA concentration was 1.0 g/L in the phosphate bath. Electrochemical impedance spectroscopy (EIS) shows that the electrochemical impedances increased gradually when the HMTA concentration varied from 1.0 to 3.0 g/L.

Promoting enterprises' green innovation is vital to realize the sustainable growth of cities and environmental protection and the rise of urban housing prices might affect the green innovation of enterprises to a certain extent. This study aims to discuss the aforementioned objective.

Based on the data of listed companies and urban housing prices of main cities in China from 2011 to 2019, this paper examines the impact of housing prices on enterprises' green innovation and analyzes the mechanism of rising housing prices on enterprises' green innovation.

The rise of urban housing prices can significantly promote the quality of green innovation of enterprises, but it has no significant impact on the quantity of green innovation. The heterogeneity test results show that the rising house prices have a more significant role in promoting the green innovation of non-state-owned enterprises, enterprises listed on the main board, enterprises in the central and western regions, and enterprises in non-first-tier cities. The mechanism research finds that the rise of urban housing prices has a financing relief effect and cost-pushing effect on the green innovation of enterprises.

Firstly, it thoroughly examines the influence of housing prices on corporate green innovation. Second, it explores the differential impact of housing prices on enterprises' green innovation based on variations among enterprises and regions, offering valuable insights for the government to formulate proper policy. Lastly, it elucidates the influencing mechanism of housing prices on enterprise green innovation from the perspectives of corporate financing and costs, providing empirical support for enterprises to appropriately perceive the opportunities and challenges posed by rising housing prices and actively promote green innovation.

The purpose of this paper is to investigate the impacts of different gray markets’ structures on both supply chain decisions and associated profits.

Within the context of gray markets, using game theory approach in this paper, supply chains have been considered as assets of manufacturers, distributors and speculators, within which manufacturers sell products to distinctive markets either directly or through authorized distributors, while speculators buy products from a lower price market and then sell them in a higher price market. Our study has examined different decision variables within such a framework.

Considering a situation where one manufacturer sells its products either directly in one market (Market 1) or through its authorized distributor (Market 2), due to different products prices in both markets, results have shown that, when market elasticity is less than its critical value, a speculator can sell a gray market product arbitrage in market 2, whereas when the market elasticity is greater than its critical value, a speculator can sell a gray market product arbitrage in market 1. In addition, manufacturers—as leaders of Stackelberg game—are always the most profitable stakeholders within a gray market supply chain.

In this study, equilibrium results for each market have been obtained, optimal results have been compared, and accordingly, valuable insights have been developed. Such results would help managers to take better managerial decisions, as well as strategizing policies in gray markets.

In this paper, we have considered a gray market where both distributors and speculators exist and act as parallel channels. To the best of our knowledge, the extant literature focuses either on distributors or speculators, but never concurrently on both. In fact, the coexistence of one distributor and one speculator in a gray market will impact their own decisions, as well as both decisions and profits of other stakeholders, and hence, will exert an impact on the manufacturer side.

Online retailers are pioneering the incentivization of customers to generate more product reviews by rewarding them. However, little is known about the impact of reward types on customers' review behavior, including review frequency and sentiment. To address this gap, we investigated the effects of different reward types on customers' review behavior and how these rewards influence customers' review behavior.

We collected secondary data and empirically tested the hypothesis by analyzing the change in reward policy. Regression and two-stage Heckman models were applied to investigate the effects, with the latter used to control potential selection issues.

The results revealed that monetary rewards can stimulate customers to generate more positive product reviews. Furthermore, the reward amount has a negative moderating effect on the aforementioned relationship. Additionally, customer tenure negatively moderates the relationship between monetary rewards and review behavior.

This study contributes to the understanding of user-generated content motivation and provides managerial implications for reward programs.

China's economy has transformed from a high-speed growth phase to a high-quality development phase. The agriculture sector has grown substantially since the economic reform in 1978. Considering the five-year plan (FYP) as a collection of policies, this study explores the relationship between the FYP and agricultural total factor productivity (TFP).

This study uses 31 provincial-level panel data of the five FYPs from 1996 to 2020. The data envelopment analysis (DEA) is used to compute Malmquist productivity indexes. The authors analyze the temporal and spatial changes and convergences of China's agricultural TFP, and investigate the impact of economic planning on China's agricultural TFP and its regional difference.

There is a slow but upward growth trend in China's agricultural TFP. The technical change has played a leading role in the growth of China's agricultural TFP. The agricultural TFP of all provinces has shown a “catch-up” effect and is developing toward their respective steady-state levels. The regional difference in productivity growth among the eastern, central and western regions exists. Test results show that the FYP has a positive effect on the agricultural TFP, and the effect has obvious regional heterogeneity. The FYP also plays a positive role in the gross value of agricultural output, and the impact effect is greater than that on the improvement of agricultural productivity.

There are many forms of industrial policy in China, among which the FYP is the guiding document of industrial policy, which makes a systematic plan for industrial development in the subsequent five years. The development objectives, guidelines and overall deployment for agriculture in the FYP not only describe the general context of China's agricultural development but also show the key ideas of agricultural development. Therefore, this study explores its impact on agricultural quality development from the perspective of FYP. The results provide evidence for examining the governance performance of the government and the objective evaluation and restraint of the FYP. As agriculture moves toward the stage of high-quality development, the Chinese government should strengthen the critical guiding role of the FYP and pay attention to quality indicators such as technical progress, efficiency improvement and regional coordination in the formulation of the FYP.