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High risk is one of the most prominent characteristics of the Chinese construction industry, and it poses a significant threat to construction projects. Owing to initiatives aimed at achieving high efficiency, low carbon emissions, etc., industrialization of the construction industry has become an inevitable trend in China. However, it remains to be discussed whether industrialization of construction can reduce the risks entailed in construction projects compared with traditional construction. The paper aims to discuss these issues.

Based on the theory of risk life cycle, this paper proposes a practical risk assessment technique to assess the risk life cycle, including the risk occurrence time and potential financial losses. This technique is then applied to assess the differences between the risks involved in an engineering, procurement and construction (EPC) project executed via traditional and industrial production modes.

The results show that the total duration of risks in the industrial construction project is half of that in the traditional project. In addition, the expected financial loss entailed in the industrial construction project is 29 percent lower than that in the traditional construction project. Therefore, industrial construction has the potential to optimize risk performance.

There is no significant difference between the traditional and industrial construction models in terms of probability of risk. The maximum total loss might occur in the procurement stage in the case of industrial production, and in the construction stage in the case of traditional production. Moreover, the total expected loss from risk in the EPC project in the industrial production mode is only half of that in the traditional production route. This study is expected to provide a new risk evaluation technique and promote an understanding of the life cycle of risk management in the construction industry.

Management education is at its peak in India. But pedagogy and modes of delivery are not always innovative compared to top international Business Schools. It is through experimentation that the paper may be able to discover what works best in our context. The purpose of this paper is to determine the effectiveness of intensive mode of delivery vs traditional semester-wide teaching of management courses among MBA students of a leading Business School, through one such experiment.

A total of 19 dimensions were used in this study. The questionnaire was tested on two different groups. An independent sample t-test was conducted for each dimension, to find out if the group that was subjected to this experiment had opinions different from the group that did not undergo the accelerated version.

The results indicated perceptions on most of the dimensions disconcerting, barring increase in commitment, engagement, focus, and concentration with intensive mode. Further, this negative perception augmented towards intensive delivery mode, after experiencing traditional delivery.

The literature does appear to show controversial outcomes related to intensive mode but more studies are in support of intensive modes of delivery format. The issue of whether students learn better in a semester/trimester of traditional length or with a compressed schedule is a key concern to the innovations in higher education scheduling today. Findings of this study pose a serious threat to all those management institutions which are planning to attempt to speed up the delivery of programmes and courses within them in order to reduce cost or other reasons.

In education literature, significant amount of research has been carried out using a time compressed in developed countries. This study is one of the first studies, which focuses on determining the effectiveness of intensive teaching against traditional trimester/semester wide teaching among MBA education in India.

In the crowd intelligence networking era, the smart connections of human, machines and things enable point-to-point trustable transactions and distributed efficient collaboration; the smart connections among government, enterprises, organizations and the public would enable active participation of the public in society management and decision-making and improve the efficiency of government management and services. All interactions among various agents can be viewed as the transaction activity. The social division of labor system drives the evolution of transaction. The transaction mode also differentiated into different patterns with the development of human society. What will be the intelligent transaction in the crowd intelligence networking era? What will be the transactions modes and rules in the crowd intelligence networking era? The answers to these questions are of great importance to the future development of transactions.

The authors review the evolution of traditional transaction and transaction modes and analyze the driving forces of it. They attempt to give the definitions of intelligent transaction and intelligent transaction mode. They also review the traditional transaction modes and rules, analyze the characteristics of the intelligent transaction and classify the intelligent transaction modes.

The authors find the intelligent transaction is mainly reflected in the intellectualization of transaction subject, transaction object and transaction process. They summarize the characteristics of intelligent transaction and develop four modes for the intelligent transactions based on the modularization level of the transaction objects and the quantity of transaction subjects, including the demand side and the supply side. The authors also show representative examples to further illustrate rules and features of these transaction modes and point out the potential research directions.

This study is among the first to analyze the characteristics of the intelligent transaction, and the proposed division framework of the intelligent transaction modes could not only add value to the future research of intelligent transaction modes and rules but also help to guide the transactions in the crowd intelligence network.

Nowadays, quality is one of the most important key success factors in the automobile industry. Improving the quality is based on optimizing the most important quality characteristics and usually launched by highly applied techniques such as failure mode and effect analysis (FMEA). According to the literature, however, traditional FMEA suffers from some limitations. Reviewing the literature, on one hand, shows that the fuzzy rule-base system, under the artificial intelligence category, is the most frequently applied method for solving the FMEA problems. On the other hand, the automobile industry, which highly takes advantages of traditional FMEA, has been deprived of benefits of fuzzy rule-based FMEA (fuzzy FMEA). Thus, the purpose of this paper is to apply fuzzy FMEA for quality improvement in the automobile industry.

Firstly, traditional FMEA has been implemented. Then by consulting with a six-member quality assurance team, fuzzy membership functions have been obtained for risk factors, i.e., occurrence (O), severity (S), and detection (D). The experts have also been consulted about constructing the fuzzy rule base. These evaluations have been performed to prioritize the most critical failure modes occurring during production of doors of a compact car, manufactured by a part-producing company in Iran.

Findings indicate that fuzzy FMEA not only solves problems of traditional FMEA, but also is highly in accordance with it, in terms of some priorities. According to results of fuzzy FMEA, failure modes E, pertaining to the sash of the rear right door, and H, related to the sash of the front the left door, have been ranked as the most and the least critical situations, respectively. The prioritized failures could be considered to facilitate future quality optimization.

This research provides quality engineers of the studied company with the chance of ranking their failure modes based on a fuzzy expert system.

This study utilizes the fuzzy logic approach to solve some major limitations of FMEA, an extensively applied method in the automobile industry.

The purpose of this paper is to investigate the optimal operational strategies in a green platform supply chain and provide suggestions on the selection of sales and financing modes for the capital-constrained manufacturer.

This study combines different sales channels with financing modes and investigates three sales-financing modes, i.e. traditional sales-prepayment financing (TSPF), traditional sales-bank financing (TSBF) and online sales e-retailer financing (OSEF). By establishing and comparing Stackelberg game models of these sales-financing modes from the perspectives of economy, environment and social welfare, the optimal strategies of emission reduction, financing, pricing and service improvement are obtained.

The results suggest that as the commission rate increases to a certain level, a threshold of the cost coefficient of emission reduction can be found such that the manufacturer should choose OSEF below this threshold and TSBF above this threshold. OSEF is Pareto optimal when this cost coefficient is low, and this mode can lead to the highest social welfare when the platform loan interest rate is relatively low. The Pareto areas in TSBF and OSEF enlarge as the default coefficient decreases.

These results can provide operational insights on how to select sales channels and financing modes when manufacturer faces financial constraints in emission reduction.

This paper combines different sales and financing modes to study their comprehensive influence on the decision-makings of chain members and the resulting performance of economy, environment and social welfare.

By introducing the mass customization service mode into the cloud logistics environment, this paper studies the joint optimization of service provider selection and customer order decoupling point (CODP) positioning based on the mass customization service mode to provide customers with more diversified and personalized service content with lower total logistics service cost.

This paper addresses the general process of service composition optimization based on the mass customization mode in a cloud logistics service environment and constructs a joint decision model for service provider selection and CODP positioning. In the model, the two objective functions of minimum service cost and most satisfactory delivery time are considered, and the Pareto optimal solution of the model is obtained via the NSGA-II algorithm. Then, a numerical case is used to verify the superiority of the service composition scheme based on the mass customization mode over the general scheme and to verify the significant impact of the scale effect coefficient on the optimal CODP location.

(1) Under the cloud logistics mode, the implementation of the logistics service mode based on mass customization can not only reduce the total cost of logistics services by means of the scale effect of massive orders on the cloud platform but also make more efficient use of a large number of logistics service providers gathered on the cloud platform to provide customers with more customized and diversified service content. (2) The scale effect coefficient directly affects the total cost of logistics services and significantly affects the location of the CODP. Therefore, before implementing the mass customization logistics service mode, the most reasonable clustering of orders on the cloud logistics platform is very important for the follow-up service combination.

The originality of this paper includes two aspects. One is to introduce the mass customization mode in the cloud logistics service environment for the first time and summarize the operation process of implementing the mass customization mode in the cloud logistics environment. Second, in order to solve the joint decision optimization model of provider selection and CODP positioning, this paper designs a method for solving a mixed-integer nonlinear programming model using a multi-layer coding genetic algorithm.

The purpose of this paper is to propose a new analytical approach in human thought computation with an application to the child language acquisition research.

Certain fuzzy statistical concepts, such as fuzzy samples, fuzzy mode, fuzzy category test, and their relevant properties, were presented. Empirical data sets on children's language and cognitive development were discussed. Finally, the paper makes a comparison result between the traditional statistical analysis and fuzzy statistical analysis.

The results show that the new method is better able to capture the intricacies and complexities of the nature and processes in acquiring languages than the traditional methods.

The paper presents fuzzy statistical analysis as a new analytical approach in child language research.

Web-based language learning (WBLL) materials have long been favored by English language instructors because they are plentiful, easily accessible, user-friendly and, most importantly, free. This research looks into the effects of learning the simple perfect tense translation in three different English translation classes that used three different teaching methods: traditional face-to-face, integrative and web-based learning. It also aims to investigate the impact of gender on every mode of instruction and to identify the most effective method for learning translation.

The study is quantitative, with a pretest/posttest quasi-experimental research design. A total of 93 third-year undergraduate students (51 female and 42 male students) participated in the pretest/posttest design. Each group was exposed to one mode of instruction for nine weeks. All groups sat for a pretest in the first week of the treatment. After the treatment, the participants were provided with a posttest, and the data obtained were analyzed using the SPSS computer software program.

The findings revealed a significant difference in both tests for all modes used. All three groups improved in their gain score, but the highest gain among these groups was the integrative method, followed by web-based learning. The result of the independent sample t-tests and ANOVA exhibited that there was no significant difference in the level of students between the two groups, both were sig. two-tailed (p = 0.342). Furthermore, it was discovered that gender did not affect students' performance in the posttest (Z = −1.564, p > 0.05) when each mode of instruction was applied. Finally, the integrative method was observed to be the most effective.

The findings can inspire translation course designers to plan necessary policies or syllabi regarding English translation courses and may serve as a platform for improving the curriculum for training and motivating the next generation of English as a Foreign Language (EFL) learners.

The findings of this study could be used to improve English instruction in countries where English is a second or foreign language. The actual gap in knowledge is that no other studies have compared all three groups in the past few years.

With the proliferation of virtual reality (VR) applications in electronic commerce, investigations on the effects of VR on consumer responses are important. The purpose of this paper is to examine the effects of online presentation modes (i.e. situational VR, pure VR and picture) on consumer responses for three product types (i.e. geometric, material and mechanical).

This study conducted a 3×3 between-subjects experiment to validate the research model and hypotheses.

The results revealed that both the situational VR mode and the pure VR mode had a greater impact on product knowledge and purchase intention than the picture mode. The situational VR mode yielded a higher level of product knowledge and purchase intention than the pure VR mode although it was not statistically significant. Furthermore, the pattern of VR modes superiority was found to be consistent across geometric, material and mechanical product types.

This research study contributes to the VR literature by investigating a new type of VR: situational VR, and offering a more comprehensive picture of consumer responses to online product presentations. The authors then drew the implications from the findings to suggest guidelines for practitioners to efficiently allocate resources and maximize the effectiveness of online presentation modes.

This study associated consumers' food choice motives and socio-demographic characteristics with their attitudes and consumptions towards food shopping with four e-commerce modes: business-to-consumer (B2C), online-to-offline delivery (O2O Delivery), online-to-offline in-store (O2O In-store) and New Retail. It also explored consumer preferences for specific food categories within the four e-commerce modes.

An online survey was administered to 954 participants from three Chinese cities: Beijing, Shanghai and Shenzhen. Descriptive analysis and linear regression were used in the data analysis.

The following food choice motives (FCMs) and socio-demographic characteristics had a significant effect on food e-commerce attitudes and/or consumption, with some or all of the four e-commerce modes: Taste Appeal, Value for Money, Safety Concerns, Quality Concerns, Processed Convenience, Purchase Convenience, Others' Reviews, City, Gender, Household Size, Age, Income, Occupation and Marital Status. Consumers also have different consumption preferences for food categories in the four e-commerce modes.

This is the first study to associate consumer FCMs and socio-demographics with their e-commerce attitudes and consumption regarding food in four e-commerce modes: B2C, O2O Delivery, O2O In-store and New Retail.