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Conflicts among top management team (TMT) members have a significant impact on sustainable development of family enterprises in China. The complex attributes of different kinds of conflicts in a TMT have dual effects on firm performance and its stability. Thus, avoiding conflicts in a TMT through a systematic conflict management strategy is very important. This paper aims to therefore investigate how to maximize the performance and income level of the TMT in family enterprises through managing conflict systematically, while adopting the best conflict coping strategies.

In this study, the authors apply conflict coping strategies as a useful tool of conflict management and propose five kinds of dynamic conflict coping strategies among TMT members. Repeated game and multi-agent simulation by computer experiment are used to dynamically simulate the rules and evolution of individual conflict coping strategy choices.

It is found that with the passage of time, different conflict coping strategies have different effects on earnings of individuals and teams at different conflict levels. It is also revealed that conflict coping strategies affect not only the earnings of individuals and teams but also their distribution; it also reflects the conflict level in TMT of a family enterprise but in reverse.

This study contributes to the existing literature on conflict management in relevance to the choice and revolution of conflict coping strategies in a Chinese business culture context. It focuses on strengthening the unity and cooperation of TMT members. Controlling the conflict level of TMT members at a reasonable level, investigating the primary cause of conflict and identifying its nature lead to better performance of the TMT and the sustainable development of Chinese family enterprises. Based on these outcomes, different conflict coping strategies can be appropriately used to solve TMT conflicts.

Credit risk evaluation is a crucial task for banks and non-bank financial institutions to support decision-making on granting loans. Most of the current credit risk methods rely solely on expert knowledge or large amounts of data, which causes some problems like variable interactions hard to be identified, models lack interpretability, etc. To address these issues, the authors propose a new approach.

First, the authors improve interpretive structural model (ISM) to better capture and utilize expert knowledge, then combine expert knowledge with big data and the proposed fuzzy interpretive structural model (FISM) and K2 are used for expert knowledge acquisition and big data learning, respectively. The Bayesian network (BN) obtained is used for forward inference and backward inference. Data from Lending Club demonstrates the effectiveness of the proposed model.

Compared with the mainstream risk evaluation methods, the authors’ approach not only has higher accuracy and better presents the interaction between risk variables but also provide decision-makers with the best possible interventions in advance to avoid defaults in the financial field. The credit risk assessment framework based on the proposed method can serve as an effective tool for relevant policymakers.

The authors propose a novel credit risk evaluation approach, namely FISM-K2. It is a decision support method that can improve the ability of decision makers to predict risks and intervene in advance. As an attempt to combine expert knowledge and big data, the authors’ work enriches the research on financial risk.

The purpose of this paper is to develop a hybrid semantic recommendation system to provide personalized government to business (G2B) e‐services, in particular, business partner recommendation e‐services for Australian small to medium enterprises (SMEs).

The study first proposes a product semantic relevance model. It then develops a hybrid semantic recommendation approach which combines item‐based collaborative filtering (CF) similarity and item‐based semantic similarity techniques. This hybrid approach is implemented into an intelligent business‐partner‐locator recommendation‐system prototype called BizSeeker.

The hybrid semantic recommendation approach can help overcome the limitations of existing recommendation techniques. The recommendation system prototype, BizSeeker, can recommend relevant business partners to individual business users (e.g. exporters), which therefore will reduce the time, cost and risk of businesses involved in entering local and international markets.

The study would be of great value in e‐government personalization research. It would facilitate the transformation of the current G2B e‐services into a new stage wherein the e‐government agencies offer personalized e‐services to business users. The study would help government policy decision‐makers to increase the adoption of e‐government services.

Providing personalized e‐services by e‐government can be seen as an evolution of the intentions‐based approach and will be one of the next directions of government e‐services. This paper develops a new recommender approach and systems to improve personalization of government e‐services.

This study aims to develop a decision making model and approach for logistics planning problem which naturally involves two or more decision units at a hierarchical structure. Such a decision problem in practice often involves uncertain and imprecise factors with the parameters of a bilevel decision model, either in the objective functions or constraints.

This paper proposes a fuzzy bilevel decision making model for a general logistics planning problem and develops a fuzzy number based Kth‐best approach to find an optimal solution for the proposed fuzzy bilevel decision problem.

The proposed approach illustrates an optimal solution in logistics management, which meets maximally/minimally the objectives of both supplier and distributor (or other parts of the logistics chain). The proposed fuzzy bilevel decision approach can have a wide range of logistics management applications.

The decision model, approach and system will be further tested for some more complicated real cases in the future.

The proposed fuzzy bilevel decision model and approach are new, which offer theoretical and practice help to logistics management.

In any organization there are main goals, with lots of projects designed to achieve these goals. It is important for any organization to determine how much these projects affect the achievement of these goals. The purpose of this paper is to develop a fuzzy multiple attribute-based group decision-support system (FMAGDSS) to evaluate projects’ performance in promoting the organization's goals utilizing simple additive weighting (SAW) algorithm and technique for order of preference by similarity to ideal solution (TOPSIS) algorithm. The proposed FMAGDSS deals with choosing the most appropriate fuzzy ranking algorithm for solving a given fuzzy multi attribute decision making (FMADM) problem with both qualitative and quantitative criteria (attributes), and uncertain judgments of decision makers.

In this paper, a FMAGDSS model is designed to determine scores and ranks of every project in promoting the organization's goals. In the first step of FMAGDSS model, all projects are assessed by experts based on evaluation criteria and the organization's goals. The proposed FMAGDSS model will then choose the most appropriate fuzzy ranking method to solve the given FMADM problem. Finally, a sensitivity analysis system is developed to assess the reliability of the decision-making process and provide an opportunity to analyze the impacts of “criteria weights” and “projects” performance’ on evaluating projects in achieving the organizations’ goals, and to assess the reliability of the decision-making process. In addition, a software prototype has been developed on the basis of FMAGDSS model that can be applied to solve every FMADM problem that needs to rank alternatives according to certain attributes.

The result of this study simplifies and accelerates the evaluation process. The proposed system not only helps organizations to choose the most efficient projects for sustainable development, but also helps them to assess the reliability of the decision-making process, and decrease the uncertainty in final decision caused by uncertain judgment of decision makers.

Future studies are suggested to expand this system to evaluate and rank the project proposals. To achieve this goal, the efficiency of the projects in line with organization's goals, should be predicted.

This study contributes to the relevant literature by proposing a FMAGDSS model to evaluate projects in promoting organization's goals. The proposed FMAGDSS has ability to choose the most appropriate fuzzy ranking algorithm to solve a given FMADM problem based on the type and the number of attributes and alternatives, considering the least computation and time consumption for ranking alternatives.

This paper first presents a research framework for e‐service evaluation within four categories: cost, benefit, functions and development, each incorporating a number of factors. Through data analysis and hypotheses testing, inter‐relationships among the factors of the four categories are examined. The results show that the development type of an e‐service has a significant effect on the degree of user satisfaction. Expertise, technique and expense are the principle factors limiting current e‐service adoption. The most significant finding is that, in the development of e‐services, certain cost factors are significantly more important than others in relation to certain benefit factors. The finding is presented as a cost‐benefit factor‐relation model. This provides an insight into whether investment in certain areas of e‐service applications is more important than in others for particular business objectives. These results have the potential to improve the strategic planning of companies by determining more effective investment areas and adopting more suitable development activities where e‐services are concerned.

In a voluntary consumer market, the consumer's decision‐making process is still a magnetic topic although a large number of related studies have been conducted. The purpose of this paper is to explore the determining factors for the future adoption of an innovation service and proposes a new model to better explain consumer's adoption intention.

Social cognitive theory is introduced in the paper to explore consumer behavior in terms of triadic and reciprocal causation among value, self‐efficacy, and adoption constructs. Based on the theoretical framework, a self‐efficacy‐based value adoption model (SVAM) is proposed to study the adoption of mobile auction (m‐auction) – one of mobile‐internet services in an empirical manner. The analysis of measurement and structural model is conducted via LISREL 8.80.

Research results indicate that the instruments are reliable and valid and the model is well accepted. Perceived functional, social, and emotional values are tested to have a significant influence on perceived value. Perceived cost is negatively related to perceived value and positively to attitude. m‐auction self‐efficacy (MASE) comes from subjective and objective general self‐efficacy, which not only directly impacts attitude but also strongly influences perception of all the antecedents of perceived value.

Although SVAM is proved to be successful in this paper, it still needs more experimental research from different research contexts and authors to be supported.

Compared with previous research on innovation adoption, results show that the model is more effective in explaining the adoption intention (R²=0.72). Just like perceived value has a significantly impact on attitude, self‐efficacy of m‐auction has an essential role in perceived values and cost and attitude. The result fully demonstrates that the value adoption model based on self‐efficacy is a better technology acceptance model, especially for a voluntary consumer market.

Investigating the characteristics of the transformation of China's agricultural growth and the institutional reforms during the whole transition period in the 1980s will help to understand China's economic and agricultural reform and offer some successful experiences to other developing countries. The purpose of this paper is to answer the following questions: Has China's agricultural output experienced a structural break toward a more stable state? When did the break point happen? What factors did play key roles during the transformation?

This study applies the nonlinear structural break regime switching analysis which includes two different models: one with the structural break only in the variance and one with the structural break in both shift parameters and the variance.

The empirical results showed that it took five years for agricultural development in China to finish the reform that began in 1980. The agricultural growth had become more stable after the break point which was around 1985‐1986. Both the primary industry and agricultural sector had transformed from a “low growth rate, high volatility” state to a “high growth rate, low volatility” state. Among the different driving factors, the Household Responsibility System (HRS) and preferential policies for agricultural products played the most important roles in the early stage of the economic reform.

The authors first study the structural break of China's agricultural output by the regime switching model. From the empirical results, the structural break point was determined to be around 1985‐1986, after which China's agricultural output had become more stable.

The purpose of this paper is to present an intelligent mobile based emergency response system (MERS) framework, a text information extraction and aggregation algorithm to integrate information from multiple sources in the MERS system, and an ontology‐supported case‐based reasoning system for the MERS system.

The paper explains the components of information extraction and aggregation process, and a CBR‐Ontology approach for the MERS system.

The result of this study will offer a new opportunity to the interaction between government, citizens, responders, and other non‐government agencies in emergency situations, and therefore improve the services of the government in an emergency situation.

The paper indicates the need for usage of mobile technologies to assist the government to get information and make decisions in responding to disasters anytime and anywhere.

Markov chains and queuing theory are widely used analysis, optimization and decision‐making tools in many areas of science and engineering. Real life systems could be modelled and analysed for their steady‐state and time‐dependent behaviour. Performance measures such as blocking probability of a system can be calculated by computing the probability distributions. A major hurdle in the applicability of these tools to complex large problems is the curse of dimensionality problem because models for even trivial real life systems comprise millions of states and hence require large computational resources. This paper describes the various computational dimensions in Markov chains modelling and briefly reports on the author's experiences and developed techniques to combat the curse of dimensionality problem.

The paper formulates the Markovian modelling problem mathematically and shows, using case studies, that it poses both storage and computational time challenges when applied to the analysis of large complex systems.

The paper demonstrates using intelligent storage techniques, and concurrent and parallel computing methods that it is possible to solve very large systems on a single or multiple computers.

The paper has developed an interesting case study to motivate the reader and have computed and visualised data for steady‐state analysis of the system performance for a set of seven scenarios. The developed methods reviewed in this paper allow efficient solution of very large Markov chains. Contemporary methods for the solution of Markov chains cannot solve Markov models of the sizes considered in this paper using similar computing machines.