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This paper aims to investigate the consumption and actual purchase behaviour of Malaysian food consumers who have experienced of buying imported food and to compare the differences between two groups, Muslim and non-Muslim.

This study collected data from the imported food buyers in Malaysia using a self-administered questionnaire. A total of 435 usable responses were used for further analysis. To identify the Malaysian consumers’ imported food consumption, exploratory factor analysis was used. A t-test and χ² test were applied to compare the difference of consumer groups between Muslim and non-Muslims.

The results have found the determinants of imported food purchasing of both Muslim and non-Muslim consumers and the most perceived quality aspects related to their food lifestyles. Malaysian consumers, regardless of religion, are most affected by the intrinsic factors like nutrients, health functionality and freshness. Muslim consumers also mostly perceive taste as a quality aspect regarding to their food lifestyles.

This study provides a number of potentially important and valuable resources for the manufacturer or exporters seeking to enter the Malaysian food market. Imported food into Malaysia should adopt the customised strategies through the products emphasising health benefits and tastes to achieve maximum marketing results.

This paper contributes important information about imported food consumption of Malaysian consumers. Therefore, it will be useful for food manufacturers or exporters from in particular non-Muslim countries to understand Muslim consumers’ perception and purchasing behaviour towards imported food.

The authors develop a framework to build an early warning mechanism in detecting financial deterioration of Chinese companies. Many studies in the financial distress and bankruptcy prediction literature rarely do they examine the impact of pre-processing financial indicators on the prediction performance. The purpose of this paper is to address this shortcoming.

The proposed framework is evaluated by using both original and discretized data, and a least absolute shrinkage and selection operator (LASSO) selection technique for choosing an appropriate subset of financial ratios for improved predictive performance. The financial ratios are then analyzed by five different data mining techniques. Managerial insights, using data from Chinese companies, are revealed by the methodology employed.

The prediction accuracy increases after we discretized the continuous variables of financial ratios. A better prediction performance can be achieved by including fewer, but relatively more significant variables. Random forest has the highest overall performance following closely by SVM and neural network.

The contribution of this study is fourfold. First, the authors add to the literature on defaults by showing variable discretization to be an essential pre-processing step to improve the prediction performance for classification problems. Second, the authors demonstrate that machine learning approaches can achieve better performance than traditional statistical methods in classification tasks. Third, the authors provide the evidence for the adoption of C5.0 over other methods because rules generated with C5.0 provide managerial insights for managers. Finally, the authors demonstrate the effectiveness of the LASSO technique for identifying the most important financial ratios from each category, enabling one to build better predictive models.

Abnormal behaviors such as rework, backlog, changes and claims generated by project organizations are unavoidable in complex projects. When abnormal behaviors emerge, the previously normal state of interactions between organizations will be altered to some extent. However, previous studies have ignored the associations and interactions between organizations in the context of abnormal organizational behaviors (AOBs), making this challenging to cope with AOBs. As a result, the objective of this paper is to explore how to reduce AOBs in complex projects at the organizational level from a network perspective.

To overcome the inherent limitations of a single case study, this research integrated two data collection methods: questionnaire survey and expert scoring method. The questionnaire survey captured the universal data on the influence possibility of AOBs between complex project organizations and the expert scoring method got the influence probability scores of AOBs between organizations in the case. Using these data, four organizational influence network models of AOBs based on a case were developed to demonstrate how to destroy AOBs networks in complex projects using network attack theory (NAT).

First, the findings show that controlling AOBs generated by key organizations preferentially and improving the ability of key organizations can weaken AOBs network, enabling more effective coping strategies. Second, the owners, government, material suppliers and designers are identified as key organizations across all four influence networks of AOBs. Third, change and claim behaviors are more manageable from the organizational level.

Project managers can target specific organizations for intervention, weaken the AOBs network by applying NAT and achieve better project outcomes through coping strategies. Additionally, by taking a network perspective, this research provides a novel approach to comprehending the associations and interactions between organizations in the context of complex projects.

This paper proposes a new approach to investigating AOBs in complex projects by simultaneously examining rework, backlog, change and claim. Leveraging NAT as a novel tool for managing the harmful effects of influence networks, this study extends the knowledge body in the field of organizational behavior (OB) management and complex project management.