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Manufacturing firms are always faced with the problem of promoting operational performance and labor‐force management. The utilization of human resources is closely correlated with operations and production performance. This study investigates the correlation between human resource management (HRM) and business performance of large‐scale manufacturing firms in Taiwan. First, 16 subjects of HRM are designed to survey the importance level and achievement level of HRM by the sample firms. Productivity indices are also defined to measure business performance. Based on the survey, four critical HRM factors including 12 subjects are extracted by factor analysis. The difference between importance level and achievement level of subjects contained in each factor is examined. Furthermore, considering importance and achievement levels of HRM as features, fuzzy clustering analysis is employed to categorize the firms into four patterns. With various HRM characteristics, each pattern has different business performance in terms of productivity. Using a pattern approach, these findings can aid the firms in each pattern to improve their productivity by improving their HRM strategies.

Bankruptcy, stock price fluctuations and making decisions to invest on the listed companies on the Tehran Stock Exchange show the need to have some tools for evaluating the financial potential of companies. One of the tools for evaluating financial power to investment in companies is using analysis of financial ratios and obtaining the patterns for predicting the bankruptcy of companies. The purpose of this study is to modify the current patterns for predicting bankruptcy in proportion to the environmental status of Iran and to present a new pattern for determining the bankruptcy of the listed companies.

To modify the patterns and present a new pattern, in this research, the hypotheses pertinent to the ability for right classification of the companies are designed by the modified patterns of predicting the company’s bankruptcy and ranking, that is according to the envelopment analysis method and by comparison of the results and presenting a new prediction pattern.

The hypotheses test results show that modification of bankruptcy patterns and presentation of a new bankruptcy pattern are confirmed by the data envelopment analysis.

The current paper is almost the first paper which combined several different methods of bankruptcy prediction.

The purpose of this paper is to explore the influence of conflict management on conflicts at work. Design/methodology/approach – A total of 148 post‐graduate students in management responded to a questionnaire online. Two cluster analyses were performed to identify dispute resolution patterns and organizational dispute states. Then, cross tabulation between the two clusters was performed (Pearson's chi‐square coefficient and Sommer's D statistic). Findings – Cluster analyses identified three styles of dispute resolution pattern – interest‐based, based on controlled power, and power‐based – and three different organizational dispute states: harmony, dissonance, and conflict. Finally, the influence of resolution patterns on dispute states was been confirmed by the cross tabulation. Research limitations/implications – Firstly, Ury et al.'s theoretical typology should be revised, especially for the rights‐based approach. Secondly, the results of our cluster analysis indicate that it might not be necessary to measure the emotional and behavioral dimension of conflict separately. Thirdly, our research confirms the impact of conflict management on conflicts at work. Practical implications – The results show that dispute resolution patterns have a non‐negligible influence on organizational conflict states. In order to increase the likelihood of a harmony state, an interest‐based dispute resolution pattern should be adopted. Originality/value – First, the statistical technique used – cluster analysis – is somewhat innovative. Secondly, this research shows that dispute resolution patterns may affect organizational dispute states.

The aim of this study is to undertake a systematic analysis of the supply chain literature to uncover the changes and patterns of international cooperation in the context of the COVID-19 pandemic.

In this study, the information on supply chain-related publications in the Web of Science (WOS) database is analyzed using statistical techniques and visual approaches. The focus is on the five countries with the highest number of supply chain publications, accounting for approximately 70% of global publications. This in-depth analysis aims to provide a clearer understanding of the cooperation patterns and their impact on the supply chain during the COVID-19 pandemic.

The results of the study reveal that the growth rate of international cooperation in supply chain research during the COVID-19 pandemic is higher compared to the 5-year and 10-year periods before the pandemic. This suggests that the pandemic has not hindered international cooperation in the field, but instead has increased collaboration. In terms of international cooperation patterns, the findings indicate that China and the USA have a strong partnership, with China being the largest partner for the USA and vice versa. The UK's largest partner is China, India's largest partner is the UK and Italy's largest partner is also the UK. This implies that trade, rather than the pandemic, is a determining factor in supply chain research.

This study examines the patterns of international cooperation in supply chain research during the COVID-19 pandemic, providing insights into the changes and mechanisms of international cooperation in this field. Moreover, the results of this study may offer practical benefits for supply chain operators and managers. By providing a deeper understanding of the international cooperation patterns in the field, this research could contribute to the recovery and growth of the global supply chain.

This study's analysis of the impact of crisis events, such as the COVID-19 pandemic, on international cooperation in supply chain research contributes to the theoretical development of the field. Additionally, by examining how academia responds to emergencies, it provides valuable insights for operations and supply chain managers in their pursuit of more effective supply chain management.

This study provides a preliminary examination of the international cooperation patterns of supply chain research in the context of the COVID-19 pandemic, representing a novel and early contribution to the existing literature, helping to expand upon current understanding in the field and provide a more comprehensive perspective. Furthermore, this study offers a practical analysis strategy for future supply chain research, fostering progress and growth in the field.

The purpose of this paper is to construct the best combination of fabric printing pattern’s attributes to obtain the optimum design to meet the markets need.

This study reviewed the morphological analysis of fashion design expert groups regarding the fashionable fabric printing patterns, as well as the attribute preferences for different groups of fabric printing patterns through focus group discussions. In addition, the conjoint analysis was used to calculate the part-worth of these patterns’ design attributes and design levels to obtain the optimum design for fabric printing patterns.

The results of this study showed that the most importance of fabric attributes is the secondary pattern (32.59 percent), while the weights of shading element (24.80 percent) and primary pattern (24.46 percent) are almost the same. This study found that a wide range of intertwining image primary pattern can be applied to create interesting and popular fabric printing pattern designs. In practical applications of this result, when the primary pattern is supported by the buyer, fabric designers may use New Classic composite plants, with simple shading and different cycling configuration, to reproduce the optimum designs of the fabric printing patterns.

This proposed a novel solution to figure out the combination basis for optimizing fabric printing pattern designs. In addition, this result helps fabric printing pattern designers to establish an optimal design method for a specific brand to create a unique printing pattern design.

Responding to emergency incidents by emergency response organizations such as fire, ambulance and police during large disaster and emergency events is very important. The purpose of this paper is to provide some insights into response patterns during the 2013 ice storm in the city of Vaughan, Ontario, Canada, using temporal and spatial analyses.

The City of Vaughan Fire and Rescue Service data set containing all responses to fire and other emergency incidents from January 1, 2009 to December 31, 2016 was used. The 2013 Southern Ontario ice storm occurred from December 20, 2013 to January 1, 2014, and, for this study, December 20–31 is considered the “study period.” Temporal, spatial and spatiotemporal analyses of responses during the study period are carried out and are compared with the same period in other years (2009–2012 and 2014–2016).

The findings show that temporal patterns of response attributes changed significantly during the 2013 ice storm. Similarly, the spatial pattern of responses during the 2013 ice storm showed some major differences with other years. The spatiotemporal analyses also demonstrate significant variations in responses in the city during different hours of the day in the ice storm days.

This study is the first study to examine the spatiotemporal patterns of responses made by a fire department during the 2013 ice storm in Canada. It provides some insights into the differences between response volumes, temporal and spatial distributions during large emergency events (e.g. ice storm) and normal situations. The results will help in mitigating the number of responses in the future through public education and technological changes. Moreover, the results will provide fire departments with information that could help them prepare for such events by possible reallocation of resources.

Most studies of conflict handling styles in organizations analyze these styles separately. These studies assume that individuals are oriented towards the use of one of the styles of conflict management. As a result, different styles are compared one by one as if they were independent. In contrast, from a more all‐embracing perspective people are seen as adopting configurations of styles. The interest in this alternative perspective lies in exploring the relations between these styles, how they combine and form patterns of conflict styles. This article presents an exploratory study that seeks to identify empirically the specific combinations of conflict handling styles that result in differentiated patterns within groups of managers. By using hierarchical and non‐hierarchical cluster analyses of a sample of managers, different patterns of conflict management were identified. The effectiveness of each of the resulting patterns was analyzed in terms of its influence on the parties' joint substantive outcomes and their mutual relationship. Results show that patterns using multiple conflict handling styles were more effective than patterns based on a single style.

Behavioral pattern mining for intelligent system such as SmEs sensor data are vitally important in many applications and performance optimizations. Sensor pattern mining (SPM) is also dynamic and a hot research issue to pervasive and ubiquitous of smart technologies toward improving human life. However, in large-scale sensor data, exploring and mining pattern, which leads to detect the abnormal behavior is challenging. The paper aims to discuss these issues.

Sensor data are complex and multivariate, for example, which data captured by the sensors, how it is precise, what properties are recorded or measured, are important research issues. Therefore, the method, the authors proposed Sequential Data Mining (SDM) approach to explore pattern behaviors toward detecting abnormal patterns for smart space fault diagnosis and performance optimization in the intelligent world. Sensor data types, modeling, descriptions and SPM techniques are discussed in depth using real sensor data sets.

The outcome of the paper is measured as introducing a novel idea how SDM technique’s scale-up to sensor data pattern mining. In the paper, the approach and technicality of the sensor data pattern analyzed, and finally the pattern behaviors detected or segmented as normal and abnormal patterns.

The paper is focussed on sensor data behavioral patterns for fault diagnosis and performance optimizations. It is other ways of knowledge extraction from the anomaly of sensor data (observation records), which is pertinent to adopt in many intelligent systems applications, including safety and security, efficiency, and other advantages as the consideration of the real-world problems.