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The activities of the oil industry from discovery to distribution of oil products have adverse effects on human and environment. Thus, the companies that are active in this industry should identify and manage their risks. The purpose of this paper is to prioritize the identified risks based on different measures such as cost, occurrence, etc. Then, selecting the most important corrective actions using goal-programming approach is another objective of this study.

To identify the health, safety and environment (HSE) risks, the Fuzzy Delphi method was used. The failure mode and effects analysis (FMEA) and fuzzy Vlse Kriterijumsk Optimizacija Kompromisno Resenje (VIKOR) methods covering the deficits of FMEA were used to rank the HSE risks. Unlike similar researches, in the proposed FMEA–VIKOR method, the risk priority number was not calculated. In addition to severity, occurrence and detection, the parameters such as time, cost and quality, being considered for ranking the risks, were weighted by the Eigenvector method. Then, a fuzzy goal-programming model was developed for determining the best solutions of risk response.

The research findings indicated that the most important risks include fire and blast because of tank and pipeline, leakage of connections and pipelines and industrial waste. Also, the most important risk responses include using and strengthening the alarm and fire extinguishing systems, using fiberglass tanks to prevent pipeline corrosion, using modern technology to have more efficient oil refining.

The main contribution of this paper is using hybrid approach of FMEA–VIKOR for risk ranking by considering different measures such as time, cost and quality besides severity, occurrence and detection. Providing a fuzzy goal-programming framework for determining the main risk responses is another value for this research.

The overall goal of the data mining process is to extract information from an extensive data set and make it understandable for further use. When working with large volumes of unstructured data in research information systems, it is necessary to divide the information into logical groupings after examining their quality before attempting to analyze it. On the other hand, data quality results are valuable resources for defining quality excellence programs of any information system. Hence, the purpose of this study is to discover and extract knowledge to evaluate and improve data quality in research information systems.

Clustering in data analysis and exploiting the outputs allows practitioners to gain an in-depth and extensive look at their information to form some logical structures based on what they have found. In this study, data extracted from an information system are used in the first stage. Then, the data quality results are classified into an organized structure based on data quality dimension standards. Next, clustering algorithms (K-Means), density-based clustering (density-based spatial clustering of applications with noise [DBSCAN]) and hierarchical clustering (balanced iterative reducing and clustering using hierarchies [BIRCH]) are applied to compare and find the most appropriate clustering algorithms in the research information system.

This paper showed that quality control results of an information system could be categorized through well-known data quality dimensions, including precision, accuracy, completeness, consistency, reputation and timeliness. Furthermore, among different well-known clustering approaches, the BIRCH algorithm of hierarchical clustering methods performs better in data clustering and gives the highest silhouette coefficient value. Next in line is the DBSCAN method, which performs better than the K-Means method.

In the data quality assessment process, the discrepancies identified and the lack of proper classification for inconsistent data have led to unstructured reports, making the statistical analysis of qualitative metadata problems difficult and thus impossible to root out the observed errors. Therefore, in this study, the evaluation results of data quality have been categorized into various data quality dimensions, based on which multiple analyses have been performed in the form of data mining methods.

Although several pieces of research have been conducted to assess data quality results of research information systems, knowledge extraction from obtained data quality scores is a crucial work that has rarely been studied in the literature. Besides, clustering in data quality analysis and exploiting the outputs allows practitioners to gain an in-depth and extensive look at their information to form some logical structures based on what they have found.

The current extensive business ecosystem, characterized by technological advances and development, impressive customers, and increasing social concerns, has exerted great pressure on business organizations. Among different business values for affording this pressure, organizational agility is a critical factor that should be carefully incorporated in business processes. The main purpose of the present study is to investigate the role of social networking technology, as a crucial collaborative tool, on organizational agility.

A model based on structural equations was designed in this regard. The constructs of this model are quality of service, varieties of services, costs and speed of service as independent variables and also agility management as a dependent variable. Based on the conceptual model, a questionnaire was prepared and distributed among the experts of social networking technology and agility management. Based on Cochran's formula the sample size was 384. The response rate was 100%. The main statistical measures such as Chi-square ratio to the degree of freedom, Non-soft Fitness Index (RMSEA), Goodness of Fit Index (GFI) and Modified fitness index (AGFI) were employed for analyzing the model.

Results of obtained data indicated that a variety of services as the main factor of social networking technology has the most impact on the agility of a company. Then, the speed of service, service quality and costs were ranked respectively in second to fourth. Providing information technology (IT) service perceptions, promoting the service climate and thorough identification of IT requirements are the main critical success factors for maintaining a robust impact of social networking technology on organizational agility. Moreover, a well-designed enterprise structure alongside employing newly developed IT infrastructures such as cloud computing certainly improves the capabilities of organizations to improve their agility.

Although the literature suggests a positive impact among IT or social networks on organizational agility, it is deficient in relation to considering the impact of social networking. Furthermore, a structural equation model (SEM) is used for assessing unobservable latent constructs and their related interrelationship.

Project management generally covers many important areas such as cost, quality and time in different industrial settings, but it is deficient in relation to integration of health, safety and environmental risks. Poor knowledge of project managers about HSE management necessitates the studying on the mutual effects of HSE and project management. Hence, investigating the impact of project management on health monitoring programs, safety prevention monitoring, environmental monitoring plans and finally the effectiveness of professional health monitoring programs and determining their importance are main objectives of this research. The paper aims to discuss these issues.

A model based on structural equations was designed and developed. The constructs of this model are project management, health monitoring and safety prevention monitoring program. Based on the conceptual model, some questionnaires were prepared and distributed among the experts of strategic project management.

The results of applied structural modeling suggest that project management focuses on each aspect of HSE management, including health monitoring programs, safety prevention monitoring programs, environmental monitoring plans and effectiveness of professional health monitoring programs. HSE management can also be strengthened by empowering project management. Checking fire protection systems, using appropriate techniques to identify contamination and disposal of waste and incorporating techniques for brainstorming or other ideas creation in the group are the most important tasks in HSE-enabled project management frameworks.

Since there is still no strategic alignment model that includes components of project management and HSE management, a model for achieving this goal is vital. This paper elaborates this alignment based on literature and using a field study.

Total quality management (TQM) is a part of the total quality assurance perspective. This system, which is considered as a type of managerial philosophy, employs all organizational levels to constantly ameliorate the quality of goods and service. The purpose of this paper is to measure the effect of hard and soft TQM factors on the behavior of customers based on the role of innovation and continuous improvement.

The research model was extracted from TQM variables in hard and soft parts, customer behavior, innovation and continuous improvement by reviewing the literature and research background. Based on this, a questionnaire was prepared and then, distributed among the statistical population including 374 project managers, quality assurance managers as well as quality control managers by using simple random sampling. All sub-criteria of questionnaire were determined using Delphi technique, to test the research model. Having gathered the questionnaire, the hypotheses were analyzed by using structural equation modeling and AMOS software.

According to the statistical analyses, TQM has a significant effect on customer behavior through continuous improvement of the quality and innovation. Also, regarding the obtained results, the highest effect was related to the effect of hard TQM factors on customer behavior through innovation as 0.62. Furtheremore, TQM soft factors such as human resource management have significant effect on customer behavior through quality improvement and innovation. Moreover, TQM hard factors are effective on customer behavior through quality improvement and innovation.

The questionnaire was designed and distributed in order to evaluate the hypotheses in this study. One of the primary rationales behind utilizing this method instead of other methods such as interview was high geographical distribution of organizations. Using other moderator variables such as knowledge management, customer knowledge management and customer emotions can be conducted in the future in this area.

Changing the organizational relationships from task orientation to the process orientation, and controlling the organizational performance by measuring process innovations and improvements, while paying attention to the customer satisfaction system are suggested in this paper. These implications should be implemented in construction projects by department of project management office. Furtheremore, providing different communication for receiving the opinions of the customer and imposing them in the product and service, paying attention to the response system and customer complaint, implementation of this process in the organization, and having a process approach for presenting and developing services are the main subjects in this regard.

Unlike previous studies on this subject, a structural equation model is used for assessing unobservable latent constructs and their related interrelationship in measuring the impact of TQM factors. Focusing on customer behavior which is a broader domain than customer satisfation through continuous improvement of the quality and innovation is another value of this research.

The purpose of this paper is to study the role of organizational forgetting in the impact of strategic thinking on the implementation of an organizational excellence model. Furthermore, the factors with main effects on the implementation success of the organizational excellence model are investigated. The two main causes of organizational forgetting, including purposefulness and randomness, along with the three main factors of strategic thinking (vision, creativity and systematic thinking) also are explored. Enablers and results, which are the two key parts of an organizational excellence model are considered as well.

A model based on structural equations is designed, in which organizational forgetting factors, strategic thinking measures and main parts of a business excellence model are incorporated based on the literature. A total of 297 Iranian companies in which an organizational excellence model had been implemented are selected for investigation. A questionnaire is designed and distributed among the experts, middle managers and top managers of these companies. Based on Cochran’s formula, the sample size of 168 is obtained, for which the response rate is 100 percent. Main statistical measures such as χ² ratio to degree of freedom, non-soft fitness index (RMSEA), fitness index (GFI) and modified fitness index (AGFI) are used to assess the performance of the proposed model.

According to the results of the statistical significance tests, the role of organizational obsessive mediators in the establishment of the organizational excellence model has been largely confirmed. Furthermore, the mediator role of organizational forgetting in the final impact of strategic thinking on implementing an organizational excellence model has been widely endorsed. Failure to use knowledge from learning, the inability of a company in coding and documenting knowledge and lack of incentives to share it are the most important factors in the forgetting of knowledge in companies.

As top managers, middle managers and experts are hard to reach due to the wide geographical spread of the organization under study, a questionnaire is designed and distributed among them. The impact of organizational forgetting on other quality management systems such as ISO 9001 and ISO 4001 needs another research to be conducted in the future.

Using new experiences, increasing the competency of employees and managers experience through organizational learning, employee and managerial assessment and organizational strategy assessment are the main practical methods for considering organizational forgetting in the process of implementing organizational excellence models.

This research addresses organizational forgetting besides strategic thinking as joint main roles for implementing organizational excellence, whereas previous research works only considered strategic thinking as a factor. Furthermore, a structural equation model is developed for appraisal of effect of different factors.

Evaluation of the quality and performance of a tunnel lining during the installation of segments are the main objects of tunneling projects. Because the quality is affected by several attributes, the purpose of this paper is an appropriate multivariate data analysis that is helpful in extracting applicable knowledge of the data collected regarding the related attributes of the initial installed rings.

Principal component analysis (PCA) is used to analyze the data obtained by the quality control team. The authors use canonical correlation analysis (CCA) to extract some linear combinations of the original attributes of the two groups that produce the largest correlations with the second set of variables.

The authors reduce the dimensionality of the original data set for further analyses, and use a small number of uncorrelated variables rather than a larger set of correlated variables to take effective and efficient action to control the quality of the tunnel lining. The authors also explore the correlation structure and relationship between two main groups of characteristics used for assessing the quality of the installed rings. Then, instead of a large number of the original characteristics in the two groups, the authors can easily control these few to attain a reasonable quality for the tunnel lining.

This is a case study, and for each ring selected for inspection, 16 different characteristics are measured and the observations are recorded. The authors use PCA and CCA to analyse the data and interpret the results. Although the methods are not new, applying them to this data results in useful and informative outcomes and interpretation.