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The widespread usage of magnetic nanoparticles (MNPs) requires their efficient synthesis during combustion process. This study aims to present a mathematical model for the oxidation of MNPs in a counter-flow non-premixed combustion system to produce MNPs, where the key sub-processes during the oxidation reaction are involved.

To accurately describe structure of flame and determine distributions of temperature and mass fractions of both reactants and products, equations of energy and mass conservations were solved based on the prevailing assumptions that three regions, i.e. preheating, reaction and oxidizer zones exist.

The numerical simulation was first validated against experimental data and characteristics of the combustion process are discussed. Eventually, the influences of crucial parameters such as reactant Lewis numbers, strain rate ratio, particle size, inert gas and thermophoretic force on structure of flame and combustion behavior were examined. The results show that maximum flame temperature can achieve 2,205 K. Replacing nitrogen with argon and helium as carrier gases can increase flame temperature by about 27% and 34%, respectively. Additionally, maximum absolute thermophoretic force was found at approximately 9.6 × 10–8 N.

To the best of authors’ knowledge, this is the first time to numerically model the preparation of MNPs in a counter-flow non-premixed combustion configuration, which can guide large-scale experimental work in a more effective way.

Appropriate access to formally-trained health workers for people living in rural and underdeveloped areas is a continuing challenge worldwide. The purpose of this paper is to investigate the willingness of formally-trained health workers to practice in underdeveloped areas and its main determinants among medical students in the western provinces of Iran.

A total of 753 medical students from four provinces in western Iran (Kermanshah, Ilam, Lorestan and Kurdistan) were surveyed cross-sectionally in 2017. A self-administrated questionnaire was used to collect data on sociodemographic characteristics, willingness to practice in underdeveloped areas, intrinsic (e.g. desire to help others and self-interest in medicine) and extrinsic (e.g. the high income of physicians and social prestige) motivations of the study population. Multivariable logistic regression was used to identify the main determinants of willingness to practice in underdeveloped areas among medical students after their graduation.

The results indicated that 58.3 percent of students were willing to practice in underdeveloped areas. While 59 percent of the study population had a strong extrinsic motivation to study medicine, the remaining 41 percent of the study population had a strong intrinsic motivation to study medicine. The logistic regression results indicated that low parental professional and educational status, an experience of living in rural areas and having strong intrinsic motivation were associated with greater willingness to practice in underdeveloped areas.

This is the first study to investigate the willingness to practice in underdeveloped areas and its main determinants among medical students in the west of Iran.

The purpose of this study is simulation of dynamic stall behavior around the Eppler 387 airfoil in the low Reynolds number flow with a direct-forcing immersed boundary (DFIB) numerical model.

A ray-casting method is used to define the airfoil geometry. The governing continuity and Navier–Stokes momentum equations and boundary conditions are solved using the DFIB method.

The purposed method is validated against numerical results from alternative schemes and experimental data on static and oscillating airfoil. A base flow regime and different vortices patterns are observed, in accordance with other previously published investigations. Also, the effects of the reduced frequency, the pitch oscillation amplitude and the Reynolds number are studied. The results show that the reduced frequency has a major effect on the flow field and the force coefficients of the airfoil. On the other hand, the Reynolds number of the flow has a little effect on the dynamic stall characteristics of the airfoil at least in the laminar range.

It is demonstrated that the DFIB model provides an accurate representation of dynamic stall phenomenon.

The results show that the dynamic stall behavior around the Eppler 387 is different than the general dynamic stall behavior understanding in the shedding phase.

Developing strategic relationships with third-party logistics (3PL) providers has long been one of the key challenges in automotive supply chains. The purpose of this paper is to propose a new approach for evaluation and indexing 3PL providers using the Kano model.

The statistical population used in this research comprises managers from the Iranian automotive industry. The Kano evaluation approach is used to analyze the data collected and to classify the criteria used in selection of the preferred 3PL providers.

The results suggest that the proposed framework, based on the Kano classification, can be a powerful tool for the automotive industry in evaluating 3PL providers. Moreover, the analyses indicate that 3PL providers need to improve their service offering in aspects that are to be found in the Kano model’s requirements, namely, must-be, one-dimensional and attractive.

This study contributes to the supply chain management literature by being the first to classify selection indices of 3PL providers in the automotive industry using the Kano model.

The purpose of this study is to investigate the relationship between total quality management (TQM), employee outcomes and performance. Moreover, how environmental uncertainty (EU) influences the relationship between employee outcomes and performance is regarded in the context of automobile part manufacturing and suppliers of Iran. Four theories namely resource-based view theory, ability, motivation, opportunity framework, contingency theory and quality management theory have been adopted in this research.

A research project is conducted in 191 automobile part manufacturing and suppliers plants using the questionnaire method. Confirmatory factor analysis is applied to assess the reliability and validity of the measurement instrument. The correlations between latent constructs are examined through partial least squares method.

The results show positive relationship between TQM, employee outcome and performance. The EU also moderates the relationship between employee outcome and performance.

It is recommended that some contextual factors such as culture be noticed in future research studies. Data were collected from Iranian automobile part manufacturing and suppliers plants, which may limit the generalization of results to other organizations in other countries.

In this paper, some beneficial insights are addressed to assist managers in recognizing the organizational problems, which weaken implementing TQM, employee outcomes and the effect of EU on organizations.

By improving the quality of management practices and employee outcomes, the society gains benefits such as customer satisfaction.

This study contributes to the TQM advance and human resource management literature and provides better foundations for employee outcomes improvement through TQM practices in the Middle East. By investigating the effect of EU, this study fills the current gap in this field.

Despite increasing attentions to sustainable service supply chain management (SSSCM), a framework for performance evaluation of sustainable service only supply chain management (SSOSCM) is still missing. This paper tries to fill this gap and provides a novel conceptual framework.

The articles related to SSSCM are fetched from the databases of SCOPUS and Web of Science. Analysis of 174 articles identified by the systematic literature review is further carried out.

This research identifies the sustainable practices for service only supply chain (SOSC) as environmental management, social management, economic management, customer management, health, safety and risk management, technical sustainability, institutional sustainability, information and technology management as well as two performance measurement criteria as operational performance and organizational performance. This paper provides a novel conceptual framework for the performance evaluation of SSOSCM. The results call for future exploration in the following three broad directions-(1) customer's perception, involvement and their behaviour towards sustainability in SOSC context; (2) trade-off, incentive mechanism and multilevel evaluation for achieving sustainability in SOSC and (3) sustainability in SOSC from various point of views.

The managers can use the framework to assess the performance of the organization while researchers can explore the discussed research gaps.

This is the first paper that provides a novel conceptual framework for the performance evaluation of SSOSCM as well as potential future research directions.

The purpose of this paper was to study the possibility of using smartphone roughness measurements for developing pavement roughness regression models as a function of pavement age, traffic loading and traffic volume variables. Also, the effects of patching and pavement distresses on pavement roughness were investigated. The work focused on establishing pavement roughness prediction models and applying these models to pavement management systems (PMS) to help decision-makers choose the best maintenance and rehabilitation (M&R) options by using cost-effective methods.

Signal processing techniques including filtering and processing techniques were used to obtain the International Roughness Index (IRI) from raw acceleration data collected from smartphone accelerometer sensors. The obtained IRI values were inputted as a dependent variable in analytical regression models as well as several independent variables with proper transformations.

According to the study results, several regression models were developed with a big variation in the coefficients of determination (R²). However, the best models included pavement age, accumulated traffic volume (∑TV) and construction quality factor (CQF) with R² equal to 0.63. It was also found that the effects of pavement distresses and patching was significant at a-level < 0.05. The patching effect on pavement roughness was found higher than the effect of other pavement distresses.

The presented results and methods in this paper could be used in the future predictions of pavement roughness and help the decision-makers to estimate M&R needs. The work focused on establishing IRI prediction models and applying these models to the PMS to help decision-makers choose the best M & R options.

To develop sound pavement roughness models, it is essential to collect roughness data using automated procedures. However, applying these procedures in developing countries faces several difficulties such as the high price and operation costs of roughness equipment and lack of technical experience. The advantage of using IRI values taken from smartphones is that the roughness evaluation survey may be expanded to cover the full road network at a cheaper cost than with automated instruments. Therefore, if the roughness survey covers more roads, the prediction model’s accuracy will be improved.

The internationalization of business has grown the production value chains and created performance challenges for industrial production. Industry 4.0, the digital transformation of industrial processes, promises to deliver performance improvements through smart functionalities. This study investigates how digital transformation translates to performance gain by adopting a systems perspective to drive smartness.

This study uses qualitative research to collect data on the lived experiences of digital transformation practitioners for theory development. It uses semi-structured interviews with industry experts and applies the Gioia methodology for analysis.

The study determined that enterprise smartness is an organizational capability developed by digital transformation, it is a function of integration and the enabler of organizational performance gains in the Industry 4.0 context. The study determined that performance gains are experienced in productivity, sustainability, safety and customer experience, which represents performance metrics for Industry 4.0.

This study contributes a model that inserts smartness in the linkage between digital transformation and organizational outcomes to the digital transformation and production management literature.

The study indicates that digital transformation programs should focus on developing smartness rather than technology implementations, which must be considered an enabling activity.

Existing studies recognized the positive impact of technology on performance in industrial production. The study addresses a missing link in the Industry 4.0 value creation process. It adopts a systems perspective to establish the role of smartness in translating technology use to performance outcomes. Smart capabilities have been the critical missing link in the literature on harnessing digital transformation in organizations. The study advances theory development by contributing an Industry 4.0 value model that establishes a link between digital technologies, smartness and organizational performance.

Supplier selection (SS) is one of the prime competencies in a sourcing decision. Taking into account the key role played by suppliers in facilitating the implementation of green supply chain management (GSCM), it is somewhat surprising that very little research attention has been imparted to the development of a strategic sourcing model for GSCM. This research aims to develop a strategic sourcing framework in which supplier organizations are prioritized and ranked based on their GSCM performance. Accordingly, the benchmark organization is identified and its strategy is explored for GSCM performance improvement.

The research develops an innovative GSCM performance evaluation framework using six parameters, namely, investment in corporate social responsibility, investment in research and development, utilization of renewable energy, total energy consumption, total carbon-di-oxide emissions and total waste generation. An integrated multicriteria decision-making (MCDM) approach is proposed in which the entropy method calculates criteria weights. The Complex Proportional Assessment (COPRAS) and the Grey relational analysis (GRA) methods are used to rank supplier organizations based on their performance scores. A real-world case of green supplier selection (GSS) is considered in which five leading India-based automobile manufacturing organizations (Supplier 1, Supplier 2, Supplier 3, Supplier 4 and Supplier 5) are selected. Surveys with industry experts at the strategic, tactical, and operational levels are carried out to collect relevant data.

The results reveal that total carbon dioxide emission is the most influential parameter, as it gains the highest weight. On the contrary, investment in research and development, and total waste generation have no significant impact on GSCM performance. Results show that Supplier 5 secures the top rank. Hence, it is the benchmark organization.

The proposed methodology offers an easy and comprehensive approach to sourcing decisions in the field of GSCM. The entropy weight-based COPRAS and GRA methods offer an error-free channel of decision-making and can be proficiently used to outrank various industrial sectors based on their GSCM performances. This research is specific to the automobile manufacturing supply chain. Therefore, research outcomes may vary across supply chains with distinct characteristics.

The basic propositions of this research are based on a real-world case. Hence, the research findings are practically feasible. The less significant parameters identified in this study would enable managers to impart more attention to vulnerable areas for improvement. This research may help policymakers identify the influential parameters for effective GSCM implementation. As this research considers all aspects of sustainability, the strategies of the benchmark supplier have a direct impact on organizations' overall sustainability. The study would enable practitioners to make various strategies for GSCM performance improvement and to develop a cleaner production system.

The originality of this research lies in the consideration of both economic, social, environmental and operational aspects of sustainability for assessing the GSCM performance of supplier organizations. Quantitative criteria are considered so that vagueness can be removed from the decision. The use of an integrated grey-based approach for developing a strategic sourcing model is another unique feature of this study.

As markets, economies and institutions are increasingly globalized, there is a growing understanding of the need to introduce intercultural learning alongside business learning. Participating in a study abroad program is potentially one of the most important experiences for any college student. Such programs provide students the opportunity to immerse themselves in different cultures and gain new perspectives. The purpose of this paper is threefold: to identify the main factors that students consider when selecting a program; to integrate quality function deployment (QFD), benchmarking and Hoshin Kanri in the analysis of student expectations and to examine the implications for research and practice.

The study was based on primary data collected from 180 students at four universities. To design an approach that helps students in the selection of a program that best satisfies their expectations, a self-designed questionnaire was used for data collection. The data were analyzed using the techniques of QFD/benchmarking. Finally, a long-term strategy is proposed based on the Hoshin Kanri theory.

The great variability in student expectations presents a challenge in designing a methodology of selection; however, it does help in identifying the most important student expectations. A key, balanced relationship among academic quality, having fun and cost cannot be ignored in this study because they represent the factors that are altogether influential in the decision to study abroad. In the current literature, the key variables of study abroad programs are discussed; however, most studies fail to incorporate student expectations. This paper will fill this gap by incorporating both key academic variables and the voice of the customer (student).

Given the diversity of the population, the authors developed several methodologies to standardize the array of student responses to the questionnaire. Using this standardization along with several total quality management (TQM) tools allows us to simplify and categorize the different student expectations. The gathering of students’ expectations directly provided by students (voice of the customer) allows international programs to focus on the real problems and expectations that have been acknowledged, thus yielding student satisfaction with their experience, most importantly, in their field of study.

For universities, the current study identifies new means by which to improve the quality of international programs with the use of TQM tools including QFD, benchmarking and the Hoshin Kanri Planning Process with an evidence-based real case.

This paper presents a conclusive application of QFD, benchmarking and Hoshin Kanri and an analysis of how these tools can help international programs with future improvements incorporating the needs of students in their programs. To the authors’ knowledge, this is the first application of these techniques to improve the international experience for business undergraduate students.