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Transport and logistics companies (TLCs) have adopted green practices to reduce their emission and waste in support of environmental protection requirements. However, not all green practices of TLCs have resulted in green performance (GP) because of inherent variations in internal abilities and external factors affecting firms in different operational environments. Using institutional (INT) and resource-based view (RBV) theories we develop a model that shows how institutional pressures and internal abilities impact TLCs’ GP.

Underpinned by INT and RBV theories, this study utilised structural equation modelling on a cross-sectional survey of 222 TLC firms operating in the Philippines.

The study reveals that TLCs’ regulatory compliance, social obligations and competitor pressures have significant direct effects on TLCs’ GP while supplier integration and resource capabilities serially mediate the relationships. The findings indicate that institutional pressures and internal abilities that shape firms’ environmental policies and procedures explain TLCs’ GP.

As a result of the cross-sectional nature of our data, findings may be time, population and prevailing situation dependent. The long-term validity of the research can be improved if longitudinal design is employed. We collected data from a single respondent considered as the best respondent. However, with time and resource availability, a jury of executives for each company would have been a better alternative respondent.

The findings of this research provide TLC managers, policymakers and other stakeholders with much-needed guidance for crafting policies and strategies for handling the challenges of caring for the planet and maintaining sustainable operations.

Improving green practices of TLCs to enable waste and emission reductions is critical to the health and well-being of people and the planet. An environment free of pollution will result in fewer health challenges, fewer medical/insurance-related expenses, and, above all, enable a higher quality of life and a more productive workforce.

Studies have extensively explored the green practices of TLCs as well as the influences of both institutional pressures and internal abilities of TLCs on their green practices. This study identifies critical factors that significantly affect the GP of TLCs and provides a conceptual framework for a better understanding of the dynamics of strategic, tactical and operational issues that TCLs may face in their effort at greening the sector.

Transport and logistics companies (TLCs) are vital to the world's economies but they account for one-third of the world's CO₂ emissions which damage the environment, economy and society. For managers and policymakers to help improve the environmental performance of TLCs, using dynamic capability (DC) theory as the lens, a theoretical model was developed to show how environmental performance affects the TLCs' support requirements and economic and social outcomes. This paper aims to discuss the aforementioned theoretical model.

All registered TLCs operating in the Philippines were sent an invitation and survey questionnaire, and 218 responded with complete and useable answers. From the collected data, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) served to evaluate the study's scale and model accuracies. Comprehensive CFA marker technique helped detect and correct for common method bias (CMB). Covariance-based structural equation modeling using AMOS 26 was implemented to test the model and hypotheses.

Using data from 218 participating TLCs operating in the Philippines, this study found that good environmental performance advances TLCs' economic and social performances, as well as their support infrastructure requirements. However, further financial support assistance from the government and non-government development organizations that can help save the environment are not required by eco performing firms.

This research has provided some theoretical contributions to the knowledge on the greening TLCs. It uses the DC theory as the lens to understand how environmental performance relates to economic and social performance of TLCs and how it affects their demand for eco-enabling mechanisms and support infrastructure if they are to remain competitive, socially responsible and eco-friendly. By providing an epistemology on environmental performance and how it affects the support requirements, and economic and social outcomes of TLCs, it offers the literature and industry a tool/framework to better understand the dynamics of eco-performing TLCs operating in a developing country.

The results of this study, together with the extant operations management principles, can be used by TLC managers, policymakers and other stakeholders in crafting strategies, mechanisms and support systems that can help TLCs maintain sustainability for future generations.

The environmentally sound practices of TLCs such as waste and emission reductions improve the health and welfare of people in and around where they operate. Health is wealth for the populace because healthy people are more productive, have more time and energy to work and are less likely to pay hefty medical expenses. Hence, realization of environmental performance by TLCs does not only do justice to the environment but also the quality of life of people.

Research on the sustainability of TLCs in developing countries especially in Southeast Asia is sparse. The literature has shown how various support systems affect the “greening” of firms, but what is not yet explored, is understanding how environmental performance affects the support requirements of TLCs so that their eco-endeavors are sustained and meaningful. As well, knowledge on how the environmental performance of TLCs affects their economic and social outcomes are under-investigated. This paper aims to fill these gaps in research by examining the sustainability of TLCs operating in the Philippines.

In profile monitoring, which is a growing research area in the field of statistical process control, the relationship between response and explanatory variables is monitored over time. The purpose of this paper is to focus on the process capability analysis of linear profiles. Process capability indices give a quick indication of the capability of a manufacturing process.

In this paper, the proportion of the non‐conformance criteria is employed to estimate process capability index. The paper has considered the cases where specification limits is constant or is a function of explanatory variable X. Moreover, cases where both equal and random design schemes in profile data acquisition is required (as the explanatory variable) is considered. Profiles with the assumption of deterministic design points are usually used in the calibration applications. However, there are other applications where design points within a profile would be i.i.d. random variables from a given distribution.

Simulation studies using simple linear profile processes for both fixed and random explanatory variable with constant and functional specification limits are considered to assess the efficacy of the proposed method.

There are many cases in industries such as semiconductor industries where quality characteristics are in form of profiles. There is no method in the literature to analyze process capability for theses processes, however recently quite a few methods have been presented in monitoring profiles. Proposed methods provide a framework for quality engineers and production engineers to evaluate and analyze capability of the profile processes.

The purpose of this study is use a bibliometric analysis to explore the relational nature of knowledge creation in WFM in operations. Companies live under constant pressure to find the best ways to plan their workforce, and the workforce emangement (WFM) is one of the biggest challenges faced by managers. Relevant research on WFM in operations has been published in a several range of journals that vary in their scope and readership, and thus the academic contribution to the topic remains largely fragmented.

To address this gap, this review aims to map research on WFM in operations to understand where it comes from and where it is going and, therefore, provides opportunities for future work. This study combined two bibliometric approaches with manual document coding to examine the literature corpus of WFM in operations to draw a holistic picture of its different aspects.

Content and thematic analysis of the seminal studies resulted in the extraction of three key research themes: workforce cross-training, planning workforce mixed methods and individual workforce characteristics. The findings of this study further highlight the gaps in the WFM in operations literature and raise some research questions that warrant further academic investigation in the future.

Likewise, this study has important implications for practitioners who are likely to benefit from a holistic understanding of the different aspects of WFM in operations.

This chapter aims to identify, analyse, classify and rank the sustainability indices and internationalisation challenges of the footwear industry in the emerging economy of Iran. This would provide deeper decision-making insights into Iranian footwear businesses. First, a list of sustainability indices and internationalisation challenges was obtained by reviewing the literature. Then, a combination of multi-criteria decision-making (MCDM) approaches was implemented. The initial sustainability indices and internationalisation challenges were screened using the fuzzy Delphi method, keeping a total of 14 criteria. The best–worst method (BWM) was employed to weigh and rank the criteria. The interpretive structural modelling (ISM) technique and cross-impact matrix applied in MICMAC were employed to visualise the conceptual model based on the levels and classification of the important criteria for the internationalisation of the Iranian footwear industry. The 14 criteria were demonstrated to be important in internationalisation. The most critical sustainability indices were reducing hazardous substances in leather tanning and labour education and training. In contrast, exchange rate instability in Iran’s economy and strict chemical regulations for clothing and footwear were found to be the most important internationalisation challenges. Hence, these criteria should be considered in the internationalisation strategies of the Iranian footwear industry. A combined multilayer sustainable decision-making approach was used to analyse the Iranian footwear industry’s essential sustainability indices and internationalisation challenges. Furthermore, implications and insights are offered to footwear businesses for future decision-making.

The purpose of this paper is to machine the pressure surface of the turbine blade made of A286 iron-based superalloy by using four directions of raster strategy, including horizontal upward, horizontal downward, vertical upward and vertical downward, to achieve appropriate surface roughness and to investigate the tool wear in each strategy.

In this study, all cutting tests were performed by DAHLIH-MCV 1020 BA vertical 3-axis machining center with ball nose end mill. After milling by each strategy, according to the surface slope, the surface was divided into 27 meshes, and roughness of surface was studied and compared. Roughness measuring after machining was implemented by using portable Mahr ps1 roughness tester, and surface texture was photographed by CCD 100× optical zoom camera. Also, to measure tool flank wear in each strategy as an indication of tool life, the surface of workpiece was divided into four equal areas. The wear of the inserts was measured by ARCS vertical non-contact measuring system at the end of each area.

The results indicate that cutting directions and toolpath strategies have significant influence on tool wear and surface roughness in machining processes and that they can be taken into consideration individually as determinative parameters. In this case, the most uniform surface texture and the lowest surface roughness are obtained by using horizontal downward direction; in addition, abrasion is a dominant tool wear mechanism in all experiments, and tool wear in the horizontal downward is lower than other strategies.

Machining of turbine blades or other airfoil-shaped workpieces is quite common in manufacturing aerospace and aircraft products. The results of this research contribute to increasing quality of machined surface and tool life in machining of turbine blade.

This work proves the significance of milling strategies in machining of the turbine blade made of A286 superalloy and, consequently, exhibits the proper strategy in terms of surface roughness and tool life. Also, this work explains and elaborates the behavior of A286 superalloy in machining processes, which has not been studied much in recent research works.

The purpose of this paper is to propose a data-driven model to predict credit risks of actors collaborating within a supply chain finance (SCF) network based on the analysis of their network attributes. This can support applying reverse factoring mechanisms in SCFs.

Based on network science, the network measures of the actors collaborating in the investigated SCF are derived through a social network analysis. Then several supervised machine learning algorithms are applied to predict the credit risks of the actors on the basis of their network level and organizational-level characteristics. For this purpose, a data set from an SCF within an automotive industry in Iran is used.

The findings of the research clearly demonstrate that considering the network attributes of the actors within the prediction models can significantly enhance the accuracy and precision of the models.

The main limitation of this research is to investigate the applicability and effectiveness of the proposed model within a single case.

The proposed model can provide a well-established basis for financial intermediaries in SCFs to make more sophisticated decisions within financial facilitation mechanisms.

This study contributes to the existing literature of credit risk evaluation by considering credit risk as a systematic risk that can be influenced by network measures of collaborating actors. To do so, the paper proposes an approach that considers network characteristics of SCFs as critical attributes to predict credit risk.

Movies critics believe that the diversity of Iranian cinematic genres has decreased over time. The paper aims to answer the following questions: What is the impact of the continuous cooperation between the key nodes on the audience's taste, uniformity of the cinematic genres and the box office? Is there any relationship between the importance of actors in the actors' network and their popularity?

In the artistic world, artists' relationships lead to a network that affects individuals' commercial or artistic success and defines the artwork's value. To study the issue that the diversity of Iranian cinematic genres has decreased over time, the authors utilized social network analysis (SNA), in which every actor is considered a node, and its collaboration with others in the same movies is depicted via edges. After preparing the desired dataset, networks were generated, and metrics were calculated. First, the authors compared the structure of the network with the box office. The results illustrated that the network density growth negatively affects box office. Second, network key nodes were identified, their relationships with other actors were inspected using the Apriori algorithm to examine the density cause and the cinematic genre of key nodes, and their followers were investigated. Finally, the relationship between the actors' Instagram follower count and their importance in the network structure was analyzed to answer whether the generated network is acceptable in society.

The social problem genre has stabilized due to continuous cooperation between the core nodes because network density negatively impacts the box office. As well as, the generated network in the cinema is acceptable by the audience because there is a positive correlation between the importance of actors in the network and their popularity.

The novelty of this paper is investigating the issue raised in the cinema industry and trying to inspect its aspects by utilizing the SNA to deepen the cinematic research and fill the gaps. This study demonstrates a positive correlation between the actors' Instagram follower count and their importance in the network structure, indicating that people follow those central in the actors' network. As well as investigating the network key nodes with a heuristic algorithm using coreness centrality and analyzing their relationships with others through the Apriori algorithm. The authors situated the analysis using a novel and original dataset from the Iranian actors who participated in the Fajr Film Festival from 1998 to 2020.

Conventional approaches such as Data Envelopment Analysis (DEA) and Fuzzy Data Envelopment Analysis (FDEA) cannot effectively account for uncertainty, which can lead to imprecise decision-making. Furthermore, these methods frequently rely on precise numbers, ignoring the inherent uncertainty of real-world data. To address this gap, the research question arises: How can we develop a methodology that combines Z-number theory and FDEA to provide a comprehensive assessment of residency preferences in European countries while accounting for uncertainty in information reliability? The proposed methodology aims to fill this gap by incorporating Z-number theory and FDEA.

The proposed study assesses residency preferences across 39 European countries, focusing on key factors like environment, sustainability, technology, education, and development, which significantly influence individuals' residency choices. Unlike conventional DEA and FDEA approaches, the proposed method introduces a novel consideration: dependability. This inclusion aims to refine decision-making precision by accounting for uncertainties related to data reliability. The proposed methodology utilizes an interval approach, specifically employing the a-cut approach with interval values in the second step. Unlike using crisp values, this interval programming resolves formulations to determine the efficiencies of decision-making units (DMUs).

The comprehensive findings provide valuable insights into the distinctive factors of European nations, aiding informed decision-making for residency choices. Malta (75.6%-76.1%-75.8%), Austria (78.2%-78%-76.1%), and the United Kingdom (79.3%-78.4%-77%) stand out with distinct characteristics at levels of a = 0-a = 0.5-a = 1, assuming the independence of variables of the overall evaluation. Individual consideration of each factor reveals various countries as prominent contenders, except for the environmental factor, which remains consistent across countries.

Traditional DEA models encounter challenges when dealing with uncertainties and inaccuracies, particularly in the evaluation of large systems. To overcome these limitations, we propose integrating Z-numbers—a powerful mathematical tool for modeling uncertainty—into the conventional DEA process. Our methodology not only assesses the effectiveness of countries across various socio-economic and environmental metrics but also explicitly addresses the inherent uncertainties associated with the data. By doing so, it aims to enhance the precision of decision-making and provide valuable insights for policymakers and stakeholders.

The present study aimed to investigate the relationship between career development and productivity of the university staff with the mediating role of self-regulation.

The research approach is quantitative-relational and is based on structural equation modeling (SEM). The population consisted of the staff of two Iranian universities in 2018 out of which 331 participants were selected using Cochran's formula and a proportionate stratified random sampling method. To gather data, the self-regulation questionnaire (SRQ), the career development questionnaire (CDQ) and a researcher-made employees' productivity questionnaire (EPQ) were used. In terms of reliability, Cronbach's alpha coefficient of the instruments was found to be 0.97, 0.84 and 0.88, respectively. Face and content validity were confirmed by a group of field experts.

The findings indicated that the staff’s self-regulation had a positive and significant effect on individual, organizational and environmental productivity factors. In addition, self-regulation had the ability to predict those factors. It was found that self-regulation had a mediating role in the relationship between career development and staff productivity. According to the results, educational institutions, especially universities, can provide their staff with the opportunity to exploit their full potentials through reinforcing their self-regulation and increasing their productivity.

Higher self-regulation capacity among university staff helps them liberate their potential energy for disinterested selfless service to the society. Higher self-regulation capacities allow individuals to increase the energy resource for self-regulation and contribute to the productivity and quality of life. The statistical population of the quantitative section is confined only to the staff working at Iranian universities. Therefore, our results should be cautiously generalized to universities in other countries.

Our findings can help in empowering human resources and consequently improving education and research processes.

Universities play a decisive role in the economic growth and development of countries because of their diverse services in the production and distribution of science and knowledge.

This study was conducted on university’s staff productivity, while most previous researches have been conducted in industrial enterprises. Thus, the present study seeks to fill this research gap by means of providing new perspectives and information on the factors affecting staff productivity and the relationship between research variables in higher education institutions.