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Knowledge management (KM) significantly affects supply chain management (SCM) and its performance in today's highly competitive corporate climate. It is crucial to consider this relationship to achieve optimal supply chain performance (SCP). This study aims to assess this impact by defining and examining the multi-dimensional relationships between KM Process Elements (KMPEs) and SCP Evaluation Criteria (SCPEC) within a comprehensive theoretical framework.

Integrating KMPEs and SCPEC becomes an uncertain decision-making problem due to data deficiency and the vagueness of decision-makers’ judgments. To address uncertainties, this study uses interval-valued neutrosophic (IVN) sets and proposes an IVN model consisting of SWARA, which is one of the effective multi-criteria decision-making (MCDM) approaches, and house of quality (HOQ) methods. IVN-SWARA is used to weight the SCPEC while IVN-HOQ establishes relationships and prioritizes the KMPEs and SCPEC.

The results show that reliability is the most significant SCP evaluation criterion. Among the KMPEs, capitalization, sharing, and transfer exhibit stronger associations with the SCPEC compared to the other elements. Capitalization as one of the KMPEs was found to be the most critical one, and efficiency is the criterion most affected by all elements of the KM process.

This study uses innovative methodologies to evaluate the adoption of KM processes on SCP under uncertain environments and involving multi-decision-makers. The proposed integrated model demonstrates flexibility and practicality in combining KM and SCM, leading to improved SCP. Notably, this study presents the development of IVN-SWARA and the use of the integrated IVN-SWARA - IVN-HOQ decision tool, which are novel contributions to the existing literature.

This study analyses the challenges in implementing lean manufacturing (LM) in the wood and furniture industry. In order to facilitate the smooth implementation of LM practices in this industry, the challenges in terms of its deployment need to be analysed and observed.

Realizing this importance, this study proposes a model, using PLS-SEM, which focusses on dealing with the challenges faced in the implementation of lean in the wood and furniture industry. The model consists of ten challenges that were determined based on a survey involving 46 SMEs companies in Malaysia.

The findings revealed that the implementation of LM is significantly affected by three main issues, namely: knowledge, resources and culture and human attitude. Furthermore, the analyses also highlighted four dominant challenges which are related to culture and human attitude issues – lack of employee commitment, lack of senior management's interest and support, difficult to implement, and LM is viewed as “current trend”. Overall, the ability to deal with the challenges involving factors of knowledge and culture and human attitude determines the success of LM implementation, especially in companies that have limited resources.

This study would help wood and furniture SMEs, government agencies, professional bodies and academics to better understand the challenges when implementing LM practices.

Overall, this study aims at investigating the relationships between the three challenges to better promote LM in the scope under study. Therefore, several activities were proposed to overcome the abovementioned challenges and subsequently contribute to the current body of knowledge.

The purpose of this paper is to contribute to the existing knowledge system of lean supply chain management (LSCM), i.e. by using main path analysis, and the authors extract the main development track of LSCM. It is advantageous for relevant scholars to deepen their understanding of this academic field from a bibliometrics view to grasp the future directions better.

Structuring a citation network with the processed data set containing 866 papers and relevant information collected from Web of Science (WoS). Conducting review analyses aiming at the main paths that are extracted from the above citation network.

There are two different evolution paths in LSCM field, i.e. improving corporate sustainability performance through combining lean and green practices, and seeking the balance between lean and agility to structure leagile supply chain for specific industries. LSCM research studies mainly focus on five aspects: (1) establishment and development of LSCM theory; (2) structuration of lean supply chain; (3) research studies of the relationship between LSCM and corporate performance; (4) supply chain evaluation system; and (5) review and vista of LSCM field. The intersection of two knowledge evolute routes would be Industry 4.0, which is an integrated theory system combining lean, agility, green and other supply chain thinking.

The data set collected from WoS cannot contain all the research studies about LSCM is the main research limitation. Sustainability, as represented by environmental performance, will continue to be a major pursuit of this field. Integrating LSCM in Industry 4.0 will be the next hotspot in LSCM field.

Providing the main research contents and common methods of LSCM field. It is conducive to deepening the understanding of relevant practitioners and scholars to LSCM field from a dynamic perspective.

To the best of the authors’ knowledge, it is the first time to reveal the knowledge diffuse trajectories of LSCM under different view with main path analysis. This study is unique that it provides a new view to understand the field of LSCM.

This chapter studies how in Iranian schools the power of resistance is evident in female students’ moral dilemmas. The Islamic Revolution of Iran was a turning point in Iranian educational system in which the role of religion and the desire for Islamization of all aspects of the society was emphasized. The data was gathered from one girl and one boy school in Tehran in 2016, and it includes lower secondary school students’ essays (Female n=175, Male n=165) about moral dilemmas. The study reveals the impact of Islamic values in school life and that the power of resistance is weak since students cannot criticize Islamic values. However, female students’ moral dilemmas indicate aspects of resistance in terms of questioning the schools rules that are based on these religious values and principles. This chapter examines female cases in detail to discuss how issues related to gender and religion are interconnected in Iranian schools.

This chapter discusses the use of an extended stage model for the evaluation of the adoption of e-business in small and medium-sized enterprises (SMEs). Empirical studies of e-business adoption are rare in Middle Eastern and developing countries and the chapter provides valuable insight into this region, by presenting an account of the use of the extended stage model to explore the level of e-business adoption among Yemeni SMEs.

In making this examination, the challenges and opportunities that accompany e-business adoption are revealed. The internal drivers and barriers, such as finance and skills, are recognised along with the external factors that include infrastructure and legislation. It also provides valuable insight into the macro-level sociopolitical determinants of e-business adoption that have not previously been appreciated; the study was undertaken during the Yemen Civil War in 2016.

Current adoption models imply that organisations adopt technologies in a linear fashion, gradually increasing complexity and capability. This study makes an important contribution by recognising that there are multiple points at which SMEs may ‘enter’ the technology-adoption ladder.

The chapter aims to investigate the link between COVID-19 and Islamic finance, investigating how Islamic countries respond to the impact of the pandemic and how Islamic banks have responded in consideration of their financial statements. The study proposes a novel perspective based on thematic analysis of blogs and newspapers to validate the relevant literature. Moreover, the documentary analysis will allow researchers to investigate Islamic banks' financial statements. We find that Islamic countries have used extraordinary Sukuk issuances both at government and cross-border level. Moreover, traditional instruments such as the Zakat have been converted for even more social uses. Concerning the literature, we find that there have been temporary tax suspensions and commodity supply measures to deal with the pandemic crisis's uncertainty. Finally, financial statements analysis reveals prudent behaviour with decreases in profits aimed at increasing risk provisions. The results provide theoretical evidence to researchers and practical evidence to policymakers, public policy investors and citizens.

In real manufacturing systems, schedules are often disrupted with uncertainty factors such as random machine breakdown, random process time, random job arrivals or job cancellations. This paper aims to investigate robust scheduling for a two-stage assembly flow shop scheduling with random machine breakdowns and considers two objectives makespan and robustness simultaneously.

Owing to its structural and algorithmic complexity, the authors proposed imperialist competitive algorithm (ICA), genetic algorithm (GA) and hybridized with simulation techniques for handling these complexities. For better efficiency of the proposed algorithms, the authors used artificial neural network (ANN) to predict the parameters of the proposed algorithms in uncertain condition. Also Taguchi method is applied for analyzing the effect of the parameters of the problem on each other and quality of solutions.

Finally, experimental study and analysis of variance (ANOVA) is done to investigate the effect of different proposed measures on the performance of the obtained results. ANOVA's results indicate the job and weight of makespan factors have a significant impact on the robustness of the proposed meta-heuristics algorithms. Also, it is obvious that the most effective parameter on the robustness for GA and ICA is job.

Robustness is calculated by the expected value of the relative difference between the deterministic and actual makespan.

The purpose of this study is to identify the drivers and future scenarios of Iran’s Social Security Organization.

The research is applied in terms of orientation and mixed in terms of methodology. In this research, the methods of theme analysis, root definitions, fuzzy Delphi and Cocoso were used. The theoretical population is the managers and senior experts of the social security organization, and the sampling method was done in a judgmental way. The tools of data collection were interviews and questionnaires. The interview tool was used to extract the main and subdrivers of the research and develop the scenarios.

Through theme analysis, 35 subdrivers were extracted in the form of economic, sociocultural, financial and investment, policy, marketing, environmental and legal themes. Due to the large number of subdrivers, these factors were screened with fuzzy Delphi. Eleven drivers had defuzzied coefficient higher than 0.7 and were selected for final prioritization. The final drivers were prioritized with the CoCoSo technique, and the two drivers of social security holdings governance and state of government revenues had the highest priority. Based on these two drivers, four scenarios of prosperity, resilient social security, unstable development and collapse have been developed.

Some of the suggestions of the research are: using the capacity of FinTechs and financial startups to invest the government revenues of the organization, using digital technologies such as business intelligence for more efficient decisions and developing corporate governance in the organization.

This paper aims to study the application of high-tolerance and flexible indigenous bacteria and fungi, along with the co-metabolism in recycled paper and cardboard mill (RPCM) wastewater treatment (WWT).

The molecular characterization of isolated indigenous bacteria and fungi was performed by 16S rRNA and 18S rRNA gene sequencing, respectively. Glucose was used as a cometabolic substrate to enhance the bioremediation process.

The highest removal efficiency was achieved for both chemical oxygen demand (COD) and color [78% COD and 45% color removal by Pseudomonas aeruginosa RW-2 (MZ603673), as well as approximately 70% COD and 48% color removal by Geotrichum candidum RW-4 (ON024394)]. The corresponding percentages were higher in comparison with the efficiency obtained from the oxidation ditch unit in the full-scale RPCM WWT plant.

Indigenous P. aeruginosa RW-2 and G. candidum RW-4 demonstrated effective capability in RPCM WWT despite the highly toxic and low biodegradable nature, especially with the assistance of glucose.

The purpose of this paper is to develop three methods including artificial bee colony algorithm (ABC-ANN), regression and adaptive neural fuzzy inference system (ANFIS) to predict the environmental indicators for land leveling and to analysis the sensitivity of these parameters.

This paper develops three methods including artificial bee colony algorithm (ABC-ANN), regression and adaptive neural fuzzy inference system (ANFIS) to predict the environmental indicators for land leveling and to analysis the sensitivity of these parameters. So, several soil properties such as soil, cut/fill volume, soil compressibility factor, specific gravity, moisture content, slope, sand per cent and soil swelling index in energy consumption were investigated. A total of 90 samples were collected from three land areas with the selected grid size of (20 m × 20 m). Acquired data were used to develop accurate models for labor, energy (LE), fuel energy (FE), total machinery cost (TMC) and total machinery energy (TM).

By applying the three mentioned analyzing methods, the results of regression showed that, only three parameters of sand per cent, slope and soil, cut/fill volume had significant effects on energy consumption. All developed models (Regression, ANFIS and ABC-ANN) had satisfactory performance in predicting aforementioned parameters in various field conditions. The adaptive neural fuzzy inference system (ANFIS) has the most capability in prediction according to least RMSE and the highest R² value of 0.0143, 0.9990 for LE. The ABC-ANN has the most capability in prediction of the environmental and energy parameters with the least RMSE and the highest R² with the related values for TMC, FE and TME (0.0248, 0.9972), (0.0322, 0.9987) and (0.0161, 0.9994), respectively.

As land leveling with machines requires considerable amount of energy, optimizing energy consumption in land leveling operation is of a great importance. So, three approaches comprising: ABC-ANN, ANFIS as powerful and intensive methods and regression as a fast and simplex model have been tested and surveyed to predict the environmental indicators for land leveling and determine the best method. Hitherto, only a limited number of studies associated with energy consumption in land leveling have been done. In mentioned studies, energy was a function of the volume of excavation (cut/fill volume). Therefore, in this research, energy and cost of land leveling are functions of all the properties of the land including slope, coefficient of swelling, density of the soil, soil moisture, special weight and swelling index which will be thoroughly mentioned and discussed. In fact, predicting minimum cost of land leveling for field irrigation according to the field properties is the main goal of this research which is in direct relation with environment and weather pollution.