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The purpose of this paper is to investigate the social acceptance of renewable and non-renewable energies in Iran using the social acceptance pyramid.

Today, social acceptance is considered a very important phenomenon in the development, implementation and achievement of energy policy goals. Low acceptance will make it difficult to achieve energy development goals; therefore, social acceptance must be taken into account when making policy. Firstly, the model criteria, using data obtained from questionnaires, are weighted by the Shannon entropy method and, finally, four sources of fossil, nuclear, wind and solar energy were ranked by means of VIKOR, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).

The results show that, in Iran, the social acceptance criterion and trust sub-criterion are the most important criteria for energy acceptance. The results of the ranking of options based on multiple-criteria decision-making (MCDM) techniques show that, given Iran's specific energy requirements, social acceptance of fossil energy is higher than wind, solar and nuclear, and wind, solar and nuclear energy come later in the rankings.

This research contributes to the literature in two ways: Firstly, social acceptance is considered a very important phenomenon in the development, implementation and achievement of energy policy goals; thus social acceptance must be taken into account when making policy. The results of the ranking of options based on MCDM techniques show that, given Iran's specific energy requirements, social acceptance of fossil energy is higher than wind, solar and nuclear, and wind, solar and nuclear energy come later in the rankings. Also, the social acceptance criterion and trust sub-criterion are the most important criteria for energy acceptance in Iran.

This study first aims to survey the state of user innovation in Iran and then identified barriers and incentives for user innovation in Iran.

In this study, a questionnaire (through 1,360 adults from Iran) prepared by De Jong (2016) was used to survey user innovation.

The results showed: the percentage of user innovators is high in Iran, there is no linear relationship between the income of a country and the percentage of the user innovators, there is a positive relationship between the education level and the percentage of user innovators in Iran, there is no linear relationship between the number of people educated in the scientific and technical fields and the percentage of user innovators. Finally, based on surveying users opinion in questionnaire, four incentives and five barriers in user innovation were identified that can help policymakers to improve their innovation policies.

Literature review in this field showed it was found that in a number of developed countries, the issue of user innovation has been addressed, but in Iran as a developing country, no such research has been conducted so far. In addition, it was found that so far, no comprehensive research has identified barriers and incentives for user innovation, so this study, first surveyed the state of user innovation in Iran and then identified barriers and incentives for user innovation in Iran.

This paper aims to investigate Supply Chain (SC) Performance Measurement Systems (PMSs) (SCPMSs) that are suitable and applicable to evaluate SC performance during unexpected events such as global pandemics. Furthermore, the contribution of Industry 4.0 Disruptive Technologies (IDTs) to implement SCPMSs during such Black Swan events is investigated in this study.

The research methodology is based upon a novel qualitative and quantitative mixed-method. A Systematic Literature Review (SLR) was initially employed to identify two complete lists of SCPMSs and IDTs. Then, a novel Interval-Valued Intuitionistic Hesitant-Fuzzy (IVIHF)-Delphi method was firstly developed in this paper to screen the extracted SCPMSs. Afterward, the Propriety, Economic, Acceptable, Resource, Legal (PEARL) indicator of the Hanlon method was innovatively applied to prioritize the identified IDTs for each finalized SCPMS.

Two high-score SCPMSs including the SC operations reference (SCOR) model and sustainable SCPMS were recommended to improve measuring the performance of the pharmaceutical SC of emerging economies such as Iran in which the societal, biological and economic issues were undeniable, particularly during unexpected events. Employing nine IDTs such as simulation, big data analytics, cloud technologies, etc., would facilitate implementing sustainable SCPMS from distinct perspectives.

This is one of the first papers to provide in-depth insights into determining the priority of contribution of IDTs in applying different SCPMSs during global pandemics. Proposing a novel multi-layer mixed-methodology involving SLR, IVIHF-Delphi, and the PEARL indicator of the Hanlon method is another originality offered by this paper.

Sensor networks have found wide applications in the monitoring of environmental events such as temperature, earthquakes, fire and pollution. A major challenge with sensor network hardware is their limited available energy resource, which makes the low power design of these sensors important. This paper aims to present a low power sensor which can detect sound waveform signatures.

A novel mixed signal hardware is presented to correlate the received sound signal with a specific sound signal template. The architecture uses pulse width modulation and a single bit digital delay line to propagate the input signal over time and analog current multiplier units to perform template matching with low power usage.

The proposed method is evaluated for a chainsaw signature detection application in forest environments, under different supply voltage values, input signal quantization levels and also different template sample points. It is observed that an appropriate combination of these parameters can optimize the power and accuracy of the presented method.

The proposed mixed signal architecture allows voltage and power reduction compared with conventional methods. A network of these sensors can be used to detect sound signatures in energy limited environments. Such applications can be found in the detection of chainsaw and gunshot sounds in forests to prevent illegal logging and hunting activities.

Science and technology diplomacy (STD) is an emerging area in the field of public policy and technology management. The purpose of this study is to overview the concept of STD based on the two approaches “Science and Technology” and “Diplomacy” to explain its necessity for Non-Aligned Movement member countries.

To design the work, principal domains and sub-domains of STD are identified based on the thematic analysis. By using MAXQDA software, the initial codes are analyzed and validated for the case study. Then, six areas and 29 sub-areas are identified based on the fuzzy Delphi and the framework is designed.

STD is defined from six scopes, including political, economics, law and legal issues, social sciences, philosophy and science and technology. Each scope has its own mechanism that is discussed in the article.

This work is one of the first in the literature of science and technology policy making.

One of most important tools for lean production is value stream mapping (VSM), which identifies and reduces errors, losses, waiting time and improves value adding time, leading to enhanced product quality through empowering production unit in terms of production risk and cost reduction in the long term. This paper aims to present a general concept for production flow, such that value stream can be fully understood. For this purpose, this research investigates the effect of value stream on operational losses and analyzes the process that has been conducted in preview step operationally, using the discovered pattern.

In this research, related processes were evaluated after the review of the relevant literature and after extracting operational basics of lean approach using a questionnaire, such that all factors affecting operational losses and VSM were assessed. Then comparison between the result of current state map and the result of future state map were done and analyzed. Cronbach’s alpha was calculated to test the designed questionnaire’s reliability, which resulted in an acceptable level of 0.845. The structural equation modeling method through SPSS and Smart–Partial Least Square was applied to analyze the data and test the hypotheses.

The results reveal that implementing required changes and corrections will lead directly to production process improvement, which, in turn, results in higher customer satisfaction because of cost reduction and quality enhancement. Also, tests of the hypotheses confirm that VSM affects six operational losses, namely, equipment failures, set-up and changeovers, idling and minor stoppages, reduced speed operation, scrap and rework and startup losses.

Though this study was done in a single manufacturing company, the implementation of this study delivers important results that can be deployed in other such manufacturing companies.

This tool helps to identify loss points, make right decisions, and choose the best methods and lean tools for improvement. The surveyed company can integrate lean principles and tools and achieve better results using this tool. Also, this tool could be used to analyze the process and to define necessary changes before changing the process components.

This study contributes to the body of knowledge in manufacturing research as the study regarding the effects of improvement opportunities identified through VSM on operational losses is still something new.

This study aims to to provide a model based on the context and conditions of the regions in Iran for monitoring regional innovation systems (RISs).

In the first stage, after reviewing the literature, interviewing the experts, and then thematic analyzing the content of the obtained data, initial Dimensions and indicators were identified. Then, a fuzzy Delphi technique and a seven-scale questionnaire were used to test the results and expert opinion. Finally, after the extraction and correction of the indicators to evaluate the RISs, a comprehensive model was identified in 5 dimensions, 16 indicators and 176 sub-indicators. In the next step, the importance weights of the main 16 indicators were determined using the analytic hierarchy process method.

The proposed RIS assessment model consists of firms’ behavior and regional interactions, economy, human capital, innovative outputs, locational features, that each dimension has its own indicators and sub-indicators, each one is discussed in the paper.

The research contribution is that the proposed RIS model of this research is the first model that is appropriate to the context of Iran.

Customer differences and similarities play a crucial role in service operations, and service industries need to develop various strategies for different customer types. This study aims to understand the behavioral pattern of customers in the banking industry by proposing a hybrid data mining approach with rule extraction and service operation benchmarking.

The authors analyze customer data to identify the best customers using a modified recency, frequency and monetary (RFM) model and K-means clustering. The number of clusters is determined with a two-step K-means quality analysis based on the Silhouette, Davies–Bouldin and Calinski–Harabasz indices and the evaluation based on distance from average solution (EDAS). The best–worst method (BWM) and the total area based on orthogonal vectors (TAOV) are used next to sort the clusters. Finally, the associative rules and the Apriori algorithm are used to derive the customers' behavior patterns.

As a result of implementing the proposed approach in the financial service industry, customers were segmented and ranked into six clusters by analyzing 20,000 records. Furthermore, frequent customer financial behavior patterns were recognized based on demographic characteristics and financial transactions of customers. Thus, customer types were classified as highly loyal, loyal, high-interacting, low-interacting and missing customers. Eventually, appropriate strategies for interacting with each customer type were proposed.

The authors propose a novel hybrid multi-attribute data mining approach for rule extraction and the service operations benchmarking approach by combining data mining tools with a multilayer decision-making approach. The proposed hybrid approach has been implemented in a large-scale problem in the financial services industry.

The purpose of this paper is to analyze zero valent iron nanoparticles (NZVIs) by a novel green method, taken from Thymus vulgaris (TV) plant extract, were synthesized and applied to degrade reactive black 5 (RB5) azo dye.

The optimum conditions for the highest removal of RB5 dye were determined. Characterization of NZVIs was done by scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy (FTIR), transmission electron microscopy (TEM) and X-ray diffraction (XRD). The NZVIs were used for the removal of dye RB5, and the parameters affecting were discussed like pH, initial concentration, contact time and NZVIs dosage.

The characterization results of NZVIs by SEM, TEM, FTIR and XRD show that polyphenols, organic acids and proteins reduce not only the green synthesis of NZVIs but also the aggregation of nanoparticles. The maximum dye removal efficiency of 99.6 per cent occurred at pH 4, NZVIs dose of 600 mg/L, and contact time of 5 min. The adsorption of RB5 dye onto the NZVIs surface and scavenging of the azo bond (−N = N) by the strong reduction of NZVIs were the proposed mechanisms for dye removal. The application of NZVIs to treat wastewater containing reactive dye shows high degradation efficiency.

The findings may greatly benefit the application of the NZVIs taken from Thymus vulgaris (TV) in the fields of dye adsorption.

The present study is novel because it incorporated the morphological and structural properties of the synthesized NZVIs using a native plant of Iran and studied the capability of green-synthesized NZVIs to remove RB5 as a water contaminant.

The native plant presented here can be developed for reduced environmental pollution before discharge to accepted water.

The NZVIs is prepared via green-synthesized method, which is prepared with leaves of TV. There are two main innovations. One is that the novel NZVIs is prepared successfully by native plant via green-synthesized method. The other is that the optimized conditions were obtained for the removal of RB5 dye as a water contaminant. Furthermore, to the best of our knowledge, no study has ever investigated the removal of RB5 by NZVIs produced using a native plant in Iran.

This paper proposes a multi-layer hybrid decision-making approach to evaluate the capability alternatives for developing a collaborative network to operate in the international market.

The present study is contextualised in the Iranian pistachio export industry. An extensive review of the state-of-the-art literature on supplier collaboration was conducted to identify key capabilities that are essential to establish a collaborative network. The set of defined capabilities were then optimised through interviews with 14 experts from the relevant industry, academics and export authorities. A combination of the fuzzy Delphi method and the best–worst method (BWM) approach was, respectively, used to reduce the number of capability alternatives and assign priority weights to these alternatives. Subsequently, a weighted aggregated sum product assessment method (WASPAS) was employed to rank and evaluate the ability to creating a collaborative network for the export of pistachio.

From the extant literature review, 18 capabilities for the formation of coordination networks in the international markets were identified. Then, the prominent indicators in forming a global network were extracted. After ranking the top pistachio export countries/regions to formalise an efficient collaborative network, it was revealed that although Iran exports approximately 30% of the global market, it falls behind the USA and European Union. The competitors have scored higher in critical criteria, including “trust and commitment”, “strategy and management”, “managerial control and standardization” and “financial resources”.

The proposed hybrid approach encompassing fuzzy Delphi–BWM–WASPAS offers to solve the capability evaluation and selection as well as ranking the possible alternative to formalise a collaborative network in an integrated fashion. This combination of methods is capable to first identify the most important factors, then measuring their importance and eventually rank the possible alternatives. The suggested framework provides an approach to deal with the uncertainty of global collaborative network formation.