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With the rapid advancement of technology, companies use new technologies to produce their products and services to maintain a competitive advantage. As companies alone cannot research and develop their technologies, they should use knowledge sources outside the organization that may exist throughout the world; hence, organizations need technology transfer. Because the success rate of technology transfer projects is low, the need to accurately assess and investigate the critical success factors of technology transfer projects is felt. In this regard, this study aims to identify and prioritize the critical success factors in technology transfer projects.

In this research, 56 critical success factor (CSF) were extracted from the context of the articles and were adjusted using experts’ opinions in different phases, as well as the fuzzy-Delphi approach. Finally, 15 factors were categorized in the form of steps of the technology transfer model: STAGE-GATE. In the next step, the set of criteria needed to prioritize CFSs was extracted from the literature and finalized with the help of the experts. Then, how each of the CSF influences the identified criteria was scored according to the organization’s export opinions. Finally, the priority of each key success factor was calculated using the additive ratio assessment (ARAS) method.

The results obtained for prioritization of the critical success factors show that experience in technology transfer in the transferee company, the existence of experienced technology transfer managers, sufficient organizational infrastructure and documenting project problems, achievements and experiences are four critical success factors of the technology transfer projects. Considering the long-term and short-term specific goals of the technology transfer process and the choice of technology in line with the company’s commercial strategy are also the critical success factors with the next priorities.

The combination of ARAS and step-wise weight assessment ratio analysis methods for identifying and prioritizing managerial decisions in the high-tech industries is a value of this research. Also, a combination of novel multi-attribute decision-making methods by the older framework of new product development is another contribution of this research.

The purpose of this paper is to propose a new method for knowledge worker productivity measurement which is based on valid principles and appropriate viewpoints.

Based on an extensive and thorough literature review the elements that need to be taken into consideration, while designing a method for knowledge worker productivity measurement, are determined and divided into principles and viewpoints. These elements must be incorporated into the design of knowledge worker productivity measurement methods so that the correctness and accuracy of these methods can be verified. The proposed model, which is based on appropriate principles and viewpoints, determines the outputs of knowledge work with respect to the tasks that a worker’s job includes. Considering nine measures, these outputs are evaluated using fuzzy numbers and, then, quantified. The inputs of knowledge work are knowledge, skills and abilities (KSAs) required to do the job. These inputs are identified and quantified using Job Element Method. Furthermore, fuzzy Data Envelopment Analysis is employed to model the productivity.

In this paper, the proposed method for knowledge worker productivity measurement follows both appropriate principles and viewpoints, simultaneously. In order to validate the obtained results and explore the applicability of the proposed method, a case study was carried out at an Iranian organization in electric power industry. Statistical analyses are employed to prove the validity of the results. Based on the obtained results, the productivity of a knowledge worker is said to be high when he/she delivers the expected amount of job outputs considering the values of his/her inputs (KSAs).

The originality of this paper is twofold. First, the extracted principles and viewpoints can serve as a guideline for the development of similar methods. Second, the proposed model offers an effective and efficient tool that can serve as the basis for the comparison among relative productivity of knowledge workers. Furthermore, the obtained results could form a basis to examine the productivity trend of each knowledge worker over different periods of time.

The present study is aimed to develop a firm export performance measurement model. Increased levels of globalization are forcing firms, especially SMEs (i.e. small and medium-sized enterprises) and entrepreneurial firms, to enter new markets and increase their exports. Consequently, export performance measurement has become a vital tool for achieving competitive advantages, and this type of measurement is considered an important topic of research in the field of international marketing. However, the literature lacks a comprehensive framework for measuring export performance, even though many criteria for gauging this performance's parameters have been discovered and applied.

A literature review was conducted, and the most important criteria for export performance measurement was identified using weights obtained by stepwise weight assessment ratio analysis method. Then, the export performance of five firms was evaluated using the additive ratio assessment method with interval-valued triangular fuzzy numbers.

The 15 most significant criteria were identified and categorized into three dimensions: economic, strategic, and other. The strategic dimension was identified as the most important, while the criteria “strategic goals achievement” and “return on investment” were found to be the most relevant.

A comprehensive framework including a definitive set of the most prominent, useful evaluation criteria was established which enables both scholars and practitioners to assess export performance.

The current study attempts to fill the gaps in the literature by presenting a comprehensive framework for export performance measurement.

Environmental awareness is increasing among people in developing countries. In this regard, companies should consider ecological goals in addition to financial goals. Since the food industry is recognised as one of the largest emitters of CO₂, profit and ecological objectives are optimised in radio-frequency identification (RFID) based closed-loop supply chain in the food industry in this paper.

Based on the literature, companies with a green entrepreneurial orientation (GEO) can turn ecological problems into opportunities using their proactiveness. In this regard, a new mixed-integer non-linear mathematical model is presented for optimising a new multi-product RFID-based closed-loop supply chain with a GEO in the food industry. The case study in this paper is Ofogh-e Kourosh company which is located in Iran. The GAMS software is used to code this model.

The optimum number of new products and materials flow was found among the closed-loop supply chain entities. Some factors as price, quality and warranty of products were considered, and the number of reopening of facilities if needed was set. The optimum node for RFID installation was found.

The paper presents a multi-objective mathematical model for optimising a multi-product RFID-based closed-loop supply chain with a GEO in the food industry. In addition, this paper gives insights into how can model this type of supply chain considering ecological and financial attributes.

This study attempts to identify the different types of activities that comprise a worker's job, and provide a framework for quantitative definition and segmentation of knowledge works (KWs).

Every KW has two main parts: working with knowledge and establishing communication. Thus, in order to provide an exact definition for the KW it is necessary to calculate the knowledge intensity score of a job (JKIS) and communication intensity score of a job (JCIS). For determining these two parameters precisely, jobs were broken hierarchically to tasks and then activities. To identify these activities, an initial list of activities mentioned in the literature was created and then completed with generalized work activities of O*NET. A six‐step framework for calculating of JKIS and JCIS was proposed and finally, different groups of knowledge workers (KWrs) with respect to JKIS and JCIS were identified by using a clustering method.

This article shows how KW can be defined and segmented based on two dimensions (i.e. knowledge intensity score of a job (JKIS) and communication intensity score of a job (JCIS)). The proposed framework was used to analyze 133 jobs in 11 organizations. Practicality and validity of framework were examined based on this empirical study.

This study is a base for the identification of appropriate managerial frameworks corresponding to each discovered group of KWrs. Using more data can improve the results obtained in this study.

This work emphasizes the importance of defining and clustering KW and proposes a practical method for this aim.

A new framework for the quantification of KW is proposed. This framework is supported by five principles inferred from the literature.