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Research and Development (R&D) activities are important for technological innovation and present opportunities for entrepreneurship. These activities depend on the flow of funding. This paper aims to review approaches used in R&D project selection and budget allocation.

This study conducts a systematic review, examining the content of 60 relevant papers (spanning 2000–2022) concerning public R&D budget allocation. The analysis focuses on allocation methodology, R&D output evaluation, budget allocation efficiency and the management of uncertainty in the allocation process.

The systematic review reveals different methods proposed for allocating government R&D budgets. These methods range from classical optimization, multi-criteria analysis and hierarchical analysis to techniques such as balanced scorecard, data envelopment analysis and analytic hierarchy process, including fuzzy approaches. Recent trends indicate an increase in the use of advanced optimization, integration and simulation algorithms. Performance indicators for reflecting R&D project outputs or goals can be categorized into four main groups: output (e.g. publications, patents, graduates), outcome, productivity (e.g. citations, patent references, articles and patents per capita) and sector-specific metrics.

Future research directions in government R&D budget allocation may include optimizing allocation to maximize social, economic and political benefits, developing ranking models, decision-making frameworks, simulations and evaluations of factors influencing allocation type and strategy. Additionally, there is a growing interest in novel budget allocation algorithms leveraging artificial intelligence and self-adjusting meta-heuristic algorithms.

The systematic review showed that some important research gaps in (government) R&D budget allocation could be considered in future studies; for example, long-term social, economic and political benefits in budget allocation optimization models, comprehensiveness of allocating government R&D budgets to universities, higher education and research institutes, R&D budget allocation to strategic technology development, e.g. renewable energy sector, supply chain issues and renewable energy value chain; new budget allocation algorithms based on artificial intelligence and self-adjusting meta-heuristic algorithms; methods for optimizing the structures of government budget allocation to R&D, considering executive and regulatory conflicts.

Business research and development (R&D) is of critical importance for innovation and economic growth. The purpose of this study is to present an application of the analytic hierarchy process (AHP) to select the most appropriate policy measure to support the business expenditure on R&D (BERD).

AHP method adopts a multi-criteria approach that can be used to analyse and prioritize the policy measures based on pairwise comparisons between several attributes that affect the selection of a policy tool. The model formulated in this study is applied to a real case of supporting decision-makers in some high-tech sectors in Iran.

The results highlight the four main financial policy measures implemented in Iran to enhance the BERD; those are, public procurement for R&D, direct subsidies for R&D, grants for R&D and income tax credit for firms have the priority values of 0.280, 0.260, 0.249 and 0.211, respectively.

The findings of this study are based on subjective evaluation of policy measures by experts of designing policy measures. Objective assessment of policy measures is important too because the preferences of policy interventions change during the time. Another significant point is that the priorities of specific policy measures depend on the effectiveness of their implementing arrangement and the previously successful experience of firms in receiving them.

This paper presents an application of the AHP to select the most appropriate policy measure to support the BERD. This method could be used to prioritize the policies and interventions that governments implement to solve different problems, especially at the innovation system level.

Although Iran is one of the largest producers and exporters of saffron in the world, the organic saffron market in Iran is still in its early stages, and there is scarce empirical evidence in this regard. Therefore, the study's primary purpose is to segment the organic saffron market in Mashhad, Iran using neobehavioristic theory and machine learning methods.

Considering the neobehavioristic theory of consumer behavior, the organic saffron market was segmented using crisp and fuzzy clustering algorithms. Also, to assess the relative importance of the factors affecting the intention to buy organic saffron in each market segment, a sensitivity analysis was performed on the output of the artificial neural network (ANN). A total of 400 questionnaires were collected in Mashhad, Iran in January and February 2020.

In contrast to the belief that psychological factors are more important in market segmentation than demographic characteristics, findings showed that the demographic characteristics of consumers, especially education and income, are the dominant variables in the segmentation of the organic food market. Among the 4 A’s marketing mix elements, the results showed that a low level of awareness and accessibility are obstacles to organic saffron market development. Advertising, distribution channel improvement, package downsizing and online business development are suggested strategies for expanding the organic saffron market in Iran.

The results of the present study will help policymakers and suppliers of organic saffron to identify their target markets and design short- and long-term marketing strategies to develop the organic saffron market.

Machine learning methods and the neobehavioristic theory of consumer behavior were used to segment the organic food market.

The purpose of this paper is to describe the current status of the knowledge-based economy (KBE) in Iran in comparison to Turkey, the challenges encountered and the appropriate policies toward Iran’s Outlook 2025 based on which the country is expected to be ranked first in science and technology within the Middle East region.

This study is a descriptive research. The methodology used for this study is qualitative/quantitative secondary research. The method will be used for two main goals. First, we used the World Bank’s Knowledge Assessment Methodology, and the data are mostly collected from secondary sources such as the World Bank’s Development Indicators for Iran and Turkey. Second, desktop research will be used to summarize and synthesize available studies on the consideration of policy-making toward KBE, especially among developing economies.

The paper provides policy considerations around four pillars: information and communications technology (ICT), innovation system, education and human resources development and economic incentives and institutional regime. It suggests that regarding ICT indicators, Iran has to join international programs to attract senior public authorities’ involvement and accountability. Regarding its innovation system, lessons for policymakers are implementing development plans and coordinating science and technology policies in the country. Moreover, the quality of education, in-company training, post-secondary technical education and scientific and technological workforce need to be improved. Finally, considering the weak macroeconomic circumstances, legislative measures are needed in addition to, establishing a promotion agency for foreign direct investment to coordinate the inflow and to grant incentives for attracting more investment.

Because of the chosen research approach, the research results have not been confirmed by an experts group. Therefore, using some group decision-making methods, such as panel of experts, could be proposed to further test the findings.

The paper includes implications for public policymakers, especially in developing countries, and for moving toward a KBE.

This paper fulfills an identified need to learn from similar countries experiences in policymaking about the same problem.

This research paper aimed to investigate the affordances of using virtual reality (VR) in teaching culture among in-service teachers of teaching Persian to speakers of other languages (TPSOL) in Iran.

This qualitative case study, conducted at two Iranian universities, used purposeful sampling to select 34 eligible in-service Persian teachers from a pool of 73. Data collection used an open-ended questionnaire and interviews.

Before the TPSOL in-service training workshop, teachers expressed their reservations regarding the use of VR to teach culture in TPSOL courses. The emerged themes were “skepticism toward effectiveness,” “practicality concerns,” “limited awareness of VR applications,” “technological apprehension” and “prevalence of traditional teaching paradigms.” During the post-workshop interview, it was discovered that the teachers’ perceptions of VR in teaching culture had undergone a positive shift. The workshop generated emergent themes that reflected positive perceptions and affordances for using VR to teach culture in TPSOL, including “enhanced cultural immersion,” “increased student engagement,” “simulation of authentic cultural experiences,” and “facilitation of interactive learning environments.”

One primary limitation is the lack of prior experience with VR for teaching practices in real-world classrooms among the participants. While the study aimed to explore the potential of VR in enhancing pedagogical approaches, the absence of participants with prior exposure to VR in educational contexts may impact the generalizability of the findings to a broader population. Additionally, the study faced practical constraints, such as the unavailability of sufficient facilities in the workshop. As a result, the instructor had to project the VR cont7ent on a monitor, potentially diverging from the immersive nature of true VR experiences. These limitations offer opportunities for future research to refine methodologies and gain a more comprehensive understanding of the implications of integrating VR into teaching practices.

Extensive research has been conducted on the effectiveness of VR in language education. However, there is a significant gap in research on TPSOL, which is considered a less commonly taught language. This study aims to address this gap by exploring the use of VR in the TPSOL through the lenses of in-service teachers. As part of a larger investigation, this qualitative inquiry focuses on the perceptions of in-service teachers about VR, with a particular emphasis on the cultural understanding of the Persian language.

The purpose of this paper is to examine the dynamical behavior of a combined three-story building with a 3D panel wall system including a soft story irregularity at the very first floor by doing a shaking table test. The upper two stories of the model were made out of the 3D panel system, while the first story was constructed only with moment-resisting RC frames.

Besides the experimental program, the numerical finite element method was implemented for the verification of the experimental results. In the experimental study, the building responses including the floors’ accelerations and drifts were considered, and the seismically vulnerable zones were reported and compared with that provided by the implemented FEM-based program.

After the shaking table test, the major cracks appeared at the end of each column and beam-column connections. Some negligible cracks were also visible around the beam-panel connections. However, no crack was seen in the upper stories. The lateral deformation of the studied building was investigated under the applied ELC25 and NGH135 earthquakes. Under the both aforementioned ground motion records, the first story drift was larger than two upper stories, since the moment-resisting frame was a soft story. The hysteretic relation between the shear and displacement for each story was studied. Under the applied ELC25 earthquake, the system remains linear and the stiffness of each story is obtainable as well.

This is the first time when the dynamical behavior of a combined system is studied and tested experimentally and numerically for data validation. Regarding the response of the assumed combined structure, the 3D panel system has a remarkable rigidity with respect to the conventional RC frames, also 3D panels have less weight than the moment-resisting frames.

The study aims to examine the association between learning culture and adaptive performance of delivery employees during crises situation. The study develops and tests a model that explains how learning culture, through change-efficacy and meaningful work, influences employees' adaptive performance (including how they handle crisis situations and deal with uncertainty).

Data was collected from 298 delivery employees working in e-commerce companies throughout India in a time-lagged manner. Regression analysis and structural equation modeling were performed to assess the influence of learning culture, change-efficacy and meaningful work on adaptive performance using SPSS 24. Further, PROCESS macro was used to test the parallel mediation effects through bootstrapping approach.

The study establishes a significant direct and indirect relationship between learning culture and adaptive performance for employees. Further, underpinning the transformative learning theory (Mezirow, 1997), and job characteristics theory (1976), this study came across two pathways for organizations to transform their learning efforts into improved adaptive performance for employees.

Organizations, particularly in crisis situations, can leverage employees' change-efficacy and meaningful work to connect learning efforts with employees' adaptive performance.

The study contributes significantly to existing theory on transformative learning and job characteristics theory while strengthening the literature on antecedents of employees' adaptive performance, particularly in crises situation.