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The purpose of this paper is to focus on modeling economy growth with indicators of knowledge-based economy (KBE) introduced by World Bank for a case study in Iran during 1993-2013.

First, for grouping and reducing the number of variables, Tukey method and the principal component analysis are used. Also for modeling, 67 per cent of data is used for training in the two approaches of ARDL bounds testing and gene expression programming (GEP) and 33 per cent of them for testing the models. Then, the result models are compared with fitness function and Akaike information criteria (AIC).

The GEP model with fitness 945.7461 for training data and 954.8403 for testing data from 1000 is better than ARDL bounds testing model with fitness 335.5479 from 1000. In addition, according to model comparison tools (AIC), the GEP model has an extremely larger weight in comparison with ARDL bounds model. Therefore, the GEP model is introduced for future use in academia.

Knowledge and information is one of the most basic sources of wealth in economists’ sight. Thus, using KBE indicators appears essential in economic growth regarding daily progress in knowledge processes and its different theories. It is also extremely important to determine an appropriate model for KBE indicators which play a highly important role in the allocation of the economic resources of the country in an optimal manner.

This paper introduced a novel expression for economy growth using KBE indicators. All the data and the indicators are extracted from Word Bank service between 1993 and 2013.

The purpose of this paper is to present a holistic approach regarding evaluation of knowledge management (KM) practices on organizational performance. The effects of seven critical success factors (CSFs), namely leadership role, organizational culture, KM strategy, processes and activities, training and education, information technology, and motivation and rewarding system, on organizational performance in the framework of four perspectives of balance scored card (BSC) approach were surveyed.

The research hypotheses were raised based on the four perspectives of this approach, namely, growth and learning, internal processes, customer and financial. By literature review, CSFs of KM and organizational performance along with their items were identified in the framework of BSC’s perspectives. Based on these constructs and their items an instrument was designed and distributed among managers and employees of the subsidiary firms of Iran National Petrochemical Company (INPC). Reliability of the instrument was calculated by Chronbach’s α for the two sections of the instrument i.e. KM practices and organizational performance. Also, using factor analysis the construct validity of the questionnaire was approved. Finally, based on the hypotheses of the study and using structural equation modeling the impacts of KM practices on organizational performance were investigated.

The results revealed that KM practices positively and meaningfully (though weak) impact overall organizational performance. This impact is significant only regarding growth and learning dimension and on the other dimensions is insignificant. Also, as customer and financial constructs were loaded on one factor based on the entity of their indicators we considered these two constructs as stakeholders construct. In addition, among the above mentioned seven CSFs, motivation and rewarding system obtained the lowest rank among the survey organizations.

The sample is restricted to only three companies, so gathering data from various parts of Iran including both manufacturing and service industries could increase the generalizability of the results obtained. Also, as in this study the data gathered were cross-sectional, a longitudinal study could help gain deeper understanding of the cause-and-effect relationship among the variables.

The most significant gap in the literature is the lack of enough application of statistical and comprehensive methods like BSC that KM makes a difference to organizational performance. This study contributes to the field of KM by empirically investigating the impact of KM practices on various measures of organizational performance in order to prove the suitability of a comprehensive approach like BSC.

The primary objectives of this study are to (1) highlight subsectors and industry groups of the construction sector that are most vulnerable to weather-related disasters (with highest labor cost escalation) and (2) analyze how immediate this labor wage escalation happens in different subsector of the construction sector.

The research methodology consists of three steps: (i) integrating various data sources to enable measurement of the county-level labor wage changes following large-scale weather-related disasters; (ii) measuring postdisaster labor wage changes at the county level; and (iii) comparing amount and timing of postdisaster labor wage changes among all sub-sectors (and industry groups) of the construction sector.

The results show that among the three construction subsectors (Heavy and Civil Engineering Construction subsector, Construction of Buildings subsector, and Specialty Trade Contractors sub-sector), Heavy and Civil Engineering Construction subsector is the most vulnerable to weather-related disasters. The industry groups under the Heavy and Civil Engineering Construction subsector showed the same vulnerability level; however, under the Construction of Buildings subsector, Industrial Building Construction industry group showed to be the most vulnerable; and under the Specialty Trade Contractors subsector, the Building Foundation and Exterior Contractors industry group is the most vulnerable. The results also showed that in approximately 75% of the damaged counties, there were increases in wages of all construction labors, over the following three quarter after the disasters. In average, labor wages in Construction of Buildings subsector and the Specialty Trade Contractors subsector decreased by 0.6% and 0.8%, respectively, in the quarter of disaster and gradually increased by 4.4% and 4.6%, respectively, in the following three quarters. On the other hand, Heavy and Civil Engineering Construction’s labor wages did not experience this decrease right after the disasters; wages increased immediately after disasters hit the counties and continually increased by 8.6% in three quarters after the disasters. It is expected that the results of this study will help policy makers, cost estimators and insurers to have a better understanding of the post-disaster construction labor wage fluctuations.

This study is unique in the way it used construction labor wage data. All data are location quotient, which makes the comparison among the affected counties (with different construction size) feasible.

The success of any organization in a knowledge-based economy depends on effective knowledge transferring and then proper use of the transferred knowledge. As is known, optimizing the knowledge transferring costs in a product portfolio plays an important role in improving productivity, competitive advantage and profitability of any organization. Therefore, this paper aims to determine an optimal product portfolio by minimizing the konlwedge transferring costs.

Here, a fuzzy binary linear programming model is used to select an optimal product portfolio. The model is capable of considering the knowledge transferring costs while taking into account the human-hours constraints for each product by a fuzzy approach. Using fuzzy ranking functions, a reasonable solution of the model can be achieved by classical or metaheuristic algorithms.

Numerical experiments indicate that the proposed fuzzy model is practically effective.

The contributions of this work essentially consist of considering knowledge transferring costs in selecting an optimal product portfolio and using the fuzzy data which make the model more realistic.

Data comprise one of the key resources currently used in organizations. High-quality data are those that are appropriate for use by the customer. The quality of data is a key factor in determining the level of healthcare in hospitals, and its improvement leads to an improved quality of health and treatment and ultimately increases patient satisfaction. The purpose of this paper is to assess the quality of emergency patients’ information in a hospital information system.

This cross-sectional study was conducted on 385 randomly selected records of patients admitted to the emergency department of Shahid Beheshti Hospital in Kashan, Iran, in 2016. Data on five dimensions of quality, including accuracy, accessibility, timeliness, completeness and definition, were collected using a researcher-made checklist and were then analyzed in SPSS. The results are presented using descriptive statistics, such as frequency distribution and percentage.

The overall quality of emergency patients’ information in the hospital information system was 86 percent, and the dimensions of quality scored 87.7 percent for accuracy, 86.8 percent for completeness, 83.9 percent for timeliness, 79 percent for definition and 62.1 percent for accessibility.

Increasing the quality of patient information at emergency departments can lead to improvements in the timely diagnosis and management of diseases and patient and personnel satisfaction, and reduce hospital costs.

The primary purpose of this study is to examine the effect of organizational culture (OC), that is, group, developmental, hierarchical and rational culture on organizational learning (OL) of employees in electricity distribution companies of Isfahan province. Further, the role of the high-performance work system (HPWS) as a mediator between OC and OL has also been explored.

Questionnaire survey method has been used for data collection, and data analysis was completed through a two-stage partial least squares structural equation modeling technique. At the first stage, the measurement model was examined for construct validity and reliability, whereas at the second stage, the structural model and by implication the research hypotheses were tested.

Results indicate that OC positively affects OL. Further, HPWS act as a mediating variable between OC and OL.

The findings contribute to the existing literature by demonstrating the mediating role of HPWS in the relationship between OC and learning.

Several variables can affect work stress. This study aims to model the cause-and-effect relationships among different variables that can predict work stress based on one of the most important fuzzy multicriteria decision-making methods used to investigate the cause-and-effect relationships among variables.

This study was conducted in 2020, including 17 experts in safety management, occupational health and work psychology, based on the fuzzy decision-making trial and evaluation laboratory method as a robust approach to identify the cause-and-effect relationships among different variables.

Shift work, lack of job satisfaction, mental health, mental overload, fatigue, job security, sleep disorders, environmental discomfort, work pressure, job knowledge (this could mean expertise/level of qualifications/familiarity with the job), work complexity and role conflict were found to be the most significant variables affecting work stress. Moreover, the cause-and-effect model of relationships among variables showed that shift work and lack of job satisfaction are root causes, and mental health, fatigue, mental workload, sleep disorder and environmental discomfort are direct causes.

Although the results of this study demonstrate that work stress can be influenced by 12 different variables, the modeling results show that some variables, such as shift work and lack of job satisfaction, can directly or indirectly impact other variables and thus result in work stress.

The purpose of this paper is to use Group Method of Data Handling (GMDH)-type artificial neural network to model the affecting factors of housing price in Isfahan city housing market.

This paper presents an accurate model based on GMDH approach to describing connection between housing price and considered affecting factors in case study of Isfahan city based on trusted data that have been collected from 1995 to 2017 for every six months. The accuracy of the model has been evaluated by mean absolute percentage error (MAPE), root mean square error (RMSE) and mean absolute error (MAE) in this case.

Due to the obtained value of MAPE, RMSE and MAE and also their interpretation, accuracy of modelling the factors affecting housing price in Isfahan city housing market using GMDH-type artificial neural network that has been conducted in this paper, is acceptable.

Due to limitation of reliable data availability about affecting factors, selected period is from 1995 to 2017. Accessing to longer periods of reliable data can improve the accuracy of the model.

The key point of this research is reaching to a mathematical formula that accurately shows the relationships between housing price in Isfahan city and effective factors. The simplified formula can help users to use it easily for analysing and describing the status of housing market in Isfahan city of Iran.

An accurate predictive model for forecasting urban housing price in Isfahan can be useful for sellers and owners to take more appropriate actions about housing supplying. Also, it can help urban housing planners and policymakers in managing of the housing market and preventing an urban housing crisis in Isfahan. The purpose of this paper is forecasting housing price in Isfahan city of Iran until 2022 using group method of data handling (GMDH).

This paper presents an accurate predictive model by applying the GMDH algorithm by using GMDH-Shell software for forecasting housing price in municipal boroughs of Isfahan city till the second half of 2022 based on creating time series and existing data. Alongside housing price, some other affecting factors have been also considered to control the forecasting process and make it more accurate. Furthermore, this research shows the housing price changes of boroughs on map using ArcMap.

Based on forecasting results, the housing price will increase at all boroughs of Isfahan till second half of the year 2022. Amongst them, Borough 15 will have the highest percentage of the price increasing (28.27%) to year 2022 and Borough 6 will have the lowest percentage of the price increasing (8.34%) to the year 2022. About ranking of the boroughs in terms of housing price, Borough number 6 and 3 will keep their current position at the top and Borough number 15 will stay at the bottom.

In this research, just few factors have been selected alongside housing price to control the forecasting process owing to limitation of reliable data availability about affecting factors.

The most remarkable point of this paper is reaching to a mathematical formula that can accurately forecast housing price in Isfahan city which has been rarely investigated in former studies, especially in simplified form. The technique used in this paper to forecast housing price in Isfahan city of Iran can be useful for other cities too.