The aim of this study is to identify the behavioral factors affecting organizational health and select the most effective ones and design a model related to it. This model has been studied in public sector organizations and the impact of these factors on organizational health has been reported. Another goal of this paper is to draw the attention of organizational health researchers to behavioral factors.
Design / Methodology / Approach: In this study, with the help of previous research studies and the use of Delphi method, the most important behavioral factors that have a high validity were extracted. These factors were then summarized and divided into related categories using exploratory factor analysis. Also, by designing the structural equation model, the relevant model was constructed and tested.
The structural equation model obtained in this study illustrates the impact of behavioral factors on organizational health. This model also divides these factors into three categories: cultural factors, spiritual factors, and leadership style factors, and measures the impact of each factor. Researchers who want to work on the health of organizations and, above all, improve it, can take advantage of this model.
Limitations and Consequences: Due to the wide range of behavioral factors, it was not possible to study them fully in this study. Therefore, a specific time period of the articles was studied. It is also suggested that other aspects of behavioral factors be considered.
Practical Consequences: The development of participatory culture in the organization can help improve its health.
Initiative or Value of the Article: In this paper, the effect of behavioral factors on organizational health is illustrated in public sector organizations by studying extensive texts and constructing a tailored model. Since these organizations have a great impact on the lives of individuals in society, it is doubly important to pay attention to their health. This model also highlights the importance of paying attention to organizational behavior.
Paper Type: Research Paper

Despite the assumptions of hard Operations Research (OR) in using pure scientific and mathematical techniques, it has always been obvious that the application of these techniques and the implementation of their results go beyond the analytical sophistication of the OR methods and are impressed by the “human factor” in decision making. Behavioral factors, emotions, or personality traits of individuals can affect the process of applying the OR methodologies which could ultimately have an effect on decision making results. This research is aimed to examine the behavioral factors affecting the OR methodologies. To this end, by applying General Morphological Analysis (GMA) method, after determining the possible compatibility of each type of problem with each of the OR methodologies as model inputs, the behavioral factors affecting the OR methodologies are identified. This allows decision-makers to be aware of the effectiveness of each of these behavioral factors and prevent their occurrence as much as possible.

The purpose of the present study was modeling villagers’ intention to adopt e-marketing and rural provincial clustering. The data was collected from 988 villages with ICT offices from 31 provinces of Iran. The results showed that the rate of agricultural income in villages with ICT offices was desirable but income of handicrafts and tourism was lower than the average. Also, rural access to city and ICT users were acceptable. In designing the research model, the “theory of planned behavior” (TPB) was developed. In this regard, a new factor called rural economic geography was added having five new variables such as rural access, rural tourism, agriculture, rural handicrafts, and population. Thus, the “Geographic Model of Planned Behavior” (GeoTPB) was presented. In the new model, the impact of geographic factor on behavioral factor was examined using structural equation modeling approach. In general, the rural economic geography had a direct impact (64%) on behavioral factors and in the next step, the behavioral factor (as full mediation) affected the intention. The Geographic Model of Planned Behavior predicted villagerschr('39') intention to e-marketing adoption up to 75%. In addition, the K-means clustering indicated that Iran rural regions can be placed in six clusters due to economical and behavioral factors. In most clusters, rural economic geography was effective on behavioral factor. The interesting finding was that Sistan and Baluchestan, Hormozgan, and Khuzestan provinces in cluster 1 were the leading regions in e-marketing adoptions. These regions were recorded as less-developed villages in the reports of the statistics center indicating that the development of technology was significantly effective on the digital literacy of the inhabitants of these regions. The findings emphasized the significance of considering behavioral factors in geographic studies to provide realistic solutions for rural development.

The aim of this study was to "study behavioral factors in the selection of optimal portfolio Iran capital market" is. In order to achieve this goal, the dominant factors for mental accounting and loss aversion behavior and investment in stock and optimal portfolio selection with high efficiency compared to standard finance Using data from 106 firms listed in the Tehran Stock Exchange during the period of 5 years from 2011 to 2014 And regression analysis, variance analysis, was measured.The results showed that the expected return on portfolio behavioral model with an emphasis on mental accounting and loss aversion (as an indicator of behavioral factors) is greater than the expected return of the Standard Model and The hypothesis was accepted.

The aim of the current study was to determine the cognitive-behavioral factors affecting education in the Faculty of Pharmacy at Iran University of Medical Sciences. This was a descriptive-analytical study with a cross-sectional design, implemented in the late 2019 and early 2020, using census sampling method. The data collection tool was a questionnaire prepared based on the previous phases of the study including a systematic review, a qualitative phase, and a Delphi study. The SPSS software was used to analyze the data. Since the dependent variable had a normal distribution, independent sample t-test was used. According to the results, Iranian professors and students believed that innovation, anxiety and psychological stress, and concentration are the most important cognitive-behavioral factors affecting education. On the other hand, foreign professors and students believed that the most important factors affecting education were attempt to search information, intrapersonal intelligence, cognitive competence, and awareness. The cognitive-behavioral factors selected by Iranian students were different from those selected by foreign students. Therefore, policymakers, planners, managers, and experts must take into account the results of this study, particularly as far as Pharm.D. programs are concerned.

Today, given the atmosphere of Iran & world''s capital markets, analyzing behavioral factors that influence decisions of investors, is very important. This applied study is to evaluate the effect of behavioral factors (biorhythm mode, self-esteem, prestige & rational behavior) and so the kind of information (public & financial) and transparency of information on the investors'' behavior, on a random sample of 292 shareholders & investors in Ahvaz stock exchange. Collecting data is through questionnaires and data analysis, with modeling structural equation using SPSS & SMART PLS software. The results show that the kind of information and behavioral factors influence the behavior of individuals who invest in the stock market significantly. But, significant relation between transparency of information and investor''s behavior was not confirmed.

As technological advancements drive economic development and energy diversification, reliance on electricity has steadily increased. With the residential sector being a major electricity consumer, understanding the behavioral patterns of this group is crucial for managing electricity consumption and mitigating pollution associated with power generation. The main purpose of this study is to evaluate household electricity consumption behavior alongside its determining economic factors based on Theory of Planned Behavior (TPB).In this framework, the relationship between electricity pricing and socio-demographic factors with saving intention is examined to assess the intention of households to conserve electricity and explain their saving behaviors. To test the research hypothesis regarding the significant and dominant effect of behavioral factors on household electricity consumption, structural equations are employed. Research data is gathered through a field study involving 405 subjects (based on Cochran-Orcutt statistics) from residential electricity customers in Mazandaran Province of Iran. According to the results, the path coefficients for attitude, perceived behavioral control, and subjective norm were estimated to be 0.442, 0.323, and 0.128, respectively. Notably, the path coefficient for electricity price was estimated to be 0.121 (lower than subjective norm), indicating a significant but less influential role compared to behavioral factors. Additionally, the path coefficient for electricity saving intention was estimated to be 0.617, again highlighting the substantial importance of behavioral factors in electricity consumption. Based on the findings of this research, it is recommended that policymakers consider behavioral factors alongside pricing measures to control electricity consumption. For instance, in the framework of nudge approach, designing bills based on behavioral patterns and sending monthly and quarterly reports to compare the consumption of residents with their neighbors can have a significant effect on reducing electricity consumption. This approach not only contributes to resource and environmental conservation but also mitigates the adverse income distribution effects stemming from electricity price hikes.

Today, banks are facing a serious challenge in relation to social branding activities on social networks. Because on one hand, customers are pessimistic about branding activities on social networks, and on the other hand, fierce competition has led banks to look for a tool to maintain their competitive advantage. Although some studies have pointed to the importance of social responsibility as one of the dominant social tools in branding activities on social networks, little research has been done on how a social brand is built with the help of these policies on social networks; Therefore, the main purpose of this study is to design a social branding model of the banking industry in social networks. The present research is in an applied, mixed and cross-sectional research with an inductive approach. The statistical population of the study in the qualitative part consists of bank managers and university professors and in the quantitative part, 384 Sepah Bank customers who are members of social networks. The questionnaire was used to collect data. The validity of the questionnaire was confirmed by content validity and its reliability was confirmed by Cronbach's alpha coefficient. To analyze the data, first the Grand Theory method was used, and then the main and secondary factors in branding have been identified. Then, in order to rank and evaluate the components affecting branding, a combined model of fuzzy hierarchical analysis and fuzzy DEMATEL technique has been used. The results of Grand Theory method showed that structural, behavioral, environmental factors, processes, output, outcome and effect and sub-components related to each factor are the main factors on branding. The results of the integrated model to prioritize, determine the intensity of impact and effectiveness and drawing a causal diagram between the components of the research showed that among the main factors of the research, the behavioral factor has gained the highest rank. The behavioral factor, which ranks first in weighting the fuzzy hierarchical analysis approach, also has the highest weight in the fuzzy DEMATEL method and is in maximum interaction with other criteria; therefore, due to its greater effectiveness, it is considered a disabled criterion. The final result of prioritization showed that the effect factor is the least important factor in branding and on the other hand, behavioral factors are determined as the most important factor in branding. This finding emphasizes that banks must properly identify and pay special attention to behavioral factors before taking any action.

Blockchain is a technology that can be used in various organizations. Blockchain, through a decentralized computer network, leads to the facilitation of high-level transactions of organizations as well as their registration, in order to better respond to people's needs. In this research, a preliminary conceptual model consisting of the behavioral factors of blockchain technology acceptance in the banking industry, which is derived from theoretical literature and research background, is presented. Behavioral factors include facilitating conditions, attitude, literacy and skill, perceived risk, technology, perceived behavioral control, external motivation, internal motivation, competition and mental norm. Then, in order to check the fit of the model, structural equation modeling and smart pls software were used using a researcher-made questionnaire extracted from the research model. For this purpose, due to the unlimited size of the statistical population, 384 samples were randomly selected and the questionnaire was distributed among them. The result indicates that all the relationships are significant and the factors cause more than 80% of changes in technology adoption. In addition, agent-based modeling and Anylogic software have been used in order to predict changes in the adoption of blockchain technology over time, affected by the identified behavioral factors. The results showed that with the improvement of behavioral factors, the adoption of blockchain technology increases over time. In this study, insight is generated for key decision makers and relevant policy makers to propagate the adoption of blockchain technology in the banking industry.

Investors generally make decisions based on risk and stock returns, and their decisions are influenced by two factors, namely macroeconomic variables and microeconomic variables. The behavioral factors affecting investment decisions are investigated in the area of behavioral finance. In other words, behavioral finance focuses on specific human behavior attributes and their utilization in asset pricing. Behavioral asset pricing is the result of applying behavioral finance theories within traditional asset pricing theories. Although there are many asset-pricing models, due to their weaknesses and incompleteness as well as the necessity of investigating behavioral factors, this study attempted to model asset pricing using behavioral models.The population of the study included all listed firms in Tehran Stock Exchange over the years 2008 to 2018, and the sample was selected through systematic elimination of the population. Given these conditions, 141 firms were selected as the sample. The hypotheses were then tested by designing multivariate regression models.The results showed that using Fama-Macbeth approach, accounting information risk, investors’ trading behavior, and investors' sentiments had a significant and direct impact on firms’ stock returns.Thus, it is argued that behavioral variables can play a significant role in Modeling Asset Pricing.