AbstractPurpose: The velocity of change in the surrounding world has forced them to identify those FACTORS which impact their change capacity. The present study was conducted to identify AND prioritize influential FACTORS on organizational change capacity.Methodology: In terms of purpose, this was explorative AND applied research. Its population included ten familiar experts with research subjects in a research AND training center. By reviewing the literature on change capacity, relevant, influential FACTORS were identified, AND they were concurred by the fuzzy Delphi technique, AND then they were ranked. After recognizing influential FACTORS, a pair comparisons questionnaire was distributed among ten experts, AND after gathering the questionnaires, effecting FACTORS were ranked using the Expert Choice11 Software package AND AHP technique.Findings: Results show that organizational culture, structure, AND style of leadership were the main FACTORS that impacted change capacity.Conclusion: The influential FACTORS can be divided into three categories: environmental, content, AND structural elements. Environmental FACTORS include environment AND innovation; content FACTORS include team working, intellectual capital, leadership, political behavior, AND human resource management; Structural elements include strategy, structure, organizational policies, information technology, knowledge management, AND technology. Value: This is the first time in the literature that the influential variables affecting the change capacity were recognized AND categorized in a conceptual classification that has not been seen in the previous research.

ABSTRACT Despite a wide range of components AND criteria affecting travel behavior presented through empirical research, the results of these studies are inconclusive, which could be due to the difference between these components AND criteria in the study areas. Therefore, this research presented a method to determine which FACTORS in different physical developments affect travel behavior due to the differences in various physical developments. The required information was collected through 271 questionnaires at the level of three neighborhoods of Monirieh, Koye Bimeh, AND Koye Golestan in Tehran, Iran, as the old, conventional, AND new neighborhoods, respectively. ANOVA test was exerted to analyze the significant difference between different development patterns in three neighborhoods. Dunnett's T3 was applied to determine which neighborhood caused the difference between groups. Also, the FACTORS affecting travel behavior were obtained based on exploratory factor analysis indicators. Finally, by comparing the results of the ANOVA test AND regression analysis, it was discovered that FACTORS such as car ownership, dependence AND pro-liking for private cars, density AND access to educational centers AND parks, access to medical AND service centers, AND variety AND density of retail stores had been introduced as the FACTORS affecting travel behavior due to the differences in development patterns. However, proximity to the public transportation station, accessibility preferences in choosing a residence, dependence, AND pro-liking for other than a private car, having a license, number of children under five years old, AND age have influenced travel behavior regardless of the variation between neighborhoods. Extended Abstract Introduction Finding FACTORS affecting travel behavior has been one of the main concerns of transportation planners. However, in the last two decades, the importance of the influence of the features of the built environment, including lAND use, along with demographic-economic characteristics, travel behavior, AND attitudes of people, has been raised by urban planners. Studies seek to find FACTORS affecting travel behavior, especially lAND use characteristics. Despite presenting a wide range of components AND criteria affecting travel behavior, the results of the studies are inconclusive, which could be due to the difference between these components AND criteria in the study areas. Therefore, this research presented a method to determine which FACTORS in different physical developments affect travel behavior due to the differences in various physical developments. In order to do this, it must first be determined whether the study areas/different development patterns have a significant difference in terms of travel behavior or not. In case of a positive answer to the previous question, the following question is which study areas caused this difference. The next question arises: -Which physical AND non-physical characteristics affect travel behavior due to distinctions between different development patterns?   Methodology The present research method is analytical AND experimental based on quantitative methods. This research chose the frequency of travel by private car, public transportation, AND walking as the travel behavior. According to the research's purpose, indicators AND criteria affecting travel behavior were extracted after reviewing the theoretical AND experimental literature. Then, the required information was collected through 271 questionnaires at the level of three neighborhoods of Monirieh, Koye Bimeh, AND Koye Golestan as the old, conventional, AND new neighborhoods, respectively. The questionnaire was compiled as a Likert scale in five parts of travel information, demographic-economic characteristics, perceptual characteristics of lAND use, travel habits, AND access preferences of people in choosing their residence. ANOVA test was used to analyze the significant difference between different groups of a characteristic (here, different development patterns or the three case studies). Dunnett T3 was exerted to determine which neighborhood caused the difference between groups. Also, the FACTORS affecting travel behavior were obtained based on exploratory factor analysis indicators. Finally, by comparing the results of the ANOVA test AND regression analysis, it was discovered which FACTORS affecting travel behavior were due to the differences in study areas AND which FACTORS affect travel behavior regardless of development patterns.   Results AND discussion This research aims to identify the FACTORS affecting travel behavior due to the differences in development patterns. In this regard, the findings in line with the first research question show that the frequency of three modes of travel, by private car, transportation, AND pedestrian, differ significantly in the three neighborhoods. Furthermore, ANOVA test results depict that there is a significant difference between these three neighborhoods in terms of FACTORS affecting travel behavior, such as perceptually environmental characteristics of the neighborhood, dependence AND pro-liking for personal cars, variety AND density of retail stores, density AND access to educational units AND parks, access to medical AND service centers, AND car ownership. Finally, by comparing the results of the ANOVA test with the regression analysis assessing the relationship between physical AND non-physical FACTORS (the same indicators in the same study areas) with travel behavior, the FACTORS affecting travel behavior owing to different development patterns were identified. FACTORS such as car ownership, dependence AND pro-liking for private cars, density AND access to educational units AND parks, access to medical AND service centers, AND variety AND density of retail stores have been introduced as the FACTORS affecting travel behavior due to the differences in development patterns. However, proximity to the public transportation station, accessibility preferences in choosing a place of residence, dependence, AND pro-liking for other than a private car, having a certificate, number of children under five years old, AND age have influenced on travel behavior regardless of the variation between neighborhoods (different physical development patterns).   Conclusion In In order to discover the FACTORS affecting travel behavior due to the differences in patterns of physical development, this research has provided a more detailed analysis of the FACTORS affecting travel behavior. It has achieved more accurate components than previous studies in this regard. Detailed analysis of studies related to travel behavior AND finding the main components affecting it, considering the extent of variables AND data, can pave the way for professionals in transportation planning AND urban planning, in addition to providing detailed methods AND criteria in the related literature.   Funding There is no funding support.   Authors’ Contribution Authors contributed equally to the conceptualization AND writing of the article. All of the authors approved thecontent of the manuscript AND agreed on all aspects of the work declaration of competing interest none.   Conflict of Interest Authors declared no conflict of interest.   Acknowledgments  We are grateful to all the scientific consultants of this paper.

ABSTRACT Considering the concept of quality of life in cities to be the same, urban planners do not have a precise understANDing of the FACTORS affecting the quality of life. Therefore, according to the size of the city, different indicators should be considered to measure the quality of life. Thus, according to the main goal of the research, which is to recognize, identify AND compile the FACTORS affecting the improvement of the quality of life in middle AND small cities on an intra-provincial scale, Qazvin AND Zia Abad cities in Qazvin province were selected as study samples. In order to achieve the goal of the research, exploratory factor analysis AND regression analysis were exerted. The findings show that the homogenization of the indicators affecting the quality of life in all cities is incorrect. Some concepts, such as the size of the city, are effective in compiling AND selecting the indicators affecting the quality of life. So, the indicators affecting the quality of life in each city can be different according to some concepts, such as the city's characteristics or the city's size. Examining study samples also shows that in small cities, social indicators are more important due to strong social solidarity among people AND the high importance of social AND cultural concepts. Moreover, in middle cities, due to the importance of finding economic concepts AND urban services in people's lives AND people's less communication with each other AND the weakening of solidarity AND social connections, indicators of urban services AND facilities AND economic FACTORS have the most importance in explaining the quality of life; this shows that the Quality of life indicators are different in all cities Extended Abstract Introduction Quality of life is a multifaceted AND dynamic concept that can be different from city to city AND region to region AND can be defined by various indicators. Not paying attention to this issue AND considering the concept of quality of life equally in all cities has caused the level of quality of life to decrease in many cities, especially middle AND small cities, in recent years. On the other hAND, considering the concept of quality of life in cities to be the same, urban planners do not have a precise understANDing of the FACTORS affecting the quality of life. Following this, the main goal of this research is to identify, identify AND compile effective FACTORS for improving the quality of life in middle AND small cities on an intra-provincial scale. In order to achieve this goal, the cities of Qazvin AND Zia Abad in Qazvin province were selected as study samples, so that according to the size of the city, in order to achieve the goal of the research, the FACTORS affecting the quality of life are analyzed on an intra-provincial scale.   Methodology The research method is applied research based on quantitative methods. In line with data analysis, after extracting 60 indicators of quality of life, first, the indicators were separated into separate AND structured FACTORS using the exploratory factor analysis method in SPSS software. AND then, step by step regression method was used to measure the effective FACTORS in improving the quality of life in cities.   Results AND discussion The research findings show that based on exploratory factor analysis, 16 FACTORS were extracted in Zia Abad city AND 15 FACTORS in Qazvin city as effective FACTORS on the quality of life in small AND middle cities. In order to present AND explain the FACTORS affecting the improvement of the quality of life in small AND middle cities, the FACTORS extracted from the factor analysis were analyzed by multivariate regression method AND step-by-step method. Furthermore, finally, (13) FACTORS were introduced in Zia   Abad AND (13) FACTORS in Qazvin as the main FACTORS influencing the quality of life. The analysis of the findings indicates that in Zia Abad, FACTORS such as life expectancy AND social relations, security, the state of urban services AND access to urban facilities, living expenses, the state of urban furniture, urban traffic, the state of offices AND housing have more effects in explaining AND improving the quality of life. In Qazvin, FACTORS such as the state of urban services, the state of green AND public spaces in the city, the state of security AND economic opportunities in the city, the density AND hope of urban, economic, AND cultural life, the state of roads AND leisure in the city are effective in explaining the quality of life. Following this, according to the above findings, quality of life is a multidimensional AND dynamic concept that directly relates to the type of cities AND the specific characteristics of cities. Therefore, the quality of life AND its indicators can differ from city to city; because every city has its characteristics AND characteristics with citizens with different cultures AND thinking, which causes the FACTORS affecting the quality of life to be different. Therefore, considering the quality of life AND its indicators to be the same in all cities is wrong, which can lead to the decline of the quality of life AND the failure of quality of life improvement programs.   Conclusion Finally, the research results indicate that quality of life is a simple concept covering various urban life aspects. This concept can be defined as a determining factor in the excellence AND progress of cities AND residents' satisfaction with their urban life. It is multifaceted AND dependent on place AND time. As a result, the perception of people in different cities AND communities about the concept of quality of life AND the FACTORS affecting it can be different. Following this, FACTORS AND indicators should be selected according to the characteristics AND conditions of that city in urban planning to improve the quality of life for each city. Thus, this research, using the classification of cities based on the size of the city, has presented FACTORS specifically for middle AND small cities, which in small cities due to strong social solidarity among people AND the great importance of social AND cultural concepts, social indicators are more important. Moreover, in middle cities, due to the importance of finding economic concepts AND urban services in people's lives AND people's less communication with each other AND the weakening of solidarity AND social connections, indicators of urban services AND facilities AND economic FACTORS have the most importance in explaining the quality of life; this shows that the quality of life indicators are different in all cities.   Funding There is no funding support.   Authors’ Contribution All of the authors approved the content of the manuscript AND agreed on all aspects of the work.   Conflict of Interest Authors declared no conflict of interest.   Acknowledgments We are grateful to all the scientific.

Introduction: The aim of this study is to determine some of the maternal RISK FACTORS that lead to preterm delivery. Recognizing these RISK FACTORS makes it possible to prevent preterm labors as the most frequent cause of neonatal morbidity AND mortality. Materials AND Methods: This is a descriptive-analytical research study. The data were collected from the recorded information in the files of mothers AND preterm neonates that had been born in Bahman 22nd AND Shahrivar 17th Hospitals in Mashhad during 2007-2009. The case group consists of 360 preterm deliveries (before week 37th of gestation). The control group consists of 360 term deliveries (after week 37th of gestation). In this study, some of the maternal RISK FACTORS leading to preterm labor were taken into consideration. The criteria selected were: maternity age, previous abortions, number of previous pregnancies, preeclampsia, the number AND gender of fetuses, diabetes AND bacterial vaginitis. Also, UTI, the premature rupture of membrane (PROM) was extracted from the registration records. Using SPSS, the two groups were compared AND the data were analyzed with the method of chi- square test.Results: In this study, the prevalence rate of preterm labor was estimated as 6.1%. The findings suggest that maternal RISK FACTORS, such as diabetes, UTI, maternity age, the number of previous pregnancies AND the gender of fetus did not cause any significant difference between the groups of the study, but we found significant p-value for the RISK FACTORS, such as bacterial vaginitis, PROM, preeclampsia, previous abortions, AND the number of fetuses. Conclusion: We can reduce the incidence of preterm labor as a result of which the rate of morbidity AND mortality of preterm neonates would decrease. This can be materialize  through early diagnosis AND treatment of maternal genitourinary infections, prevention of premature rupture (PROM) AND preeclampsia as well as preventing repeated abortions AND iatrogenic multiple gestation with induction of ovulation.