Component Analysis Regression Research Articles

Understanding Composite-Based Structural Equation Modeling Methods From the Perspective of Regression Component Analysis

Regression component analysis (RCA) replaces the factors in a factor analysis model with weighted composites of the model’s observed variables. The weight matrix may be calculated from the factor model’s parameter estimates. Thus, RCA parameter estimates can be obtained using factor model software, but RCA composites have determinate scores, rather than the indeterminate scores of factors. Analytically, RCA equates to modeling with “regression method” factor scores, except that, while those scores will be inconsistent with the original factor model, they are strictly consistent with the RCA model. When the original factor model is strictly correct in the population and the composites in RCA are standardized, RCA parameter estimates replicate those from regression-weighted forms of partial least squares (PLS) path modeling and generalized structured component analysis (GSCA)—affirming that those methods also equate to modeling with regression method factor scores under the same conditions. Parallel measurement allows RCA to replicate both correlation weight and regression weight versions of PLS and GSCA. These results suggest that RCA and regression-weighted forms of PLS and GSCA are all consistent approaches for modeling data that conforms to a factor model. All analytical methods are described using one consistent symbol palette. Complete R syntax is provided.

XNAS: A Regressive/Progressive NAS for Deep Learning

Deep learning has achieved great and broad breakthroughs in many real-world applications. In particular, the task of training the network parameters has been masterly handled by back-propagation learning. However, the pursuit on optimal network structures remains largely an art of trial and error. This prompts some urgency to explore an architecture engineering process, collectively known as Neural Architecture Search (NAS). In general, NAS is a design software system for automating the search of effective neural architecture. This article proposes an X-learning NAS (XNAS) to automatically train a network’s structure and parameters. Our theoretical footing is built upon the subspace and correlation analyses between the input layer, hidden layer, and output layer. The design strategy hinges upon the underlying principle that the network should be coerced to learn how to structurally improvethe input/output correlation successively (i.e., layer by layer). It embraces both Progressive NAS (PNAS) and Regressive NAS (RNAS). For unsupervised RNAS, Principal Component Analysis (PCA) is a classic tool for subspace analyses. By further incorporating teacher’s guidance, PCA can be extended to Regression Component Analysis (RCA) to facilitate supervised NAS design. This allows the machine to extract components most critical to the targeted learning objective. We shall further extend the subspace analysis from multi-layer perceptrons to convolutional neural networks, via introduction of Convolutional-PCA (CPCA) or, more simply, Deep-PCA (DPCA). The supervised variant of DPCA will be named Deep-RCA (DRCA). The subspace analyses allow us to compute optimal eigenvectors (respectively, eigen-filters) and principal components (respectively, eigen-channels) for optimal NAS design of multi-layer perceptrons (respectively, convolutional neural networks). Based on the theoretical analysis, an X-learning paradigm is developed to jointly learn the structure and parameters of learning models. The objective is to reduce the network complexity while retaining (and sometimes improving) the performance. With carefully pre-selected baseline models, X-learning has shown great successes in numerous classification-type and/or regression-type applications. We have applied X-learning to the ImageNet datasets for classification and DIV2K for image enhancements. By applying X-learning to two types of baseline models, MobileNet and ResNet, both the low-power and high-performance application categories can be supported. Our simulations confirm that X-learning is by and large very competitive relative to the state-of-the-art approaches.

WATER QUALITY PARAMETERS FOR POLLUTION SOURCE IDENTIFICATION IN WARWADE DAM WATER, DUTSE, JIGAWA STATE, NIGERIA: STATISTICAL AND SPATIOTEMPORAL EVALUATION

This study looked at the diminishing water quality of Warwade Dam, which is one of Dutse's largest dams. Statistical methodologies such as descriptive statistics, principal component analysis (PCA), regression analysis, correlation analysis, and cluster analysis were used to examine selected water quality characteristics. pH, conductivity, TDS, temperature, and DO captured the highest variability (98.94%) in the data set, according to the PCA scree plot. Ca and Mg were the most variable from the PCA scree plot among the metals studied (99.96 percent). The correlational analysis revealed that the characteristics differed in terms of spatio-temporal and/or limnological aspects. Variables within a cluster are relatively similar, whereas variables outside a cluster are very dissimilar, according to cluster analysis employing hierarchical dendrogram to establish linkages, relationships, and differences among parameters. The validation test was passed by 67 percent of the parameters in the general linear regression model (very strong fit at P value 0.05). The PCA scree plot of 99.39 percent variability revealed that the dam concentrations of nitrate, nitrite, and chloride were identical

R-IMNet: Spatial-Temporal Evolution Analysis of Resource-Exhausted Urban Land Based on Residual-Intelligent Module Network

The transformation of resource-exhausted urban land is an urgent problem for sustainable urban development in the world today. Obtaining the urban land use type and analyzing the changes in their land use can lead to better management of the relationship between economic development and resource utilization. In this paper, a residual-intelligent module network was proposed to solve the problems of low classification accuracy and missing objects edge information in traditional computer classification methods. The classification of four Landsat-TM/OLI images from 1993–2020 for Jiaozuo city (the first batch of resource-exhausted cities in China) was realized by this method. The results (overall accuracy was 98.61%, in 2020 images) were better than the comparison models (support vector machine, 2D-convolutional neural network, hybrid convolution networks; overall accuracy was 87.12%, 96.16%, 98.46%, respectively) and effectively reduced the loss of information on the edge of the ground objects. On this basis, six main land use types were constructed by combining field surveys and other methods. The characteristics and driving forces of spatial-temporal change in land use were explored from the aspect of social, economic and policy factors. The results showed that from 1993 to 2020 the cultivated land, forest land, water body and other land types in the study area decreased by 690.97 km2, 57.54 km2, 47.04 km2 and 59.43 km2, respectively. The construction land and bare land increased by 839.38 km2 and 15.57 km2, respectively. The transfer of land use types was mainly from cultivated land to construction land, with a cumulative conversion of 920.95 km2 within 27 years. The driving forces of land use in the study area were analyzed by principal component analysis (PCA) and regression analysis. The spatial-temporal evolution of land use types was affected by policy changes, the level of social development and the adjustment in the economy, industry and agriculture structure. The investment in fixed assets and per capita net income in rural areas were the top two influencing factors and their cumulative contribution rate was 94.62%. The findings of this study can provide scientific reference and theoretical support for land use planning, land reclamation in mining areas, ecological protection and sustainable development in Jiaozuo and other resource-exhausted cities in the world.

Lung Cancer: Spectral and Numerical Differentiation among Benign and Malignant Pleural Effusions Based on the Surface-Enhanced Raman Spectroscopy.

We present here that the surface-enhanced Raman spectroscopy (SERS) technique in conjunction with the partial least squares analysis is as a potential tool for the differentiation of pleural effusion in the course of the cancerous disease and a tool for faster diagnosis of lung cancer. Pleural effusion occurs mainly in cancer patients due to the spread of the tumor, usually caused by lung cancer. Furthermore, it can also be initiated by non-neoplastic diseases, such as chronic inflammatory infection (the most common reason for histopathological examination of the exudate). The correlation between pleural effusion induced by tumor and non-cancerous diseases were found using surface-enhanced Raman spectroscopy combined with principal component regression (PCR) and partial least squares (PLS) multivariate analysis method. The PCR predicts 96% variance for the division of neoplastic and non-neoplastic samples in 13 principal components while PLS 95% in only 10 factors. Similarly, when analyzing the SERS data to differentiate the type of tumor (squamous cell vs. adenocarcinoma), PLS gives more satisfactory results. This is evidenced by the calculated values of the root mean square errors of calibration and prediction but also the coefficients of calibration determination and prediction (R2C = 0.9570 and R2C = 0.7968), which are more robust and rugged compared to those calculated for PCR. In addition, the relationship between cancerous and non-cancerous samples in the dependence on the gender of the studied patients is presented.

Constraints and facilitators in elite athletes’ dual careers – A mixed methods approach

ABSTRACT Many elite athletes invest time and resources into a professional career besides their sporting career to prepare for their time after sports. This is called a dual career. The present study aims to identify the constraints and facilitators that elite athletes encounter in the different stages of their dual career. Moreover, the effects of these constraints and facilitators on athletes’ dual careers are investigated. The study employs an exploratory mixed-methods approach. First, a qualitative study is performed by conducting semi-structured interviews with n= 11 athletes from Germany. Following this, a quantitative study using an online survey is conducted with a sample of n= 200 German elite athletes. Data are analysed using descriptive analysis, principal component analysis, one-way ANOVA and regression analysis. The combined results show that athletes in university feel most constrained by intra-personal constraints and have the lowest levels of intra-personal facilitators. Structural facilitators provided by educational and vocational institutions have a positive effect on athletes’ dual careers whereas constraints, especially feelings of pressure have a negative effect. These findings highlight the importance of providing structural support to athletes such as psychological support, financial support and contact persons in the athletes’ environments.

Analysis of the ex-vivo transformation of semen, saliva and urine as they dry out\xa0using ATR-FTIR spectroscopy and chemometric approach

The ex-vivo biochemical changes of different body fluids also referred as aging of fluids are potential marker for the estimation of Time since deposition. Infrared spectroscopy has great potential to reveal the biochemical changes in these fluids as previously reported by several researchers. The present study is focused to analyze the spectral changes in the ATR-FTIR spectra of three body fluids, commonly encountered in violent crimes i.e., semen, saliva, and urine as they dry out. The whole analytical timeline is divided into relatively slow phase I due to the major contribution of water and faster Phase II due to significant evaporation of water. Two spectral regions i.e., 3200–3400 cm−1 and 1600–1000 cm−1 are the major contributors to the spectra of these fluids. Several peaks in the spectral region between 1600 and 1000 cm−1 showed highly significant regression equation with a higher coefficient of determination values in Phase II in contrary to the slow passing Phase I. Principal component and Partial Least Square Regression analysis are the two chemometric tool used to estimate the time since deposition of the aforesaid fluids as they dry out. Additionally, this study potentially estimates the time since deposition of an offense from the aging of the body fluids at the early stages after its occurrence as well as works as the precursor for further studies on an extended timeframe.

Understanding client satisfaction in elderly care: new insights from social resource theory

Social resource theory suggests that social interaction can be conceived as resource transaction or exchange with behaviours falling within six fundamental resource categories (i.e. love, status, information, money, goods, and services) organised along two underlying dimensions: particularism–universalism and concreteness–abstractness. With the purpose of extending knowledge about quality of care, this study adopts a novel approach in that it describes and categorises care behaviours using social resource theory instead of using single instances of care behaviour. The categorisation is further used to predict client satisfaction in care services targeting older people. Daily interactions between care staff and older persons were observed in two different residential care facilities using a structured non-participant observation design. The data were analysed using principal component analysis, correlation, and regression analysis. The results confirmed the hypothesis that satisfaction with care services is predicted by resource transactions that are high on the underlying dimensions of particularism and abstractness. Thus, the resource categories of love and status (resource categories high on particularism and abstractness) were shown to be strong predictors of client satisfaction. The use of social resource theory is a novel and appropriate approach to examine person-centred care and satisfaction with care. Also, in addition to addressing potential problems in previous self-report studies on care staff behaviour, the observational technique was highly practical to this service area where dealing with clients not always able to provide feedback directly.

The Influence Of Facebook Marketing On Consumer Buying Intention Of Clothing: Evidence From Young Adults

As a result of the rapid improvements paved through global advertising pathways, marketers have started to promote their products through social media platforms and among that; Facebook had become the most popular mode of communicating out of all. The study was conducted with the objective of comprehensively investigating the influence of Facebook marketing on consumer buying intention of clothing using evidence of young adults. Predominantly, the research focused upon identifying the superiority of Facebook as a social media communication platform and its relevant impact upon advertising. Majority of the businesses aims toward increasing the reach, whereas Facebook users expect clear experience through Facebook rather than watching typical pop ups, web banners and adds. However,
 
 
 
 
 Facebook marketing was identified to be positively and negatively influencing the companies and users. Therefore, focus was to analyze the impact to consumer’s buying
 intention from Facebook marketing. The companies can identify the effectiveness of the
 advertising and the improvement to be done. The study has undertaken a comprehensive survey to achieve the objectives of the research and those data was analyzed through component factor analysis, regression analysis and correlation coefficient. As per the results of the analysis, researchers have accomplished the stated objectives of the study. In conclusion, it was identified that there was a strong relationship between independent and dependent variables except E-WOM. Among that, Likes and sharing are the most important variables in Facebook marketing. According to outcomes, E- WOM was not an important variable, nonetheless researchers recommend focusing on E-WOM in order to convert this variable in to positive way to make better results, as previous researches stated that E-WOM can make massive impact to consumer purchase intention.

Mobile banking adoption by business executives in Nigeria

Challenges with the adoption of mobile banking technologies are best understood by studies on adoption. This however requires understanding the factors that influence its adoption in a given region. Technology Acceptance Model (TAM) was adopted to understand the underlying factors that influence mobile banking adoption by business executives in Nigeria. TAM was extended to include economic issues. Data were collected from 66 business executives in Nigeria using a structured questionnaire based on the model; the questionnaire was further analyzed using principal component analysis and regression analysis. Results showed that economic issues and social influence were major determinants for mobile banking adoption by business executives in Nigeria. However, economic issues were considered as the main predictor with a p-value of 0.000. This study contributes to the existing knowledge by giving consideration to business executives’ adoption of mobile banking in a developing country.

An evaluation of FTIR spectroscopy for prediction of royal jelly content in hive products

In this work, a rapid FTIR-ATR spectroscopic method combined with chemometrics was developed to predict royal jelly content in hive products; namely, dietary supplements such as royal jelly, propolis, honey and pollen. FTIR spectra of royal jelly, propolis, honey and pollen as well as their mixtures and dietary supplements were characterized. Amide II spectral absorption band with a peak point around 1547 cm−1 in the spectral range 1580–1500 cm−1 was used for quantitative estimation based on the calibration method using Partial Least Squares Regression (PLSR) and principal component regression (PCR) analyses. The results from the developed FTIR technique were also validated by those from HPLC-PDA technique, revealing a good correlation between FTIR and HPLC results (R2 = 0.99). The results of the present study suggest that the developed FTIR spectroscopy technique can be used to rapidly, easily and accurately predict the royal jelly content in the hive products.

The influence of mascot’s modeling characteristics on emotional image

We intends to investigate the impact of the characteristics of mascots modelling on the emotional imagery based on the theory of perceptual engineering. Specifically, we used the method of semantic difference image cognitive analysis on representative samples by virtue of SPSS software, which completes principal component analysis and regression analysis, typical vocabulary screening, as well as extraction of projects and category of the project modelling using morphological analysis in combination with the theory of science I analysis the correlation coefficient. Eventually, we will translate user’s perceptual demand description into rational modelling characteristics, so as to acquire a better understanding of the correlation between mascots modelling characteristic and emotional image, all of which provide certain reference basis for designers.

Prenatal exposure to perfluoroalkyl substances and associations with symptoms of attention-deficit/hyperactivity disorder and cognitive functions in preschool children.

Perfluoroalkyl substances (PFASs) are persistent organic pollutants that are suspected to be neurodevelopmental toxicants, but epidemiological evidence on neurodevelopmental effects of PFAS exposure is inconsistent. We investigated the associations between prenatal exposure to PFASs and symptoms of attention-deficit/hyperactivity disorder (ADHD) and cognitive functioning (language skills, estimated IQ and working memory) in preschool children, as well as effect modification by child sex. This study included 944 mother-child pairs enrolled in a longitudinal prospective study of ADHD symptoms (the ADHD Study), with participants recruited from The Norwegian Mother, Father and Child Cohort Study (MoBa). Boys and girls aged three and a half years, participated in extensive clinical assessments using well-validated tools; The Preschool Age Psychiatric Assessment interview, Child Development Inventory and Stanford-Binet (5th revision). Prenatal levels of 19 PFASs were measured in maternal blood at week 17 of gestation. Multivariable adjusted regression models were used to examine exposure-outcome associations with two principal components extracted from the seven detected PFASs. Based on these results, we performed regression analyses of individual PFASs categorized into quintiles. PFAS component 1 was mainly explained by perfluoroheptane sulfonate (PFHpS), perfluorooctane sulfonate (PFOS), perfluorohexane sulfonate (PFHxS) and perfluorooctanoic acid (PFOA). PFAS component 2 was mainly explained by perfluorodecanoic acid (PFDA), perfluoroundecanoic acid (PFUnDA) and perfluorononanoic acid (PFNA). Regression models showed a negative association between PFAS component 1 and nonverbal working memory [β = -0.08 (CI: -0.12, -0.03)] and a positive association between PFAS component 2 and verbal working memory [β = 0.07 (CI: 0.01, 0.12)]. There were no associations with ADHD symptoms, language skills or IQ. For verbal working memory and PFAS component 2, we found evidence for effect modification by child sex, with associations only for boys. The results of quintile models with individual PFASs, showed the same pattern for working memory as the results in the component regression analyses. There were negative associations between nonverbal working memory and quintiles of PFOA, PFNA, PFHxS, PFHpS and PFOS and positive associations between verbal working memory and quintiles of PFOA, PFNA, PFDA and PFUnDA, with significant relationships mainly in the highest concentration groups. Based on our results, we did not find consistent evidence to conclude that prenatal exposure to PFASs are associated with ADHD symptoms or cognitive dysfunctions in preschool children aged three and a half years, which is in line with the majority of studies in this area. Our results showed some associations between PFASs and working memory, particularly negative relationships with nonverbal working memory, but also positive relationships with verbal working memory. The relationships were weak, as well as both positive and negative, which suggest no clear association - and need for replication.

Факторы макроэкономической динамики при переходе к информационной экономике

The study was made of the relationship between GDP growth rates and individual components of its final use by combining factor analysis using the principal component method and regression analysis. The period from 1955 to 1980 was studied using data on the average annual growth rate of components over fiveyear periods in the countries of the non-socialist world. The growth rates of the various components of the use of GDP at different stages as the fourth technical and economic structure of the fourth dominates and the development of crisis phenomena in the economy has had a variable impact on economic growth. During the crisis period, the impact of the growth of all components of using GDP on economic growth has dropped sharply. The greatest role in this period was played by the growth of current expenditures of the state. Comparison of the structure of the use of GDP using factor analysis by the method of principal components was carried out by the average annual values for the period from 1950 to 1980. As the development crisis approaches, as the models show, the influence of factors of the structure of using GDP on economic growth decreases. During the entire period, high levels of government spending had a negative effect on economic growth. The impact of exports and imports was positive for the entire period studied. Overcoming the crisis in the economies of developed countries led to a transition to a positive impact of investment on economic growth.

Production environment effect on agronomic performance of okra breeding lines and their F2 populations

ABSTRACTThe type of production environment can affect agronomic performance of West African okra [Abelmoschus caillei (A.Chev) Stevels] breeding lines and their progenies. The study was conducted to (1) compare agronomic performance of West African okra lines and F2 populations under upland and bottomland environments, (2) identify sources of genes with potential to improve pod yield, (3) estimate genetic and phenotypic correlations for evaluated traits, and (4) estimate expected response to selection for pod yield and other agronomic traits through intergeneration correlation and regression. Twenty-three genotypes (9 F2 populations and their corresponding 14 parents) were grown in two production environments at the research farms of the Federal University of Agriculture, Abeokuta, Nigeria. Results were analyzed using principal component analysis, correlation and regression analyses. Unique trait profiles in genotypes responses to production environments occurred. Most genotypes indicated adaptation to the bottomland environment by reducing days to physiological maturity. Hybrid AGA97/066/5780 × NGAE-96–-0062-2, which combines early maturity with superior expression of pod yield between production environments, may be used to improve earliness in West African okra. Principal component analysis revealed three major factors that explained more than 66% and 69% of interrelation among traits in upland and bottomland, respectively. Changes of genotypic correlations between pod yield and other agronomic traits occurred between environments indicating the influence of environmental factors to trait correlation. A significant, positive correlation, and regression coefficients, between parents and the F2 generation for pod yield occurred between production environments, indicating the effectiveness of selection for pod yield in the early segregating generation. Generally, bottomland production environment offered a higher potential for agronomic trait expression in breeding lines and F2 population of West African okra.

CARBON ABSORPTION CAPABILITY THE WATERS IN THE RUPAT STRAIT OF RIAU PROVINCE

The purpose of this study isto analyze the uptake of carbon in polluted waters and unpolluted based on suspended sediment concentration in Rupat Strait. This study used multivariate statistics of Principle Component Analysis (PCA) and regression analysis to clarity the relationship between suspended sediments and primary productivity with carbon uptake. The results indicate that carbon uptake in polluted waters range from 0.023-0.026 tonC/m2/year and unpolluted ranged from 0.034- 0.038 tonC/m2/year. PCA analysis shows that the two main axes (F1 and F2) can represent 82.26% of the total diversity of distribution, indicate the contribution of F1 is 70.08% and F2 is 12.18%. The relationship between total suspended sediment with carbon uptake clearly show if total suspended sediment is higher, the carbon uptake will be lower. On the other hand the relationship between primary productivity and carbon uptake, indicate abundance of primary productivity, the carbon uptake will behigher.

How farmers\u2019 characteristics influence spontaneous spreading of stone bunds in the highlands of Ethiopia: a case study in the Girar Jarso woreda

This study aims to identify key differences between farmers who spontaneously implement stone bunds (i.e. farmers implementing stone bunds by their own initiative) and farmers who do not. Data were collected in the Girar Jarso woreda in the central highlands of Ethiopia, through a household survey with 80 farmers: 40 with spontaneously implemented stone bunds and 40 without. Independent samples t test, principal component analysis and regression analysis were used to analyse the data. Results show that five key-factors explain differences between the two groups of farmers: (1) readiness to change, (2) available resources, (3) social capital, (4) type of family, and (5) commitment. These factors together explain 73% of the variance in the data set and show that particularly characteristics related to the farmer’s intrinsic motivation play a crucial role to spontaneously implement and integrate stone bunds into the farming system. Furthermore, results show that young farmers are most committed to soil conservation: they are often intrinsically motivated dynamic farmers who are ready to change their future and improve productivity and food security. The study suggests that government extension programmes should therefore focus more on these young and dynamic farmers and foster their readiness to change. This implies that extension workers and government officials should better understand the crucial role of farmers’ intrinsic motivation when dealing with sustainable land management, and also reformulate extension strategies and messages. This is particularly important when developing a scaling-up strategy that helps to sustainably increase agricultural production and achieve food security of small-holder farmers in Ethiopia.

Resident Attitudes Toward Future Tourism Development: "it's a Question of Trust"

Extensive research has examined resident attitudes toward local tourism development, yet many gaps still remain. The most prominent framework used to examine resident attitudes is social exchange theory (SET), with trust being one of its main constructs. This study expands on previous research about resident attitudes by examining the antecedent of trust—trustworthiness (ability, benevolence, and integrity)—as it relates to future tourism development. Residents in a small town in the south-eastern US were surveyed to determine their level of trustworthiness with the local Chamber of Commerce, which was proposing the development of a new international horse park. Principle component analysis, correlation analysis, and regression analysis were conducted. Analysis of the data indicates that trustworthiness is a unidimensional structure and positively predicts resident support.

Comparative binder efficiency modeling of dry granulation binders using roller compaction

Roller compaction parameters’ impact on granules and tableting properties of coprocessed Avicel® DG [ADG], a physical mixture of the two components at the same composition present in ADG [PADCP], and microcrystalline cellulose and Kollidon® VA-64 Fine physical mixture [KVA64] was quantified by analysis of variance (ANOVA) and multivariate methods. Roller force, roller gap, and roller speed levels were selected for evaluation. A 33 full-factorial experimental design with three center points for roller force, roller gap, and roller speed was used. The response parameters studied were granule-to-fines (GF) ratio, compressibility index (CI), tablet thickness (TT), tablet friability (TF), tablet breaking force (TBF) and disintegration time (DT). A model acetaminophen tablet formulation was roller granulated and tableted at 10 kg scale. Principal component analysis of ADG and PADCP formulations were separated from KVA64 formulations, indicating different granule and tableting properties were binder dependent. This difference in binder performance was also confirmed by ANOVA. The ANOVA also showed that there were no statistical performance differences between coprocessed ADG and its comparable physical blend with the exception of TT. Principal component regression (PCR) analyses of ADG and PADCP revealed that these excipients exhibited a statistically significant negative effect on granules-to-fine (GF) ratio, TT, TBF, and DT. KVA64 demonstrated a positive effect on these parameters. The KVA64 physical mixture demonstrated an overall better performance and binding capability. This study strongly suggests that there is no performance advantage of coprocessed Avicel® DG when compared to a physical mixture of the two components at the same composition.

How alliance partner characteristics can affect performance? An empirical evidence from emerging economies context

The growth of alliances in developing countries has generated significant interest among scholars. Forming alliance is a reliable solution for companies based in developing countries to gain an advantage in international markets. Most of the previous studies present generic models or sets of characteristics for partner selection. In contrast, the current study suggests the partner characteristics which alliances' leaders should care about to be financially prosperous in international markets. Grounded in the resource-based view and review of recent studies, the theoretical framework of partner characteristics consisted of 13 variables. The data was gathered from Iranian alliances and analyses were done through the principal component and multiple regression analyses. The findings stress the importance of cognitive capability and knowledge stock as the most effective partner characteristics in export performance of alliances. Specifically, results introduce a framework that addresses why managers select partners with certain, specific characteristics to improve the export performance of alliances.