Simple Random Sampling Design Research Articles

Effect on Temperature Influences of the Life Table Studies for Aphid, Aphis craccivora Koch. in Cowpea under Laboratory Condition

Aim: To study the life table aphid, Aphis craccivora in cowpea at different temperature. Study Design: Simple random sampling design with two treatments and 12 replications. Place and Duration Study: Agricultural College and Research Institute, Madurai and 2019-2020. Methodology: We used a simple random sampling design, which included two temperature variables (30 and 35°C) and 12 repetitions for A. craccivora. The female adult aphids were placed in dishes with leaf dishes and 12 hours light appropriate temperature. Female adult aphids were placed in dishes containing leaf discs and incubated at the appropriate temperature, with a 12-hour light phase. They were transferred to new dishes as needed. We evaluated the pre-reproductive and reproductive periods under a stereomicroscope every 24 hours, and we determined the number of nymphs produced and their longevities at each temperature. Results: Aphid, Aphis craccivora Koch. is most important pest in Pulses ecosystem and life table studies on cowpea, the net reproductive rate (22.53 individuals / female at 300 C; 14.23 individuals / female at 350 C, mean generation time 11.41days at 300C; 10.39 at 350C, population doubling time (2.710 days at 300C, 2.539 at 350C in Cowpea. The age specific survivorship curve revealed that the population followed Type III curve and 50 per cent mortality of A. craccivora 9 days at 300C and 7.5 days at 350C in cowpea. Conclusion: The life table aphids insects parameters of net reproductive rate, population doubling time, mean generation time and age specific curve was maximum in 300C than 350C in Cowpea.

A new family of robust quantile-regression-based mean estimators using Sarndal approach

In presence of extreme-values or outliers, the ratio-type mean estimators are prominently developed by using the robust coefficients of regression and covariance matrices. In this article, we consider quantile regression coefficients while estimating the finite population mean. Incorporating Sarndal concept, we offer quantile-regression-based mean estimators under a simple random sample design. We calculate the mean squared error (MSE) for the suggested estimators and discover that they perform better than the existing estimators. Moreover, numerical representations and a simulation study support our discoveries in theory.

The Effect of E-service Quality and Customer Satisfaction and Trust to Customer Online Shopping Behavior

China's e-commerce sector is experiencing rapid growth. Among Chinese cities, Beijing stands out with its robust development, consistently ranking among the top three in the nation. Nonetheless, Beijing's e-commerce enterprises encounter various challenges. This research endeavors to examine the interplay between e-service quality, customer satisfaction, trust, and online shopping behavior within the Beijing e-commerce landscape. Descriptive method was considered by the researchers. The respondents of the study are the users of online shopping platforms that operates in Beijing China who have experience using major online consumption platforms. The sample size is 385 people which was calculated by Raosoft software. The respondents of the study were chosen using the simple random sampling design. Moreover, constructed survey questionnaires served as a tool with five-key point Likert scale. The Statistical Package for Social Sciences (SPSS) was employed in the statistical analysis of data. The following statistical tools were used in the study: frequency and percentage, mean, reliability and validity analysis, Analysis of Variance and Multiple Linear Regression. The study resulted in a strong positive relationship between customer satisfaction, trust and over-all e-service quality. Specifically, customer satisfaction and trust resulted in a very strong relationship to customer behaviors. For the comparison of means when grouped according to profile variables, customer satisfaction level and over-all e-service quality have a significant difference when grouped to frequency of purchase. Based on the results of the study, the researchers proposed a development program to help improve the quality of e-services and thus increase customer re-purchase rates.

Venture Capital and Financial Performance of E-Commerce-Driven Firms in Kenya

Purpose: Businesses that rely on e-commerce for growth frequently find that their ability to obtain sufficient capital is critical to their success. Funding from venture capital firms is essential in this context. Due to insufficient capital, e-commerce-driven businesses in Kenya have continuously underperformed financially. Numerous of these businesses, such as Rupu, OLX Kenya, and ePay, encountered financial difficulties and eventually lost the ability to continue operating. This study's main objectives were to evaluate the impact of capital management support, financing options, and cost of capital on the financial performance of Kenyan e-commerce businesses. Additionally, the study sought to fully evaluate the moderating impact of regulatory structure on venture capital funding and the financial performance of Kenyan e-commerce-driven firms. The trade-off, stakeholder, financial liberalization, and agency theories all lend credence to this research. The research philosophy of positivism, which emphasizes the use of empirical data to evaluate theories and hypotheses, was applied to perform this study.
 Methodology: The study examined a population of 45 e-commerce-driven businesses that obtained venture capital financing between 2017 and 2022 using a descriptive cross-sectional survey research approach. A stratified simple random sampling design technique was employed to choose 45 e-commerce-driven businesses as a sample. Surveys were distributed to collect primary data. Important stakeholders, including investors, executives, and founders, were among the participants in the study. Descriptive statistics were employed to summarize the data, such as measures of central tendency (particularly the mean), variability (expressed by the standard deviation), and frequency distributions. Regression analysis, an inferential statistic, was also employed in the study to investigate the relationship between venture capital funding and financial success. The gathered data were coded before entering into the Statistical Package for Social Science (SPSS) program to make the analysis process easier. The analysis's final results were provided as tables.
 Findings: The findings showed that the cost of capital, financing strategies, and capital management support significantly impact Kenyan e-commerce enterprises' financial performance. The aggregate score of 3.73 in cost of capital indicated consensus among participants on the impact of venture capital on e-commerce companies' financial performance. Participants perceived a significant correlation, with a standard deviation of 1.15 reflecting varying opinions. Most agree that lower venture capital costs lead to better financial success, influencing organizations' financial decisions significantly. The study concluded that venture capital funding has a considerable impact on the financial performance of e-commerce-driven enterprises in Kenya. The report advised management to thoroughly understand the total cost of capital related to venture capital funding.
 Unique Contribution to Theory, Practice and Policy: The study on venture capital and financial performance of e-commerce-driven firms in Kenya has made significant contributions to theory, practice, and policy. It has enhanced theoretical understanding by examining the relationship between venture capital investments and the financial performance of e-commerce firms, providing insights into the dynamics of these markets. In practice, the study has offered valuable guidance to e-commerce entrepreneurs and investors by identifying factors that influence financial performance and informing strategic decision-making. Additionally, the findings have implications for policy formulation, as they highlight the importance of fostering an enabling environment for venture capital investments in the e-commerce sector to spur economic growth and innovation in Kenya.

Marketing Efficiency of Shallot Commodity in Malang Raya

Analyzing the marketing efficiency of shallot commodities is the main objective in this research.The research was located in Malang Raya included Malang City, Batu City, and Malang Regency. The method of taking farming respondents with a simple random sample design was taken based on the parallel formula in 37 people. Selected marketer’s respondent using the snowball sampling method, involving 13 individuals in total. The margin analysis is conducted to analyze the components of marketing costs as well as the share received by each market actors involved in shallot marketing. Profit marketing margin analysis, the ratio of profit was taken over the costs incurred by each marketing institution in the four marketing channels could be said to be generally efficient. The price share that was received by farmers in marketing channel IV is the highest sahre among the other channels. This is a result of farmers selling their shallots to intermediary traders directly. In contrast to marketing channel III, which gives farmers the lowest share. This occurs because of the high selling price difference between retailers and farmers and involves three marketing institutions.

THE INFLUENCE OF THE BIG FIVE PERSONALITY ON THE PSYCHOLOGICAL WELL-BEING OF CHILDREN IN AGAINST THE LAW

This study aims to determine the influence of big five personalities on psychological well-being in Law-Facing Children at LPKA. This study uses the psychological well-being scale and the big five inventory scale. The subjects in this study amounted to 35 ABH, the method used is quantitative with probability sampling techniques with a simple random sampling design. The hypothesis test used is a simple linear regression test. The results showed that there is an influence of the big five personalities on psychological well-being. This research is expected to be able to help the subject, the environment and the LPKA to understand and strive for the subject's psychological well-being.Keywords: psychological well-being, big five personality, anak berhadapan hukum (ABH), lembaga pembinaan khusus anak (LPKA)

Assessing of case-cohort design: a case study for breast cancer patients in Xinjiang, China.

To assess the effectiveness and clinical value of case-cohort design and determine prognostic factors of breast cancer patients in Xinjiang on the basis of case-cohort design. The survival data with different sample characteristics were simulated by using Cox proportional risk models. To evaluate the effectiveness for the case-cohort, entire cohort, and simple random sampling design by comparing the mean, coefficient of variation, etc., of covariate parameters. Furthermore, the prognostic factors of breast cancer patients in Xinjiang were determined based on case-cohort sampling designs. The models were comprehensively evaluated by likelihood ratio test, the area under the receiver operating characteristic curve (AUC), and Akaike Information Criterion (AIC). In a simulations study, the case-cohort design shows better stability and improves the estimation efficiency when the censored rate is high. In the breast cancer data, molecular subtypes, T-stage, N-stage, M-stage, types of surgery, and postoperative chemotherapy were identified as the prognostic factors of patients in Xinjiang. These models based on the different sampling designs both passed the likelihood ratio test (p<0.05). Moreover, the model constructed under the case-cohort design had better fitting effect (AIC=3,999.96) and better discrimination (AUC=0.807). Simulations study confirmed the effectiveness of case-cohort design and further determined the prognostic factors of breast cancer patients in Xinjiang based on this design, which presented the practicality of case-cohort design in actual data.

Weight calibration in the joint modelling of medical cost and mortality.

Joint modelling of longitudinal and time-to-event data is a method that recognizes the dependency between the two data types, and combines the two outcomes into a single model, which leads to more precise estimates. These models are applicable when individuals are followed over a period of time, generally to monitor the progression of a disease or a medical condition, and also when longitudinal covariates are available. Medical cost datasets are often also available in longitudinal scenarios, but these datasets usually arise from a complex sampling design rather than simple random sampling and such complex sampling design needs to be accounted for in the statistical analysis. Ignoring the sampling mechanism can lead to misleading conclusions. This article proposes a novel approach to the joint modelling of complex data by combining survey calibration with standard joint modelling. This is achieved by incorporating a new set of equations to calibrate the sampling weights for the survival model in a joint model setting. The proposed method is applied to data on anti-dementia medication costs and mortality in people with diagnosed dementia in New Zealand.

Knowledge, practices, and self-reported health outcomes related to chemical use and safety among beauty salons and parlor workers in Polokwane, Limpopo Province.

daily, workers in beauty salons and parlors use different cosmetic products made of various chemicals. This study aimed to determine the knowledge, practices, and self-reported health outcomes related to chemical use and safety among workers in beauty salons and parlors. a quantitative, cross-sectional descriptive study was conducted among 145 participants, randomly selected using a simple random sampling design. A self-administered questionnaire was used to assess the Knowledge, practices, and self-reported health outcomes related to chemical safety. Pearson´s correlation was used to assess the correlation between perception, awareness, and other study determinants. fifty-seven percent of participants had been working with chemicals for more than two years, and only 5% were smokers. There was a positive relationship between age and years of experience in the beauty and salon industry (r= 0.385; p < 0.001), while significant positive correlations between knowledge score with educational levels (r=0.444; p= <0.001) and formal training as a salon and beauty parlor worker (r=0.504; p= <0.001) were also found. Participants also reported symptoms such as headache (43%), nausea (23%), skin irritation (48%), eye irritation (39%), and respiratory illness-related symptoms (62%). there is a need for health and safety training in beauty salons and parlors, with emphasis on the correct use of personal protective equipment (PPE). The findings of this study may serve as the baseline for the development of safety policies for all beauty salons and parlors in South Africa.

Estimation methods based on ranked set sampling for the arctan uniform distribution with application

&lt;abstract&gt;&lt;p&gt;The arctan uniform distribution (AUD) is a brand-new bounded distribution that may be used for modeling a variety of existing bounded real-world datasets. Ranked set sampling (RSS) is a useful technique for parameter estimation when accurate measurement of the observation is challenging and/or expensive. In the current study, the parameter estimator of the AUD is addressed based on RSS and simple random sampling (SRS) techniques. Some of the popular conventional estimating techniques are considered. The efficiency of the produced estimates is compared using a Monte Carlo simulation. It appears that the maximum product spacing method has an advantage in assessing the quality of proposed estimates based on the outcomes of our simulations for both the SRS and RSS datasets. In comparison to estimates produced from the SRS datasets, it can be seen that those from the RSS datasets are more reliable. This implies that RSS is a more effective sampling technique in terms of generating estimates with a smaller mean squared error. The benefit of the RSS design over the SRS design is further supported by real data results.&lt;/p&gt;&lt;/abstract&gt;

A Critical Discourse Analysis on the Variations of Language Used by Tomboy Characters in Pakistani Dramas

The current study examined the language variations of tomboys' characters in Pakistani dramas to identify the representation of tomboys and the factors that affect their characters by discussing linguistic methods, behaviors of society, stereotypical thinking of society and the role of patriarchy in it. The theoretical underpinning for the current investigation was based on Fairclough's (1989) three-dimensional model for Critical Discourse Analysis. The study employed qualitative research method to investigate the research questions. The data was collected by using a simple random sampling design to collect episodes from two different Pakistani dramas. The tomboys’ characters’ chosen dialogues and the language elements used were divided into three categories based on subjects in the selected episodes. The data was analysed by using the CDA model. leading to conclusions on the role of patriarchy and societal norms in shaping the representation of tomboys in Pakistani dramas. The results of the study showed the negative attitudes of society toward tomboys’ characters. They were often excluded from social groups and activities; discriminated and viewed as less feminine competent. It was concluded that tomboys’ characters could serve as role models for girls who might not conform to traditional gender expectations.

Practical Considerations for Sandwich Variance Estimation in 2-Stage Regression Settings.

In this paper, we present a practical approach for computing the sandwich variance estimator in 2-stage regression model settings. As a motivating example for 2-stage regression, we consider regression calibration, a popular approach for addressing covariate measurement error. The sandwich variance approach has rarely been applied in regression calibration, despite its requiring less computation time than popular resampling approaches for variance estimation, specifically the bootstrap. This is probably because it requires specialized statistical coding. Here we first outline the steps needed to compute the sandwich variance estimator. We then develop a convenient method of computation in R for sandwich variance estimation, which leverages standard regression model outputs and existing R functions and can be applied in the case of a simple random sample or complex survey design. We use a simulation study to compare the sandwich estimator to a resampling variance approach for both settings. Finally, we further compare these 2 variance estimation approaches in data examples from the Women's Health Initiative (1993-2005) and the Hispanic Community Health Study/Study of Latinos (2008-2011). In our simulations, the sandwich variance estimator typically had good numerical performance, but simple Wald bootstrap confidence intervals were unstable or overcovered in certain settings, particularly when there was high correlation between covariates or large measurement error.

New robust class of estimators for population mean under different sampling designs

New robust class of estimators that auxiliary variable information such as coefficient of variation, kurtosis and quarters are used in are suggested by considering robust methods of the population mean under different sampling designs. The estimators that using LTS, Huber MM, LMS, Tukey-M, LAD and Hampel M robust methods are investigated under simple random sampling (SRS), ranked set sampling (RSS) and median ranked set sampling (MRSS) designs. A real data example and detail simulation study are applied to see efficiency of proposed robust class of estimators in SRS, RSS and MRSS designs. The efficiency of suggested estimators are compared with regard to mean squared error (MSE) and percent relative efficiency (PRE). The numerical study is used to observe performances of the estimators concerning body mass index (BMI) dataset. In the simulation study we considered seven continuous probability distributions, five sample sizes with different sampling designs. In general, our results show that the proposed robust class of estimators performs better under MRSS.

Estimating the prevalence of osteoporosis using ranked-based methodologies and Manitoba's population-based BMD registry

Osteoporosis is a metabolic bone disorder that is characterized by reduced bone mineral density (BMD) and deterioration of bone microarchitecture. Osteoporosis is highly prevalent among women over 50, leading to skeletal fragility and risk of fracture. Early diagnosis and treatment of those at high risk for fracture is very important in order to avoid morbidity, mortality and economic burden from preventable fractures. The province of Manitoba established a BMD testing program in 1997. The Manitoba BMD registry is now the largest population-based BMD registry in the world, and has detailed information on fracture outcomes and other covariates for over 160,000 BMD assessments. In this paper, we develop a number of methodologies based on ranked-set type sampling designs to estimate the prevalence of osteoporosis among women of age 50 and older in the province of Manitoba. We use a parametric approach based on finite mixture models, as well as the usual approaches using simple random and stratified sampling designs. Results are obtained under perfect and imperfect ranking scenarios while the sampling and ranking costs are incorporated into the study. We observe that rank-based methodologies can be used as cost-efficient methods to monitor the prevalence of osteoporosis.

Predictive performance of machine learning model with varying sampling designs, sample sizes, and spatial extents

Using machine learning and earth observation data to capture real-world variability in spatial predictive mapping depends on sample size, design, and spatial extent. Nonetheless, there is still ambiguity in answering some basic questions: a) How many samples are necessary for fitting the model? b) Which sampling techniques are suitable for modeling? c) Do results vary with changes in spatial extents? These questions are crucial for spatial modeling projects and require proper investigation. In the present study, we evaluated two sampling designs with different sample sizes, considering three nested spatial extents. Specifically, we adopted the conditioned Latin Hypercube Sampling and Simple Random Sampling designs. Based on this, a Random Forest model was used to predict Above-Ground forest Biomass at local, regional, and national spatial extents, comparing different sample sizes (n = 25, 50, 100, 200, 300, and 500). We defined one national extent, five regional extents within the national extent, and a local extent inside each regional extent. Each sampling design and size combination was tested 100 iterations. The results showed that there was no significant difference between the different sampling designs. The accuracy metrics showed marginal differences for 25 and 50 sample sizes, which were then reduced to minimal and provided similar results. However, a deeper analysis of all 100 repetitions exposed a noteworthy pattern: cLHS outperformed the SRS in terms of RMSE and variability. Regarding the sampling size, the R2 values increased with increasing sample size. Nevertheless, beyond a minimum of 300 to 500 samples, the improvement in accuracy became insignificant, emphasizing the diminishing returns with excessively large sample sizes. Moreover, increasing the size of the spatial extent reduced the accuracy of the model, possibly due to the effect of environmental factors or landscape nature. Therefore, this study demonstrates the potential impact of sample size, sampling design, and spatial extents on model accuracy and emphasizes the importance of reducing the sample size to reduce the model's complexity.

Students’ Mastery of English Grammar towards Effective Writing and Speaking Competence

English language proficiency is one of the most pressing issues, particularly in the field of education. Speaking and writing are two of the most important macroskills to master, especially in today's increasingly connected world. The current study seeks to ascertain students' comprehension and mastery of English grammar and explores whether grammar explains the students' proficiency in speaking and writing in Grade 10 Junior High School. The respondents were chosen for the study using a simple random sampling design. Additionally, the researcher's questionnaire, with three qualified validators tested for validity and a pilot tested for reliability, produced a satisfactory Cronbach's alpha result and served as the basis for gathering the study's data. Findings show that students' competence in English grammar, writing, and speaking is satisfactory. The outcome demonstrates that mastery of English grammar accounts for developing an acceptable level of speaking and writing abilities. The study concluded that students' command of English grammar explains their writing and speaking abilities.&#x0D;

Different estimation methods for the generalized unit half-logistic geometric distribution: Using ranked set sampling

The generalized unit half-logistic geometric distribution (GUHLGD) is a modern two-parameter unit distribution with attractive shape flexibility for the corresponding probability density and hazard rate functions. Due to its versatility, it may be used to model a variety of current bounded real-world datasets. On the other hand, an effective sampling strategy for both parametric and non-parametric inferences is the ranked set sampling (RSS) method. This article focuses on estimating the parameters of the GUHLGD based on the RSS method as well as the simple random sampling (SRS) method. Eleven traditional estimation methods are taken into consideration, including the percentile, Cramér–von Mises, maximum likelihood, Anderson–Darling, right-tailed Anderson–Darling, left-tailed Anderson–Darling, least squares, weighted least squares, minimum spacing absolute-log distance, maximum product of spacing, and minimum spacing absolute distance methods. A Monte Carlo simulation is employed to compare the performance of the resultant estimates based on some accuracy measures. We draw the conclusion that, for both sampling procedures, the maximum likelihood estimation methodology is the best option among the rest based on the partial and total ranking measures. The estimates based on the RSS method are more efficient than the others based on the SRS method. Results from actual data further support the advantage of the RSS design over the SRS design.

On Past Extropy and Negative Cumulative Extropy Properties of Ranked Set Sampling and Maximum Ranked Set Sampling with Unequal Samples

Ranked set sampling is considered as an alternative to simple random sampling and maximum ranked set sampling is a very useful modification of ranked set sampling. In this paper we focused on information content of ranked set sampling and maximum ranked set sampling with unequal samples in terms of past extropy measure and also considered the information content of negative cumulative extropy and its dynamic version based on maximum ranked set sampling and simple random sampling designs. We also compare ranked set sampling data, maximum ranked set sampling data with simple random sampling and with each other. Also here we obtained a new discrimination information measure among simple random sampling data, ranked set sampling data and maximum ranked set sampling data for past extropy measure.

Analysis of ℝ=P[Y&lt;X&lt;Z] Using Ranked Set Sampling for a Generalized Inverse Exponential Model

In many real-world situations, systems frequently fail due to demanding operating conditions. In particular, when systems reach their lowest, highest, or both extremes operating conditions, they usually fail to accomplish their intended functions. This study considers estimating the stress–strength reliability, for a component with a strength (X) that is independent of the opposing lower bound stress (Y) and upper bound stress (Z). We assumed that the strength and stress random variables followed a generalized inverse exponential distribution with different shape parameters. Under ranked set sampling (RSS) and simple random sampling (SRS) designs, we obtained four reliability estimators using the maximum likelihood method. The first and second reliability estimators were deduced when the sample data of the strength and stress distributions used the sample design (RSS/SRS). The third reliability estimator was determined when the sample data for Y and Z were received from the RSS and the sample data for X were taken from the SRS. The fourth reliability estimator was derived when the sample data of Y and Z were selected from the SRS, while the sample data of X were taken from the RSS. The accuracy of the suggested estimators was compared using a comprehensive computer simulation. Lastly, three real data sets were used to determine the reliability.

Entrepreneurial Innovation Strategies and Performance of Dairy Micro and Small Enterprises in Nyeri County, Kenya

Micro and small enterprises (MSEs) have failed to achieve set objectives of generating sustainable growth and employment. The growth and survival rate of micro and small-sized enterprises (MSEs) in Kenya has been one of the major concerns of the policy makers, practitioners and scholars. The high failure rate among small and medium enterprises businesses in Kenya has created an urgent need to identify strategies that will improve their levels of performance. The strategy and entrepreneurship literature suggests that an entrepreneurial competitive strategy improves firm performance, but empirical results are mixed. This study investigated how entrepreneurial competitive strategies: innovativeness, risk-taking and competitive aggressiveness), affect performance of MSEs in Kenya. In doing so, the study examined the moderating effect of external environment on these relationships. The objectives of the study was to determine: the influence of innovativeness, risk taking propensity, competitive aggressiveness, moderating influence of external environment on performance of micro and small enterprises of Dairy MSEs in Nyeri County. The study used a descriptive survey design. The study was anchored on Resource Based Theory, Diffusion Innovation Theory and Entrepreneurship Theories. The target population will be all the managers/Owners in the 146 Dairy MSEs in Nyeri, County Kenya. The study adopted two sampling designs namely stratified and simple random sampling design. The sample size will comprise of 146 respondents. The study used questionnaires to collect primary data. Pilot test was done to three respondents in three Dairy MSEs to refine the research instruments. Content validity was determined by experts. The reliability of the instrument was tested using Cronbach's Alpha coefficient. A Cronbach's coefficient alpha of .80 or higher was adequate in this study to allow the instrument for data collection. Data analysis was carried out using descriptive statistics. To establish the overall relationship between the independent and dependent variables in the conceptual framework. The study identified and recommended the factors that influence entrepreneurial innovation strategies on performance of dairy micro and small enterprises in Nyeri County, Kenya. These were replicated in areas with similar agro ecological zones facing similar challenges. The study found that entrepreneurship product innovation, process innovation and market innovation had a positive and significant influence on the performance of dairy micro and small enterprises in Nyeri County, Kenya. The study concluded that product innovations helps in developing improved versions of existing products in a market, enhancing the functionality or desire for an item by taking consumer feedback to make improvements or discovering additional features and technology to add. Innovative processes typically describe changes to how a company manufactures a product or performs a transaction, so it can yield better results and profits. The concept of marketing innovation identifies a framework for strategies to make important changes in a product/service, function, price, etc. to achieve higher business objectives such as attracting customers, enhancing image and gaining competitive advantages. The study recommended that the organization should determine its objectives first by being clear with their product goals. Understand its market at all times including its customers and competitors so as to understand what else is going on within its market segment. The organization should adopt new technology to increase efficiency and effectiveness such as automation tools that automates the manual tasks in a given process. The organizations can go for rebranding to match their marketing content to a new product or process the company is selling or using. Retain existing customers to help when selling innovative products.