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Discussion of the Need to Practice Agility and Embrace a New Method

Discussion of the Need to Practice Agility and Embrace a New Method

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Discussion of the Need to Practice Agility and Embrace a New Method

            While working at the data security firm, I have learned about the most effective method of safeguarding against data breach is the use of hybrid solution. Initially, our security firm had implemented asymmetric encryption to curb data breach. However, the former technique has not been effective since the firm lost significant information through either unauthorized access or theft. Therefore, I have learned that a hybrid data encryption and decryption method would improve the development of algorithm since it combines both the asymmetric and standard cryptographic. The newer data security approach reinforces the cipher weaknesses of each encryption technique. Consequently, I have the responsibility of sharing this newly learned data security system with my colleagues at the workplace.

            Although my colleague at work is unwilling to learn and apply this effective data security technique, I would advise her to embrace organizational agility in several ways. Firstly, the ability and readiness to learn from past data breach experiences need to benefit our IT security firm (Harraf, Wanasika, Tate, & Talbott, 2015). Secondly, I would persuade her that improving one’s self-awareness is an integral part of thinking with agility, thus attempting to challenge formerly learned knowledge of acting, and further unlearn from it. For example, I would challenge her to embrace this new hybrid data security by creating a stable autonomy and role clarity, which would compel her to maintain a collaborative learning culture (Harraf et al., 2015). I believe that if she used strategic agility, we would successfully change our team’s priorities. Therefore, we would help empower each other to solve this data breach security that we have recently witnessed if we get the necessary support from the management team.

References

Harraf, A., Wanasika, I., Tate, K., & Talbott, K. (2015). Organizational agility. Journal of Applied Business Research, 31(2), 675-686. doi:10.19030/jabr.v31i2.9160

Formulation of Strategy and Strategic Management

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Formulation of Strategy and Strategic Management

Why Strategic Management Becomes Important to Today’s Corporations

Strategic management is of paramount importance in today’s corporations. It assists in providing the company with the right direction and required leadership to define the strategies that align with the mission, vision, and objectives (Wheelen et al., slide 4). Strategic management offers employees a sense of unification with the business organization, causing them to express their total loyalty for the attainment of commonly defined goals. Likewise, strategic management is better placed to invoke organizational workers to show their best in respect to work performance and productivity (Wheelen et al., slide 17). Therefore, the organizational leadership needs to plan for change based on the consideration of strategic management as a continuously changing process.

Necessary Information for Proper Formulation of Strategy

Before attempting to proceed with formulation of strategy, its precise definition o is critical. All the information should be customized in respect to an organization’s mission and objectives while considering the immediate environment (Wheelen et al., slide 19). Additionally, a proper definition of the strategic objectives would guide the corporate leadership towards a particular adaption action plan, outlining a roadmap and assigning roles. Indeed, a strategic plan would demonstrate the need for the organization to measure its effectiveness and progress over a formulated competitive strategy (Wheelen et al., slide 26). Apart from that aspect, information on formulation of strategy is a necessity because it acts as a force that drives an organization to examine its prospect of adaptation in the predictable future (Wheelen et al., slide 20). Consequently, the same information would help prepare corporate for change through allocation of appropriate capital budgeting, instead of waiting in actively for the outcompeting market forces.

Work Cited

Wheelen, Thomas, L., et al. Strategic Management and Business Policy: Globalization, Innovation, and Sustainability. 15th ed., Pearson Education, Inc., 2018.

Methods of Data Analysis

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Methods of Data Analysis

Introduction

Data analysis is a critical component in understanding and extracting the actual meaning from business insights in the modern business setting. The reason is that data analysis provides the basis for business success. However, large volumes of data are created daily, with just less than 1% being analyzed and adopted to improve the business’s value. Regardless, this still provides essential information for achieving desired goals of any organization. Thus, knowing how to collect, analyze and interpret data remains a minefield.

            In light of the above, a researcher from Mini Project Part 2 Company seeks support in data analysis. The company has a new dataset of 1000 California properties that contain the same variables as their first dataset. Now the researcher is interested in knowing which characteristics of California properties combined that best explain the variation observed in the median housing value of homes in California. They would like to help develop multiple linear models, including the predictors that best explain median housing value and can appropriately predict the median housing value of a new neighborhood. They will also share this model with real estate agents, so it should be simple to understand it effortlessly. Thus, the proposed model should be complex enough for good predictions and description of the population (with all the suitable properties) but simple enough that it is easily understood. Hence, it is essential to ensure that the analysis stepsare clear and justifiable such that there are no questions about why you chose the model that you present compared to any other possibility.

Methods

The data for the study was collected from the Quercus project page. A population of 20,433 California homes was selected before sampling the data to obtain a sample size of 1,000 California homes for the study.

Variable Selection

The study utilized 14 variables to help come up with the appropriate model. The variables were randomly selected from the company data and processed through four stages to build a reliable model. In the first case, all the 14 variables were subjected to regression analysis. The model was then tested for reliability. Scatterplots were used to assess the residual data. Afterwards, several transformations were made to remove data that exhibited excessive multicollinearity. Finally, r-standard plots were performed to examine disparity of the data from the mean to help assess the quality of the final model. The following are the variables utilized in the model;

Longitude – which represents the longitude where the home/region is located

Latitude – that represents the latitude where the home or region is located

Housing medium age – that represents the median age of houses in the area of this

Total rooms – the variable represents the total number of rooms in the homes in this area

Total bedrooms – the variable represents the total number of bedrooms in the homes in this area Population – this variable represents the population of the area where this home is located

Households – the variable represents the number of households in the area this home is located

Median income – this represents the median income of households in the area (in ten-thousand dollars) Median house value – the median house value in the area where this home is located

Near Bay – the variable represents the indicator of whether the home or region is located near a bay

Near ocean – it represents the indicator of whether the home/region is located near the ocean

One ocean represents whether the home/region is located within a one-hour drive of the ocean.

Inland – it represents the indicator of whether the home/region is located inland. Further, the X variable was employed to act as an identifier for each observation made on data.

Accordingly, the following model was used in setting up the assessment;

(i)

Model Validation

There are different approaches towards achieving model validation. The study utilized the split data method to implement data validation by utilizing the functions in R-Studio Statistics software. In this approach, the housing.csv data was split into two parts: training data and validation. The predicted probability (score) for sample validation was then performed using the considered model. The score file was ranked in a descending order using the estimated probability. The ranked file was split into deciles and observations in each decile ascertained and the cumulative events assessed for each decile. The cumulative events’ gain score was determined, which was divided by the percentage score of data for each decile determined. Lastly, KS Statistics was performed to measure the degree of separation between the negative and positive distribution.

Model Violations/Diagnosis

The model was tested to assess the existence of multicollinearity in the model. Multicollinearity exists because of the inter-dependence in independent variables. Thus, this situation renders the model invalid when the degree of correlation is high.

Results

In this section, the results of the analysis are presented. The aim is to illustrate the information obtained from analyzing the company data. The section also describes the data, processes involved in obtaining the results, and assessing the quality of the model.

Description of Data

Regression Analysis – based on the regression model under (i), the analysis produced the following regression output.

Figure 1: Results of Regression Analysis

Figure 1 shows a summary of regression output as obtain from the R-Studio analysis report. As such, the following is the regression model;

(ii)

The minimum value was -255,804, and the maximum value of 334,858. The quantile ranges are -40354 to 30449.

Distribution of Data

Distribution assessment sought to ascertain how the data is distributed. The scatterplot was utilized in achieving this, as displayed in figure 2 below.

Figure 2: Scatter Plot 1

Figure 2 shows the distribution of the housing medium value. Figure 3 below is a normal probability plot. This was used to assess the distribution of sample data against the normal line. The quantile to quantile plot assesses how data is distributed from the expected normal distribution line.

Figure 3: Normal Q-Q Plot

Figures 5 and 6 show the r-standard plots for the data. The r-standard plots were utilized to assess the standard dispersion of data from the mean. Figure 5 shows the r-standard distribution against deciles, and figure 6 shows the r-standard distribution against the quantiles.

Figure 4: Housing Data Distribution

Figure 5: R-Standard Plot

The goodness of Final Model

The goodness of the final model was assessed by examining the goodness of fit. This is illustrated in figure 6 below. The best model is determined by how best it cuts through the majority of data.

Figure 6: improved version Scatter plot 2

Discussion and Conclusion

The section presents a summary of the findings and interpretation of the analysis results as demonstrated under the results section. The purpose is to find meaning and assess the relevance of the information towards addressing the current problem faced by the company. It also discusses the flaws in the data.

Interpretation and Importance

The final regression analysis revealed the following regression model;

(ii)

            The model implies that the housing value decreases by 3.367 units when other factors are not part of the environment. Equally, latitude and longitude have negative implications on the housing value as any change in these factors results in a significant decrease in the value by 3.845 and 3.442 units, respectively. The exact consequences are realized with total rooms, change in population size, and households which significantly decreases house value by 3.507, 2.845, and 1.611. However, a unit change in total bedrooms, median housing age, and median income increases housing value by 1.296, 1.012, and 3.713 units. Equally, houses near the Bay, ocean, and ocean have a higher value than those away from this location. Further, the significance of each coefficient for the independent variables at p-value <0.05 reveals that only latitude, longitude, median housing age, total bedrooms, population, median income, and nearby ocean are the only statistically significant factors. The rest are insignificant at a 5% significance level. Thus, it implies that these are the only factors influencing the company’s housing value changes.

            Regarding the quality of data, most of the housing value data were positively skewed, as revealed under Figure 2. Examining the quantile to quantile plot (figure 3) revealed that the data is away from the expected normal distribution although positive skewed. Equally, the r-standard distribution plot indicates that most of the data were dispersed from the mean.

Thus, the most appropriate model for consideration is the following;

The model is the best fit as it cuts through most of the data points, as illustrated in figure 6.  Therefore, this has implications that there is a possibility of fluctuation in value with changes in data distribution.

Limitation of Analysis

In summary, the analysis was not without challenges. Most of the data were sourced from open sources without validation. Further, examination of the study indicates that other factors should be considered in ascertaining appropriate predictors for the housing value. However, the study was limited on the current factors for lack of time to incorporate others. Thus, future studies should focus on incorporating a wide range of other factors.

Appendices

R-Studio Programming Code

Data Preparation

Data = read.csv (“housing.csv”)

set.seed(123)

rows = sample(1:nrow(Data), 1000, replace = F)

newdata = Data[rows,]

housing = newdata[sample(1:nrow(newdata),500, replace = F), ]

testdata = newdata[which(!(newdata$X %in% housing)), ]

Regression Operations

full = lm(median_house_value ~ longitude + latitude + housing_median_age + total_rooms + total_bedrooms + population + households + median_income+ near_bay + near_ocean + oneh_ocean + inland, data = housing)

summary(full)

#realize shows N/A for “inland” row, so we get rid off inland

full2 = lm(median_house_value ~ longitude + latitude + housing_median_age + total_rooms + total_bedrooms + population + households + median_income+ near_bay + near_ocean + oneh_ocean, data = housing)

summary(full2)

#now we have got rid off the predictors shows correlation as 1

#check conditions to see if residual plots can tell us what is wrong with the model

pairs(housing[, c(2,3,4,5,6,7,8,9)])

plot(housing$median_house_value~fitted(full2))

abline(a=0,b=1)

# first column, second column shows non-linear relationship, so get rid off longtitude and latitude

#full_3 with no longitude or latitude 

full3 = lm(median_house_value ~ housing_median_age + total_rooms + total_bedrooms + population + households + median_income+ near_bay + near_ocean + oneh_ocean, data = housing)

pairs(housing[, c(4,5,6,7,8,9)])

plot(housing$median_house_value~fitted(full3))

abline(a=0,b=1)

#Residual Plot

plot(rstandard(full3) ~ fitted(full3))

plot(rstandard(full3) ~ housing[, 4])

plot(rstandard(full3) ~ housing[, 5])

plot(rstandard(full3) ~ housing[, 6])

qqnorm(rstandard(full3))

qqline(rstandard(full3))

#Probably remove

w = which (housing$median_house_value >= 500001)

# lots of 500001, strange.

nd = housing[-w, ]

full4 = lm(median_house_value ~ housing_median_age + total_rooms + total_bedrooms + population + households + median_income+ near_bay + near_ocean + oneh_ocean, data = nd)

pairs(housing[, c(4,5,6,7,8,9)])

plot(housing$median_house_value~fitted(full3))

abline(a=0,b=1)

#residual plot

plot(rstandard(full4) ~ fitted(full4))

plot(rstandard(full4) ~ nd[, 4])

plot(rstandard(full4) ~ nd[, 5])

plot(rstandard(full4) ~ nd[, 6])

plot(rstandard(full4) ~ nd[, 7])

plot(rstandard(full4) ~ nd[, 8])

plot(rstandard(full4) ~ nd[, 9])

#looks abit better

#Transformation

#multicolinear

library(car)

vif(full4)

#total_rooms, total_bedrooms, population, households

#try to remove see what happens

full5 = lm(median_house_value ~ housing_median_age + median_income+ near_bay + near_ocean + oneh_ocean, data = nd)

pairs(nd[,c(4,9)])

plot(nd$median_house_value~fitted(full5))

abline(a=0,b=1)  

plot(rstandard(full5)~fitted(full5))

plot(rstandard(full5)~nd[,4])

#model selection

#see word doc

#model violation

#see word doc

Web-Based Application and Infrastructure for Apache

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Web-Based Application and Infrastructure for Apache

Whereas the landscape of web servers is composed of many divergent technologies, Apache is debatably considered one of the best web-server. Its development is open-source Apache HTTP software, which retrieves source codes freely through viewing and collaboration. Unlike other web servers like NGINX, Apache HTTP is regarded the most popular at 50% since it can handle large quantities of data traffics with minimal configurations. Therefore, Apache can be deployed with ease on Linux, macOS, and Windows.

Overall Product Architecture and Key Components of Web-Based Application

Product architecture entails the organization or the chunking of Apache’s functional elements. In particular, it might encompass the interactions of all chunks or elements. Certainly, it takes an integral role in scheming, selling, as well asrevampingthe offer of a new product.Therefore, the web-based infrastructure means the involved strategies of mapping the function into the product’s form. 

Figure 1. Picture of web-based product architecture

Generally, there are two basic types of product architecture, including modular as well as integral. Concerning the modular type of product architecture, there is a well-described component whose purpose is to interface functionally within self-contained modules. The product is organized into several modules in a bid to create and develop a particular goal. Usually, the interaction of all encompassed modules brings about the overall purpose of a product, leading to many benefits like outsourcing and allocating tasks. In short, the modular type of product architecture brings many other advantages, ranging from economies of scale, standardization to mass customization.

However, regarding the integral product architecture, functions of the product are ideally distributed among the physical elements. Therefore, there is a greater anticipated complexity of mapping in the association of components coupled with functions. Thus, it is easier to optimize the whole product architectural type since each component is adapted for a specific function.

            A web-based application (Apache) has four critical components associated with streaming architecture as follows.

  1. The message broker or stream processor – this particular component derives data from a producer source, converts it into a standard message format, and additional streams it continuously while other components eavesdrop and consume the passed-on messages. The latest hyper-performing message brokers include Apache Kafka, as illustrated by figure 2. Therefore, streaming brokers support extremely high performance based on persistence coupled with high capacity Gigabytes per second.

Figure 2. The message broker or stream processor

  1. Batch together with Real-time ETL features– this component is relevant forcombining, changing, and configuring numerous messages before the features of SQL-oriented analytics tools could structure them. The ETL tools are useful for receiving queries from varied users, thus creating the predictableoutcomes. Undeniably, this is done by queuing together with applying the queries, as shown in figure 3.Therefore, Apache Storm is an example of this kind of component streaming architecture.

Figure 2. Batch and real-time ETL tools

  1. Data analytics or Serverless query engine – this component is responsible for analyzing the streamed and consumed data to provide value regarding the analytics tool. For example, Kafka Connect is useful in streaming topics directly into Elastic search, creating the correct data types automatically through mappings.
  2. Streaming data storage – the advent of low-cost storage technologies has occasioned many organizations to commence the process of storing their streaming event data in Kafka, as demonstrated by figure 4 in the data lake storage.

Figure 4. Modern streaming architecture

Outlining the Types of Problems Apache Solves for the Enterprise

  1. The implementation of modular architecture in Apache offers it the capacity to resolve the problem of shifting the dominant requirements and environments of a business. When undertaking a technological transformation in a business, a flexible and scalable architecture will give room for impacting new workflows.
  2. The use of Apache brings about a better user experience associated with shifting the application to the mobile platform. Therefore, the shift to a mobile-based platform is easily aided by web-based architecture, leading to increased productivity coupled with a timely decision-making process.
  3. Apache gives room for the input of solid defense systems, which are associated with solving the challenging security issues on the web. For example, there are embedded application vulnerability tests whose capabilities go beyond the act of recognizing loopholes in the system.
  4. With the emergence of big data, it has become essential for an enterprise application to utilize curating, organizing, and centralizing data projects in Hadoop platforms.
  5. The changing technology has necessitated the introduction of Artificial Intelligence through the usability of highly adapted enterprise applications and Software as a Service (SaaS). Indeed, Apache functions in line with the system requirements of the Internet of Things (IoT) and micro services.
  6. The implementation of web-based applications has resolved interoperability standards by offering a platform where various applications can be lined smartly. For example, it is possible to connect the functions of both Leave Management and Payroll Systems.

How the Apache is Deployed and Set up for the Client and Server-Side

Apache is deployed and set up by communicating over networks from client to server-side through TCP/IP protocol. The frequently used protocol is HyperText Transfer Protocol-Secure (HTTP/S) to define how messages are formatted and further conveyed across the web. In particular, the protocol defines specific commands for both the client and server regarding how best to respond to requests. Therefore, HyperText Protocol Secure usually occurs across port 443 while the unsecured port happens across port 80. Besides, the Apache server set-up is aided by the configuration files, where all the applied modules help control the server’s behavior. Indeed, Apache listens to the configured IP addresses inside the requested configuration files. Apache can successfullyconsent to precise route trafficswhile being linked to particular address ports.Since the Apache Listen directive runs automatically on port 80, it can be changed to run on different ports per the hosting of many domains. Apache returns acknowledgment notices (ACK) to the original sender when their messages in the form of data have successfully reached destinations. In case of an encountered error, while receiving data, the protocol returns Not Acknowledged notices (NAK), thus confirming the need for retransmission.

Defining Common Operating Processes, Procedures, and Trends for Managing Apache in a Large-Scale Enterprise Network

With the introduction of Apache Hadoop as an open-source software platform, it has now become easy to manage the storage and processing of large-scale enterprise networks. Therefore, Apache Hadoop comprises the following operating processes as its functional modules.

  1. Hadoop typical – It is a library of utilities relevant for the operating functions of a Hadoop.
  2. Hadoop Distributed File System (HDFS) – the distributed file system stores data by providing a high aggregate bandwidth over a network cluster
  3. Hadoop YARN – It is a resource-management platform associated with the computation of clusters. It helps schedule diverse applications from the users.
  4. Hadoop MapReduce – It is an operational process for programming large-scale enterprise networks.

Fundamentally, there are two primary components of Apache Hadoop: HDFS and MapReduce parallel processing frameworks. All these open source projects are developed under the inspiration of technologies created inside Google.

Figure 5. The high-level architecture of Apache web-application

            The implementation of the web-based application is a precursor for supporting future growth in the IT industry due to its increased demand, interoperability, and improved reliability necessities. With web-based application making use of object-oriented programming in its architecture, the definition of all the attached functions is as follow.

  1. The delivery of consistent data via HTTP on the client-side code
  2. Ensuring that requests contain the most valid data.
  3. Offering complete authentication to users.
  4. Limiting the access of users concerning view-based permissions.
  5. Creating, updating, and deleting records.

Apart from that, the web-based application has undergone tremendous evolution as much as technology does. The use and creation of service-oriented architecture in Apache is considered one of the emerging trends that make web-based applications a service platform. For example, the existence of each HTTP API has occasioned one facet of codes to procure requests from another part of the code even though they run on different servers. Additionally, a single-page application is regarded as an emerging trend in web-based application architecture. The web is seen as User Interface throughout the application of rich JavaScript. The single-page application stays in the browsers of the user while conducting various interactions. Consequently, the user experiences a more natural appeal coupled with a reduced number of page load interruptions.

Security Concerns for Apache and How IT Managers Ensure Secure Operation

  1. Injection – IT managers would need to control and vet each user input for possible attack arrays.
  2. Broken authentication – IT managers would execute multi-factor authentication, thus preventing automated and credentialing cases coupled with credential attacks.
  3. Sensitive exposure of data –IT managers would need to take stock, scale down, and further lock up sensitive data.
  4. Broken access control – IT managers would apply server-less API relevant for discouraging attackers from modifying the access control check.
  5. Security misconfiguration – IT managers would employ a comprehensive security configuration tool feature that constantly monitors, resolves, normalizes, and reports cases of misconfigurations.
  6. Cross-site scripting – IT managers would need to hide“prevent XSS”susceptibilities from showing in an application, and this can be done by escaping the user input.

Common Web-Based Application and Infrastructure Failures that Impact the IT Industry

DNS Issues and Network Connectivity. With instances of failed network connectivity and inefficient firewall, there would be faulty DNS queries. DNS problems coming from improper network protection cause errors 404s coupled withinappropriate pathways, which preclude visitors from reaching their destined websites. However, this problem is amicably resolved by employing DNS observingsafeguards.

Slow Servers and Loading Time. Hosting the websites under a slow server would negatively impact the general process of analyzing tools in Apache. Therefore, this issue can be resolved by co-hosting the webs under shared accounts, which are relatively fast.

Poorly Written Code. The common problem associated with the performance of a web application is the experience of poorly written code. Such a problem of inefficient codes occasions memory leaks and inappropriate synchronization, thus leading to deadlock application. Besides, this issue could be resolved by applying optimal coding practices like the use of profilers and code reviews.

Lack of Load Balancing. Web-based applications might be trailed withsluggish response times, which originate entirely from poor data load spreading. Therefore, new visitors are erroneously allotted in the server, leading to drown out server capacity.Nonetheless, the implementation of tools like NeoLoad and AppPerfect would help pinpoint the weaknesses in the architecture infrastructure, leading to high scalability.

Traffic Spikes. The execution of a web-based application like Apache is faced with numerous spikes resulting from promotional videos. Such marketing videos take up extra traffic, thus causing the servers to slow down. As a result, the performance of the whole IT industry might be hindered, leading to the reduced popularity of Apache among its implementers. Nonetheless, the whole traffic spike can be resolved by setting an early warning system that feigns usercontrollike NeoSense. 

Duplication of Specific HTML Title Tags. The use of the web-based application is associated with duplication of specific HTML title tags, causing sites to lose traffic visibility. For example, many web developers assign similar HTML title tags, occasioning the search engines to consider such websites duplicates of the other activity. Besides, one may resolve the issue by cross-checking their title tags on Google Search Console for duplications or errors.

Failure of Optimizing Bandwidth Usage. Since most developers entirely depend on local networks while testing their websites, the process of adding videos, visual, audio, or other high-volume data activities might impact the performance of the IT industry negatively. Therefore, developers need to optimize the usage of the bandwidth to conduct a performance boost. Likewise, they need to impact a modification of JavaScript to compress and optimize server-side HTTP based on the desired resolution and size of the images.

Describing the Alternative System from Competitors based on Strengths and Weaknesses

Apache vs. NGINX. With Apache using thread-based structure, their heavy traffics are encountered that impact performance problem. However, NGINX addresses the c10k issue. Also, NGINX possesses an event-based architecture that does not create a new process for each request as much as Apache does. Instead, NGINX conducts each incoming request in a solo thread. As much as Apache is reliably secure, it does not manage high traffic more effectively than NGINX. Thus, Apache is more suitable for medium and small businesses since it is more easily configured than NGINX due to its numerous modular architecture and beginner-friendly capability.

Apache vs. Tomcat. Tomcat is explicitly meant for Java apps, and Apache is usually purposed for an HTTP server. Therefore, Apache is cross-platform, which can be subject to other programming languages like Perl, PHP, and Python. Tomcat is less efficient for serving static webs as compared to Apache. For instance, Tomcat usually pre-loads the unnecessary java Virtual Machine on websites. Consequently, Apache is more configurable than Tomcat because one might utilize a general-objective HTTP server when running WordPress.

Future Expected Projects as Web-Based Application Evolves

With the designation of Apache HTTP web-server to serve only static web pages, the recent introduction of Apache Tomcat has evolved the infrastructure and attached module components of this product architecture. As a result, Tomcat is adapted to serve java applications, even though there are instances of reduced efficiency. Therefore, software developers are responsible for developing highly adapted types of Apache web-server while focusing centrally on upgrading its efficiency during the configuration phase. In brief, developers need to design web-based applications with the required traffic to avoid cases of increasing scalability.

Works Cited

Jugo, Igor et al. “Analysis and Evaluation of Web Application Performance Enhancement Techniques.” 14th International Conference on Web Engineering at Toulouse, France, 2014, pp. 1-43. doi:10.1007/978-3-319-08245-5_3

Sarhan, Qusay, and Idrees S. Gawdan. “Web Applications and Web Services: A Comparative Study.” Science Journal of the University of Zakho, vol. 6, no. 1, 2018, pp. 1-66. doi:10.25271/2018.6.1.375

The Influence of Employee Status On Salary Prediction for IT Firms: Case of Grant Technologies, Inc.

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The Influence of Employee Status On Salary Prediction for IT Firms: Case of Grant Technologies, Inc.

Introduction/Problem Statement

Proper recruitment of employees is critical in advancing a business strategy. Currently, recruitment processes are drastically changing and becoming highly complex tasks that require intensive interviews and evaluation. Salary is an important aspect when it comes to employment(Frost, n.d). It determines whether a given firm can attract qualified employees. Thus, it influences the quality of employees that a firm would higher.

Among software engineering firms, challenges of predicting employee salary are prominent. A prediction refers to an assumption regarding the future based on existing knowledge and experience(Frost, n.d). It is essential in helping firms plan their future. Thus, this paper aims at predicting the salary of employees for software engineering firms based on different factors.  Some of these factors include test scores, age, years of experience, and gender. 

Methodology

Collection of Data

The regression analysis in this paper seeks to understand if years of experience, test score, age of the employee, and gender can predict employee salary among software engineering firms. In an attempt to implement the project, data was collected from a sample of 30 web developers from Grant Technologies, Inc. An aptitude test was utilized in collected the test scores for each web developer. Thus, this study seeks to conduct a multiple regression to assess the validity of the model and statistical significance of independent variables (test score, years of experience, age, gender) in influencing the dependent variable’s behavior (salary).  The study defines employees in terms of age, job experience, test scores, and gender.

Regression Model

The following regression model was used in achieving the objectives of the paper;

(i)

Therefore, the above regression model was used to assess how age, age, years of experience, and test score affect employee salary for software engineering firms. Gender was denoted by 1 (if male), 2 (if female) and 0 (undefined).

Results

The following shows the results of regression analysis displayed in the regression table;

Table 1: Regression Analysis

  Coefficients Standard Error t Stat P-value
Intercept 334.75 104.86 3.19 0.00
Gender 9.35 23.66 0.40 0.70
Age (years) 1.68 2.48 0.68 0.51
Test Score -0.45 0.96 -0.47 0.64
Years of Experience 12.37 4.48 2.76 0.01

As shown in Table 1, the intercept score is 334.75 with a p-value = 0.00. The gender, age (years), test-score, and years of experience coefficients scores 9.35, 1.68, -0.45, and 12.37. As such, this yields the following regression model;

(ii)

p-value = 0.01

The ANOVA F-test results are as displayed in the following table;

Table 2: ANOVA F-test Results

ANOVA          
  df SS MS F Significance F
Regression 4 205043.2269 51260.8067 8.838 0.000136667
Residual 25 144993.0305 5799.72122    
Total 29 350036.2574      

p-value = 0.01

Interpretation

Regression analysis model (ANOVA F-test)

The regression analysis model (ii) shows that if all factors are held constant, web developers at Grant Technologies, Inc. will earn $334.75. Employee’s age increases employee salary by 9.35, gender by 1.68, and years of experience by 12.37. However, test scores have a negative influence on employee salary as it reduces it by 0.45 units. As illustrated by the R-squared value, these independent variables can predict 58.6% of all the employee salary changes. Thus, the rest is predicted by variables outside the model. The F–test results show that the calculated value (F-test Calculated) is 0.00014 against a p-value of 0.01 or 10% significance level. Since F-Calculated < F-Critical (3.49) or (F-calculated <3.49 at alpha = 0.01), it implies that we fail to reject the null hypothesis. Thus, this implies that the model is statistically significant at 10%. The independent variables can predict 90% of the changes in employee salary.

Implications

The study indicates that age, experience, gender, and test scores have a significant influence on employee salary. Test scores negatively affect employee salary while the rest of the variables have a positive influence. Thus, employers must look at these factors when evaluating the salary of their employees.

Short Comings

Although the model scores a high R-squared value of 58.6%, it leaves out a significant score of 41.4%. Thus, the current independent variables cannot thoroughly explain all the changes in the model. There are other factors outside the model that should be considered that the study did not incorporate. These may include inflation rate and economic stability, among others.

Works Cited

Frost, J. “Regression Tutorial with Analysis Examples.” Statistics by Jim, 13 June 2019, statisticsbyjim.com/regression/regression-tutorial-analysis-examples/.

Appendix

Definition of variables

Age– refers to the age of the employee

Test score– refers to aptitude tests obtained from employee responses

Years of experience– refers to time employee has worked within the same profession

Gender – the particular gender of employee (Male =1, Female = 2, Undefined = 0)

Salary – the amount the firm pays to an employee

Table 1. Employee Data

NO Salary (US $) Gender Age (years) Test Score Years of Experience
1 300.7 1 25 76 4
2 330.1 1 18 67 2
3 480.0 1 30 80 4
4 500.0 1 40 10 5
5 500.0 2 43 80 10
6 300.0 1 41 76 10
7 454.7 2 23 76 9
8 469.8 2 28 76 8
9 485.0 1 28 73 9
10 500.1 1 29 70 9
11 515.2 1 29 80 10
12 530.4 0 36 100 12
13 545.5 1 38 100 8
14 560.7 1 38 76 14
15 575.8 1 38 73 14
16 591.0 0 56 85 17
17 606.1 2 56 80 19
18 621.2 1 45 82 16
19 636.4 2 45 81 18
20 651.5 2 49 83 17
21 666.7 2 48 82 19
22 700.0 2 41 86 15
23 450.0 2 38 90 15
24 350.0 1 38 93 12
25 400.0 1 18 93 1
26 332.2 1 21 79 1
27 480.0 1 33 84 3
28 415.1 2 33 88 4
29 419.3 2 33 82 4
30 423.6 2 36 82 6

Source: Grant Technologies, Inc. Database. www.granttechnologies.com

Final Regression and Significance Test

Summary Output

SUMMARY OUTPUT          
           
Regression Statistics          
Multiple R 0.765        
R Square 0.586        
Adjusted R Square 0.520        
Standard Error 76.156        
Observations 30        
           
ANOVA          
  df SS MS F Significance F
Regression 4 205043.2269 51260.8067 8.838495 0.000136667
Residual 25 144993.0305 5799.72122    
Total 29 350036.2574      
           
  Coefficients Standard Error t Stat P-value Lower 95%
Intercept 334.75 104.86 3.19 0.00 118.79
Gender 9.35 23.66 0.40 0.70 -39.37
Age (years) 1.68 2.48 0.68 0.51 -3.44
Test Score -0.45 0.96 -0.47 0.64 -2.43
Years of Experience 12.37 4.48 2.76 0.01 3.15

Residual Output

RESIDUAL OUTPUT    
     
Observation Predicted Y Residuals
1 401.187 -100.487
2 368.766 -38.666
3 407.771 72.229
4 468.573 31.427
5 513.161 -13.161
6 502.262 -202.262
7 469.028 -14.361
8 465.051 4.758
9 469.427 15.525
10 472.462 27.633
11 480.311 34.927
12 498.408 31.973
13 461.637 83.887
14 546.705 13.962
15 548.061 27.748
16 600.609 -9.657
17 646.309 -40.214
18 580.482 40.756
19 615.023 21.358
20 608.464 43.060
21 631.977 34.690
22 568.940 131.060
23 562.096 -112.096
24 514.280 -164.280
25 344.642 55.358
26 356.007 -23.762
27 398.629 81.371
28 418.540 -3.418
29 421.252 -1.906
30 451.028 -27.456

Residual Plots – Line Check

Applying Statistical Methods in Money-ball Search

Applying Statistical Methods in Money-ball Search

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Date

Applying Statistical Methods in Money-ball Search

Addressing How the Oakland A’s Used Statistical Methods

Following Lewis (2003), the inspiration behind Money-ball motivated Bill Beane to come up with his draft regarding baseball players’ position on specific statistical factors. His actions entailed imposing an on-base percentage (OBP)coupled with slugging percentage on Oakland Athletics (Lewis, 2003). Beane went ahead to combine these two statistical variants into a new tool recognized as on-base plus slugging (OPS) to impact a fruitful change. Apart from that, Beane’s new approach did not consider to feature power as an essential factor for determining a player’s ability to run and score. In his belief, unlike patience, power is developed over a given period along with player capacity to be at the desired base (Lewis, 2003).Therefore, he relied on baseball players from college due to their experience compared to high school ones. The high school players lacked the stable power that required to maneuver during the game, implying limited potential to win the game.

Beane adopted Bill James’ sabermetrics ideology as a measure of deciding on his team’s lineup. In this regard, he chose his line of action that would impact the best opportunities for winning. For instance, he fully understood that any guaranteed win was pegged on his team’s possible ability to score more runs compared to other competing teams (Lewis, 2003). As much as many coaches are accustomed to placing significant importance on battling averages, Beane considered it otherwise. Instead, he chose to focus centrally on the order of runs scored per single game.In this case, it is worth noting that in a given match, the task of the hitter is creating home runs, doubling, getting on the base and stealing the bases. Subsequently, the newly derived sabermetrics – OPS – helps measure a hitter’s success in respect to the number of runs one would create throughout the match (Lewis, 2003). As a way of assessing the number of created runs by the hitter, Beane adopted the application of the following formula in his tall task to success.

 (Hits + Walks) x Total Bases

At-bats + Walks

Precisely, the formula has a success rate of 90%, where it provides a total of the team’s tangible scored runs occurring within 5% (Lewis, 2003). Therefore, James’ sabermetrics on OBP needs to be meticulously examined because walks are the critical part of the fashioned runs formula. 

Discussing the Importance of Approaching Business Problems in Innovative Ways

Motivating creativity and exploring innovative ways of approaching a business has helped improve an organization’s productivity beyond recognized borders. According to Triady & Utami (2015), innovation is a critical component for an organization because it enhances its competitiveness. Adopting an innovation implies that an organization will save time, cost and other resources along its production line.  Most importantly, the reassurance of employees towards positive and out-thinking ostensibly offers them a chance to commit time and resources effectively. Equally, proper utilization of employees’ capability is the first option towards inculcating innovation in business. For example, employees are capacitated to explore new innovative meansabout several business solutions’ cost-effectiveness (Triady & Utami, 2015; Lewis, 2003).Therefore, creativity would advance the much-needed processof solving problems to stay ahead of the competition. In brief, creative problem-solving brings about a better competitive edge that any business might adopt torealize a striving success.

References

Lewis, M. (2003). Money-ball: The Art of Winning the Unfair Game. New York:

W.W. Norton and Company.

Triady, S. M., & Utami, F. A. (2015). Analysis of decision-making process in Money-ball: The art of winning an unfair game. The Winners, 16(1), 57. https://www.doi:10.21512/tw.v16i1.1555

logarithmic graph function

Student’s Name

Instructor’s Name

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Date

Week 13 Discussion

A logarithmic graph function is an equation of the form that reads y equals to the log of x, base “b” or y equals the logbase “b” ofx. In all these two equation forms, xand b are higher than zero while b is not equal to one (Makgakga and Sepang 78). On the other hand, an exponential graph relates to an exponent variable as 10 raised to power x. An exponential graph can be expressed approximately by an exponential function (Makgakga and Sepang 78). Likewise, it is well described by an exceptionally rapidly increasing exponential growth rate, either in size or extent.

The association between the graphs of exponential and logarithmic functions is illustrated in the following ways. The graphs of these two functions are not the same. Specifically, the logarithmic graph function is the inverse function of an exponential graph function. Therefore, it implies thata^x = b is an exponential graph function, whereas log base a (b) = xis a logarithmic graph function (Makgakga and Sepang 81). Their operations are inverses of one another in the sense that a person can regard the actual logarithm as the isolating variable of interest and vice versa when expressing an exponential function.

Moreover, the inverse of a logarithmic graph function is a function of an exponential graph equation. The inverse of an equation is derived by switching the x and y coordinates so that the graph reflects the line y=x(HELM 4).For instance, the actual exponential function is dictated by y = f(x) = ex while the actual logarithmic graph function is expressed as f(x) = loge x = lnx(HELM 4). In short, x is considered greater than zero. In summary, the graph function to the right-hand side of the logarithmic graph curve is a literal reflection of the exponential graph curve.

Works Cited

Helping Engineers Learn Mathematics (HELM). “Exponential and Logarithmic Functions.” Workbook 6, 2005,pp. 1-78.

Makgakga, Sello, and Percy Sepang. “Teaching and Learning the Mathematical Exponential and Logarithmic Functions: A Transformation Approach.” Mediterranean Journal of Social Sciences, vol. 4, no. 13, 2013, pp. 77-85.

How the Incorporation of Green Bonds Impacts Economic Sustainability Globally?

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How the Incorporation of Green Bonds Impacts Economic Sustainability Globally?

Introduction

As much as green finance centrally focuses on how a specific investment can be environmentally friendly, recent studies have stressed the importance of deriving greater economic sustainability from green bonds. In this line of thought, investing in green bonds would significantly impact sustainability due to the following reasons. Green finance not only delivers additional sustainability but also impacts the economy on a larger scale. Therefore, the most recent approach to green financing seeks to facilitate the transformation of the entire process of initiating sustainability by integrating various sustainable projects. The combination of several green bond projects would equally lessen the high costs attached to an unsustainable system’s side effects. With the creation of green bond markets, there has been assured mitigation of both finance and risk constraints. In this regard, the introduction of green financing through green bond markets has positively impacted sustainability by slowing down the growth of environmental degradation.

Green Bond Market

With the intensification of environmental degradation globally, it is essential to invest in green bonds because it seeks to improve sustainability (Alonso-Conde and Javier Rojo-Suárez 4). In particular, it is now of high economic advantage to invest in green financial products like bonds. Investments in green financing have increased across the globe, with many nations channeling their efforts towards sustainability. For example, the move towards economic sustainability has been earmarked by massive initiatives in green bond markets, as illustrated in figure 1 (Alonso-Conde and Javier Rojo-Suárez 4). The figure expounds on the trends in the primary green bond market, ranging from 2012 to 2019. The trend has significantly improved up to 2019, with many issuers of bonds diversifying their projects in green financing (See figure 1). Graph (a) displays the growth in both new issues and market size in $ billion; it signifies the recent diversification of financial products to receive green fixed incomes (Alonso-Conde and Javier Rojo-Suárez 4). On the other hand, graph (b) portrays the geographical distribution of green bond issuers worldwide, from Germany to the US.

Figure 1. Green bonds market and its universally geographical diversification (Alonso-Conde and Javier Rojo-Suárez 4)

Illustratively, the graph implies an increasing appetite for green bonds among the corporate and government issuers with a 60% growth in 2019 (Alonso-Conde and Javier Rojo-Suárez 4). More so, graph (b) indicates that the Europeans are leading in the move towards green financing, with an additional 32% of new issuers across the market. The US green bond market follows 20% for new issuers, and the Chinese market is recording a new number of issuers at 7.5% (Alonso-Conde and Javier Rojo-Suárez 4). All these indicators in the growth of an additional number of green bond issuers happen in 2019. From these illustrated figures, it can be opined that the green bond market has emerged as a well-established market among investors. The green bond market has continued to grow exponentially since 2019 because it receives immense support from the UN-SDG on climate awareness (Alonso-Conde and Javier Rojo-Suárez 4). Although the market is still young and promising with developing countries’ dominance, there has been a recent comparative embrace among other nations like Brazil, Mexico, and China (Alonso-Conde and Javier Rojo-Suárez 4). Therefore, the emergence of the green bonds markethas fortunately bolstered cross-regional trades by pinpointing various international trading opportunities a country might harness.

            Besides, this juvenile green bond market necessitates increasing regulation from the relevant governments. The market requires a comprehensive and broad set of government control since its growth is pegged on the degree of issuance, supervision, and liquidity (Alonso-Conde and Javier Rojo-Suárez 5).Considering this kind of bond market as a subject of energy transition when allocating huge finances, many European nations have increasingly tightened their regulatory actions. In turn, the ever-growing regulation situation has compelled the need for a public-private regulatory framework to identify and analyze the challenging gaps in governance (Alonso-Conde and Javier Rojo-Suárez 5). In the long run, the government is responsible for optimizing the wide-ranging interests of both the investors and stakeholders. For example, the European Commission published an Action Plan on Financing Sustainable Growth in 2018. The Action Plan is capacitated to offer a comprehensive strategy on improving the link between finance and sustainability (Alonso-Conde and Javier Rojo-Suárez 5). Categorically, the action plan encourages the redirection of capital flows towards sustainable growth, intending to attain inclusive growth. Also, the action plan is aimed at handling the risks, which result from adverse climate change, depletion of resources, and degradation of the environment. More so, the said action plan has equally promoted transparency coupled with a long-term visualization of both financial and economic activity (Alonso-Conde and Javier Rojo-Suárez 5). Subsequently, a European Union Green Bond Standard (EU-GBS) benchmark encourages the financing of low carbon projects.

Theoretical Background on the Green Bonds and its Government Regulation

Literature on green bonds concentrates wholly on the close association between its pricing and the attached financial market (UN- ECLAC 2). In particular, existing literature establishes the relation of fixed income together with currency markets. However, the authors draw a weak connection between the green bond and the value of stock and energy. This literature provides a systematic analysis of the derived returns, liquidity, and volatility of the green bonds (UN- ECLAC 2). Besides, they offer discussions on third-party authorization’s primary responsibilities initiated on both personal and institutional challenges. Third-party authorization is critical for private issuers because corporate green bonds are regarded as less favorable regarding volatility and liquidity (UN- ECLAC 2). Furthermore, the corporate bonds might have higher interest rates as compared to their conventional counterpart. Therefore, this empirical literature reviews the pricing of the green bond in the market. For example, the green bonds have a higher comparative liquidity-adjusted yield premium than the regular bonds (UN- ECLAC 5). With lower interest rates pegged on the greater liquidity, the green bonds attract higher daily interest spreads, leading to favorable market prices. Subsequently, the authors have opined that both the financial and corporate green bonds trade more often than non-green bonds (UN- ECLAC 5). Most importantly, the marginal sales of the government-associated green bonds are more significant due to the size of the issue and maturity. However, the type of currency does not influence the pricing of the green bonds. Instead, the set rating on the Environmental Social Governance (ESG) dictates the market prices of the green bond (UN- ECLAC 5). In this regard, this literature provides an inquiry on the dynamics of volatility coupled with spillovers attached to the green bond markets. Thus, government regulation on the green bond market is a measure of controlling the bond’s sensitivity in respect to its volatility factor. As much as a considerably more significant part of literature discusses an investor’s perspectives in the green bond market, talks on the offered incentives on issuance are significant (UN- ECLAC 6). Primarily, governments provide financial incentives to issuers; these green bonds decrease the cost of invested capital through financing, leading to lower risks on the availability of capital (UN- ECLAC 7). All these measures are relevant to encourage the issuance of green bonds because they enhance the protection of the environment by creating value among investors.

Measuring and Tracking the Additionality of Green Bonds Globally

The primary features of assessing the impact of the green bond are based on the following two significant yardsticks. First, evaluating the impact of such an example of a green fixed income depends partly on establishing whether the finances attached to the bond flows towards a verifiably green project (Tu, Sarker, and Rasoulinezhad 4). Second, assessing the impacts of the bonds also relies on the determination of the extracted sustainable value in respect to the green label project (Tu, Sarker, and Rasoulinezhad 4). In this regard, it is essential to designate an appropriate framework for channeling the required green financing towards specific projects. Such a move would prevent diverting green resources into more minor “green” investment projects. The reason is that measuring and tracking the addition of a green bond inside a less “green” project is difficult (UNEP 10). Thus, this test of addition is underscored on the possibility of measuring if more significant financial flows would result in higher sustainability.Furthermore, the incorporation of green financing stimulateshigh levels of sustainability via reducing the relative costs of financing in extra sustainable investments (UNEP 11). Thus, green bonds encourage high economic sustainability by lowering the relative cost of sustainability undertakings.

Underscoring Sustainability as a Worldwide Transition through the Use of Green Bonds

Achieving green financingrequires a clear comprehension of both the start and endpoint associated with the direction of sustainability. Such understanding is gained by having expeditious knowledge on the following questions concerning the green bonds (Tu, Sarker, and Rasoulinezhad 5).

  1. What a sustainable economy looks like and itsprospective pathway to sustainability.
  2. What barriers or prospective catalysts impedeor encourage sustainability vision.
  3. How finances help overcome barriers or activate catalysts.
  4. The measurement metrics for guiding finance along the attained green path

The path and sustainability of a green bond entirely rely on the circumstances surrounding the issuance of such a bond. For example, the case study of the green bond market in China has utilized Shanxi and Sichuan energy systems to illustrate the same pathway to sustainability (Alonso-Conde and Rojo-Suárez 11). Apart from that, there might be impeding barriers and catalysts, including technological, behavioral, financial, or political, which negatively impact the market of green bonds. Hypothetically, the introduction of green financing through government bonds would decrease the technological cost like the R&D. Moreover, there are assured financial incentives that catalyze the behavioral change of issuers in a bond market. Consequently, the provision of government incentives helps reduce capital costs, leading to the financing of projects that would otherwise be unfinanced (Sartzetakis 2).  Also, lower capital costs would encourage the government to initiate policy changes on the regulation of green bond markets. As much as there are many sustainable energy investments financed using lower costs, the application of green bonds helps avoid the financial risks associated with the shift to a low carbon system (Sartzetakis 7). Thus, the shift to low carbon energy entails several expensive infrastructural changes that impact the economy differently.

Implications of Green Bonds on Economic Sustainability

Though the application of green bonds helps increase the number of financed projects, the green bond helps deliver the added value or addition (Maltais and Nykvist 4). Whereas some pundits argue that the usability of green bonds does not imply additional value, others have disagreed over the implied economic benefits. In this regard, the consideration of green financing saves on the initial costs of capital, thus ensuring that a vast number of projects are entirely financed (Sartzetakis 13). Therefore, the legitimate concern that the issuance of green bonds offers a false impression regarding its sustainability variable is true based on the following analysis. From the Swedish perspective, it is understood that the actors trade green bonds the same way they would do to the conventional bonds (Nelson 6). They argue that the green bonds function just like other financial instruments, and therefore these bonds have similar environmental impacts on economic sustainability (Nelson 6). Further, they have constantly opined that they falsely created an impression about the green bonds is the specific reason why many investors have pumped large capital bases into novel investments. Practically, there is strong evidence that indicatesmost investors and issuers have changedtheir interaction activities in the capital markets (Maltais and Nykvist 8). Indeed, such alterations in the bond market have occasioned positive effects onmany organizations’ operation sustainability. Furthermore, numerous similarities pinpoint the way green bond market and active ownership function among external stakeholders (Maltais and Nykvist 8). Even though many bonds lack voting rights, all buyers of these green bonds have engaged in sustainability dialogues. Consequently, the trading of green bonds only signifies a small percentage of the bond market; there is an effort among the investors to expand the rising number of opportunities.

Conclusion

The introduction of green bonds is designed as a familiar and low-risk financial instrument because its issuance significantly contributes to economic sustainability at a comparatively low cost. Additionally, the market requires a comprehensive and broad set of government control since its growth is pegged on the degree of issuance, supervision, and liquidity. The ever-growing regulation situation has compelled the need for a public-private regulatory framework to identify and analyze the challenging gaps in governance. In the long run, the government is responsible for optimizing the wide-ranging interests of both the investors and stakeholders. Also, lower capital costs would encourage the government to initiate policy changes on the regulation of green bond markets. As much as there are many sustainable energy investments financed using lower costs, the application of green bonds helps avoid the financial risks associated with the shift to a low carbon system. Though many pundits do not consider green bonds responsible for transiting capital from unsustainable to sustainable investments, green bonds’ trading offers incentives to issuers. In brief, the provided incentives allow the issuers to finance any number of projects.

Works Cited

Alonso-Conde, Ana-Belén, and Javier Rojo-Suárez. “On the Effect of Green Bonds on the Profitability and Credit Quality of Project Financing.” MDPI – Sustainability, vol. 12, no. 6695, 2020, pp. 1-23.  

Maltais, Aaron, and Bjorn Nykvist. “Understanding the Role of Green Bonds in Advancing Sustainability.” Journal of Sustainable Finance and Investment, 2020, pp. 1-20.

Nelson, David. “Green Finance in China: Achieving Sustainability through Finance.” Climate Policy Initiative, 2020, pp. 1-24.

Sartzetakis, Eftichios. “Green Bonds as an Instruments to Finance Low Carbon Transition.” Economic Change and Restructuring, 2020, pp. 1-34.

Tu, Chuc, Sarker, Tapan, and Ehsan Rasoulinezhad. “Factors Influencing the Green Bond Market Expansion: Evidence from a Multi-Dimensional Analysis.” MDPI – Journal of Risk and Financial Management, 2020, pp. 1-14.

UN Economic Commission for Latin America and the Caribbean (ECLAC). “The Rise of Green Bonds.” Financing for Development in Latin America and the Caribbean, 2017, pp. 1-46.

UNEP. “Green Bonds: Country Experiences, Barriers, and Options.” UNEP inquiry in Support of the G20 Green Finance Study Group, 2020, pp. 1-39.

Sub-contractor’s Bid Evaluation

Student’s Name

Instructor’s Name

Course

Date

Sub-contractor’s Bid Evaluation

Question 1

Sub-contractors might quote lower bids because they have left out some vital items during the bid process. During the progress of the project, they might come on board to ask the contractor for more money to cater for the missed aspects. It implies that little money is spent when all items are purchased once, but purchasing fewer items underway might seem more costly. Thus the contractor may end up spending more than the planned budget. Also, the lowest bidder might have quoted for materials that are cheap or of lower quality. Such an arrangement might endanger the safety of the work in the long-run, leading to frequent accidents at construction sites. Likewise, the lowest bidder might not possess the adequate skills relevant for the contract, leading to a substandard work output that really inconveniences the contractor.

Question 2

The electrical sub-contractor must be certified by the National Inspection Council for Electrical Installation Contracting (NICEIC). Such a body assesses both the domestic and commercial electricians for enhancing a proper safety of workmanship. The sub-contractor should be certified by the national body that checks the installation standards for further affirmation in a bid to control the required quality. Besides, a better customer service would impact positively on the reputation of the sub-contractor based on his or her previous accomplished works (Ramalingam 1). Therefore, a sub-contractor should possess service attributes like punctuality and reliability, work tidiness, and on-time task completion to improve the customer service.

Question 3

 quotation during the bid is an ethical breach that I personally experienced during my first attachment in a certain company. Categorically, there were reports of corruption from an electrical company resulting from the sub-contractor’s falsification and overcharging of receipts. During their bid, the company charged extremely high quotations for their quotations. The company engineers on-site forged the receipts of payment, and thus charged the clients higher prices than actual prices. When discovered, the client authorized the main contractor to terminate the relevant company from any further contraction work.

Works Cited

Ramalingam, Shobha. “Subcontractor Selection Process through Vendor Bids: A Case of an Outsourcing Service in Construction.”  IIM Kozkhikode Society and Management Review, 2020.
IIM Kozhikode Society & Management Review
IIM Kozhikode Society & Management Review
IIM Kozhikode Society & Management Review

Strategy, Balanced Scorecard, and Strategic Profitability Analysis

Strategy, Balanced Scorecard, and Strategic Profitability Analysis

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Due Date

Strategy, Balanced Scorecard, and Strategic Profitability Analysis

Until recently, performance measures of a company have been based on evaluation of financial accounting. In this case, organizations incorporate qualitative and quantitative criteria and short-term and long-term goals when implementing evaluation performance for the company (Johansson & Carr, 2018). Thus, the most preferred approach to attain this is the balanced scored card.

A balanced scorecard can evaluate employee performance on different quantitative factors by utilizing the existing qualitative and available financial information. Quantitative measures emphasize the previous results, primarily as provided in the financial statements (Johansson & Carr, 2018; Kaplan & Norton, 1993). However, qualitative measures seek to address the current outcomes on employee activities to evaluate them to help influence the company’s financial performance in the future (Carey & Knowles, 2020; Motacki & Burke, 2011). As such, the subsequent discussion examines the balanced scored for Limpers Limited. Limpers Limited is a sales company operating in a competitive environment. Recently, the management team wanted to evaluate the quality of different strategies undertaken by the firm. Hence, the subsequent section seeks to discuss the generic strategies the company uses, understand what comprises reengineering, and understand the four perspectives of the balanced scorecard. The paper further aims to analyze the changes in operating income to evaluate strategy and identify the unused capacity.

The Generic Strategies the Company is using.

Generic strategies for a company refers to the general approaches utilized by the company to position itself in the industry. In the case of Limpers Limited, the company has been using various notable strategies. Some of these are implementing cost-cutting measures to increase its price competitiveness. Other methods include differentiation and best value approaches. Although these strategies have helped the company remain effective, additional procedures are needed to improve its operational efficiency. Thus, these strategies are explained in the balanced scorecard.

What Comprises Reengineering

Reengineering encompasses the examination and redesigning of processes and related workflows of a business for an organization. Notably, business processes refer to a set of work activities that employees perform for achieving their goals (Wang, National Research Council Canada, & International Conference on Flexible Automation and Intelligent Manufacturing, 2004; Martin, 2002). Hence, reengineering is performed to enhance flexibility, responsiveness, efficiency, and effectiveness in the company operations.

The Four Perspectives of Balanced Scorecard

Next is a general description of perspectives and illustration of Limpers Limited’s measures to achieve its strategy. These perspectives are discussed as follows.

Financial Perspective

This perspective aims to evaluate the profitability of the strategy and the creation of the shareholder’s value. The main strategic objective for Limpers Limited is to reduce costs concerning competitors and increase its sales performance. Therefore, the financial perspective focuses on evaluating the income attained from cost reduction and increased sales.

Customer Perspective

The perspective seeks to identify targeted customers. It also identifies market segments besides understanding different measures undertaken by the company within the respective components (Nejati & Nejati, 2009). Limpers Limited uses market share and performance measures as ascertained from communication networks to monitor its customers. Thus, this also helps to establish new customers and satisfaction ratings for their customers.

Internal-Business-Process Perspective

This perspective focuses on internal operations that target creating customer value, thus enhancing financial performance. According to Limpers Limited, this perspective is determined through benchmarking with its competitors based on published financial information, current market prices, customer and supplier feedback, utilization of financial analysts and experts from the industry (Carey & Knowles, 2020). The perspective is explained in three primary processes that include innovation, operational and post-sales processes. Through innovation, Limpers Limited can create products and services and improve customer services by invoking innovations in the market (Iqbal, 2019). Equally, the operational process ensures that the company can produce and deliver customer-focused products. Finally, post-sales–service processes enhance evaluation of aftersales performance, thus improving existing activities.

Learning and Growth Perspective

The perspective seeks to identify organizational capabilities that should be acquired to realize internalprocesses that are superior. Thus, this enhances the creation of customer and shareholder value (Iqbal, 2019). Thus, the Learning and growth perspective for Limpers Limited is realized through information system capabilities, employee capability, and motivation capabilities.

Analysis of Changes in Operating Income

Table 1 below shows changes in operating income to evaluate strategy for Limpers Limited in 2020.

Table 1: Limpers Limited – Balanced Score Card

Strategic Objectives Measures Initiatives Target Performance Actual Performance
Financial Perspective Increase shareholder valueGrow income Income from the growth of revenue Income from production gains   Cost management and utilization of unused capacityBuilding a strong relationship with customers $ 2, 800,0001,689,000     7.1%   $3,000,0001,980,000     8.1%  
Customer Perspective Increase the market shareIncrease satisfaction for the customer Market share across communication platforms, new customers, and customer ratings Identify the future customer needsIdentify new customersIncrease sales focus 7%       2%     93% of customers give high ratings 7.8%       2.1%     85% gave high ratings
Internal Business Process Perspective Improve product deliveryMeet specific delivery dates and quality Improve the process for service customersReengineer order delivery processes   90%       87% 92%       88%
Learning and Growth Perspective Empower the workforceEnhance system information capability Empower line workers to manage the processEnsure manufacturing processes produce real-time feedback Have coaching team of supervisorsImprove offline and online data collection. 93%       84% 94%       83%

As indicated in Table 1, the firm surpassed its target performance by realizing financial perspectives from 7.1% to 8.1%, a 1% increase. Customer base strategies, which included identifying future needs of the customer and new customers, also supposed the target by 0.8% and 0.1%, respectively. However, the strategy to increase customer sales focus fell under the mark. The internal business process perspective strategies significantly surpassed the target performance. Equally, from the learning and growth perspective, the firm sought to empower 93% of its workforce and enhance understanding of the system by 84%. The second strategy fell below the target performance with only 83% capability enhancement achieved in the information system.

Some unused capacity includes underutilized employee skills and experience. The company can manage this through target allocation of duties and responsibilities based on employee specialization, thus enhancing their performance.

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