IMPLEMENTATION OF RIGHT SKILL TARGETS ON THE HUMAN EXPERIENCE MANAGEMENT SYSTEM CASE STUDY AT PT PLN (PERSERO)

 

Muhammad Raihan Ferdiaz

Institut Teknologi Bandung

m_raihan_ferdiaz@sbm-itb.ac.id

 

Abstract

PT PLN (Persero) consistently encourages transformation and innovation in Human Capital governance. To support the transformation as a whole, it is critical to have a qualified HR system that can support the formation of superior talents who will act as the driving force for transformation. The Role of Human System Management is very important in advancing the company. Human Capital Transformation will be carried out comprehensively and integrated with the technology and value employee experience bases throughout the PLN, and in accordance with the implementation of the HC roadmap in 2022, namely the Human Experience Management System (HXMS). The Purpose of this study is to understand the implementation right skill target on Human Experience Management System at PT PLN (Persero) from distinction between Human Capital System Management and Human Experience Management System, To analyze and evaluate the impact implementation Right Skill on Human Experience Management System and To optimize implementation of right skill target on Human Experience Management System. This research was conducted using qualitative methods. Qualitatives methods as research methods in a natural setting area that allows the researcher to obtain detailed data from actual experience and Data Analysis evaluation using Nvivo. The results of this study will show that Human Experience plays a crucial role as a change enabler in PLN Transformation program because it is recognized as a more valuable asset than the individual. And to optimizing the implementation of Right Skill Target on Human Experience Management System is develop an integrated system that enables businesses to manage various aspects of human resources.

 

Keywords: human experience, human capital, right skill target, nvivo, pln

 

Introduction

PT PLN (Persero) consistently encourages transformation and innovation in Human Capital governance. PLN's performance is maintained as a result of this transformation. Clearly, even in the midst of a pandemic, PLN made a profit of IDR 5.9 trillion (audited) in 2020 and IDR 6.6 trillion (audited) in semester 1 2021. Improving Human Capital Governance PLN was recognized in the Indonesia Human Capital Award (IHCA) VII - 202. The compliance of electric power needs for people is the fundamental requirement for the economic growth of the country. Because of its crucial needs, then all supporting infrastructure such as Human resource and Technology related to it has to be optimally operated. PT PLN (Persero) is the State-owned Enterprises that are engaged in the provision of electricity in Indonesia. The future business potential of PLN will rise in tandem with the expected increase in electricity demand. In response, PLN implemented a transformation known as "Power Beyond Generation," which is also in line with the company's Long-Term Plan. To support the transformation as a whole, it is critical to have a qualified HR system that can support the formation of superior talents who will act as the driving force for transformation. The Role of Human System Management is very important in advancing the company. Human System Management are the key success of a company to run the business as well as to achieve the company’s goals (Gupta & Singhal, 1993).

Human capital readiness can be seen from three aspects, namely the process carried out has a good level of maturity, supported by a comprehensive and effective human capital system in supporting transformation, and the readiness of human resources in carrying out PLN transformation programs.

The employee-first approach has gained more popularity in the recent times. Organization who believes this have experienced more dividends into the company account. The constant change in today’s workplace is witnessing the transition from physical space to the digital realm. Organizations are increasingly putting the effort in recognising the role of technology not only in automating the work but alsi un enhancing the employee experience (Rosenberg, 2005). The term employee experience is a sum of all interactions occurring between employee and the organization. These interactions are influenced by three things, like the physical space that employee uses every day, the culture of the organization and the tools and technology provided by the employes (Mosadeghrad, 2014).

The term employee experience is seemingly attractive in the business in recent years. It is highly appreciated with its advocacy in satisfying the organization wants and needs. However, professionals and practitioners embrace the concept of employee experience, drawn to its potential in solving the issues related to workplace interactions, few challenges have also raised. Questions arise defining the term employee experience and how this can be differentiated with the term engagement, satisfaction and commitment (Plaskoff, 2017).

Human Resources are crucial to the organization's success. Great Organizations are always built by Great People. As a result, talented human resources are the main competitive advantage and an important aspect for companies in facing business competition in the global era that requires proper adaptability in the face of various turbulent and complex changes.

 

Method

This research was conducted using qualitative methods. Qualitatives methods as research methods in a natural setting area that allows the researcher to obtain detailed data from actual experience. In order to produce the trustworthy qualitative data, there are several things that must be considered in conducting this research method, as set forth by Donald and Pamela as follows:

-          Form the pobing questions with appropriate literature support

-          Apply a qualitative method in its natural setting (field study) rather than a highly controlled setting (laboratory).

-          Choosing the sample participants that are relevant to represent the target population.

-          Structuring the data analysis in an appropriate manner.

-          Benchmark the data with other resources.

-          Conducting peer-researcher debriefing on results for asses clarity, additional insights, and reduces bias.

Qualitative research was chosen because it can produce an in depth understanding of a situation from the person concerned. The purpose of qualitative research is to conduct research at the chosen area by taking data that depth and detail in describing the events, situations and interactions between resources. This final project uses qualitative methods to focus on obtaining information from people and organization.

Data Analysis Method

Most researchers engaged in qualitative data analysis have heard of Qualitative Data Analysis Software (QDAS) or computer assisted qualitative data analysis (CAQDAS) and know that Nvivo is one of the options for storing, managing, and analysing qualitative data. Nvivo has retained the core features of handling text data via coding, writing, linking, adding demographics, searching for patterns, and reporting or exporting data. Since the construction of these early tools, the subsequent software developers incorporated additional capabilities to analyse a wide range of data types (Pdf files, audio, video, images, surveys, reference manager, web pages, social media, etc) with increasingly complex searches and modes of output (Textual, numeric, and visual – via graphs, charts and maps).

When performing data analysis with Nvivo related to coding, the following steps can be followed:

1.      Choose Source and then click on the transcript you want to analyze

2.      Right-click the analyzed transcript fly to bring up a new worksheet.

3.      Click Next and Code with source

4.      Select New Nodes to save the coding results in the new node, then select the location of the new node, name the node, and click Finish.

5.      The result of the procedure.

Diagram Description automatically generated

Figure 1 Qualitative Analysis Process

after defining the nodes. Additionally, a comparative analysis of the node classification was performed using matrix codes. The outcomes of the two analyses are also connected to the themes that have been examined. Because Nvivo only presents an overview of the relationship results of the analysis of existing qualitative data, it should be understood that the strength of relationships between these themes cannot be measured by the level of signification.

Nvivo can then use a comparative diagram to visually represent all the results of the project map analysis of nodes, codes, and relationships after all the steps have been completed. In this research there is a comprehensive conceptual framework to assist the process of finding root cause of the problem, analyze significant factors affecting to the Human Capital business, and based on the result will be the recommendation of strategic process. This research would adopt a conceptual framework that contains: strategy analysis, strategy formulation, and implementation. Right Skill target interview that was conducted to 8 employees of PT PLN (Persero), from BOD, BOD-1, BOD-2 and BOD-3.

Table 1 List of Interviewers

Positions

Division / Directorate

Name

Director

Legal and Human Capital

Yusuf Didi Setiarto

Executive Vice President

Human Strategic Division

Ridho Hutomo

Executive Vice President

Human Talent Development Division

Dedi Budi Utomo

General Manager

Unit Induk Penyaluran dan Pengatur Beban Sulawesi

Djarot Setyawan

General Manager

Unit Induk Pembangunan Sulawesi

Defiar Anis

Vice President

Talent Development Area 9

Willy Siska

Manager

Talent Development UIP3B Sulawesi and UIP Sulaweso

Muhammad Usman

Assistant Manager

Talent Development UIP3B Sulawesi

Yogie Wiratmoko

Officer

Talent Development UIP3B Sulawesi

Andi Ilman Alqadri

How to measure the accuracy or consistency of qualitative research is one of the fundamental issues that every qualitative researcher needs to pay attention to. Researchers can make use of the Coding Comparison Query feature in Nvivo software to determine the study's level of reliability. Using this feature, two users or two groups of users' coding can be compared. This feature offers two methods for evaluating the dependability of qualitative research: computing the percentage of deals to gauge the degree of user agreement, or calculating Cohen's Kappa to gauge user dependability. The kappa coefficient is regarded by many academics as being more helpful than the percentage of agreement figures. The average kappa coefficient or deal percentage across various sources or nodes must be calculated in order to reflect the overall reliability of high-quality research because Nvivo software calculates the kappa coefficient and deal percentage separately for each combination of nodes and data sources. To enable further calculations, the output of the Coding Comparison Query can be exported from Nvivo as a spreadsheet. The weights of various data sources must be taken into account when calculating the average kappa coefficient or the percentage of agreement for one node across multiple resources, data sources, and nodes. There are two possible outcomes for weighting each research data source, the same weighting across all data sources or different weighting across each data source based on its size. Additionally, the following principles are used to interpret the kappa coefficient: (Fleis, Levin & Paik, 2003; QSR International, 2016).

Table 2 Coefficient Interpretation Guidelines

Kappa Value

Interpretasi

Less than 0,40

Poor Agreement

0,40 – 0,75

Fair to Good Agreement

More than 0,75

Excellent Agreement

 

 

 

Results and Discussion

Analysis

Researcher selects appropriate tools to further explain the issue in order to conduct a deeper analysis of the research objective. In order to analyze the implementation of Right Skill Target on Human Experience Management System (Case Study at PT PLN (Persero)), which is the purpose of this study,

The researcher as a human instrument, has a record of various preparations, his feelings, his expectations, and his views on himself as the key to data collection before going into the field. The limitations faced by qualitative researchers in analyzing data have actually been bright spots, specifically by using computer-based analytical tools. (Zamawe, 2015) stated that "In recent times the use of electronics to analyze data has only applied to quantitative research, and it has not been applied to qualitative research.", However, a program called Computer Assisted Qualitative Data Analysis Software (CAQDAS) has started to be created that can be used to analyze qualitative data. In accordance with this viewpoint, (Basak, 2015) claims that using CAQDAS software by academic researchers will boost research productivity. This is demonstrated by the fact that CAQDAS software is very useful for researchers when performing quality data analysis because it can import data, copy code into text, and retrieve data. According to (Hamrouni & Akkari, 2012), NVivo is the most effective CAQDAS software for qualitative data analysis because it offers more comprehensive and ideal tools. If a piece of software can search, connect objects, code, query, annotate, and map research data, it is referred to as a CAQDAS. Researchers are interested in using NVivo software to help with data analysis because of its benefits for qualitative data analysis. Additionally, while data is being gathered, a process known as emergent design is used to develop the research focus. The process of gathering data continues until it is deemed that the research is finished. Consequently, the outcomes of the data analysis will be discussed in this chapter.

Nvivo Analysis

Utilizing Nvivo 12 Plus for Windows, the analysis in this study was designed to produce the best possible technical results. In order to analyze qualitative data effectively and efficiently, Nvivo's qualitative data management process is crucial (Bandur, 2016). Coding and nodes are the most noteworthy aspects of using Nvivo. Koding is the process of populating nodes with data pertaining to concept categories (codes) that have developed in the node system. Nodes are therefore storage spaces for data that is pertinent to the ideas found in each category of system nodes.

In Nvivo the analyzed data sources can be divided into internal research data sources, external research data sources, researchers' notes during data collection (Memos), and matrix frameworks (Matrices Framework). Furthermore, Qualitative Data Analysis with nvivo uses several stages, starting with the stage of collecting data, filling in nvivo to facilitate the data processing process, the next is transcribing on nvivo and all of that is done manually, in this transcribing process it simplifies us more in processing data, the next is the coding stage and this is the most difficult stage in this analysis,  because in the coding process this is the essence of the procedure using Nvivo, in this coding process it is divided into several stages as well, namely open and axial coding, The final stage, Visualizing, deals with how to visualize the coding results that we have entered into the application and how the outcomes we require will be attained. According to (Bandur, 2016), interpreting coding as an iterative process, specifically the ongoing perseverance of qualitative researchers in data analysis Researchers classify data based on concepts that appear in the data, compare concepts and/or categories of data, and then group concepts and categories of data that are related to one another. When researchers are unable to discover any new ideas in the data, this process will eventually come to an end. Coding is used to examine research issues. Additionally, Koding seeks to compile all pertinent data regarding a specific case from various sources.

Researchers were able to obtain the results of interviews with top PLN executives during the first step, which involved data collection. After receiving approval, the researcher first drafts a letter addressed to the appropriate agency on behalf of the campus before conducting an in-depth interview. The researcher conducted her first interview with the Executive Vice President of the Talent Development Division of PLN Head Office, and it lasted about an hour. The researcher summarized the interview's findings and identified a number of key points that will be taken into account when processing the data in the future. Appendix 1 contains a summary of the interview conversation.

The Executive Vice President of Human Capital Services, the Vice President of Talent Development Area 9, and the Manager of the Talent Development Sub-Section of UIKL and UIP Sulawesi were the interviewees I addressed in the subsequent round. Appendices 2, 3, and 4 contain a summary of the conversation with the source. The researcher codes the data after importing all the interview information into the NVivo program. According to Richard (2016), coding is the process of locating the transcript's main ideas as well as topics that were discovered during the search for those main ideas. Data reduction through coding is used to describe participant characteristics or attributes. Nodes play a crucial role in the management and analysis of qualitative data with NVivo because they store the theme categories that researchers examine during the coding process. Nodes, in the opinion of  (Jackson & Bazeley, 2019), are storage areas for themes, participants, research settings, and research organizations. The relationship patterns of each theme and or concept generated based on the data can be seen by researchers by looking at nodes created based on categories and subcategories of units of analysis. Deductive node creation relies on literature reviews or theoretical concepts, whereas inductive nodes are based on field data without being connected to themes derived from literature reviews. Additionally, participants and research environments can be represented by nodes. Cases in the context of NVivo are broader, including research participants, research sites, and even themes that appear in the research. In NVivo, cases are not related to case studies but are defined as units of analysis in the research conducted. Form these cases requires classification, as shown in Figure 4.1

Figure 3 Codes Classification

Differences of HCMS and HXMS

 

 

The first step in evaluating the HXMS implementation is to determine the respondent's level of HXMS knowledge. Based on the findings of the qualitative data analysis performed using Nvivo's Text Search Query, it is known that the majority of respondents explained that HXMS is closely related to "Experience," which makes up 3,56 % of all research data sources, followed by the word "Employee" which is 2,64 %, and Company which makes up 1,87 %. The word clouds for the top 60 terms from the data source for this study are displayed in the image below.


Figure 4 Word Frequency Query

A text search query can be used to see how these words are used across different research data sources. Researchers sought to comprehend how the word "experience," which predominates across a variety of sources of research data, was used in this study. The following is how the search results are displayed in the Word Tree.

Figure 5 Word Tree from word “Pengalaman”

Based on this figure, it was discovered that PLN is currently focusing on experience, which has developed into a valuable asset that is more expensive than people themselves. HXMS is therefore capable of producing what is known as value creation and places a strong emphasis on the experiences of individuals from each.

Based on the conducted interview session, the primary differences between the old system, known as HCMS, and the new system, known as HXMS, were also identified. According to the analysis's findings and Nvivo, the primary point of differentiation is the role and experience.

Figure 6 Background Implementing

This is the main justification for implementing this HXMS in PLN. Since one of the sources claimed that "The problem or problem that has been felt so far is that the experience in PLN has not been able to capitalize," and since other sources also claimed that "The role of people and organizations as enablers of change in transformation in PLN," this assertion is further supported.

The correlation between the distinctions between the old and new systems and the rationale for changing this system can be drawn from this. The majority of respondents stated that the HXMS implementation is still in the category of being immature or needing improvement at PLN. This is due to the employees' inconsistent understanding of the newest management system, which is another interesting finding related to the HXMS implementation that is already operational at PLN. This, however, serves to motivate management to keep deepening its comprehension of the value of integrating HXMS into PLN.

It transpires that a number of factors could either support or hinder the HXMS policy's implementation at PLN in terms of how well it functions. According to the interview's findings, there are currently felt challenges to respondents found that the most obstructive risks and obstacles in this implementation are how Human Capital managers communicate and internalize this system to all employees, and we must be able to develop a long-term business strategy with the core values owned by the company. The researcher then poses additional questions about methods for enhancing the Right Skill Goals in the Implementation of HXMS as well as questions about how to lessen the effects of things that impede. Researchers feel compelled to address this question because the right skill goal is a fundamental one that can be felt directly by employees in the workplace. Right skill is closely related to a person's level of capability, making it possible to correlate that a good company is obtained from competent employees.


Figure 7 Implementation Right Skill Target on HXMS

The importance of all forms of development on this right skill target must also be emphasized. This is because all forms of development must eventually result in value creation, which necessitates our ability to deliver advantages and value across a range of developed programs.

Tables 3 Differences HCMS and HXMS

Differences

HCMS

HXMS

System

Based on Human Capital

Based on Experience

Target and Objective

Development of human resources as a resource to maximize their contribution

By providing tools and technology that have an impact on business, can create meaningful employee personal experiences.

Key Point

Prioritizing the analysis of the development of human resources and companies

 

Putting an emphasis on employee experience, which will have an impact on employee engagement and motivation to contribute as much as possible to the company.

Dimension

Business Process, Organization, Acquisition, Development, Reward and Recognition, Exit and Leave Management, HC Information System.

Right Size, Right Skill. Right Spend. Right System

Mekanism

Decentralized

Sentralized

From the tables key distinctions between the HCMS and HXMS systems are discernible. The primary distinction between the current system and its targets and objectives, focal points, dimensions, and mechanisms.

 

Business Solution

Based on prior business analysis and findings, this chapter will describe the solutions and rank potential solutions. The proposed action and implementation plan will be developed from the available solutions.

Implementation Right Skill Target on HXMS

Based on the project map analysis of the critical factors in implementing the Right Skill target of Human Experience Management system which discusses the causes of PLN's failure to achieve its target, will come up with business alternative solutions. The author had discussions with several individuals from the relevant division during the creation of the matrix. The most likely and viable alternative to the current situations and conditions is the criterion to look for business solutions. Reminding everyone that the HXMS is being conducted and involved 51 Units throughout unit PLN in Indonesia, the current situation and conditions call for an immediate and precise solution to the problem's root.

Figure 8 Project Map Implementation HXMS

The researcher also includes a project map, which can be seen in Figure 4.5 that shows the stages of solving the problem. The various components of a project are depicted graphically in a project map. Using coding themes as a guide, project maps are developed that can be used to explore and present data connections. Based on the image, it is learned that understanding the distinctions between HCMS and HXMS, as well as how to deduce each system's goals and focus from these distinctions, are necessary first steps in solving the problem. The next step is to analyze the rationale for switching from the old HCMS to the new HXMS system. The rationale for the switch is based on the system's primary function, followed by the frequent issues with the old system and the impact of the change as well as the period of time between the two systems..

Additionally, the scale of the HXMS implementation at PLN shows that the leader's influence over the implementation is significant. If this influence is further diminished, it will become clear that PLN is currently concentrating more on the Right Skill target. The risks and challenges associated with this HXMS system change can also be seen from the implementation that has been going on at PLN. In this section, it will also be discussed in relation to risk mitigation and the impact of the impacts that will happen. The outcomes of this implementation include strategies that PLN will bring in order for all of these systems to function properly.

Figure 9 Risk and Mitigation

A hierarchical diagram can be used to represent the project map. There are two varieties of hierarchical diagrams: tree maps and sunbursts. A hierarchical diagram is a diagram that displays hierarchical data as a collection of four multilevel rectangles of different sizes. Size denotes quantity, such as the amount of coding present on the node or the reference amount of coding. The size of the rectangles should be viewed in relation to one another, not as an absolute number, as hierarchical diagrams have the best scale given the available space. The largest region is shown in the top left corner of the graph, while the smallest region is shown in the bottom right. A hierarchical diagram was used in this study because the researcher wanted to see how the interviewees' responses predominated and because it could be used to identify the issue and its indicators based on how much coding was present in the data source.

Figure 10 Diagram Hirarkis Tree Map

Six stages—Differences, Basis of Change, Implementation, Risks and Constraints, Strategies, and Expectations of Monitoring Success—are shown in the hierarchical diagram to determine the implementation of right skill goals in HXMS. Implementation makes up a significant portion of the six questions, followed by Hope and Success Monitoring, Basic Changes, Strategies, Risks and Constraints, and Differences. This shows that the interviewer believes that implementation is the most crucial issue and that it merits further discussion.

There are two sub-categories in the final stage, which is to reexamine problem solving: re-checking and conviction. Rechecking is subordinate to confidence in solutions. This shows that the researcher quickly checks the student's solution to the problem after the student has done so and is satisfied with it.

Implementation Plan & Justification

Use the comparison diagram feature of the NVivo QSR software to compare and contrast the interviewer subjects.

Optimize Implementation Right Skill Target on HXMS

To compare the same two types of project items, this feature can create a comparison diagram.

 

 

 

                           A                                                             B

 

C

Figure 11 Comparison Digram Subject

Table 4 Comparison of Research Subjects' Problem Solving Stages

Statements Resulting from Data Sources (Node and Child Nodes)

Subyek

EVP HTD

EVP HSC

VP HTD

MSB HTD

 

Perbedaan HCMS dan HXMS

++

++

+++

++

 

Dasar Perubahan

+++

+++

++

++

 

Implementasi

++

++

++

++

 

Resiko dan Kendala

+++

+++

++

++

 

Strategi

+++

++

+++

++

 

Harapan dan Monitoring

+++

+++

+++

++

 


According to Table 4, each node and child node created in NVivo contained coding from different research data sources, indicating that the four research subjects

Figure 12 Optimize Implementation Plan of Right Skill Target on HXMS

carried out the stages of problem-solving in essentially the same manner. The recommendations made by the research participants for the implementation of Right Skill targets in HXMS are also summarized in Table 4. The process is part of the thought process that is engaged in.

According to the figure 4.10, about Optimize Implementation Plan of Right Skill Target on HXMS, PLN must develop a system to support the management of its talent management in order to support the implementation of the appropriate skill targets in HXMS. Profile Success Factor, a system that PLN will soon release, is based on research done using interview data and academic sources. It is anticipated that this system will close gaps in the application of appropriate skill targets. Because of this Success Factor, it may be simpler for businesses to identify the best talent, allowing for the best possible use of experience, and how to measure the accuracy or consistency of qualitative research is one of the fundamental things that every qualitative researcher needs to pay attention to. Researchers can use the Coding Comparison Query feature of NVivo software to determine the study's level of reliability. This feature is used to compare the coding produced by two users, or two groups of users. This feature offers two methods for assessing the dependability of qualitative research: calculating percentage agreements to measure the degree of user agreement, or using Cohen's Kappa to assess user dependability.  As a result, the Cohen's Kappa coefficient and a percentage of approval rate between coders A and B were obtained in the output of the Coding Comparison Query using NVivo to assess the validity of the qualitative research.

Deal percentage is the number of deal units divided by the total number of units of measurement in the data item, and it is shown as a percentage. The percentage of data source content that two users agree can be coded on the node is known as the "deal percentage," or in other words, the percentage of content where this is possible. To put it simply, the percentage of agreement is determined by adding the percentages from the sum of the A and B columns to the percentages from the A and B columns.

As previously mentioned, Nvivo calculates the Kappa coefficient and deal percentage separately for each combination of nodes and data sources. Therefore, in order to reflect the overall qualitative research reliability, the average kappa coefficient or deal percentage across multiple sources or nodes must be calculated. In order to perform additional calculations, the output Coding Comparison Query can be exported as a spreadsheet. We must take into account the weights of various data sources when calculating the average kappa coefficient or the percentage of agreement for a single node across multiple data sources, or across multiple data sources and nodes. Each research data source can be weighted in one of two ways: equally or differently depending on the size of the source.

It should be noted that the unit or units of measurement differ depending on the type of data source. Documents, datasets, memos, and external data sources all have character size units. The appendix of this study contains the results of the outpur Coding Comparison Query, a calculation of the average Kappa coefficient, and a breakdown of the proportion of deals that were weightless. With a percentage of deals reaching 0,7809, these calculations led to the average Kappa coefficient in this study being obtained. Looking at the table will help you interpret the kappa coefficient's value. The reliability of this research is rated as excellent agreement when considering the guidelines or the interpretation of kappa values and the conclusion that is reached

 

Kesimpulan

Based on the test results in this research, the author finds the answer for the answer for the question and the following conclusion are made. The differences between Human Capital Management System and Human Experience Management System is on focus and the purpose of the system. HCMS focuses on the personal development of its employees and HXMS focuses on developing its employee experience and is able to provide value creation for the company. The reason PLN Changing a Human Capital Management System is it possible to transform human resources from being objects in an organization to being subjects. Experience plays a crucial role as a change enabler in PLN's Transformation program because it is recognized as a more valuable asset than the individual. To optimizing the implementation of Right Skill Target on Human Experience Management System to better internalize to all employees by strengthening the PLN 1 cultural program “Conveying one information every day” so that employees can comprehend this system.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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